Title
Extensive Evaluation Of A Continental-Scale High-Resolution Hydrological Model Using Remote Sensing And Ground-Based Observations
Abstract
Extreme hydrologic events are getting more frequent under a changing climate, and a reliable hydrological modeling framework is important to understand their mechanism. However, existing hydrological modeling frameworks are mostly constrained to a relatively coarse resolution, unrealistic input information, and insufficient evaluations, especially for the large domain, and they are, therefore, unable to address and reconstruct many of the water-related issues (e.g., flooding and drought). In this study, a 0.0625-degree (similar to 6 km) resolution variable infiltration capacity (VIC) model developed for China from 1970 to 2016 was extensively evaluated against remote sensing and ground-based observations. A unique feature in this modeling framework is the incorporation of new remotely sensed vegetation and soil parameter dataset. To our knowledge, this constitutes the first application of VIC with such a long-term and fine resolution over a large domain, and more importantly, with a holistic system-evaluation leveraging the best available earth data. The evaluations using in-situ observations of streamflow, evapotranspiration (ET), and soil moisture (SM) indicate a great improvement. The simulations are also consistent with satellite remote sensing products of ET and SM, because the mean differences between the VIC ET and the remote sensing ET range from -2 to 2 mm/day, and the differences for SM of the top thin layer range from -2 to 3 mm. Therefore, this continental-scale hydrological modeling framework is reliable and accurate, which can be used for various applications including extreme hydrological event detections.
Year
DOI
Venue
2021
10.3390/rs13071247
REMOTE SENSING
Keywords
DocType
Volume
hydrological modeling, high resolution, remote sensing product, continental-scale
Journal
13
Issue
Citations 
PageRank 
7
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Bowen Zhu110.73
Xianhong Xie222.03
Chuiyu Lu300.34
Tianjie Lei422.08
Yibing Wang565.56
kun jia63011.31
Yunjun Yao710530.36