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

Abstract

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.