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• Sorted by Date • Sorted by Last Name of First Author •
He, Qing, Fok, Hok Sum, Chen, Qiang, and Chun, Kwok Pan, 2018. Water Level Reconstruction and Prediction Based on Space-Borne Sensors: A Case Study in the Mekong and Yangtze River Basins. Sensors, 18(9):3076, doi:10.3390/s18093076.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2018Senso..18.3076H,
author = {{He}, Qing and {Fok}, Hok Sum and {Chen}, Qiang and {Chun}, Kwok Pan},
title = "{Water Level Reconstruction and Prediction Based on Space-Borne Sensors: A Case Study in the Mekong and Yangtze River Basins}",
journal = {Sensors},
keywords = {water level, TRMM, GRACE Drought Severity Index (DSI), TRMM-based Standardized Precipitation Index (SPI), Mekong River Basin, Yangtze River Basin},
year = 2018,
month = sep,
volume = {18},
number = {9},
eid = {3076},
pages = {3076},
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 Ni{\~n}o 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 improvement in WL reconstructions for
river basins affected by ENSO events and hydrological extremes.}",
doi = {10.3390/s18093076},
adsurl = {https://ui.adsabs.harvard.edu/abs/2018Senso..18.3076H},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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