Lange et al. (2020) used global climate models, global hydrological models and the global river flood model CaMa-Flood to project how global warming might change the exposure of land and population to river floods around the world. A summary of that study including results at the global and national level is provided in the associated global ISIpedia article. Here we present additional results at the national and grid level for Kenya. Important limitations of these country-specific results are discussed in the last section of this report.
What are our projections for Kenya?
Compared to pre-industrial conditions, global warming has already reached 1°C today. For this warming level, we simulated that 0.08% of the land area and 0.06% of the population of Kenya is exposed to at least one river flood per year. For 2°C global warming, we projected these numbers to change to 0.21% of the land area and 0.19% of the population of Kenya.
To better understand how we arrived at those numbers, you can explore the details of our simulation results in Figure 1. The toggles land/population and global warming/time allow you to change what is represented by which axis of the line plot. For example, you can view how land exposure to flooding varies with global warming and how population exposure to flooding changes over time for different climate change scenarios (RCP2.6 and RCP6.0).
By default, Figure 1 shows the median of the results obtained with all climate model–hydrological model combinations. Lines and dots represent the exposure projected for an average year. The shading represents how much the exposure typically deviates from that average as it varies from year to year. To see results for an individual model combination, use the drop-down menus for model selection. You will find that depending on which climate model you select, exposure variation with global warming is shown for different ranges of global warming. This is because different climate models reach different levels of global warming towards the end of the 21st century under the two future climate change scenarios used in our study. The highest warming level reached by all climate models is 2°C. That is why median results over all climate models are only shown up to that warming level.
Explore how land and population exposure to river flooding in Kenya vary with global warming and time
Show exposure of
Show how exposure varies with
Click on any dot to see a disaggregation of the national results to the grid level. Compare results obtained with different models by selecting All in the drop-down menus or by clicking on their headers. Use the toggles to change what is represented by the axes of the line plot. The NDC line represents the expected global warming level for the given year assuming the mean of the unconditional worst, unconditional best, and conditional NDC pathways. See this article for more info. Use the Download Data button to get numeric results for all model combinations.
FIG 1 / Adapted from Lange et al. (2020).
Have you noticed that the time series shown in Figure 1 are less smooth than the corresponding time series at the global level? The difference is most visible for individual model combinations. It is related to our decision to only consider river floods with magnitudes exceeding the 100-year return level under pre-industrial climate conditions. By definition, those events are rare. More than 20 years can easily pass between two such events hitting a country, in particular if the country is small. The larger the country, the less likely this becomes. At the global level, this is very unlikely because almost every year a large river flood event occurs somewhere on Earth. As a consequence, the results of our projections are statistically more uncertain at the national level than at the global level. More simulations would be needed to reduce that uncertainty.
If you click on a dot in Figure 1 you will see results at the grid level that correspond to the national results represented by the dot. The grid you see on that map is the 0.5° x 0.5° latitude–longitude grid that was used for the hydrological model simulations. The grid-level results are statistically even more uncertain than the national-level results. Nevertheless, visible differences between grid cells are not necessarily insignificant.
In many cases, you will find similarities between the spatial pattern of the exposure to river floods and the river network of the country. You can explore those patterns further in Figure 2, which shows the multi-model median exposure to river flooding at the grid level in an average year at 2°C global warming.
Use the toggle to switch from land exposure to population exposure.
FIG 2 / Adapted from Lange et al. (2020).
What are the limitations of our projections?
The country-level and grid-level results presented here are based on global hydrological model simulations that were analysed by Lange et al. (2020) to quantify how the exposure to extreme climate impact events changes with global warming. The results shown here were not reviewed in detail by experts of the hydrology of Kenya. The models used for the global simulations were not tailored to the specifics of the country. They were also not evaluated at that level. It is possible that relevant factors for river flood projections for Kenya were not included or strongly simplified in the hydrological models used in Lange et al. (2020). For example, for some countries it may be problematic that glaciers were not represented and vegetation-related processes strongly simplified in many of the global hydrological model simulations.
Additional limitations of the country-level and grid-level results presented here are their higher statistical uncertainty compared to the global-level results. And the limitations of the latter apply here too. Those are related to a simplified representation of flood protection measures, not taking into account future population change and uncertain precipitation projections.
This article was written in collaboration with the ISIpedia Editorial Team.
Please contact the ISIpedia Editorial Team (email@example.com) for more information or questions about this report.
1 Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
2 School of Geographic Sciences, East China Normal University, Shanghai, China