The foundation of stormwater management is an understanding of how a particular land area and drainage system can affect or be affected by the stormwater passing through it. In particular, when alterations to the land area or drainage network are planned or being made, 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 these complex processes involving stormwater runoff.
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 proper design of stormwater Best Management Practices (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 monitoring conditions 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 governing protection of water and waterways. They are used, for example, to predict
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 a large number of situations.
The most commonly used stormwater models can generally be classified as either hydrologic, hydraulic, or water quality models.
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.
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.
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.
Models range from simple to complex. Modelers should refer to the page discussing available models for an up-to-date listing of models most frequently used in Minnesota. The following discussion provides a brief background on model input data and indicates where to find model input data. Simple models may require some of the following data be input, while complex models may require additional input that is not described in this section. Designers and modelers are encouraged to review the sources of the information presented for more detailed information.
Models are often required to predict the effects of a wide range of precipitation events, ranging from water quality events (equal to a precipitation depth of approximately 1 inch) to an extreme event such as back-to-back 100 year rainfalls. The purpose of this section is to describe the information that modelers will need to predict the rainfall and runoff for these events which, in turn, may be used to size a BMP, predict downstream effects, or some other hydraulic purpose. These events are further defined in the Unified Sizing Criteria section of the Minnesota Stormwater Manual.
Stormwater models are used for the fundamental purpose of evaluating existing stormwater facilities for both hydraulic performance and water quality performance, and sizing alternative facilities to meet level of service objectives. Historically stormwater designs were intended to convey the runoff from a major event, typically the 1 percent storm, casually termed the 100-year event. Design engineers typically make use of precipitation exceedance probabilities to calculate the risks associated with lack of storm sewer capacity, channel erosion, over-bank flooding, and extreme flooding. A storm magnitude with a return period (T) has the probability of being equaled or exceeded in any given year equal to 1/T. For example a “100-year” event at a given location has a 1/100 (or 1 percent) chance of being equaled or exceeded in any given year.
More recently, since the mid-1990’s, stormwater facilities must also be sized to manage the runoff from small storms, generally with precipitation volumes less than a 1-year event, for the purpose of treating the runoff to meet water quality objectives. Stormwater treatment and streambank erosion control require effective management of a long-term sequence of rainfall runoff events rather than performance under a single design storm event.
All stormwater models require the input of hydrologic parameters, typically in the form of rainfall; parameters to convert rainfall to runoff such as land use and infiltration; and relationships that yield equivalent rainfall-runoff volumes, intensities, durations and frequencies. The modeling approach will vary based on whether the model is set up to predict the runoff of a single event or a series of events, or a long-term continuous simulation. Single events are important for the sizing of a conveyance system and a BMP. Continuous simulation models are important when assessing the downstream effects of a stormwater discharge. For example channel erosion protection needs to be based more on continuous simulations of more frequent storms to properly represent the duration of erosive periods, particularly if detention used to control peak rate of runoff with limited volume control (WEF, 2012).
This section lays out the types of statistical precipitation data that is available.
Statistical evaluations of precipitation volume, intensity, duration, and frequency have been performed over the past 50 years, with the more recent evaluations benefiting from a larger, more statistically accurate database of measured precipitation data.
Until recently, the most commonly referenced precipitation frequency study in Minnesota was Technical Paper No. 40 (TP-40). The precipitation-frequency estimates in NOAA Atlas 14 supersede the estimates produced in TP-40, as well as those in TP-49 and NWS HYDRO-35. These historic studies are included for purpose of comparison and transition to Atlas 14, as required by the Minnesota Department of Transportation and an increasing number of watershed districts/watershed management organizations. Studies associated with approximating the precipitation statistics for areas within Minnesota are included below, along with their publication dates and links to their documentation.
In addition to these 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.
Prior to development of Atlas 14 in 2013 the more recent work listed above was developed to 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. The major differences between the NOAA Atlas 14 analysis and those performed in previous studies include the following.
In April 2013, the National Oceanographic and Atmospheric Administration (NOAA) released Atlas 14: Precipitation-Frequency Atlas of the United States, Volume 8, Version 2.0. The precipitation frequency estimates contained in Volume 8 were developed for eleven Midwestern states, including Minnesota. As part of this new analysis, precipitation depths for ten separate recurrence intervals and 19 separate storm durations were produced. The recurrence intervals and storm durations are noted below.
NOAA Atlas 14 recurrence intervals and storm durations
Link to this table.
|Recurrence intervals||Storm durations|
The results of the revised precipitation-frequency analysis can be found on NOAA’s Precipitation Frequency Data Server (PFDS). The site is intuitive, allowing the user to select any location within the State of Minnesota and obtain precipitation depths for the duration and frequency combinations noted in the table above. The site also provides several supplementary documents for the specific area that the user may find useful, including isopluvial maps, temporal distributions, and a seasonality analysis. MnDOT has produced several documents and presentations regarding the use of NOAA Atlas 14 and the PFDS that can be found on their website, including Technical Memorandum 15-10-B-02, which summarizes the shift to use of Atlas 14.
Snowfall was considered in the NOAA Atlas 14 analysis, both in the form of snow or when converted to snow water. However, it was determined that the difference in the final precipitation frequency estimates between snow forms was trivial for all stations across Minnesota for all durations. Also, although noted that a regional approach was completed during the statistical analysis by using precipitation values from nearby stations, the results on the PFDS are point estimates. In order to apply these precipitation estimates over an area (i.e., a watershed), appropriate areal reduction factors must be used. These reduction factors are typically a function of the size of area and the duration of the precipitation.
In general, the results of NOAA Atlas 14 show increased precipitation depths when compared to results published in TP-40, especially at higher recurrence intervals. Much of southern Minnesota show a precipitation estimate increase as much as 2 inches for the 100-year 24-hour event.
To show a comparison between the precipitation estimates from NOAA Atlas 14, TP-40, and Bulletin 71, six sites across Minnesota were selected.
The table below shows precipitation estimates (in inches) for the 24-hour duration from the three publications. Three recurrence intervals were selected, signifying the events that may be of interest for engineers when examining the risk associated with channel erosion (2-year event), storm sewer surcharging (10-year event), or flood hazard areas (100-year event). The table shows that precipitation estimates in NOAA Atlas 14 represent a 0 to 9 percent increase as compared to the other two studies for the 2-year event, a 0 to 15 percent increase for the 10-year event, and a 7 to 37 percent increase for the 100-year event.
Comparison of precipitation totals from TP-40, Bulletin 71, and Atlas 14. All values are in inches per hour.
Link to this table
|Recurrence interval||Method||Cloquet||Itasca county||Lamberton||Minneapolis-St. Paul Airport||Rochester Airport||St. Cloud Airport|
While NOAA Atlas 14 is considered a robust study that utilizes the most recent precipitation data, its use may not be required at this time. MnDOT is requiring the use of Atlas 14 for trunk highway projects, where feasible. Many watershed organizations throughout the state are in the process of reviewing the data in Atlas 14 and are considering whether to incorporate this new precipitation data into their requirements. Modelers are encouraged to contact the appropriate organization (e.g., MPCA, MNDOT, watershed organizations) for information on the usage of Atlas 14. The links below show the location of watershed organizations throughout the state and provide a link to their individual websites.
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.
Most models developed will need to assess the pollutant removal and volume management of a BMP. Small storms are the focus of these water quality requirements because research has shown that pollution migration associated with frequently occurring events accounts for a large percentage of the annual pollutant load. The Minnesota Construction General Permit (CGP) as well as certain local regulatory agencies will require that developments retain the runoff generated from a rainfall up to 1 inch in depth.
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 County, 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 (EOR, 2005).
The potential exists that a snowmelt event may need to be modeled for the purpose of assessing the pollutant removal by a BMP or for the pollutant loading from a large watershed containing multiple BMPs. The following technique may be utilized to analyze snowmelt.
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 (Lyons-Johnson, 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).
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).
Between 2011 and 2013, new performance goals were developed as part of the Minimal Impact Design Standards (MIDS). The precipitation depth is applied over all new and/or fully reconstructed impervious areas to determine the volume that must be retained on site for water quality purposes. The MIDS performance standards apply to sites creating 1 or more acres of impervious surface.
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 how 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. A plot of the distribution of the rainfall will determine the timing and extent of the peak rainfall, which is assumed to represent the timing of the peak runoff generated by the rainfall. This information is developed for models that predict the peak flow for the purpose of sizing the conveyance capacity of a storm sewer or a BMP.
Historic rainfall records are not considered reliable for the purpose of developing rainfall distribution curves (called hyetographs) over a wide range of watershed sizes that are typically evaluated in a single computer simulation. The most reliable approach to predict the resulting runoff from a rain event would be to assess the streamflow data against the rainfall to understand runoff. It is rare that streamflow records are available. Therefore hydrologists have developed approaches that are used to develop “synthetic” rainfall distributions to equate rainfall statistics to runoff statistics.
The advantage of using a synthetic event is that it is appropriate for determining both peak runoff rate and runoff volume. The most commonly used approaches are described in this section. A MnDOT memorandum recommends use of a rainfall distribution derived from Atlas 14 data or the NRCS MSE 3 distribution, both of which are described below.
The standard rainfall distribution used in Minnesota was the SCS (now the NRCS) Type II distribution. This distribution was developed in the 1960s using the precipitation-frequency estimates contained in Technical Paper No. 40 (described in Precipitation). The release of NOAA Atlas 14 provides updated and more reliable precipitation-frequency estimates. The Type II rainfall distribution was created using data over a large geographic area; therefore, the resulting peak discharge for a smaller area may be over- or under-estimated. Consequently, the use of the SCS Type II rainfall distribution is becoming archaic. The Minnesota Department of Transportation (MnDOT) is recommending the use of NOAA 14 data to create a nested rainfall distribution, as described below. Other distributions are available; the Huff Distribution (as described in Bulletin 71) and updated distributions from NRCS are described further in this section.
The precipitation-frequency data presented in NOAA Atlas 14 can be used directly to develop a rainfall distribution. The basis of the distribution is to nest the high-intensity, short duration precipitation values within the lower-intensity, longer duration values. For study basins with a time of concentration less than 24 hours, a 24-hour rainfall distribution is appropriate since the largest peak discharge is generally produced when the storm duration is equal to the time of concentration. A cumulative discharge curve for each desired frequency is produced by taking the fraction of each storm duration over the 24-hour duration and centering it around a 12-hour time period. The fractions of the NOAA Atlas 14 precipitation depths for each duration over the 24-hour duration are subsequently centered around the 50 percent ratio. Consult the MnDOT website for presentations and tutorials on how to create the nested rainfall distribution.
The Midwest Atlas, also called Bulletin 71, has not been widely used in Minnesota. The rainfall distribution information presented in this section is included for the purpose of comparison for those modelers utilizing Bulletin 71, or considering transitioning from Bulletin 71 to Atlas 14. The Bulletin 71 Rainfall distribution was developed by examining the time-distribution characteristics of storms in Illinois. According to Bulletin 71, the time-distribution relationships should be applicable to the other Midwestern states studied, including Minnesota. Four distribution curves were developed and classified as first-, second-, third-, and fourth-quartile storms based on which quarter of the total storm period the greatest percentage of the rainfall occurred. The area over which the rain fell was also examined. The following table shows the median time distributions for all four quartiles for both rainfall at a point, and rainfall over a 10- to 50-square mile area.
Cumulative percent storm rainfall Bulletin 71
Link to this table.
|Cumulative percent of storm time||Rainfall at a point||Rainfall on areas of 10 to 50 square miles (%)|
Modelers should assess which distribution is the best fit for the watershed being modeled. The Third Quartile distribution for the 24 hour event often is more appropriate for watersheds with longer times of concentration (10 to 50 square mile ones noted in Rainfall Frequency Atlas of the Midwest), while the First Quartile can be the better fit for a 6 hour distribution in a small urban watersheds of a few square miles or less.
A distribution similar study was completed in NOAA Atlas 14. The study noted that the majority of storms in the North Plains, which includes Minnesota, are first-quartile events, as shown in the following table. The NOAA Atlas 14 results presented on their Precipitation Frequency Data Server include temporal distributions for each of the quartile events for each area in Minnesota for the six-, 12-, 24-, and 96-hour durations.
Cumulative Percent of Storm Rainfall for Given Storm Type – Atlas 14. Source: U.S. Department of Commerce. NOAA Atlas 14, Precipitation-Frequency Atlas of the United States, Volume 8, Version 2, 2013.
Link to this table.
|Duration||All cases||First quartile cases||Second quartile cases||Third quartile cases||Fourth quartile cases|
|6-hour||8828||3967 (45%)||2547 (29%)||1554 (17%)||760 (9%)|
|12-hour||9010||4593 (51%)||2110 (23%)||1505 (17%)||802 (9%)|
|24-hour||8370||4170 (50%)||1765 (21%)||1378 (16%)||1057 (13%)|
|96-hour||8415 (47%)||3990 (47%)||1551 (18%)||1389 (17%)||1485 (18%)|
The NRCS has developed rainfall distribution curves for three NRCS distribution regions in Minnesota based on Atlas 14 data. These are available in spreadsheet format on the NRCS Minnesota website.
The figures below compare the SCS Type II, NOAA Atlas 14 Nested, and Huff (first quartile) cumulative rainfall distribution graphs for the 100-year event at the six locations that were assessed in 2005 for the first edition of the MN stormwater manual (EOR, 2005). Further information regarding rainfall distribution can be found on the NRCS Minnesota Hydrology and Hydraulics web page.
As described in the Rainfall Distribution of this Minnesota Stormwater Manual, rainfall records are typically used to estimate the rate and volume of runoff that is generated for both large and small rain events. There are many factors that influence the generation of runoff as well as the prediction of runoff. The generation of runoff is influenced by soil type, moisture conditions of the soil, area of impervious cover, land slope, and other physical features. Models will utilize techniques to predict runoff which require the input of some or all of the parameters described in this section.
The runoff coefficient is a unitless factor that represents the fraction of rainfall that becomes runoff. It is used in the Rational Method to estimate peak runoff rates for very small drainage areas, typically less than 50 acres (WEF, 2012). The simple equation for peak discharge (Q, in 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, with the smaller C values related to the smaller rain events. Tabled values for C are shown below for 5- to 10-year events.
Runoff coefficients for 5- to 10-year storms (Source: Haan et al., 1994)
Link to this table
|Land use description||Runoff coefficient (C)|
|< 5% slope||0.30|
|5 to 10% slope||0.35|
|> 10% slope||0.50|
|< 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|
|Multi-family||0.40 to 0.75|
|Single family||0.30 to 0.50|
|Impervious areas||0.70 to 0.95|
|< 5% slope||0.50|
|5 to 10% slope||0.60|
|> 10% slope||0.72|
|N 5% slope||0.30|
|5 to 10% slope||0.36|
|> 10% slope||0.42|
1For clay and silt loam soils.
Curve number (CN) is a unit-less parameter that represents the runoff potential of a specific land area for use in models that utilize the SCS method of predicting runoff. It was developed by the USDA Natural Resource Conservation Service (NRCS), formerly called the Soil Conservation Service (SCS). Although the name of the organization has changed, CN is still often referred to as “SCS Curve Numbers”. Curve numbers range from 0 to 100, with the smaller numbers representing low runoff potential and the higher numbers representing high runoff potential. The factors that are considered when selecting a CN include the following.
Additional information and CN tables are available from NRCS.
Curve number tables are published in TR-55 (Urban Hydrology for Small Watersheds), but are also available in textbooks and within modeling software.
|Meadow - good condition||30||58||72||78|
|Commercial 85% impervious||89||92||94||95|
|Industrial 72% impervious||81||88||91||93|
|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|
|Roads (including right of way)|
|Straight row - good||67||78||85||89|
|Contoured row - good||65||75||82||86|
|Pasture - good||39||61||74||80|
The selection of appropriate curve numbers is of great importance when using any model that predicts hydrology based on the SCS Method. Regulators including watershed management organizations, watershed districts, and municipalities often require that the post-construction rate and volume of runoff match the pre-construction rate and volume of runoff. The intent is to prevent degradation of wetland, lakes and streams that is commonly caused by additional runoff from developments, such as streambank erosion, increased frequency of flooding, etc.
The hydrologic soil group of the native soils should be used for existing conditions, but developed conditions may alter the soil condition by compaction, fill, or soil amendments. 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. A full description of curve numbers is available from the NRCS in the TR-55 documentation manual. Included in this document is a table of curve numbers and a formula for computing the curve number for conditions that are not contained in the summary table.
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:
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).
The NRCS has developed a Runoff Curve Number Computation Spreadsheet that can be used to develop a CN for sites that are being converted from a rural to urban land use.
The sizing of stormwater management facilities depends on the selected curve numbers or runoff coefficients. Development of a model that predicts the existing conditions can mean two things depending on the applicable regulation. Some regulators require an assessment of the land cover in place immediately before the proposed project (“pre-development” condition). Other regulators use a more natural condition to reflect change from pre-European settlement times (“pre-settlement” or “native” condition). There are reasons for selecting either condition, as described below; note, though, that the engineer should always check the local regulations and/or determine the implications of choosing either condition before selecting curve numbers.
When designing stormwater management facilities, runoff volumes are typically compared between proposed and existing conditions. The “pre-settlement” or “native” condition is the more conservative assumption for assessment of existing conditions. Much of Minnesota retains native conditions, as shown in studies documenting that forests and grasslands still cover up to 60 percent of many of the major watersheds in Minnesota (MIDS Workgroup). Native conditions reflect the presence of high infiltration and evapotranspiration rates that keep runoff volumes low in native land cover. If native conditions are used as the existing condition, then the difference between the pre-settlement and post-development volumes could be large, resulting in additional pond storage than may be necessary in pre-development conditions. Regulators that require post-project stormwater discharges match the native condition typically intend to match the natural water quality and rate conditions of downstream lakes and streams.
The pre-development condition is the assumption that land disturbance has previously occurred with the land use in place at the time of project initiation. This is the definition used under most circumstances by the MPCA in the Construction General Permit (CGP). Under this scenario, runoff conditions after construction need to match those of the land use immediately prior to the development using matching curve numbers or runoff coefficients. There is potential that a new project could improve runoff conditions, typically where the prior land use did not accommodate any runoff management. That is, implementation of good runoff management to an area that had previously developed without it would likely reduce total runoff amount compared to existing development. Note that the MPCA could alter its definition of pre-development under certain circumstances, such as a TMDL established load limit.
In pre-development conditions, non-native soils may have been introduced to the land, the existing soil may have been compacted for development, or the land may have been used for agricultural purposes. In these cases, the infiltration capacity has most likely been reduced. Care must be taken when selecting curve numbers or runoff coefficients to ensure the appropriate amount of runoff is produced that matches the site conditions (MIDS Workgroup). NRCS (TR-55) notes that heavily disturbed sites, including agricultural areas, curve numbers should be selected from the “Poor Condition” subset under the appropriate land use to account for common factors that affect infiltration and runoff. Lightly disturbed areas require no modification. Where practices have been implemented to restore soil structure, no permeability class modification is recommended.
In 2011, Barr Engineering developed a long-term simulation model using XP-SWMM for the purpose of estimating runoff volumes from a theoretical 10-acre site in three regions of Minnesota. The work was conducted at the request of the MIDS workgroup, who were interested in understanding the effectiveness of the various runoff volume performance goals commonly used in the state. This work assessed the differences between native hydrology and developed hydrology in order to understand if performance goals were effective in mimicking natural hydrology. The following conclusions were made.
Historic land use and land cover data is available at some of the links noted in the Resources for Model Input Data section.
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 5 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 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.
Models are used to predict the infiltration to estimate the volume of rainfall that does not become runoff and/or to estimate the volume of runoff that is infiltrated through a BMP. Often the infiltration rate of rainfall on a pervious surface is a component of the hydrologic approach used by the model selected. However, this is not true for the more complex modeling software, which may require the input of Green-Ampt or other infiltration parameters.
The infiltration rate is most often 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 rate can be made based on the soil texture. The infiltration rate represents the long-term infiltration capacity of a constructed infiltration practice and is 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.
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 Classification|
|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.||
|GW - well-graded gravels, sandy gravels
GP - gap-graded or uniform gravels, sandy gravels
GM - silty gravels, silty sandy gravels
SP - gap-graded or poorly graded sands
|0.45||1.14||SM - silty sands, silty gravelly sands|
|0.3||0.76||loam, silt loam||MH - micaceous silts, diatomaceous silts, volcanic ash|
|0.2||0.51||Sandy clay loam||ML - silts, very fine sands, silty or clayey fine sands|
GC - clayey gravels, clayey sandy gravels
*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 Wisconsin Department of Natural Resources Conservation Practice Standard: Site Evaluation for Stormwater Infiltration.
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 (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. Note that concentrations of most chemicals in stormwater show a positively skewed distribution, resulting in mean concentrations being larger than median concentrations. Thus, median concentrations found in this table are less than the mean concentrations shown below.
|Land use||Phosphorus EMC (mg/L)||TSS EMC (mg/L)|
|Residential - high density||0.3||0.4||0.5||120||140||160|
|Residential - low density||0.4||0.5||0.6||130||150||170|
|Residential - medium density||0.2||0.3||0.4||100||120||140|
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
In addition to the precipitation and other model input data presented in the Model Input Guidance Section, there is additional data that will be needed for development of stormwater models, including the following.
Statistical rainfall data that sets the design precipitation events is available from various sources. Modelers that are looking for site specific precipitation records for the purpose of calibration or modeling of a specific rain event in Minnesota should review the extensive data assembled by the Minnesota Climatology Work Group.
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 Minnesota Geospatial Information Office. To acquire detailed topographic data for a site, a local survey may need to be completed.
There is no single location to obtain storm sewer information. Municipalities or counties typically maintain data for public facilities. Private or non-available data will require a field survey.
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 Minnesota Geospatial Information Office, or from MetroGIS. Information on surficial geology can be obtained from the Minnesota Geological Survey.
Modelers will require either land use or land cover data, but rarely will require both for development of a model. 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 Minnesota Spatial Information Office, and MetroGIS.
Monitoring data is not required for model input. However, monitoring data is necessary if the model is to be calibrated to existing conditions. Other “low-tech” data that could be used to compare model results against actual conditions could include high water mark and other visual observations after a flood or other significant rain event. Water quality data is available from the MPCA. Streamflow data of certain monitored streams is available from the USGS and from the DNR.
Much of the data required is available at regional, state, and national resources listed below. Users are also encouraged to check local resources, such as watershed district, county, and / or city websites for local data.
Sources of data for use in stormwater models.
Link to this table
|Data||MetroGIS||Met Council||MN DNR2||MGS||MN geospatial information office||MPCA||NRCS||USGS Geospatial data gateway||USGS|
|Topographic data (contours, terrain models)||X||X||X||X||X|
|Statewide topographic data1||X||X||X||X|
|Recent terrain data (since 2010)||X|
|LiDAR / high resolution DEM (cell size < 3 meters)||X||X||X||X||X|
|Surficial geology data||X||X||X||X||X|
|Statewide land use / land cover data||X||X||X||X||X|
|Recent land use / land cover data (since 2010)||X||X||X|
|Historic land use / land cover||X||X|
|Future land use / land cover||X|
|Lake levels, groundwater, stream flow and water quality||X||X||X||X||X|
1Generally coarse topographic data; can be used to determine general drainage in region and/or in hydrologic investigations
2Historic information, only. Is available at the MN DNR Data Deli.