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Article

Satellite-Based Precipitation Datasets Evaluation Using Gauge Observation and Hydrological Modeling in a Typical Arid Land Watershed of Central Asia

by 1,2,3,4,5,6, 1,2,5,6,7,*, 1,2,5,6,7, 1,2,3,4,5,6, 1,2,3, 1,2,5,6, 1,5,6,7, 1,5,6 and 3,4,5,6
1
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2
Key Laboratory of GIS & RS Application, Xinjiang Uygur Autonomous Region, Urumqi 830011, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
4
Department of Geography, Ghent University, 9000 Ghent, Belgium
5
Sino-Belgian Joint Laboratory of Geo-Information, Urumqi 830011, China
6
Sino-Belgian Joint Laboratory of Geo-Information, 9000 Ghent, Belgium
7
CAS Research Center of Ecology and Environment in Central Asia, Urumqi 830011, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(2), 221; https://doi.org/10.3390/rs13020221
Received: 7 December 2020 / Revised: 5 January 2021 / Accepted: 6 January 2021 / Published: 11 January 2021
(This article belongs to the Special Issue Remote Sensing in Hydrology and Water Resources Management)
Hydrological modeling has always been a challenge in the data-scarce watershed, especially in the areas with complex terrain conditions like the inland river basin in Central Asia. Taking Bosten Lake Basin in Northwest China as an example, the accuracy and the hydrological applicability of satellite-based precipitation datasets were evaluated. The gauge-adjusted version of six widely used datasets was adopted; namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Global Precipitation Measurement Ground Validation National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) Morphing Technique (CMORPH), Integrated Multi-Satellite Retrievals for GPM (GPM), Global Satellite Mapping of Precipitation (GSMaP), the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA). Seven evaluation indexes were used to compare the station data and satellite datasets, the soil and water assessment tool (SWAT) model, and four indexes were used to evaluate the hydrological performance. The main results were as follows: 1) The GPM and CDR were the best datasets for the daily scale and monthly scale rainfall accuracy evaluations, respectively. 2) The performance of CDR and GPM was more stable than others at different locations in a watershed, and all datasets tended to perform better in the humid regions. 3) All datasets tended to perform better in the summer of a year, while the CDR and CHIRPS performed well in winter compare to other datasets. 4) The raw data of CDR and CMORPH performed better than others in monthly runoff simulations, especially CDR. 5) Integrating the hydrological performance of the uncorrected and corrected data, all datasets have the potential to provide valuable input data in hydrological modeling. This study is expected to provide a reference for the hydrological and meteorological application of satellite precipitation datasets in Central Asia or even the whole temperate zone. View Full-Text
Keywords: satellite datasets; accuracy evaluation; hydrological applicability; SWAT; Bosten Lake Basin satellite datasets; accuracy evaluation; hydrological applicability; SWAT; Bosten Lake Basin
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MDPI and ACS Style

Peng, J.; Liu, T.; Huang, Y.; Ling, Y.; Li, Z.; Bao, A.; Chen, X.; Kurban, A.; De Maeyer, P. Satellite-Based Precipitation Datasets Evaluation Using Gauge Observation and Hydrological Modeling in a Typical Arid Land Watershed of Central Asia. Remote Sens. 2021, 13, 221. https://doi.org/10.3390/rs13020221

AMA Style

Peng J, Liu T, Huang Y, Ling Y, Li Z, Bao A, Chen X, Kurban A, De Maeyer P. Satellite-Based Precipitation Datasets Evaluation Using Gauge Observation and Hydrological Modeling in a Typical Arid Land Watershed of Central Asia. Remote Sensing. 2021; 13(2):221. https://doi.org/10.3390/rs13020221

Chicago/Turabian Style

Peng, Jiabin, Tie Liu, Yue Huang, Yunan Ling, Zhengyang Li, Anming Bao, Xi Chen, Alishir Kurban, and Philippe De Maeyer. 2021. "Satellite-Based Precipitation Datasets Evaluation Using Gauge Observation and Hydrological Modeling in a Typical Arid Land Watershed of Central Asia" Remote Sensing 13, no. 2: 221. https://doi.org/10.3390/rs13020221

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