Each year, many African countries experience natural hazards such as floods and, because of their low adaptative capabilities, they hardly have the means to face the consequences, and therefore suffer huge economic losses. Extreme rainfall plays a key role in the occurrence of these hazards. Therefore, climate projection studies should focus more on extremes in order to provide a wider range of future scenarios of extremes which can aid policy decision making in African societies. Some researchers have attempted to analyze climate extremes through indices reflecting extremes in climate variables such as rainfall. However, it is difficult to assess impacts on streamflow based on these indices alone, as most hydrological models require daily data as inputs. Others have analyzed climate projections through general circulation models (GCMs) but have found their resolution too coarse for regional studies. Dynamic downscaling using regional climate models (RCMs) seem to address the limitation of GCMs, although RCMs might still lack accuracy due to the fact that they also contain biases that need to be eliminated. Given these limitations, the current study combined both dynamic and statistical downscaling methods to correct biases and improve the reproduction of high extremes by the models. This study’s aim was to analyze extreme high flows under the projection of extreme wet rainfall for the horizon of 2041 of a Kenyan South Coast catchment. The advanced delta change (ADC) method was applied on observed data (1982–2005), control (1982–2005) and near future (2018–2041) from an ensemble mean of multiple regional climate models (RCMs). The created future daily rainfall time series was introduced in the HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) hydrological model and the generated future flow were compared to the baseline flow at the gaging station 3KD06, where the observed flow was available. The findings suggested that in the study area, the RCMs, bias corrected by the ADC method, projected an increase in rainfall wet extremes in the first rainy season of the year MAMJ (March–April–May–June) and a decrease in the second rainy season OND (October–November–December). The changes in rainfall extremes, induced a similar change pattern in streamflow extremes at the gaging station 3KD06, meaning that an increase/decrease in rainfall extremes generated an increase/decrease in the streamflow extremes. Due to lack of long-term good quality data, the researchers decided to perform a frequency analysis for up to a 50 year return period in order to assess the changes induced by the ADC method. After getting a longer data series, further analysis could be done to forecast the maximum flow to up to 1000 years, which could serve as design flow for different infrastructure.
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