Abstract
Water level monitoring is important for understanding the global hydrological cycle. Remotely-sensed indices that capture localized instantaneous responses have been extensively explored for water level reconstruction during the past two decades. However, the potential usage of the Palmer’s Drought Severity Index (PDSI) and El Niño Southern Oscillation (ENSO) indices for water level reconstruction and prediction has not been explored. This paper examines the relationship between observed water level and PDSI based on a soil-moisture water balance model and three ENSO indices for the lower Mekong River estuary on a monthly temporal scale. We found that the time-lagged information between the standardized water level and the ENSO indices that enabled us to reconstruct the water level using the ENSO indices. The influence of strong ENSO events on the water level can help capture the hydrological extremes during the period. As a result, PDSI-based water level reconstruction can be further improved with the assistance of ENSO information (called ENSO-assisted PDSI) during ENSO events. The water level reconstructed from the PDSI and ENSO indices (and that of remote sensing) compared to observed water level shows a correlation coefficient of around 0.95 (and <0.90), with an RMS error ranging from 0.23 to 0.42 m (and 0.40 to 0.79 m) and an NSE around 0.90 (and <0.81), respectively. An external assessment also displayed similar results. This indicates that the usage of ENSO information could lead to a potential improvement in water level reconstruction and prediction for river basins affected by the ENSO phenomenon and hydrological extremes.
Highlights
Water resources, including fresh water supply, hydrological extremes and agricultural irrigation, is critical for human sustainability in the near future [1,2]
An external assessment displayed similar results. This indicates that the usage of El Niño Southern Oscillation (ENSO) information could lead to a potential improvement in water level reconstruction and prediction for river basins affected by the ENSO phenomenon and hydrological extremes
This study aims to investigate the methodology for reconstructing water levels based on the hydrological drought index (i.e., Palmer’s Drought Severity Index (PDSI)) and ENSO indices and their combination in the lower Mekong River Basin, where it is mainly controlled by the alternating wet and dry seasons in the front of the South China Sea, making it an ideal experimental region for this study
Summary
Water resources, including fresh water supply, hydrological extremes and agricultural irrigation, is critical for human sustainability in the near future [1,2]. The instantaneous responses only represent the localized information (e.g., precipitation, soil moisture and land surface temperature) within a hydrological cycle, which may contain some noises that may cause the causal information to be unclear This can be one of the reasons for the relatively less accurate water level estimations based on remote sensing data. This study aims to investigate the methodology for reconstructing water levels based on the hydrological drought index (i.e., PDSI) and ENSO indices and their combination in the lower Mekong River Basin, where it is mainly controlled by the alternating wet and dry seasons in the front of the South China Sea, making it an ideal experimental region for this study. Temperature (LST) [34]) are included as baseline predictors for a comparative analysis with the PDSI and ENSO indices employed in this study
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