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

Abstract

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

Estimation of the Water Level in the Ili River from Sentinel-2 Optical Data Using Ensemble Machine Learning

“…Due to the widespread reduction in hydrometric stations, remote sensing data are becoming increasingly popular for assessing the river discharge regimes [38]. In general, the use of satellite data increases the accuracy of calculation [39], reconstructing [40] and forecasting the volume of water discharge with a sufficiently high value of the Nash-Sutcliffe model coefficient of efficiency (NSE), such as in [41], where the value of NSE = 0.8 was achieved.…”