Prepared in cooperation with the 

New Jersey Department of Environmental Protection

Method to Support Total Maximum Daily Load Development 

Using Hydrologic Alteration as a Surrogate to Address 

Aquatic-Life Impairment in New Jersey Streams

Scientific Investigations Report 2013–5089

U.S. Department of the Interior

U.S. Geological Survey
A

F

E

D

C

B

Cover. Photographs showing examples of hydrologic alteration near U.S. Geological Survey streamflow-gaging stations in the Raritan River 

watershed, New Jersey: A, gated flow-regulating dam, South Branch Rockaway Creek at Whitehorse Station, NJ; B, concrete weir in stream 

channel, Stony Brook at Princeton, NJ; C, stream subject to reservoir releases and streamwater diversions, South Branch Raritan River at 

Stanton, NJ; D, gated flow-regulating dam, Manalapan Brook at Spotswood, NJ; E, masonry dam, North Branch Raritan River at Far Hills, NJ; 

and F, concrete weir across stream channel, Millstone River at Blackwells Mills, NJ. (Photographs by U.S. Geological Survey.)
Method to Support Total Maximum Daily Load 

Development Using Hydrologic Alteration 

as a Surrogate to Address Aquatic-Life 

Impairment in New Jersey Streams

By Jonathan G. Kennen, Melissa L. Riskin, Pamela A. Reilly, and Susan J. Colarullo

Prepared in cooperation with the 

New Jersey Department of Environmental Protection

Scientific Investigations Report 2013–5089

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013

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Suggested citation:

Kennen, J.G., Riskin, M.L., Reilly, P.A., and Colarullo, S.J., 2013, Method to support Total Maximum Daily Load 

development using hydrologic alteration as a surrogate to address aquatic life impairment in New Jersey streams: 

U.S. Geological Survey Scientific Investigations Report 2013–5089, 86 p., http://pubs.usgs.gov/sir/2013/5089/
iii

Acknowledgments

The authors thank the many individuals of the New Jersey Department of Environmental 

Protection, especially Barbara Hirst, Donna Milligan, Dean Bryson, Kimberly Cenno, Jeffrey 

Hoffman, Alfred Korndoerfer, and Victor Poretti, who dedicated time and resources in support of 

this cooperative study. Additional technical support from Donald Rice and James Henriksen of 

the U.S. Geological Survey (USGS), New Jersey Water Science Center and Fort Collins Science 

Center, respectively, is immensely appreciated. Special thanks to Brian Cade of the USGS Fort 

Collins Science Center for his help and insight regarding quantile-regression modeling. We 

are indebted to the many USGS scientists, including Blaine White, Richard Edwards, Andrew 

Watson, Jason Shvanda, John Trainor, Brian Painter, Aric Vanselous, Heidi Hoppe, Robert 

Atkinson, Sarina Howell, Charlie Shivers, Jerilyn Collenburg, Vincent Brown, Patrick Bowen, 

and Michal Niemoczynski, who, on an annual basis, carry out the continuous-record monitoring 

of New Jersey streams. Their hard work and dedication to collecting and publishing timely 

hydrologic information represents the scientific backbone of studies such as this. Additionally, 

Jason May, Brian Caskey, Tanja Williamson, and Dale Simmons of the USGS provided many 

useful suggestions that helped improve this report. 

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................2

The TMDL Process in New Jersey ....................................................................................................4

Purpose and Scope ..............................................................................................................................5

Description of Study Area ...................................................................................................................5

Statewide Ambient Biomonitoring Network (AMNET)...................................................................7

Statewide Watershed Runoff Model ..........................................................................................................8

Hydroecological Integrity Assessment Process ......................................................................................9

Identifying Baseline Periods of Record and Stream Class in the Raritan River Basin ....................10

Application of Quantile Regression to Identify Baseline Periods of Record .....................................12

Sensitivity Analysis and Calibration of Watershed Model Parameters..............................................19

Reevaluation of Raritan River Basin Stream Classes............................................................................19

Simulation of Unaltered Streamflow ........................................................................................................20

Determining Deviation of Streamflow from Baseline Conditions ........................................................21

Class A Streams..................................................................................................................................21

Comparisons with the Observed Baseline ............................................................................21

Comparisons with the Simulated Baseline............................................................................24

Class C Streams ..................................................................................................................................26

Management Implications..........................................................................................................................27

Reservoirs and Dams ........................................................................................................................27

Impervious Surface Cover.................................................................................................................29

Water Abstraction ..............................................................................................................................30
iv

Linking Hydrologic Alteration to Invertebrate-Assemblage Response ..............................................31

Site Selection and Data Aggregation..............................................................................................31

Aquatic-Invertebrate Analysis .........................................................................................................31

Use of Ecological Impairment to Define a Hydrologic Disturbance Gradient .........................34

Data-Reduction Procedures and Analytical Approach................................................................34

Evaluation of Aquatic-Invertebrate Metrics as a Basis for Flow-Ecology Response Model 

Development ...................................................................................................................................37

Bivariate Flow-Ecology Response Relations .................................................................................37

Multivariate Flow-Ecology Response Models ...............................................................................39

Data Limitations............................................................................................................................................42

Differences between Observed and Simulated Baseline Periods of Record...........................42

Flow-Ecology Response Models......................................................................................................45

Summary and Conclusions.........................................................................................................................46

References Cited..........................................................................................................................................49

Appendix 1. Definitions of the 171 hydrologic indices .......................................................................61

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, 

New Jersey .....................................................................................................................................69

Figures

1. Location of study sites in the Raritan River Basin study area, New Jersey.......................6

2. Hydrograph for U.S. Geological Survey streamgage Raritan River below Calco 

Dam at Bound Brook, New Jersey, 1945–2006 ......................................................................11

3. Percent impervious surface cover and location of wells and discharge sites 

that may influence streamflow quantity and quality in the North Branch Raritan 

River at Far Hills, New Jersey, study basin in the Raritan River Basin study area,

New Jersey..................................................................................................................................13

4. Quantile Regressions for A, daily streamflow and B, hourly precipitation at U.S.

Geological Survey streamgage Raritan River at Far Hills, New Jersey ............................14

5. Percent impervious surface cover and location of wells and discharge sites that 

may influence streamflow quantity and quality in the Raritan River at Manville, 

New Jersey study basin in the Raritan River Basin study area, New Jersey..................16

6. Hydrograph for U.S. Geological Survey streamgage Raritan River at Manville, 

New Jersey, 1922–2005..............................................................................................................17

7. Quantile regression for A, daily streamflow and B, hourly precipitation at Raritan

River at Manville, New Jersey .................................................................................................18

8. Interactive window for selecting nonredundant hydrologic indices for stream

classes A and C as identified in the New Jersey Hydrologic Assessment Program......20

9. Location of hydrologic and ecological sites used for the flow-ecology response

analysis for the Raritan River Basin study area, New Jersey.............................................32
v

Tables

1. Baseline periods of record for Raritan River Basin study sites determined 

by using quantile regression.....................................................................................................58

2. Site abbreviations and baseline dataset types for the 29 class A and C streams

used in the hydrologic analysis................................................................................................22

3. Aquatic-invertebrate sampling sites and their corresponding U.S. Geological

Survey streamgage station numbers used in the flow-ecology response analysis........33

4. Flow-ecology response relations of selected New Jersey Department of

Environmental Protection (NJDEP) Ambient Monitoring Network (AMNET) 

metrics significantly correlated (Spearman’s rho) with the reduced set 

of hydrologic indices..................................................................................................................35

5. Flow-ecology response relations of selected invertebrate-assemblage metrics

significantly correlated (Spearman’s rho) with the reduced set of hydrologic 

indices ..........................................................................................................................................36

6. Two- and three-variable multiple regression models relating selected New Jersey 

Department of Environmental Protection Ambient Monitoring Network (AMNET) 

and Invertebrate Data Analysis System (IDAS) metrics to hydrologic indices................40

7. Deviations of flow-management targets for the observed and simulated baseline

records for the Raritan River at Manville, NJ, study site that fall outside the

established 25th-to-75th-percentile range.............................................................................42

8. Deviations of flow-management targets for the observed and simulated baseline

records for the Raritan River at Stanton, NJ, study site that fall outside the

established 25th-to-75th-percentile range.............................................................................43

9. Deviations of flow-management targets for the observed and simulated baseline

record for the Neshanic River at Reaville, NJ, study site that fall outside the

established 25th-to-75th-percentile range.............................................................................44
vi

Conversion Factors and Datums

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

inch (in.) 25,400 micrometer (μm)

foot (ft) 0.3048 meter (m)

Area

acre 0.004047 square kilometer (km2)

square foot (ft2) 0.09290 square meter (m2)

square mile (mi2) 2.590 square kilometer (km2) 

Volume

gallon (gal) 3.785 liter (L) 

Flow rate

foot per second (ft/s) 0.3048 meter per second (m/s)

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3/s)

cubic foot per second per day 

[(ft3/s)/d]

0.02332 cubic meter per second per day 

[(m3/s)/d]

million gallons per day (Mgal/d) 3,785 cubic meters per day (m3/d)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

°C=(°F-32)/1.8

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Altitude, as used in this report, refers to distance above the vertical datum.

Concentrations of chemical constituents in water are given in either milligrams per liter (mg/L) 

or micrograms per liter (μg/L).

Acronyms, abbreviations, and symbols

AIC Akaike Information Criterion

AMNET Ambient Biomonitoring Network

BMPs best management practices

CWA Clean Water Act

ELOHA Ecological Limits of Hydrologic Alteration

ERHIs ecologically relevant hydrologic indices

EPT Ephemeroptera, Plecoptera, and Trichoptera

FBI Family Biotic Index
vii

GIS geographic information system

HEC-HMS U.S. Army Corps of Engineers, Hydrologic Engineering Center—Hydrologic 

Modeling System

HIP Hydroecological Integrity Assessment Process

HIT Hydrologic Index Tool

HSPF U.S. Environmental Protection Agency Hydrologic Simulation Program—Fortran

IDAS Invertebrate Data Analysis System

ISC impervious surface cover

MIKE SHE Danish Hydraulic Institute Integrated Hydrologic Modeling System

MLR multiple linear regression

NAWQA National Water Quality Assessment

NFRP Natural Flow Regime Paradigm

NJDEP New Jersey Department of Environmental Protection

NJHAT New Jersey Hydrologic Assessment Tool

NJIS New Jersey Impairment Score

NJSCT New Jersey Stream Classification Tool

NJPDES New Jersey Pollutant Discharge Elimination System

NWIS National Water Information System

OB observed baseline hydrograph

POR period of record

PRESS predicted residual sum of squares

QR quantile regression

SB simulated baseline hydrograph

SWRM Statewide Watershed Runoff Model

TMDL total maximum daily load

TOPMODEL physically based, semidistributed topographical watershed model

USEPA U.S. Environmental Protection Agency

USGS U.S. Geological Survey

VIF variance inflation factor

WaterSMART Sustain and Manage America’s Resources for Tomorrow

> greater than

< less than
viii

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Method to Support Total Maximum Daily Load 

Development Using Hydrologic Alteration as a 

Surrogate to Address Aquatic-Life Impairment in 

New Jersey Streams

By Jonathan G. Kennen, Melissa L. Riskin, Pamela A. Reilly, and Susan J. Colarullo

Abstract

More than 300 ambient monitoring sites in New Jersey have been identified by the New Jersey Department of 

Environmental Protection (NJDEP) in its integrated waterquality monitoring and assessment report (that is, the 305(b) 

Report on general water quality and 303(d) List of waters that 

do not support their designated uses) as being impaired with 

respect to aquatic life; however, no unambiguous stressors 

(for example, nutrients or bacteria) have been identified. 

Because of the indeterminate nature of the broad range of possible impairments, surrogate measures that more holistically 

encapsulate the full suite of potential environmental stressors 

need to be developed. Streamflow alteration resulting from 

anthropogenic changes in the landscape is one such surrogate. 

For example, increases in impervious surface cover (ISC) 

commonly cause increases in surface runoff, which can result 

in “flashy” hydrology and other changes in the stream corridor 

that are associated with streamflow alteration. The NJDEP 

has indicated that methodologies to support a hydrologically 

based Total Maximum Daily Load (hydro-TMDL) need to be 

developed in order to identify hydrologic targets that represent a minimal percent deviation from a baseline condition 

(“minimally altered”) as a surrogate measure to meet criteria 

in support of designated uses.

The primary objective of this study was to develop an 

applicable hydro-TMDL approach to address aquatic-life 

impairments associated with hydrologic alteration for New 

Jersey streams. The U.S. Geological Survey, in cooperation 

with the NJDEP, identified 51 non- to moderately impaired 

gaged streamflow sites in the Raritan River Basin for evaluation. Quantile regression (QR) analysis was used to compare 

flow and precipitation records and identify baseline hydrographs at 37 of these sites. At sites without an appropriately 

long period of record (POR) or where a baseline hydrograph 

could not be identified with QR, a rainfall-runoff model 

was used to develop simulated baseline hydrographs. The 

hydro-TMDL approach provided an opportunity to evaluate 

proportional differences in flow attributes between observed 

and baseline hydrographs and to develop complementary flow-ecology response relations at a subset of Raritan River Basin sites where available flow and ecological 

information overlapped.

The New Jersey Stream Classification Tool (NJSCT) 

was used to determine the stream class of all 51 study sites by 

using either an observed or a simulated baseline hydrograph. 

Two New Jersey stream classes (A and C) were evaluated to 

help characterize the unique hydrology of the Raritan River 

Basin. In general, class C streams (1.99–40.7 square miles) had 

smaller drainage areas than class A streams (0.7–785 square 

miles). Many of the non-impaired and moderately impaired 

class A and C streams in the Raritan River Basin were found to 

have significant hydrologic alteration as indicated by numerous flow values that fell outside the established 25th-to-75thand the more conservative 40th-to-60th-percentile boundaries. 

However, percent deviations for the class C streams (defined 

as moderately stable streams with moderately high base-flow 

contributions) were, in general, much larger than those for the 

class A streams (defined as semiflashy streams characterized 

by moderately low base flow). The greater deviations for class 

C streams in the hydro-TMDL assessments likely resulted 

from comparisons that were based solely on simulated baseline 

hydrographs, which were developed without considering any 

anthropogenic influences in the basin. In contrast, comparisons for many of the class A streams were made by using an 

observed baseline, which already includes an implicit level of 

ISC and other human influences on the landscape.

By using the hydro-TMDL approach, numerous flow 

deviations were identified that were indicative of streams that 

are highly regulated by reservoirs or dams, streams that are 

affected by increasing amounts of surface runoff resulting 

from ISC, and streams that are affected by water abstraction 

(that is, groundwater or surface-water withdrawals used for 
2 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

agricultural and human supply). Eight of the reservoir- and 

(or) dam-affected sites showed flow deviations that are indicative of flow-managed systems. For example, indices that 

account for the timing and magnitude of high and low flows 

were often found to fall outside the 25th-to-75th-percentile 

range. In general, at regulated class C streams, annual summer low flows are arriving later and tend to be lower, and high 

flows are arriving earlier with higher magnitudes of longer 

duration. At class A streams, high and low flows are arriving 

later with an overall increase in discharge with respect to the 

prereservoir baseline conditions. 

The drainage basins of eight of the study sites had 

large values of ISC (>10 percent), most likely as a result of 

expanding urban development. In general, the magnitude and 

frequency of high flows at class A and C sites with high ISC 

are increasing and were commonly found to fall outside the 

25th-to-75th-percentile range. Additionally, magnitudes of low 

flows are becoming lower and, although the timing of high 

flows was highly variable, low-flow events appeared to be 

arriving earlier than would be expected under normal low-flow 

conditions. Three of the study sites appeared to be affected by 

hydrologic changes associated with water abstraction. At these 

sites, the timing of flows appeared to be altered. For example, 

low flows tended to arrive earlier and high flows arrived later 

at two of the three sites. Additionally, the magnitude and duration of low flows were commonly less than the 25th-percentile 

value and the duration of high flows appeared to increase. 

A reduced set of hydrologic and ecological variables 

was used to develop univariate and multivariate flow-ecology 

response models for the aquatic-invertebrate assemblage. 

Many hydrologic variables accounting for the duration, 

magnitude, frequency, and timing of flows were significantly 

correlated with ecological response. Multiple linear regression (MLR) models were developed to provide a more holistic 

evaluation of the combined effects of hydrologic alteration 

and to identify models with two or three hydrologic variables 

that account for a significant proportion of the variability in 

invertebrate-assemblage condition as represented by assemblage metric scores. MLR models, derived on the basis of 

hydrologic attributes, accounted for 35 to 75 percent of the 

variability in assemblage condition.

The hydro-TMDL method developed herein for non- to 

moderately impaired Raritan River Basin streams utilizes a 

“surrogate” approach in place of the traditional “pollutant of 

concern” approach commonly used for TMDL development. 

Managers can use the results obtained by using the hydroTMDL method to offset the effects of impervious-surface 

runoff and altered streamflow and to implement measures 

designed to achieve the necessary load reductions for the 

“pollutant of concern” (that is, percentage deviations of 

stream-class-specific flow-index values outside the established 

25th-to-75th-percentile range). In this case, such deviations 

could represent all or a subset of the altered flow indices that 

prevent the stream from meeting designated aquatic-life criteria. This hydro-TMDL uses a reference, or attainment stream 

approach for developing the TMDL endpoint. That is, either 

observed or simulated baseline hydrographs were selected as 

appropriate reference conditions on the basis of results of QR 

analysis and watershed modeling procedures, respectively. 

For any stream in the Raritan River Basin evaluated as part of 

this study, the hydro-TMDL can be expressed as the greatest 

amount of deviation in flow a stream can exhibit without violating the stream’s designated aquatic-life criteria. Use of this 

surrogate approach is appropriate because flows that fall outside the established percentile ranges are ultimately a function 

of many anthropogenic modifications of the landscape, including the amount of stormwater runoff generated from impervious surfaces within a given basin, the presence of manmade 

structures designed to retain or divert water, the magnitude 

of ground- and surface-water abstraction, and the presence of 

water-supply processes implemented to support human needs. 

In addition, the stream-type-specific flow indices used as the 

basis for the hydro-TMDL approach are useful for representing the hydrologic conditions of class A and C streams/basins 

because they incorporate the full spectrum of flow conditions 

(very low to very high) that occur in the stream system over 

a long period of time, as well as those flow properties that 

change as a result of seasonal variation.

Ultimately, an estimate of the maximum percentage flow 

reduction that could be allowed will be needed to address 

the aquatic-life impairments in many of the study streams 

in the Raritan River Basin and will be necessary for identifying appropriate target flow conditions for hydro-TMDL 

implementation. As described in this report, a target flow 

value equal to the 25th- or 75th-percentile flow rate could 

be selected as the point useful for setting specific hydrologic 

targets. This selection, however, is a management decision 

that could vary depending on the designated use of the stream 

or other regulatory factors (for example, water-supply protection, trout production, antidegradation policies, or special 

protection designations). In New Jersey streams where no 

unambiguous stressors can be identified, State monitoring 

agencies, such as the NJDEP, could choose to require the 

implementation of a flow-based TMDL that not only supports 

designated uses, but meets the regulatory requirements under 

the Clean Water Act, and represents a balance between water 

supply intended to meet human needs and the conservation of 

ecosystem integrity. 

Introduction

Biotic impairment in New Jersey streams is considered 

by the New Jersey Department of Environmental Protection 

(NJDEP) to be an indicator that some type of anthropogenic 

process has occurred and has resulted in aquatic-assemblage 

degradation. More than 300 New Jersey streams are listed in 

the New Jersey 2010 integrated water-quality monitoring and 

assessment report (includes 305(b) Report on general water 

quality and 303(d) List of waters that do not support their 
Introduction 3

designated uses) (N.J. Department of Environmental Protection, 2012a) as “biologically impaired”; however, no specific, 

unambiguous stressor has been identified. Unlike impairments caused by contaminants such as phosphorus or metals, 

which can be specifically quantified, the cause of aquatic-life 

impairment in many New Jersey streams is not readily apparent. Therefore, directly measurable and quantifiable surrogate 

parameters need to be identified to provide State and other 

resource managers with the ability to allocate a percentage 

of the remedial response necessary to address aquatic-life 

impairment. Some State agencies have had success with using 

sediment loads (for example, Virginia Department of Environmental Quality, 2004) or stormwater pollutants (for example, 

Massachusetts Department of Environmental Protection, 

2003; Maine Department of Environmental Protection, 2004) 

as surrogates for biotic impairment on a site-specific basis; 

however, hydrologic alteration more holistically encapsulates 

the full suite of potential environmental stressors and has a 

greater potential for application to the Total Maximum Daily 

Load (TMDL; that is, the maximum amount of a pollutant 

that a waterbody can receive without violating water-quality 

standards) in New Jersey streams. In streams where a single 

unambiguous stressor (for example, sediment, nutrients, 

bacteria, or another specific pollutant) has not been identified, 

it can be inferred that increases in impervious surface cover 

(ISC) can facilitate increased surface runoff, which can result 

in an increase in the loads of associated pollutants washed 

from impervious surfaces, reduced groundwater recharge, 

“flashy” stream hydrology, habitat degradation, riparian corridor encroachment, and channel modification. These types of 

stressor responses have been seen in numerous studies relating urbanization and increases in ISC to aquatic-assemblage 

impairment in streams (Roy and others, 2003; Coles and others, 2004; Cuffney and others, 2005; Meyer and others, 2005; 

Kennen and others, 2005; Wang and others, 2008; Cuffney 

and others, 2010; and many others). These stressors may be 

acting in individual, synergistic, and (or) cumulative ways to 

cause impairment. As a result, novel approaches are required 

to develop a TMDL that will address aquatic-life impairment 

and also can serve as a basis for implementing management 

actions. The challenge, therefore, is to develop surrogate 

indicators that can be expressed as quantitative targets. The 

common thread that links most of the stressors identified is 

hydrologic alteration, which can be viewed as a master variable that represents the cumulative effects of multiple stressors contributing to aquatic-life impairments in these stream 

systems. Consequently, an alternative approach for developing 

TMDLs for aquatic-life impairment in New Jersey streams 

could incorporate a modeling approach that accounts for 

changes in hydrologic variability and provides a quantitative 

pathway for evaluating the deviation of altered flow regimes 

from relatively unaltered (baseline) streamflow. 

The flow characteristics of many New Jersey streams 

have changed substantially over the past several decades 

(Watson and others, 2005), primarily in response to increases 

in ISC and water use as a result of population growth. 

Alterations in the natural streamflow regime arise from many 

factors, including changes in climate (Sefton and Boorman, 

1997; Legesse and others, 2003; Palmer and others, 2008), 

but in most New Jersey basins, hydrologic instability can be 

attributed primarily to anthropogenic influences (Kennen and 

Ayers, 2002; Kennen and others, 2008; Kennen and others, 

2010). With an estimated 2010 population of roughly 8.8 million (U.S. Census Bureau, 2012) distributed over an area of 

7,354 square miles (mi2), New Jersey is the most densely 

populated state in the United States, and has undergone 

periods of accelerated development during the last several 

decades. Urban and suburban development have substantially 

increased the fraction of ISC, yielding rapid surface runoff and 

changing patterns and timing of streamflow (Kennen and others, 2008). Increased ISC caused by development also inhibits 

recharge to aquifers, decreasing the magnitude of reliable 

base flow and increasing the frequency and duration of low 

flows. Diversion of both surface- and groundwater in heavily 

developed basins further exacerbates hydrologic instability by 

introducing unnatural variations in streamflow characteristics. 

These observed changes in the hydrologic regime are likely to 

be compounded by future economic development within the 

State of New Jersey unless regulatory measures are enacted to 

mitigate the effects of hydrologic alteration.

Much evidence exists that hydrology accounts for a 

significant portion of the variability in the composition, 

structure, and function of aquatic systems (Richter and others, 

1996; Ward and Stanford, 1989; Bunn and Arthington, 2002; 

Poff and Zimmerman, 2010). Studies indicate that management practices that promote natural hydrologic patterns and 

processes are likely to reduce the effects of hydrologic alteration on stream biota (for example, Poff and others, 1997). 

Therefore, both ecological theory and abundant evidence of 

ecosystem degradation in flow-altered rivers support the need 

for a scientifically sound and empirically robust foundation 

for flow-based management of streams and rivers (Poff and 

others, 2010). It is not always a question of how much water 

a stream “needs,” but of the degree to which flow regimes can 

be altered before a measurable effect on ecosystem integrity 

is observed. Hydrologic alterations have resulted in varying 

degrees of long-term biotic impairment to aquatic ecosystems 

in New Jersey by changing the availability and persistence of 

stream habitat, the composition and quality of channel substrate, the rate of nutrient flux and uptake, and the connectivity 

between streams and biologically productive riparian zones in 

adjacent flood plains.

 By targeting hydrologic variability, as represented by 

the anthropogenic alteration of streamflows, the hydro-TMDL 

approach also addresses many of the concomitant geomorphologic impairments that cumulatively degrade aquatic life. 

The environmental costs of hydrologic alteration on stream 

geomorphology are many, and include habitat fragmentation 

(Dynesius and Nilsson, 1994), loss of wetlands and flood 

plains that help buffer against flood flows and attenuate loads 

(Rosenberg and others, 1997), and increased streambank erosion and sediment transport resulting from alteration of natural 
4 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

seasonal patterns of discharge (Vorosmarty and Sahagian, 

2000). In particular, increased peak streamflow and streamflow 

variability have been linked to degradation in the composition, 

structure, and function of aquatic assemblages, which are good 

predictors of the biological integrity of aquatic ecosystems 

(Ward and Stanford, 1989; Poff and Allan, 1995; Richter and 

others, 1996; Clausen and Biggs, 1997; Pusey and others, 

2000; Bunn and Arthington, 2002; Kennen and others, 2010). 

Kennen and Ayers (2002) have shown that increases in peak 

discharge, streamflow variability, ISC, and human population density contribute to impairment of stream assemblages 

in New Jersey. When hydrologic alterations persist over long 

periods, they can result in substantial loss of abundance and 

diversity in both the aquatic and riparian habitats. Even though 

recent studies have indicated that some aquatic invertebrates 

appear to be resilient to stress associated with short-term 

reductions in streamflow (Miller and others, 2007; James and 

others, 2008), aquatic assemblages that have undergone severe 

and chronic flow disturbance might reach a point beyond 

resistance or resilience and may never recover to their predisturbance ecological function (McCabe and Gotelli, 2000; Paul 

and Meyer, 2001; Biggs and others, 2005; Urban and others 

2006; Miller and others, 2007; Utz and others, 2009; Cuffney 

and others, 2010). Managing watersheds to reduce hydrologic 

alteration not only reduces the loading of associated contaminants but also helps to restore natural aquatic species (Poff 

and Ward, 1989; Bunn and Arthington, 2002). The goal of 

State monitoring agencies is to promote a balance between 

water supply intended to meet human needs and conservation 

of biological integrity. Therefore, a science-based framework 

for assembling data to develop appropriate hydrologic-alteration-based TMDLs that account for assemblage impairment 

through changes in hydrologic processes and provides options 

for making sound environmental recommendations supports 

this goal.

Maintenance or restoration of hydrologic variability is 

critical to protecting biodiversity and enhancing the integrity of aquatic, riparian, and wetland ecosystems, and is the 

foundation of the Natural Flow Regime Paradigm (NFRP) 

presented by Poff and others (1997). The NFRP emphasizes 

the need to account for the full range of natural intra- and 

interannual variation in the hydrologic regime, as summarized 

by broad categories of flow characteristics such as magnitude, 

frequency, duration, timing, and rate of change, all of which 

are important in maintaining aquatic-ecosystem integrity 

(Richter and others, 1997). For example, seasonal timing of 

flow can be crucial to supporting the emergence and reproduction of certain species in perennial streams (Grossman, 

1982; Poff and Ward,1989; Peckarsky and others, 2000), but 

seasonal flow between years generally is not so predictable 

that it favors one species over most others and constrains 

the natural diversity of the aquatic ecosystem. Some optimal 

balance between flow seasonality and interannual variability 

that is unique to the particular basin needs to be maintained if 

the basin is to support an aquatic assemblage comparable to 

that present prior to the onset of hydrologic disturbance. An 

intermittent stream that supports a different suite of species 

and is characterized by an entirely different flow “signature” 

than a perennial snowmelt stream, for example, may require 

the development of an entirely different restoration strategy 

than the perennial stream. One goal may be to restore streamflow to its natural condition; however, that goal may not be 

achievable, especially in some moderately to highly degraded 

urban systems or in streams with reservoirs designed for 

water-supply purposes. The same may be true for streams in 

basins where streamflow-restoration needs change over time 

as a result of climatic variability or ongoing anthropogenic 

alterations of the landscape.

In this study, conducted by the U.S. Geological Survey (USGS) in cooperation with the NJDEP, an applicable 

hydrologically based TMDL (hereafter referred to as “hydroTMDL”) approach was developed to address aquatic-life 

impairments associated with hydrologic alteration of New Jersey streams. This approach includes (1) applying a Statewide 

Watershed Runoff Model (SWRM) (Kennen and others, 2008) 

to simulate streamflow at selected New Jersey locations and 

develop a “least impaired” hydrologic endpoint by modifying 

parameters in the model that are associated with anthropogenic 

alteration of streamflow (for example, ISC); (2) identifying 

a set of ecologically relevant flow parameters within predefined hydrologic stream classes (Kennen and others, 2007) 

for direct comparison of streamflow between the simulated 

“least impaired” (baseline, or reference) hydrograph and the 

current, anthropogenically modified (present-day) hydrograph 

by using the Hydroecological Integrity Assessment Process; 

(3) developing percentage differences for a subset of streamtype-specific hydrologic indices to evaluate the proportion of 

flow needed to meet designated aquatic-life uses (for example, 

do the streams fall within the targeted 25th-to-75th-percentile 

range of flow and, if not, how far does the stream deviate from 

this flow target); (4) developing predictive linear and nonlinear 

flow-ecology response models linking aquatic assemblages to 

altered flow; and (5) developing higher (that is, multivariate) 

models that incorporate multiple predictor hydrologic variables. The ultimate goal of this study was to develop methods 

to support a hydro-TMDL that provides a viable scientific 

pathway that can be used by the NJDEP and other State 

management agencies to understand the extent of hydrologic 

alteration of river systems and reduce the effect of hydrologic 

stress so that the beneficial uses of a stream can be restored.

The TMDL Process in New Jersey

New Jersey is one of a few states in the Nation that 

require an implementation plan that identifies specific remedial actions as part of a complete TMDL for all water bodies 

that have been listed under section 303(d) of the U.S. Environmental Protection Agency (USEPA) guidance and requirements pursuant to the Clean Water Act (CWA). These sites 

may be impaired as a result of a suite of anthropogenic modifications, including hydrologic alteration. Biotic impairment in 
Introduction 5

New Jersey streams is commonly accompanied by pronounced 

alterations in the natural streamflow record, indicating that 

biological degradation of aquatic assemblages could also 

result from physical disturbances, such as flow alteration, 

rather than solely from contaminants or other identified “pollutants.” Some studies have shown that in lightly to moderately urbanized watersheds, water quality typically bears little 

relation to biotic impairment (for example, May and others, 

1997; Horner and May, 1999), lending additional support to 

the likelihood that the biotic integrity of aquatic systems is at 

least partly influenced by physical changes associated with 

flow rather than water quality alone.

Load-reduction goals established through TMDLs are 

achieved through the issuance of waste-load allocations for 

point-source discharges and load allocations for nonpointsource discharges. Because nonpoint-source pollution, by 

definition, does not have discrete, identifiable sources, load 

allocations would consist of the identification of categories of 

nonpoint sources that contribute to the parameters of concern, 

such as hydrologic alteration, and the resulting degradation of 

biological assemblages in receiving streams. The load allocation will then necessarily include specific load-reduction 

measures that can be instituted to address the impairment. 

The NJDEP has indicated that hydrologic targets need to be 

identified that represent a minimal percent deviation from a 

baseline (reference or “minimally altered”) condition to meet 

designated aquatic-life uses. Regulating and managing watersheds by reducing hydrologic alteration not only reduces the 

potential loading of associated pollutants contained in streamflow runoff, but also helps restore the natural hydrologic variability that is known to support many sensitive aquatic species 

(Poff and others, 2007). This approach is consistent with the 

USEPA Water Quality Management and Planning Regulations 

for the implementation of TMDLs that allow the expression 

of a TMDL in terms of a surrogate measure (that is, 40 CFR 

130.2(i)), such as percent change in a hydrologic attribute relative to baseline stream conditions, as an appropriate surrogate 

for assessing hydrologic impairments to aquatic-life use. 

Purpose and Scope

This report establishes an applicable hydro-TMDL 

approach to address aquatic-life impairments associated with 

hydrologic alteration of New Jersey streams. The goal of every 

TMDL is to achieve water-quality standards for the impaired 

water body; therefore, it is important to identify the appropriate target so that the water body will meet the water-quality 

standard or designated aquatic-life use. For a hydrologicalteration-based TMDL, the surrogate target is developed by 

comparing streamflow characteristics to an observed baseline 

hydrograph (minimally altered stream, where the period of 

record (POR) reflects minimal hydrologic alteration) or a 

simulated baseline hydrograph (where anthropogenic modification in a specific basin has been minimized by adjusting 

model parameters). Ecologically relevant flow parameters are 

identified for streams within the Raritan River Basin that have 

known aquatic-assemblage impairments. These attributes are 

used to (1) identify a series of critical flow parameters as surrogates for defining aquatic-life impairment and (2) compare 

and normalize flow statistics of hydrologically altered streams 

by using those from minimally altered reference streams to 

develop hydrologic targets (percent deviation from reference 

streamflows) for use by water managers for the purpose of 

streamflow protection, maintenance, or restoration. Numeric 

targets are based on hydrologic attributes that deviate from 

the expected in comparison to a reference stream or from a 

modeled parameter that was assessed by using a hydrograph 

simulated from less anthropogenically modified conditions. 

These targets (for example, the 25th and 75th percentiles 

of flow), therefore, are based on percent deviation from the 

expected hydrologic response under least modified conditions 

as opposed to the required minimal loading of a specific pollutant. Percent deviations that take the place of load scenarios 

are developed by comparing current conditions to baseline 

hydrology, and the proportional deviation (that is, the difference between baseline and observed streamflow) is presented 

as a basis for evaluating mitigation options. In addition, flowecology models predicting aquatic-assemblage response along 

a gradient of hydrologic degradation in a specified hydrologic 

region (watershed, state, region, or province), as in Poff and 

others (2010), that can be used to support broader regional 

flow targets for a subset of classified streams are developed 

(Kennen and others, 2007). Although this study focuses on 

the Raritan River Basin, the methods developed in this study 

should have broad transferability to river systems throughout 

the United States and abroad.

Description of Study Area

All hydrologic and corresponding ecological study sites 

targeted to support hydro-TMDL development based on 

hydrologic alteration in this study are in the Raritan River 

Basin. The Raritan River Basin is in central and northern 

New Jersey and is the largest drainage basin entirely within 

the State of New Jersey, encompassing an area of 1,105 mi2

(fig. 1) (Reiser, 2004). The basin contains six major drainages: the Millstone River (287 mi2

), South Branch Raritan 

River (279 mi2), North Branch Raritan River (190 mi2), South 

River (133 mi2), Bound Brook (65 mi2), and Lawrence Brook 

(46.3 mi2). Spruce Run Reservoir, Round Valley Reservoir, 

and Budd Lake are the major impoundments in the basin, with 

Spruce Run Reservoir releasing water to the South Branch 

Raritan River and Round Valley Reservoir. The basin drains 

all or parts of 100 municipalities in Hunterdon, Mercer, 

Middlesex, Monmouth, Morris, Somerset, and Union Counties 

(Reiser, 2004). Site-selection criteria were based on a USGS–

NJDEP cooperative approach in which sites were identified 

where anthropogenic degradation of the aquatic system generally was slight to moderate and where, at a subset of sites, 

biological samples were collected at or near (slightly upstream 

or downstream from) an existing USGS continuous real-time 

streamflow-gaging station. Fifty-one subbasins in the Raritan 
6 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

MORRIS

HUNTERDON

MIDDLESEX

SOMERSET

WARREN

MERCER

MONMOUTH

OCEAN

ESSEX

UNION

BURLINGTON

PASSAIC

SUSSEX

EXPLANATION

01406000

Physiographic province

Valley and Ridge

New England

Piedmont

Coastal Plain

Boundary of Raritan River Basin study area

Location of U.S. Geological Survey 

streamgage and station number

75°15' 75° 74°45' 74°30' 74°15'

40°45'

40°30'

40°15'

Base from U.S. Geological Survey digital line graph files, 1:24,000,

Universal Transverse Mercator projection, Zone 18, NAD 83 

0 5 10 MILES

0 105 KILOMETERS

FALL

LINE

01406000 01405300

01405400

01405500

01406500

01400730

01401500

01401000

01400953

01400932

01402000 01398000

01397000

01403060

01403000

01403900

01400500

01403150

01403160

01399690 01399700

01399670

01396800

01396660

01396500

01396580

01399500

01398500

01399190

01399200

01396190

01399510

01399525

01399830

01398107

01400000

01400350

01400300

01401650

01402600

01397500 01402590

01398045

01404500

01405000

01405030

01404000

01403500

01403400

01403540

01403535

75° 74°

41°

40°

39° DELAWARE

PENNSYLVANIA

NEW YORK

AREA OF DETAIL

ATLANTIC OCEAN

0 10 MILES

0 10 KILOMETERS

Physiographic province boundary from New Jersey

 Department of Environmental Protection, 2002

Figure 1. Location of study sites in the Raritan River Basin study area, New Jersey.
Introduction 7

River Basin were initially chosen for this study on the basis of 

available hydrographic information, proximity of hydrologic 

and ecological sampling locations, and impairment criteria.

New Jersey spans four physiographic provinces—the 

Valley and Ridge, New England, Piedmont, and Coastal Plain. 

The Raritan River Basin study area drains an area spanning 

three physiographic provinces—the New England, Piedmont, 

and Coastal Plain (fig. 1). The New England physiographic 

province features broad, flat-topped highlands and long, narrow valleys that range in elevation from 490 to 1,500 feet (ft). 

The Piedmont Physiographic Province, characterized by 

northwestward-dipping sedimentary rocks that form broad, 

gently sloping lowlands and rolling hills, has an average elevation of 390 ft. The Coastal Plain Physiographic Province is an 

area dominated by flat to gently rolling topography and unconsolidated sedimentary deposits that range in elevation from 

about 80 to 390 ft. These physiographic features have a strong 

influence on streamflow, with flow in the high-relief northern 

portions of the State being dominated by surface runoff, and 

flow in Coastal Plain streams largely sustained by groundwater 

contributions. Large-scale natural spatial variations in climate, 

geology, soils, and vegetation imposed by a wide range in 

physiographic conditions within New Jersey have given rise 

to several classes of streamflow behavior, each characterized 

by a natural flow regime unique to that class (see Kennen and 

others (2007) for a review of the hydrologic classes present in 

New Jersey). 

The diverse flow regimes encountered in the study 

area are caused by nonuniform patterns of urbanization that 

produce a broad spectrum of flow behaviors. In addition, portions of the Raritan River Basin drain large impervious areas, 

which yield runoff rapidly and produce peak streamflows that 

greatly exceed those associated with preurbanization flood 

events. These floods can scour fine-grained sediment from 

channel bottoms, erode stream banks, and inundate productive 

flood plains at times of the year when these events may not be 

beneficial to the life cycles of certain aquatic species. Groundwater withdrawals have substantially reduced base-flow contributions to some coastal streams (Zapecza, 1989), modifying 

concentrations of nutrients and potentially altering seasonal 

spawning cycles of long-lived aquatic species. Groundwater 

withdrawals may have a less pronounced effect, however, on 

aquatic-assemblage health in upland basins, where stream base 

flow is not largely sustained by groundwater. Large interbasin 

transfers of surface water through tightly coupled watersupply systems may also adversely affect the biotic integrity 

of aquatic ecosystems, but the complex routing and timing of 

these transfers makes it difficult to evaluate how extensively 

they influence biological impairment in a given watershed. 

It is within the constraints of this complex set of natural and 

anthropogenic factors that State watershed planners, managers, 

and policy makers must make decisions regarding trade-offs 

between streamflow needed for economic development and 

human use and streamflow necessary to maintain the integrity 

of aquatic ecosystems in New Jersey.

Statewide Ambient Biomonitoring Network 

(AMNET)

In 1992, the NJDEP Bureau of Freshwater and Biological 

Monitoring developed the Ambient Biomonitoring Network 

(AMNET) program to provide long-term monitoring of 

aquatic-invertebrate assemblages to support sound policy decisions in water-quality/watershed management (N.J. Department of Environmental Protection, 2007). Collecting samples 

of aquatic invertebrates is a primary, cost-effective biomonitoring tool as these organisms are ubiquitous in distribution, 

are more stationary than fish but less transient than algae and 

other microscopic assemblages, and are easily collected and 

quantified (N.J. Department of Environmental Protection, 

2008). Aquatic invertebrates also integrate environmental 

stressors over time and provide an ecological measure of environmental conditions (Kennen and others, 2012). As a result, 

the invertebrate assemblage holistically reflects conditions in 

its environment.

The AMNET consists of more than 800 stream sites in 

New Jersey where samples are collected and analyzed once 

every 5 years. Approximately 150 to 200 sites are located 

in each of the five major drainage basins, or Water Regions 

(upper and lower Delaware, Northeast, Raritan, and Atlantic) 

throughout the State (N.J. Department of Environmental Protection, 2008). The AMNET program is designed so that sampling occurs at each site within a given Water Region within 

1 year’s time, with all aquatic-invertebrate samples collected 

during an index period from April to November. Sampling is 

curtailed through the coldest months (December to March) 

because of difficulties encountered in obtaining representative 

samples during this period (N.J. Department of Environmental Protection, 2008). At the time of data compilation for this 

study (2008), three rounds of sampling had been conducted 

and the data analyzed for most sites. AMNET “round 3” data 

were used to evaluate the relative level of biotic impairment in 

the Raritan River study basins and to support the development 

of flow-ecology response models that relate the aquatic-invertebrate assemblage to changes in flow processes for a subset of 

streams in this study. Round 3 samples for the Raritan Water 

Region were collected from April to early September 2004, 

during the established index period. 

The standardized sample-collection procedures used for 

the AMNET program are a modification of the USEPA “Rapid 

bioassessment protocols for use in streams and wadeable 

rivers” (Barbour and others, 1999). Assessments of impairment can be made on the basis of standardized procedures, 

which can show perturbations measured as changes or differences in assemblage structure by means of comparisons 

with assemblage characteristics at designated reference sites 

(Barbour and others, 1999). These standardized procedures 

use invertebrate-assemblage metrics or traits (Poff and others, 2006; Vieira and others, 2006) to provide information on 

structural and functional composition, diversity, dominance, 
8 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

and tolerance (or intolerance) of aquatic organisms to 

anthropogenic disturbance.

For each site the NJDEP derives five invertebrate-assemblage metrics, which are ranked and summarized to produce 

a New Jersey Impairment Score (NJIS) (see N.J. Department of Environmental Protection (1994, 2007)). The NJIS, 

which ranges from severely impaired to non-impaired, is one 

of the criteria used by the NJDEP to evaluate stream biotic 

integrity, especially for those streams previously identified as 

having some level of aquatic-life impairment. It is also used 

to support the biennial New Jersey integrated water quality 

monitoring and assessment report (that is, the 305(b) Report 

and 303(d) List), which supports the development of waterquality criteria to protect aquatic life and human health (N.J. 

Department of Environmental Protection, 2007). The metrics 

from which the NJIS is derived are (1) taxa richness (based on 

the number of families); (2) percent dominance—the percentage of the total abundance composed of the dominant family 

in the subsample; (3) EPT index—the number of families 

represented within the orders Ephemeroptera, Plecoptera, 

and Trichoptera; (4) percent EPT—the percentage of the total 

abundance (at family level) composed of EPT taxa; and (5) 

modified Family Biotic Index—a weighted sum of the Family Tolerance Values, based on Hilsenhoff’s tolerance scores 

assigned to families, which range from 0 (intolerant) to 10 

(tolerant) (Hilsenhoff, 1987; N.J. Department of Environmental Protection, 2007). Detailed descriptions of the individual 

metrics used to derive the NJIS and results of each sampling 

round can be found on the NJDEP Bureau of Freshwater and 

Biological Monitoring Web page at http://www.state.nj.us/dep/

wms/bfbm/publications.html. 

Statewide Watershed Runoff Model

To help understand the linkages between hydrologic 

alteration resulting from increased urbanization and biotic 

impairment of aquatic assemblages, the USGS, New Jersey 

Water Science Center, developed the physically based SWRM 

to simulate streamflow characteristics at AMNET sites (Kennen and others, 2008). The SWRM incorporates advantages of 

existing and well-documented modeling tools by integrating 

the Natural Resources Conservation Service (NRCS) empirically based TR-55 model (Natural Resources Conservation 

Service, 1986) of runoff from impervious areas with the physically based, semidistributed watershed model (TOPMODEL) 

equations of Beven and Kirkby (1979), as modified by Wolock 

(1993) and Kennen and others (2008). The SWRM, like traditional rainfall-runoff process models, tracks the movement 

of water as it enters the basin in the form of rain or snow and 

exits through evapotranspiration, direct withdrawal, or streamflow. The SWRM is also highly parsimonious, and typically 

requires less parameterization than many of the traditional 

process models. 

TOPMODEL is a physically based, semidistributed 

watershed model that offers computational efficiencies over 

many of the more parameter-intensive, fully distributed 

hydrologic models such as MIKE SHE (Graham and Butts, 

2005), HSPF (Bicknell and others, 1997), and HEC-HMS 

(U.S. Army Corps of Engineers, 2000; Merritt and others, 

2004). The TOPMODEL component of the SWRM routes 

precipitation that falls on pervious areas of the watershed. 

The major processes considered are subsurface flow, Dunne 

saturation excess overland flow, and return flow. TOPMODEL, 

chosen because of its simple parameterization using available 

digital topographic and soils data, was developed during the 

late 1970s in response to concerns about large computational 

burdens associated with spatially distributed watershed 

models. Such fully distributed models require that parameters 

be specified in each cell over a spatially discretized watershed 

to handle the complexity of rainfall-runoff processes. 

TOPMODEL, however, was one of the first models to reduce 

the number of parameters to a more manageable size. In its 

most simplistic form, TOPMODEL uses only three primary 

watershed parameters, each of them physically based. This 

small number of parameters helps ensure that their values do 

not become statistical artifacts of the calibration process. 

TOPMODEL is well documented (Beven, 2001) and 

has been successfully applied in many environments (Boyer 

and others, 1996; Beaujouan and others, 2001; Engel and 

others, 2002); however, it does not account for runoff from 

impervious surfaces, surface- and groundwater withdrawals, 

wastewater contributions, or lake-storage effects. The foundation of TOPMODEL is built on the assumption that the land 

surface is pervious to rainfall. The SWRM approach builds on 

TOPMODEL by adding separate calculations derived from the 

NRCS TR-55 method (Natural Resources Conservation Service, 1986) to estimate runoff for portions of the basin that are 

covered with impervious material. These equations are based 

on runoff curve numbers specific to pavement, and are used to 

compute impervious surface area runoff as a function of the 

estimated ISC. This estimated impervious surface area runoff 

is then input to the TOPMODEL mass-balance equation at the 

beginning of each time step to partition runoff derived from 

pervious areas into its surface and subsurface components. The 

SWRM also incorporates equations to add wastewater contributions and remove surface- and groundwater withdrawals 

from appropriate flow partitions, and implements an exponential decay function to account for the effects of lake-storage 

delay on watershed runoff (Kennen and others, 2008).

Because the SWRM is a physically based model that can 

reliably predict streamflow (that is, can be used to develop 

a simulated hydrograph) under natural conditions or a variety of hydrologic disturbance scenarios, it is also useful for 

estimating potential changes in streamflow indices that could 

contribute to aquatic-assemblage impairment. Its use of a 

process-based approach is consistent with the recommendations proposed by Arthington and others (2006), who advocate 

development of flow-response relations between indicators 
Hydroecological Integrity Assessment Process 9

of ecological condition and degrees of flow modification. In 

basins lacking long-term streamgage records, many researchers, including Snelder and others (2005), Sanborn and Bledsoe 

(2006), Henriksen and others (2006), Stuckey (2006), Waldron 

and Archfield (2006), and Poff and others (2010), support 

the use of flow modeling using physically based or statistical 

models to develop baseline hydrographs and estimate streamflow indices.

The SWRM, as applied in this study, builds on efforts of 

previous researchers. Gibson and others (2005), for example, 

used the Precipitation Runoff Modeling System to evaluate flow-regime alterations under climate change. Similarly, 

Thoms and Parsons (2003) applied the Integrated Quantity 

Quality Model to estimate the effects of land-use change on 

flow measures and Bobba and others (2000) used mathematical models to assess the effects of point and nonpoint sources 

on the quality of surface water. Statistical approaches, as 

opposed to process-oriented or flow models, also have been 

used to predict hydroecological indices. Sanborn and Bledsoe (2006) estimated streamflow indices for pristine streams 

in Colorado, Washington, and Oregon by using regression 

techniques. Van Sickel and others (2006) also used statistical 

methods, but estimated both flow and biological conditions 

for streams throughout the Willamette River Basin in Oregon. 

The SWRM approach is an integrated application of a robust 

process-oriented watershed model and is ideal for simulating 

hydrographs and developing linkages between anthropogenic 

processes and streamflow response.

By offering a physically based linkage between anthropogenic change and streamflow response, the SWRM represents 

an important tool for assessing the extent and degree of biotic 

impairment of aquatic-invertebrate assemblages as a result of 

watershed and streamflow alterations. It provides State watershed managers with a systematic means for developing basinspecific plans necessary to protect, maintain, or restore flow 

conditions that best balance the competing demands of human 

growth and maintaining biotic integrity in targeted watersheds. 

Kennen and others (2008) successfully used the SWRM to 

predict several flow-dependent variables, including the average number of storms per year that produce quick flow (that 

portion of streamflow that moves rapidly to the stream during 

a storm event as surface runoff; Woodruff and Hewlett, 1970), 

the ratio of 25-percent exceedance flow to 75-percent exceedance flow (a measure of stream flashiness), the minimum size 

of storms needed to produce quick flow, the average annual 

quick flow, and the statistical relation between streamflow 

characteristics and the biotic integrity of aquatic ecosystems in 

New Jersey, as reflected by the impairment score derived from 

data collected at New Jersey AMNET sites. As the hydrologic 

basis of this study, the SWRM is used to derive the simulated 

baseline (minimally altered) hydrographs necessary to evaluate changes in streamflow processes and supports the development of streamflow criteria for implementing a hydro-TMDL 

and flow-ecology response models. 

Hydroecological Integrity Assessment 

Process

The Natural Flow Regime Paradigm (Poff and others, 

1997) synthesizes existing scientific knowledge to argue that 

the natural flow regime plays a critical role in sustaining native 

biodiversity and ecosystem integrity in rivers. Decades of 

observation of the effects of human alteration of natural flow 

regimes have established that altering the hydrologic regimes 

in rivers can be ecologically deleterious (for example, Johnson 

and others, 1976; Tyus, 1990; Hill and others, 1991; Arthington and others, 1992; Sparks, 1995; Toth, 1995; Castleberry 

and others, 1996; Stanford and others, 1996; Richter and others, 1997; and many others). These authors argue that streamflow quantity and timing are critical factors that affect the 

ecological integrity of river systems. Many studies have documented ecological responses to alterations of the natural flow 

regime; examples include fish life-cycle disruption, encroachment of vegetation, loss of sensitive aquatic-invertebrate species, and loss of fish access to backwaters and wetlands (Poff 

and others, 1997). Work by Olden and Poff (2003) provided 

a statistically based scientific pathway to help investigators identify hydrologic indices that adequately differentiate 

stream types on the basis of five critical elements of the flow 

regime: magnitude, frequency, duration, timing, and rate of 

change. The Hydroecological Integrity Assessment Process 

(HIP) (Kennen and others, 2007) builds on this scientifically 

based foundation. Flow characteristics of streams differ with 

basin size, local gradient of the stream channel, physiographic 

province, vegetative cover, climate, and the type of human 

activity causing the flow disturbance. Treating all basins as 

if they behave in a hydrologically similar way confounds 

efforts to identify linkages among streamflow alteration, level 

of urbanization, and aquatic-assemblage response because 

differing classes of streams can show distinctly different 

responses to the same anthropogenic stressors. In recognition 

of these differences, gaged basins in the Raritan River system 

were classified according to hydrologic similarity by using the 

HIP classification scheme (Kennen and others, 2007). When 

streams are grouped into classes on the basis of similar hydrologic characteristics, deterministic streamflow influences are 

removed from the analysis and changes in streamflow behavior can be evaluated within each individual class. 

The HIP represents a suite of software tools that can be 

used to identify and relate aquatic-assemblage response to ecologically relevant hydrologic indices (ERHIs) determined from 

water-depletion or hydrologic-alteration scenarios (Kennen 

and others, 2007). The HIP incorporates many of the NFRP 

principles, which recognize that the diversity and integrity of 

aquatic ecosystems depend on preserving natural streamflow 

variability. The structure and function of riverine ecosystems 

are strongly influenced by hydrologic variability, which can 

be used to characterize the entire range of flows, as well as the 
10 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

specific hydrologic events critical to maintaining the integrity 

of river ecosystems.

The HIP consists of (1) a statewide classification of 

streams into hydrologic stream types on the basis of streamflow data from long-term gaging-station records for a subset 

of streams in relatively unmodified basins; (2) an identification 

of stream-type-specific indices that address 10 subcomponents 

(that is, a subset of important ERHIs) of the flow regime; 

(3) an ability to establish environmental flow standards; (4) 

an evaluation of hydrologic alteration; and (5) a capacity to 

conduct alternative analyses (Kennen and others, 2007, 2009). 

HIP includes the Hydrologic Index Tool (HIT), the New Jersey 

Stream Classification Tool (NJSCT), and the New Jersey 

Hydrologic Assessment Tool (NJHAT) (Henriksen and others, 

2006). The HIT is a stand-alone program that uses USGS 

daily mean and peak flow discharges from the National Water 

Information System (NWIS) databases (available at http://

waterdata.usgs.gov/nwis/sw) to generate a series of ERHIs 

that are used to help characterize and classify stream flow 

regimes that are thought to be important in shaping ecological processes in streams (Olden and Poff, 2003; Kennen and 

others, 2007). 

In this study, the HIP software was used to assess the 

baseline hydrographs (the minimally altered or reference 

hydrographs) identified through either quantile regression 

(QR) analysis (refer to section on Application of Quantile 

Regression to Identify Baseline Time Periods for Streams in 

the Raritan River Basin) or simulated using the SWRM for 

each study basin. It was also used to derive a suite of ERHIs 

for the baseline hydrograph and the current, anthropogenically modified hydrograph (that is, the observed or present-day 

hydrograph) in an effort to evaluate proportional differences 

between them for use in hydro-TMDL development. The 

HIP accomplishes this task by using flow statistics, trend 

analysis, and 10 primary stream-class-specific indices chosen 

from the available hydrologic indices (Henriksen and others, 

2006; Kennen and others, 2007). The software generates 171 

hydrologic indices (see appendix 1 for definitions of all 171 

indices) that are widely accepted as having ecological significance (Olden and Poff, 2003) by using daily mean and peak 

flow discharge data. If peak flows are not available, then eight 

HIT indices (FH11, DH22, DH23, DH24, TA3, TH3, TL3, and 

TL4) are not calculated.

By using the HIP approach, four distinct New Jersey 

stream classes were identified on the basis of streamflow 

behaviors observed in 88 minimally altered streams. Stream 

classes include semiflashy streams characterized by moderately low base flow (class A), stable streams supported 

by large base-flow contributions (class B), moderately 

stable streams with moderately high base-flow contributions (class C), and flashy streams that receive little base 

flow and yield large volumes of runoff over short periods of 

time (class D). (For more information about stream class, see 

Kennen and others (2007)). The New Jersey Stream Classification Tool (NJSCT) can also be used to classify streams 

that were not included in the original analysis and is based on 

direct comparison of statistically significant streamflow attributes for specific stream classes using discriminant function 

analysis (Kennen and others, 2007, 2009). For this study, only 

stream classes A and C in the Raritan River Basin were used 

for the hydro-TMDL assessment because of their relatively 

similar streamflow characteristics.

The New Jersey Hydrologic Assessment Tool (NJHAT; 

Kennen and others, 2007) was used in this study to examine 

baseline time periods and help identify periods in the hydrograph that show a response to human activities. Through close 

examination of contrasting stream hydrographs with NJHAT 

and using statistical tools (for example, QR), streamflow characteristics were directly compared. If changes in the streamflow characteristics could not be related directly to changes 

in precipitation by using QR, it was assumed that changes in 

the hydrograph were the result of anthropogenic activities (for 

example, surface-water withdrawals or diversions, groundwater pumping, or land-use changes) that altered natural patterns 

of streamflow somewhere in the study basin or in a tributary 

stream. Observed changes in critical streamflow indices 

relative to those calculated for natural or simulated baseline 

conditions were used to provide a quantitative indication of 

the proportional difference to which natural flow patterns have 

been modified by anthropogenic alterations or have deviated 

from natural streamflow patterns.

Identifying Baseline Periods of Record 

and Stream Class in the Raritan River 

Basin

Accurately determining base-flow components and 

baseline conditions of stream hydrographs depends on expert 

judgment and institutional knowledge; however, evaluation 

of baseline periods of streamflow record can be biased if 

information about patterns of population growth, timing of 

reservoir releases or water withdrawals, and discharge records 

in existing databases is in error or is incomplete. Hydrograph 

analysis also requires that hydrologists be able to discern 

subtle patterns in the streamflow record and judge which are 

indicative of systematic changes in the natural flow regime. 

Study basins for this project were chosen in cooperation with 

the NJDEP, and the Raritan River Basin was targeted because 

of existing TMDL concerns and the need to choose an area 

with streams that are known to be relatively similar hydrologically (primarily New Jersey stream classes A and C). 

Examples of the stream sites evaluated for inclusion in this 

study and the analytical process used to classify streams 
Identifying Baseline Periods of Record and Stream Class in the Raritan River Basin 11

and examine hydrographs for discerning baseline periods of 

streamflow record are presented below. 

For the 1945–2006 hydrograph of the Raritan River 

below Calco Dam at Bound Brook, NJ (USGS station number 

01403060, fig. 2), streamflow at the gage appears relatively 

unaltered in its mean behavior. If the statistical distribution 

of flow from year to year is examined, however, it becomes 

evident that streamflow in the Raritan River below Calco Dam 

underwent identifiable changes in behavior beginning in the 

1950s. On the basis of this example (fig. 2), however, determination of the period of unaltered flow by means of direct 

observation of the hydrograph may not be the most appropriate way to identify baseline time periods for streams whose 

behavior has been modified by anthropogenic influences.

A more objective and informative approach to defining 

the onset of streamflow alteration resulting from anthropogenic alteration of the landscape (for example, urbanization 

and development) might focus on examining the extremes of 

the probability distribution of daily streamflow. It is in these 

extremes, or “tails,” of the probability distribution where 

flow alterations are likely to be most evident. For example, 

increased ISC typically associated with road and parking-lot 

construction tends to inhibit infiltration of rainfall into the soil 

and underlying aquifers (Dunne and Leopold, 1978; Klein, 

1979; Schueler, 1994; Arnold and Gibbons, 1996) as well 

as change discharge and stormwater runoff patterns (Espey 

and others, 1966; Leopold, 1968; Seaburn, 1969; Hirsch and 

others, 1990; Arnold and Gibbons, 1996). This reduction in 

infiltration not only yields rapid overland flow during storms 

and produces higher flood-flow events, it also decreases 

groundwater contributions in the days, months, or even years 

following the storm event. Loss of infiltration capacity can 

also generate critical low-flow conditions in the stream during 

intervals of the year when streamflow would have been sustained by groundwater storage in the absence of urbanization. 

A second example is surface-water diversions from reservoirs 

for municipal and agricultural water use, which can produce 

low-flow events by removing water from reservoir storage, 

from which it is no longer available for release during dry 

periods. Both peak-flow and low-flow events are of particular 

concern because such flows provide the conditions necessary to support natural assemblage complexity (Stanford and 

others, 1996; Poff and others, 1997; Richter and others, 1997; 

Mathews, 2005). Any measurable change in these extreme 

flow components may compromise the biotic integrity of 

aquatic ecosystems by modifying substrate quality, altering 

water temperature and nutrient concentrations, and reducing 

connectivity between the stream channel and adjacent productive riparian zones, thereby degrading conditions essential for 

maintaining the abundance and diversity of aquatic assemblages (Grossman, 1982; Poff and Ward, 1989; Lytle and 

Poff, 2004). 

1945195019551960196519701975198019851990199520002005

100,000

10,000

1,000

100

10

Year

Logarithm of streamflow, in cubic feet per second

Figure 2. Hydrograph for U.S. Geological Survey streamgage Raritan River below Calco Dam at Bound Brook (station number 

01403060), New Jersey, 1945–2006.
12 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Application of Quantile Regression to 

Identify Baseline Periods of Record

In some cases, direct inspection of stream hydrographs 

can be used to identify the baseline period of record (POR) 

during which a stream has remained relatively undisturbed 

by anthropogenic alterations of the landscape. In most cases, 

however, a more objective approach is needed to define a 

baseline time period (for example, Esralew and Baker, 2008). 

Although the timing, frequency, and magnitude of peak- and 

low-flow events are observable in the historical hydrograph, it 

is often difficult to discern how the properties of these events 

have changed through time without the aid of statistical 

analysis. To determine how the statistical properties of daily 

streamflow might have changed from year to year, probability 

density functions can be constructed for each year by using all 

daily flows that occurred within that year. QR (for example, 

Cade and Noon, 2003) can be used to more objectively 

identify baseline periods of record for streams for which 

flow has remained relatively unaltered by human activity or 

estimate the onset of streamflow alteration in hydrologically 

modified basins.

Consider the area draining to the North Branch Raritan 

River at Far Hills, NJ (USGS station number 01398500, 

fig. 3). This drainage basin has few wells, a small number 

of New Jersey Pollutant Discharge Elimination System 

(NJPDES) surface- and groundwater sites, and no reservoirs 

or other impoundments. With only 5.3 percent total 

impervious surface cover (ISC) in 2002 (N.J. Department of 

Environmental Protection, 2008), it is not considered to be an 

intensively developed basin. Five AMNET sites distributed 

throughout the upstream reaches of the basin showed minimal 

biological impairment. On the basis of the HIP-identified 

baseline streamflow, the stream draining the North Branch 

Raritan River at Far Hills, NJ, was classified as stream class C 

(see Kennen and others, 2007, app. 4, p. 38). 

QR analysis of daily observed streamflows at the 

streamgage from 1922 to 2006 are presented in figure 4A, 

where each set of vertical points can be viewed as the probability density for the corresponding year against which 

streamflows are plotted. Streamflow shows a consistently 

positive skew across the years of record, with a long tail in 

the direction of higher quantile flows. Such a positive skew is 

common, particularly for flashy streams that are not dominated by base-flow contributions. If a line of least squares 

is fit to all of the points by using traditional regression, 

allowing the slope of the line to change at breakpoints every 

10 years, it will trend through the mean daily streamflow at 

each breakpoint year. It is not mean streamflow, however, 

that is of greatest interest, but rather extreme flow behavior. 

The conditional mean provided by conventional regression 

does not contain information necessary to determine whether 

significant alterations have occurred in the statistical behavior of extreme flow events, because it considers only average 

changes. Given the strong asymmetries in the distributions 

of daily streamflows throughout the years, the mean provides only limited information about typical flow response in 

the basin.

In contrast, QR (fig. 4) fits a line to a particular quantile 

rather than to the conditional mean and is especially useful 

for assessing changes in extreme high- or low-flow events. 

Quantiles are related to the more traditional concept of 

exceedance probabilities, with 10-percent exceedance probability corresponding to the 90th quantile, 20-percent exceedance probability corresponding to the 80th quantile, and so 

on. Regression lines for nine equally spaced (10th, 20th, 30th, 

40th, 50th, 60th, 70th, 80th, and 90th) daily streamflow quantiles were plotted on a log scale. Breakpoints, where the slope 

of the regression line was allowed to change, are seen at years 

1930, 1940, 1950, 1960, 1970, 1980, 1990, and 2000. The QR 

plot (fig. 4A) shows that streamflows are not symmetric about 

the 50th quantile, as they would be if the streamflow values 

were normally distributed. In the presence of asymmetry, the 

conditional mean, which is determined by means of traditional regression by minimizing the sum of squared deviations, would yield a biased estimate of the average streamflow 

response for the basin.

To determine whether observed changes in streamflow 

quantiles over time could be related to temporal climatic 

influences, precipitation was also analyzed by using QR 

(fig. 4B). Because precipitation intensity governs the partitioning of rainfall into overland runoff and subsurface flow and 

ultimately dictates how flow is distributed probabilistically 

during any given year, hourly rather than daily precipitation 

values were used for the QRs. If patterns in hourly precipitation quantiles closely follow patterns in daily streamflow 

quantiles, then changes in precipitation likely explain much 

of the observed changes in streamflow. Relations between 

rainfall and streamflow would be particularly evident in the 

higher quantiles, because intense storms are highly correlated 

with peak-flow events. This relation is especially true when 

the flow geometry of the basin is highly convergent, with the 

main flow channel oriented parallel to the direction of longer 

basin dimension, as in the Raritan River Basin. The correlation 

between rainfall and streamflow tends to be weaker in lower 

rainfall-runoff quantiles, because small storms generally do 

not produce immediate runoff in undeveloped basins unless 

the basin overlies valley-fill or coastal-plain deposits where 

depth to groundwater and subsurface storage are typically 

small (Zapecza,1989). 

All hourly rainfall values, including zero values, were 

required to be included in the precipitation regression in order 

to accurately reflect the probability distribution of hourly 

rainfall. Omission of zero-valued hourly precipitation measurements would treat unusually dry years associated with low 
Application of Quantile Regression to Identify Baseline Periods of Record 13

74°40' 74°35'

40°50'40°45'

Base from U.S. Geological Survey digital line graph files, 1:24,000,Universal Transverse Mercator projection, Zone 18, NAD 83 

0 2 1 MILES

0 21 KILOMETERS

74°40' 74°35'

40°50'40°45'

0 21 MILES

0 21 KILOMETERS

Impervious surface cover from New Jersey

Department of Environmental Protection, 2008

01398500

MORRIS

SOMERSET

EXPLANATION

01398500

Raritan River Basin study area01398500 study-basin boundaryStreamU.S. Geological Survey streamgage and station numberAmbient Biomonitoring Network site impairment score

Non-impairedModerately impairedSeverely impaired

Groundwater well (residential, industrial, or agricultural use)Public-supply wellPermitted groundwater discharge sitePermitted surface-water discharge site

2002 impervious surface 

cover, in percentLess than 1010–24.925–49.950–75Greater than 75

EXPLANATION

Raritan River Basinstudy area

01398500study basin

NEW JERSEY

Figure 3. Percent impervious surface cover and location of wells and discharge sites that may influence streamflow quantity and quality in the North Branch Raritan River at Far Hills, New Jersey (station number 01398500), study basin in the Raritan River Basin study area, New Jersey.
14 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Year

10,000

1,000

100

10

1

EXPLANATION

Quantile—

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

Flow, in cubic feet per second

A. Quantile regression on daily streamflow 

0.001

0.01

0.1

1

10

1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Year

EXPLANATION

Quantile—

0.99

0.98

0.975

0.95

0.925

0.9

0.875

0.85

0.825

0.8

0.7

Hourly precipitation, in inches

B. Quantile regression on hourly precipitation 

Figure 4. Quantile regressions for A, daily streamflow and B, hourly precipitation at U.S. Geological Survey 

streamgage Raritan River at Far Hills, New Jersey (station number 01398500).
Application of Quantile Regression to Identify Baseline Periods of Record 15

storm frequency not much differently than slightly dry years, 

and possibly bias the results of the QR. The large number of 

same-valued zero hourly rainfalls associated with dry days, 

however, caused matrix singularities that compromised the 

simplex method (a standard iterative procedure in linear programming for solving an optimization problem; see Dantzig, 

1951) used to perform the QR. To overcome this discrepancy, 

all zero-valued hourly precipitation measurements were randomly perturbed by a small amount, ranging between 0 and 

0.0001 millimeters. To enhance resolution of the precipitation 

plots, the rainfall axis in the QR plots did not include these 

small values.

The high degree of correspondence between changes in 

hourly precipitation quantiles and changes in daily streamflow quantiles (fig. 4) appears to indicate that alterations in 

observed daily streamflow at the North Branch Raritan River 

at Far Hills site can be attributed largely to climatic fluctuations, and that streamflow essentially remains in its nearnatural, or relatively unaltered (baseline), state. The strong 

correspondence between rainfall and streamflow quantiles 

provides clear, objective evidence that properties of daily 

streamflow within the basin have not been substantially altered 

since 1922, when the POR for the gage began. Minimal 

streamflow disturbance is also supported by the impairment 

status at the five AMNET sites (fig. 3), which indicates that 

most aquatic-invertebrate assemblages in the basin have been 

minimally affected by human activities or changes directly 

associated with flow alteration. By using the entire 1922–2006 

POR, NJSCT classified the stream as type C (table 1, at end of 

report). The 1922–2006 reference baseline POR identified by 

using QR for the North Branch Raritan at Far Hills, NJ (USGS 

station 01398500), is in agreement with that found by Esralew 

and Baker (2008; table 2, p. 11). It is not in direct agreement, 

however, with that previously used to classify streams for 

the New Jersey HIP study (Kennen and others, 2007, app. 

4), in which a more conservative baseline period of 1922–75 

was used to avoid even the most minor anthropogenic effects 

that might influence streamflow (for example, groundwater 

withdrawals). The quantile plots show, however, that most 

fluctuations in streamflow after 1975 can be directly attributed to changes in precipitation. The absence of observable 

anthropogenic flow disturbance during the available record 

explains why both the 1922–75 and 1922–2006 PORs result in 

the same stream classification (class C; table 1). 

On the basis of results of the QRs, the stream draining to the North Branch Raritan River at Far Hills, NJ, gage 

would be considered to represent a stream near the “least 

altered” end of the hydrologic-disturbance gradient. Many 

other Raritan River subbasins, however, would be expected 

to show some evidence of flow alteration caused by human 

activities, especially larger basins downstream from the North 

Branch Raritan River at Far Hills, NJ, gage that integrate 

broader areas of anthropogenic disturbance. Candidates for 

basins that likely represent more anthropogenically modified 

locations along the disturbance gradient include those that 

contain a large number of moderately impaired AMNET sites; 

are characterized by a greater percentage of ISC (typically 

greater than 5 percent); and have a long POR that begins prior 

to development, ends roughly at the present time (2012), and 

includes a sufficient number of predevelopment years to establish reliable flow indices for relatively unaltered (baseline) 

flow. Although few basins meet all these criteria and sites chosen for this study were mostly non- to moderately impaired, 

the basin draining to the streamgage on the Raritan River at 

Manville, NJ (USGS station number 01400500; fig. 5), may 

lie closer to the “altered” end of the disturbance gradient than 

most other streams in the Raritan River Basin evaluated in 

this study. 

Although the area that drains to the Raritan River at 

Manville streamgage was characterized by only 7.0 percent 

ISC in 2002, the available 1922–2006 streamflow record for 

this streamgage indicates that dense urbanization immediately 

upstream from the gage may have contributed to the long-term 

alteration of the mean and variance of observed streamflow 

(fig. 6) at this site. Unlike the North Branch Raritan River at 

Far Hills basin (fig. 3), the Raritan River at Manville basin 

drains a relatively large area that encompasses substantial 

anthropogenic alteration (fig. 5), including concentrated 

urbanization, diversions at the Spruce Run and Round Valley 

Reservoirs, permitted surface-water discharges, and groundwater pumping. As a consequence of these factors, most 

Raritan River basins in this study would be expected to lie 

somewhere between the North Branch Raritan River at Far 

Hills (fig. 3) and the Raritan River at Manville (fig. 5) basin 

endpoints in their degree of hydrologic alteration.

Whereas none of the AMNET sites within the basin 

draining to the Raritan River at Manville streamgage shows 

severe biotic impairment, most indicate moderate levels of 

impairment (fig. 5). The impaired sites tend to be in downstream reaches of the drainage basin where the effects of 

upstream alterations of flow have accumulated and where ISC 

tends to be concentrated. Some, however, are in upstream 

areas where a large number of domestic-supply wells are 

present, or lie immediately downstream from the Spruce 

Run and Round Valley operational reservoirs. Over such a 

large drainage area, it is nearly impossible for flow to remain 

unaltered in the presence of such diverse and widely diffuse 

anthropogenic activity. QR analysis of daily observed streamflows at the Raritan River at Manville, NJ, streamgage (fig. 

7A) indicates that mean daily flow changed and streamflow 

variation decreased sometime during the 1960s, but it is not 

clear how the statistical distribution of daily flow processes 
16 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey 01400500 MORRIS HUNTERDON SOMERSET WARREN MIDDLESEX MERCER SUSSEX Round Valley Reservoir Spruce Run Reservoir Budd Lake 74°45' 75° 40° 45' 40° 30' Base from U.S. Geological Survey digital line graph files, 1:24,000,

Universal Transverse Mercator projection, Zone 18, NAD 83 

0 5 MILES

0 5 KILOMETERS

0 5 MILES

0 5 KILOMETERS

74°45' 75°

40°45'40°30'

Impervious surface cover from New Jersey

Department of Environmental Protection, 2008

EXPLANATION

01400500

Raritan River Basin study area01400500 study-basin boundaryStreamU.S. Geological Survey streamgage and station numberAmbient Biomonitoring Network site impairment score

Non-impairedModerately impairedSeverely impaired

Groundwater well (residential, industrial, or agricultural use)Public-supply wellPermitted groundwater discharge sitePermitted surface-water discharge site

2002 impervious surface 

EXPLANATION

cover, in percentLess than 1010–24.925–49.950–75Greater than 75

Raritan River Basinstudy area

01400500study basin

NEW JERSEY

Figure 5. Percent impervious surface cover and location of wells and discharge sites that may influence streamflow quantity and quality in the Raritan River at Manville, New Jersey (station number 01400500) study basin in the Raritan River Basin study area, New Jersey.
Application of Quantile Regression to Identify Baseline Periods of Record 17

has changed, or precisely when the change occurred. The QRs 

of daily streamflow (fig. 7A) indicate that after about 1960, 

changes in streamflow appeared to show little relation to 

changes in hourly precipitation (fig. 7B) across all quantiles, 

indicating anthropogenic mechanisms were likely responsible 

for the observed flow alteration. ISC for the basin, though 

estimated at 7.0 percent in 2002, is concentrated just above 

the streamgage and is likely to have had a pronounced effect 

on observed streamflow. Reservoir operations, which began 

during the early 1960s, also likely contributed to the observed 

flow disturbances.

Breakpoints at 1960, 1963, and 1965 (fig. 7) were 

included in the QRs to determine as precisely as possible 

when urban development or reservoir operations began to 

affect streamflow behavior. Noticeable changes appear to have 

begun in 1960, indicating that flow alterations associated with 

reservoir construction may have influenced streamflow even 

before the onset of reservoir operations. By using observed 

streamflows over the longest continuous baseline POR 

(1922–60), NJSCT classified the stream as being in class A

(table 1), which is consistent with the classification reported in 

the New Jersey HIP study (Kennen and others, 2007). The Raritan River at Manville, NJ, streamgage provides an excellent 

example showing that statistical examination conducted using 

the probabilistic distribution of streamflow over time provides 

a more precise determination and understanding of the baseline 

POR than a historical examination of the streamflow record 

alone, which might not reveal or explain subtle changes related 

to climatic or anthropogenic drivers. By using all available 

streamflow and precipitation data, the same QR approach used 

for the North Branch Raritan River at Far Hills and Raritan 

River at Manville, NJ, streamgages (figs. 4 and 7, respectively) 

was used for the remaining 49 Raritan River Basin study 

basins to determine whether significant changes in the statistical properties of streamflow occurred and, if so, whether they 

could be directly related to changes in precipitation. 

192219251930193519401945195019551960196519701975198019851990199520002005

100,000

10,000

1,000

100

10

Year

Logarithm of streamflow, in cubic feet per second

Figure 6. Hydrograph for U.S. Geological Survey streamgage Raritan River at Manville, New Jersey (station number 01400500), 

1922–2005.
18 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

100,000

10,000

1,000

100

10

EXPLANATION

Quantile—

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

Year

Flow, in cubic feet per second

EXPLANATION

Quantile—

0.975

0.95

0.925

0.9

0.875

0.85

0.825

0.8

0.7

A. Quantile regression on daily streamflow 

0.001

0.01

0.1

1

10

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Year

Hourly precipitation, in inches

B. Quantile regression on hourly precipitation 

Figure 7. Quantile regressions for A, daily streamflow and B, hourly precipitation at Raritan River at Manville, 

New Jersey (station number 01400500).
Reevaluation of Raritan River Basin Stream Classes 19

Sensitivity Analysis and Calibration of 

Watershed Model Parameters

To effectively develop, or synthesize, a baseline hydrograph 

that can serve as a strong basis for comparison with the 

hydrograph that is representative of altered flow conditions, 

the hydrologic parameters used in a watershed model (for 

example, the SWRM) need to be appropriately calibrated. 

A procedure for quantifying parameter sensitivity that is an 

extension of the generalized sensitivity analysis originally 

proposed by Hornberger and Spear (1981) was implemented 

for the SWRM. Because correlation among parameters manifests as lack of sensitivity, sensitivity analysis on the SWRM 

parameters was used to identify parameters that provide 

redundant information and therefore should be excluded from 

calibration procedures (Kennen and others, 2008). Failure to 

remove correlated parameters from the analysis can result in a 

biased calibration that produces poor parameter estimates. By 

using a maximum likelihood approach, in which Monte Carlo 

methods are used to sample uniformly over the SWRM parameter space, the “best” parameter sets for all 51 gaged drainage 

basins were obtained by using 100 parameter realizations. 

Only 100 realizations were used because of the large number 

of basins in the analysis and the fact that two sets of these 

realizations had to be generated. The first set was to determine 

to which parameters the model was sensitive and to remove 

correlation among parameters. The second set was to obtain 

the optimal (“best”) parameter set that minimized the sum of 

squared error in daily streamflow over the POR unique to the 

particular basin being considered. This analysis indicated that 

the 51 gaged basins consistently showed the highest sensitivity 

to parameter m, which is a function of soil porosity (Kennen 

and others, 2008). The “best” parameter sets for each of the 

study basins were identified and used to develop simulated 

hydrographs; however, parameters for two of the study sites 

(the two Royce Brook tributary sites, USGS station numbers 

01402590 and 01492600) could not be fully calibrated, presumably as a result of the intermittent nature of these streams, 

a condition that is not easily handled with a saturation-excess 

algorithm like the one used in the SWRM. 

Reevaluation of Raritan River Basin 

Stream Classes

The New Jersey Stream Classification Tool (NJSCT) 

was used to reevaluate the stream class of all 51 cooperatively 

identified study basins on the basis of the existing POR. The 

purpose of the reevaluation was twofold. First, additional 

streamflow data have become available since the original classification was done by Kennen and others (2007); these new 

data potentially could affect some stream classifications. Second, the initial New Jersey HIP work relied on examination 

of long-term flow records in concert with best professional 

judgment as a basis for establishing the extent of the baseline 

POR and for identifying when alteration resulting from human 

activities first began to influence the hydrograph. In this study, 

a more comprehensive, statistically based approach (that is, 

quantile regression) was used to identify when the major 

effects of anthropogenic streamflow alterations (for example, 

dams, surface-water withdrawals, interbasin transfers, and 

other water-management infrastructure) began to alter the 

natural flow regime. Streams in a given stream class typically 

exhibit similar hydroecological characteristics and, therefore, 

are expected to exhibit a hydrologic “signature” that is unique 

for a given stream class. On the basis of the work of Henriksen 

and others (2006), a suite of 10 nonredundant hydroecological 

indices accounting for the five major components of streamflow for each of the two New Jersey stream classes considered in this study (classes A and C) was identified (fig. 8), 

and these indices were used to help characterize the unique 

hydrology of these stream classes. All 51 Raritan River study 

basins included in this assessment were classified by using the 

NJSCT software (fig. 1) on the basis of either the quantileregression-derived baseline POR or the simulated baseline 

POR from the SWRM. The latter approach was necessary 

for study basins classified as “unknown” (table 1), because 

an observed baseline POR could not be established from the 

existing hydrologic record. 

Of the 51 Raritan River study basins chosen for this 

study, 12 were identified as members of stream class D (sites 

that are typically characterized by high peak flows) and therefore were not considered for further analysis in the current 

study. In addition, the basins of the two Royce Brook tributary 

sites (USGS stations 01402590 and 01402600), mentioned 

in the previous section, were removed from analysis because 

the SWRM could not be calibrated and, therefore, no baseline 

POR could be simulated for comparison (table 1). On the 

basis of the results of QR analysis, baseline streamflow data 

for 16 of the remaining 37 basins was sufficient to characterize the streams as either class A or class C by using the 

NJSCT (table 1), and these 16 basins were retained for further 

analysis. The remaining 21 basins (referred to as “unknown” 

in table 1) did not have an observed period of unaltered flow 

(could not be identified by using QR) to accurately define a 

stream class. For these streams, simulated unaltered (baseline) 

hydrographs needed to be developed by using the SWRM in 

order to accurately identify the stream class. This step was 

necessary because a basis for hydrologic comparison (whether 

to a known baseline established from the existing POR or to 

one that was simulated by using the SWRM) needed to be 

established for all class A and C streams in order to evaluate 

the proportion of potential deviation of hydrologic indices 

from those associated with less altered streamflow conditions. 

Of the 21 streams classified as “unknown,” 13 were identified 

as either class A or C on the basis of the simulated baseline 

hydrograph (table 1) and were retained for further analysis. 

Ultimately, 29 streams were identified as either class A or 

class C streams and were found to be acceptable for use in the 

comparative hydrologic analysis portion of this study.
20 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Figure 8. Interactive window for selecting nonredundant hydrologic indices for stream classes A and C as identified in the New Jersey 

Hydrologic Assessment Program (NJHAT). (See appendix 1 or Kennen and others (2007) for hydrologic index definitions.)

Simulation of Unaltered Streamflow

Once the parameters for the unclassified basins were 

appropriately calibrated by using the approach described 

above, modifications were made to portions of the SWRM to 

set percent ISC and water use (that is, groundwater withdrawals and surface-water diversions and discharges) to zero for all 

years to reflect unaltered flow conditions. The availability of 

either daily (1902–48) or hourly (1948–present (2006)) rainfall 

data from 1902 to 2006 made it possible to generate long-term 

hydrographs that were NWIS-formatted and ready for input 

directly into NJSCT and NJHAT for further assessment of 

stream class and analysis of the unaltered streamflow indices, 

respectively. Because the NJHAT/NJSCT software accepts 

only 100 years of data at a time, the period from 1907 to 2006 

was chosen as the POR for analysis. At the time the data were 

run through the SWRM, however, the flow record for 2006 

was incomplete (was available only to June 30, 2006), so the 

record for 2006 could not be considered a complete water 

year and was subsequently eliminated from the streamflow 

analysis. Ultimately, the POR from 1907 to 2005 was used as 

the final input for all streamflow simulations performed using 

the SWRM. The SWRM was run in predictive mode and all 

streamflow simulations were completed by using the parameter set identified by maximum likelihood sensitivity analysis 

and the calibration procedures described earlier. In general, 

high-flow events were smaller in magnitude under unaltered 

flow conditions than under observed conditions and low-flow 

events were higher in magnitude (which would be expected 

when flows that have been altered through anthropogenic 

activity are compared to those that have not), with the greatest difference being between the unaltered baseline flows (that 

is, the simulated baseline) and the observed daily flow record 

in basins that have undergone extensive development. On the 

basis of results of analysis of the simulated baseline hydrographs derived by using the SWRM, a total of 18 class A and 

11 class C study sites (n=29) were identified by using QR and 

the SWRM analyses. 

Because the PORs for four of the sites (USGS station 

numbers 01396580, 01400730, 01403000, and 01403900) 

had missing data, the observed data could not be run through 

NJHAT all at once. Instead, the broken PORs for the observed 

flow at these sites were entered into NJHAT separately and the 

mean flow values for the NJHAT flow indices were used for 

all subsequent comparative analyses.
Determining Deviation of Streamflow from Baseline Conditions 21

Determining Deviation of Streamflow 

from Baseline Conditions

Direct comparisons were performed between existing 

streamflow records and either the hydrologic baseline identified by using quantile regression (QR) (referred to here as 

the observed baseline, OB) or the SWRM-modeled baseline 

(referred to here as the simulated baseline, SB) by using the 

greatly reduced subset of the hydroecological indices generated by NJHAT for stream classes A and C (fig. 8). For sites 

with an OB record, the record represents an existing baseline/

reference time period or hydrograph during which the stream 

was minimally altered with respect to the amount of anthropogenic change that had already occurred. Simulated baselines 

were created by using the SWRM to reduce the effects of 

anthropogenic stressors (for example, urban land use, impervious surface, compacted soils, etc.) on the hydrologic response 

and simulate a “natural” baseline period of record (POR) for 

comparison to the existing hydrologic record. The natural 

or simulated baseline typically represents the hydrographic 

response profile of a time period when anthropogenic disturbance was minimal (that is, when impervious surface cover 

(ISC) in the basin was near zero and rates of both surface 

and groundwater use were small relative to rates of natural 

flow). In this study, ISC and water use were set to zero in 

the model, indicating no existing anthropogenic disturbance. 

These comparisons were completed for all study streams to 

evaluate whether the observed streamflow has deviated from 

the baseline streamflow (for both OB and SB sites) in an effort 

to determine whether a measurable change (based on proportional deviation from the OB or SB) had occurred in one or 

more aspects of the flow regime at each study site (that is, to 

evaluate whether the hydro-TMDL method used to evaluate 

each study stream was able to identify statistically significant 

deviations of the flow indices from the established 25thto-75th-percentile range). In this study, statistical comparisons 

were computed on the basis of the 10 primary nonredundant 

flow indices, which include various measures of the five 

major components of the flow regime: magnitude, frequency, 

duration, timing, and rate of change for stream classes A 

and C (fig. 8; Kennen and others, 2007). Hydrologic indices 

from observed values that fell outside the established 25thto-75th-percentile range (chosen as the ecologically acceptable 

range on the basis of the work of Richter and others, 1996, 

1997) were used to identify study sites that have likely been 

hydrologically altered by processes associated with anthropogenic changes in the landscape or water-supply infrastructure 

(for example, dams, reservoirs, surface-water diversions, 

impoundments, intakes, and various conveyance devices). 

Consequently, those sites with flow indices that fell outside the 

25th-to-75th-percentile range are considered, for the purposes 

of this report, to not meet the established hydro-TMDL flow 

criteria and, therefore, do not fully support established aquaticlife uses. Hydrologic indices also were evaluated with respect 

to the more conservative 40th-to-60th-percentile range as part 

of the comparative analysis to address NJDEP concerns that 

the 25th-to-75th-percentile range may not fully meet more 

stringent standards for streams that need greater protection as 

a result of existing State regulations—for example, Category 

One Waters, which are protected from any measurable change 

in water quality because of exceptional ecological, recreational, or water-supply significance (http://www.nj.gov/dep/

wms/bwqsa/c1waters.htm).

Class A Streams

Class A stream sites included semiflashy streams characterized by moderately low base flow. Eighteen class A sites 

were examined in this report. Only seven study sites identified 

as class A (South Branch Raritan River at Stanton (SBRarSt), 

Neshanic River at Reaville (NeshRea), North Branch Raritan 

River near Raritan (NBRarRar), Raritan River at Manville 

(RarMan), Millstone River at Plainsboro (MillPla), Millstone 

River at Blackwells Mills (MillBlak), and Raritan River below 

Calco Dam at Bound Brook (RarCalco), NJ) (table 2) had a 

hydrologic baseline POR that could be statistically differentiated from the existing observed POR (table 1) by using QR 

(that is, an OB record). These comparisons may be the most 

relevant from a management perspective because they represent direct comparisons between an established baseline POR 

derived from observed rather than simulated data. The drainage areas for the above seven class A sites ranged from 25.7 to 

785 mi2; of these, five were larger than 100 mi2. Percent ISC 

at these streams ranged from 5.3 to 10.2 (table 1) (see Kennen and others, 2007, for a broader discussion of differences 

among New Jersey stream classes). 

Comparisons with the Observed Baseline

At SBRarSt (01397000), the annual minimum 30-day 

moving-average flow (DL4) exceeded the 75th-percentile 

value by nearly 50 percent (35.8 cubic feet per second (ft3/s) 

(app. 2)). The Julian date timing of annual minimum flow 

(TL1) exceeded the 75th-percentile value by 4 percent and 

represents an 11-Julian-day delay for the timing of the annual 

low flow. Evaluations performed by using the more conservative 40th-to-60th-percentile range indicated, as would 

be expected, that more flow attributes exceeded the upper 

percentile boundary. For example, the mean monthly flow for 

July (MA18) exceeded the 60th-percentile value by 43 percent 

(54.1 ft3/s), but did not exceed the 75th-percentile value. Mean 

maximum high flow for May (MH5) exceeded the 60thpercentile value by more than 17 percent under this scenario, 

which is equivalent to 115 ft3/s greater than the acceptable 

maximum. The percentage change in annual timing of flows 

also was greater for the 40th-to-60th-percentile scenario, 

indicating a nearly 10- and 16-Julian-day shift in the timing of 

high- and low-flow periods, respectively. 

At NeshRea (01398000), flow constancy (TA1) fell 

below the 25th-percentile value. In general, constancy is a 
22 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Table 2. Site abbreviations and baseline dataset types for the 29 class A and C streams used in the hydrologic analysis.

[USGS, U.S. Geological Survey; NJSCT, New Jersey Stream Classification Tool; SB, simulated baseline hydrograph derived by using the Statewide Watershed 

Runoff Model; OB, observed baseline hydrograph identified by using quantile-regression analysis; mi2, square miles; ISC, impervious surface cover]

USGS 

station 

number

USGS station name Site 

abbreviation

NJSCT 

stream 

class

Baseline 

dataset 

type

Drainage 

area

(mi2)

Percent 

ISC

01396500 South Branch Raritan River near High Bridge, NJ SBRarHi A SB 65.3 7.2

01396580 Spruce Run at Glen Gardner, NJ SpRGlen C SB 11.3 3.4

01396660 Mulhockaway Creek at Van Syckel, NJ MulhVan C SB 11.8 4.8

01396800 Spruce Run at Clinton, NJ SpRClin A SB 41.3 3.6

01397000 South Branch Raritan River at Stanton, NJ SBRarSt A OB 147.0 6.0

01398000 Neshanic River at Reaville, NJ NeshRea A OB 25.7 5.3

01398500 North Branch Raritan River near Far Hills, NJ NBRarFar C SB 26.2 5.3

01399500 Lamington (Black) River near Pottersville, NJ LamPot C/D SB 32.8 7.0

01399670 South Branch Rockaway Creek at Whitehouse Station, NJ SBRkWhS C SB 12.3 8.2

01399690 South Branch Rockaway Creek at Whitehouse, NJ SBRkWh C SB 13.2 9.1

01399700 Rockaway Creek at Whitehouse, NJ RockWh C SB 37.1 5.6

01400000 North Branch Raritan River near Raritan, NJ NBRarRar A OB 190.0 6.3

01400500 Raritan River at Manville, NJ RarMan A OB 490.0 7.0

01400730 Millstone River At Plainsboro, NJ MillPla A OB 65.8 10.2

01400932 Baldwins Creek at Baldwin Lake near Pennington, NJ BaldPen A SB 2.5 5.1

01400953 Honey Branch near Pennington, NJ HoneyPen A SB 0.7 1.0

01401000 Stony Brook at Princeton, NJ StonyPrn A SB 44.5 5.0

01401500 Millstone River near Kingston, NJ MillKing A SB 171.0 10.3

01402000 Millstone River at Blackwells Mills, NJ MillBlak A OB 258.0 9.6

01403000 Raritan River at Bound Brook, NJ RarBound A SB 779.0 7.8

01403060 Raritan River below Calco Dam at Bound Brook, NJ RarCalco A OB 785.0 7.9

01403150 West Branch Middle Brook near Martinsville, NJ WBMdMart C SB 2.0 11.0

01403160 West Branch Middle Brook near Somerville, NJ WBMdSom A SB 3.8 8.5

01403900 Bound Brook at Middlesex, NJ BoundMid A SB 48.4 28.6

01405300 Matchaponix Brook at Spotswood, NJ MatchSpt A SB 43.9 14.7

01405400 Manalapan Brook at Spotswood, NJ ManalSpt C SB 40.7 8.2

01405500 South River at Old Bridge, NJ SROldBr A SB 94.6 12.0

01406000 Deep Run near Browntown, NJ DeepBrn C SB 8.1 11.1

01406500 Tennent Brook near Browntown, NJ TennBrn C SB 5.2 16.6
Determining Deviation of Streamflow from Baseline Conditions 23

measure of similarity of monthly means within a year and 

reflects conditions of relative hydrologic stability (Colwell, 

1974; Poff and Ward, 1989). Thus, the term “constancy,” the 

degree to which monthly means vary from month to month, 

is inversely related to streamflow variability (Olden and Poff, 

2003). A highly regulated stream with low streamflow variability would have high flow constancy (near 1), whereas a 

nonregulated stream with high streamflow variability would 

have low constancy (near 0). The observed constancy value 

(0.197; high variability) at this site was 54.4 percent below the 

25th-percentile value (0.432), indicating that with the onset 

of anthropogenic influences, the stream appears to be becoming more variable from month to month. NeshRea has many 

domestic, regulated, and unregulated (that is, they fall within 

the <100,000-gallon-per-day (gal/d) permitting criterion) 

agricultural water withdrawals that may cumulatively affect 

streamflow variability over time. TL1 fell below the 25thpercentile value by 2.3 percent, which indicates that minimum 

flows are occurring approximately 6 days earlier than they did 

before streamflow alteration. The Julian date of the annual 

maximum flow exceeded the 60th-percentile value by 5.6 percent, which is equivalent to about 3 days; thus, the annual low 

flows at this site appear to be occurring earlier, whereas the 

annual high flows occur later. 

The frequency of high-flow pulse count (FH4) at 

NeshRea has decreased by nearly 10 percent (with respect to 

the 40th-percentile value), which indicates that there are about 

three fewer high flows annually than during observed baseline 

conditions. The annual maximum of 3-day moving-average 

flows (DH2) exceeded the 60th-percentile value by 6.5 percent, which represents a 34.4-ft3

/s increase. The changes in 

these flow measures indicate that there has been a general 

change in the timing and duration of seasonal flow processes 

in the Neshanic River. 

Few flow indices exceeded the 25th-to-75th-percentile 

range at NBRarRar (01400000); with respect to the more 

conservative 40th-to-60th-percentile range, however, many 

hydrologic indices were exceeded (app. 2). DL4 did exceed 

the 75th-percentile value by 6.0 percent (about 4 ft3/s) and also 

exceeded the 60th-percentile value by nearly 30 percent (about 

17 ft3/s) (app. 2). TA1 exceeded the 75th-percentile value by 

1.4 percent and the observed flow constancy value was 0.561, 

indicating a stream with a moderate amount of flow variability. 

The mean maximum flow for May (MH5) exceeded the 60thpercentile value by 20 percent concurrently with an 8.8-percent change in the Julian date of annual maximum flow (TH1), 

which is equal to a 5-Julian-day delay in the high-flow period. 

Results of these hydrologic comparisons indicate that highflow magnitudes are increasing and high flows are occurring 

later in the season, which may affect the life-history cycles of 

some sensitive aquatic organisms. DH2 exceeded the 60thpercentile value by 8.0 percent, which is equivalent to about 

189 ft3/s. This additional exceedance in the duration of highflow may indicate a further exacerbation of impaired flow conditions at this site because high-flow magnitudes are occurring 

both later in the season and for a longer duration. Although 

the estimated ISC for this site is only 6.3 percent (table 1), 

much of this ISC is concentrated in large areas immediately 

upgradient from the streamgage, which could potentially affect 

average flows. Many more of the hydrologic indices evaluated 

at this site likely will deviate from the 25th-to-75th-percentile 

range if anthropogenic activity, and concomitantly ISC, in this 

basin continues to increase. For example, examination of the 

results of analysis of the flow record by means of QR indicates 

distinct changes in flow properties at NBRarRar beginning in 

2000. Increased ISC, which predictably decreases flow variability and increases flow across all quantiles, could explain 

these changes in flow characteristics. 

At RarMan (01400500), DL4 exceeded the 75th-percentile value by 57 percent (84.2 ft3

/s) and the 60th-percentile 

value by more than 73 percent (97.8 ft3/s). The timing of flow 

indices including both TA1 and TH1 exceeded the 75th-percentile value. TA1showed the greatest deviation from natural 

flow timing and exceeded the 75th- and 60th-percentile values 

by 18 and 23 percent, respectively (app. 2). The observed constancy value of 0.652 may indicate that the stream is reflecting 

greater flow regulation and less month-to-month variability 

than during baseline conditions. TH1 exceeded the 75th- and 

60th-percentile values by 4.0 and 33 percent, respectively. 

The 75th-percentile exceedance is equivalent to a 3-day 

delay beyond the established maximum (the 75th-percentile 

value) and 18 days past the more conservative maximum (the 

60th-percentile value). The latter may represent a considerable delay in the streamflow timing, especially for sensitive 

aquatic organisms (for example, mayflies, Ephemeroptera) that 

rely on the timing of high and low flows for crucial life-cycle 

stages. Reservoir operations, which began in the early 1960s, 

may have contributed to the observed flow disturbances. The 

exceedance of TA1, which represents a shift toward greater 

flow regulation, would also be expected at any gage downstream from water-supply reservoirs (for example, Spruce Run 

and Round Valley Reservoirs). 

The basin draining to MillPla (01400730) has undergone 

moderate development that may have altered streamflow 

properties. Many hydrologic indices deviated from the 25thto-75th-percentile range, some by a large percentage. For 

example, MA18 and TH1 exceeded the 75th-percentile values 

by 91 percent and 176 percent, respectively. The 176-percent exceedance for TH1, which represents a 124-day delay 

in spring high flows, may be an extreme example; however, 

it may instead indicate that flow has been so greatly modified that natural spring flows no longer occur in this system. 

The 91-percent exceedance of MA18 is 104.3 ft3/s above 

the 75th-percentile value. DL4 and the annual maximum of 

30-day moving average flows (DH4) also exceeded the 75thpercentile values by 9.8 and 4.6 percent, respectively. TL1 

fell below the 25th-percentile value by 3.8 percent, which is 

9 days earlier than in the baseline record. Because hydrologic 

monitoring at this site was discontinued during 1975–85, then 

restarted in 1986, it is unclear whether these results are fully 

representative of altered flow conditions, as 10 or more years 

of post-baseline record typically are needed for substantive 
24 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

hydrologic comparisons. Of the streams with an OB record 

assessed in this study, however, MillPla had the greatest number of flow-index values that fell outside the established 25thto-75th-percentile range. The increases in the magnitude and 

duration of flows at this site can probably be attributed to the 

relatively high ISC (10.2 percent as of 2002; table 1) and the 

rapid suburbanization that has occurred and still is occurring, 

resulting in increased runoff to streams and decreased stormwater infiltration. Inadequate stormwater controls are common 

in many older suburban communities in this basin. Results 

indicate that the average magnitude of high flows and duration 

of low flows are increasing, summer low flows are occurring 

earlier, and spring high flows are occurring later than during 

baseline conditions. 

At MillBlak (01402000), timing and frequency of high 

and low flows were the hydrologic attributes that most commonly fell outside the 25th-to-75th-percentile range. TH1 

exceeded the 75th-percentile value by 4.7 percent, the equivalent of slightly more than a 3-day delay in spring high flows. 

FH4 exceeded the 75th-percentile value by 2.3 percent, which 

is a difference of about 0.5 days per year. TL1 arrived 2.5 days 

earlier than under baseline conditions (1.0 percent below the 

25th-percentile value and nearly 4 percent below the 40th-percentile value). TH1, FH4, and TL1 were the only flow indices 

that fell outside the 25th-to-75th-percentile range (app. 2); 

however, additional deviations in the magnitude and frequency of high flows were identified for the more conservative 

40th-to-60th-percentile range. For example, MH5 exceeded 

the 60th-percentile value by 35 percent (390 ft3/s) and DH2 

exceeded the 60th-percentile value by almost 9.0 percent 

(295 ft3/s). The frequency of high flows increased to more than 

32 percent above the 60th-percentile value, which represents 

an additional 5.5 days per year that flow is greater than a 

threshold value equal to seven times the median flow for the 

entire record. Additionally, TH1 exceeded the 60th-percentile 

value by 34 percent, an 18.8-day delay in high flows. The 

flow exceedances above the 60th-percentile value identified 

at this site may indicate that the drainage basin of this stream 

is being altered hydrologically. TH1 at RarCalco (01403060) 

exceeded the 75th-percentile value by 12 percent (9 days) and 

the 60th-percentile value by 44 percent (24 days). The timing 

of low flows fell below the 25th-percentile value by 2.5 percent, indicating that low flows are occurring nearly 6 days 

earlier than in the observed baseline record. Changes in the 

timing of spring high flows and summer low flows appear to 

be common in many streams where annual variability has been 

modified by the construction of dams. TA1 exceeded the 75thpercentile value by only 0.18 percent; however, if the more 

conservative 60th-percentile value is applied, exceedance is 

4.3 percent. Although this exceedance is relatively small, the 

potential difference between the observed TA1 value for the 

75th- relative to the 60th-percentile value could indicate that 

this site may be beginning to show greater monthly changes 

in annual flow timing than before hydrologic alteration that 

could continue to increase if not addressed through mitigative 

measures. The values of three additional hydrologic indices 

fell outside the more conservative 40th-to-60th-percentile 

range that did not fall outside the 25th-to-75th-percentile 

range. For example, mean spring flows (MH5) exceed the 

60th-percentile value by 14 percent (510 ft3/s) and the annual 

maximum of the 3-day moving average flows (DH2) was 

nearly 11 percent above the 60th-percentile value (1,133 ft3/s; 

app. 2). Additionally, the high flood pulse count exceeded the 

60th-percentile value by almost 8 percent, which is equivalent 

to an additional 1.5 days of flooding per year than under baseline conditions. This stream is influenced by a wide array of 

human activities and its basin has an ISC of nearly 8 percent 

(table 1). Streamflow at this site is also highly influenced by 

the 1938 construction of the Calco Dam, which was designed 

to carry and disperse wastewater into the Raritan River. This 

dam, however, was removed in 2011 (http://www.nj.gov/dep/

newsrel/2012/12_0085.htm).

Comparisons with the Simulated Baseline

Eleven streams in the class A group did not have a 

baseline POR that was long enough to be statistically differentiated from the observed streamflow record; they are South 

Branch Raritan River near High Bridge (SBRarHi), Spruce 

Run at Clinton (SpRClin), Baldwins Creek at Baldwin Lake 

near Pennington (BaldPen), Honey Branch near Pennington 

(HoneyPen), Stony Brook at Princeton (StonyPrn), Millstone 

River near Kingston (MillKing), Raritan River at Bound 

Brook (RarBound), West Branch Middle Brook near Somerville (WBMdSom), Bound Brook at Middlesex (BoundMid), 

Matchaponix Brook at Spotswood (MatchSpt), and South 

River at Old Bridge (SROldBr) (table 1). In these cases, the 

observed record was compared to an SB record derived by 

using the SWRM. Values of more hydrologic parameters fell 

outside the 25th-to-75th-percentile range and often by a higher 

percentage at sites where comparisons were made by using the 

SB record than at sites where comparisons were made with 

the OB record. It is hypothesized that the higher percentage 

deviations observed with the SB may be related to setting ISC 

in the Watershed model to 0 percent, indicating no existing 

anthropogenic disturbance (see the Data Limitations section 

of this report). The size of the drainage areas of the 11 class A 

streams whose comparisons are based on an SB record ranges 

from 0.7 to 779 mi2, and the ISC in these basins ranges from 

1.0 to 28.6 percent (table 1).

The two indices whose values most frequently exceeded 

the 75th-percentile value and by the greatest percentage at 

the 11 SB streamgages were MH5 (mean maximum flow for 

May) and DH2 (annual maximum of 3-day moving average flows) (app. 2). MH5 exceeded the 75th-percentile value 

at 10 of the 11 SB streams with deviations that exceeded 

the 75th-percentile value by 25 to 395 percent. The largest 

deviation at WBMdSom (01403160) is equivalent to nearly 

48 ft3/s above the 75th-percentile mean maximum flow. This 

large exceedance, however, may be somewhat skewed as a 

result of the availability of only 5 years of flow data, which 

is considered by some to be insufficient to fully assess the 
Determining Deviation of Streamflow from Baseline Conditions 25

degree of flow disturbance in the basin. StonyPrn (01401000) 

had the next highest deviation for MH5, which exceeded the 

75th-percentile value by 158 percent (206 ft3/s). The increase 

in magnitude of spring high flows at this site is likely related 

to increases in ISC resulting from increasing urbanization, as 

these two factors are commonly linked (for example, Roy and 

others, 2005; Kennen and others, 2005; Wenger and others, 

2008; Cuffney and others, 2010). The 75th-percentile value 

of MH5 was exceeded by 142 percent (117 ft3/s) at SpRClin 

(01396800). In this basin, flow alterations likely are related 

to a combination of hydrologic stressors including Spruce 

Run Reservoir releases and ongoing anthropogenic activity in 

the basin. The mean of monthly July flow (MA18) exceeded 

the 75th-percentile value by nearly 120 percent (38.1 ft3/s) at 

SpRClin. DH2 exceeded the 75th-percentile value by 314 percent (329 ft3

/s) (app. 2). The annual fall rate (RA3) at SpRClin 

fell below the 25th-percentile value by 343 percent, which is 

equivalent to a 6-ft3/d decrease. The Julian date of the annual 

maximum flow (TH1) exceeded the 75th-percentile value by 

56 percent (43 days)—nearly a 1-1/2-month delay. The Julian 

date of annual minimum flow (TL1) was similarly delayed, 

by about 42 days (app. 2). The annual minimum of 30-day 

moving average flow (DL4) was below the 25th-percentile 

value by almost 24 percent (1.5 ft3/s). The cumulative changes 

in hydrologic processes that have occurred at SpRClin relative 

to the SB record appear to be extensive, especially the large 

deviations in the magnitude and duration of high and low 

flows, respectively. These changes represent a general overall 

decline in natural flow characteristics that is commonly associated with dams and diversion structures (Graf, 1999; Poff and 

others, 2007); this type of streamflow alteration was recently 

found to be one of the most important indicators of diminished 

ecological integrity throughout the United States (Carlisle and 

others, 2010). The annual maximum of 3-day moving-average 

flows (DH2, a measure of the duration of high flows) exceeded 

the 75th-percentile value at 9 of the 11 sites, with deviations 

ranging from 133 to 355 percent (app. 2). The maximum, at 

BoundMid (01403900), is 506 ft3/s above (about 3.5 times) the 

expected 75th-percentile value for a stream with near-natural 

flow conditions and minimal anthropogenic disturbance as 

simulated with the SWRM. Values of most flow parameters 

at this site showed a general upward trend; therefore, the 

observed increase in DH2 relative to the SB is not surprising 

given the large amount of ISC (28.6 percent; table 1) and the 

concomitant increase in the amount of stormwater runoff. The 

value of the flow index DH2 was also found to greatly exceed 

(by 314 percent) the 75th-percentile value at SpRClin (app. 2). 

Many other class A streams with simulated baseline records 

had exceedances of the 75th-percentile value nearing 200 percent; however, exceedances were highest for BoundMid 

and SpRClin.

DL4 fell below the 25th-percentile value at six of the 

class A SB sites by 14 to 99 percent. The greatest deviation from the 25th-percentile value, 0.277 ft3

/s (99 percent), 

occurred at HoneyPen (01400953). This site has the smallest 

drainage area in the study (0.7 mi2); because the observed 

value is so small (0.003 ft3/s) relative to the 25th-percentile 

value, it yielded a large percentage deviation. Therefore, the 

overall significance of this change, especially for such a small 

system, should be interpreted with caution. Similarly, DL4 at 

BaldPen (01400932), in another small basin, fell below the 

25th-percentile value by a large percentage (97 percent); however, these large deviations reflect a relatively small absolute 

change in DL4. In contrast, the annual minimum at BoundMid 

was about 18.7 ft3/s (56 percent) lower than the 25th-percentile 

value. BoundMid is in a larger (48.4 mi2), highly urban basin 

with high ISC (28.6 percent), and these characteristics are 

directly reflected in the number of high- and low-flow indices 

that deviated from the established percentile ranges. DL4 at 

MillKing (01401500) was 44.3 percent (33.6 ft3/s) below the 

25th-percentile value. Many public-supply wells are distributed throughout the basin in which this site is located, which, 

in concert with unregulated water use (for example, agricultural wells with pumpage that falls below the 100,000-gal/d 

permitted-use criterion in New Jersey) could indirectly and 

cumulatively affect long-term low flows. For example, MA18 

at MillKing fell below the 25th-percentile value by nearly 

28 percent (about 26.5 ft3/s). DL4 at RarBound (01403000) 

exceeded the 75th-percentile value by 19 percent (43.6 ft3/s). 

At this site, the combined effects of human activity throughout 

the upper basin appear to have produced a streamflow record 

that exhibits severe flow disturbances.

RA3, which represents the change in flow for days in 

which the change is negative for the entire flow record, was 

3.9 to 439 percent below the 25th-percentile value at eight of 

the SB sites (app. 2). The largest (439-percent) difference, at 

BoundMid, is equivalent to a 5-(ft3/s)/d change in the fall rate 

relative to the SB condition. As mentioned above, the high ISC 

in this basin results in a flashy stream (greatly increases stream 

rise and fall rates) and likely explains the extreme deviation 

of this hydrologic index as well as the ongoing flooding this 

basin has experienced over the last two decades. SpRClin also 

had a high deviation for RA3 (343 percent below the 25th 

percentile, or a 6.2-(ft3/s)/d difference).

The Julian date of the annual minimum flow (TL1) fell 

outside the 25th-to-75th-percentile range at 7 of the 11 SB 

sites. The largest deviation was 16 percent above the 75thpercentile value at SpRClin, which is equivalent to a 42-day 

delay in annual low flow. Large deviations for TL1 were also 

found at SROldBr (01405500), where the observed value was 

4.5 percent below the 25th-percentile value, which indicates 

that annual low flows are occurring about 11 days earlier than 

during the baseline period. At BaldPen, the annual minimum 

occurred about 10 days earlier. 

TH1 fell outside the 25th-to-75th-percentile range at 6 of 

the 11 SB sites (app. 2). The largest deviation in TH1, 120 percent (equivalent to a high-flow delay of about 134 days), was 

at Honey Branch near Pennington. TH1 at Bound Brook at 

Middlesex and SpRClin exceeded the 75th-percentile value 

by 66 percent (74 days) and 56 percent (43 days), respectively. MA18 was outside the 25th-to-75th-percentile range 

at 5 of the 11 SB sites. MA18 at HoneyPen fell below the 
26 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

25th-percentile value by almost 55 percent, which is equivalent to about 0.30 ft3

/s. As mentioned previously, because 

this basin is the smallest in this study, relatively small differences in discharge can yield large percent differences in 

index values. This stream experiences a number of 0-ft3/s 

flow days per year. MA18 at MillKing was nearly 28 percent 

(26.5 ft3/s) below the 25th-percentile value. MA18 at Baldwins Creek near Pennington was 18 percent (about 0.09 ft3

/s) 

below the 25th-percentile value; however, these percent differences only appear to be large because the observed values 

are small.

Flow constancy (TA1) was outside the 25th-to-75thpercentile range at five of the SB sites. The largest TA1 

deviation was 56 percent above the 75th-percentile value at 

HoneyPen. The constancy value for HoneyPen (0.69; app. 2) 

indicates that annual month-to-month streamflow variability 

is much less than that for BaldPen, with a comparably 

sized drainage basin and an observed constancy value 

nearer to 0.3, indicating greater overall monthly streamflow 

variability at BaldPen. 

Class C Streams

Class C stream sites represent moderately stable streams 

with moderately high base-flow contributions. Eleven class C 

sites were examined as part of the hydro-TMDL assessment 

(table 2). None of these study sites had a hydrologic baseline 

POR that could be statistically differentiated from the existing observed POR (table 1); therefore, all hydrologic comparisons for these sites were made by using the SB hydrograph. The drainage areas of class C streams generally were 

smaller than those of class A streams and ranged from 2.0 to 

40.7 mi2, and percent ISC ranged from 3.4 to 16.6. Percent 

deviations outside the defined flow-management range (25thto-75th-percentile) at the class C streams were, in general, 

much larger than those found for the class A streams. This 

difference may be directly related to the fact that all comparisons for the class C streams were made with a simulated 

rather than an observed baseline.

The variability in monthly flow for January (MA24) 

exceeded the 75th-percentile value at all 11 of the class C 

streams, with deviations ranging from 179 to 1,931 percent 

(app. 2). The largest deviation was at Rockaway Creek 

at Whitehouse (RockWh, 01399700). MA24 at South 

Branch Rockaway Creek at Whitehouse Station (SBRkWhS, 01399670), which is upstream from the RockWh 

site, exceeded the 75th-percentile value by 1,046 percent. 

South Branch Rockaway Creek at Whitehouse (SBRkWh, 

01399690) had the third largest exceedance, 909 percent. 

These three sites also had the largest exceedances for MH14 

(median of annual maximum flows) and DH11 (annual 

maximum of 1-day moving-average flows). At the three 

Rockaway sites, MH14 exceeded the 75th-percentile value 

by 1,405 percent (RockWh), 1,211 percent (SBRkWhS), and 

1,164 percent (SBRkWh). DH11 exceeded the 75th-percentile 

value at these sites by 1,277 percent (SBRkWhS), 1,102 percent (RockWh), and 965 percent (SBRkWh). Flow at all three 

of these sites is greatly influenced by releases from the Round 

Valley Reservoir. 

Many indices accounting for the timing of flows were 

also outside the targeted 25th-to-75th-percentile range at these 

three sites. The variability in Julian date of annual minimum 

flow (TL2) exceeded the 75th-percentile value at all three 

of the Rockaway sites by 2, 4, and nearly 20 Julian days at 

SBRkWhS, RockWh, and SBRkWh, respectively (app. 2). 

The annual variability in Julian date of annual maximum flow 

(TH2) exceeded the 75th-percentile value by 4 days at SBRkWhS and was less than the 25th-percentile value at SBRkWh 

(4.5 days) and RockWh (9 days), indicating that the annual 

low flows appear to be occurring later and the high flows are 

occurring earlier than during the baseline period at two of the 

three sites.

Low flow pulse duration (DL16) was less than the 25thpercentile value at 9 of the 11 sites (app. 2). Deviations ranged 

from 18 to 63 percent below the 25th-percentile value; the 

largest deviation, at West Branch Middle Brook near Martinsville (WBMdMart, 01403150), is equivalent to flows being 

less than the 25th-percentile threshold for 11 days. Mean minimum March flows (ML3) at this site also were low, 1.3 ft3

/s 

below the 25th-percentile value (app. 2). Because the drainage 

area of this site is small (2.0 mi2), changes in discharge at or 

exceeding 1.3 ft3/s could substantially affect streamflow and 

alter low-flow periods sufficiently to increase the likelihood 

of the stream becoming dry in the spring. The variability of 

monthly January flows (MA24) and the median of annual 

maximum flows (MH14) at WBMdMart exceeded the 75thpercentile values by 641 and 711 percent, respectively. ISC 

at WBMdMart is 11 percent (table 2), so it is also possible 

that increased runoff resulting from ISC may be contributing to the greatly increased flashiness of the stream (that is, 

greater monthly flow variability combined with higher annual 

maximum flows). Timing indices for WBMdMart fell within 

the 25th-to-75th-percentile range, which might further indicate 

that flow alterations are linked to land-use change rather than 

to increasing water use or abstraction.

The second largest deviation for DL16 was at the North 

Branch Raritan River near Far Hills (NBRarFar, 01398500), 

where the annual average low flow pulse duration was less 

than the 25th-percentile value by about 60 percent, the equivalent of about 12 days. QR plots for NBRarFar (POR 1922–

2006; fig. 4) show little evidence of flow alteration resulting 

from human activity. The results of the NJHAT analyses, 

however, indicate some high- and low-flow magnitude and 

duration index values outside the 25th-to-75th-percentile range 

(app. 2). 

TL2 fell outside the 25th-to-75th-percentile range at 

7 of the 11 class C stream sites (app. 2); the value of this 

index exceeded the 75th-percentile value at 4 sites and was 

less than the 25th-percentile value at 3 sites. The largest 
Management Implications 27

percentage deviation above the 75th-percentile value was at 

SBRkWh (45 percent), which is equivalent to an 18-day delay 

in the annual minimum. The second largest deviation was 

28 percent below the 25th-percentile value at Tennent Brook 

near Browntown (TennBrn, 01406500), which represents 

the occurrence of the annual low flows 10 days earlier than 

during the baseline period. Only 9 years of hydrologic 

record were available for this site; therefore, some of these 

findings may be slightly skewed because a longer POR 

tends to be more robust for developing hydrologic index 

values for comparison between the observed conditions and 

the simulated baseline hydrograph. However, not only is 

the timing of flows at TennBrn altered, but the variability 

of January flows (MA24), the median of annual maximum 

flows (MH14), and the high-flow durations (DH11) increased 

(app. 2). Concomitantly, the mean minimum monthly flows 

for March (ML3) and the low flow pulse duration (DL16) 

decreased. In general, the high flows have increased and occur 

more frequently, indicating an increase in stream flashiness, 

and spring low flows have decreased. With an ISC of 16.6 

percent in this basin (table 1), stormwater runoff may be 

contributing to the increases in high flows. The timing of low 

and high flows has also been altered, which may indicate 

that water abstraction associated with human water use has 

increased. TennBrn appears to be representative of a stream 

that has been modified by urbanization and increased ISC. 

Flow constancy (TA1, a measure of similarity of monthly 

means within a year that reflects conditions of relative 

hydrologic stability) deviated from the 25th-to-75th-percentile 

range at 5 of the 11 class C stream sites (app. 2). As stated 

previously, constancy ranges from 0 to 1, and a highly 

regulated stream with low streamflow variability would 

have a constancy near 1. At TennBrn, NBRarFar, and Deep 

Run near Browntown (DeepBrn, 01406000), constancy fell 

below the 25th-percentile value by 70, 2.3, and 15.4 percent, 

respectively, indicating that these streams are more variable 

than during the baseline period. In contrast, constancy at 

RockWh and Manalapan Brook at Spotswood (ManalSpt, 

01405400) exceeded the 75th-percentile value by 7.5 and 

8.0 percent, respectively, indicating that these streams are 

more constant than during the baseline period and show flow 

alterations consistent with regulated systems. As discussed 

previously, RockWh is likely influenced by releases from the 

Round Valley Reservoir and Cushetunk Lake Dam; therefore, 

it is not surprising that constancy at this site has increased. 

The observed constancy value at ManalSpt was 0.502 (low 

variability), whereas the 75th-percentile value was 0.465, 

indicating that the stream is slightly less variable from month 

to month than during the baseline period. This site is located 

downstream from Lake Manalapan, Helmetta Pond, and the 

DeVoe Lake Dam in Spotswood; discharge at the latter site 

includes flow through sluice gates that are open at various 

times throughout the year, which likely accounts for the 

increase in constancy at this site.

Management Implications

Many of the sites in this study appear to fall into one of 

three primary flow-alteration categories: those that may have 

been affected by (1) reservoir and (or) dam operations, (2) 

impervious surface cover (ISC), or (3) groundwater abstraction. In some cases, flow alteration may result from a combination of these factors.

Reservoirs and Dams 

Stream sites in this study with upstream reservoirs or dam 

operations include SBRarSt, RarMan, RarCalco, SpRClin, 

SBRkWhS, SBRkWh, RockWh, and ManalSpt. Even though 

reservoirs and dams provide many benefits to society and 

maintaining adequate streamflow can be important in maintaining hydrologic functionality and supporting downstream 

human water uses, artificially stabilizing water levels throughout the year may produce unintended consequences on the 

aquatic assemblages that inhabit these systems. For example, 

water is released from New Jersey water-supply reservoirs to 

meet established minimum passing-flow requirements, which 

maintain the mandated flow of water in streams downstream 

from a reservoir. Such releases, however, are not intended to 

mimic seasonality or temporal variability of natural streamflow and therefore may not meet the life-history requirements 

of some aquatic fauna. Research over the last two decades 

has shown that changes in the annual timing of flow events 

are particularly relevant for synchronization of life-history 

processes, especially for those aquatic species whose life cycle 

(spawning and reproduction) is tied to annual fluctuations in 

streamflow. Lytle and Poff (2004) suggest that, even though it 

is difficult to forecast individual flow events, it is likely that 

aquatic organisms adapt to the long-term average timing, especially if such occurrences are in regions where there is some 

level of flow predictability (for example, high spring flows or 

low summer flows in the northeastern United States). A difference of a few days for such hydrologic measures certainly 

falls well within the variability of annual fluctuations in yearly 

precipitation; however, if such changes in flow timing are 

exacerbated by predicted climate change in the region, they 

can potentially disrupt aquatic organism life-history processes. 

Synchronizing reproductive processes with high- or low-flow 

periods helps to optimize reproductive success and avoid 

high mortality rates during extreme events such as floods or 

droughts (Lytle, 2002; Boulton, 2003).

An opportunity exists to define the timing and quantity 

of reservoir releases not only to meet the needs of human consumption, but also to increase streamflow variability to better 

accommodate the flow needs of aquatic species that depend 

on annual high- and low-flow periods as life-cycle cues for 

reproduction and migration. A protection-based hydro-TMDL 

for sites downstream from water-supply reservoirs may 
28 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

consist of a framework that incorporates a holistic hydrologic 

management plan that maintains current streamflow levels 

to maximize downstream human water use, but also modifies reservoir releases such that they are synchronized with 

natural fluctuations in annual streamflow variability. It might 

be possible to design varied release rates that incorporate components of natural hydrologic variability. Such an approach 

could be part of a decision-making strategy that incorporates 

timing, magnitude, frequency, and duration of flows in a way 

that promotes the long-term health and survival of aquatic 

organisms. With careful consideration of both downstream 

human water-supply needs and ecological needs (for example, 

spawning and reproduction of trout), release periods that more 

closely mimic natural streamflow hydrology and hydrologic 

variability that more closely represents prereservoir conditions 

could be promoted.

The Round Valley Reservoir in Clinton Township, New 

Jersey, was created in 1960 when the New Jersey Water Supply Authority (NJWSA) constructed two dams and flooded 

a large valley in Hunterdon County. This reservoir, which 

became operational in 1965, has a capacity of 55 billion gallons of water and provides a dependable supply of water to 

approximately 1.2 million people in central New Jersey. Additionally, the NJWSA supplements reservoir inflows by pumping water into the reservoir through the Hamden Pump Station 

located on the South Branch of the Raritan River. The NJWSA 

is required to maintain a minimum flow of 61.6 ft3/s (40 million gallons per day (Mgal/d)) at the South Branch of the 

Raritan River at Stanton (USGS station number 01397000). 

Releases from Round Valley Reservoir enter Rockaway 

Creek directly upstream from the SBRkWhS gaging station. 

Reservoirs such as Round Valley Reservoir can alter flow 

processes at such sites by creating non-natural water-release 

patterns. The changes seen in flow processes at SBRkWhS, 

SBRkWh, and RockWh, all of which are influenced by Round 

Valley Reservoir releases, are highly similar to those observed 

at sites downstream from Spruce Run Reservoir. These three 

stream sites show an overall increase in streamflow (for 

example, increases in MA24, MH14, and DH11) that is commonly associated with dam and reservoir releases designed to 

maintain a minimum flow (Richter and Thomas, 2007). 

Reservoir release strategies that incorporate Natural Flow 

Regime Paradigm principles (that is, incorporate release rates 

that mimic natural streamflow variability) could offset some 

of the changes in flow processes (for example, magnitude and 

timing of high and low flows) currently observed. Flows at 

these sites, however, are also related to release rates over the 

dam at Cushetunk Lake, which is located downstream from 

Round Valley reservoir and about 1,000 feet upstream from the 

SBRkWhS gage. The extreme departures (in some cases, more 

than 1,000 percent) of low and high flows from baseline conditions seen at these sites, however, might be beyond the scope 

of any existing flow-management measures that are designed 

to modify flows on the Rockaway River to a point at which 

natural flow patterns are restored. Like reservoirs, dams and 

impoundments are known to alter the annual pattern of flows 

(that is, regular cycles of low and high flows) on which some 

aquatic invertebrates and fish depend for life-cycle cues. For 

example, changes in annual flow variability, especially processes that remove flood peaks and release the water later during normally low flow periods, are a management technique 

that commonly results in inflated minimum flows (Magilligan 

and Nislow, 2005), which greatly delay annual low flows and 

may modify seasonal flow patterns. Such alteration of natural 

hydrologic variability may ultimately compromise the longterm survival of some endemic or sensitive aquatic fauna. For 

example, regulation of flows and conversion of rivers to slowor still-water habitat has been shown to affect diversity and the 

functional organization of aquatic assemblages, increasing the 

dominance of generalist species and favoring introduced species (Bunn and Arthington, 2002). Additionally, dams, which 

are commonly constructed for water-supply purposes (that is, 

they are designed as water-retention devices), tend to dampen 

the effects of moderate high flows and, in some cases, increasing low flows on downstream areas (Wenger and Freeman, 

2007). Although seemingly beneficial for downstream water 

use, such changes can come at the expense of hydrologic variability at upstream reaches, where detention storage can result 

in alteration of habitat, geomorphology, and stream assemblages (for example, Freeman and Marcinek, 2006). Like 

those from reservoirs, the flows released from some dams are 

commonly based on minimum passing-flow criteria that have 

been implemented to prevent streamflow from falling below 

specific flow rates. 

Aquatic fauna are dependent on more than just streamflow (that is, stream level); they are also dependent on annual 

fluctuations in the duration and magnitude of high and low 

flows, which are rarely considered in flow releases (for 

example, see The Nature Conservancy’s Sustainable Rivers Project at http://www.nature.org/ourinitiatives/habitats/

riverslakes/sustainable-rivers-project.xml). In general, both 

reservoirs and dams are known to move streams away from 

“natural” hydrologic cycles, and such changes may ultimately 

delay or modify reproductive processes, limit species distribution, disrupt fish migration, and hinder survival, especially for 

those aquatic taxa that are particularly sensitive to hydrologic 

alteration and are dependent on hydrologic variability to support life-cycle needs.

The removal of impoundments is becoming a common 

management approach to restoring natural streamflow variability and is a technique that is currently being practiced in 

many areas throughout the country in both large (for example, 

Elwha River dam removal; Duda and others, 2011) and small 

(for example, Manatawny Creek Dam Removal Project; 

Bushaw-Newton and others, 2002; Thomson and others, 2005; 

New Jersey Department of Environmental Protection, 2011) 

systems as a method for restoring the natural flow and ecological integrity of streams and rivers and as a way to re-establish 

uninterrupted flow and improve water quality and biological 

integrity. Increased awareness of the adverse effects of dams 

in concert with an aging infrastructure and dam maintenance 

costs has led to a heightened interest in dam removal in the 
Management Implications 29

United States (Orr and others, 2008), where more than 500 

dams have been removed in the past two decades (Stanley and 

Doyle, 2003). The ecological benefits of dam removal include 

increased fish passage and broader access to spawning habitats, reestablishment of riverine taxa, declines in nonendemics, 

and restoration of stream integrity (for example, Catalano and 

Bozek, 2007); however, large amounts of trapped sediment 

released after dam removal can adversely affect biotic integrity for months or even years after dam removal (Thomson and 

others, 2005). This effect is temporary, however, as aquatic 

assemblages will continue to recover as fine sediments are 

reduced in downstream habitats. Ideally, the ecological costs 

and benefits of dam removal would be considered, and where 

the long-term benefits of restoring river flows outweigh the 

relatively short-term ecological effects, dam removal may be 

one of the few management measures that can be implemented 

in some streams (for example, ManalSpt) to restore natural 

flows and improve ecological integrity. Still, dam or impoundment removal may be impractical in areas where multiple 

historic dams or impoundments, typically located on the same 

stream segment, have greatly altered streamflow. 

Impervious Surface Cover

It is well established that increases in urban land use 

are highly correlated with increases in ISC, which have been 

shown to alter stream hydrology and affect ecological assemblages (Kennen and Ayers, 2002; Kennen and others, 2005; 

Roy and others, 2005; Wenger and others, 2008; Brown and 

others, 2009a,b; see also Schueler and others (2009) for a 

comprehensive review). Recent findings by Cuffney and others (2010) indicate that aquatic invertebrates may be adversely 

affected by even smaller amounts of ISC (for example, 

<10 percent) than previous studies have indicated. Stormwater 

moves rapidly across impervious surfaces and is transmitted to 

streams through storm drain pipes and other water-conveyance 

devices, resulting in a characteristic pattern of higher peak 

flows and “flashy” streamflow response (Henshaw and Booth, 

2000; Fongers and Fulcher, 2002; Walsh and others, 2005b). 

MillPla, WBMdMart, BoundMid, MatchSpt, DeepBrn, and 

TennBrn are examples of study sites whose drainage areas are 

associated with relatively high percentages of ISC (>10 percent; table 1). 

There are many challenges associated with mitigating 

the effects of urbanization on hydrology. Ideally, watershedprotection strategies would be developed and implemented 

to prevent hydrologic alterations before they occur. In most 

cases, however, such plans are not considered until water 

resources and stream biological integrity have already started 

to decline. In urban and suburban systems, a comprehensive 

management strategy that addresses runoff volume, duration, 

velocity, frequency, groundwater recharge, and protection of 

stream channels would likely be most effective. If development patterns and stormwater-management strategies that preserve the predevelopment hydrologic regime are encouraged, 

the adverse effects resulting from urban development may be 

minimized. 

Runoff-reduction approaches that comprehensively 

address runoff volume, duration, velocity, and frequency and 

groundwater recharge are growing in acceptance (National 

Research Council, 2008), and a number of States (for example, 

Virginia) are implementing strategies that provide numerous 

options for the design and specifications of best management 

practices (BMPs) (see the Virginia Runoff Reduction Method 

Web site at http://www.dcr.virginia.gov/laws_and_regulations/

lr2f.shtml). Dampening the effect of urban runoff by increasing infiltration is one way to offset increases in high-flow 

magnitude. BMPs that facilitate the infiltration of precipitation and slowly release it will likely reduce the intensity and 

duration of floods and flood peaks, respectively. This increased 

infiltration can often be accomplished with minimal efforts 

designed to protect groundwater-recharge areas, reduce the 

extent of ISC in the watershed, and protect and enhance the 

amount of forested riparian buffers, especially in those areas 

where buffers are not directly bypassed by existing storm 

drains. Appropriately designed water-detention basins and biofiltration systems including grassed waterways and filter strips 

are examples of possible measures that have been suggested to 

reduce surface runoff and increase infiltration of precipitation 

(for example, Walsh and others, 2005a).

Construction of BMPs (through either retrofits or direct 

application) that retain the first 1 to 1.5 inches of stormwater 

and then slowly discharge it to a stormwater system (and (or) 

the stream if necessary) can help to reduce the duration of 

high-flow events (for example, see Carter and others, 2009). 

Systems that are designed to successfully retain stormwater 

would increase infiltration and groundwater recharge, thus 

reducing stormwater runoff to streams.

Land-protection strategies that restrict development in 

least impaired watersheds (for example, by using tools such as 

land-use planning and zoning) may help to protect or restore 

hydrologic variability and stream biological integrity. Reuse of 

existing urban lands through redevelopment and revitalization 

could create opportunities for reversing adverse hydrologic 

effects by reducing, disconnecting, and (or) treating ISC–for 

example, incorporating low-impact development (LID) and 

restoring green infrastructure (a network of open green space 

that conserves the infiltration capacity of natural ecosystems; 

Benedict and McMahon, 2001) throughout the built environment. These types of comprehensive strategies can provide 

incremental improvements in the hydrology of local waters 

and, when used in conjunction with established State TMDL 

limits, the approach may result in considerable reduction in the 

load of contaminants delivered to downstream waters (Nisenson, 2005).

A runoff-prevention or -reduction strategy that incentivizes stormwater retrofits, detention storage, or other BMPs 

as a way to minimize the direct effects of high flow and 

thereby replicate a more natural hydrologic response may be 

highly beneficial to the long-term health of aquatic fauna. For 

example, providing incentives to homeowners in the basin to 
30 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

disconnect impervious surfaces from streams by using rain 

barrels or constructing rain gardens (that is, small-scale bioretention areas that are designed to allow rainwater runoff to 

infiltrate from impervious areas such as rooftops, parking lots, 

and driveways) could help to reduce runoff in small basins 

such as WBMdMart. This type of decentralized BMP approach 

is less costly than large engineering infrastructure, captures 

rainwater where it falls and reduces stormwater volumes, peak 

flows, and contaminant loads (Bitting and Kloss, 2008) and 

has been used successfully to improve stream conditions in 

small watersheds in Ohio (for example, Roy and others, 2006). 

Other methods that could be used to reduce the magnitude 

of high-flow events include the disconnection or redistribution of stormwater sewer lines to stream outfalls, swales, or 

retention devices.

Water Abstraction

Some examples of streams in the Raritan River Basin 

that may have been affected by water abstraction (that 

is, withdrawals of groundwater or surface water for agricultural and human supply) include NeshRea, MillKing, 

and HoneyPen. According to the New Jersey Division of 

Water Supply, agricultural water use registration must be 

obtained if a landowner has the capability to withdraw 

ground- and (or) surface water in excess of 100,000 gal/d 

for agricultural, aquaculture, or horticultural purposes 

(http://www.nj.gov/dep/watersupply/a_ag.html). Unregulated 

water withdrawals or the extraction of large volumes of water 

that fall just under that limit could be affecting flow at some 

of these study sites. Unregulated and unsustainable use of 

groundwater can compromise environmental-flow objectives 

by reducing surface-water flows (Richter, 2009). These types 

of water withdrawals are compounded by the fact that agricultural water use typically spikes at times of the year (generally 

spring) when irrigation demands are highest and the need for 

streamflow to support the life history (migration and reproduction) of many endemic fauna is greatest. Water abstraction 

is another stressor that poses the fundamental challenge of 

balancing human needs with the Clean Water Act requirements to sustain and protect natural aquatic resources within 

rivers. Evaluating the ecological effects of water abstraction at unregulated streams can be challenging, however, 

because it involves many geographically dispersed users who 

pump water intermittently; because such use generally is not 

metered, the pumpage is typically unknown (Chessman and 

others, 2011).

Improved accountability for agricultural and human water 

use through metering, especially for those withdrawals that are 

less than the regulated minimum, or the reduction of water use 

during critical time periods (for example, during summer lowflow periods) might ease hydrologic stress or possibly even 

“reset” the timing of the seasonal low-flow patterns. Balancing 

water extraction for municipal, commercial, and agricultural 

uses with that needed to support ecological integrity may be 

useful in stream systems like MillKing that, with minimal 

additional hydrologic stress, could experience even greater 

hydrologic deviations at both the high- and low-flow ends of 

the hydrograph. If, for example, low flows become lower and 

wetted stream widths decrease, aquatic-invertebrate or fish 

eggs deposited near the river edge may become dewatered 

and desiccate, or streamflow may be inadequate to properly 

disperse newly emerging larvae, resulting in stranding or 

death. The implementation of guidelines that largely restrict 

withdrawals to higher flow periods when more water is available or to times that do not coincide with critical life-cycle 

periods may also be helpful. Aquatic invertebrates can also be 

indirectly affected by withdrawals because of changes in water 

quality associated with extended periods of low flow, such as 

increases in water temperature, changes in conductivity, and 

decreases in dissolved-oxygen concentration (Boulton, 2003). 

Changes to aquatic-invertebrate assemblages have also been 

observed as a result of increased periphyton biomass, which 

can occur naturally in rivers during summer low flows but also 

can be exacerbated if velocities are reduced by abstraction 

(Suren and others, 2003). Suren and others (2003) suggest that 

the degree of enrichment of a river should perhaps be considered when assessing minimum flow for the development of 

flow-management guidelines.

Results of recent studies assessing the effects of water 

abstraction on aquatic-invertebrate assemblages vary. Many 

of the differences appear to depend on the amount of flow 

diverted (type or intensity of the abstraction); particular 

climatic conditions; and the timing, proximity, and relative 

location of sample collection with respect to the diversion 

(upstream or downstream). The application of widespread 

metering of irrigation and water-supply wells would help to 

improve current knowledge about the location and magnitude of these withdrawals; however, implementation of such 

methods can be prohibitively expensive. The Georgia State 

Legislature, however, implemented the Georgia Agricultural 

Water Conservation and Metering Program in 2003 and, by 

the end of 2010, more than 10,000 water meters had been 

installed on agricultural irrigation-supply wells in the middle 

and lower Chattahoochee and Flint River Basins (Torak and 

Painter, 2011). Such detailed geospatial information is essential for informing farmers and water managers about water 

use, crop selection, and the effects of climate (for example, 

drought conditions) and pumpage changes on groundwater 

and surface-water resources. Additionally, more complete 

water-use data from such efforts could be integrated with corresponding national water-use datasets through the National 

Water Census, which is part of the USGS WaterSMART 

(Sustain and Manage America’s Resources for Tomorrow) 

Availability and Use Assessment Program (U.S. Geological 

Survey, 2011; Alley and others, 2013); see http://water.usgs.

gov/watercensus/.

Grouping sites into stressor categories (for example, 

reservoirs and dams, ISC, water abstraction) provides waterresource managers with a general understanding of some of 

the mechanistic pathways that potentially may alter natural 

streamflow processes in New Jersey. Managers and decision 
Linking Hydrologic Alteration to Invertebrate-Assemblage Response 31

makers can then use this information to establish sustainable 

ranges in high- and low-flow criteria to support ecological and 

human uses at these sites and, by extension, at other sites that 

may fall into one or more of these stressor categories. Some 

study sites are likely altered through multiple hydrologic 

stressor pathways; however, disaggregation of these complex 

pathways is beyond the scope of this report. Nevertheless, 

these general stressor groups represent a starting point for the 

development of hydroecological targets.

Linking Hydrologic Alteration to 

Invertebrate-Assemblage Response 

Developing flow-ecology response relations that emphasize the linkage between changes in hydrologic processes 

and ecological response is a fundamental component of the 

ELOHA–Ecological Limits of Hydrologic Alteration process 

(Poff and other, 2010). In general, these relations correlate 

measures of ecological condition (for example, aquatic-invertebrate species richness) to measures of streamflow condition 

(for example, DL4–the annual minimum of 30-day movingaverage flow). Described below are a series of technical 

and analytical steps taken to develop flow-ecology response 

relations for a subset of Raritan River Basin study sites where 

aquatic-invertebrate and hydrologic data overlap.

Site Selection and Data Aggregation

As previously stated, selection criteria were based on a 

U.S. Geological Survey (USGS)–N.J. Department of Environmental Protection (NJDEP) cooperative approach that identified sites in the Raritan River Basin that, in general, had only 

slight to moderate anthropogenic degradation of the aquatic 

system (that is, none was considered to be severely impaired 

as determined from its NJIS score) and were at or near an 

existing USGS real-time continuous streamflow-gaging station. A database was designed to link all available aquaticinvertebrate data with hydrologic information for these sites 

where overlap occurred. The primary purpose of this database 

was to merge hydrologic and ecological data for the purpose 

of developing flow-ecology response relations. This work 

required (1) the acquisition of Ambient Biomonitoring Network (AMNET) data (round 3) with all available metrics from 

the NJDEP; (2) the creation of the database by compiling data 

from multiple sources in order to link all available data from 

USGS and NJDEP, including current and historic AMNET 

data, National Water Quality Assessment (NAWQA) program 

ecological data, and New Jersey volunteer invertebrate-monitoring data; and (3) proximally linking invertebrate-monitoring sites with corresponding USGS streamgages. The initial 

AMNET dataset included five NJIS metrics—taxa richness, 

EPT index, percent dominance, percent EPT, and the modified 

Family Biotic Index. To increase the number of invertebrate 

metrics available for developing flow-ecology response relations, however, the invertebrate data were processed further 

by using the Invertebrate Data Analysis System (IDAS; 

Cuffney and Brightbill, 2011), which provides a larger suite of 

structural, functional, and behavioral assemblage metrics for 

comparison with hydrologic indices.

In order to relate hydrologic to ecological data, a set of 

aquatic-invertebrate sampling sites needed to be identified that 

are near the hydrologic study sites. Distance-based criteria 

were established to identify AMNET or other aquatic-invertebrate sampling sites for inclusion in the analysis. The following conditions were required: (1) all invertebrate sampling 

sites had to be located on the same stream segment (that is, 

there could be no stream confluences with other major tributaries between the streamflow-gaging station and the invertebrate 

sampling site, and (2) the invertebrate sampling site needed 

to be within 2.5 stream miles of the USGS real-time continuous streamflow-gaging station. To maintain consistency with 

the comparative hydrologic analysis, only stream classes A 

(semiflashy streams with moderately low base flow) and C 

(moderately stable streams with moderately high base flow) 

were targeted as before and as cooperatively agreed upon with 

the NJDEP. A geographic information system (GIS) approach 

was used to pair aquatic-invertebrate sampling sites and 

streamflow-gaging stations spatially by using the conservative 

criteria above. GIS analysis identified 23 of the 29 class A and 

C streams used in the hydrologic analysis that met the above 

requirements and were subsequently paired for hydroecological analysis (fig. 9). Most of the assembled invertebrate data 

were collected from April to September in 2003, 2004, and 

2005. In two instances, aquatic-invertebrate samples collected 

from two time periods at the same location (that is, AMNET 

site AN0368 and USGS NAWQA station number 01403300) 

were associated with separate, but proximal, continuousrecord locations (table 3) following the pre-established selection criteria listed above.

Aquatic-Invertebrate Analysis

Aquatic-invertebrate assemblage data for the 23 class 

A and C stream sites compiled for this study combined State 

AMNET data, NAWQA data, and data from volunteer monitoring networks by using IDAS. All datasets were comparable 

in terms of sampling protocols (that is, the methods were similar regarding sampled habitat, number of composite samples 

collected, and total sampled area) and laboratory procedures 

(sorting, subsample count level, and taxonomic resolution) 

(see N.J. Department of Environmental Protection (2007, 

2012b) and Moulton and others (2002) for detailed sampling 

protocols). For the NAWQA sites, the assemblage data were 

randomly resampled to a fixed count (n = 100) using IDAS 

to match the subsample size of the volunteer and AMNET 

datasets. The data were reviewed extensively to ensure that 

the aggregated data from the three separate sources included 

the same taxonomic groups, followed the same spelling and 
32 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

MORRIS

HUNTERDON

MIDDLESEX

SOMERSET

WARREN

MERCER

MONMOUTH

ESSEX

UNION

BURLINGTON

PASSAIC

SUSSEX

OCEAN

Location of U.S. Geological Survey (USGS)

streamgage and station number

Location of aquatic-invertebrate sampling site and identifier

New Jersey Department of Environmental Protection Ambient 

Biomonitoring Network

USGS National Water Quality Assessment Program

Upper Raritan Watershed Association

01403300

URWA NB06

AN0377

EXPLANATION

01406000 Physiographic province

Valley and Ridge

New England

Piedmont

Coastal Plain

Boundary of Raritan River Basin study area

75°15' 75° 74°45' 74°30' 74°15'

40°45'

40°30'

40°15'

Base from U.S. Geological Survey digital line graph files, 1:24,000,

Universal Transverse Mercator projection, Zone 18, NAD 83 

01403300

01403900

URWA NB06

FALL

LINE

75° 74°

41°

40°

39° DELAWARE

PENNSYLVANIA

NEW YORK

AREA OF DETAIL

ATLANTIC OCEAN

0 10 MILES

0 10 KILOMETERS

Base from U.S. Geological Survey digital line graph files, 1:24,000,

Universal Transverse Mercator projection, Zone 18, NAD 83 

0 5 10 MILES

0 105 KILOMETERS

Physiographic province boundary from New Jersey

 Department of Environmental Protection, 2002

01406000 01405300

01405400

01405500

01406500

01400730

01401000

01400953

01400932

01402000

01398000

01397000

01403060

01403000

01403900

01400500

01400000

01403150

01403160

01399700 01399690

01399670

01396800

01396660

01396500

01396580

01399500

01401500

01398500

AN0453

AN0451

AN0440

AN0397

AN0393

AN0333

AN0326

AN0377

AN0417

AN0416

AN0360

AN0874

AN0369

AN0368

AN0321

AN0317

AN0319

AN0410

Figure 9. Location of hydrologic and ecological sites used for the flow-ecology response analysis for the Raritan River Basin 

study area, New Jersey.
Linking Hydrologic Alteration to Invertebrate-Assemblage Response 33

Table 3. Aquatic-invertebrate sampling sites and their corresponding U.S. Geological Survey streamgage station 

numbers used in the flow-ecology response analysis.

[USGS, U.S. Geological Survey; ecology site identifier beginning with AN, New Jersey Department of Environmental Protection Ambient Monitoring Network (AMNET) site number; ecology site identifier beginning with URWA, Upper Raritan Watershed Association 

site number; AMR3, sample collected during round 3 of the AMNET program; AMR2, sample collected during round 2 of the AMNET 

program; NAWQA, U.S. Geological Survey National Water Quality Assessment; NJIS, New Jersey impairment score]

USGS station 

number

Ecology site 

identifier

Data 

source

Sample 

date NJIS Impairment Dominant taxon

01396500 AN0317 AMR3 4/27/2004 30 Non-impaired Chironomidae

01396580 AN0319 AMR3 4/7/2004 21 Moderately impaired Chironomidae

01396660 AN0321 AMR3 4/22/2004 21 Moderately impaired Lumbriculidae

01397000 AN0326 AMR3 4/29/2004 30 Non-impaired Lepidostomatidae

01398000 AN0333 AMR3 5/11/2004 21 Moderately impaired Naididae

01398500 URWA NB06 URWA 6/22/2005 21 Moderately impaired Hydropsychidae, Chironomidae

01399500 AN0360 AMR3 5/25/2004 30 Non-impaired Chironomidae

01399670 AN0368 AMR2 5/13/1999 21 Moderately impaired Chironomidae

01399690 AN0368 AMR3 5/4/2004 15 Moderately impaired Chironomidae

01399700 AN0369 AMR3 4/20/2004 15 Moderately impaired Chironomidae

01400000 AN0374 AMR3 6/2/2004 30 Non-impaired Gammaridae

01400500 AN0377 AMR3 6/3/2004 12 Moderately impaired Gammaridae

01401000 AN0393 AMR3 9/9/2004 21 Moderately impaired Elmidae

01401500 AN0397 AMR3 9/21/2004 18 Moderately impaired Chironomidae

01402000 AN0410 AMR3 9/23/2004 15 Moderately impaired Asellidae

01403000 01403300 NAWQA 7/29/2003 24 Non-impaired Elmidae

01403060 01403300 NAWQA 8/8/2005 27 Non-impaired Elmidae

01403150 AN0416 AMR3 6/3/2004 9 Moderately impaired Chironomidae

01403160 AN0417 AMR3 5/25/2004 18 Moderately impaired Chironomidae

01403900 01403900 NAWQA 7/28/2003 21 Moderately impaired Hydropsychidae

01405300 AN0451 AMR3 8/24/2004 9 Moderately impaired Chironomidae

01405400 AN0440 AMR3 8/5/2004 15 Moderately impaired Chironomidae

01406000 AN0453 AMR3 9/2/2004 9 Moderately impaired Chironomidae
34 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

abbreviation procedures, and had appropriate taxonomic resolution (that is, organisms were identified to the lowest taxonomic level) before data analysis was attempted.

The IDAS software (Cuffney and Brightbill, 2011) was 

used to resolve all taxonomic issues (taxonomic identification 

level and nomenclature) and to resolve taxonomic ambiguities 

(this process is commonly referred to as taxonomic harmonization). Ambiguities in the taxonomic hierarchy (that is, 

organisms that are not completely identified because of small 

size, incomplete development, damage, or poor preservation) 

were typically resolved by distributing the abundance of the 

ambiguous parents among their children in accordance with 

the relative abundance of each child (Cuffney and Brightbill, 

2011). This approach, which represents a compromise between 

removing redundant taxonomic information and conserving quantitative information on taxa richness and abundance 

(Taylor, 1997), is one of the methods suggested by Cuffney 

and others (2007). When this approach was not possible, other 

approaches for resolving taxonomic ambiguities were applied. 

For example, rare organisms or those with difficult taxonomy 

were sometimes aggregated to family or higher. The dipteran 

family, Chironomidae, is considered an important bioindicator group, yet is historically a difficult group to identify to 

genus or species. As a result, data for this group were typically 

assigned to six taxa levels (five subfamilies plus Chironomidae) from the various family- to genus-level identifications 

that occurred within the original data. In some cases, where 

appropriate, genus-level identification was maintained. After 

data preparation, the IDAS program was used to calculate 

184 aquatic-invertebrate assemblage metrics, many of which 

are commonly used in stream bioassessment (Rosenberg and 

Resh, 1993; Davis and Simon, 1995; Barbour and others, 

1999) and some of which are comparable to those used by the 

NJDEP for the AMNET program (for example, N.J. Department of Environmental Protection, 2007). Tolerance and 

functional group metrics were calculated by using values from 

Barbour and others (1999). Tolerances were calculated on the 

basis of richness (average of tolerance values assigned to each 

taxon) (Cuffney and Brightbill, 2011).

Use of Ecological Impairment to Define a 

Hydrologic Disturbance Gradient 

Within stream classes A and C are study basins associated with varying intensities of anthropogenic disturbance 

as indicated by their New Jersey Impairment Score (NJIS) 

(table 3). This multimetric index is derived from combining 

ecological measures of the aquatic-invertebrate assemblage, 

including dominance, tolerance, richness, and characteristics 

of taxonomic groups (see previous section on the Statewide 

Ambient Biomonitoring Network) (N.J. Department of Environmental Protection, 2007). In the context of this report, the 

NJIS (or its component metrics) or IDAS metrics can be used 

as a general guideline to rank the relative level of ecological 

impairment in the study basin, or within stream classes, and 

indirectly establish a hydrologic disturbance gradient along 

which the effects of urbanization and development increase 

(for example, Kennen and others, 2002; Coles and others, 

2004; Cuffney and others, 2005; Kennen and others, 2005). 

If at least one basin representing minimally impaired conditions (for example, South Branch Raritan River near High 

Bridge, NJ (01396500)) exists, that basin can represent an 

impairment endpoint for stream classes A and C. By using the 

hydrologic information for each class, changes in a subset of 

the HIP indices can be evaluated on the basis of their ranking 

along the NJIS disturbance gradient. In other words, changes 

can be evaluated by relating ecological metrics (the response 

variables) to hydrologic attributes (the explanatory variables) 

to derive flow-ecology response relations. Linear and multivariate statistical analyses can be used to determine whether 

a quantitative link exists between the metric scores and the 

mean change in the flow indices for a specific stream class or 

combination of stream classes. A subset of hydrologic indices 

that are ecologically relevant and nonredundant are presented. 

Hydrologic indices determined to be statistically significant 

predictors of stream impairment represent the basis of the 

flow-ecology response model building phase presented in 

this report.

Data-Reduction Procedures and Analytical 

Approach

Scatterplots and correlation matrices were used to 

examine data distributions and to detect potential outliers in 

the hydrologic and invertebrate metric data. Several hydrologic indices (MA1, 15, 20; MH5; ML4, 8; DL4; DH1) were 

significantly influenced by sites with large drainage areas that 

consequently had higher-than-average flow values. Therefore, hydrologic indices found to be affected by drainage area 

(those with a Pearson product-moment correlation coefficient 

(r) > 0.70 with drainage area) were standardized by drainage 

area in order to “downweight” the influence of the larger study 

basins on the analyses. Standard data-reduction techniques 

were used to achieve a workable dataset and eliminate redundancy among the 184 IDAS metrics. In general, invertebrate 

metric response variables with a limited range of response to 

hydrologic variables (r < 0.50) were removed from consideration. Remaining invertebrate metric and hydrologic variable 

groups were correlated separately to identify redundancies. 

Several parsimonious surrogate invertebrate and hydrologic 

variables were selected to represent intercorrelated (r > 0.70) 

groups of variables. If all quantitative factors were equal 

between two invertebrate or hydrologic variables, the general 

management applicability of the variables to the Raritan River 
Linking Hydrologic Alteration to Invertebrate-Assemblage Response 35

Basin and ease of measurement were considered. In effect, an 

attempt was made to identify and choose the most parsimonious variables for analysis. Correlations of aquatic-invertebrate 

metrics with hydrologic variables were then examined by 

means of Spearman correlation and variables that had a 

rho < 0.40 were removed from further analysis. All univariate 

and multivariate analyses were completed by using a combination of Primer v6 (Clarke and Gorley, 2006) and SAS (version 

9.2; SAS Institute Inc., 2006). The remaining set of hydrologic 

and ecological variables was used to develop bivariate and 

multivariate flow-ecology response models (see tables 4 and 5 

for definitions and variable codes for the final set of ecological 

metrics used in the analyses). Spearman correlations and MLR 

models were developed on the basis of combined analysis of 

class A and C streams (n = 23). MLR model performance was 

assessed by using the mean sum of squares (R2), root mean 

squared error (RMSE), Akaike Information Criterion (AIC), 

predicted sum of squares (PRESS), and regression coefficients 

(SAS Institute Inc., 1991). A consistent MLR model-fitting 

approach was adopted for each invertebrate metric response 

variable. Stepwise selection criteria (with forward-selection 

and backward-elimination procedures) based on AIC and 

R2 for all two- and three-variable models was used. Model 

residuals, potential outliers, variance inflation factors (VIFs), 

and interaction terms were evaluated. When the final or “best” 

MLR model was selected, consideration was given to the AIC 

and the overall model fit. That is, MLR models were generally ranked in order of their R2 value, with higher rankings 

assigned to models with the lowest AIC and (or) no interaction 

term. To help evaluate the relative importance of each variable 

within the final MLR models, partial R2 values were determined for each variable. 

Table 4. Flow-ecology response relations of selected New Jersey Department of Environmental Protection (NJDEP) Ambient 

Monitoring Network (AMNET) metrics significantly correlated (Spearman’s rho) with the reduced set of hydrologic indices.

[Hydrologic index definitions can be found in appendix 1; EPT, Ephemeroptera, Plecoptera, Trichoptera; D/D, downward/decrease; U/I, upward/increase]

Metric 

abbreviation Metric description Predicted metric response1

Hydrologic 

index rho p-value

NJIS2 New Jersey Impairment Score D/D

MA27 -0.484 0.0193

ML21 -0.436 0.0377

FH1 -0.466 0.0249

EPT Index Total richness of EPT taxa D/D

MA27 -0.493 0.0170

ML15 0.486 0.0187

ML21 -0.525 0.0101

Percent EPT3 Percentage abundance of EPT taxa D/D

MA27 -0.480 0.0204

ML18 -0.462 0.0264

DL11 0.420 0.0463

FBI Modified Family Biotic Index4 U/I

MA27 0.476 0.0216

ML15 -0.487 0.0184

ML18 0.456 0.0289

1

Predicted response pattern (direction of temporal change) for the aquatic-invertebrate metric with increasing anthropogenic disturbance within the Raritan 

River Basin, New Jersey.

2

The NJDEP uses five metrics to calculate the New Jersey Impairment Score (NJIS): Taxa Richness (total families), EPT Index, Percent Dominance (percent of total abundance composed of the dominant family in the subsample), Percent EPT, and Modified Family Biotic Index (FBI). These metrics are summarized (New Jersey Department of Enironmental Protection, 1994; 2008) to produce the NJIS (New Jersey Department of Enironmental Protection, 1994; 

2007). The NJIS, which ranges from non-impaired to severely impaired, is one of the criteria used by the NJDEP to assess stream biotic integrity. 

3

Includes the hydropsychid family.

4

A weighted sum of the Family Tolerance Values (FTV), based on Hilsenhoff’s tolerance scores assigned to families ranging from 0 (intolerant) and 10 

(tolerant) (Hilsenhoff, 1987; New Jersey Department of Enironmental Protection, 2007). 
36 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Table 5. Flow-ecology response relations of selected invertebrate-assemblage metrics significantly correlated (Spearman’s rho) with 

the reduced set of hydrologic indices.

[Hydrologic index definitions can be found in appendix 1. A detailed explanation of all invertebrate metrics can be found in Cuffney and others (2005). Tolerance values (RichTOL and Tol_rich) and functional feeding groups (for example, scrapers (SC) and shredders (SH)) are derived from Appendix B in the U.S. 

Environmental Protection Agency (USEPA) Rapid Bioassessment Protocols (Barbour and others, 1999). <, less than; EPT, Ephemeroptera, Plecoptera, Trichoptera; D/D, downward/decrease; U/I, upward/increase]

Metric 

abbreviation Metric description Predicted metric response

Hydrologic 

index rho p-value

RichTOL Average USEPA tolerance values based on richness U/I

MA25 0.554 0.0061

DH19 0.593 0.0029

Tol_rich Number of taxa in the tolerant class U/I

MA25 0.545 0.0071

ML18 0.527 0.0098

MH17 0.554 0.0061

ODIPNIR Richness composed of nonmidge dipterans and noninsects U/I

MA27 0.700 0.0002

MH17 0.609 0.0021

FH4 0.652 0.0001

MOLCRUR Richness composed of molluscs and crustaceans U/I DH19 0.595 0.0028

EPTRp Percent of total richness composed of EPT taxa D/D

MA27 -0.697 0.0002

ML15 0.649 0.0008

DH19 -0.526 0.0099

pSC_Rich Percent of total richness composed of scraper taxa2 D/D ML21 -0.519 0.0112

SH_Abund Total abundance composed of shredder taxa3 D/D

MA14 0.579 0.0038

MA154 0.584 0.0034

TL1 0.602 0.0024

DIP Total abundance of Diptera taxa U/I

ML21 0.603 0.0023

DH15 -0.528 0.0097

NCHDIP Total abundance of nonmidge Diptera taxa U/I

MH54 0.597 0.0026

FH1 0.502 0.0147

DH9 0.524 0.0103

1

Predicted response pattern (direction of temporal change) for the aquatic-invertebrate metric with increasing anthropogenic disturbance within the Raritan 

River Basin, New Jersey.

2

Scrapers (SC) are aquatic organisms that shear and feed on attached algae and associated material located on submerged underwater surfaces such as 

rocks, twigs, and leaf debris (for example, Stenelmis sp., Phaenopsectra sp., Amnicola sp., and Glossosoma sp.) (Vannote and others, 1980; Merritt and 

Cummins, 1996). 

3

Shredders (SH) are aquatic organisms that chew primarily large pieces of decomposing vascular plant tissue ( > 1 millimeter diameter), with a significant 

dependence on the associated microbial biomass (for example, Crictopus sp., Lepidostoma sp., Brillia sp., and Pycnopsyche sp.) (Vannote and others, 1980; 

Merritt and Cummins, 1996). 

4

Hydrologic index standardized to drainage area.
Evaluation of Aquatic-Invertebrate Metrics as a Basis for Flow-Ecology Response Model Development 37

Evaluation of Aquatic-Invertebrate 

Metrics as a Basis for Flow-Ecology 

Response Model Development

Stream impairment levels in the study area, as indicated 

by the NJIS for each site, ranged from moderately impaired 

(NJIS = 9–21) to non-impaired (NJIS = 24–30), with no sites 

listed as severely impaired (table 3). Six sites were listed 

as non-impaired and 17 sites were classified as moderately 

impaired. The initial compilation of aquatic-invertebrate 

data for the 23 sites yielded 556 taxa. After ambiguities with 

IDAS were removed, the number of taxa for the processed 

dataset was reduced to 536. The number of taxa at a site 

ranged from 14 to 33 (median 23), Chironomidae was the 

dominant family at 12 sites (table 3), with percent dominance 

ranging from 22 to 64 percent. The range of values for the 

aquatic-invertebrate metrics varied and was associated with 

invertebrate-assemblage conditions ranging from poor to very 

good. For example, the percent richness of Ephemeroptera, 

Plecoptera, and Trichoptera taxa (EPTRp) ranged from 3.3 to 

53.1 (median 24), and percent richness as nonmidge Diptera 

and noninsects (ODIPNIRp) ranged from 15.6 to 53 (median 

30.8). The percent abundance of EPT (EPTp) ranged from 

0.9 to 65.2 (median 16.8), and percent abundance of nonmidge 

Diptera and noninsects (ODIPNIp) ranged from 9.1 to 93.0 

(median 26.3). The modified Family Biotic Index (FBI), which 

measures the relative tolerances of aquatic invertebrates to 

organic enrichment, ranged from 2.7 to 7.4 (median 5.5). 

A tolerant taxon is one that is likely to be found at a site 

that has been altered by some type of environmental stressor 

(U.S. Environmental Protection Agency, 2006). In contrast, 

a sensitive taxon is one that tends to decline in abundance or 

occurrence probability along a defined stressor gradient (for 

example, urbanization or streamflow modification). Ecological tolerance values assigned to a taxon typically indicate how 

well the taxon tolerates the presence of organic enrichment in 

the stream (Hilsenhoff, 1987). Tolerance values range from 0 

(extremely sensitive organism) to 10 (tolerant organism), with 

low values indicative of intolerant taxa that will disappear 

quickly from assemblages as water quality degrades. High 

values indicate tolerant taxa that will remain in the aquaticinvertebrate assemblage as water quality degrades (Cuffney 

and Brightbill, 2011). In the IDAS software, the tolerance values chosen for each taxon were based on values derived from 

Barbour and others (1999) and the Mid-Atlantic Coastal Plain 

Work Group (U.S. Environmental Protection Agency, 1997), 

which included representatives from the States of New Jersey, 

Delaware, Maryland, North Carolina, and South Carolina. 

For each ecological site, tolerance values were assigned 

to each taxon and the taxa richness of the intolerant, moderately tolerant, and tolerant class of aquatic invertebrates was 

quantified in IDAS. The intolerant-class richness (Intol_rich) 

ranged from 0 to 18 (median 4), the moderately tolerant-class 

richness (Modtol_rich) ranged from 7 to 17 (median 12), 

and the tolerant-class richness (Tol_rich) ranged from 0 to 8 

(median 3). Although the study sites exhibit both non-impaired 

(Intol_rich = 18) and impaired (Intol_rich = 0) conditions, 

the aquatic-invertebrate data indicate that the majority of the 

Raritan River Basin ecological sites included in this study are 

characterized by moderately tolerant taxa.

Bivariate Flow-Ecology Response Relations

Flow-ecology response relations were developed to 

evaluate potential linkages between flow indices and aquaticinvertebrate assemblage metrics that could potentially be useful to water managers. Flow-ecology response relations were 

evaluated for a subset of aquatic-invertebrate metrics (tables 4 

and 5) for the 23 study sites that met the previously described 

criteria by using Spearman’s correlation and regression procedures. For the development of all flow-ecology relations, 

class A and C streams were combined because too few sites 

with both hydrologic and ecological data existed in any single 

stream class to provide sufficient power for univariate or multivariate analysis. Class A and C streams have many similar 

characteristics but, in general, differ in size and proportion of 

base flow.

Many hydrologic measures accounting for the duration, 

magnitude, timing, and frequency of flows were significantly 

correlated with ecological response (tables 4 and 5). Many 

invertebrate-assemblage metrics (n = 184) accounting for 

richness, abundance, and function of the aquatic-invertebrate 

assemblage were computed by using the IDAS software 

(Cuffney and others, 2005); however, only a subset (n = 9) 

that met the screening criteria was retained for further analysis (table 5). In addition, a smaller suite of AMNET metrics 

(n = 4) used by the NJDEP, which includes the NJIS, was also 

retained for development of flow-ecology response relations 

(table 4). Although some of the AMNET and IDAS metrics 

were comparable (for example, EPT richness (AMNET) and 

percent of total richness composed of EPT taxa (IDAS)), 

stronger relations with some hydrologic variables were seen 

for the IDAS metrics than for the AMNET metrics. This difference may result from the different approaches used to deal 

with taxonomic hierarchy and resolution when the taxonomic 

data were processed. NJDEP round 3 AMNET metrics are 

typically derived from data aggregated to the family level, 

whereas IDAS metrics are derived from data aggregated to the 

lowest practical taxonomic level (most commonly the genus 

level) where appropriate. These differences in data resolution 

may ultimately affect the comparability of the calculated richness values of the aquatic-invertebrate data used to determine 

the score for some of the NJIS component metrics relative 

to those calculated for the entire aquatic-invertebrate dataset 

using IDAS.

In general, invertebrate flow-ecology relations followed 

response patterns that would be expected in streams with some 

degree of hydrologic disturbance. The metrics most highly 

correlated to flow processes were the total richness composed 

of nonmidge Diptera and noninsects (ODIPNIR) as well as 

the percentage of total richness composed of EPT species 
38 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

(EPTRp) (table 5). A significant relation was identified with 

the variability in magnitude of April flow values (MA27) for 

both of these metrics. As the variability in MA27 increased, 

there was an increase in the richness of more tolerant aquatic 

invertebrates (ODIPNIR, rho = 0.700) and a decrease in 

the percent richness of aquatic invertebrates considered to 

be highly sensitive to changes in stream condition (EPTRp, 

rho = -0.697). Many of the AMNET metrics used in the analysis (for example, NJIS, EPT index, percent EPT), with the 

exception of FBI, showed a negative correlation with MA27 

(table 4). Higher high flows are common in streams in urbanizing drainage basins, and in the Raritan River Basin this type 

of alteration in flow appears to negatively affect the richness 

and abundance of aquatic-invertebrate fauna (for example, 

EPT taxa) (table 5). In contrast, the average U.S. Environmental Protection Agency tolerance values for samples based on 

richness (RichTOL) and the overall richness of tolerant invertebrate taxa (Tol_rich) increased with the variability in the 

magnitude of February flow values (MA25) (table 5). These 

relations may indicate that changing variability in the magnitude of streamflows, especially during critical flow periods, 

may have an appreciable effect on assemblage integrity (for 

example, Kennen and others, 2010).

A common effect of urbanization on stream hydrology 

is that the frequency and magnitude of high flows generally 

increase and flow durations generally decrease as a result of 

rapid runoff from impervious surfaces (Walsh and others, 

2005b). This increased flashiness is a consistent trait of the 

“urban stream syndrome,” and can be caused by a combination 

of increased runoff from ISC and more efficient transport of 

stormwater runoff within the catchment by piped stormwaterdrainage systems that drain directly to streams (Dunne and 

Leopold, 1978; Walsh and others, 2005a). In streams draining 

conventionally urban areas, the frequency of small to moderate 

flows from direct surface runoff may increase, even after small 

rain events (Walsh and others, 2004). This increased drainage 

of stormwater to streams alters the natural flow regime and 

has a direct effect on the aquatic-invertebrate assemblage. For 

example, when the average frequency of days (annually) in 

which the flow exceeds seven times the median flow (FH4), 

metrics that account for the richness of more tolerant taxa (for 

example, ODIPNIR) tend to increase (rho = 0.652, p > 0.001). 

Similarly, as the average frequency of annual high-flow events 

exceeding the 75th-percentile value (FH1) increases, NJIS 

decreases (rho = -0.466) and the abundance of nonmidge Diptera, NCHDIP, increases (rho = 0.502). 

The magnitude of low and high flows, timing of low 

flows, and duration of high flows were significantly linked 

to the abundance and richness of certain aquatic-invertebrate 

taxa (tables 4 and 5). As the variability across annual minimum flows (ML21) increased, the abundance of Diptera taxa 

(DIP, rho = 0.603) increased and the percent richness composed of scraper taxa (pSC_Rich, rho = -0.519), EPT index 

(rho = -0.525), and impairment score (NJIS, rho = -0.436) 

decreased. Scrapers feed on materials attached to submerged 

underwater surfaces such as rocks, twigs, and leaf debris 

(Vannote and others, 1980; Merritt and Cummins, 1996); 

therefore, a decrease in minimum flows may decrease habitat 

and food availability for many scraper organisms. Reductions 

in minimum annual streamflows, as indicated by ML21, may 

result in lower low flows that favor more tolerant taxa or taxa 

that prefer slower velocities (Jowett, 1997), or those taxa that 

are more tolerant of stressors (for example, oxygen depletion 

and increased water temperatures), such as some Diptera species. Chironomidae, for example, was the dominant family at 

12 of the Raritan River Basin ecological sites (table 3). Many 

chironomid taxa are particularly adaptable to degraded stream 

habitats, and some, such as those that survive in sediment, 

even have the capacity to respire in oxygen-depleted environments (Paine and Gaufin, 1956), even under extreme low-flow 

conditions. In contrast, the EPT and the EPTRp indices were 

positively correlated to the low-flow index (ML15) (tables 4 

and 5, respectively). These significant relations may indicate 

that the presence of some sensitive taxa (for example, Ephemeroptera) is highly coupled with changes in minimum annual 

flows. In addition, Carlisle and others (2010) found a shift in 

the functional traits of aquatic-invertebrate taxa in streams 

with diminished minimum flows. Taxa favoring slow-moving 

waters and fine substrates associated with lower flows replaced 

riffle-loving aquatic invertebrates that prefer coarse substrates 

in streams (such as EPT taxa), and aquatic invertebrates with 

the ability to move quickly within a stream (strong swimmers, 

fast crawlers) replaced taxa lacking these traits. The timing 

of minimum flows may also affect the aquatic-invertebrate 

assemblage in the Raritan River Basin study area. The abundance of shredder taxa (SH_Abund) was positively correlated 

with the Julian date of annual minimum flows (TL1), but when 

the annual minimum flow occurred before September 1st, the 

abundance of shredder taxa was reduced (rho = 0.602).

As the variability in base flow (ML18) increases as a 

result of increased surface runoff and diminished groundwater recharge, streams become flashier with higher peak flows 

and lower low flows. Again, lower low flows tend to support increases in more tolerant taxa (Tol_rich, rho = 0.527; 

FBI, rho = 0.456) while promoting a decrease in the percent 

abundance of more sensitive taxa (percent EPT, rho = -0.462). 

Altered low flows at many of the Raritan River Basin study 

sites likely have an adverse effect on sensitive aquatic invertebrates with life-history and behavioral constraints that rely 

on flow cues for support of crucial life-cycle stages (Lytle 

and Poff, 2004). Additionally, flows with higher magnitudes 

followed a similar pattern—that is, a positive correlation was 

found between the 25-percent median flow exceedance values 

for the entire hydrologic record (MH17) and an increase in 

the richness of more tolerant species (Tol_rich, rho = 0.554; 

ODIPNIR, rho = 0.609), which may indicate that some moderately impaired Raritan River Basin study area streams at the 

more highly altered end of the disturbance gradient are becoming more flashy. It has been demonstrated that in conventionally drained urban catchments, more frequent high-flow 

events, such as MH17, may be an important driver of channel 

incision, thereby reducing instream habitat while consequently 
Evaluation of Aquatic-Invertebrate Metrics as a Basis for Flow-Ecology Response Model Development 39

reducing the abundance of sensitive species (MacRae and 

Rowney, 1992; Walsh and others, 2005b).

In general, as streams become more urbanized (that is, 

they reflect flows of higher frequency and magnitude and 

shorter duration), the richness and abundance of tolerant 

organisms tend to increase and intolerant organisms decrease 

(Harding and others, 1998; Walsh and others, 2005b). Many 

of the IDAS metrics evaluated in this study reflect this relation 

(table 5); however, this was not always the case. For example, 

as the average duration of high flows above the 75th-percentile 

value for each year (DH15) increases, there is a corresponding decrease in Diptera taxa (rho = -0.528). Similarly, as 

high-flow duration seven times the median for the entire flow 

record (DH19) increased, the percent richness of EPT species 

(EPTRp, rho = -0.526) decreased (table 5). Even though most 

results of this study indicate that Diptera taxa appear to be 

resilient to stress associated with reductions in streamflow and 

appear to be highly adaptable to “flashy” streams, the inverse 

relation of Diptera taxa with DH15 as well as the decreases in 

EPTRp with increases in DH19 may indicate the presence of a 

change point for dipteran and other aquatic species in regard to 

increased duration and magnitude of the more extreme highflow events. Such high flows of long duration may heavily 

scour the stream bottom and ultimately result in a general, but 

often temporary reduction in the richness of dipteran as well 

as many other taxa.

Multivariate Flow-Ecology Response Models 

Multiple linear regression (MLR) analysis of hydrologic 

attributes and invertebrate-assemblage metrics was used 

to develop a series of equations defining the probability of 

assemblage alteration from hydrologic disturbance. MLR 

analysis is typically used to predict or model a response 

variable (for example, EPT metric scores) from one or more 

continuous explanatory variables (for example, hydrologic 

characteristics). The result is a regression equation that 

identifies the minimum set of explanatory variables needed to 

account for the observed variation in the response variable. By 

using multiple explanatory variables to estimate values of a 

response variable, errors in prediction can be minimized while 

still accounting for a large proportion of the variance in the 

response variable.

As would be expected, MLR models accounted for a 

relatively high proportion of the variability in the assemblage 

metric response. As more variables are incorporated into a 

regression model, higher R2 values will typically result. Therefore, although simple univariate flow-ecology response models 

using only one hydrologic variable (for example, tables 4 

and 5) may be significant, multiple regression models fit with 

two to three hydrologic variables (table 6) typically represent 

a more integrative model with greater predictive capability. 

In addition, multivariate flow-ecology response models may 

provide a more holistic perspective of the types of hydrologic 

changes that are affecting the aquatic-invertebrate assemblages 

in the Raritan River Basin.

Two- and three-variable MLR models were derived on 

the basis of hydrologic attributes that explained from 35 to 

75 percent of the variability in metric scores (table 6). Twentyfour hydrologic variables were significantly (p < 0.05) related 

to the ecological metrics for the aquatic-invertebrate assemblage, and all these variables had low variance inflation factors 

(VIF < 10.0). Combined, these hydrologic measures accounted 

for all five major components of the flow regime–magnitude, 

frequency, duration, timing, and rate of change. In general, 

multivariate models derived by using the IDAS metrics performed better (had a higher R2

) than those derived for comparable AMNET metrics. No significant multivariate model 

could be derived for the AMNET metric percent EPT. The 

three ecological metrics with the strongest relation to changes 

in streamflow were SH_Abund (R2 = 0.751, p < 0.0001), 

NCHDIP (R2 = 0.749, p < 0.0001), and EPTRp (R2 = 0.667, 

p = 0.0001) (table 6). All were three-variable models and all 

were metrics derived by using the IDAS program. The variability in monthly February streamflow (MA25), mean annual 

high flow for May (MH5), high flow discharge index (MH17), 

and mean annual September flow (MA20) each appeared four 

or more times in the represented models and accounted for 

58 to 71 percent, 45 to 79 percent, 25 to 38 percent, and 18 to 

49 percent of the overall variability, respectively. Mean annual 

high flow for May (MH5) accounted for the greatest amount of 

variability in any single model (79 percent in the two-variable 

model for SH_Abund) (table 6).

Predictive MLR models were derived by linking assemblage structure and function (that is, ecological metrics or 

a multimetric) with hydrologic attributes, and these models 

appear to indicate that hydrologic alteration resulting from 

landscape change, water abstraction, and streamflow regulation have modified stream biotic integrity in the Raritan River 

Basin, even for the subset of non- to moderately impaired sites 

targeted in this analysis. Most prominent were changes in the 

duration and magnitude of high and low flows that individually and cumulatively appeared to account for the greatest 

amount of variability in the aquatic-invertebrate metrics. 

Frequency, timing, and rate-of-change variables were much 

less prominent in the MLR models and generally accounted 

for a smaller proportion of the model variance (table 6). Most 

susceptible to hydrologic alteration were indices accounting 

for changes in sensitive taxa (for example, EPTRp), structural attributes such as those based on invertebrate richness 

(for example, ODIPNIR), and functional components (for 

example, pSC_Rich). The AMNET multimetric NJIS appeared 

to be slightly less responsive to overall changes in flow for 

streams in the Raritan River Basin than many of the individual 

taxa-based metrics. This finding is not unexpected, however, 

given that NJIS is a composite multimetric that is designed 

to provide a holistic assessment of stream health and was 

not specifically designed to assess streamflow alteration. The 

functional measure SH_Abund (total abundance of shredder taxa) was the metric most strongly related to changes in 

streamflow among all MLR models developed (table 6). All 

of these changes in biotic-assemblage metrics may be, in part, 
40 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Table 6. Two- and three-variable multiple regression models relating selected New Jersey Department of Environmental 

Protection Ambient Monitoring Network (AMNET) and Invertebrate Data Analysis System (IDAS) metrics to hydrologic 

indices.—Continued

[All model intercepts were significant at the p < 0.001 level. Hydrologic index definitions can be found in appendix 1; ecological metrics are 

defined in tables 4 and 5; R2, coefficient of determination; p, probability value; VIF, variance inflation factor]

Ecological 

metric

Number in 

model Source Model R2 Model p Partial R2 p-value Variable influence

Hydrologic 

index VIF

NJIS 2 AMNET 0.3503 0.0134

0.2202 0.0039 – MA25 1.89

0.1301 0.0500 + MH17 1.89

NJIS 2 AMNET 0.3643 0.0135

0.2166 0.0039 – ML22 1.46

0.1477 0.0492 – MA201 1.46

FBI 2 AMNET 0.4249 0.0040

0.2846 0.0010 – ML22 1.71

0.1403 0.0390 – MH17 1.71

EPT Index 2 AMNET 0.3787 0.0086

0.2384 0.0311 – DL6 1.04

0.1403 0.0463 – FH8 1.04

EPT Index 3 AMNET 0.5669 0.0015

0.2626 0.0039 – MA201 1.94

0.2472 0.0021 – DL6 1.09

0.0571 0.0037 – DH19 1.82

RichTOL 2 IDAS 0.4453 0.0028

0.3144 0.0014 + MA25 3.15

0.1309 0.0402 – DH11 3.15

RichTOL 3 IDAS 0.5133 0.0029

0.3144 0.0106 + MA25 1.94

0.1271 0.0383 – MH17 2.16

0.0718 0.0179 + DH19 1.98

Tol_rich 2 IDAS 0.4675 0.0015

0.3451 0.0029 + MA25 1.01

0.1314 0.0365 + DH92 1.01

Tol_rich 3 IDAS 0.5973 0.0005

0.3451 0.0065 + MA25 5.49

0.1442 0.0173 + FH4 5.35

0.1080 0.0035 + RA7 7.59

ODIPNIR 2 IDAS 0.4255 0.0039

0.2338 0.0063 – ML41 1.01

0.1917 0.0178 + MH51 1.01

ODIPNIR 3 IDAS 0.5455 0.0022

0.3502 0.0082 + MA27 1.40

0.0982 0.0079 – MA201 5.82

0.0971 0.0654 + RA7 6.68

MOLCRUR 2 IDAS 0.4648 0.0019

0.3362 0.0007 + DH19 1.07

0.1286 0.0403 + TL1 1.07

MOLCRUR 3 IDAS 0.5214 0.0025

0.2963 0.0028 + MH51 2.23

0.1650 0.0003 – ML41 2.48

0.0601 0.0020 – MA26 3.48

EPTRp 2 IDAS 0.5205 0.0009

0.3127 0.0002 + DL41 1.97

0.2078 0.0098 – MA201 1.97

EPTRp 3 IDAS 0.6669 0.0001

0.3696 0.0001 + ML19 9.88

0.1498 0.0050 – DL11 8.93

0.1475 0.0113 – MA201 1.48

pSC_Rich 2 IDAS 0.3086 0.0300

0.1581 0.0177 – DL6 1.10

0.1505 0.0502 – MA201 1.10

pSC_Rich 3 IDAS 0.4915 0.0043

0.3200 0.0026 – RA7 4.63

0.1448 0.0025 + DH15 1.46

0.0267 0.0026 + ML22 3.78
Evaluation of Aquatic-Invertebrate Metrics as a Basis for Flow-Ecology Response Model Development 41

Table 6. Two- and three-variable multiple regression models relating selected New Jersey Department of Environmental 

Protection Ambient Monitoring Network (AMNET) and Invertebrate Data Analysis System (IDAS) metrics to hydrologic 

indices.—Continued

[All model intercepts were significant at the p < 0.001 level. Hydrologic index definitions can be found in appendix 1; ecological metrics are 

defined in tables 4 and 5; R2, coefficient of determination; p, probability value; VIF, variance inflation factor]

Ecological 

metric

Number in 

model Source Model R2 Model p Partial R2 p-value Variable 

influence

Hydrologic 

index VIF

SH_Abund 2 IDAS 0.5921 0.0001

0.4663 0.0028 + MH51 1.18

0.1258 0.0220 + TL1 1.18

SH_Abund 3 IDAS 0.7512 < 0.0001

0.4358 0.0003 + MA151 1.20

0.1627 0.0023 + TL1 1.19

0.1527 0.0014 + DL7 1.04

DIP 2 IDAS 0.5413 0.0004

0.3594 0.0005 + ML182 1.01

0.1819 0.0107 + MA151 1.01

DIP 3 IDAS 0.6262 0.0003

0.3594 0.0004 + ML182 1.25

0.1395 0.0167 – DH15 1.06

0.1273 0.0198 + ML41 1.19

NCHDIP 2 IDAS 0.6365 <0.0001

0.4988 0.0005 + MH51 1.11

0.1377 0.0123 + DH92 1.11

NCHDIP 3 IDAS 0.7491 <0.0001

0.2821 <0.0001 + MH17 4.81

0.2545 0.0003 + FH6 2.03

0.2125 <0.0001 – FH3 5.96

1

Standardized by drainage area

2

Log10 transformed

a direct result of hydrologic alteration, or may be a result of 

changes in landscape factors associated with hydrologic alteration (Lytle and Poff, 2004), such as artificially maintained 

passing flows at dams or impoundments, greater surface- and 

groundwater use, and increasing ISC. Magnitude of average, 

high, and low flows and duration and timing of low and high 

flows were identified as important hydrologic variables in the 

MLR analysis. For example, magnitude of low flow (that is, 

ML22, ML4, ML19, and ML18) accounted for a significant 

amount of the variability in many of the two- and threevariable models, especially for NJIS, FBI, ODIPNIR, EPTRp, 

and DIP (table 6). Periods of low flow tend to favor taxa that 

prefer slower velocities (Jowett, 1997) or are more tolerant 

of stressors (for example, oxygen depletion and higher water 

temperatures) associated with more slowly flowing water. In 

particular, minimum flows appeared to be highly important in 

maintaining invertebrate-assemblage integrity, and spring low 

flow (that is, ML4) accounted for a significant portion of the 

variability (55 percent) in the two-variable ODIPNIR model 

(table 6). As low flows become lower and occur later, the 

richness of tolerant taxa such as many nonmidge dipterans and 

other noninsect taxa tends to increase, whereas the richness 

of more sensitive EPT taxa tends to decrease. The variability 

and magnitude of mean annual flow values (MA25 and MA20, 

respectively) and the mean maximum flow for May across all 

years (MH5) accounted for a significant proportion of the variability in the two- and three-variable MLR models, especially 

for NJIS, EPT index, EPTRp, RichTOL, ODIPNIR, MOLCRUR, and NCHDIP (table 6). Increasing magnitudes and 

variability in annual and high flows are indicative of streams 

that are becoming more flashy (Walsh and others, 2005b), 

and this type of flow alteration appears to negatively affect 

the richness, abundance, and function of aquatic-invertebrate 

fauna. For example, EPTRp taxa tend to be negatively related 

to changes in the magnitude of annual flows, whereas RichTOL is positively related to an increase in annual high-flow 

variability. In general, the magnitude, frequency, and variability of high flows are increasing and the magnitude of low 

flows is decreasing with increasing assemblage impairment, 

indicating that many of the study streams in the Raritan River 

Basin are becoming flashier and tend to have high flows of 

shorter duration and lower low flows. Streams with more 

unpredictable high-flow events in combination with extreme 

low flows are indicative of catchments that are affected by 

changes in the landscape associated with urbanization (Konrad 

and Booth, 2005; Walsh and others, 2005b), flow regulation 

(Freeman and Marcinek, 2006), and water abstraction (Chessman and others, 2011). Such changes in runoff and streamflow 

patterns alter the natural flow regime and greatly affect sensitive native and endemic species.
42 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Data Limitations

A major premise of the streamflow modeling approach 

used in support of the hydro-TMDL methodology is that the 

Statewide Watershed Runoff Model (SWRM) results reasonably and relatively accurately represent (or mimic) the natural 

hydrograph and are an appropriate surrogate for a baseline 

hydrograph in locations in the Raritan River Basin for which 

an observed baseline record did not exist or could not be differentiated from an existing long-term U.S. Geological Survey 

hydrologic record. As previously mentioned in the section 

“Determining Deviation of Streamflow from Baseline Conditions,” observed baseline (OB) sites are sites where an existing 

baseline/reference or “minimally altered” time period of 

change could be differentiated (by using quantile regression) 

from an existing long-term period of record (POR). When 

such a record was not available, a simulated baseline (SB) 

was developed by using the SWRM. SB hydrographs were 

developed to reduce the effects of anthropogenic stressors 

(for example, urban land use, impervious surface cover (ISC), 

compacted soils, etc.) on the hydrologic response and simulate 

a “natural” baseline POR. For example, in this study ISC in 

the SWRM model was typically set to 0 percent, indicating no 

pre-existing anthropogenic disturbance.

Differences between Observed and Simulated 

Baseline Periods of Record

Results of the hydro-TMDL comparisons appear to indicate that greater deviations from the 25th-to-75th-percentile 

ranges occurred more often at the SB sites than at the OB 

sites (app. 2). On the basis of this observation, it was hypothesized that the SB record derived by using 0 percent ISC 

may ultimately represent a more conservative endpoint from 

a regulatory perspective than an OB record, as it is unlikely 

that any given stream in New Jersey has not undergone, at 

some point in its history, at least some anthropogenic alteration of the landscape. In an effort to test this hypothesis, an 

SB record was simulated for three sites for which an existing 

OB record was previously identified (using the same POR) 

and the two were directly compared to evaluate the differences in percent deviations between corresponding hydrologic 

variables. For example, table 7 shows percent deviations 

using both the OB and SB records for the Raritan River at 

Manville, NJ (RarMan). For the hydrologic variables whose 

values fell outside the 25th-to-75th-percentile range for the SB 

record but not the OB record, the OB 25th-to-75th-percentile 

ranges were included in the tables to exhibit the difference in 

ranges between the two hydrologic-record types. The annual 

Table 7. Deviations of flow-management targets for the observed and simulated baseline records for the 

Raritan River at Manville, NJ, study site that fall outside the established 25th-to-75th-percentile range.

[ft3/s, cubic feet per second; (ft3/s)/d, cubic feet per second per day; D, dimensionless; JD, Julian day; --, no deviations above the 

75th or below the 25th percentile; hydrologic indices are defined in appendix 1; values less than 1 were rounded to the thousandths 

place to show precision]

U.S. Geological Survey station number 01400500 Raritan River at Manville, NJ 

Hydrologic 

index Units 25th percentile Observed NJHAT value 75th percentile

Percent 

deviation 

below the 

25th percentile

Percent 

deviation 

above the 

75th percentile

Stream class A, observed baseline dataset

MH5 ft3/s 960.0 2,360 3,820 -- --

DL4 ft3/s 81.1 231.2 147.0 -- 57.3

DH2 ft3/s 4,897 6,643 7,737 -- --

TA1 D 0.432 0.652 0.553 -- 17.9

TL1 JD 246.8 262.6 262.5 -- 0.040

TH1 JD 42.8 73.2 70.4 -- 4.0

RA3 (ft3/s)/d -168.0 -57.0 -19.0 -- --

Stream class A, simulated baseline dataset

MH5 ft3/s 730.6 2,360 1,380 -- 71.0

DL4 ft3/s 86.9 231.2 222.0 -- 4.1

DH2 ft3/s 1,168 6,643 1,825 -- 264.0

TA1 D 0.432 0.652 0.553 -- 17.9

TL1 JD 246.8 262.6 262.5 -- 0.040

TH1 JD 42.8 73.2 70.4 -- 4.0

RA3 (ft3/s)/d -21.9 -57.0 -7.1 160.3 --
Data Limitations 43

minimum of 30-day moving-average flow (DL4), flow 

constancy (TA1), and the Julian date of the annual maximum 

(TH1) for the OB record fell outside the established 25thto-75th-percentile range. In contrast, the same indices for 

the SB record fell outside the 25th-to-75th-percentile range, 

but the percent deviations differed. Additionally, four other 

hydrologic variables fell outside the established percentile 

range for the SB record (table 7). Most notable was the difference in percentage deviation for DL4, which was an order of 

magnitude greater for the OB than for the SB record (57.3 and 

4.1 percent, respectively). This difference is likely explained 

by the implementation of the minimum passing flows established at this site after reservoir operations began in the early 

1960s. Most New Jersey reservoirs release water to meet 

minimum passing-flow criteria that are typically designed to 

support human water use farther downstream. Such releases, 

however, are not intended to mimic the natural variability of 

the stream and invariably result in sustained periods of higher 

flow throughout the low-flow period, as was identified in the 

OB record comparisons for RarMan (table 7, app. 2). Because 

streamflow simulations done by using the SWRM do not 

include a routine for reservoir operations, values of hydrologic indices such as DL4 would be expected to be lower for 

the SB than for the OB record, as it is simulating prereservoir 

conditions. Values of hydrologic variables like mean maximum flow for May (MH5) and the annual maximum of 3-day 

moving average of flow (DH2) that did not fall outside the 

25th-to-75th-percentile range for the OB record, but did fall 

outside the range for the SB record (by 71.0 and 264.0 percent, 

respectively), also exemplify differences between the simulated prereservoir and post-reservoir baseline records. Reservoirs tend to dampen the effects of periodic high-flow events 

because they are designed to capture and retain water; therefore, variability in the magnitude and duration of high flows 

will ultimately be reduced for streams with an OB record 

established after passing-flow criteria were implemented.

The South Branch Raritan River at Stanton, NJ 

(SBRarSt), is another site where, on the basis of the OB 

record, three hydrologic indices (DL4, TA1, and the Julian 

date of the annual minimum (TL1)) fell outside the established 25th-to-75th-percentile range (table 8). Three additional 

indices for the SB record also fell outside the 25th-to-75thpercentile range, including mean monthly flow for June 

(MA18), MH5, and DH2. Like RarMan, SBRarSt is downstream from the Spruce Run Reservoir (constructed in 1963) 

and is therefore subject to a similar type of flow augmentation. For example, the percent deviation for DL4, the annual 

minimum of 30-day moving-average flow, is greater for the 

Table 8. Deviations of flow-management targets for the observed and simulated baseline records for the 

Raritan River at Stanton, NJ, study site that fall outside the established 25th-to-75th-percentile range.

[ft3/s, cubic feet per second; D, dimensionless; JD, Julian day; --, no deviations above the 75th or below the 25th percentile; hydrologic indices are defined in appendix 1; values less than 1 were rounded to the thousandths place to show precision]

U.S. Geological Survey station number 01397000 South Branch Raritan River at Stanton, NJ 

Hydrologic 

index Units 25th percentile Observed NJHAT value 75th percentile

Percent

deviation 

below the 25th 

percentile

Percent 

deviation 

above the 75th

percentile

Stream class A, observed baseline dataset

MA18 ft3/s 73.8 179.9 211.1 -- --

MH5 ft3/s 400.0 785.0 940.0 -- --

DL4 ft3/s 45.5 107.6 71.8 -- 49.8

DH2 ft3/s 1,149 1,703 1,992 -- --

TA1 D 0.432 0.634 0.553 -- 14.6

TL1 JD 246.8 273.5 262.5 -- 4.2

Stream class A, simulated baseline dataset

MA18 ft3/s 88.4 179.9 176.6 -- 1.9

MH5 ft3/s 223.3 785.0 379.3 -- 107.0

DL4 ft3/s 51.9 107.6 96.1 -- 11.9

DH2 ft3/s 300 1,703 450 -- 278.5

TA1 D 0.432 0.634 0.553 -- 14.6

TL1 JD 246.8 273.5 262.5 -- 4.2
44 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

OB (49.8 percent) record than for the SB (11.9 percent) record 

(table 8). Releases from the Spruce Run Reservoir are highly 

regulated and, therefore, DL4 will tend to be higher for an 

OB than an SB record because flows during low-flow periods 

are artificially maintained above those that would be expected 

naturally during low-flow periods. Values of MH5 and DH2 

fell outside the 25th-to-75th-percentile range for the SB record 

by 107.0 and 278.5 percent, respectively. These deviations 

are comparable to those for these two hydrologic variables 

at RarMan and similarly illustrate the differences between a 

prereservoir SB record and a post-reservoir OB record. MA18 

for the SB record also deviated from the established 25thto-75th-percentile range (table 8), but by only 1.9 percent, a 

value well within the expected range of annual variability for 

this hydrologic variable.

Two hydrologic variables, constancy (TA1) and the Julian 

date of the annual minimum flow (TL1), deviated from the 

established 25th-to-75th-percentile range for the OB record at 

NeshRea (table 9). Unlike the two previous examples, values 

of DL4 did not fall outside the 25th-to-75th-percentile range 

for the OB record because the reservoir influence is absent 

(that is, there are no established minimum passing-flow criteria) immediately upstream from the NeshRea gage. NeshRea, 

however, is likely affected by water abstraction resulting 

from water withdrawals from numerous domestic and publicsupply wells located in the basin upstream from the site. These 

withdrawals could potentially explain the identified deviation of low-flow timing (table 9) because, whether the withdrawals are made for agricultural or human water use, their 

timing would not necessarily be synchronized with natural 

seasonal flow patterns, especially if the wells are unregulated. 

Additionally, agricultural water use typically spikes at times 

of the year (generally, spring) when irrigation demands are 

highest and when the need for streamflow to support the life 

history (migration and reproduction) of many endemic fauna 

is greatest. 

The values of four additional hydrologic variables were 

found to fall outside the established 25th-to-75th-percentile 

range for the SB record but not for the OB record. These variables include MH5, high flow pulse count (FH4), DL4, and 

DH2 (table 9). As previously stated, the upper and lower percentage boundaries for the SB record typically represent more 

conservative endpoints than those for the OB record because 

the SWRM uses an ISC of 0 percent, which greatly reduces or 

eliminates the effects of impervious surface runoff. For example, the mean May maximum flows (MH5) for the SB record 

exceeded the 75th-percentile value by nearly 30 percent (table 

9). On the basis of the observed NJHAT value for MH5 (129.5 

ft3/s), it is clear that the 75th-percentile value for the SB record 

(99.7 ft3/s) represents a more conservative endpoint than the 

75th-percentile value for the OB record (233 ft3/s). Therefore, 

it is not unexpected that higher percent deviations from the 

Table 9. Deviations of flow-management targets for the observed and simulated baseline record for the 

Neshanic River at Reaville, NJ, study site that fall outside the established 25th-to-75th-percentile range.

[ft3/s, cubic feet per second; d/yr, days per year; D, dimensionless; JD, Julian day; --, no deviations above the 75th or below the 25th 

percentile; hydrologic indices are defined in appendix 1; values less than 1 were rounded to the thousandths place to show precision]

U.S. Geological Survey station number 01398000 Neshanic River at Reaville, NJ

Hydrologic 

index Units 25th percentile Observed NJHAT value 75th percentile

Percent 

deviation 

below the 

25th percentile

Percent 

deviation 

above the 

75th percentile

Stream class A, observed baseline dataset

MH5 ft3/s 37.3 129.5 233.0 -- --

FH4 d/yr 26.8 27.5 45.0 -- --

DL4 ft3/s 1.1 1.9 2.2 -- --

DH2 ft3/s 332.2 563.0 648.9 -- --

TA1 D 0.432 0.197 0.553 54.4 --

TL1 JD 246.8 241.1 262.5 2.3 --

Stream class A, simulated baseline dataset

MH5 ft3/s 29.9 129.5 99.7 -- 29.9

FH4 d/yr 4.8 27.5 15.8 -- 74.1

DL4 ft3/s 2.0 1.9 3.7 6.1 --

DH2 ft3/s 202.0 563.0 359.5 -- 56.6

TA1 D 0.432 0.197 0.553 54.4 --

TL1 JD 246.8 241.1 262.5 2.3 --
Data Limitations 45

flow-management target range in high-flow magnitudes, frequencies, and duration would be found for the SB record than 

for the OB record (table 9), because the OB record represents 

a baseline for a known time period where some amount of 

anthropogenic alteration (for example, ISC) already exists. 

Even minor changes (in some cases less than 5 percent) in the 

amount of ISC in a basin can result in significant increases in 

runoff (Schueler, 1994; Cuffney and others, 2010). 

The duration and frequency of high flows for the SB 

record exceeded the 75th-percentile value by 74.1 and 

56.6 percent, respectively. These differences are also likely 

attributable to the lack of impervious surface runoff associated 

with the more conservative simulated hydrograph. That is, the 

SB record tends to be less flashy than the OB record because it 

does not include an implicit amount of ISC. The annual minimum of 30-day moving-average flow (DL4) for the SB record 

fell below the 25th-percentile value by 6.1 percent. Given the 

absence of ISC in the SB record, more groundwater infiltration would be expected in the SB than in the OB, therefore 

the SB 25th-percentile value (2.0) would be higher than the 

OB 25th-percentile value (1.1), because the OB record is a 

response to less infiltration and greater runoff, and the actual 

value would then fall below the simulated limits. Additionally, 

the SWRM did not use actual daily values for permitted withdrawals and discharges; rather, withdrawals and discharges 

(unpublished data on file at the U.S. Geological Survey office, 

West Trenton, New Jersey) were quantified by distributing the 

annual average values evenly throughout the year (Kennen and 

others, 2008). Therefore, for certain times of the year, the OB 

record may actually reflect a narrower range and shorter duration of low flows as a result of regulated and unregulated daily 

withdrawals on pumping. The SB record, therefore, would be 

less likely than the OB record to accurately account for this 

variation in seasonal water use.

The data in tables 7, 8, and 9 appear to validate the 

hypothesis that values of hydrologic indices more frequently 

deviate from the 25th-to-75th-percentile range and often 

by a higher percentage for sites with an SB record than for 

sites with an OB record. These comparisons indicate that OB 

records may be the most realistic from a management perspective because they represent an established baseline POR 

that was not derived from simulated data. However, both 

types of record are useful and can be important depending on 

specific management intentions. OB records for minimally to 

moderately altered New Jersey streams like those discussed 

above include an implicit amount of anthropogenically driven 

watershed disturbance (for example, urban land use, ISC, 

water abstraction, reservoirs, etc.) that has, to some degree, 

altered the existing hydrograph. In some cases, however, the 

more conservative SB record may better represent the management target for high-quality reference streams or for special 

protection waters that are highly valued for water-supply 

purposes and therefore require a higher level of protection or 

restoration. The SB record could also be useful in helping to 

identify streams that currently are not of concern hydrologically, but that could be in the near future if steps are not taken 

to protect the stream corridor from changes associated with 

urban development and other anthropogenic processes. In 

other words, the SB record can be used to help identify those 

class A or C streams with hydrologic index values that fall 

just within the established 25th-to-75th-percentile range so 

that appropriate management measures can be implemented to 

prevent further degradation.

Understanding the limitations associated with using an 

SB hydrograph as a basis for streamflow comparison is an 

important consideration for managers and stakeholders interested in streamflow protection and (or) remediation. Additionally, knowing how these records are affected by anthropogenic 

influences (that is, dams, wells, ISC) can help explain discrepancies that can be seen only if simulated data are available. 

Although an OB record appears to be preferable for comparing current with baseline conditions, long-term streamflow 

information is not available for every stream segment in New 

Jersey. When the SB record is the only hydrologic basis of 

comparison available, it should be applied with an awareness 

of its limitations as an accurate representation of the baseline 

period of record.

The approach used to simulate baseline streamflow 

(that is, to derive an SB record) with the SWRM model was 

consistent for all the sites. Although a closer calibration 

might have been achieved for some streams by adjusting the 

percentage of ISC used in the model to simulate a moderate 

level of anthropogenic disturbance, this additional complexity 

was considered to be outside the scope of the study. The HIP 

methodology developed cooperatively with the N.J. Department of Environmental Protection (NJDEP) to divide the State 

into distinct hydrologic regions (Kennen and others, 2007), 

however, greatly enhanced our ability to derive useful baseline 

hydrographs for specific stream types that represent the basis 

of hydroecological comparison for the hydro-TMDL methodology. The simulated baseline results also are limited, to some 

degree, in that the discharges and withdrawals used as input to 

the model are based on average annual values. Had the actual 

daily values for permitted withdrawals and discharges been 

available, the saturation deficit could have been determined 

more accurately, ultimately improving the accuracy of the 

simulated hydrographs.

Flow-Ecology Response Models

Generally, most bivariate and multivariate ecological 

response models developed during this study represented a 

strong response to changes in hydrologic processes (tables 4 

and 5, respectively). The large implicit variability of hydrologic and ecological data, however, may have hampered our 

ability to develop even stronger response relations. For example, ecological data are inherently variable and differences in 

substrate complexity among streams, year-to-year changes in 

sampling crews, or differences in stream size could increase 

variability and affect response models. Aquatic-invertebrate 

data for this study, however, were considered to be comparable 
46 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

in terms of sampling protocols (sampled habitat, number of 

composite samples, and total sampled area) and laboratory 

procedures, including sorting, subsample count level, and 

taxonomic resolution, because, for the most part, aquaticinvertebrate samples were collected in similar habitats by 

using comparable sampling techniques. Additionally, the data 

were reviewed extensively to ensure that all data aggregated 

from separate sources (that is, NJDEP and the National 

Water Quality Assessment Program (NAWQA)) included 

the same taxonomic groups and had appropriate taxonomic 

resolution before data analysis was attempted. This task was 

accomplished by using the Invertebrate Data Analysis System 

(IDAS) software (Cuffney and Brightbill, 2011), which is 

designed to resolve all taxonomic issues (taxonomic identification level and nomenclature), to remove ambiguous taxa 

(Cuffney and others, 2007), and to randomly subsample raw 

counts to an equal 100-specimen count (the highest possible 

count based on the NJDEP monitoring program protocols for 

New Jersey). To address any innate variability related to basin 

size, all hydrologic variables correlated with basin size were 

standardized by basin area. 

Improvement in the overall fit between flow variables 

and assemblage metrics would likely require a larger number of sites having both aquatic-invertebrate and long-term 

hydrologic data (see Poff and others, 2010). The ability to 

develop flow-altered hydrographs for any NJDEP ecological 

monitoring site would greatly increase the “N” for developing such relations. The level of effort required for this type of 

modeling is beyond the scope of the current investigation, but 

certainly is a process that would provide the needed information if pursued in the future for New Jersey. Such efforts 

are already underway in the Apalachicola-ChattahoocheeFlint River Basin (Georgia), where relations between simulated daily water withdrawals and the predicted change in 

the richness of fish species are being developed to provide 

quantitative river management alternatives (Freeman and 

others, 2012). Incorporating multiple aquatic assemblages, 

particularly fish assemblages, may also be useful as a result of 

their longer life span and sensitivity to physical disturbances 

and habitat change (for example, Kennen and others, 2012). 

Other limitations may include our inability to specifically 

account for the effects of ongoing human-induced impacts 

on water availability (for example, surface- and groundwater 

abstraction) in the SWRM that likely reduced our ability to 

fully account for the amount of water in the streams and may, 

in some cases, accentuate the high and low flows for streams 

at specific times of the year. These types of hydrologic 

alterations no doubt have a cumulative effect on the aquatic 

assemblage; however, their effect is extremely difficult to 

separate from variability in yearly precipitation and concomitant anthropogenic effects because the records for such data 

are typically scarce, lacking, or unregulated for movement 

of water or direct withdrawals less than a specific amount, 

especially in some transitional agricultural areas in New 

Jersey where withdrawals that do not exceed a preset rate are 

not regulated.

Summary and Conclusions

As a result of the large number of stream sites in New 

Jersey with aquatic-life impairment and no specific chemical 

or physical stressor identified, as well as the understanding 

that applying a standard, pollutant-based Total Maximum 

Daily Load (TMDL) to more than 300 such sites is fiscally 

challenging, the U.S. Geological Survey, in cooperation with 

the N.J. Department of Environmental Protection, developed a 

hydrologically based TMDL (hydro-TMDL) methodology that 

accounts for a broad suite of streamflow alterations in order to 

address aquatic-life impairments in New Jersey streams. Additionally, flow-ecology response models based on stream class 

were produced to complement the hydro-TMDL approach and 

provide managers with a set of response relations that identifies important individual or multiple hydrologic attributes 

that can be used to support broader regional flow targets for 

other class A and C New Jersey streams that were not part of 

this study. 

Unaltered (“minimally impaired”) hydrographs represented the regulatory endpoint of the hydro-TMDL approach 

used in this study. In some cases, an observed baseline 

hydrograph was differentiated from the existing period of 

record (POR) by using quantile regression (QR) procedures. 

In this approach, flow quantiles and precipitation quantiles 

were directly compared across the POR; this is an objective 

and statistically robust procedure for identifying anomalous 

periods in the record during which changes in flow occurred 

that were not directly driven by variability in annual precipitation. When either the length of the existing POR was insufficient to identify a baseline POR or the baseline hydrograph 

could not be statistically differentiated from the existing 

hydrograph using QR, a simulated baseline hydrograph was 

developed. The simulated baseline was produced by using 

the Statewide Watershed Runoff Model (SWRM) and represents an essential step in the Ecological Limits of Hydrologic 

Alteration (ELOHA) approach. Two ranges encapsulating the 

25th-to-75th (less conservative) and the 40th-to-60th (more 

conservative) percentiles of flow were the TMDL boundaries 

established for the hydro-TMDL comparisons presented. The 

25th-to-75th-percentile range has been broadly accepted as 

the established flow range needed to support healthy aquatic 

assemblages, and as an extension, aquatic-life uses. Recent 

work by Richter and others (2011) has expanded upon this 

concept with a “presumptive standard” that provides ecological flow practitioners with a sustainability boundary that 

restricts hydrologic alteration to within some percentage range 

and is presented as a “stopgap” measure for use in streamflow 

management where no other flow-protection strategy currently 

exists. Results of the hydro-TMDL approach applied in this 

study are highly complementary to that presented by Richter 

and others (2011), but more directly identify proportional differences between baseline hydrographs and observed hydrographs for a subset of non- to moderately impaired streams 

within the Raritan River Basin, New Jersey, as a basis for 

streamflow management and meeting Clean Water Act goals. 
Summary and Conclusions 47

Because the physical and chemical properties of many 

of the Raritan River Basin streams assessed were altered 

prior to the current study, many of the biological assemblages 

have shifted from native populations to populations that are 

more tolerant of the existing conditions associated with extant 

landscape modification. That is, the biological assemblages 

found in natural systems are often, but not always, more complex (for example, higher diversity, richness, and abundance) 

than those found in disturbed ecosystems. As ecosystems 

are hydrologically altered and habitat, substrate conditions, 

and water quality are degraded, more tolerant species are 

able to survive while sensitive species typically decline. As 

additional changes in the landscape occur, however, even the 

more resilient species are displaced as conditions degrade 

to a point where few species can survive. This study, however, targeted only those sites in the Raritan River Basin that 

represented minimal to moderate aquatic-invertebrate impairment. If hydrologic alteration was identified on the basis of 

the hydro-TMDL assessment, management techniques (for 

example, streamflow best management practices (BMPs) such 

as stormwater retrofits, pervious pavement, variable reservoir 

release rates, etc.) could still be implemented in an attempt to 

offset those deviations that fall outside the established range of 

flows identified during comparative evaluations. 

Many flow values that fall outside the 25th-to-75th 

and 40th-to-60th percentiles were identified as part of this 

study for many non-impaired as well as moderately impaired 

streams. In general, more deviations in flow indices were seen 

in streams identified as moderately impaired than in those 

identified as non-impaired. Additionally, percent deviations 

outside the established boundaries tended to be greater and 

more numerous at streams where comparisons were based on 

simulated baseline (SB) hydrographs than where comparisons 

were based on observed baseline (OB) hydrographs derived 

from an existing long-term POR by using QR. The occurrence 

of such large percentage deviations is not unexpected as the 

simulated baseline represents a hydrograph derived with the 

assumption of little or no historic anthropogenic alteration of 

the drainage basin. From a regulatory perspective, this may 

ultimately represent an unattainable endpoint from which to 

implement a hydro-TMDL for surface-water management. 

That is, if a given flow deviation value for a subset of critical 

flow indices is so far outside a manageable percentage that no 

amount of mitigation can return the stream to compliance from 

a hydro-TMDL perspective, then it may be more appropriate 

to use modeled baseline hydrographs with a small amount of 

anthropogenic alteration added (for example, 2 to 5 percent 

impervious surface cover (ISC) or 5 to 10 percent urban 

land use) for comparison, such that the simulated endpoints 

are more comparable to observed baselines derived from an 

existing streamflow POR. A small amount of anthropogenic 

influence is implicit within the observed baseline even for 

non-impaired basins; therefore, in retrospect, it might have 

been appropriate to supplement the simulated baseline hydrographs by incorporating a minimal amount of anthropogenic 

alteration into the flow-modeling process. Even though it was 

possible to identify periods of significant changes in flow at 

sites with observed baselines by using the QR approach, these 

changes were occurring in addition to an existing background 

level of anthropogenic activity associated with processes 

that likely occurred many times over the prior century. It is 

unlikely that the basin of any given river or stream in New 

Jersey has not undergone, at some point in its history, at least 

some amount of anthropogenic alteration of the landscape (for 

example, changes driven by legacy fire, damming/milling, logging, or agricultural activities).

The major challenge facing water managers who may 

wish to maintain environmental flows is the complexity associated with determining how much alteration of natural flows 

can be tolerated without compromising the ecological health 

of a stream. In this study, annual flow variability, specifically 

the variability across minimum and maximum streamflow, 

was shown to be particularly important for the health of the 

aquatic-invertebrate assemblage. Loss of permeable surfaces 

in the basin can decrease infiltration rates and reduce recharge 

to shallow groundwater, resulting in reduced base-flow inputs 

to streams. Such changes can exacerbate low-flow conditions 

and affect aquatic-assemblage integrity. For example, stream 

sites in the Raritan River Basin study area that show increased 

variability in the magnitude and timing of low flows (low flow 

index), variability in base flow index, variability across annual 

minimum flows, and the Julian date of annual minimum flows, 

respectively) and in the magnitude of historically higher flows 

(variability of monthly February and April flows) exhibit 

hydrologic conditions that differ greatly from the natural flow 

regime and tend to be populated with taxa that are known to 

be resilient and tolerant of environmental stressors. These 

results indicate that as the magnitude of high and low flows is 

altered, some species with life-history and behavioral constraints that rely on annual flow patterns or fluctuations in flow 

for reproduction may become less abundant and species with 

greater resilience to changes in natural streamflow variability 

may become more abundant.

All of the sites evaluated in this study are located in 

drainage basins that have undergone some degree of urbanization or anthropogenic influence leading to varying levels 

of hydrologic stress. The source of the modifications is more 

readily identifiable for some of the sites than for others, but all 

are affected, to some degree, by changes in hydrology. Sites 

near dams or reservoirs or sites in basins that have a large 

amount of ISC or multiple pumped wells may, in some cases, 

be more easily managed because the source(s) of hydrologic 

stress can be identified.

There are many examples of sites in this study where the 

hydrology may have been altered by reservoir and (or) dam 

operations. Water-supply reservoirs are typically operated to 

store water captured during higher flow periods for off-stream 

use during low-flow periods, with the effect of dampening 

moderate to high flows and, in some cases, increasing low 

flows. Changes in the timing of spring high flows and summer low flows appear to be a common thread among streams 

whose annual flow variability has been modified by the 
48 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

construction of dams. The releases of water from reservoirs 

and (or) dams are typically based on minimum-passingflow requirements designed to maintain a regulated amount 

of water in streams downstream from a reservoir with little 

regard to the seasonality or timing of flows. It has been suggested that the primary challenge in setting flow-protection 

standards is to employ a practical method that limits water 

withdrawals and dam operations in such a way as to protect 

essential flow variability. Therefore, management strategies 

that most closely mimic natural flows, developed in collaboration with reservoir authorities (for example, the New Jersey 

Water Supply Authority), could potentially enhance or restore 

ecological integrity. For example, the timing and magnitudes 

of flow releases could be implemented in a way that supports 

“ecologically sustainable passing flows,” which would aim 

to restore a more variable system that supports the long-term 

survival and life history of aquatic organisms rather than 

to maintain a “passing-flow” standard that was established, 

in part, to maintain streamflow to meet downstream human 

uses. A protection-based hydro-TMDL for flow-regulated 

sites might consist of a framework that incorporates a holistic hydroecological management plan that seeks to maintain 

current streamflow levels to maximize human water use, but 

also modifies reservoir or dam releases such that they are 

more closely synchronized with natural fluctuations in annual 

streamflow variability. For example, flow BMPs could not 

only maintain the quantity of flow but also augment the timing 

of reservoir releases to coincide with fish spawning cycles and 

migration periods, to coincide with aquatic-invertebrate emergence and reproduction, or to increase stream variability and 

reduce the frequency of long-duration low-flow events.

The drainage basins of some of the Raritan River Basin 

sites evaluated in this study have relatively high ISC percentages (>10 percent) that appear to be directly linked to hydrologic alteration at these sites. Over the past several decades, 

growth has expanded beyond cities and older suburbs into 

many areas that were once rural. Lowland urban development 

typically leads to increased ISC, losses of riparian vegetation, 

and subsequent increases in sediment inputs to streams as a 

result of erosion. Increased ISC promotes increased surface 

runoff which can, in turn, result in reduced groundwater 

recharge, flashier hydrology, habitat degradation, riparian-corridor encroachment, channel modifications, and an increase in 

runoff of associated contaminants from impervious surfaces. 

The frequency of moderate flooding can increase substantially 

after land use in a basin shifts from forested to increasingly 

urban. Flashy streamflows are typically associated with urban 

runoff and create a system in which stormflow frequency and 

intensity increase and base flows decrease. Within the Raritan 

River Basin study area, more frequent higher magnitude flows 

(high flood pulse counts and the high-flow discharge index), or 

increased flashiness, increase the proportion of tolerant organisms and reduce the abundance of sensitive species. These 

types of responses have been seen in numerous studies relating urbanization and increases in ISC to aquatic-assemblage 

impairment. These more frequent, higher magnitude flows 

are not limited to large storm events but can also occur after 

small to moderate storm events as a result of increased inputs 

of stormwater from overland flow by conventional drainage directly to streams. During these smaller storm events, 

streamflow can reach bankfull width and erode banks, increase 

sedimentation, and create a general loss of habitat for sensitive 

species. Controlling overland runoff (in the basin) by utilizing flow-alteration strategies may help to reduce the effects of 

these smaller flow events.

Flow-alteration strategies that could be implemented to 

restore watershed hydrology to more closely resemble natural 

streamflow at sites in urbanizing basins could include both 

structural and nonstructural methods. Nonstructural methods aim for a general reduction in the volume of runoff from 

developments and include disconnection of downspouts from 

hard surfaces, conservation of natural areas, and watershed 

planning. Structural methods target runoff-volume reduction 

by capturing and reusing stormwater through infiltration into 

porous pavement or retention ponds, infiltration trenches, 

rain gardens, and swales, and rainwater harvesting by means 

of rain barrels, tanks, and cisterns. Such measures promote 

increased base flow and reduce rapid stormwater runoff and 

the magnitude of high-flow events. Planting trees in riparian 

zones to attenuate stormwater flow through flood plains is 

another viable option to decrease rapid runoff and reduce the 

flashiness of such systems, as long as riparian zones are not 

directly bypassed by stormwater pipes and conveyances.

Increasing the amount of green infrastructure in urban 

environments has been presented as means to offset increased 

stormwater runoff from impervious surfaces. Green infrastructure refers to a network of open green space that conserves 

the infiltration capacity of natural ecosystems in addition to 

providing benefits to human populations by bringing natural 

aspects of the environment to inhabited space. Examples of 

green infrastructure include the planting of trees or other types 

of vegetation in and around roads, pavements, or buildings, 

or even the creation of green roofs on homes or businesses. 

Use of green infrastructure for urban stormwater retrofits can 

reduce stormwater contamination by reducing the conversion 

of rainfall to runoff by capturing rainwater where it falls in 

an effort to reduce stormwater volumes, peak-flow rates, and 

contaminant loads (water quality). In catchments where these 

methods are utilized, a large percentage of runoff from small 

to moderate storms may be diverted from overland flow to 

groundwater recharge, thereby reducing the effect of more 

frequent high flows on sensitive taxa in streams in an effort to 

return the streams to a more natural flow regime.

Some streams in the Raritan River Basin may have been 

affected by water abstraction (that is, groundwater or surfacewater withdrawals used for agricultural and human supply). 

In New Jersey, agricultural water-use registration must be 

obtained if a landowner has the capability to withdraw ground 

and (or) surface water in excess of 100,000 gallons per day 

(gal/d) for agricultural, aquacultural, or horticultural purposes. 
References Cited 49

Unregulated water withdrawals or the extraction of large volumes of water slightly less than the 100,000-gal/d limit could 

affect flow at some of these study sites. Focusing management efforts on (1) developing stricter regulatory guidelines 

about the quantity of water being removed at locations at or 

near the affected streams (that is, either lower the minimum 

gallon-per-day limit or require registration for all water withdrawals, regardless of size), and (2) implementing guidelines 

that restrict withdrawals to periods of relatively high flow or 

to times that do not coincide with critical life-cycle periods, 

would provide better water accounting and give managers 

greater flexibility in the development and implementation of 

flow-based BMPs.

Continued and persistent stressors (for example, unmitigated runoff, increases in impervious surfaces, increases in 

stormwater runoff, etc.) on stream hydrology can negatively 

affect biological integrity and, if the system is pushed to a 

point beyond its capacity to withstand flow stress, implementing management measures that protect native and endemic 

stream assemblages, even in the non- to moderately impaired 

Raritan River Basin streams assessed in this study, may 

become difficult. Depending on the resistance and resilience of 

stream assemblages, frequent high-energy flow events coupled 

with prolonged periods of low or no flow can dramatically 

affect lotic ecosystem structure and function, often resulting 

in a highly simplified trophic structure with low taxonomic 

diversity and a dominance of relatively few tolerant taxa. In 

contrast, moderate- to low-magnitude flow events (natural 

flow patterns) appear to strongly influence processes that act at 

the population level of ecosystem organization, and may ultimately be responsible for maintaining healthy and diverse lotic 

ecosystems. Even though recent studies have indicated that 

aquatic invertebrates appear to be resilient to stress associated 

with short-term reductions in streamflow, results of this study 

may indicate that changes in streamflow indices, especially 

those that fall outside the more conservative 40th-to-60thpercentile range, may result in greatly altered streamflows that 

modify natural-assemblage complexity and push the aquatic 

assemblages beyond their capacity for resistance or resilience. 

Consequently, sensitive aquatic organisms may become less 

abundant, whereas species with greater resilience to temporal 

changes in stream variability (tolerant organisms) may become 

more abundant.

Scientific evidence strongly indicates that hydrology 

accounts for a significant portion of the variability in the composition, structure, and function of aquatic systems. Management practices that promote natural hydrologic patterns and 

processes are likely to reduce the effects of hydrologic alteration on stream biota. The stream-type-based hydrologic deviations and flow-ecology response relations presented in this 

report can be used by managers and policy makers to establish 

a hydro-TMDL that can be used to address designated aquaticlife uses in the Raritan River Basin with potential application 

to other parts of New Jersey and elsewhere. In many streams 

across the United States where no unambiguous stressors are 

identified, State monitoring agencies could implement surrogate measures (for example, flow-based TMDL approaches) 

that support designated uses, meet the regulatory requirements 

under the Clean Water Act, and support a balance between 

water supply intended to meet human needs and the conservation of biological integrity.

References Cited

Alley, W.M., Evenson, E.J., Barber, N.L., Bruce, B.W., Dennehy, K.F., Freeman, M.C., Freeman, W.O., Fischer, J.M., 

Hughes, W.B., Kennen, J.G., Kiang, J.E., Maloney, K.O., 

Musgrove, MaryLynn, Ralston, Barbara, Tessler, Steven, 

and Verdin, J.P., 2013, Progress toward establishing a 

national assessment of water availability and use: U.S. Geological Survey Circular 1384, 34 p. (Also available at http://

pubs.usgs.gov/circ/1384).

Arnold, C.L., and Gibbons, C.J., 1996, Impervious surface coverage—The emergence of a key environmental indicator: American Planners Association Journal, 

v. 62, p. 243–258 (Also available at http://dx.doi.

org/10.1080/01944369608975688).

Arthington, A.H., Bunn, S.E., Poff, N.L., and Naiman, R.J., 

2006, The challenge of providing environmental flow 

rules to sustain river ecosystems: Ecological Applications, v. 16, p. 1,311–1,318 (Also available at http://dx.doi.

org/10.1890/1051-0761(2006)016[1311:TCOPEF]2.0

.CO;2).

Arthington, A.H., King, J.M., O’Keefe, J.H., Bunn, S.E., Day, 

J.A., Pusey, B.J., Bluhdorn, D.R., and Thame, R., 1992, 

Development of an holistic approach for assessing environmental flow requirements of riverine ecosystems, in

Pigram, J.J., and Hooper, B.A., eds., Water allocation for the 

environment—Proceedings of an international seminar and 

workshop: Armidale, Australia, University of New England, 

The Centre for Water Policy Research, p. 69–76.

Barbour, M.T., Gerritsen, J., Snyder, B.D., and Stribling, J.B., 

1999, Rapid bioassessment protocols for use in streams and 

wadeable rivers—Periphyton, benthic macroinvertebrates, 

and fish (2d ed.): U.S. Environmental Protection Agency 

Report, EPA 841-B-99-002, 226 p. (Also available at 

http://water.epa.gov/scitech/monitoring/rsl/bioassessment/

download.cfm).

Beaujouan, V., Durand, P., and Ruiz, L., 2001, Modelling the 

effect of the spatial distribution of agricultural practices on 

nitrogen fluxes in rural catchments: Ecological Modelling, 

v. 137, no. 1, p. 93–105 (Also available at http://dx.doi.

org/10.1016/S0304-3800(00)00435-X).
50 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Benedict, M., and McMahon, E., 2001, Green infrastructure—

Smart conservation for the 21st century: Washington, D.C., 

Sprawl Watch Clearinghouse Monograph Series, 32 p. 

Beven, K.J., 2001, Rainfall-runoff modelling—The primer: 

Chichester, New York, John Wiley, 372 p.

Beven, K.J., and Kirkby, M.J., 1979, A physically based variable contributing area model of basin hydrology: Hydrologic Science Bulletin, v. 24, no. 1, p. 43–69 (Also available 

at http://dx.doi.org/10.1080/02626667909491834).

Bicknell, B.R., Imhoff, J.C., Kittle, J.L., Jr., Donigian, A.S., 

Jr., and Johanson, R.C., 1997, Hydrological simulation 

program, Fortran—User’s manual for version 11: U.S. Environmental Protection Agency, Athens, Georgia, National 

Exposure Research Laboratory Report EPA/600/R-97/080, 

755 p.

Biggs, B.J.F., Nikora, V.I., and Snelder, T.H., 2005, Linking scales of flow variability to lotic ecosystem structure 

and function: River Research and Applications, v. 21, 

p. 283–298 (Also available at http://dx.doi.org/10.1002/

rra.847).

Bitting, J., and Kloss, C., 2008, Managing wet weather with 

green infrastructure—Municipal handbook: U.S. Environmental Protection Agency, Low Impact Development 

Center, Olympia, Wash., EPA/833/F-08/008, 23 p.

 Bobba, A.G., Singh, V.P., and Bengtsson, L., 2000, Application of environmental models to different hydrological systems: Ecological Modelling, v. 125, p. 15–49 (Also available at http://dx.doi.org/10.1016/S0304-3800(99)00175-1).

Boulton, A.J., 2003, Parallels and contrasts in the effects of 

drought on stream macroinvertebrate assemblages: Freshwater Biology, v. 48, p. 1,173–1,185 (Also available at 

http://dx.doi.org/10.1046/j.1365-2427.2003.01084.x).

Boyer, E.W., Hornberger, G.M., Bencala, K.E., and McKnight, 

D., 1996, Overview of a simple model describing variation of dissolved organic carbon in an upland catchment: 

Ecological Modelling, v. 86, p. 183–188 (Also available at 

http://dx.doi.org/10.1016/0304-3800(95)00049-6).

Brown, L.R., Cuffney, T.F., Coles, J.F., Fitzpatrick, F.A., 

McMahon, G., Steuer, J.J., Bell, A.H., and May, J.T., 2009a, 

Urban streams across the USA—Lessons learned from studies in 9 metropolitan areas: Journal of the North American 

Benthological Society, v. 28, p. 1,051–1,069 (Also available 

at http://dx.doi.org/10.1899/08-153.1).

Brown, L.R., Gregory, M.B., and May, J.T., 2009b, Relation of 

urbanization to stream fish assemblages and species traits in 

nine metropolitan areas of the United States: Urban Ecosystems, v. 12, p. 391–416 (Also available at http://dx.doi.

org/10.1007/s11252-009-0082-2).

Bunn, S.E., and Arthington, A.H., 2002, Basic principles and 

ecological consequences of altered flow regimes for aquatic 

biodiversity: Environmental Management, v. 30, no. 4, 

p. 492–507 (Also available at http://dx.doi.org/10.1007/

s00267-002-2737-0).

Bushaw-Newton, K.L., Hart, D.D., Pizzuto, J.E., Thomson, 

J.R., Egan, J., Ashley, J.T., Johnson, T.E., Horwitz, R.J., 

Keeley, M., Lawrence, J., Charles, D.F., Gatenby, C., 

Kreeger, D.A., Nightengale, T., Thomas, R.L., and Velinsky, 

D.J., 2002, An integrative approach towards understanding 

ecological responses to dam removal—The Manatawny 

Creek study: Journal of the American Water Resources 

Association, v. 38, p. 1,581–1,599.

Cade, B.S., and Noon, B.R., 2003, A gentle introduction to 

quantile regression for ecologists: Frontiers in Ecology and 

the Environment, v. 1, p. 412–420 (Also available at http://

dx.doi.org/10.1890/1540-9295(2003)001[0412:AGITQR]2.

0.CO;2). 

Carlisle, D.M., Wolock, D.M., and Meador, M.R., 2010, 

Alteration of streamflow magnitudes and potential ecological consequences—A multiregional assessment: Frontiers in 

Ecology and the Environment, v. 9, p. 264–270 (Also available at http://dx.doi.org/10.1890/100053).

Carter, T., Wenger, S., Seymour, R.M., and Stewart, D., 2009, 

Etowah Habitat Conservation Plan runoff limits manual, 

Final draft, September 2007, 146 p., accessed February 6, 

2013, at http://www.etowahhcp.org/runoff/runoff_limits_

manual_draft_2009_09_15.pdf.

Castleberry, D.T., Cech, J.J., Jr., Erman, D.C., Hankin, D., 

Healey, M., Kondolf, G.M., Mangel, M., Mohr, M., Moyle, 

P.B., Nielsen, J., Speed, T.P., and Williams, J.G., 1996, 

Uncertainty and instream flow standards: Fisheries, v. 21, 

p. 20–21.

Catalano, M.J., and Bozek, M.A., 2007, Effects of dam 

removal on fish assemblage structure and spatial distributions in the Baraboo River, Wisconsin: North American 

Journal of Fisheries Management, v. 27, p. 519–530 (Also 

available at http://dx.doi.org/10.1577/M06-001.1).

Chessman, B., Royal, M., and Muschal, M., 2011, The challenge of monitoring impacts of water abstraction on macroinvertebrate assemblages in unregulated streams: River 

Research and Applications, v. 27, p. 76–86.

Clarke, K.R., and Gorley, R.N., 2006, PRIMER v6—Users 

Manual/Tutorial PRIMER-E: Plymouth, England, 190 p.

Clausen, B., and Biggs, B.J.F., 1997, Relationships between 

benthic biota and hydrological indices in New Zealand streams: Freshwater Biology, v. 38, p. 327–342 

(Also available at http://dx.doi.org/10.1046/j.1365-

2427.1997.00230.x).
References Cited 51

Coles, J.F., Cuffney, T.F., McMahon, G., and Beaulieu, K., 

2004, The effects of urbanization on the biological, physical, and chemical characteristics of coastal New England 

streams: U.S. Geological Survey Professional Paper 1695, 

47 p. (Also available at http://pubs.usgs.gov/pp/pp1695/).

Colwell, R.K., 1974, Predictability, constancy, and contingency of periodic phenomena: Ecology, v. 55, 

p. 1,148–1,153.

Cuffney, T.F., and Brightbill, R.A., 2011, User’s manual for 

the National Water-Quality Assessment Program Invertebrate Data Analysis System (IDAS) software, version 5.0: 

U.S. Geological Survey Techniques and Methods 7–C4, 

126 p. (Available only online at http://pubs.usgs.gov/

tm/7c4/).

Cuffney, T.F., Bilger, M.D., and Haigler, A.M., 2007, Ambiguous taxa—Effects on the characterization and interpretation 

of invertebrate assemblages: Journal of the North American 

Benthological Society, v. 26, p. 286–307 (Also available at 

http://dx.doi.org/10.1899/0887-3593(2007)26[286:ATEOT

C]2.0.CO;2).

Cuffney, T.F., Brightbill, R.A., May, J.T., and Waite, I.R., 

2010, Responses of benthic macroinvertebrates to environmental changes associated with urbanization in nine metropolitan areas: Ecological Applications, v. 20, p. 1,384–1,401 

(Also available at http://dx.doi.org/10.1890/08-1311.1).

Cuffney, T.F., Zappia, H., Giddings, E.M.P., and Coles, J.F., 

2005, Effects of urbanization on benthic macroinvertebrate 

assemblages in contrasting environmental settings—Boston, Massachusetts; Birmingham, Alabama; and Salt Lake 

City, Utah: American Fisheries Society Symposium, v. 47, 

Bethesda, Maryland, p. 361–407 (Also available at http://

water.usgs.gov/nawqa/urban/pdf/361-408_Cuffney.pdf).

Dantzig, G.B., 1951, Maximization of linear function of variables subject to linear inequalities, in Koopmans, T.C., ed., 

Activity analysis of production and allocation: New York, 

John Wiley and Sons, p. 339–347.

Davis, W.S., and Simon, T.P., 1995, Biological assessment and 

criteria—Tools for water resource planning and decision 

making: Boca Raton, Fla., Lewis Publishers, 415 p.

Dewson, Z.S., James, A.B.W., and De’ath, R.G., 2007, 

Invertebrate responses to short-term water abstraction in 

small New Zealand streams: Freshwater Biology, v. 52, 

p. 357–369 (Also available at http://dx.doi.org/10.1111/

j.1365-2427.2006.01682.x).

Duda, J.J., Warrick, J.A., and Magirl, C.S., 2011, Elwha River 

dam removal—Rebirth of a river: U.S. Geological Survey 

Fact Sheet 2011–3097, 4 p.

Dunne, T., and Leopold, L.B., 1978, Water in environmental 

planning: New York, Freeman Press, 818 p.

Dynesius, M., and Nilsson, C., 1994, Fragmentation and 

flow regulation of river systems in the northern third of the 

world: Science, v. 266, p. 753–762 (Also available at http://

dx.doi.org/10.1126/science.266.5186.753).

Engel, V.C., Stieglitz, M., Williams, M., and Griffin, K.L., 

2002, Forest canopy hydraulic properties and catchment 

water balance—Observations and modeling: Ecological 

Modelling, v. 154, no. 3, p. 263–288 (Also available at 

http://dx.doi.org/10.1016/S0304-3800(02)00068-6).

Espey, W.H., Jr., Morgan, C.W., and Masch, F.D., 1966, A 

study of some effects of urbanization on storm runoff from 

a small watershed: Austin, Texas, Technical Report 44D 

07-6501 CRWR-2, Center for Research in Water Resources, 

University of Texas, Austin, Texas, 110 p. (Also available at 

http://www.twdb.texas.gov/publications/reports/numbered_

reports/doc/R23/R23.pdf).

Esralew, R.A., and Baker, R.J., 2008, Determination of baseline periods of record for selected streamflow-gaging stations in New Jersey for determining Ecologically Relevant 

Hydrologic Indices (ERHI): U.S. Geological Survey Scientific Investigations Report 2008-5077, 70 p. (Also available 

at http://pubs.usgs.gov/sir/2008/5077/pdf/sir2008-5077.pdf).

Fongers, D., and Fulcher, J., 2002, Hydrologic impacts due 

to development—The need for adequate runoff detention and stream protection: Lansing, Michigan, Michigan 

Department of Environmental Quality, 23 p. (Also available at http://www.michigan.gov/documents/deq/lwmimpact_202416_7.pdf).

Freeman, M.C., Buell, G.R., Hay, L.E., Hughes, W.B, Jacobson, R.B., Jones, J.W., Jones, S.A., Lafontaine, J.H., Odom, 

K.R., Peterson, J.T., Riley, J.W., Schindler, J.S., Shea, C., 

and Weaver, J.D., 2012, Linking river management to species conservation using dynamic landscape-scale models: 

River research and applications, accessed April 20, 2012 

at http://dx.doi.org/10.1002/rra.2575.

Freeman, M.C., and Marcinek, P.A., 2006, Fish assemblage 

responses to water withdrawals and water supply reservoirs 

in Piedmont streams: Journal of Environmental Management, v. 38, p. 435–450 (Also available at http://dx.doi.

org/10.1007/s00267-005-0169-3).

Gibson, C.A., Meyer, J.L., Poff, N.L., Hay, L.E., and Georgakakos, A., 2005, Flow regime alterations under changing 

climate in two river basins—Implications for freshwater ecosystems: River Research and Applications, v. 21, 

p. 849–864 (Also available at http://dx.doi.org/10.1002/

rra.855).

Graf, W.L., 1999, Dam nation—A geographic census of 

American dams and their large-scale hydrologic impacts: 

Water Resources Research, v. 35, p. 1,305–1,311.
52 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Graham, D.N., and Butts, M.B., 2005, Flexible, integrated 

watershed modeling with MIKE SHE, in Singh, V.P., and 

Frevert, D.K., eds., Watershed models: Boca Raton, Florida, 

CRC Press, p. 245–272.

Grossman, G.D., 1982, Dynamics and organization of a 

rocky intertidal fish assemblage—The persistence and 

resilience of taxocene structure: American Naturalist, 

v. 119, p. 611–637 (Also available at http://www.jstor.org/

stable/2461182?origin=JSTOR-pdf).

Harding, J.S., Benfield, E.F., Bolstad, P.V., Helfman, G.S., and 

Jones, E.B.D., III, 1998, Stream biodiversity—The ghost of 

land use past: Proceedings of the National Academy of Sciences, v. 95, p. 14,843–14,847. 

Henriksen, J.A., Heasley, J., Kennen, J.G., and Nieswand, 

S.P., 2006, Users’ manual for the Hydroecological Integrity 

Assessment Process software (including the New Jersey 

Assessment Tools): U.S. Geological Survey Open-File 

Report 2006–1093, 71 p. (Also available at http://pubs.

er.usgs.gov/usgspubs/ofr/ofr20061093).

Henshaw, P.C., and Booth, D.B., 2000, Natural restabilization of stream channels in urban watersheds: Journal 

of the American Water Resources Association, v. 36, 

no. 6, p. 1,219–1,236 (Also available at http://dx.doi.

org/10.1111/j.1752-1688.2000.tb05722.x).

Hill, M.T., Platts, W.S., and Beschta, R.L., 1991, Ecological 

and geomorphological concepts for instream and out-ofchannel flow requirements: Rivers, v. 2, p. 198–210.

Hilsenhoff, W.L., 1987, An improved biotic index of organic 

stream pollution: The Great Lakes Entomologist, v. 20, 

p. 31–39.

Hirsch, R.M., Walker, J.F., Day, J.C., and Kallio, R., 1990, The 

influence of man on hydrologic systems, in Wolman, W.G., 

and Riggs, H.C., eds., Surface water hydrology—The geology of North America, v. O-1: Boulder, Colorado, Geological Society of America, p. 329–359.

Hornberger, G.M., and Spear, R.C., 1981, An approach to the 

preliminary analysis of environmental systems: Journal of 

Environmental Management, v. 12, p. 7–18.

Horner, R.R., and May, C.W., 1999, Regional study supports 

natural land cover protection as leading Best Management 

Practice for maintaining stream ecological integrity, in

Proceedings of Comprehensive Stormwater and Aquatic 

Ecosystem Management Conference, Auckland, New Zealand, February 22-26, 1999, v. 1, p. 233–247.

James, A.B.W., Dewson, Z.E., and De’ath, R.G., 2008, Do 

stream invertebrates use instream refugia in response to 

severe short-term flow reduction in New Zealand streams?: 

Freshwater Biology, v. 53, p. 1,316–1,334 (Also available at 

http://dx.doi.org/10.1111/j.1365-2427.2008.01969.x).

Johnson, W.C., Burgess, R.L., and Keammerer, W.R., 1976, 

Forest overstory vegetation and environment on the Missouri River floodplain in North Dakota: Ecological Monographs, v. 46, p. 59–84 (Also available at http://dx.doi.

org/10.2307/1942394).

Jowett, J.G., 1997, Environmental effects of extreme flows, 

in Mosely, M.P., and Pearson, C.P., eds., Floods and 

droughts—The New Zealand experience: Christchurch, 

Caxton Press, p. 104–116.

Kennen, J.G., and Ayers, M.A., 2002, Relation of environmental characteristics to the composition of aquatic assemblages 

along a gradient of urban land use in New Jersey, 1996–98: 

U.S. Geological Survey Water-Resources Investigations 

Report 02-4069, 77 p. (Also available at http://pubs.usgs.

gov/wri/wri024069/pdf/wri024069.pdf).

Kennen, J.G., Chang, M., and Tracy, B.H., 2005, Effects of 

landscape change on fish assemblage structure in a rapidly growing metropolitan area in North Carolina, USA, 

in Brown, L.R., Gray, R.H., Hughes, R.M., and Meador, 

M.R., eds., Effects of urbanization on stream ecosystems: 

American Fisheries Society Symposium, v. 47, Bethesda, 

Maryland, p. 39–52.

Kennen, J.G., Henriksen, J.A., Heasley, J., Cade, B.S., and 

Terrell, J.W., 2009, Application of the Hydroecological 

Integrity Assessment Process for Missouri streams: U.S. 

Geological Survey Open-File Report 2009–1138, 57 p. 

(Also available at http://pubs.usgs.gov/of/2009/1138/).

Kennen, J.G., Henriksen, J.A., and Nieswand, S.P., 2007, 

Development of the Hydroecological Integrity Assessment Process for determining environmental flows for New 

Jersey streams: U.S. Geological Survey Scientific Investigations Report 2007–5206, 55 p. (Also available at http://

pubs.usgs.gov/sir/2007/5206/pdf/sir2007-5206-508.pdf).

Kennen, J.G., Kauffman, L.J., Ayers, M.A., and Wolock, 

D.M., 2008, Use of an integrated flow model to estimate ecologically relevant hydrologic characteristics at 

stream biomonitoring sites: Ecological Modelling, v. 211, 

p. 57–76 (Also available at http://dx.doi.org/10.1016/j.

ecolmodel.2007.08.014). 

Kennen, J.G., Riva-Murray, K., and Beaulieu, K.M., 2010, 

Determining hydrologic factors that influence stream macroinvertebrate assemblages in the northeastern US: Ecohydrology, v. 3, p. 88–106 (Also available at http://dx.doi.

org/10.1002/eco.99).

Kennen, J.G., Sullivan, D.J., May, J.T., Bell, A.H., Beaulieu, 

K.M., and Rice, D.E., 2012, Temporal changes in aquaticinvertebrate and fish communities in streams of the north 

central and northeastern US: Ecological Indicators, v. 18, 

p. 312–329 (Also available at http://dx.doi.org/10.1016/j.

ecolind.2011.11.022).
References Cited 53

Klein, R.D., 1979, Urbanization and stream quality impairment: Water Resources Bulletin, v. 15, p. 948–963 (Also 

available at http://dx.doi.org/10.1111/j.1752-1688.1979.

tb01074.x).

Konrad, C.P., and Booth, D.B., 2005, Hydrologic changes in 

urban streams and their ecological significance, in Brown, 

L.R., Gray, R.H., Hughes, R.M., and Meador, M.R., eds., 

Effects of urbanization on stream ecosystems: American 

Fisheries Society, Bethesda, Md., p. 157–177. 

Legesse, D., Vallet-Coulomba, C., and Gasse, F., 2003, 

Hydrological response of a catchment to climate and 

land use changes in Tropical Africa—Case study South 

Central Ethiopia: Journal of Hydrology, v. 275, p. 67–85 

(Also available at http://dx.doi.org/10.1016/S0022-

1694(03)00019-2).

Leopold, L.B., 1968, Hydrology for urban land planning—A 

guidebook on the hydrologic effects of urban land use: U.S. 

Geological Survey Circular 554, 18 p. (Also available at 

http://onlinepubs.er.usgs.gov/djvu/CIR/circ_554.djvu). 

Lytle, D.A., 2002, Flash floods and aquatic insect life history evolution—Evaluation of multiple models: Ecology, v. 8, p. 370–385 (Also available at http://dx.doi.

org/10.2307/2680021).

Lytle, D..A., and Poff, N.L., 2004, Adaptation to natural 

flow regimes: Trends in Ecology & Evolution, v. 19, 

p. 94–100 (Also available at http://dx.doi.org/10.1016/j.

tree.2003.10.002).

MacRae, C., and Rowney, A., 1992, The role of moderate flow 

events and bank structure in the determination of channel response to urbanization—45th Annual Conference, 

Resolving Conflicts and Uncertainty in Water Management: 

Proceeding of the Canadian Water Resources Association, 

Kingston, Ontario, June 1992, p. 12.1–12.21.

Magilligan, F.J., and Nislow, K.H., 2005, Changes in hydrologic regime by dams: Geomorphology, v. 71, p. 61–78.

Maine Department of Environmental Protection, 2004, Modeling report to support Total Maximum Daily Load (TMDL) 

development for Penjajawoc Stream (AKA Meadow 

Brook) –Draft report: Prepared by Tetra Tech, Inc., Fairfax, Virginia, 26 p., accessed July 9, 2013, at http://www.

bangormaine.gov/image_upload/ModelingReport.pdf.

Massachusetts Department of Environmental Protection, 2003, 

Draft storm water pollutant total maximum daily load for 

headwaters of the Shawsheen River: Boston, Mass., Massachusetts Department of Environmental Protection, Department of Watershed Management, TMDL Report MA83-08-

2003-01, June 9, 2003, 31 p., accessed March 14, 2013, at 

http://www.mass.gov/dep/water/resources/shawhead.pdf.

Mathews, R., 2005, A six-step framework for ecologically sustainable water management: Universities Council on Water 

Resources–Journal of Contemporary Water Research and 

Education, v. 131, p. 60–65 (Also available at http://dx.doi.

org/10.1111/j.1936-704X.2005.mp131001010.x).

May, C.W., Horner, R.R., Karr, J.R., Mar, B.W., and Welch, 

E.B., 1997, Effects of urbanization on small streams in the 

Puget Sound Lowland Ecoregion: Watershed Protection 

Techniques, v. 2, no. 4, p. 485–494.

McCabe, D.J., and Gotelli, N.J., 2000, Effects of disturbance 

frequency, intensity, and area on assemblages of stream 

macroinvertebrates: Oecologia, v. 124, p. 270–279 (Also 

available at http://dx.doi.org/10.1007/s004420000369).

Merritt, R.W., and Cummins, K.W., 1996, An introduction 

to the aquatic insects of north America (3d ed.): Dubuque, 

Iowa, Kendall/Hunt Publishing Company, 772 p. 

Merritt, W.S., Croke, B.F.W., Jakeman, A.J., Letcher, R.A., 

and Perez, P., 2004, A biophysical toolbox for assessment 

and management of land and water resources in rural catchments in Northern Thailand: Ecological Modelling, v. 171, 

p. 279–300 (Also available at http://dx.doi.org/10.1016/j.

ecolmodel.2003.08.010).

Meyer, J.L., Paul, M.J., and Taulbee, W.K., 2005, Stream 

ecosystem function in urbanizing landscapes: Journal of the 

North American Benthological Society, v. 24, p. 602–612 

(Also available at http://dx.doi.org/10.1899/04-021.1).

Miller, S.W., Wooster, D., and Li, J., 2007, Resistance and 

resilience of macroinvertebrates to irrigation and water 

withdrawals: Freshwater Biology, v. 52, p. 2,494–2,510 

(Also available at http://dx.doi.org/10.1111/j.1365-

2427.2007.01850.x).

Moulton, S.R., Kennen, J.G., Goldstein, R.M., and Hambrook, 

J.A., 2002, Revised protocols for sampling algal, invertebrate and fish communities as part of the National WaterQuality Assessment Program: U.S. Geological Survey 

Open-File Report 2002–150, 75 p. 

National Research Council, 2008, Urban stormwater management in the United States: Washington, D.C., The National 

Academies Press, 610 p. (Also available at http://books.nap.

edu/openbook.php?record_id=12465).

Natural Resources Conservation Service, 1986, Urban hydrology for small watersheds (TR-55): U.S. Department of 

Agriculture Technical Release 55, June 1986, 164 p.

New Jersey Department of Environmental Protection, 1994, 

Ambient Biomonitoring Network, Arthur Kill, Passaic, 

Hackensack, and Wallkill River drainage basins: Trenton, 

N.J., New Jersey Department of Environmental Protection, 

22 p.
54 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

New Jersey Department of Environmental Protection, 2008, 

Ambient Biomonitoring Network–Generalized executive summary–Watershed management areas and benthic 

macroinvertebrate data water monitoring report: Trenton, 

N.J., New Jersey Department of Environmental Protection, 

Bureau of Freshwater and Biological Monitoring, 12 p. 

(Also available at http://www.state.nj.us/dep/wms/bfbm/

GenExecSum.html). 

New Jersey Department of Environmental Protection, 2008, 

Bureau of Freshwater and Biological Monitoring–Rapid 

bioassessment protocol: accessed February 20, 2013, at 

http://www.nj.gov/dep/wms/bfbm/rbpinfo.html.

New Jersey Department of Environmental Protection, 2008, 

Bureau of Geographic Information Systems (BGIS)–ArcGis 

2002 Land use/land cover polygon by watershed management area (WMA): accessed December 28, 2010, at http://

www.state.nj.us/dep/gis/lulc02cshp.html.

New Jersey Department of Environmental Protection, 2011, 

DEP agreement will remove three dams, open up segment 

of Raritan River to fish spawning for first time in decades: 

News release, accessed March 12, 2012, at http://www.

nj.gov/dep/newsrel/2011/11_0010.htm.

New Jersey Department of Environmental Protection, 2012b, 

New Jersey’s four-tiered approach to volunteer monitoring: 

Trenton, N.J., New Jersey Department of Environmental 

Protection, Division of Watershed Management, 4 p. (Also 

available at http://www.state.nj.us/dep/wms/bwqsa/vm/docs/

WatershedWatchFinal.pdf).

New Jersey Department of Environmental Protection, 2012a, 

New Jersey Integrated Water Quality Monitoring and 

Assessment Report: Trenton, N.J., New Jersey Department 

of Environmental Protection, Division of Water Monitoring and Standards, 75 p. (Also available at http://www.

state.nj.us/dep/wms/bwqsa/2010_revised_final_integrated_

report_complete.pdf).

New Jersey Department of Environmental Protection, New 

Jersey Geological Survey, 2002, Digital Geologic Series 

DGS02-7: Physiographic Provinces of New Jersey: 

accessed December 28, 2010, at http://www.state.nj.us/dep/

njgs/geodata/dgs02-7.htm.

New Jersey Department of Environmental Protection, 2007, 

Standard operating procedures–Ambient biological monitoring using benthic macroinvertebrates–Field, lab, and 

assessment methods: Trenton, N.J., New Jersey Department 

of Environmental Protection, Bureau of Freshwater and 

Biological Monitoring, 40 p.

Nisenson, L., 2005, Using smart growth techniques as stormwater best management practices: Washington, D.C., U.S. 

Environmental Protection Agency, EPA 231-B-05-002, 

108 p.

Olden, J.D., and Poff, N.L., 2003, Redundancy and the choice 

of hydrologic indices for characterizing streamflow regimes: 

River Research and Applications, v. 19, p. 101–121 (Also 

available at http://dx.doi.org/10.1002/rra.700).

Orr, C.H., Kroiss, S.J., Rogers, K.L., and Stanley, E.H., 2008, 

Downstream benthic responses to small dam removal in a 

coldwater stream: River Research and Applications, v. 24, 

p. 804–822.

Paine, G.H., and Gaufin, A.R., 1956, Aquatic Diptera as 

indicators of pollution in a Midwestern stream: The Ohio 

Journal of Science, v. 56, no. 5, p. 291–304.

Palmer, M.A., Liermann, C.A.R., Nilsson, C., Flörke, M., 

Alcamo, J., Lake, P.S., and Bond, N., 2008, Climate change 

and the world’s river basins: Frontiers in Ecology and the 

Environment, v. 6, p. 81–89 (Also available at http://dx.doi.

org/10.1890/060148).

Paul, M.J., and Meyer, J.L., 2001, Streams in the urban 

landscape: Annual Review of Ecological Systems, v. 32, 

p. 333–365. 

Peckarsky, B.L., Taylor, B.W., and Caudill, C.C., 2000, 

Hydrologic and behavioral constraints on oviposition of 

stream insects–Implications for adult dispersal: Oecologia, v. 125, p.186–200 (Also available at http://dx.doi.

org/10.1007/s004420000446).

Poff, N.L., and Allan, J.D., 1995, Functional organization of 

stream fish assemblages in relation to hydrologic variability: 

Ecology, v. 76. no. 2, p. 606–627 (Also available at http://

dx.doi.org/10.2307/1941217).

Poff, N.L., Allan, J.D., Bain, M.B., Karr, J.R., Prestegaard, 

K.L., Richter, B.D., Sparks, R.E., and Stromberg, J.C., 

1997, The natural flow regime—A paradigm for river 

conservation and restoration: BioScience, v. 47, p. 769–784 

(Also available at http://dx.doi.org/10.2307/1313099).

Poff, N.L., Olden, J.D., Merritt, D., and Pepin, D., 2007, 

Homogenization of regional river dynamics by dams 

and global biodiversity implications: Proceedings of the 

National Academy of Sciences, v. 104, p. 5,732–5,737 (Also 

available at http://dx.doi.org/10.1073/pnas.0609812104).

Poff, N.L., Olden, J.D., Vieira, N.K.M., Finn, D.S., Simmons, M.P., and Kondratieff, B.C., 2006, Functional trait 

niches of North American lotic insects—Traits-based 

ecological applications in light of phylogenetic relationships: Journal of the North American Benthological Society, 

v. 25, no. 4, p. 730–755 (Also available at http://dx.doi.

org/10.1899/0887-3593(2006)025[0730:FTNONA]2.0

.CO;2).
References Cited 55

Poff, N.L., Richter, B.D., Arthington, A.H., Bunn, S.E., 

Naiman, R.J., Kendy, E., Acreman, M., Apse, C., Bledsoe, 

B.P., Freeman, M.C., Henriksen, J.A., Jacobson, R.B., Kennen, J.G., Meritt, D.M., O’Keeffe, J., Olden, J.D., Rogers, 

K.H., Tharme, R.E., and Warner, A.T., 2010, The ecological 

limits of hydrologic alteration (ELOHA)—A new framework for developing regional environmental flow standards: 

Freshwater Biology: v. 55, p. 147–170 (Also available at 

http://dx.doi.org/10.1111/j.1365-2427.2009.02204.x).

Poff, N.L., and Ward, J.V., 1989, Implications of streamflow variability and predictability for lotic community 

structure—A regional analysis of streamflow patterns: 

Canadian Journal of Fisheries and Aquatic Sciences, v. 46, 

p. 1,805–1,818 (Also available at http://dx.doi.org/10.1139/

f89-228).

Poff, N.L., and Zimmerman, J.K.H., 2010, Ecological 

responses to altered flow regimes—A literature review 

to inform environmental flows science and management: 

Freshwater Biology, v. 55, p. 194–200 (Also available at 

http://dx.doi.org/10.1111/j.1365-2427.2009.02272.x).

Power, M.E., and Stewart, A.J., 1987, Disturbance and 

recovery of an algal assemblage following flooding 

in an Oklahoma stream: American Midland Naturalist, v. 117, p. 333–345 (Also available at http://dx.doi.

org/10.2307/2425975).

Pusey, B.J., Kennard, M.J., and Arthington, A.H., 2000, 

Discharge variability and the development of predictive 

models relating stream fish assemblage structure to habitat in northeastern Australia: Ecology of Freshwater Fish, 

v. 9, p. 30–50 (Also available at http://dx.doi.org/10.1034/

j.1600-0633.2000.90105.x).

Reiser, R.G., 2004, Evaluation of streamflow, water quality, 

and permitted and nonpermitted loads and yields in the 

Raritan River Basin, New Jersey, water years 1991–98: U.S. 

Geological Survey Water-Resources Investigations Report 

03-4207, 224 p. (Also available at http://pubs.usgs.gov/wri/

wri03-4207/). 

Richter, B.D., 2009, Re-thinking environmental flows—From 

allocations and reserves to sustainability boundaries: River 

Research and Applications, v. 26, p. 1,052–1,063 (Also 

available at http://dx.doi.org/10.1002/rra.1320).

Richter, B.D., Baumgartner, J.V., Powell, J., and Braun, D.P., 

1996, A method for assessing hydrologic alteration within 

ecosystems: Conservation Biology, v. 10, p. 1,163–1,174 

(Also available at http://dx.doi.org/10.1046/j.1523-

1739.1996.10041163.x).

Richter, B.D., Baumgartner, J.V., Wigington, R., and Braun, 

D.P., 1997, How much water does a river need?: Freshwater 

Biology, v. 37, p. 231–249 (Also available at http://dx.doi.

org/10.1046/j.1365-2427.1997.00153.x).

Richter, B.D., Davis, M.M., Apse, C., and Konrad, C., 2011, 

A presumptive standard for environmental flow protection: 

River Research and Applications, v. 28, p. 1,312–1,321 

(Also available at http://dx.doi.org/10.1002/rra.1511).

Richter, B.D., and Thomas, T.A., 2007, Restoring environmental flows by modifying dam operations: Ecology and 

Society, v. 12, no. 1, article 12 (Also available at http://

www.ecologyandsociety.org/vol12/iss1/art12/).

Robinson, C.T., and Minshall, G.W., 1986, Effects of disturbance frequency on stream benthic community structure in 

relation to canopy cover and season: Journal of the North 

American Benthological Society, v. 5, p. 237–248 (Also 

available at http://dx.doi.org/10.2307/1467711).

Rosenberg, D.M., Berkes, F., Bodaly, R.A., Hecky, R.E., 

Kelly, C.A., and Rudd, J.W.M., 1997, Large-scale impacts 

of hydroelectric development: Environmental Reviews, v. 5, 

p. 27–54 (Also available at http://dx.doi.org/10.1139/er-5-

1-27).

Rosenberg, D.M., and Resh, V.H., 1993, Freshwater biomonitoring and benthic macroinvertebrates: New York, Routledge, Chapman and Hall, Inc., 488 p.

Roy, A.H., Cabezas, H., Clagett, M.P., Hoagland, N.T., Mayer, 

A.L., Morrison, M.A., Shuster, W.D., Templeton, J.J., and 

Thurston, H.W., 2006, Retrofit stormwater management: 

Navigating multidisciplinary hurdles at the watershed 

scale—Stormwater, v. 7, p. 16–29 (Also available at http://

www.stormh2o.com/SW/Articles/16660.aspx).

Roy, A.H., Freeman, M.C., Freeman, B.J., Wenger, S.J., 

Ensign, W.E., and Meyer, J.L., 2005, Investigating hydrologic alteration as a mechanism of fish assemblage shifts in 

urbanizing streams: Journal of the North American Benthological Society, v. 24, no. 3, p. 656–678.

Roy, A.H., Rosemond, A.D., Paul, M.J., Leigh, D.S., and 

Wallace, J.B., 2003, Stream macroinvertebrate response 

to catchment urbanisation (Georgia, U.S.A.): Freshwater 

Biology, v. 48, p. 1–18 (Also available at http://dx.doi.

org/10.1046/j.1365-2427.2003.00979.x).

Sanborn, S.C., and Bledsoe, B.P., 2006, Predicting streamflow regime metrics for ungaged streams in Colorado, 

Washington, and Oregon: Journal of Hydrology, v. 325, 

p. 241–261 (Also available at http://dx.doi.org/10.1016/j.

jhydrol.2005.10.018).

SAS Institute Inc., 2006, SAS/STAT® users guide, version 

9.2 (4th ed.), v. 2: Cary, North Carolina, SAS Institute Inc., 

1,682 p.

SAS Institute Inc., 1991, SAS® system for regression (2d ed.): 

Cary, North Carolina, SAS Institute Inc., 210 p.
56 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Schlosser, I.J., 1985, Flow regime, juvenile abundance, 

and the assemblage structure of stream fishes: Ecology, v. 66, p.1,484–1,490 (Also available at http://dx.doi.

org/10.2307/1938011).

Schueler, T.R., 1994, The importance of imperviousness: 

Watershed Protection, v. 1, no. 3, p. 100–111.

Schueler, T.R., Fraley-McNeal, L., and Cappiella, K., 2009, 

Is impervious cover still important? —A review of recent 

research: Journal of Hydrologic Engineering, v. 14, no. 4, 

p. 309–315.

Seaburn, G.E., 1969, Effects of urban development on direct 

runoff to East Meadow Brook, Nassau County, New York: 

U.S. Geological Survey Professional Paper 627-B, 14 

p. (Also available at http://pubs.er.usgs.gov/publication/

pp627B).

Sefton, C.E.M., and Boorman, D.B., 1997, A regional investigation of climate change impacts on UK streamflows: 

Journal of Hydrology, v. 195, p. 26–44 (Also available at 

http://dx.doi.org/10.1016/S0022-1694(96)03257-X).

Snelder, T.H., Biggs, B.J.F., and Woods, R.A., 2005, Improved 

eco-hydrological classification of rivers: River Research 

and Applications, v. 21, no. 66, p. 609–628 (Also available 

at http://dx.doi.org/10.1002/rra.826).

Sparks, R.E., 1995, Need for ecosystem management of large 

rivers and their floodplains: BioScience, v. 45, p. 168–182 

(Also available at http://dx.doi.org/10.2307/1312556).

Stanford, J.A., Ward, J.V., Liss, W.J., Frissell, C.A., Williams, R.N., Lichatowich, J.A., and Coutant, C.C., 1996, A 

general protocol for restoration of regulated rivers: Regulated Rivers: Research and Management, v. 12, p. 391–414 

(Also available at http://dx.doi.org/10.1002/(SICI)1099-

1646(199607)12:4/5<391::AID-RRR436>3.3.CO;2-W).

Stanley, E.H., and Doyle, M.W., 2003, Trading off–The ecological effects of dam removal: Frontiers in Ecology and the 

Environment, v. 1, p. 15–22.

Stuckey, M.H., 2006, Low-flow, base-flow, and meanflow regression equations for Pennsylvania streams: 

U.S. Geological Survey Scientific Investigations Report 

2006–5130, 84 p. (Also available at http://pubs.usgs.gov/

sir/2006/5130/).

Suren, A.M., Biggs, B., Duncan, M.J., Burgey, L., and Lambert, P., 2003, Benthic community dynamics during summer 

low-flows in two rivers contrasting enrichment–2. Invertebrates: New Zealand Journal of Marine and Freshwater 

Research, v. 37, p. 71–83 (Also available at http://dx.doi.org

/10.1080/00288330.2003.9517147).

Taylor, B.R., 1997, Optimization of field and laboratory methods for benthic invertebrate biomonitoring—Final report to 

Canada Centre for Mineral and Energy Technology: Taylor 

Mazier Associates, St. Andrews, Nova Scotia, January 1997, 

67 p.

Thoms, M.C., and Parsons, M., 2003, Identifying spatial and 

temporal patterns in the hydrological character of the Condamine-Balonne River, Australia, using multivariate statistics: 

River Research and Applications, v. 19, p. 443–457 (Also 

available at http://dx.doi.org/10.1002/rra.737).

Thomson, J.R., Hart, D.D., Charles, D.F., Nightengale, T.L., 

and Winter, D.M., 2005, Effects of removal of a small dam 

on downstream macroinvertebrate and algal assemblages in a 

Pennsylvania stream: Journal of the North American Benthological Society, v. 24, p. 192–207.

Torak, L.J., and Painter, J.A., 2011, Summary of the Georgia 

Agricultural Water Conservation and Metering Program and 

evaluation of methods used to collect and analyze irrigation 

data in the middle and lower Chattahoochee and Flint River 

basins, 2004–2010: U.S. Geological Survey Scientific Investigations Report 2011–5126, 25 p.

Toth, L.A., 1995, Principles and guidelines for restoration of 

river/floodplain ecosystems—Kissimmee River, Florida 

(2d ed.), in Cairns, J., ed., Rehabilitating damaged ecosystems: Boca Raton, Fla., Lewis Publishers/CRC Press, 

p. 49–73.

Tyus, H.M., 1990, Effects of altered stream flows on fisheries 

resources: Fisheries v. 15, p. 18–20.

Urban, M.C., Skelly, D.K., Burchsted, D., Price, W., and 

Lowry, S., 2006, Stream communities across a rural-urban 

landscape gradient: Diversity and Distributions, v. 12, 

p. 337–350.

Utz, R.M., Hilderbrand, R.H., and Boward, D.M., 2009, Identifying regional differences in threshold responses of aquatic 

invertebrates to land cover gradients: Ecological Indicators, 

v. 9, p. 556–567. 

U.S. Army Corps of Engineers, 2000, HEC-HMS Hydrologic 

Modeling System, User’s manual: Davis, California, U.S. 

Army Corps of Engineers Hydrologic Engineering Center, 

149 p. 

U.S. Census Bureau, 2012, 2010 Census of Population and 

Housing, Population and Housing Unit Counts, CPH-2-32, 

New Jersey: U.S. Government Printing Office, Washington, 

D.C., 65 p. plus appendix, accessed August 6, 2013, at http://

www.census.gov/prod/cen2010/cph-2-32.pdf.

U.S. Environmental Protection Agency, 1997, Field and laboratory methods for macroinvertebrates and habitat assessment of low gradient, nontidal streams: Wheeling, W.Va., 

Mid-Atlantic Coastal Streams Workgroup, Environmental 

Services Divisions, Region 3, 23 p.
References Cited 57

U.S. Environmental Protection Agency, 2006, Estimation and 

application of macroinvertebrate tolerance values: Washington, D.C., National Center for Environmental Assessment, 

Office of Research and Development, EPA/600/P-04/116F, 

80 p.

U.S. Geological Survey, 2011, WaterSMART—Departmental 

highlights: accessed June 2, 2012, at http://www.doi.gov/budget/appropriations/2011/highlights/upload/WaterSMART.pdf.

Vannote, R.L., Minshall, G.W., Cummins, K.W., Sedell, J.R., 

and Cushing, C.E., 1980, The river continuum concept: 

Canadian Journal of Fisheries and Aquatic Sciences, v. 37, 

p. 130–137 (Also available at http://dx.doi.org/doi:10.1139/

f80-017). 

Van Sickel, J., Baker, J., Herlihy, A., Bayley, P., Gregory, S., 

Haggerty, P., Ashkenas, L., and Li, J., 2006, Projecting the 

biological condition of streams under alternative scenarios of 

human land use: Ecological Applications, v. 14, p. 368–380 

(Also available at http://dx.doi.org/10.1890/02-5009).

Vieira, N.K.M., Poff, N.L., Carlisle, D.M., Moulton, S.R., II, 

Koski, M.L., and Kondratieff, B.C., 2006, A database of 

lotic invertebrate traits for North America: U.S. Geological 

Survey Data Series 187, 15 p. (Also available at http://pubs.

usgs.gov/ds/ds187/pdf/ds187.pdf).

Virginia Department of Environmental Quality, 2004, Benthic 

TMDL for Toms Brook in Shenandoah County: Richmond, 

Virginia, Virginia Department of Environmental Quality, 

prepared by Department of Biological Systems Engineering, 

Virginia Polytechnic Institute and State University, Blacksburg, Virginia, 93 p.

Vorosmarty, C.J., and Sahagian, D., 2000, Anthropogenic disturbance of the terrestrial water cycle: BioScience, v. 50, p. 

753–765 (Also available at http://dx.doi.org/10.1641/0006-

3568(2000)050[0753:ADOTTW]2.0.CO;2).

Waldron, M.C., and Archfield, S.A., 2006, Factors affecting firm yield and the estimation of firm yield for selected 

streamflow-dominated drinking-water-supply reservoirs in 

Massachusetts: U.S. Geological Survey Scientific Investigations Report 2006–5044, 39 p. (Also available at http://pubs.

usgs.gov/sir/2006/5044/).

Walsh, C.J., Fletcher, T.D., and Ladson, A.R, 2005a, Stream 

restoration in urban catchments through redesigning stormwater systems—Looking to the catchment to save the stream: 

Journal of the North American Benthological Society, 

v. 24, no. 3, p. 690–705 (Also available at http://dx.doi.

org/10.1899/04-020.1).

Walsh, C.J., Leonard, A.W., Ladson, A.R., and Fletcher, T.D., 

2004, Urban stormwater and the ecology of streams: Canberra, Australia, Cooperative Research Centre for Freshwater 

Ecology and Cooperative Research Centre for Catchment 

Hydrology, 44 p.

Walsh, C.J., Roy, A.H., Feminella, J.W., Cottingham, P.D., 

Groffman, P.M., and Morgan, R.P., II, 2005b, The urban 

stream syndrome: Current knowledge and the search 

for a cure: Journal of the North American Benthological 

Society, v. 24, p. 706–723 (Also available at http://dx.doi.

org/10.1899/04-028.1).

Wang, L., Brendon, T., Seelbach, P., Cooper, A., Allan, D., 

Clark, R., Jr., and Wiley, M., 2008, Landscape based identification of human disturbance gradients and reference conditions for Michigan streams: Environmental Monitoring 

and Assessment, v. 141, p. 1–17, http://dx.doi.org/10.1007/

s10661-006-9510-4.

Ward, J.V., and Stanford, J.A., 1989, Riverine ecosystems—

The influence of man on catchment dynamics and fish ecology: Canadian Special Publications in Fisheries and Aquatic 

Sciences, v. 106, p. 56–64.

Watson, K.M., Reiser, R.G., Nieswand, S.P., and Schopp, 

R.D., 2005, Streamflow characteristics and trends in New 

Jersey, water years 1897–2003: U.S. Geological Scientific 

Investigations Report 2005–5105, 131 p. (Also available at 

http://pubs.usgs.gov/sir/2005/5105/pdf/NJsir2005-5105_

report.pdf).

Wenger, S.J., and Freeman, M., 2007, Stressors to imperiled 

fishes in the Etowah Basin—Mechanisms, sources and management under the Etowah HCP: For the Etowah Regional 

Habitat Conservation Plan, September 25, 2006, 42 p., 

http://rivercenter.uga.edu/publications/pdf/stressors_2007.

pdf.

Wenger, S.J., Peterson, J.T., Freeman, M.C., Freeman, 

B.J., and Homans, D.D., 2008, Stream fish occurrence 

in response to impervious cover, historic land use, and 

hydrogeomorphic factors: Canadian Journal of Fisheries 

and Aquatic Sciences, v. 65, p. 1,250–1,264 (Also available 

at http://dx.doi.org/10.1139/F08-046).

Wolock, D.M., 1993, Simulating the variable-source-area 

concept of streamflow generation with the watershed model 

TOPMODEL: U.S. Geological Survey Water-Resources 

Investigations Report 93–4124, 33 p. (Also available at 

http://pubs.er.usgs.gov/publication/wri934124).

Woodruff, J.F., and Hewlett, J.D., 1970, Predicting and mapping the average hydrologic response for the eastern United 

States: Water Resource Research, v. 6, p. 1,312–1,326 (Also 

available at http://dx.doi.org/10.1029/WR006i005p01312).

Zapecza, O.S., 1989, Hydrogeologic framework of the New 

Jersey Coastal Plain: U.S. Geological Survey Professional 

Paper 1404-B, 49 p., 24 pl. (Also available at http://pubs.

er.usgs.gov/usgspubs/pp/pp1404B).
58 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey Table 1. Baseline periods of record for Raritan River Basin study sites determined by using quantile regression.—Continued [USGS, U.S. Geological Survey; NJSCT, New Jersey Stream Classification Tool; POR, period of record; ISC, impervious surface cover; QR, quantile regression; mi2, square miles; unknown, streams without a quantile-regression-defined baseline POR for characterizing stream class; none, stream for which no baseline POR could be derived either because the streamflow record was too short, the entire POR was considered anthropogenically altered, or the quantile-regression analysis was inconclusive; --, streams without a defined baseline period; NA, not applicable—stream not reclassified; NC, not calibrated— streams for which calibration of the Statewide Watershed Runoff Model (SWRM) was unsuccessful and, therefore, no simulated baseline POR was defined] USGS station number USGS station name NJSCT1 stream class Reclassified2 streams Drainage area (mi2) Total POR (observed) Percent total ISC (2002) Baseline POR from QR Number of years of baseline streamflow 01396190 South Branch Raritan River at Four Bridges, NJ unknown

D 31.0 2000–2006 10.4 none --

01396500 South Branch Raritan River near High Bridge, NJ 

A NA 65.3 1919–2006 7.2 1919–20063 88

01396580 Spruce Run at Glen Gardner, NJ

C NA 11.3 1979–20054 3.4 1979–2005 26

01396660 Mulhockaway Creek at Van Syckel, NJ

C NA 11.8 1978–2006 4.8 1978–1985 8

01396800 Spruce Run at Clinton, NJ unknown

A 41.3 1961–2006 3.6 none --

01397000 South Branch Raritan River at Stanton, NJ

A NA 147.0 1920–2006 6.0 1920–1963 44

01397500 Walnut Brook near Flemington, NJ

D NA 2.2 1937–1961 3.0 1937–1961 25

01398000 Neshanic River at Reaville, NJ

A NA 25.7 1931–2006 5.3 1931–19605 29

01398045 Back Brook tributary near Ringoes, NJ

D NA 2.0 1978–1988 1.6 1978–1988 11

01398107 Holland Brook at Readington, NJ

D NA 9.0 1979–1996 5.2 1979–19905 12

01398500 North Branch Raritan River near Far Hills, NJ

C NA 26.2 1922–2006 5.3 1922–2006 85

01399190 Lamington (Black) River at Succasunna, NJ unknown

D 7.4 1977–1987 13.1 none --

01399200 Lamington (Black) River at Ironia, NJ unknown

D 10.9 1976–1987 13.5 none --

01399500 Lamington (Black) River near Pottersville, NJ C/D6,7 NA 32.8 1922–2006 7.0 1922–20063 8501399510 Upper Cold Brook near Pottersville, NJ

D NA 2.2 1973–1996 2.8 1973–1996 24

01399525 Axle Brook near Pottersville, NJ

D NA 1.2 1978–1988 0.9 1978–1988 11

01399670 South Branch Rockaway Creek at Whitehouse Station, NJ unknown

C 12.3 1978–2006 8.2 none --

01399690 South Branch Rockaway Creek at Whitehouse, NJ unknown

C 13.2 1978–1987 9.1 none --

01399700 Rockaway Creek at Whitehouse, NJ unknown

C 37.1 1978–1984 5.6 none --

01399830 North Branch Raritan at North Branch, NJ unknown

B 174.0 1978–1981 5.7 none --
Table 1 59

Table 1. Baseline periods of record for Raritan River Basin study sites determined by using quantile regression.—Continued[USGS, U.S. Geological Survey; NJSCT, New Jersey Stream Classification Tool; POR, period of record; ISC, impervious surface cover; QR, quantile regression; mi2, square miles; unknown, streams without a quantile-regression-defined baseline POR for characterizing stream class; none, stream for which no baseline POR could be derived either because the streamflow record was too short, the entire POR was considered anthropogenically altered, or the quantile-regression analysis was inconclusive; --, streams without a defined baseline period; NA, not applicable—stream not reclassified; NC, not calibrated—streams for which calibration of the Statewide Watershed Runoff Model (SWRM) was unsuccessful and, therefore, no simulated baseline POR was defined]

USGS 

station 

number

USGS station name NJSCT1 stream class Reclassified2 streams Drainage area (mi2) Total POR (observed) Percent total ISC (2002) Baseline POR from QR Number of years of baseline streamflow

01400000 North Branch Raritan River near Raritan, NJ A NA 190.0 1924–2006 6.3 1924–19625 8301400300 Peters Brook near Raritan, NJ unknown D 4.2 1979–1996 18.6 none --01400350 Macs Brook at Somerville, NJ unknown D 0.8 1983–1995 28.4 none --01400500 Raritan River at Manville, NJ A NA 490.0 1922–2006 7.0 1922–1960 3901400730 Millstone River At Plainsboro, NJ unknown A 65.8 1964–19894 10.2 1964–1975 1101400932 Baldwins Creek at Baldwin Lake near Pennington, NJ unknown A 2.5 1963–1970 5.1 none --01400953 Honey Branch near Pennington, NJ unknown A 0.7 1968–1975 1.0 none --01401000 Stony Brook at Princeton, NJ unknown A 44.5 1954–2006 5.0 none --01401500 Millstone River near Kingston, NJ A NA 171.0 1934–1949 10.3 1934–1949 1601401650 Pike Run at Belle Mead, NJ D NA 5.4 1981–2006 7.3 1981–2006 2601402000 Millstone River at Blackwells Mills, NJ A NA 258.0 1922–2006 9.6 1922–19703 4901402590 Royce Brook tributary at Frankfort, NJ8 unknown NC 0.3 1969–1975 21.5 none --01402600 Royce Brook tributary near Belle Mead, NJ8 unknown NC 1.2 1967–1996 31.4 none --01403000 Raritan River at Bound Brook, NJ unknown A 779.0 1907–19664 7.8 none --01403060 Raritan River below Calco Dam at Bound Brook, NJ unknown A 785.0 1945–2006 7.9 1945–19635 1801403150 West Branch Middle Brook near Martinsville, NJ C NA 2.0 1980–2006 11.0 1980–2006 2701403160 West Branch Middle Brook near Somerville, NJ unknown A 3.8 1983–1986 8.5 none --01403400 Green Brook at Seeley Mills, NJ D NA 6.2 1980–2006 12.8 1980–2006 2701403500 Green Brook at Plainfield, NJ D NA 9.7 1939–1984 20.0 1939–1984 4601403535 East Branch Stony Brook at Best Lake at Watchung, NJ D NA 1.6 1981–2000 10.8 1981–2000 20
60 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey Table 1. Baseline periods of record for Raritan River Basin study sites determined by using quantile regression.—Continued [USGS, U.S. Geological Survey; NJSCT, New Jersey Stream Classification Tool; POR, period of record; ISC, impervious surface cover; QR, quantile regression; mi2, square miles; unknown, streams without a quantile-regression-defined baseline POR for characterizing stream class; none, stream for which no baseline POR could be derived either because the streamflow record was too short, the entire POR was considered anthropogenically altered, or the quantile-regression analysis was inconclusive; --, streams without a defined baseline period; NA, not applicable—stream not reclassified; NC, not calibrated— streams for which calibration of the Statewide Watershed Runoff Model (SWRM) was unsuccessful and, therefore, no simulated baseline POR was defined] USGS station number USGS station name NJSCT1 stream class Reclassified2 streams Drainage area (mi2) Total POR (observed) Percent total ISC (2002) Baseline POR from QR Number of years of baseline streamflow 01403540 Stony Brook at Watchung, NJ

D NA 5.5 1975–2006 12.6 1975–20063 32

01403900 Bound Brook at Middlesex, NJ unknown

A 48.4 1973–20064 28.6 none --

01404000 Bound Brook at Bound Brook, NJ unknown

B 49.0 1924–1930 28.4 none --

01404500 Lawrence Brook at Patricks Corner, NJ unknown

D 29.0 1923–1926 16.3 none --

01405000 Lawrence Brook at Farrington Dam, NJ

D NA 34.4 1928–1989 16.5 1928–1989 62

01405030 Lawrence Brook at Westons Mills, NJ

D NA 44.9 1990–2006 20.5 1990–2006 17

01405300 Matchaponix Brook at Spotswood, NJ unknown

A 43.9 1958–1967 14.7 none --

01405400 Manalapan Brook at Spotswood, NJ

C NA 40.7 1958–2006 8.2 1958–2006 49

01405500 South River at Old Bridge, NJ

A NA 94.6 1940–1988 12.0 1940–1988 49

01406000 Deep Run near Browntown, NJ

C NA 8.1 1933–1940 11.1 1933–1940 8

01406500 Tennent Brook near Browntown, NJ

C NA 5.2 1933–1941 16.6 1933–1941 9

1Stream class was calculated from the baseline POR derived from quantile-regression analysis. See section “Hydroecological Integrity Assessment Process” for stream-class definitions and classification 

methods.

2Stream initially of unknown stream class that was classified by using a simulated baseline POR derived from the SWRM model.3Indicates baseline POR derived from quantile regression differs from that identified in Esralew and Baker (2008). This difference likely results from the application of quantile regression analysis that is 

well suited for differentiating portions of the existing POR that are directly related to changes in precipitation (climate) from those that are driven by anthropogenic influences.

4The hydrologic period of record is incomplete or highly fragmented.5Quantile regression was inconclusive—in such cases, the baseline POR from Kennen and others (2007) and (or) Esralew and Baker (2008) was used.6Stream whose stream class differs from that originally published in Henriksen and others (2006).7Stream considered to be of borderline stream class. That is, the identified stream class for such streams may differ depending on the range of the baseline streamflow record that is used as input into 

NJSCT.

8Stream that could not be calibrated within the SWRM framework, and was subsequently removed from any further analysis.
Appendix 1. Definitions of the 171 hydrologic indices 61

Appendix 1. Definitions of the 171 hydrologic indices

The following information for the 171 hydrologic indices is from Olden and Poff (2003). 

The U.S. Geological Survey revised a limited number of the formulas and (or) definitions when 

deemed appropriate for a given study (for example, MA6, MA7, and MA8). Olden and Poff 

(2003) contains 12 additional references from which the indices were derived; 2 of these references (Colwell, 1974; Poff, 1996) are cited here because they provide examples and additional 

explanation for complex indices.

The alphanumeric code preceding each definition refers to the category of the flow 

regime (magnitude, frequency, duration, timing, and rate of change) and type of flow event 

(A, average; L, low; and H, high) the hydrologic index was developed to describe. Indices are 

numbered successively within each category. For example, MA1 is the first index describing 

magnitude of the average flow condition and FL1 is the first index describing frequency of the 

low flow condition.

Code prefix Event

MA Magnitude, average flow event

ML Magnitude, low flow event

MH Magnitude, high flow event

FL Frequency, low flow event

FH Frequency, high flow event

DL Duration, low flow event

DH Duration, high flow event

TA Timing, average flow event

TL Timing, low flow event

TH Timing, high flow event

RA Rate of change, average event

Following each definition, in parentheses, are (1) the units of the index, and (2) the type of 

data (temporal or spatial data) from which the upper and lower percentile limits (for example, 

25th and 75th) are derived. Temporal data are from a multiyear daily flow record from a single 

streamgage. For example, index MA1, mean for the entire flow record, uses 365 mean daily 

flow values for each year in the flow record to calculate the mean for the entire flow record. 

Consequently, there are 365 values for each year from which to calculate upper and lower 

percentile limits. However, formulas for 60 of the indices do not produce a range of values 

from which percentile limits can be calculated. For example, MA5 (skewness), the mean for the 

entire flow record divided by the median for the entire record, results in a single value; therefore, upper and lower percentile limits cannot be directly calculated. The New Jersey Hydrologic Assessment Tool uses available spatial data, which are values for each streamgage for all 

the streams within a stream class, to compute upper and lower percentile limits (for example, 

25th and 75th) for hydrologic metrics that are identified as spatial.

Exceedence and percentile are used in the calculation for a number of indices. Note 

the difference: a 90-percent exceedance means that 90 percent of the values are equal to 

or greater than the value, whereas 10 percent of the values are equal to or greater than the 

90th-percentile value.
62 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Code Definition

MA1 Mean of the daily mean flow values for the entire flow record (cubic feet per second–temporal).

MA2 Median of the daily mean flow values for the entire flow record (cubic feet per second–temporal).

MA3 Mean (or median–Use Preference option) of the coefficients of variation (standard deviation/mean) for each year. Compute 

the coefficient of variation for each year of daily flows. Compute the mean of the annual coefficients of variation 

(percent–temporal).

MA4 Standard deviation of the percentiles of the entire flow record divided by the mean of percentiles. Compute the 5th, 10th, 15th, 

20th, 25th, 30th, 35th, 40th, 45th, 50th, 55th, 60th, 65th, 70th, 75th, 80th, 85th, 90th, and 95th percentiles for the entire flow 

record. Percentiles are computed by interpolating between the ordered (ascending) flow values. Compute the standard deviation 

and mean for the percentile values. Divide the standard deviation by the mean to get MA4. (percent–spatial)

MA5 The skewness of the entire flow record is computed as the mean for the entire flow record (MA1) divided by the median (MA2) 

for the entire flow record (dimensionless–spatial).

MA6 Range in daily flows is the ratio of the 10-percent to 90-percent exceedance values for the entire flow record. Compute the 

5-percent to 95-percent exceedance values for the entire flow record. Exceedance is computed by interpolating between the 

ordered (descending) flow values. Divide the 10-percent exceedance value by the 90-percent value (dimensionless–spatial).

MA7 Range in daily flows is computed as for MA6 except using the 20-percent and 80-percent exceedance values. Divide the 

20-percent exceedance value by the 80-percent value (dimensionless–spatial).

MA8 Range in daily flows is computed as for MA6 except using the 25-percent and 75-percent exceedance values. Divide the 

25-percent exceedance value by the 75-percent exceedance value (dimensionless–spatial).

MA9 Spread in daily flows is the ratio of the difference between the 90th and 10th percentile of the flow data to median of the entire 

flow record. Compute the 5th, 10th, 15th, 20th, 25th, 30th, 35th, 40th, 45th, 50th, 55th, 60th, 65th, 70th, 75th, 80th, 85th, 90th, 

and 95th percentiles for the entire flow record. Percentiles are computed by interpolating between the ordered (ascending) flow 

values. Compute MA9 as (90th–10th)/MA2 (dimensionless–spatial).

MA10 Spread in daily flows is computed as for MA9 except using the 20th and 80th percentiles (dimensionless–spatial).

MA11 Spread in daily flows is computed as for MA9 except using the 25th and 75th percentiles (dimensionless–spatial).

MA12–

MA23

Means (or medians–Use Preference option) of monthly flow values. Compute the means for each month over the entire flow 

record. For example, MA12 is the mean of all January flow values over the entire record (cubic feet per second–temporal).

MA24–

MA35

Variability (coefficient of variation) of monthly flow values. Compute the standard deviation for each month in each year over the 

entire flow record. Divide the standard deviation by the mean for each month. Average (or median–Use Preference option) these 

values for each month across all years (percent–temporal).

MA36 Variability across monthly flows. Compute the minimum, maximum, and mean flows for each month in the entire flow 

record. MA36 is the maximum monthly flow minus the minimum monthly flow divided by the median monthly flow 

(dimensionless–spatial).

MA37 Variability across monthly flows. Compute the first (25th percentile) and the third (75th percentile) quartiles (every month 

in the flow record). MA37 is the third quartile minus the first quartile divided by the median of the monthly means 

(dimensionless–spatial).

MA38 Variability across monthly flows. Compute the 10th and 90th percentiles for the monthly means (every month in the flow record). 

MA38 is the 90th percentile minus the 10th percentile divided by the median of the monthly means (dimensionless–spatial).

MA39 Variability across monthly flows. Compute the standard deviation for the monthly means. MA39 is the standard deviation times 

100 divided by the mean of the monthly means (percent–spatial).

MA40 Skewness in the monthly flows. MA40 is the mean of the monthly flow means minus the median of the monthly means divided by 

the median of the monthly means (dimensionless–spatial).

MA41 Annual runoff. Compute the annual mean daily flows. MA41 is the mean of the annual means divided by the drainage area (cubic 

feet per second/square mile–temporal).

MA42 Variability across annual flows. MA42 is the maximum annual flow minus the minimum annual flow divided by the median of 

mean annual flows (dimensionless–spatial).

MA43 Variability across annual flows. Compute the first (25th percentile) and third (75th percentile) quartiles and the 10th and 90th 

percentiles for the annual means (every year in the flow record). MA43 is the third quartile minus the first quartile divided by 

the median of the annual means (dimensionless–spatial).
Appendix 1. Definitions of the 171 hydrologic indices 63

Code Definition

MA44 Variability across annual flows. Compute the first (25th percentile) and third (75th percentile) quartiles and the 10th and 90th 

percentiles for the annual means (every year in the flow record). MA44 is the 90th percentile minus the 10th percentile divided 

by the median of the annual means (dimensionless–spatial).

MA45 Skewness in the annual flows. MA45 is the mean of the annual flow means minus the median of the annual means divided by the 

median of the annual means (dimensionless–spatial).

ML1–

ML12

Mean (or median–Use Preference option) minimum flows for each month across all years. Compute the minimum daily flow for 

each month over the entire flow record. For example, ML1 is the mean of the minimums of all January flow values over the 

entire record (cubic feet per second–temporal).

ML13 Variability (coefficient of variation) across minimum monthly flow values. Compute the mean and standard deviation for the 

minimum monthly flows over the entire flow record. ML13 is the standard deviation times 100 divided by the mean minimum 

monthly flow for all years (percent–spatial).

ML14 Mean of annual minimum annual flows. ML14 is the mean of the ratios of minimum annual flows to the median flow for each 

year (dimensionless–temporal).

ML15 Low flow index. ML15 is the mean (or median–Use Preference option) of the ratios of minimum annual flows to the mean flow 

for each year (dimensionless–temporal).

ML16 Median of annual minimum flows. ML16 is the median of the ratios of minimum annual flows to the median flow for each year 

(dimensionless–temporal).

ML17 Base flow. Compute the mean annual flows. Compute the minimum of a 7-day moving average flow for each year and 

divide them by the mean annual flow for that year. ML17 is the mean (or median–Use Preference option) of those ratios 

(dimensionless–temporal).

ML18 Variability in base flow index 1. Compute the standard deviation for the ratios of minimum 7-day moving average flows to mean 

annual flows for each year. ML18 is the standard deviation times 100 divided by the mean of the ratios (percent–spatial).

ML19 Base flow. Compute the ratios of the minimum annual flow to mean annual flow for each year. ML19 is the mean (or median–Use 

Preference option) of these ratios times 100 (dimensionless–temporal).

ML20 Base flow. Divide the daily flow record into 5-day blocks. Find the minimum flow for each block. Assign the minimum flow as 

a base flow for that block if 90 percent of that minimum flow is less than the minimum flows for the blocks on either side. 

Otherwise, set it to zero. Fill in the zero values using linear interpolation. Compute the total flow for the entire record and the 

total base flow for the entire record. ML20 is the ratio of total base flow to total flow (dimensionless–spatial).

ML21 Variability across annual minimum flows. Compute the mean and standard deviation for the annual minimum flows. ML21 is the 

standard deviation times 100 divided by the mean (percent–spatial).

ML22 Specific mean annual minimum flow. ML22 is the mean (or median–Use Preference option) of the annual minimum flows divided 

by the drainage area (cubic feet per second/square mile–temporal).

MH1–

MH12

Mean (or median–Use Preference option) maximum flows for each month across all years. Compute the maximum daily flow for 

each month over the entire flow record. For example, MH1 is the mean of the maximums of all January flow values over the 

entire record (cubic feet per second–temporal).

MH13 Variability (coefficient of variation) across maximum monthly flow values. Compute the mean and standard deviation for the 

maximum monthly flows over the entire flow record. MH13 is the standard deviation times 100 divided by the mean maximum 

monthly flow for all years (percent–spatial).

MH14 Median of annual maximum flows. Compute the annual maximum flows from monthly maximum flows. Compute the ratio of 

annual maximum flow to median annual flow for each year. MH14 is the median of these ratios (dimensionless–temporal).

MH15 High flow discharge index. Compute the 1-percent exceedance value for the entire data record. MH15 is the 1-percent exceedance 

value divided by the median flow for the entire record (dimensionless–spatial).

MH16 High flow discharge index. Compute the 10-percent exceedance value for the entire data record. MH16 is the 10-percent 

exceedance value divided by the median flow for the entire record (dimensionless–spatial).

MH17 High flow discharge index. Compute the 25-percent exceedance value for the entire data record. MH17 is the 25-percent 

exceedance value divided by the median flow for the entire record (dimensionless–spatial).

MH18 Variability across annual maximum flows. Compute the logs (log10) of the maximum annual flows. Find the standard deviation 

and mean for these values. MH18 is the standard deviation times 100 divided by the mean (percent–spatial).
64 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Code Definition

MH19 Skewness in annual maximum flows. Use the equation:

MH19 = N2 × sum(qm3

)− 3N × sum(qm)× sum(qm2)+ 2 × sum(qm)

3

N × (N −1)× (N − 2)× stddev3

Where: 

N = Number of years

qm = Log10 (annual maximum flows)

stddev = Standard deviation of the annual maximum flows

(dimensionless–spatial).

MH20 Specific mean annual maximum flow. MH20 is the mean (or median–Use Preference option) of the annual maximum flows 

divided by the drainage area (cubic feet per second/square mile–temporal).

MH21 High flow volume index. Compute the average volume for flow events above a threshold equal to the median flow for the entire 

record. MH21 is the average volume divided by the median flow for the entire record (days–temporal).

MH22 High flow volume. Compute the average volume for flow events above a threshold equal to three times the median flow for the 

entire record. MH22 is the average volume divided by the median flow for the entire record (days–temporal).

MH23 High flow volume. Compute the average volume for flow events above a threshold equal to seven times the median flow for the 

entire record. MH23 is the average volume divided by the median flow for the entire record (days–temporal).

MH24 High peak flow. Compute the average peak flow value for flow events above a threshold equal to the median flow for the entire 

record. MH24 is the average peak flow divided by the median flow for the entire record (dimensionless–temporal).

MH25 High peak flow. Compute the average peak flow value for flow events above a threshold equal to three times the median flow for 

the entire record. MH25 is the average peak flow divided by the median flow for the entire record (dimensionless–temporal).

MH26 High peak flow. Compute the average peak flow value for flow events above a threshold equal to seven times the median flow for 

the entire record. MH26 is the average peak flow divided by the median flow for the entire record (dimensionless–temporal).

MH27 High peak flow. Compute the average peak flow value for flow events above a threshold equal to 75th percentile value for the 

entire flow record. MH27 is the average peak flow divided by the median flow for the entire record (dimensionless–temporal).

FL1 Low flood pulse count. Compute the average number of flow events with flows below a threshold equal to the 25th 

percentile value for the entire flow record. FL1 is the average (or median–Use Preference option) number of events 

(number of events/year–temporal).

FL2 Variability in low pulse count. Compute the standard deviation in the annual pulse counts for FL1. FL2 is 100 times the standard 

deviation divided by the mean pulse count (percent–spatial).

FL3 Frequency of low pulse spells. Compute the average number of flow events with flows below a threshold equal to 5 percent 

of the mean flow value for the entire flow record. FL3 is the average (or median–Use Preference option) number of events 

(number of events/year–temporal).

FH1 High flood pulse count. Compute the average number of flow events with flows above a threshold equal to the 75th 

percentile value for the entire flow record. FH1 is the average (or median–Use Preference option) number of events 

(number of events/year–temporal).

FH2 Variability in high pulse count. Compute the standard deviation in the annual pulse counts for FH1. FH2 is 100 times the standard 

deviation divided by the mean pulse count (number of events/year–spatial). 

FH3 High flood pulse count. Compute the average number of days per year that the flow is above a threshold equal to three times the 

median flow for the entire record. FH3 is the mean (or median–Use Preference option) of the annual number of days for all 

years (number of days/year–temporal).

FH4 High flood pulse count. Compute the average number of days per year that the flow is above a threshold equal to seven times the 

median flow for the entire record. FH4 is the mean (or median–Use Preference option) of the annual number of days for all 

years (number of days/year–temporal).

FH5 Flood frequency. Compute the average number of flow events with flows above a threshold equal to the median flow value for the 

entire flow record. FH5 is the average (or median–Use Preference option) number of events (number of events/year–temporal).

FH6 Flood frequency. Compute the average number of flow events with flows above a threshold equal to three times the 

median flow value for the entire flow record. FH6 is the average (or median–Use Preference option) number of events 

(number of events/year–temporal).

FH7 Flood frequency. Compute the average number of flow events with flows above a threshold equal to seven times the 

median flow value for the entire flow record. FH7 is the average (or median–Use Preference option) number of events 

(number of events/year–temporal).
Appendix 1. Definitions of the 171 hydrologic indices 65

Code Definition

FH8 Flood frequency. Compute the average number of flow events with flows above a threshold equal to 25-percent 

exceedance value for the entire flow record. FH8 is the average (or median–Use Preference option) number of events 

(number of events/year–temporal).

FH9 Flood frequency. Compute the average number of flow events with flows above a threshold equal to 75-percent 

exceedance value for the entire flow record. FH9 is the average (or median–Use Preference option) number of events 

(number of events/year–temporal).

FH10 Flood frequency. Compute the average number of flow events with flows above a threshold equal to median of the 

annual minima for the entire flow record. FH10 is the average (or median–Use Preference option) number of events 

(number of events/year–temporal).

FH111 Flood frequency. Compute the average number of flow events with flows above a threshold equal to flow corresponding 

to a 1.67-year recurrence interval. FH11 is the average (or median–Use Preference option) number of events 

(number of events/year–temporal).

DL1 Annual minimum daily flow. Compute the minimum 1-day average flow for each year. DL1 is the mean (or median–Use 

Preference option) of these values (cubic feet per second–temporal). 

DL2 Annual minimum of 3-day moving average flow. Compute the minimum of a 3-day moving average flow for each year. DL2 is the 

mean (or median–Use Preference option) of these values (cubic feet per second–temporal). 

DL3 Annual minimum of 7-day moving average flow. Compute the minimum of a 7-day moving average flow for each year. DL3 is the 

mean (or median–Use Preference option) of these values (cubic feet per second–temporal). 

DL4 Annual minimum of 30-day moving average flow. Compute the minimum of a 30-day moving average flow for each year. DL4 is 

the mean (or median–Use Preference option) of these values (cubic feet per second–temporal). 

DL5 Annual minimum of 90-day moving average flow. Compute the minimum of a 90-day moving average flow for each year. DL5 is 

the mean (or median–Use Preference option) of these values (cubic feet per second–temporal).

DL6 Variability of annual minimum daily average flow. Compute the standard deviation for the minimum daily average flow. DL6 is 

100 times the standard deviation divided by the mean (percent–spatial).

DL7 Variability of annual minimum of 3-day moving average flow. Compute the standard deviation for the minimum 3-day moving 

averages. DL7 is 100 times the standard deviation divided by the mean (percent–spatial).

DL8 Variability of annual minimum of 7-day moving average flow. Compute the standard deviation for the minimum 7-day moving 

averages. DL8 is 100 times the standard deviation divided by the mean (percent–spatial).

DL9 Variability of annual minimum of 30-day moving average flow. Compute the standard deviation for the minimum 30-day moving 

averages. DL9 is 100 times the standard deviation divided by the mean (percent–spatial). 

DL10 Variability of annual minimum of 90-day moving average flow. Compute the standard deviation for the minimum 90-day moving 

averages. DL10 is 100 times the standard deviation divided by the mean (percent–spatial).

DL11 Annual minimum daily flow divided by the median for the entire record. Compute the minimum daily flow for each year. DL11 is 

the mean of these values divided by the median for the entire record (dimensionless–temporal).

DL12 Annual minimum of 7-day moving average flow divided by the median for the entire record. Compute the minimum of a 

7-day moving average flow for each year. DL12 is the mean of these values divided by the median for the entire record 

(dimensionless–temporal).

DL13 Annual minimum of 30-day moving average flow divided by the median for the entire record. Compute the minimum of a 

30-day moving average flow for each year. DL13 is the mean of these values divided by the median for the entire record 

(dimensionless–temporal).

DL14 Low exceedance flows. Compute the 75-percent exceedance value for the entire flow record. DL14 is the exceedance value 

divided by the median for the entire record (dimensionless–spatial).

DL15 Low exceedance flows. Compute the 90-percent exceedance value for the entire flow record. DL15 is the exceedance value 

divided by the median for the entire record (dimensionless–spatial).

DL16 Low flow pulse duration. Compute the average pulse duration for each year for flow events below a threshold equal to the 25th 

percentile value for the entire flow record. DL16 is the median of the yearly average durations (number of days–temporal).

DL17 Variability in low pulse duration. Compute the standard deviation for the yearly average low pulse durations. DL17 is 100 times 

the standard deviation divided by the mean of the yearly average low pulse durations (percent–spatial).

DL18 Number of zero-flow days. Count the number of zero-flow days for the entire flow record. DL18 is the mean (or median–Use 

Preference option) annual number of zero-flow days (number of days/year–temporal).

DL19 Variability in the number of zero-flow days. Compute the standard deviation for the annual number of zero-flow days. DL19 is 

100 times the standard deviation divided by the mean annual number of zero-flow days (percent–spatial).
66 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Code Definition

DL20 Number of zero-flow months. While computing the mean monthly flow values, count the number of months in which there 

was no flow over the entire flow record (percent–spatial).

DH1 Annual maximum daily flow. Compute the maximum of a 1-day moving average flow for each year. DH1 is the mean 

(or median–Use Preference option) of these values (cubic feet per second–temporal).

DH2 Annual maximum of 3-day moving average flows. Compute the maximum of a 3-day moving average flow for each year. 

DH2 is the mean (or median–Use Preference option) of these values (cubic feet per second–temporal).

DH3 Annual maximum of 7-day moving average flows. Compute the maximum of a 7-day moving average flow for each year. 

DH3 is the mean (or median–Use Preference option) of these values (cubic feet per second–temporal).

DH4 Annual maximum of 30-day moving average flows. Compute the maximum of 30-day moving average flows. Compute the 

maximum of a 30-day moving average flow for each year. DH4 is the mean (or median–Use Preference option) of these values 

(cubic feet per second–temporal).

DH5 Annual maximum of 90-day moving average flows. Compute the maximum of a 90-day moving average flow for each year. 

DH5 is the mean (or median–Use Preference option) of these values (cubic feet per second–temporal).

DH6 Variability of annual maximum daily flows. Compute the standard deviation for the maximum 1-day moving averages. 

DH6 is 100 times the standard deviation divided by the mean (percent–spatial).

DH7 Variability of annual maximum of 3-day moving average flows. Compute the standard deviation for the maximum 3-day moving 

averages. DH7 is 100 times the standard deviation divided by the mean (percent–spatial).

DH8 Variability of annual maximum of 7-day moving average flows. Compute the standard deviation for the maximum 7-day moving 

averages. DH8 is 100 times the standard deviation divided by the mean (percent–spatial).

DH9 Variability of annual maximum of 30-day moving average flows. Compute the standard deviation for the maximum 30-day 

moving averages. DH9 is 100 times the standard deviation divided by the mean (percent–spatial).

DH10 Variability of annual maximum of 90-day moving average flows. Compute the standard deviation for the maximum 90-day 

moving averages. DH10 is 100 times the standard deviation divided by the mean (percent–spatial).

DH11 Annual maximum of 1-day moving average flows divided by the median for the entire record. Compute the maximum of a 

1-day moving average flow for each year. DH11 is the mean of these values divided by the median for the entire record 

(dimensionless–temporal).

DH12 Annual maximum of 7-day moving average flows divided by the median for the entire record. Compute the maximum 

daily average flow for each year. DH12 is the mean of these values divided by the median for the entire record 

(dimensionless–spatial).

DH13 Annual maximum of 30-day moving average flows divided by the median for the entire record. Compute the maximum of a 

30-day moving average flow for each year. DH13 is the mean of these values divided by the median for the entire record 

(dimensionless–temporal).

DH14 Flood duration. Compute the mean of the mean monthly flow values. Find the 95th percentile for the mean monthly flows. 

DH14 is the 95th percentile value divided by the mean of the monthly means (dimensionless–spatial).

DH15 High flow pulse duration. Compute the average duration for flow events with flows above a threshold equal to the 75th percentile 

value for each year in the flow record. DH15 is the median of the yearly average durations (days/year–temporal).

DH16 Variability in high flow pulse duration. Compute the standard deviation for the yearly average high pulse durations. DH16 is 

100 times the standard deviation divided by the mean of the yearly average high pulse durations (percent–spatial).

DH17 High flow duration. Compute the average duration of flow events with flows above a threshold equal to the median flow 

value for the entire flow record. DH17 is the average (or median–Use Preference option) duration of the events 

(days–temporal).

DH18 High flow duration. Compute the average duration of flow events with flows above a threshold equal to three times the 

median flow value for the entire flow record. DH18 is the average (or median–Use Preference option) duration of the events 

(days–temporal).

DH19 High flow duration. Compute the average duration of flow events with flows above a threshold equal to seven times the 

median flow value for the entire flow record. DH19 is the average (or median–Use Preference option) duration of the events 

(days–temporal).

DH20 High flow duration. Compute the 75th percentile value for the entire flow record. Compute the average duration of flow events 

with flows above a threshold equal to the 75th percentile value for the median annual flows. DH20 is the average (or median–

Use Preference option) duration of the events (days–temporal).

DH21 High flow duration. Compute the 25th percentile value for the entire flow record. Compute the average duration of flow events 

with flows above a threshold equal to the 25th percentile value for the entire set of flows. DH21 is the average (or median–Use 

Preference option) duration of the events (days–temporal). 
Appendix 1. Definitions of the 171 hydrologic indices 67

Code Definition

DH221 Flood interval. Compute the flood threshold as the flow equivalent for a flood recurrence of 1.67 years. Determine the median 

number of days between flood events for each year. DH22 is the mean (or median–Use Preference option) of the yearly median 

number of days between flood events (days–temporal).

DH231 Flood duration. Compute the flood threshold as the flow equivalent for a flood recurrence of 1.67 years. Determine the number 

of days each year that the flow remains above the flood threshold. DH23 is the mean (or median–Use Preference option) of the 

number of flood days for years in which floods occur (days–temporal).

DH241 Flood-free days. Compute the flood threshold as the flow equivalent for a flood recurrence of 1.67 years. Compute the maximum 

number of days that the flow is below the threshold for each year. DH24 is the mean (or median–Use Preference option) of the 

maximum yearly no-flood days (days–temporal).

TA1 Constancy. Constancy is computed with the formulation of Colwell (see example in Colwell, 1974). A matrix of values is 

compiled where the rows are 11 flow categories and the columns are 365 (no February 29th) days of the year. The cell values 

are the number of times that a flow falls into a category on each day. The categories are: 

log(flow) < 0.1 × log(mean flow), 

0.1 × log(mean flow) ≤ log(flow) < 0.25 × log(mean flow)

0.25 × log(mean flow) ≤ log(flow) < 0.5 × log(mean flow)

0.5 × log(mean flow) ≤ log(flow) < 0.75 × log(mean flow)

0.75 × log(mean flow) ≤ log(flow) < 1.0 × log(mean flow)

1.0 × log(mean flow) ≤ log(flow) < 1.25 × log(mean flow)

1.25 × log(mean flow) ≤log(flow) < 1.5 × log(mean flow)

1.5 × log(mean flow) ≤ log(flow) < 1.75 × log(mean flow)

1.75 × log(mean flow) ≤ log(flow) < 2.0 × log(mean flow)

2.0 ×log(mean flow) ≤ log(flow) < 2.25 × log(mean flow)

log(flow) ≥ 2.25 × log(mean flow)

The row totals, column totals, and grand total are computed. Using the equations for Shannon information theory parameters, 

constancy is computed as:

1− (uncertainty with respect to state)

log(number of state)

(dimensionless–spatial).

TA2 Predictability. Predictability is computed from the same matrix as constancy (see example in Colwell, 1974). It is computed as: 

1− (uncertainty with respect to interaction of time and state − uncertainty with respect to time)

log(number of state)

(dimensionless–spatial).

TA31 Seasonal predictability of flooding. Divide years into 2-month periods (that is, Oct.–Nov., Dec.–Jan., and so forth). Count 

the number of flood days (flow events with flows > 1.67-year flood) in each period over the entire flow record. TA3 is the 

maximum number of flood days in any one period divided by the total number of flood days (dimensionless–temporal).

TL1 Julian date of annual minimum. Determine the Julian date that the minimum flow occurs for each water year. Transform the dates 

to relative values on a circular scale (radians or degrees). Compute the x and y components for each year and average them 

across all years. Compute the mean angle as the arc tangent of y-mean divided by x-mean. Transform the resultant angle back 

to Julian date (Julian day–spatial).

TL2 Variability in Julian date of annual minima. Compute the coefficient of variation for the mean x and y components and convert to 

a date (Julian day–spatial).

TL32 Seasonal predictability of low flow. Divide years into 2-month periods (that is, Oct.–Nov., Dec.–Jan., and so forth). Count 

the number of low-flow events (flow events with flows ≤ 5-year flood threshold) in each period over the entire flow 

record. TL3 is the maximum number of low-flow events in any one period divided by the total number of low-flow events 

(dimensionless–spatial).

TL42 Seasonal predictability of non-low flow. Compute the number of days that flow is above the 5-year flood threshold as the ratio of 

number of days to 365 or 366 (leap year) for each year. TL4 is the maximum of the yearly ratios (dimensionless–spatial).

TH1 Julian date of annual maximum. Determine the Julian date that the maximum flow occurs for each year. Transform the dates to 

relative values on a circular scale (radians or degrees). Compute the x and y components for each year and average them across 

all years. Compute the mean angle as the arc tangent of y-mean divided by x-mean. Transform the resultant angle back to Julian 

date (Julian day–spatial).
68 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Code Definition

TH2 Variability in Julian date of annual maxima. Compute the coefficient of variation for the mean x and y components and convert to 

a date (Julian days–spatial).

TH31 Seasonal predictability of nonflooding. Computed as the maximum proportion of a 365-day year that the flow is less than the 

1.67-year flood threshold and also occurs in all years. Accumulate nonflood days that span all years. TH3 is maximum length 

of those flood-free periods divided by 365 (dimensionless–spatial).

RA1 Rise rate. Compute the change in flow for days in which the change is positive for the entire flow record. RA1 is the mean 

(or median–Use Preference option) of these values (cubic feet per second/day–temporal).

RA2 Variability in rise rate. Compute the standard deviation for the positive flow changes. RA2 is 100 times the standard deviation 

divided by the mean (percent–spatial).

RA3 Fall rate. Compute the change in flow for days in which the change is negative for the entire flow record. RA3 is the mean 

(or median–Use Preference option) of these values (cubic feet per second/day–temporal).

RA4 Variability in fall rate. Compute the standard deviation for the negative flow changes. RA4 is 100 times the standard deviation 

divided by the mean (percent–spatial).

RA5 Number of day rises. Compute the number of days in which the flow is greater than the previous day. RA5 is the number 

of positive gain days divided by the total number of days in the flow record (dimensionless–spatial).

RA6 Change of flow. Compute the log10 of the flows for the entire flow record. Compute the change in log of flow for days in which the 

change is positive for the entire flow record. RA6 is the median of these values (cubic feet per second–temporal).

RA7 Change of flow. Compute the log10 of the flows for the entire flow record. Compute the change in log of flow for days in which the 

change is negative for the entire flow record. RA7 is the median of these log values (cubic feet per second/day–temporal).

RA8 Number of reversals. Compute the number of days in each year when the change in flow from one day to the next changes 

direction. RA8 is the average (or median–Use Preference option) of the yearly values (days–temporal).

RA9 Variability in reversals. Compute the standard deviation for the yearly reversal values. RA9 is 100 times the standard deviation 

divided by the mean (percent–spatial).

1

1.67-year flood threshold (Olden and Poff, 2003)–For indices FH11, DH22, DH23, DH24, TA3, and TH3, compute the log10 of the peak annual flows. 

Compute the log10 of the daily flows for the peak annual flow days. Calculate the coefficients for a linear regression equation for logs of peak annual flow with 

respect to logs of average daily flow for peak days. Using the log peak flow for the 1.67-year recurrence interval (60th percentile) as input to the regression 

equation, predict the log10 of the average daily flow. The threshold is 10 to the log10 (average daily flow) power (cubic feet per second).

2

5-year flood threshold (Olden and Poff, 2003)–For TL3 and TL4, compute the log10 of the peak annual flows. Compute the log10 of the daily flows for the 

peak annual flow days. Calculate the coefficients for a linear regression equation for logs of peak annual flow with respect to logs of average daily flow for 

peak days. Using the log peak flow for the 5-year recurrence interval (80th percentile) as input to the regression equation, predict the log10 of the average daily 

flow. The threshold is 10 to the log10 (average daily flow) power (cubic feet per second).
Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey 69

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey, by U.S. Geological Survey 

station number, determined by using observed New Jersey Hydrologic Assessment Tool (NJHAT) values for specific nonredundant 

hydrologic indices that fall outside the established 25th-to-75th-percentile range and the more conservative 40th-to-60th-percentile 

range.

Station index for appendix 2

U.S. Geological Survey station number and name Page 

number

Observed baseline

01397000 South Branch Raritan River at Stanton, NJ 72

01398000 Neshanic River at Reaville, NJ 72

01400000 North Branch Raritan River near Raritan, NJ 76

01400500 Raritan River at Manville, NJ 77

01400730 Millstone River At Plainsboro, NJ 77

01402000 Millstone River at Blackwells Mills, NJ 80

01403060 Raritan River below Calco Dam at Bound Brook, NJ 81

Simulated baseline

01396500 South Branch Raritan River near High Bridge, NJ 70

01396580 Spruce Run at Glen Gardner, NJ 70

01396660 Mulhockaway Creek at Van Syckel, NJ 71

01396800 Spruce Run at Clinton, NJ 71

01398500 North Branch Raritan River near Far Hills, NJ 73

01399500 Lamington (Black) River near Pottersville, NJ 73

01399670 South Branch Rockaway Creek at Whitehouse Station, NJ 74

01399690 South Branch Rockaway Creek at Whitehouse, NJ 75

01399700 Rockaway Creek at Whitehouse, NJ 76

01400932 Baldwins Creek at Baldwin Lake near Pennington, NJ 78

01400953 Honey Branch near Pennington, NJ 78

01401000 Stony Brook at Princeton, NJ 79

01401500 Millstone River near Kingston, NJ 79

01403000 Raritan River at Bound Brook, NJ 80

01403150 West Branch Middle Brook near Martinsville, NJ 81

01403160 West Branch Middle Brook near Somerville, NJ 82

01403900 Bound Brook at Middlesex, NJ 82

01405300 Matchaponix Brook at Spotswood, NJ 83

01405400 Manalapan Brook at Spotswood, NJ 84

01405500 South River at Old Bridge, NJ 85

01406000 Deep Run near Browntown, NJ 85

01406500 Tennent Brook near Browntown, NJ 86
70 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey, by U.S. Geological Survey 

station number, determined by using observed New Jersey Hydrologic Assessment Tool (NJHAT) values for specific nonredundant 

hydrologic indices that fall outside the established 25th-to-75th-percentile range and the more conservative 40th-to-60th-percentile 

range.—Continued

[ft3/s, cubic feet per second; (ft3/s)/d, cubic feet per second per day; days, number of days; d/yr, days per year; D, dimensionless; JD, Julian day; %, percent; 

--, no deviations above the 75th or 60th or below the 25th or 40th percentile range; hydrologic indices defined in appendix 1; values less than 1 were rounded to 

the thousandths place to show precision]

U.S. Geological Survey station number 01396500

South Branch Raritan River near High Bridge, NJ

(Stream class A, simulated baseline dataset)

Hydrologic 

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MH5 ft3/s 109.9 314.5 207.3 -- 51.7

DH2 ft3/s 154.6 726.5 305.2 -- 138.0

TA1 D 0.299 0.564 0.465 -- 21.3

RA3 (ft3/s)/d -3.2 -8.0 -0.873 153.1 --

Hydrologic 

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA18 ft3/s 49.6 66.2 61.1 -- 8.3

MH5 ft3/s 140.7 314.5 176.1 -- 78.5

DL4 ft3/s 25.1 38.3 35.2 -- 8.8

DH2 ft3/s 195.8 726.5 254.8 -- 185.1

TA1 D 0.485 0.564 0.531 -- 6.2

TL1 JD 254.1 261.1 257.9 -- 1.2

TH1 JD 47.6 45.8 54.9 3.8 --

RA3 (ft3/s)/d -2.2 -8.0 -1.4 265.5 --

U.S. Geological Survey station number 01396580

Spruce Run at Glen Gardner, NJ

(Stream class C, simulated baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MA24 % 5.1 80.5 12.1 -- 566.2

MH14 D 1.8 26.4 2.5 -- 953.4

DL16 days 15.6 7.2 56.0 53.6 --

DH11 D 1.7 30.6 3.2 -- 844.9

TH2 JD 68.3 71.8 70.1 -- 2.5

RA6 ft3/s 0.008 0.232 0.049 -- 373.5

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA24 % 6.6 80.5 9.2 -- 779.2

ML3 ft3/s 15.3 14.3 17.6 7.1 --

MH14 D 1.9 26.4 2.2 -- 1,093

DL16 days 23.0 7.2 35.1 68.4 --

DH11 D 2.1 30.6 2.7 -- 1,022

TA1 D 0.336 0.299 0.4 11.0 --

TL2 JD 31.7 30.1 33.3 5.0 --

TH2 JD 68.3 71.8 70.1 -- 2.5

RA6 ft3/s 0.015 0.232 0.030 -- 673.3
Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey 71

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey, by U.S. Geological Survey 

station number, determined by using observed New Jersey Hydrologic Assessment Tool (NJHAT) values for specific nonredundant 

hydrologic indices that fall outside the established 25th-to-75th-percentile range and the more conservative 40th-to-60th-percentile 

range.—Continued

[ft3/s, cubic feet per second; (ft3/s)/d, cubic feet per second per day; days, number of days; d/yr, days per year; D, dimensionless; JD, Julian day; %, percent; 

--, no deviations above the 75th or 60th or below the 25th or 40th percentile range; hydrologic indices defined in appendix 1; values less than 1 were rounded to 

the thousandths place to show precision]

U.S. Geological Survey station number 01396660

Mulhockaway Creek at Van Syckel, NJ

(Stream class C, simulated baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MA24 % 6.5 79.6 16.4 -- 386.0

MH14 D 2.1 27.4 3.3 -- 742.4

DL16 days 13.6 6.1 56.0 55.0 --

DH11 D 2.0 29.5 4.1 -- 625.1

TL2 JD 29.4 28.2 40.8 3.9 --

RA6 ft3/s 0.010 0.244 0.059 -- 313.6

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA24 % 8.7 79.6 12.2 -- 552.2

ML3 ft3/s 15.6 14.0 17.9 10.3 --

MH14 D 2.3 27.4 2.8 -- 886.1

DL16 days 21.4 6.1 36.8 71.4 --

DH11 D 2.7 29.5 3.6 -- 727.4

TA1 D 0.336 0.332 0.411 1.2 --

TL2 JD 31.7 28.2 33.3 11.1 --

TH2 JD 68.3 72.3 70.1 -- 3.1

RA6 ft3/s 0.019 0.244 0.037 -- 559.5

U.S. Geological Survey station number 01396800

Spruce Run at Clinton, NJ

(Stream class A, simulated baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MA18 ft3/s 12.1 69.9 31.8 -- 119.5

MH5 ft3/s 46.6 199.0 82.3 -- 141.8

DL4 ft3/s 6.5 8.0 13.7 23.7 --

DH2 ft3/s 61.1 433.5 104.8 -- 313.8

TL1 JD 253.3 305.1 263.5 -- 15.8

TH1 JD 45.8 120.1 77.1 -- 55.7

RA3 (ft3/s)/d -1.8 -8.0 -0.677 342.7 --

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA18 ft3/s 15.5 69.9 21.3 -- 227.8

MH5 ft3/s 59.4 199.0 72.1 -- 176.0

DL4 ft3/s 8.6 8.0 11.1 6.8 --

DH2 ft3/s 75.1 433.5 93.5 -- 363.7

TL1 JD 256.8 305.1 258.3 -- 18.1

TH1 JD 53.7 120.1 67.5 -- 78.1

RA3 (ft3/s)/d -1.4 -8.0 -0.961 482.2 --
72 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey, by U.S. Geological Survey 

station number, determined by using observed New Jersey Hydrologic Assessment Tool (NJHAT) values for specific nonredundant 

hydrologic indices that fall outside the established 25th-to-75th-percentile range and the more conservative 40th-to-60th-percentile 

range.—Continued

[ft3/s, cubic feet per second; (ft3/s)/d, cubic feet per second per day; days, number of days; d/yr, days per year; D, dimensionless; JD, Julian day; %, percent; 

--, no deviations above the 75th or 60th or below the 25th or 40th percentile range; hydrologic indices defined in appendix 1; values less than 1 were rounded to 

the thousandths place to show precision]

U.S. Geological Survey station number 01397000

South Branch Raritan River at Stanton, NJ

(Stream class A, observed baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

DL4 ft3/s 45.5 107.6 71.8 -- 49.8

TA1 D 0.432 0.634 0.553 -- 14.6

TL1 JD 246.8 273.5 262.5 -- 4.2

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA18 ft3/s 93.3 179.9 125.8 -- 43.0

MH5 ft3/s 483.0 785.0 670.0 -- 17.2

DL4 ft3/s 49.2 107.6 63.7 -- 69.0

DH2 ft3/s 1,454 1,703 1,701 -- 0.137

TA1 D 0.485 0.634 0.531 -- 19.4

TL1 JD 254.1 273.5 257.9 -- 6.1

TH1 JD 47.6 64.7 54.9 -- 17.7

U.S. Geological Survey station number 01398000

Neshanic River at Reaville, NJ

(Stream class A, observed baseline dataset)

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

TA1 D 0.432 0.197 0.553 54.4 --

TL1 JD 246.8 241.1 262.5 2.3 --

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

FH4 d/yr 30.4 27.5 36.2 9.5 --

DL4 ft3/s 1.4 1.9 1.8 -- 0.759

DH2 ft3/s 410.3 563.0 528.6 -- 6.5

TA1 D 0.485 0.197 0.531 59.4 --

TL1 JD 254.1 241.1 257.9 5.1 --

TH1 JD 47.6 58.0 54.9 -- 5.6
Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey 73

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey, by U.S. Geological Survey 

station number, determined by using observed New Jersey Hydrologic Assessment Tool (NJHAT) values for specific nonredundant 

hydrologic indices that fall outside the established 25th-to-75th-percentile range and the more conservative 40th-to-60th-percentile 

range.—Continued

[ft3/s, cubic feet per second; (ft3/s)/d, cubic feet per second per day; days, number of days; d/yr, days per year; D, dimensionless; JD, Julian day; %, percent; 

--, no deviations above the 75th or 60th or below the 25th or 40th percentile range; hydrologic indices defined in appendix 1; values less than 1 were rounded to 

the thousandths place to show precision]

U.S. Geological Survey station number 01398500

North Branch Raritan River near Far Hills, NJ

(Stream class C, simulated baseline dataset)

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MA24 % 6.9 57.5 15.2 -- 279.2

MH14 D 2.1 13.4 3.4 -- 298.4

DL16 days 19.7 7.8 57.5 60.2 --

DH11 D 2.0 16.6 3.6 -- 358.6

TA1 D 0.432 0.422 0.553 2.3 --

RA6 ft3/s 0.010 0.187 0.063 -- 196.8

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA24 % 8.6 57.5 12.0 -- 379.4

ML3 ft3/s 44.2 40.0 54.5 9.4 --

MH14 D 2.4 13.4 2.7 -- 398.0

DL16 days 28.1 7.8 42.7 72.2 --

DH11 D 2.5 16.6 3.1 -- 444.1

TA1 D 0.336 0.422 0.411 -- 2.7

RA6 ft3/s 0.019 0.187 0.037 -- 405.4

U.S. Geological Survey station number 01399500

Lamington River near Pottersville, NJ

(Stream class C, simulated baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MA24 % 7.2 38.3 13.7 -- 179.1

MH14 D 2.1 6.6 3.0 -- 122.3

DL16 days 12.1 10.0 35.0 17.6 --

DH11 D 1.8 7.8 3.6 -- 117.9

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA24 % 16.3 38.3 20.1 -- 90.0

MH14 ft3/s 3.7 6.6 4.6 -- 44.4

DL16 D 16.3 10.0 24.6 38.7 --

DH11 days 3.8 7.8 4.8 -- 61.5

TA1 D 0.336 0.470 0.411 -- 14.4

TH2 D 68.3 64.7 70.1 5.2 --
74 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey, by U.S. Geological Survey 

station number, determined by using observed New Jersey Hydrologic Assessment Tool (NJHAT) values for specific nonredundant 

hydrologic indices that fall outside the established 25th-to-75th-percentile range and the more conservative 40th-to-60th-percentile 

range.—Continued

[ft3/s, cubic feet per second; (ft3/s)/d, cubic feet per second per day; days, number of days; d/yr, days per year; D, dimensionless; JD, Julian day; %, percent; 

--, no deviations above the 75th or 60th or below the 25th or 40th percentile range; hydrologic indices defined in appendix 1; values less than 1 were rounded to 

the thousandths place to show precision]

U.S. Geological Survey station number 01399670

South Branch Rockaway Creek at Whitehouse Station, NJ

(Stream class C, simulated baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MA24 % 5.2 102.3 8.9 -- 1,046

ML3 ft3/s 14.2 11.0 17.6 22.3 --

MH14 D 1.6 26.5 2.0 -- 1,211

DH11 D 1.6 28.2 2.1 -- 1,277

TL2 JD 29.4 42.9 40.8 -- 5.2

TH2 JD 64.5 76.8 72.5 -- 5.9

RA6 ft3/s 0.015 0.266 0.144 -- 84.7

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA24 % 5.9 102.3 7.2 -- 1,319

ML3 ft3/s 14.9 11.0 16.3 26.0 --

MH14 D 1.7 26.5 1.8 -- 1,351

DL16 days 5.9 5.7 8.2 3.9 --

DH11 D 1.8 28.2 1.9 -- 1,396

TL2 JD 31.7 42.9 33.3 -- 28.9

TH2 JD 68.3 76.8 70.1 -- 9.5

RA6 ft3/s 0.037 0.266 0.083 -- 220.5
Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey 75

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey, by U.S. Geological Survey 

station number, determined by using observed New Jersey Hydrologic Assessment Tool (NJHAT) values for specific nonredundant 

hydrologic indices that fall outside the established 25th-to-75th-percentile range and the more conservative 40th-to-60th-percentile 

range.—Continued

[ft3/s, cubic feet per second; (ft3/s)/d, cubic feet per second per day; days, number of days; d/yr, days per year; D, dimensionless; JD, Julian day; %, percent; 

--, no deviations above the 75th or 60th or below the 25th or 40th percentile range; hydrologic indices defined in appendix 1; values less than 1 were rounded to 

the thousandths place to show precision]

U.S. Geological Survey station number 01399690

South Branch Rockaway Creek at Whitehouse, NJ

(Stream class C, simulated baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MA24 % 5.2 108.4 10.7 -- 909.5

ML3 ft3/s 13.6 10.8 20.3 20.7 --

MH14 D 1.6 27.8 2.2 -- 1,164

DH11 D 1.6 23.0 2.2 -- 964.8

TL2 JD 29.4 59.2 40.8 -- 45.2

TH2 JD 64.5 60.0 72.5 6.9 --

RA6 ft3/s 0.006 0.268 0.125 -- 114.4

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA24 % 6.4 108.4 8.3 -- 1,211

ML3 ft3/s 15.0 10.8 17.2 28.5 --

MH14 D 1.8 27.8 1.9 -- 1,389

DL16 days 6.0 5.1 7.1 14.4 --

DH11 D 1.8 23.0 2.0 -- 1,030

TL2 JD 31.7 59.2 33.3 -- 77.8

TH2 JD 68.3 60.0 70.1 12.0 --

RA6 ft3/s 0.018 0.268 0.063 -- 325.4
76 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey, by U.S. Geological Survey 

station number, determined by using observed New Jersey Hydrologic Assessment Tool (NJHAT) values for specific nonredundant 

hydrologic indices that fall outside the established 25th-to-75th-percentile range and the more conservative 40th-to-60th-percentile 

range.—Continued

[ft3/s, cubic feet per second; (ft3/s)/d, cubic feet per second per day; days, number of days; d/yr, days per year; D, dimensionless; JD, Julian day; %, percent; 

--, no deviations above the 75th or 60th or below the 25th or 40th percentile range; hydrologic indices defined in appendix 1; values less than 1 were rounded to 

the thousandths place to show precision]

U.S. Geological Survey station number 01399700

Rockaway Creek at Whitehouse, NJ

(Stream class C, simulated baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MA24 % 2.5 113.5 5.6 -- 1,931

ML3 ft3/s 40.8 35.0 59.1 14.3 --

MH14 D 1.3 24.3 1.6 -- 1,405

DL16 days 7.8 6.8 24.9 13.6 --

DH11 D 1.2 22.5 1.9 -- 1,102

TA1 D 0.165 0.474 0.441 -- 7.5

TL2 JD 34.1 49.2 44.8 -- 9.9

TH2 JD 62.7 53.5 74.7 14.7 --

RA6 ft3/s 0.007 0.241 0.050 -- 382.0

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA24 % 3.3 113.5 4.3 -- 2,570

ML3 ft3/s 44.2 35.0 49.6 20.8 --

MH14 D 1.3 24.3 1.5 -- 1,544

DL16 days 11.0 6.8 18.9 38.4 --

DH11 D 1.3 22.5 1.5 -- 1,357

TA1 D 0.336 0.474 0.411 -- 15.3

TL2 JD 31.7 49.2 33.3 -- 47.7

TH2 JD 68.3 53.5 70.1 21.7 --

RA6 ft3/s 0.014 0.241 0.029 -- 731.0

U.S. Geological Survey station number 01400000

North Branch Raritan River near Raritan, NJ

(Stream class A, observed baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

DL4 ft3/s 37.8 74.9 70.7 -- 6.0

TA1 D 0.432 0.561 0.553 -- 1.4

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MH5 ft3/s 532 1,130 940 -- 20.2

DL4 ft3/s 44.8 74.9 58.0 -- 29.2

DH2 ft3/s 1,943 2,549 2,360 -- 8.0

TA1 D 0.485 0.561 0.531 -- 5.6

TL1 JD 254.1 253.8 257.9 0.128 --

TH1 JD 47.6 59.8 54.9 -- 8.8
Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey 77

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey, by U.S. Geological Survey 

station number, determined by using observed New Jersey Hydrologic Assessment Tool (NJHAT) values for specific nonredundant 

hydrologic indices that fall outside the established 25th-to-75th-percentile range and the more conservative 40th-to-60th-percentile 

range.—Continued

[ft3/s, cubic feet per second; (ft3/s)/d, cubic feet per second per day; days, number of days; d/yr, days per year; D, dimensionless; JD, Julian day; %, percent; 

--, no deviations above the 75th or 60th or below the 25th or 40th percentile range; hydrologic indices defined in appendix 1; values less than 1 were rounded to 

the thousandths place to show precision]

U.S. Geological Survey station number 01400500

Raritan River at Manville, NJ

(Stream class A, observed baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

DL4 ft3/s 81.1 231.2 147.0 -- 57.3

TA1 D 0.432 0.652 0.553 -- 17.9

TH1 JD 42.8 73.2 70.4 -- 4.0

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA18 ft3/s 204.9 340.3 339.4 -- 0.266

DL4 ft3/s 88.2 231.2 133.4 -- 73.3

TA1 D 0.485 0.652 0.531 -- 22.8

TL1 JD 254.1 262.6 257.9 -- 1.8

TH1 JD 47.6 73.2 54.9 -- 33.3

U.S. Geological Survey station number 01400730

Millstone River at Plainsboro, NJ

(Stream class A, observed baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MA18 ft3/s 27.6 218.6 114.3 -- 91.2

DL4 ft3/s 13.1 34.4 31.4 -- 9.8

DH4 ft3/s 436.9 975.3 932.8 -- 4.6

TL1 JD 246.8 237.5 262.5 3.8 --

TH1 JD 42.8 194.3 70.4 -- 175.8

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA18 ft3/s 33.6 218.6 83.2 -- 162.6

MH5 ft3/s 216.4 343.0 293.4 -- 16.9

DL4 ft3/s 17.2 34.4 28.1 -- 22.6

DH2 ft3/s 629.4 975.3 779.0 -- 25.2

TL1 JD 254.1 237.5 257.9 6.5 --

TH1 JD 47.6 194.3 54.9 -- 253.6
78 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey, by U.S. Geological Survey 

station number, determined by using observed New Jersey Hydrologic Assessment Tool (NJHAT) values for specific nonredundant 

hydrologic indices that fall outside the established 25th-to-75th-percentile range and the more conservative 40th-to-60th-percentile 

range.—Continued

[ft3/s, cubic feet per second; (ft3/s)/d, cubic feet per second per day; days, number of days; d/yr, days per year; D, dimensionless; JD, Julian day; %, percent; 

--, no deviations above the 75th or 60th or below the 25th or 40th percentile range; hydrologic indices defined in appendix 1; values less than 1 were rounded to 

the thousandths place to show precision]

U.S. Geological Survey station number 01400932

Baldwins Creek at Baldwin Lake near Pennington, NJ

(Stream class A, simulated baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MA18 ft3/s 0.488 0.399 1.8 18.2 --

MH5 ft3/s 3.3 11.2 6.8 -- 64.2

DL4 ft3/s 0.155 0.004 0.582 97.4 --

DH2 ft3/s 6.4 42.2 14.2 -- 197.0

TA1 D 0.432 0.314 0.553 27.3 --

TL1 JD 246.8 236.6 262.5 4.2 --

TH1 JD 42.8 71.4 70.4 -- 1.3

RA3 (ft3/s)/d -0.122 -0.300 -0.023 145.9 --

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA18 ft3/s 0.810 0.399 1.2 50.7 --

MH5 ft3/s 4.1 11.2 5.6 -- 99.7

DL4 ft3/s 0.247 0.004 0.379 98.4 --

DH2 ft3/s 7.8 42.2 10.5 -- 302.1

TA1 D 0.485 0.314 0.531 35.3 --

TL1 JD 254.1 236.6 257.9 6.9 --

TH1 JD 47.6 71.4 54.9 -- 29.9

RA3 (ft3/s)/d -0.078 -0.3 -0.041 284.6 --

U.S. Geological Survey station number 01400953

Honey Branch near Pennington, NJ

(Stream class A, simulated baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MA18 ft3/s 0.505 0.229 1.4 54.7 --

DL4 ft3/s 0.280 0.003 0.446 98.9 --

TA1 D 0.165 0.689 0.441 -- 56.2

TL1 JD 241.0 232.4 266.9 3.6 --

TH1 JD 31.0 245.3 111.5 -- 120.1

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA18 ft3/s 0.656 0.229 1.0 65.1 --

MH5 ft3/s 1.6 4.1 2.6 -- 57.0

DL4 ft3/s 0.334 0.003 0.399 99.1 --

DH2 ft3/s 12.0 17.8 14.3 -- 24.3

TA1 D 0.485 0.689 0.531 -- 29.8

TL1 JD 254.1 232.4 257.9 8.6 --

TH1 JD 47.6 245.3 54.9 -- 346.4

RA3 (ft3/s)/d -0.080 -0.120 -0.040 50.0 --
Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey 79

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey, by U.S. Geological Survey 

station number, determined by using observed New Jersey Hydrologic Assessment Tool (NJHAT) values for specific nonredundant 

hydrologic indices that fall outside the established 25th-to-75th-percentile range and the more conservative 40th-to-60th-percentile 

range.—Continued

[ft3/s, cubic feet per second; (ft3/s)/d, cubic feet per second per day; days, number of days; d/yr, days per year; D, dimensionless; JD, Julian day; %, percent; 

--, no deviations above the 75th or 60th or below the 25th or 40th percentile range; hydrologic indices defined in appendix 1; values less than 1 were rounded to 

the thousandths place to show precision]

U.S. Geological Survey station number 01401000

Stony Brook at Princeton, NJ

(Stream class A, simulated baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MH5 ft3/s 62.0 337.0 130.8 -- 157.7

DH2 ft3/s 158.2 801.0 306.4 -- 161.4

TL1 JD 241.0 236.8 266.9 1.8 --

RA3 (ft3/s)/d -3.5 -4.0 -0.385 13.0 --

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MH5 ft3/s 83.0 337.0 110.3 -- 205.5

DH2 ft3/s 197.9 801.0 250.6 -- 219.7

TA1 D 0.485 0.225 0.531 53.6 --

TL1 JD 254.1 236.8 257.9 6.8 --

RA3 (ft3/s)/d -2.0 -4.0 -0.866 99.6 --

U.S. Geological Survey station number 01401500

Millstone River near Kingston, NJ

(Stream class A, simulated baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MA18 ft3/s 122.1 95.6 317.5 27.7 --

MH5 ft3/s 280.8 869.0 666.1 -- 30.5

DL4 ft3/s 75.7 42.1 124.6 44.3 --

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA18 ft3/s 161.7 95.6 239.6 40.9 --

MH5 ft3/s 343.1 869.0 507.0 -- 71.4

DL4 ft3/s 86.3 42.1 110.8 51.2 --

TL1 JD 254.1 266.5 257.9 -- 3.3

TH1 JD 47.6 36.3 54.9 23.7 --

RA3 (ft3/s)/d -16.1 -19.0 -8.6 17.8 --
80 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey, by U.S. Geological Survey 

station number, determined by using observed New Jersey Hydrologic Assessment Tool (NJHAT) values for specific nonredundant 

hydrologic indices that fall outside the established 25th-to-75th-percentile range and the more conservative 40th-to-60th-percentile 

range.—Continued

[ft3/s, cubic feet per second; (ft3/s)/d, cubic feet per second per day; days, number of days; d/yr, days per year; D, dimensionless; JD, Julian day; %, percent; 

--, no deviations above the 75th or 60th or below the 25th or 40th percentile range; hydrologic indices defined in appendix 1; values less than 1 were rounded to 

the thousandths place to show precision]

U.S. Geological Survey station number 01402000

Millstone River at Blackwells Mills, NJ

(Stream class A, observed baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

FH4 days/yr 9.5 22.5 22.0 -- 2.3

TL1 JD 246.8 244.3 262.5 1.0 --

TH1 JD 42.8 73.7 70.4 -- 4.7

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MH5 ft3/s 588.0 1,500 1,110 -- 35.1

FH4 days/yr 13.0 22.5 17.0 -- 32.4

DH2 ft3/s 2,880 3,665 3,370 -- 8.8

TL1 JD 254.1 244.3 257.9 3.9 --

TH1 JD 47.6 73.7 54.9 -- 34.2

U.S. Geological Survey station number 01403000

Raritan River at Bound Brook, NJ

(Stream class A, simulated baseline dataset)

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MH5 ft3/s 1,179 4,120 2,218 -- 85.8

DL4 ft3/s 65.0 272.1 228.5 -- 19.1

DH2 ft3/s 2,075 9,061 3,895 -- 132.6

TA1 D 0.432 0.642 0.553 -- 16.0

RA3 (ft3/s)/d -43.8 -110.0 -10.5 151.1 --

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA18 ft3/s 280.5 454.2 387.2 -- 17.3

MH5 ft3/s 1,476 4,120 1,899 -- 116.9

DL4 ft3/s 95.2 272.1 172.0 -- 58.2

DH2 ft3/s 2,499 9,061 3,218 -- 181.6

TA1 D 0.485 0.642 0.531 -- 20.8

RA3 (ft3/s)/d -29.2 -110.0 -17.7 276.2 --
Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey 81

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey, by U.S. Geological Survey 

station number, determined by using observed New Jersey Hydrologic Assessment Tool (NJHAT) values for specific nonredundant 

hydrologic indices that fall outside the established 25th-to-75th-percentile range and the more conservative 40th-to-60th-percentile 

range.—Continued

[ft3/s, cubic feet per second; (ft3/s)/d, cubic feet per second per day; days, number of days; d/yr, days per year; D, dimensionless; JD, Julian day; %, percent; 

--, no deviations above the 75th or 60th or below the 25th or 40th percentile range; hydrologic indices defined in appendix 1; values less than 1 were rounded to 

the thousandths place to show precision]

U.S. Geological Survey station number 01403060

Raritan River below Calco Dam at Bound Brook, NJ

(Stream class A, observed baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

TA1 D 0.432 0.554 0.553 -- 0.181

TL1 JD 246.8 240.7 262.5 2.5 --

TH1 JD 42.8 79.1 70.4 -- 12.3

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MH5 ft3/s 1,980 4,150 3,640 -- 14.0

FH4 days/yr 11.0 20.5 19.0 -- 7.9

DH2 ft3/s 8,467 11,710 10,577 -- 10.7

TA1 D 0.485 0.554 0.531 -- 4.3

TL1 JD 254.1 240.7 257.9 5.3 --

TH1 JD 47.6 79.1 54.9 -- 44.0

U.S. Geological Survey station number 01403150

West Branch Middle Brook near Martinsville, NJ

(Stream class C, simulated baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MA24 % 12.2 172.8 23.3 -- 640.7

ML3 ft3/s 2.3 1.0 3.7 57.6 --

MH14 D 3.1 91.4 11.3 -- 710.8

DL16 days 16.9 6.2 44.8 63.0 --

DH11 D 3.5 117.6 11.3 -- 941.2

RA6 ft3/s 0.020 0.452 0.129 -- 250.4

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA24 % 14.6 172.8 19.6 -- 783.4

ML3 ft3/s 2.7 1.0 3.3 64.5 --

MH14 D 4.3 91.4 6.8 -- 1,237

DL16 days 22.0 6.2 34.7 71.7 --

DH11 D 4.1 117.6 6.6 -- 1,691

TL2 JD 31.7 36.0 33.3 -- 8.0

TH2 JD 68.3 74.2 70.1 -- 5.8

RA6 ft3/s 0.039 0.452 0.077 -- 487.0
82 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey, by U.S. Geological Survey 

station number, determined by using observed New Jersey Hydrologic Assessment Tool (NJHAT) values for specific nonredundant 

hydrologic indices that fall outside the established 25th-to-75th-percentile range and the more conservative 40th-to-60th-percentile 

range.—Continued

[ft3/s, cubic feet per second; (ft3/s)/d, cubic feet per second per day; days, number of days; d/yr, days per year; D, dimensionless; JD, Julian day; %, percent; 

--, no deviations above the 75th or 60th or below the 25th or 40th percentile range; hydrologic indices defined in appendix 1; values less than 1 were rounded to 

the thousandths place to show precision]

U.S. Geological Survey station number 01403160

West Branch Middle Brook near Somerville, NJ

(Stream class A, simulated baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MH5 ft3/s 6.2 60.0 12.1 -- 394.5

DH2 ft3/s 11.2 71.2 24.5 -- 190.2

TL1 JD 241.0 269.3 266.9 -- 0.900

RA3 (ft3/s)/d -0.285 -0.480 -0.042 68.4 --

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA18 ft3/s 1.4 1.2 2.2 13.8 --

MH5 ft3/s 7.4 60.0 10.0 -- 501.4

DL4 ft3/s 0.491 0.432 0.700 12.0 --

DH2 ft3/s 14.0 71.2 18.0 -- 294.8

TA1 D 0.485 0.186 0.531 61.6 --

TL1 JD 254.1 269.3 257.9 -- 4.4

TH1 JD 47.6 71.2 54.9 -- 29.5

RA3 (ft3/s)/d -0.174 -0.480 -0.088 175.9 --

U.S. Geological Survey station number 01403900

Bound Brook at Middlesex, NJ

(Stream class A, simulated baseline dataset)

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MH5 ft3/s 77.0 280.7 122.5 -- 129.1

DL4 ft3/s 33.5 14.8 45.3 55.9 --

DH2 ft3/s 90.6 648.3 142.6 -- 354.6

TH1 JD 31.0 185.4 111.5 -- 66.4

RA3 (ft3/s)/d -1.1 -6.2 -0.326 438.6 --

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA18 ft3/s 53.3 62.7 62.2 -- 0.802

MH5 ft3/s 87.9 280.7 106.0 -- 164.9

DL4 ft3/s 36.4 14.8 40.1 59.3 --

DH2 ft3/s 102.4 648.3 128.6 -- 404.1

TA1 D 0.485 0.425 0.531 12.4 --

TL1 JD 254.1 259.9 257.9 -- 0.793

TH1 JD 47.6 185.4 54.9 -- 237.5

RA3 (ft3/s)/d -0.798 -6.2 -0.492 672.8 --
Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey 83

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey, by U.S. Geological Survey 

station number, determined by using observed New Jersey Hydrologic Assessment Tool (NJHAT) values for specific nonredundant 

hydrologic indices that fall outside the established 25th-to-75th-percentile range and the more conservative 40th-to-60th-percentile 

range.—Continued

[ft3/s, cubic feet per second; (ft3/s)/d, cubic feet per second per day; days, number of days; d/yr, days per year; D, dimensionless; JD, Julian day; %, percent; 

--, no deviations above the 75th or 60th or below the 25th or 40th percentile range; hydrologic indices defined in appendix 1; values less than 1 were rounded to 

the thousandths place to show precision]

U.S. Geological Survey station number 01405300

Matchaponix Brook at Spotswood, NJ

(Stream class A, simulated baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MA18 ft3/s 23.8 47.0 40.8 -- 15.1

MH5 ft3/s 71.1 171.0 136.5 -- 25.2

DL4 ft3/s 14.8 12.8 24.6 14.0 --

DH2 ft3/s 100.9 506.7 196.7 -- 157.6

TA1 D 0.554 0.444 0.674 19.9 --

TL1 JD 240.8 240.3 252.8 0.2 --

TH1 JD 78.7 39.5 198.5 49.8 --

RA3 (ft3/s)/d -3.9 -4.0 -0.553 3.9 --

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA18 ft3/s 27.2 47.0 34.0 -- 38.2

MH5 ft3/s 84.5 171.0 119.9 -- 42.6

DL4 ft3/s 17.2 12.8 20.8 25.8 --

DH2 ft3/s 135.5 506.7 165.8 -- 205.7

TA1 D 0.485 0.444 0.531 8.5 --

TL1 JD 254.1 240.3 257.9 5.4 --

TH1 JD 47.6 39.5 54.9 17.0 --

RA3 (ft3/s)/d -2.1 -4.0 -1.0 89.5 --
84 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey, by U.S. Geological Survey 

station number, determined by using observed New Jersey Hydrologic Assessment Tool (NJHAT) values for specific nonredundant 

hydrologic indices that fall outside the established 25th-to-75th-percentile range and the more conservative 40th-to-60th-percentile 

range.—Continued

[ft3/s, cubic feet per second; (ft3/s)/d, cubic feet per second per day; days, number of days; d/yr, days per year; D, dimensionless; JD, Julian day; %, percent; 

--, no deviations above the 75th or 60th or below the 25th or 40th percentile range; hydrologic indices defined in appendix 1; values less than 1 were rounded to 

the thousandths place to show precision]

U.S. Geological Survey station number 01405400

Manalapan Brook at Spotswood, NJ

(Stream class C, simulated baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MA24 % 4.7 60.4 10.9 -- 455.2

ML3 ft3/s 46.2 43.0 75.4 7.0 --

MH14 D 1.6 10.4 2.2 -- 376.9

DL16 days 11.5 6.7 33.0 41.4 --

DH11 D 1.5 13.3 2.6 -- 405.4

TA1 D 0.299 0.502 0.465 -- 8.0

TL2 JD 29.4 42.4 40.8 -- 3.9

TH2 JD 64.5 75.5 72.5 -- 4.1

RA6 ft3/s 0.011 0.205 0.081 -- 153.1

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA24 % 5.8 60.4 8.2 -- 637.7

ML3 ft3/s 54.5 43.0 62.8 21.1 --

MH14 D 1.8 10.4 2.0 -- 429.3

DL16 days 16.7 6.7 24.2 59.7 --

DH11 D 1.9 13.3 2.3 -- 489.3

TA1 D 0.336 0.502 0.411 -- 22.1

TL2 JD 31.7 42.4 33.3 -- 27.3

TH2 JD 68.3 75.5 70.1 -- 7.6

RA6 ft3/s 0.022 0.205 0.048 -- 327.1
Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey 85

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey, by U.S. Geological Survey 

station number, determined by using observed New Jersey Hydrologic Assessment Tool (NJHAT) values for specific nonredundant 

hydrologic indices that fall outside the established 25th-to-75th-percentile range and the more conservative 40th-to-60th-percentile 

range.—Continued

[ft3/s, cubic feet per second; (ft3/s)/d, cubic feet per second per day; days, number of days; d/yr, days per year; D, dimensionless; JD, Julian day; %, percent; 

--, no deviations above the 75th or 60th or below the 25th or 40th percentile range; hydrologic indices defined in appendix 1; values less than 1 were rounded to 

the thousandths place to show precision]

U.S. Geological Survey station number 01405500

South River at Old Bridge, NJ

(Stream class A, simulated baseline dataset)

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MH5 ft3/s 129.2 397.0 246.4 -- 61.1

DH2 ft3/s 184.9 896.7 318.5 -- 181.5

TL1 JD 253.3 242.0 263.5 4.5 --

TH1 JD 45.8 78.2 77.1 -- 1.3

RA3 (ft3/s)/d -3.0 -10.0 -0.930 235.2 --

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MH5 ft3/s 166.2 397.0 220.0 -- 80.4

DH2 ft3/s 225.4 896.7 272.9 -- 228.6

TL1 JD 254.1 242.0 257.9 4.8 --

TH1 JD 47.6 78.2 54.9 -- 42.3

RA3 (ft3/s)/d -2.1 -10.0 -1.4 370.8 --

U.S. Geological Survey station number 01406000

Deep Run near Browntown, NJ

(Stream class C, simulated baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MA24 % 8.5 90.9 21.1 -- 330.6

MH14 D 2.6 19.8 4.6 -- 327.5

DL16 days 15.8 7.9 43.0 50.3 --

DH11 D 2.5 22.6 5.4 -- 318.9

TA1 D 0.299 0.253 0.465 15.4 --

TL2 JD 29.4 21.6 40.8 26.5 --

RA6 ft3/s 0.015 0.288 0.095 -- 203.2

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA24 % 10.9 90.9 16.3 -- 457.3

ML3 ft3/s 10.9 9.8 13.4 9.9 --

MH14 D 3.0 19.8 3.7 -- 436.5

DL16 days 22.4 7.9 31.3 64.8 --

DH11 D 3.5 22.6 4.3 -- 424.7

TA1 D 0.336 0.253 0.411 24.7 --

TL2 JD 31.7 21.6 33.3 32.0 --

RA6 ft3/s 0.029 0.288 0.057 -- 405.3
86 Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jersey

Appendix 2. Deviations of flow-management targets for the Raritan River Basin study area, New Jersey, by U.S. Geological Survey 

station number, determined by using observed New Jersey Hydrologic Assessment Tool (NJHAT) values for specific nonredundant 

hydrologic indices that fall outside the established 25th-to-75th-percentile range and the more conservative 40th-to-60th-percentile 

range.—Continued

[ft3/s, cubic feet per second; (ft3/s)/d, cubic feet per second per day; days, number of days; d/yr, days per year; D, dimensionless; JD, Julian day; %, percent; 

--, no deviations above the 75th or 60th or below the 25th or 40th percentile range; hydrologic indices defined in appendix 1; values less than 1 were rounded to 

the thousandths place to show precision]

U.S. Geological Survey station number 01406500

Tennent Brook near Browntown, NJ

(Stream class C, simulated baseline dataset) 

Hydrologic

index Units 25th percentile1 Observed NJHAT value2 75th percentile1

Percent deviation 

below the 

25th percentile

Percent deviation 

above the 

75th percentile

MA24 % 11.5 108.2 24.7 -- 337.5

ML3 ft3/s 6.2 2.1 9.0 66.0 --

MH14 D 3.1 27.8 5.7 -- 387.6

DL16 days 17.7 8.4 53.0 52.4 --

DH11 D 3.0 29.4 5.7 -- 414.4

TA1 D 0.554 0.167 0.674 69.9 --

TL2 JD 35.4 25.5 42.2 27.9 --

TH2 JD 71.9 52.4 78.2 27.2 --

RA6 ft3/s 0.014 0.452 0.143 -- 216.1

Hydrologic

index Units 40th percentile1 Observed NJHAT value2 60th percentile1

Percent deviation 

below the 

40th percentile

Percent deviation 

above the 

60th percentile

MA24 % 13.8 108.2 20.2 -- 434.7

ML3 ft3/s 7.0 2.1 8.1 70.1 --

MH14 D 3.6 27.8 4.5 -- 512.5

DL16 days 28.7 8.4 42.6 70.6 --

DH11 D 3.7 29.4 4.7 -- 528.7

TA1 D 0.336 0.167 0.411 50.3 --

TL2 JD 31.7 25.5 33.3 19.6 --

TH2 JD 68.3 52.4 70.1 23.2 --

RA6 ft3/s 0.033 0.452 0.080 -- 465.0

1

Percentile range derived from either the quantile regression baseline period (observed baseline) or the Statewide Watershed Runoff Model (SWRM) simulated baseline.

2

Value derived from the analysis of the entire period of altered streamflow record.
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Kennen and others—Method to Support Total Maximum Daily Load Development to Address Aquatic-Life Impairment in New Jeresy—Scientific Investigations Report 2013–5089