Abstract
Abstract. Many climate impact assessments require high-resolution precipitation time series that have a spatio-temporal correlation structure consistent with observations, for simulating either current or future climate conditions. In this respect, weather generators (WGs) designed and calibrated for multiple sites are an appealing statistical downscaling technique to stochastically simulate multiple realisations of possible future time series consistent with the local precipitation characteristics and their expected future changes. In this study, we present the implementation and validation of a multi-site daily precipitation generator re-built after the methodology described in Wilks (1998). The generator consists of several Richardson-type WGs run with spatially correlated random number streams. This study aims at investigating the capabilities, the added value and the limitations of the precipitation generator for a typical Alpine river catchment in the Swiss Alpine region under current climate. The calibrated multi-site WG is skilful at individual sites in representing the annual cycle of the precipitation statistics, such as mean wet day frequency and intensity as well as monthly precipitation sums. It reproduces realistically the multi-day statistics such as the frequencies of dry and wet spell lengths and precipitation sums over consecutive wet days. Substantial added value is demonstrated in simulating daily areal precipitation sums in comparison to multiple WGs that lack the spatial dependency in the stochastic process. Limitations are seen in reproducing daily and multi-day extreme precipitation sums, observed variability from year to year and in reproducing long dry spell lengths. Given the performance of the presented generator, we conclude that it is a useful tool to generate precipitation series consistent with the mean climatic aspects and likely helpful to be used as a downscaling technique for climate change scenarios.
Highlights
In Switzerland, precipitation is a key weather variable with high relevance for sectors such as energy production, infrastructure, tourism, agriculture and ecosystems
Based on our ensemble of synthetic time series, each containing 51 years, we analyse the reproduction of key precipitation characteristics. This validation goes beyond the reproduction of pure model parameters used to calibrate the weather generators (WGs) (Sect. 3.3.2), as it includes precipitation statistics that are not directly used in the specification and calibration of the model
Note that we present this analysis for the same time period as used for calibrating our WG
Summary
In Switzerland, precipitation is a key weather variable with high relevance for sectors such as energy production, infrastructure, tourism, agriculture and ecosystems. Owing to a complex topography, daily precipitation varies strongly in space and time (Frei and Schär, 1998; Isotta et al, 2013). The spatial distribution of daily precipitation frequency and intensity depends on the topography, with higher frequencies and intensities along the northern Alpine ridge during summer, and a strong north–south gradient with heavier intensities in southern Switzerland from spring to autumn. Precipitation amounts and frequencies are typically largest in summer, mainly due to convective processes (Frei and Schär, 1998). Given the expected changes in the hydrological cycle over the twenty-first century (Allen and Ingram, 2002; Held and Soden, 2006), the need for reliable and quantitative future local precipitation projections in Switzerland is continuously growing. To effectively assess the impacts related to changes in precipitation, often highly localised daily data are needed that are ideally both consistent in time and in space In Switzerland, various impact assessment reports rely on the statistically downscaled pre-
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