ERDC/CHL TR-20-3 

Nested Physics-Based Watershed Modeling at 

Seven Mile Creek 

Minnesota River Integrated Watershed Study Coastal and Hydraulics Laboratory 

Charles W. Downer, Mark Wahl, Nawa Raj Pradhan, 

Brian Skahill, Stephen Turnbull, and Ryan Pickett

March 2020

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 ERDC/CHL TR-20-3 

March 2020 

Nested Physics-Based Watershed Modeling at 

Seven Mile Creek 

Minnesota River Integrated Watershed Study

Charles W. Downer, Mark Wahl, Nawa Raj Pradhan, Brian Skahill, Stephen Turnbull, and 

Ryan Pickett

Coastal and Hydraulics Laboratory

US Army Engineer Research and Development Center

Vicksburg, MS 39180-6199

Final report

Approved for public release; distribution is unlimited.

Prepared for US Army Corps of Engineers, Saint Paul District

Saint Paul, MN 55101

Under USACE MVP; MIPR 138667; “Minnesota River Basin Watershed Study - Nested 

GSSHA Model Development for the Seven Mile Creek Watershed”
ERDC/CHL TR-20-3 ii

Abstract

The Minnesota River Basin (MRB) Integrated Study Team (IST) was 

tasked with assessing the condition of the MRB and recommending 

management options to reduce suspended sediments and improve the 

water quality in the basin. The Gridded Surface Subsurface Hydrologic 

Analysis (GSSHA) was chosen by the IST as the fine scale model for the 

Seven Mile Creek Watershed to help quantify the physical effects from best 

management practices within the MRB. The predominately agricultural 

Seven Mile Creek Watershed produces high total suspended solids and 

nutrients loads, contributing roughly 10% of the total load to the 

Minnesota River. GSSHA models were developed for a small experimental 

field research site called Red Top Farms, a Hydrologic Unit Code (HUC)-

12 model for the entire Seven Mile Creek Watershed, and a sub-basin of 

the Seven Mile Creek Watershed. After calibration, the resulting models 

were able to simulate measured tile drain flows, stream flow, suspended 

sediments, and to a lesser extent, nutrients. A selected suite of alternative 

land-use scenarios was simulated with the models to determine the 

watershed response to land-use changes at the small and medium scale 

and to test whether the type, size, and spatial distribution of land uses will 

influence the effectiveness of land management options.

DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. 

Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. 

All product names and trademarks cited are the property of their respective owners. The findings of this report are not to 

be construed as an official Department of the Army position unless so designated by other authorized documents.

DESTROY THIS REPORT WHEN NO LONGER NEEDED. DO NOT RETURN IT TO THE ORIGINATOR.
ERDC/CHL TR-20-3 iii

Contents

Abstract.................................................................................................................................... ii

Figures and Tables...................................................................................................................v

Preface...................................................................................................................................viii

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

1.1 Background........................................................................................................1

1.2 Objective.............................................................................................................2

1.3 Approach ............................................................................................................3

2 Input and Observed Data ................................................................................................5

3 Spatial Data......................................................................................................................6

3.1 Terrain ................................................................................................................6

3.2 Land cover..........................................................................................................6

3.3 Soil characteristics ............................................................................................8

3.4 Geologic conditions .........................................................................................10

3.5 Drainage network ............................................................................................11

4 Forcing Data ...................................................................................................................14

5 Observed Data................................................................................................................19

6 Hydrologic Processes ....................................................................................................20

6.1 Precipitation.................................................................................................... 20

6.2 Overland flow .................................................................................................. 20

6.3 Frozen soil ........................................................................................................21

6.4 Channel routing ...............................................................................................21

6.5 Infiltration.........................................................................................................21

6.6 Groundwater ................................................................................................... 22

6.7 Tile drains........................................................................................................ 22

6.8 Evapotranspiration ......................................................................................... 25

6.9 Soil erosion ..................................................................................................... 26

6.10 In-stream sediment transport .................................................................. 26

6.11 Nutrients .................................................................................................... 26

7 Hydrologic Simulation ...................................................................................................27

7.1 Modeling framework........................................................................................27

7.1.1 Seven Mile Creek model ...........................................................................................27

7.1.2 NE Fork Sub-basin model .........................................................................................28

7.1.3 Red Top Farms model...............................................................................................28

7.2 Initialization..................................................................................................... 30

7.3 Calibration/validation......................................................................................31
ERDC/CHL TR-20-3 iv

7.3.1 Red Top Farms tile parameter calibration...............................................................32

7.3.2 Seven Mile Creek model calibration ........................................................................36

7.3.3 NE Fork Sub-watershed model calibration ..............................................................40

7.3.4 Sediment calibration.................................................................................................42

7.3.5 Nutrients....................................................................................................................45

7.4 Long-term simulations.................................................................................... 49

7.5 Alternate land-use scenarios ..........................................................................51

7.6 Simulation specifics ....................................................................................... 52

7.6.1 Tile drains ..................................................................................................................52

7.6.2 Parameter values......................................................................................................52

7.6.3 Nutrients....................................................................................................................53

7.7 Results............................................................................................................. 54

7.7.1 Discharge...................................................................................................................54

7.7.2 Water balance ...........................................................................................................57

7.7.3 Sediment ...................................................................................................................60

7.7.4 Nutrients....................................................................................................................64

8 Discussion of Results ....................................................................................................66

8.1 Flow regimes................................................................................................... 66

8.2 Sediment......................................................................................................... 67

8.3 Effect of row cropping..................................................................................... 67

8.4 Tile drains........................................................................................................ 68

8.4.1 Water budget.............................................................................................................68

8.4.2 Flow regime ...............................................................................................................68

8.4.3 Sediment ...................................................................................................................71

8.4.4 Nutrients....................................................................................................................71

9 Future Efforts..................................................................................................................72

9.1 Precipitation data ........................................................................................... 72

9.2 Hydrologic calibration/validation................................................................... 72

9.3 Nutrients ......................................................................................................... 72

9.4 Future scenarios............................................................................................. 73

10 Summary .........................................................................................................................74

References.............................................................................................................................76

Acronyms and Abbreviations...............................................................................................79

Report Documentation Page
ERDC/CHL TR-20-3 v

Figures and Tables

Figures

Figure 1. Minnesota River Basin..................................................................................................1

Figure 2. Seven Mile Creek Watershed located in Nicollet County, Minnesota. ...................2

Figure 3. Land-use map obtained from USDA, National Agricultural statistics 

service, Cropland Data Layer (Han et al. 2012). .......................................................................7

Figure 4. Cropland data layer acreage for 2012 in Nicollet, Minnesota. ...............................8

Figure 5. Identification of soils within the Seven Mile Creek Watershed...............................9

Figure 6. Bed rock elevations.....................................................................................................11

Figure 7. Drainage network consisting of streams, ditches, and subsurface drain 

tiles throughout Seven Mile Creek. Ravines with significant erosion are 

highlighted. ...................................................................................................................................12

Figure 8. Locations of surveyed channel cross sections........................................................13

Figure 9. Streams and ditches defined in the model according to surveyed crosssectional geometry. .....................................................................................................................13

Figure 10. Location of USGS/MCES/MPCA precipitation, flow, and water 

quality gages.................................................................................................................................15

Figure 11. Radar rainfall pixels intersecting the Seven Mile Creek Watershed 

model.............................................................................................................................................16

Figure 12. Location of winter precipitation gages used in the Seven Mile Creek 

model.............................................................................................................................................17

Figure 13. Reported location of private tiles in the Seven Mile Creek Watershed.............23

Figure 14. Typical tile drain development.................................................................................24

Figure 15. Comparison of SUPERLINK and LINK/NODE tile routing models 

at Red Top Farms.........................................................................................................................25

Figure 16. Nested modeling framework within the A) Seven Mile Creek 

Watershed identifying the B) NE Fork and C) Red Top Farms experimental 

watershed. ....................................................................................................................................28

Figure 17. Location of Red Top Farms model domain in relation to the Seven Mile 

Creek study area..........................................................................................................................29

Figure 18. Tile drain network and soil type distribution in the East Field of the 

Red Top Farm model. ..................................................................................................................30

Figure 19. Observed versus measured tile flow for the East Field of the Red Top 

Farms model.................................................................................................................................35

Figure 20. Extended Red Top Farms East Field tile flow results. ..........................................36

Figure 21. Comparison of simulated discharge (blue lines) from the 50 m 

resolution Seven Mile Creek GSSHA model with observed flow (black lines) for 

four gages for the June 2006 event..........................................................................................37

Figure 22. Comparison of simulated discharge versus observed measurements 

at the four Seven Mile Creek gage locations from 2004. ......................................................38
ERDC/CHL TR-20-3 vi

Figure 23. Comparison of simulated and observed discharge at the NE Fork 

model for the June 2006 event (calibration period)...............................................................41

Figure 24. Comparison of simulated and observed discharge at the NE Fork 

model for the summer 2004 period (validation period).........................................................41

Figure 25. Relationship between discharge and TSS at Seven Mile Creek used to 

estimate the volume of sediment associated with the calibration event. ...........................43

Figure 26. Sedographs derived from an empirical relationship between discharge 

and suspended sediment used to estimate the sediment volume from the June 

2006 calibration event. ..............................................................................................................43

Figure 27. Location of permitted manure spreading fields, shown in black........................46

Figure 28. Nutrient concentrations at the outlet of the NE Fork for June 2006

event..............................................................................................................................................48

Figure 29. Simulated N concentrations at the outlet of Seven Mile Creek for the 

June 2006 event..........................................................................................................................48

Figure 30. Simulated P concentrations at the outlet of Seven Mile Creek for the 

June 2006 event..........................................................................................................................49

Figure 31. Long-term simulation results for the Seven Mile Creek Watershed 

compared with observed discharges at SMC1 and SMC3....................................................50

Figure 32. Simulated discharge from each of the alternative land-use scenarios.............55

Figure 33. Simulated discharge from each of the alternative land-use scenarios.............56

Figure 34. Simulated discharge from each of the alternative land-use scenarios.............56

Figure 35. Simulated discharge from each alternative land-use scenarios in NE 

Fork................................................................................................................................................56

Figure 36. Simulated discharge from each alternative land-use scenarios in NE 

Fork................................................................................................................................................57

Figure 37. Simulated discharge from each alternative land-use scenarios in NE 

Fork................................................................................................................................................57

Figure 38. Water balance as a percentage of total precipitation within the Seven 

Mile Creek Watershed for present conditions (Base) compared with the results 

from the four alternate land-use scenarios. (Note: Totals were not available for 

the AG scenario.)..........................................................................................................................59

Figure 39. Output from the GSSHA model showing locations of net sediment 

erosion/deposition (meter) throughout the Seven Mile Creek Watershed for the 

present conditions (Base scenario). .........................................................................................61

Figure 40. Erosion can be observed from the simulations locally concentrated 

around tile outlets at select locations.......................................................................................62

Figure 41. A comparison between present-day conditions (Base) with the native 

regime (pre-development) of the respective surface and groundwater 

components of flow. ....................................................................................................................66

Figure 42. Annual sediment yields associated with the various land uses.........................67

Figure 43. Comparison of outflow at Seven Mile Creek between simulated flow 

with and without tile drains. .......................................................................................................69

Figure 44. Comparison of outflow at the NE Fork between simulated flow with 

and without tile drains. ...............................................................................................................70
ERDC/CHL TR-20-3 vii

Tables

Table 1. Soil physical properties from SSURGO.......................................................................10

Table 2. Summary of the available HMET parameters required for the long term 

simulation. ....................................................................................................................................14

Table 3. Red Top Farms parameterization. ..............................................................................34

Table 4. Goodness-of-fit parameters for the overall Seven Mile Creek model....................39

Table 5. Calibrated parameter values.......................................................................................39

Table 6. Goodness-of-fit characteristics of NE Fork Models. .................................................42

Table 7. Final erodibility values for the NE Fork model (CL – clay loam; L – loam; 

MSL – mucky silt loam)...............................................................................................................44

Table 8. Final calibrated erodibility values for the Seven Mile Creek model with 

LUST categories (CL – clay loam; L – loam; MSL – mucky silt loam). ..................................45

Table 9. Nutrient parameter values, K – soil/water partition coefficient. ...........................47

Table 10. Soil vertical hydraulic conductivity values, K, cm hr-1 used in the 

scenarios.......................................................................................................................................53

Table 11. Summary of the water balance in 106 m3 for the various long-term 

Seven Mile Creek simulations....................................................................................................59

Table 12. Summary of the water balance in 106 m3 for the NE Fork simulations..............59

Table 13. Simulated annual sediment loading rate (TSS) within the Seven Mile

Creek Watershed and NE Fork Sub-basin Models for present conditions (Base) 

compared with the results from the four alternate land-use scenarios...............................63

Table 14. Distribution of land uses in the model scenarios (%)............................................64

Table 15. Simulated nutrient loads within the Seven Mile Creek model and NE 

Fork for present conditions (Base) compared with the results from the four 

alternate land-use scenarios......................................................................................................65
ERDC/CHL TR-20-3 viii

Preface

This work for performed for the US Army Corps of Engineers, Saint Paul 

District, under USACE MVP, MIPR 138667; “Minnesota River Basin 

Watershed Study - Nested GSSHA Model Development for the Seven Mile 

Creek Watershed.” The US Army Corps of Engineers, Saint Paul District,

sponsor point of contact was Mrs. Ann Banitt. COL Karl Jansen was the 

Commander at the time of publication of this report.

At the time of publication of this report, Dr. Hwai-Ping (Pearce) Cheng 

was Chief, Hydrologic Systems Branch, and Dr. Cary Talbot was Chief, 

Flood and Storm Protection Division of the US Army Engineer Research 

and Development Center, Coastal and Hydraulics Laboratory (ERDCCHL). The Deputy Director of ERDC-CHL was Mr. Jeffrey R. Eckstein, 

and Dr. Ty V. Wamsley was the Director. 

COL Teresa A. Schlosser was the Commander of ERDC, and the director 

was Dr. David Pittman.
ERDC/CHL TR-20-3 1

1 Introduction

1.1 Background

The Minnesota River Basin is located primarily in southern Minnesota, 

and extends into parts of Iowa, South Dakota, and North Dakota 

(Figure 1). The basin drains approximately 38,850 km2 of land to its 

main artery, the Minnesota River. From the origin in Big Stone Lake at 

the Minnesota/South Dakota border, the Minnesota River traverses

571 km to its confluence with the Mississippi River at Fort Snelling.

Figure 1. Minnesota River Basin. 
ERDC/CHL TR-20-3 2

The Seven Mile Creek Watershed lies in the Middle Minnesota River Basin 

in Nicollet County in Minnesota (Figure 2). The Seven Mile Creek drainage 

area includes 91 km2. The watershed is predominately agricultural and 

characterized by uplands and ravines at the interface with the River 

Warren Escarpment in the downstream portion. The soils are primarily 

clay loam characterized as a Des Moines Lobe Till. The Middle Minnesota 

River contributes approximately 9% of the total suspended solids (TSS), 

10% of the nitrogen (N), and 5% of the phosphorous (P) loads to the 

Minnesota River (Minnesota State University Mankato 2003). The 

watershed includes substantial hydrological modifications including 

extensive networks of subsurface drains.

Figure 2. Seven Mile Creek Watershed located in Nicollet County, Minnesota. 

1.2 Objective

The Minnesota River Basin Integrated Study Team (IST) was tasked with 

assessing the condition of the Minnesota River Basin and recommending 

management options to improve the water quality in the basin. The IST 

was asked to identify and quantify strategies that specifically target the 

reduction of sediment transported through and out of the Minnesota River 

Watershed. State Study Partners have developed large-scale lumped 

parameter basin models using the SWAT1 and the HSPF2 lumped 

parameter hydrologic models for most of the Minnesota River Basin.

 

1 Soil and Water Assessment Tool

2 Hydrologic Simulation Program-Fortran
ERDC/CHL TR-20-3 3

These models are limited in their ability to assess the impact of best 

management practices (BMPs) at a field scale and project what the 

cumulative impacts are downstream. The objective of this modeling effort is 

to physically define the impacts of land management options at a fine scale.

1.3 Approach

A nested modeling approach was prescribed. The study team has defined 

three categories of models that vary their level of fidelity, input needs, and 

degree of complexity.

• Tier 1: Fundamentally simple. Provides a relative sense of 

cause/effects. Requires few input data.

• Tier 2: Provides a level of certainty to model economics. Needs more 

input data than Tier 1.

• Tier 3: Detailed, physically based processes. More input data required 

than Tier 2.

The Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model was 

chosen as a Tier 3 model for the Seven Mile Creek Watershed to help 

quantify the physical effects from BMPs within the Minnesota River Basin. 

GSSHA is a physically based, spatially distributed numerical model used to 

simulate important stream flow processes (Downer and Ogden 2004),

evaluate flood inundation (Sharif et al. 2010), soil moisture (Downer and 

Ogden 2003), constituent fate and transport (Downer 2009), and snow 

accumulation (Follum and Downer 2013). The GSSHA model development 

focused on the interactions that define how a particular basin behaves with 

respect to changes in land use, climate, and BMPs. Nested Tier 3 

hydrologic models include a field scale Tier 3 GSSHA Model of Red Top 

Farms; a HUC-121 model for the entire Seven Mile Creek Watershed; and a 

finer resolution model covering a sub-basin of the Seven Mile Creek 

Watershed. The models integrate the surface and groundwater 

interactions as well as the drain tile features of GSSHA.

A selected suite of alternative land-use scenarios was simulated with 

GSSHA to determine the watershed response to land-use changes at the 

small and medium scale and to test whether the type, size, and spatial 

 

1 HUC = Hydrologic Unit Code
ERDC/CHL TR-20-3 4

distribution of land uses will influence the effectiveness of land 

management strategies.

This report describes only the Tier 3 GSSHA modeling results. Results of 

the overall study effort are available from USACE1.

 

1 USACE. In preparation. Minnesota River Basin Interagency Report.
ERDC/CHL TR-20-3 5

2 Input and Observed Data

According to Section 8 of the Scope of Work (SOW), the US Army Corps of 

Engineers, Saint Paul District (USACE-MVP), was responsible for 

providing all required/requested data needed to accomplish the modeling 

needs. USACE-MVP was also charged with quality assurance. Unless 

otherwise noted, all data referenced in this report were provided by 

USACE-MVP, and the US Army Engineer Research and Development 

Center (ERDC) assumed the data were the best data available for 

conducting the study. ERDC assessed the quality and completeness of the 

provided data. Due to gaps in the needed data, ERDC conducted an 

extensive, if not exhaustive, effort to supplement the data provided by 

USACE-MVP and located and incorporated data and information related 

to defining drainage tile properties, sub-surface properties for simulating 

groundwater, and additional precipitation data, among others. The 

sources of these other data are noted in the report. All data 

provided/collected by ERDC underwent extensive quality control. Data 

were checked for completeness of record, recorded quality, internal 

consistency, and consistency with other data sets. Specific actions taken 

for each data type are discussed in individual sections below.
ERDC/CHL TR-20-3 6

3 Spatial Data

3.1 Terrain

Ground elevation, slope, and aspect are derived from a digital elevation 

model (DEM) at 1/3 arc-second resolution (approximately 10 m) from the 

National Elevation Dataset (NED) provided by the US Geological Survey

(USGS). NED data are referenced to the North American Datum of 1983.

The DEM was resampled to match the 50 m grid size of the GSSHA model 

for the entire Seven Mile Creek Watershed and resampled to 25 m to 

match the resolution of the inset sub-watershed model.

3.2 Land cover

Land use data were obtained from the US Department of Agriculture

(USDA) National Agricultural Statistics Service

(http://nassgeodata.gmu.edu/CropScape/) (Han et al. 2012). The watershed is largely 

agricultural with 2-year rotations of corn and soy bean. Land uses were 

assumed static for the purposes of this study. Eight categories where used 

in the models. As shown in Figure 3, the distribution of these eight land 

uses in the Seven Mile Creek Watershed model is the following:

• Corn – 47%

• Soybeans – 32%

• Alfalfa – 0.5%

• Open Water – 0.9 %

• Developed Space – 5 %

• Deciduous Forest – 4%

• Grassland – 0.9%

• Herbaceous Wetland – 10%.
ERDC/CHL TR-20-3 7

Figure 3. Land-use map obtained from USDA, National Agricultural statistics service, 

Cropland Data Layer (Han et al. 2012). 

Seven Mile Creek 

Land Cover
ERDC/CHL TR-20-3 8

Eight unique land coverages were identified within the watershed with 

associated acreages provided in Figure 4.

Figure 4. Cropland data layer acreage for 2012 in Nicollet, Minnesota. 

3.3 Soil characteristics

Three dominant soils were identified in the Seven Mile Creek Basin: clay 

loam (CL), loam (L) and mucky silt loam (MSL), as shown in Figure 5. Soil 

types were derived from USDA Natural Resources Conservation Service

Soil Survey Geographic Database (SSURGO) (NCSS Staff 2014). The 

associated soil physical properties were obtained from the SSURGO site 

and are provided in Table 1. As shown in Figure 5, the predominant soils 

are tight poorly drained soils, clay and silt loams. Well-drained loam soils 

are found mostly along the stream channels.
ERDC/CHL TR-20-3 9

Figure 5. Identification of soils within the Seven Mile Creek Watershed. 
ERDC/CHL TR-20-3 10

Table 1. Soil physical properties from SSURGO. 

3.4 Geologic conditions

Bed rock elevations shown in Figure 6 were derived from well-boring logs 

from the Minnesota Well Index, Minnesota Department of Health 

(http://www.health.state.mn.us/divs/eh/cwi/). The boring logs were used to determine 

the depth to the impermeable layer (aquifer bottom) for simulating 

interactions with the ground water table.

Water table elevations were not readily available, so preliminary water table 

elevations were inferred. The water table was assumed to be near the 

surface for poorly drained soils. This inferred water table was drawn down 

during an initialization period in which the models were spun up in order 

for the water table and soil moisture to stabilize prior to the calibration and 

validation periods. The final state at the end of the initialization period 

provides more realistic starting conditions for simulations going forward.
ERDC/CHL TR-20-3 11

Figure 6. Bed rock elevations. 

3.5 Drainage network

The drainage network at Seven Mile Creek consists of streams and ditches 

in addition to subsurface drains as depicted in Figure 7. USACE-MVP 

provided the shape files with locations of both public and private tile. No 

additional information on the tile drain system was provided or located.

The figure also identifies ravines where significant erosion can be seen, 

which occur near the streams in proximity to the watershed outlet.
ERDC/CHL TR-20-3 12

Figure 7. Drainage network consisting of streams, ditches, and subsurface drain tiles 

throughout Seven Mile Creek. Ravines with significant erosion are highlighted. 

Stream and ditch cross sections were defined in the GSSHA models.

Locations of surveyed cross sections are shown in Figure 8. Cross sections 

in the model are specifically defined according to information provided by 

Ann Banitt, USACE-MVP. A typical ditch cross section is shown in Figure 9.
ERDC/CHL TR-20-3 13

Figure 8. Locations of surveyed channel cross sections. 

Figure 9. Streams and ditches defined in the model according to surveyed crosssectional geometry. 
ERDC/CHL TR-20-3 14

4 Forcing Data

Long-term simulations in GSSHA require inputs for precipitation, air 

temperature, atmospheric pressure, relative humidity, wind speed, cloud 

cover, and solar radiation. Nearly continuous hourly hydro-meteorological 

(HMET) data were available from 2003 to 2008. The meteorological 

records were compiled to produce the necessary input files for the longterm simulation from the available data indicated in Table 2. HMET data 

were provided by USACE-MVP. 

Table 2. Summary of the available 

HMET parameters required for the 

long term simulation. 

HMET Parameters Units 

Precipitation mm

Air Temperature  ̊F

Barometric Pressure in Hg

Wind Speed knots

Relative Humidity %

Total Sky Cover 10ths

Direct Radiation W/m2

Global Radiation W/m2

An assessment of the precipitation data provided by USACE-MVP, from 

state monitoring locations in the watershed, indicated that these were of 

only fair to poor quality, spotty, of coarse space and time resolution, and 

completely lacking any winter precipitation. ERDC conducted an extensive 

effort to fill in the gaps in the precipitation data set, and the final Seven 

Mile Creek precipitation gage file was compiled from multiple sources of 

data with varying time scales to cover a time period from February 2003 to 

August 2008. The primary source of data was from Red Top Farms, which 

had a single gage with a temporal resolution of 15 minutes and 1 hour, 

depending on the time period. Red Top Farms data were provided by Bill 

Vanryswick, Dept. of Agriculture. Precipitation data were also available 

from four monitoring gages located in the basin: SMC1, SMC2, SMC3 and 

SMC4 (Monitoring sites 1, 2, and 3 in Figure 10). Availability and quality 

of these data varied significantly. Coverage from these gages was spotty at 

best. When available, data were hourly. Notes on the quality of the data 

indicated that many were of poor quality. When these higher spatial 
ERDC/CHL TR-20-3 15

resolution and lower temporal resolution data were available for periods 

when the Red Top Farms data were collected at 15 min, the hourly data 

were converted to 15 min data evenly distributing over the hour of 

collection. These data were then supplemented with radar data for the 

summer periods of 2006, 2007, and 2008 obtained from the Minnesota 

Department of Natural Resources. Radar rainfall was used to help define 

the spatial and temporal distribution of highly convective storms that 

typically are not captured by ground based stations. Those storm events 

that used radar products were derived from the National Oceanic and 

Atmospheric Administration NTP1 16-bit reflectivity products with storm 

total precipitation aerially adjusted using Minnesota’s High Density 

observer gage network. When radar data were available, they replaced the 

available point gage data. The watershed was covered by 12 (2 km) ×

(2 km) radar pixels, as shown in Figure 11.

Figure 10. Location of USGS/MCES2/MPCA3 precipitation, flow, and water 

quality gages. 

 

1 Network Time Protocol

2 Metropolitan Council of Environmental Services

3 Minnesota Pollution Control Agency

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ERDC/CHL TR-20-3 16

Figure 11. Radar rainfall pixels intersecting the Seven Mile Creek Watershed model. 

None of the sources of precipitation described above had winter 

precipitation, approximately October through April, depending on the 

year. Because GSSHA is run in a continuous mode and snow 

accumulation, melt and runoff may be a significant portion of the water 

budget, estimates of winter precipitation were required. USACE MVP 

provided winter data from four sites: KMKT at Mankato Airport, Amboy, 

LeSueur, and Sherburn. These data were available on an hourly basis and 

used to fill any missing time periods but did not replace any of the 

previous data. None of these are within the Seven Mile Creek Basin. The 

relative locations of the gages are shown in Figure 12. As seen in Figure 12,

the winter precipitation gages provide relatively poor coverage of the 

watershed, and therefore there is significant uncertainty in the amount 

and distribution of winter precipitation in the basin. As the winter 

precipitation provides the source of water for the spring thaw and runoff 

period, the lack of certainty in the winter precipitation data can cause 

increased uncertainty in spring runoff predictions.
ERDC/CHL TR-20-3 17

Figure 12. Location of winter precipitation gages used in the Seven Mile 

Creek model. 

Raw precipitation data were processed in Microsoft Excel utilizing a macro 

written at USACE-ERDC Coastal and Hydraulics Laboratory, which 

converts the data into a GSSHA rain gage file. After development, all data 

were checked by visually inspecting the file for correct format and errors 

as well as being checked by simulating the rainfall record in the GSSHA 

model. During simulations, the GSSHA model checks both the HMET and 

precipitation files for errors and reports on types and locations of errors.

In addition to these checks on data formatting and processing, the rainfall 

record was also compared to observed stream records to try to eliminate

phantom precipitation events, where large single values of rainfall were 

recorded but no corresponding stream flow was observed. The gages were 

also checked for internal consistency, to help eliminate gages that were not 

functioning properly for a given event. As the precipitation input file was 

extensive, and complicated by having numerous precipitation types and 

number and location of gage configurations, several iterations of model 

checking, followed by hand corrections, were required to produce a 

precipitation file best suited for the modeling effort.

In general, the precipitation gage data were judged of good quality for use at 

Red Top Farms, except for periods when radar rainfall data supplemented 
ERDC/CHL TR-20-3 18

the gage data of rather poor quality for simulations in the larger basins. The 

Red Top Farms data were consistently recorded at higher temporal 

resolution and reported to be of good quality. The values at Red Top Farms 

are probably more applicable to the sub-basin watershed model, upstream 

of SMC1 in Figure 10, than for the Seven Mile Creek Basin model due to the 

spatial variability of precipitation likely in the larger basin. As described 

above, the other gage data, while providing some measure of spatial 

variability, were spotty and of rather poor quality, with spotty recording, 

poor temporal resolution, and much of the data being recorded as poor 

quality. No winter precipitation gages were available in the basin. The lack 

of measured precipitation in the basin during the winter months leads to a 

large uncertainty in the estimation of winter precipitation and subsequent

snow melt, runoff, and hydrology response.
ERDC/CHL TR-20-3 19

5 Observed Data

Flow data were provided by the Brown-Nicollet-Cottonwood (BNC) 

Water Quality Board at 15 min increments at four gage locations 

throughout the watershed at the monitoring sites shown in Figure 10.

Data quality varied among the gages and throughout the period. The 

records from the gages had many periods of missing data. Much of the 

data were reported as being of poor quality or as estimated. Based on the 

amount of missing data and reported quality of data, it was felt that Gage 

3, near the outlet, had the highest quality data; Gage 1, on the outlet of 

the northeast (NE) fork of Seven Mile Creek, had the second best data;

Gage 2, on the northwest fork of Seven Mile Creek was considered of 

generally poorer quality compared to Gages 1 and 3; and Gage 4, on the 

southeast branch of Seven Mile Creek (not shown in the figure) had only 

spotty daily data, and was generally considered of poor quality. For these 

reasons, Gage 2 and 4 data were de-emphasized in calibration efforts and 

were not used for judging hydrologic response.

Grab samples of relevant water quality variables, TSS, N, and P, were

available at all four of the gages referenced above. Grab samples 

corresponded to both low/base flow, as well as samples from events. In 

general, the samples do not allow a good definition of any particular storm 

event, and the peak discharge is rarely, if ever, captured. Available grab 

data from all four sites were utilized in model calibration and assessment 

despite the quality of the flow data associated with it.
ERDC/CHL TR-20-3 20

6 Hydrologic Processes

In terms of application of watershed models to the Minnesota River Basin 

in general, and the Seven Mile Creek Basin specifically, the GSSHA model 

is unique in that it is a fully distributed, physics-based model that 

explicitly simulates point processes at the grid level and then integrates 

point processes responses to produce the overall system response.

Therefore, in GSSHA each physical process is explicitly simulated. GSSHA 

is also an option-driven model. Not all processes are needed to simulate all 

watersheds, and the user can specify which processes to simulate as well as 

the manner in which to simulate them. As described above, the Seven Mile 

Creek Watershed, while relatively small, is rather complex, hydrologically 

speaking. The streamflow is a combination of overland flow, groundwater 

baseflow, and tile drainage flow originating from winter snowfall, diffuse 

spring rainfall and snowmelt events, and intense summer thunderstorms.

Below-zero winter temperatures means the ground freezes, inhibiting 

infiltration, increasing runoff, and reducing flow into tile drains. Overland 

and ravine sediment erosion are of concern as well as overland and instream nutrient loading. In an attempt to realistically simulate the 

hydrologic processes occurring in the Seven Mile Creek Watershed, as well 

as the hydrologic response of the basin, the following processes were 

simulated in the described fashion.

6.1 Precipitation

Precipitation was incorporated into the model using available rainfall gage 

data, as described above, and distributed over the models using Thiessen 

polygons. The distribution of Thiessen polygons varies with the gages 

being used during individual events. Snowfall accumulation and melt is 

simulated in the model using the Hybrid method, as described by Follum 

and Downer (2013).

6.2 Overland flow

Overland flow is simulated as two-dimensional (2D) lateral flow. The 

diffusive wave equation, which captures backwater effects, is solved using 

the ADE1 method. Overland roughness values were assigned according to 

land cover within the range suggested by Senarath and Ogden (2000).

 

1 alternating direction explicit 
ERDC/CHL TR-20-3 21

Micro-depressions are represented as retention storage. Overland flow is 

routed through snowpack according to Darcy’s law (Darcy 1856). 

6.3 Frozen soil

Frozen soil is simulated with Continuous Frozen Ground Index (CFGI) 

model (Molnau and Bissell 1983), which simulates the soil as either frozen, 

or not frozen, based on an accounting of heat deficit, using the hourly air 

temperature in each grid cell. Frozen soils are considered impervious to 

infiltration. In addition, groundwater flux into tile drains ceases when the 

CFGI exceeds the threshold value in the cell and the ground is considered 

frozen. See http://www.gsshawiki.com/Frozen_Soil:Frozen_Soil for more detail.

6.4 Channel routing

A one-dimensional (1D) diffusive wave channel routing scheme is used to 

simulate stream flow. Channel cross sections were explicitly defined

utilizing 49 surveyed cross sections.

6.5 Infiltration

The standard Green and Ampt (Green and Ampt 1911) model has proven 

effective for modeling infiltration into soils for single events in welldrained uniform soils, but several important common natural and manmade phenomena can invalidate the assumption of vertically uniform 

soils. Soil layering, non-uniform initial soil moisture, surface crusts, 

lenses, and high water tables violate the conditions necessary to apply the 

traditional Green and Ampt method. For these reasons, the three-layer 

Green and Ampt (multi-layer G&A) infiltration option in GSSHA (Downer 

2002) was used to simulate infiltration in the Seven Mile Creek catchment. 

Soil layering was available from the SSURGO physical properties, Table 1.

Three soil types were defined:

• Type 1: Clay Loam (layer 1), Loam (layer 2), Loam (layer 3)

• Type 2: Clay Loam (layer 1), Clay Loam (layer 2), Clay Loam (layer 3)

• Type 3: Silty Clay Loam (layer 1), Silty Clay Loam (layer 2), Silt Loam 

(layer 3).

For parameter assignment, soil types were combined with five dominant 

land-use types (corn/soy, alfalfa/grass, wetlands/water, developed, forest) 

to create 12 Land Use/Soil Type (LUST) categories to assign parameters.
ERDC/CHL TR-20-3 22

6.6 Groundwater

A lateral 2D simulation of saturated groundwater flow is included in the 

model. The watershed boundary for each model is used as a no-flow 

boundary condition. Groundwater recharge is provided by the multi-layer 

G&A model. Stream losses and gains are governed by a river flux boundary 

condition. The saturated water table provides the flux to tile drains. The 

tile drains provide a sink to the groundwater solution.

6.7 Tile drains

Tile drains were explicitly simulated in the models according to Downer 

and Pradhan (2014a). The tile drain networks were defined from USACEMVP-provided shape files that described the public and private tile drain 

systems. The public tile drains are a matter of public record and are 

considered accurate. The private tile drains are considered an estimate of 

possible/probable tile locations obtained from a variety of sources. These 

tile locations are shown in Figure 13.
ERDC/CHL TR-20-3 23

Figure 13. Reported location of private tiles in the Seven Mile Creek Watershed. 

To the extent possible, this network was implemented in the GSSHA 

hydrologic models of the watershed. Information from the Red Top 

Farms tile network was used to populate the physical properties of the 

tiles in the GSSHA model. Drains in fields were assumed to be 15 cm.

Mains were assumed to be 30 cm. All drains were placed approximately 

1.2 m below the local land surface. Typical development of the tile model 

is shown in Figure 14.
ERDC/CHL TR-20-3 24

Figure 14. Typical tile drain development. 

Pipe roughness and hydraulic conductivity were assigned and then 

calibrated using observed tile flow data at Red Top Farms and observed 

stream flow in the basin. Excluding the Red Top Farms model, the tile 

drains in the models represent effective networks, meant to capture the 

effect of the tiles. The exact number, location, and properties of the actual 

tile network are impossible to know in such a complex network.

Initially, tiles were simulated using the SUPERLINKS dynamic routing 

model (Ogden et al. 2011). However, due to excessive simulation times, 

another method, a simple link/node model, was added to GSSHA and 

utilized for the long-term future scenario simulations. In the link/node 

routing model, once the water enters the tile drain, it is assumed to 

instantly be routed to the tile network outlet. Given the primary control on 

the tile flow is the movement in the sub-surface, both in the groundwater 

and into the tile drain, and that most segments of tile drain are short, and 

smooth, the assumption is reasonable for routing tile flow at the field 

scale. Comparison of flows at Red Top Farms and for the whole basin 

model indicated that the link/node routing model produced comparable 

results at drastically reduced simulation times (Figure 15).
ERDC/CHL TR-20-3 25

Figure 15. Comparison of SUPERLINK and LINK/NODE tile routing models 

at Red Top Farms. 

6.8 Evapotranspiration

Evapotranspiration (ET), a critical component of the seasonal water 

balance, is necessary for simulating soil wetting and drying. The Penman 

method (Penman 1948) was specified within GSSHA to calculate ET on an

hourly basis, the same temporal resolution as the HMET inputs. The 

required model parameters were assigned according to the land-use map, 

Figure 3. Surface albedo values are based on recommended values from 

the GSSHA user manual (Downer and Ogden 2006). Canopy transmission 

coefficients were assigned according to light interception studies 

(Hutchison and Matt 1977) for deciduous forests. Canopy stomatal 

resistance was based on two published studies (Eliáš 1979; Verma and 

Baldocchi 1986). The Penman method is fairly sensitive to stomatal 

resistance (Lemeur and Zhang 1990), so considerable attention was given 

to stomatal resistance during initial calibration. HMET data described 

earlier are also used in ET calculations.
ERDC/CHL TR-20-3 26

6.9 Soil erosion

Overland sediment erosion is routed using a 2D mass balance approach 

(Downer et al. 2014b). The simulation considers detachment of sediment 

due to rainfall impact (Gabet and Dunne 2003; Wicks and Bathurst 1996)

as well as by surface runoff. In this study, the modified Kilinc and 

Richardson equations (1973) to determine sediment transport potential 

(Julien 1995) were used. Amounts of soils eroded by rainfall impact 

and/or overland flow shear below the transport capacity of the flow are 

routed with a 2D advection dispersion equation to the 1D channel network 

where the sediments are routing toward the watershed outlet. Eroded soils 

in excess of the transport capacity are settled using a trap efficiency 

equation. In GSSHA, any number, size, and density of particles can be 

simulated. In the Seven Mile Creek models soils were partitioned amongst 

sand, silt, and clay sizes, with a specific gravity of 2.65, that of quartz.

6.10 In-stream sediment transport

Once in the stream, sediment is routed as either wash load or bed load.

Sediment sizes smaller than sand, silt, and clay are routed with a 1D 

advection dispersion equation. Sand, and larger size particles, are routed 

as bed load with Yang’s Formula.

6.11 Nutrients

Nutrients, N and P, were simulated as first-order reactants in the model.

For the purposes of this study, N is approximately analogous to 

nitrate+nitrite, and P is analogous to total phosphorous. N and P, in both 

the dissolved and sorbed phase, were simulated in the soils (1D vertical), 

the overland (2D lateral), and in the streams (1D). Groundwater 

concentrations were spatially variable but temporally static. These were 

assigned along with the other relevant variables, as described below.
ERDC/CHL TR-20-3 27

7 Hydrologic Simulation

The Watershed Modeling System (WMS 9.1) was used for watershed 

delineation. Flow directions and accumulations were computed using the 

Topographic Parameterization Program method (Garbrecht and Martz 

1993). This information, along with spatial data pertaining to terrain, soils, 

land cover, and temporal data related to HMET data, are fed to GSSHA.

7.1 Modeling framework

A set of nested models was developed for analysis. The models include 

one of the entire Seven Mile Creek project, a model of the NE fork of Seven 

Mile Creek, and a model of the Red Top Farms experimental watershed.

The Red Top Farm model is located within the NE Fork model, which lies 

within in the overall Seven Mile Creek extents. The nested framework is 

illustrated in Figure 16.

7.1.1 Seven Mile Creek model

The drainage area of the Seven Mile Creek is contained within one HUC12 

boundary of 97.85 km2. The Seven Mile Creek model was developed using 

a 50 m computational grid with 39,142 grid cells. The model has 91 stream 

links and 874 subsurface tile drainage nodes. The Seven Mile Creek Model 

is shown in Figure 16, A.
ERDC/CHL TR-20-3 28

Figure 16. Nested modeling framework within the A) Seven Mile Creek Watershed 

identifying the B) NE Fork and C) Red Top Farms experimental watershed. 

A) Seven Mile Creek Watershed 

 

 

B) NE Fork 

 

C) Red Top Farms 

7.1.2 NE Fork Sub-basin model

The NE Fork Sub-basin model is defined as the section of the basin 

upstream of the SMC1 gage, or monitoring Site 1 in Figure 10. The basin 

encompasses 39.04 km2. This model is simulated at 25 m resolution, 

containing 62,467 grid cells. This model has 176 stream links and 312 

sub-surface tile drainage nodes. The NE Fork sub model is shown in 

Figure 16, B.

7.1.3 Red Top Farms model

Red Top Farms is an experimental watershed located within the NE Fork 

section. The site includes two tiled agricultural fields with 

controlled/measured crops and application of fertilizers (Figure 16, C).

Red Top Farms has a rich dataset associated with it including measured 

precipitation, tile flow, and water quality data collected between 1994 and 

2010. The location is unique in that tile flow and water quality are 

measured in the field and relatable to soils, land use, and crop rotation. 

The Red Top Farms GSSHA model was developed with the intent to 

facilitate hydrologic process understanding and to support GSSHA model 

parameterization throughout the entire Seven Mile Creek Watershed 

NE Fork

Red Top Farms
ERDC/CHL TR-20-3 29

system. Although the Red Top Farms area is small, the model had to be 

large enough to develop groundwater boundary conditions. The Red Top 

Farms model domain, depicted in Figure 17, encompasses 12.58 km2 with 

20,133 grid cells at 25 m resolution. The model includes the ditch network 

within the model domain with nine channel links.

Figure 17. Location of Red Top Farms model domain in relation to the Seven Mile 

Creek study area. 

The Red Top Farms GSSHA model was developed in a manner such that 

state observations of response from the approximate 12.59 km2 acre Red 

Top Farms East Field named “RM” could be compared with their model 

simulated counterparts. The Red Top Farms GSSHA model of the East 

Field, including the tile drains reflected in the model, is shown in Figure 18.
ERDC/CHL TR-20-3 30

Figure 18. Tile drain network and soil type distribution in the East Field of the 

Red Top Farm model. 

Similar to the larger models, the East Field was simulated with the 

following three soil types:

• Type 1: Clay Loam (layer 1), Loam (layer 2), Loam (layer 3)

• Type 2: Clay Loam (layer 1), Clay Loam (layer 2), Clay Loam (layer 3)

• Type 3: Silty Clay Loam (layer 1), Silty Clay Loam (layer 2), Silt Loam 

(layer 3).

7.2 Initialization

Assumptions about the water table and initial soil moisture conditions 

based strictly on the soil descriptions result in improbable discontinuities 

at boundaries between soil types. To equilibrate the model, the simulation 

was initialized over a period from late September 2003 to the first of May 

2004. At the end of each initialization period, the final state is fed back 

into the model as new initial conditions. Initialization had to be repeated 

several times during model calibration when parameter sets where 

updated. This process was repeated until the base flow in the channels was 

reasonable and the water table was stable and smooth. This process 

East Field
ERDC/CHL TR-20-3 31

provides the initial groundwater table and soil moisture conditions at the 

start of the calibration period. The calibration and validation periods 

include an additional simulation start-up period before the 

calibration/validation results are used.

7.3 Calibration/validation

Calibration/validation periods were selected with the intent of having a 

dry period with little or no tile flow for calibrating surface runoff processes 

as well as a period during wet conditions characterized by a high water 

table for calibrating tile flow. Periods with the most complete 

precipitation, discharge, sediment, and nutrient records were identified as 

2004, 2006, 2007, and 2008. The wet period was chosen as May 1 to July 

15, 2004, and the dry period was chosen to be June 15-June 30, 2006,

since these timeframes had adequate observed data for 

calibration/validation and best met the desired hydrologic conditions.

The Secant Levenberg-Marquardt (SLM) method (Levenberg 1944; 

Marquardt 1963) for model independent parameter estimation and the 

Shuffled Complex Evolution (SCE) general purpose global optimization 

method (Duan et al. 1992, 1993)as described by Skahill et al. (2012a, 

2012b) were used to calibrate the GSSHA models to observed data. The 

basics of SLM are provided in Skahill et al. (2009). The BNC Water 

Quality Board provided observed flow at 15 min increments at four gage 

locations throughout the watershed along with less frequent sediment and 

nutrient concentrations. Sediment loads were calibrated using the ERDC 

implementation of the SCE method (Skahill et al. 2012a,b). Flow was 

calibrated using the ERDC implementation (Skahill et al. 2012a,b) of the 

SLM method. The SLM method employs a nonlinear least squares 

minimization local search algorithm. To better accommodate scaling 

issues resulting from mismatches of units among the various parameters, 

and in an attempt to decrease the degree of nonlinearity of the parameter 

estimation problem, the logs of the adjustable model parameters were 

estimated instead of their native values; past experience has demonstrated 

that greater efficiency and stability of the parameter estimation process 

can often be achieved through this means (Doherty and Skahill 2006).

Initially, model calibration was hampered by long simulation times. While 

these issues were eventually resolved, as described above, the calibration 

strategy was based on this limitation at the time. To facilitate a quicker 

hydrologic calibration, calibration of different model components was 
ERDC/CHL TR-20-3 32

separated with the basic strategy to use the Red Top Farm model to 

determine parameters relevant to tile drainage during a wet period and to 

use the Seven Mile Creek model to determine surface and groundwater 

parameters during a dry period, when little or no tile flow was expected to 

be a factor. Once all the model parameters were determined for the Seven 

Mile Creek model in this manner, they would be validated for the same wet 

period used to determine the tile drain parameters in the Red Top Farm 

model. Parameters determined in this fashion were then applied to the NE 

Fork Sub-basin model with adjustment of parameters as needed.

7.3.1 Red Top Farms tile parameter calibration

Simulation of Red Top Farms was beyond the SOW, and this model was 

developed with the purposes of testing/demonstrating the ability of the 

GSSHA model to explicitly simulate tile drainage flow and to develop an 

appropriate tile drain parameter set that can be used in the Seven Mile 

Creek and NE Fork models. Flow from the tile drainage network provided 

in Figure 18 was used as the basis for comparison with observed data. No 

surface flow is measured or included. Observed measurements of flow at 

the outlet of the East Field were compared with simulated values from the 

model every 30 min for the period May 1, 2004 – July 15, 2004, excluding 

the period May 5, 2004, 1200, through May 8, 2004, 1200, resulting in a 

total of 3456 model-observation compares to constitute the objective 

function. In attempts to stabilize the variance of the residuals, the 

objective function in fact consisted of the sum of weighted squared 

differences between 3456 modeled and transformed flow values, with all 

weights assigned a value of 1. The Box-Cox transformation with λ = 0.3 

(Box and Jenkins 1976; Misirli et al. 2003) was employed to transform the 

observed and modeled flows.

To support calibration of the Red Top Farms GSSHA model, five 

parameters were specified as adjustable, viz.,

1. Groundwater Hydraulic Conductivity – the GSSHA model input 

parameter named GW_UNIF_HYCOND, which specifies and assigns a 

uniform value throughout the entire model domain for the 

groundwater hydraulic conductivity.

2. Tile Roughness – the roughness value uniformly assigned to all of the 

tile represented in the GSSHA model developed to simulate the East 

Field of Red Top Farms.
ERDC/CHL TR-20-3 33

3. Tile Conductance – the conductance value uniformly assigned to all of 

the tile represented in the GSSHA model developed to simulate the 

East Field of the Red Top Farms.

4. Canopy Stomatal Resistance – the GSSHA model input parameter 

named CANOPY_RESIST, only for those grid cells representing the 

East Field of the Red Top Farms, and which parameterizes 

evapotranspiration calculations within the GSSHA model to support 

continuous simulation.

5. Soil Hydraulic Conductivity – a multiplier value that is uniformly 

applied to base values specified for the soil hydraulic conductivity for 

all three layers of soil for the three distinct soil types characterized 

within the East Field of the Red Top Farms. The base hydraulic 

conductivity values were obtained from GSSHA model parameter 

guidance provided with the GSSHA documentation:

http://www.gsshawiki.com/Infiltration:Parameter_Estimates.

The SLM local search was configured for efficiency in the Red Top Farms 

model. In particular, no full update of the model sensitivity matrix was 

employed; and column cyclic updating was deactivated for the duration of 

the optimization. The Red Top Farms model calibration period spanned 

the period from May 1 to July 15, 2004. Calibration of tile roughness, 

hydraulic conductivity, and ET parameters resulted in an 84% reduction in 

the sum of the squared residuals. The final estimated model parameter set 

is listed in Table 3 below along with an estimate of uncertainty. Note that 

the reported 95% confidence limits provide only an indication of 

parameter uncertainty. They rely on a linearity assumption that may not 

extend as far in parameter space as the confidence limits themselves.
ERDC/CHL TR-20-3 34

Table 3. Red Top Farms parameterization. 

Parameter 

Initial 

Value 

Final 

Value Sensitivity 

95% Confidence 

Limits 

Lower Upper 

Manning 

Roughness Tile 0.0091 0.0031 0.00182 0.0029 0.0031

Hydraulic 

Conductivity 

(cm/hr)

Tile 3.918 1,718 0.00288 1685 1752

Groundwater 1.0043 0.0051 0.00220 0.0049 0.0052

MUSYM_L107A_RM

upper 0.099 98.03 0.00164 94.52 101.7

intermediate 0.651 646.0 0.00164 623.8 671.0

lower 0.651 646.0 0.00164 623.8 671.0

MUSYM_112_RM

upper 0.099 98.03 0.00164 94.52 101.7

intermediate 0.099 98.03 0.00164 94.52 101.7

lower 0.099 98.03 0.00164 94.52 101.7

MUSYM_134_RM

upper 0.099 98.03 0.00164 94.52 101.7

intermediate 0.099 98.03 0.00164 94.52 101.7

lower 0.336 333.3 0.00164 321.4 345.7

Evapotranspiration Stomatal Resistance (s/m) 100.67 56.86 0.00184 54.82 58.98

Note that of the values shown in Table 3, only tile properties, Manning 

roughness and hydraulic conductivity are used in subsequent modeling 

efforts. The observations at Red Top Farms included no surface flow.

Additionally, the Red Top Farms model was configured solely to simulate 

the tile drains, with no intention of using the other values. During 

calibration, the values were allowed to float to better match the tile flow.

Without a constraint of matching surface flow, the soil hydraulic 

conductivity parameters were allowed to become high. They are not and 

are not intended to be representative of conditions in the watershed.

Calibration of surface features is best done to surface water flow, not tile 

flow.

The information encapsulated in Figure 19 summarizes model-tomeasurement misfit at the final estimated parameter set. The related 

computed Nash and Sutcliffe efficiency (NSE) score of 0.86 indicates a 

Red Top Farms East Field GSSHA calibrated model of predictive value.
ERDC/CHL TR-20-3 35

Figure 19. Observed versus measured tile flow for the East Field of the Red Top 

Farms model. 

This parameters set was further tested by extending the simulation period 

through the next wet season, 2005. The results of this experiment are 

shown in Figure 20. As shown in the figure, even after simulating a dry 

season, and a snow accumulation and melt season, the model, with the 

calibrated parameter set, demonstrates sufficient skill in simulating the 

measured tile flow from the East Field of the Red Top Farms model. This 

demonstrates that the GSSHA model can be used to simulate tile drain 

flow for a well-defined tile drainage system.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 1

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Flows (cfs)

Precip. (in)

Date and Time

Modeled Flows Observed Flows Precipitation
ERDC/CHL TR-20-3 36

Figure 20. Extended Red Top Farms East Field tile flow results. 

7.3.2 Seven Mile Creek model calibration

The field scale Red Top Farms model was used to inform parameters 

related to generation of tile flow in larger model domains. The larger 

Seven Mile Creek model was initially calibrated to a single event in June of 

2006 in which tile flow was assumed to be minimal as the preceding 

period is relatively dry and the flow gage at Red Top Farms indicates no 

tile flow during this period. Radar rainfall estimates were available to drive 

the model during this period. Calibration over this period focused on 

capturing the surface runoff generating characteristics. Fifteen-minute 

observed flows at the two gaged locations within the watershed were used 

to assess the goodness-of-fit (Table 4). Manning roughness, soil hydraulic 

conductivity, overland flow retention depths, and groundwater parameters 

were adjusted during calibration. An 85% reduction in the sum of the 

squared weighted residuals resulted. 

A comparison of observed and simulated discharge for the single June 

2006 event is presented in Figure 21 below. The gage labeled “SMC1”

corresponds to the outlet at the NE Fork Sub-basin model while the one 

labeled “SMC3” is at the Seven Mile Creek Watershed outlet. Figure 10

identifies locations of the monitoring sites. As seen in Figure 21, the model 
ERDC/CHL TR-20-3 37

does a fairly good job of capturing the hydrograph shape, with the 

ascending limb, peak flow, and recession well represented. The NSE is 

0.72 for SMC1 and 0.89 for SMC3, indicating very good reproduction of 

measured flow for this period. Storm total volumes are also well presented.

Given the accuracy in reproduction of the flow for this period, this model 

should be usable for determining the sediment erosion parameter values.

Figure 21. Comparison of simulated discharge (blue lines) from the 50 m resolution 

Seven Mile Creek GSSHA model with observed flow (black lines) for four gages for the 

June 2006 event. 

The same 2004 period considered initially for the calibration of the Red 

Top Farms model (May 1 - July 15) was revisited to verify the results and 

perform final calibration of the overall model during wet conditions for 

fine tuning the combined contributions from surface runoff, subsurface 

flow, and tile flow. Point gage data from Red Top Farms were used to drive 

the models for this period. The cost function was marginally reduced by 

adjusting groundwater parameters in this phase. Comparison of simulated 

and observed values is shown in Figure 22. Summary statistics are shown 

in Table 4. As seen in the figure, the model does a good job of representing 

the overall model response at Gage 3 and to a lesser extent at Gage 1, 

where the larger flows are well represented, but some of the smaller flows 

are not. The statistics attest to this visual impression. NSE for Gage 3 is 

0.65, not as strongly positive as the dry period but still good. NSE for Gage 

1 is 0.45, still strongly positive but not as high as Gage 3 or for the previous 

period. Total flow at Gage 1 is underestimated by 40%.

There are several potential/likely reasons for the differences between 

measured and simulated flow for this dry and wet calibration/validation 

periods. The calibrated model was driven by radar rainfall, is temporally Observations vs Simulations

Time Q, [m3/s]

0 20 40 60 80 100

sim1

obs1

Jun 15 00:00:00 Jun 17 00:00:00 Jun 19 00:00:00 Jun 21 00:00:00 Jun 23 00:00:00 Jun 25 00:00:00 Jun 26 12:00:00

GoF's: VE = 0.424

r = 0.784 R2 = 0.615

d = 0.734 NSE = -0.266 MAE = 15.358 MSE = 285.36 RMSE = 16.893 PBIAS = -50.9 RSR = 1.125

Observations vs Simulations

Time Q, [m3/s]

0 200 400 600 800 1000

sim3

obs3

Jun 15 00:00:00 Jun 17 00:00:00 Jun 19 00:00:00 Jun 21 00:00:00 Jun 23 00:00:00 Jun 25 00:00:00 Jun 26 12:00:00

GoF's: VE = 0.414

r = 0.89 R2 = 0.793

d = 0.773 NSE = 0.179 MAE = 54.617 MSE = 3897.247 RMSE = 62.428 PBIAS = -57.2 RSR = 0.906

SMC1 SMC3
ERDC/CHL TR-20-3 38

removed away from the effects of snow accumulation and melt, and has 

little suspected tile flow as conditions are relatively dry, and data from the 

Red Top Farms site indicate no tile flow occurs during this period. As 

noted previously, confidence in the winter precipitation is not high due to 

the lack of winter precipitation gages in the actual basin. The winter 

precipitation sets the stage for spring melt and runoff. Errors in winter 

precipitation will result in additional errors in spring melt runoff. In 

addition, these errors will be exacerbated by errors in tile flow, which are 

derived from the groundwater, which is derived from the winter 

precipitation and spring melt. Additionally, as noted previously, the exact 

existing tile drainage system is not represented in the model, only the best 

understanding of what the tile drainage system may be. Errors in volume 

for the validation are probably a combination of errors in winter 

precipitation, use of a different type of precipitation data to drive the 

model (point versus radar), and tile drain flow. Still, the model is thought 

be to of sufficient quality to make predictive assessment of the land-use

alternatives.

Figure 22. Comparison of simulated discharge versus observed measurements at the 

four Seven Mile Creek gage locations from 2004. 

 
ERDC/CHL TR-20-3 39

Table 4. Goodness-of-fit parameters for the overall Seven Mile Creek model. 

SMC1 SMC3 

Calibration Validation Calibration Validation

Pearson's correlation coefficient (r) 0.976 0.75 0.944 0.86

Coefficient of determination (R2) 0.953 0.563 0.891 0.74

Index of agreement (d) 0.953 0.819 0.95 0.853

Nash-Sutcliffe efficiency (NSE) 0.725 0.447 0.849 0.655

Mean absolute error (MAE) 10.011 28.471

Mean square error (MSE) 3.284 10.414 916.187 1485.337

Root mean square error (RMSE) 95.553 211.853 30.269 38.54

Percent bias (PBIAS) 9.775 14.555 -20.5 -1.3

Volumetric efficiency (VE) 33.8 -39.7 0.728 0.391

Ratio of RMSE (rsr) 0.602 0.378 0.388 0.588

Initial and final parameter values are listed below in Table 5 along with 

confidence bounds. Again, note that the reported 95% confidence limits 

provide only an indication of parameter uncertainty.

Table 5. Calibrated parameter values. 

Parameter 

Initial 

Value 

Final 

Value Sensitivity 

95% 

Confidence 

Limits 

Lower Upper 

Manning 

Roughness

Stream Roughness 0.050 0.167 0.018 0.137 0.204

Ditch Roughness 0.050 0.500 0.037 0.433 0.577

Row Crop Overland 0.025 0.271 0.0306 0.224 0.329

Forest Overland 0.025 0.644 0.0321 0.468 0.886

Wetland Overland 0.025 0.563 0.0250 0.190 1.663

Tile 0.011 0.011 - - -

Hydraulic 

Conductivity 

(cm/hr)

Streambed 1.000 4.941 0.9710 - -

Ditch bed 0.000 0.000 0.00091 0.000 -

Groundwater 20.00 12.62 1.1021 - -

Tile 2000 2010 0.866 - -

upper 0.040 0.008 0.0318 0.005 0.011
ERDC/CHL TR-20-3 40

Clay 

loam

intermediate 0.573 0.109 0.0318 0.078 0.153

lower 0.760 0.145 0.0318 0.103 0.202

loam

upper 0.606 0.115 0.0318 0.082 0.161

intermediate 0.760 0.145 0.0318 0.103 0.202

lower 0.760 0.145 0.0318 0.103 0.202

Mucky 

silt 

loam

upper 0.048 0.009 0.0318 0.007 0.013

intermediate 0.360 0.068 0.0318 0.049 0.096

lower 0.360 0.068 0.0318 0.049 0.096

Retention 

Depth (mm)

Row Crop 1.00 0.890 0.0179 0.321 2.465

Deciduous Forest 5.00 3.647 0.0181 0.686 19.40

Herbaceous Wetland 50.0 16.753 0.0161 8.567 32.76

Groundwater 

Properties

Sat/Unsat 0.750 0.955 0.099827 0.888 1.027

Porosity 0.600 0.400 0.0664 0.371 0.431

7.3.3 NE Fork Sub-watershed model calibration

The parameter values developed for the Seven Mile Creek model were 

transposed to the NE Fork Sub-watershed model and tested during the 

calibration/validation periods. The model was compared to 15 min flow 

data from SMC1. Analysis of these simulation periods indicated that no 

adjustment of parameter values obtained from the Seven Mile Creek 

model was required. Results of this testing are shown in Figure 23 and 

Figure 24 for the dry and wet seasons, respectively. Goodness-of-fit 

statistics for the two periods are provided in Table 6. As seen in Figure 23

and Table 6, the calibration model has a very good fit to the observed data, 

with an extremely high NSE value of 0.95. As seen in Figure 24 and in 

Table 6, the validation is of lesser, although acceptable, accuracy, with an

NSE of 0.433, still strongly positive. Volumes are underestimated for the 

validation period. The overall result is very similar to the results at this 

gage for the larger Seven Mile Creek model. The reasons for the 

underprediction of flows at Gage 1 for this model are the same as 

described above.
ERDC/CHL TR-20-3 41

Figure 23. Comparison of simulated and observed discharge at the NE Fork model for 

the June 2006 event (calibration period). 

Figure 24. Comparison of simulated and observed discharge at the NE Fork model for 

the summer 2004 period (validation period). 
ERDC/CHL TR-20-3 42

Table 6. Goodness-of-fit characteristics of NE Fork Models. 

25m NE Fork 

Calibration Validation 

Pearson's correlation coefficient (r) 0.994 0.775

Coefficient of determination (R2) 0.989 0.601

Index of Agreement (d) 0.981 0.806

NSE 0.905 0.433

Prediction efficiency (Pe) 0.932 0.669

MAE 2.569 9.848

MSE 33.056 217.588

RMSE 5.749 14.751

PBIAS 31 -46

VE 0.689 0.412

rsr 0.308 0.753

7.3.4 Sediment calibration

As noted above, observed sediment data were sparse. There were 11 

sediment observations available from the four Seven Mile Creek gage 

locations over the same June 2006 period considered for the flow 

calibration. As a result, sediment loads were inferred based on the 

relationship between discharge and TSS. That relationship is illustrated in 

Figure 25. A similar approach was used by Barr Engineering (2009) to 

estimate sediment loads at a single gage. From this established 

relationship, sediment loads associated with the June 2006 calibration 

event were derived.
ERDC/CHL TR-20-3 43

Figure 25. Relationship between discharge and TSS at Seven Mile Creek used to 

estimate the volume of sediment associated with the calibration event. 

Figure 26 shows the inferred sediment loadings at three gage locations 

within the Seven Mile Creek Watershed. There was insufficient flow data 

at the fourth gage (SMC4) to infer sediment loads at that location. The 

empirical relationship may not hold for flows in excess of 8.5 m3 s

-1

.

Figure 26. Sedographs derived from an empirical relationship between discharge and 

suspended sediment used to estimate the sediment volume from the June 2006 

calibration event. 
ERDC/CHL TR-20-3 44

Calibration was done using the SCE general purpose global optimization 

method (Duan et al. 1992, 1993) as described by Skahill et al. (2012a, 

2012b). The soil erodibility factor/soil cropping factor/conservation factor 

parameter was used to adjust the sediment transport capacity to match the 

total volume of sediment from the model to the inferred volume derived 

from the observations.

For the NE Fork Sub-basin model, five erodibility values were calibrated to 

the total inferred sediment discharge at SMC1, 17.31 m3. The final 

simulated value of sediment discharge was within 1% of this value. The 

final erodibility values are as listed in Table 7. Due to the distribution of 

land uses and soil types in the watershed, erodibility was calibrated for 

only selected LUST categories. In Table 7, values with an asterisk were 

adjusted during calibration. Values without an asterisk were assigned 

based on literature values and not adjusted.

Table 7. Final erodibility values for the NE Fork 

model (CL – clay loam; L – loam; MSL – 

mucky silt loam). 

Land Use Soil Type Erodibility K 

Corn/Soy CL/L 0.000351*

Alfalfa CL 0.00028

Wetlands CL/L/MSL 0.00268*

Developed CL/L/MSL 0.00808*

Forest CL/L/MSL 0.000706*

Corn/Soy MSL 0.0000318*

Alfalfa L 0.00038

Alfalfa MSL 0.00048

*calibrated values

For the Seven Mile Creek Watershed model, estimated total sediment 

values from SMC 1, 2, and 3 were used. During the automated calibration 

process, 80% of the weight was put on the value at SMC3, with 10% each 

on the values at SMC2 and SMC1. Estimated values of total sediment 

discharge at these three gages were 17.31, 24.25, and 1735.22 m3, for SMC 

1, 2, and 3, respectively. The final simulated values were within 10% of the 

estimated values. Final calibrated erodibility values for the LUST

categories are shown in Table 8.
ERDC/CHL TR-20-3 45

Table 8. Final calibrated erodibility values for the 

Seven Mile Creek model with LUST categories (CL – 

clay loam; L – loam; MSL – mucky silt loam). 

Land Use Soil Type Erodibility

Corn/Soy CL 0.0002

Corn/Soy L 0.00219

Corn/Soy MSL 0.000003

Alfalfa CL/L/MSL 0.000012

Wetlands CL 0.000161

Wetlands L 0.00161

Wetlands MSL 0.000478

Developed CL/L/MSL 0.0015

Forest CL/MSL 0.000947

Forest L 0.0084

Calibrated erodibility tended to be related more to soil type than to land 

use. In general, the loam soils were more erodible than the CL/MSL soil 

types. This is not predicted by standard guidance for selecting erodibility, 

with increasing erodibility for finer soil textures. In this case, the opposite 

seems to be true. Testing of soil erodibility in the field indicated a similar 

pattern, with the loam soils being more erodible that the silts1. Possible 

explanations are that the finer soils are more cohesive and that the 

location of ravines and gullies (Figure 7) is largely located in the loam soil 

type (Figure 5). Exposed soils in ravines/gullies may be much more 

erodible than soils with some type of land cover. Another possible 

explanation is that the limited observed sediment data skewed the results.

For the Seven Mile Creek model, the estimated contribution from SMC1

and SMC2 was only approximately 2% of the total load at SMC3. In

calibration, this has the effect of forcing the model to produce most of the 

sediment in the lower portion of the watershed, where the loam soils are 

located.

7.3.5 Nutrients

A combination of land use, manure spreading, and feed lot location index 

maps were used to assign parameter values for nutrients. Permitted 

 

1 Chis Lenhart. Personal communication, University of Minnesota. 2015.
ERDC/CHL TR-20-3 46

manure spreading fields cover approximately 40% of the watershed. The 

location of tracts of land where manure spreading was permitted as of 

2008 (but not necessarily utilized) is shown in Figure 27. Manure 

spreading locations and amount vary over time. Manure spreading is 

thought to increase P in the soils, with a corresponding reduction in the 

partition coefficient1. From prior experience, feed lots are a potential 

hotspot for high soil concentrations of both N and P. Data to populate the 

nutrient parameters were particularly scarce. No soil concentration or 

partition coefficient data were available. Initial soil concentration values 

were assigned based on measured values from a previous study in a similar 

setting in Wisconsin (Johnson et al. 2009; Pradhan et al. 2014). These 

values may not be representative of the actual values at Seven Mile Creek.

The soil P concentration was increased, amount depending on land use, 

for areas with manure spreading. Groundwater concentrations were 

assigned based on average observed tile drain values at Red Top Farms 

and low stream flow values at SMC1 and SMC3. These values were 

relatively consistent over time, indicating that the assumption in GSSHA 

of constant groundwater concentrations is probably reasonable at 

Seven Mile Creek.

Figure 27. Location of permitted manure spreading fields, 

shown in black. 

 

1 Chuck Reagan. Personal communication. Minnesota Pollution Control Agency. 2015. 
ERDC/CHL TR-20-3 47

The calibration period for nutrients is the period June 15-30, 2006. Only a 

few observed data points were available, with none corresponded to high 

flows. Soil uptake and partition coefficients were adjusted to better fit the 

available points. The model and parameter set do not necessarily represent 

the conditions at Seven Mile Creek but more represent a plausible set of 

conditions for the watershed based on extremely limited data. Final 

water/soil partition coefficients were 0.001 and 300.00 for N and P, 

respectively. Spatially distributed parameter values are shown in Table 9.

In the NE Fork Sub-basin model, KN is 10-3 d-1 and UptakeP is 10-4.

Final calibration model results are shown in Figure 28 for the NE Fork 

Sub-basin model, and Figure 29 and Figure 30 for the Seven Mile Creek 

model. As is seen in the figures, the observed data are sparse. Results for N 

look better than results for P, which are probably over estimated. At SMC1 

in the Seven Mile Creek Watershed model, the concentrations tend to drift 

higher as the streams start to go dry. This is an artifact of the numerical 

computations. As there is very little flow at this point in the simulation, 

there is very little effect on mass calculations.

Table 9. Nutrient parameter values, K – soil/water partition coefficient. 

Land Use 

Manure 

Spreading 

Soil N 

mg kg-1 

Soil P 

mg kg-1 

GW N 

mg L-1 

GW P 

mg L-1 

Soil 

KN 

Soil 

KP 

UptakeN 

d-1 

UptakeP 

d-1 

Corn No 2200 629 13 0.07 10-2 17 10-5 10-7

Soy no 2200 629 13 0.07 10-2 17 10-5 10-7

Alfalfa no 2750 761 13 0.07 10-2 26 10-5 10-7

Open water no 0 0 13 0.07 10-2 30 10-5 10-7

Developed no 1000 500 13 0.07 10-2 30 10-5 10-7

Forest no 4500 710 13 0.07 10-2 20 10-5 10-7

Grassland no 3200 611 13 0.07 10-2 37 10-5 10-7

Wetland no 3200 611 13 0.07 10-2 30 10-5 10-7

Feedlot 16000 3215 13 0.07 10-2 10 10-5 10-7

Corn yes 2200 1530 13 0.07 10-2 13 10-5 10-7

Soy yes 2200 1530 13 0.07 10-2 13 10-5 10-7

Alfalfa yes 2750 1662 13 0.07 10-2 18 10-5 10-7

Open water yes 0 901 13 0.07 10-2 20 10-5 10-7

Developed yes 1000 1401 13 0.07 10-2 20 10-5 10-7

Forest yes 4500 1611 13 0.07 10-2 15 10-5 10-7

Grassland yes 3200 901 13 0.07 10-2 23 10-5 10-7

Wetland yes 3200 901 13 0.07 10-2 20 10-5 10-7
ERDC/CHL TR-20-3 48

Figure 28. Nutrient concentrations at the outlet of the NE Fork for June 2006 event. 

NE Fork Model – Nutrient Concentations

Figure 29. Simulated N concentrations at the outlet of Seven Mile Creek for the 

June 2006 event. 

Seven Mile Creek Model – Nitrogen Concentrations 

 

 

Nitrogen Phosphorus
ERDC/CHL TR-20-3 49

Figure 30. Simulated P concentrations at the outlet of Seven Mile Creek for the 

June 2006 event. 

 

7.4 Long-term simulations

The calibrated/verified Seven Mile Creek Watershed model and NE Fork 

Sub-basin model were used to simulate flow over the better part of a 

3-year period (2004-2006). The models simulated frozen ground and 

snow accumulation throughout the winter periods to characterize existing 

conditions in the basin. The results of these simulations are shown in 

Figure 31.
ERDC/CHL TR-20-3 50

Figure 31. Long-term simulation results for the Seven Mile Creek Watershed 

compared with observed discharges at SMC1 and SMC3. 

 

As is seen in the figure, the models compare well for the calibration and 

validation periods in 2004 and 2006. Comparisons at other times vary.

Note that the quality of the observed precipitation and flow data varies 

considerably during the simulation; 2005 is considered a poor-quality data 

year. Also note that the model parameter set was developed using hourly 

distributed radar rainfall. These data were only available for select summer 

events as described above. At other times, the rainfall is 15 min point data a 

Red Top Farms, or combined with observed values from SMC1, SMC2, and 

SMC3. During summer periods especially, this coverage may not accurately 

reflect the rainfall distribution in the watershed. As noted, the point data are 

also variable in quality, with 2005 being a poor coverage year. Model results 

will be superior if radar data are used for the period of interest. Still, the 

precipitation record used in this period can be considered typical if not an 

accurate reproduction of actual events, and the simulated flow and 

sediment results are accurate for the precipitation record provided, as 
ERDC/CHL TR-20-3 51

demonstrated during the calibration/validation period. These model results 

of this period of record are useful for comparison of hydrology and sediment 

from different future land-use scenarios.

The long-term simulations took several days to complete. Runtimes for the 

Seven Mile Creek Watershed model at 50 m resolution took between 91 to 

131 hours to finish with multiple runs going simultaneously on an Intel® 

Xeon® CPU X5650 at 2.67 GHz with 24.0 GB random access memory 

(RAM). The higher resolution NE Fork Sub-basin Models took 

considerably longer with runtimes from 7 to 19 days on an Intel Xeon CPU 

E5-2630 at 2.30 GHz with 16.0 GB RAM.

7.5 Alternate land-use scenarios

The physically based, spatially distributed nature of the GSSHA model 

makes it ideal for evaluating targeted landscape modifications. The 

calibrated Seven Mile Creek Watershed model was used to simulate 

hydrology as well as nutrient and sediment displacement throughout the 

watershed, for the present-day land use to identify relative differences 

among four different future scenarios. The various scenarios were defined 

by the IST using the LANDFIRE program to generate the hypothetical 

scenarios. The modeling sub-team made the decisions about how the 

scenarios are configured and how they should be represented in the model.

The four future scenarios are the following:

1. Pre-development (PreDev) – scenario involving native vegetation 

believed to be representative the condition of the land prior to human 

settlement in the area.

2. Agricultural (AG) – scenario in which commodity prices dictate 

agricultural intensification in order to maximize the production on the 

land.

3. Water Quality (WQ) – scenario in which the land is managed in order 

to protect the water resources within the watershed.

4. Biodiversity (BD) – scenario in which the land is managed to maximize 

wildlife habit.
ERDC/CHL TR-20-3 52

7.6 Simulation specifics

7.6.1 Tile drains 

Tile drains are closely associated with the agricultural areas in the 

watershed. In the base model simulations, the existing tile drain system is 

included in all the simulations. The same tile drain network is used for all 

future land-use scenarios. The tile drain network is not modified for 

changes in land use. This has the implied assumptions that restoration 

efforts will remove the tile drains (possibly) and that additional 

agricultural areas will not include tile drains (unlikely but possibly 

negligible considering the minimum amount of farmable area not 

currently in production). For the Pre-Dev, BD, and WQ land-use 

scenarios, the tile drain system is turned off; no tile flow is calculated for 

these scenarios. For the BD and WQ cases simulating the scenarios with 

tile flow on had the effect of cycling the increased infiltrated water into and 

out of the tile drains as the infiltrated water was collected in the tiles and 

then resurfaced, only to be infiltrated again and collected in other tiles.

This result is non-intuitive, and the tiles were subsequently eliminated 

from these scenarios.

7.6.2 Parameter values 

To a great degree, the calibrated/verified parameter sets were not 

modified to simulate the future scenarios. One exception exists in that the 

parameter value of the soil vertical hydraulic conductivity and overland 

roughness for the grass land use. In the base model, the land-use grass 

largely represents alfalfa and hay fields, agricultural land uses where the 

soil matrix is largely destroyed through tillage/compaction and the vertical 

soil hydraulic conductivity is low, representative of an agricultural land 

use. For future land uses with significant ecologic restoration, water 

quality and biodiversity, and for the pre-development scenario, the term 

grass is assumed to correspond to some type of native prairie grass. In the 

restoration case, it is reasonable to assume that over time the soil matrix 

will be partially re-established, at least in the top layer. For this reason, the 

soil vertical hydraulic conductivity for the top layer of soil for grass in 

these future scenarios is increased. In the PreDev scenario, it is fair to 

assume that the entire soil column, A, B, and C, horizons will be more 

representative of natural conditions in the watershed. Correspondingly, in 

the PreDev case, the soil vertical hydraulic conductivity of grass is 

increased in all three layers. Currently, there is no native prairie in the 
ERDC/CHL TR-20-3 53

watershed, and thus no such land use in the model. In this watershed, the 

only land use with a presumed intact soil matrix is forest. Typically, a 

forest is expected to have a higher soil vertical hydraulic conductivity than 

a grassland, due to the decomposition of leaf litter and additional 

bioturbation. For that reason, the soil vertical hydraulic conductivity 

values for prairie grass were raised closer to that of the forest values in the 

corresponding soil types. Values of soil vertical hydraulic conductivity, 

K cm hr-1, for forest and grass in the relevant soil types used in the 

scenarios are shown in Table 10.

Table 10. Soil vertical hydraulic conductivity values, K, cm hr-1 used in the scenarios. 

Scenario Base All WQ/BD Pre-Dev Base ALL WQ/BD Pre-Dev

Land Use Grass Forest Grass Grass Grass Forest Grass Grass

Soil Clay loam Clay loam Clay loam Clay loam Loam Loam Loam Loam

Layer 1 K 0.0077 0.1500 0.1100 0.1100 0.1152 0.6060 0.3500 0.3500

Layer 2 K 0.1090 0.5730 0.1090 0.5730 0.1445 0.7600 0.1445 0.7600

Layer 3 K 0.1446 0.7600 0.1446 0.7600 0.1445 0.7600 0.1445 0.7600

Similarly, the overland roughness values developed for alfalfa were 

thought to not be representative of the conditions in a native prairie 

environment. Whereas alfalfa fields may look lush from the top, at the 

ground level there is little actual foliage. Overland roughness values are

similar to other crops. A native prairie is very dense, with much higher 

values of overland roughness expected. In terms of roughness, the prairie 

is thought to most closely resemble the herbaceous wetlands in the 

present-day model. As shown in Table 5, the overland roughness in the 

herbaceous wetland is 0.562. For comparison, the value for alfalfa is 0.24 

and forest is 0.643. For the restoration scenarios, the land-use grass is 

given an overland roughness value of 0.562.

7.6.3 Nutrients 

For nutrients, land use alone was used to populate the parameter values, 

and the appropriate values from Table 9 were applied. Because the 

application of manure is unknown in the future, spreading was assumed to 

occur in fields described as corn and not to occur in fields described as soy.

This is a somewhat arbitrary way of trying to account for the general 

distribution of manure spreading in the different scenarios. The possible 

location of feedlots is unknown. Feedlots were not considered in the 
ERDC/CHL TR-20-3 54

scenarios. Also, the prescribed values for groundwater concentration do 

not vary amongst the scenarios. The nutrient concentration values 

prescribed represent the as-is (Base) condition and may be representative 

of the AG scenario, but likely are high for the WQ and BD scenarios and 

possibly quite high for the PreDev scenario. Additionally, soil nutrient 

concentrations were not changed for the scenarios. It is possible that these 

values are not representative of the future scenario values.

7.7 Results

7.7.1 Discharge

Simulations were run for each alternate land-use scenario over the same 

period of record (May 1, 2004, to January 7, 2007) that was considered in 

the long-term simulation of present conditions described above.

Hydrographs for each scenario are provided in Figure 32, Figure 33, and 

Figure 34 for each year of the simulation for the Seven Mile Creek 

Watershed model. As seen in the figures, the hydrographs from the Base

and AG models tend to be spikey with high peaks, fast recessions and little 

base flow. This is a result of agricultural drainage and loss of the soil 

matrix. As more natural areas are included, the BD and WQ scenarios, 

hydrograph peaks decrease, recessions extend, and base flow increases.

For the PreDev case, the hydrographs are markedly different with low 

peaks, with extended recessions and significant base flow. In the PreDev

case, surface flows from large events like those that occur in late 2004 are 

greatly reduced (Figure 32). The BD and WQ cause a similar, though 

reduced, effect. This water is being infiltrated and converted to 

groundwater. While there is some groundwater discharge during and 

immediately after the event (Figure 32), much of the water is stored in the 

ground. During subsequent springs (Figures 33 and 34), some of this

water may be discharged, as groundwater discharge to the stream and or 

seeps, resulting in the increased discharge seen in the early melt season. A 

similar but lesser effect is seen for the BD and WQ scenarios (Figures 33 

and 34). The discharge shown in Figures 33 and 34 during the early 

spring, February/March, is not precipitation runoff; it is a combination of 

snow melt and groundwater discharge. In addition to the snow melt and 

groundwater discharge to the stream, the model indicates seep water 

bound up in the snow pack. As the snow pack begins to melt, the seep 

water is released along with the snow melt. Precipitation is still falling as 

snow, but the daytime temperatures are getting high enough to melt the 

snow, releasing snow melt and any water bound up in the snow pack. A 
ERDC/CHL TR-20-3 55

high water table in the restoration alternatives adds substantial 

groundwater discharge to this flow. The Base and AG scenarios actually 

result in lower early spring runoff, as the groundwater table has been 

effectively lowered prior to the beginning of winter precipitation.

Results from the NE Fork Sub-basin model simulations are shown in 

Figure 35, Figure 36, and Figure 37 for the 3 years, respectively. The NE 

Fork Sub-basin model shows similar result except that there is very little 

runoff from the PreDev model. In this case, the varying result is a function 

of varying contributions from the different sources of flow into the stream.

In the NE Fork Sub-basin model, base flow in the stream is negligible, 

both from the observed data and from the model calibration and 

validation results. All flow into the stream at SMC1 is a combination of 

overland flow and tile discharge. For the PreDev model, there are no tiles, 

so all of the additional groundwater recharge is adding to the water table,

but that water is not being discharged in the ditches above SMC1. Instead, 

it is discharged into the stream between SMC1 and SMC3, resulting in the 

increased base flow shown in the Seven Mile Creek Watershed model in 

Figure 32 through Figure 34. The exception to this is the early spring 

flows, February/March, where the restoration scenarios result in higher 

flows. The reasoning is the same as discussed above. In this case, the seep 

water bound up in the snow pack is the source of increased flow in the

restoration alternatives, PreDev, BD, WQ, over the Base and Ag scenarios.

Once the snow melts and the water table drops, approximately April 1 for 

2005 and 2006, the pattern changes.

Figure 32. Simulated discharge from each of the alternative land-use scenarios. 
ERDC/CHL TR-20-3 56

Figure 33. Simulated discharge from each of the alternative land-use scenarios. 

Figure 34. Simulated discharge from each of the alternative land-use scenarios. 

Figure 35. Simulated discharge from each alternative land-use scenarios in NE Fork. 
ERDC/CHL TR-20-3 57

Figure 36. Simulated discharge from each alternative land-use scenarios in NE Fork. 

Figure 37. Simulated discharge from each alternative land-use scenarios in NE Fork. 

7.7.2 Water balance

Water balances from the alternate scenarios are compared with the Base 

scenario to identify the relative hydrologic response associated with the 

different scenarios; results for the Seven Mile Creek Watershed model are 

summarized in Table 11 and Figure 38. For the Base scenario, runoff is 

approximately 20% of total precipitation; evaporation is approximately 

82% of total precipitation, with approximately 12% of precipitation going 

to groundwater recharge. For the PreDev and restoration scenarios, BD

and WQ, discharge is slightly reduced, on the order of 10%. Total 

infiltration increases to almost 100% for the PreDev scenario. ET is 

reduced, and baseflow increases to approximately 22% of the total 

precipitation. In general, results from the AG case are very similar to the 

Base case, with slightly less infiltration, slightly reduced flow (due to 
ERDC/CHL TR-20-3 58

reduction in baseflow), and increased ET, when compared to the Base

case. As noted previously, the AG and Base models are very similar.

Results for the NE Fork Sub-basin model are summarized in Table 12. In 

general, the results are consistent with the Seven Mile Creek Watershed 

model results, shown in Table 11, with the PreDev, BD, and WQ scenarios 

resulting in increased infiltration and groundwater recharge, and reduced 

ET, while the AG scenario has the opposite effect. The results are more 

significant for the NE Fork model. Because of the lack of baseflow in the 

NE Fork model, restoration cases convert in this watershed result in 

substantial reductions in streamflow due to the increased infiltration.

At the Seven Mile Creek Watershed outlet, a significant source of water is 

the baseflow, the groundwater recharging the stream. Contributions to 

Seven Mile Creek stream flow at SMC3 are 52% groundwater, 33% surface 

water, and 15% tile flow. In the Seven Mile Creek model, the baseflow is 

81% of total discharge in the PreDev scenario, with only 19% surface flow. 

The BD and WQ scenarios also increase base flow, to approximately 75% 

of total discharge. The AG scenario results in only 36% baseflow, with tile 

flow 18% and surface flow 46%, making up the difference.

In the NE Fork Sub-basin model, baseflow is not a source of streamflow,

but seepage can be, depending on the scenario. For the Base case, stream 

flow contributions at SMC1 are 43% surface water and 57% tile flow. For 

the PreDev scenario, the flow is 100% surface water flow. For the AG case,

tile flow is increased to 80% of the total discharge at SMC1. Surface water 

flows to the stream consist of a combination of direct precipitation runoff, 

seepage, and tiles discharging on the overland. The actual contribution of 

streamflow from any source is complicated by runoff, run-on, reinfiltration, and re-emergence from the groundwater. As seepage can be 

high in the early spring under the restoration scenarios, it may contribute 

significantly to streamflow during these periods. With the Base and AG

models, seepage is negated by the tile drains.
ERDC/CHL TR-20-3 59

Figure 38. Water balance as a percentage of total precipitation within the Seven Mile 

Creek Watershed for present conditions (Base) compared with the results from the 

four alternate land-use scenarios. (Note: Totals were not available for the AG scenario.) 

Table 11. Summary of the water balance in 106 m3 for the various long-term 

Seven Mile Creek simulations. 

Scenario 

Total 

Precipitation 

Total 

Discharge 

Total 

Infiltration 

Groundwater 

Recharge ET 

Base 161 32 117 20 133

Base-no tile 161 28 117 17 135

PreDev 161 27 160 35 117

AG 161 31 119 21 134

WQ 161 26 147 29 126

BD 161 27 144 29 126

Table 12. Summary of the water balance in 106 m3 for the NE Fork simulations. 

Scenario 

Total 

Precipitation 

Total 

Discharge 

Total 

Infiltration 

Groundwater 

Recharge 

Evapotranspiration 

Base 65 9.5 45 7.5 53

Base-no tile 65 5.0 45 2.8 56

PreDev 65 1.7 66 11.0 49

AG 65 9.8 49 9.8 55

WQ 65 3.1 52 8.3 50

BD 65 2.2 60 9.4 51
ERDC/CHL TR-20-3 60

These results are instructive and indicate the value of the modeling 

approach for better understanding the system dynamics. The results help 

explain how the basin functions and the effects of certain practices on the 

watershed. The discharge and water balance indicate that for the entire 

Seven Mile Creek Basin, base flow is a significant part of the water budget, 

but not so upstream of SMC1. In terms of process, they are quite different 

basins, and the results from one are not translatable to the other. The 

contribution of tile flow is more significant in the sub-basin than in the 

overall basin. Without the tile contribution, the streams would likely be 

dry much of the time, flowing only briefly after rainfall events large 

enough to produce overland flow, and during the spring snow melt season.

It is unlikely that this result could be discerned without separating the 

processes of surface water flow, base flow, and tile flow. Surface water 

runoff is important at both levels. Agricultural practices decrease 

infiltration and increase surface flow.

7.7.3 Sediment

Table 13 provides the annual sediment yields associated with each of the 

scenarios. Yearly sediment yield varied considerably over the 3 years, with 

2006 delivering 72% of the total yield for the Seven Mile Creek Basin and 

48% for the NE Fork model, for the Base case, with much of this sediment 

being delivered during the June 2006 calibration event, a particularly 

large event during the observed period of record. This is typical of 

sediment delivery, where much of the total sediment load is produced in a 

few large events. The average annual sediment loading rate at SMC3 is 

247 kg hectare-1 yr-1. The average annual sediment loading rate at SMC1 is 

13.6 kg hectare-1 yr-1, indicating that most of the sediment load originates 

between SMC1 and SMC3, consistent with the observed sediment 

measurements and the model calibration.

An advantage of physically based, spatially distributed approach is that it 

can be used to identify sediment sources and sinks throughout the 

watershed. Figure 39 shows the locations of sediment erosion and 

deposition throughout the Seven Mile Creek Watershed. As can be seen in 

Figure 39, the ravines near the watershed outlet, as well as the locations 

where tiles discharge on the overland flow plane, are erosion hotspots, 

shown in red in the figure. Figure 39 clearly shows erosion occurring in the 

measured ravines, as noted in Figure 7. Figure 40 shows an expanded view 

of the erosion patterns around tile discharge locations. The concentrated 

flow from the tile drains is a major source of erosion in the model. Other 
ERDC/CHL TR-20-3 61

notable features in Figure 40 are the lack of erosion that occurs in the 

wetland areas (Figure 3).

Figure 39. Output from the GSSHA model showing locations of net sediment 

erosion/deposition (meter) throughout the Seven Mile Creek Watershed for the 

present conditions (Base scenario). 

Base Scenario
ERDC/CHL TR-20-3 62

Figure 40. Erosion can be observed from the simulations locally concentrated around 

tile outlets at select locations. 

To further assess the effects of the tile drains on erosion, the Base scenario 

was run without tile drains to quantify their contribution to sediment loads.

The results are consistent with the source of flow in the models. For the 

Seven Mile Creek model, the effect was negligible; for the NE Fork Subwatershed, the sediment loading rate was reduced by a third (Table 13).
ERDC/CHL TR-20-3 63

Table 13. Simulated annual sediment loading rate (TSS) 

within the Seven Mile Creek Watershed and NE Fork Subbasin Models for present conditions (Base) compared with 

the results from the four alternate land-use scenarios. 

TSS Loading Rate (Kg hectare-1 yr-1) 

Scenario Seven Mile Creek

NE Fork Subwatershed

Base 247 13.6

Base w/o Tile 247 9.0

AG 332 13.3

PreDev 166 4.1

BD 289 10.0

WQ 224 17.6

As described in the Scenario Development Section 7.5, the four scenarios 

were simulated with using the calibrated erodibility parameter values for 

each of the LUST categories in the models (Table 7 and Table 8). Tile 

drains were simulated in the Base and Ag scenarios but not in the 

restoration scenarios, PreDev, BD, and WQ. Results, in terms of sediment 

loading rate kilogram hectare-1 yr-1 are shown in Table 13. For the Seven 

Mile Creek Watershed model, in comparison to the Base, the AG scenario 

produced significant increases in sediment loads in the Seven Mile Creek 

Watershed but not in the NE Fork Watershed. The increase in loading rate 

is 34%. In the NE Fork model, there is no significant difference between 

the Base and AG scenarios. For the PreDev case, there is a decrease in 

loading rate by approximately a third for the Seven Mile Creek Watershed 

model and a two-thirds decrease for the NE Fork Sub-watershed model. In 

the PreDev, surface runoff is reduced, and tile flow is eliminated. This has 

the effect of reducing erosion in both models with more effect seen at the 

SMC1 gage, where tile flow is a greater percentage of the total flow. The 

WQ scenario results in slightly reduced sediment load at the Seven Mile 

Creek model and an increase in sediment load in the NE Fork model. The 

BD scenario had an opposite effect. Both of these alternatives result in 

increased infiltration and reduced runoff, so it would be expected that 

both would reduce sediment load at both scales. The problem likely lies in 

the sediment erosion parameters for the “forest/loam” LUST. 

Two potential issues are the erosion values for grass and forest loam. The 

calibrated erosion value for grass corresponds to alfalfa fields and is 

probably significantly higher than grass in the restoration scenarios that 
ERDC/CHL TR-20-3 64

represent some form of native prairie vegetation. Table 14 shows the 

distribution of land uses in the scenarios. As indicated in the table, the 

amount of grass is much higher in the restoration and PreDev scenarios.

Due to the high percentage of grass in these scenarios, the use of the 

erodibility values derived for alfalfa may result in a significant 

overestimation of erosion in these scenarios. The suitability of these 

assumptions should be reassessed. The other potential issue is the erosion 

value for the forest/loam LUST category. As the forest/loam LUST group 

occurs mainly in conjunction with the ravines, the sediment erosion 

parameter derived from the calibration may be higher than it would be 

without the ravines. This may result in an overestimation of sediment 

erosion from other forest/loam areas in the restoration scenarios. As 

shown in Table 14, in the present-day (Base) conditions, forest is only 4% 

of the land use, much of that occurring along the streams in the loamy 

soils, which also happens to be areas of the highest erosion.

Table 14. Distribution of land uses in the model scenarios (%). 

Scenario Corn/Soy Developed Alfalfa/Grass Wetlands/Water Forest 

Base 79 5 1 11 4

AG 76 5 6 7 5

PreDev 0 0.2 80 7 13

BD 47 5 28 8 11

WQ 36 5 41 8 11

7.7.4 Nutrients

Due to lack of confidence in the parameter values and excessive simulation 

times nutrient simulations were conducted for the June 2006 calibration 

period only. Results from the simulations are summarized in Table 15. As 

shown in the table, the model did not show much differentiation between 

the different land-use scenarios, with only slight, though intuitively in the 

correct direction, differences noted. More difference was noted in the NE 

Fork Sub-basin model of P than for the other cases. As the parameters 

were not consistent across the two models the, mass balances for the two 

models, especially for N, are not necessarily comparable . There are many

assumptions built into these model results that may be overriding the 

effects of the land-use changes. These assumptions should be reassessed.
ERDC/CHL TR-20-3 65

Table 15. Simulated nutrient loads within the Seven Mile Creek model and 

NE Fork for present conditions (Base) compared with the results from the 

four alternate land-use scenarios. 

Scenario 

7 Mile Creek 

N (kg) 

7 Mile Creek 

P (kg) 

NE Fork 

N (kg) 

NE Fork 

P (kg) 

Base 628 33 0.38 7.43

PreDev 612 32 0.36 6.97

AG 628 33 0.38 7.43

WQ 614 33 0.36 7.01

BD 612 33 0.36 7.01
ERDC/CHL TR-20-3 66

8 Discussion of Results

The GSSHA model was employed at Seven Mile Creek in an effort to better 

understand the existing system and to assess the effects of possible 

restoration scenarios that may be applicable in the watershed.

8.1 Flow regimes

Discharge from the watershed is a mixture of surface water (33%), tile flow 

(15%), and groundwater (52%), so that at the watershed outlet,

groundwater is the primary overall source of flow with surface flow and 

tile being less significant sources of water at the outlet (Figure 41). Still, 

surface water and tile flow account for approximately half of the discharge.

In the upper watershed, the flow regime is quite different, with surface 

water being 43% of the total discharge, tile being 57% of discharge, and 

groundwater being negligible. Currently, the predominance of overland 

flow sources results in high discharge during events, causing erosion of 

ravines and high sediment loads.

Prior to development, flow at the outlet was primarily base flow, 81%. Flow 

in the sub-basin was surface water. Peak flows at both sites were much 

lower with extended flow over the year. There is little resemblance 

between the historic and present-day flow conditions.

Figure 41. A comparison between present-day conditions (Base) with the native regime 

(pre-development) of the respective surface and groundwater components of flow. 
ERDC/CHL TR-20-3 67

8.2 Sediment

The observed data and the model results indicate that at the watershed 

scale the primary source of sediments is from ravines and gullies near the 

stream channels and is the source of more than 90% of the sediment load 

at the watershed outlet. These gullies/ravines are the result of increased 

overland surface runoff due to a combination of increased surface runoff 

from fields and from tiles discharging above the nick points. Scenario 

modeling indicates that much of the sediment load can be eliminated by 

converting the row crops back to native prairie (Figure 42). Other ways to 

reduce the surface discharge should be investigated, such as 

repairing/eliminating ravines/gullies.

Figure 42. Annual sediment yields associated with the various land uses. 

8.3 Effect of row cropping

The effects of row cropping on the flow and sediment are dramatic. Row 

crops increase the surface runoff and increase erosion. At the watershed 

scale, the source of increased erosion is related to gullies and ravines. At 

the field scale, the increased erosion is due to increased overland 

discharge. Row crops reduce infiltration, reduce groundwater recharge, 

and increase ET. These findings were consistent across scales.
ERDC/CHL TR-20-3 68

8.4 Tile drains

Tile drains are designed to quickly lower the water table below the root 

depths after rainfall events. The modeling indicates they do this well at 

Seven Mile Creek.

8.4.1 Water budget 

Tile drains result in significantly more discharge, more infiltration, with 

less surface flow, evaporation, and lower groundwater recharge. The effect 

is larger at the small scale than the watershed scale. The most dramatic 

effect is seen on groundwater recharge.

8.4.2 Flow regime 

Tile increases overall flow, this effect being larger at the smaller scale. Tile 

has the effect of increasing flow on the receding limb of the hydrograph 

(Figure 43 and Figure 44). Tiles appear to result in reduced early season 

snow runoff by lowering the groundwater table and reducing both 

groundwater discharge to the stream, as well as seepage, which may be tied 

up in the snowpack and released as it melts. This effect may be exaggerated 

in the model. The process of snow melt runoff is poorly understood and one 

of the greatest remaining challenges in hydrology. Still, the tile drains, in 

combination with row crops, result in higher late spring and summer flows 

and lower early spring flows. At the large scale, the effects of tile are less 

pronounced (Figure 43); at the small scale the effects are more pronounced 

(Figure 44) where the tile drains are shown to substantially increase the 

peak flows and extend the recession limb of the hydrograph.
ERDC/CHL TR-20-3 69

Figure 43. Comparison of outflow at Seven Mile Creek between simulated flow with 

and without tile drains. 
ERDC/CHL TR-20-3 70

Figure 44. Comparison of outflow at the NE Fork between simulated flow with and 

without tile drains. 
ERDC/CHL TR-20-3 71

8.4.3 Sediment 

Tiles are shown to significantly affect erosion in the fields. In the sub-basin 

model, removing the tiles reduced sediment load by 33%. At the watershed 

scale, removing the tiles had no effect on total sediment load. This is due 

to two reasons. First, most of the sediment load is generated during large 

events when tile flow is only a small part of total flow. Second, without tile, 

surface runoff from the fields is increased. In either case, the overland 

discharge still concentrates in low spots, ravines, which causes the bulk of 

erosion in Seven Mile Creek. Reducing tile flow will reduce erosion in the 

fields, but it will not stop the already developed ravines from continuing to 

erode and may in fact exacerbate the problem by shifting more of the 

runoff to overland flow.

8.4.4 Nutrients 

While the nutrient modeling results were inconclusive, some inferences 

can be drawn from the hydrologic and sediment results. Since tile 

increased overall discharge from the fields it likely increases both N and P

loads during events. Since increasing tile flow results in increased erosion 

from fields, it likely results in increased P load to the stream. In addition, 

tile results in a more continuous addition of high N groundwater to the 

streams. All indications are that tile increases both N and P load. Lack of 

verifiable modeling results precludes more quantitative statements.
ERDC/CHL TR-20-3 72

9 Future Efforts

While this modeling effort produced useful models for simulating and 

understanding conditions in the Seven Mile Creek Watershed and 

assessing possible future conditions in the watershed, the effort also 

identified numerous areas where the modeling could possibly be improved 

with additional effort. Some key issues were identified.

9.1 Precipitation data

As discussed in detail in the data section (Section 4), the precipitation data 

were problematic. The lack of consistent data across the simulation period, 

as well as the lack of winter precipitation/snow estimates within the 

watershed, is thought to degrade the modeling results. The inclusion of 

radar data for selected events appeared to improve the modeling effort.

Using radar data for simulation of all events of interest may increase the 

confidence in the subsequent results. It might also be possible to use the 

Red Top Farms rain gage to calibrate the radar data, or to use the 

measurement at Red Top Farms at 15 min and the spatial distribution of 

rainfall and build a combined data set. Likewise, finding a better source of 

winter precipitation data or snow cover, SNODAS1 has been suggested, 

would likely improve the results.

9.2 Hydrologic calibration/validation

Utilizing any improved precipitation input could potentially improve the 

calibration and some adjustment to the parameter set may occur.

Additionally, utilizing the link/node tile drain model would allow for longer 

calibration periods. Longer calibrations, with more events, and different 

size events can improve the robustness of the model parameter set.

9.3 Nutrients

Two big issues hamper the nutrient simulations: a lack of (1) measured or 

good estimates of soil N and P concentrations and (2) observed N and P

concentrations in the streams. Measurements of, or better estimates of, N

and P for the different land uses would solve the first issue. Extending the 

simulation period may help to solve the second. Given the difficulty with 

obtaining input data for the N and P simulations, an alternative approach 

 

1 Snow Data Assimilation System
ERDC/CHL TR-20-3 73

to assessment might be to use the flow, sediment, and available N and P

observations to develop relations between these parameters and then 

utilizing the flow and sediment results from the model to estimate N and P 

based on these relationships.

9.4 Future scenarios

Two issues are thought to primarily affect the future scenario simulations: 

(1) the tile drainage network in the future scenarios and (2) the adjustment 

of parameters to address the addition of a new land type use type in the 

model, native prairie. 

In the current set of models, the tile drainage system is either on or off.

Ideally, the tile system would be modified to match the different future 

scenarios, with tile removed from areas taken out of production and added 

to areas put into production.

As the native prairie is not present in the watershed, suitable parameter 

values must be determined or estimated for this land use in the future 

scenario modeling. In this effort, the soil hydraulic conductivity and 

overland roughness were adjusting using surrogates for the prairie that 

were in the watershed: forest for soil hydraulic conductivity and 

herbaceous wetland for overland roughness. While this is reasonable, it is

not necessarily accurate. The Minnesota Department of Natural Resources

is currently investigating soil parameter values for native prairie. The 

results of their investigation may improve the parameter values of soil 

hydraulic conductivity in these models. Similar efforts may provide better 

estimates of overland roughness, overland retention depth, and 

particularly the soil erosion coefficients used in the models. A related issue 

is the value of the soil erosion coefficient used for the forest/loam LUST 

category, which may not be representative of the combination of land use 

and soil type in areas not affected by ravines and gullies. These two values 

of erodibility should probably be reassessed in light of the results of the 

BD and WQ scenarios, which were inconclusive.

The final issue that should be assessed in relation to the future scenarios is 

modification of the ditch system. In the restoration scenarios, and especially 

the PreDev scenario, it may be appropriate to remove the ditch system or 

parts of it from the network. Removing the ditch will likely result in 

dramatic differences in both water and sediment delivery. The ditches are 

an efficient means of removing water and sediments from the watershed.
ERDC/CHL TR-20-3 74

10 Summary

Seven Mile Creek is a smaller tributary into the larger Minnesota River 

Basin that is thought to be representative of the conditions encountered 

throughout the basin, with the predominant land use being agriculture, with 

significant alteration of hydrology due to land-use change and drainage 

features and increased sediment and nutrient yield. Nested GSSHA 

hydrologic models were built of the Seven Mile Creek Watershed, the NE 

Fork Sub-basin of the watershed, and small experimental watershed, Red 

Top Farms, to try to understand dominant hydrologic processes in the 

watershed and estimate the effect of modern agricultural practices on 

hydrology, sediment, and nutrient yield, as well as assess the potential 

impacts of alternative land-use scenarios. Observed data were obtained for 

the period 2003 through 2010. The quality of the observed data varies 

significantly. Two periods, spring 2004 and summer 2006, were chosen as 

some of the best observed data periods that represent wet and dry 

hydrologic conditions in the basin. The suite of models was 

calibrated/verified to these periods and then used to simulate the period 

2004-2006 to determine water, sediment, and nutrient yields for presentday conditions, as well as for alternative land-use scenarios, including 

restoration alternatives, and increased agriculture alternatives. A 

predevelopment case was also simulated to try to estimate the historic 

system response. The Red Top Farm model showed significant skill in 

reproducing measured tile flow from an instrumented field. This is the first 

known instance of actually reproducing measured tile flow in a hydrologic 

model. The larger models showed skill in reproducing both wet and dry 

season flows, as well as estimated sediment yield. Due to a lack of input 

data/observed data, the ability to adequately simulate nutrients is suspect.

Observed data and the long-term simulations indicated that the bulk of the 

sediment load is derived in the lower portion of the watershed. Erodibility 

parameter values indicated that the loamy soils, in the lower portions of 

the watershed, were more erodible. The tile drains were shown to 

exacerbate the erosion problems by concentrating flow. This is a very 

significant factor at the sub-watershed scale.

When utilized to simulate the alternate land uses, the models indicate that 

the historic conditions were significantly different from the present-day 

hydrologic conditions with the present-day condition yielding higher 

hydrograph peaks and lower base flow, as well as significantly more 
ERDC/CHL TR-20-3 75

sediment than historically occurred at the basin outlet. In the subwatershed above SMC1, the model indicated that in this section of the 

stream historical flows were small and intermittent in this region. It is 

likely that the historic stream, if any existed, was quite small. The drainage 

system simulated in the PreDev scenario is certainly not representative of 

the historic conditions, and the actual historic channel probably produced 

even lower flows and sediment delivery. Partial restoration alternatives 

resulted in more natural hydrologic patterns, with lower peak flows and 

higher baseflow, but the results for sediment were mixed. Some of the 

assumptions built into these simulations should be reassessed. Simulation 

of the AG alternative scenario indicated that removing the last vestiges of 

natural area in the watershed will have a significant impact on hydrology, 

water balance, and sediment yield and that it is important to preserve 

some natural areas to maintain present-day water quality.
ERDC/CHL TR-20-3 76

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Calibration of a GSSHA Hydrologic Model Using ERDC Automated Model 

Calibration Software – Efficient Local Search. ERDC/CHL TR-12-3. Vicksburg, 

MS: US Army Engineer Research and Development Center.

Verma, S., and D. Baldocchi. 1986. “Eddy Fluxes of CO2, Water Vapor, and Sensible Heat 

over a Deciduous Forest.” Boundary-Layer Meteorology 36(1-2): 71-91.

Wicks, J. M., and J. C. Bathurst. 1996. “SHESED: A Physically Based, Distributed Erosion 

and Sediment Yield Component for the SHE Hydrological Modeling System.” 

Journal of Hydrology 175(1-4): 213-238.
ERDC/CHL TR-20-3 79

Acronyms and Abbreviations

1D one-dimensional 

2D two-dimensional 

AG agricultural

BD biodiversity

BMP best management practice

BNC Brown-Nicollet-Cottonwood 

CFGI Continuous Frozen Ground Index 

CHL Coastal and Hydraulics Laboratory

CL clay loam

d index of agreement

DEM digital elevation model

ERDC US Army Engineer Research and Development Center

ET evapotranspiration

GSSHA Gridded Surface Subsurface Hydrologic Analysis 

HMET hydro-meteorological 

IST integrated study team

L loam

LUST Land Use/Soil Type 

MAE mean absolute error 

MSE mean square error 

MSL mucky silt loam

MVP Saint Paul District

N nitrogen

NED National Elevation Dataset 

NSE Nash and Sutcliffe efficiency 

P phosphorus

PBIAS percent bias 
ERDC/CHL TR-20-3 80

PreDev pre-development 

R2 coefficient of determination

RAM random access memory

RMSE root mean square error 

rsr ratio of RMSE 

SCE shuffled complex evolution 

SLM Secant Levenberg-Marquardt 

SOW Scope of Work

SSURGO Soil Survey Geographic Database 

TSS total suspended solids

USACE US Army Corps of Engineers

USDA US Department of Agriculture 

USGS US Geological Survey 

VE volumetric efficiency 

WMS Watershed Modeling System 

WQ water quality
REPORT DOCUMENTATION PAGE Form Approved

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PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS.

1. REPORT DATE 

March 2020

2. REPORT TYPE

Final Report

3. DATES COVERED (From - To)

4. TITLE AND SUBTITLE

Nested Physics-Based Watershed Modeling at Seven Mile Creek:

Minnesota River Integrated Watershed Study

5a. CONTRACT NUMBER

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

Charles W. Downer, Mark Wahl, Nawa Raj Pradhan, Brian Skahill, Stephen Turnbull, and 

Ryan Pickett

5d. PROJECT NUMBER

5e. TASK NUMBER

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

Coastal and Hydraulics Laboratory

US Army Engineer Research and Development Center

Vicksburg, MS 39180-6199

8. PERFORMING

ORGANIZATION REPORT

NUMBER

ERDC/CHL TR-20-3

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

US Army Corps of Engineers, Saint Paul District

Saint Paul, MN 55101

10. SPONSOR/MONITOR'S ACRONYM(S)

USACE MVP

11. SPONSOR/MONITOR'S

REPORT NUMBER(S)

12. DISTRIBUTION/AVAILABILITY STATEMENT

Approved for public release; distribution is unlimited.

13. SUPPLEMENTARY NOTES

USACE MVP; MIPR 138667; “Minnesota River Basin Watershed Study - Nested GSSHA Model Development for the Seven Mile 

Creek Watershed”

14. ABSTRACT

The Minnesota River Basin (MRB) Integrated Study Team (IST) was tasked with assessing the condition of the MRB and recommending 

management options to reduce suspended sediments and improve the water quality in the basin. The Gridded Surface Subsurface 

Hydrologic Analysis (GSSHA) was chosen by the IST as the fine scale model for the Seven Mile Creek Watershed to help quantify the 

physical effects from best management practices within the MRB. The predominately agricultural Seven Mile Creek Watershed produces 

high total suspended solids and nutrients loads, contributing roughly 10% of the total load to the Minnesota River. GSSHA models were 

developed for a small experimental field research site called Red Top Farms, a Hydrologic Unit Code (HUC)-12 model for the entire 

Seven Mile Creek Watershed, a sub-basin of the Seven Mile Creek Watershed. After calibration, the resulting models were able to 

simulate measured tile drain flows, stream flow, suspended sediments, and to a lesser extent, nutrients. A selected suite of alternative 

land-use scenarios was simulated with the models to determine the watershed response to land-use changes at the small and medium scale 

and to test whether the type, size, and spatial distribution of land uses will influence the effectiveness of land management options.

15. SUBJECT TERMS 

Computer simulation, Environmental management, Hydrologic models, Minnesota River Watershed (S.D. and Minn.), Watershed 

management, Watersheds

16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF 

ABSTRACT

SAR

18. NUMBER 

OF 

PAGES

91

19a. NAME OF RESPONSIBLE PERSON

Charles W. Downer a. REPORT

Unclassified

b. ABSTRACT

Unclassified

c. THIS PAGE

Unclassified 19b. TELEPHONE NUMBER (Include area code)

561-682-2922

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