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 heoretical 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, patiallyconsistent 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 ocations 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 mportance of using locally calibrated inputs to enhance spatial precision and minimize model ncertainty. 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, articularly 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.