Improved SCS-CN Model Incorporating Storm Duration and Rainfall-based Initial Abstraction for Runoff Estimation

The need of incorporating storm intensity or duration in Soil Conservation Service Curve Number (SCS-CN) methodology for improved direct surface runoff estimation for a watershed has been highlighted by many engineers and hydrologists since long and despite this fact, it is still poorly explored. Therefore, this study aims to present storm duration-based improved SCS-CN models for estimating more accurate direct surface runoff from rainfall events. The accuracy and consistency of improved models are tested on a large rainfall-runoff dataset (18,660 rainfall events) derived from 39 watersheds in the USDA-ARS. Furthermore, the quantitative model’s performance is also evaluated employing six widely accepted statistical measures viz. root mean square error (RMSE), mean absolute error (MAE), normalized root mean square error (NRMSE), Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), observations standard deviation ratio (RSR), and several grading criteria. These models are compared with the original SCS-CN model (M1) and its simple form (M2) with traditionally fixed initial abstraction ratio (λ) as 0.2. The resulting lowest values of RMSE, NRMSE, MAE, NSE, PBIAS and highest values of RSR and ranking grading system (RGS) for the proposed models (M3-M5) reveal that their performance is better than M1 and M2 models. The proposed M5 model incorporating both storm duration and varying initial abstraction (Ia) as a certain percentage of rainfall, performed the best followed by M3 incorporating only storm duration. According to RGS, M5 also ranked first with the highest marks (195) followed by M3 (140). Due to high accuracy in predicted runoff, M5 can be recommended for both small and large watersheds as it overcomes the following issues: fixed λ (=0.2), assumption of constant rainfall intensity (time-independent), fixation of Ia at 2% of rainfall and applicability to only small watersheds, restricting the application of original SCS-CN and its modified versions.

Modified NRCS Abstraction Method for Flood Hydrograph Generation

The NRCS abstraction method is based on two assumptions. The first is that the ratio of actual water retention after ponding to maximum potential retention after ponding is equal to the ratio of actual surface runoff to potential surface runoff. The second assumption is that the initial abstraction for the watershed is twenty percent of the maximum potential retention. This study shows that both assumptions violate continuity principles and proposes a modification that renders an elementary relationship accounting for all abstraction forms by dividing them into a variable and constant components. Consequently, the surface runoff computation becomes dependent on the soil initial moisture content and implicitly influenced by the initial abstraction, while retaining the advantage of the subjective selection of curve number from extensive database from which the NRCS method has gained popularity. A new time of concentration model is also proposed to extend the computation for flood hydrograph generation.

Application of the SCS–CN Method to the Hancheon Basin on the Volcanic Jeju Island, Korea

This study investigates three issues regarding the application of the SCS–CN (Soil Conservation Service–Curve Number) method to a basin on the volcanic Jeju Island, Korea. The first issue is the possible relation between the initial abstraction and the maximum potential retention. The second is the determination of the maximum potential retention, which is also closely related to the estimation of CN. The third issue is the effect of the antecedent soil moisture condition (AMC) on the initial abstraction, maximum potential retention and CN. All of these issues are dealt with based on the analysis of several rainfall events observed in the Hancheon basin, a typical basin on Jeju Island. In summary, the results are that, firstly, estimates of initial abstraction, ratio λ, maximum potential retention, and CN were all found to be consistent with the SCS–CN model structure. That is, CN and the maximum potential retention showed a strong negative correlation, and the ratio λ and the maximum potential retention also showed a rather weak negative correlation. On the other hand, a significant positive correlation was found between CN and the ratio λ. Second, in the case where the accumulated number of days is four or five, the effect of antecedent precipitation amount is clear. The antecedent five-day rainfall amount for the AMC-III condition is higher than 400 mm, compared to the AMC-I condition of less than 100 mm. Third, an inverse proportional relationship is found between the AMC and the maximum potential retention. On the other hand, a clear linear proportional relation is found between the AMC and CN. Finally, the maximum potential retention for the Hancheon basin is around 200 mm, with the corresponding CN being around 65. The ratio between the initial abstraction and the maximum potential retention is around 0.3. Even though these results are derived by analyzing a limited number of rainfall events, they are believed to properly consider the soil characteristics of Jeju Island.

Improvement of SCS-CN Initial Abstraction Coefficient in the Czech Republic: A Study of Five Catchments

The SCS-CN method is a globally known procedure used primarily for direct-runoff estimates. It also is integrated in many modelling applications. However, the method was developed in specific geographical conditions, often making its universal applicability problematic. This study aims to determine appropriate values of initial abstraction coefficients λ and curve numbers (CNs), based on measured data in five experimental catchments in the Czech Republic, well representing the physiographic conditions in Central Europe, to improve direct-runoff estimates. Captured rainfall-runoff events were split into calibration and validation datasets. The calibration dataset was analysed by applying three approaches: (1) Modifying λ, both discrete and interpolated, using the tabulated CN values; (2) event analysis based on accumulated rainfall depth at the moment runoff starts to form; and (3) model fitting, an iterative procedure, to search for a pair of λ, S (CN, respectively). To assess individual rainfall characteristics’ possible influence, a principal component analysis and cluster analysis were conducted. The results indicate that the CN method in its traditional arrangement is not very applicable in the five experimental catchments and demands corresponding modifications to determine λ and CN (or S, respectively). Both λ and CN should be viewed as flexible, catchment-dependent (regional) parameters, rather than fixed values. The acquired findings show the need for a systematic yet site-specific revision of the traditional CN method, which may help to improve the accuracy of CN-based rainfall-runoff modelling.

The Effects of Rainfall Intensities and Duration on SCS-CN Model Parameters under Simulated Rainfall

The Soil Conservation Service curve number (SCS-CN) method has been widely used in rainfall-runoff modelling; however, the direct effects of rainfall intensities and duration, which are major factors in hydrological engineering design, on its parameters (initial abstraction ratio (λ) and potential maximum storage (S), the latter is transformed into curve number (CN)) have seldom been studied. In this study, we chose simulated rainfall experiments on runoff plots (30 mm/h, 180 min; 60 mm/h, 90 min; 90 mm/h, 60 min) to obtain synchronized rainfall-runoff data and calculated the parameters using general model fitting and event analysis methods every ten minutes throughout the duration. The results indicate that the parameters changed with rainfall intensities and duration. S decreased as the rainfall intensities increased. Under the same rainfall intensity, the S increased with the duration overall. The corresponding λ changed with rainfall intensities and duration, and has no obvious relationship under different rainfall intensities. Initial abstraction in the event analysis method is the same as the actual situation and we chose these parameters obtained with the event analysis method as our selected parameters. These selected parameters and the parameters obtained using Fu et al.’s method which was based on the standardized procedure in NEH-630 were evaluated by the Nash–Sutcliffe efficiency (NSE), the percentage deviation coefficient (PBIAS), and the ratio of the root mean square error to the standard deviation of measured data (RSR). The results show that the statistics for the selected parameters satisfied the evaluating standard, and have a better value, while the statistics for the parameters obtained by Fu et al.’s method declined as the rainfall intensity increased, and PBAIS was out of the standard range under 90 mm/h rainfall intensity condition. This indicates that the rainfall intensities and duration have important effects on the parameters of the model, and the parameters vary dynamically with the rainfall intensity and duration. These results could be useful for hydrological design in which engineers consider the return period (i.e., rainfall intensities and duration).

The L-moment based regional approach to curve numbers for Slovak and Polish Carpathian catchments

The main objective of the paper was to propose and evaluate the performance of a regional approach to estimate CN values and to test the impact of different initial abstraction ratios. The curve number (CN) was analyzed for five Slovak and five Polish catchments situated in the Carpathian Mountains. The L-moment based method of Hosking and Wallis and the ANOVA test were combined to delineate the area in two homogenous regions of catchments with similar CN values. The optimization condition enabled the choice of the initial abstraction ratio, which provided the smallest discrepancy between the tabulated and estimated CNs and the antecedent runoff conditions. The homogeneity in the CN within the regions of four Slovak and four Polish catchments was revealed. Finally, the regional CN was proposed to be at the 50% quantile of the regional theoretical distribution function estimated from all the CNs in the region.The approach is applied in a group of Slovak and Polish catchments with physiographic conditions representative for the Carpathian region. The main benefit of introducing a common regional CN is the opportunity to apply this procedure in catchments of similar soil-physiographic characteristics and to verify the existing tabulated CN. The paper could give rise to an alternative way of estimating the CN values in forested catchments and catchments with a lack of data or without observations.

Estimation of Initial Abstraction for Hydrological Modeling Based on Global Land Data Assimilation System–Simulated Datasets

Initial abstraction (Ia) is a sensitive parameter in hydrological models, and its value directly determines the amount of runoff. Ia, which is influenced by many factors related to antecedent watershed condition (AWC), is difficult to estimate due to lack of observed data. In the Soil Conservation Service curve number (SCS-CN) method, it is often assumed that Ia is 0.2 times the potential maximum retention S. Yet this assumption has frequently been questioned. In this paper, Ia/S and factors potentially influencing Ia were collected from rainfall–runoff events. Soil moisture and evaporation data were extracted from GLDAS-Noah datasets to represent AWC. Based on the driving factors of Ia, identified using the Pearson correlation coefficient and maximal information coefficient, artificial neural network (ANN)-estimated Ia was applied to simulate the selected flood events in the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model. The results indicated that Ia/S varies over different events and different watersheds. Over 75% of the Ia/S values are less than 0.2 in the two study areas. The driving factors affecting Ia vary over different watersheds, and the antecedent precipitation index appears to be the most influential factor. Flood simulation by the HEC-HMS model using statistical Ia gives the best fitness, whereas applying ANN-estimated Ia outperforms the simulation with median Ia/S. For over 60% of the flood events, ANN-estimated Ia provided better fitness in flood peak and depth, with an average Nash–Sutcliffe efficiency coefficient of 0.76 compared to 0.71 for median Ia/S. The proposed ANN-estimated Ia is physically based and can be applied without calibration, saving time in constructing hydrological models.

Determination of SCS-CN initial abstraction ratio in a catchment prone to flash floods

The soil conservation service - curve number method is a globally used approach to simulations of surface runoff for its simplicity and applicability. Nevertheless, relevant simulations require proper setting of the model's components. This work focuses on optimization of initial abstraction ratio λ in the Husí potok sub-catchments in Czech Republic. Due to favorable morphology, the watershed is prone to flash floods and accurate modeling of surface runoff is of high interest. The analysis was conducted using pairs of discharge and rainfall measurements. The results outline that the traditional value λ= 0.2 is too high in this watershed and should be reduced.