Transferability of climate simulation uncertainty to hydrological climate change impacts

Abstract

Abstract. Increasing number of climate models are being produced to cover the uncertainty, which makes it infeasible to use all of them in climate change impact studies. In order to thoughtfully select subsets of climate simulations from a large ensemble, several envelope-based methods have been proposed. The subsets are expected to cover a similar uncertainty envelope as the full ensemble in terms of climate variables. However, it is not a given that the uncertainty in hydrological impacts will be similarly well represented. Therefore, this study investigates the transferability of climate uncertainty related to the choice of climate simulations to hydrological impacts. Two envelope-based selection methods, K-means clustering and Katsavounidis–Kuo–Zhang (KKZ) method, are used to select subsets from an ensemble of 50 climate simulations over two watersheds with very different climates using 31 precipitation and temperature variables. Transferability is evaluated by comparing uncertainty coverage between climate variables and 17 hydrological variables simulated by a hydrological model. The importance of properly choosing climate variables in selecting subsets is investigated by including and excluding temperature variables. Results show that KKZ performs better than K-means at selecting subsets of climate simulations for hydrological impacts, and the uncertainty coverage of climate variables is similar to that of hydrological variables. The subset of first 10 simulations covers over 85 % of total uncertainty. As expected, temperature variables are important for the snow-related watershed, but less important for the rainfall-driven watershed. Overall, envelope-based selection of around 10 climate simulations, based on climate variables that characterize the physical processes controlling hydrology of the watershed, is recommended for hydrological impact studies.