Abstract
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.
Highlights
Water resources management is a crucial issue in climate change research
The daily data from the weather generator are based on the statistical characteristics of the observed rainfall, which cannot truly reflect the changes in future rainfall characteristics (Jones et al, 2010) [2], so indicators such as changes in probability of precipitation, consecutive dry days (CDD), and other important assessment results related to water sources still need to be refined
The quantile mapping bias correction method is usually adopted to reduce the bias between Global Circulation Models (GCMs) data and station data; there is still a gap after bias correction which is caused by the different wet days in these two sets of data
Summary
Water resources management is a crucial issue in climate change research. To analyze the impact of climate change on future water resources, researchers need to follow several procedures to obtain appropriate information. Information and Adaptation Knowledge Platform (TCCIP)”, as inputs of the weather generator, and uses the output daily temperature and daily rainfall data to simulate the flow of watersheds. It uses the system dynamic model to evaluate the baseline and future changes in the supply and demand of water resource systems in different areas of Taiwan. The daily data from the weather generator are based on the statistical characteristics of the observed rainfall, which cannot truly reflect the changes in future rainfall characteristics (Jones et al., 2010) [2], so indicators such as changes in probability of precipitation, consecutive dry days (CDD), and other important assessment results related to water sources still need to be refined
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