Spatial downscaling of precipitation from GCMs for climate change projections using random cascades: A case study in Italy

Abstract

We present a Stochastic Space Random Cascade (SSRC) approach to downscale precipitation from a General Circulation Models (GCMs), developed for the assessment of water resources under climate change scenarios for the Oglio river (1440 km2), in the Italian Alps. The snow‐fed Oglio river displays complex physiography and high environmental gradient and statistical downscaling methods are required for climate change assessment. First, a back cast analysis is carried out to evaluate the most representative within a set of four available GCMs (R30, ECHAM4, PCM, HadCM3). Monthly precipitation for the window 1990–2000 from 270 gauging stations (one every 25 km2) in northern Italy is used and scores from objective indicators are calculated. The SSRC model is then tuned upon the Oglio river catchment for spatial downscaling (2 km2) of daily precipitation from the NCAR Parallel Climate Model, giving the comparatively best results for the area. Scale Recursive Estimation coupled with the Expectation Maximization algorithm is used for model estimation. The seasonal parameters of the multiplicative cascade are accommodated by statistical distributions conditioned upon the climatic forcing, based on a regression analysis. The SSRC approach reproduces well the spatial clustering, intermittency, self‐similarity, and spatial correlation structure of precipitation fields, with relatively low computational burden. Downscaling of future precipitation scenarios (A2 scenario from the Parallel Climate Model) is then carried out and some preliminary conclusions are drawn.