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Open AccessArticle

Multi-Site Statistical Downscaling Method Using GCM-Based Monthly Data for Daily Precipitation Generation

State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
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Water 2020, 12(3), 904; https://doi.org/10.3390/w12030904
Received: 23 February 2020 / Revised: 18 March 2020 / Accepted: 20 March 2020 / Published: 23 March 2020
(This article belongs to the Section Hydrology)
Global Climate Models (GCMs) can provide essential meteorological data as inputs for simulating and assessing the impact of climate change on catchment hydrology. However, downscaling of GCM outputs is often required due to their coarse spatial and temporal resolution. As an effective downscaling method, stochastic weather generators can reproduce daily sequences with statistically similar statistical characteristics. Most weather generators can only simulate single-site meteorological data, which are spatially uncorrelated. Therefore, this study introduces a method for multi-site precipitation downscaling based on a combination of a single-site stochastic weather generator, CLIGEN (CLImate GENerator), and a modified shuffle procedure constrained with multi-model ensemble GCM monthly precipitation outputs. The applicability of the downscaling method is demonstrated in the Huangfuchuan Basin (arid to semi-arid climate) for a historical period (1976–2005) and a projection period (2021–2070, historical, the representative concentration path (RCP) 2.6, RCP4.5, RCP4.8 scenarios) to generate spatially correlated daily precipitation. The results show that the proposed downscaling method can accurately simulate the mean of daily, monthly and annual precipitation and the wet spell lengths, and the inter-station correlation among 10 sites in the basin. In addition, this combination method generated the projected precipitation and showed an increasing trend for future years. These findings could help us better cope with the potential risks of climate change. View Full-Text
Keywords: CLIGEN; weather generator; multi-site statistical downscaling; spatial correlation; precipitation CLIGEN; weather generator; multi-site statistical downscaling; spatial correlation; precipitation
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Su, X.; Shao, W.; Liu, J.; Jiang, Y. Multi-Site Statistical Downscaling Method Using GCM-Based Monthly Data for Daily Precipitation Generation. Water 2020, 12, 904.

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