Abstract
A suitable routing model for predicting future monthly water discharge (WD) is essential for operational hydrology, including water supply, and hydrological extreme management, to mention but a few. This is particularly important for a remote area without a sufficient number of in-situ data, promoting the usage of remotely sensed surface variables. Direct correlation analysis between ground-observed WD and localized passive remotely-sensed surface variables (e.g., indices and geometric variables) has been studied extensively over the past two decades. Most of these related studies focused on the usage of constructed correlative relationships for estimating WD at ungauged locations. Nevertheless, temporal prediction performance of monthly runoff (R) (being an average representation of WD of a catchment) at the river delta reconstructed from the basin’s upstream remotely-sensed water balance variables via a standardization approach has not been explored. This study examined the standardization approach via linear regression using the remotely-sensed water balance variables from upstream of the Mekong Basin to reconstruct and predict monthly R time series at the Mekong Delta. This was subsequently compared to that based on artificial intelligence (AI) models. Accounting for less than 1% improvement via the AI-based models over that of a direct linear regression, our results showed that both the reconstructed and predicted Rs based on the proposed approach yielded a 2–6% further improvement, in particular the reduction of discrepancy in the peak and trough of WD, over those reconstructed and predicted from the remotely-sensed water balance variables without standardization. This further indicated the advantage of the proposed standardization approach to mitigate potential environmental influences. The best R, predicted from standardized water storage over the whole upstream area, attained the highest Pearson correlation coefficient of 0.978 and Nash–Sutcliffe efficiency of 0.947, and the lowest normalized root-mean-square error of 0.072.
Highlights
Owing to a lack of funding for facility maintenance [1], there has been a continuous decline in the number of ground-observed hydrological sites for observing water discharge (WD) worldwide since the1970s [2]
With the passive remotely-sensed (RS) surface variables (e.g., normalized difference vegetation index (NDVI) and land surface temperature (LST)) along with artificial intelligence (AI)-based models as baseline results, we examined the temporal prediction performance of monthly runoff (R) at the Mekong Delta reconstructed from the upstream remotely-sensed water balance variables (RS WBVs) and their standardizations, which should present more direct causal relationships and mitigate the environmental influences, such as El Niño–Southern Oscillation (ENSO) events and hydrological extremes
We found that the reconstructed and predicted Rs based on the remote sensing (RS)
Summary
Owing to a lack of funding for facility maintenance [1], there has been a continuous decline in the number of ground-observed hydrological sites for observing water discharge (WD) worldwide since the1970s [2]. Normalized difference vegetation index, river width, floodplain inundation, and land surface temperature are common land surface variables derived from passive RS [3,4,5,6]. Regardless of their relationships with ground-observed WD, the passive RS surface variables are directly correlated with water level or WD as long as they are well correlated with each other in the RS field of study. RS surface variables having direct causal relationships with water level and WD should be sought for the estimation [7]
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