Catching Clouds: Mapping the Potential of Fog Water Harvesting in La Palma (Canary Islands)

Abstract

Fog water harvesting represents a sustainable and low-impact alternative for water supply in regions exposed to frequent fog and limited or highly seasonal rainfall. Despite its growing relevance, spatial assessments to identify suitable sites for fog collection remain scarce, particularly in insular and mountainous territories. This study addresses this gap by applying the potential of the WaterWorld model to map fog harvesting opportunities in La Palma (Canary Islands, Spain), a volcanic island characterized by steep topography and persistent orographic cloud formation driven by trade winds. We conducted simulations using multiple high-resolution datasets-including AEMET's ROCIO+_CAN grid, Cabildo de La Palma weather station records, and the Digital Climate Atlas of the Canary Islands-to calculate fog impaction and standardised fog impaction across the landscape. Results consistently indicate that the northeastern mid-elevation slopes (800-1,600 m a.s.l.) exhibit the highest fog input values, with theoretical yields for vertical mesh collectors reaching up to 0.26 m<^>3/m<^>2 annually. While these values cannot be directly compared to rainfall totals, they nonetheless indicate a substantial, spatially consistent non-conventional water input that can complement limited or seasonal rainfall in specific microclimatic zones. The analysis also reveals that vegetation density strongly influences fog accessibility: forested areas, while fog-rich, may reduce collection efficiency due to canopy interception. The most suitable locations for fog harvesting are therefore open or semi-open areas near the cloud belt, such as forest edges or natural clearings. Additionally, the comparison between simulations underscores the importance of using locally calibrated inputs to enhance spatial precision and minimize model uncertainty. A local bivariate analysis was also conducted to assess the sensitivity of fog impaction to key climatic variables such as humidity, temperature, wind, and precipitation, revealing spatially heterogeneous relationships that inform site-specific planning. Beyond its hydrological relevance, the methodology offers a replicable approach for identifying fog harvesting zones in other fog-prone, data-scarce environments. The findings contribute to climate-adaptive water planning and offer a valuable decision-support tool for integrating non-conventional resources into decentralized supply strategies, particularly in areas facing aquifer overexploitation or logistical constraints. Due to the model limitations and the complexity of the simulated hydrological processes, results obtained here should be considered exploratory and interpreted with caution, requiring further validation through local studies.Graphical Abstract This visual summary serves as a pivotal entry point into the research, offering a concise yet engaging overview of the study's core findings and methodologies. Focused on the island of La Palma (Canary Islands), the study begins by defining the study area through geographic and elevation-based mapping (Study Area panel), where fog-prone mid-elevation slopes are influenced by trade winds and orography. The methodology (Analyses panel) incorporates high-resolution climate and vegetation datasets-including precipitation, wind, and land cover-fed into the WaterWorld hydrological model, which simulates fog inputs across the landscape.The central process (WaterWorld model simulation panel) differentiates fog impaction and deposition, focusing on quantifying harvestable fog using a standardized fog impaction output under 5% mesh efficiency. Results (Conclusion panel) are displayed as a spatial map of fog input potential (in m<^>3/m<^>2 net/year), highlighting northeastern municipalities as priority areas for fog water harvesting, with fog impaction ranging from 135 to 165 mm/year -representing about 20% of total precipitation- and potential mesh yields reaching up to 0.26 m<^>3/m<^>2 net annually. These findings support fog harvesting as a cost-effective water source, particularly in remote or groundwater-stressed regions, while offering a transferable modelling framework for other mountainous and insular environments facing water scarcity.