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Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada.

Jeong, Dae Il; St-Hilaire, André; Ouarda, Taha B. M. J.; Gachon, Philippe (2012). Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada. Stochastic Environmental Research and Risk Assessment , vol. 26 , nº 5. p. 633-653. DOI: 10.1007/s00477-011-0523-3.

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Résumé

This study compares three linear models and one non-linear model, specifically multiple linear regression (MLR) with ordinary least squares (OLS) estimates, robust regression, ridge regression, and artificial neural networks (ANNs), to identify an appropriate transfer function in statistical downscaling (SD) models for the daily maximum and minimum temperatures (Tmax and Tmin) and daily precipitation occurrence and amounts (Pocc and Pamount). This comparison was made over twenty-five observation sites located in five different Canadian provinces (British Columbia, Saskatchewan, Manitoba, Ontario, and Québec). Reanalysis data were employed as atmospheric predictor variables of SD models. Predictors of linear transfer functions and ANN were selected by linear correlations coefficient and mutual information, respectively. For each downscaled case, annual and monthly models were developed and analysed. The monthly MLR, annual ANN, annual ANN, and annual MLR yielded the best performance for Tmax, Tmin, Pocc and Pamont according to the modified Akaike information criterion (AICu). A monthly MLR is recommended for the transfer functions of the four predictands because it can provide a better performance for the Tmax and as good performance as the annual MLR for the Tmin, Pocc, and Pamount. Furthermore, a monthly MLR can provide a slightly better performance than an annual MLR for extreme events. An annual MLR approach is also equivalently recommended for the transfer functions of the four predictands because it showed as good a performance as monthly MLR in spite of its mathematical simplicity. Robust and ridge regressions are not recommended because the data used in this study are not greatly affected by outlier data and multicollinearity problems. An annual ANN is recommended only for the Tmin, based on the best performance among the models in terms of both the RMSE and AICu.

Type de document: Article
Mots-clés libres: artificial neural network; climate change; statistical downscaling; precipitation; temperature; transfer function; multiple linear regression
Centre: Centre Eau Terre Environnement
Date de dépôt: 11 janv. 2021 15:35
Dernière modification: 11 janv. 2021 15:35
URI: http://espace.inrs.ca/id/eprint/10645

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