Use of CLIGEN to Simulate Decreasing Precipitation Trends in the Southwest of Western Australia

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Vaghefi, P
Yu, B
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CLIGEN is a stochastic weather generator to statistically reproduce daily weather variables. Weather observations from regions with significant climate variability could be used to determine how to manipulate the input to CLIGEN to simulate climate change scenarios. Most previous studies have used simplistic approaches, such as changing the average daily precipitation on wet days by a fixed percentage or multiplying the CLIGEN-generated daily precipitation by a fixed factor. The aim of this study was to develop a method based on available historical data to adjust CLIGEN parameter values when precipitation has gradually but significantly decreased. In the southwest of Western Australia (SWWA), annual precipitation has shown a significant decreasing trend since the mid-1920s. Although the annual precipitation has clearly decreased during the last 100 years, trends in monthly and daily precipitation are not as strong and consistent. This study focused on CLIGEN parameter values for the daily precipitation amount to investigate correlations between variations of daily and annual precipitation for SWWA. Long-term (90 years) daily precipitation data from seven rainfall stations in this region were used to examine decadal variations in precipitation and to evaluate the changes to CLIGEN parameter values with a gradual decrease in annual precipitation. Average precipitation on wet days, standard deviations, skewness coefficients, and probabilities of a wet day following a wet day and a wet day following a dry day were analyzed for each of the 61 overlapping time series of 30 years each for these sites in SWWA. This article shows that for the wet months in winter, there are similarities between statistically significant changes in annual precipitation and changes in mean daily precipitation, standard deviation, the probability of a wet day following a dry day, and mean monthly precipitation. There is little evidence to suggest the need to adjust skewness coefficients, wet-following-wet and wet-following-dry probabilities to simulate changes in the mean monthly precipitation for this region. Three regionalization methods developed and evaluated for adjusting CLIGEN parameters are the site-specific method, the average method, and a Wilgarrup method using the site with the most significant changes to adjust CLIGEN parameters. This article also shows a linear relationship between trends in the mean monthly precipitation and mean daily precipitation. These results indicate that CLIGEN parameter values can be adjusted to reproduce the declining trend for the region in SWWA

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