Evaluation and Comparison of Reanalysis Data for Runoff Simulation in the Data-Scarce Watersheds of Alpine Regions

Xiaofeng Wang; Jitao Zhou; Jiahao Ma; Pingping Luo; Xinxin Fu; Xiaoming Feng; Xinrong Zhang; Zixu Jia; Xiaoxue Wang; Xiao Huang

doi:10.3390/rs16050751

Remote Sensing (Feb 2024)

Evaluation and Comparison of Reanalysis Data for Runoff Simulation in the Data-Scarce Watersheds of Alpine Regions

Xiaofeng Wang,
Jitao Zhou,
Jiahao Ma,
Pingping Luo,
Xinxin Fu,
Xiaoming Feng,
Xinrong Zhang,
Zixu Jia,
Xiaoxue Wang,
Xiao Huang

Affiliations

Xiaofeng Wang: School of Land Engineering, Chang’an University, Xi’an 710054, China
Jitao Zhou: School of Land Engineering, Chang’an University, Xi’an 710054, China
Jiahao Ma: School of Geological Engineering and Mapping, Chang’an University, Xi’an 710054, China
Pingping Luo: School of Water and Environment, Chang’an University, Xi’an 710054, China
Xinxin Fu: School of Geological Engineering and Mapping, Chang’an University, Xi’an 710054, China
Xiaoming Feng: Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
Xinrong Zhang: School of Earth Science and Resources, Chang’an University, Xi’an 710054, China
Zixu Jia: School of Land Engineering, Chang’an University, Xi’an 710054, China
Xiaoxue Wang: School of Land Engineering, Chang’an University, Xi’an 710054, China
Xiao Huang: School of Land Engineering, Chang’an University, Xi’an 710054, China

DOI: https://doi.org/10.3390/rs16050751
Journal volume & issue: Vol. 16, no. 5
p. 751

Abstract

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Reanalysis datasets provide a reliable reanalysis of climate input data for hydrological models in regions characterized by limited weather station coverage. In this paper, the accuracy of precipitation, the maximum and minimum temperatures of four reanalysis datasets, the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS), time-expanded climate forecast system reanalysis (CFSR+), the European Centre for Medium-Range Weather Forecast Reanalysis (ERA). and the China Meteorological Forcing Dataset (CMFD), were evaluated by using data from 28 ground-based observations (OBs) in the Source of the Yangtze and Yellow Rivers (SYYR) region and were used as input data for the SWAT model for runoff simulation and performance evaluation, respectively. And, finally, the CMADS was optimized using Integrated Calibrated Multi-Satellite Retrievals for Global Precipitation Measurement (AIMERG) data. The results show that CMFD is the most representative reanalysis data for precipitation characteristics in the SYYR region among the four reanalysis datasets evaluated in this paper, followed by ERA5 and CFSR, while CMADS performs satisfactorily for temperature simulations in this region, but underestimates precipitation. And we contend that the accuracy of runoff simulations is notably contingent upon the precision of daily precipitation within the reanalysis dataset. The runoff simulations in this region do not effectively capture the extreme runoff characteristics of the Yellow River and Yangtze River sources. The refinement of CMADS through the integration of AIMERG satellite precipitation data emerges as a potent strategy for enhancing the precision of runoff simulations. This research can provide a reference for selecting meteorological data products and optimization methods for hydrological process simulation in areas with few meteorological stations.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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