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A Novel Spatiotemporal Statistical Downscaling Method for Hourly Rainfall

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Abstract

Finer spatiotemporal resolution rainfall data is essential for assessing hydrological impacts of climate change on medium and small basins. However, existing methods pay less attention to the inter-day correlation and diurnal cycle, which can strongly influence the hydrological cycle. To address this problem, we present a spatiotemporal downscaling method that is capable of reproducing the inter-day correlation, the diurnal cycle, and rainfall statistics on daily and hourly scales. The large-scale datasets, which we obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis dataset (NNR) and general circulation model (GCM) outputs, and local rainfall data are analyzed to assess the impacts of climate change on rainfall. Our proposed method consists of two steps: spatial downscaling and temporal downscaling. We apply spatial downscaling first to obtain the relationship between large-scale datasets and daily rainfall at a site scale using a k-nearest neighbor method (KNN). Then, we conduct an hourly downscaling of daily rainfall in the second step using a genetic algorithm-based KNN (GAKNN) with the inter-day correlation and the diurnal cycle. Furthermore, we analyzed changes in rainfall statistics for the periods 2046–2065 and 2081–2100 under the A2, A1B, and B1 scenarios of the third generation Coupled Global Climate Model (CGCM3.1) and Bergen Climate Model version 2 (BCM2.0). An application of our proposed method to the Shihmen Reservoir basin (Taiwan) has shown that it could accurately reproduce local rainfall and its statistics on daily and hourly scales. Overall, the results demonstrated that the proposed spatiotemporal method is a powerful tool for downscaling hourly rainfall data from a large-scale dataset. The understanding of future changes of rainfall characteristics through our proposed method is also expected to assist the planning and management of water resources systems.

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Authors and Affiliations

  1. Department of Civil Engineering, National Taiwan University, Taipei, 10617, Taiwan

    Gwo-Fong Lin, Ming-Jui Chang & Chian-Fu Wang

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  1. Gwo-Fong Lin
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  2. Ming-Jui Chang
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  3. Chian-Fu Wang
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Correspondence to Gwo-Fong Lin.

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Lin, GF., Chang, MJ. & Wang, CF. A Novel Spatiotemporal Statistical Downscaling Method for Hourly Rainfall. Water Resour Manage 31, 3465–3489 (2017). https://doi.org/10.1007/s11269-017-1679-5

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  • DOI: https://doi.org/10.1007/s11269-017-1679-5

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