EQUIVALENCE BETWEEN TOPMODEL AND THE NRCS CURVE NUMBER METHOD IN PREDICTING VARIABLE RUNOFF SOURCE AREAS1

An equivalence is proposed between two rainfall‐runoff methods with a long history of use in the United States and Europe. In watersheds where variable source areas dominate runoff, the two methods can have comparable probability distribution functions of moisture deficit, and therefore predict similar saturated runoff source areas. A novel approach is introduced to determine the S parameter in the Natural Resources Conservation Service (NRCS) method. This approach constrains S by the physical soil and topography characteristics of the catchment and depth to water table. The NRCS curve number method is at the core of many rainfall‐runoff models in hydrology. As a simple lumped parameter method, it is often scrutinized because it is not obvious how to derive S from catchment hydromorphological characteristics. The novel approach provides a clear physical meaning for S, allowing better estimation of this parameter in humid shallow water table environments where the variable source area can be the dominant runoff mechanism.     

HYDROLOGIC PARAMETERIZATION OF WATERSHEDS FOR RUNOFF PREDICTION USING SWAT1

ABSTRACT: The use of continuous time, distributed parameter hydrologic models like SWAT (Soil and Water Assessment Tool) has opened several opportunities to improve watershed modeling accuracy. However, it has also placed a heavy burden on users with respect to the amount of work involved in parameterizing the watershed in general and in adequately representing the spatial variability of the watershed in particular. Recent developments in Geographical Information Systems (GIS) have alleviated some of the difficulties associated with managing spatial data. However, the user must still choose among various parameterization approaches that are available within the model. This paper describes the important parameterization issues involved when modeling watershed hydrology for runoff prediction using SWAT with emphasis on how to improve model performance without resorting to tedious and arbitrary parameter by parameter calibration. Synthetic and actual watersheds in Indiana and Mississippi were used to illustrate the sensitivity of runoff prediction to spatial variability, watershed decomposition, and spatial and temporal adjustment of curve numbers and return flow contribution. SWAT was also used to predict stream runoff from actual watersheds in Indiana that have extensive subsurface drainage. The results of this study provide useful information for improving SWAT performance in terms of stream runoff prediction in a manner that is particularly useful for modeling ungaged watersheds wherein observed data for calibration is not available.     

IMPROVED RAINFALL/RUNOFF ESTIMATES USING REMOTELY SENSED SOIL MOISTURE1

ABSTRACT: Remotely sensed soil moisture data measured during the Southern Great Plains 1997 (SGP97) experiment in Oklahoma were used to characterize antecedent soil moisture conditions for the Soil Conservation Service (SCS) curve number method. The precipitation‐adjusted curve number and the soil moisture were strongly related (r²= 0.70). Remotely sensed soil moisture fields were used to adjust the curve numbers and the runoff estimates for five watersheds, in the Little Washita watershed; the results ranged from 2.8 km² to 601.6 km². The soil moisture data were applied at two spatial scales, a finer one (800 m) measuring spatial resolution and a coarser one (28 km). The root mean square error (RMSE) and the mean absolute error (MAE) of the runoff estimated by the standard SCS method was reduced by nearly 50 percent when the 800 m soil moisture data were used to adjust the curve number. The coarser scale soil moisture data also significantly reduced the error in the runoff predictions with 41 percent and 28 percent reductions in MAE and RMSE, respectively. The results suggest that remote sensing of soil moisture, when combined with the SCS method, can improve rainfall runoff predictions at a range of spatial scales.     

WATERSHED WEIGHTING OF EXPORT COEFFICIENTS TO MAP CRITICAL PHOSPHOROUS LOADING AREAS1

ABSTRACT: The Export Coefficient model (ECM) is capable of generating reasonable estimates of annual phosphorous loading simply from a watershed's land cover data and export coefficient values (ECVs). In its current form, the ECM assumes that ECVs are homogeneous within each land cover type, yet basic nutrient runoff and hydrological theory suggests that runoff rates have spatial patterns controlled by loading and filtering along the flow paths from the upslope contributing area and downslope dispersal area. Using a geographic information system (GIS) raster, or pixel, modeling format, these contributing area and dispersal area (CADA) controls were derived from the perspective of each individual watershed pixel to weight the otherwise homogeneous ECVs for phosphorous. Although the CADA‐ECM predicts export coefficient spatial variation for a single land use type, the lumped basin load is unaffected by weighting. After CADA weighting, a map of the new ECVs addressed the three fundamental criteria for targeting critical pollutant loading areas: (1) the presence of the pollutant, (2) the likelihood for runoff to carry the pollutant offsite, and (3) the likelihood that buffers will trap nutrients prior to their runoff into the receiving water body. These spatially distributed maps of the most important pollutant management areas were used within New York's West Branch Delaware River watershed to demonstrate how the CADA‐ECM could be applied in targeting phosphorous critical loading areas.     

HYDROLOGY OF A SMALL IMPOUNDMENT AND WATERSHED IN CENTRAL OKLAHOMA1

ABSTRACT: West Bitter Creek floodwater retarding structure site 3 in South Central Oklahoma was instrumented and records obtained and analyzed to obtain information concerning an impoundment water budget that is useful to landowners and designers of these impoundments. On-site loss of water from the impoundment was only 17 percent of the inflow during three years when the annual precipitation averaged 26 inches and the annual inflow averaged 1.4 inches. Runoff from an eroded area with no farm ponds was about 70 percent greater per unit area than from a portion of the watershed where 71 percent of the drainage area was controlled by farm ponds. A previous study indicated, however, that the ponds were reducing runoff only 13 percent. Loss of top soil increases runoff considerably. Only 24 percent of the total runoff into the impoundment was base flow. The flow rate into the impoundment was less than 0.05 cfs 70 percent of the time, and the inflow rate exceeded 10 cfs only 1 percent of the time. SCS runoff curve numbers varied between 57 and 96 for the impoundment watershed with an inverse relation between precipitation amount and curve number apprently caused by partial area runoff from impervious and semi-impervious areas. A comparison of measured event runoff versus event runoff computed by the SCS curve numbers gave an r² of only 0.44. However, the total computed surface runoff for eight years of record was less than 1 percent below the measured runoff which indicated the curve number method was a good tool for predicting long term runoff for the watershed.     

ACCURACY AND PRECISION OF NRCS MODELS FOR SMALL WATERSHEDS1

ABSTRACT: In the last 30 years, the National Resource Conservation Service's TR‐55 and TR‐20 models have seen a dramatic increase in use for stormwater management purposes. This paper reviews some of the data that were originally used to develop these models and tests how well the models estimate annual series peak runoff rates for the same watersheds using longer historical data record lengths. The paper also explores differences between TR‐55 and TR‐20 peak runoff rate estimates and time of concentration methods. It was found that of the 37 watersheds tested, 25 were either over‐ or under‐predicting the actual historical watershed runoff rates by more than 30 percent. The results of this study indicate that these NRCS models should not be used to model small wooded watersheds less than 20 acres. This would be especially true if the watershed consisted of an area without a clearly defined outlet channel. This study also supports the need for regulators to allow educated hydrologists to alter pre‐packaged model parameters or results more easily than is currently permitted.     

USING CURVE NUMBERS TO DETERMINE BASELINE VALUES OF GREEN-AMPT EFFECTIVE HYDRAULIC CONDUCTIVITIES1

ABSTRACT: Since the trend in infiltration modeling is currently toward process-based approaches such as the Green-Ampt equation, more emphasis is being placed on methods of determining appropriate parameters for this approach. The SCS curve number method is an accepted and commonly used empirical approach for estimating surface runoff, and is based on numerous data from a variety of sources. The time and expense of calibrating process-based infiltration parameters to measured data are often prohibitive. This study uses curve number predictions of runoff to develop equations to estimate the “baseline” hydraulic conductivities (K_b) for use in the Green-Ampt equation. Curve number predictions of runoff were made for 43 soils. K_b values in the Water Erosion Prediction Project (WEPP) model were then calibrated so that the annual runoff predicted by WEPP was equal to the curve number predictions. These calibrated values were used to derive an equation that estimated K_b based on the percent sand, percent clay, and cation exchange capacity of the soil. Estimated values of K_b from this equation compared favorably with measured values and values calibrated to measured natural runoff plot data. WEPP predictions of runoff using both optimized and estimated values of K_b were compared to curve number predictions of runoff and the measured values. The WEPP predictions using the optimized values of K_b were the best in terms of both average error and model efficiency. WEPP predictions using estimated values of K_b were shown to be superior to predictions obtained from the curve number method. The runoff predictions all tended to be biased high for small events and low for larger events when compared to the measured data. Confidence intervals for runoff predictions on both an annual and event basis were also developed for the WEPP model.     

EVALUATION OF CURVE NUMBER PROCEDURES TO PREDICT RUNOFF IN GLEAMS1

ABSTRACT: Proper selection of curve number values will improve the capability of the SCS-Curve Number procedure in predicting runoff. Both CREAMS and GLEAMS models use the Smith and Williams (1980) approach of converting CNII (curve number value for average antecedent moisture conditions) into CNI (curve number value for dry antecedent moisture conditions) in calculating the soil retention parameter (S). CREAMS and GLEAMS have been found to under predict runoff because of the internal conversion of CNII to CNI. This study shows modifications of the GLEAMS model using CMI without converting it to CM and it also shows the seasonal curve number approaches with and without converting CNII to CNI. Results indicate that using CNII without internal conversion to CNI provides better runoff and erosion predictions than the original version of GLEAMS and versions with seasonal curve numbers when tested with four years of field data in the Coastal Plain physiographic region of Maryland.     

STORM RUNOFF PREDICTION BASED ON A SPATIALLY DISTRIBUTED TRAVEL TIME METHOD UTILIZING REMOTE SENSING AND GIS1

ABSTRACT: In this study, remotely sensed data and geographic information system (GIS) tools were used to estimate storm runoff response for Simms Creek watershed in the Etonia basin in northeast Florida. Land cover information from digital orthophoto quarter quadrangles (DOQQ), and enhanced thematic mapper plus (ETM+) were analyzed for the years 1990, 1995, and 2000. The corresponding infiltration excess runoff response of the study area was estimated using the U.S. Department of Agriculture (USDA), Natural Resources Conservation Service Curve Number (NRCS‐CN) method. A digital elevation model (DEM)/GIS technique was developed to predict stream response to runoff events based on the travel time from each grid cell to the watershed outlet. A comparison of predicted to observed stream response shows that the model predicts the total runoff volume with an efficiency of 0.98, the peak flow rate at an efficiency of 0.85, and the full direct runoff hydrograph with an average efficiency of 0.65. The DEM/GIS travel time model can be used to predict the runoff response of ungaged watersheds and is useful for predicting runoff hydrographs resulting from proposed large scale changes in the land use.     

SUSCEPTIBILITY OF INDIANA WATERSHEDS TO HERBICIDE CONTAMINATION1

ABSTRACT: An index of watershed susceptibility to surface water contamination by herbicides could be used to improve source water assessments for public drinking water supplies, prioritize watershed restoration projects, and direct funding and educational efforts to areas where the greatest environmental benefit can be realized. The goal of this study is to use streamflow and herbicide concentration data to develop and evaluate a method for estimating comparative watershed susceptibility to herbicide loss. United States Geological Survey (USGS) concentration data for five relatively water soluble herbicides (alachlor, atrazine, cyanazine, metolachlor, and simazine) were analyzed for 16 Indiana watersheds. Correlation was assessed between observed herbicide losses and: (1) a herbicide runoff index using GIS‐based land use, soil type, SCS runoff curve number, tillage practice, herbicide use estimates, and combinations of these factors; and (2) predicted herbicide losses from a non‐point source pollution model (NAPRA‐Web, an Internet‐based interface for GLEAMS). The highest adjusted R²value was found between herbicide concentration and the runoff curve number alone, ranging from 0.25 to 0.56. Predictions from the simulation model showed a poorer correlation with observed herbicide loss. This indicates potential for using the runoff curve number as a simple herbicide contamination susceptibility index.     

COMPARISON OF 1-D AND 2-D MODELING OF OVERLAND RUNOFF AND SEDIMENT TRANSPORT1

ABSTRACT: One-dimensional and two-dimensional modeling approaches were compared for their abilities in predicting overland runoff and sediment transport. Both 1-D and 2-D models were developed to test the hypothesis that the 2-D modeling approach could improve the model predictions over the 1-P approach, based on the same mathematical representations of physical processes for runoff and sediment transport. The models developed in this study were applied to overland areas with cross slopes. A hypothetical case and an experimental study reported by Storm (1991) were used. Based on the simulation results from the selected hypothetical case and experimental study, the 2-D model provided better representation of spatial distribution of flow depths and sediment concentrations than the 1-D model. However, no significant differences in predictions of total runoff volume and sediment yield at the outlet area were found between the 1-D and 2-D models.     

IMPACTS OF CALIFORNIA'S CLIMATIC REGIMES AND COASTAL LAND USE CHANGE ON STREAMFLOW CHARACTERISTICS1

ABSTRACT: To investigate the impacts of urbanization and climatic fluctuations on stream flow magnitude and variability in a Mediterranean climate, the HEC‐HMS rainfall/runoff model is used to simulate stream flow for a 14‐year period (October 1, 1988, to September 30, 2002) in the Atascadero Creek watershed located along the southern coast of California for 1929, 1998, and 2050 (estimated) land use conditions (8, 38 and 52 percent urban, respectively). The 14‐year period experienced a range of climatic conditions caused mainly by El Nino‐Southern Oscillation variations. A geographic information system is used to delineate the watershed and parameterize the model, which is calibrated using data from two stream flow and eight rainfall gauges. Urbanization is shown to increase peak discharges and runoff volume while decreasing stream flow variability. In all cases, the annual and 14‐year distributions of stream flow are shown to be highly skewed, with the annual maximum 24 hours of discharge accounting for 22 to 52 percent of the annual runoff and the maximum ten days of discharge from an average El Nino year producing 10 to 15 percent of the total 14‐year discharge. For the entire period of urbanization (1929 to 2050), the average increase in annual maximum discharges and runoff was 45 m³/s (300 percent) and 15 cm (350 percent), respectively. Additionally, the projected increase in urbanization from 1998 to 2050 is half the increase from 1929 to 1998; however, increases in runoff (22 m³/s and 7 cm) are similar for both scenarios because of the region's spatial development pattern.     

INFLUENCES OF SOIL DATASET RESOLUTION ON HYDROLOGIC MODELING1

ABSTRACT: Soils represent a fundamental abiotic parameter in defining the characteristics of an ecosystem. The U.S. Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) produces the most detailed digital spatial soil datasets that are publicly available. The Soil Survey Geographic (SSURGO) database contains basic attributes for the continuous coverage of soils across the United States. In its standard format, the SSURGO database is incompatible for use within the ArcView Soil and Water Assessment Tool (SWAT). A modified version of the State Soil and Geographic (STATSGO) database is the template soils dataset used by ArcView SWAT. This paper presents the methodology and development of a SSURGO database preprocessor extension for the ArcView SWAT model. A case study for the Upper Sabinal River Watershed near Uvalde, Texas, is given. Results indicate that hydro‐logic output parameter differences occur when comparing the STATSGO and SSURGO database information in the ArcView SWAT model under identical modeling conditions. Specifically, the SSURGO model produced a greater daily mean water yield with evapotranspiration and surface runoff being found consistently lower across the watershed. The most likely causes assigned to this phenomenon were higher percolation and resulting ground water return flow values due to significantly larger saturated hydraulic conductivity values associated with the SSURGO 2.x database.     

Time Series Analysis of Nebraska Daily Rainfall Data to Simulate Atrazine Runoff1

Abstract: Measured atrazine concentrations in Nebraska surface water have been shown to exceed water‐quality standards, posing risks to humans and to the ecosystem. To assess this risk, atrazine runoff was simulated at the field‐scale in Nebraska based on the pesticide component of the AGNPS model. This project’s objective was to determine the frequency that the atrazine concentration at the field outlet exceeded three different atrazine water‐quality criteria. The simulation was conducted for different farm management practices, soil moisture conditions, and five Nebraska topographic regions. If the criteria were exceeded, a risk to the drinking water consumer or freshwater aquatic life was hypothesized to exist. Three pesticide fate and transport processes were simulated with the model. Degradation was simulated using first‐order kinetics. Adsorption/desorption was modeled assuming a linear soil‐water partitioning coefficient. Advection (runoff) was based primarily on the USDA‐NRCS curve number method. Daily rainfall from the National Weather Service was used to compute the soil moisture conditions for the 1985‐2000 growing seasons. After each runoff event, the pesticide runoff concentration was compared with each of the three atrazine water‐quality criteria. The results show that environmental receptors (i.e., freshwater aquatic species) are exposed to unacceptable atrazine runoff concentrations in 20‐50% of the runoff events.     

TurfPQ, a pesticide runoff model for turf

Environmental assessments of golf courses and other turf systems must often rely on mathematical modeling. However, in the case of pesticide runoff, successful modeling applications are rare. Available models were developed for agricultural applications and have seen very limited testing for turf. TurfPQ is a pesticide runoff model developed exclusively for turf. The model is based on a curve number calculation for runoff volume and linear partitioning of pesticide into adsorbed and dissolved components during a precipitation or irrigation event. Calibration is optional, so the model can be applied, using default parameter values, to situations where runoff and chemical loss data are unavailable. TurfPQ was tested with default parameter values for 52 pesticide runoff events involving six pesticides measured in plot studies in four states. The model typically produced conservative overpredictions of pesticide runoff, particularly with strongly adsorbed pesticides. Mean predicted pesticide runoff was 2.9% [corrected] of application, compared with an observed mean of 2.1%. TurfPQ captured the dynamics of the pesticide runoff events well with R2 = 0.65 [corrected]. Sensitivity analyses indicated that prediction errors could be reduced by better estimates of adsorption parameters and runoff curve numbers. However, even with default parameters, TurfPQ predictions are at least as accurate as those produced by more complex models.     

ROLE OF BASEFLOW IN RAINFALL-RUNOFF RELATIONS1

ABSTRACT. The role of initial baseflow, or the baseflow at the beginning of storm precipitation, in modifying mathematical rainfall-runoff relations is analyzed by using data from 95 storms over a drainage basin in Illinois. A regression model is set up with total runoff, surface runoff, baseflow runoff, and peak flow as dependent variables, and storm precipitation, initial baseflow, effective and total storm durations, and highest and lowest temperatures during the storm as independent variables. Stepwise regression analyses show that storm precipitation and initial baseflow are the most important variables for making dependent variable estimates. The standard error estimates using only storm precipitation and initial baseflow as predictors show a seasonal trend with a peak in July, August, or September. An understanding of the role of baseflow as an indicator of average soil moisture condition over the basin can be of great help in short-term reservoir regulation and flood warning.     

WATERSHED SCALE MODELING OF CRITICAL SOURCE AREAS OF RUNOFF GENERATION AND PHOSPHORUS TRANSPORT1

ABSTRACT: A curve number based model, Soil and Water Assessment Tool (SWAT), and a physically based model, Soil Moisture Distribution and Routing (SMDR), were applied in a headwater watershed in Pennsylvania to identify runoff generation areas, as runoff areas have been shown to be critical for phosphorus management. SWAT performed better than SMDR in simulating daily streamflows over the four‐year simulation period (Nash‐Sutcliffe coefficient: SWAT, 0.62; SMDR, 0.33). Both models varied streamflow simulations seasonally as precipitation and watershed conditions varied. However, levels of agreement between simulated and observed flows were not consistent over seasons. SMDR, a variable source area based model, needs further improvement in model formulations to simulate large peak flows as observed. SWAT simulations matched the majority of observed peak flow events. SMDR overpredicted annual flow volumes, while SWAT underpredicted the same. Neither model routes runoff over the landscape to water bodies, which is critical to surface transport of phosphorus. SMDR representation of the watershed as grids may allow targeted management of phosphorus sources. SWAT representation of fields as hydrologic response units (HRUs) does not allow such targeted management.     

COMPOSITE VS. DISTRIBUTED CURVE NUMBERS: EFFECTS ON ESTIMATES OF STORM RUNOFF DEPTHS1

ABSTRACT: The U.S. Department of Agriculture Curve Number (CN) method is one of the most common and widely used techniques for estimating surface runoff and has been incorporated into a number of popular hydrologic models. The CN method has traditionally been applied using compositing techniques in which the area weighted average of all curve numbers is calculated for a watershed or a small number of sub-watersheds. CN compositing was originally developed as a time saving procedure, reducing the number of runoff calculations required. However, with the proliferation of high speed computers and geographic information systems, it is now feasible to use distributed CNs when applying the CN method. To determine the effect of using composited versus distributed CNs on runoff estimates, two simulations of idealized watersheds were developed to compare runoff depths using composite and distributed CNs. The results of these simulations were compared to the results of similar analyses performed on an urbanizing watershed located in central Indiana and show that runoff depth estimates using distributed CNs are as much as 100 percent higher than when composited CNs are used. Underestimation of runoff due to CN compositing is a result of the curvilinear relationship between CN and runoff depth and is most severe for wide CN ranges, low CN values, and low precipitation depths. For larger design storms, however, the difference in runoff computed using composite and distributed CNs is minimal.     

Evaluation of the MIKE SHE Model for Application in the Loess Plateau,China1

Abstract: Quantifying the hydrologic responses to land use/land cover change and climate variability is essential for integrated sustainable watershed management in water limited regions such as the Loess Plateau in Northwestern China where an adaptive watershed management approach is being implemented. Traditional empirical modeling approach to quantifying the accumulated hydrologic effects of watershed management is limited due to its complex nature of soil and water conservation practices (e.g., biological, structural, and agricultural measures) in the region. Therefore, the objective of this study was to evaluate the ability of the distributed hydrologic model, MIKE SHE to simulate basin runoff. Streamflow data measured from an overland flow‐dominant watershed (12 km²) in northwestern China were used for model evaluation. Model calibration and validation suggested that the model could capture the dominant runoff process of the small watershed. We found that the physically based model required calibration at appropriate scales and estimated model parameters were influenced by both temporal and spatial scales of input data. We concluded that the model was useful for understanding the rainfall‐runoff mechanisms. However, more measured data with higher temporal resolution are needed to further test the model for regional applications.