Introduction to stormwater modeling

The foundation of stormwater management is an understanding of how a particular land area and drainage system can affect be affected by the stormwater passing through it. In particular, when (or preferably before) alterations are made to the land area or drainage network, stormwater managers need to understand and anticipate how the alteration is likely to affect the volume, flow rate, and quality of runoff moving through the system, and in turn, how the stormwater is likely to impact the people, property, and natural resources of the area. Modeling is a tool that can be used to understand and evaluate complex processes.

Purpose of stormwater modeling

Some kind of stormwater model is needed whenever an estimate of the expected volume, rate, or quality of stormwater is desired. Modeling is also often necessary for the design of BMPs and hydraulic structures and for evaluation of the effectiveness of water quality treatment by BMPs. If monitoring data exists for the specific combination of precipitation and site conditions under consideration, modeling may not be necessary. However, in many cases the conditions to be analyzed do not fit precisely with the conditions monitored in the past and modeling will be necessary.

In general, models can be physical or numerical. A physical model is a constructed replica of the system, whereas a numerical model is based on equations that approximate the processes occurring in the system. Typically, it is not realistic to construct a physical model that would provide reliable hydrologic predictions for a watershed or drainage system, so numerical (nearly always computer-based) models are the standard tool for stormwater management.

Note that this Manual cannot possibly contain a thorough analysis of modeling. Instead, the purpose is to introduce a stormwater manager to the terms of modeling and some cursory assessment of model calibration. For a brief description of various available models, see Available stormwater models and selecting a model.

In practice, stormwater models are most commonly used either as planning and decision making aids for water management authorities, or as tools for developers who wish to design for and demonstrate compliance with regulations and principles governing protection of water and waterways. They are used, for example, to predict:

• water quality effects of various land management scenarios;
• effects of water control structures on water surface elevations in a channel;
• performance of stormwater management structures such as ponds, wetlands, trenches, etc.;
• wetland impacts resulting from channel excavation; and
• lateral extents of a floodplain along a channel

These examples show some of the potential uses of modeling, but the list is by no means exhaustive. Modeling in general is a versatile tool that can be applied to any number of situations.

Types of models

The most commonly used stormwater models can generally be classified as either hydrologic, hydraulic, or water quality models.

• Hydrologic models are used to estimate runoff volumes, peak flows, and the temporal distribution of runoff at a particular location resulting from a given precipitation record or event. Essentially, hydrologic models are used to predict how the site topography, soil characteristics, and land cover will cause runoff either to flow relatively unhindered through the system to a point of interest, or to be delayed or retained somewhere upstream. Many hydrologic models also include relatively simple procedures to route runoff hydrographs through storage areas or channels, and to combine hydrographs from multiple watersheds.
• Hydraulic models are used to predict the water surface elevations, energy grade lines, flow rates, velocities, and other flow characteristics throughout a drainage network that result from a given runoff hydrograph or steady flow input. Generally, the output (runoff) from a hydrologic model is used in one way or another as the input to a hydraulic model. The hydraulic model then uses various computational routines to route the runoff through the drainage network, which may include channels, pipes, control structures, and storage areas. Combined hydraulic and hydrologic models provide the functions of both hydraulic models and hydrologic models in one framework. A combined model takes the results from the hydrologic portion of the model and routes it through the hydraulic portion of the model to provide the desired estimates.
• Water quality models are used to evaluate the effectiveness of a BMP, simulate water quality conditions in a lake, stream, or wetland, and to estimate the loadings to water bodies. Often the goal is to evaluate how some external factor (such as a change in land use or land cover, the use of best management practices, or a change in lake internal loading) will affect water quality. Parameters that are frequently modeled include total phosphorus, total suspended solids, and dissolved oxygen.

Limitations of modeling and the importance of calibration

Hydrologic, hydraulic, and water quality models are not exact simulations of the processes occurring in nature. Rather, they are approximate representations of natural processes based on a set of equations simplifying the system and making use of estimated or measured data. The accuracy of a model, therefore, is limited by the quality of the simplifications made to approximate the system processes and the quality of the input data. In some cases, the impact of these limitations can be reduced by using a more complex model or paying to acquire more or better input data. However, it is also important to recognize that oftentimes, it is simply not possible to significantly increase accuracy with such means, because the necessary computational and data collection technology does not exist, and in any case the climatic forces driving the simulation can only be roughly predicted. There also could be time and funding constraints.

File:Illustration of model calibration.jpg

Importance of model calibration. Models that are not calibrated to data are likely to be in serious error.

Recognizing the high degree of error or uncertainty inherent in many aspects of stormwater modeling can help to focus efforts where they do the most good. Generally, the goal of stormwater modeling is to provide a reasonable prediction of the way a system will respond to a given set of conditions. The modeling goal may be to precisely predict this response or to compare the relative difference in response between a number of scenarios. The best way to verify that a model fulfills this need (to the required degree of accuracy) is to check it against actual monitoring data or observations.

The process of model calibration involves changing the estimated input variables so that the output variables match well with observed results under similar conditions. The process of checking the model against actual data can vary greatly in complexity, depending on the confidence needed and the amount of data available. In some cases, the only feasible or necessary action may be a simple “reality check,” using one or two data points to verify that the model is at least providing results that fall within the proper range. In other cases, it may be necessary to perform a detailed model calibration, to ensure the highest possible accuracy for the output data. For some models, calibration is unnecessary due to the design of the model.

Calibration should not result in the use of model parameters that are outside a reasonable range. Additionally, models should not be calibrated to fit so tightly with observed data that the model loses its flexibility to make estimates under other climatic conditions.

Minnesota model input guide

The section on unified sizing criteria outlines recommendations for sizing best management practices. The following sources of information will allow designers to use the above referenced models for estimating hydrologic, hydraulic, or water quality parameters.

Data resources

The following data and sources of data can be used for model inputs.

Precipitation

The most commonly referenced precipitation frequency study in Minnesota is the U.S. Weather Bureau’s 1961 Technical Publication 40 (TP-40, Hershfield, 1961). Despite potential doubts regarding the adequacy of TP-40, which is viewed by some as outdated and not reflective of recent climate trends, TP-40 remains the dominant source for Minnesota precipitation magnitude and return frequency. Isopluvial maps showing precipitation depths corresponding to the following 24-hour return events over the entire state are included in TP-40.

• 1-year design storm
• 2-year design storm
• 5-year design storm
• 10-year design storm
• 25-year design storm
• 100-year design storm

Design engineers typically make use of precipitation exceedence probability to calculate the risks of design failure for channel protection, over-bank flooding, and extreme flooding. A storm magnitude of a return period (T) has the probability of being equaled or exceeded in any given year is equal to 1/T. For example a “100-year” event at a given location has a chance of 1/100 or 0.01 or 1 percent of being equaled or exceeded in any given year.

More recent work by others to update, test and/or validate the TP-40 findings include precipitation frequency studies conducted by the Midwest Climate Center (Huff and Angels’ 1992 Bulletin 71), Metropolitan Council’s Precipitation Frequency Analysis for the Twin Cities Metropolitan Area (study updates in 1984, 1989, and 1995), and Mn/DOT’s November 1998 study Intensity of Extreme Rainfall over Minnesota in coordination with Richard Skaggs from the University of Minnesota. In addition to the frequency analysis studies, an impressive source of historical (and current) precipitation data and other climate data for Minnesota has been compiled by the Minnesota Climatology Working Group.

Climate trends

According to Dr. Mark Seeley, University of Minnesota, sufficient data exist to support recently observed trends of climate change in Minnesota. Notable changes over the last 30 years include:

• warmer winters;
• higher minimum temperatures;
• increased frequency of tropical dew points;
• greater annual precipitation with:
  • more snowfall;
  • more frequent heavy rainstorm events; and
  • more days with rain.

The increasing precipitation and snowfall trends suggest the need for an updated Minnesota precipitation study.

Topographic data

General topographic information can be obtained from USGS topographic maps. The USGS topographic maps display topographic information as well as the location of roads, lakes, rivers, buildings, and urban land use. Paper or digital maps can be purchased from local vendors or ordered on the USGS Web site. Counties often have more detailed topographic information available in a format suitable for use in Geographic Information Systems (GIS). Additionally, topographic data suitable for GIS use for the metro area and statewide may be available from MetroGIS and the MN DNR. To acquire detailed topographic data for a site, a local survey may need to be completed.

Soils and surficial geology

Data on soils can be obtained from county soil surveys completed by the USDA Natural Resources Conservation Service (NRCS). These reports describe each soil type in detail and include maps showing the soil type present at any given location. A list of soil surveys available for Minnesota can be found on the NRCS Web site. Soils information could also be obtained by conducting an onsite soil survey, by conducting soil borings, and by evaluating well logs. Other sources of soils information, such as dominant soil orders, may be obtained from the Land Management Information Center, the MN DNR, or from MetroGIS. Information on surficial geology can be obtained from the Minnesota Geological Survey.

Land cover and land use

Land cover and land use information can be obtained from the local planning agency such as the county or city of interest but may also be available in the sources listed by the Land Management Information Center, the DNR, and MetroGIS.

Monitoring data

Monitoring data can be used as model input and for model calibration. Data on lake levels, ground water levels, stream flow, and water quality can be obtained from local monitoring studies or from such agencies as the Department of Natural Resources (DNR), United States Geologic Survey (USGS), Minnesota Pollution Control Agency (MPCA), and the Metropolitan Council.

Input guidance

This section discusses several model inputs.

Rainfall distribution

Storm distribution is a measure of how the intensity of rainfall varies over a given period of time. For example, in a given 24 hour period, a certain amount of rainfall is measured. Rainfall distribution describes where that rain fell over that 24 hour period; that is, whether the precipitation occurred over a one hour period or over the entire 24 hours.

The standard rainfall distribution used for urban areas in Minnesota for sizing and evaluation of BMPs is the Natural Resource Conservation Service’s (NRCS) recommended SCS Type II rainfall distribution for urban areas. This is a synthetic event, created by the SCS (now the NRCS), of a 24-hour duration rainfall event in which the peak intensity falls in the center of the event (at 12 hours).

The advantage of using the synthetic event is that it is appropriate for determining both peak runoff rate and runoff volume. Drawbacks of using a synthetic event are that they rarely occur in nature and are difficult to explain. Observed precipitation data can be used if analysis with a natural distribution is desired.

Further information regarding rainfall distribution can be found in the Minnesota Department of Transportation’s Drainage Manual and in the Hydrology Guide for Minnesota prepared by the Soil Conservation Service (now the NRCS).

Water quality event

Small storms are often the focus of water quality analysis because research has shown that pollution migration associated with frequently occurring events accounts for a large percentage of the annual load. This is because of the “first flush” phenomenon of early storm wash-off and the large number of events with frequent return intervals. Rain events between 0.5 inches and 1.5 inches are responsible for about 75 percent of runoff pollutant discharges (MPCA, 2000).

The rainfall depth corresponding to 90 percent and 95 percent of the annual total rainfall depth shows surprising consistency among six stations chosen to represent regional precipitation across the State. The six stations analyzed were Minneapolis/St. Paul International Airport, St. Cloud Airport, Rochester Airport, Cloquet, Itasca, and the Lamberton SW Experiment Station. The rainfall depth which represents 90 percent and 95 percent of runoff producing events was 1.09 inches (+/- 0.04 inches) and 1.46 inches (+/- 0.08 inches), respectively. This rainfall depth can be used for water quality analysis throughout the state.

Larger events such as the spring snowmelt, however, can be the single largest water and pollutant loading event in the year. In Minnesota, this spring snowmelt occurs over a comparatively short period of time (i.e., approximately two weeks) in March or April of each year – depending on the region of the state. The large flow volume during this event may be the critical water quality design event in much of the state.

Technical Bulletin 333, Climate of Minnesota (Kuehnast, 1982), shows that the average annual date of snowmelt can be represented by the last date of a 3 inch snow cover. This document also includes figures that allow estimation of the average depth of snowpack at the start of spring snowmelt plus the water content of the snowpack during the month of March.

The estimated infiltration volume can be determined from research in cold climates by Baker (1997), Buttle and Xu (1988), Bengtsson (1981), Dunne and Black (1971), Granger et al. (1984) and Novotny (1988). This research shows that infiltration does in fact occur during a melt at volumes that vary considerably depending upon multiple factors including: moisture content of the snow pack, soil moisture content at the time the soil froze, plowing, sublimation, vegetative cover, soil properties, and other snowpack features. For example, snowmelt investigations by Granger et al. (1984) took measurements from 90 sites, located in Saskatchewan Canada, representing a wide range of land use, soil textures, and climatic conditions. From this work, general findings showed that even under conservative conditions (wet soils, ~35 percent moisture content, at the time of freeze) about 0.4 inches of water infiltrated during the melt period from a one-foot snowpack with a 10 percent moisture content (1.2 inches of equivalent moisture) in areas with pervious cover. This would not apply to impervious surfaces (see Overview of basic stormwater concepts).

Other procedures for estimating water quality treatment volume based on annual snow depth are described by the Center for Watershed Protection (CWP) (Caraco and Claytor, 1997), which is available as a free download from the CWP. More snowfall and snowmelt data can be found in a report sponsored by the Minnesota Department of Transportation.

For purposes of determining the volume of runoff or snowmelt that should be managed by the site BMPs, designers must make two water quality volume computations: snowmelt and rainfall runoff. The BMP would then be sized for the larger of the two results. Areas with low snowfall will likely find that the rainfall based computations are the larger value, while those areas with greater snowpack will find that snowmelt is larger.

In some cases snowmelt would be selected as the design parameter for computing the volume, whereas other options lead to rainfall as the critical design parameter (see Unified sizing criteria).

Extreme flood events

Because a spring melt event generates a large volume of water over an extended period of time, evaluation of the snowmelt event for channel protection and over-bank flood protection is generally not as important as the extreme event analysis. This warrants attention because of the possibility that a major melt flooding event could, and sometimes does, happen somewhere in the state.

Conservative design for extreme storms can be driven by either a peak rate or volume event depending upon multiple hydraulic factors. Therefore, depending upon the situation, either the 100-yr, 24-hr rain event or the 100-yr, 10-day snowmelt runoff event can result in more extreme conditions. For this reason, both events should be analyzed.

Protocol for simulation of the 100-yr, 24-hr rainfall event is well established in Minnesota. High water elevations (HWL) and peak discharge rates are computed with storm magnitudes based on TP-40 frequency analysis and the SCS Type II storm distribution. Protocol has been established for the analysis of HWL and peak discharge resulting from a 7.2 inch 100-yr, 10-day snowmelt runoff event. However, this event has received a considerable amount of criticism. Although not well documented, it is thought that the theoretical snowmelt event was devised by assuming a 6 inch 100-yr, 24-hr rainfall event occurs during a 10-day melt period in which 1 foot of snow (with a 10 percent moisture content) exists at the onset. A typical assumption accompanying the event is that of completely frozen ground (no infiltration) during the melt period for which the result is 100 percent delivery of volumes. So what do we use? Climate records show that the highest rain event during this common melt period over the past 100+ years was 4.75 inches. An alternative method to consider is to add 4.75 inches of precipitation to the site’s snowmelt volume (including infiltration). Designers should compare this to the 7.2 inch, 10-day snowmelt volumes and then determine which is best for the site.

Protocols for computation of extreme snowmelt events should be established as part of a state-wide precipitation study that has been discussed to update TP-40.

Runoff coefficient

The Rational Method is used to estimate peak runoff rates for very small sites. The simple equation for peak discharge (cubic feet per second) is Q=CiA, where C is a runoff coefficient, i is rainfall intensity in inches per hour, and A is drainage area in acres. The chosen value of C must represent losses to infiltration, detention, and antecedent moisture conditions. Additionally, C varies with the frequency of the rainfall event. Tabled values for C are shown below.

Runoff coefficients for 5- to 10-year storms (Source: Haan et al., 1994)
Link to this table

Land use description	Runoff coefficient (C)
Forest
< 5% slope	0.30
5 to 10% slope	0.35
> 10% slope	0.50
Open space
< 2% slope	0.05 to 0.10
2 to 7% slope	0.10 to 0.15
> 7% slope	0.15 to 0.20
Industrial	0.50 to 0.90
Residential
Multi-family	0.40 to 0.75
Single family	0.30 to 0.50
Impervious areas	0.70 to 0.95
Row crops1
< 5% slope	0.50
5 to 10% slope	0.60
> 10% slope	0.72
Pasture
N 5% slope	0.30
5 to 10% slope	0.36
> 10% slope	0.42

¹For clay and silt loam soils.

Curve numbers

Curve numbers are used in the SCS Method to represent the runoff expected after initial abstractions and infiltration into the soil. Curve numbers are based on land use and hydrologic soil group. The SCS (now the NRCS) developed tables with curve numbers appropriate for urban, agricultural, arid and semiarid rangeland, and undisturbed land uses. Hydrologic soil group can be determined from soil surveys.

Curve number tables are published in TR-55 (Urban Hydrology for Small Watersheds), but are also available in textbooks and within modeling software.

Curve numbers vary for smaller storms (see discussion in SLAMM documentation). A short summary of some more commonly used curve numbers is given in the following table

Curve numbers for antecedent moisture condition II (Source USDA-NRCS).
Link to this table

Land use description	Hydrologic soil group
	A	B	C	D
Meadow - good condition	30	58	72	78
Forest
Poor	45	66	77	83
Fair	36	60	73	79
Good	30	55	70	77
Open space
Poor	68	79	86	89
Fair	49	69	79	84
Good	39	61	74	80
Commercial 85% impervious	89	92	94	95
Industrial 72% impervious	81	88	91	93
Residential
1/8 acre lots (65% impervious)	77	85	90	92
1/4 acre lots (38% impervious)	61	75	83	87
1/2 acre lots (25% impervious)	54	70	80	85
1 acre lots (20% impervious)	51	68	79	84
Impervious areas	98	98	98	98
Roads (including right of way)
Paved	83	89	92	93
Gravel	76	85	89	91
Dirt	72	82	87	89
Row crops
Straight row - good	67	78	85	89
Contoured row - good	65	75	82	86
Pasture - good	39	61	74	80
Open water	99	99	99	99

The selection of appropriate curve numbers is of great importance when using the SCS Method. Sizing of facilities and comparisons of existing or pre-development conditions to proposed developed conditions can depend highly on the selected curve numbers. MPCA uses the land cover in place immediately before the proposed project as the “pre-development condition”. Many other regulators use a more natural condition to reflect change from pre-European settlement times. The hydrologic soil group of the native soils should be used for pre-development conditions, but developed conditions may alter the soil condition by compaction, fill, or soil amendments. In the more conservative, natural definition of pre-development condition, land use would be meadow or woods in good condition as appropriate to the natural state of the site. Special care should be taken to identify areas of soil group D and areas of open water as these areas have high levels of runoff. Care must also be taken when selecting curve numbers for agricultural land as its use can change considerably annually and even over the course of a season.

Composite Curve Numbers

According to the NRCS (TR-55, 1986), curve numbers describe average conditions for certain land uses. Urban area curve numbers are a composite of grass areas (assumed to be pasture in good condition) and directly connected impervious areas. TR-55 guidance documentation recommends that curve numbers be adjusted under certain conditions:

• When the percentage of impervious cover differs from the land use contained in curve number tables.
• When the impervious area is unconnected.
• When weighted curve number is less than 40.
• When computing snowmelt on frozen ground.

NRCS advises that the curve number procedure is less accurate when runoff is less than ½ inch. Other procedures should be followed to check runoff from these smaller events. One technique could be to compute runoff from pervious and impervious areas separately, with unique rather than composite curve numbers. Specific guidance is available in NRCS Technical Release 55 (available at NRCS National Water and Climate Center).

Antecedent Moisture Conditions

Antecedent moisture conditions (AMC) describe the moisture already present in the soil at the time of the rain event. AMC level I represents dry conditions, level II represents normal conditions, and level III represents wet conditions. Normal conditions are defined as 1.4 to 2.1 inches of rainfall in the growing season in the five days preceding the event of interest. Most evaluations of expected future site conditions use the curve numbers appropriate to AMC II. However, if the specific conditions of interest are expected to differ, curve numbers appropriate to AMC I or III should be used.

Infiltration rates

Infiltration is the process of water entering the soil matrix. The rate of infiltration depends on soil properties, vegetation, and the slope of the surface, among other factors. Discussions of infiltration often include a discussion of hydraulic conductivity. Hydraulic conductivity is a measure of ease with which a fluid flows through the soil, but it is not the infiltration rate. The infiltration rate can be determined using the hydraulic conductivity through the use of the Green-Ampt equation. The Green-Ampt equation relates the infiltration rate as it changes over time to the hydraulic conductivity, the pressure head, the effective porosity, and the total porosity. Typical values used in the Green-Ampt equation can be found in Rawls, et al. (1983).

A simple estimate of infiltration rates can be made based on the hydrologic soil group or soil texture. These infiltration rates represent the long-term infiltration capacity of a constructed infiltration practice and are not meant to exhibit the capacity of the soils in the natural state. The recommended design infiltration rates fit within the range of infiltration rates observed in infiltration practices operating in Minnesota.

The length of time a practice has been in operation, the location within the basin, the type of practice, localized soil conditions and observed hydraulic conditions all affect the infiltration rate measured at a given time and a given location within a practice. The range of rates reflects the variation in infiltration rate based on these types of factors.

Caution: The table for design infiltration rates has been modified. Field testing is recommended for gravelly soils (HSG A; GW and GP soils; gravel and sandy gravel soils). If field-measured soil infiltration rates exceed 8.3 inches per hour, the Construction Stormwater permit requires the soils be amended. Guidance on amending these soils can be found here.

Design infiltration rates, in inches per hour, for A, B, C, and D soil groups. Corresponding USDA soil classification and Unified soil Classifications are included. Note that A and B soils have two infiltration rates that are a function of soil texture.*
The values shown in this table are for uncompacted soils. This table can be used as a guide to determine if a soil is compacted. For information on alleviating compacted soils, link here. If a soil is compacted, reduce the soil infiltration rate by one level (e.g. for a compacted B(SM) use the infiltration rate for a B(MH) soil).
Link to this table

Hydrologic soil group	Infiltration rate (inches/hour)	Infiltration rate (centimeters/hour)	Soil textures	Corresponding Unified Soil ClassificationSuperscript text
A	Although a value of 1.63 inches per hour (4.14 centimeters per hour) may be used, it is Highly recommended that you conduct field infiltration tests or amend soils.b See Guidance for amending soils with rapid or high infiltration rates and Determining soil infiltration rates.		gravel sandy gravel	GW - Well-graded gravels, fine to coarse gravel GP - Poorly graded gravel
	1.63a	4.14	silty gravels gravelly sands sand	GM - Silty gravel SW - Well-graded sand, fine to coarse sand
	0.8	2.03	sand loamy sand sandy loam	SP - Poorly graded sand
B	0.45	1.14	silty sands	SM - Silty sand
	0.3	0.76	loam, silt loam	MH - Elastic silt
C	0.2	0.51	Sandy clay loam, silts	ML - Silt
D	0.06	0.15	clay loam silty clay loam sandy clay silty clay clay	GC - Clayey gravel SC - Clayey sand CL - Lean clay OL - Organic silt CH - Fat clay OH - Organic clay, organic silt

¹For Unified Soil Classification, we show the basic text for each soil type. For more detailed descriptions, see the following links: The Unified Soil Classification System, CALIFORNIA DEPARTMENT OF TRANSPORTATION (CALTRANS) UNIFIED SOIL CLASSIFICATION SYSTEM

• NOTE that this table has been updated from Version 2.X of the Minnesota Stormwater Manual. The higher infiltration rate for B soils was decreased from 0.6 inches per hour to 0.45 inches per hour and a value of 0.06 is used for D soils (instead of < 0.2 in/hr).
  

Source: Thirty guidance manuals and many other stormwater references were reviewed to compile recommended infiltration rates. All of these sources use the following studies as the basis for their recommended infiltration rates: (1) Rawls, Brakensiek and Saxton (1982); (2) Rawls, Gimenez and Grossman (1998); (3) Bouwer and Rice (1984); and (4) Urban Hydrology for Small Watersheds (NRCS). SWWD, 2005, provides field documented data that supports the proposed infiltration rates. (view reference list)
^aThis rate is consistent with the infiltration rate provided for the lower end of the Hydrologic Soil Group A soils in the Stormwater post-construction technical standards, Wisconsin Department of Natural Resources Conservation Practice Standards.
^bThe infiltration rates in this table are recommended values for sizing stormwater practices based on information collected from soil borings or pits. A group of technical experts developed the table for the original Minnesota Stormwater Manual in 2005. Additional technical review resulted in an update to the table in 2011. Over the past 5 to 7 years, several government agencies revised or developed guidance for designing infiltration practices. Several states now require or strongly recommend field infiltration tests. Examples include North Carolina, New York, Georgia, and the City of Philadelphia. The states of Washington and Maine strongly recommend field testing for infiltration rates, but both states allow grain size analyses in the determination of infiltration rates. The Minnesota Stormwater Manual strongly recommends field testing for infiltration rate, but allows information from soil borings or pits to be used in determining infiltration rate. A literature review suggests the values in the design infiltration rate table are not appropriate for soils with very high infiltration rates. This includes gravels, sandy gravels, and uniformly graded sands. Infiltration rates for these geologic materials are higher than indicated in the table.
References: Clapp, R. B., and George M. Hornberger. 1978. Empirical equations for some soil hydraulic properties. Water Resources Research. 14:4:601–604; Moynihan, K., and Vasconcelos, J. 2014. SWMM Modeling of a Rural Watershed in the Lower Coastal Plains of the United States. Journal of Water Management Modeling. C372; Rawls, W.J., D. Gimenez, and R. Grossman. 1998. Use of soil texture, bulk density and slope of the water retention curve to predict saturated hydraulic conductivity Transactions of the ASAE. VOL. 41(4): 983-988; Saxton, K.E., and W. J. Rawls. 2005. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions. Soil Science Society of America Journal. 70:5:1569-1578.

Infiltration rates observed in Minnesota.
Link to this table

Source of data	Range of infiltration rates (in/hr)	Number of monitoring sites	Brief description of site	Year construction	Monitoring dates
South Washington Watershed District	0.02 to 3.021	1	Infiltration trench located in regional basin CD-P85. These trenches are an average of 13 feet deep. Underlying material is sand and gravelly sand.	2004	1999 to 2005
Rice Creek Watershed District	0.03 to 0.59	4	Monitoring data collected at 3 rain gardens and an infiltration island located at Hugo City Hall. Soils in the basin consist of silty fine sand with a shallow depth to the water table. Trench receives significant pretreatment of stormwater prior to infiltration.	2002	2002 to 2003
Brown's Creek Watershed District	0.01 to 0.20	2	Monitoring data collected at 2 infiltration basins. Soils in the basins consist of silty sand and silt clay interspersed with clayey sandy silt.		2000 to 2005
Field's of St. Croix, Lake Elmo, MN.	0.02 to 0.14	3	Monitoring data collected at 3 infiltration basins located in a residential development. Soils in the basins consist of sandy loam and silt loam (HSG B).		2001 to 2003
Bradshaw Development, Stillwater, MN	0.26 to 0.28	1	Monitoring data collected in 1 infiltration basin located in a commercial develolment. Soils in the basin consist of a silty sand.	2005	2005
Gortner Ave. Rain Water Gardens, University of Minnesota, tested by St. Anthony Falls Laboratory, Water, Air, Soil Pollution, 2013: Assessment of the Hydraulic and Toxic Metal Capacities of Bioretention Cells After 2 to 8 Years of Service2	0.104 to 5.76	1	Assessment of 40 locations within one bioretention basin. Testing was conducted 2 years after installation. The Raingarden receives runoff from adjunct grassed areas and a street. The underlying soils consist of sandy loam and silt loam over sand.	2004	2006 and 2010
St. Anthony Falls Laboratory, Minnesota Local Road Research Board, Minnesota Department of Transportation	0.29 to 1.55	5	Five highway ditches studied, with up to 20 measurements taken at each highway ditch segment, for a total of 96 measurements.		2011-2012
JAWA, 2009: Performance Assessment of Rain Gardens	1.293	1	Stillwater Infiltration Basin, 65 measurements		2012
JAWA, 2009: Performance Assessment of Rain Gardens Water, Air, Soil Pollution, 2013: Assessment of the Hydraulic and Toxic Metal Capacities of Bioretention Cells After 2 to 8 Years of Service	2.663	1	Burnsville Rain Garden is 28 square meters and was constructed in 2003 in a residential neighborhood. Underlying soils are sandy loam over sand. In 2006, 23 infiltration measurements were taken in a single rain garden in the third year of operation.	2003	2006
JAWA, 2009: Performance Assessment of Rain Gardens Water, Air, Soil Pollution, 2013: Assessment of the Hydraulic and Toxic Metal Capacities of Bioretention Cells After 2 to 8 Years of Service	6.303	1	Cottage Grove Rain Garden is 70 square meters in area, constructed in 2002 to receive runoff from a parking lot. Underlying soils are sands and gravels. In 2006 in the fourth year of operation, 20 measurements were taken in the single rain garden.	2002	2006
JAWA, 2009: Performance Assessment of Rain Gardens Water, Air, Soil Pollution, 2013: Assessment of the Hydraulic and Toxic Metal Capacities of Bioretention Cells After 2 to 8 Years of Service	0.633	3	Ramsey-Metro Watershed District Rain Gardens range from 29 to 147 square meters. These were constructed in 2006 and receive runoff from commercial buildings and city streets. Underlying soils are sandy loam layers over sand. A total of 32 measurements were taken in the three rain gardens.	2006	2006 and 2010
JAWA, 2009: Performance Assessment of Rain Gardens	0.643	1	Thompson Lake Rain Garden is 278 square meters, and was constructed in 2003 to receive runoff from a parking lot. Underlying soils are loamy sands over sands and silt loams. A total of 30 measurements were taken in the single rain garden in the third year of operation.	2003	2006
JAWA, 2009: Performance Assessment of Rain Gardens	0.663	1	University of Minnesota Duluth Rain Garden is 1,350 square meters in area and was constructed in 2005 to receive runoff from a parking lot. Underlying soils consist of sandy loam over clay. A total of 33 measurements were taken in the second year of operation.	2005	2006
St. Anthony Falls Laboratory	0.463	1	Albertville Swale, 9		2012
Journal of Environmental Management, 2013: Remediation to improve infiltration into compact soils	0.94	1	French Regional Park was included in a study that tested the initial infiltration rates of highly traveled, compacted turf areas to assess whether modification of the soils would improve the infiltration capacity. The site is near a beach, in an area that previously had been a single family residential area. The results shown represent initial infiltration at 18 monitoring locations prior to soil modification. Soils are highly disturbed, consisting primarily of loam overlaying a clay loam.		2009
Journal of Environmental Management, 2013: Remediation to improve infiltration into compact soils	1.07	1	Maple Lake Park was included in a study that tested the initial infiltration rates of highly traveled, compacted turf areas to assess whether modification of the soils would improve the infiltration capacity. The site was a newly developed residential area that previously had been a sand/gravel excavation area. The results shown represent initial infiltration at 31 monitoring locations prior to soil modification. Soils at the time of testing were unknown.		2009
Journal of Environmental Management, 2013: Remediation to improve infiltration into compact soils	0.84	1	Lake Minnetonka Regional Park was included in a study that tested the initial infiltration rates of highly traveled, compacted turf areas to assess whether modification of the soils would improve the infiltration capacity. The site selected was assumed to be highly compacted due to the relatively small growth of the trees in addition to areas of bare soils and/or dying turf. The results shown represent initial infiltration at 14 monitoring locations prior to soil modification. Soils consisted of a loam layer over clay loams.		2009
St. Anthony Falls Laboratory	0.283	16	Woodland Cove, Minnetrista, 138 measurements	Planned development	2010

¹The high end of this range (3.1 inches per hour) is not representative of typical rates for similar soil types. This facility is periodically subject to 25 foot depths of water, is underlain by more than 100 feet of pure sand and gravel without any confining beds and the depth to the water table is greater than 50 feet below the land surface. In addition, two infiltration enhancement projects have been constructed in the bottom of the facility to promote infiltration: five dry wells and two infiltration trenches have been operating in CD-P85 at various periods of the monitoring program.
²Source: Optimizing Stormwater Treatment Practices
³Geometric mean. Source: Stormwater Research at University of Minnesota

Event mean concentrations

Event mean concentrations (EMCs) of a particular pollutant (i.e. total phosphorus, total suspended solids) are the expected concentration of that pollutant in a runoff event. Along with runoff volume, EMCs can be used to calculate the total load of a pollutant from a specific period of time. EMCs are frequently based on land use and land cover, with different predicted pollutant concentrations based on the land use and/or land cover of the modeled area.

Event mean concentrations for total phosphorus.
Link to this table

Land cover/land use	Range (mg/L)	Recommended value (mg/L)	Notes
Commercial	0.20 - 0.34	0.200	If applicable to models being used, adjust curve numbers/runoff coefficients when calculating loads
Industrial	0.23 - 0.55	0.235	If applicable to models being used, adjust curve numbers/runoff coefficients when calculating loads Adjust upward if specific phosphorus sources exist
Residential	0.26 - 0.38	0.325	Concentrations vary widely depending on local conditions
High-density/Multi-family residential	0.28 - 0.40	Calculate1	Insufficient information to recommend a specific emc Concentrations vary widely depending on local conditions
Medium density residential	0.18 - 0.40	Calculate1	Insufficient information to recommend a specific emc Concentrations vary widely depending on local conditions
Low density residential	0.24 - 0.40	Calculate1	Insufficient information to recommend a specific emc Concentrations vary widely depending on local conditions
Freeways/transportation	0.25 - 0.45	0.280	Concentrations vary widely depending on inputs Adjust upward for areas receiving large inputs of road salt or sediment or having very heavy traffic loads Adjust downward for low traffic areas or areas with reduced inputs (e.g. little road salt application, limited truck traffic)
Mixed	0.16 - 0.84	0.290	Residential land use was the primary land use in most studies that cited values for mixed land use If the study area can be delineated into specific land uses and impervious area for each land use is know, we recommend calculating the emc
Parks and recreation		Use value for open space or calculate	emc will be a function of vegetative cover Adjust upward if street tree canopy cover is high or pervious areas are primarily grass on compacted soils
Open space	0.12 - 0.31	0.190
Conventional roof	0.01 - 0.20	0.030
Institutional	0.14 - 0.422	See note	Use low values in range (0.200 mg/L or less) for facilities such as campuses, where there is considerable pervious area Use high values in range (0.30 mg/L or greater) for areas with considerable impervious surface, such as sports facilities or facilities with large parking areas
Forest/shrub/grassland	0.03 - 0.45	0.090	Concentrations are likely to vary with season in areas with fall leaf drop
Open water and wetlands		see Notes (next column)	If data exist, use the phosphorus concentration for the water body of interest If data for a specific lake do not exist, use data from similar lakes in the area emcs for wetlands will typically be higher than for lakes in an area. Consider using a value equal to 2 times the value for lakes in an area.
Cropland (row crops)	0.126-1.348	2	Median from our review = 0.533
Pasture	0.35-0.45	2

¹The link takes you to information on calculating event mean concentrations for areas with multiple land uses.
²Our literature review was not extensive enough to warrant a specific recommend emc for this land use

EMCs can range by an order of magnitude for a given land use, an it is therefore best to have site-specific or comparable local data for calibration purposes. The EMCs in the Pitt et al. study were from the National Stormwater Quality Database (NQSD, Version 1.1). Note also that EMCs are concentration data, which are only part of the overall loading equation. Although some land uses might have a high EMC, for example open space at 0.27 to 0.31 milligrams/liter, little runoff occurs from this land so overall phosphorus loading is low.

References

• Baker, J.M., 1997. Vanishing Ponds Not a Sure Sign of Spring. Agricultural Research, April, p.9.
• Bengtsson, L., 1981. Snowmelt-Generated Runoff in Urban Areas. In Proceedings, Second International Conference on Urban Storm Drainage, Urbana, Illinois, June 14-19, 1981, pp.444-451.
• Bouwer, H. and R. C. Rice. 1989. Effect of Water Depth in Ground-water Recharge Basins on Infiltration. Journal of Irrigation and Drainage Engineering, Vol. 115, No. 4, pp. 556-567.
• Buttle, J.M. and F. Xu, 1988. Snowmelt Runoff in Suburban Environments. Nordic Hydrology, 19:19-40.
• Caraco, D. and R. Claytor, 1997. Stormwater BMP Design Supplement for Cold Climates. Center for Watershed Protection, Ellicott City, Maryland.
• Dunne, T. and R.D. Black, 1971. Runoff Processes During Snowmelt. Water Resources Research, 7(5): 1160-1172.
• Granger, R.J., D.M. Gray and G.E. Dick, 1984. Snowmelt Infiltration to Frozen Soils. Canadian Journal of Earth Science, 21:669-677.
• Haan, C., B. Barfield, J. Hayes, 1994. Design Hydrology and Sedimentology for Small Catchments. Academic Press, San Diego.
• Hershfield, D.M., 1961. Rainfall Frequency Atlas of the United States for Durations from 30 Minutes to 24 Hours and Return Periods from 1 to 100 Years. U.S. Weather Bureau, Technical Publication 40.
• Huff and Angels, 1992. Bulletin 71. Midwest Climate Center.
• Metropolitan Council, 1995. Precipitation Frequency Analysis.
• Minnehaha Creek Watershed District, 2003. Hydrologic, Hydraulic, and Pollutant Loading Study.
• Minnesota Department of Transportation, Drainage Manual, 2000.
• Minnesota Department of Transportation, 1998. Intensity of Extreme Rainfall over Minnesota.
• Minnesota Pollution Control Agency, 2000. Protecting Water Quality in Urban Areas. Best Management Practices for Dealing with Storm Water Runoff from Urban, Suburban, and Developing Areas of Minnesota. St. Paul, MN.
• Natural Resources Conservation Service, 1986. Urban Hydrology for Small Watersheds, Technical Release 55 (TR-55).
• Natural Resources Conservation Service. 1975. Minnesota Hydrology Guide.
• National Resource Conservation Service, 1997. National Engineering Handbook, Part 630, Hydrology. NRCS National Water and Climate Center.
• Novotny, V., 1988. Modeling Urban Runoff Pollution During Winter and Off-Winter Periods. In Advances in Environmental Modeling, A. Marani (Editor), Elsevier Publishing, Amsterdam, pp.43-58.
• Pitt, R., A. Maestre, and R. Morquecho, 2004. The National Stormwater Quality Database (NSQD, Version 1.1). Department of Civil and Environmental Engineering. University of Alabama, Tuscaloosa.
• Rawls, W., D. Brakensiek, and N. Miller, 1983. Green-Ampt infiltration parameters from soils data. Journal of Hydraulic Engineering. Vol. 109, No. 1, pp. 62 – 70.
• Rawls, W., D. Brakensiek, and K. Saxton, 1982. Estimation of Soil Water Properties. Transactions of the American Society of Agricultural Engineers. Vol. 25, No. 5, pp. 1316 – 1320 and 1328.
• Rawls, W., D. Giminez, and R. Grossman, 1998. Use of Soil Texture, Bulk Density and Slope of water Retention Curve to Predict Saturated Hydraulic Conductivity. ASAE. Vol. 41(2), pp. 983 – 988.
• Soil Conservation Service, 1975. Hydrology Guide for Minnesota.
• U.S. Environmental Protection Agency, 1997. Compendium of Tools for Watershed Assessment and TMDL Development. EPQ 841-B-97-006, Office of Water (4503F), Washington, D.C.