Assessment of empirical and regression methods for infilling missing streamflow data in Little Ruaha catchment Tanzania

This study therefore made a contribution by appraising empirical and regression rainfall runoff based methods in the Little Ruaha River catchment, a sub-catchment of the Great Ruaha River sub-basin both within the Rufiji River Basin. The methods employed included simple linear regression (with untransformed and log-transformed data), multiple linear regression (with untransformed and log-transformed data), rainfall-runoff relationship using double mass curve technique, flow duration matching and drainage-area ratio. In addition, rainfall runoff modeling using HBV-Light was done for further comparison. With exception to the rainfall-runoff relationship and HBV-Light model, all other methods relied upon data transfer from donor stations (upstream & downstream station(s)) for infilling a downstream/upstream station. Data quality and consistency checks were performed, and performances of infilling methods were evaluated based on three performance criteria namely Nash-Sutcliffe efficiency coefficient (NSE), Coefficient of determination (R2) and standard error of estimate (SE) during calibration and validation periods. Four gauging stations (2 each upstream and downstream) were separately used to infill artificially created gaps to the target station. Overall, the calibration and validation daily results at 1KA21A indicated that the flow duration matching technique and multiple linear regression methods performed better than other methods with NSE (71%; 93%) and NSE (55%; 75%) resp...

Source: Physics and Chemistry of the Earth, Parts ABC - July 11, 2018 Category: Science Source Type: research