SDSM - a decision support tool for the assessment of regional climate change impacts

By Robert L. Wilby, C. Dawson, E. Barrow. Environmental Modelling & Software, 2002

1,459 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/ed9e4eb38682e35782cad3d4a530b7a07c493285

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Citations in the corpus

Combing Random Forest and Least Square Support Vector Regression for Improving Extreme Rainfall Downscaling (2019, methodology): “There are numerous variant approaches of regression-based downscaling techniques such as logistic regression model [12], local polynomial regression [13], linear and non-linear regression [14], canonical correlation analysis [15], principal components [16], artificial neural network [17,18], support”
Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada (2012, methodology): “Multiple linear regression (MLR) (Enke and Spekat 1997; Wilby et al. 1998a; Hellström et al. 2001; Huth et al. 2001; Palutikof et al. 2002; Wilby et al. 2002; Huth 2004; Hessami et al. 2008), MLR based on principal component analysis (PCA) (Kidson and Thompson 1998; Buishand and Brandsma 2001; Hanss”
Statistical downscaling of precipitation using quantile regression (2013, methodology): “The standard regression predictors were selected using stepwise regression as implemented in the SDSM model (Wilby et al., 2002) and the ASD model (Hessami et al.”
Peanut Shell for Energy: Properties and Its Potential to Respect the Environment (2018, background): “8 and 4 ◦C before the end of the 21st century [8].”
Climate, extreme heat, and electricity demand in California (2008, methodology): “Statistical downscaling through regression is a common approach that has been well-documented in the literature (Karl et al.1990; Wilby et al. 1998; Huth et al. 2002; Wilby et al. 2002; Wilby and Dawson 2004), with the method used here described in Dettinger et al.”
Automated regression-based statistical downscaling tool (2008, methodology): “Predictor selection methods In SDSM (Wilby et al., 2002), selection of predictors is an iterative process, partly based on the user’s subjective judgment.”
An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment (2016, background): “Rainfall occurs when the probability of wet-day occurrence is greater than or equal to a uniform random number like in Wilby et al. (2002), thus incorporating an additional stochastic process.”
Multisite multivariate modeling of daily precipitation and temperature in the Canadian Prairie Provinces using generalized linear models (2016, background): “Most of the WGs focus on individual sites (e.g., Rajagopalan and Lall 1999; Wilby et al. 2002) and are therefore unable to represent the spatial structure of the observed climatic variables.”