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.     

APPLICATION OF THE GREEN-AMPT INFILTRATION EQUATION TO WATERSHED MODELING1

ABSTRACT: A computer model was developed, based on the Green-Ampt infiltration equation, to computed rainfall excess for a single precipitation event. The model requires an estimate of parameters related to hydraulic conductivity, wetting front section, and fillable porosity of the soil layers. Values of parameters were estimated from soil textural averages or regression equations based on percent sand, percent clay, and porosity. Average values of effective porosity and wetting front suction were largely acceptable due to the relatively low variability and low model sensitivity to the parameters. Hydraulic conductivity was the most erratic constituent of the loss rate computation due to the high variability and the high sensitivity of the computed infiltration to the parameter. The performance of the Green-Ampt infiltration model was tested through a comparison with the SCS curve number procedure. Seven watersheds and 23 storms with precipitation of one inch or greater were used in the comparison. For storms with less than one inch of rainfall excess, the SCS curve number procedure generally gave the best results; however, for six of the seven storms with precipitation excess greater than one inch, the Green-Ampt procedure delivered better results. In this comparison, both procedures used the same initial abstractions. The separation of rainfall losses into infiltration, interception, and surface retention is, in theory, an accurate method of estimating precipitation excess. In the second phase of the study using nine watersheds and 39 storms, interception and surface retention losses were computed by the Horton equations. Green-Ampt and interception parameters were estimated from value sin the literature, while the surface retention parameter was calibrated so that the computed runoff volumes matched observed volumes. A relationship was found between the surface retention storage capacity and the 15-day antecedent precipitation index, month of year, and precipitation amount.     

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.     

PARAMETER UNCERTAINTY IN RAINFALL-RUNOFF MODELING FOR POLDER SYSTEM DESIGN1

ABSTRACT: An approach is developed for incorporating the uncertainty of parameters for estimating runoff in the design of polder systems in ungaged watersheds. Monte Carlo Simulation is used to derive a set of realizations of streamflow hydrographs for a given design rainstorm using the U. S. Soil Conservation Service (SCS) unit hydrograph model. The inverse of the SCS curve number, which is a function of the antecedent runoff condition in the SCS model, is the random input in the Monte Carlo Simulation. Monte Carlo realizations of streamfiow hydrographs are used to simulate the performance of a polder flood protection system. From this simulation the probability of occurrence of flood levels for a particular hydraulic design may be used to evaluate its effectiveness. This approach is demonstrated for the Pluit Polder flood protection system for the City of Jakarta, Indonesia. While the results of the application indicate that uncertainty in the antecedent runoff condition is important, the effects of uncertainty in rainfall data, in additional runoff parameters, such as time to peak, in the hydraulic design, and in the rainfall-runoff model selected should also be considered. Although, the SCS model is limited to agricultural conditions, the approach presented herein may be applied to other flood control systems if appropriate storm runoff models are selected.     

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.     

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.     

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.     

SOIL EROSION AND SEDIMENT YIELD PREDICTION ACCURACY USING WEPP1

ABSTRACT: The objectives of this paper are to discuss expectations for the Water Erosion Prediction Project (WEPP) accuracy, to review published studies related to WEPP goodness of fit, and to evaluate these in the context of expectations for WEPP's goodness of fit. WEPP model erosion predictions have been compared in numerous studies to observed values for soil loss and sediment delivery from cropland plots, forest roads, irrigated lands and small watersheds. A number of different techniques for evaluating WEPP have been used, including one recently developed where the ability of WEPP to accurately predict soil erosion can be compared to the accuracy of replicated plots to predict soil erosion. In one study involving 1,594 years of data from runoff plots, WEPP performed similarly to the Universal Soil Loss Erosion (USLE) technology, indicating that WEPP has met the criteria of results being “at least as good with respect to observed data and known relationships as those from the USLE,” particularly when the USLE technology was developed using relationships derived from that data set, and using soil erodibility values measured on those plots using data sets from the same period of record. In many cases, WEPP performed as well as could be expected, based on comparisons with the variability in replicate data sets. One major finding has been that soil erodibility values calculated using the technology in WEPP for rainfall conditions may not be suitable for furrow irrigated conditions. WEPP was found to represent the major storms that account for high percentages of soil loss quite well—a single storm application that the USLE technology is unsuitable for—and WEPP has performed well for disturbed forests and forest roads. WEPP has been able to reflect the extremes of soil loss, being quite responsive to the wide differences in cropping, tillage, and other forms of management, one of the requirements for WEPP validation. WEPP was also found to perform well on a wide range of small watersheds, an area where USLE technology cannot be used.     

ESTIMATING RUNOFF VOLUMES FROM URBAN AREAS1

ABSTRACT: Estimations of runoff volumes from urban areas can be made by the equation Q = a A σ(P_e– b), where Q is the runoff volume, a is the part of the total area A Contributing to runoff, P_e is the rainfall amount for a single event, and b is the initial rainfall losses. For the evaluation of a and b, rainfall/runoff measurements were made in five areas of sizes between 0.035 km² and 1.450 km². By linear regression analysis of rainfall volumes versus runoff volumes, the initial rainfall losses were found to vary from 0.38 mm to 0.70 mm for the different areas. The parts of the areas contributing to runoff were found to be proportional to the impermeable parts of the mas. The equation is applicable in urban areas with well defined paved surfaces and roofs and with a negligible amount of runoff from permeable 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.     

A COMPUTERIZED DATA BASE FOR FLOOD PREDICTION MODELING1

ABSTRACT: A computerized geographic information system (GIS) was created in support of data requirements by a hydrologic model designed to predict the runoff hydrograph from ungaged basins. Some geomorphologic characteristics (i.e., channel lengths) were manually measured from topographic maps, while other parameters such as drainage area and number of channels of a specified order, land use, and soil type were digitized and manipulated through use of the GIS. The model required the generation of an integrated Soil Conservation Service (SCS) curve number for the entire basin. To this end, soil associations and land use (generated from analysis of Landsat satellite data) were merged in the GIS to acquire a map representing SCS runoff curve numbers. The volume of runoff obtained from the Watershed Hydrology Simulation (WAHS) Model using this map was compared to the volume computed by hydrograph separation and found to be accurate within 19 percent error. To quantify the effect of changing land use on basin hydrology, the GIS was used to vary percentages from the drainage area from forest to bare soil. By changing the basin runoff curve numbers, significant changes in peak discharge were noted; however, the time to peak discharge remained essentially independent of change in area of land use. The GIS capability eliminated many of the more traditional manual phases of data input arid manipulation, thereby allowing researchers to concentrate on the development and calibration of the model and the interpretation of presumably more accurate results.     

Variable Source Area Hydrology Modeling with the Water Erosion Prediction Project Model

In nondegraded watersheds of humid climates, subsurface flow patterns determine where the soil saturates and where surface runoff is occurring. Most models necessarily use infiltration‐excess (i.e., Hortonian) runoff for predicting runoff and associated constituents because subsurface flow algorithms are not included in the model. In this article, we modify the Water Erosion Prediction Project (WEPP) model to simulate subsurface flow correctly and to predict the spatial and temporal location of saturation, the associated lateral flow and surface runoff, and the location where the water can re‐infiltrate. The modified model, called WEPP‐UI, correctly simulated the hillslope drainage data from the Coweeta Hydrologic Laboratory hillslope plot. We applied WEPP‐UI to convex, concave, and S‐shaped hillslope profiles, and found that multiple overland flow elements are needed to simulate distributed lateral flow and runoff well. Concave slopes had the greatest runoff, while convex slopes had the least. Our findings concur with observations in watersheds with saturation‐excess overland flow that most surface runoff is generated on lower concave slopes, whereas on convex slopes runoff infiltrates before reaching the stream. Since the WEPP model is capable of simulating both saturation‐excess and infiltration‐excess runoff, we expect that this model will be a powerful tool in the future for managing water quality.     

History and Development of the NRCS Lag Time Equation1

Abstract: Many of the hydrologic methods that are used in engineering practice today resulted from the Spring Flood of 1936, which blanketed the Northeastern portion of the United States. Because of the flood damage that was caused by this rainfall‐snowmelt event, many federal agencies including the U.S. Army Corp of Engineers and the Soil Conservation Service (SCS) implemented the hydrologic theories that were available in the literature at this time and developed hydrologic procedures for design flow estimation. Sherman had recently published his unit hydrograph theory in 1932, and later in 1938 Snyder, who had been charged by the Water Resource Council to develop a synthetic unit hydrograph, published his famous paper. The SCS unit hydrograph theory was developed by Victor Mockus in the late 1950s. Most if not all of the theories at that time reported the rainfall‐runoff process for floods as a surface phenomenon, and as such those theories all required some type of a timing parameter to estimate watershed response time. This article documents the development of the SCS lag equation.     

URBAN STORMWATER QUANTITY/QUALITY MODELING USING THE SCS METHOD AND EMPIRICAL EQUATIONS1

ABSTRACT: Runoff depth and pollutant loading (Biological Oxygen Demand [BOD₅], Total Suspended Solids [TSS], Total Kjeldahl Nitrogen [TKN] and lead [Pb]) computations of urban stormwater runoff from four small sites (i.e., 14.7–58.3 ac) in South Florida were performed using the Soil Conservation Service (SCS) hydrology method and empirical equations developed by the U.S. Environmental Protection Agency (EPA). Each site had different predominant land uses (i.e., low density residential, high density residential, highway and commercial). Quantity and quality data from 95 storm events at these sites were measured by the U.S. Geological Survey (USGS), and used for calibration of the methodology to derive appropriate input parameters. Calibrated input parameters were developed for each land use to test the applicability of the methodology in small sub-tropical urban watersheds, and to provide hydrologists with a way to select appropriate parameter values for planning studies. A total of 16 independent rainfall events were used for verification of the methodology. Comparisons of predicted versus measured data for both hydrographs and pollutant loadings were performed.     

PREDICTING STORMFLOW AND PEAKFLOW FROM SMALL BASINS IN HUMID AREAS BY THE R-INDEX METHOD1

ABSTRACT: A relatively simple nonlinear equation was fitted to 468 stormflows larger than 0.05 area inches on 11 forested basins from New Hampshire to South Carolina, providing a predictive method for use on forest and wildlands in humid regions. Stormflow in area inches (Q?) was: where R is the mean value of Q/P for all P larger than one inch, P is storm rainfall in inches, and I is the initial flow rate in ft³/sec/mi². S.E. was 0.3 inch of stormflow. Peakflow was similarly estimated, S.E. 26 ft³/sec/mi². The R-index method is proposed as a practical tool in forest and wildland management. Similar to the SCS runoff curve number method, the R-index method requires no prior assumptions about infiltration capacities of forest lands, but calls for the mapping of all first-order streams for the average storage capacity index R, i.e., the mean hydrologic response of the source areas. Tested against the runoff curve method on four independent basins, predictions by the R-index method were considerably more accurate when field information normally available to planners and managers was used in both methods.     

EFFECTS OF INITIAL ABSTRACTION AND URBANIZATION ON ESTIMATED RUNOFF USING CN TECHNOLOGY1

ABSTRACT: Few studies have been conducted to explore the effects of initial abstraction on estimated direct runoff despite the widespread use of the curve number (CN) method in many hydrologic models to estimate direct runoff. In this study, use of a 5 percent ratio of initial abstraction (I_a) to storage (S) to estimate daily direct runoff with modified CN values for a 5 percent I_a/S value was investigated using the Long‐Term Hydrologic Impact Assessment (L‐THIA) geographic information system (GIS). In addition, the effects on estimated runoff of altering the hydrologic soil group due to urbanization were investigated. The L‐THIA model was applied to the Indiana Little Eagle Creek watershed with 5 percent and 20 percent I_a/S values, considering hydrologic soil group alteration due to urbanization. The results indicate that uses of a 5 percent l_a/S and modified CN values and Hydrologic Soil Group D for urbanized areas in model runs can improve long term direct runoff prediction.     

ALAWAT: A spatially allocated watershed model for approximating stream,sediment, and pollutant flows in Hawaii,USA

The Ala Wai Canal Watershed Model (ALAWAT) is a planning-level watershed model for approximating direct runoff, streamflow, sediment loads, and loads for up to five pollutants. ALAWAT uses raster GIS data layers including land use, SCS soil hydrologic groups, annual rainfall, and subwatershed delineations as direct model parameter inputs and can use daily total rainfall from up to ten rain gauges and streamflow from up to ten stream gauges. ALAWAT uses a daily time step and can simulate flows for up to ten-year periods and for up to 50 subwatersheds. Pollutant loads are approximated using a user-defined combination of rating curve relationships, mean event concentrations, and loading/washoff parameters for specific subwatersheds, land uses, and times of year. Using ALAWAT, annual average streamflow and baseflow relationships and urban suspended sediment loads were approximated for the Ala Wai Canal watershed (about 10,400 acres) on the island of Oahu, Hawaii. Annual average urban suspended sediments were approximated using two methods: mean event concentrations and pollutant loading and washoff. Parameters for the pollutant loading and washoff method were then modified to simulate the effect of various street sweeping intervals on sediment loads.     

THE IMPORTANCE OF ACCURATE CURVE NUMBERS IN THE ESTIMATION OF STORM RUNOFF1

ABSTRACT: Storm runoff as calculated by the runoff curve number method is shown to be of varying sensitivity to both input rainfall and curve number. Using an assumed input error of 10%, a runoff error chart is given. Up to about 9 inches of rainfall, runoff is more sensitive to curve number than to rainfall. The importance of accurate curve number selection in this range is stressed.     

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.