Determining an optimal run-off coefficient method for estimating peak discharge in the Lesti River catchment

The run-off coefficients provide vital hydrological data used for river discharge forecasts and flood risk management. Selecting an appropriate method to determine this coefficient is essential for accurately estimating peak discharge. This study compared the effectiveness of the Hassing, Cook, and U.S. Forest Service methods integrating GIS in estimating run-off coefficients in the Lesti River catchment area from 2013 to 2019. The findings revealed that the run-off coefficient was determined to be 0.188–0.243 using the U.S. Forest Service method, 0.194–0.213 using the Hassing method, and 0.466–0.480 using the Cook method. These results showed a rapid increase in the run-off coefficient within the Lesti River catchment area, signifying a heightened susceptibility to flooding. This is particularly concerning as the Lesti River is a primary tributary to the Brantas River. The comparison of estimated versus observed peak discharge emphasised the superiority of the runoff coefficient associated with the Hassing method over alternative methodologies when utilised as input data for peak discharge estimation. This was evident by the notable measurement error values of 11% for MAPE and 0.58 for MAE. The Hassing method emerged as the most appropriate and reliable for reflecting run-off characteristics in the Lesti River catchment area. Additionally, it proved to be the most accurate for estimating run-off coefficients in the Nakayasu process for peak discharge estimation. Consequently, applying the Hassing method offers a viable strategy for effectively mitigating flood risks in the Lesti catchment area.