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A Novel Spatiotemporal Statistical Downscaling Method for Hourly Rainfall

Author

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  • Gwo-Fong Lin

    (National Taiwan University)

  • Ming-Jui Chang

    (National Taiwan University)

  • Chian-Fu Wang

    (National Taiwan University)

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.

Suggested Citation

  • Gwo-Fong Lin & Ming-Jui Chang & Chian-Fu Wang, 2017. "A Novel Spatiotemporal Statistical Downscaling Method for Hourly Rainfall," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(11), pages 3465-3489, September.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:11:d:10.1007_s11269-017-1679-5
    DOI: 10.1007/s11269-017-1679-5
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    References listed on IDEAS

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    1. Subimal Ghosh & Sudhir Katkar, 2012. "Modeling Uncertainty Resulting from Multiple Downscaling Methods in Assessing Hydrological Impacts of Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(12), pages 3559-3579, September.
    2. Hung-Wei Tseng & Tao-Chang Yang & Chen-Min Kuo & Pao-Shan Yu, 2012. "Application of Multi-site Weather Generators for Investigating Wet and Dry Spell Lengths under Climate Change: A Case Study in Southern Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4311-4326, December.
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    Cited by:

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