Author(s): Shrikant Charhate, Vidyanand Sayagavi, Rajendra Magar
Linked Author(s): Shrikant Charhate
Keywords: Hydrological parameters, meteorological parameters, average rainfall, runoff, temperature
Abstract: Rainfall-runoff is an important hydrological process that is complex in its nature involving in-depth study of hydrological and meteorological parameters. Hydrological processes are dynamic, chaotic and non�linear. Accurate estimation of hydrological parameters could be carried out with the study of influence of meteorological parameter by performing sensitivity analysis and so on. Generally the process governs with rainfall as the major factor, however in monsoon season some meteorological parameters may have the greater influence which may lead to enhance the model accuracy in the estimation of runoff. Some meteorological parameters; temperatures, relative humidity and pan evaporation could influence the process. The rainfall and runoff may affect evaporation, humidity and temperature. Meteorological parameters like maximum temperature and minimum temperature affect humidity, evaporation and runoff of a catchment. This paper is mainly focuses on the study of influence of meteorological parameters in the rainfall-runoff processes and importance of meteorological parameters in such process. The tools used here are Multiple Linear Regression and soft computing tool, Artificial Neural Network. The study area selected for the purpose is Shivade catchment of upper Krishna basin, Maharashtra State in India. The meteorological parameters collected includes pan evaporation, average temperatures, average wind speed, relative humidity and hydrological parameters; rainfall and runoff. The correlation analysis amongst all the parameters is carried out to perform overall sensitivity analysis. The effect of all the parameters on each other is studied. The meteorological parameters namely maximum temperature and minimum temperature along with some other parameters have been studied using MLR and ANN model building. Amongst the two methods, ANN models exhibited better estimation of both hydrological and meteorological parameters. The statistical analysis and the qualitative analysis showed influence of parameters on each other, however few parameters do not show correlation but are significant in the processes as observed in model development
Year: 2017