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The Benefits of Continental-Scale High-Resolution Hydrological Modeling in the Detection of Extreme Hydrological Events in China.

Authors :
Zhu, Bowen
Xie, Xianhong
Wang, Yibing
Zhao, Xuehua
Source :
Remote Sensing; May2023, Vol. 15 Issue 9, p2402, 14p
Publication Year :
2023

Abstract

High-resolution hydrological modeling is crucial for detecting extreme hydrological events and understanding fundamental terrestrial processes. However, spatial resolutions in current hydrological modeling studies have been mostly constrained to relatively coarse resolution (~10–100 km), and they therefore have a difficult time addressing flooding or drought issues with fine resolutions. In this study, a continental-scale high-resolution hydrological modeling framework (0.0625°, ~6 km) driven by remote sensing products was used to detect extreme hydrological event occurrences in China and evaluated based on the Variable Infiltration Capacity (VIC) model. The results showed that the developed model provided more detailed information than the coarser resolution models (a 0.25° and 1°), thereby capturing the timing, duration, and spatial extent of extreme hydrologic events regarding the 2012 Beijing flood and 2009/10 drought in Hai River Basin. Here, the total water storage changes were calculated based on the VIC model (−0.017 mm/year) and Gravity Recovery and Climate Experiment (GRACE) satellite (−0.203 mm/year) to reflect the water availability caused by climate change and anthropogenic factors. This study found that the 0.0625° dataset could capture detailed changes, thereby providing reliable information during occurrences of extreme hydrological events. The high-resolution model integrated with remote sensing products could be used for accurate evaluations of continental-scale extreme hydrological events and can be valuable in understanding its long-term occurrence and water resource security. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
15
Issue :
9
Database :
Complementary Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
163724381
Full Text :
https://doi.org/10.3390/rs15092402