Abstract
The monitoring of hydrological extremes requires water level measurement. Owing to the decreasing number of continuous operating hydrological stations globally, remote sensing indices have been advocated for water level reconstruction recently. Nevertheless, the feasibility of gravimetrically derived terrestrial water storage (TWS) and its corresponding index for water level reconstruction have not been investigated. This paper aims to construct a correlative relationship between observed water level and basin-averaged Gravity Recovery and Climate Experiment (GRACE) TWS and its Drought Severity Index (GRACE-DSI), for the Yangtze river basin on a monthly temporal scale. The results are subsequently compared against traditional remote sensing, Palmer’s Drought Severity Index (PDSI), and El Niño Southern Oscillation (ENSO) indices. Comparison of the water level reconstructed from GRACE TWS and its index, and that of remote sensing against observed water level reveals a Pearson Correlation Coefficient (PCC) above 0.90 and below 0.84, with a Root-Mean-Squares Error (RMSE) of 0.88–1.46 m, and 1.41–1.88 m and a Nash-Sutcliffe model efficiency coefficient (NSE) above 0.81 and below 0.70, respectively. The ENSO-reconstructed water levels are comparable to those based on remote sensing, whereas the PDSI-reconstructed water level shows a similar performance to that of GRACE TWS. The water level predicted at the location of another station also exhibits a similar performance. It is anticipated that the basin-averaged, remotely-sensed hydrological variables and their standardized forms (e.g., GRACE TWS and GRACE-DSI) are viable alternatives for reconstructing water levels for large river basins affected by the hydrological extremes under ENSO influence.
Highlights
Lakes, reservoirs, rivers, and wetlands are regions sensitive to changing climate
The water level (WL) is a basic hydrological variable observed traditionally for a river basin. This variable is useful for monitoring the water cycle and its extremes, including floods and droughts, with a view to achieving a stable water supply [2], and paving a pathway for human sustainability in the near future [3]
Despite poor temporal sampling rates compared with that in ground-based measurements, remote sensing measurements have been promoted for their near-global coverage, relatively low cost, and easy accessibility [6]
Summary
Reservoirs, rivers, and wetlands are regions sensitive to changing climate. Given the unstable fresh water supply due to changing climate, water level and/or volume monitoring has become an important task [1]. The water level (or stage) (WL) is a basic hydrological variable observed traditionally for a river basin. This variable is useful for monitoring the water cycle and its extremes, including floods and droughts, with a view to achieving a stable water supply [2], and paving a pathway for human sustainability in the near future [3]. Continuous WL time series are necessary [4]. Despite poor temporal sampling rates compared with that in ground-based measurements, remote sensing measurements have been promoted for their near-global coverage, relatively low cost, and easy accessibility [6]. Measurements can be taken at multiple virtual stations for different river sections, which is not possible with local measurements at scattered locations along a river [7]
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