Observation-driven model for calculating water harvesting potential from advective fog in (semi-)arid coastal regions

Abstract. Motivated by finding complementary water sources in (semi-)arid regions, we develop and assess an observational-driven model to calculate fog harvesting water potential. We aim to integrate this model with routine meteorological data collected under complex meteorological and topographic conditions to characterize the advective fog phenomenon. Based on the mass balance principle, the Advective fog Model for (semi-)Arid Regions Under climate change (AMARU) offers insights into fog water harvesting volumes in time and space domains. The model is based on a simple thermodynamic approach to calculate the dependence of the liquid water content (q_l) on height. Based on climatological fog collection records, we introduce an empirical efficiency coefficient. When combined with q_l, this coefficient facilitates the estimation of fog harvesting volumes (L m^-2). AMARU's outputs are validated against in-situ observations collected over Chile's coastal (semi-)arid regions at various elevations and years (2018–2023). The model's representations of the seasonal cycle of fog harvesting follow observations with errors of ∼10 %. The model satisfactorily estimates the maximum q_l (∼0.8 g kg^-1) available for fog harvesting potential in the vertical column. To assess spatial variability, we combine the model with satellite-retrieved data, enabling the mapping of fog harvesting potential along the Atacama coast. Our approach enables the application of the combined observational-AMARU model to other (semi-)arid regions worldwide that share similar conditions. Through the quantification of fog harvesting, our model contributes to water planning, ecosystem delimitation efforts, and the study of the climatological evolution of cloud water, among others.