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.
Highlights
The Inter-Governmental Panel on Climate Change (IPCC)’s fifth assessment report indicates that the global temperature has increased by about 0.89 °C over the period from 1901 to 2012 and, during this period, the temperature increase was mainly concentrated between 1951 and 2012, and was about 0.72 °C [1]
CLIGEN is a stochastic weather generator established by the United States Department of Agriculture (USDA) to provide a climate input for Water Erosion
CLIGEN is a stochastic weather generator established by the United States Department of Agriculture (USDA) to provide a climate input for Water Erosion Prediction Project (WEPP)
Summary
The Inter-Governmental Panel on Climate Change (IPCC)’s fifth assessment report indicates that the global temperature has increased by about 0.89 °C over the period from 1901 to 2012 and, during this period, the temperature increase was mainly concentrated between 1951 and 2012, and was about 0.72 °C [1]. For future climate change projections, Global Climate Models are widely used in the fields of hydrology, biology and environmental studies. Due to the incomplete understanding of the physical mechanism of climate change and the large amount of calculations, GCM output often has coarse spatial and temporal resolutions. On one hand, they will affect the simulation accuracy of climate characteristics at the catchment scale; on the other hand, the GCM’s output usually does not
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