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Project

Statistical Downscaling for Climate Change Impact Analysis on Hydrological Extremes

Future climate gained attention due to climate change caused by anthropogenic activities. In this, Global Climate Models (GCMs) provided primary information of studying water systems but their resolution is coarse for studying local processes in river catchments, hence, we need data al local scale. For this, two procedures are available: Dynamical Downscaling and Statistical Downscaling. With Dynamical Downscaling the Regional Climate Models (RCMs) are produced, but RCMs are also limited in their resolution; therefore, Statistical Downscaling is needed together with the results of the RCMs. RCMs explain better the processes at the regional scale, and hence, is a good practice to use them together with a downscaling method for studying river catchments. Nevertheless, due to their boundary conditions nested on GCMs, the use of RCMs build up the uncertainties more than by only using GCMs; to take into account these uncertainties in the study, ensembles of RCMs are advised. With this ensembles, we can study processes in river catchments and still account for the uncertainties, however, we need to be aware that these uncertainties are only the response uncertainties. Later, by forcing hydrological models with data series from the downscaled RCMs we can study the extremes in a catchment either high or low extremes, depending on the focus of our research, that is, flooding or droughts.

Date:16 Sep 2019 →  16 Sep 2023
Keywords:Statistical Downscaling, Climate change, Hydrological extremes
Disciplines:Climate change
Project type:PhD project