Water Level Reconstruction and Prediction Based on Space-Borne Sensors: A Case Study in the Mekong and Yangtze River Basins

Abstract

Water level (WL) measurements denote surface conditions that are useful for monitoring hydrological extremes, such as droughts and floods, which both affect agricultural productivity and regional development. Due to spatially sparse in situ hydrological stations, remote sensing measurements that capture localized instantaneous responses have recently been demonstrated to be a viable alternative to WL monitoring. Despite a relatively good correlation with WL, a traditional passive remote sensing derived WL is reconstructed from nearby remotely sensed surface conditions that do not consider the remotely sensed hydrological variables of a whole river basin. This method’s accuracy is also limited. Therefore, a method based on basin-averaged, remotely sensed precipitation from the Tropical Rainfall Measuring Mission (TRMM) and gravimetrically derived terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE) is proposed for WL reconstruction in the Yangtze and Mekong River basins in this study. This study examines the WL reconstruction performance from these two remotely sensed hydrological variables and their corresponding drought indices (i.e., TRMM Standardized Precipitation Index (TRMM-SPI) and GRACE Drought Severity Index (GRACE-DSI)) on a monthly temporal scale. A weighting procedure is also developed to explore a further potential improvement in the WL reconstruction. We found that the reconstructed WL derived from the hydrological variables compares well to the observed WL. The derived drought indices perform even better than those of their corresponding hydrological variables. The indices’ performance rate is owed to their ability to bypass the influence of El Niño Southern Oscillation (ENSO) events in a standardized form and their basin-wide integrated information. In general, all performance indicators (i.e., the Pearson Correlation Coefficient (PCC), Root-mean-squares error (RMSE), and Nash–Sutcliffe model efficiency coefficient (NSE)) reveal that the remotely sensed hydrological variables (and their corresponding drought indices) are better alternatives compared with traditional remote sensing indices (e.g., Normalized Difference Vegetation Index (NDVI)), despite different geographical regions. In addition, almost all results are substantially improved by the weighted averaging procedure. The most accurate WL reconstruction is derived from a weighted TRMM-SPI for the Mekong (and Yangtze River basins) and displays a PCC of 0.98 (and 0.95), a RMSE of 0.19 m (and 0.85 m), and a NSE of 0.95 (and 0.89); by comparison, the remote sensing variables showed less accurate results (PCC of 0.88 (and 0.82), RMSE of 0.41 m (and 1.48 m), and NSE of 0.78 (and 0.67)) for its inferred WL. Additionally, regardless of weighting, GRACE-DSI displays a comparable performance. An external assessment also shows similar results. This finding indicates that the combined usage of remotely sensed hydrological variables in a standardized form and the weighted averaging procedure could lead to an improveme...

Research Trends of Hydrological Drought: A Systematic Review

“…Log-normal and Weibull distributions were usually recommended and most frequently used for low flow evaluation, but there are no specific standards on which techniques to use for which data. The most commonly used and recommended log-normal and Weibull distributions [39,[81][82][83]. The Weibull distribution for low values occurs when the extreme value is from a parent distribution which is restricted in the interest direction which is in the event of low flows, they are bounded by zero flow on the left.…”

“…Hydrological models are driven by atmospheric model output, ongoing research site data, and product reanalysis. Most of the literature focused on analysing historical and generated streamflow data for hydrological drought, including estimating the probability density function of the drought characteristics [82,88,89].…”

Optimization of Reconfigurable Satellite Constellations Using Simulated Annealing and Genetic Algorithm

“…Earth observation has experienced unprecedented growth through the use of satellite data [ 1 , 2 ]. Space-based, spatio-temporal data is now regularly used to remotely measure fresh water elevation [ 3 ], explore potential mineral deposits [ 4 ], monitor changes in land-cover and land-use [ 5 , 6 ], to name a few practical applications. In particular, Earth observation for situational awareness often involves mobile targets, such as hurricanes [ 7 , 8 ] or emergency areas whose locations cannot be determined a priori [ 9 , 10 ].…”

Upstream GPS Vertical Displacement and its Standardization for Mekong River Basin Surface Runoff Reconstruction and Estimation

“…Given the foregoing GRACE-inferred physical quantity that is comparable to the GPS-VD, it is anticipated that the latter can be a potential alternative for reconstructing the RWD; no paper in this regard has ever been published in recent literature.Essentially, the GRACE-S and its standardization have been recently demonstrated to have a good correlation with water level [52] and R [53]. The reason for the standardization is that it enhances the regional characteristics of the averaged time series [54] when local means and variances are largely different from the regional one [55], thus mitigating systematic influences due to geographic environment [56]. Given the aforementioned similarity between GRACE-inferred quantities and GPS-VD observations, the GPS-VD and its standardization can presumably achieve a similar quality to that of GRACE in capturing the time series of R and standardized R via correlative analysis, respectively.The Mekong River Delta (MRD), a geographic region that is vital for food (e.g., [57]) and water security (e.g., [58]) in Southeast Asia, is the MRB downstream region immediately before the freshwater is discharged into the coastal ocean.…”

“…Essentially, the GRACE-S and its standardization have been recently demonstrated to have a good correlation with water level [52] and R [53]. The reason for the standardization is that it enhances the regional characteristics of the averaged time series [54] when local means and variances are largely different from the regional one [55], thus mitigating systematic influences due to geographic environment [56]. Given the aforementioned similarity between GRACE-inferred quantities and GPS-VD observations, the GPS-VD and its standardization can presumably achieve a similar quality to that of GRACE in capturing the time series of R and standardized R via correlative analysis, respectively.…”