Natural scientists are keen on identifying biodiversity hot spots under threat and places where climate change impacts are severe, forest loss is acute, or water pollution is high. Social scientists, in turn, seek to identify hot spots of societal impact of such environmental changes, including economic problems, health problems, social instability, or even political violence. As social scientists, we are thus interested in what happens to societies worldwide when they experience water stress, and where those hot spots are. At the most basic level, does more water stress (due to climate change or unsustainable water use) cause a higher risk of societal conflict, or does it increase the chances of cooperative problem-solving?
At first glance, answering this question looks like an impossible task because, unlike deforestation or soil humidity, it is difficult to spot societal impacts of water stress globally through direct observation. That said, research efforts to measure and explain societal responses to water stress have made considerable progress due to that. This research, which is reviewed in the paper just published (https://www.nature.com/articles/s41893-020-0479-8), is particularly useful along two lines.
First, it helps us move away from speculative debates about coming water wars in a climate stressed world, which in the absence of solid scientific evidence could result in self-fulfilling prophecies and an unnecessary militarization of water management. Second, it contributes to broader assessments of climate-change impacts, notably those of IPCC Working Group II, which tend to be fairly systematic and precise with respect to climate change effects on water systems, but less so when it comes to societal influences of increased water stress.
As pointed out in our article, there are two fundamentally different approaches to studying the impact of water stress on societies. One measures conflict events in societies, irrespective of their cause, and then uses statistical models to estimate the impact of various factors (including water stress) on the probability of conflict. The other uses large news media archives to identify events pertaining directly to water issues and characterizes them in terms of their cooperative or conflictive nature. It then employs statistical models to estimate what factors drive freshwater related cooperation and conflict. We prefer the latter approach as it allows us to look at both cooperative and conflict societal responses to water stress, thus avoiding a conflict bias. What is more, it captures water-related events per se, rather than indirectly inferring from statistical models that conflict of any kind is (partially) correlated with water stress.
Besides showing what we have learned from existing research so far, we point to big data gaps that should be overcome by a concerted international research effort, ideally based on computational (automated) approaches to coding vast amounts of text data, and on social media information.