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

Abstract

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.

Comparison of CMIP5 and CMIP6 Multi-Model Ensemble for Precipitation Downscaling Results and Observational Data: The Case of Hanjiang River Basin

“…A multi-site statistical downscaling method introduced by Su et al [12] was used to spatially and temporally downscale the GCM outputs. The method framework consists of three steps, as illustrated in Figure 2.…”

“…The second step is temporal downscaling, in which the spatially downscaled monthly data are temporally downscaled into daily data using a single-site climate generator (CLIGEN). The final step re-creates the spatial correlation of the precipitation sequences for the meteorological stations based on the shuffle method [12].…”

“…The IDW method interpolates the original large-scale GCM data at the four nearest neighboring grid points to the target meteorological station. The IDW method is expressed by Equations ( 1)-(3) [12,27]:…”

“…Over the past few decades, several studies have utilized the multi-site downscaling method. Su et al [12] introduced a method for multi-site precipitation downscaling that combines a single-site stochastic weather generator with a modified shuffle procedure constrained with GCM multi-model ensemble (MME) monthly precipitation outputs. The results demonstrated that the proposed downscaling method can accurately simulate the daily, monthly, and annual precipitation means and lengths of wet spell lengths.…”

“…In this study, we evaluated and compared the performances of the precipitation downscaling results for the MME of CMIP5 (CMIP5-MME) and the MME of CMIP6 (CMIP6-MME) in simulating different precipitation characteristics from 1970 to 2005 in the Hanjiang River Basin (HRB), China. First, a multi-site downscaling method produced by Su et al [12] was used to downscale the GCM outputs of the CMIP5 and CMIP6 both spatially and temporally. Second, the reliability of the downscaled precipitation data was assessed.…”