Search Results
GPD was fitted to the subsets of extreme events (i.e., >95th percentile) in the RG and SREs datasets. The extremes in the SREs were obtained in a similar way to the RGs extremes. To make the stations and all the rainfall products comparable, we normalized the modeled return values with the RG-modeled return values at the stations, then averaged over all stations for each dataset. The normalized return values of the RG data were taken as the reference for evaluating the SREs. The performance of
GPD was fitted to the subsets of extreme events (i.e., >95th percentile) in the RG and SREs datasets. The extremes in the SREs were obtained in a similar way to the RGs extremes. To make the stations and all the rainfall products comparable, we normalized the modeled return values with the RG-modeled return values at the stations, then averaged over all stations for each dataset. The normalized return values of the RG data were taken as the reference for evaluating the SREs. The performance of
ENS is computed in a similar manner as ( section 2f ) ensures that both quantities are representative of the same environment. Parameter H ENS is finally evaluated in millimeters per day per kilometer, as E is computed as a water flux rate in millimeters per day [see section 2a , Eq. (2) ] and the grid spacing for the spatial derivate is provided in kilometers. 3. Results a. Model validation In this section the performance of the ensemble and ensemble subsets ( section 2b ) in reproducing
ENS is computed in a similar manner as ( section 2f ) ensures that both quantities are representative of the same environment. Parameter H ENS is finally evaluated in millimeters per day per kilometer, as E is computed as a water flux rate in millimeters per day [see section 2a , Eq. (2) ] and the grid spacing for the spatial derivate is provided in kilometers. 3. Results a. Model validation In this section the performance of the ensemble and ensemble subsets ( section 2b ) in reproducing
( IPCC 2012 ). For African countries, impacts of natural hazards are projected to be 20–30 times larger than in industrialized countries ( IPCC 2014 ). The most severe climate change impacts can be expected for regions of high population density and poverty rates ( Müller et al. 2014 ), as often observed in African cities. Hirabayashi et al. (2013) found a high consistency among global climate models predicting large increases in flood frequency in Africa under the strongest climate change scenario
( IPCC 2012 ). For African countries, impacts of natural hazards are projected to be 20–30 times larger than in industrialized countries ( IPCC 2014 ). The most severe climate change impacts can be expected for regions of high population density and poverty rates ( Müller et al. 2014 ), as often observed in African cities. Hirabayashi et al. (2013) found a high consistency among global climate models predicting large increases in flood frequency in Africa under the strongest climate change scenario
