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

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 comp...

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Vydané v:Water resources research Ročník 47; číslo 3
Hlavní autori: Groppelli, B., Bocchiola, D., Rosso, R.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Washington Blackwell Publishing Ltd 01.03.2011
John Wiley & Sons, Inc
Predmet:
Case studies
Climate
Climate change
Climate models
Data assimilation
Discharge measurement
Environmental assessment
Environmental gradient
Gaging stations
GCMs
General circulation models
Hydrology
Precipitation
Rain
Regression analysis
River catchments
Rivers
Satellites
Snow
statistical downscaling
Statistical methods
Stream discharge
Stream flow
Water resources
Watersheds
ISSN:0043-1397, 1944-7973
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Popis
Shrnutí: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. Key Points Statistical Downscaling of GCMs Parameters calibration/validation by gauges Focus on local precipitation
Bibliografia:istex:864E41A3485839E1A40B41496E8C5CA0CE33EDBF
ark:/67375/WNG-7Q9CZ7G6-P
ArticleID:2010WR009437
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ISSN:0043-1397
1944-7973
DOI:10.1029/2010WR009437