An R package for assessment of statistical downscaling methods for hydrological climate change impact studies. (September 2017)

• Record Type: Journal Article
• Authors: Hanel, Martin; Kožín, Roman; Heřmanovský, Martin; Roub, Radek
• Is Part Of: Environmental modelling & software. Volume 95(2017)
• Page Start: 22
• Page End: 28
• Publication Date: 2017-09
• Subjects: Bias correction -- Time scale -- Runoff -- Multiscale bias correction -- Multiscale delta change; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.03.036
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Due to inherent bias the climate model simulated precipitation and temperature cannot be used to drive a hydrological model without pre-processing – statistical downscaling. This often consists of reducing the bias in the climate model simulations (bias correction) and/or transformation of the observed data in order to match the projected changes (delta change). The validation of the statistical downscaling methods is typically limited to the scale for which the transformation was calibrated and the driving variables (precipitation and temperature) of the hydrological model. The paper introduces an R package "musica" which provides ready to use tools for routine validation of statistical downscaling methods at multiple time scales as well as several advanced methods for statistical downscaling. The musica package is used to validate simulated runoff. It is shown that using conventional methods for downscaling of precipitation and temperature often leads to substantial biases in simulated runoff at all time scales.