Daily runoff and its potential error sources reconstructed using individual satellite hydrological variables at the basin upstream

Basin-scale hydropower operation and water resource allocation rely on in situ river discharge measured at a mouth, which is referred to as runoff. Due labor intensiveness tight financial constraints, satellite hydrological variables have been advocated for reconstructing monthly runoff via regressing with nearby over the past two decades. Nevertheless, daily reconstruction by upstream scale has yet be examined. A data standardization approach proposed reconstructed using of Mekong Basin. It was found that accuracy predicted against substantially increased, particular, troughs (peaks) during dry (wet) seasons, respectively, when compared direct linear regression. The backwater impact negligible after standardization, implying possibility choosing basin exit entrance delta. Results generated from neural network–based models do not improve consistently or even bit worse than best forecasted runoff, yielding lowest relative error 8.6%, obtained standardized storage index. Detrended cross-correlation analysis indicated yielded larger 0.8 within most window sizes. Further improvement lies methodology mitigating influence due climate variability extreme events.