Our short answer is yes. This article summarises how we arrived at this conclusion and lets you explore the long answer. We explain how we did our research, which data we used, how those data were produced and how they were analysed. First we outline what was already known when we started working on this question. At the end, we discuss some of the limitations of our results. This article is about our motivation, methodology and results at the global and national level. In the column to the left you can access more detailed results for individual countries.

Why did we expect climate change to change the exposure to river flooding?

Warmer air can hold more water vapour. This thermodynamic law is mathematically expressed by the Clausius–Clapeyron relation. Because of this law, we expect a warmer atmosphere to hold more moisture. A moister atmosphere in turn is expected to produce more intense precipitation. The more intense precipitation is expected to result in more frequent river flooding. According to this thermodynamic reasoning, one would expect river flood frequencies to increase everywhere on Earth in response to global warming.

In addition to changing the water contents of the atmosphere, climate change alters atmospheric circulation patterns. The interplay of changing circulation patterns and thermodynamics complicates the projection of precipitation patterns and river flooding. We use numerical models to simulate the details of those changes and produce river flood projections. In a previous modelling study, Hirabayashi et al. (2013) projected a spatially heterogeneous pattern of river flood frequency increases and decreases for different world regions. At the global scale, however, they projected an increase of the number of people annually exposed to river floods, in line with the simple thermodynamic reasoning outlined above.

Our study Lange et al. (2020) uses similar methods to project land and population exposure to river floods but puts these projections into the greater context of changing extreme climate impact events in response to global warming. Our new river flood projections per se can be considered a refinement of the projections provided by Hirabayashi et al. (2013) since we used a more refined modelling chain including climate models, hydrological models and a river flood model.

How did we project land and population exposure to river floods?

In order to arrive at our projections, we needed to define what we consider a river flood event, how we measure the exposure to such an event, and how we simulate these events.

We decided to consider only exceptionally large events since those have the greatest impact. We used flood magnitudes that occur only once in 100 years under pre-industrial climate conditions as a threshold. Only flood events with a magnitude greater than that threshold were considered.

Land exposure to river floods was measured by the inundated land area and population exposure to river floods was measured by the population living in those flooded areas. For our population exposure projections we used the global population distribution of year 2005. By using this fixed distribution we ensured that our projections represent only climate change related impacts, not those related to future population change.

In order to simulate river floods, we used climate models, hydrological models and a river flood model. Climate projections made with four global climate models (IPSL-CM5A-LR, GFDL-ESM2M, MIROC5, HadGEM2-ES) were used to make hydrological projections with eight global hydrological models. Based on the output from these models, we used the global river flood model CaMa-Flood to derive flood magnitudes and the associated inundated land area.

In the following we present results of our projections both in the form of time series and at different levels of global warming relative to pre-industrial conditions. The results at different warming levels were derived by pooling results from all years with a global mean temperature sufficiently close to the respective warming level.

For a full methodological description with more details please see our paper (Lange et al. 2020).

What are the results of our projections?

Compared to pre-industrial conditions, global warming has already reached 1°C today. For this warming level, we simulate that 0.17% of the global land area (excluding Greenland and Antarctica) and 0.24% of the global population is exposed to at least one river flood per year. For 2°C global warming, we project these numbers to increase to 0.32% of the global land area and 0.34% of the global population. These are increases by a factor of 1.88 and 1.44, respectively. The number of people annually exposed to river flooding is projected to increase from 18.1 million at 1°C to 26.14 million at 2°C global warming, again, in terms of the 2005 global population.

In Figure 1 you can explore those projections at the global level. 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 vary with global warming and time at the global and national level


Show exposure of


Show how exposure varies with


Click on any dot to see a disaggregation of the global results to the national 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. 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.

Adapted from Lange et al. (2020).


FIG 1

What you can learn from Figure 1 is that in global terms the exposure to flooding increases as the world warms. Higher greenhouse gas emissions under RCP6.0 compared to RCP2.6 lead to stronger global warming, which results in more flooding. The exact magnitude of the increase in flooding in response to global warming is uncertain though. This uncertainty is represented by the range of results obtained with different model combinations.

If you click on a dot in Figure 1 you will see results at the national level that correspond to the global results represented by the dot. You will find that exposure to flooding varies considerably from country to country. You can explore these differences further in Figure 2, which shows the multi-model median exposure to river flooding in an average year at 2°C global warming for different countries. Next to that, Figure 2 ranks countries by those exposure values. According to that ranking, the largest exposure to river floods in terms of the national land area percentage annually exposed at 2°C global warming is projected for Uganda (1), South Sudan (2), Netherlands (3), Rwanda (4), Tanzania (5), Sierra Leone (6), Bangladesh (7), Myanmar (8), Finland (9) and Indonesia (10).

Explore how land and population exposure to river flooding at 2°C global warming vary from country to country



Use the toggles to switch from a country ranking by exposure to annual average exposure values and from land exposure to population exposure. Use the Download Data button to get numeric results for the current view.

Adapted from Lange et al. (2020).


FIG 2

What are the limitations of our projections?

The limitations of our projections are related to our flood definition, our measure of exposure and to the way we simulated river floods.

In our flood definition we only considered river floods with magnitudes exceeding the 100-year return level under pre-industrial climate conditions. Hence we implicitly assumed universal protection against river floods of lower magnitude and did not account for future changes in protection levels. We did this not only to simplify our study and isolate the pure effect of climate change on exposure to flooding but also because detailed information about existing protection levels were not available for all regions of the world. It is known though that river flood defences in most developing regions are currently insufficient to prevent floods with 100-year return levels. We therefore expect to underestimate the exposure to river flooding in developing countries, but overestimate it in some industrialised countries.

Our exposure projection does not take into account future population change. While this was done to isolate the pure effect of climate change it is certainly not realistic. People moving into or out of flood-prone areas as well as different population growth rates in different world regions will influence future population exposure to river flooding.

Finally, it is important to keep in mind that the models used here are simplifications of reality. Since different models use different simplifications, the results shown above are model-dependent. For river floods, the greatest contribution to this modelling uncertainty comes from the global climate models. Precipitation projections from these models are uncertain due to their coarse resolution and their simplified representation of cloud dynamics. The uncertainty is particularly large for localised extreme precipitation events, which often lead to flooding. Furthermore, some important factors for river discharge modelling were not included or strongly simplified in the hydrological models, for example the influence of vegetation and glaciers.

Acknowledgements

This summary article was written in collaboration with the ISIpedia Editorial Team.

Contact

Please contact the ISIpedia Editorial Team (isipedia.editorial.team@pik-potsdam.de) for more information or questions about this report.

References

Hirabayashi, Y., R. Mahendran, S. Koirala, L. Konoshima, D. Yamazaki, S. Watanabe, H. Kim, and S. Kanae. 2013. “Global Flood Risk Under Climate Change.” Nature Climate Change 3 (9): 816–21. https://doi.org/10.1038/nclimate1911.
Lange, S., J. Volkholz, T. Geiger, F. Zhao, I. Vega, T. Veldkamp, C. P. O Reyer, et al. 2020. “Projecting Exposure to Extreme Climate Impact Events Across Six Event Categories and Three Spatial Scales.” Earth’s Future 8: 1–22. https://doi.org/10.1029/2020EF001616.

Affiliations

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