Effects of Spatial Distribution of Prairie Vegetation in an Agricultural Landscape on Curve Number Values

The curve number (CN) method is used to calculate runoff in many hydrologic models, including the Soil and Water Assessment Tool (SWAT). The CN method does not account for the spatial distribution of land cover types, an important factor controlling runoff patterns. The objective of this study was to empirically derive CN values that reflect the strategic placement of native prairie vegetation (NPV) within row crop agricultural landscapes. CNs were derived using precipitation and runoff data from a seven‐year period for 14 small watersheds in Iowa. The watersheds were planted with varying amounts of NPV located in different watershed positions. The least squares and asymptotic least squares methods (LSM) were used to derive CNs using an initial abstraction coefficient (λ) of 0.2 and 0.05. The CNs were verified using leave‐one‐out cross‐validation and adjustment for antecedent moisture conditions (AMC) was tested. The asymptotic method produced CN values for watersheds with NPV treatment that were 8.9 and 14.7% lower than watersheds with 100% row crop at λ = 0.2 and λ = 0.05, respectively. The derived CNs produced Nash‐Sutcliffe efficiency values ranging from 0.4 to 0.7 during validation. Our analyses show the CNs verified best for the asymptotic LSM, when using λ of 0.05 and adjusting for AMC. Further, comparison of derived CNs against an area weighted CN indicated that the placement of vegetation does impact the CN value. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Curve Number Derivation for Watersheds Draining Two Headwater Streams in Lower Coastal Plain South Carolina,USA

The objective of this study was to assess curve number (CN) values derived for two forested headwater catchments in the Lower Coastal Plain (LCP) of South Carolina using a three‐year period of storm event rainfall and runoff data in comparison with results obtained from CN method calculations. Derived CNs from rainfall/runoff pairs ranged from 46 to 90 for the Upper Debidue Creek (UDC) watershed and from 42 to 89 for the Watershed 80 (WS80). However, runoff generation from storm events was strongly related to water table elevation, where seasonally variable evapotranspirative wet and dry moisture conditions persist. Seasonal water table fluctuation is independent of, but can be compounded by, wet conditions that occur as a result of prior storm events, further complicating flow prediction. Runoff predictions for LCP first‐order watersheds do not compare closely to measured flow under the average moisture condition normally associated with the CN method. In this study, however, results show improvement in flow predictions using CNs adjusted for antecedent runoff conditions and based on water table position. These results indicate that adaptations of CN model parameters are required for reliable flow predictions for these LCP catchments with shallow water tables. Low gradient topography and shallow water table characteristics of LCP watersheds allow for unique hydrologic conditions that must be assessed and managed differently than higher gradient watersheds.     

Calibration and Verification of SWMM for Low Impact Development

The Storm Water Management Model was used to simulate runoff and nutrient export from a low impact development (LID) watershed and a watershed using traditional runoff controls. Predictions were compared to observed values. Uncalibrated simulations underpredicted weekly runoff volume and average peak flow rates from the multiple subcatchment LID watershed by over 80%; the single subcatchment traditional watershed had better predictions. Saturated hydraulic conductivity, Manning's n for swales, and initial soil moisture deficit were sensitive parameters. After calibration, prediction of total weekly runoff volume for the LID and traditional watersheds improved to within 12 and 5% of observed values, respectively. For the validation period, predicted total weekly runoff volumes for the LID and traditional watersheds were within 6 and 2% of observed values, respectively. Water quality simulation was less successful, Nash–Sutcliffe coefficients >0.5 for both calibration and validation periods were only achieved for prediction of total nitrogen export from the LID watershed. Simulation of a 100‐year, 24‐h storm resulted in a runoff coefficient of 0.46 for the LID watershed and 0.59 for the traditional watershed. Results suggest either calibration is needed to improve predictions for LID watersheds or expanded look‐up tables for Green–Ampt infiltration parameter values that account for compaction of urban soil and antecedent conditions are needed.     

MONTHLY RUNOFF PREDICTIONS BASED ON RAINFALL FORECASTS IN A SMALL OKLAHOMA WATERSHED1

ABSTRACT: Conditions under which monthly rainfall forecasts translate into monthly runoff predictions that could support water resources planning and management activities were investigated on a small watershed in central Oklahoma. Runoff response to rainfall forecasts was simulated using the hydrologic model SWAT. Eighteen scenarios were examined that represented combinations of wet, average, and dry antecedent rainfall conditions, with wet, normal, and dry forecasted rainfall. Results suggest that for the climatic and physiographic conditions under consideration, rainfall forecasts could offer potential application opportunities in surface water resources but only under certain conditions. Pronounced wet and dry antecedent rainfall conditions were shown to have greater impact on runoff than forecasts, particularly in the first month of a forecast period. Large forecast impacts on runoff occurred under wet antecedent conditions, when the fraction of forecasted rainfall contributing to runoff was greatest. Under dry antecedent conditions, most of the forecasted rainfall was absorbed in the soil profile, with little immediate runoff response. Persistent three‐month forecasts produced stronger impacts on runoff than one‐month forecasts due to cumulative effects in the hydrologic system. Runoff response to antecedent conditions and forecasts suggest a highly asymmetric utility function for rainfall forecasts, with greatest decision‐support potential for persistent wet forecasts under wet antecedent conditions when the forecast signal is least dampened by soil‐storage effects. Under average and dry antecedent conditions, rainfall forecasts showed little potential value for practical applications in surface water resources assessments.     

Development of the Spatial Rainfall Generator (SRGEN) for the Agricultural Policy/Environmental Extender Model

Accurate spatial representation of climatic patterns is often a challenge in modeling biophysical processes at the watershed scale, especially where the representation of a spatial gradient in rainfall is not sufficiently captured by the number of weather stations. The spatial rainfall generator (SRGEN) is developed as an extension of the “weather generator” (WXGEN), a component of the Agricultural Policy/Environmental eXtender (APEX) model. SRGEN generates spatially distributed daily rainfall using monthly weather statistics available at multiple locations in a watershed. The spatial rainfall generator as incorporated in APEX is tested on the Cowhouse watershed (1,178 km²) in central Texas. The watershed presented a significant spatial rainfall gradient of 2.9 mm/km in the lateral (north‐south) directions based on four rainfall gages. A comparative analysis between SRGEN and WXGEN indicates that SRGEN performs well (PBIAS = 2.40%). Good results were obtained from APEX for streamflow (NSE = 0.99, PBIAS = 8.34%) and NO₃‐N and soluble P loads (PBIAS ≈ 6.00% for each, respectively). However, APEX underpredicted sediment yield and organic N and P loads (PBIAS: 24.75‐27.90%) with SRGEN, although its uncertainty in output was lower than WXGEN results (PBIAS: ?13.02 to ?46.13%). The overall improvement achieved in rainfall generation by SRGEN is demonstrated to be effective in the improving model performance on flow and water quality output.     

Discussion

Epps et al. (2013) derived Curve Number (CN) values for two forested headwater watersheds in the Lower Coastal Plain of South Carolina during the 2008‐2011 period from rainfall‐runoff data, resulting in 23 events for the Upper Debidue Creek (UDC) watershed and in 20 events for Watershed 80 (WS80). D'Asaro and Grillone analyzed the P, CN data of the UDC watershed finding an evident “complacent” behavior, characterized by a declining CN with increasing P but without approaching a stable value at large storms. In this case, the traditional runoff CN equation does not fit well with the rainfall‐runoff data that indicate a partial source area watershed behavior and are more aptly modeled by the equation introduced by D'Asaro and Grillone (2012), who introduced a C parameter in the well‐known runoff CN formula. The C value, that represents the source area (fraction of drainage area) of the basin that produces runoff with a fixed CN < 100, is here considered to vary with the water table elevation (WTE), to which it is well correlated.     

Sensitivity of SCS Models to Curve Number Variation1

ABSTRACT: The Soil Conservation Service (SCS) models, including the TR-20 computer program and the simplified methods in TR-55, are widely used in hydrologic design. The runoff curve number (CN), which is an important input parameter to SCS models, is defined in terms of land use tretments, hydrologic, condition, antecedent soil moisture, and soil type. The objective of this study was to evaluate the sensitivity of the SCS models to errors in CN estimates. The results show that the effects of CN variation decrease as the design rainfall depth increases, such as for the larger storm events. The value and use of the sensitivity curves are demonstrated using a comparison of Landsat and conventionally derived curve numbers for three watersheds in Pennsylvania.     

Reclaimed Mineland Curve Number Response to Temporal Distribution of Rainfall1

Warner, Richard C., Carmen T. Agouridis, Page T. Vingralek, and Alex W. Fogle, 2010. Reclaimed Mineland Curve Number Response to Temporal Distribution of Rainfall. Journal of the American Water Resources Association (JAWRA) 46(4): 724-732. DOI: 10.1111/j.1752-1688.2010.00444.x Abstract: The curve number (CN) method is a common technique to estimate runoff volume, and it is widely used in coal mining operations such as those in the Appalachian region of Kentucky. However, very little CN data are available for watersheds disturbed by surface mining and then reclaimed using traditional techniques. Furthermore, as the CN method does not readily account for variations in infiltration rates due to varying rainfall distributions, the selection of a single CN value to encompass all temporal rainfall distributions could lead engineers to substantially under- or over-size water detention structures used in mining operations or other land uses such as development. Using rainfall and runoff data from a surface coal mine located in the Cumberland Plateau of eastern Kentucky, CNs were computed for conventionally reclaimed lands. The effects of temporal rainfall distributions on CNs was also examined by classifying storms as intense, steady, multi-interval intense, or multi-interval steady. Results indicate that CNs for such reclaimed lands ranged from 62 to 94 with a mean value of 85. Temporal rainfall distributions were also shown to significantly affect CN values with intense storms having significantly higher CNs than multi-interval storms. These results indicate that a period of recovery is present between rainfall bursts of a multi-interval storm that allows depressional storage and infiltration rates to rebound.     

Impacts of Climate Variability and Land Use Alterations on Frequency Distributions of Terrestrial Runoff Loading to Coastal Waters in Southern California1

Abstract: The transport of water, sediment, dissolved and particulate chemicals, and bacteria from coastal watersheds affects the nearshore marine and estuarine waters. In southern California, coastal watersheds deliver water and associated constituents to the nearshore system in discrete pulses. To better understand the pulsed nature of these watersheds, frequency distributions of simulated runoff events are presented for: (1) three land use conditions (1929, 1998, 2050); (2) three time periods (all water years 1989‐2002), only El Nino years (1992, 1993, 1995, 1998); and only non‐El Nino years; and (3) three regions (watershed, uplands, and lowlands). At the watershed scale, there was a significant increase (>200%) in mean event runoff from 1929 to 2050 (0.4‐1.3 cm) due to localized urbanization, which shifted the dominant sources of runoff from the mountains in 1929 (78% of watershed runoff) to the coastal plane for 2050 conditions (51% of watershed runoff). Inter‐annual climate variability was strong in the rainfall and runoff frequency distributions, with mean event rainfall and runoff 66 and 60% larger in El Nino relative to non‐El Nino years. Combining urbanization and climate variability, 2050 land conditions resulted in El Nino years being five times more likely to produce large (>3.0 cm) runoff events relative to non‐El Nino years. Combining frequency distributions of event runoff with regional nutrient export relationships, we show that in El Nino years, one in five events produced runoff ≥2.5 cm and temporary nearshore nitrate and phosphate concentrations of 12 and 1.4 μM, respectively, or approximately 5‐10 times above ambient conditions.     

Using simulated rainfall to evaluate field and indoor surface runoff phosphorus relationships

While numerous studies have evaluated the efficacy of outdoor rainfall simulations to predict P concentrations in surface runoff, few studies have linked indoor rainfall simulations to P concentrations in surface runoff from agricultural fields. The objective of this study was to evaluate the capacity of indoor rainfall simulation to predict total dissolved P concentrations [TP(<0.45)] in field runoff for four dominant agricultural soils in South Dakota. Surface runoff from 10 residue-free field plots (2 m wide by 2 m long, 2-3% slope) and packed soil boxes (1 m long by 20 cm wide by 7.5 cm high, 2-3% slope) was compared. Surface runoff was generated via rainfall simulation at an intensity of 65 mm h(-1) and was collected for 30 min. Packed boxes produced approximately 24% more runoff (range = 2.8-3.4 cm) than field plots (range = 2.3-2.7 cm) among all soils. No statistical differences in either TP(<0.45) concentration or TP(<0.45) loss was observed in runoff from packed boxes and field plots among soil series (0.17 < P < 0.83). Three of four soils showed significantly more total P lost from packed boxes than field plots. The TP(<0.45) concentration in surface runoff from field plots can be predicted from TP(<0.45) concentration in surface runoff from the packed boxes (0.68 < r(2) < 0.94). A single relationship was derived to predict field TP(<0.45) concentration in surface runoff using surface runoff TP(<0.45) concentration from packed boxes. Evidence is provided that indoor runoff can adequately predict TP(<0.45) concentration in field surface runoff for select soils.     

Runoff Curve Numbers at the Agricultural Field‐Scale and Implications for Continuous Simulation Modeling

This study used monitoring in the waterways of agricultural fields to understand the use of the runoff curve number (CN) in continuous simulation models. The CN has a long history as a design tool for estimating runoff volumes for large, single storms on small watersheds, but its use in continuous simulation models to describe runoff from smaller storms and relatively small areas is more recent and controversial. We examined 788 nonwinter rainfall events on four agricultural fields over five years (2004‐2008) during which runoff was generated in 87 events. The largest 20 runoff events on each field generated approximately 90% of the total runoff volume. The runoff event CNs showed an inverse correlation with storm depth that could not consistently be explained by previous precipitation. We review how small areas of higher runoff generation within larger areas will systematically increase the apparent CN of the larger area as the storm size decreases. If this variation is not incorporated into a model explicitly, continuous simulation modelers must understand that when source areas are aggregated or when runoff generation is spatially variable, the overall CN is not unique when smaller storms are included in the calibration set.     

A Hydrologic Data Screening Procedure for Exploring Monotonic Trends and Shifts in Rainfall and Runoff Patterns

A thorough understanding of past and present hydrologic responses to changes in precipitation patterns is crucial for predicting future conditions. The main objectives of this study were to determine temporal changes in rainfall‐runoff relationship and to identify significant trends and abrupt shifts in rainfall and runoff time series. Ninety‐year rainfall and runoff time series datasets from the Gasconade and Meramec watersheds in east‐central Missouri were used to develop data screening procedure to assess changes in the rainfall and runoff temporal patterns. A statistically significant change in mean and variance was detected in 1980 in the rainfall and runoff time series within both watersheds. In addition, both the rainfall and runoff time series indicated the presence of nonstationary attributes such as statistically significant monotonic trends and/or change in mean and variance, which should be taken into consideration when using the time series to predict future scenarios. The annual peak runoff and the annual low flow in the Meramec watershed showed significant temporal changes compared to that in the Gasconade watershed. Water loss in both watersheds was found to be significantly increasing which is potentially due to the increase in groundwater pumping for water supply purposes.     

PCN BASED METAL PARTITIONING IN URBAN SNOWMELT,RAINFALL/RUNOFF,AND RIVER FLOW SYSTEMS1

ABSTRACT: Drawing an analogy between the popular Soil Conservation Service curve number (SCS‐CN) method based infiltration and metal sorption processes, a new partitioning curve number (PCN) approach is suggested for partitioning of heavy metals into dissolved and particulate bound forms in urban snowmelt, rainfall/runoff, and river flow environments. The parameters, the potential maximum desorption, ψ, and the PCN analogous to the SCS‐CN parameters S and CN, respectively, are introduced. Under the condition of snowmelt, PCN (or ψ) is found to generally rely on temperature, relative humidity, pH, and chloride content; during a rainstorm, ψ is found to depend on the alkalinity and the pH of the rainwater; and in the river flow situation, PCN is found to generally depend on the temperature, pH, and chloride content. The advantage of using PCN instead of the widely used partitioning parameter, K_d, is found to lie in the PCN's efficacy to distinguish the adsorption (or sorption) behavior of metals in the above snowmelt, rainfall/runoff, and river flow situations, analogous to the hydrological behavior of watersheds.     

THE APPLICATION OF HYDROLOGIC MODELS TO SMALL WATERSHEDS HAVING MILD TOPOGRAPHY1

ABSTRACT: The application of hydrologic models to small watersheds of mild topography is not well documented. This study evaluates the applicability of hydrologic models described by Huggins and the Soil Conservation Service to small watersheds by comparing the simulated and actual hydrograph for both gaged and ungaged situations. The annual maximum rainfall events plus storms exceeding 2.5 inches from 25 years of rainfall and runoff data for two small watersheds were selected for the model evaluations. These storms had a variety of patterns and occurred on many different watershed conditions. Simulated and actual hydrographs were compared using a parameter which contained volume, peak, and shape factors. One-half of the selected storms were used to calibrate the models. For both models, there were no significant differences between the simulated and actual runoff volumes and peak runoff rates. Parameters obtained during the calibration process and relationships developed to estimate antecedent moisture and to modify tabulated runoff curve numbers were used to simulate the runoff hydrograph from the remaining storms. These remaining storms or test storms were simulated only once in order to imitate an ungaged situation. In general, both the Huggins and SCS model performed similarly on the test storms, but the level of model performance was lower than that for the calibration storms. For both models, the two-day antecedent rainfall was more important than the five-day in determining antecedent moisture and modifying tabulated curve numbers. The time of concentration which resulted in good hydrograph simulations was about three times larger than that estimated using published empirical relationships.     

Characterization of Storm Flow Dynamics of Headwater Streams in the South Carolina Lower Coastal Plain1

Epps, Thomas H., Daniel R. Hitchcock, Anand D. Jayakaran, Drake R. Loflin, Thomas M. Williams, and Devendra M. Amatya, 2012. Characterization of Storm Flow Dynamics of Headwater Streams in the South Carolina Lower Coastal Plain. Journal of the American Water Resources Association (JAWRA) 1‐14. DOI: 10.1111/jawr.12000 Abstract: Hydrologic monitoring was conducted in two first‐order lower coastal plain watersheds in South Carolina, United States, a region with increasing growth and land use change. Storm events over a three‐year period were analyzed for direct runoff coefficients (ROC) and the total storm response (TSR) as percent rainfall. ROC calculations utilized an empirical hydrograph separation method that partitioned total streamflow into sustained base flow and direct runoff components. ROC ratios ranged from 0 to 0.32 on the Upper Debidue Creek (UDC) watershed and 0 to 0.57 on Watershed 80 (WS80); TSR results ranged from 0 to 0.93 at UDC and 0.01 to 0.74 at WS80. Variability in event runoff generation was attributed to seasonal trends in water table elevation fluctuation as regulated by evapotranspiration. Groundwater elevation breakpoints for each watershed were identified based on antecedent water table elevation, streamflow, ROCs, and TSRs. These thresholds represent the groundwater elevation above which event runoff generation increased sharply in response to rainfall. For effective coastal land use decision making, baseline watershed hydrology must be understood to serve as a benchmark for management goals, based on both seasonal and event‐based surface and groundwater interactions.     

FLOW PERSISTENCE AS A MODIFIER OF SEASONAL RUNOFF PATTERNS1

ABSTRACT: The effect of flow persistence on seasonal patterns of watershed runoff was modeled by using runoff of the immediate antecedent month as an index. Monthly runoff was expressed as a function of monthly rainfall, season of the year, and runoff of the antecedent month. The three independent variables were expressed functionally as sliding polynomials, thus producing a piece-wise, form-free, three-dimensional causative structure. A model form allowing complete interactivity of the three independent variables could not be optimized because of insufficient data with high values of both antecedent runoff and monthly rainfall. A model with reduced interactivity was successfully optimized. Data sets from five watersheds ranging from 0.14 to 398 square miles were analyzed. Results were presented as a series of contour maps that showed contours of monthly runoff in the data space of season and monthly rain. In the series of maps, the patterns of the runoff contours changed with changing values of antecedent runoff. During the wet season of the year the contours changed significantly with antecedent runoff, but changes in the dry season were minimal. The quantitative change of runoff was more readily portrayed with cross-sections through the contoured surfaces.     

Runoff Storage Potential of Drained Upland Depressions on the Des Moines Lobe of Iowa

We present estimates of the volumetric storage capacities of currently drained upland depressions and catchment depressional specific storage and runoff storage indices for the Des Moines Lobe of Iowa (DML‐IA) subregion of the Prairie Pothole Region of North America. Storage capacities were determined using hydrologically enforced Light Detection and Ranging‐derived digital elevation models, and a unique geoprocessing algorithm. Depressional specific storage was estimated for each 12‐digit Hydrologic Unit Code (HUC12) watershed in the region from total catchment‐specific depressional storage volume and catchment area. Runoff storage indices were calculated using catchment depressional specific storage values and estimates of the amount of rainfall likely to fall within each watershed during sub‐annual and 1‐, 2‐, 5‐, and 10‐year 24‐h events. The 173,171 identified drained depressions in the DML‐IA can store up to 903.5 Mm³ of runoff. Most of this capacity is in depressions located in the north of the region. Specific storage varies from nearly 109 mm in the younger landscapes to <10 mm in older more eroded areas. For 95% of the HUC12 watersheds comprising the region, depressional storage will likely be exhausted by rainfall‐derived runoff in excess of a 1‐year 24‐h event. Rainfall amounts greater than a 5‐year 24‐h event will exceed all available depressional storage. Therefore, the capacity of drained depressions in the DML‐IA to mitigate flooding resulting from infrequent, but large, storm events is limited.     

Pesticide transport pathways from a sloped Litchi orchard to an adjacent tropical stream as identified by hydrograph separation

This study was performed to identify the transport pathways of pesticides from a sloped litchi ( Sonn.) orchard to a nearby stream based on a three-component hydrograph separation (baseflow, interflow, surface runoff). Dissolved silica and electrical conductivity were chosen as representative tracers. During the study period (30 d), 0.4 and 0.01% of the applied mass of atrazine and chlorpyrifos, respectively, were detected in the stream after 151 mm of rainfall. Baseflow (80-96%) was the dominant hydrological flow component, followed by interflow (3-18%) and surface runoff (1-7%). Despite its small contribution to total discharge, surface runoff was the dominant atrazine transport pathway during the first days after application because pesticide concentrations in the surface runoff flow component declined quickly within several days. Preferential transport with interflow became the dominant pathway of atrazine. Because chlorpyrifos was detected in the stream water only twice, it was not included in the hydrograph separation. A feature of the surface runoff pathway was the coincidence of pesticide and discharge peaks. In contrast, peak concentrations of pesticides transported by interflow occurred during the hydrograph recession phases. Stormflow generation and pesticide transport depended on antecedent rainfall. The combination of high-resolution pesticide concentration measurements with a three-component hydrograph separation has been shown to be a suitable method to identify pesticide transport pathways under tropical conditions.     

Spatial and Temporal Evaluation of Hydrological Response to Climate and Land Use Change in Three South Dakota Watersheds

This study analyzed changes in hydrology between two recent decades (1980s and 2010s) with the Soil and Water Assessment Tool (SWAT) in three representative watersheds in South Dakota: Bad River, Skunk Creek, and Upper Big Sioux River watersheds. Two SWAT models were created over two discrete time periods (1981‐1990 and 2005‐2014) for each watershed. National Land Cover Datasets 1992 and 2011 were, respectively, ingested into 1981‐1990 and 2005‐2014 models, along with corresponding weather data, to enable comparison of annual and seasonal runoff, soil water content, evapotranspiration (ET), water yield, and percolation between these two decades. Simulation results based on the calibrated models showed that surface runoff, soil water content, water yield, and percolation increased in all three watersheds. Elevated ET was also apparent, except in Skunk Creek watershed. Differences in annual water balance components appeared to follow changes in land use more closely than variation in precipitation amounts, although seasonal variation in precipitation was reflected in seasonal surface runoff. Subbasin‐scale spatial analyses revealed noticeable increases in water balance components mostly in downstream parts of Bad River and Skunk Creek watersheds, and the western part of Upper Big Sioux River watershed. Results presented in this study provide some insight into recent changes in hydrological processes in South Dakota watersheds. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Managing Uncertainty in Runoff Estimation with the U.S. Environmental Protection Agency National Stormwater Calculator

The U.S. Environmental Protection Agency National Stormwater Calculator (NSWC) simplifies the task of estimating runoff through a straightforward simulation process based on the EPA Stormwater Management Model. The NSWC accesses localized climate and soil hydrology data, and options to experiment with low‐impact development (LID) features for parcels up to 5 ha in size. We discuss how the NSWC treats the urban hydrologic cycle and focus on the estimation uncertainty in soil hydrology and its impact on runoff simulation by comparing field‐measured soil hydrologic data from 12 cities to corresponding NSWC estimates in three case studies. The default NSWC hydraulic conductivity is 10.1 mm/h, which underestimates conductivity measurements for New Orleans, Louisiana (95 ± 27 mm/h) and overestimates that for Omaha, Nebraska (3.0 ± 1.0 mm/h). Across all cities, the NSWC prediction, on average, underestimated hydraulic conductivity by 10.5 mm/h compared to corresponding measured values. In evaluating how LID interact with soil hydrology and runoff response, we found direct hydrologic interaction with pre‐existing soil shows high sensitivity in runoff prediction, whereas LID isolated from soils show less impact. Simulations with LID on higher permeability soils indicate that nearly all of pre‐LID runoff is treated; while features interacting with less‐permeable soils treat only 50%. We highlight the NSWC as a screening‐level tool for site runoff dynamics and its suitability in stormwater management.