Prepared in cooperation with the Legislative-Citizen Commission on Minnesota Resources

Streamflow Distribution Maps for the Cannon River Drainage 

Basin, Southeast Minnesota, and the St. Louis River Drainage 

Basin, Northeast Minnesota

Pamphlet to accompany 

Scientific Investigations Map 3390

U.S. Department of the Interior

U.S. Geological Survey
Cover. Streamflow distribution maps for the Cannon River and St. Louis River drainage basins. 
Streamflow Distribution Maps for the 

Cannon River Drainage Basin, Southeast 

Minnesota, and the St. Louis River 

Drainage Basin, Northeast Minnesota

By Erik A. Smith, Chris A. Sanocki, David L. Lorenz, and Katrin E. Jacobsen

Prepared in cooperation with the Legislative-Citizen Commission on 

Minnesota Resources

Scientific Investigations Map 3390

U.S. Department of the Interior

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

RYAN K. ZINKE, Secretary

U.S. Geological Survey

William H. Werkheiser, Deputy Director 

 exercising the authority of the Director

U.S. Geological Survey, Reston, Virginia: 2017

For more information on the USGS—the Federal source for science about the Earth, its natural and living 

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For an overview of USGS information products, including maps, imagery, and publications, 

visit https://store.usgs.gov.

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U.S. Government.

Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Smith, E.A., Sanocki, C.A., Lorenz, D.L., and Jacobsen, K.E., 2017, Streamflow distribution maps for the Cannon River 

drainage basin, southeast Minnesota, and the St. Louis River drainage basin, northeast Minnesota: U.S. Geological 

Survey Scientific Investigations Map 3390, pamphlet 16 p., 2 sheets, https://doi.org/10.3133/sim3390.

ISSN 2329-132X (online)
iii

Acknowledgments

Funding for this study was provided by a grant from the Environmental and Natural Resource 

Trust Fund of Minnesota (Legislative-Citizen Commission on Minnesota Resources) and from 

U.S. Geological Survey Cooperative Matching Funds.

Jeffrey Ziegeweid is acknowledged for generating the flow-duration curve statistics using the 

estFDC package. Sarah Levin and Robert Kay of the U.S. Geological Survey are greatly acknowledged for colleague reviews of the report.

v

Contents

Acknowledgments........................................................................................................................................iii

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

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

Purpose and Scope ..............................................................................................................................2

Study Area Description........................................................................................................................2

Previous Studies ...................................................................................................................................3

Streamflow Distribution Maps.....................................................................................................................7

Flow-Duration Curve Statistics...........................................................................................................7

Soil-Water-Balance Models ...............................................................................................................9

Cannon River Drainage Basin....................................................................................................9

St. Louis River Drainage Basin ................................................................................................11

Adjusting the Flow-Duration Curve Statistics using the Soil-Water-Balance 

Model Output .........................................................................................................................11

Limitations and Assumptions............................................................................................................12

Summary........................................................................................................................................................13

References Cited..........................................................................................................................................13

Figures

1. Map showing the Cannon River study area, including the entire drainage basin 

above the Cannon River at Welch, Minnesota, streamgage (U.S. Geological 

Survey station number 05355200), main stem of the Cannon River and the 

Straight River, major cities/towns, and other administrative boundaries...........................4

2. Map showing the six Minnesota groundwater provinces, as outlined by the 

Minnesota Department of Natural Resources (2017), along with the Cannon 

River drainage basin (above the Cannon River at Welch, Minnesota, streamgage 

[U.S. Geological Survey station number 05355200]) and the St. Louis River drainage 

basin (above the St. Louis River at Scanlon, Minnesota, streamgage 

[U.S. Geological Survey station number 04024000])................................................................5

3. Map showing the St. Louis River study area, including the entire drainage basin 

above the St. Louis River at Scanlon, Minnesota, streamgage (U.S. Geological 

Survey station number 04024000), main stem of the St. Louis River, the 

Whiteface River, and the Cloquet River, major cities/towns, and other 

administrative boundaries...........................................................................................................6

4. Map showing the five hydrologic regions in Minnesota, as defined by the 

regional regression equations from Ziegeweid and others (2015), along with 

the Cannon River drainage basin (above the Cannon River at Welch, Minnesota 

[U.S. Geological Survey station number 05355200]), and the St. Louis River 

drainage basin (above the St. Louis River at Scanlon, Minnesota 

[U.S. Geological Survey station number 04024000])................................................................8
vi

Tables

1. Site information for the U.S. Geological Survey streamgages used for this study 

in the Cannon River and St. Louis River drainage basins, Minnesota.................................2

2. Annual mean base flow estimates rates (from 1996 to 2010) using three 

hydrograph separation techniques for the Cannon River at Welch, Minnesota 

(U.S. Geological Survey station number 05355200), used in model calibration 

analysis in comparison to the Soil-Water-Balance model estimated annual 

potential recharge rates............................................................................................................10

3. Annual mean base flow estimates rates (from 1996 to 2010) using three 

hydrograph separation techniques for the St. Louis River at Scanlon, Minnesota 

(U.S. Geological Survey station number 04024000), used in model calibration 

analysis for comparison to the Soil-Water-Balance model estimated annual 

potential recharge rates............................................................................................................12

Conversion Factors

International System of Units to U.S. customary units

Multiply By To obtain

Length

meter (m) 3.281 foot (ft)

meters per kilometer (m/km) 5.28 foot per mile (ft/mi)

kilometer (km) 0.6215 mile (mi)

millimeter (mm) 25.4 inch (in)

Area

square meter (m2

) 10.76 square foot (ft2)

square kilometer (km2

) 0.3861 square mile (mi2)

Volume

cubic meter (m3

) 35.31 cubic foot (ft3)

Flow rate

cubic meter per second (m3

/s) 35.31 cubic foot per second (ft3/s)

millimeters per year (mm/yr) 0.03937 inches per year (in/yr)

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

(NAVD 88), unless otherwise indicated.

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

Abbreviations

< less than

ET evapotranspiration

FDC flow-duration curve

HSPF Hydrologic Simulation Program-FORTRAN

LUC land-use classification

Minn. Minnesota

NLCD National Land Cover Database

SSURGO Soil Survey Geographic [database]

STATSGO State Soil Geographic [database]

SWB Soil Water Balance [model]

USGS U.S. Geological Survey

WRAPS watershed restoration and protection strategies

Streamflow Distribution Maps for the Cannon River 

Drainage Basin, Southeast Minnesota, and the St. Louis 

River Drainage Basin, Northeast Minnesota

By Erik A. Smith, Chris A. Sanocki, David L. Lorenz, and Katrin E. Jacobsen

Abstract

Streamflow distribution maps for the Cannon River 

and St. Louis River drainage basins were developed by 

the U.S. Geological Survey, in cooperation with the Legislative-Citizen Commission on Minnesota Resources, to 

illustrate relative and cumulative streamflow distributions. 

The Cannon River was selected to provide baseline data to 

assess the effects of potential surficial sand mining, and the 

St. Louis River was selected to determine the effects of ongoing Mesabi Iron Range mining. Each drainage basin (Cannon, 

St. Louis) was subdivided into nested drainage basins: the 

Cannon River was subdivided into 152 nested drainage basins, 

and the St. Louis River was subdivided into 353 nested drainage basins. For each smaller drainage basin, the estimated 

volumes of groundwater discharge (as base flow) and surface 

runoff flowing into all surface-water features were displayed 

under the following conditions: (1) extreme low-flow conditions, comparable to an exceedance-probability quantile of 

0.95; (2) low-flow conditions, comparable to an exceedanceprobability quantile of 0.90; (3) a median condition, comparable to an exceedance-probability quantile of 0.50; and (4) a 

high-flow condition, comparable to an exceedance-probability 

quantile of 0.02.

Streamflow distribution maps were developed using 

flow-duration curve exceedance-probability quantiles in 

conjunction with Soil-Water-Balance model outputs; both the 

flow-duration curve and Soil-Water-Balance models were built 

upon previously published U.S. Geological Survey reports. 

The selected streamflow distribution maps provide a proactive 

water management tool for State cooperators by illustrating 

flow rates during a range of hydraulic conditions. Furthermore, after the nested drainage basins are highlighted in terms 

of surface-water flows, the streamflows can be evaluated in 

the context of meeting specific ecological flows under different flow regimes and potentially assist with decisions regarding groundwater and surface-water appropriations. Presented 

streamflow distribution maps are foundational work intended 

to support the development of additional streamflow distribution maps that include statistical constraints on the selected 

flow conditions.

Introduction

Water resource management in a drainage basin requires 

knowledge of the water budget. The water budget states that 

the rate of change of water volume in a drainage basin is balanced by the flow in and out of all the surface water bodies 

(for example, lakes, streams, reservoirs, and wetlands) and 

underlying groundwater that make up the drainage basin 

(Healy and others, 2007). In Minnesota, the County Geologic 

Atlas program provides basic information on surficial and 

buried aquifers, including maps that detail the distribution 

and the lithologic and hydrogeologic properties of rock and 

sediments that lie below the land surface (Setterholm, 2014); 

this information can help estimate the volume of groundwater 

in storage within a particular geological unit. For streams and 

rivers with active streamgage networks, the volume and rate of 

surface-water flows at a streamgaged location are well quantified. For surficial aquifers, the volume and rate of groundwater 

flow in and out of surface water bodies are affected by the 

amount of surface-water flow and flow to and from deeper 

aquifers (Winter and others, 1998).

Water budgets provide a means for evaluating water 

availability and sustainability in the absence of robust groundwater modeling to determine sustainable groundwater yields 

within a basin or long-term monitoring of streamflow to determine continuous surface-water flows. These water budgets can 

be an important initial step for determining the water balance; 

however, predevelopment water budgets should not be used as 

the sole source of information for determining the amount of 

groundwater withdrawal that can happen in a watershed or to 

preserve minimum ecological streamflows (Bredehoeft, 1997).

The initial water budget generally requires accurate 

estimates of the spatial and temporal distribution of the water 

budget components, including potential recharge, surface 

runoff, evapotranspiration (ET), inflow from surface-water 

bodies and groundwater, and outflow from surface-water bodies and groundwater. The Soil-Water-Balance (SWB) model 

was developed to estimate the volume of potential recharge 

using widely available geographic information system datasets 

(Westenbroek and others, 2010) and includes estimates of 

ET and surface runoff. The SWB modeling assumes most 
2 Streamflow Distribution Maps for the Cannon River Drainage Basin and the St. Louis River Drainage Basin, Minnesota

potential recharge eventually flows into nearby surface-water 

systems as base flow. Combined with surface runoff, this 

analysis can be the first step for a water budget.

Another common problem with developing water budgets 

is that streamgages are not available to compute streamflow at 

every stream location; however, methods have been developed 

to estimate flow-duration curve (FDC) statistics at stream locations in Minnesota (Ziegeweid and others, 2015) with little 

or no long-term monitoring data, such as the upper reaches 

of large drainage basins. An FDC is a cumulative frequency 

curve that shows the fraction of time that specified streamflow 

is equaled or exceeded (Searcy, 1959). An FDC is built by 

sorting streamflow observed during a given period by magnitude and calculating the probability that a specified streamflow 

value will be equaled or exceeded. The fraction of time that 

the streamflow is calculated to have been equaled or exceeded 

is termed exceedance probability. At an ungaged location, the 

FDC is constructed from regression-based estimates of streamflow for 13 exceedance probabilities (0.9999, 0.999, 0.99, 

0.95, 0.9, 0.75, 0.5, 0.25, 0.1, 0.05, 0.02, 0.001, and 0.0001) 

that cover the range of streamflow (Lorenz and Ziegeweid, 

2016). The FDC statistics define exceedance probabilities, and 

they are not a substitute for calculating continuous streamflow; 

instead, the FDC can provide a mechanism to assess water 

availability and determine initial estimates for sustained flow 

requirements (Unthank and others, 2012).

The contribution of base flow to total streamflow can 

be calculated for lower flow conditions, such as droughts, by 

integrating the SWB water budget components and the FDC 

exceedance probabilities. Additionally, the combined contribution of base flow and surface runoff at higher flow conditions, 

such as floods, can be estimated with this approach. To help 

guide the efforts made by the State of Minnesota to define 

water use sustainability from surficial aquifers and effectively 

manage streamflow during low-flow conditions, the U.S. Geological Survey (USGS), in cooperation with the LegislativeCitizen Commission on Minnesota Resources, combined SWB 

and FDC components to create a series of streamflow distribution maps for two pilot drainage basins. The selected streamflow distribution maps represent the relative contribution of 

base flow and surface runoff to total volume of streamflow 

for typical conditions, not necessarily the contribution for any 

particular time. These maps will help guide potential State 

cooperators such as the Minnesota Department of Natural 

Resources and the Minnesota Pollution Control Agency for 

proactive water management, ensure streamflows meet ecological needs at different flow conditions, and assist with decisions for groundwater appropriations at low-flow conditions.

Purpose and Scope

The purpose of this report is to describe the development of a series of estimated streamflow distribution maps 

at selected low-flow conditions and one high-flow condition 

for the Cannon River and St. Louis River drainage basins in 

Minnesota. The scope of the streamflow distribution maps was 

limited to the entire drainage basin above the farthest downstream continuous USGS streamgage. For the Cannon River, 

the maps included the entire drainage basin above the Cannon 

River at Welch, Minnesota (USGS station number 05355200). 

For the St. Louis River, the maps included the entire drainage basin above the St. Louis River at Scanlon, Minn. (USGS 

station number 04024000). Tasks specific to the estimated 

streamflow distribution maps were (1) analyzing the flow data 

collected at both gages (table 1); (2) generating FDC statistics 

for 505 smaller drainage basins within the 2 larger drainage 

basins using the Minnesota regional regression equations 

(Ziegeweid and others, 2015); (3) constructing and calibrating 

SWB models for each drainage basin for the period of January 1, 1996, through December 31, 2010, primarily based on 

the published Minnesota statewide Soil-Water-Balance model 

(Smith and Westenbroek, 2015); (4) adjusting the FDC statistics based on the results of the SWB models; and (5) producing estimated streamflow distribution maps based on selected 

flow conditions representative of approximately median, low 

flow, extreme low flow, and high flow conditions.

Study Area Description

The Cannon River drainage basin in southeastern Minnesota consists of the Cannon River and the Straight River 

(Wotzka and Watkins, 2016). The drainage basin above the 

Cannon River at Welch, Minn., streamgage is 3,471 square 

Table 1. Site information for the U.S. Geological Survey streamgages used for this study in the Cannon River and St. Louis River 

drainage basins, Minnesota.

[See figure 1 for station location; USGS, U.S. Geological Survey; Minn., Minnesota; ft3/s, cubic foot per second]

Station name

USGS 

station

number

Drainage area 

(square miles)

Latitude Longitude

Continuous period of record

Maximum recorded 

streamflow at 

streamgage and date (degrees, minutes, seconds)

Cannon River at 

Welch, Minn.

05355200 1,340 44° 33′ 50′′ 92° 43′ 55′′ 1931 to current year (2017) 36,100 ft3/s

April 8, 1965

St. Louis River at 

Scanlon, Minn.

04024000 3,430 46° 42′ 12′′ 92° 25′ 07′′ 1908 to current year (2017) 45,300 ft3/s

June 21, 2012
Introduction 3

kilometers (km2) (1,340 square miles [mi2]) (U.S. Geological Survey, 2017). The Cannon River flows northeast towards 

the Mississippi River near Red Wing, Minn., and the Straight 

River joins the Cannon River in Faribault, Minn. (fig. 1; 

table 1). The Cannon River drainage basin covers parts of 

nine counties (fig. 1), including Blue Earth, Dakota, Freeborn, 

Goodhue, LeSueur, Rice, Scott, Steele, and Waseca.

The Cannon River drainage basin overlies two groundwater provinces (Minnesota Department of Natural Resources, 

2017; fig. 2): the south-central province (groundwater province 2) and the southeastern province (groundwater province 3). The upstream sections of both the Cannon River and 

the Straight River are underlain by the south-central province, 

a glacial drift dominated landscape classified as thick clayey 

till overlying Paleozoic sandstone, limestone, and dolostone 

aquifers (Minnesota Department of Natural Resources, 2017). 

The glacial drift has a strong effect on infiltration and runoff, and the infiltration pathways are more diffuse through 

intermittent sand and gravel bodies, whereas the fine-grained 

units in this area impede infiltration (Runkel and others, 

2014). Discharge to surface waters in the upstream part of the 

drainage basin follows a more diffuse network of preferential 

groundwater flow pathways (Runkel and others, 2014).

The more downstream parts of the Cannon River, in addition to the lower parts of the Straight River near the confluence 

of the two rivers, are underlain by the southeastern province. 

The southeastern province is an area with thin clayey till overlying Paleozoic bedrock and has karst characteristics, particularly in the eastern section of the drainage basin (Runkel and 

others, 2014; Minnesota Department of Natural Resources, 

2017). The downstream parts of the Cannon River have a more 

pronounced connection to limestone and dolostone aquifers, in 

particular the Prairie du Chien Group (Groten and Alexander, 

2013). High-purity sands within the St. Peter Sandstone exist 

in a substantial proportion of the Cannon River drainage basin 

(not shown). These high-purity sands are often extracted for 

use in hydraulic fracturing.

The St. Louis River drainage basin is one of the largest in 

northern Minnesota (fig. 2; Anderson and others, 2013). The 

drainage basin above the St. Louis River at Scanlon, Minn., 

streamgage is 8,884 km2 (3,430 mi2) (U.S. Geological Survey, 

2017). The St. Louis River flows for 323 kilometers (km) 

(201 miles [mi]) towards Lake Superior before discharging 

into Lake Superior at Duluth, Minn. (fig. 3; table 1). The last 

24 km (15 mi) of the St. Louis River comprise the St. Louis 

River estuary. Several tributaries join the St. Louis River along 

its reach, including the Embarrass River (not shown), Whiteface River, Floodwood River (not shown), and the Cloquet 

River (fig. 3) (Lindholm and others, 1979). The St. Louis 

River drainage basin covers parts of five counties (fig. 3), 

including Aitkin, Carlton, Itasca, Lake, and St. Louis; however, most of the basin is contained within St. Louis County.

The St. Louis River drainage basin overlies the central groundwater province (groundwater province 4) and 

the arrowhead province (groundwater province 6; fig. 2). 

The upstream part of the St. Louis River drainage basin is 

underlain by the arrowhead province, which is made up of 

Precambrian rocks exposed at the surface or covered by less 

than 9.2 meters (30 feet) of glacial till (Minnesota Department 

of Natural Resources, 2017). Groundwater in this region typically flows through faults and fractures within the Precambrian 

bedrock, although sand and sandstone aquifers are present 

in small areas. The lower parts of the St. Louis River drainage basin are underlain by the central province. The central 

province is an area with sand aquifers and thicker sand and 

clay glacial drift overlying the Precambrian and Cretaceous 

bedrock. Both the sand deposits within the glacial till and the 

bedrock deposits function as aquifers (Minnesota Department 

of Natural Resources, 2017). St. Louis River gradients are 

highly variable, ranging from 0.1 to 6.5 meters per kilometer 

(Lindgren and others, 2006). Much of the upper and middle 

sections of the St. Louis River drainage include shallow gradients, resulting in a highly fragmented stream network made up 

of small, short tributaries (Lindgren and others, 2006). In the 

lower reaches, gradients increase to near 6.5 meters per kilometer, particularly in a 24-km (15-mi) reach below Cloquet, 

Minn. (fig. 3) (Lindholm and others, 1979).

The St. Louis River estuary, the final 24 km (15 mi) of 

the St. Louis River, has been listed as an Area of Concern 

by the International Joint Commission (Lindgren and others, 

2006) because of the effects of heavy industry (for example, 

mining) and untreated urban effluent since the 19th century. 

Mining activity has happened within the St. Louis River drainage basin in the past 150 years. The Mesabi Iron Range (not 

shown), noted for rich deposits of iron ore, parallels much of 

the northern drainage basin boundary (Lindholm and others, 

1979). Ongoing (2017) and potential future expansion of iron 

ore mining along the Mesabi Iron Range has been noted as an 

area of study and concern within the St. Louis River drainage 

basin (Fletcher and Christin, 2015).

Previous Studies

Several water-resource reports that include parts of the 

Cannon River drainage basin have been published during the 

last 45 years. A preliminary water budget that accounted for 

precipitation, ET, runoff, and a general compilation of water 

usage was included as part of a characterization of the Cannon 

River surface-water and groundwater resources by Anderson and others (1974). The high-purity silica sand deposits 

from the Middle Ordovician age were mapped across several 

midwestern states, including the Cannon River drainage basin 

in southeastern Minnesota by Ketner (1979). More recently, 

a comprehensive regional analysis of geologic controls on 

nitrate concentrations, in the context of the hydrogeologic 

attributes of surficial and bedrock deposits, was completed by 

Runkel and others (2014). Synoptic dye traces in a Cannon 

River tributary, Trout Brook, concluded that 30 to 40 percent 

of all Trout Brook streamflow was spring fed (Groten and 

Alexander, 2013). As part of the statewide watershed restoration and protection strategies (WRAPS) process, a Hydrologic 
4 Streamflow Distribution Maps for the Cannon River Drainage Basin and the St. Louis River Drainage Basin, Minnesota

05355200

Mankato

Rochester

Waseca

Chaska

Austin

Red Wing

Owatonna

Hastings

St. Peter

Le Center

Faribault

Albert Lea

Mantorville

RICE

GOODHUE

SCOTT DAKOTA

STEELE

MOWER

WASECA DODGE

LE SUEUR

FREEBORN

OLMSTED

FARIBAULT

BLUE EARTH

CARVER

SIBLEY

WABASHA

NICOLLET

WASHINGTON

FILLMORE

MCLEOD HENNEPIN

WISCONSIN

MINNESOTA

94°00’ 93°30’ 93°00’ 92°30’

44°30’

44°00’

WISCONSIN

NORTH

DAKOTA

SOUTH

DAKOTA

CANADA

IOWA

MINNESOTA

Study area

Base map modified from U.S. Geological Survey,

Minnesota Department of Natural Resources 

and other digital data, various scales

Universal Transverse Mercator projection, zone 15 North

North American Datum of 1983

EXPLANATION

Drainage basin boundary

U.S. Geological Survey streamgage 

and number (table 1)

0 5 10 15 20 MILES

0 5 10 15 20 KILOMETERS

Cannon River

Cannon River

Straight River

05355200

Figure 1. The Cannon River study area, including the entire drainage basin above the Cannon River at Welch, Minnesota, 

streamgage (U.S. Geological Survey station number 05355200), main stem of the Cannon River and the Straight River, major 

cities/towns, and other administrative boundaries.
Introduction 5

96° 94° 92° 90°

48°

46°

44°

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

NORTH

DAKOTA

SOUTH 

DAKOTA

WISCONSIN

IOWA

MICHIGAN

CANADA

UNITED STATES

LAKE SUPERIOR

ST. LOUIS

CANNON

MINESOTA

Base map modified from U.S. Geological Survey, Minnesota Department of Natural Resources and other digital data, 

various scales

Univeral Transverse Mercator projection, zone 15 North

North American Datum of 1983

Drainage basin boundary

1

2

3

4

5

6

Groundwater provinces

EXPLANATION

Figure 2. The six Minnesota groundwater provinces, as outlined by the Minnesota Department of Natural Resources (2017), 

along with the Cannon River drainage basin (above the Cannon River at Welch, Minnesota, streamgage [U.S. Geological Survey 

station number 05355200]) and the St. Louis River drainage basin (above the St. Louis River at Scanlon, Minnesota, streamgage 

[U.S. Geological Survey station number 04024000]).
6 Streamflow Distribution Maps for the Cannon River Drainage Basin and the St. Louis River Drainage Basin, Minnesota

93°30’ 93°00’ 92°30’ 92°00’ 91°30’

48°00’

47°30’

47°00’

46°30’

WISCONSIN

NORTH

DAKOTA

SOUTH

DAKOTA

CANADA

IOWA

MINNESOTA

Study area

EXPLANATION

0 10 20 30 40 MILES

0 10 20 30 40 KILOMETERS

LAKE SUPERIOR

MINNESOTA

WISCONSIN

Base map modified from U.S. Geological Survey, 

Minnesota Department of Natural Resources and 

other digital data, various scales

Univeral Transverse Mercator projection, zone 15 North

North American Datum of 1983

Duluth

Aitkin

Carlton

Two Harbors

Grand Rapids

Ely

Hibbing

Cloquet

Hoyt Lakes

ST. LOUIS

LAKE

AITKIN

ITASCA

PINE

CARLTON

KOOCHICHING

MILLE LACS

COOK

St. Louis Riv erSt. Louis Riv er

St. Louis River

Cloquet River

Whiteface River

04024000

St. Louis River drainage 

basin boundary

U.S. Geological Survey 

streamgage and 

number (table 1)

04024000

Figure 3. The St. Louis River study area, including the entire drainage basin above the St. Louis River at Scanlon, 

Minnesota, streamgage (U.S. Geological Survey station number 04024000), main stem of the St. Louis River, the Whiteface 

River, and the Cloquet River, major cities/towns, and other administrative boundaries.
Streamflow Distribution Maps 7

Simulation Program-FORTRAN (HSPF) model that simulated 

drainage basin hydrology and water quality was constructed, 

calibrated, and validated by LimnoTech (2015) for the Cannon 

River WRAPS (Wotzka and Watkins, 2016).

The St. Louis River drainage basin also has had several 

reports published in the last 4 decades. Similar to the Cannon River, a preliminary water budget was included as part 

of a characterization of the St. Louis River surface-water 

and groundwater resources by Lindholm and others (1979). 

Additionally, the Fond du Lac Indian Reservation water 

resources were characterized by Ruhl (1989). The physical 

characteristics and comprehensive water quality synopses for 

the St. Louis River were summarized by Lindgren and others 

(2006); similar to the Lindgren and others (2006) report, a 

separate Minnesota Pollution Control Agency-led summary 

monitoring and assessment report was published by Anderson 

and others (2013). After the severe flooding of the St. Louis 

River in 2012, Czuba and others (2012) characterized the 

high-water marks for several communities in and around the 

St. Louis River, also creating flood-peak inundation maps and 

water-surface profiles for several of the most affected communities. Similar to the Cannon River, an HSPF model that 

simulated drainage basin hydrology and water quality was 

constructed, calibrated, and validated by Tetra Tech (2016).

Streamflow Distribution Maps

Methods used generally are cited from previously 

published reports (Smith and Westenbroek, 2015; Ziegeweid 

and others, 2015; Lorenz and others, 2016). The SWB model 

developed for Minnesota at a 1-km2 resolution (Smith and 

Westenbroek, 2015) was used as a baseline model for the 

Cannon River and St. Louis River SWB models. Regression equations developed in Ziegeweid and others (2015) 

were used to estimate the FDC quantiles, based on hydrologic regions developed in Lorenz and Ziegeweid (2016). If 

techniques varied substantially from previously documented 

methods because of available data, model domain resolution, 

or time periods, those techniques are described in detail in this 

report. Estimated streamflow distribution maps were produced for four flow conditions as referenced to the long-term 

discharge characteristics for the streamgages on the Cannon 

River at Welch, Minn. (USGS station number 05355200), and 

the St. Louis River at Scanlon, Minn. (USGS station number 04024000). These profiles covered the following flow 

conditions: (1) extreme low-flow conditions, comparable to an 

exceedance-probability quantile of 0.95; (2) low-flow conditions, comparable to an exceedance-probability quantile of 

0.90; (3) median flow, comparable to an exceedance-probability quantile of 0.50; and (4) a high-flow condition, comparable 

to an exceedance-probability quantile of 0.02.

The FDC statistics were generated using the estFDC 

method, part of the DVstats package (Lorenz, 2016) for the 

R statistical environment (RStudio Team, 2016). The estFDC 

method estimates streamflow at selected exceedance probabilities for ungaged sites in Minnesota, based on the regression 

equations developed from Ziegeweid and others (2015). The 

SWB model executable used for this study was compiled by 

Steve Westenbroek of the USGS on June 8, 2015; the SWB 

lookup table was based on the published Minnesota statewide 

Soil-Water-Balance model (Smith and Westenbroek, 2015), 

with minor modifications described in the “Soil-Water-Balance 

Models” section.

Flow-Duration Curve Statistics

The FDC statistics were calculated to predict streamflow 

at selected exceedance-probability quantiles. The two drainage 

basins, the Cannon River and the St. Louis River, were subdivided into 152 nested drainage basins in the Cannon River 

above the Welch, Minn., streamgage, and 353 nested drainage basins in the St. Louis River above the Scanlon, Minn., 

streamgage. The selected exceedance-probability quantiles 

(0.95, 0.90, 0.50, and 0.02) were used as a representation of 

the spatial streamflow contribution from different parts of 

the drainage basins. The 13 exceedance probabilities were 

developed in Ziegeweid and others (2015) using the weighted 

left-censored regression method (Helsel and Hirsch, 2002). 

Regional regression equations were developed for five hydrologic regions in Minnesota; the Cannon and St. Louis drainage 

basins contain three of the five regions, including region BC 

that combines regions B and C (Lorenz and others, 2010). An 

illustration of the regional boundaries, regions D and E were 

present in the Cannon River and region BC was present in the 

St. Louis River, is shown in figure 4.

The regional regression equations for the flow-duration 

curves rely upon explanatory variables related to basin 

characteristics. The regional regression equations used in this 

study are based on five basin characteristics (Ziegeweid and 

others, 2015): (1) drainage area, in square miles (DRNAREA); 

(2) percentage of drainage basin area consisting of lakes and 

wetlands (STORAGE); (3) percentage of drainage basin area 

consisting of hydrologic soil group C (SOILC); (4) percentage 

of mean basin low-lying flatland (PFLATLOW); and (5) percentage of area of forested land (FOREST). The FDC statistics 

for the 505 nested drainage basins were calculated from the 

imported explanatory basin characteristics using the estFDC 

method from the R package DVstats (Lorenz, 2016). Once the 

13 exceedance-probability quantiles that define the FDC were 

calculated for each of the 505 nested drainage basins, these 

quantiles were exported from R into Microsoft® Excel for 

further postprocessing; all the FDC statistics are available on 

USGS ScienceBase (Smith, 2017a, 2017b).
8 Streamflow Distribution Maps for the Cannon River Drainage Basin and the St. Louis River Drainage Basin, Minnesota

96° 94° 92° 90°

48°

46°

44°

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

NORTH

DAKOTA

SOUTH 

DAKOTA

WISCONSIN

IOWA

MICHIGAN

CANADA

UNITED STATES

LAKE SUPERIOR

ST. LOUIS

CANNON

MINESOTA

Base map modified from U.S. Geological Survey, Minnesota Department of Natural Resources and other digital data, 

various scales

Univeral Transverse Mercator projection, zone 15 North

North American Datum of 1983

Drainage basin boundary

A

BC

D

E

F

Hydrologic regions

EXPLANATION

Figure 4. The five hydrologic regions in Minnesota, as defined by the regional regression equations from Ziegeweid and others 

(2015), along with the Cannon River drainage basin (above the Cannon River at Welch, Minnesota [U.S. Geological Survey station 

number 05355200]), and the St. Louis River drainage basin (above the St. Louis River at Scanlon, Minnesota [U.S. Geological 

Survey station number 04024000]).
Streamflow Distribution Maps 9

Soil-Water-Balance Models

The SWB model uses a modified Thornthwaite-Mather 

SWB accounting method (Thornthwaite and Mather, 1955, 

1957) to estimate potential recharge to groundwater and surface runoff on a daily basis (Westenbroek and others, 2010). 

The SWB approach calculates the values of the water-budget 

components based on relations among surface runoff, land 

cover, hydrologic soil group, maximum soil-water capacity, 

ET estimates, and temperature. Each of the water-budget components is handled by one or more modules within the SWB 

model. Within the SWB approach, potential recharge is calculated within each grid cell of the model domain separately 

based on the differences among sources (precipitation, snowmelt, inflow), sinks (interception, outflow, ET), and changes 

in soil moisture (∆soil moisture). Outflow from each grid cell, 

also known as surface runoff, is calculated by the U.S. Department of Agriculture Natural Resources Conservation Service 

curve number rainfall-runoff relation, also known as the curve 

number method (Cronshey and others, 1986).

The SWB model uses the soil and land-cover lookup 

table to assign model cell properties related to soils and land 

cover (Westenbroek and others, 2010). The soil and land-cover 

lookup table cross-references the 15 land-cover classes in 

Minnesota, as derived from the National Land Cover Database (NLCD) classification (Homer and others, 2007; Fry 

and others, 2011) to five soil classes (four hydrologic soil 

group classes plus a special organic soil class) to assign the 

curve number, daily maximum recharge (in inches per day), 

and the root-zone depth (in feet). Hydrologic soil group data 

used in the SWB model were obtained from two separate 

soil geographic databases, both distributed by and available for download from the U.S. Department of Agriculture 

Natural Resources Conservation Service (Natural Resources 

Conservation Service, 2014): (1) the Soil Survey Geographic 

(SSURGO) database and (2) the State Soil Geographic 

(STATSGO) database.

Individual SWB models were used as a method for 

estimating potential recharge and surface runoff for the Cannon River and St. Louis River drainage basins. The SWB 

models calibrated for this study were largely based on the 

Smith and Westenbroek (2015) SWB model, which estimated 

the average potential groundwater recharge across Minnesota from 1996 through 2010 at a 1-km2

 resolution. Further 

calibration simulations for this study compared the potential 

recharge estimate from the SWB model to annual base-flow 

estimates derived from hydrograph separation techniques 

using the USGS groundwater toolbox (Barlow and others, 

2015). Additionally, the sum of annual potential recharge and 

runoff from the individual SWB models was compared to the 

annual mean flow for the two drainage basins at the previously 

mentioned streamgages.

For this report, the published statewide SWB model 

(Smith and Westenbroek, 2015) was rerun for the period 

1996–2010 (Smith, 2017a, 2017b). All meteorological data 

were provided by the Daymet dataset, which provided key 

climatological data such as daily precipitation, minimum daily 

temperature, and maximum daily temperature (Thornton and 

others, 1997). The grid was refined to a 100-square meter resolution, rather than the original 1-km2

 resolution, including the 

other inputs such as the hydrologic soil groups and available 

soil-water capacity. The soil and land-cover lookup table from 

the published statewide SWB model (Smith and Westenbroek, 

2015) was used for the Cannon and St. Louis River drainage 

basin SWB models with only slight adjustments as explained 

in the next two sections. One feature of the soil and land-cover 

lookup table of the statewide model, interception, was zeroed 

out for all values; interception for SWB was defined as the 

part of precipitation intercepted by the plant canopy and lost to 

ET but was a minimal component of the statewide model and 

technically has overlap with curve numbers.

Cannon River Drainage Basin

Further refinement of the Cannon River SWB model 

(Smith, 2017a) consisted of manual calibrations comparing the 

potential recharge estimates to the annual base-flow estimates 

from the Cannon River at Welch, Minn., streamgage (USGS 

station number 05355200). The annual base-flow estimates 

were generated using three different hydrograph separation 

techniques: PART (Rutledge, 1998), HYSEP fixed-interval 

method (Sloto and Crouse, 1996), and HYSEP sliding method 

(Sloto and Crouse, 1996). Additionally, the combined potential recharge and surface runoff estimates were compared to 

annual mean streamflow from the same streamgage. As a baseline SWB model, the published statewide SWB model with the 

increased 100-m resolution was used, and manual adjustments 

were completed only on the soil and land-cover lookup table. 

The construction of the original model was preserved as much 

as possible; only multiplier adjustments were made to parts of 

the lookup table.

For an individual iteration, subgroups of curve numbers 

and root-zone depths within the lookup table were altered by 

a multiplier to determine if the new set of parameter values 

improved the difference between the SWB model results and 

both the base-flow estimates (from the hydrograph separation techniques) and total flow. Improvements in the SWB 

model calibration from the statewide model were evaluated 

using relative errors. The relative error between the 15-year 

mean annual potential recharge estimates from SWB and 

the 15-year mean annual base flow estimates from the three 

different hydrograph separation techniques was considered 

with little emphasis on individual years. The relative error 
10 Streamflow Distribution Maps for the Cannon River Drainage Basin and the St. Louis River Drainage Basin, Minnesota

(dimensionless) of the potential recharge estimate from the SWB model for each drainage basin 

in table 2 was calculated by using equation 1:

relative error potential r ech e mean base flow estimate = arg − − ( )

mean base − flow estimate

 (1)

where the potential recharge, in inches per year, is obtained from the SWB model and the mean 

base-flow estimate is the mean of the three base-flow estimates determined using hydrograph 

separation techniques, in inches per year.

After several iterations, the final calibration yielded relative errors for the overall mean 

base-flow estimate and total flow estimate of less than (<) 0.01 ft3/s; for the individual years, 

Table 2. Annual mean base flow estimates rates (from 1996 to 2010) using three hydrograph separation techniques for the Cannon 

River at Welch, Minnesota (U.S. Geological Survey station number 05355200), used in model calibration analysis in comparison to 

the Soil-Water-Balance model estimated annual potential recharge rates. Additional comparison shown between the annual mean 

streamflow and the Soil-Water-Balance model estimated annual sum of potential recharge and surface runoff rates.

[in/yr, inch per year; <, less than]

Year

Base-flow estimate (in/yr) Soil-Water-Balance model 

(in/yr) Annual mean 

streamflow 

(in/yr)

Relative error

PARTa

(in/yr)

HYSEP 

fixed-interval 

methodb

HYSEP 

sliding 

methodb

Mean 

base-flow 

estimate

Potential 

recharge 

rate

Potential 

recharge rate 

and surface 

runoff

Mean 

base-flow 

estimatec

Total flow 

estimated

1996 5.79 5.34 5.38 5.50 5.18 6.79 7.11 −0.06 −0.04

1997 8.99 8.72 8.52 8.74 8.82 11.31 11.85 0.01 −0.05

1998 9.11 8.20 8.26 8.52 7.61 9.46 11.98 −0.11 −0.21

1999 8.53 8.21 7.81 8.18 7.92 10.24 10.73 −0.03 −0.05

2000 6.04 5.44 5.46 5.65 4.62 6.97 8.00 −0.18 −0.13

2001 8.77 9.04 8.46 8.76 9.10 13.12 12.24 0.04 0.07

2002 6.37 5.93 5.84 6.05 7.08 10.00 8.05 0.17 0.24

2003 4.53 4.19 4.12 4.28 1.52 2.84 5.42 −0.64 −0.48

2004 7.10 6.76 6.28 6.71 8.36 11.44 9.88 0.24 0.16

2005 6.77 6.07 6.11 6.32 5.82 8.80 8.75 −0.08 0.01

2006 6.10 5.77 5.73 5.87 5.41 7.57 7.68 −0.08 −0.01

2007 7.80 6.45 6.66 6.97 10.68 13.93 10.17 0.53 0.37

2008 5.88 5.49 5.51 5.63 3.75 5.32 7.19 −0.33 −0.26

2009 3.14 2.98 2.96 3.03 4.56 6.34 3.82 0.51 0.66

2010 10.21 9.04 8.55 9.27 9.26 13.03 13.94 −0.00 −0.07

1996–2010 7.01 6.51 6.38 6.63 6.65 9.14 9.12 <0.01 <0.01

a

Rutledge (1998).

b

Sloto and Crouse (1996).

c

The relative error is the soil-water-balance potential recharge estimate to the mean of the three different base-flow estimates (equation 1). 

d

The relative error is the soil-water-balance estimate (potential recharge and surface runoff) to the annual mean streamflow (equation 1).
Streamflow Distribution Maps 11

the mean base-flow estimate relative errors ranged from -0.64 

to 0.53 ft3/s, and the total flow estimate ranged from -0.48 to 

0.66 ft3/s (table 2). Compared to the original Minnesota statewide SWB model, the following differences were made for the 

Cannon River for the soil and land-cover lookup table: (1) all 

curve numbers for the NLCD land-use classifications (LUCs) 

of grassland/herbaceous (LUC 71), pasture/hay (LUC 81), and 

cultivated crops (LUC 82) were multiplied by 1.08; and (2) all 

root-zone depths for the NLCD LUCs of grassland/herbaceous (LUC 71), pasture/hay (LUC 81), and cultivated crops 

(LUC 82) were multiplied by 0.82. Besides these changes and 

the zeroing out of the interception values mentioned earlier, 

all other features of the Cannon River SWB model (Smith, 

2017a) were identical to the published Minnesota statewide 

SWB model (Smith and Westenbroek, 2015).

St. Louis River Drainage Basin

Similar to the Cannon River SWB model, further refinement of the St. Louis River SWB model (Smith, 2017b) consisted of manual calibrations comparing the potential recharge 

estimates to the annual base-flow estimates from the St. Louis 

River at Scanlon, Minn. (USGS station number 04024000). 

The same annual base-flow estimates were generated, and 

the combined potential recharge and surface runoff estimates 

were compared to annual mean streamflow from the Scanlon 

streamgage. The baseline St. Louis River SWB model was run 

with the published statewide SWB model using an increased 

100-m resolution, and manual adjustments were completed 

only on the soil and land-cover lookup table and the SWB 

control file.

For an individual iteration, subgroups of curve numbers 

and root-zone depths within the lookup table, in addition to 

the rainfall and snowfall multipliers from the control file, were 

altered to determine improvements in the fit between the same 

calibration criteria outlined for the Cannon River SWB model. 

Model calibration improvements were evaluated using relative 

errors.

After several iterations, the final calibration yielded relative errors for the overall mean base flow estimate and total 

flow estimate of <0.01 ft3/s; for the individual years, the mean 

base flow estimate relative errors ranged from -0.53 to 0.70 

ft3/s, and the total flow estimate ranged from -0.21 to 0.63 ft3/s 

(table 3). Compared to the original Minnesota statewide SWB 

model, the following differences were made for the St. Louis 

River in the soil and land-cover lookup table: (1) all curve 

numbers for the NLCD LUCs of deciduous forest (LUC 41), 

evergreen forest (LUC 42), and mixed forest (LUC 43) 

were multiplied by 1.25; and (2) all curve numbers for the 

NLCD LUCs of woody wetlands (LUC 90) and emergent 

herbaceous wetlands (LUC 95) were increased from 60 to 83. 

Within the SWB control file, the rainfall and snowfall correction factors were increased from 1.00 to 1.092. In addition to 

the zeroing out of the interception values, all other features of 

the St. Louis River SWB model (Smith, 2017b) were identical 

to the published Minnesota statewide SWB model (Smith and 

Westenbroek, 2015).

Adjusting the Flow-Duration Curve Statistics 

using the Soil-Water-Balance Model Output

The SWB components of potential recharge and outflow 

were used as surrogates for base flow and surface runoff, 

respectively; therefore, the sum of the 15-year means for 

these two SWB water budget components was used to calculate the long-term mean total flow from the SWB model. 

In ArcGIS 10.4 (Esri, 2017), the mean SWB flow for each 

of the 505 drainage basins was calculated and exported to 

Microsoft® Excel. The 13 exceedance-probability quantiles 

that define the FDC, previously calculated for each of the 

505 drainage basins, were further processed to determine the 

mean FDC flow (Smith, 2017a, 2017b).

The mean FDC flow is defined as the area underneath the 

FDC curve. The mean FDC flow was calculated by multiplying each flow percentile by the respective FDC statistic. 

Each flow percentile was assumed to represent the flow at 

the halfway point between the surrounding percentiles (or 0 

or 100 in the two end-members); for example, in the 13 FDC 

statistics for this study, the 5th percentile (0.05) represents the 

mean flow value for 0.035 to 0.075 percent, the 10th percentile 

(0.10) represents the mean flow value for 0.075 to 0.175 percent, and so forth. So, if the FDC exceedance probability 

for the 5th percentile was 1,000 ft3/s, 1,000 ft3/s would be 

multiplied by 0.04 (difference between 0.035 and 0.075); if 

the FDC exceedance probability for the 10th percentile was 

750 ft3/s, 750 ft3/s would be multiplied by 0.10 (the difference 

between 0.075 and 0.175). These calculations for all 13 FDC 

exceedance probabilities would be carried out and summed to 

calculate the mean FDC flow. This calculation was completed 

for all 505 drainage basins.

The next step was to calculate the ratio of mean SWB 

total flow to mean FDC flow. The ratios differed for each 

nested drainage basin and ranged from 0.075 to 1.757. 

The ratio was multiplied by the previously calculated FDC 

exceedance probabilities, thereby defining an adjusted set of 

13 FDC exceedance probabilities. From these adjusted FDC 

exceedance probabilities, a subset of four flow conditions was 

selected for the streamflow distribution maps; however, any 

of the 13 FDC exceedance probabilities could be mapped as 

a typical flow condition across the drainage basin for a given 

exceedance probability. The four chosen flow conditions, 

(1) extreme low-flow conditions, comparable to an exceedance-probability quantile of 0.95; (2) low-flow conditions, 

comparable to an exceedance-probability quantile of 0.90; 

(3) median flow, comparable to an exceedance-probability 

quantile of 0.50; and (4) a high-flow condition, comparable 

to an exceedance-probability quantile of 0.02, were selected 

for illustrative purposes and were based upon feedback from 

Minnesota Department of Natural Resources and Minnesota 

Pollution Control Agency personnel (sheets 1 and 2).
12 Streamflow Distribution Maps for the Cannon River Drainage Basin and the St. Louis River Drainage Basin, Minnesota

Table 3. Annual mean base flow estimates rates (from 1996 to 2010) using three hydrograph separation techniques for the St. Louis 

River at Scanlon, Minnesota (U.S. Geological Survey station number 04024000), used in model calibration analysis for comparison to 

the Soil-Water-Balance model estimated annual potential recharge rates. Additional comparison shown between the annual mean 

streamflow and the Soil-Water-Balance model estimated annual sum of potential recharge and surface runoff rates.

[in/yr, inch per year; <, less than]

Year

Base-flow estimate (in/yr) Soil-Water-Balance model 

(in/yr) Annual mean 

streamflow 

(in/yr)

Relative error

PARTa

(in/yr)

HYSEP 

fixed-interval 

methodb

HYSEP 

sliding 

methodb

Mean 

base-flow 

estimate

Potential 

recharge 

rate

Potential 

recharge rate 

and surface 

runoff

Mean 

base-flow 

estimatec

Total flow 

estimated

1996 10.89 9.78 9.74 10.14 8.98 11.66 14.31 −0.11 −0.19

1997 8.97 8.01 8.05 8.34 6.76 9.74 11.24 −0.19 −0.13

1998 6.05 5.78 5.78 5.87 7.59 10.41 8.63 0.29 0.21

1999 11.15 10.10 9.95 10.40 10.18 14.53 16.00 −0.02 −0.09

2000 5.92 5.49 5.53 5.65 4.05 6.67 7.81 −0.28 −0.15

2001 9.66 9.44 8.77 9.29 9.74 12.86 13.23 0.05 −0.03

2002 6.25 5.89 5.75 5.96 5.54 8.72 9.29 −0.07 −0.06

2003 4.45 4.14 4.20 4.26 2.01 4.38 5.54 −0.53 −0.21

2004 6.35 5.65 5.69 5.90 5.54 7.72 8.03 −0.06 −0.04

2005 6.40 6.01 6.08 6.16 6.19 8.52 8.44 0.00 0.01

2006 4.89 4.30 4.31 4.50 3.33 5.22 6.47 −0.26 −0.19

2007 5.58 5.12 5.07 5.26 8.92 11.99 7.34 0.70 0.63

2008 7.61 6.73 6.70 7.01 7.26 9.42 9.65 0.03 −0.02

2009 6.18 5.71 5.76 5.88 7.00 9.58 7.56 0.19 0.27

2010 4.48 3.89 4.04 4.14 5.41 8.03 5.85 0.31 0.37

1996–2010 6.99 6.40 6.36 6.58 6.57 9.30 9.29 <0.01 <0.01

a

Rutledge (1998).

b

Sloto and Crouse (1996).

c

The relative error is the soil-water-balance potential recharge estimate to the mean of the three different base-flow estimates (equation 1). 

d

The relative error is the soil-water-balance estimate (potential recharge and surface runoff) to the annual mean streamflow (equation 1). 

Limitations and Assumptions

The limitations and assumptions for the streamflow distribution maps inherently depend on the underlying FDC statistics and SWB model results. A full understanding of model 

limitations and assumptions is necessary to better evaluate 

the performance of any hydrologic model. The SWB model 

was originally developed to allow for recharge calculations 

based on readily available data and standardized parameters 

for short-term periods (Westenbroek and others, 2010). Both 

SWB models used in this study were intended to obtain estimates of the amount of potential recharge and surface runoff; 

however, in this study, mean potential recharge and surface 

runoff estimates (that is, the mean for the 15-year period 

1996−2010) were calculated for the 15-year period from 1996 

to account for climate cycles and variability in surficial aquifer 

groundwater flow rates.

The SWB conceptual model for this study assumed that 

the base-flow component from the hydrograph separation 

technique was correct, even though this base-flow estimate 

was a simulated result. For comparisons made in this study, 

all SWB potential recharge was assumed to become base flow 

to the respective streamgaged drainage basin; furthermore, 

all groundwater exchange between the surficial aquifer and 

deeper aquifers was assumed to be at steady state. Finally, 

drainage basin boundaries of the streamgaged surface-water 

bodies were assumed to coincide with the boundaries of contributing recharge areas to the surficial aquifer, even though 

these boundaries do not always coincide (Kanivetsky, 1979).

General limitations and assumptions of the SWB model 

are discussed in great detail in the conceptual SWB report 

(Westenbroek and others, 2010) and the Minnesota statewide 

SWB model (Smith and Westenbroek, 2015). First, potential 

recharge is assumed to eventually become actual recharge. 
References Cited 13

Next, because the path or distance to the water table is not 

known, the SWB model only represents water leaving the 

root zone. The model assumes that recharge is instantaneous 

within the daily time step, but actual recharge can sometimes 

take months or even years. Even if the recharge reaches the 

water table in days to weeks, it can take much longer for 

the same recharge water to become base flow to the nearest 

surface-water body. Theoretical limitations to the SWB model 

also relate to the aggregation of meteorological data to daily 

time steps; however, except for groundwater-flow models for 

small areas, daily time steps are considered reasonable and 

are recommended for water-budget tabulations (Healy and 

Scanlon, 2010).

Several limitations warrant consideration when using the 

FDC statistics presented in this report. The applicability and 

accuracy of the underlying regional equations depend on if the 

basin characteristics calculated for an ungaged stream location 

are within the range of the values for variables used to develop 

the regression equations. Inconsistencies may happen because 

regional regression equations were developed separately and 

have variable estimation intervals depending on the size and 

variability of the datasets used to develop regression equations (Ziegeweid and others, 2015). Additionally, adjustments 

to the regression equations based on the mean flow from the 

SWB model assumes that the bias in the regression equation estimates are constant across the FDC. Estimates at the 

extremes often exhibit more bias than estimates at the mean 

flow, so manipulation of the low-flow estimates would lead to 

a higher bias.

Several factors affect the estimation accuracy of the 

underlying regression equations to develop the FDC statistics. 

Estimation accuracy depends on the sample size, the accuracy of each recorded streamflow, and how well the chosen 

distribution fits the actual distribution of the data (Lorenz 

and others, 2010). The accuracies of regression estimates 

are affected by errors in explanatory variables, and systematic errors in the computation of the response variable can 

bias estimates.

Special attention must be given to censored values and 

the number of significant figures used. Because of the uncertainty in measuring and estimating flows less than 0.1 ft3

/s, 

the censoring threshold used to develop the left-censored 

regression equations was set at 0.1 ft3/s; thus, any regression 

estimates that were 0.1 ft3/s or less were reported as less than 

0.1 ft3/s. Because the precision of response and explanatory 

variable data used to develop the equations commonly was 

limited to three significant figures, selected-statistic streamflows estimated from the regression equations also were 

limited to three significant figures. The discussion from Ziegeweid and others (2015) is recommended for a further summary 

of limitations and assumptions for FDC statistics.

Summary

Streamflow distribution maps for the Cannon River and 

St. Louis River drainage basins were developed by the U.S. 

Geological Survey, in cooperation with the Legislative-Citizen 

Commission on Minnesota Resources, to illustrate relative and 

cumulative streamflow distributions. The Cannon River was 

selected to provide baseline data to assess the effects of potential surficial sand mining, and the St. Louis River was selected 

to determine the effects of ongoing Mesabi Iron Range mining. These maps (available at https://doi.org/10.3133/sim3390) 

can help guide potential State cooperators such as the Minnesota Department of Natural Resources and the Minnesota 

Pollution Control Agency for proactive water management, 

ensure streamflows meet ecological needs at different flow 

conditions, and assist with decisions for groundwater appropriations at low-flow conditions.

These maps were developed using flow-duration curve 

(FDC) statistics in conjunction with Soil-Water-Balance 

(SWB) model output, specifically potential recharge and surface runoff. The mean flow calculated from the FDC statistics 

was compared to the mean total flow (potential recharge estimates and the surface runoff estimates) from the SWB models. 

The ratio of mean SWB total flow to mean FDC flow was 

calculated, and these ratios were used to readjust the 13 FDC 

exceedance probabilities. The selected streamflow distribution 

maps illustrate streamflow contributions from different parts 

of the drainage basin for typical conditions, not necessarily the 

contribution for any particular time.

A subset of the FDC exceedance probabilities defining 

the following flow conditions was mapped for 505 nested 

drainage basins (152 nested drainage basins in the Cannon 

River and 353 nested drainage basins in the St. Louis River): 

(1) extreme low-flow conditions, comparable to an exceedance-probability quantile of 0.95; (2) low-flow conditions, 

comparable to an exceedance-probability quantile of 0.90; 

(3) a median condition, comparable to an exceedance-probability quantile of 0.50; and (4) a high-flow condition, comparable to an exceedance-probability quantile of 0.02.

References Cited

Anderson, C.R., Niemala, Scott, Anderson, Jesse, Grayson, 

Stacia, Monson, Bruce, Christopherson, Dave, Lundeen, 

Ben, Jasperson, Jeff, Kennedy, Mike, Parson, Kris, and 

Kelly, Mike, 2013, St. Louis River watershed monitoring 

and assessment report: Minnesota Pollution Control Agency 

document number wq–ws3–04010201b, 200 p. [Also 

available at https://www.pca.state.mn.us/sites/default/files/

wq-ws3-04010201b.pdf.]
14 Streamflow Distribution Maps for the Cannon River Drainage Basin and the St. Louis River Drainage Basin, Minnesota

Anderson, W.H., Jr., Farrell, D.F., Broussard, W.L., and 

Felsheim, P.E., 1974, Water resources of the Cannon 

River watershed, southeastern Minnesota: U.S. Geological Survey Hydrologic Atlas 522, 3 pls. [Also available at 

https://pubs.er.usgs.gov/publication/ha522.]

Barlow, P.M., Cunningham, W.L., Zhai, Tong, and Gray, 

Mark, 2015, U.S. Geological Survey groundwater toolbox, 

a graphical and mapping interface for analysis of hydrologic 

data (version 1.0)—User guide for estimation of base flow, 

runoff, and groundwater recharge from streamflow data: 

U.S. Geological Survey Techniques and Methods, book 3, 

chap. B10, 27 p. [Also available at https://doi.org/10.3133/

tm3B10.]

Bredehoeft, John, 1997, Safe yield and the water budget 

myth: Ground Water, v. 35, no. 6, p. 929. [Also available at 

https://doi.org/10.1111/j.1745-6584.1997.tb00162.x.]

Cronshey, Roger, McCuen, R.H., Miller, Norman, Rawls, 

Walter, Robbins, Sam, and Woodward, Don, 1986, Urban 

hydrology for small watersheds—TR–55 (2nd ed.): Washington, D.C., U.S. Department of Agriculture, Soil Conservation Service, Engineering Division, Technical Release 

55, 164 p. [Also available at https://www.nrcs.usda.gov/

Internet/FSE_DOCUMENTS/stelprdb1044171.pdf.]

Czuba, C.R., Fallon, J.D., and Kessler, E.W., 2012, Floods 

of June 2012 in northeastern Minnesota: U.S. Geological Survey Scientific Investigations Report 2012–5283, 

42 p. with 3 app. [Also available at https://pubs.usgs.gov/

sir/2012/5283/.]

Esri, 2017, ArcGIS 10.4 software: Esri web page, accessed 

May 24, 2017, at http://www.esri.com/.

Fletcher, A., and Christin, Z., 2015, The value of nature’s benefits in the St. Louis River watershed: Tacoma, Wash., U.S. 

Environmental Protection Agency and Fond du Lac Band of 

Lake Superior Chippewa, joint study, 105 p. [Also available 

at https://www.glifwc.org/Events/Earth%20Economics%20

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For more information about this publication, contact

Director, USGS Upper Midwest Water Science Center 

2280 Woodale Drive

Mounds View, MN 55112

(763) 783–3100

For additional information visit https://mn.water.usgs.gov

Publishing support provided by the 

Rolla Publishing Service Center
Smith and others— Streamflow Distribution Maps for the Cannon River Drainage Basin and the St. Louis River Drainage Basin, Minnesota—SIM 3390

ISSN 2329-132X (online)

https://doi.org/10.3133/sim3390