Soil Conservation Service Curve Number Model Research Articles

Characteristics of impervious surface and its effect on direct runoff: a case study in a rapidly urbanized area

Abstract In recent years, many cities have experienced serious urban flood and non-point pollution issues due to hydrological process changes in rapidly urbanizing areas. Understanding the relationship between impervious surface and direct runoff is important for urban planning to protect the urban hydrological system. In this study, we used a mixed spectral decomposition method to interpret the long-term series of impervious surface of Shenyang, China. Direct runoff was evaluated by an improved SCS-CN (Soil Conservation Service curve number) model, and the relative influences of five underlying surface factors on the direct runoff of each period were analyzed by boosted regression trees. The overall impervious area was significantly increased in both the study area and built-up area from 1984 to 2015. The impervious ratio showed a decreasing trend in the built-up area and increasing trend in the whole study area. The runoff coefficient of the built-up area showed a significantly decreasing trend. The runoff ratio of the built-up area to the whole study area was increased dramatically, reaching 0.26 by 2015. NDVI (normalized distribution vegetation index), vegetation, and impervious surface were the most important urban surface conditions in the study area for direct runoff generation. The relative influence of impervious surface showed a rapidly increasing trend and then gradually decreased from 2000.

Surface runoff prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA, and GIS-based SCS-CN models in tropical region

The effects of climate and land use/land cover (LULC) dynamics have directly affected the surface runoff and flooding events. Hence, current study proposes a full-packaged model to monitor the changes in surface runoff in addition to forecast of the future surface runoff based on LULC and precipitation variations. On one hand, six different LULC classes were extracted from Spot-5 satellite image. Conjointly, land transformation model (LTM) was used to detect the LULC pixel changes from 2000 to 2010 as well as predict the 2020 ones. On the other hand, the time series-autoregressive integrated moving average (ARIMA) model was applied to forecast the amount of rainfall in 2020. The ARIMA parameters were calibrated and fitted by latest Taguchi method. To simulate the maximum probable surface runoff, distributed soil conservation service-curve number (SCS-CN) model was applied. The comparison results showed that firstly, deforestation and urbanization have been occurred upon the given time, and they are anticipated to increase as well. Secondly, the amount of rainfall has non-stationary declined since 2000 till 2015 and this trend is estimated to continue by 2020. Thirdly, due to damaging changes in LULC, the surface runoff has been also increased till 2010 and it is forecasted to gradually exceed by 2020. Generally, model calibrations and accuracy assessments have been indicated, using distributed-GIS-based SCS-CN model in combination with the LTM and ARIMA models are an efficient and reliable approach for detecting, monitoring, and forecasting surface runoff.

An enhanced SMA based SCS-CN inspired model for watershed runoff prediction

Incorporation of initial soil moisture (V 0) in the Soil Conservation Service Curve Number (SCS-CN) methodology helps to avoid the sudden jumps in Curve Number (CN) and, in turn, in computed runoff. It invoked the development of an enhanced (yet simple) Soil Moisture Accounting (SMA) procedure-based-SCS-CN inspired model, by incorporating initial moisture (V 0). Its performance is tested using a dataset of 152 small to large watersheds of USDA (total 38,169 storm events), and compared with original SCS-CN method, Mishra and Singh (Acta Geophys Polon 50(3):457–477, 2002), Michel et al. (Water Resour Res 41(2):W02011, 2005) and Singh et al. (Water Resour Manag 29(11): 4111–4127, 2015) model using four statistical indices (RMSE, R 2, PBIAS and NSE) and rank grading system (RGS). The proposed model scores highest (= 691 marks out of maximum 2280 marks) (Rank I) followed by Singh et al. (Water Resour Manag 29(11):4111–4127, 2015) model with 642 marks (Rank II), Michel et al. (Water Resour Res 41(2):W02011, 2005) model with 376 marks (Rank III) and Mishra and Singh model with 362 marks (= Rank IV). The original SCS-CN model, however, performs the poorest of all with 209 marks (Rank V).

Improved Algorithm of SCS-CN Model Parameters in Typical Inland River Basin in Central Asia

Rainfall-runoff relationship is the most important factor for hydrological structures, social and economic development on the background of global warmer, especially in arid regions. The aim of this paper is find the suitable method to simulate the runoff in arid area. The Soil Conservation Service Curve Number (SCS-CN) is the most popular and widely applied model for direct runoff estimation. In this paper, we will focus on Wen-quan Basin in source regions of Boertala River. It is a typical valley of inland in Central Asia. First time to use the 16m resolution remote sensing image about high-definition earth observation satellite “Gaofen-1” to provide a high degree accuracy data for land use classification determine the curve number. Use surface temperature/vegetation index (TS/VI) construct 2D scatter plot combine with the soil moisture absorption balance principle calculate the moisture-holding capacity of soil. Using original and parameter algorithm improved SCS-CN model respectively to simulation the runoff. The simulation results show that the improved model is better than original model. Both of them in calibration and validation periods Nash-Sutcliffe efficiency were 0.79, 0.71 and 0.66,038. And relative error were3%, 12% and 17%, 27%. It shows that the simulation accuracy should be further improved and using remote sensing information technology to improve the basic geographic data for the hydrological model has the following advantages: 1) Remote sensing data having a planar characteristic, comprehensive and representative. 2) To get around the bottleneck about lack of data, provide reference to simulation the runoff in similar basin conditions and data-lacking regions.

Coupling Modified Linear Spectral Mixture Analysis and Soil Conservation Service Curve Number (SCS-CN) Models to Simulate Surface Runoff: Application to the Main Urban Area of Guangzhou, China

Land surface characteristics, including soil type, terrain slope, and antecedent soil moisture, have significant impacts on surface runoff during heavy precipitation in highly urbanized areas. In this study, a Linear Spectral Mixture Analysis (LSMA) method is modified to extract high-precision impervious surface, vegetation, and soil fractions. In the modified LSMA method, the representative endmembers are first selected by combining a high-resolution image from Google Earth; the unmixing results of the LSMA are then post-processed to reduce errors of misclassification with Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). The modified LSMA is applied to the Landsat 8 Operational Land Imager (OLI) image from 18 October 2015 of the main urban area of Guangzhou city. The experimental result indicates that the modified LSMA shows improved extraction performance compared with the conventional LSMA, as it can significantly reduce the bias and root-mean-square error (RMSE). The improved impervious surface, vegetation, and soil fractions are used to calculate the composite curve number (CN) for each pixel according to the Soil Conservation Service curve number (SCS-CN) model. The composite CN is then adjusted with regional data of the terrain slope and total 5-day antecedent precipitation. Finally, the surface runoff is simulated with the SCS-CN model by combining the adjusted CN and real precipitation data at 1 p.m., 4 May 2015.

Testing of Coupled SCS Curve Number Model for Estimating Runoff and Sediment Yield for Eleven Watersheds

Abstract: The soil conservation service (SCS) methodology for computing direct run-off, using soil-cover-moisture complexes involves the selection of a runoff curve number (CN) for such complex events. This method has been further simplified by introducing an assumption on initial abstraction, with only one unknown parameter CN, which is represented by the potential retention capacity of the watershed (S). In this study, coupled SCN-CN with USLE model was used for the estimation of the runoff and sediment yield for eleven watersheds of different land uses (urban, agricultural, and forest) from Damodar Valley Corporation (DVC), Hazaribagh district, Jharkhand, India. For the validation, runoff-sediment yield model is employed to a large set of rainfall-runoff-sediment yield data (68 storm events) observed from eleven watersheds. Model performance was assessed by using Nash and Sutcliffe statistical method. The efficiency of results was varying from 60.42 to 92.99 % for sediment yield and 54.23 to 96.12 % for runoff; this efficiency showed a reliable performance of model for estimating the sediment yield and runoff.

Assessment of the water balance of the Barekese reservoir in Kumasi, Ghana

The Barekese Reservoir constructed across the Offin River provides 80% of the total public pipe borne water supplied to the Kumasi metropolis and its environs. The reservoir was designed to produce both potable water and hydropower, however, the hydropower component has not been implemented since its construction in 1971.There is also reported land cover degradation in the catchment area which has the propensity to alter the hydrologic cycle and hence runoff into the reservoir. A 10 year water balance has been assessed for the Barekese Reservoir using an integrated Remote Sensing and GIS approach for estimation of surface runoff based on Soil Conservation Service Curve Number (SCS-CN). The SCS-CN model was calibrated against observed discharges recorded at Offinso located 10.3km upstream from Barekese and the result of the calibration used to simulate runoff into the reservoir. The SCS-CN model produced an R 2 value of 0.84 and an efficiency of 82.68%. Monthly observed reservoir levels were used for the calibration and validation of the water balance model. The water balance model produced an R 2 value of 0.84 and an efficiency of 81.9%. The monthly water budget revealed that total catchment runoff and direct precipitation respectively constituted 94.32% and 5.68% of the inflows while spilled water, water withdrawal and evaporation respectively amounted to 72.19%, 20.85% and 6.96% of the outflows. This result reveals that the reservoir is being underutilized. The current average production of treated water is 109,000m 3 day but the reservoir can safely yield the design capacity of 220,000m 3 day and an additional average hydropower of 368.6kW in six months during the rainy season provided the economic analysis for the hydropower generation is found to be justifiable. Keywords : Water balance, Barekese Reservoir, SCS-CN model, Offinso, Hydropower

Development of a Modified SMA Based MSCS-CN Model for Runoff Estimation

The Soil Conservation Service Curve Number (SCS-CN) method developed by the USDA-Soil Conservation Service (SCS, 1972) is widely used for the estimation of direct runoff for a given rainfall event from small agricultural watersheds. The initial soil moisture plays an important role in re-structuring of the SCS-CN method and enables us to prevent unreasonable sudden jump in runoff estimation and this has prompted the concept of soil moisture accounting (SMA) procedure to develop improved SCS-CN based models. Applying the concept of SMA procedure and changed parameterization, Michel et al. Water Resour Res 41(2):1–6 (2005) developed an improved SCS-CN model (MSCS-CN), which could be thought of an improvement over the existing SCS-CN method; however, their model still inherits several conceptual limitations and inconsistencies. Therefore, in this study an attempt is made to propose an improved SMA based SCS-CN-inspired model (MMSCS-CN) model incorporating a continuous function for initial soil moisture and test its suitability over the MSCS-CN and SCS-CN model using a large dataset from US watersheds. Using, Nash and Sutcliffe efficiency (NSE) and root mean square error (RMSE) of these models, the overall performance is further evaluated using rank grading system, and it is found that the MMSCS-CN scores highest mark (95; overall rank I) followed by MSCS-CN with 61 (overall rank II), and SCS-CN model with 51 mark (overall rank III) out of the maximum 105. This study shows that the proposed MMSCS-CN model has several advantages and performs better than the MSCS-CN and the existing SCS-CN model.

Impact of landuse/land cover change on run-off in a catchment of Narmada river in India

The landuse/land cover change has a significant influence on the hydrological response of a river basin. The run-off characteristics are naturally changing due to reduction of initial abstraction that increases run-off volume. Thus, it is necessary to quantify the changes in the run-off characteristics of a catchment that occur due to changes in landuse/land cover. These changes can be sudden or continuous. Physically based conceptual models have developed to find out surface run-off, and one of the models used in the present study is the Soil Conservation Service curve number (SCS-CN) model which has a broad range of applications. The present study is on the application of the SCS-CN model to analyse the impact of various landuse/land cover change in the run-off characteristics of a catchment of Narmada basin in Madhya Pradesh, India. The study includes the preparation of a landuse/land cover map of different years from the satellite imageries, drainage network, and development of a database by using a geographic information system (GIS) and a run-off generation with the model to estimate the impact of landuse change on run-off.

Simulating debris flow deposition using a two-dimensional finite model and Soil Conservation Service-curve number approach for Hanlin gully of southern Gansu (China)

Evaluating the flood discharge and deposition process of a debris flow is important for risk assessment, management, and design of possible supporting works for geo-hazard mitigation. The movement and deposition process of a typical debris flow gully in southern Gansu province, China, was simulated using the Soil Conservation Service-curve number (SCS-CN) approach and a two-dimensional finite model (FLO-2D PRO model) coupled with geographic information systems. Runoff volumes and depths were obtained by the use of the SCS-CN model using different precipitations and different intervals. The deposition, velocity, impact force, and influence zone of the debris flow were simulated with the FLO-2D PRO model based on the results of the SCS-CN method. Simulation results for a storm that occurred on 12 October 2010 suggest a maximum flow velocity of 23.1 m/s, a maximum deposition depth of 27.9 m, and a hazard zone of about 0.414 km2. These results were consistent with measured results from the documented debris flow. Verification demonstrated that model results could be used to help predict disaster-causing debris flows, thus helping to protect the lives, property, and economy of the local population.

Quantifying Excess Stormwater Using SCS-CN–Based Rainfall Runoff Models and Different Curve Number Determination Methods

AbstractEstimation of excess storm water is among the most basic hydrological challenges for hydrologists and engineers. The initial abstraction ratio λ (= 0.2) assumed in the original U.S. Soil Conservation Service curve number (SCS-CN) is ambiguous and must be calibrated from rainfall-runoff measurements for better runoff estimation. Eight different models including the original SCS-CN model, modified models inspired by it, and three newly proposed models were investigated to assess the accuracy of runoff estimation using rainfall-runoff measurements from 15 watersheds in South Korea. Different methods for CN determination were evaluated to see the effect of CN and λ on runoff estimation. The optimized λ and CN exhibited better results than fixed values as in the original SCS-CN model. Using three different goodness-of-fit statistics to assess the accuracy of the estimates, our proposed models outperformed in all watersheds in the study area when compared with the original SCS-CN model and some of its m...

Application of a continuous Rainfall-Runoff model to the basin of Kosynthos river using the hydrological software HEC-HMS

<div> <h1 style="text-align: justify;"><span style="font-size:11px;"><span style="font-family:arial,helvetica,sans-serif;">In this paper, the application of a continuous rainfall-runoff model to the basin of Kosynthos River (district of Xanthi, Thrace, northeastern Greece), as well as the comparison of the computational runoff results with field discharge measurements are presented. The rainfall losses are estimated by the widely known Soil Conservation Service-Curve Number model, while the transformation of rainfall excess into direct runoff hydrograph is made by using the dimensionless unit hydrograph of Soil Conservation Service. The baseflow is computed by applying an exponential recession model. The routing of the total runoff hydrograph from the outlet of a sub-basin to the outlet of the whole basin is achieved by the Muskingum-Cunge model. The application of this complex hydrologic model was elaborated with the HEC-HMS 3.5 Hydrologic Modeling System of the U.S. Army Corps of Engineers. The results of the comparison between computed and measured discharge values are very satisfactory.</span></span></h1> </div> <p> </p>

Validation of the rainfall-runoff SCS-CN model in a catchment with limited measured data in Zimbabwe

An evaluation of available opportunities to revive irrigation on a long abandoned irrigation scheme in a dry region of Zimbabwe is presented by assessing water availability at catchment level. The aim is to enhance the livelihoods, income and nutrition of the communities that depend on the irrigation scheme through a sustainable management of revitalised irrigation infrastructure and ensure food security. Runoff generated in the catchment, with potential to flow into the dam that supplies water to the scheme, is estimated using the Soil Conservation Service curve number (SCS-CN) model. The model simulates runoff at catchment level using daily rainfall data. An overview of the methodology and the various steps followed are provided. Daily rainfall and dam water levels are the only measured data available for the catchment. The dam water levels are used to determine the dam water volumes using rating tables. The dam water volumes are used to calculate the daily water inflows into the dam and these are compared with simulated water discharge rates obtained from the model. The plotted hydrographs of both simulated and measured values coincided very well in shape with great precision validating the SCS-CN model for simulating runoff in ungauged catchments. Key words: Irrigation, water availability, curve number, water balance, runoff, dam level.

Estimating Composite Curve Number Using an Improved SCS-CN Method with Remotely Sensed Variables in Guangzhou, China

The rainfall and runoff relationship becomes an intriguing issue as urbanization continues to evolve worldwide. In this paper, we developed a simulation model based on the soil conservation service curve number (SCS-CN) method to analyze the rainfall-runoff relationship in Guangzhou, a rapid growing metropolitan area in southern China. The SCS-CN method was initially developed by the Natural Resources Conservation Service (NRCS) of the United States Department of Agriculture (USDA), and is one of the most enduring methods for estimating direct runoff volume in ungauged catchments. In this model, the curve number (CN) is a key variable which is usually obtained by the look-up table of TR-55. Due to the limitations of TR-55 in characterizing complex urban environments and in classifying land use/cover types, the SCS-CN model cannot provide more detailed runoff information. Thus, this paper develops a method to calculate CN by using remote sensing variables, including vegetation, impervious surface, and soil (V-I-S). The specific objectives of this paper are: (1) To extract the V-I-S fraction images using Linear Spectral Mixture Analysis; (2) To obtain composite CN by incorporating vegetation types, soil types, and V-I-S fraction images; and (3) To simulate direct runoff under the scenarios with precipitation of 57mm (occurred once every five years by average) and 81mm (occurred once every ten years). Our experiment shows that the proposed method is easy to use and can derive composite CN effectively.

SCS-CN Based Quantification of Potential of Rooftop Catchments and Computation of ASRC for Rainwater Harvesting

Rooftop rainwater harvesting, among other options, play a central role in addressing water security and reducing impacts on the environment. The storm or annual storm runoff coefficient (RC/ASRC) play a significant role in quantification of potential of rooftop catchments for rainwater harvesting, however, these are usually selected from generic lists available in literature. This study explores methodology/procedures based on one of the most popular and versatile hydrological model, Soil Conservation Service Curve Number (SCS-CN) (SCS 1986) and its variants, i.e., Hawkins SCS-CN (HSCS-CN) model (Hawkins et al. 2001), Michel SCS-CN (MSCS-CN) model (Michel et al. Water Resour Res 41:W02011, 2005), and Storm Water Management Model-Annual Storm Runoff Coefficient (SWMM-ASRC) (Heaney et al. 1976) and compares their performance with Central Ground Board (CGWB) (CGWB 2000) approach. It has been found that for the same amount of rainfall and same rooftop catchment area, the MSCS-CN model yields highest rooftop runoff followed by SWMM-ASRC > HSCS-CN > SCS-CN > CGWB. However, the SCS-CN model has close resemblance with CGWB approach followed by HSCS-CN model, SWMM-ASRC, and MSCS-CN model. ASRCs were developed using these models and it was found that MSCS-CN model has the highest value of ASRC (= 0.944) followed by SWMM-ASRC approach (=0.900), HSCS-CN model (=0.830), SCS-CN model (=0.801), and CGWB approach (=0.800). The versatility of these models lies to the fact that CN values (according to rooftop catchment characteristics) would yield rooftop runoff and therefore ASRC values based on sound hydrological perception and not just on the empiricism. The models have inherent capability to incorporate the major factors responsible for runoff production from rooftop/urban, i.e., surface characteristics, initial abstraction, and antecedent dry weather period (ADWP) for the catchments and would be better a tool for quantification rather than just using empirical runoff coefficients for the purpose.

Coupling the modified SCS-CN and RUSLE models to simulate hydrological effects of restoring vegetation in the Loess Plateau of China

Abstract. Predicting event runoff and soil loss under different land covers is essential to quantitatively evaluate the hydrological responses of vegetation restoration in the Loess Plateau of China. The Soil Conservation Service curve number (SCS-CN) and Revised Universal Soil Loss Equation (RUSLE) models are widely used in this region to this end. This study incorporated antecedent moisture condition (AMC) in runoff production and initial abstraction of the SCS-CN model, and considered the direct effect of runoff on event soil loss by adopting a rainfall-runoff erosivity factor in the RUSLE model. The modified SCS-CN and RUSLE models were coupled to link rainfall-runoff-erosion modeling. The effects of AMC, slope gradient and initial abstraction ratio on curve number of SCS-CN, as well as those of vegetation cover on cover-management factor of RUSLE, were also considered. Three runoff plot groups covered by sparse young trees, native shrubs and dense tussock, respectively, were established in the Yangjuangou catchment of Loess Plateau. Rainfall, runoff and soil loss were monitored during the rainy season in 2008–2011 to test the applicability of the proposed approach. The original SCS-CN model significantly underestimated the event runoff, especially for the rainfall events that have large 5-day antecedent precipitation, whereas the modified SCS-CN model was accurate in predicting event runoff with Nash-Sutcliffe model efficiency (EF) over 0.85. The original RUSLE model overestimated low values of measured soil loss and underpredicted the high values with EF values only about 0.30. In contrast, the prediction accuracy of the modified RUSLE model improved with EF values being over 0.70. Our results indicated that the AMC should be explicitly incorporated in runoff production, and direct consideration of runoff should be included when predicting event soil loss. Coupling the modified SCS-CN and RUSLE models appeared to be appropriate for evaluating hydrological effects of restoring vegetation in the Loess Plateau. The main advantages, limitations and future study scopes of the proposed models were also discussed.

Application of the SCS-CN Model to Runoff Estimation in a Small Watershed with High Spatial Heterogeneity

For reasons of simplicity, the most commonly used hydrological models are based on the Soil Conservation Service Curve Number (SCS-CN) model, which is probably a good choice for the estimation of runoff on the Loess Plateau of China; however, the high spatial heterogeneity, mainly caused by a fragmented landform and variations in soil type, may limit its applicability to this region. Therefore, applicability of the SCS-CN model to a small watershed, Liudaogou on the plateau, was evaluated and the most appropriate initial abstraction ratio ( I a /S) value in the model was quantified by the inverse method. The results showed that the standard SCS-CN model was applicable to the estimation of runoff in the Liudaogou watershed and the model performance was acceptable according to the values of relative error and Nash-Sutcliffe efficiency. The most appropriate I a /S value for the watershed was 0.22 because with this modified I a /S value, the model performance was slightly improved. The model performance was not sensitive to the modification of the I a /S value when one heavy rainfall event (50.1 mm) was not considered, which implied that the model, using a standard I a /S value, can be recommended for the Liudaogou watershed because single rainfall events exceeding 50 mm seldom occurred in that region. The runoff amount predicted for the Liudaogou watershed by the SCS-CN model, using the modified I a /S value, increased gradually with increasing rainfall when rainfall values were lower than 50 mm, whereas the predicted amount increased rapidly when the rainfall exceeded 50 mm. These findings may be helpful in solving the problem of serious soil and water loss on the Loess Plateau of China.

Improved SCS-CN–Inspired Model

The present study enhances the Soil Conservation Service curve number (SCS-CN) predictions by improving the model structure, considering the following issues of concern: implementation of antecedent moisture condition procedure, fixation of initial abstraction ratio (λ) at 0.2, usage of the potential maximum retention parameter, and incorporation of storm intensity or duration in runoff estimation. A five-parameter M3 model is proposed, with storm duration and a new parameter Sabs (potential maximum retention), to overcome most of the above limitations prevailing in the SCS-CN model. For simplicity and practical applications obviating storm-duration data, a four-parameter M4 model is also proposed. The performance of the suggested and the available models has been evaluated using the data of 82 small watersheds in the United States of America. As demonstrated, the M3 model performs the best, whereas the conventional SCS-CN model performs the poorest among all the models studied.

Rainfall–runoff simulation using a normalized antecedent precipitation index

The normalized antecedent precipitation index (NAPI) model by Heggen for the prediction of runoff yield is analytically derived from the water balance equation. Heggen's model has been simplified further to a rational form and its performance verified with the Soil Conservation Service Curve Number (SCS-CN) model. The simplified model has three coefficients specific to a watershed, and requires two inputs: rainfall and the derived parameter, NAPI. The characteristic behaviour of the NAPI has resonance with the curve number (CN) of the SCS model. The proposed NAPI model was applied to three watersheds in the semi-arid region of India to simulate runoff yield. The model showed improved correlation between the observed and predicted runoff data compared to the SCS-CN model. The F test and paired t test also confirmed the reliability of the model with significance levels of 0.01 and 0.001%, respectively. The proposed model could be used successfully for rainfall–runoff modelling in a watershed. Citation Ali, S., Ghosh, N. C. & Singh, R. (2010) Rainfall–runoff simulation using a normalized antecedent precipitation index. Hydrol. Sci. J. 55(2), 266–274.

Comparative evaluation of SCS-CN-inspired models in applications to classified datasets

One of the popular methods for estimating the depth of surface runoff for a given rainfall event is the Soil Conservation Service Curve Number (SCS-CN) method. Of late, several inconsistencies in its soil moisture accounting procedure have been pointed out by Michel et al. (2005), and a more rational procedure suggested. Recently, a modification incorporating an expression for estimation of initial soil moisture store level, a crucial parameter, was suggested by Sahu et al. (2007). The present study compares this modification with the original SCS-CN model and the other available variants on a large set of data of 76 small agricultural watersheds of the United States and finally suggests an improved model. The comprehensive comparison between these models reveals the proposed improvement to perform better than all other versions in all classified applications based on land use, soil type, combinations of land use and soil type, and precipitation regimes. A simplified version of the model is further suggested for practical applications.