Mapping Current and Future Seawater Desalination Plants Globally Using Species Distribution Models

Abstract

AbstractDesalinized seawater is a vital freshwater source for regions with coastal water scarcity. Mapping seawater desalination plants enables a spatially detailed water resource assessment. Here, which is the first of its kind, we investigated the potential application of species distribution models (SDMs), which are widely used in ecology, to predict the global spatial distribution of seawater desalination plants. Two regression SDMs, a generalized linear model and a generalized additive model, along with two machine learning SDMs, a random forest model and a generalized boosted regression model, were trained and tested using the cross‐validation method at 0.5°. For each SDM, we considered four explanatory variables: aridity, distance to coastline, gross domestic product per capita, and annual domestic and industrial water withdrawal. Our results showed that machine learning SDMs had a relatively strong performance in capturing the historical locations of seawater desalination plants. We then mapped the future distribution of seawater desalination plants under different shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs). Our predictions showed that the number of predicted locations of seawater desalination plants increased by 31%, 47%, 55%, 57% in 2030, 2050, 2070, and 2090, respectively, relative to 2014. The largest increase occurred under SSP3_RCP7.0, while the lowest increase was found under SSP1_RCP2.6, which is mainly determined by the differences in the volume of annual domestic and industrial water withdrawal. Our study provides an insight into how SDMs can be used to predict the geographic locations of water management facilities.