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Article

Converting Climate Change Gridded Daily Rainfall to Station Daily Rainfall—A Case Study at Zengwen Reservoir

1
National Science and Technology Center for Disaster Reduction, New Taipei City 23143, Taiwan
2
Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Olga Petrucci
Water 2021, 13(11), 1516; https://doi.org/10.3390/w13111516
Received: 23 April 2021 / Revised: 22 May 2021 / Accepted: 24 May 2021 / Published: 28 May 2021
(This article belongs to the Special Issue Water Resources Vulnerability and Resilience in a Changing Climate)
With improvements in data quality and technology, the statistical downscaling data of General Circulation Models (GCMs) for climate change impact assessment have been refined from monthly data to daily data, which has greatly promoted the data application level. However, there are differences between GCM downscaling daily data and rainfall station data. If GCM data are directly used for hydrology and water resources assessment, the differences in total amount and rainfall intensity will be revealed and may affect the estimates of the total amount of water resources and water supply capacity. This research proposes a two-stage bias correction method for GCM data and establishes a mechanism for converting grid data to station data. Five GCMs were selected from 33 GCMs, which were ranked by rainfall simulation performance from a baseline period in Taiwan. The watershed of the Zengwen Reservoir in southern Taiwan was selected as the study area for comparison of the three different bias correction methods. The results reveal that the method with the wet-day threshold optimized by objective function with observation rainfall wet days had the best result. Error was greatly reduced in the hydrology model simulation with two-stage bias correction. The results show that the two-stage bias correction method proposed in this study can be used as an advanced method of data pre-processing in climate change impact assessment, which could improve the quality and broaden the extent of GCM daily data. Additionally, GCM ranking can be used by researchers in climate change assessment to understand the suitability of each GCM in Taiwan. View Full-Text
Keywords: general circulation model; GCMs ranking; statistical downscaling daily data; probability of precipitation; two-stage bias correction method general circulation model; GCMs ranking; statistical downscaling daily data; probability of precipitation; two-stage bias correction method
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MDPI and ACS Style

Teng, T.-Y.; Liu, T.-M.; Tung, Y.-S.; Cheng, K.-S. Converting Climate Change Gridded Daily Rainfall to Station Daily Rainfall—A Case Study at Zengwen Reservoir. Water 2021, 13, 1516. https://doi.org/10.3390/w13111516

AMA Style

Teng T-Y, Liu T-M, Tung Y-S, Cheng K-S. Converting Climate Change Gridded Daily Rainfall to Station Daily Rainfall—A Case Study at Zengwen Reservoir. Water. 2021; 13(11):1516. https://doi.org/10.3390/w13111516

Chicago/Turabian Style

Teng, Tse-Yu, Tzu-Ming Liu, Yu-Shiang Tung, and Ke-Sheng Cheng. 2021. "Converting Climate Change Gridded Daily Rainfall to Station Daily Rainfall—A Case Study at Zengwen Reservoir" Water 13, no. 11: 1516. https://doi.org/10.3390/w13111516

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