Future Climatic Projections and Hydrological Responses in the Upper Beijiang River Basin of South China Using Bias‐Corrected RegCM 4.6 Data

Abstract

Global climate change has a significant impact on the water cycle and natural ecosystems. The quantification of the impact of climate change on precipitation, temperature, and water resources has attracted substantial attention (IPCC, 2013). Global climate models (GCMs) have been developed and applied increasingly in recent decades (Stouffer et al., 2017;IPCC, 2013;Taylor et al., 2012). However, the low spatial resolution of GCMs is inadequate for high-precision and high-resolution climate information required in regional studies. Therefore, regional climate models (RCMs) have been developed by dynamically downscaling GCMs to obtain climate data with a high temporal and spatial resolution (Giorgi, 2019;Giorgi et al., 2009;Walton et al., 2020;Xu, Han, & Yang, 2019). RCMs have significant value in areas with high altitude, complex terrain, or sparse and uneven rainfall stations. Recently, many studies have been carried out using future climate data obtained from RCMs to drive hydrological models and obtain future watershed hydrological characteristics; these studies provided valuable results (Gorguner et al., 2019;Zhou et al., 2018). Commonly used RCMs include the Weather Research and Forecast model, Regional Climate Model (RegCM), Hadley Centre Global Environmental Model Version 3 Regional Climate Model, and Providing Regional Climates for Impacts Studies; among them, the RegCM is the most widely used model (Gao & Giorgi, 2017;Gao et al., 2018). However, due to different spatial resolution of GCMs, different parameterization schemes of the RegCM, and different land-use/land cover types, substantial differences occur between the RegCM output and field observations, especially in mountainous watersheds with complex topography, such as the Upper Beijiang River Basin (UBRB) in South China (Tong et al., 2017(Tong et al., , 2020. Some studies have found that using the RegCM output in hydrological research without any bias correction amplifies the rainfall-runoff deviation, causing uncertainties (Ozturk et al., 2017;da Silva et al., 2019). Therefore, it is crucial to perform bias correction, which is also referred to as statistical downscaling of the model output, to ensure the model's applicability to the hydrological research.

Evaluation and Comparison of Reanalysis Data for Runoff Simulation in the Data-Scarce Watersheds of Alpine Regions

“…Notably, during the calibration period (2009-2014), the highest daily average flow reached 3350.00 m 3 /s at the source of the Yellow River on 24 July 2012, and 3320.00 m 3 /s at the source of the Yangtze River on 24 July 2009. Employing the 95% quantile [36] of runoff values for the study period as the extreme runoff threshold (as illustrated in Figure 6), it was observed that, in most instances, both reanalysis datasets and observed data tend to underestimate peak flows during runoff simulations at the sources of both the Yellow River and the Yangtze. This discrepancy may arise from the SWAT model's limitation in accurately representing the glacier and soil thawing processes in the SYYR region during the thaw period.…”