U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2007–1159

Estimating Water Storage Capacity of Existing and 

Potentially Restorable Wetland Depressions in a 

Subbasin of the Red River of the North
Front cover photographs: Upper left, seasonal wetland surrounded by cropland; lower right, seasonal wetland surrounded 

by restored grassland.
Estimating Water Storage Capacity of 

Existing and Potentially Restorable 

Wetland Depressions in a Subbasin of the 

Red River of the North

By Robert A. Gleason, Brian A. Tangen, Murray K. Laubhan, Kevin E. Kermes, and 

Ned H. Euliss, Jr.

Open-File Report 2007–1159

U.S. Department of the Interior

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

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia: 2007

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Suggested citation:

Gleason, R.A., Tangen, B.A., Laubhan, M.K., Kermes, K.E., and Euliss, N.H., Jr., 2007, Estimating water storage 

capacity of existing and potentially restorable wetland depressions in a subbasin of the Red River of the North: 

U.S. Geological Survey Open-File Report 2007–1159, 36 p.
iii

Contents

Executive Summary.......................................................................................................................................1

General Introduction .....................................................................................................................................1

Chapter A: Development of Wetland Volume and Interception Area Estimators ...............................3

Overview.................................................................................................................................................3

Methods..................................................................................................................................................4

Study Area.....................................................................................................................................4

Topographic Surveys...................................................................................................................4

Statistical Analyses/Model Development ...............................................................................5

Results and Discussion........................................................................................................................7

Evaluation of Surface Area to Volume Relationships ............................................................7

Evaluation of Surface Area to Interception Area Relationships..........................................8

Estimating Depressional Storage and Interception Areas .................................................11

Conclusion............................................................................................................................................11

Chapter B: Wetland Water Storage Simulation Model .........................................................................14

Overview...............................................................................................................................................14

Methods................................................................................................................................................14

Model Development/Assumptions..........................................................................................14

Data Inputs and Sources...................................................................................................................15

General Model Calculations ....................................................................................................15

Calibration of Wetland Water Storage Model ......................................................................15

Results and Discussion......................................................................................................................15

Conclusion............................................................................................................................................21

Chapter C: Application of the Wetland Water Storage Simulation Model to the 

Upper Mustinka Subbasin ............................................................................................................21

Overview...............................................................................................................................................21

Methods................................................................................................................................................21

Study Area...................................................................................................................................21

Data Input and Sources............................................................................................................22

Simulation Period.......................................................................................................................22

Restoration Scenario ................................................................................................................22

Results and Discussion......................................................................................................................22

Conclusion............................................................................................................................................22

Acknowledgments.......................................................................................................................................24

References Cited..........................................................................................................................................24

Appendix 1. Acquiring input data, defining model scenarios, obtaining runoff curve 

numbers, creating input database, and interpreting model simulations ..............................28

Appendix 2. SAS program to create input database for simulation model ....................................31

Appendix 3. SAS program for simulation model .................................................................................32

Appendix 4. Description of SAS code used to create input database for simulation 

model................................................................................................................................................33

Appendix 5. Description of SAS code for simulation model .............................................................34

Appendix 6. SAS program used to create input database for 1987 simulation model..................35

Appendix 7. SAS program for 1987 (25 percent restoration scenario) simulation model ............36
iv

Figures

1–3. Maps showing:

1. Area of major river drainage basins and physiographic regions within the 

Prairie Pothole Region of the United States....................................................................2

2. Location of sections (1.6 × 1.6 km) where wetlands were surveyed in the 

upper Mustinka subbasin within Grant County, Minn. ..................................................4

3. National Wetlands Inventory and Restorable Wetlands Working Group 

wetland data for the upper Mustinka subbasin within Grant County, Minn..............5

4. Plan and profile view of wetland and catchment areas as defined in this report.............6

5. Representation of a typical spatial distribution of wetlands and catchments 

throughout a landscape...............................................................................................................7

6–9. Graphs showing:

6. Comparison of surface area-volume relationships among the model 

developed for the upper Mustinka subbasin and models for the three 

physiographic regions of the Prairie Pothole Region....................................................9

7. Relationship between 123 wetland surface areas obtained from the 

National Wetlands Inventory and areas for the same sites obtained 

from ground-based topographic surveys ........................................................................9

8. Comparison of surface area-interception area relationships among the 

model developed for the upper Mustinka subbasin and models for the 

three physiographic regions of the Prairie Pothole Region .......................................10

9. Catchment area-wetland area ratios (catchment area/wetland area) 

relative to wetland size.....................................................................................................10

10–11. Maps showing:

10. Total storage volumes (ha-m) per hectare per section (1.6 × 1.6 km) in the 

upper Mustinka subbasin of Grant County, Minn.........................................................12

11. Percentage of section (1.6 × 1.6 km) area covered by interception areas ..............13

12. Diagram showing catchment and wetland zones used in wetland water 

storage simulation model ..........................................................................................................16

13. Graphs showing simulation model calibration for the Cottonwood Lake study area 

wetlands for the time period of April 14 to September 21, 1993..........................................18
Tables

1. Predictive models developed for restorable wetlands in the upper Mustinka 

subbasin of Grant County, Minn. (data from this study), and the three 

physiographic regions of the Prairie Pothole Region.............................................................8

2. Maximum storage volumes and interception areas of existing and potentially 

restorable wetlands in the upper Mustinka subbasin of Grant County, Minn..................11

3. Precipitation events used in the calibration of the simulation model for the 

time period of April 14 to September 21, 1993........................................................................17

4. Results from model simulations performed on National Wetlands Inventory 

and Restorable Wetlands Working Group wetland data for the upper Mustinka 

subbasin of Grant County, Minn., for the growing seasons of 1987 and 1993 ..................23

5. Percent of total precipitation stored by depressional wetlands in the upper 

Mustinka subbasin of Grant County, Minn. ............................................................................23

1–1. Definition of input database variables for simulation model...............................................29

1–2. Definition of variables created in the wetland hydrology simulation model ....................30

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

acre 0.4047 hectare (ha)

Volume

acre-foot (acre-ft) 0.123 hectare (ha)

SI to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft)

kilometer (km) 0.6214 mile (mi)

Area

hectare (ha) 2.471 acre

Volume

hectare-meters (ha-m) 8.107 acre-foot (acre-ft)
Estimating Water Storage Capacity of Existing and 

Potentially Restorable Wetland Depressions in a 

Subbasin of the Red River of the North

By Robert A. Gleason, Brian A. Tangen, Murray K. Laubhan, Kevin E. Kermes, and Ned H. Euliss, Jr.

50 percent of the farmed and drained wetlands in the upper 

Mustinka subbasin. The model simulations were performed 

during the growing season (May–October) for relatively wet 

(1993; 0.67 m of precipitation) and dry (1987; 0.32 m of 

precipitation) years. Results from the simulations indicated 

that the 25 percent restoration scenario would increase water 

storage by 27–32 percent and that a 50 percent scenario 

would increase storage by 53–63 percent. Additionally, we 

estimated that wetlands in the subbasin have potential to store 

11.57–20.98 percent of the total precipitation that fell over 

the entire subbasin area (52,758 ha). Our simulation results 

indicated that there is considerable potential to enhance water 

storage in the subbasin; however, evaluation and calibration of 

the model is necessary before simulation results can be applied 

to management and planning decisions.

In this report we present guidance for the development 

and application of models (e.g., surface area-volume predictive 

models, hydrology simulation model) to simulate wetland 

water storage to provide a basis from which to understand and 

predict the effects of natural or human-induced hydrologic 

alterations. In developing these approaches, we tried to use 

simple and widely available input data to simulate wetland 

hydrology and predict wetland water storage for a specific 

precipitation event or a series of events. Further, the hydrology simulation model accounted for land use and soil type, 

which influence surface water inputs to wetlands. Although 

information presented in this report is specific to the Mustinka 

subbasin, the approaches and methods developed should be 

applicable to other regions in the Prairie Pothole Region.

General Introduction

The Prairie Pothole Region of the northern Great Plains 

encompasses a large portion of major river drainage basins 

including the Red of the North, Missouri, and Mississippi 

Rivers (fig. 1). In recent years, the magnitude and frequency 

of flooding in these drainage basins have caused concern that 

land use changes and wetland drainage have increased flooding (Brun and others, 1981; Miller and Frink, 1984; Miller 

and Nudds, 1995; Manale, 2000). Most notable is flooding 

Executive Summary

Concern over flooding along rivers in the Prairie 

Pothole Region has stimulated interest in developing spatially 

distributed hydrologic models to simulate the effects of wetland water storage on peak river flows. Such models require 

spatial data on the storage volume and interception area of 

existing and restorable wetlands in the watershed of interest. 

In most cases, information on these model inputs is lacking 

because resolution of existing topographic maps is inadequate 

to estimate volume and areas of existing and restorable 

wetlands. Consequently, most studies have relied on wetland 

area to volume or interception area relationships to estimate 

wetland basin storage characteristics by using available 

surface area data obtained as a product from remotely sensed 

data (e.g., National Wetlands Inventory). Though application 

of areal input data to estimate volume and interception areas 

is widely used, a drawback is that there is little information 

available to provide guidance regarding the application, limitations, and biases associated with such approaches. Another 

limitation of previous modeling efforts is that water stored 

by wetlands within a watershed is treated as a simple lump 

storage component that is filled prior to routing overflow to a 

pour point or gaging station. This approach does not account 

for dynamic wetland processes that influence water stored in 

prairie wetlands. Further, most models have not considered the 

influence of human-induced hydrologic changes, such as land 

use, that greatly influence quantity of surface water inputs and, 

ultimately, the rate that a wetland basin fills and spills.

The goals of this study were to (1) develop and improve 

methodologies for estimating and spatially depicting wetland storage volumes and interceptions areas and (2) develop 

models and approaches for estimating/simulating the water 

storage capacity of potentially restorable and existing wetlands 

under various restoration, land use, and climatic scenarios. 

To address these goals, we developed models and approaches 

to spatially represent storage volumes and interception areas 

of existing and potentially restorable wetlands in the upper 

Mustinka subbasin within Grant County, Minn. We then 

developed and applied a model to simulate wetland water 

storage increases that would result from restoring 25 and 
Figure 1. Area of major river drainage basins and physiographic regions within the Prairie Pothole Region of the 

United States.

State and provincial boundary

Prairie Pothole Region boundary

Glaciated Plains boundary

Missouri Coteau boundary

Hydrologic region boundary

Prairie Coteau boundary

Major rivers

Missouri River region

Souris, Rainy, and

Red River of the North subregion

Upper Mississippi

River region

State and provincial boundary

Prairie Pothole Region boundary

Glaciated Plains boundary

Missouri Coteau boundary

Hydrologic region boundary

Prairie Coteau boundary

Major rivers

State and provincial boundary

Prairie Pothole Region boundary

Glaciated Plains boundary

Missouri Coteau boundary

Hydrologic region boundary

Prairie Coteau boundary

Major rivers

Missouri River region

Souris, Rainy, and

Red River of the North subregion

Upper Mississippi

River region

along the Red River of the North that in recent years has 

caused enormous economic losses and inflicted serious social 

hardships. Most of the Red River of the North basin has been 

intensively farmed, and over 50 percent of the wetlands have 

been drained (Tiner, 1984; Dahl, 1990; Dahl and Johnson, 

1991). Wetland depressions are important landscape features 

of river drainages in the Prairie Pothole Region, and alteration of wetlands (e.g., draining, changing land use in watershed) has been implicated with increases in the frequency 

and magnitude of flood events (Brun and others, 1981; Miller 

and Nudds, 1995). Consequently, flooding along rivers in the 

Prairie Pothole Region has stimulated much interest in developing spatially distributed hydrologic models to simulate the 

effects of wetland water storage on peak river flows (Moore 

and Larson, 1979; Bengtson and Padmanabhan, 1999; Simonovic and Juliano, 2001; Vining, 2002).

Attempts to model the impact of wetland water stores 

on peak river flows are often constrained by lack of information on wetland volumes and interception areas. Estimating 

wetland volumes in the Prairie Pothole Region is problematic 

because resolution of available databases (e.g., topographic 

and digital elevation maps) is inadequate to estimate depressional volumes. Given these constraints, modeling efforts 

generally have estimated wetland volumes by applying models 

available in the literature that capture definable relationships 

between volume and wetland surface area (Haan and Johnson, 

1967; Best, 1978; Best and Moore, 1979; Hubbard, 1982; 

Bell and others, 1999; Bengtson and Padmanabhan, 1999; 

Wiens, 2001). These models are applied to available wetland 

surface area data (e.g., National Wetlands Inventory (NWI)) to 

estimate storage volumes. Though this approach is frequently 

used, definable relationships between volume and wetland 

surface area vary throughout the Prairie Pothole Region. For 

example, the Prairie Pothole Region consists of three primary 

physiographic regions, the Glaciated Plains, Missouri Coteau, 

and Prairie Coteau (fig. 1) that differ greatly in landform 

(e.g., ground moraine, dead-ice moraine (Bluemle 2000)) and, 

consequently, in topographic relief. Morphological variations 

between these regions result in differences in wetland surface 

area and volume relationships (Hubbard, 1982). Given these 

topographic differences, models developed to predict wetland 

volumes in one landform may not work well in a different 

landform. Currently, there is little information available to 

provide guidance regarding the application and biases associated with such approaches. Similarly, there is little information 

available when using comparable approaches to estimate wetland interception areas. Here, we define interception area as 

the area of the wetland plus the area of the upland catchment 

 Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions, Red River of the North
or contributing area. Interception area is an important model 

component because it accounts for areas in the watershed that 

do not route water directly to ditches or streams.

Another limitation of previous modeling efforts is that 

water stored by wetlands within a watershed is treated as a 

simple lump storage component that is filled before overflow 

is routed to a pour point or gaging station. This approach does 

not account for dynamic wetland processes that influence 

water stored in prairie wetlands. Further, most models have 

not considered the influence of human-induced hydrologic 

changes, such as land use, that greatly influence quantity of 

surface water inputs and, ultimately, the rate that wetland 

basins fill and spill. In this report we rely on the conceptual 

model that the primary sources of water for Prairie Pothole 

Region wetlands are direct precipitation and surface runoff 

from the catchment (i.e., area contributing surface runoff to a 

wetland basin), which is highly influenced by land use (e.g., 

grassland, cropland). The primary sources of water loss are 

evapotranspiration and surface outflow (i.e., overflow) when 

a wetland basin is filled beyond capacity. We consider two 

types of wetland water storage, existing and potential. Existing refers to the capacity of existing wetlands to store water, 

and potential refers to the capacity of restorable wetlands to 

store water. Existing wetlands are defined as intact, palustrine 

wetlands identified by the NWI, and potentially restorable 

wetlands are drained wetlands in agricultural landscapes 

mapped by the Restorable Wetlands Working Group (RWWG; 

2002). The storage potential of the drained wetlands is based 

on the premise that the wetland hydrology is restored by plugging surface drains, breaking drainage tile, etc.

The goals of this study were to (1) develop and improve 

methodologies for estimating and spatially depicting wetland 

storage volume and interception areas and (2) develop models 

and approaches for simulating water storage capacity of potentially restorable and existing wetlands under various restoration, land use, and climatic scenarios. To address these goals, 

we focused our work on the upper Mustinka subbasin located 

in Grant County, Minn. This subbasin was selected because of 

the availability of a geographic information systems database that identifies restorable wetlands (Restorable Wetlands 

Working Group, 2002) that are otherwise not accounted for in 

traditional wetland databases (e.g., NWI). Although information presented in this report is specific to this subbasin, the 

approaches and methods used can be applied to other regions 

in the Prairie Pothole Region.

This report is organized into three primary chapters. In 

chapter A, we develop wetland volume and interception area 

models to estimate maximum storage potential and interception area of existing and potentially restorable wetlands in the 

upper Mustinka subbasin. In chapter B, we develop a wetland 

water storage simulation model that can be used for simulating 

various restoration, land use, and climatic scenarios. Lastly, 

in chapter C, we apply models developed in chapters A and 

B to simulate the impact of wetland restoration scenarios on 

water storage in the upper Mustinka subbasin. Each chapter 

consists of Overview, Methods, Results and Discussion, and 

Conclusion sections. Chapters do not include an introduction; 

rather, the Overview—which includes a brief statement of the 

problem, goals, approach to solve the problem, and relevant 

findings—is intended to serve this purpose.

Chapter A: Development of Wetland 

Volume and Interception Area 

Estimators

Overview

Flooding along rivers in the Prairie Pothole Region has 

stimulated interest in developing spatially distributed hydrologic models to simulate the effects of wetland water storage 

on peak river flows. Such models require spatial data on 

the storage volumes and interception areas of existing and 

restorable wetlands in the watershed of interest; however, 

spatially distributed hydrologic models often rely on areal 

input data from geographic information systems (GIS) that do 

not provide estimates of wetland storage volumes or interception areas. Given these constraints, many modeling efforts 

have relied on estimating wetland volumes and interception 

areas by applying models that capture definable relationships 

between volume and wetland surface area. These models are 

then applied to available wetland area data (e.g., National 

Wetlands Inventory (NWI)) to estimate storage volumes. The 

overall goal of this study was to develop and apply predictive 

models to estimate the maximum water storage capacity and 

interception area of wetlands in the upper Mustinka subbasin, Grant County, Minn. Models we developed provided 

reasonably good estimates of wetland volumes and interception areas; however, we did identify several sources of error 

that need to be considered when applying the models. These 

sources of error include variation among methods used to 

define wetland boundaries (e.g., interpretation of aerial photography, ground survey) and models that do not account for 

morphological differences among physiographic regions in the 

Prairie Pothole Region. Using our models, we estimated that 

existing and restorable wetlands located in the upper Mustinka 

subbasin could intercept precipitation from up to 42 percent of 

the subbasin’s (52,758 ha) land area and have the potential to 

store 4,706 ha-m of water. Application of these models to the 

upper Mustinka subbasin demonstrated the spatial impact and 

maximum potential of wetlands to intercept precipitation and 

store water.

Chapter A: Development of Wetland Volume and Interception Area Estimators 
Mustinka River

Mustinka River

Watershed boundaries

Grant County

Upper Mustinka subbasin

Lower Mustinka subbasin

Sections containing sampled wetlands

Prairie Pothole Region

Upper Mustinka subbasin within Grant County, Minn.

Hydrologic region boundary

Figure 2. Location of sections (1.6 × 1.6 km) where wetlands were surveyed in the upper Mustinka subbasin 

within Grant County, Minn.

Methods

Study Area

We surveyed restorable wetlands located within the upper 

Mustinka subbasin in the southern portion of Grant County, 

Minn. (fig. 2). Grant County is located in the eastern portion 

of the Glaciated Plains physiographic region in the southeast 

reach of the Red River of the North drainage basin (fig. 2). 

The upper Mustinka subbasin encompasses 52,758 ha and 

includes numerous watersheds that contribute water to the 

Mustinka River that eventually flows into the Red River of the 

North.

To identify restorable wetlands we used GIS data provided by the RWWG (2002). The RWWG was formed in 2002 

and began mapping restorable wetlands in the Prairie Pothole 

Region of Minnesota and Iowa. The restorable wetlands GIS 

data consists of restorable (e.g., drained, farmed) wetlands 

identified through photo-interpretation and includes welldrained or altered wetlands that are not well accounted for 

by the NWI (fig. 3). To select study sites, we first identified 

watersheds that had relatively high densities of restorable 

wetlands and size distributions similar to that of the entire 

county. This method resulted in the selection of two watersheds that best met these criteria. We then identified sections 

of land within the watersheds with high densities of restorable 

wetlands. Following contact with landowners, we acquired 

permission to sample 95 potentially restorable wetlands on 

portions of 11 sections (1.6 × 1.6 km) of land (fig. 2).

Topographic Surveys

We conducted detailed topographic surveys of field sites 

to characterize the morphology of 95 restorable wetlands. We 

used a Trimble® 5700 GPS total station (Trimble, Sunnyvale, 

CA) to conduct surveys that included the location and relative 

elevation of the entire wetland basin and catchment, as well as 

tile and/or surface drains. We then used the software program 

ForeSightTM (Tripod Data Systems, Inc., 1997) to compute the 

surface area (ha) and volume (ha-m) of the wetland basin and 

the surface area (ha) of the catchment. The wetland basin area 

is delineated by the maximum elevation/spill point of the wetland, that is, the elevation at which surface water would flow 

out of the wetland basin and catchment and into an adjacent 

catchment, ditch, etc. (fig. 4). Wetland volume was determined 

by specifying the maximum elevation of the wetland in ForeSight and calculating the fill volume for the wetland polygon. 

 Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions, Red River of the North
Restorable Wetlands Working Group wetlands

National Wetlands Inventory depressional wetlands

National Wetlands Inventory nondepressional wetlands

Upper Mustinka subbasin

Lower Mustinka subbasin

Grant County

Restorable Wetlands Working Group wetlands

National Wetlands Inventory depressional wetlands

National Wetlands Inventory nondepressional wetlands

Upper Mustinka subbasin

Lower Mustinka subbasin

Grant County

Restorable Wetlands Working Group wetlands

National Wetlands Inventory depressional wetlands

National Wetlands Inventory nondepressional wetlands

Upper Mustinka subbasin

Lower Mustinka subbasin

Grant County

Figure 3. National Wetlands Inventory and Restorable Wetlands Working Group wetland data for the upper 

Mustinka subbasin within Grant County, Minn.

If the wetland was drained, volumes were calculated as if the 

hydrology was restored (e.g., fill-in ditch, break tile, etc.). The 

catchment area is defined as the area that contributes surface 

runoff to a wetland basin and was defined by constructing 

and interpreting contour maps and digital elevation models by 

using the survey data.

Upland areas sloping toward a wetland basin were considered part of the catchment, while areas sloping away from 

the wetland basin were considered outside of the catchment 

(fig. 4). The interception area is defined as the surface area 

of the catchment plus the surface area of the wetland basin 

(fig. 4). The interception area represents an area where precipitation is not routed “downstream” in a watershed through 

ditches or surface flow unless the wetland overflows. Figure 4 

depicts the catchment, wetland, and interception areas as 

defined in this report. Figure 5 illustrates a typical spatial distribution of wetlands and catchments throughout a landscape; 

note that by definition catchment areas do not overlap and 

that there are areas in a landscape that do not contribute direct 

runoff to wetland basins. Also, note how much of the actual 

interception area (wetland plus catchment) would be overlooked if only the wetland basin were considered.

In the following sections of the report we refer to models 

developed with data from Gleason and Tangen (2006). These 

models were developed with survey data collected and analyzed by using the same methods as outlined above. The data 

used to develop these models were consolidated from multiple 

studies conducted across the entire Prairie Pothole Region 

(all physiographic regions) by scientists of the U.S. Geological Survey’s Northern Prairie Wildlife Research Center in 

Jamestown, N. Dak. Information pertaining to this data may be 

obtained from the authors.

Statistical Analyses/Model Development

We performed linear regression analysis by using SAS 

software (SAS Institute, Inc., 2001) to determine the relationship of wetland surface area to volume and interception area. 

The data indicated that the response variables were distributed 

lognormal; therefore, we log transformed the responses to 

normalize and stabilize the variances. For all regression models, surface areas and volumes were transformed by using the 

natural logarithm (approximating model Y=β0XB1). We applied 

the wetland interception area and volume predictive models to 

wetland areas mapped by the NWI and the RWWG to estimate 

existing and potential interception area and maximum water 

storage capacity of palustrine wetlands in the upper Mustinka 

subbasin.

Chapter A: Development of Wetland Volume and Interception Area Estimators 
Wetland basin area defined by

the maximum elevation / spill

point (from topographic survey).

Catchment area defined by using

topographic survey data. Surface flow

inside this boundary is focused to the

wetland basin.

Wetland boundary defined

by hydrophytes / surface water.

Interception area equals the wetland

basin area plus the catchment area.

Catchment area

Wetland basin area based on the

maximum elevation / spill point.

Wetland boundary based

on hydrophytes / surface water.

Figure 4. Plan and profile view of wetland and catchment areas as defined in this report. For the models 

presented in this report, the wetland basin area is defined by the maximum elevation or spill point of a wetland 

(from topographic survey data); this boundary is constant. Remote sensing methods (e.g., National Wetlands 

Inventory (NWI)) typically define wetland boundaries by using easily identifiable features such as surface water 

and/or hydrophytes; this boundary depends on current hydrologic conditions and is dynamic. The catchment 

area (from topographic survey data) is defined as the area that contributes runoff to a wetland. The interception 

area is the combination of the wetland basin area and the catchment area. Block arrows indicate direction of 

flow (i.e., runoff/surface flow). As demonstrated in the figure, boundaries based on surface water/vegetation may 

underestimate the actual topographic size of a wetland basin during periods when water levels are low. Applying 

area-based models to boundaries based on surface water/hydrophytes (e.g., NWI) to predict wetland volume 

could result in the underestimation of the actual water storage represented by the maximum elevation/spill point.

 Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions, Red River of the North
Catchment Wetland basin

Figure 5. Representation of a typical spatial distribution of wetlands and catchments throughout a landscape. Catchment 

areas do not overlap, and there are areas in a landscape that do not contribute direct runoff to wetland basins (green area 

outside of wetland catchments). Block arrows show general directions of flow/runoff, and dotted arrows represent the 

overflow/spill point of the wetland/catchment (see fig. 4). Also, note how much of the actual interception area (wetland basin 

plus catchment) would be overlooked if the catchment areas were excluded.

Results and Discussion

Evaluation of Surface Area to Volume 

Relationships

Regression techniques indicated that wetland volume is 

strongly related (r 2 = 0.96) to wetland basin area (table 1). 

When our volume model is compared to a similar model 

developed with data from the Glaciated Plains physiographic 

region (Gleason and Tangen, 2006), we found good concordance among models (fig. 6); however, there are notable differences in models among regions (fig. 6) that demonstrate the 

importance of regionalized models to account for dissimilarity 

in relief and basin morphometry that varies throughout the 

physiographic regions of the Prairie Pothole Region.

Selecting a volume model developed for specific regions 

or landforms will reduce error (fig. 6); however, when estimating the maximum volume of a wetland, greater error may 

result if the wetland area input into the model is based on a 

depressional boundary that is topographically lower than the 

true maximum wetland elevation (i.e., overflow/spill elevation 

(see fig. 4)). For example, the NWI spatial wetland information is based on interpretation of aerial photography that 

identifies wetland features such as hydrophytes and surface 

water. Figure 4 demonstrates how wetlands delineated in this 

manner may underestimate the actual surface area and thus 

volumes predicted by using models based on wetland area. 

Key to reducing or adjusting for this type of error is to understand the relationship between the remotely sensed wetland 

areas (e.g., NWI) used as model input data to ground-based 

areas from which the model was developed. Using data from 

previous studies (Gleason and Tangen, 2006) on 123 wetlands 

with surface areas defined by using topographic survey data 

as well as spatial data from the NWI, we found surface areas 

from the NWI to be consistently smaller than ground-based 

area estimates derived from topographic surveys, regardless 

of physiographic region. Wetland surface areas from the NWI 

were found to account for only 58.6 ± 5.8 percent (mean ± 

95 percent confidence interval) of the surveyed wetland areas. 

Chapter A: Development of Wetland Volume and Interception Area Estimators 
Since this “bias” did not vary significantly with wetland size 

(fig. 7), it can be used to adjust wetland areas obtained from 

the NWI, prior to applying the model to predict volumes; 

however, this correction factor is based on the limited range of 

wetland sizes (0.18–11.29 ha) that were surveyed. Over this 

size range we found that this bias did not vary significantly 

with wetland surface area, but for larger, more stable wetlands this may not be the case. For example, it seems unlikely 

that the NWI would account for only 58 percent of a 100-ha 

wetland. Therefore, caution should be used when applying this 

correction factor to NWI wetland areas outside the size range 

that we sampled.

Evaluation of Surface Area to Interception Area 

Relationships

Interception area was found to be strongly related 

(r 2 = 0.70) to wetland basin area (table 1). We compared our 

interception area model to a similar model developed with 

data from the Glaciated Plains physiographic region (Gleason 

and Tangen, 2006). The models have the same general shape, 

but we found poor concordance among the Glaciated Plains 

models (fig. 8) compared to the consistency we observed for 

the volume models (fig. 6). Similar to the volume models, 

however, the results show the importance of limiting regional 

variation by selecting an interception area model according to 

the physiographic region of interest.

The model developed for the upper Mustinka subbasin 

predicted consistently higher interception areas than did the 

other model developed for the Glaciated Plains; the model 

also predicted greater interception areas than the models 

developed for the Missouri and Prairie Coteaus. The inconsistency among models from the same physiographic region is 

likely due, in part, to the fact that we surveyed restorable (i.e., 

drained, farmed (Restorable Wetlands Working Group, 2002)) 

wetlands in Grant County. Generally, the smaller wetlands are 

drained because of logistical and monetary constraints, and 

these wetlands were typically associated with relatively large, 

flat catchments that likely skewed the upper Mustinka subbasin model presented in figure 8. In general, the smaller the 

wetland, the larger the catchment area to wetland area ratio; 

this relationship is depicted in figure 9.

Table 1. Predictive models developed for restorable wetlands in the upper Mustinka subbasin of Grant County, Minn. (data from 

this study), and the three physiographic regions of the Prairie Pothole Region (Gleason and Tangen, 2006). The wetland volume 

and interception area models can be used in conjunction with spatial wetland data (e.g., National Wetlands Inventory) to predict 

wetland volumes and interception areas on large spatial scales.

[UMS, Upper Mustinka subbasin; PPR, Prairie Pothole Region]

Predicted variable Physiographic 

region Source1 n

Size2

Model3 MSE4

Coefficient of 

determination 

(r 2 Mean ) Range

Wetland volume Glaciated Plains UMS 95 0.12 0.002–3.8 V1 = 0.201A1.5029 0.50 0.96

Interception area Glaciated Plains UMS 95 3.04 .06–14.24 IA1 = 5.06A0.5921 .60 .70

Wetland area Glaciated Plains UMS 95 .12 .002–3.8 A1 = 2.567V0.6356 .32 .96

Wetland volume Glaciated Plains PPR 288 .92 .002–9.25 V1 = 0.25A1.4742 .69 .91

Wetland volume Missouri Coteau PPR 186 1.28 .01–11.29 V1 = 0.398A1.542 .73 .91

Wetland volume Prairie Coteau PPR 23 2.22 .24–7.08 V1 = 0.458A1.5611 .39 .94

Interception area Glaciated Plains PPR 288 3.11 .06–17.60 IA1 = 3.43A0.5765 .55 .70

Interception area Missouri Coteau PPR 186 3.98 .14–28.91 IA1 = 3.6A0.6148 .49 .78

Interception area Prairie Coteau PPR 23 6.08 1.07–16.11 IA1 = 3.39A0.7694 .34 .84

Wetland area5 Glaciated Plains PPR 288 .92 .002–9.25 A1 = 2.15V0.6155 .45 .91

Wetland area5 Missouri Coteau PPR 186 1.28 .01–11.29 A1 = 1.62V0.59 .45 .91

Wetland area5 Prairie Coteau PPR 23 2.22 .24–7.08 A1 = 1.64V0.6043 .25 .94

1

If source is UMS, then the model was developed with data collected for this study from the upper Mustinka subbasin. If source is PPR, then model was 

developed with data collected from multiple studies across the entire Prairie Pothole Region (Gleason and Tangen, 2006).

2

For wetland volume/area models, size refers to wetland area (ha); for interception area models, size refers to interception area (ha).

3

V1 = predicted wetland volume, V = measured maximum wetland volume (ha-m); IA1 = predicted interception area, A1 = predicted wetland area, A = 

measured maximum wetland area (ha).

4

Mean square error (MSE) from log transformed (natural logarithm) data.

5

The wetland area model is used to recalculate wetland surface area in the wetland water storage simulation model presented in chapter B.

 Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions, Red River of the North
Wetland area (ha)

0 2 4 6 8 10

Predicted wetland volum

e (ha-m)

0

2

4

6

8

10

12

14

16

18

Prairie Coteau

Missouri Coteau

Glaciated Plains

Glaciated Plains (upper Mustinka)

Figure 6. Comparison of surface area-volume relationships among the model developed for the upper Mustinka subbasin and models 

for the three physiographic regions of the Prairie Pothole Region (Gleason and Tangen, 2006). Predicted volumes for the Prairie Coteau 

and Missouri Coteau are generally greater than those for the Glaciated Plains, and the regional differences among predicted volumes 

increase with wetland surface area. The model developed for this study (Glaciated Plains; upper Mustinka) shows good concordance 

with the model developed with data from across the Glaciated Plains.

Wetland area (ha)

0 2 4 6 8 10

Bias (%)

0

20

40

60

80

100

120

140

160

180

Figure 7. Relationship between 123 wetland surface areas obtained from the National Wetlands Inventory (NWI) and areas for 

the same sites obtained from ground-based topographic surveys. A bias ((wetland area from NWI/wetland area from survey) × 100) 

greater than 100 percent represents an overprediction of wetland area by the NWI, while a bias of less than 100 percent represents an 

underprediction. Overall, the wetland areas obtained from the NWI are smaller than areas determined by using the maximum wetland 

elevation, and there is no significant correlation between wetland size and NWI bias.

Chapter A: Development of Wetland Volume and Interception Area Estimators 
Wetland area (ha)

0 2 4 6 8 10

Interception area (ha)

0

5

10

15

20

25

Prairie Coteau

Glaciated Plains (upper Mustinka)

Missouri Coteau

Glaciated Plains

Figure 8. Comparison of surface area-interception area relationships among the model developed for the upper Mustinka subbasin 

and models for the three physiographic regions of the Prairie Pothole Region (Gleason and Tangen, 2006). There is poor concordance 

among the two models developed for the Glaciated Plains. The model developed for the upper Mustinka subbasin more closely 

resembles the model developed for the Prairie Coteau. This discrepancy is likely due to the relatively small size of the wetlands sampled 

in Grant County compared to their respective catchments.

Wetland area (ha)

0 2 4 6 8 10 1

Ratio (catchment ar

e

a / wetland area)

2

0

20

40

60

80

100

120

Figure 9. Catchment area-wetland area ratios (catchment area/wetland area) relative to wetland size (data from current study and 

from Gleason and Tangen, 2006 (n = 503)). There was no strong relationship between ratios and wetland size; however, ratios were 

noticeably higher for a portion of the smaller wetlands.

10 Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions, Red River of the North
Table 2. Maximum storage volumes and interception areas of existing and potentially restorable wetlands in the 

upper Mustinka subbasin of Grant County, Minn.

[NWI, National Wetlands Inventory]

Wetland type Area (ha) Volume (ha-m) Interception

Area (ha) % area of subbasin1

Existing NWI wetlands 2,397 3,397 10,859 20.6

Restorable wetlands <0.7 ha 479 51 4,568 8.7

Restorable wetlands >0.7 ha 2,340 1,258 6,876 13.0

Total 5,216 4,706 22,303 42.3

1

Percent area of the upper Mustinka subbasin (52,758 ha).

Estimating Depressional Storage and 

Interception Areas

We applied models (table 1) to estimate the maximum 

potential storage volume and interception area of depressional 

(i.e., palustrine) wetlands mapped by the NWI and restorable 

wetlands mapped by the RWWG in the upper Mustinka subbasin (fig. 3). Based on the NWI and the RWWG databases, 

there were 2,060 existing depressional wetlands totaling 

2,397 ha and 6,161 potentially restorable wetlands totaling 

2,819 ha in the upper Mustinka subbasin (fig. 3). Prior to estimating maximum storage volumes and interception areas of 

existing NWI wetlands, we adjusted NWI areas by following 

procedures described earlier (see Evaluation of Surface Area 

to Volume Relationships). Also, we only included volume and 

interception area estimates for 54 percent (randomly selected) 

of the restorable wetlands that were less than 0.7 ha in area. 

This adjustment was based on a ground-based error estimate 

that indicated that 54 percent of the restorable wetlands less 

than 0.7 ha in area may be depressions capable of storing 

water. Our ground-based assessment indicated that, in addition 

to being small (i.e., <0.7 ha), most sites identified as being 

nondepressions were likely inundated by ephemeral runoff 

from precipitation or snow melt; were typically influenced by 

roadbeds, ditches, and elevated fence lines; and were often 

associated with sloping drainage areas.

We predicted that existing wetlands could store 

3,397 ha-m of water and that restorable wetlands have potential to store an additional 1,309 ha-m of water (table 2). Hence, 

restoration of wetlands has potential to increase existing storage in the subbasin by approximately 38 percent. Examination 

of the spatial variation in existing and restorable wetland water 

storage in the subbasin depicts which watersheds have the 

greatest potential to store water (fig. 10). Interception areas of 

existing and restorable wetlands accounted for 20.6 percent 

(10,859 ha) and 21.7 percent (11,444 ha) of the total area of 

the upper Mustinka subbasin, respectively (table 2; fig. 11).

Conclusion

The overall goal of this study was to develop and apply 

predictive models to estimate maximum wetland water storage 

capacity and interception areas. To develop these models we 

used relatively simple survey methods to measure wetland 

morphologic characteristics and calculate wetland surface 

areas, interception areas, and volumes. Models developed from 

these data provided reasonably good estimates of wetland 

volumes and interception areas. We also identified how to 

correct for errors associated with remotely sensed data (i.e., 

NWI correction factor; see above Evaluation of Surface Area 

to Volume Relationships) and highlighted the need to apply 

region-specific models. Application of these models to the 

upper Mustinka subbasin demonstrated the spatial impact and 

maximum potential of wetlands to intercept precipitation and 

store water. Hence, application of our models to areal GIS 

data provided a spatially distributed framework from which to 

evaluate the potential of wetlands to intercept and store precipitation. With moderate calibration and testing, our models 

should have wide applicability in the development of spatially 

distributed hydrologic models used to predict the effects of 

wetland water storage on peak river flows.

Chapter A: Development of Wetland Volume and Interception Area Estimators 11
Figure 10. Total storage volumes (ha-m) per hectare per section (1.6 × 1.6 km) in the upper Mustinka subbasin of Grant County, Minn. Existing storage is based on data from

 the National Wetlands Inventory selected for depressional (palustrine) wetlands, while potential storage is based on restorable wetland data from the Restorable Wetlands Working Group (e.g, drained, farmed). All storage volumes were calculated by applying wetland areas to the upper Mustinka subbasin wetland volume model in table 1.

Existing storage Potential storage Existing and potential storage

0.0000-0.02500.0251-0.05000.0500-0.10000.1001-0.2000

0.2001-0.4500Section boundaries

Existing storage Potential storage Existing and potential storage

0.0000-0.02500.0251-0.05000.0501-0.10000.1001-0.20000.2001-0.45000.0000

Section boundaries

-0.0250

0.0251-0.05000.1001-0.20000.2001-0.4500Section boundaries

12  Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions, Red River of the North
E

xisting

a

n

d pot

enti

al

interce

ption Potential i

nterc

e

pti

o

n

0-

2

0

21-40

41-60

61-80

81-10

0

E

xisting int

erc

eption

E

xisting

a

n

d pot

enti

al

interce

ption

E

xisting

a

n

d pot

enti

al

interce

ption Potential i

nterc

e

pti

o

n Potential i

nterc

e

pti

o

n

0-

2

0

21-40

41-60

61-80

81-10

0

0-

2

0

21-40

41-60

61-80

81-10

0

E

xisting int

erc

eption

E

xisting int

erc

eption

Section boundaries

Figure 11. Percentage of section (1.6 × 1.6 km) area covered by interception areas. Existing interception is based on data from the National Wetlands Inventory selected for depressional (palustrine) wetlands, while potential interception is based on restorable wetland data from the Restorable Wetlands Working Group (e.g., drained, farmed). All interception areas were calculated by applying wetland areas to the upper Mustinka subbasin interception area model in table 1.

Chapter A: Development of Wetland Volume and Interception Area Estimators 13
Chapter B: Wetland Water Storage 

Simulation Model

Overview

Attempts to model the impacts of wetland water storage 

on peak river flows have been constrained by lack of methods 

to simulate how precipitation is intercepted, processed, and 

stored by wetlands. Most often, modeling efforts have treated 

water stored by wetlands within a watershed as a simple lump 

storage component that is filled before overflow is routed to a 

pour point or gaging station. This approach does not account 

for dynamic hydrologic processes that influence water stored 

in prairie wetlands. Further, most models have not considered 

the influence of human-induced hydrologic changes, such as 

land use, that greatly influence quantity of surface water inputs 

and, ultimately, the rate that wetland basins fill and spill.

The goal of our study was to develop a simple model to 

simulate wetland water storage to provide a basis from which 

to understand and predict the effects of natural or humaninduced hydrologic alterations. Requisites for the model 

included that it (1) runs by using the standard statistical software program SAS (SAS Institute, Inc., 2001), (2) uses simple 

and widely available input data to simulate wetland hydrology and predict wetland volume for a specific precipitation 

event or a series of events, and (3) must account for effects 

of land use and soil type on surface water inputs. The model 

we developed to predict water stored by wetlands includes a 

function of water inputs from direct precipitation and surface 

water inflow and water losses from evapotranspiration and 

surface water outflow; the model met all predefined requisites. 

Further, simulation tests demonstrated that the model provided reasonably good estimates of actual changes in wetland 

water storage, and on average, volume estimates were within 

0.08 ha-m of known values.

Methods

Model Development/Assumptions

Our goal was to develop a model that uses simple input 

variables (e.g., daily precipitation, daily evapotranspiration 

rate estimate, surface runoff estimates) to simulate and predict 

wetland water storage for a specific precipitation event (e.g., 

a 3-day rain event) or a series of events (e.g., for 1 or more 

years). We also wanted the model to account for differences 

in land use (e.g., row crop, small grain, meadow, pasture, etc.) 

and soil types that influence surface water inputs. To develop 

the model we first considered major hydrologic components 

that influence water received or lost from wetlands; these 

major hydrologic components are expressed in the following 

water storage equation:

WS = SS + (P + GWI + SWI) - (ET + GWO + SWO)

where

WS = water stored,

SS = starting storage,

P = precipitation,

GWI = ground water inflow,

SWI = surface water inflow,

ET = evapotranspiration,

GWO = ground water outflow, and

SWO = surface water outflow.

Ideally all sources of water inputs (i.e., P, GWI, SWI) 

and outputs (i.e., ET, GWO, SWO) would be included in a 

model to predict wetland water storage; however, early in the 

development of the model, we decided not to include a ground 

water flow function (i.e., GWO and GWI) because ground 

water flow in prairie wetlands is highly complicated (Winter, 

1989). For example, wetlands may serve as a recharge area for 

ground water (i.e., GWO), a discharge area for ground water 

(i.e., GWI), or both a recharge and discharge area for ground 

water (e.g., GWO and GWI). Spatial databases available on 

wetlands (e.g., National Wetlands Inventory (NWI)), to which 

our model would be applied, do not provide information necessary to classify the relationship of a wetland to ground water 

flow systems. Nevertheless, the lack of a ground water function in the model is not expected to greatly compromise model 

performance. For example, because of the slow rate of ground 

water movement in prairie wetlands (0.025 to 2.5 m yr –1 (Winter and Rosenberry, 1995)), dynamic changes in wetland water 

storage are not strongly correlated to GWO or GWI. Rather, 

direct P and SWI are known to be the primary sources of water 

to prairie wetlands, and ET accounts for the largest loss of 

water from prairie wetlands (Winter, 2003). Hence, in an overall water budget, GWI and GWO contribute the least. When 

excluding the ground water function, the model we developed 

to predict wetland water storage for a specific precipitation 

event, t, is expressed in the following equation:

WSt = (WSt-1 + Pt + SWIt) - (ETt + SWOt),

where

WSt = water stored for event t,

WSt-1 = water stored prior to event t,

Pt = direct precipitation inputs during event t,

SWIt = surface runoff inputs during event t,

ETt = evapotranspiration during event t, and

SWOt = surface water outflows during event t.

An important consideration when developing the above 

model was that it could be run by using simple yet easily 

obtained precipitation, soil, and wetland data. Below, we 

provide a general overview of data inputs necessary to run 

the model and a general overview of methods used to calculate model components in the above equation. More detailed 

information on model inputs and calculations are presented 

in appendixes 1–7, including SAS code used to create input 

databases and run the model.

14  Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions, Red River of the North
literature; although it would increase labor and model complexity, a daily ET rate would likely improve model performance to some degree. Once maximum water storage volume 

of the basin is attained, any additional water input is treated as 

SWOt. Following each simulation event, t, the model recalculates the ponded and nonponded areas of the wetland basin 

(fig. 12) and uses these areas for the next simulation event.

Calibration of Wetland Water Storage Model

We calibrated the model by using precipitation, water 

level, and topographic data from 15 wetlands at the Cottonwood Lake study area located in Stutsman County, N. Dak. 

The Cottonwood Lake area is a long-term study site located 

near the eastern edge of the Missouri Coteau and includes a 

complex of wetlands that have been intensively monitored for 

over 30 years.

Daily precipitation data and water level data for the 

Cottonwood Lake study area was provided by the U.S. Geological Survey, Northern Prairie Wildlife Research Center, 

Jamestown, N. Dak. After reviewing this data, we decided 

to simulate changes in water storage in 15 wetlands for the 

period of April 14 to September 21, 1993. This period was 

selected for simulation because of the completeness of available precipitation and water level data and because 1993 had a 

high frequency of precipitation events. For this calibration, the 

precipitation data were grouped by event, defined as consecutive days with or without precipitation (table 3). Precipitation 

events grouped in this way better account for antecedent moisture conditions that influence generation of surface runoff.

All of the Cottonwood Lake area wetlands included in the 

simulation are surrounded by grass and have similar soil types 

(i.e., similar land use and hydrologic soil groups). We used 

procedures described in appendix 1 to determine runoff curve 

numbers for the catchment and the nonponded portion of each 

wetland basin. For example, the runoff curve number for all 

wetland catchments was 58 (e.g., land use = meadow, hydrologic soil group = B, AMC = II, hydrologic condition = good), 

and the runoff curve number for the nonponded portion of the 

wetland was 78 (land use = meadow, hydrologic soil group = 

D, AMC = II, hydrologic condition = good). For all wetlands, 

the ponded portion of the wetland received 100 percent of 

the precipitation. The daily evapotranspiration rate was set at 

0.358 cm d–1 (Parkhurst and others, 1998).

Results and Discussion

Overall, the model performed well with simulated volumes resembling changes of actual volumes (figs. 13A, 13B, 

and 13C). Comparison of predicted volumes to actual volumes 

for 10 points (i.e., 10 “actual” volumes in figs. 13A–C) over 

the simulation period indicated a mean difference (absolute) of 

0.08 ha-m (SE = 0.01). Of the 15 Cottonwood Lake wetlands, 

8 are classified as semipermanent, and 7 are classified as 

seasonal (classification follows Stewart and Kantrud, 1971). 

Data Inputs and Sources

Wetland area (ha): Wetland area can be determined through 

field surveys or from existing databases such as the NWI.

Maximum Wetland Storage Volume (ha-m): Maximum potential water storage can determined through field surveys or 

estimated by using wetland area to volume relationships 

described in chapter A.

Catchment Area (ha): Wetland catchment area can be determined through field surveys or estimated by using 

wetland area to catchment area relationships described in 

chapter A.

Wetland Area and Catchment Area Land Use: Land use (e.g., 

cropland, grassland) can be acquired through field surveys 

or from existing databases such as the U.S. Geological 

Survey’s (USGS) National Land Cover Dataset (http://

landcover.usgs.gov/).

Wetland Area and Catchment Area Hydrologic Soil Group: 

Hydrologic soil group of wetland and catchment areas can 

be determined by using Standard Soil Surveys or other 

databases such as the U.S. Department of Agriculture’s 

(USDA) Soil Survey Geographic (SSURGO) database 

(www.ncgc.nrcs.usda.gov/products/datasets/ssurgo/).

Wetland Area and Catchment Area Surface Runoff Curve 

Number: Specific runoff curve numbers can be determined by using the USDA National Engineering Handbook (ftp://ftp.wcc.nrcs.usda.gov/downloads/hydrology_

hydraulics/neh630/). Surface runoff curves are unique to 

specific land uses and hydrologic soil groups.

Daily Precipitation: Precipitation data can be gathered through 

field measurement or obtained from a variety of weather 

monitoring networks.

General Model Calculations

For a given precipitation event, t, the model incorporates 

estimates of water input from three sources: (1) surface water 

runoff contributed from the catchment, (2) surface water 

runoff contributed from the nonponded portion of the wetland, 

and (3) direct precipitation contributed to the ponded portion 

of the wetland basin. Catchment zones used in the model (e.g., 

catchment, ponded and nonponded portion of wetland basin) 

are illustrated and described in figure 12. Surface water inputs 

(SWIt) derived from the catchment and nonponded portion of 

the wetland are estimated by using surface runoff curve numbers that are unique to specific hydrologic soil groups and land 

use types (e.g., grass and cultivated). Runoff curve numbers 

account for cover and soil factors that influence the generation 

and contribution of surface runoff to the wetland. Details on 

selection of runoff curve numbers are described in appendix 1. 

In contrast, the ponded portion of the basin was estimated 

as receiving 100 percent of the precipitation. For simplicity, 

to account for evapotranspiration (ETt) we used a constant 

average daily rate based on seasonal averages available in the 

Chapter B: Wetland Water Storage Simulation Model 15
Nonponded area

(CATCH_A_2)

Ponded area

(AREA_HA_2)

Wetland area

(AREA_HA )

Interception area

(INT_A)

Catchment area

(CATCH_A ) Nonponded area

(CATCH_A_2)

Ponded area

(AREA_HA_2)

Wetland area

(AREA_HA )

Nonponded area

(CATCH_A_2)

Ponded area

(AREA_HA_2)

Wetland area

(AREA_HA )

Interception area

(INT_A)

Catchment area

(CATCH_A )

Interception area

(INT_A)

Catchment area

(CATCH_A )

Figure 12. Catchment and wetland zones used in wetland water storage simulation model. 

The interception area (INT_A) refers to the highest elevation of the wetland basin (AREA_HA) 

and its entire contributing area or catchment (CATCH_A); see also figure 4. The area of the 

wetland basin is determined from topographic survey data and is constant regardless of 

the current state of the hydrophytic vegetation or water level. Therefore, the wetland basin 

comprises two areas, the ponded (AREA_HA_2) and nonponded (CATCH_A_2) portions. The 

ponded portion represents the area of standing water, and the nonponded portion represents 

the area of the wetland basin that does not contain standing water. If a wetland were filled to 

the maximum water holding capacity, then there would not be a nonponded portion.

16  Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions, Red River of the North
Table 3. Precipitation events used in the calibration of the simulation 

model for the time period of April 14 to September 21, 1993.

Event number Number of days in event Precipitation (cm)

1 9 0

2 3 .56

3 1 0

4 3 .51

5 6 0

6 6 6.63

7 10 0

8 4 2.06

9 1 0

10 5 3.15

11 5 0

12 5 3.86

13 2 0

14 2 .71

15 1 0

16 2 1.12

17 5 0

18 5 4.14

19 1 0

20 13 8.51

21 1 0

22 17 19.23

23 4 0

24 3 .13

25 2 0

26 1 .03

27 2 0

28 5 2.41

29 1 0

30 1 .03

31 5 0

32 2 .08

33 1 0

34 6 5.51

35 3 0

36 3 .20

37 13 0

38 2 .05

Chapter B: Wetland Water Storage Simulation Model 17
P1

V

olum

e (ha-m) 0

1

2

3

4

5

Max volume

0

2

4

6

8

Predicted volume

Actual volume

Precipitation

P2

Volume (ha-m) 0

1

2

3

4

5

Max volume

0

2

4

6

8

P3

*Event

Volume (ha-m) 0.00

0.05

0.10

0.15

0.20

0.25

0.30

Max volume

0

2

4

6

8

P4

0.0

0.5

1.0

1.5

2.0

2.5

Max volume

Precipitation (in)

0

2

4

6

8

P6

0.0

0.5

1.0

1.5

2.0

Max volume Preciptiation)

0

2

4

6

8

P7

*Event

1

7

0

8

Max volume

Precipitation (in)

0

2

4

6

8

*Event = 1-38 (see table 3)

Figure 13. Simulation model calibration for the Cottonwood Lake study area wetlands for the time period of April 14 to 

September 21, 1993. A, P1, P2, P3, P4, P6, and P7; B, P8, T2, T3, T4, T5, and T6; and C, T7, T8, and T9.

A

18  Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions, Red River of the North
P8

Volume (ha-m)

0.0

0.5

1.0

1.5

2.0

2.5

Max volume

0

2

4

6

8

Predicted volume

Actual volume

Precipitation

T2

Volu

me (ha-m)

0.0

0.1

0.2

0.3

0.4

0.5

Max volume

0

2

4

6

8

T3

*Event

Volume (ha-m)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

Max volume

0

2

4

6

8

T4

0.00

0.01

0.02

0.03

0.04

Max volume

Precipitation (in)

0

2

4

6

8

T5

0.00

0.02

0.04

0.06

0.08

Max volume Precipitation (in)

0

2

4

6

8

T6

*Event

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Max volume

Precipitation (in)

0

2

4

6

8

*Event = 1-38 (see table 3)

Figure 13. Simulation model calibration for the Cottonwood Lake study area wetlands for the time period of April 14 

to September 21, 1993. A, P1, P2, P3, P4, P6, and P7; B, P8, T2, T3, T4, T5, and T6; and C, T7, T8, and T9.—Continued

B

Chapter B: Wetland Water Storage Simulation Model 19
T7

Volume (ha-m)

0.00

0.02

0.04

0.06

0.08

0.10

Max volume

Precipitation (in)

0

2

4

6

8

T8

Volume (ha-m)

0.0

0.1

0.2

0.3

0.4

0.5

Max volume

Precipitation (in)

0

2

4

6

8

Predicted volume

Actual volume

Precipitation

T9

*Event

Volume (ha-m)

0.0

0.5

1.0

1.5

2.0

2.5

Max volume Precipitation (in)

0

2

4

6

8

*Event = 1-38 (see table 3)

Figure 13. Simulation model calibration for the Cottonwood Lake study area wetlands for the time 

period of April 14 to September 21, 1993. A, P1, P2, P3, P4, P6, and P7; B, P8, T2, T3, T4, T5, and T6; and 

C, T7, T8, and T9.—Continued

C

20 Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions, Red River of the North
Results from the calibration suggested that mean differences in 

predicted and actual volumes were slightly greater in semipermanent wetlands (0.13 ha-m, SE = 0.02) than in seasonal 

(0.03 ha-m, SE = 0.006). These differences, however, are 

proportionally greater in the seasonal wetlands since the actual 

volumes are much smaller. This initial calibration suggests 

that the model performed well and satisfactorily calculated 

changes in wetland water storage.

Conclusion

The overall goal of this study was to develop a simple 

model to simulate changes in wetland water storage for specific precipitation events. Prerequisites of this model included 

that it could be run by using simple yet widely available data 

and that the model could account for changes in land use that 

influence surface water inputs. The model we developed met 

these requirements and provided reasonably good estimates of 

changes in wetland water storage; however, additional calibration and testing is necessary to understand how the model will 

perform over a range of precipitation events. Furthermore, 

results suggest that future research should explore whether 

volume and interception area predictive models should be 

segregated by wetland classification or size classes since 

the model did not provide equivalent results among wetland 

classes. Correspondingly, relationships among catchment 

slope lengths and grades and catchment runoff should be 

explored. For example, a small wetland surrounded by a large, 

relatively flat catchment may not receive surface runoff from 

the entire catchment area, whereas a large wetland surrounded 

by a proportionally smaller catchment with steep slopes may 

receive runoff from the entire catchment.

For simplicity, our model was calibrated by using summer precipitation events. The model likely will need to be 

modified to simulate springtime snowmelt events. The surface 

runoff curves applied in our model were not developed to 

estimate runoff from frozen soils. Additionally, our calibration tests did not include wetlands that were in croplands; 

rather, calibrations were performed on wetlands in grasslands. 

Although not tested, the model is designed to account for 

differences in land use. Furthermore, obtaining detailed information to calculate daily evapotranspiration rates would likely 

improve model performance. With appropriate calibration and 

testing, this model should have wide applicability as a decision 

support tool to evaluate the effects of various wetland and land 

use restoration scenarios designed to provide a broad suite of 

ecological services, including flood water attenuation.

Chapter C: Application of the Wetland 

Water Storage Simulation Model to the 

Upper Mustinka Subbasin

Overview

The overall goal of this study was to demonstrate the 

application of models developed in chapters A and B by 

simulating the potential of wetlands to intercept, process, and 

store water in the upper Mustinka subbasin, Grant County, 

Minn. We simulated wetland water storage increases that 

would result from restoring 25 and 50 percent of the farmed 

and drained wetlands in the upper Mustinka subbasin. The 

model simulations were performed during the growing season 

(May–October) for relatively wet (1993; 0.67 m of precipitation) and dry (1987; 0.32 m of precipitation) years. Model 

results indicated that the 25 percent restoration scenario would 

increase water storage by 27–32 percent and that a 50 percent 

scenario would increase storage by 53–63 percent. Additionally, we estimated that wetlands in the upper Mustinka subbasin have potential to store 11.57–20.98 percent of the total 

precipitation that fell there (a land area of 52,758 ha). Our 

simulation results indicated that there is considerable potential to enhance water storage in the upper Mustinka subbasin; 

however, evaluation and calibration of the model are necessary before simulation results are deemed reliable for use in 

management decisions and planning.

Methods

Study Area

We applied the simulation model developed in 

chapter B to existing depressional and potentially restorable 

wetlands within the upper Mustinka subbasin (fig. 3). Grant 

County is located in the eastern portion of the Glaciated Plains 

physiographic region in the southeast reach of the Red River 

drainage basin (fig. 2). The upper Mustinka subbasin encompasses 52,758 ha, and based on the National Wetlands 

Inventory (NWI) and RWWG (2002) databases, it contains 

2,060 existing depressional wetlands totaling 2,397 ha and 

6,161 potentially restorable wetlands totaling 2,819 ha.

Chapter C: Application of the Wetland Water Storage Simulation Model to the Upper Mustinka Subbasin  21
Data Input and Sources

Precipitation data used for the simulation was obtained 

for Elbow Lake, Minn., which is centrally located in the upper 

Mustinka subbasin. The daily evapotranspiration rate was set 

at 0.358 cm d–1 (Parkhurst and others, 1998). Wetland area of 

existing and potentially restorable wetlands was determined by 

using NWI and RWWG databases. Maximum wetland storage 

volume and interception area of each basin was estimated by 

using equations presented in table 1 and procedures described 

in chapter A. All potentially restorable wetlands mapped by 

the RWWG were considered drained and given a cropland 

land use category. For existing wetlands mapped by the NWI, 

we determined land use (i.e., cropland or grassland) by using 

the 1992 National Land Cover Dataset (http://landcover.usgs.

gov/). Hydrologic soil group and surface runoff curve numbers for wetland and catchment areas were determined by 

following procedures described in chapter B and appendix 1. 

The following details are provided: for wetlands classified as 

farmed, catchment areas (figs. 4 and 12) were assigned the 

runoff curve number 81 (land use = row cropping (straight), 

hydrologic soil group = B, AMC = II, hydrologic condition = 

poor); the nonponded areas (fig. 12) of the wetland basin were 

assigned the runoff curve number 91 (land use = row cropping 

(straight), hydrologic soil group = D, AMC = II, hydrologic 

condition = poor). Wetlands classified as grass were assigned 

the runoff curve number 58 for the catchment (land use = 

meadow, hydrologic soil group = B, AMC = II, hydrologic 

condition = good) and 78 for the nonponded portion of the 

wetland basin (land use = meadow, hydrologic soil group = D, 

AMC = II, hydrologic condition = good). For all wetlands, 

the ponded portion of the wetland received 100 percent of the 

precipitation.

Simulation Period

The model simulation was performed during the growing season (May–October) for relatively wet (1993; 0.67 m of 

precipitation) and dry (1987; 0.32 m of precipitation) years. 

Precipitation was treated as depositing uniformly over the 

entire study area. For the simulation, precipitation data were 

grouped by event, defined as consecutive days with or without 

precipitation (see chapter B). Appendixes 6 and 7 provide 

example SAS programs for the creation of the 1987 input data 

set and the 1987 model simulation for a 25 percent restoration 

scenario.

Restoration Scenario

We set the restoration scenario (i.e., percent of farmed 

wetlands (based on total number of wetlands) restored) at 0, 

25, and 50 percent. Wetlands restored were randomly selected, 

and the sample was constrained to wetlands between 0.5 

and 20 ha. The minimum restoration size cutoff was applied 

because restoration of a large number of small wetlands is not 

likely to be cost effective, whereas the maximum cutoff was 

set based on the limitations associated with applying volume 

and interception area estimators to wetlands larger than the 

model range (see chapter A). Prior to restoration, wetlands 

obtained from the RWWG database were treated as farmed 

and drained with no water storage. If selected for restoration, 

they were treated as hydrologically intact (i.e., store water) 

and in grass. In contrast, all NWI wetlands were hydrologically intact with a land use of grass or farmed. Hence, in this 

case, restoration of a farmed wetland consisted of converting 

the land use to grass. At the start of both simulation periods 

(i.e., May) we assumed that wetlands were 50 percent full 

because of water remaining from the previous year and inputs 

from springtime precipitation and runoff.

Results and Discussion

Based on simulation results (table 4), a 25 percent restoration scenario would increase water storage by 27–32 percent, 

and a 50 percent restoration scenario would increase storage 

by 53–63 percent; the higher percent storage increases within 

scenarios occurred during the 1993 simulation period. Most 

gains in storage were associated with the restoration of drained 

wetlands (50 percent scenario increase = 1,187 to 2,330 ha-m), 

whereas only moderate increases in storage were associated 

when converting farmed but hydrologically intact wetlands 

to grass (50 percent scenario = 12 to 225 ha-m (table 4)). 

Assuming that the precipitation was uniform across the study 

area (52,758 ha) for the simulation time periods (0.32 m in 

1987; 0.67 m in 1993), depressional wetlands stored from 

11.57 to 20.98 percent of the precipitation that fell in the upper 

Mustinka subbasin (table 5). These simulations suggest that 

there is considerable potential for existing and potentially 

restorable wetlands to intercept and store water in the upper 

Mustinka subbasin. For our simulation we treated wetlands as 

being 50 percent full at the start of each simulation. Hence, we 

did not simulate maximum storage potential, and estimates of 

water storage are likely conservative since wetlands typically 

draw down in the fall and provide increased storage in the 

spring.

Conclusion

The goal of this study was to evaluate the application 

of models developed in chapters A and B by simulating the 

potential of wetlands to intercept, process, and store water in 

the upper Mustinka subbasin; our simulation results indicated 

that there is considerable potential to enhance water storage 

there. Results of our model simulations could provide a basis 

for understanding how restoring wetlands may increase water 

storage in a watershed of interest; however, evaluation and 

calibration of the model is always necessary before simulation 

results are deemed reliable for use in management decisions 

and planning. As indicated in chapter B, the wetland water 

storage simulation model requires calibration within the 

22  Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions, Red River of the North
Table 4. Results from model simulations performed on National Wetlands Inventory (NWI) and Restorable Wetlands Working Group 

(RWWG) wetland data for the upper Mustinka subbasin of Grant County, Minn., for the growing seasons of 1987 and 1993. Precipitation 

totals for the May–October time period were 0.32 m (1987) and 0.67 m (1993).

Restoration 

scenario Source Land use

Wetland area4 May–Oct. 1987 May–Oct. 1993

Mean Min Max n

volume4 volume4

Stored Spilled Stored Spilled

1

0% NWI FARM 0.61 0.002 17.02 1,551 1,393.36 24.62 2,305.37 711.36

1

0% NWI GRASS 1.94 .016 19.99 482 891.07 0 1,780.98 114.76

2

25% NWI FARM .62 .006 14.63 1,147 1,042.09 18.11 1,719.86 535.69

2

25% NWI GRASS 1.33 .002 19.99 886 1,248.86 .0001 2,478.68 178.24

2

25% RWWG GRASS 1.99 .501 19.77 286 599.11 0 1,178.81 95.79

3

50% NWI FARM .63 .006 14.6327 765 697.09 12.27 1,154.03 355.12

3

50% NWI GRASS 1.11 .002 19.99 1,268 1,599.70 .0001 3,158.31 245.02

3

50% RWWG GRASS 1.91 .501 19.86 571 1,187.31 0 2,330.89 195.10

1

The 0% restoration scenario represents existing water storage (i.e., all NWI wetlands; no RWWG drained wetlands).

2

25% of the RWWG wetlands >0.5 ha are treated as hydrologically intact (restored) with the associated catchments planted to grass; the NWI wetlands classified as grass consist of existing NWI wetlands (grass) as well as the 25% of those NWI wetlands previously classified as farmed that are now considered restored 

(i.e., catchments planted to grass). The NWI wetlands classified as farmed represent the 75% that were not restored.

3

50% of the RWWG wetlands >0.5 ha are treated as hydrologically intact (restored) with the associated catchments planted to grass; the NWI wetlands classified as grass consist of existing NWI wetlands (grass) as well as the 50% of those NWI wetlands previously classified as farmed that are now considered restored 

(i.e., catchments planted to grass). The NWI wetlands classified as farmed represent the 50% that were not restored.

4

Wetland areas are presented in hectares; volumes are presented in hectare-meters.

Table 5. Percent of total precipitation stored by depressional wetlands 

in the upper Mustinka subbasin of Grant County, Minn.

[NWI, National Wetlands Inventory; RWWG, Restorable Wetlands Working Group]

Restoration 

scenario Source Land use

1987 1993

% of total 

precipitation 

stored

% of total 

precipitation 

stored

1

0% NWI FARM 8.39 6.53

1

0% NWI GRASS 5.37 5.04

13.76 11.57

2

25% NWI FARM 6.28 4.87

3

25% NWI GRASS 7.52 7.02

4

25% RWWG GRASS 3.61 3.34

17.41 15.22

5

50% NWI FARM 4.20 3.27

6

50% NWI GRASS 9.63 8.94

7

50% RWWG GRASS 7.15 6.60

20.98 18.81

1

All depressional NWI wetlands classified as farmed and grass.

2

75% of NWI farmed wetlands.

3

100% of NWI grass wetlands + 25% of restored NWI wetlands.

4

25% of RWWG wetlands >0.5ha.

5

50% of NWI farmed wetlands.

6

100% of NWI grass wetlands + 50% of restored NWI wetlands.

7

50% of RWWG wetlands >0.5ha.

Chapter C: Application of the Wetland Water Storage Simulation Model to the Upper Mustinka Subbasin  23
watershed of interest. Hence, a limitation of our results is that 

we did not calibrate the model for the upper Mustinka subbasin.

As demonstrated in this study, simulating various restoration scenarios provides useful information to guide land 

management decisions. Many of the variables that we included 

to perform simulations are easily obtained from available 

databases. This model includes many factors (e.g., land use, 

catchment area, physiographic region) that have previously 

been overlooked by modelers. In our study we made no 

attempt to relate potential storage of existing and restorable 

wetlands to the impacts of flooding or to analyze the direct 

impacts of wetland drainage. Rather, the purpose of this study 

was to develop and improve methods to estimate and simulate 

potential water storage.

Acknowledgments

Funding for this project was provided by the U.S. Geological Survey and U.S. Fish and Wildlife Service Science 

Support Program. For their assistance and cooperation with 

logistics and fieldwork, we thank Lynn Foss, Donald Kirby, 

and Robert Koltes. We also thank the private landowners for 

access to land. We wish to thank the following people for 

review of this report: Diane Eckles, Laurie Fairchild, Phil 

Gerla, Rex Johnson, James LaBaugh, Wesley Newton, and 

Kevin Vining. Additionally, we thank the Restorable Wetlands 

Working Group for providing spatial wetland data and Deb 

Buhl for statistical assistance.

References Cited

Bell, A., Eckhardt, D., and Pucherelli, M., 1999, Wetlands 

inventory and drained wetlands water storage capacity estimation for the St. Joe-Calio Coulee subbasin of the greater 

Devils Lake basin, North Dakota: Denver, U.S. Department 

of the Interior, Bureau of Reclamation, Technical Memorandum No. 8260-99-02.

Bengtson, M.L., and Padmanabhan, G., 1999, Hydrologic 

model for assessing the influence of wetlands on flood 

hydrographs in the Red River Basin—development and 

application: Fargo, North Dakota Water Research Institute, 

North Dakota State University, report submitted to the International Joint Commission Red River Task Force.

Best, R.G., 1978, Utilization of color-infrared aerial photography to characterize prairie potholes, in PECORA IV, 

Proceedings of the Symposium on Application of Remote 

Sensing Data to Wildlife Management: Sioux Falls, S. Dak., 

National Wildlife Federation, Scientific and Technical 

Series 3, p. 180–187.

Best, R.G., and Moore, D.G., 1979, Landsat interpretation 

of prairie lakes and wetlands of eastern South Dakota, in

Satellite Hydrology, Proceedings of the Fifth Annual 

William T. Pecora Memorial Symposium on Remote 

Sensing: American Water Resources Association Technical 

Publication Series TPS 81-1, p. 499–506.

Bluemle, J.P., 2000, The face of North Dakota—The geologic 

story (3d ed.): Grand Forks, North Dakota Geological Survey, Educational Series 26.

Brun, L.J., Richardson, J.W., Enz, J.W., and Larsen, J.K., 

1981, Stream flow changes in the southern Red River valley 

of North Dakota: North Dakota Farm Research Bulletin, 

v. 38, p. 11–14.

Dahl, T.E., 1990, Wetland losses in the United States, 1780’s 

to 1980’s: Washington, D.C., U.S. Department of the Interior, Fish and Wildlife Service.

Dahl, T.E., and Johnson, C.E., 1991, Status and trends of wetlands in the coterminous United States, mid-1970’s to mid1980’s: Washington, D.C., U.S. Department of the Interior, 

Fish and Wildlife Service.

Gleason, R.A., and Tangen, B.A., 2006, Floodwater storage, in

Gleason, R.A., Laubhan, M.K., and Euliss, N.H., Jr., (eds.), 

Ecosystem services derived from wetland conservation 

practices in the United States Prairie Pothole Region with 

an emphasis on USDA Conservation Reserve and Wetland 

Reserve Programs: U.S. Geological Survey, Biological 

Resources Discipline, Scientific Investigations Report, 

submitted 2006.

Haan, C.T., and Johnson, H.P., 1967, Geometrical properties 

of depressions in north-central Iowa: Iowa State Journal of 

Science, v. 42, p. 149–160.

Hubbard, D.E., 1982, Surface area-volume relationship of 

wetland basins in the interior of the Coteau Des Prairies, 

in Proceedings of the South Dakota Academy of Sciences, 

v. 61, p. 154–158.

Manale, A., 2000, Flood and water quality management 

through targeted, temporary restoration of landscape functions—paying upland farmers to control runoff: Journal of 

Soil and Water Conservation, v. 55, p. 285–295.

Miller, J.E., and Frink, D.L., 1984, Changes in flood response 

of the Red River of the North basin, North DakotaMinnesota: U.S. Geological Survey, Water Resources 

Division, Water-Supply Paper 2243.

Miller, M.W., and Nudds, T.D., 1995, Prairie landscape change 

and flooding in the Mississippi River Valley: Conservation 

Biology, v. 10, p. 847–853.

24  Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions, Red River of the North
Moore, I.D., and Larson, C.L., 1979, Effects of drainage projects on surface runoff from small depressional watersheds 

in the north central region: University of Minnesota, Water 

Resources Research Center Bulletin 99.

Parkhurst, R.S., Winter, T.C., Rosenberry, D.O., and Sturrock, 

A.M., 1998, Evaporation from a small prairie wetland in the 

Cottonwood Lake area, North Dakota—an energy-budget 

study: Wetlands, v. 18, p. 272–287.

Restorable Wetlands Working Group, 2002, Minnesota restorable wetlands: accessed 2002 at http://prairie.ducks.org/

index.cfm?&page=minnesota/restorablewetlands/home.htm

Simonovic, S.P., and Juliano, K.M., 2001, Role of wetlands 

during low frequency flooding events: Canadian Water 

Resources Journal, v. 26, p. 377–397.

Stewart, R.E., and Kantrud, H.A., 1971, Classification of natural ponds and lakes in the glaciated prairie region: Washington, D.C., U.S. Department of the Interior, Fish and Wildlife 

Service publication 92.

Tiner, R.W., Jr., 1984, Wetlands of the United States—current 

status and recent trends: Washington, D.C., U.S. Fish and 

Wildlife Service, U.S. Government Printing Office.

Tripod Data Systems, Inc., 1997, Foresight for Windows: 

Corvallis, Oreg., Tripod Data Systems, Inc.

References Cited  25

SAS Institute, Inc., 2001, SAS software release 8.02: Cary, 

N.C., SAS Institute, Inc.

Vining , K.C., 2002, Simulation of streamflow and wetland 

storage, Starkweather Coulee subbasin, North Dakota, water 

years 1981-98: U.S. Geological Survey Water-Resources 

Investigations Report 02-4113.

Wiens, L.H., 2001, Surface area volume relationship for 

prairie wetlands in the upper Assiniboine River basin, 

Saskatchewan: Canadian Water Resources Journal, v. 26, 

p. 503–513.

Winter, T.C., 1989, Hydrologic studies of wetlands in the 

northern prairie, in van Der Valk, A. (ed.), Northern prairie 

wetlands: Ames, Iowa State University Press, p. 16–54.

Winter, T.C., 2003, Hydrological, chemical, and biological 

characteristics of a prairie pothole wetland complex under 

highly variable climate conditions—the Cottonwood Lake 

Area, east-central North Dakota: U.S. Geological Survey 

Professional Paper 1675.

Winter, T.C., and Rosenberry, D.O., 1995, The interaction of 

ground water with prairie pothole wetlands in the Cottonwood Lake area, east central North Dakota, 1979-1990: 

Wetlands, v. 15, p. 193–211.
Appendixes 1—7
Appendix 1. Acquiring input data, defining model scenarios, obtaining runoff 

curve numbers, creating input database, and interpreting model simulations.

The model uses simple input variables to simulate wetland hydrology. Proficiency with the statistical software program 

SAS (SAS Institute, Inc., 2001) is required to modify and run the attached programming code. This appendix describes the 

model and defines the input and model variables. The following appendixes provide the SAS code necessary to create the input 

database and run the model.

Step 1. Obtain precipitation data and determine daily evapotranspiration, land use and soil types, restoration and starting volume 

scenarios, etc.

Obtain daily precipitation, wetland (e.g., National Wetlands Inventory (NWI), Restorable Wetlands Working Group 

(RWWG)), soil, and land use information for the area of interest. The soil and land use information will be used to 

determine the Natural Resources Conservation Service (NRCS) curve numbers.

If working in the Prairie Pothole Region, all necessary models (i.e., volume, area) are provided in this report; 

models may also be obtained from additional sources available in the literature (e.g., Best, 1978; Hubbard, 1982; 

Wiens, 2001).

Determine approximate mean daily evapotranspiration rate for depressional wetlands of the region.

Determine the starting volume scenario.

Estimation of the starting volume scenario can be based on precipitation, snowfall, or general observations. For 

example, during extreme drought when a majority of wetlands are dry, the starting scenario would be 100 percent (i.e., 100 percent of the maximum volume is available for water storage). Similarly, during an extremely wet 

period the scenario would be 0 percent (i.e., wetlands are full), and during an average period the scenario would be 

50 percent (i.e., wetlands are 50 percent full).

Estimation of the starting volume scenario can be based on data from a series of instrumented wetlands. For 

example, one could measure the spring water volume for a representative set of wetlands in the region of interest 

and use the mean volume (e.g., 25 percent, 50 percent, etc.) for the starting scenario. Similarly, one could measure water volumes in the fall and create a springtime runoff event by using precipitation data (from snowfall); 

however, this type of “spring dump” would require information related to runoff over frozen soils and wintertime 

evaporation rates.

A restoration scenario can also be added to the model. This act would simply entail randomly selecting a percentage of 

wetlands in the region of interest to restore (e.g., 1 percent, 10 percent, 50 percent) or selecting certain areas of interest. A restoration scenario can also include wetland size. For example, it is likely not practical to restore large numbers of very small wetlands to mitigate flooding. As part of the restoration scenario, one may want to consider larger 

wetlands that store more water per restoration and would be more cost effective.

Determine the proper runoff curve numbers for specific land use and soil combinations for each zone (fig. 12) in the 

model. Because of general soil characteristics, the model incorporates unique curve numbers for the catchment area 

(CATCH_A) and the nonponded portion of the wetland area (CATCH_A_2).

Information for determining runoff by using curve numbers is available from the NRCS in the National Engineering Handbook (NEH) at the following Web site: ftp://ftp.wcc.nrcs.usda.gov/downloads/hydrology_hydraulics/

neh630/.

NEH chapter 10 describes the curve number process and provides information on land use and treatment categories, hydrologic soil groups, and hydrologic condition. The general procedure is as follows:

Q = (P-0.2S)

2

 / (P+0.8S)

where

Q = accumulated direct runoff; direct flow volume expressed as a depth,

P = accumulated rainfall (potential maximum runoff), and

S = surrogate for initial abstraction; it is the potential maximum retention; the curve number is used to aid in the 

estimation of S

A.

B.

C.

D.

a.

b.

E.

F.

a.

b.

28  Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions, Red River of the North
S = (1000/CN) - 10

where

CN = curve number

Steps to determine curve number (CN):

determine hydrologic soil group

determine land use and treatment (practice) categories

determine hydrologic condition category

Calculate the minimum amount of precipitation required to generate runoff for each curve number. This computation can be done by simply plotting the results from the runoff equation for a range of precipitation amounts and 

identifying where Q = 0.

Step 2. Create input database and run model.

Create SAS database (by using SAS code in appendix 2) containing the variables listed and described in table 1–1. 

Appendix 4 describes, in detail, the SAS code in appendix 2. Appendix 6 provides example SAS code used to create 

the input database for the 1987 simulation (see chapter 3).

Using database created in step 2A, run the model by using the SAS program in appendix 3. Table 1–2 lists and 

describes the model variables. Appendix 5 describes, in detail, the SAS code in appendix 3. Appendix 7 provides 

example SAS program used for 1987 (25 percent restoration scenario) simulation model (see chapter C).

Step 3. Interpretation of simulation.

Volume Stored

The variable VOL_STORE (volume stored) is the predicted wetland volume for a specific time period (event); it can be 

used to graph wetland volume over time or determine wetland volume for a specific period. Since this variable is additive, simply summing it will not provide the total volume stored for the simulation period.

To determine the total volume stored for a simulation, multiply the sum of the individual interception areas by the total precipitation (potential storage), then subtract the sum of the volume spilled variable (VOL_SPILL). The volume stored for a simulation period is equal to the potential storage (i.e., precipitation × interception area) minus the volume spilled (VOL_SPILL). In 

this method, the volume stored includes water lost to evaporation as well as water that infiltrated into the catchment soils.

Volume Spilled

To determine the amount of water that wetlands did not store (i.e., spill volume), simply sum the variable VOL_SPILL; it is 

not additive.

1.

2.

3.

c.

A.

B.

Table 1–1. Definition of input database variables for simulation model.

[NWI, National Wetlands Inventory; RWWG, Restorable Wetlands Working Group; RWI, Restorable Wetlands Inventory]

Abbreviation Definition Units

AREA_HA wetland area obtained from NWI, RWWG ha

CATCH_A wetland catchment area (INT_A – AREA_HA) ha

DAY day of month --

ET_M daily estimate of evapotranspiration × EVENT_DAYS m

EVENT precipitation event defined as consecutive days with or without measurable precipitation --

EVENT_DAYS number of days in EVENT --

INT_A estimate of wetland interception area from interception area model (table 1) ha

JUL_DATE Julian date --

LANDUSE land use category (e.g., farm, grass) --

MONTH month --

Appendix 1  29
Table 1–2. Definition of variables created in the wetland hydrology simulation model.

Abbreviation Definition Units

A_S_1 available storage volume on start date for model, based on scenario (VOL_M – START_VOL) ha-m

A_S_2 available storage volume (adjusted), based on model event (VOL_M – VOL_2) ha-m

AREA_HA_2 estimated wetland area using wetland volume-area model (table 1) ha

CATCH_A_2 AREA_HA – AREA_HA_2 ha

INPUT total water inputs into basin for an event (surface runoff + direct precipitation; INPUT_CATCH_A + 

INPUT_CATCH_A2 + INPUT_PONDED) ha-m

INPUT_CATCH_A surface water runoff into the basin from the catchment area ha-m

INPUT_CATCH_A2 surface water runoff into the ponded portion from the nonponded portion of the basin ha-m

INPUT_PONDED Water inputs into the ponded portion of the basin from direct precipitation ha-m

LOSS total water lost to evapotranspiration for an event ha-m

PREV_VOL start volume for events 2-X, based on volume from previous day ha-m

START_VOL start volume for model. On first day it is based on data or scenario (e.g., 50% full); on all other days it is 

based on previous day (i.e., PREV_VOL) ha-m

VOL_2 START_VOL adjusted for inputs and losses, by event ha-m

VOL_SPILL volume of water spilled when wetland overflows, by event ha-m

VOL_STORE volume of water stored, by event ha-m

WATER_1 START_VOL + INPUT ha-m

WATER_2 WATER_1 - LOSS ha-m

Abbreviation Definition Units

PP2 variable in SAS program used to identify whether there was measurable precipitation on the previous day 

(PRECIP_2) when creating the EVENT variable --

PREC_I daily precipitation in

PRECIP_2 codes days with and without measurable precipitation --

PRECIP_M daily precipitation m

RANDOM_NWI unique random number assigned to wetlands obtained from NWI data --

RANDOM_RWI unique random number assigned to wetlands obtained from RWWG data --

SAS_DATE date in SAS format --

SOURCE source of wetland data, NWI or RWI (RWWG) --

START codes start and nonstart days for model --

V_SCEN starting volume scenario, or the average wetland starting volume (as a % of maximum) for first day of model %

VOL_M estimated maximum wetland volume from volume model (table 1) ha-m

WETLAND unique wetland identifier --

YEAR year --

Table 1–1. Definition of input database variables for simulation model.—Continued

[NWI, National Wetlands Inventory; RWWG, Restorable Wetlands Working Group; RWI, Restorable Wetlands Inventory]

30 Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions, Red River of the North
Appendix 2. SAS program to create input database for simulation model 

(contact the authors with any questions regarding the programming or 

model input variables).

DATA PRECIPITATION;

SET [see A below];

SAS_DATE=MDY(MONTH,DAY,YEAR);

JUL_DATE=JULDATE(SAS_DATE);

IF PREC_I GT 0 THEN PRECIP_2=’1’;

IF PREC_I = 0 THEN PRECIP_2=’0’;

PROC SORT DATA=PRECIPITATION;BY JUL_DATE;

DATA PRECIPITATION_2;

SET PRECIPITATION;

BY JUL_DATE;

RETAIN EVENT PP2;

IF JUL_DATE=[see B below] THEN EVENT=1;

IF JUL_DATE NE [see B below] AND PP2 = PRECIP_2 THEN EVENT=EVENT+0;

IF JUL_DATE NE [see B below] AND PP2 NE PRECIP_2 THEN EVENT=EVENT+1;

PP2=PRECIP_2;

PROC SORT DATA=PRECIPITATION_2;BY EVENT;

PROC UNIVARIATE DATA=PRECIPITATION_2 NOPRINT; BY EVENT;

VAR PREC_I;

OUTPUT OUT=PRECIPITATION_3 SUM=PREC_I N=EVENT_DAYS;

DATA WETLAND;

SET [see C below];

INT_A=[see D below]*AREA_HA**[see D below];

VOL_M=[see E below]*AREA_HA**[see E below];

CATCH_A=INT_A-AREA_HA;

V_SCEN=[see F below];

DO YEAR=[see G below] TO [see G below];

DO MONTH=[see H below] TO [see H below];

DO DAY=[see I below] TO [see I below];OUTPUT;END;END;END;

DO EVENT=[see J below] TO [see J below]; OUTPUT;END;

PROC SORT DATA=PRECIPITATION_3; BY EVENT;

PROC SORT DATA=WETLAND; BY EVENT;

DATA INPUT_VARIABLES;

MERGE PRECIPITATION_3 WETLAND; BY EVENT;

ET_M=[see K below]*EVENT_DAYS;

PREC_M=PREC_I*0.0254;

IF JUL_DATE=[see L below] THEN START=’YES’;

IF START NE ‘YES’ THEN START=’NO’;

*[ input data required (see below) ]

A – insert precipitation database including day, month, and year

B – insert starting Julian date for model

C – insert database that includes wetland data (e.g., NWI areas, RWWG areas, etc.)

D – insert information for wetland interception area predictive model (table 1)

E – insert information for wetland volume predictive model (table 1)

F – insert starting volume scenario (percent of total volume)

G – insert year or range of years for model

H – insert month or range of months for model

I – insert day or range of days for the model

J – insert range of events for model (see data step PRECIPITATION_2)

K – insert daily evapotranspiration estimate

L – insert starting Julian date for model

Appendix 2  31
Appendix 3. SAS program for simulation model (contact the authors with any 

questions regarding the programming or model variables).

DATA MODEL;

SET [see A below];

BY WETLAND;

RETAIN PREV_VOL;

IF START=’YES’ THEN START_VOL=VOL_M*V_SCEN;

IF START=’NO’ THEN START_VOL=PREV_VOL;

A_S_1=VOL_M-START_VOL;

AREA_HA_2=[see B below]*START_VOL**[see B below];

CATCH_A_2=AREA_HA-AREA_HA_2;

IF LANDUSE=’insert landuse #1 here’ AND PREC_I GT [see C below] THEN INPUT_CATCH_A=((((PREC_I-(0.2*

[see D below]))**2)/(PREC_I+(0.8*[see D below]))*0.0254) ) *CATCH_A);

IF LANDUSE=’insert landuse #1 here’ AND PREC_I LE [see C below] THEN INPUT_CATCH_A=0;

IF LANDUSE=’insert landuse #1 here’AND PREC_I GT [see C below] THEN INPUT_CATCH_A_2=

((((PREC_I-(0.2*[see D below]))**2)/(PREC_I+(0.8*[see D below]))*0.0254)*CATCH_A_2);

IF LANDUSE=’insert landuse #1 here AND PREC_I LE [see C below] THEN INPUT_CATCH_A_2=0;

IF PREC_I GT 0 THEN INPUT_PONDED=PREC_M*AREA_HA_2;

IF PREC_I = 0 THEN INPUT_PONDED=0;

IF LANDUSE=’insert landuse #2 here’ AND PREC_I GT [see C below] THEN INPUT_CATCH_A=((((PREC_I-(0.2*

[see D below]))**2)/(PREC_I+(0.8*[see D below]))*0.0254) )*CATCH_A);

IF LANDUSE=’insert landuse #2 here’ AND PREC_I LE [see C below] THEN INPUT_CATCH_A=0;

IF LANDUSE=’insert landuse #2 here’AND PREC_I GT [see C below] THEN INPUT_CATCH_A_2=

((((PREC_I-(0.2*[see D below]))**2)/(PREC_I+(0.8*[see D below]))*0.0254)*CATCH_A_2);

IF LANDUSE=’insert landuse #2 here AND PREC_I LE [see C below] THEN INPUT_CATCH_A_2=0;

INPUT_PONDED=PREC_M*AREA_HA_2;

INPUT=INPUT_CATCH_A + INPUT_CATCH_A_2 + INPUT_PONDED;

WATER_1=(START_VOL+INPUT);

IF WATER_1 GT 0 THEN LOSS=([see B below]*WATER_1**[see B below])*ET_M;

IF WATER_1 LE 0 THEN LOSS=0;

IF LOSS LE WATER_1 THEN WATER_2= START_VOL+INPUT-LOSS;

IF LOSS GT WATER_1 THEN WATER_2=0;

IF WATER_2 GT 0 THEN VOL_2=WATER_2;

IF WATER_2 LE 0 THEN VOL_2=0;

A_S_2=VOL_M-VOL_2;

IF A_S_2 GE 0 THEN VOL_STORE=VOL_2;

IF A_S_2 LT 0 THEN VOL_STORE=VOL_M;

IF A_S_2 GE 0 THEN VOL_SPILL=0;

IF A_S_2 LT 0 THEN VOL_SPILL=ABS(A_S_2);

PREV_VOL=VOL_STORE;RUN;

*[ input data required (see below) ]

A – insert model input data set (see appendix 2)

B – insert information for wetland area predictive model (table 1)

C – insert minimum amount of precipitation (inches) needed to generate runoff (unique for each runoff curve)

D – insert S value calculated by using runoff curve number (see appendix 1)

32  Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions, Red River of the North
Appendix 4. Description of SAS code (appendix 2) used to create input 

database for simulation model.

DATA PRECIPITATION; names data step

SET [see A below]; sets precipitation input data set

SAS_DATE=MDY(MONTH,DAY,YEAR); formats date for SAS

JUL_DATE=JULDATE(SAS_DATE); creates Julian date variable

IF PREC_I GT 0 THEN PRECIP_2=’1’; codes days with precipitation

IF PREC_I = 0 THEN PRECIP_2=’0’; codes days with no precipitation

PROC SORT DATA=PRECIPITATION;BY JUL_DATE; sorts data

DATA PRECIPITATION_2; names data step

SET PRECIPITATION; sets input data

BY JUL_DATE; specifies “by” variable

RETAIN EVENT PP2; specifies “retain” variables

IF JUL_DATE=[see B below] THEN EVENT=1; sets date for beginning of event #1

IF JUL_DATE NE [see B below] AND PP2 = PRECIP_2 THEN EVENT=EVENT+0; creates events

IF JUL_DATE NE [see B below] AND PP2 NE PRECIP_2 THEN EVENT=EVENT+1; creates events

PP2=PRECIP_2; creates PP2 variable

PROC SORT DATA=PRECIPITATION_2;BY EVENT; sorts data

PROC UNIVARIATE DATA=PRECIPITATION_2 NOPRINT; BY EVENT;*sums precipitation for each

VAR PREC_I; *event and creates

OUTPUT OUT=PRECIPITATION_3 SUM=PREC_I N=EVENT_DAYS; * EVENT_DAYS variable

DATA WETLAND; names data step

SET [see C below]; sets input data

INT_A=[see D below]*AREA_HA**[see D below]; uses model from Table 1 to create INT_A variable

VOL_M=[see E below]*AREA_HA**[see E below]; uses model from Table 1 to create V0L_M variable

CATCH_A=INT_A-AREA_HA; creates CATCH_A variable

V_SCEN=[see F below]; creates V_SCEN variable

DO YEAR=[see G below] TO [see G below]; creates YEAR variable (use year(s) in precipitation data set)

DO MONTH=[see H below] TO [see H below]; creates MONTH variable (use month(s) in precipitation data)

DO DAY=[see I below] TO [see I below]; creates DAY variable (use day(s) in precipitation data set)

OUTPUT;END;END;END; ends “DO” statements used to create YEAR, MONTH, DAY variables

DO EVENT=[see J below] TO [see J below]; creates EVENT variable (see PRECIPITATION_3 data set)

OUTPUT;END; ends “DO” statement used to create EVENT variable

PROC SORT DATA=PRECIPITATION_3; BY EVENT; sorts data

PROC SORT DATA=WETLAND; BY EVENT; sorts data

DATA INPUT_VARIABLES; names final input variable data set used in model simulation

MERGE PRECIPITATION_3 WETLAND; BY EVENT; merges data sets created above

ET_M=[see K below]*EVENT_DAYS; creates ET_M variable for each event

PREC_M=PREC_I*0.0254; creates PREC_M variable

IF JUL_DATE=[see L below] THEN START=’YES’; codes start date for model

IF START NE ‘YES’ THEN START=’NO’; codes “non-start” days for model

*[ input data required (see below) ]

A – insert precipitation database including day, month, and year

B – insert starting Julian date for model

C – insert database that includes wetland data (e.g., NWI areas, RWWG areas, etc.)

D – insert information for wetland interception area predictive model (table 1)

E – insert information for wetland volume predictive model (table 1)

F – insert starting volume scenario (percent of total volume)

G – insert year or range of years for model

H – insert month or range of months for model

I – insert day or range of days for the model

J – insert range of events for model (see data step PRECIPITATION_2)

K – insert daily evapotranspiration estimate

L – insert starting Julian date for model

Appendix 4  33
Appendix 5. Description of SAS code (appendix 3) for simulation model.

DATA MODEL; names data step

SET [see A below]; sets input database

BY WETLAND; specifies “by” variable

RETAIN PREV_VOL; specifies “retain” variable

IF START=’YES’ THEN START_VOL=VOL_M*V_SCEN; creates START_VOL for day 1 of model

IF START=’NO’ THEN START_VOL=PREV_VOL; creates START_VOL for day 2-x of model

A_S_1=VOL_M-START_VOL; creates A_S_1 variable

AREA_HA_2=[see B below]*START_VOL**[see B below]; uses model (Table 1) to create AREA_HA_2

CATCH_A_2=AREA_HA-AREA_HA_2; creates CATCH_A_2 variable

The following steps calculates INPUT_CATCH_A, and INPUT_CATCH_A2 for precipitation events that generate surface runoff (each curve number has a minimum amount of precipitation required to generate runoff). INPUT_PONDED and INPUT 

are calculated for all precipitation events. The code contains the runoff equation (Appendix A) and applies it to the catchment 

area and the nonponded portion of the wetland basin. This code must be customized for each land use / S-value combination 

(see Appendix A).

IF LANDUSE=’insert landuse #1 here’ AND PREC_I GT [see C below] THEN INPUT_CATCH_A=((((PREC_I-(0.2*

[see D below]))**2)/(PREC_I+(0.8*[see D below]))*0.0254)*CATCH_A);

IF LANDUSE=’insert landuse #1 here’ AND PREC_I LE [see C below] THEN INPUT_CATCH_A=0;

IF LANDUSE=’insert landuse #1 here’ AND PREC_I GT [see C below] THEN INPUT_CATCH_A_2=((((PREC_I-

(0.2*[see D below]))**2)/(PREC_I+(0.8*[see D below]))*0.0254)*CATCH_A_2);

IF LANDUSE=’insert landuse #1 here’ AND PREC_I LE [see C below] THEN INPUT_CATCH_A_2=0;

INPUT_PONDED=PREC_M*AREA_HA_2;

IF LANDUSE=’insert landuse #2 here’ AND PREC_I GT [see C below] THEN INPUT_CATCH_A((((PREC_I-(0.2*

[see D below]))**2)/(PREC_I+(0.8*[see D below]))*0.0254)*CATCH_A);

IF LANDUSE=’insert landuse #2 here’ AND PREC_I LE [see C below] THEN INPUT_CATCH_A=0;

IF LANDUSE=’insert landuse #2 here’ AND PREC_I GT [see C below] THEN INPUT_CATCH_A_2((((PREC_I-

(0.2*[see D below]))**2)/(PREC_I+(0.8*[see D below]))*0.0254)*CATCH_A_2);

IF LANDUSE=’insert landuse #2 here’ AND PREC_I LE [see C below] THEN INPUT_CATCH_A_2=0;

IF PREC_I GT 0 THEN INPUT_PONDED=PREC_M*AREA_HA_2;

IF PREC_I = 0 THEN INPUT_PONDED=0;

INPUT=INPUT_CATCH_A + INPUT_CATCH_A_2 + INPUT_PONDED;

WATER_1=(START_VOL+INPUT); creates WATER_1 variable

*The following step calculates LOSS (i.e., loss to evapotranspiration). The ponded wetland area is adjusted (using model from 

table 1) according to precipitation inputs; this area is multiplied by ET_M.

IF WATER_1 GT 0 THEN LOSS=([see B below]*WATER_1**[see B below])*ET_M;

IF WATER_1 LE 0 THEN LOSS=0;

IF LOSS LE WATER_1 THEN WATER_2= START_VOL+INPUT-LOSS; creates WATER_2 variable

IF LOSS GT WATER_1 THEN WATER_2=0; creates WATER_2 variable

IF WATER_2 GT 0 THEN VOL_2=WATER_2; creates VOL_2 variable

IF WATER_2 LE 0 THEN VOL_2=0; creates VOL_2 variable

A_S_2=VOL_M-VOL_2; creates A_S_2 variable

IF A_S_2 GE 0 THEN VOL_STORE=VOL_2; creates VOL_STORE variable

IF A_S_2 LT 0 THEN VOL_STORE=VOL_M; creates VOL_STORE variable

IF A_S_2 GE 0 THEN VOL_SPILL=0; creates VOL_SPILL variable

IF A_S_2 LT 0 THEN VOL_SPILL=ABS(A_S_2); creates VOL_SPILL variable

PREV_VOL=VOL_STORE;RUN; creates PROV_VOL variable

*[ input data required (see below) ]

A – insert model input data set

B – insert information for wetland area predictive model (table 1)

C – insert minimum amount of precipitation (inches) needed to generate runoff (unique for each runoff curve)

D – insert S value calculated by using runoff curve number

34  Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions, Red River of the North
Appendix 6. SAS program used to create input database 

for 1987 simulation model.

DATA DATA1;

SET NWI_DATA;

SOURCE=’NWI’;

IF AREA_HA GT 20 THEN DELETE;

DATA DATA2;

SET RWWG_DATA;

IF AREA_HA GT 20 THEN DELETE;

SOURCE=’RWI’;

LANDUSE=’FARM’;

PROC SORT DATA=DATA1; BY WETLAND;

PROC SORT DATA=DATA2; BY WETLAND;

DATA WETLAND;

MERGE DATA1 DATA2; BY WETLAND;

IF AREA_HA LE 0 THEN DELETE;

IF SOURCE=’NWI’ AND LANDUSE=’FARM’ THEN RANDOM_NWI=RANUNI(0);

IF SOURCE=’NWI’ AND LANDUSE=’GRASS’ THEN RANDOM_NWI=1;

IF SOURCE=’RWI’ AND AREA_HA GE 0.5 THEN RANDOM_RWI=RANUNI(0);

IF SOURCE=’RWI’ AND AREA_HA LT 0.5 THEN RANDOM_RWI=0;

DATA PRECIPITATION_87;

SET ELBOW_LAKE_PRECIP;

IF YEAR NE 1987 THEN DELETE;

IF MONTH LT 5 THEN DELETE;

IF MONTH GT 10 THEN DELETE;

SAS_DATE=MDY(MONTH,DAY,YEAR);

JUL_DATE=JULDATE(SAS_DATE);

IF PREC_I GT 0 THEN PRECIP_2=’1’;

IF PREC_I = 0 THEN PRECIP_2=’0’;

PROC SORT DATA=PRECIPITATION_87;BY JUL_DATE;

DATA PRECIPITATION_2_87;

SET PRECIPITATION_87;

BY JUL_DATE;

RETAIN EVENT PP2;

IF JUL_DATE=87121 THEN EVENT=1;

IF JUL_DATE NE 87121 AND PP2 = PRECIP_2 THEN EVENT=EVENT+0;

IF JUL_DATE NE 87121 AND PP2 NE PRECIP_2 THEN EVENT=EVENT+1;

PP2=PRECIP_2;

PROC SORT DATA=PRECIPITATION_2_87;BY EVENT;

PROC UNIVARIATE DATA=PRECIPITATION_2_87 NOPRINT; BY EVENT;

VAR PREC_I;

OUTPUT OUT=PRECIPITATION_3_87 SUM=PREC_I N=EVENT_DAYS

DATA WETLAND_87;

SET WETLAND;

INT_A=5.06*AREA_HA**0.5921;

VOL_M=0.201*AREA_HA**1.5029;

CATCH_A=INT_A-AREA_HA;

V_SCEN=0.5;

DO EVENT= 1 TO 65;

OUTPUT;END;

PROC SORT DATA=PRECIPITATION_3_87; BY EVENT;

PROC SORT DATA=WETLAND_87; BY EVENT;

DATA INPUT_VARIABLES_87;

MERGE PRECIPITATION_3_87 WETLAND_87; BY EVENT;

Appendix 6  35
ET_M=0.00358*EVENT_DAYS;

PREC_M=PREC_I*0.0254;

IF EVENT=1 THEN START=’YES’;

IF START NE ‘YES’ THEN START=’NO’;

Appendix 7. SAS program for 1987 (25 percent restoration 

scenario) simulation model.

DATA INPUT;

SET INPUT_VARIABLES_1987;

IF SOURCE=’RWI’ AND AREA_HA LT 0.5 THEN DELETE;

IF SOURCE=’RWI’ AND RANDOM_RWI GT 0.25 THEN DELETE;

IF SOURCE=’RWI’ THEN LANDUSE=’GRASS’;

IF SOURCE=’NWI’ AND RANDOM_NWI LE 0.25 THEN LANDUSE=’GRASS’;

PROC SORT DATA=INPUT; BY WETLAND;

DATA MODEL;

SET INPUT;

BY WETLAND;

RETAIN PREV_VOL;

IF START=’YES’ THEN START_VOL=VOL_M*V_SCEN;

IF START=’NO’ THEN START_VOL=PREV_VOL;

A_S_1=VOL_M-START_VOL;

AREA_HA_2=2.57*START_VOL**0.6356;

CATCH_A_2=AREA_HA-AREA_HA_2;

IF LANDUSE=’FARM’ AND PREC_I GT 0.5 THEN INPUT_CATCH_A=

((((PREC_I-(0.2*2.35))**2)/(PREC_I+(0.8*2.35))*0.0254)*CATCH_A)

IF LANDUSE=’FARM’ AND PREC_I LE 0.5 THEN INPUT_CATCH_A=0;

IF LANDUSE=’FARM’ AND PREC_I GT 0.25 THEN INPUT_CATCH_A2=

((((PREC_I-(0.2*0.989))**2)/(PREC_I+(0.8*0.989))*0.0254)*CATCH_A_2);

IF LANDUSE=’FARM’ AND PREC_I LE 0.25 THEN INPUT_CATCH_A2=0;

IF PREC_I GT 0 THEN INPUT_PONDED=PREC_M*AREA_HA_2;

IF PREC_I = 0 THEN INPUT_PONDED=0;

IF LANDUSE=’GRASS’ AND PREC_I GT 1.5 THEN INPUT_CATCH_A=

((((PREC_I-(0.2*7.24))**2)/(PREC_I+(0.8*7.24))*0.0254)*CATCH_A)

IF LANDUSE=’GRASS’ AND PREC_I LE 1.5 THEN INPUT_CATCH_A=0;

IF LANDUSE=’GRASS’ AND PREC_I GT 0.5 THEN INPUT_CATCH_A2=

((((PREC_I-(0.2*2.82))**2)/(PREC_I+(0.8*2.82))*0.0254)*CATCH_A_2);

IF LANDUSE=’GRASS’ AND PREC_I LE 0.5 THEN INPUT_CATCH_A2=0;

IF PREC_I GT 0 THEN INPUT_PONDED=PREC_M*AREA_HA_2;

IF PREC_I = 0 THEN INPUT_PONDED=0;

INPUT=INPUT_CATCH_A + INPUT_CATCH_A_2 + INPUT_PONDED;

WATER_1=(START_VOL+INPUT);

IF WATER_1 GT 0 THEN LOSS=(2.57*WATER_1**0.6356)*ET_M;

IF WATER_1 LE 0 THEN LOSS=0;

IF LOSS LE WATER_1 THEN WATER_2= START_VOL+INPUT-LOSS;

IF LOSS GT WATER_1 THEN WATER_2=0;

IF WATER_2 GT 0 THEN VOL_2=WATER_2;

IF WATER_2 LE 0 THEN VOL_2=0;

A_S_2=VOL_M-VOL_2;

IF A_S_2 GE 0 THEN VOL_STORE=VOL_2;

IF A_S_2 LT 0 THEN VOL_STORE=VOL_M;

IF A_S_2 GE 0 THEN VOL_SPILL=0;

IF A_S_2 LT 0 THEN VOL_SPILL=ABS(A_S_2);

PREV_VOL=VOL_STORE;RUN;

36  Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions, Red River of the North
Prepared by the Helena Publishing Service Center

For additional information contact:

Director, Northern Prairie Wildlife Research Center

U.S. Geological Survey

8711 37th Street Southeast

Jamestown, North Dakota 58401

(701) 253-5500

http://www.npwrc.usgs.gov/
Gleason and others—Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions, Red River of the North—Open-File Report 2007–1159

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