Thu Apr 10, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Zhou, Linghao, Fok, Hok Sum, Ma, Zhongtian, and Chen, Qiang, 2019. Upstream Remotely-Sensed Hydrological Variables and Their Standardization for Surface Runoff Reconstruction and Estimation of the Entire Mekong River Basin. Remote Sensing, 11(9):1064, doi:10.3390/rs11091064.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2019RemS...11.1064Z,
author = {{Zhou}, Linghao and {Fok}, Hok Sum and {Ma}, Zhongtian and {Chen}, Qiang},
title = "{Upstream Remotely-Sensed Hydrological Variables and Their Standardization for Surface Runoff Reconstruction and Estimation of the Entire Mekong River Basin}",
journal = {Remote Sensing},
keywords = {runoff, water balance standardization, GRACE satellite gravimetry, remote sensing hydrology, Mekong River Basin},
year = 2019,
month = may,
volume = {11},
number = {9},
eid = {1064},
pages = {1064},
abstract = "{River water discharge (WD) is an essential component when monitoring a
regional hydrological cycle. It is expressed in terms of surface
runoff (R) when a unit of river basin surface area is
considered. To compensate for the decreasing number of
hydrological stations, remotely-sensed WD estimation has been
widely promoted over the past two decades, due to its global
coverage. Previously, remotely-sensed WD was reconstructed
either by correlating nearby remotely-sensed surface responses
(e.g., indices and hydraulic variables) with ground-based WD
observations or by applying water balance formulations, in terms
of R, over an entire river basin, assisted by hydrological
modeling data. In contrast, the feasibility of using remotely-
sensed hydrological variables (RSHVs) and their standardized
forms together with water balance representations (WBR) obtained
from the river upstream to reconstruct estuarine R for an entire
basin, has been rarely investigated. Therefore, our study aimed
to construct a correlative relationship between the estuarine
observed R and the upstream, spatially averaged RSHVs, together
with their standardized forms and WBR, for the Mekong River
basin, using estuarine R reconstructions, at a monthly temporal
scale. We found that the reconstructed R derived from the
upstream, spatially averaged RSHVs agreed well with the observed
R, which was also comparable to that calculated using
traditional remote sensing data (RSD). Better performance was
achieved using spatially averaged, standardized RSHVs, which
should be potentially attributable to spatially integrated
information and the ability to partly bypass systematic biases
by both human (e.g., dam operation) and environmental effects in
a standardized form. Comparison of the R reconstructed using the
upstream, spatially averaged, standardized RSHVs with that
reconstructed from the traditional RSD, against the observed R,
revealed a Pearson correlation coefficient (PCC) above 0.91 and
below 0.81, a root-mean-squares error (RMSE) below 6.1 mm and
above 8.5 mm, and a Nash-Sutcliffe model efficiency coefficient
(NSE) above 0.823 and below 0.657, respectively. In terms of the
standardized water balance representation (SWBR), the
reconstructed R yielded the best performance, with a PCC above
0.92, an RMSE below 5.9 mm, and an NSE above 0.838. External
assessment demonstrated similar results. This finding indicated
that the standardized RSHVs, in particular its water balance
representations, could lead to further improvement in estuarine
R reconstructions for river basins affected by various
systematic influences. Comparison between hydrological stations
at the Mekong River Delta entrance and near the estuary mouth
revealed tidally-induced backwater effects on the estimated R,
with an RMSE difference of 4-5 mm (equivalent to 9-11\% relative
error).}",
doi = {10.3390/rs11091064},
adsurl = {https://ui.adsabs.harvard.edu/abs/2019RemS...11.1064Z},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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