To understand who is most negatively affected by natural disasters in the Pacific region today, let’s begin by exploring the last 25 years disaster exposure of countries in the region measured alongside their relative economic power.
Disaster Impact and GDP
GDP per capitaAffected persons as a share of population (per-year mean)
When measuring disaster exposure as a percent of population, it becomes clear that risk mostly clusters at the lower end of the per-capita GDP spectrum. These smaller economies are less capable of summoning resources needed mitigate risk and rebuild.
Furthermore, there are four countries (Vanuatu, Palau, Marshall Islands, and Micronesia) whose worst disaster years resulted in >100% population affected. This means that there were multiple high-impact disaster events in a given year. These become “whole-nation” events, leaving the population with fewer unaffected resources and a smaller economic buffer. Of these four countries, only Palau has a GDP per-capita above the median for the region.
With that context, let’s try to understand what types of disaster are creating the most impact.
Disasters by Type
Num of affected people by year
With 3.8 million people affected during the period, flood-related phenomena clearly emerge as the highest-impact event category. The next highest is drought, which affected 2.9 million people, nearly all of which came from a devastating drought in Papua New Guinea in 2015-2016, the single largest event in the dataset.
Still, despite a major drought event in a country with 10x the population of any other in the region, flooding impacted more people in the region during the period.
Looking to the future
Because flooding is a major disaster category in the region, forecasting future flood exposure could prove valuable. We have 25 years of flood impact data, but in the context of the changing climate, we have to account for how flooding frequencies may change.
To do this, we can use a method described by in a paper by Vitousek et al. 2017 to determine a flood amplification factor (AF) for each country in the region:
AF = 2^(SLR / D)
SLR - relative sea-level rise. This is determined per-country from the nearest IPCC AR6 sensors.
D - the regionally-defined doubling interval.
The SLR sea level projections for the 2050 horizon are essentially locked in, meaning that the window to reduce them by cutting emissions has already closed. Because of this, AF is likely conservative.
AF does not predict a change in storm frequency. It describes a change in coastal flooding frequency for a given storm frequency/intensity. Because virtually all flooding in the Pacific is coastal flooding, it is appropiate to use this multiplier against all of the baseline flooding measured in the region. The way it is typically described is that a flood amplification factor of 10 would predict that flooding events that only occur once in 50 years at the baseline would be expected to occur every 5 years.
AF produces a range of expected values, and the chart below plots the central point in the range (represented by the dotted line). Hovering or tapping a projection will show the upper and lower bounds of the expected range.
Flood-Affected Population to 2050
Cumulative people affected by flooding — history (2000–2025) and projected (2025–2050)
The projections are computed by calculating AF per year based on a linearly scaling SLR value over the period between 2025 and 2050. That yearly AF value is then applied to the average number of flood affected people per year during the baseline period and multiplied by the projected population. The projection represents a trend line, not the actual expected shape of future flooding. Flooding will continue to occur as punctuated events, but with greater frequency.
These curves are shaped both by the climate-driven flood AFand expected changes in population. The countries with the sharpest curves upwards have both high expected flood amplification and a growing population. If flooding continues to grow in this way, that might cause population growth to slow, which would help flatten the curve. Some countries, like Palau, are expecting to decrease their population, which will help mitigate the human cost of increased coastal flooding.