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Assessing reliability of precipitation data over the Mekong River Basin: A comparison of ground-based, satellite, and reanalysis datasets
Chen, AF (Chen, Aifang)1; Chen, DL (Chen, Deliang)1,2,3; Azorin-Molina, C (Azorin-Molina, Cesar)1
Source PublicationINTERNATIONAL JOURNAL OF CLIMATOLOGY
2018-09-01
Volume38Issue:11Pages:4314-4334
DOI10.1002/joc.5670
Abstract

Accurate precipitation data are the basis for hydro-climatological studies. As a highly populated river basin, with the biggest inland fishery in Southeast Asia, freshwater dynamics is extremely important for the Mekong River Basin (MB). This study focuses on evaluating the reliability of existing gridded precipitation datasets both from satellite and reanalysis, with a ground observations-based gridded precipitation dataset as the reference. Two satellite products (Tropical Rainfall Measuring Mission [TRMM] and the Precipitation Estimation from Remote Sensing Information using an Artificial Neural NetworkClimate Data Record [PERSIANN-CDR]), as well as three reanalysis products (Modern-Era Retrospective analysis for Research and Applications [MERRA2], the European Centre for Medium-Range Weather Forecasts interim reanalysis [ERA-Interim], and the Climate Forecast System Reanalysis [CFSR]) were compared with the Asian PrecipitationHighly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) over the MB. The APHRODITE was chosen as the reference for the comparison because it was developed based on ground observations and has also been selected as reference data in previous studies. Results show that most of the assessed datasets are able to capture the major climatological characteristics of precipitation in the MB for the 10-year study period (1998-2007). Generally, both satellite data (TRMM and PERSIANN-CDR) show higher reliability than reanalysis products at both spatial and temporal scales across the MB, with the TRMM outperforming when compared to the PERSIANN-CDR. For the reanalysis products, MERRA2 is more reliable in terms of temporal variability, but with some underestimation of precipitation. The other two reanalysis products CFSR and ERA-Interim are relatively unreliable due to large overestimations. CFSR is better positioned to capture the spatial variability of precipitation, while ERA-Interim shows inconsistent spatial patterns but more realistically resembles the daily precipitation probability. These findings have practical implications for future hydro-climatological studies.

Subject Area地理学
WOS IDWOS:000443683600019
Language英语
Indexed BySCIE
KeywordRain-gauge Observations Hydrological Cycle Wavelet Analysis Tibetan Plateau Dense Network Analysis Tmpa Time-series Products Climate Performance
WOS Research AreaMeteorology & Atmospheric Sciences
WOS SubjectMeteorology & Atmospheric Sciences
Cooperation Status国际
ISSN0899-8418
Department环境变化与地表过程重点实验室
PublisherWILEY
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.itpcas.ac.cn/handle/131C11/8552
Collection图书馆
Corresponding AuthorChen, DL (Chen, Deliang)
Affiliation1. Univ Gothenburg, Dept Earth Sci, Reg Climate Grp, Box 460, S-40530 Gothenburg, Sweden.
2.Chinese Acad Sci, Key Lab Tibetan Environm Changes & Land Surface P, Inst Tibetan Plateau Res, Beijing, Peoples R China;
3.CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing, Peoples R China.
Recommended Citation
GB/T 7714
Chen, AF ,Chen, DL ,Azorin-Molina, C . Assessing reliability of precipitation data over the Mekong River Basin: A comparison of ground-based, satellite, and reanalysis datasets[J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY,2018,38(11):4314-4334.
APA Chen, AF ,Chen, DL ,&Azorin-Molina, C .(2018).Assessing reliability of precipitation data over the Mekong River Basin: A comparison of ground-based, satellite, and reanalysis datasets.INTERNATIONAL JOURNAL OF CLIMATOLOGY,38(11),4314-4334.
MLA Chen, AF ,et al."Assessing reliability of precipitation data over the Mekong River Basin: A comparison of ground-based, satellite, and reanalysis datasets".INTERNATIONAL JOURNAL OF CLIMATOLOGY 38.11(2018):4314-4334.
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