Geomorphology-Wavelet based approach to rainfall runoff modeling for data scarce semi-arid regions, Kolar river catchment, India
Climate change due to anthropogenic activities causing serious threat to environment and its natural processes. Whole world is suffering with water scarcity and its management related problems. So, a reliable method for rainfall-runoff prediction is prerequisite for the water resource management of the watershed. The conceptual and physical mathematical model uses various parameters such as land use land cover, soil type classification, rainfall, atmospheric data such as temperature, evapotranspiration, solar radiation and wind speed, etc. But these data may not be available for developing countries and data scares semi-arid watershed. Also, the problem is even more critical for ungauged station and where manual record is maintained of water level and rainfall data. To address this issue, trend analysis is performed using Mann-Kendall test and Sen’s slope test which shows significant trend change stressing the need for new method for runoff prediction for better water resource management. In this study, a total of four models namely nonlinear autoregressive model with exogenous inputs lumped (LNARX), nonlinear autoregressive model with exogenous geomorphometrically processed inputs (GNARX), wavelet nonlinear autoregressive model with exogenous inputs (WLNARX) and nonlinear autoregressive model with exogenous geomorphometrically processed inputs (WGNARX). Ten models with different input combinations were selected based on their performance are analyzed for all the four networks. All the four models are trained using Levenberg- Marquardt (LM) algorithm. The performance criterion used are Root mean square, error (RMSE), NSE, and R2. The best performing model for these networks is model no. 6 with WGNARX network with NSE 0.97 and RMSE 0.97 and with least value of RMSE. Most of the models have performed better than the same models without wavelet transformed inputs. This method can be applied to data scarce region where data available are available for shorter duration.