National Water Quality Program

An Enhanced Hydrologic Stream Network Based 

on the NHDPlus Medium Resolution Dataset

Scientific Investigations Report 2019–5127

U.S. Department of the Interior

U.S. Geological Survey

An Enhanced Hydrologic Stream Network 

Based on the NHDPlus Medium Resolution 

Dataset

By John W. Brakebill, Gregory E. Schwarz, and Michael E. Wieczorek

National Water Quality Program

Scientific Investigations Report 2019–5127

U.S. Department of the Interior

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

DAVID BERNHARDT, Secretary

U.S. Geological Survey

James F. Reilly II, Director

U.S. Geological Survey, Reston, Virginia: 2020

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Suggested citation:

Brakebill, J.W., Schwarz, G.E., and Wieczorek, M.E., 2020, An enhanced hydrologic stream network based on the 

NHDPlus medium resolution dataset: U.S. Geological Survey Scientific Investigations Report 2019–5127, 49 p., 

https://doi.org/10.3133/sir20195127.

Associated data for this publication:

Schwarz, G.E., 2019. E2NHDPlusV2_us: Database of Ancillary Hydrologic Attributes and Modified Routing for 

NHDPlus Version 2.1 Flowlines: U.S. Geological Survey data release, https://doi.org/10.5066/P986KZEM.

ISSN 2328-0328 (online)
iii

Foreword

Sustaining the quality of the Nation’s water resources and the health of our diverse ecosystems 

depends on the availability of sound water-resources data and information to develop 

effective, science-based policies. Effective management of water resources also brings more 

certainty and efficiency to important economic sectors. Taken together, these actions lead to 

immediate and long-term economic, social, and environmental benefits that make a difference 

to the lives of the almost 400 million people projected to live in the United States by 2050. 

(http://water.usgs.gov/nawqa/applications/).

In 1991, Congress established the National Water-Quality Assessment (NAWQA) to address 

where, when, why, and how the Nation’s water quality has changed, or is likely to change 

in the future, in response to human activities and natural factors. Since 1991, NAWQA has 

been a leading source of scientific data and knowledge used by national, regional, state, and 

local agencies to develop science-based policies and management strategies to improve and 

protect water resources used for drinking water, recreation, irrigation, energy development, 

and ecosystem needs. Plans for the third decade of NAWQA (2013–23) address priority waterquality issues and science needs identified by NAWQA stakeholders (such as the Advisory 

Committee on Water Information) and the National Research Council. The plans are designed to 

meet increasing challenges related to population growth, increasing needs for clean water, and 

changing land use and weather patterns.

Federal, state, and local agencies have invested billions of dollars to reduce the amount of 

pollution entering rivers and streams that millions of Americans rely on for a variety of water 

needs and biota rely on for habitat. Understanding the sources and transport of pollution is 

crucial for designing strategies to improve water quality. Studies have indicated that, for 

example, contaminants such as excess total nitrogen and total phosphorus from sources such as 

wastewater treatment plants or commercial fertilizer contribute to poor water-quality conditions 

in streams. Understanding of the role of nutrients in determining water-quality conditions is 

important, and models, supported by adequate data, are important tools for developing the 

information needed to manage water quality.

Digital hydrologic networks are a key component to any spatial analysis or modeling study. This 

report describes a topologically reconditioned (enhanced) version of the routing and stream 

connections to the value-added attributes of NHDPlusV2.1 with the intent of improving the 

networking capabilities and assigned attributes utilized by SPARROW modeling and other 

hydrologic routing and attribute accumulating applications.

We hope this publication will provide you with insights and information to meet your water 

resource needs and will foster increased citizen awareness and involvement in the protection 

and restoration of our Nation’s waters. The information in this report is intended primarily for 

those interested or involved in resource management and protection, conservation, regulation, 

and policymaking at the regional and national level.

Dr. Donald W. Cline

Associate Director for Water

U.S. Geological Survey

v

Contents

Foreword ........................................................................................................................................................iii

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

Introduction.....................................................................................................................................................1

Purpose and Scope ..............................................................................................................................3

Material and Methods...................................................................................................................................3

NHDPlusV2.1..........................................................................................................................................3

Constructing a Modified Network......................................................................................................8

Filling In Missing Values .............................................................................................................8

Routing Modifications...............................................................................................................11

Addition/Removal Events..........................................................................................................13

New Variables for Interpreting Network Flow Characteristics .........................................15

Flowline Termination ........................................................................................................15

Foreign Drainage ..............................................................................................................16

Diversion-Affected Flowlines .........................................................................................16

Revised Variables ......................................................................................................................16

Hydro-sequence ...............................................................................................................16

Streamflow.........................................................................................................................16

Slope ................................................................................................................................16

Additional Ancillary Information .............................................................................................16

Aggregations ..............................................................................................................................17

Validation.......................................................................................................................................................18

Summary and Conclusions.........................................................................................................................21

References Cited..........................................................................................................................................22

Appendix 1. Description of Addition and Removal Events Spreadsheet ........................................26

Appendix 2. Description of Methods Used to Update Streamflow Estimates................................29

Appendix 3. Description of Methods Used to Update Slope Estimates ..........................................32

Appendix 4. Description of Attributes in E2NHDPlusV2_us ..............................................................43

Appendix 5. Description of Selected Ancillary Geospatial Dataset Variables Assigned 

to the Catchments and Flowlines of NHDPlusV2.1 ...................................................................45
vi

Figures

1. Diagram showing example of how routing of NHDPlusV2.1 flowlines can be

affected by an upstream diversion ............................................................................................6

2. Map showing NHDPlusV2 production units.............................................................................9

3. Map showing example of routing errors associated with diversions affecting

measured streamflow at a U.S. Geological Survey streamgage in Michigan..................12

4. Schematic diagram of A, A simple routed transfer and B, a routed transfer with 

a diversion....................................................................................................................................14

5. Map showing location of Addition/Removal events included in the enhanced

hydrologic stream network, by event class ...........................................................................15

6. Graph showing NHDPlus upstream watershed areas in relation to total upstream

accumulation values ..................................................................................................................19

7. Graphs showing Pearson’s Correlation for basin percent of GAGESII NLCD 2006

land-use classes in relation to values from the total upstream aggregation method ....20

Tables

1. Primary spatial features of the NHDPlusV2 network, divided into sub-categories,

with number of occurrences of each........................................................................................4

2. Classes of diversions in NHDPlusV2.1 and their defining characteristics .........................5

3. Revised names given to NHDPlusV2 variables......................................................................10

4. Summary of modification method codes for NHDPlusV2 variables...................................11

5. Diversion-related modifications to NHDPlusV2.....................................................................13

6. Number of discrepancy values from attempt to reconstruct original NHDPlus

estimates......................................................................................................................................19
vii

Conversion Factors

International System of Units to U.S. customary units

Multiply By To obtain

Length

meter (m) 3.281 foot (ft)

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2) 10.7639 square foot (ft2)

square kilometer (km2) 247.1 acre

Mass

kilogram per year (kg/yr) 2.205 pound avoirdupois per year (lb/yr)

metric ton (t) 2204.62 pound, avoirdupois (lb)

millimeter per year (mm/yr) 0.0393701 inch per year (in/yr)

Application rate

metric ton per square kilometer (t/km2) 8.92179122 pound per acre (lb/acre)

Datum

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

Abbreviations

AR Addition and Removal

DEM Digital Elevation Model

EPA U.S. Environmental Protection Agency

NAWQA National Water-Quality Assessment

NED National Elevation Dataset

NHDPlus National Hydrography Dataset Plus

NHDPlusV2 National Hydrography Dataset Plus version 2

NRSA National River and Stream Assessment

NWIS National Water Information System

SPARROW SPAtially Referenced Regressions On Watershed attributes

USGS U.S. Geological Survey

WSA Wadeable Stream Assessment

An Enhanced Hydrologic Stream Network Based on the 

NHDPlus Medium Resolution DataSet

By John W. Brakebill, Gregory E. Schwarz, and Michael E. Wieczorek

Abstract

The National Hydrography Dataset Plus, Version 2.1 

(NHDPlusV2.1) is an attribute-rich digital stream network 

for the conterminous United States, serving as a foundational 

infrastructure for reporting hydrologic information at both 

regional and national scales. SPAtially Referenced Regressions On Watershed attributes (SPARROW) is a process-based 

statistical model that relies on a digital hydrologic network 

like NHDPlusV2.1 to establish spatial relations between 

quantities of monitored contaminant loads and contaminant 

sources, accounting for the physical characteristics along flow 

paths affecting contaminant transport. The U.S. Geological 

Survey National Water Quality Assessment project adopted 

and modified the medium-resolution NHDPlusV2.1 network 

for use as the primary framework supporting SPARROW 

modeling. This report describes the enhancements made to 

improve the routing capabilities and the value-added attributes 

of NHDPlusV2.1 to support modeling and other hydrologic 

analyses. These enhancements include corrections to inconsistencies in network/routing information, filling in missing 

attribute values of associated characteristics, accounting of 

water use affecting flow, new variables useful for interpreting network data, revised flowline attributes such as slope 

and flow, and incorporation of ancillary spatial data into the 

network. The resulting dataset containing the enhancements 

to the network is named E2NHDPlusV2_US. Although the 

enhancements described in the report were developed for use 

in SPARROW modeling, the enhancements are expected to 

be useful for a wide variety of hydrologic studies within the 

United States.

Introduction

Digital hydrologic networks are a key component of any 

hydrologic spatial analysis or modeling study. A network of 

connected surface-water pathways, and the areas they drain, 

is often used as a foundation for a standard, spatial framework 

for the characterization and analysis of aquatic and watershed 

processes. Explicit, discrete spatial units can help interested 

parties visualize and locate the assigned physical properties of 

a watershed system (Hill and others, 2016; Sprague and Gronberg, 2012; Brakebill and others, 2011). Geospatial data representing landscape, aquatic, and subsurface watershed characteristics, such as contaminant monitoring and potential supply, 

land cover and use, slope, soil characteristics, and annual 

streamflow, are often used to help describe and characterize 

these watershed systems. These geospatial descriptions help 

formulate a better understanding of the intrinsic connections 

between land and water, and their interactions (Alexander and 

others, 2007; Brakebill and others, 2011). Spatial referencing 

afforded by these methods has demonstrated improvement 

in the predictive capabilities of hydrologic models (Smith 

and others, 1997). Properly connected hydrologic networks 

provide additional ability to simulate and route the movement 

of water and associated constituents or properties throughout 

the landscape. Hydrologic connectivity also can help facilitate 

the computation of total upstream landscape information for 

any location along a river system, including river systems with 

braided channels or diversions. 

The medium resolution National Hydrography Dataset 

Plus, Version 2.1 (NHDPlusV2.1) is an attributed geospatial 

stream network developed by the U.S. Environmental Protection Agency (EPA) and U.S. Geological Survey (USGS) from 

USGS 1:100,000 scale topographic maps (McKay and others, 

2012). The data structure of NHDPlusV2.1 includes upstream 

and downstream navigational capabilities, feature naming, and 

“value-added attributes” (VAAs; McKay and others, 2012) 

of relevant stream and watershed characteristics. Attributed 

navigational variables relate stream segments to each other 

according to spatial topology (hydrologic sequencing) and 

are essential for routing streamflow and performing other 

aggregations of associated attributed information. Unique 

identifiers (FeatureID and ComID) are used as primary keys 

for identification of waterbody features (McKay and others, 

2012; Moore and Dewald, 2016) and relate the waterbodies 

to specific stream segments (flowlines) in the routed network. 

The NHDPlusV2.1 contains polygons (catchments) derived 

from the USGS National Elevation Dataset (NED) that delineate areas draining each flowline (McKay and others, 2012). 

Additional variables derived from the NHDPlusV2.1 data that 

are useful for modeling include estimates of mean annual and 

monthly flow, mean annual and monthly stream velocity, and 

channel slope.
2 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

The comprehensive NHDPlusV2.1 hydrologic network 

with VAAs has wide applications for hydrologic analyses. It 

provides and presents a profound foundational infrastructure 

for organized hydrologic information. Previous versions of 

NHDPlus have served as the hydrologic network for regional 

studies (Hill and others, 2016; Tsang and others, 2014; Moore 

and Dewald, 2016), including regional applications of the 

USGS-developed SPAtially Referenced Regressions On 

Watershed attributes (SPARROW) empirical water-quality 

model (Moore and Dewald, 2016; Hoos and others, 2013, 

Ator and others, 2011), and NHDPlus is the spatial network 

for the National Oceanic and Atmospheric Administration 

(NOAA) National Water Model (NOAA, 2016). The USGS 

National Water Quality Assessment (NAWQA) Cycle III 

project adopted the medium-resolution NHDPlusV2.1 network 

to serve as the primary framework supporting SPARROW 

modeling because it provides a structure for the Nation’s 

drainage system, has rapidly become a uniform reporting unit 

for hydrologic information, and is available at a national scale 

(Wieczorek and others, 2018; Schwarz and Wieczorek, 2018; 

Schwarz, 2019; Wieczorek and others, 2019). The NHDPlusV2.1 also provides a unique navigational infrastructure 

and framework to add additional spatially referenced attributes 

to suit relative modeling and assessment applications, including elevation, precipitation, mean annual streamflow, and 

velocity (McKay and others, 2012).

Despite these advantages and the many improvements 

since the NHDPlus framework inception, the NHDPlusV2.1 

network and associated data exhibit some noteworthy limitations that indicate the need for modifications and enhancements. Existing inconsistent VAAs and attributed navigational 

information, or duplicate flowlines, can have significant local 

effects on routing and analysis. Values for each flowline or 

ancillary information associated with the network (inherent or 

additional) could be missing for some observations, depending on the origin of the variable or the method of association. 

Some values, like stream name, simply are not known. Other 

VAAs, like precipitation, may be missing because a catchment 

does not exist for that flowline, thus rendering null any aerial 

geospatial overlay operations designed to capture mean values, 

by catchment. 

Divergences in the stream network, where streamflow 

(a portion or all the streamflow) is diverted from the natural course of a river, are numerous (more than 64,000 are 

represented). These divergences present the opportunity for 

topological errors in the data, such as inadvertently routing 

streamflow in the wrong direction or through an inappropriate 

pathway and can result in large errors in watershed parameters 

derived from the network (for example, drainage area and 

mean annual precipitation of the basin). Accurate routing and 

basin parameterization are essential to the development of any 

hydrologic model. Routing errors can potentially induce large 

discrepancies between the monitored (or calibration) values 

and the corresponding model estimates for empirical models. 

Even if monitoring sites are excluded from diversion-affected 

rivers, routing errors can still magnify errors in the predictions 

of the affected flowlines. Additionally, some corrections or 

modifications in network routing can render network attributes 

obsolete or insignificant, particularly variables dependent on 

the assigned primary pathway, such as channel slopes, hydrologic sequencing, and variables pertaining to aggregations or 

accumulations of local stream segments or watershed values. 

Any corrections or modifications in routing require redevelopment of these types of attributes.

The NHDPlusV2.1 network includes attributes which 

define flowlines as primary, secondary, or divergent pathways. 

Divergent flowlines also can represent pathways that have 

been artificially diverted to other locations. This distinction 

provides a framework for attribution of the fraction of flow 

assigned downstream along the primary and secondary pathways. However, these diversions and pathways could benefit 

from a more robust assessment of the proportion of flow that 

is routed through each of the diverting pathways (or flowline 

if that is used). The assessment has typically been based on 

circumstantial factors such as stream name and not observed 

streamflow data (McKay and others, 2012).

Addition and Removal (AR) events are locations along 

the network where artificial transfers of water that mimic the 

rerouting of streamflow occur. The AR events are designed 

to capture and account for irrigation withdrawals and returns, 

drinking-water withdrawals, permitted discharges, or other 

streamflow redirections without creating the necessary physical flowlines to support the transfer. The AR events included 

with NHDPlusV2.1 represent only a small sample of all such 

events, but generally for a stream network of this scale, a significant volume of transfers is captured. However, the sources 

for rerouted flow estimates referenced in McKay and others 

(2012) could be expanded as opportunities arise to incorporate 

revisions into the network to account for additional AR events 

or routing modifications. The U.S. Environmental Protection 

Agency (EPA) Permit Compliance System (EPA, 2018) database, and representations of aqueducts, canals, conduits, pipes, 

irrigation ditches, and flowlines corresponding to flow paths 

that lack flow direction, also could be explored to define and 

refine AR withdrawal and discharge events.

A topologically reconditioned (enhanced) version of the 

NHDPlusV2.1 routing and stream connections was created 

with the intention of improving the networking capabilities 

and assigned attributes utilized by SPARROW modeling and 

other hydrologic routing and attribute accumulating applications. The enhancements include corrections to recently 

discovered inconsistencies in network/routing information, 

filling in of previously missing attribute values of associated 

characteristics, revisions in flow routing, expanded accounting 

of water use affecting flow, creation of new variables useful 

for interpreting network data, revised flowline attributes such 

as slope and flow, and incorporation of ancillary spatial data 

into the network. The resulting enhanced network, available in 

a single data file for the entire conterminous United States, has 

the designation E2NHDPlusV2_us. Additional assigned ancillary data can be found in Wieczorek and others (2019).
Material and Methods 3

Purpose and Scope

This report describes a topologically reconditioned 

version of the NHDPlusV2.1 routing network, including 

enhanced attributes to improve flow routing and the physical description of flowlines. The modifications pertain only 

to existing NHDPlusV2.1 data-table values; no changes, 

enhancements, nor modifications were made to the spatial 

aspects of NHDPlusV2.1 (McKay and others, 2012). Shapefile flowlines depicting stream channel position and shape, 

and polygon shapefiles that define and delineate catchments, 

were unaltered. The enhancements described here also do not 

modify any presumed artificial flowpaths through waterbodies, 

despite the arbitrary nature of these pathways and any limitations they induce in describing contaminant processing in 

the waterbodies.

The approaches described in this report assume the preexistence of the NHDPlusV2.1 network and its associated 

attributes; however, many of the attributes can be generated 

without reference to existing values. The enhanced dataset 

(E2NHDPlusV2_us, superseding ENHDPlus_us; Schwarz and 

Wieczorek, 2018) is the result of methods described and referenced within this report and represents a national framework 

useful for the hydrologic indexing of spatial data like contaminant sources, terrestrial and aquatic properties, characteristics, 

and information (Schwarz and Wieczorek, 2018; Wieczorek 

and others, 2018). The data are useful to hydrologic modeling, 

such as the SPARROW water-quality model, and other analyses relating spatial features to hydrologic data. Data described 

here and associated with the network flowlines and catchments 

are publicly available through separate data releases by USGS, 

which are referenced in this report. 

Material and Methods

NHDPlusV2.1

The NHDPlusV2.1 data consist of multiple files containing spatial and tabular information, organized by 21 production units within the conterminous United States. Data are 

included for some regions of Canada and Mexico where the 

source of a United States stream is outside the United States, 

or the United States stream flows across a foreign border and 

returns to the United States downstream. 

Three primary spatial hydrologic features are included in 

the NHDPlusV2.1 data: flowlines, catchments, and waterbodies (described below). A fourth spatial feature, NHDArea, has 

many characteristics similar to waterbodies; for purposes of 

this report, NHDAreas were joined with waterbodies. Associated with these spatial features are tabular files representing 

numeric and textual attributes of each feature. Counts of the 

three primary features, and subcategories of these features, are 

given in table 1 (McKay and others, 2012).

Flowlines are linear spatial features representing the 

paths of flowing water. For most flowlines, the identified 

path is an actual stream; however, some flowlines represent 

artificial pathways through a waterbody (further described in 

this section). Flowlines possess either routed or non-routed 

navigational properties. Routed flowlines have navigational 

information that determines the connectivity of each flowline 

to all the other flowlines within the routed network. Nonrouted flowlines, in contrast, have no navigational information 

(no connection) and without this connection cannot be used in 

hydrologic models to route flow. 

Catchments are polygons that represent the incremental 

drainage area contributing runoff directly to the flowline it 

contains. An exception would be in coastal regions where the 

catchment represents the total area draining each individual 

coastline segment. Catchments can also represent off-network 

sink features that capture the total drainage area to a sink 

because there are no upstream features (McKay and others, 

2012). Catchments are derived using a complex process using 

a flow direction grid derived from 30-meter elevation data 

(McKay and others, 2012). Some short-length flowlines (or 

“connector” flowlines) do not have an assigned catchment, 

mainly because the resolution of the elevation data used to 

create the catchment was too small to capture an accurate or 

relevant drainage area (McKay and others, 2012). For these 

flowlines, the assigned catchment area is zero, no physical 

catchment exists, and the flowline serves the purpose of a connector. Catchments can, however, be defined without reference 

to a routed flowline effectively, as residual areas remaining 

after catchments for routed flowlines have been delineated. 

Such catchments are referred to as sinks. 

Bodies of water (waterbodies) such as lakes, ponds, reservoirs, and wide rivers also are represented as polygons; some 

are associated with flowlines traversing through the center of 

the polygon. Most waterbodies associated with flowlines are 

lakes; however, waterbodies classified as flowing streams also 

are common and can be interpreted as polygons representing 

the left and right banks of large rivers. Waterbodies classified 

as streams typically relate to wide rivers for which the banks 

of the river have been digitized as polygons. Waterbodies that 

straddle two NHDPlusV2 production regions in some cases 

are split into separate polygons along the regional boundaries.

Most routed flowlines are streams with catchments, 

although many represent artificial pathways through a waterbody. Flowlines routed along coastlines have navigational 

information and typically have associated catchments. 

Although coastal flowlines do not possess estimates of streamflow, the flowline simply represents the shoreline, a destination 

for runoff collected in the containing catchment. Most nonrouted flowlines represent canals or ditches for which flow 

direction was not determined.

Flowlines and waterbodies are uniquely referenced in 

NHDPlusV2.1 by an identifying code (ComID). The referencing variable for catchments is called the FeatureID. However, 

a FeatureID’s value also is the same value as the ComID of 

the flowline it contains. Catchments that are sinks have no 
4 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

Table 1. Primary spatial features of the NHDPlusV2 network, divided into sub-categories, with number of occurrences of each.

[*, There is a discrepancy of one flowline that has a catchment, but is not counted as a separate catchment. This is due to an erroneous duplication of a flowline 

in two regions.]

Number of 

occurrences

Number of 

occurrences

Routed flowlines 2,691,344 Waterbodies 465,249

With catchments 2,638,935 Attached to routed flowlines 132,900

With headwater area 940,413 Type:

In waterbody 359,660 Canal 259

Type: Ice 46

Artificial path 378,207 Lake 122,594

Canal/ditch 25,930 Playa 170

Connector 25,316 Reservoir 396

Coastline 23,590 Stream 6,510

Pipe 183 Swamp 2,481

Stream 2,238,112 Unspecified 444

Unspecified 6 Unattached to routed flowlines 332,349

Type:

Non-routed flowlines 302,180 Canal 254

Type: Ice 1,156

Artificial path 6,959 Lake 251,271

Canal/ditch 177,815 Playa 1,512

Connector 746 Reservoir 4,826

Coastline 3,102 Stream 429

Pipe 2,239 Swamp 63,835

Stream 111,317 Unspecified 9,066

Unspecified 2

Catchments* 2,647,454

Sinks 8,520

contained flowline. For these features, the FeatureID is set to a 

negative value in the range of -600,000 to -6,000. The ComIDs 

for flowlines and waterbodies are distinct, even in cases 

where a flowline is contained within a waterbody; however, 

in cases where a waterbody is split along regional boundaries, 

all the separate parts will have the same ComID. Flowlines 

have an additional referencing scheme called a Reachcode. 

Reachcodes have the advantage over ComIDs in that they are 

relatively stable across different versions of NHDPlus (the first 

eight digits of a Reachcode refer to hydrologic regions); however, a complication of using Reachcodes as a unique identifier is that multiple flowlines can have the same Reachcode, 

making associations of ancillary data to specific catchments 

and flowlines using a one-to-one relation difficult.

Flowlines and waterbodies also are referenced by their 

Geographic Names Information System (GNIS) (USGS, 

2017) name. These names are not unique, nor are they available for all features. GNIS names have been used to identify 

the primary pathway for flowlines with braided channels. 

Flowlines and waterbodies have an additional attribute coded 

that describes the flowline or waterbody physical type. There 

are many different “type” codes for these features, so they 

can be collapsed into broader subcategories as described in 

table 1. The coastline type identifies flowlines that represent 

flow routing for coastlines in bays, estuaries, the ocean, and 

the Great Lakes. These flowlines have navigational information and catchments, even though they do not pass flow along 

the shoreline. A small fraction of the catchments is classified as sinks, meaning they have no flowline contained in 

them. These are closed basins which contribute no flow to the 

navigable network. Waterbodies are mostly lakes, reservoirs, 

and swamps. 

The NHDPlusV2.1 data include a set of attributes useful for navigating upstream and downstream in the network. 

Network topology is defined by flowlines connected by 

nodes, which are the uniquely integer-numbered endpoints of 

flowlines that represent the points where flowlines originate 

and connect. The upstream end of the flowline is termed the 
Material and Methods 5

“From-node,” and the downstream end is the “To-node.” All 

flowlines immediately upstream from a given flowline will 

have a To-node equal to the given flowline’s From-node. It 

is useful to consider nodes as distinct features related to, but 

separate from, flowlines. 

The StartFlag and TermFlag network variables identify 

the most upstream and downstream flowlines in the network. 

A headwater flowline, which has a StartFlag of one, has a 

From-node value in which there is no other flowline in the 

network with a matching To-node value. Similarly, a terminal 

flowline, having TermFlag of one, has a To-node value that is 

unmatched by any other flowline’s From-node value. 

The HydroSeq variable is a unique sequence number that 

places each flowline in hydrologic order. It is a compact numbering system based on the node topology numbering system. 

At any flowline location, all upstream flowlines will have a 

higher hydrologic sequence number, and all downstream flowlines will have lower hydrologic sequence number. The final 

HydroSeq values are smallest at the downstream end of the 

network and largest at the upstream end of the network.

Diversions, locations in the stream network where a 

portion or all of the flow is diverted from the river’s natural 

course, are a common feature of an actual stream system and 

may have many causes and associated classes of features 

(table 2). The NHDPlusV2.1 network contains more than 

64,000 diversions. 

Diversions in NHDPLusV2.1 are defined by locations 

where more than one flowline has the same node as its Fromnode. Many of these divergences are local with the diverging 

paths reconnecting with one or two flowlines downstream. The 

NHDPlusV2 divergence code (Divergence) identifies which 

of the multiple downstream pathways is the primary pathway; 

a divergence code value of 0 indicates the flowline is the only 

flowline emanating from the upstream node, implying the 

flowline is on a primary path by default. A value of 1 indicates 

the flowline is the primary path emanating from diversion at 

the upstream node. A diversion code of 2 indicates the flowline 

emanates from a diversion but is on a secondary pathway. 

Generally, the primary pathway was assigned according to 

GNIS stream name or by determining the shortest pathway to 

the point where the diverged paths reconnect. Only one flowline at a diversion is coded as the primary pathway.

The fraction of flow exiting a From-node that is directed 

down a given flowline is estimated and termed the “diversion 

fraction.” The sum of diversion fractions across the diverted 

pathways equals one to account for all flow passing through 

the diversion. NHDPlusV2.1 estimated diversion fractions 

(DivFrac) for a handful of diversions; diversion fractions 

were primarily derived from streamflow monitoring station 

(streamgage) data. However, most of the diversion fractions 

were determined simply by setting the diversion fraction equal 

to 1 for the primary pathway and to 0 for all other pathways 

(Mckay and others, 2012). If a diversion did not exist, then a 

diversion fraction of 1 is accurate; however, given inaccuracies discovered in the primary pathway designation and the 

recognition that some flow will likely pass downstream along 

a secondary pathway, the potential for errors in apportioned 

flow through the correct pathway can be significant. 

Although some diversions involve secondary pathways that are isolated and appear to terminate in the ground 

Table 2. Classes of diversions in NHDPlusV2.1 and their defining characteristics.

Type of diversion Defining characteristic for routing

Braided channels/islands Can be important for flow uncertainty depending on monitoring station location; generally affects local 

routing only.

Natural diversions to ground Generally not important in affecting flow on main pathways and can be ignored.

Diversions in drainage ditches Not generally important unless main pathways become incorporated into field drainage pathways; difficult to assess because channels are not naturally formed and flow is intermittent.

Multiple outlets at lakes/reservoirs Can be important for flow accounting; operational control in reservoirs makes it difficult to assess 

mean routing of flow.

Irrigation diversions to ground Important in aggregate in the West; difficult to assess because the channels are not naturally formed 

and flow is seasonal.

Cross-basin transfers Important for flow accounting; many have gaging stations assigned.

Diversions in coastal areas Common but tidal effects make them difficult to assess in non-tidal areas.

Divides An uncommon feature where NHDPlus shows two flowlines connected across a divide, but the 

flowlines actually flow in opposite directions, tend to occur in headwater areas of low relief, and are 

typically associated with a marsh.

Diversions at stream confluences Common in low relief areas where small tributaries connect with larger streams; affects local routing 

only; frequent cause of incorrect flow direction and primary path routing.

“Oxbow” diversions Common in large rivers in flat relief; difficult to assess fraction diverted as width of feature a poor 

indicator of flow; typically occur in wooded areas making remote visual assessment difficult.
6 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

or have no outflow, most diversions reconnect to the main 

pathway downstream. The simplest such case is a diversion 

that forms two downstream pathways, only to reconnect at 

the next downstream node (fig. 1). The two diverted flowlines 

described here form what is called a diversion group. In this 

case, although flow in each of the flowlines of the diversion 

group is sensitive to the apportioning of flow at the diversion, 

the direction of flow in each flowline and the magnitude of 

flow leaving the group is unaffected by the diversion. 

A more complicated topological situation occurs when 

an additional tributary merges with the diverted flowlines. 

Although the direction of flow at the inflows and outflows 

to the diversion group is known, the locations of the additional diversions and the direction of flow in all the flowlines 

composing the larger group are ambiguous. For a group with a 

single outflow, the direction of flow for the group of flowlines 

that connect with the outflow must be the same as the outflow. However, the diversion can logically be placed at either 

inflow, and the flowline that connects the two inflows can flow 

in either direction. Moreover, the ambiguity associated with 

these topological groupings implies NHDPlusV2.1 cannot 

be certain exactly which inflow node of a group is the site of 

a diversion. The NHDPlusV2.1 algorithm used to define the 

primary path determined the direction of flow for the group as 

a whole, but as with the assignment of primary paths, the algorithm also can assign flow to travel in the wrong direction. The 

diversion-induced complications of flow apportioning and, 

with multiple inflows to the diversion group, flow direction are 

referred to collectively as a routing problem.

Diversions where one of the diverted pathways terminates in the ground, implying the diverted paths are not 

resolved downstream, are not likely to cause major problems 

Inflow

Outflow

Secondary 

path

Primary 

path

Location where

diverted flow

recombines

Location where diverted 

flow recombines 

Streamflow direction 

Diversion group and 

direction of flow

Diversion location

EXPLANATION

Figure 1. Example of how routing of 

NHDPlusV2.1 flowlines can be affected 

by an upstream diversion.
Material and Methods 7

for modeling. This is because the primary path is invariably 

the pathway that does not terminate, and the pathway that 

does terminate cannot be too large or a large closed waterbody 

would have formed. However, the latter feature can occur in 

areas where no flow escapes. If the primary pathway delivers flow to a coast, the flow that is lost to termination in the 

ground is likely to be small and can be ignored.

In contrast, diversions that create groups with multiple 

inflows can raise significant issues for modeling. Depending 

on the assumed flow direction and apportioning at diversions, 

the implied flow in any given flowline of these groups can 

vary greatly. If a streamgaging station is located anywhere 

along this group of flowlines, the assumptions incorporated 

into the network can affect the modeling of the flow or contaminant load at the station, resulting in large model residual 

errors. Accumulations also are sensitive to routing issues, 

making it difficult to determine the accuracy of stream conditions derived from a processing of upstream characteristics. 

NHDPlusV2.1 AR events identify flowlines where water 

is withdrawn or discharged and include an estimate of flow 

quantities for the addition (discharge) or removal (diversion). 

These quantities are used to modify streamflow estimated by 

routed runoff, or what might be called natural streamflow, thus 

converting natural streamflow into actual streamflow—the 

streamflow that is measured at streamgages. 

Disconnected and routed transfers are two types of AR 

events distinguished here. Disconnected withdrawal and 

discharge events, when paired, represent the NHDPlusV2.1 

flowlines that bracket a nonconservative use of water that may 

also alter the water’s quality. Routed transfers are assumed to 

conserve total flow and are not presumed to alter water quality. 

The disconnected withdrawal/discharge events were 

implemented in NHDPlusV2.1 by including the attribution of 

such events in the network attribute file—without alteration 

of the network. The events are further characterized according 

to their functional purpose, such as supporting public supply 

or irrigation, and whether the withdrawal or discharge could 

be associated with a complement discharge or withdrawal 

AR event. These designations can be useful in modeling to 

account for changes in water quality along the network and 

determining whether a withdrawal is a net withdrawal or a 

total withdrawal. In contrast, routed transfer events involve the 

creation of additional flowlines to support the routing of flow 

between two NHDPlusV2.1 flowlines that otherwise would 

not be connected. 

The NHDPlusV2 variable StreamLevel generates an 

ordering of network streams in the inverse, upstream direction. The determination of a flowline’s StreamLevel requires 

the definition of two additional variables: cumulative length 

and principal path. Cumulative length (also referred to as 

arbolate sum; McKay and others, 2012) is the sum of lengths 

of all upstream flowline segments, inclusive of the flowline’s 

incremental length. To simplify the concept at diversions, 

only the primary pathway flowline accumulates area from 

upstream segments; all secondary flowlines (diversions) have 

cumulative length reinitialized at the flowline’s incremental 

length, with no accumulation from upstream segments.

The principal path represents a priority pathway which 

is distinct from a major path. It is identified based on both 

upstream and downstream relations. The designation of principal path is assigned to the single flowline, among all conflating flowlines having the same To-node, that either possess the 

same GNIS stream name as that of the downstream primary 

path flowline or, if the stream names do not match, the flowline that has the longest cumulative length. If multiple flowlines have a matching GNIS name, then the flowline among 

these that has the longest cumulative length is designated as 

being on the principal path.

The NHDPlus data include physical properties of the 

spatial features, such as flowline length, catchment area, 

minimum elevation in a catchment, waterbody surface area, 

and waterbody elevation. Flowlines have additional attributes derived from the accumulation of physical properties: 

the accumulated length along the primary upstream pathway 

and the accumulated upstream drainage area (inclusive of 

the catchment area associated with the flowline). Headwater 

flowlines have special properties to describe the portion of the 

catchment that drains directly to the From-node: the area of 

that headwater node portion and the minimum elevation within 

that portion. The headwater node area is useful for interpolating area at an arbitrary point along a headwater stream 

segment. The “minimum area” in the headwater-node portion, 

referred to in NHDPlusV2 as the maximum raw elevation, is 

an estimate of the maximum elevation of the delineated headwater stream channel.

There are two concepts of upstream drainage area: diversion-apportioned and total upstream. Diversion-apportioned 

upstream area is determined by apportioning accumulated 

drainage area at a node to a downstream flowline, according to 

the flowline’s diversion fraction. This approach is akin to the 

way streamflow would accumulate. In contrast, total upstream 

area represents the total area of all catchments upstream from 

the given flowline, inclusive of the flowline’s catchment area. 

Owing to diversions, this area concept is much more difficult 

to compute; care is required to avoid the double counting of an 

area where diverted pathways reconnect.

In addition to navigational properties, many useful 

watershed (catchment) and stream (flowline) characteristics 

represented by ancillary gepspatial data are referenced to the 

NHDPlusV2.1 network either by index, or by intersection or 

overlay with catchment polygons, or by extensive processing 

of network data. Monitoring locations, monthly and annual 

mean precipitation, temperature, and runoff are examples of 

indexing or spatial intersections and overlays, and channel 

elevation, slope, and monthly and annual mean estimates of 

flow and stream velocity are representative of extensive processing (McKay and others, 2012).
8 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

Constructing a Modified Network

The enhancement and transformation of NHDPlusV2.1 

(McKay and others, 2012) into the E2NHDPlusV2_us 

(Schwarz, 2019) network was undertaken in a series 

of intermediate steps. A national-level dataset was first 

created from the regional production units that compose 

the NHDPlusV2.1 data tables (McKay and others, 2012). 

Multiple data files from the NHDPlusV2 repository arranged 

and distributed by 21 production units nationally (McKay and 

others, 2012) (fig. 2) were downloaded and decompressed 

into the native file structure. Each record in the newly created 

database refers to a unique ComID with flowline, catchment, 

waterbody observations, and VAAs all joined into a single 

national file. This preliminary national network was then 

processed to fix discovered errors in routing and to fill in 

missing values. No cartographic alterations were made to the 

original NHDPlusv2 flowline line work. The result was the 

dataset ENHDplusV2_us (Schwarz and Wieczorek, 2018). 

The next phase of enhancement used ENHDPlusV2_us 

as a foundation and incorporated procedures that modified the 

network routing capabilities, including the enhancement of 

addition/removal events and the assignment of more precise 

diversion fractions to flowlines, described in the “Routing 

Modifications” section of this report. New variables were 

created that describe the extent to which streamflow can be 

affected by drainage from outside the United States borders 

(foreign) and by upstream diversions, and maintain an ability 

to distinguish between flowline, catchment, and waterbody 

features (table 3). Selected variable names and content also 

were modified to conform with the revised routing structure 

and to provide more descriptive names. Node identification numbers used to define the topological relation between 

flowlines were recomputed into a compact numerical set of 

numbers in order to expedite and facilitate procedures used to 

navigate the network. Variables not listed as being renamed (in 

table 3) were retained with the original NHDPlusV2 names. 

These enhancements also included the generation of channel slope and flow using modified methods from the original 

NHDPlusV2.1 methodology. Although not strictly part of the 

process required to generate E2NHDPlusV2_us, a revised 

hydrologic conditioning of an extensive set of ancillary variables useful in empirical modeling and other hydrologic analyses were additionally generated (Wieczorek and others, 2018, 

Wieczorek and others, 2019) that can be used in conjunction 

with this network.

Filling In Missing Values

The SPARROW model infrastructure requires that every 

observation in the network used to simulate the routing of 

water, and (or) the parameters (variables) used for the assessment, possess non-missing values (Schwarz and others, 2006). 

However, not every observation in NHDPlusV2.1 contained a 

value (inherent or additional) for each variable, depending on 

the origin of the variable and (or) the method of association. 

Some variables for certain observations were missing values 

simply because the values were not known. For example, not 

every stream or waterbody had an associated name. For some 

observations, flowlines with missing values did not have the 

proper networking connections to logically pass information 

up or downstream, preventing the ability to make educated 

assumptions of what the missing value might be. Additionally, 

the process used to populate original NHDPlusV2.1 flowlines 

with ancillary attribute data like precipitation intersected data 

values represented by a geospatial data layer with the catchment polygons. However, as noted earlier, some flowlines 

did not have an associated catchment because they were too 

short in length to be estimated. For these flowlines, a missing 

precipitation value would have resulted because a value could 

not have been computed from the ancillary spatial dataset. 

Lastly, flowlines may have been assigned missing value placeholders because the catchment (or part of it) lies outside the 

spatial extent of the ancillary spatial dataset being referenced 

or assigned. Depending on the nature of the variable, the lack 

of information inherent from the original NHDPlusV2 or inadequate ancillary information associated with the network could 

affect certain modeling applications. 

Missing-value placeholders used in NHDPlusV2.1 

signify that the attribute value was missing or undetermined 

(-9998) or that there was no applicable value was assigned 

(-9999). In cases where replacing a missing-value placeholder 

would be appropriate, a simple substitution was used to assign 

values. If the reason for a missing-value code was specific, a 

condition was deduced and applied for assigning a new value 

(Schwarz and Wieczorek, 2018). For example, the diversion 

fraction variable (DivFrac) was unknown for almost all 

flowlines; however, a simple condition for assigning a new 

value was to set DivFrac equal to 1 if the divergence code 

pertained to a primary path (the Divergence variable takes a 

value of 0 or 1) and to DivFrac equal to 0 for secondary paths 

(Divergence code of 2). 

For most variables, the fill-in process was based on 

non-missing values from neighboring flowlines. To apply the 

process, it was necessary to distinguish variables as two types: 

intensive variables and extensive variables. Intensive variables 

are values for which the metric does not scale with area. For 

example, temperature is measured in degrees, and precipitation is in decimeters (millimeter × 100) per year; neither 

metric is scaled with area. Extensive variables, in contrast, 

have units that tend to scale with the area of the catchment 

that they describe. An example of an extensive variable would 

be incremental catchment flow, measured in cubic feet per 

second, which is designated by cfs. 

There were two approaches to filling-in missing data 

values using neighboring non-missing values (Schwarz, 2019). 

The default method, called the interpolation fill-in method, 

passed non-missing values from flowlines immediately 

downstream or upstream from the flowline containing the 

missing data value. The default method first operated in the 

upstream direction. If the variable in question was an intensive 

variable, a catchment-area weighted average of the variable 
Material and Methods 9 Figure 2. Map showing NHDPlusV2 production units.
10 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

Table 3. Revised names given to NHDPlusV2 variables.

[--, denotes name was not revised]

NHDPlus variable name Revised name for 

flowline features

Revised name for 

catchment features

Revised name for waterbody/

nhdarea features

ComID FL_ComID -- WB_ComID

WBAreaComI -- -- WB_ComID

GNIS_ID FL_GNIS_ID -- WB_GNIS_ID

GNIS_Name FL_GNIS_Name -- WB_GNIS_Name

Ftype FL_FTypeAB -- WB_FType

Fcode FL_Fcode -- WB_FCode

TerminalFl TermFlag -- --

ArbolateSu TotLngthKm -- --

AreaSqKm IncAreaKm2 CatAreaKm2 WB_AreaKm2

DivDASqKm CumAreaKm2 -- --

TotDASqKM TotAreaKm2 -- --

MaxElevRaw MaxElRawCm -- --

MinElevRaw MinElRawCm -- --

MaxElevSmo MaxElSmoCm -- --

MinElevSmo MinElSmoCm -- --

HwNodeSqKm HWNodeAKm2 -- --

FeatureID -- FL_ComID --

Q0001E MAFlowUcfs -- --

V0001E MAVelUfps -- --

Qincr0001E IncFlwUcfs -- --

Elevation -- -- WB_ElevaFt

for all flowlines having the same From-node as the upstream 

flowline’s downstream To-node was passed to the downstream 

flowline and became the fill-in value for that flowline. In computing the area-weighted average, if a flowline had a value but 

no catchment area, then the weight for that value was determined assuming the flowline had an area of 0.00001 square 

kilometers, designated by km2, (approximately one-hundredth 

the area of the smallest catchment with non-zero area in 

NHDPlusV2.1). For extensive variables, the sum of the values 

across the downstream flowlines was first normalized by the 

sum of the downstream flowlines’ catchment areas. The normalized value was then passed upstream, where it was extrapolated by multiplying by the catchment area of the flowline 

with the missing value. If the missing value was in a flowline 

with no catchment, in which case catchment area was zero, the 

missing value was set to zero, regardless of the downstream 

value. If there were successive flowlines with missing values, 

the default process passed the initiating downstream flowline’s non-missing value, or normalized value, to each of the 

successive flowlines, with subsequent expansion according to 

catchment area if the variable is extensive.

The default process was continued throughout the network to fill in as many missing values as possible. Thereafter, 

if missing values remained, the default process was applied 

going in the downstream direction, passing to the downstream 

flowline possessing a missing value the catchment-area 

weighted average or the normalized sum determined for all 

flowlines having the same To-node as the downstream flowline’s From-node. Performing the default process in the downstream direction could be effective in cases where the missing 

value was caused by alignment issues, most likely in coastal 

areas where the variable’s spatial coverage did not fully extend 

over the catchment coverage.

If missing values remained after application of the 

interpolation fill-in process, then the method switched to 

obtaining neighboring values from flowlines using an extrapolation method in the same spatial region as the missing-value 

flowline. In the case of intensive variables, the fill-in value 

was the area-weighted average of all non-missing values in the 

region; in the case of the extensive variables, the fill-in value 

was the product of the missing-value flowline’s area and the 

sum of the non-missing values in the same region, normalized 

by the sum of the catchment areas for the associated nonmissing value catchments. The method initially attempted to 

fill in values using the region defined by the 8-digit hydrologic 

cataloging unit (HUC), which was defined for each flowline 
Material and Methods 11

Table 4. Summary of modification method codes for NHDPlusV2 

variables. 

[Method code of 1 = simple substitution rule; 0 = flowline upstream or 

downstream fill-in; 8 = fill-in via area-weighted average of 8-digit cataloging 

unit]

Variable Method code Count

CumAreaKm2 1 9,243

TotAreaKm2 1 9,243

CatAreaKm2 1 52,410

HWNodeAKm2 1 1,750,931

Divfrac 1 2,691,310

Slope 1 47,812

IfSink 1 684,080

MAVelUfps 1 23,574

WB 1 188,446

StartFlag 1 3,197

TermFlag 1 3,197

StreamOrde 1 1

StreamLeve 1 1

LevelPathI 1 1

MinElRawCm 0 73,611

MinElRawCm 8 1,963

MaxElSmoCm 0 3,412

MaxElSmoCm 8 757

MinElSmoCm 0 3,412

MinElSmoCm 8 757

PrecipV 0 50,963

PrecipV 8 1,447

TempV 0 50,963

TempV 8 1,447

RunOffV 0 50,963

RunOffV 8 1,447

by the first eight characters of the assigned ReachCode. If 

the 8-digit region contained only missing values, then the 

method switched to the 6-digit HUC region. Sinks missing a ReachCode assignment necessitated the determination 

of these flowlines by use of geographic information system 

(GIS) software to intersect catchments with the 8-digit HUCs 

(Steves and Nebert, 1994) and setting the first eight characters 

of Reachcode to the HUC that includes the largest share of the 

catchment (table 4).

Routing Modifications

It was not feasible to assess all possible routing assumptions (corrections, substitutions, and modifications) or errors 

that may occur that could affect proper routing in the assigned 

primary path and (or) in the assumed direction of flow. A 

priority was established to focus on flowlines identified in 

NHDPlusV2.1 (McKay and others, 2012) that contained 

streamgages where the diversion-apportioned drainage area 

of the flowline differed greatly from the streamgage drainage 

area, as reported in the USGS National Water Information 

System. Additionally, the correlation between drainage areas 

of all flowlines affected by an upstream diversion (diversion 

group) with flowlines having a streamgage (Stewart and others, 2006) helped to identify other diversion groups warranting 

investigation for routing accuracy, regardless of the drainage 

area inconsistencies identified. Flowlines of lesser priority 

for investigation were those that were sensitive to diversion 

assumptions defined by the primary path identifier that carried 

a significant amount of flow through a complex set of alternative flowpaths. Figure 3 depicts a case in Michigan where 

routing assumptions affected the assumed flow. The original 

NHDPlusV2 routed the main source of flow from the Farmers Creek area (A) south and entering Lake Lapeer (B). The 

problem was a diversion assumed to be at a point along Farmers Creek (A), identifying a minor tributary as the only source 

of flow to streamgage 04146000. To rectify the routing for this 

example, the direction of flow was reversed (switched Fromnode and To-node values of flowlines) between locations (A) 

and (B). With this change, flow was routed north through 

Farmers Creek (A) and through the flowline, which included 

the streamgage.

Edits to the network, specifically edits to the flow direction, resulted in inconsistencies in the hydrologic sequence 

number which need to be recomputed to maintain hydrologic 

order. Although changes in hydrologic order may not have 

been necessary in simple edits, for situations involving the 

change of direction of multiple flowlines, a change in hydrologic ordering was always required. 

A multi-step manual process was used to validate flowline routing and diversion fractions. The process began by 

identifying the latitude and longitude of a target diversion and 

using this location to acquire remote imagery through Google 

EarthTM. The map imagery was enhanced with an overlay of 

USGS topographic maps, which provided feature names and 

topography helpful in assessing flow direction. In cases where 

the imagery allowed the identification of stream ripples, flow 

direction was verified visually. An attempt was made to apportion flow at diversions. Stewart and others (2006) include 

estimates of mean flow and the period for which that mean is 

determined. Upon inspection, if all paths emanating from a 

target diversion had a streamgage, and if mean flow was available for a period of no less than 2,000 days, then the diversion 

fraction for the diverting flowlines was based on these flows. 

Streamgages were rarely located on each of the diverted 

paths; therefore, visual information from Google EarthTM was 

then used to obtain a more comprehensive assessment of flow 

apportionment. By comparing channel width at the narrowest 

points downstream from the diversions, it was then possible 

to obtain a rough estimate of the fraction of flow diverted to 
12 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

Lake Lapeer

Farmers Creek

04146000

Assumed

diversion

B

A

0 0.75 1.5 2.25 3 MILES

0 1 2 3 4 KILOMETERS

Base from U.S. Environmental Protection Agency and U.S. Geological Survey 1:100,000

National Hydrography Data Plus version 2 (NHDPlusV2)

43°3'

43°

42°57'

−83°24' −83°21' −83°18' −83°15' −83°12'

EXPLANATION

Reversed direction of flow 

U.S. Geological Survey

streamgage and identifier 

Diversions

Flowline direction of flow 

Water bodies

04146000

Figure 3. Example of routing errors associated with diversions affecting measured streamflow at a U.S. Geological Survey 

streamgage in Michigan. (A, Farmers Creek area; B, Lake Lapeer area)

each pathway, the fraction being computed by the ratio of 

the pathway width to the sum of pathway widths across all 

diverted pathways. Comparisons of diversion fractions based 

on this method with mean flows at streamgage locations 

showed reasonable correlation; however, no comparisons were 

performed on flowlines classified as canals. In some cases, it 

was concluded that the primary pathway needed to be altered, 

but it was not possible to assess the amount of flow allotted 

to each diversion pathway. In these cases, the explicit coding of the diversion fraction equal to one implies the primary 

pathway was confirmed and that all upstream flow was routed 

through the primary pathway. 

The methods for estimating diversion fractions using 

imagery was sensitive to the flow conditions at the time the 

imagery was generated. To reduce uncertainty, historical 

imagery was scanned to ensure conditions were not overly 

dry or wet. Historical imagery also was useful for obtaining 

“leaf off” scenes to assess channel width at locations that were 

not visible in warmer months owing to the presence of thick 

forest canopy. 

The results of diversion assessments were recorded 

with the following items: the ComID, modified value of the 

diversion code (0 for no diversion, 1 for primary pathway, 

and 2 for secondary pathway); the estimated diversion 

fraction for each pathway, a 0/1 code to identify flowlines 

requiring a reversal of flow direction; the revised value of 

the hydro-sequence number; and a comment (Schwarz, 

2019). The comment column describes noted problems in 

the routing that may not be possible to fix, like an extensive 

modification of hydro-sequence numbering, or the existing 

line work in NHDPlusV2.1 that did not properly describe 

the actual channels. If streamgages were used to determine 

divergence fraction, the station identifiers were recorded in the 

comments column.

The number of diversion-related modifications applied 

by NHDPlus region and the totals for the Nation are listed 

in table 5. The values reported for modifications to diversion codes and fractions pertain to distinct diversions, which 

generally consist of multiple flowlines. The values reported 

for modified flow direction pertain to the number of individual 

flowlines with a reversed direction, multiples of which could 

be associated with a contiguous set of reversed flowlines.
Material and Methods 13

Table 5. Diversion-related modifications to NHDPlusV2. 

[Numbers for modified diversion code and fraction pertain to distinct diversions consisting of multiple flowlines; values for modified flow direction 

pertain to individual flowlines and do not include flowlines requiring a 

modification of the hydro-sequence attribute]

NHDPlus Region

Modified 

diversion 

code

Modified 

diversion 

fraction

Modified 

flow 

direction

01 41 37 23

02 14 41 2

03N 47 85 16

03S 20 43 1

03W 14 23 8

04 171 252 82

05 77 136 22

06 62 106 23

07 736 1,309 267

08 19 12 5

09 270 378 93

10L 43 57 2

10U 18 42 0

11 39 38 12

12 16 17 3

13 3 4 0

14 10 22 0

15 7 17 0

16 13 34 1

17 22 61 3

18 47 95 4

Total 1,689 2,809 567

Addition/Removal Events

Addition/Removal (AR) events are identified at locations 

along the E2NHDPlusV2_us network where flow is gained 

or lost due to anthropogenic changes to natural streams, 

specifically the addition or removal/diversion of water from 

the streams. These alterations can have multiple purposes: 

move water across basin divides to improve its economic or 

beneficial value; support specific economic activity, such as 

public supply or irrigation; or support navigable waterways 

like canals. Modifications to routing and (or) the attribution of 

network variables support the navigation through the network 

and account for the AR events; however, the AR events do not 

represent modifications of water quality nor quantity between 

the withdrawal and discharge locations. 

AR events were incorporated by adding virtual flowlines 

to the network, along with appropriate modifications of the 

navigation variables (the From- and To-nodes, hydrologic 

sequence numbers, and diversion fractions), to support the 

additional flowlines. AR routed transfers were assumed to 

attach to the network at the upstream node of the flowline 

to which they are referenced (fig. 4A). A simple routed 

transfer consists of at least two “pseudo” flowlines: a flowline 

that connects to the network at the withdrawal node and a 

flowline that connects to the network at the discharge node. 

This framework allows for additional pseudo flowlines to 

be included to account for diversions (fig. 4B), whereby a 

given withdrawal was transferred to multiple locations on the 

network. Pseudo flowlines were assigned COMIDs in a special 

range (less than -90000000) to distinguish them in the network 

from standard flowlines. Pseudo flowlines also were assigned 

a length of zero, the implication being that no instream losses 

could occur on the pseudo portion of the revised network, 

which would be consistent with the assumed properties of 

routed transfers. Multiple pseudo flowlines were permitted 

to merge either at a common node of the pseudo network or 

as discharge to a common node on the NHDPlusV2 network. 

Disconnected AR withdrawal/discharge events (including 

non-routed transfers) were assumed to connect to the specific 

flowline they reference.

Only one AR withdrawal/discharge event was assigned 

to a given flowline. If there were multiple withdrawals or 

discharges associated with the same flowline, they were incorporated only as a single combined event. This convention was 

not greatly restrictive if the multiple withdrawals/discharges 

were similar in nature, for example, summing the withdrawals from multiple public-supply facilities. The convention 

was more restrictive if the multiple withdrawals/discharges 

were dissimilar. In the latter case, it was necessary to spread 

the dissimilar withdrawals/discharges over multiple neighboring flowlines in the network, sacrificing the strict locational 

accuracy of the events.

Multiple sources of information were used to define and 

characterize AR events classes. The principal requirements 

for inclusion were that the event had a measure of mean flow 

withdrawn or discharged and that the event was indexed to 

NHDPlusV2.1. A principal source of flow information was 

streamgage records; many irrigation districts and transfers in 

the U.S. West have monitoring at the inflows and outflows to 

facilitate water-rights accounting (Idaho Department of Water 

Resources, http://idwr.idaho.gov/water-rights/overview.html). 

Additionally, public-supply facilities typically disseminate 

estimates of water withdrawals and discharges online as part 

of their efforts to inform the public of their activities.

Location information for AR events was available from 

numerous sources. A component of the regional files composing the NHDPlusV2 data consists of listings of AR events 

deemed important for flow accounting in the network; these 

data were directly incorporated into the determination of flow 

estimates provided with the original NHDPlusV2 data. The 

AR event classes included routed interbasin transfers, prominent withdrawals and discharges associated with public supply 

in the U.S. East and several events in the U.S. West associated 

with irrigation (McKay and others, 2012) (fig. 5).
14 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

NHDPlus

upstream

connection

NHDPlus

downstream

connection

Simple 

routed

transfer

Routed

transfer

with a 

diversion

Addition /

R emoval 

start

A B

Addition /

R emoval 

end

Figure 4. A, A simple routed transfer and B, a routed transfer with a diversion.

An additional source of AR events is the pair of USGS 

publications documenting major interbasin transfers in the 

conterminous United States, one for the West (Petsch, 1985) 

and one for the East (Mooty and Jeffcoat, 1986). These publications identify prominent routed transfers for various purposes, some of which duplicate those in the NHDPlusV2 AR 

event tables. The events from publications do not link directly 

to the NHDPlusV2 network and require additional locational 

research either by finding them visually in Google EarthTM and 

translating the location to NHDPlusV2 or by finding an undirected flowline in NHDPlusV2 that corresponds to the transfer. 

A useful feature of these events is that they include estimates 

of mean flow.

AR events modified in E2NHDPlusV2_us were specified 

using a spreadsheet (appendix 1; Schwarz, 2019), which was 

processed to incorporate the events into the enhanced network. 

The incorporation of AR events into the enhanced network 

led to the creation of pseudo flowlines and associated navigational information—nodes, diversion fractions, for example—

to affect the routed transfer. Also created were numerous 

additional variables describing the events, including the source 

region of the event; the quantity of flow associated with the 

withdrawal, transfer, or discharge event; the purpose or type of 

event in terms of public supply, irrigation, or transfer; a code 

that signifies the event as either a disconnected withdrawal, a 

disconnected discharge, or routed transfer; and the diversion 

fraction associated with a withdrawal or routed transfer. The 

incorporated routed-transfer AR events were tested, accumulating flow and area to insure the topological integrity of the 

modified network. Should the opportunity arise to incorporate 

revisions into the network to account for additional AR events 

or other routing modifications, previous AR routed-transfer 

events should not be reapplied; doing so would duplicate 

the virtual flowlines created to implement these transfers. If 

the existing routed transfers need modification such as for 

additional diversions or confluences among the existing virtual 

flowlines or to change the location of withdrawal or discharge, 

then it will be necessary to rebuild the network in its entirety, 

beginning with the compilation of the NHDPlusV2 data. 
Material and Methods 15

EXPLANATION

Addition/Removal 

events, by class

Routed transfer

Withdrawal

Discharge

Figure 5. Location of Addition/Removal events included in the enhanced hydrologic stream network, by event class.

New Variables for Interpreting Network Flow 

Characteristics

Three groups of additional variables useful for interpreting results were derived from network applications related to 

flow characteristics. The first group (Flowline Termination) 

identifies flowlines terminating at foreign borders, shorelines, 

or in the ground. The second group of variables (Foreign 

Drainage) describes the area of contributing foreign drainage 

in each catchment and the cumulative upstream basin areas of 

each flowline. The last group of variables (Diversion-Affected 

Flowlines) identifies flowlines having basin aggregates that 

can be sensitive to the assumed flow apportionment made to 

upstream diversions. 

Flowline Termination

The variable StreamLevel can help to identify and 

distinguish terminating flowlines at the coast from other 

terminations. However, StreamLevel did not differentiate 

between termination to ground, so-called internal termination, 

or termination at a foreign border, called external termination. 

It was not possible to visually identify external terminating 

flowlines from the geospatial dataset because several apparent 

terminating flowlines at U.S. borders were topologically 

connected via common nodes to other flowlines that 

reenter the United States at different locations. However, 

the identification of exterior terminating flowlines was 

straightforward given the recognition that all such flowlines 

must be flowlines with To-nodes that have no matching 

From-node, and the To-node must be located near a linear 

U.S. external border, of which there were only10—5 near 

Canada and 5 near Mexico. If the latitude and longitude of the 

To-node for a terminating flowline was within a 0.011-degree 

buffer of one of these linear foreign borders, the borders being 

described by simple linear equations of latitude and longitude 

coordinates, then the flowline was classified as having an 

external termination, and the variable IfExtTerm was set 

to a value of 1. A companion variable, IfIntTerm, identifies 

flowlines that have a termination in the interior of the network, 

and IfIntTerm was assigned similar to IfExtTerm, except that 

it takes a value of 1 if the non-coastline terminating flowline 

also is not an external terminating flowline. 
16 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

Foreign Drainage

Three derivative attributes were created to describe 

the effect of foreign drainage on the conditions of a given 

flowline. They include (1) the amount of foreign area in the 

incremental catchment (IncUSArKm2), (2) the cumulative 

amount of foreign drainage at and upstream from the flowline 

(CumUSArKm2), and (3) the percent of upstream drainage 

that is foreign (PctForgnAr). This information is useful if 

the network is used with models that are based on input 

data that are only available domestically, in which case the 

characteristics for flowlines having substantial amounts of 

foreign drainage need to be closely inspected. 

The incremental foreign drainage was determined 

by intersecting the NHDPlusV2.1 catchment layer, which 

includes foreign drainage, with the 2011 National Land Classification Dataset (NLCD; Homer and others, 2015), which is 

confined to U.S. borders. Areas within both the NHDPlusV2.1 

incremental catchment and NLCD were defined as incremental 

domestic drainage. The cumulative upstream domestic drainage area was determined by accumulating the incremental 

domestic area downstream using the 0/1 diversion fraction 

implied by the primary path diversion classification (Divergence code) to apportion accumulated area to the diverting 

downstream flowlines. The variable Percent foreign area was 

determined by differencing the cumulative upstream domestic 

area from the diversion-apportioned cumulative drainage area 

(the CumAreaKm2 variable), normalizing by the cumulative 

drainage area and converting to percent. 

Diversion-Affected Flowlines

Another group of new variables was created to assist in 

identifying flowlines affected by upstream diversions. These 

variables were useful for indicating which flowlines have 

greater uncertainty in cumulative properties owing to the 

potential error associated with assigning an upstream diversion fraction. This uncertainty also could be elevated to an 

extreme level if there are multiple upstream diversions that 

remain unresolved, perhaps as a result of a braiding pattern. 

These added variables provide information on the presence 

of a diversion effect and the level of uncertainty upstream 

diversions could induce in accumulative properties. This is 

critical information, given that the routing of approximately 

2.5 percent of the flowlines in the dataset can be affected by 

diversions. As more information on diversion fractions is 

included in the network, the uncertainty caused by diversions 

should attenuate. Schwarz (2019) contains variable names and 

descriptions identifying diversion-affected flowlines. 

Revised Variables

The routing changes induced by diversion and 

flow direction modifications, and the inclusion of AR 

routed-transfer events, necessitate revisions to several 

E2NHDPlusV2_us variables in order to maintain consistency 

with the modified routing. 

Hydro-sequence

As part of the network revision, particularly the operations of reversing flow direction of selected flowlines and 

the addition of virtual flowlines to effect AR routed-transfer 

events, the hydro-sequence variable (HYDSEQ) used to order 

the network was revised. Because routing changes may affect 

hydrologic sequencing across multiple regions, new sequence 

numbers were computed for the entire network simultaneously, rather than regionally as was done for the original 

NHDPlusV2 network. The result was unique sequence numbers in hydrologic order for the entire dataset.

Streamflow

The regeneration of streamflow follows much of the 

Enhanced Unit Runoff Method (EROM) described in the 

NHDPlusV2 documentation (McKay and others, 2012) 

but was modified to correct some logical inconsistencies in 

the conditioning of flow from streamgage measurements 

(appendix 2). In the application of the EROM method to the 

revised network, the entire network was processed simultaneously, making it necessary to remove the boundary condition flow component from the incremental catchment flows 

at the boundary locations. The EROM method also added or 

subtracted flow quantities based on AR events. The revised 

network incorporated a number of AR events that were not 

included in the original NHDPlusV2, in addition to revised 

quantities of many events that were common to both datasets.

Slope

A method used to compute channel slope for each 

flowline is described in appendix 3. This method is similar to 

the one used to generate slope estimates for NHDPlusV2.1 

(McKay and others, 2012). The revised method expanded the 

search for set points (the smallest elevation among all pathways converging at one node) beyond a given path to include 

all nodes upstream from the current set point. The revised 

method includes evaluating for potential diversions.

Additional Ancillary Information

The NHDPlusV2.1 flowlines and catchments, and 

subsequently the enhanced network, (E2NHDPlusV2_us) 

provide unique frameworks for additional spatially referenced attributes suited for relative modeling and assessment 

applications. By associating additional characteristics, 

models like SPARROW can be used to empirically assess 

statistical correlations between stream-monitoring and specific 

contaminant sources, watershed characteristics, and transport 

processes at regional and national scales (Brakebill and others, 

2011; Robertson and Saad, 2011; Preston and others, 2009; 

Rowe and others, 2010). 

Many additional ancillary datasets have been acquired or 

developed as separate geospatial data layers, either as points, 

polygons, or a cell-based rasters representing a wide variety of 
Material and Methods 17

landscape, subsurface, or hydrologic information and characteristics (Wieczorek and others, 2018; Wieczorek and others, 

2019; Schwarz, 2019; McKay and others, 2012; Saad and others, 2018; Skinner and Wise, 2019). Values representing these 

characteristics were assigned to the catchments or the flowlines of NHDPlusV2.1 networks using standard GIS intersection and overlay techniques. Examples include streamflow and 

water-quality monitoring locations, municipal point sources, 

dam locations, land use, atmospheric deposition, natural 

features like lithology, and commercial fertilizer and manure 

applications. Examples of land surface and subsurface transport characteristics include soil properties, slope, precipitation, 

and temperature. Instream transport processes include streamflow and water-chemistry monitoring locations, estimates of 

mean-annual streamflow and water velocity, stream length, 

mean-annual water travel time, reservoir surface area, and 

stream-reach type. In addition to flowline networking features 

and some instream processes, ancillary streamflow estimates 

and some landscape and climate information native to the 

NHDPlusV2 suite of data were updated or used (McKay and 

others, 2012; Schwarz and Schwarz, 2019). These data were 

applied at various stages of SPARROW model development 

and exploration. Appendix 4 of this report contains a comprehensive list of variables developed for E2NHDPlusV2_us. 

Appendix 5 lists selected ancillary geospatial dataset variables 

assigned to the catchments of and flowlines of NHDPlusV2.1 

(Wieczorek and others, 2019). 

Automated methods of transference or association of 

these additional ancillary geospatial data to the network by 

either GIS intersection or overlay operation created challenges 

in obtaining a complete set of ancillary values for every 

flowline in the network, a requirement for the SPARROW 

infrastructure. In some cases, the spatial extent of many 

ancillary geospatial datasets represented only areas within 

the U.S. border, creating areas of missing information within 

some catchments. Differences in spatial resolutions between 

the network and ancillary data also created inconsistencies 

between the coastline boundaries of catchments and the 

coastline extent of the ancillary variable when overlaying or 

intersecting polygons or rasters. In cases where the coverage 

of ancillary information did not fully overlay the NHDPlus 

catchments, the transfer of values to the network was 

incomplete, creating areas of missing data. Missing values 

also arose when a flowline was too small to allow for the 

generation of a catchment, making it impossible to transfer 

area-based ancillary data. Some geospatial data were missing 

data qualifiers for open-water areas. Methods for estimating 

missing values are explained in the “Fill-in Missing Values” 

section of this report. 

Aggregations

An aggregation of ancillary variables associated with the 

network allows for assessments, including total upstream characteristics and contributions, to occur at any location along the 

network. A secondary purpose of the automated procedures 

was to navigate the network and perform aggregations of the 

ancillary variables. Aggregation always operated in a downstream direction, so that the aggregated values pertain to an 

aggregation of all flowlines upstream and inclusive of any 

given flowline. 

Four types of aggregations can be performed on sets 

of associated ancillary data (Schwarz and Wieczorek, 2018; 

Wieczorek and others, 2019): accumulation, area-average, 

minimum, and maximum. Variables classified as accumulation variables can be aggregated by accumulating the values; 

variables classified as averages can be aggregated by simple 

area-average values. Variables for which an upstream minimum or maximum could be computed can be treated as average variables for purposes of the fill-in procedures described 

in section “Filling In Missing Data Values.”

Two methods were used to determine accumulation and 

area average. The default method used the NHDPlus diversion 

fractions (either the coded values or the default 0/1 values 

assigned according to the primary path at a diversion) to 

allocate a cumulation along diverted paths at a diversion. 

For accumulation variables, this is a simple apportion of the 

cumulated values at a diversion. For average variables, the 

incremental value of the variable would first be multiplied 

by incremental catchment area. This product would then be 

accumulated and apportioned at diversions. To recover an 

area-weighted average, the variable’s accumulated product 

would be divided by its diversion-apportioned accumulated 

incremental catchment area.

The second method of accumulation/area-averaging computed a total aggregation, representing an aggregation of all 

values of the variable upstream and inclusive of the flowline. 

This method did not apportion at diversions but was implemented in such a way that values were never double counted 

because they appear in a diverted path. As with the standard, 

diversion-apportioned aggregation, an area average would 

be undertaken by forming the product of the variable and its 

incremental area, performing separate total accumulations of 

the product and the incremental catchment area, and recovering the area average by dividing the total accumulated value of 

the product by the total accumulated catchment area. 

The estimation of minimum and maximum aggregations was more straightforward. The algorithm descended 

the network while recording the maximum (and minimum) 

of the values stored in the flowline’s upstream node and the 

incremental flowline value. If the upstream node had no value, 

the flowline’s value would then become the default maximum 

(and minimum). The flowline’s maximum (and minimum) 

value would be passed to the downstream node only if the 

value was greater (or less) than the value recorded in the node. 

If the downstream node was not assigned a value, the flowline 

value of the maximum (and minimum) would then be automatically passed to the node.

Last, results were visually inspected and verified. The 

focus was on areas of the network with braided streams, as 

well as complex areas at various positions within the stream 

network to ensure accuracy. 
18 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

Validation

A series of checks was performed to ensure the logical 

consistency of the modifications such as ensuring only one 

flowline at a diversion was coded as the primary pathway, 

diversion fractions across the pathways summed to one, and 

the flow connectivity of the network was not compromised by 

the altering flow direction (the process of flipping the Fromand To-nodes) after reordering the network with the revised 

hydro-sequence numbers.

Changes and modifications to the routing network rendered some attribute values obsolete, particularly variables 

dependent on the assigned primary pathway, such as channel 

slopes, hydrologic sequencing, and variables pertaining to 

aggregations or accumulations of incremental (local) segment 

or catchment values. 

Most modifications to the routing network were performed in region 07 specifically and the Midwest generally (table 5). Few modifications were applied in the more 

semi-arid regions of the United States. The numbers reported 

for modified diversion fraction include cases where there is 

confirmation of the original NHDPlusV2 assessment of the 

primary path. Most of the modifications reported in the table, 

particularly for regions 04, 05, 06, 07 and 09, were not predicated on an observed problem at streamgages. Thus, the ratio 

of the number of modified diversion codes to the number of 

modified diversion fraction, 60 percent, is an approximate estimate of the error rate for NHDPlusV2 diversion assignment. 

This ratio, estimated nationally at 61 percent, indicates that 

the algorithms used to assign diversion codes in the original 

NHDPlusV2 are incorrect and revisions to the method should 

be considered. 

The modification process performed revisions on several 

NHDPlus variables so that they remain consistent with the 

revised routing. Changes in routing imply diversion-allotted 

cumulative variables, such as drainage area (CumAreaKm2) 

and streamflow (MAFlowUcfs or Q0001E from the EROM 

table), must be recomputed from incremental flowline values. 

Additionally, the computationally challenging cumulative 

variables expressed as upstream totals (TotAreaKm2 and 

TotLngthKm [Arbolatesu]) must be recomputed. Other 

NHDPlus attributes that potentially affected by network 

modification are StartFlag, TermFlag, TerminalPa, 

StreamCalc, StreamOrde, StreamLeve, and LevelPathI (See 

“Methods and Material” and appendix 4 for descriptions). 

These variables were regenerated to be in accordance with 

the revised network. The methods used to revise the derived 

NHDPlus variables have been evaluated to determine that 

the results reproduce the existing attributes for non-coastline 

flowlines using the existing network topology (hydroseq, 

From-/To-nodes, divergence), excluding the alterations 

described in table 5 and a few isolated incidences. The results 

of this evaluation are reported in table 6. An investigation 

of each attribute discrepancy, excluding flow and velocity, 

showed the discrepancy to be the result of an error in the 

original NHDPlusV2 dataset.

Comparing reported drainage areas from USGS 

streamgages with accumulated areas at the same location has 

proven to be an effective way to evaluate and verify proper 

stream connectivity and routing. These steps were completed 

using the best available data and quality assurance measures 

throughout the process. Computed watershed areas (divergence routed and total upstream accumulation; fig. 6) were 

compared with those distributed with the NHDPlusV2 data 

suite (McKay and others, 2012). This comparison demonstrated that, by comparing the accumulated drainage areas 

with those reported in NHDPlusV2, catchment, divergence 

routed, and total upstream watershed-level metrics were 

calculated correctly. The outliers in the plots (fig. 6) could be 

explained by the modifications done to the original routing of 

the NHDPlus dataset where flow directions were reversed and 

diversions were edited. Overall, the results show an overwhelmingly positive correlation with R-squared value of more 

than 0.99.

Routing and accumulating upstream watershed area also 

were compared with streamgage drainage areas published 

in the GAGESII dataset, which used digitized watershed 

boundaries defining the drainage outlet as the point locations 

of selected streamgages, combined with ancillary geospatial 

data (Falcone, 2011; Falcone and others, 2010). GAGESII 

basin outlets can occur anywhere along the flowline, which 

can result in varying metrics, depending on the source data 

being compared. In some cases, NHDPlusV2_us flowlines 

are 95 kilometers in length. In addition, some source data 

can be widely variable within an NHDPlusV2 flowline 

catchment. More than 4,700 GAGESII basins were selected 

for comparison. By matching GAGESII outlet points with 

NHDPlusV2 COMIDs, only those GAGESII basins that 

had a basin area within 1 percent of the NHDPlusV2 total 

accumulated basin area were selected for comparison. Results 

demonstrate a significant positive Pearson’s R-squared 

value (fig. 7). Outliers from the comparisons likely can be 

attributed either (1) to the difference in the basin delineation 

for GAGESII compared to that of the network accumulation 

catchment representations in NHDPlusV2 or (2) to the fact 

that the NHDPlus network reports data from the downstream 

end of flowline, whereas GAGESII reports information at the 

exact locations of the streamgages.
Validation 19

Table 6. Number of discrepancy values from attempt to 

reconstruct original NHDPlus estimates.

[Results exclude coastline flowlines and do not include modifications of 

NHDPlus described in table 5]

Variable Number of flowline discrepancies

StartFlag 0

TermFlag 0

TerminalPa 15

StreamCalc 77

StreamOrde 81

StreamLeve 200

LevelPathI 115

TotAreaKm2 1

CumAreaKm2 0

TotLngthKm 0

MAFlowUcfs 157,066

MAVelUfps 14,702

2,500,000

1,000,000

Total upstream accumulation area, in meters

0

0 500,000 1,500,000 2,500,000

NHDPlus upstream watershed area, in meters

3,000,000

Figure 6. NHDPlus upstream watershed areas in relation to total upstream 

accumulation values.
20 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet 40302010

0 Accumulated NLCD water Pearson Correlation: 0.99984

0 10 20 30 40

GAGESII NLCD water

10

6420 Accumulated NLCD

urban high intensityPearson Correlation: 0.99841

0 2 4 6 10

GAGESII NLCD urban high intensity

8

8

100

604020

0 Accumulated NLCD shrub Pearson Correlation: 0.99942

0 20 40 60 100

GAGESII NLCD shrub

80

80

301510

50 Accumuated NLCD

woody wetland

Pearson Correlation: 0.99963

0 5 10 15 30

GAGESII NLCD woody wetland

25

20 25

20

40302010

0 Accumulated NLCD

urban open spacePearson Correlation: 0.99891

0 10 20 30 40

GAGESII NLCD open space

402010

0

Accumulated NLCD barren Pearson Correlation: 0.99992

0 10 20 30 40

GAGESII NLCD barren

30604020

0

Accumulated NLCD grassland Pearson Correlation: 0.99938

0 20 40 60

GAGESII NLCD grassland

80

80

6420 Accumulated NLCD

herbaceous wetlandPearson Correlation: 0.99864

0 2 4 6

GAGESII NLCD herbaceous wetland

8

8

60503010

0 Accumulated NLCD

urban low intensityPearson Correlation: 0.99823

0 20 40 50 60

GAGESII NLCD urban low intensity

4020

10 30

100

604020

0 Accumulated NLCD

deciduous forest

Pearson Correlation: 0.99992

0 20 40 60 100

GAGESII NLCD deciduous forest

80

80

604020

0

Accumulated NLCD pasture Pearson Correlation: 0.9988

0 20 40 60

GAGESII NLCD pasture

500003000010000

0 Accumulated basin area Pearson Correlation: 0.99951

0 10000 20000 30000

GAGESII basin area

40000 50000

252010

50 Accumulated NLCD

medium intensity

Pearson Correlation: 0.99431

0 10 15 20 25

GAGESII NLCD urban medium intensity

15

5

804020

0 Accumulated NLCD

evergreen forest

Pearson Correlation: 0.99892

0 20 40 60

GAGESII NLCD evergreen forest

60

80

604020

0

Accumulated NLCD row crop Pearson Correlation: 0.99984

0 20 40 60

GAGESII NLCD row crop

80

80

302010

0 Accumulated NLCD

mixed forest

Pearson Correlation: 0.99993

0 10 20 30

GAGESII NLCD mixed forest

40

40

Figure 7. Pearson’s Correlation for basin percent of GAGESII NLCD 2006 land-use classes in relation to values from the total upstream aggregation method.
Summary and Conclusions 21

Summary and Conclusions

Digital hydrologic networks provide common spatial 

units that can be used to frame the descriptions of aquatic 

and watershed processes and properties. These descriptions 

have been applied to hydrologic analysis and modeling. The 

National Hydrography Dataset Plus (NHDPlus), Version 

2.1 (NHDPlusV2.1) represents an attribute-rich geographic 

information system (GIS) stream network developed at 

the 1:100,000 scale. The structure includes networking, 

feature naming, and “value-added attributes” of relevant 

characteristics that make the dataset useful for hydrologic 

modeling and interpretations. The U.S. Geological Survey 

National Water-Quality Assessment project adopted the 

medium-resolution NHDPlusV2 network as the primary 

framework supporting SPAtially Referenced Regression On 

Watershed (SPARROW) regional and national modeling. The 

modeling technique relies on accurate navigational attributes 

and structures to establish relations between associated 

quantities of monitored contaminant loads, contaminant 

sources, and physical characteristics affecting contaminant 

transport. Versions of the NHDPlus network have proven 

useful for assessing and applying stream and watershed 

characteristics for hydrologic applications at regional scales; 

limitations in NHDPlusV2 networking capabilities do 

exist. Modifications to the NHDPlusV2 routing and stream 

connections were performed with the intention of providing 

important networking improvements and enhancements 

utilized by SPARROW modeling and other hydrologic 

routing and accumulating applications. 

The steps required to create and update the enhanced 

network (E2NHDPlusV2_us; Schwarz, 2019) were undertaken in a precise order, ensuring that correct values of routing 

attributes were updated before they could be used as input 

affecting other attributes. It was first necessary to compile the 

NHDPlusV2.1 files, compactify the nodes of the network, 

apply the fixes that correct known errors, and fill in missing values. The next step differentiated non-coastal terminal 

flowlines that terminate in the U.S. interior from those that terminate at a U.S. border. Following this, the Addition/Removal 

(AR) events were created—a step that inserts virtual flowlines 

into the network to account for routed transfers. The critical 

step in generating a new set of hydro-sequence variables to 

account for changes in flow direction and the inclusion of the 

AR virtual flowlines was then performed. This step also produced revised estimates of the diversion-fraction apportioned 

drainage area. 

Once modification of the network was completed, the 

additional variables related to foreign drainage and diversionaffected flow were created and made consistent with the 

revised routing. The value-added attributes that identify flowpath relations were reevaluated, and then smoothed elevation, 

channel slope, and mean streamflow and velocity were generated using the revised algorithms. At each step, the entire network was processed concurrently, avoiding the complications 

from making changes consistent across multiple production 

regions, which occurs if the regions are processed separately. 

The last step generated a copy of the enhanced national 

network as a set of regional files, one file for each production 

region, which was then merged with regional spatial data for 

incorporation into a GIS. 

Given the existence of the enhanced network, there may 

arise the opportunity to incorporate further revisions into the 

network to account for additional AR events or routing modifications. In implementing these changes, it is important that 

previous AR routed-transfer events are not reapplied; doing 

so would duplicate the virtual flowlines created to implement 

these transfers. If the existing routed transfers need modification, for example, to account for additional diversions or confluences among the existing virtual flowlines or to change the 

location of withdrawal or discharge, then it will be necessary 

to rebuild the network in its entirety, beginning with the compilation of the NHDPlusV2 data. A reapplication of the other 

AR events, the withdrawal and discharge events, would not 

affect the integrity of the enhanced network. Similarly, care is 

required when performing modified routing to the enhanced 

network; a reapplication of diversion fraction and divergence 

code modifications is innocuous, but reapplying the changes in 

flow direction would negate the previous modifications.

Assuming the AR events and routing modifications 

pertain only to new instances of routed transfers and flow 

direction reversals, then the revision process could start with 

these steps. All the steps that follow AR and routing modification processing as part of the creation of the enhanced network 

must be repeated. The creation of new variables and the network revision process are sensitive to these changes and must 

be regenerated to restore the enhanced network to a valid state.

An approach was developed to efficiently and accurately associate and aggregate landscape information for all 

flowline catchments and their upstream areas in the E2NHDPlusV2_us dataset. Because the modifications to the routing 

involved a quality-control inspection of routing assumptions 

incorporated into the original NHDPlusV2.1 dataset, based on 

published flow data and local knowledge from hydrologists, 

a more accurate accounting of basin characteristics for every 

flowline, waterbody, coastal catchment, and sink from headwater streams to downstream units could be computed. The 

described methods produced accurately acquired summaries 

through braided streams without double counting of values of 

streams or their catchments. 

The sources of AR events listed here are not exhaustive, 

and numerous additional events could be used to fully describe 

the artificial withdrawal and discharge of water to the stream 

network. Examples of sources include the U.S. Environmental 

Protection Agency (EPA) Permit Compliance System (EPA, 

2018) database, which reports on outfall locations and flow 

discharge for thousands of permitted facilities, and numerous irrigation districts in the U.S. West that have non-directed 

drainage ditch flowlines in NHDPlusV2.1 and may have pertinent streamgaging information. Although not incorporated 

in the network, representations of aqueducts, canals, conduits, 
22 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

pipes, irrigation ditches, and flowlines corresponding to flow 

paths that lack flow direction could be used to define and 

refine AR withdrawal and discharge events.

Results from an evaluation of the modified routing 

network database demonstrate a positive correlation with 

published datasets containing similar information, such as 

accumulated land use and drainage area. These and other associated ancillary data provide an important asset for hydrologists and other researchers to understand and characterize the 

Nation’s rivers and streams. Updates, additions, and modifications to ancillary data assigned to the NHDPlus network 

likely will continue as needed to support regional and national 

modeling and assessment activities.

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https://pubs.usgs.gov/sir/2011/5167/.

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conterminous United States: Journal of the American 

Water Resources Association, v. 52, no. 1, p. 120–128, 

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S.E., Johnston, C.M., and Dennis, R.L., 2013, Simulating 

stream transport of nutrients in the eastern United States, 

2002, using a spatially-referenced regression model and 

1:100,000-scale hydrography: U.S. Geological Survey 

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and Rea, A., 2012, NHDPlus Version 2: User Guide: U.S. 

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Association (JAWRA), v. 52, iss. 4, p. 890–900, accessed 

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transfers of water in the eastern United States: U.S. Geological Survey Open-File Report 86–148, 38 p.

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2016, The National Water Model, [not paged], accessed 

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in the western conterminous United States: U.S. Geological 

Survey Open-File Report 85–166, 34 p.

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understanding of the Nation’s water quality: U.S. Geological Survey Fact Sheet 2009–3019, 6 p. [Also available at 

http://pubs.usgs.gov/fs/2009/3019/.] 
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Robertson, D.M., and Saad, D.A., 2011, Nutrient inputs to 

the Laurentian Great Lakes by source and watershed 

estimated using SPARROW watershed models: Journal of 

the American Water Resources Association, v. 47, 

no. 5, p. 1011–1033, accessed April 15, 2017, at 

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D.W., 2010, Design of Cycle 3 of the National Water-Quality Assessment Program, 2013–2022: Part 1: Framework of 

water-quality issues and potential approaches: U.S. Geological Survey Open-File Report 2009–1296, 54 p. [Also 

available at https://pubs.usgs.gov/of/2009/1296/.]

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region of Canada and the United States, 2002 base year: 

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2018–5051, 14 p., accessed February 17, 2019, at 

https://doi.org/10.3133/sir20185051.

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NHDPlus Version 2.1 Flowlines: U.S. Geological Survey data release, accessed October 10, 2019, at 

https://doi.org/10.5066/P986KZEM.

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modified routing for NHDPlus Version 2.1 flowlines: 

ENHDPlusV2, 2018: U.S. Geological Survey data release, 

accessed October 3, 2018, at https://doi.org/10.5066/

P9PA63SM.

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application, and user documentation: U.S. Geological Survey Techniques and Methods, book 6, chap. B3, available 

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to streams of the conterminous United States, 2012: U.S. 

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streamgages linked to the medium Resolution NHD: U.S. 

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usgswrd/XML/streamgages.xml.

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Cooper, A.R., 2014, 3:589 An approach for aggregating upstream catchment information to support research 

and management of fluvial systems across large landscapes: SpringerPlus, v. 3, accessed May 12, 2015, at 

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Search: U.S. Environmental Protection Agency Web site, 

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object/a88f4496-63ed-43cb-a352-c48e89efbc24/html.

Wieczorek, M.E., Jackson, S.E., and Schwarz, G.E., 2019, 

Select Attributes for NHDPlus Version 2.1 Reach Catchments and Modified Network Routed Upstream Watersheds for the Conterminous United States (ver 2.0, October 2019): U.S. Geological Survey data release, 

https://doi.org/10.5066/F7765D7V.

Wieczorek, M.E., Jackson, S.E., and Schwarz, G.E., 2018, 

Select attributes for NHDPlus Version 2.1, Reach catchments and modified network routed upstream watersheds 

for the conterminous United States: U.S. Geological Survey 

data release, https://doi.org/10.5066/F7765D7V.

Appendixes 1–5
26 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

Appendix 1. Description of Addition and Removal Events Spreadsheet

The specification of Addition and Removal (AR) events 

is through the AR events spreadsheet (Schwarz, 2019). The 

spreadsheet contains information on the network locations of 

withdrawal and discharge components of the events, connections between pseudo flowlines, flows associated with each 

event, characteristics of the event needed for incorporation 

into models, and reference information that documents information sources. For disconnected withdrawal or discharge 

events (and non-routed transfers), each observation in the 

spreadsheet represents an individual withdrawal or discharge. 

For routed transfers, an observation can be either the withdrawal or discharge point of the transfer, where a pseudo flowline is created, or the observation can represent connections 

of pseudo flowlines that occur between transfer withdrawals 

and discharges. Pseudo flowlines have no nodal structure to 

describe the connections between them. Consequently, the 

observations in the spreadsheet pertaining to routed transfers 

represent connections in terms of FromComid and ToComid 

(table 1.1). Every routed transfer will consist of at least three 

entries in the spreadsheet: two observations corresponding to 

the withdrawal and discharge events and an observation that 

shows the connection between the two pseudo flowlines created from these events.

The spreadsheet includes several variables useful for 

incorporating the AR events in models. A list of these variables with a description of each one is presented in table 1.1. 

The AReventID is used to link multiple observations pertaining to a common event, for example, the disconnected withdrawal and discharge events associated with a common use, 

such as single municipal public-supply or irrigation district. 

The ARSrcReg can be used to identify AR events that initiate 

outside the region in which the NHDComid flowline resides 

(given by the variable Region). This could be useful in modeling for determining where boundary conditions may be needed 

to replace a routed transfer because the transfer is outside the 

model study area. The AR events spreadsheet currently consists of 294 observations.

FromComid and ToComid identify connections between 

NHDPlusV2 flowlines and AR events, and between pseudo 

flowlines associated with routed transfers. For a disconnected 

withdrawal, FromComid is the NHDPlusV2 flowline Comid 

where the withdrawal is located, and ToComid is blank. For a 

disconnected discharge, ToComid is the NHDPlusV2 Comid 

where the discharge is located, and FromComid is blank. For 

routed transfers, FromComid and ToComid are generally 

valued, with FromComid corresponding to an NHDPlusV2 

Comid for the initial routed-transfer withdrawal and ToComid 

corresponding to an NHDPlusV2 Comid for the terminating 

routed-transfer discharge. In these cases, the complement 

ToComid or FromComid relates to a pseudo flowline with 

a unique identification number having a value less than 

−90000000. The ARQFrom variable represents the amount 

of flow, expressed in cubic feet per second, in the flowline 

designated by the FromComid. If FromComid has a positive 

value, meaning the observation pertains to a withdrawal event 

(either disconnected or routed transfer), this is the flow in the 

NHDPlusV2 flowline from which the withdrawal is taken 

before the withdrawal is made. For disconnected discharge 

events, there is no FromComid, and the ARQFrom is blank. 

ARQuantity is the flow, expressed in cubic feet per second, 

routed to the ToComid flowline. If the event is a disconnected 

withdrawal event, in which case the ToComid value is blank, 

ARQuantity represents the amount of flow withdrawn from the 

FromComid. In contrast, if the event pertains to a discharge 

event, either for a disconnected discharge or to complete 

a routed transfer (in which case ToComid is a positive 

number), the stated flow is the amount discharged and added 

to the ToComid flowline. For discharges of routed transfers, 

ARQuantity is not generally relevant because the flow 

discharged will be determined by the amount routed to the 

ToComid; however, the amount can be relevant as a boundary 

condition if the source region for the transfer (ARSrcReg) is 

outside the model study area. If ARQuantity and ARQFrom 

are valued, then ARQuantity cannot exceed ARQFrom.

The ARQFrom and ARQuantity variables can be used to 

define the fraction of flow diverted either from the network or 

to a pseudo-flowline diversion path. If the FromComid is positive, meaning the observation is either a disconnected withdrawal or the withdrawal point for a routed transfer, then the 

share of flow diverted from the network is given by the ratio 

of ARQuantity to ARQFrom. If the observation represents a 

connection between two pseudo flowlines as part of a routed 

transfer, and ARQuantity is less than ARQFrom, then the flow 

in the FromComid is diverted, and the fraction diverted to the 

ToComid flowline is the ratio of ARQuantity to ARQFrom. 

These ratios can be used in modeling to remove flow from the 

network without requiring the absolute diverted flow be less 

than the flow in the NHDPlusV2 flowline.

The variables IfPublic and IfIrr are 0/1 dichotomous 

variables used to identify flowlines as either public supply 

(IfPublic = 1 and IfIrr = 0) or irrigation (IfPublic = 0 and 

IfIrr = 1). Both variables are zero if the observation pertains to 

a transfer, either routed or non-routed, or another unspecified 

activity. ARDWType describes the type of withdrawal/

discharge event. ARDWType is 0 if the observation pertains 

to a routed transfer (both FromComid and ToComid are coded 

with values), 1 if the observation pertains to public-supply 

withdrawal or discharge (IfPublic = 1), 2 if the observation 

pertains an irrigation withdrawal or discharge (IfIrr = 1), and 

3 if the event is a non-routed transfer withdrawal or discharge 

(FromComid or ToComid is blank, and IfPublic and IfIrr 

are 0). The ARDWType variable can be used in modeling to 

differentiate the quality of discharge between the two principal 

withdrawal uses, public supply and irrigation. The AREventCd 
Appendix 1 27

Table 1.1. Description of variables in the Addition/Removal spreadsheet found in Schwarz and Wieczorek, 2018.

Variable Description

ARObs Sequential number for each observation.

AREventID Sequential number for groupings of observations signifying a single event (a single row for non-transfer withdrawal or 

discharge events; at least observations for non-routed transfer events (the withdrawal observation and the discharge 

observation; and at least three observations for a routed transfer (adding -90000000’s pseudo flowlines to the network; 

for routed transfers at least one -90000000 flowline must be specified). 

Region The up-to-3-character NHDPlusV2 production region code for the end point of the event.

ARSrcReg The up-to-3-character NHDPlusV2 production region code for the initial point of the event.

FromComid The NHDPlusV2 comid corresponding to where a withdrawal initiates, or the upstream comid of a pseudo-flowline created as part of a routed transfer. Leave blank for discharge events. For virtual flowlines created to support a transfer, 

designate a unique number in the -90000000’s; check that there are no conflicts.

ToComid The NHDPlusV2 comid corresponding to where a discharge occurs, or the downstream comid of a pseudo-flowline created as part of a routed transfer. Blank for withdrawal events. See numbering convention described for FromComid.

ARQFrom Flow (cubic feet per second) in FromComid, prior to withdrawal; blank for discharge events (ToComid is blank).

ARQuantity For withdrawal events (ToComid is blank), this is flow (cfs) removed from the FromComid; for transfer events with both 

FromComid and ToComid specified, this is the flow originally in FromComid that is routed through ToComid (note, 

it does not necessarily equal the flow in ToComid as there may be other upstream FromComid’s that route to the same 

ToComid; for discharge to the non-virtual network (ToComid is > 0), this is the flow added to ToComid (in addition to 

flow that is already in the ToComid).

Units The units for flow (always use cfs).

IfPublic 0/1 variable equals 1 if the row corresponds to either a withdrawal or discharge (either FromComid or ToComid is blank) 

and the use or source of the water is a public supply facility.

IfIrr 0/1 variable equals 1 if the row corresponds to either a withdrawal or discharge (either FromComid or ToComid is blank) 

and the use or source of the water is irrigation.

ARDWType Withdrawal/discharge type. Takes the value: 0 if a routed transfer, 1 if withdrawal/discharge is associated with public supply (IfPublic = 1), 2 if withdrawal/discharge pertains to irrigation (IfIrr = 1), and 3 if a non-routed transfer (both IfPublic 

and IfIrr are 0).

AREventCd Value is 1 if a routed transfer (transfer with either FromComid or ToComid in the -90000000s), 2 if withdrawal (public 

supply, irrigation, or non-routed transfer), and 3 if discharge (public supply, irrigation, or non-routed transfer).

ARName Descriptive name for the AR event component.

Notes Text explaining the event context, along with information sources for the ARQFrom and ARQuantity (specify station ids 

with range of years on which flow is based, if available).

OFRID_E Reference item number if event refers to, Mooty, W.S., and Jeffcoat, H.H., 1986, Inventory of interbasin transfer of water 

in the Eastern United States, U.S. Geological Survey Open-File Report 86–148, 47 p.

OFRID_W Reference item number if event refers to, Petsch Jr., H.E., 1989, Inventory of interbasin transfers of water in the western 

conterminous United States, U.S. Geological Survey Open-File Report 85–166, 45 p.

NHDComid NHDPlusV2 comid equal to either FromComid or ToComid, if either is coded with a comid from NHDPlusV2, and blank 

otherwise.

Lat The latitude of the midpoint of NHDComid.

Lon The longitude of the midpoint of NHDComid.
28 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

describes the type of observation and takes a value of 1 if 

the observation is part of a routed transfer, 2 if the event is a 

discharge (including non-routed transfers), and 3 if the event is 

a withdrawal (including non-routed transfers). 

The Notes variable contains information describing the 

context of the observation and documents the source of the 

flow information. The flow documentation is typically given 

in parentheses, with the first part representing the source of the 

withdrawal or discharge flow and the second part corresponding to the NHDPlusV2 flow. The form of the flow documentation may be a URL (Universal Resource Locator) linking to a 

web page that provides flow data (typically for public-supply 

facilities), a streamgage number and period used to form an 

average flow, or other reference.

It is not necessary for an AR routed transfer to reconnect 

to the original network. It is possible that a routed transfer 

ends prior to returning water to the network because the entire 

consumption of the routed water is associated with some use. 

In this case, FromComid will have a negative value corresponding to the terminal pseudo flowline, and ToComid is 

blank. To be consistent with other coding conventions, the 

terminal pseudo flowline is classified as a disconnected withdrawal event (AREventCd = 2) and has a designated use given 

by the ARDWType code, and possibly one of the IfPublic or 

IfIrr variables is non-zero. The terminal pseudo flowline is 

identified as a terminal flowline in the network.

All disconnected withdrawal observations in the AR 

events spreadsheet are either paired with a disconnected discharge observation or the discharge occurs off the network and 

terminates in the ocean. If future observations are added to the 

spreadsheet that are not paired, it may be necessary to create 

another variable that distinguishes them from paired observations. For paired observations, the withdrawal of water from 

the network can be interpreted as a total withdrawal; any water 

that is not consumed by this withdrawal is returned to the 

network by another AR event. However, if a withdrawal observation is not paired with discharge observation, and discharge 

does in fact occur from this withdrawal, then the observation 

must be adjusted to reflect the net consumption of water. 

Reference Cited

Schwarz, G.E., 2019, E2NHDPlusV2_us: Database of 

Ancillary Hydrologic Attributes and Modified Routing for 

NHDPlus Version 2.1 Flowlines: U.S. Geological Survey 

data release, accessed October 10, 2019, at https://doi.

org/10.5066/P986KZEM.
Appendix 2 29

Appendix 2. Description of Methods Used to Update Streamflow Estimates

The method to update streamflow utilized the intermediate incremental catchment flow in NHDPlusV2.1 that was 

dependent on unit runoff (Qincr0001A), added an adjustment 

for stream evaporation (Qincr0001B), and produced selective substitutions of regression estimates based on reference 

streamgage unit runoff (Qincr0001C). The original EROM 

method (McKay and others, 2012) was applied to the network 

(NHDPlusV2) in individual production units, approximately 

equivalent to two-digit hydrologic regions. Because some of 

these regions are hydrologically upstream from other regions, 

the original EROM method incorporated boundary conditions 

into the incremental catchment flow where inflows from an 

upstream region enter a given region. 

In the application of the EROM method to the revised 

network (E2NHDPlusV2_us), the entire network was processed simultaneously, making it necessary to remove the 

boundary condition flow component from the incremental 

catchment flows at the boundary locations. This adjustment 

required accumulating the values of Qincr0001C, starting from 

the headwaters of the upstream region (or regions, in the case 

of multiple upstream regions terminating at the same point) 

through to the most downstream node of flowlines leaving 

each region. In the downstream regions, for flowlines representing the continuation of flow from the upstream region, the 

accumulated flow in the upstream node was subtracted from 

the flowline’s incremental catchment flow Qincr0001C that 

included a boundary condition. In performing this operation, 

it was discovered that the original EROM method failed to 

incorporate the flow from the Ohio River into the Mississippi 

River, an oversight that was corrected in the revised flows. 

It was also determined that the boundary condition for the 

boundary between regions 14 and 15 was too small to account 

for the incoming flow from region 14 but that the difference 

was incorporated into the incremental flow of the next flowline 

downstream from the region 15 boundary flowline.

Given a consistent set of incremental level-C catchment flows (in this application, with boundary conditions 

removed), the EROM method added or subtracted flow quantities known as Addition/Removal (AR) events. These events 

represent withdrawals or discharges of flow owing to water 

use or inter-basin transfers. The revised network (E2NHDPlusV2_us) incorporated a number of AR events not included 

in the original NHDPlusV2 and revised quantities of many 

events that were common to both. The resulting adjustments to 

incremental catchment flow Qincr0001C produced the revised 

incremental values Qincr0001D. 

In assessing the veracity of the original EROM method it 

was determined that incremental flows were incorrectly computed for locations where the AR adjustment caused cumulative flow to go negative. The incremental flow Qincr0001D 

reported at these locations equaled the amount by which the 

AR adjustment exceeded level-C cumulative flow (Q0001C, 

inclusive of the flowline’s value of Qincr0001C) rather than 

the amount that the level-D flow (computed as the analog of 

the NHDPlusV2 EROM variable, Qincr0001D; McKay and 

others, 2012) delivered from upstream flowlines, which must 

be reduced to drive level-D flow to zero. This error indicated 

the accumulation of level-D incremental flows was inconsistent with the reported cumulative flow. This error did not affect 

the derivation of revised flow undertaken in this study because 

the revised flow was based on the level-C incremental flows.

The last step of the EROM method used flow from 

streamgage measurements to adjust the AR-adjusted incremental flows. Two adjustments were applied, one downstream 

and one upstream. [It was discovered that the documented 

EROM method for this step was not the one used to derive the 

final EROM estimates contained in the existing NHDPlusV2.1 

(the Q0001E flow estimates). Through detailed analysis, it was 

possible to determine the actual method used to produce the 

EROM estimates, although this method contained many logical flaws that were corrected in the revision.]

The downstream adjustment descended the network to 

accumulate level-D incremental flows. If a monitored flowline 

was encountered, the measured flow was substituted for the 

accumulated flow in proceeding the accumulation past the 

monitored flowline. The accumulation of incremental level-D 

flows continued downstream using the adjusted flow as the 

base for accumulation. An upstream adjustment was applied to 

any flowline within a monitored nested basin having cumulative area at least one-half as large as the cumulative area at 

the nearest downstream monitored flowline. For these qualifying flowlines, the downstream-adjusted level-D flows were 

adjusted on the basis of two factors: a base adjustment factor, 

common to all qualifying flowlines, and a flowline-specific 

apportionment ratio that apportions the base adjustment to an 

individual qualifying flowline. To obtain the adjustment, the 

two factors were multiplied, and the product was added to the 

downstream-adjusted, cumulative, level-D flow.

The original EROM method used as the base adjustment 

factor the difference between measured flow at streamgages 

and the monitored flowline’s value of non-adjusted, cumulative, level-D flow. The apportionment factor for this method 

used the ratio of the qualifying flowline’s cumulative area to 

the sum of cumulative area over all qualifying flowlines in the 

monitored nested basin. The ratio is generally a small number 

that grows in a downstream direction but remains small, even 

at the monitored flowline. 

A problem with the EROM methodology just described 

is that the streamgage adjustment formula, if applied to the 

monitored reach, would not result in a final flow estimate 

that equaled measured flow at that flowline. This inconsistency arose for two reasons. First, the base adjustment 

for the monitored nested basin is given by the difference 

between streamgaged flow and cumulative, level-D flow at 
30 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

the monitored location, whereas the actual discrepancy in 

flow should be the difference between measured flow and 

the downstream-adjusted, cumulative, level-D flow, prior to 

applying the downstream adjustment at the monitored flowline. Second, the apportionment factor for the monitored 

flowline is given by the ratio of its cumulative area to the sum 

of the cumulative area of all qualifying flowlines in the nested 

basin, a ratio that is considerably less than one. Because the 

downstream adjustment set flow at a monitored flowline is 

equal to the streamgaged flow, the adjustments tended to show 

a discontinuous jump between the monitored flowline and the 

flowlines on the main flow path immediately upstream.

The revised EROM method used in this analysis corrected the original EROM methodology by smoothing the 

upstream adjustment. First, the base adjustment for the monitored nested basin was determined by the difference between 

measured flow at the streamgaged and the downstreamadjusted, cumulative, level-D flow at the monitored flowline, 

prior to applying the adjustment at the monitored flowline. 

Second, the apportionment factor was given by the ratio of the 

qualifying flowline’s sum of cumulative area, as determined 

from a downstream accumulation of the qualifying flowlines’ 

cumulative area, to the sum of cumulative area across all 

qualifying flowlines in the monitored nested basin. As a result, 

the ratio was the same as the original EROM method’s ratio 

for the most upstream, adjustment-qualifying flowlines; the 

numerator of the ratio was the drainage area of the flowline 

under both methods. However, the revised ratio increased 

more rapidly in the downstream direction than the original 

ratio, with the ratio equaling one at the monitored flowline. 

Thus, the upstream adjustment if applied at the monitored 

flowline would exactly equal the difference between measured 

flow at the streamgage and the downstream-adjusted, cumulative, level-D flow, yielding an adjusted flow that exactly 

equaled measured flow. The result of the modified adjustments 

was a smoother transition of flow in flowlines approaching the 

monitored flowline.

Table 2.1 of this appendix shows the relative performance 

of flow estimation across different estimation methodologies 

and networks. The performance measures were determined 

using a sequestration method, whereby the flow-estimation 

algorithm was applied using a smaller set of streamgages than 

was used to derive the results. Flow estimates at the locations 

of the excluded streamgages were then compared to measured 

flows at streamgages to determine a percent error (100 times 

the ratio of the difference between measured and estimated 

flow to estimated flow). The first set of columns in table 2.1 

pertains to the original EROM methodology applied to the 

original NHDPlusV2 network. The middle set of columns 

pertains to the revised EROM methodology described above, 

also applied to the original NHDPlusV2 network. The last 

set of columns pertains to the revised EROM methodology 

applied to the revised network described in this present analysis. Although not uniform across all regions, the new EROM 

methodology tended to outperform the original methodology, 

holding the network fixed. Significant improvements in root 

mean-squared percent error were observed using the revised 

EROM methodology for regions 03S, 11, 13 and 15 (fig. 2 in 

the body of the report), with the overall root mean-squared 

percent error falling from 58.1 to 56.5 percent. Additional 

improvement in estimates from using the revised network 

was more modest, with only region 15 showing a significant 

reduction in root mean-squared percent error. Switching to 

the revised network lowered the overall root mean-squared 

percent error by only 0.1 percent.

The method for regenerating stream velocity differs 

slightly from that used in the original production of NHDPlusV2. In the original NHDPlusV2 the determination of 

velocity differs depending on the availability of channel slope. 

Equation 12 of Jobson (1996) is used if slope is known, and 

equation 14 is used if slope is not known. This distinction is 

made even though mean annual flow is zero, a questionable 

approach given that both Jobson equations show that, if flow is 

zero, there is no effect of slope on the determination of velocity. Consequently, in regenerating velocity it was assumed that 

if mean annual flow is zero, then velocity is set to the small 

value 0.06561, which is consistent with the unknown-slope 

equation evaluated at zero mean annual flow. The estimation 

of seasonal velocity based on the Jobson equations depends 

on mean annual flow and seasonal flow. The equations have 

the property that there is no effect of slope on velocity if either 

mean annual or seasonal flow is zero. Consequently, if either 

mean annual or seasonal flow was zero, velocity was set to the 

value 0.06561. 

The original NHDPlusV2.1 flow estimates accounted 

for losing streams by including an adjustment that interpolated flow between measured flows at monitored flowlines, an 

adjustment that implied incremental catchment flow was negative for losing streams (McKay and others, 2012). Because 

streamflow was established for the existing network routing, 

it was possible that flowlines with negative incremental flow 

made flow accumulated for the revised network routing negative. The problem of negative flow is exacerbated by frequent 

inconsistencies in the original NHDPlus data, as described 

in table 6 of this report, where flow cannot be reproduced 

by accumulating incremental flow. Consequently, negative 

accumulated flow for the revised network was not uncommon 

when such flows were not constrained to be non-negative during the process. Improvements could be made if an algorithm 

for the estimation of cumulative and incremental flow that 

is not as sensitive to network routing were developed. The 

smoothed elevations (MinElSmoCm and MaxElSmoCm) used 

to estimate channel slope also are sensitive to network routing 

and could benefit from a revised algorithm.
Appendix 2 31

Table 2.1. Relative performance of the EROM and revised EROM flow estimation methods, applied to the original NHDPlusV2 and 

revised E2NHDPlusV2_us networks.

Region

Number of 

sequestered 

gages

Original EROM method applied to 

NHDPlusV2 network

New EROM method applied to 

NHDPlusV2 network

New EROM method applied to 

E2NHDPlusV2_us network

Mean 

percent 

error

Standard 

deviation 

percent 

error

Root mean 

square

percent 

error

Mean 

percent 

error

Standard 

deviation 

percent 

error

Root mean 

square 

percent 

error

Mean 

percent 

error

Standard 

deviation 

percent 

error

Root mean 

square 

percent 

error

1 52 3.1 12.1 12.3 3.3 12.2 12.5 3.3 12.2 12.5

2 129 -0.7 34.9 34.8 -0.4 34.4 34.3 -0.2 34.6 34.5

03N 54 3.8 15.8 16.1 3.7 15.6 15.9 3.8 15.6 15.9

03S 32 12.6 51.9 52.6 7.1 30.5 30.8 7.1 30.5 30.8

03W 44 6.5 26.2 26.7 6.8 26.1 26.7 6.6 26.1 26.7

4 69 -4.9 17.1 17.6 -5.6 16.9 17.6 -5.6 16.9 17.6

5 115 3.2 12.7 13 3.1 12.6 12.9 3.1 12.6 12.9

6 25 -0.2 9.5 9.3 0.1 9.4 9.2 0.1 9.4 9.2

7 82 6 25 25.6 5.7 25 25.5 4.7 24.4 24.7

8 21 3.4 14 14 3.5 13.9 14 3.6 13.9 14

9 13 14 22.6 25.8 12.1 25 26.9 11.5 23.8 25.6

10L 71 8.2 66.3 66.3 8.5 63.2 63.4 8.2 63.1 63.2

10U 70 -0.1 49.9 49.6 0.9 48.6 48.3 0.9 48.6 48.3

11 76 -7.9 56.5 56.7 -13.8 50.4 52 -15.6 49.2 51.3

12 64 13.7 79.6 80.2 10 78.5 78.5 10 78.5 78.5

13 21 0.7 111.9 109.2 -2.5 107.2 104.8 -2.5 107.2 104.8

14 63 -21 32.9 38.9 -20.1 32.5 38 -18.6 32.6 37.3

15 27 -19.5 48.5 51.5 -21.1 41.7 46 -22.9 37.8 43.6

16 37 -1.9 42.9 42.3 0.2 43.2 42.6 0.4 43.1 42.6

17 125 -4.1 34.6 34.7 -4.3 33.8 33.9 -5.6 34.1 34.4

18 107 15.8 143.1 143.3 14.5 141.6 141.7 14.4 141.8 141.9

All 1,297 1.4 58.1 58.1 0.7 56.5 56.5 0.4 56.5 56.4

References Cited

Jobson, H.E., 1996, Prediction of traveltime and longitudinal 

dispersion in rivers and streams: U.S. Geological Survey 

Water-Resources Investigations Report 96–4013, 69 p.

McKay, L., Bondelid, T., Dewald, T., Johnston, J., Moore, R., 

and Rea, A., 2012, NHDPlus Version 2: User Guide: U.S. 

Environmental Protection Agency, 182 p., accessed May 1, 

2018, at https://s3.amazonaws.com/nhdplus/NHDPlusV21/

Documentation/NHDPlusV2_User_Guide.pdf.
32 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

Appendix 3. Description of Methods Used to Update Slope Estimates

The NHDPlusV2.1 method used to compute channel 

slope established an elevation for each flowline node based on 

the largest value of the minimum catchment elevations among 

all flowlines conflating at the node. For headwater nodes, the 

elevation was simply established by the minimum elevation 

within the sub-catchment area that flowed to the headwater 

node. The method then started from the most downstream 

node and navigated the network upstream along a path having 

the greatest flow to find the next upstream node along a path 

with an elevation value that was higher than that at the downstream node. 

Nodes so identified are called set points of the slope 

determination method. The difference in elevations between 

these two successive set-point nodes, divided by the sum 

of the flowline lengths between the two nodes determined 

the slope for all flowlines along the connected pathway. The 

elevations of all nodes along the connecting path were then reestablished by linear interpolation using the estimated slope. 

Slopes for tributaries of the level path were determined 

using the interpolated or established elevation at the node 

at which the original level path and the tributary level path 

intersected and climbed the tributary level path to find the 

next upstream node having an elevation value greater than the 

intersecting node. In this way, by climbing the network along 

level paths to find the next local maximum elevation, slopes 

were estimated for all flowlines in the network. If no upstream 

node with an elevation value greater than the downstream 

node’s established elevation was observed, a default slope of 

0.00001 was used for all flowlines upstream from the established node. Flowlines classified as being in a waterbody also 

were assigned the default slope (McKay and others, 2012). 

A revised method used here initialized the flowline nodes 

with the same initial elevations as the NHDPlusV2.1 method; 

these elevations were the maximum of the minimum initial 

catchment elevations (MinElRawCm), corresponding to all 

catchments directly upstream from a given node, rounded to 

the nearest centimeter. For headwater flowlines, the upstream 

node was initialized with the flowline’s reported maximum 

initial catchment elevation (MaxElRawCm); this elevation 

is the minimum raw elevation corresponding to the headwater node drainage area—the area determined by the digital 

elevation model to flow through the headwater flowline’s 

upstream node. 

The revised method then expanded the search for set 

points beyond a given path to include all nodes upstream from 

the current set point. Given the initial set-point elevations at 

the terminus nodes of the network, determined according to 

the initial elevations for these nodes, the algorithm climbed 

the network in reverse hydrologic order (as determined by the 

NHDPlus hydrosequence variable) to identify a node having an initial elevation that is greater than all initial elevations downstream from it. The nodes so identified represent 

a collection of set points having monotonically increasing 

elevations as the network is traversed in the upstream direction. Like the NHDPlusV2.1 method, the determination of 

set-point nodes excluded nodes having initial elevations that 

represented local minima. 

The determination of set points did not guarantee that 

headwater nodes would be classified as set points. Such 

nodes were classified as set points only if they had an initial 

elevation that exceeded all downstream initial elevations. An 

alternative, regression-based method was used to determine 

elevation in the case of headwater nodes and any subsequent 

nodes immediately downstream that did not qualify as set 

points nor had an upstream set point on which to interpolate 

an elevation.

In the next step, the interpolation algorithm descended 

the network in hydrologic order to determine for each node 

the upstream set point having the minimum elevation and the 

maximum flowline distance between the node and this set 

point. If the node was classified as a set point, then the node 

referenced its own initial elevation as the minimum upstream 

set-point elevation, and the distance was set to zero. If there 

were multiple upstream set points having the same minimum 

elevation, then the set point with the greatest stream distance 

was referenced. This convention indicates that if there were 

diversions creating multiple paths connecting the node to an 

upstream set point, the effective path forming the connection 

was the one having the greatest distance. 

In the last step, the interpolation climbed the network 

to determine an elevation for each node that is not a set point 

but referenced a set point somewhere upstream. Starting from 

the most downstream flowline in the network, which had an 

initialized value for the downstream node elevation, the algorithm climbed the network to determine the elevation for each 

upstream node, E_u (eq. 3.1). If the node was a set point, then 

the elevation of the node was set equal to the node’s initial 

elevation, E_0. If the node was not a set point, then the elevation was determined by linearly interpolating an elevation 

based on the determined downstream node elevation, E_d; the 

elevation of the minimum-elevation upstream set point, E_s; 

the distance from that set point to the upstream node, D_s; and 

the length of the flowline corresponding to the upstream node, 

L, according to the equation

E_u=E_d+(E_s-E_d ) L⁄((L+D_s ) ). (3.1)

If the node corresponded to a diversion, and the node 

was not a set point, then there were multiple downstream node 

elevations, E_d, and applying the equation to each resulted 

in multiple alternative values for the node elevation, E_u. In 

such cases, E_u was assigned according to the maximum of 

the alternative values. The selection of the maximum elevation 
Appendix 3 33

assured that the elevation at the diversion node exceeded the 

elevations for all other nodes downstream.

Because each non-terminus set point represented the 

smallest elevation upstream from the downstream set-point 

elevation, a linear interpolation of elevation between that 

point and the downstream set point, following the connected 

path, resulted in elevations at all nodes along the path that 

were strictly below the smallest elevation set points in each 

tributary basin, if such a set point existed. Thus, if set points 

existed, a positive slope between them was guaranteed. Only 

if a tributary had no elevation above the downstream set-point 

elevation, meaning the tributary had no upstream set point, the 

regression-based slope would have been required.

An example network that demonstrates the relation 

between set points and interpolated elevation is shown in 

figure 3.1. In the example, an examination of the initial node 

elevations (shown only for set points) determines the basin 

has two basin-determined set points—one with an initial nodal 

elevation of 20 and the other with 25 (units are arbitrary). 

From this, we can assume that all other nodes have elevations 

less than or equal to the elevation of the terminus, which is 

10. All flowlines along the red path reference the upstream set 

point that has an elevation of 20, and all the green flowlines 

reference the upstream set-point elevation of 25. Note, also, 

that the green path contains a diversion, which complicates 

the determination of elevation for the node coinciding with the 

diversion. Blue flowlines correspond to tributaries that have 

a headwater node elevation that is less than or equal to the 

elevation of the terminus node (10), indicating they have no 

upstream set point and therefore must have slope determined 

using the regression method.

The equations in figure 3.1 show the application of 

equation 3.1 to determine elevation, Eu , for each node in the 

downstream path of a set point. In applying these equations, 

the length of each flowline is given by the value equilibrated 

with the variable L , and Ds in equation 3.1 is evaluated by 

summing the lengths for all flowlines in the path between the 

downstream node of the flowline and the upstream set-point 

node. This approach leads to an ambiguity at the diversion 

node, which is resolved by taking the maximum of the alternative estimated elevations.

The determination of slope and elevation for flowlines 

having no upstream set point was based on regression analysis. Separate regressions relating the logarithm of slope to a 

set of explanatory variables were estimated for each of the 

21 NHDPlus production regions. The regressions were based 

on flowline observations for which the downstream node is a 

set point and the upstream node elevation is interpolated (not 

based on an initial elevation). The specific subset of flowlines 

used as observations in the regression indicates that the input 

data have the same characteristics as the set of flowlines to be 

filled in; therefore, both sets pertain to flowlines having no set 

point immediately upstream. 

The explanatory variables for the regression include 

the logarithm of the distance to the upstream set point, the 

logarithm of the minimum slope pertaining to the flowline 

immediately downstream (use the logarithm of 1E-7 if the 

flowline is terminal), the logarithm of total upstream drainage 

area (use the logarithm of 0.5 square kilometer if the flowline 

has no upstream drainage area), and the logarithm of elevation 

of the downstream node (use the logarithm of 1 centimeter if 

the downstream node has a non-positive elevation). The coefficients for the logarithms of distance to the upstream node and 

upstream drainage area are constrained in the regression to be 

non-positive; the coefficients for the logarithms of downstream 

slope and elevation are constrained to be non-negative.

The regression results for the slope equation are shown 

in appendix table 3.1. The results show that the estimated 

coefficients are generally highly significant with the expected 

sign. The elevation coefficient is constrained to equal zero 

for regions 04, 07, 09 and 14; the distance coefficient is 

constrained to equal zero for regions 13 and 16. The R2

 of 

the regressions ranges from 0.24 to 0.53, with an average of 

0.41, and the root mean squared error (in logarithm space) 

ranges from 1.36 to 1.83 with an average of 1.56. These fits, 

while significant, indicate a considerable amount of residual 

uncertainty. The adjustment factor in the rightmost column 

corresponds to the retransformation-bias correction factor 

used to convert slope predictions from log to real space and is 

computed as the average of the exponentiated residuals from 

the log-space regression model.

The regression estimates from table 3.1 were used to 

estimate slope for flowlines with no upstream set point. In 

forming these estimates, the method begins with the most 

downstream flowlines in network without an upstream set 

point—the flowlines that have a set point for a downstream 

node but no upstream set point. However, to use the regression equation for prediction, an estimate of the distance to an 

upstream set point is required, a distance that is not known 

because there is no upstream set point. To determine the 

distance to be incorporated into the slope equations, a second set of regional regression equations are estimated, each 

having as the dependent variable the logarithm of distance to 

the upstream set point—the same variable used in the corresponding region’s slope equation. The explanatory variables 

included in the second set of regressions are the same as those 

used in the slope regressions, except for the logarithm of distance to the upstream set point. The estimation results for the 

distance equations are given in appendix table 3.2. 

The relevant relations for a given regional pair of models 

can be described as follows. Let s denote the logarithm of 

slope; d

 , the logarithm of distance to the upstream set point; 

X0 , a row vector representing the set of explanatory variables 

excluding the logarithm of distance to the upstream set point; 

β0 and θ0 , vectors of parameters associated with X0 in the 

slope and distance equations; βd , the coefficient associated 

with the distance variable in the slope equation; and e and 

u , residuals in the slope and distance equations, assumed 

to be independent with mean 0 and having standard errors 

σ e and σ u . Independence of the residuals follows from the 

assumption that the slope equation is a valid regression model, 
34 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

= { } = 17.72 + (25 - 17.72) 3/13, max 21.65

18.3 + (25 - 18.3) 10/20

E

u ⋅

⋅

E

u = 16.29 + (25 - 16.29) · 6/26 = 18.3

E

u = 10 + (20 - 10) · 10/35 = 12.86

E0 = 10

L = 10

L = 12

E

s

 = 20

L= 8

L = 3

L = 10

L = 5

L = 6

E

s L = 10 = 25

Eu = 12.86 + (20 - 12.86) · 12/25 = 16.29

Eu = 16.29 + (20 - 16.29) · 5/13 = 17.72

Upstream basin-determined node set points

Terminus node set point

Figure 3.1. The calculation of node elevation by the interpolation method using basin-determined upstream set points.
Appendix 3 35

Table 3.1. Regional regression results relating the logarithm of slope to various flowline predictors for regions in the conterminous 

United States.

[Observations are restricted to flowlines having a set point only for the downstream node. Regions correspond to the 21 NHDPlus production regions. Distance 

to the upstream set-point node corresponds to the longest possible channel path connecting the node to the flowline. Downstream slope is the minimum slope 

among all flowlines having an upstream node that connects to the flowline. Drainage area is diversion-flow apportioned drainage area. RMSE is root mean 

squared error. Adjustment factor is the average of the exponentiated model residuals.]

Region Intercept

Coefficient estimates (standard errors)

Number of 

observations R-square RMSE Adjustment factor

Log distance 

to upstream 

set-point

Log downstream slopeLog upstream area

Log downstream node 

elevation

1 -1.997 -0.317 0.097 -0.38 0.133 5,387 0.286 1.825 3.454

-0.329 -0.025 -0.011 -0.011 -0.013

2 -4.461 -0.261 0.07 -0.342 0.317 7,645 0.398 1.655 2.793

-0.245 -0.018 -0.01 -0.008 -0.009

03N -3.239 -0.395 0.106 -0.322 0.322 8,426 0.528 1.468 2.508

-0.223 -0.016 -0.009 -0.006 -0.009

03S -3.143 -0.432 0.046 -0.286 0.258 3,673 0.435 1.537 2.534

-0.338 -0.025 -0.012 -0.011 -0.012

03W -2.939 -0.28 0.089 -0.392 0.155 6,611 0.48 1.412 2.229

-0.27 -0.019 -0.01 -0.008 -0.012

4 -0.642 -0.364 0.173 -0.254 0 7,665 0.236 1.691 2.983

-0.235 -0.02 -0.009 -0.009 0

5 -6.16 -0.415 0.084 -0.407 0.665 8,964 0.452 1.517 2.363

-0.471 -0.017 -0.009 -0.007 -0.037

6 -7.309 -0.399 0.133 -0.445 0.813 3,151 0.478 1.535 2.677

-0.671 -0.029 -0.014 -0.012 -0.053

7 0.638 -0.471 0.145 -0.301 0 11,522 0.339 1.604 2.46

-0.2 -0.016 -0.008 -0.006 0

8 -0.357 -0.625 0.13 -0.208 0.212 11,148 0.381 1.555 2.532

-0.22 -0.016 -0.008 -0.006 -0.009

9 -0.12 -0.458 0.137 -0.239 0 2,431 0.334 1.606 2.68

-0.394 -0.034 -0.016 -0.015 0

10L -8.291 -0.292 0.108 -0.218 0.629 12,299 0.356 1.364 1.956

-0.275 -0.012 -0.008 -0.004 -0.019

10U -11.769 -0.134 0.178 -0.257 0.829 13,507 0.429 1.402 2.011

-0.387 -0.011 -0.008 -0.004 -0.03

11 -8.207 -0.251 0.14 -0.293 0.65 11,735 0.419 1.497 2.298

-0.278 -0.014 -0.008 -0.005 -0.018

12 -4.945 -0.242 0.098 -0.29 0.31 4,125 0.447 1.505 2.211

-0.34 -0.023 -0.013 -0.009 -0.013

13 -5.663 0 0.195 -0.291 0.212 1,860 0.459 1.449 2.283

-0.432 0 -0.018 -0.01 -0.033

14 -2.224 -0.08 0.122 -0.348 0 3,892 0.394 1.467 2.192

-0.253 -0.021 -0.011 -0.008 0

15 -2.141 -0.105 0.208 -0.304 0.056 3,165 0.42 1.402 2.646

-0.521 -0.024 -0.014 -0.009 -0.034

16 -15.456 0 0.251 -0.347 1.057 3,247 0.418 1.64 2.755

-2.104 0 -0.012 -0.011 -0.173

17 1.064 -0.407 0.288 -0.289 0.079 6,283 0.363 1.761 3.568

-0.336 -0.024 -0.01 -0.009 -0.013

18 -0.603 -0.281 0.292 -0.297 0.106 5,602 0.392 1.784 3.924

-0.324 -0.024 -0.01 -0.01 -0.012
36 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

Table 3.2. Regional regression results relating the logarithm of flowline channel distance to upstream set-point node to various 

explanatory variables for production units in the conterminous United States.

[Observations are restricted to flowlines having a set-point only for the downstream node. Regions correspond to the 21 NHDPlus production regions. Distance 

to the upstream set-point node corresponds to the longest possible channel path connecting the node to the flowline. Downstream slope is the minimum slope 

among all flowlines having an upstream node that connects to the flowline. Drainage area is diversion-flow apportioned drainage area. RMSE is root mean 

squared error]

Region

Coefficient estimates (standard errors) Number of 

observations R-square RMSE

Intercept Log downstream slope

Log upstream 

area

Log downstream 

node elevation

1 11.504 -0.032 0.126 -0.017 5,387 0.092 0.997

-0.088 -0.006 -0.006 -0.007

2 11.678 -0.021 0.143 -0.059 7,645 0.121 1.049

-0.079 -0.006 -0.005 -0.006

03N 11.925 -0.046 0.113 -0.102 8,426 0.171 1.014

-0.082 -0.006 -0.004 -0.006

03S 11.677 0.002 0.19 -0.04 3,673 0.206 0.997

-0.104 -0.008 -0.006 -0.008

03W 11.846 -0.012 0.151 -0.055 6,611 0.191 0.9

-0.091 -0.006 -0.004 -0.007

4 12.167 -0.039 0.116 -0.071 7,665 0.087 0.982

-0.304 -0.005 -0.005 -0.029

5 15.058 0.001 0.14 -0.347 8,964 0.15 0.942

-0.245 -0.006 -0.004 -0.023

6 12.35 -0.028 0.1 -0.091 3,151 0.079 0.937

-0.346 -0.009 -0.007 -0.032

7 11.83 -0.04 0.092 -0.025 11,522 0.087 0.909

-0.253 -0.005 -0.003 -0.025

8 11.983 -0.067 0.085 -0.059 11,148 0.113 0.948

-0.071 -0.005 -0.003 -0.005

9 18.142 -0.044 0.209 -0.666 2,431 0.307 0.947

-1.022 -0.01 -0.008 -0.095

10L 13.36 -0.056 0.022 -0.171 12,299 0.035 1.004

-0.163 -0.006 -0.003 -0.014

10U 13.656 -0.081 0.039 -0.216 13,507 0.059 1.115

-0.285 -0.006 -0.003 -0.024

11 13.918 -0.057 0.078 -0.256 11,735 0.118 1.013

-0.137 -0.005 -0.003 -0.012

12 12.537 -0.03 0.118 -0.094 4,125 0.156 1.025

-0.124 -0.009 -0.006 -0.009

13 12.227 -0.041 0.089 -0.072 1,860 0.111 1.102

-0.329 -0.013 -0.008 -0.025

14 14.948 -0.053 -0.016 -0.268 3,892 0.013 1.125

-1.173 -0.009 -0.007 -0.095

15 12.647 -0.045 0.004 -0.079 3,165 0.014 1.029

-0.31 -0.011 -0.006 -0.025

16 22.606 -0.068 0.018 -0.905 3,247 0.08 1.025

-1.314 -0.007 -0.007 -0.108

17 12.182 -0.028 0.087 -0.072 6,283 0.089 0.939

-0.092 -0.005 -0.005 -0.007

18 11.663 -0.06 0.065 -0.029 5,602 0.079 0.983

-0.087 -0.006 -0.005 -0.006
Appendix 3 37

whereby the residual e is assumed to be independent of all predictor variables, including d

 . The 

log slope and distance equations can be expressed as

 s X    0 0  d e   , d (3.2)



d X   0 0  u. (3.3)

As explained above, in applying these relations to the prediction of slope, the distance variable is not directly measured; only the distance to the most upstream node in the network, which 

is not a set-point node, is measured. The implication is that u , the distance equation residual, 

is distributed as a truncated distribution, with the lower bound given by d X 0 0  0 , where d0 is 

the truncated distance to the most upstream node in the stream network. Upon substitution of the 

distance equation into the slope equation, slope is shown to be a function of the slope residual, u . 

Assuming u is distributed truncated normal with a lower bound of d0  X00 , taking the expectation of slope, S , in real space, conditioned on the slope residual e , gives




  1  

  u2 
  2 2 2 exp u    

  

E S e X     exp 0 0       2 2

 X u 0 0    e u

  , d d X 0 0   	 du 0

  d X 1  0 0     0 
       u  

 (3.4)  d X    2

u d  
 1  0 0 0

  

E S e X        2     u

  exp  0 0   0 d d   u e ,  d X  1  0 00 


  u 

where z is the standard normal cumulative distribution function. The term in braces in 

the first line of equation 3.4 represents the truncated normal distribution having a lower bound 

of d0 0  X 0 . Note that the effect of using a truncated distribution is to lengthen the effective 

upstream distance to be greater than d0 . Given that upstream distance has a negative coefficient 

in the slope equation, this lengthening results in a lower slope estimate than would be determined 

otherwise. 

The regression-based estimate of slope, unconditioned on knowing the residual e , is determined by evaluating equation 3.4 for given values of the exogenous variables, X0 , using the 

coefficient estimates contained in tables 3.1 and 3.2, where the exponentiated slope residual term, 

exp  e , is replaced by a smearing estimate given by the mean of the exponentiated residuals 

shown as the adjustment factor in table 3.1. 

Owing to the nonlinear functional form associated with the coefficients, the replacement of 

the coefficients with estimated values induces a bias in the slope estimate. To correct this bias a 

parametric bootstrap is performed, where the coefficients β0 , βd , and θ0 are randomly generated 100 times from a multivariate normal distribution with mean vector and covariance matrix 

given by their respective estimates obtained from the regression results for the log slope and 

distance models. In generating these random values, θ0 is treated as being independent of β0 and 

βd , in accordance with the independence of e and u . Random values of the scale parameter σ2

u

are generated by multiplying the estimate of σ2

u from the distance regression by 100 randomly 

generated values from a chi-square distribution with degrees of freedom given by the distance 

regression results; the result is normalized by the number of degrees of freedom.

The bias-corrected slope estimate is obtained assuming a ratio form for the bias. Under 

this approach (see Schwarz and others, 2006), a multiplicative bias-correction term is computed as the ratio of the estimate of x using the regression coefficient estimates to the mean 

value of the bootstrap-generated values; the expe term cancels from this formulation. 
38 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

Thus, if S (β

ˆˆˆ 2

0 0 ,β d , , θ σˆμ; X0 ) is the regression-based estimate of equation x, with e set to zero, 

S    r  r r  2 r

0 0 , , d  , ; u X0  is the bootstrap-iteration r evaluation of this function using parametric bootstrap 

iteration r of R randomly generated sets of the coefficients    r  r r     0 0 , , 2 r ,   d u , and eˆ

i is the i -th of N

estimated residuals from the regression model, the bias-corrected estimate of slope is 

S ( ˆ ˆ ,β ˆ 2β 2 N

S N ˆ exp(eˆ ) 0 0= ∑ d , , θ σˆμ; 0 −1 ) i

R−

( . 1

∑R

S β(r)

,β(r r ) 2 1 1 0 0 , , θ( ) σ (r) i= r= d u ; )

X

 (3.5)

X0

Equation 3.5 is first applied to determine slope at the most downstream flowlines having no upstream 

set point. Given the estimate of slope, and knowledge of the downstream node elevation and flowline length, 

it is straightforward to estimate the upstream node elevation. As with the interpolation method, if there are 

multiple flowlines meeting at a diversion, the node is assigned the largest elevation determined among the 

diverting flowlines. The method progresses upstream to determine slope and upstream node elevation based 

on revised values of the predictor variables, X0 and d0 . 

The revised method obtains slopes based on set points available for both the upstream and downstream 

nodes in 71 percent of the flowlines. Slope was determined for 23 percent of the flowlines by interpolation 

between set points beyond their upstream or downstream nodes. Slope was assigned for 6 percent of 

flowlines on the basis of the regression method, a considerable improvement over the original NHDPlus 

slopes which have fill-in values (the default slope 0.00001) assigned to 14 percent of all flowlines. The 

method is computationally efficient and operates simultaneously on the entire 2.6 million flowlines 

composing the national NHDPlusV2 flow-directed network. Computation requires no more than four passes 

through the network and can be completed in about 2 minutes.

Figure 3.2 shows the method of slope determination for flowlines in region 08, a region of fairly low 

relief at the mouth of the Mississippi River. Most small tributaries have slope determined directly from 

upstream or downstream set points. Interpolated slopes tend to be associated with larger channels, which are 

of lower relief than tributary channels. The regression-based fill-in values are primarily located in tributary 

headwaters near the Gulf of Mexico, where relief is low, even for tributaries. 

The distributions of the revised slopes with the original NHDPlus slopes are shown in figure 3.3 for 

comparison. The figure shows that the original NHDPlusV2 slope estimates (fig. 3.3A) have two distributional anomalies: a spike in the distribution associated with the default fill-in slope value, 0.00001, and a 

discontinuous bump in the distribution for mid- to upper-range slope estimates (see areas circled in red). The 

revised estimates (fig. 3.3B) successfully eliminated these distributional anomalies.

One reason the default slope is so prevalent in the original NHDPlusV2 estimates is that it is automatically applied for any flowline classified as a waterbody, the reasoning being that waterbodies are effectively “flat.” The revised approach does not distinguish waterbodies. Consequently, the revised slopes for 

waterbodies can be interpreted as the slopes that would prevail if these flowlines were converted to flowing 

streams. This interpretation may be problematic for some applications, particularly the estimation of travel 

time for waterbodies. However, given that travel time for waterbodies is typically estimated using a method 

that depends on volume and flow, and not using the typical method for streams that depends on slope, this 

problem is moot. 

Figure 3.4 shows the revised and original NHDPlus slopes compared to slopes determined at approximately 2,846 sampling sites of the U.S. Environmental Protection Agency’s National River and Stream 

Assessment (NRSA) and Wadeable Stream Assessment (WSA) that are indexed to the NHDPlusV2 network. 

The comparison shows good agreement between the revised slope estimates and the measured slopes. The 

bulge of disagreement in the slope values at a NRSA/WSA slope of 0.01 is due to rounding error in the 

NRSA/WSA estimates. As can be expected, the revised slopes show greater disagreement for smaller NRSA/

WSA values. The original NHDPlus default slopes, appearing as a horizontal grouping at the value 0.00001, 

are shown to distribute over four orders of magnitude of NRSA/WSA slope estimates, indicating considerable bias associated with these default values. 
Appendix 3 39

0 50 100 150 200 MILES

0 90 180 270 360 KILOMETERS

TEXAS

LOUISIANA

OKLAHOMA

ARKANSAS

MISSOURI

KENTUCKY

TENNESSEE

ALABAMA

MISSISSIPPI

Gulf of Mexico

Base from U.S. Environmental Protection Agency and U.S. Geological Survey 1:100,000

National Hydrography Data Plus version 2 (NHDPlusV2)

38°

36°

34°

32°

30°

92° 90° 88° 86°

EXPLANATION

Slope determination method

Raw elevation

Linear interpolation

Regression

Figure 3.2. NHDPlusV2.1 streams in production unit 08 in the United States, and the specific 

method used to determine slope using the revised method.
40 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet 17.5 15.0 12.5 10.0 7.5 5.0 2.5

0

17.515.012.510.0

7.55.02.5

0

Revised percent Original percent

-7.46 -6.74 -6.02 -5.30 -4.58 -3.86 -3.14 -2.42 -1.70 -0.98 -0.26 0.46

Log base 10 slope

3.02.52.01.51.00.5

0

3.02.52.01.51.00.5

0

Revised percent Original percent

-7.46 -6.74 -6.02 -5.30 -4.58 -3.86 -3.14 -2.42 -1.70 -0.98 0.26

Log base 10 slope

0.46

Figure 3.3. Slope distributions of the original NHDPlusV2 and revised methods.
Appendix 3 41

1

0.01

0.0001

0.000001

NHDPlus slope

0.000001 0.00001 0.0001 0.001 0.01 0.1 1

WSA/NRSA slope

Line of equal value

Original NHDPlusV2 slopes 

Revised slopes

EXPLANATION

Figure 3.4. NHDPlus slopes determined according to the original NHDPlusV2 method and the revised method in relation to slopes 

measured at 2,846 sites indexed to NHDPlusV2 from the U.S. Environmental Protection Agency's Wadeable Streams Assessment 

and National River and Stream Assessment. WSA, Wadeable Streams Assessment; NRSA, National River and Stream Assessment.
42 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

References Cited

McKay, L., Bondelid, T., Dewald, T., Johnston, J., Moore, R., 

and Rea, A., 2012, NHDPlus Version 2: User Guide: U.S. 

Environmental Protection Agency, 182 p., accessed May 1, 

2018, at https://s3.amazonaws.com/nhdplus/NHDPlusV21/

Documentation/NHDPlusV2_User_Guide.pdf.

Schwarz, G.E., Hoos, A.B., Alexander, R.B., and Smith, R.A., 

2006, The SPARROW surface water-quality model: Theory, 

application, and user documentation: U.S. Geological Survey Techniques and Methods, book 6, chap. B3, accessed 

month, day, year, at http://pubs.usgs.gov/tm/2006/tm6b3/.

U.S. Environmental Protection Agency, National Aquatic 

Resource Surveys, Wadeable Streams Assessment: U.S. 

Environmental Protection Agency, accessed March 30, 

2017, at https://www.epa.gov/national-aquatic-resourcesurveys/wadeable-streams-assessment.

U.S. Environmental Protection Agency, National Aquatic 

Resource Surveys, National Rivers and Streams Assessment: U.S. Environmental Protection Agency, accessed 

March 30, 2017, at https://www.epa.gov/national-aquaticresource-surveys/data-national-aquatic-resource-surveys.
Appendix 4 43

Appendix 4. Description of Attributes in E2NHDPlusV2_us

Table 4.1. Description of attributes in E2NHDPlusV2_us.—Continued

[km, kilometer; km2, square kilometer; cm, centimeter; mm, millimeter; deg, degree; neg, negative value; deg C, degrees Celsius; cfs, cubic feet per second; 

char, characters; %, percent; f/s, foot per second; f, foot]

E2NHDPlusV2_us variable name NHDPlusV2.1 variable name Description

ComID ComID Unique feature identifier

FL_ComID ComID Comid for flowline (neg=closed catch)

FL_GNIS_ID GNIS_ID Flowline GNIS identifier

FL_GNIS_Na GNIS_Name Stream GNIS name (<=65 char)

LENGTHKM LENGTHKM Flowline length (km)

REACHCODE REACHCODE Reach code

WB_Comid WBAreaComI Comid for waterbody

FL_FCode FCode Flowline feature code

nhdplusreg NHDPlus region code

FL_FTypeAb Flowline feature type abbreviation

IfWDig If flowline in digitized network (0/1)

StreamLeve StreamLeve Strm levl (lowst=main,1=cst,4=grnd/brdr)

StreamOrde StreamOrde Stream order (1-10)

StreamCalc StreamCalc Major path stream order (1-10)

FromNode FromNode Original NHDPlusV2 from node

ToNode ToNode Original NHDPlusV2 to node

Hydroseq Hydroseq Hydro sequence (upstream order)

LevelPathI LevelPathI Levl path (hydsq most dnstrm FL in path)

TerminalPa TerminalPa Hydseq value of terminal FL in path

TotLngthKm ArbolateSu Total upstream flowline length (km)

Divergence Divergence Divergnce code (0=none,1=prime,2=second)

StartFlag StartFlag If flowline is headwater

TermFlag TerminalFl If terminate at coast/grnd/border

FromMeas FromMeas Reachcode measure at dnstrm node (0-100)

ToMeas ToMeas Reachcode measure at upstrm node (0-100)

IncAreaKm2 AreaSqKm Incremental drainage area (km2)

TotAreaKM2 TotDASqKM Total upstream area (km2)

CumAreaKm2 DivDASqKm Cumulative div-based drainage area (km2)

DivFrac DivFrac Diversion fraction

MaxElRawCm MaxElevRaw Maximum catchment raw elevation (cm)

MinElRawCm MinElevRaw Minimum catchment raw elevation (cm)

MaxElSmoCm MaxElevSmo Max catchment smoothed elevation (cm)

MinElSmoCm MinElevSmo Min catchment smoothed elevation (cm)

SLOPE SLOPE Channel slope (vert length/horiz length)

HWNodeAKm2 HwNodeSqKm Area upstream of headwater node (km2)

CatAreaKm2 AreaSqKm Incremental catchment area (km2)

IfSink If closed catchment

PrecipV PrecipV Mean annual precipitation (mm*100)

TempV TempV Mean annual temperature (deg C*100)

RunOffV RunOffV Mean annual runoff (mm)
44 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

Table 4.1. Description of attributes in E2NHDPlusV2_us.—Continued

[km, kilometer; km2, square kilometer; cm, centimeter; mm, millimeter; deg, degree; neg, negative value; deg C, degrees Celsius; cfs, cubic feet per second; 

char, characters; %, percent; f/s, foot per second; f, foot]

E2NHDPlusV2_us variable name NHDPlusV2.1 variable name Description

MAFlowUcfs Q0001E Cum mean annual flow (unit mthd,cfs)

MAVelUfps V0001E Stream velocity at mean ann. flow (f/s)

IncFlwUcfs Qincr0001E Incr catch mn ann flow (unit mthd,cfs)

WB_GNIS_ID GNIS_ID Waterbody GNIS identifier

WB_GNIS_Na GNIS_Name Waterbody GNIS name (<=65 char)

WB_AreaKm2 AreaSqKm Waterbody area (km2)

WB_ElevaFt Elevation Waterbody elevation (f)

WB_FCode FCode Waterbody feature code

WB_FTypeAb Waterbody feature type abbreviation

ifarea If NHDAREA feature (0/1)

alt_reg WB feature alternative NHDPlusreg

cfromnode Compactified from node

ctonode compactified to node

termground If all dwnstrm flowpaths term to grounnd

divunrslvd If unresolved divrsion path

orig_CumAreaKm2 Pre-modification CumAreaKm2

inc_CumAreaKm2 Difference incremental area (km2)

orig_MAFlowUcfs Pre-modification MAFlowUcfs

inc_MAFlowUcfs Difference incremental flow (cfs)

ModDivFrac Modified value of diversion fraction

ModFDir If modified to reverse flow direction

orig_divergence Pre-modification divergence code

orig_divfrac Pre-modification divergence fraction

orig_hydroseq Pre-modification hydro sequence

incusarkm2 Amount incareakm2 in US (km2)

CUMUSARKM2 Amount cumareakm2 in US (km2)

PCTFORGNAR Percent cumareakm2 not in US (%)

ifintterm If flowline terminates to ground (0/1)

ifextterm If flwline terminates at US border (0/1)

pctunrslvd Indx non-div2grnd unrslvd drnage (0-100)

divmajorpa If major path on non-trmgrnd unrslvd div

div2ground If termground w non-termground upstream

divindetrm If non-trmgrnd & completes div2grnd loop

divgroup Hydro-independent diversion group id

divfrslvd If div-affected FL reslvd w mod divfrac
Appendix 5 45

Appendix 5. Description of Selected Ancillary Geospatial Dataset Variables 

Assigned to the Catchments and Flowlines of NHDPlusV2.1

Table 5.1. Description of selected ancillary geospatial dataset variables assigned to the catchments of and flowlines of NHDPlusV2.1.

—Continued

Theme Dataset Variable Citation

Atmospheric 

Deposition

Community Multiscale Air Quality (CMAQ) Wet 

Atmospheric 

Deposition

2012 Wet Zhang, W., Capps, S.L., Hu, Y., Nenes, A., Napelenok, 

S.L., and Russell, A.G., 2012, Development of the 

high-order decoupled direct method in three dimensions for particulate matter: enabling advanced 

sensitivity analysis in air quality models: Geoscience 

Model Development, v. 5, no. 2, p. 355–368, 

https://doi.org/10.5194/gmd-5-355-2012, 2012.

Atmospheric 

deposition

Community Multiscale Air Quality (CMAQ) Dry 

Atmospheric 

Deposition

2012 Dry Schwede, D.B., and Lear, G.G., 2014, A novel hybrid 

approach for estimating total deposition in the United 

States, U.S. Environmental Protection Agency Papers, 

accessed June 20, 2019, at http://digitalcommons.unl.

edu/usepapapers/219

Bankful 

width/

depth

Bankfull_width_

area_depth

Bankful Width/Depth Wieczorek, M.E., Jackson, S.E., and Schwarz, G.E., 

2019, Select Attributes for NHDPlus Version 2.1 Reach 

Catchments and Modified Network Routed Upstream 

Watersheds for the Conterminous United States (ver 

2.0, October 2019): U.S. Geological Survey data release, https://doi.org/10.5066/F7765D7V: Bankfull Hydraulic Geometry Related to Physiographic Divisions.

Climate Parameter-elevation 

Regressions on 

Independent Slopes 

Model (PRISM)

Mean Temperature and Precipitation, 

2000–2014

PRISM, PRISM Group at Oregon State University, 

http://www.prism.oregonstate.edu/

Climate Parameter-elevation 

Regressions on 

Independent Slopes 

Model (PRISM)

Average number of wet days (CAT_

CWD)–30 year average

Easterling, D.R., Karl, T.R., Lawrimore, J.H., and Del 

Greco, S.A., 1999, United States Historical Climatology Network Daily Temperature, Precipitation, and 

Snow Data for 1871–1997, https://info.ornl.gov/sites/

publications/Files/Pub57691.pdf

Climate Water Balance Model 

Output

Actual Evapotranspiration, Potential 

evapotranspiration, Precipitation, 

Runoff, Snowfall, Soil Moisture, Temperature. 2000–2014, AVERAGE.

Wolock, D.M., and McCabe, G.J., 2018, Water Balance 

Model inputs and outputs for the conterminous United 

States, 1900–2015: U.S. Geological Survey data release, https://doi.org/10.5066/F71V5CWN.

Climate Water Balance Model 

Output

Actual Evapotranspiration, Potential 

evapotranspiration, Precipitation, 

Runoff, Snowfall, Soil Moisture, 

Temperature. 2000–2014, detrended 

to 2012.

Wolock, D.M., and McCabe, G.J., 2018, Water Balance 

Model inputs and outputs for the conterminous United 

States, 1900–2015: U.S. Geological Survey data release, https://doi.org/10.5066/F71V5CWN.

Conservation 

Practices

National Agricultural 

Statistics Service 

(NASS)

CPRAC1: Land artificially drained by 

ditches 2012.

CPRAC4: Land drained by tile 2012.

CPRAC7: Land on which conservation 

tillage was used 2012.

CPRAC10: Land on which conventional 

tillage was used 2012.

CPRAC13: Land on which no-till practices were used 2012.

CPRAC16: Land planted to a cover crop 

(excluding CRP) 2012.

CPRAC19: Land under a conservation 

easement 2012.

U.S. Department of Agriculture, National Agricultural 

Statistics Service, 2016, Land Use Practices Data at 

the National, State, and County Level, accessed June 

2017 at https://www.nass.usda.gov/Publications/

AgCensus/2012/.
46 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

Table 5.1. Description of selected ancillary geospatial dataset variables assigned to the catchments of and flowlines of NHDPlusV2.1.

—Continued

Theme Dataset Variable Citation

Conservation 

Practices

National Resources 

Inventory (NRI)

Grouped practices identified as improving or worsening water qualityWieczorek, M.E., Jackson, S.E., and Schwarz, G.E., 

2019, Select Attributes for NHDPlus Version 2.1 Reach 

Catchments and Modified Network Routed Upstream 

Watersheds for the Conterminous United States 

(ver. 2.0, October 2019): U.S. Geological Survey data 

release, https://doi.org/10.5066/F7765D7V: Percent of 

Agriculture Land Subject to select national resource 

inventory conservation practices, 1997–2012.

Conservation 

Practices

Irrigated Agriculture Dataset for 

the United States 

(MIrAD-US)

2012 Irrigation Brown, J.F., and Pervez, M.S., 2014, Merging remote 

sensing data and national agricultural statistics to 

model change in irrigated agriculture: Agricultural 

Systems, accessed May 17, 2016, at doi:10.1016/j.

agsy.2014.01.004. Data Accessed May 17, 2016, at 

https://earlywarning.usgs.gov/USirrigation

Conservation 

Practices

National Resources 

Inventory (NRI)

1992 Tile Drainage Nakagaki, N., Wieczorek, M.E., and Qi, S.L., 2016, 

Estimates of subsurface tile drainage extent for the 

conterminous United States, early 1990s: U.S. Geological Survey data release, http://dx.doi.org/10.5066/

F7RB72QS.

Conservation 

Practices

U.S. Department of 

Agriculture, Farm 

Service Agency 

(FSA)

2012 Percent Farm land in Conservation 

Reserve Program (CRP) (fraction of 

CRP acres/total cropland)

United States Department of Agriculture, Farm Service 

Agency, 2012, Conservation Reserve Program Statistics; CRP Practice Acres by County, accessed March 9, 

2018, at https://www.fsa.usda.gov/programs-andservices/conservation-programs/reports-and-statistics/

conservation-reserve-program-statistics/index

Conservation 

Practices

NRCS CEAP model 

output

LOSS in TP, TN, and Sediment due to U.S. Department of Agriculture, Natural Resources Conservation Service, Resource Assessment Division, Conservation Effects Assessment Project (CEAP)—Cropland, 2003–06 Farmer Survey CEAP-1 APEX output at 

HUC-4 resolution, conterminous U.S., July 2016.

Distance to 

Streams

NHDPlus Flow 

Direction

Length Wieczorek, M.E., Jackson, S.E., and Schwarz, G.E., 

2018. Select attributes for NHDPlus Version 2.1 reach 

catchments and modified network routed upstream 

watersheds for the conterminous United States: U.S. 

Geological Survey data release, https://doi.org/10.5066/

F7765D7V. Mean Flow Path Distance to NHDPlus 

Version 2 Streams.

Enhanced 

Vegetation Index 

(EVI)

Moderate Resolution 

Imaging Spectroradiometer (MODIS)

2012 Seasonal greenness Vermote, E., 2015, MOD09A1 MODIS/Terra Surface 

Reflectance 8-Day L3 Global 500m SIN Grid V006 

[MOD09A1]. NASA EOSDIS Land Processes DAAC. 

doi: 10.5067/MODIS/MOD09A1.006.

Fertilizer U.S. Geological 

Survey Estimates 

of Fertilizer Use.

2012 N and P, Conditional and 

Unconditional

Stewart, J.S., Brakebill, J.W., Schwarz, G.E., and Preston, 

S.D., 2018, Dataset used for estimating catchment-level 

nitrogen and phosphorus fertilizer use from commercial fertilizer sales data for the conterminous U.S., 

2012. U.S. Geological Survey data release, 

https://doi.org/10.5066/F73J3C50

Fertilizer U.S. Geological Survey Estimates of 

County Fertilizer 

Use.

2012 N and P Brakebill, J.W., and Gronberg, J.M., 2017, County-level 

estimates of nitrogen and phosphorus from commercial 

fertilizer for the conterminous United States, 1987–

2012: U.S. Geological Survey data release, 

https://doi.org/10.5066/F7H41PKX.
Appendix 5 47

Table 5.1. Description of selected ancillary geospatial dataset variables assigned to the catchments of and flowlines of NHDPlusV2.1.

—Continued

Theme Dataset Variable Citation

Geology Surficial Material of 

the US.

All Classes Soller, D.R., Reheis, M.C., Garrity, C.P., and Van Sistine, 

D.R., 2009, Map database for surficial materials in the 

conterminous United States: U.S. Geological Survey 

Data Series 425, scale 1:5,000,000 [https://pubs.usgs.

gov/ds/425/].

Geology Lithology Lithologic classes Anning, D.W., and Ator, S.W., 2017, Generalized lithology for the conterminous United States: U.S. Geological Survey data release, https://doi.org/10.5066/

F7R78D4N.

Hydrologic Base Flow Index 

(bfi48grd)

Value (base flow index value as percent) Wolock, D.M., 2003, Base-flow index grid for the 

conterminous United States. U.S. Geological Survey digital data, accessed December 21, 2015, at 

http://water.usgs.gov/lookup/getspatial?bfi48grd.

Land Use/

Cover

National Land Cover 

Database (NLCD)

All Land Classes Homer, C.G., Dewitz, J.A., Yang, L., Jin, S., Danielson, 

P., Xian, G., Coulston, J., Herold, N.D., Wickham, 

J.D., and Megown, K., 2015, Completion of the 2011 

National Land Cover Database for the conterminous 

United States-Representing a decade of land cover 

change information: Photogrammetric Engineering and 

Remote Sensing, v. 81, no. 5, p. 345–354, accessed 

April 22, 2015, at https://www.mrlc.gov/nlcd2011.php.

Land Use/

Cover

Cropland Data Layer 

(CDL)

All Land Classes USDA National Agricultural Statistics Service Cropland Data Layer, 2012, Published crop-specific data 

layer [Online], accessed April 22, 2015, at 

https://nassgeodata.gmu.edu/CropScape/

Land Use/

Cover

U.S. Conterminous 

Wall-to-Wall 

Anthropogenic 

Land Use Trends 

(NWALT), 

1974–2012

All Land Classes Falcone, J.A., 2015, U.S. conterminous wall-to-wall 

anthropogenic land use trends (NWALT), 1974–2012: 

U.S. Geological Survey Data Series 948, 33 p. plus 

appendixes 3–6 as separate files, accessed February 12, 

2015, at http://dx.doi.org/10.3133/ds948

Land Use/

Cover

National Land Cover 

Database (NLCD) 

Percent Impervious

Percent Impervious Xian, G., Homer, C., Dewitz, J., Fry, J., Hossain, N., and 

Wickham, J., 2011, The change of impervious surface 

area between 2001 and 2006 in the conterminous United States: Photogrammetric Engineering and Remote 

Sensing, v. 77, no. 8, p. 758–762, accessed December 

21, 2015, at https://www.mrlc.gov/nlcd11_data.php.

Land Use/

Cover

U.S. Geological 

Survey Landslide 

Hazard

Landslide susceptibility Godt, J.W., Landslide Hazards: U.S. Geological Survey, 

accessed April 30, 2014, at https://landslides.usgs.gov/

hazards/nationalmap/

Land Use/

Cover

National Land Cover 

Database (NLCD) 

Tree Canopy

Percent canopy in 100m buffer of stream 

network

Tipton, J., Moisen, G., Patterson, P., Jackson, T.A., and 

Coulston, J., 2012, Sampling intensity and normalizations: Exploring cost-driving factors in nationwide 

mapping of tree canopy cover, in McWilliams, W., and 

Roesch, F.A., eds., 2012, Monitoring Across Borders: 2010 Joint Meeting of the Forest Inventory and 

Analysis (FIA) Symposium and the Southern Mensurationists. e-Gen. Tech. Rep. SRS-157. Asheville, 

NC: U.S. Department of Agriculture, Forest Service, 

Southern Research Station: p. 201–208.
48 An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet

Table 5.1. Description of selected ancillary geospatial dataset variables assigned to the catchments of and flowlines of NHDPlusV2.1.

—Continued

Theme Dataset Variable Citation

Manure U.S. Geological 

Survey County Estimates of Manure

2012 N and P Gronberg, J.M., and Arnold, T.L., 2017, County-level 

estimates of nitrogen and phosphorus from animal 

manure for the conterminous United States, 2007 and 

2012: U.S. Geological Survey Open-File Report 2017–

1021, 6 p. [Also available at https://doi.org/10.3133/

ofr20171021.]

Municipal 

Discharge

U.S. Environmental 

Protection Agency 

(EPA) Integrated 

Compliance Information System 

(ICIS) and Permit 

Compliance System (PCS).

2012 N, P, flow Skinner, K.D., and Wise, D.R., 2018, Point-source nutrient loads to streams of the conterminous United States, 

2012: U.S. Geological Survey data release,

https://doi.org/10.5066/P9PYVPFT.

Natural 

Phosphorus

U.S. Geological Survey Estimates of 

Natural Phosphorus (Pmapnatl)

Bed Sediment Phosphorus Nardi, M.R., 2014, Watershed Potential to Contribute 

phosphorus from geologic materials to receiving 

streams, conterminous United States. U.S. Geological 

Survey, accessed June 20, 2017, at https://water.usgs.

gov/GIS/metadata/usgswrd/XML/pmapnatl.xml.

Natural 

Phosphorus

U.S. Geological 

Survey Estimates 

of Natural Phosphorus

Natural occurring phosphorous in 

4 different soil horizons (A horizon, 

C horizon and A horizon, C horizon 

per Geologic Mapping Unit)

Terziotti, S., 2019, Distribution of phosphorus in soils and 

aggregated within geologic mapping units, conterminous United States: U.S. Geological Survey data 

release, https://doi.org/10.5066/P918DF1E. 

Physiography U.S. Geological Survey Physiograohuc 

Divisions (Physio)

Physiographic Regions Fenneman, N.M., and Johnson, D.W., 1946, Physiographic divisions of the conterminous U.S., accessed 

October 2, 2017, at https://water.usgs.gov/lookup/

getspatial?physio

Population 

Census

U.S. Geological Survey Estimates of 

Housing Density

2000, 2010 Falcone, J.A., 2016, U.S. block-level population density 

rasters for 1990, 2000, and 2010: U.S. Geological Survey data release, http://dx.doi.org/10.5066/F74J0C6M.

Reservoir 

Information

NHDPlus, National 

Inventory Dams 

(NID), and National Land Cover 

Data (NLCD)

Surface area Wieczorek, M.E., Jackson, S.E., and Schwarz, G.E., 

2018, Select attributes for NHDPlus Version 2.1 reach 

catchments and modified network routed upstream 

watersheds for the conterminous United States: U.S. 

Geological Survey data release, https://doi.org/10.5066/

F7765D7V. Attributes for Surface Water Impoundments Used in NAWQA SPARROW Model

Reservoir 

Information

National Hydrologic 

Database Plus 

(NHDPlus)

Lake residence time McKay, L., Bondelid, T., Dewald, T., Johnston, J., 

Moore, R., and Rea, A., “NHDPlus Version 2: 

User Guide”, 2012, accessed March 4, 2015, at 

https://s3.amazonaws.com/nhdplus/NHDPlusV21/

Documentation/NHDPlusV2_User_Guide.pdf

Septic U.S. Geological 

Survey Estimates 

of Populations on 

Septic (SepN_

CONUS_bg_2010)

Estimate of nitrogen from septic LaMotte, A.E., 2018, Estimated nitrogen from septic for 

the conterminous United States, 2010: U.S. Geological 

Survey data release, https://doi.org/10.5066/P9QTGSI7

Soil propertiesState Soil Geographic 

Database 

(STATSGO)

Pct Clay, Sand, Silt, KFACT, grain size, 

permeability, rainfall runoff factor, 

soil thickness, depth to water table, 

available water capacity, mean salinity

Wolock, D.M., 1997, STATSGO soil characteristics 

for the conterminous United States: U.S. Geological 

Survey Open-File Report 656, XX p., accessed May 4, 

2008, at https://water.usgs.gov/lookup/getspatial?muid
Appendix 5 49

Table 5.1. Description of selected ancillary geospatial dataset variables assigned to the catchments of and flowlines of NHDPlusV2.1.

—Continued

Theme Dataset Variable Citation

Soil propertiesState Soil Geographic 

Database 

(STATSGO2)

Hydrologic Groups, Soil Ph Soil Survey Staff, U.S. Department of Agriculture, 

Natural Resources Conservation Service, General 

Soil Map (STATSGO2), 2010, accessed October 14, 

2010, at http://websoilsurvey.sc.egov.usda.gov/App/

WebSoilSurvey.aspx

Water use U.S. Geological 

Survey County 

Estimates of 

Water Use

2010 surface water withdrawal, 

groundwater withdrawal, irrigation

Maupin, M.A., Kenny, J.F., Hutson, S.S., Lovelace, J.K., 

Barber, N.L., and Linsey, K.S., 2014, Estimated use 

of water in the United States in 2010: U.S. Geological 

Survey Circular 1405, 56 p. [Also available at 

https://dx.doi.org/10.3133/cir1405.]

Water use U.S. Geological Survey Estimates of 

Population Served 

by Public Supply 

Water Use

Population Served Price, C.V., and Maupin, M.A., 2014, Documentation for 

the U.S. Geological Survey Public-Supply Database 

(PSDB)—A database of permitted public-supply wells, 

surface-water intakes, and systems in the United States: 

U.S. Geological Survey Open-File Report 2014–1212, 

22 p. [Also available at https://dx.doi.org/10.3133/

ofr20141212.]

Water use U.S. Geological 

Survey Estimates 

of Thermoelectric 

Water Use

Total Withdrawal Diehl, T.H., Harris, M.A., Murphy, J.C., Hutson, S.S., and 

Ladd, D.E., 2013, Methods for estimating water consumption for thermoelectric power plants in the United 

States: U.S. Geological Survey Scientific Investigations 

Report 2013–5188, 78 p. [Also available at 

http://dx.doi.org/10.3133/sir20135188.]

Watersheds National Watershed 

Boundary Dataset 

(WBD)

HUC12 Moore, R.B., 2019, Crosswalk between NHDPlus V2.1 

and its WBD Snapshot: U.S. Geological Survey data 

release, https://doi.org/10.5066/P9CFXHGT

Wildfire U.S. Geological 

Survey Estimates 

of Areas Burned 

by Wildfire 

(Gaplf2011)

2000–2012 U.S. Geological Survey Gap Analysis Program, 2016, 

GAP/LANDFIRE National Terrestrial Ecosystems 

2011, accessed December 22, 2017, at 

https://doi.org/10.5066/F7ZS2TM0.

For additional information, contact:

NAWQA Science Team

U.S. Geological Survey

12201 Sunrise Valley Drive, MS 413

Reston, VA 20191-0002

Email: gs-w_opp_nawqa_science_team@usgs.gov

Publishing support provided by the 

West Trenton and Pembroke Publishing Service Centers
Brakebill and others—An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution DataSet—Scientific Investigations Report 2019–5127

ISSN 2328-0328 (online)

https://doi.org/10.3133/sir20195127