PCR-GLOBWB Global Drought RP500

Global hydrological model used to carry out simulations of daily river discharge and runoff. Model forced using daily meteorological fields of precipitation, temperature, and radiation for four different time periods, namely: (a) 1960-1999, which represents the baseline climate; (b) 2010-2049 (representing 2030); (c) 2030-2069 (representing 2050); and (d) 2060-2099 (representing 2080). The meteorological data for the baseline climate are taken from the WATCH Forcing data (WFD) (Weedon et al., 2011). The future meteorological data are provided by the ISI-MIP project, and consist of bias-corrected data (Hempel et al., 2013) for an ensemble of five Global Climate Models (GCMs) from the ISIMIP project (Taylor et al., 2012). The GCMs used are GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M. For this study, we used climate projections based on 2 representative concentration pathways (RCPs), namely RCP2.6 and RCP8.5. The resolution of the input meteorological datasets for the current and future climate conditions is 0.5° x 0.5°.

The resulting drought hazard maps express probabilities of occurrence of the intensity of drought conditions. The intensity is measured as the number of months of long-term mean discharge which would be needed to overcome the maximum accumulated deficit volume under a certain return period. The deficit volume is calculated as the monthly flow deficit below the 20-percentile climatological flow. The different return period maps were generated by performing extreme value analysis on the yearly extreme values of the number of months of long-term mean discharge needed to overcome a discharge deficit. Please note that the used climate forcing in future climate still contains bias due to inter and intra annual variability in rainfall. This is not resolved in the bias correction scheme. Therefore, the drought hazard maps prepared with the GCM data still contain bias. This bias should be corrected by a comparison between 1960-1999 GCM runs of risk estimates and 1960-1999 EU-WATCH runs of risk estimates.

Further reading in: Veldkamp, T.I.E., Wada, Y., de Moel, H., Kummu, M., Eisner, S., Aerts, J.C.J.H., Ward, P.J. (in press). Changing mechanism of global water scarcity events: impacts of socioeconomic changes and inter-annual hydro-climatic variability. Global Environmental Change. DOI: 10.1016/j.gloenvcha.2015.02.011

Data e Risorse

Campo Valore
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metadata-date 2018-11-20T18:44:28Z
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Tag
  • amerigeo
  • amerigeoss
  • disaster
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  • drought
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  • gfdrr
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  • global
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isopen False
metadata_created 2025-11-24T16:23:06.774934
metadata_modified 2025-11-24T16:23:06.774939
notes Global hydrological model used to carry out simulations of daily river discharge and runoff. Model forced using daily meteorological fields of precipitation, temperature, and radiation for four different time periods, namely: (a) 1960-1999, which represents the baseline climate; (b) 2010-2049 (representing 2030); (c) 2030-2069 (representing 2050); and (d) 2060-2099 (representing 2080). The meteorological data for the baseline climate are taken from the WATCH Forcing data (WFD) (Weedon et al., 2011). The future meteorological data are provided by the ISI-MIP project, and consist of bias-corrected data (Hempel et al., 2013) for an ensemble of five Global Climate Models (GCMs) from the ISIMIP project (Taylor et al., 2012). The GCMs used are GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M. For this study, we used climate projections based on 2 representative concentration pathways (RCPs), namely RCP2.6 and RCP8.5. The resolution of the input meteorological datasets for the current and future climate conditions is 0.5° x 0.5°. The resulting drought hazard maps express probabilities of occurrence of the intensity of drought conditions. The intensity is measured as the number of months of long-term mean discharge which would be needed to overcome the maximum accumulated deficit volume under a certain return period. The deficit volume is calculated as the monthly flow deficit below the 20-percentile climatological flow. The different return period maps were generated by performing extreme value analysis on the yearly extreme values of the number of months of long-term mean discharge needed to overcome a discharge deficit. Please note that the used climate forcing in future climate still contains bias due to inter and intra annual variability in rainfall. This is not resolved in the bias correction scheme. Therefore, the drought hazard maps prepared with the GCM data still contain bias. This bias should be corrected by a comparison between 1960-1999 GCM runs of risk estimates and 1960-1999 EU-WATCH runs of risk estimates. Further reading in: Veldkamp, T.I.E., Wada, Y., de Moel, H., Kummu, M., Eisner, S., Aerts, J.C.J.H., Ward, P.J. (in press). Changing mechanism of global water scarcity events: impacts of socioeconomic changes and inter-annual hydro-climatic variability. Global Environmental Change. DOI: 10.1016/j.gloenvcha.2015.02.011
num_resources 173
num_tags 15
title PCR-GLOBWB Global Drought RP500