Original Soil Conservation Service Curve Number Research Articles

A comparison of the SCS-CN-based models for hydrological simulation of the Aghanashini River, Karnataka, India

Abstract This present study investigates different techniques for estimating the surface runoff using the Soil Conservation Service Curve Number (SCS-CN) method for the Aghanashini River in Karnataka, India. The SCS-CN method is a simplified approach for runoff estimation, but it does not take into account the actual moisture content in the soil. Consequently, insignificant moisture level changes could induce significant variations in the runoff. The study analyzes six different models based on the SCS-CN method, including the original SCS-CN model and several variations with added features (SCS-CN with slope correction, SCS-CN with λ-optimization, Mishra and Singh, Michel-Vazken -Perrin (MVP), Activation Soil Moisture Accounting SCS-CN). The accuracy of each model was compared using several goodness-of-fit statistics. Furthermore, based on the flood frequency analysis, three large flood events were reported in 2005, 2013, and 2014. The results showed that the MVP model was the best-performing method in simulating runoff. The outcomes of this study can provide valuable information to the local authorities in making informed decisions about flood forecasting and water conservation.

Evaluation of parameters of SCS-CN-based models for prediction of runoff from watersheds of Jharkhand, India

Soil Conservation Service Curve number (SCS-CN) method is one of the popular methods to estimate the volume of direct surface runoff for a given rainfall. Recently, an improved version of SCS-CN model called as Sahu-Mishra-Eldho (SME) model was reported incorporating hydrologically sounder procedure for accounting antecedent moisture in the Mishra-Singh (MS) model, a modified version of the SCS-CN model. The present study estimates and evaluates the model parameters of the SME model, the MS model and the original SCS-CN model for the rainfall-runoff datasets of the selected four watersheds of Jharkhand (India). The model parameters were estimated by using the non-linear Marquardt algorithm of constrained least squares. The results indicated that for MS model and SME model, the optimum value of in all the four watersheds is zero. The optimum values of S for MS model for Adda-1, Chitankhari, Indra and Karimati watersheds are found to be 266.78, 256.61, 194.47 and 233.72 mm, respectively while the optimum values of S0 for SME model are found to be 194.39, 329.60, 214.51 and 259.90 mm, respectively. Further, the optimum values of in SME model are found to be 0.35, 1.15, 0.80 and 0.87, respectively. The value of greater than 1 in Chitakhari watershed indicates that there is a seepage inflow to this watershed from the nearby watershed.

Activation soil moisture accounting (ASMA) for runoff estimation using soil conservation service curve number (SCS-CN) method

In this study, the concept of activation soil moisture (ASM) has been conceptualised by coupling the Soil Moisture Accounting (SMA) concept with the static infiltration component (Fc) for simulating rainfall-runoff process. The ASM has been defined as the height of soil moisture barrier (or the amount of soil moisture deficit), which must be fulfilled before runoff can start. Most of the SCS-CN inspired methods, including the original one do not consider ASM in their formulation to simulate rainfall-runoff process. To account for ASM, here, we develop an activation soil moisture accounting (ASMA) based method (ASMA-SCS-CN) by coupling the SMA concept of Michel-Vazken-Perrin (MVP) method with the static infiltration (Fc) based Mishra-Singh (MS) method, which presents a fuller picture of SMA system. The performance of the ASMA-SCS-CN method is compared with the original SCS-CN method, MS method and MVP method by applying a large dataset of 56,343 storm events from 164 small to large watersheds in the United States using goodness-of-fit statistics in terms of Nash-Sutcliffe efficiency (NSE), the root mean square error (RMSE), normalized RMSE (nRMSE), percent bias (PBIAS), mean absolute error (MAE), standard error (SE) and RMSE-observations standard deviation ratio (RSR). The ASMA-SCS-CN method has the highest median value of NSE (0.71; varying from 0.11 to 0.97) with inter-quartile range (IQR) as (0.62–0.80) followed by MVP with NSE (0.67; varying from 0.10 to 0.0.96) and IQR as (0.57–0.74), MS with NSE (0.61; varying from 0.02 to 0.97) and IQR range as (0.46–0.72), and SCS-CN with NSE (0.58; varying from 0,01 to 0.92) and IQR as (0.44–0.69). The ASMA-SCS-CN method is found to have lowest mean and median values of RMSE, nRMSE, MAE, SE and RSR than the MVP, MS and SCS-CN method. The PBIAS values of the ASMA-SCS-CN and MVP methods are lower than that of MS and SCS-CN method. In addition, the performance of all four methods is further evaluated based on the watershed characteristics such as landuse, soil type, drainage area, and mean rainfall and the results show that in all cases the ASMA-SCS-CN method performs much better than the rest of the methods. Overall, the improved performance of ASMA-SCS-CN can be attributed to the inclusion of SMA along with the static infiltration component for representing the complete picture of SMA system in modelling rainfall-runoff process.

Improved SMA-based SCS-CN method incorporating storm duration for runoff prediction on the Loess Plateau, China

Abstract In the Soil Conservation Service Curve Number (SCS-CN) method for estimating runoff, three antecedent moisture condition (AMC) levels produce a discrete relation between the curve number (CN) and soil water content, which results in corresponding sudden jumps in estimated runoff. An improved soil moisture accounting (SMA)-based SCS-CN method that incorporates a continuous function for the AMC was developed to obviate sudden jumps in estimated runoff. However, this method ignores the effect of storm duration on surface runoff, yet this is an important component of rainfall-runoff processes. In this study, the SMA-based method for runoff estimation was modified by incorporating storm duration and a revised SMA procedure. Then, the performance of the proposed method was compared to both the original SCS-CN and SMA-based methods by applying them in three experimental watersheds located on the Loess Plateau, China. The results indicate that the SCS-CN method underestimates large runoff events and overestimates small runoff events, yielding an efficiency of 0.626 in calibration and 0.051 in validation; the SMA-based method has improved runoff estimation in both calibration (efficiency = 0.702) and validation (efficiency = 0.481). However, the proposed method performed significantly better than both, yielding model efficiencies of 0.810 and 0.779 in calibration and validation, respectively.

An Event-Based Sediment Yield and Runoff Modeling Using Soil Moisture Balance/Budgeting (SMB) Method

The Soil Conservation Service Curve Number (SCS-CN) method is frequently used for the estimation of direct surface runoff depth from the small watersheds. Coupling the SCS-CN method with the Soil Moisture Balance (SMB) method, new simple 2-parameters rainfall-runoff model and 3-parametrs rainfall-sediment yield models are derived for computation of runoff and sediment yield respectively. The proposed runoff (R2) and sediment yield (S2) models have been tested on a large set of rainfall-runoff and sediment yield data (98 storm events) obtained from twelve watersheds from different land use/land cover, soil and climatic conditions. The improved runoff (R2) and sediment yield (S2) models show superior results as compared to the existing Mishra et al. (S1) and original SCS-CN (R1) models. The results and analysis justify the use of the proposed models for field applications.

Influence of land cover data sources on estimation of direct runoff according to SCS-CN and modified SME methods

The aim of this work was to assess the accuracy of direct runoff depth calculated according to an original Soil Conservation Service – Curve Number (SCS-CN) method and a modified Sahu-Mishra-Eldho (SME) method based on different sources of information on land cover. Main novelty of this work is using Database of topographic objects (BDOT10k) instead of Corine Land Cover (CLC) for the assessment of land cover in a catchment. The research was performed in the Kamienica river, a right tributary of the Dunajec, Poland. The effect of land cover data on runoff depth was assessed with the original SCS-CN method, and modified SME method. The value of CN parameter was estimated using two databases on land use, CLC and BDOT10k. BDOT10k was proven to be a considerably more precise land cover database that enabled more accurate interpretation. The study demonstrated the highest accuracy of direct runoff calculation with SME-CN method, irrespective of the approach used for determination of the catchment land use. The use of SME-CN instead of the original SCS-CN method provides much more accurate prediction of direct runoff in a catchment.

Spatially distributed long‐term hydrologic simulation using a continuous SCS CN method‐based hybrid hydrologic model

AbstractA continuous Soil Conservation Service (SCS) curve number (CN) method that considers time‐varied SCS CN values was developed based on the original SCS CN method with a revised soil moisture accounting approach to estimate run‐off depth for long‐term discontinuous storm events. The method was applied to spatially distributed long‐term hydrologic simulation of rainfall‐run‐off flow with an underlying assumption for its spatial variability using a geographic information systems‐based spatially distributed Clark's unit hydrograph method (Distributed‐Clark; hybrid hydrologic model), which is a simple few parameter run‐off routing method for input of spatiotemporally varied run‐off depth, incorporating conditional unit hydrograph adoption for different run‐off precipitation depth‐based direct run‐off flow convolution. Case studies of spatially distributed long‐term (total of 6 years) hydrologic simulation for four river basins using daily NEXRAD quantitative precipitation estimations demonstrate overall performances of Nash–Sutcliffe efficiency (ENS) 0.62, coefficient of determination (R2) 0.64, and percent bias 0.33% in direct run‐off and ENS 0.71, R2 0.72, and percent bias 0.15% in total streamflow for model result comparison against observed streamflow. These results show better fit (improvement in ENS of 42.0% and R2 of 33.3% for total streamflow) than the same model using spatially averaged gauged rainfall. Incorporation of logic for conditional initial abstraction in a continuous SCS CN method, which can accommodate initial run‐off loss amounts based on previous rainfall, slightly enhances model simulation performance; both ENS and R2 increased by 1.4% for total streamflow in a 4‐year calibration period. A continuous SCS CN method‐based hybrid hydrologic model presented in this study is, therefore, potentially significant to improved implementation of long‐term hydrologic applications for spatially distributed rainfall‐run‐off generation and routing, as a relatively simple hydrologic modelling approach for the use of more reliable gridded types of quantitative precipitation estimations.

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).

A Modified SCS-CN Method Incorporating Storm Duration and Antecedent Soil Moisture Estimation for Runoff Prediction

In one of the widely used methods to estimate surface runoff - Soil Conservation Service Curve Number (SCS-CN), the antecedent moisture condition (AMC) is categorized into three AMC levels causing irrational abrupt jumps in estimated runoff. A few improved SCS-CN methods have been developed to overcome several in-built inconsistencies in the soil moisture accounting (SMA) procedure that lies behind the SCS-CN method. However, these methods still inherit the structural inconsistency in the SMA procedure. In this study, a modified SCS-CN method was proposed based on the revised SMA procedure incorporating storm duration and a physical formulation for estimating antecedent soil moisture (V0). The proposed formulation for V0 estimation has shown a high degree of applicability in simulating the temporal pattern of soil moisture in the experimental plot. The modified method was calibrated and validated using a dataset of 189 storm-runoff events from two experimental watersheds in the Chinese Loess Plateau. The results indicated that the proposed method, which boosted the model efficiencies to 88% in both calibration and validation cases, performed better than the original SCS-CN and the Singh et al. (2015) method, a modified SCS-CN method based on SMA. The proposed method was then applied to a third watershed using the tabulated CN value and the parameters of the minimum infiltration rate (fc) and coefficient (β) derived for the first two watersheds. The root mean square error between the measured and predicted runoff values was improved from 6 mm to 1 mm. Moreover, the parameter sensitivity analysis indicated that the potential maximum retention (S) parameter is the most sensitive, followed by fc. It can be concluded that the modified SCS-CN method, may predict surface runoff more accurately in the Chinese Loess Plateau.

Improved SCS-CN Method Based on Storage and Depletion of Antecedent Daily Precipitation

The accurate simulation or prediction of storm runoff is one of the most important bases of water resource management and environmental quality assessment of water and soil. The soil conservation service-curve number (SCS-CN) method cannot effectively determine the effect of antecedent precipitation storage and depletion on runoff, which limits the accuracy of the method’s runoff prediction. Thus, potential initial abstraction and decay constant were developed in this study to improve the SCS-CN method. Potential initial abstraction determined the maximum rainfall storage before runoff and the threshold of daily effective rainfall, and the decay constant was used to describe the dynamic depletion of antecedent daily effective rainfall induced by evapotranspiration and seepage. The improved SCS-CN method was evaluated withthe runoff data observed in four cropping systems and three drainage areas, and it proved that the improved SCS-CN method predicted runoff more accurately than the original SCS-CN method. The inproved SCS-CN method increased both NSE and R2 values by more than 20 % for the four cropping systems, and increased R2 values by 11.9, 13.9 and 9.6 % for the plot, field, and catchment, respectively, compared with the original SCS-CN method. The decay constant did not vary with cropping systems and drainage areas.

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...

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.

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.