Comparative evaluation of models to estimate direct runoff volume from an agricultural watershed

Abstract

Generally, runoff records are the most important input data in water resource management; however, their availability is very limited especially in developing country as compared to rainfall records, especially under medium and small-scale catchments. In our study, we estimated runoff from ungauged agricultural watershed with the curve number method and empirical mathematical models were compared with SCS-CN. Empirical mathematical models (Inglis and De Souza Formula (IDS), Turc relationship (TR), Indian Irrigation Department (DII) model, Coutagine relationship (CR), Khosla method (KH), Justin Equation (JE), Lacey relationship (LR), and Indian Council of Agricultural Research (ICAR)) model were used to estimate annual runoff (in cm). It was found that IDS model has capability to simulate annual runoff as very close to Soil Conservation Service Curve Number (SCS-CN) model and has lowest Root Mean Square Error (RMSE) value as 7.75, and ranking of this model (based on K factor (value of 0.001) analysis) was topmost (or 1 st) in comparison to other eight models. This study suggests that empirical mathematical model has potential for annual runoff estimation from ungauged watershed.

Modeling runoff in Bhima River catchment, India: A comparison of artificial neural networks and empirical models

“…The Department of Irrigation in India produced an empirical equation depending on data acquired from the Reihand River, which was used by Rawat et al (2021). Equation ( 8) represents the relationship between rainfall and runoff as follows:…”

“…This envisages the necessity of soft computing techniques for better prediction of the basin yield to achieve sustainable development of the watersheds. Rawat et al (2021) assessed annual runoff in an ungauged agricultural watershed using the SCS-CN and empirical mathematical methods. Results showed that the Inglis and DeSouza IDS model can simulate annual runoff as closely as the SCS-CN model and has the lowest RMSE value of 7.75, and the ranking of this model was ranked first compared with the other eight models.…”

Investigating impact of land-use and land cover changes on hydro-ecological balance using GIS: insights from IIT Bombay, India

“…The SCS-CN method, as developed by the US Department of Agriculture (USDA) Soil Conservation Service (SCS), primarily aimed at estimating rainfall excesses from rainfall [18]. The method has been readily accepted and used widely in numerous hydrologic studies to estimate surface runoff, such as for ungauged agricultural watersheds, quantifying vulnerability of soil erosion, and the studies monitoring spatio-temporal variability in LULC patterns [41,43,[52][53][54]. The SCS-CN method for the Indian conditions was used in the estimation of surface runoff from the IITB campus.…”