Flood hydrograph simulation is affected by uncertainty in Rainfall – Runoff )RR( parameters. Uncertainty of RR parameters in Gharasoo catchment, part of the great Karkheh river basin, is evaluated by Monte–Carlo (MC) approach. A conceptual-distributed model, called ModClark, was used for basin simulation, in which the basin’s hydrograph was determined using the superposition of runoff generated...

drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. this paper presents the performances of autoregressive integrated moving average (arima), direct multi-step neural network (dmsnn), recursive multi-step neural network (rmsnn), hybrid stochastic neural network of directive ap...

A large number of local and global databases for soil, land use, crops, and climate are now available from different sources, which often differ, even when addressing the same spatial and temporal resolutions. As the correct database is unknown, their impact on estimating water resource components (WRC) has mostly been ignored. Here, we study the uncertainty stemming from the use of multiple da...

Flood hydrograph simulation is affected by uncertainty in Rainfall – Runoff )RR( parameters. Uncertainty of RR parameters in Gharasoo catchment, part of the great Karkheh river basin, is evaluated by Monte–Carlo (MC) approach. A conceptual-distributed model, called ModClark, was used for basin simulation, in which the basin’s hydrograph was determined using the superposition of runoff generated...