Thu Apr 10, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Yi, Shuang, Li, Hao-si, Han, Shin-Chan, Sneeuw, Nico, Yuan, Chunyu, Song, Chunqiao, Yeo, In-Young, and McCullough, Christopher M., 2025. Quantification of the Flood Discharge Following the 2023 Kakhovka Dam Breach Using Satellite Remote Sensing. Water Resources Research, 61(3):2024WR038314, doi:10.1029/2024WR038314.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025WRR....6138314Y,
author = {{Yi}, Shuang and {Li}, Hao-si and {Han}, Shin-Chan and {Sneeuw}, Nico and {Yuan}, Chunyu and {Song}, Chunqiao and {Yeo}, In-Young and {McCullough}, Christopher M.},
title = "{Quantification of the Flood Discharge Following the 2023 Kakhovka Dam Breach Using Satellite Remote Sensing}",
journal = {Water Resources Research},
keywords = {Kakhovka reservoir, dam failure, discharge model, GRACE, remote sensing, Ukrainian-Russian war},
year = 2025,
month = mar,
volume = {61},
number = {3},
pages = {2024WR038314},
abstract = "{Fourteen months post the Ukrainian-Russian war outbreak, the Kakhovka
Dam collapsed, leading to weeks of catastrophic flooding. Yet,
scant details exist regarding the reservoir draining process. By
using a new technique for processing gravimetric satellite
orbital observations, this study succeeded in recovering
continuous changes in reservoir mass with a temporal resolution
of 2{\textendash}5 days. By integrating these variations with
satellite imagery and altimetry data into a hydrodynamic model,
we derived the effective width and length of the breach and the
subsequent 30-day evolution of the reservoir discharge. Our
model reveals that the initial volumetric flow rate is
$(5.7\mathit{\pm }0.8)\times {10}^{4}$ m$^{3}$/s, approximately
28 times the average flow of the Dnipro River. After 30 days,
the water level in the reservoir had dropped by $12.6\mathit{\pm
}1.1$ m and its water volume was almost completely depleted by
$20.4\mathit{\pm }1.4$ km$^{3}$. In addition, this event
provides a rare opportunity to examine the discharge
coefficient{\textemdash}a key modeling parameter{\textemdash}of
giant reservoirs, which we find to be 0.8{\textendash}1.0,
significantly larger than the {\ensuremath{\sim}}0.6 value
previously measured in the laboratory, indicating that this
parameter may be related to the reservoir scale. This study
demonstrates a paradigm of utilizing multiple remote sensing
techniques to address observational challenges posed by extreme
hydrological events.}",
doi = {10.1029/2024WR038314},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025WRR....6138314Y},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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