Statistical learning-based spatial downscaling models for precipitation distribution
Issue Date
2022-06-07Subjects
climate changeclimate projections
climate change education
Subject Categories::F850 Environmental Sciences
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The downscaling technique produces high spatial resolution precipitation distribution in order to analyze impacts of climate change in data-scarce regions or local scales. In this study, based on three statistical learning algorithms, such as support vector machine (SVM), random forest regression (RF), and gradient boosting regressor (GBR), we proposed an effcient downscaling approach to produce high spatial resolution precipitation. In order to demonstrate effciency and accuracy of our models over traditional multilinear regression (MLR) downscaling models, we did a downscaling analysis for daily observed precipitation data from 34 monitoring sites in Bangladesh. Validation revealed that R2 of GBR could reach 0.98, compared with RF (0.94), SVM (0.88), and multilinear regression (MLR) (0.69) models, so the GBR-based downscaling model had the best performance among all four downscaling models. We suggest that the GBR-based downscaling models should be used to replace traditional MLR downscaling models to produce a more accurate map of high-resolution precipitation for flood disaster management, drought forecasting, and long-term planning of land and water resources.Citation
Wu Y, Zhang Z, Crabbe MJC, Das LC (2022) 'Statistical learning-based spatial downscaling models for precipitation distribution', Advances in Meteorology, 2022 (3140872)Publisher
HindawiJournal
Advances in MeteorologyAdditional Links
https://www.hindawi.com/journals/amete/2022/3140872/Type
ArticleLanguage
enISSN
1687-9309Sponsors
This research was supported by the European Commission’s Horizon 2020 Framework Program (861584) and the Taishan distinguished professorship fund.ae974a485f413a2113503eed53cd6c53
10.1155/2022/3140872
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