Assessing reliability of precipitation data over the Mekong River Basin: A comparison of ground‐based, satellite, and reanalysis datasets

• Published in: International journal of climatology 2018-09, Vol.38 (11), p.4314-4334
• Main Authors: Chen, Aifang, Chen, Deliang, Azorin‐Molina, Cesar
• Format: Article
• Language: English
• Subjects: 2017; analysis tmpa; Artificial neural networks; Atmospheric precipitations; climate; Climate studies; Climatic data; Climatology; Daily precipitation; Data integration; Datasets; dense network; Dynamics; Evaluation; Fisheries; Freshwater; Freshwater fish; Ground-based observation; Hydrologic data; hydrological cycle; Inland fisheries; Inland water environment; Mekong River Basin; Meteorologi och atmosfärsvetenskap; Meteorology & Atmospheric Sciences; Meteorology and Atmospheric Sciences; n ml; Neural networks; plateau; Precipitation; Precipitation data; Precipitation estimation; precipitation evaluation; Precipitation probability; Probability theory; Products; Rain; rain gauge observations; Rainfall; reanalysis data; Reliability; Reliability analysis; Remote sensing; River basins; Rivers; Satellite data; Satellite observation; Satellites; Spatial variability; Spatial variations; Temporal variability; Temporal variations; tibetan; time-series; Tropical climate; Tropical rainfall; Tropical Rainfall Measuring Mission (TRMM); Variability; Water resources; water-sui; wavelet analysis; Weather forecasting
• ISSN: 0899-8418; 1097-0088
• DOI: 10.1002/joc.5670

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 Network—Climate 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 Precipitation—Highly 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. This study focuses on evaluating the reliability of existing gridded precipitation datasets both from satellite (Tropical Rainfall Measuring Mission [TRMM] and the Precipitation Estimation from Remote Sensing Information using an Artificial Neural Network—Climate Data Record [PERSIANN‐CDR]) and reanalysis (Modern‐Era Retrospective a