Author:
Tang Xuan,Li Hongxia,Qin Guanghua,Huang Yuanyuan,Qi Yongliang
Abstract
Satellite-based precipitation products (SBPPs) are essential for rainfall quantification in areas where ground-based observation is scarce. However, the accuracy of SBPPs is greatly influenced by complex topography. This study evaluates the performance of Integrated Multi-satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) in characterizing rainfall in a mountainous catchment of southwestern China, with an emphasis on the effect of three topographic variables (elevation, slope, aspect). The SBPPs are evaluated by comparing rain gauge observations at eight ground stations from May to October in 2014–2018. Results show that IMERG and GSMaP have good rainfall detection capability for the entire region, with POD = 0.75 and 0.93, respectively. In addition, IMERG overestimates rainfall (BIAS = −48.8%), while GSMaP is consistent with gauge rainfall (BIAS = −0.4%). Comprehensive analysis shows that IMERG and GSMaP are more impacted by elevation, and then slope, whereas aspect has little impact. The independent evaluations suggest that variability of elevation and slope negatively correlate with the accuracy of SBPPs. The accuracy of GSMaP presents weaker dependence on topography than that of IMERG in the study area. Our findings demonstrate the applicability of IMERG and GSMaP in mountainous catchments of Southwest China. We confirm that complex topography impacts the performance of SBPPs, especially for complex topography in mountainous areas. It is suggested that taking topographical factors into account is needed for hydrometeorological applications such as flood forecasting, and SBPP evaluations and retrieval technology require further improvement in the future for better applications.
Funder
the National Key Research and Development Program of China
the National Natural Science Foundation of China
Subject
General Earth and Planetary Sciences
Reference53 articles.
1. Flood risk and its reduction in China;Kundzewicz;Adv. Water Resour.,2019
2. Flash flood forecasting, warning and risk management: The HYDRATE project;Borga;Environ. Sci. Policy,2011
3. Application of System Dynamics to Water Security Research;Chen;Water Resour. Manag.,2013
4. Mosavi, A., Ozturk, P., and Chau, K.-w. (2018). Flood Prediction Using Machine Learning Models: Literature Review. Water, 10.
5. Flood Management Current State, Challenges and Prospects in Pakistan: A Review;Aslam;Mehran Univ. Res. J. Eng. Technol.,2018
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