Hydrological and Statistical Characterization of the Water-Conflict Nexus in the Lake Chad Basin

The existence, nature, and relevance of the interconnections between water and conflict have been debated from both the viewpoints of natural and social sciences. Representing these interconnections in a quantitative way is a challenging task, because of their intrinsic complexity. Yet, the allegations of actors in intra-state conflicts taking advantage of environmental stress makes the environmental aspects of this type of conflicts worthy of interest, especially for case studies where resources such as water and land have a primal role in the local population's livelihoods. We investigate these aspects for the Lake Chad Basin, in the period 2000-2015. Starting from custom-made spatially distributed hydrological simulations, we construct water availability indicators explicitly accounting for human dimensions of water demand and water utilization, with a strong focus on agriculture. Then, we use spatial econometric regression models to test water scarcity as a covariate of conflict occurrence, including it in a set of both biophysical and social stress variables, and specifically accounting for space- and time-specific conflict mechanisms. As a complement, we develop a spatial-clustering-based methodology to identify specific patterns of water availability in association to specific conflict dynamics. While we find that, in line with previous literature, water scarcity does not play a predominant role among the drivers of conflict, the results of the clustering analysis show that complex, context-specific interconnections may exist between water and conflict, mediated by particular water utilization processes and specific conflictual mechanisms. Advanced hydrological simulations and statistical tools, combined with an heuristic, critical approach toward the description of socio-hydrological processes, makes quantitative results able to support qualitative insights. This can help closing the gap between the biophysical modeling of environmental stress and the qualitative representation of social stress, and building more comprehensive knowledge frameworks for complex socio-environmental issues.