Multiple Spatial and Temporal Scales Evaluation of Eight Satellite Precipitation Products in a Mountainous Catchment of South China
Satellite precipitation products (SPPs) have emerged as an important information source of precipitation with high spatio-temporal resolutions, with great potential to improve catchment water resource management and hydrologic modelling, especially in data-sparse regions. As an indirect precipitatio...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/5/1373 |
| _version_ | 1797614465345126400 |
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| author | Binbin Guo Tingbao Xu Qin Yang Jing Zhang Zhong Dai Yunyuan Deng Jun Zou |
| author_facet | Binbin Guo Tingbao Xu Qin Yang Jing Zhang Zhong Dai Yunyuan Deng Jun Zou |
| author_sort | Binbin Guo |
| collection | DOAJ |
| description | Satellite precipitation products (SPPs) have emerged as an important information source of precipitation with high spatio-temporal resolutions, with great potential to improve catchment water resource management and hydrologic modelling, especially in data-sparse regions. As an indirect precipitation measurement, satellite-derived precipitation accuracy is of major concern. There have been numerous evaluation/validation studies worldwide. However, a convincing systematic evaluation/validation of satellite precipitation remains unrealized. In particular, there are still only a limited number of hydrologic evaluations/validations with a long temporal period. Here we present a systematic evaluation of eight popular SPPs (CHIRPS, CMORPH, GPCP, GPM, GSMaP, MSWEP, PERSIANN, and SM2RAIN). The evaluation area used, using daily data from 2007 to 2020, is the Xiangjiang River basin, a mountainous catchment with a humid sub-tropical monsoon climate situated in south China. The evaluation was conducted at various spatial scales (both grid-gauge scale and watershed scale) and temporal scales (annual and seasonal scales). The evaluation paid particular attention to precipitation intensity and especially its impact on hydrologic modelling. In the evaluation of the results, the overall statistical metrics show that GSMaP and MSWEP rank as the two best-performing SPPs, with KGE<sub>Grid</sub> ≥ 0.48 and KGE<sub>Watershed</sub> ≥ 0.67, while CHIRPS and SM2RAIN were the two worst-performing SPPs with KGE<sub>Grid</sub> ≤ 0.25 and KGE<sub>Watershed</sub> ≤ 0.42. GSMaP gave the closest agreement with the observations. The GSMaP-driven model also was superior in depicting the rainfall-runoff relationship compared to the hydrologic models driven by other SPPs. This study further demonstrated that satellite remote sensing still has difficulty accurately estimating precipitation over a mountainous region. This study provides helpful information to optimize the generation of algorithms for satellite precipitation products, and valuable guidance for local communities to select suitable alternative precipitation datasets. |
| first_indexed | 2024-03-11T07:11:47Z |
| format | Article |
| id | doaj.art-b13b64c2ce144e9897db6a460703016c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T07:11:47Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b13b64c2ce144e9897db6a460703016c2023-11-17T08:32:11ZengMDPI AGRemote Sensing2072-42922023-02-01155137310.3390/rs15051373Multiple Spatial and Temporal Scales Evaluation of Eight Satellite Precipitation Products in a Mountainous Catchment of South ChinaBinbin Guo0Tingbao Xu1Qin Yang2Jing Zhang3Zhong Dai4Yunyuan Deng5Jun Zou6College of Geography and Tourism, Hengyang Normal University, Hengyang 421002, ChinaFenner School of Environment and Society, The Australian National University, Canberra, ACT 2601, AustraliaCollege of Geography and Tourism, Hengyang Normal University, Hengyang 421002, ChinaBeijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, ChinaCollege of Geography and Tourism, Hengyang Normal University, Hengyang 421002, ChinaCollege of Geography and Tourism, Hengyang Normal University, Hengyang 421002, ChinaCollege of Geography and Tourism, Hengyang Normal University, Hengyang 421002, ChinaSatellite precipitation products (SPPs) have emerged as an important information source of precipitation with high spatio-temporal resolutions, with great potential to improve catchment water resource management and hydrologic modelling, especially in data-sparse regions. As an indirect precipitation measurement, satellite-derived precipitation accuracy is of major concern. There have been numerous evaluation/validation studies worldwide. However, a convincing systematic evaluation/validation of satellite precipitation remains unrealized. In particular, there are still only a limited number of hydrologic evaluations/validations with a long temporal period. Here we present a systematic evaluation of eight popular SPPs (CHIRPS, CMORPH, GPCP, GPM, GSMaP, MSWEP, PERSIANN, and SM2RAIN). The evaluation area used, using daily data from 2007 to 2020, is the Xiangjiang River basin, a mountainous catchment with a humid sub-tropical monsoon climate situated in south China. The evaluation was conducted at various spatial scales (both grid-gauge scale and watershed scale) and temporal scales (annual and seasonal scales). The evaluation paid particular attention to precipitation intensity and especially its impact on hydrologic modelling. In the evaluation of the results, the overall statistical metrics show that GSMaP and MSWEP rank as the two best-performing SPPs, with KGE<sub>Grid</sub> ≥ 0.48 and KGE<sub>Watershed</sub> ≥ 0.67, while CHIRPS and SM2RAIN were the two worst-performing SPPs with KGE<sub>Grid</sub> ≤ 0.25 and KGE<sub>Watershed</sub> ≤ 0.42. GSMaP gave the closest agreement with the observations. The GSMaP-driven model also was superior in depicting the rainfall-runoff relationship compared to the hydrologic models driven by other SPPs. This study further demonstrated that satellite remote sensing still has difficulty accurately estimating precipitation over a mountainous region. This study provides helpful information to optimize the generation of algorithms for satellite precipitation products, and valuable guidance for local communities to select suitable alternative precipitation datasets.https://www.mdpi.com/2072-4292/15/5/1373precipitationsatellite observationevaluationhydrologic modellingXiangjiang River basin |
| spellingShingle | Binbin Guo Tingbao Xu Qin Yang Jing Zhang Zhong Dai Yunyuan Deng Jun Zou Multiple Spatial and Temporal Scales Evaluation of Eight Satellite Precipitation Products in a Mountainous Catchment of South China Remote Sensing precipitation satellite observation evaluation hydrologic modelling Xiangjiang River basin |
| title | Multiple Spatial and Temporal Scales Evaluation of Eight Satellite Precipitation Products in a Mountainous Catchment of South China |
| title_full | Multiple Spatial and Temporal Scales Evaluation of Eight Satellite Precipitation Products in a Mountainous Catchment of South China |
| title_fullStr | Multiple Spatial and Temporal Scales Evaluation of Eight Satellite Precipitation Products in a Mountainous Catchment of South China |
| title_full_unstemmed | Multiple Spatial and Temporal Scales Evaluation of Eight Satellite Precipitation Products in a Mountainous Catchment of South China |
| title_short | Multiple Spatial and Temporal Scales Evaluation of Eight Satellite Precipitation Products in a Mountainous Catchment of South China |
| title_sort | multiple spatial and temporal scales evaluation of eight satellite precipitation products in a mountainous catchment of south china |
| topic | precipitation satellite observation evaluation hydrologic modelling Xiangjiang River basin |
| url | https://www.mdpi.com/2072-4292/15/5/1373 |
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