The contribution of fog to the water balance of Mediterranean climate-type coastal forests

The contribution of fog to the water balance of Mediterranean climate-type coastal forests | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 8 August 2025 V1 Latest version Share on The contribution of fog to the water balance of Mediterranean climate-type coastal forests Authors : Jorge Herrera-Bórquez 0009-0008-7828-3908 [email protected] , Camilo del Río , Patricio Pliscoff 0000-0002-5971-8880 , Vicente Espinoza , and Felipe Lobos-Roco 0000-0002-8786-0083 Authors Info & Affiliations https://doi.org/10.22541/au.175463508.87585823/v1 516 views 167 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract In Mediterranean climate-type coastal ecosystems, where precipitation rates are lower than evapotranspiration, fog contributes to the ecosystem’s water balance. Although the interception of fog water by vegetation is well-documented, its contribution to the overall water balance remains unclear. In this study we aim to understand the role of fog water as an input to the water balance in a representative Mediterranean climate-type coastal forest in Chile. By combining the water balance approach with a fog harvesting model , we characterize the key components of water storage, precipitation, fog, and evapotranspiration across different slopes of a coastal forest. Main results indicate that forest fog collection efficiency should average ~45% to maintain the forest water balance. However, in-situ measurements of fog dripping show a collection efficiency of ~12%, suggesting that more complex mechanisms, such as foliar water absorption, may also contribute to the total forest fog collection efficiency. Moreover, we demonstrate that fog acts as a consistent water input in these forests, contributing ~18% to their monthly water balance complementing precipitation by an additional 20% to 40%, depending on slope orientation (~120 mm y -1 ). This research provides evidence to clarify the role of fog in supporting the resilience of Mediterranean climate-type forests, which are highly threatened by climate change, land cover conversion, urban sprawl and fires. The contribution of fog to the water balance of Mediterranean climate-type coastal forests Jorge Herrera-Bórquez 1,2 , Camilo del Río 1,2 , Patricio Pliscoff 3 , Vicente Espinoza 1,4 , Felipe Lobos-Roco 1,5 . 1 Centro UC Desierto de Atacama, Pontificia Universidad Católica de Chile, Santiago, Chile. 2 Instituto de Geografía, Pontificia Universidad Católica de Chile, Santiago, Chile. 3 Centro de estudios territoriales, Universidad de los Andes, Santiago, Chile. 4 Meteorology and Air Quality Group, Wageningen University, Wageningen, the Netherlands. 5 Facultad de Agronomía y Sistemas Naturales, Pontificia Universidad Católica de Chile, Santiago, Chile. Abstract In Mediterranean climate-type coastal ecosystems, where precipitation rates are lower than evapotranspiration, fog contributes to the ecosystem’s water balance. Although the interception of fog water by vegetation is well-documented, its contribution to the overall water balance remains unclear. In this study we aim to understand the role of fog water as an input to the water balance in a representative Mediterranean climate-type coastal forest in Chile. By combining the water balance approach with a fog harvesting model , we characterize the key components of water storage, precipitation, fog, and evapotranspiration across different slopes of a coastal forest. Main results indicate that forest fog collection efficiency should average in-situ measurements of fog dripping show a collection efficiency of foliar water absorption, may also contribute to the total forest fog collection efficiency. Moreover, we demonstrate that fog acts as a consistent water input in these forests, contributing precipitation by an additional 20% to 40%, depending on slope orientation (~120 mm y -1 ). This research provides evidence to clarify the role of fog in supporting the resilience of Mediterranean climate-type forests, which are highly threatened by climate change, land cover conversion, urban sprawl and fires. Keywords: Cloud forest, Fog collection, Water balance, Mediterranean climate-type forest, Fog-harvesting Model, Remote sensing Introduction The Mediterranean climate-type coastal forests of Chile are characterized by hardy sclerophyllous vegetation especially adapted to withstand prolonged dry seasons lasting from early Spring to Autumn (from October to April) (Matskovky et al., 2021). These forests host a high diversity of woody species, which, depending on factors such as environmental humidity, may vary in composition and exhibit greater size and longevity. Among them are evergreen and summer-deciduous sclerophyllous species, such as Lithraea caustica , Peumus boldus , Cryptocarya alba , and Quillaja Saponaria , along with various succulent species and annual and perennial herbaceous plants (Luebert and Pliscoff, 2017 ). These ecosystems play a vital role in the region’s ecological gradient, contributing to a southward increase in biodiversity, vegetation density, and biomass (Armesto et al., 2007). At higher altitudes over the coastal mountains, the water balance in Mediterranean climate-type forests depends mainly on the equilibrium between precipitation and evapotranspiration (Zearpour et al., 2024). However, precipitations are insufficient to reach the evapotranspiration demand, leading to an apparent water imbalance. For example, in this region, the total annual precipitation averages 330 mm, with most rainfall occurring during the winter months (from June to August) (Santibañez, 2016), while evapotranspiration reaches ~500 mm, with peaks in summer (from December to February) (Balocchi et al., 2020). This apparent water imbalance can be offset by additional water sources and storage mechanisms. In this context, forests’ location and topography play a key role, enabling exposure to advective or orographic fog (Keim-Vera et al., 2024). The presence of fog in these forests is closely related to the inland advection of the Marine Boundary Layer (MBL), typically accompanied by stratocumulus clouds (Sc). Accordingly, the fog results from the interaction between the marine SCu and the local topography at heights ranging from 250 to 1,000 m ASL with a distinctive southwest-facing pattern (Lobos-Roco et al., 2025). The regular presence of fog creates persistent humid conditions in the forest environment through direct contact with vegetation surfaces, forming water droplets which effectively moisten the ecosystem (Sánchez et al., 2023). Fog formation follows well-defined daily and seasonal patterns, primarily driven by temperature and pressure gradients between oceanic and continental air masses (Wood, 2012). Over the region, fog is mainly controlled by a strong inversion layer formed at the lower atmosphere by thermal differences between a cold ocean and a hot free troposphere. This inversion layer increases over 20 K in summer, leading to ideal conditions for SCu cloud formation (Espinoza et al., 2024), which are advected inland by the typical land-sea circulation of the Chilean coast (Lobos-Roco et al., 2021). While the presence and interaction of marine fog with Mediterranean climate-type and fog-dependent forests have been well-documented in previous studies (Garreaud et al., 2008; Cuevas et al., 2023), most research has focused on describing the fog occurrence and its ecological importance. However, the specific mechanisms through which fog influences the forest water balance remain insufficiently understood and are rarely quantified in detail, except for Pacheco et al. (2024). In this context, it has been hypothesized that fog might contribute as a water input through direct interception by the vegetation canopy, where fog droplets accumulate on leaves and branches until they become large enough to drip to the forest floor, providing an additional water source for the root system (Sánchez-Salfán et al., 2023). This hypothesis is based on the apparent water imbalance resulting from annual precipitation that fails to reach evapotranspiration rates. Given our limited understanding of fog’s role in the forest ecohydrology, this research aims to quantify fog’s contribution as a water input in a specific Mediterranean climate-type coastal forest, the “Bosques de Zapallar”. Specifically, we aim to address two fundamental questions: how much fog water is intercepted and used by Mediterranean climate-type coastal forests in Chile?, and how does this fog water contribute to the overall ecosystem water balance? To answer these questions, we applied a water balance approach combining satellite data, numerical modeling, and field observations. Methods Study area We focused our research on a representative Mediterranean climate-type forest in coastal Chile, known as the ”Bosques de Zapallar”. This forest is located on a mountain at 150 to 800 m ASL and less than 2 km from the shore. All possible combinations of slope orientations are found in this study area’s. as shown in Figure 1a. This area enables us to analyse and understand the relationships between climate, topography and vegetation. Figure 1. Study area illustrating key features of the Mediterranean climate-type forest in central Chile and the instrumentation setup. (a) “Bosques de Zapallar” slope orientation across the study area. (b) “Parque el Boldo” forest showing the placement of the analogue rain gauges, the Standard Fog Collector (SFC) and the meteorological station. 2.2. Water balance model approaches To determine the amount of fog water intercepted by the Mediterranean climate-type forests in Chile and its role in their water balance, we combined a water balance model with a fog water collection model. The water balance model applied to the Bosques de Zapallar quantifies how ecosystem water storage fluctuates over time by tracking inputs and outputs within the forest system. The water balance equation reads: \(\Delta S\ =\ \left(P\ +\ f\right)-ET\), (1) where ΔS represents the soil water storage, \(P\) is the precipitation, \(f\) is the fog water input, and ET is the forest evapotranspiration. Since this study assumes that fog contributes to the forest water balance, the term \(f\) is included to represent the fog water intercepted and collected by vegetation. To deepen into the fog water collection term, we used the A dvective fog M odel for A rid and semi-arid R egions U nder climate change (AMARU, Lobos-Roco et al., 2025). This model estimates the fog water harvesting potential in environments where fog results from the advection of SCu clouds. The model is designed to use routine meteorological data of air temperature, relative humidity, air pressure and wind speed and direction at two different locations placed over a topographic transect. The main model equation reads: \(\int_{t0}^{t1}f=(F_{in(z)}\ \eta)\ dt\), (2) where \(F_{in(z)}\) is the fog influx, which corresponds to the total amount of liquid water that faces a collection surface during an advective fog event at z level, and \(\eta\) represents an empirical dimensionless coefficient of collection efficiency. The model is integrated over time ( t 0 to t 1 ), resulting in L m -2 per month equivalent to mm per month. The AMARU model is designed to represent fog water collection using a Standard Fog Collector (SFC, Schemenauer & Cereceda, 1994), whose efficiency is between 20% and 36% (Carvajal et al., 2020; Montecinos et al., 2018). Since SFC cannot represent the forest canopy water collection efficiency, we combined Equations (1) and (2) as a function of \(\eta\) as follows: \(\eta_{\text{cm}}\ =\ \frac{\mathrm{\Delta}S+ET-P}{F_{\text{in}}}\), (3) where \(\eta_{\text{cm}}\) corresponds to the forest canopy fog water collection efficiency obtained from the water balance model. Finally, fog canopy efficiency can also be estimated through in-situ canopy tree water collection and the estimated fog influx through the following relation: \(\eta_{(co,\ cm)}=\frac{f_{(co,\ \ cm)}}{F_{\text{in}}}\), (4) where \(f_{\text{co}}\) corresponds to the observed fog water collected by the canopy, measured through in-situ analog rain gauges, and\(f_{\text{cm}}\) corresponds to the modeled fog water collection derived from the water balance. The resulting efficiency terms,\(\eta_{\text{co}}\) and \(\eta_{\text{cm}}\), represent the observed and modeled canopy fog collection efficiency, respectively. Both estimates follow the same methodological framework, allowing for a consistent comparison between observed and modeled fog collection efficiencies. 2.3. Water balance sources The forest water balance model is fed by gridded assimilated and satellite data to define water balance. Moreover, additional meteorological ground observations and fog collection data serve as inputs for the AMARU model (Lobos-Roco et al., 2025). Below, we detail the data collection process, organized according to each water balance term. 2.3.1. Evapotranspiration ( ET) Evapotranspiration rates (mm) are obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) with product MOD16A2, which offers satellite data at 500m spatial resolution. This product provides measurements at 8-day intervals since 2021, which are integrated into total monthly amounts. 2.3.2. Precipitation ( \(P\) ) Regional precipitation rates (mm) are based on the Weather Research and Forecasting´s (WRF) results (Skamarock et al., 2019), obtained by the Argentine National Meteorological Service (SMN, 2022). This model provides accumulated precipitation hourly data at 4km spatial resolution, integrated into total monthly amounts. In addition, local precipitation measurements were collected using a meteorological station installed at the study site (Fig. 1b). The station has recorded precipitation at 10-min local time (LT) intervals since 2022. Unfortunately, precipitation data provided by local measurements are inaccurate during months of higher precipitation, failing to capture the largest precipitation event during winter (August 2023) and resulting in a lower correlation coefficient (R = 0.36). To obtain a better comparison (R = 0.61), we removed data referring to the month of August. To capture the spatial variability of precipitation, we compared the WRF results with data obtained from another meteorological station (Quintero, 32°47’ S - 71°31’ O, 14 m ASL). This comparison yielded a higher R value (0.92), including data referring to August 2023. Given these results and the lack of consistent in-situ precipitation data within the forest, WRF data were adopted as the official precipitation input for the study area. For further details, see Appendix 1. 2.3.3. Soil water storage ( ΔS ) The Soil water storage is estimated using daily data from the Global Land Evaporation Amsterdam Model (GLEAM v3.6a), which provides satellite-based estimates of land surface hydrological fluxes and states. GLEAM provides soil moisture products at a spatial resolution of 0.25° (~25 km), covering the period from 1980 to the present. In this study, we use the surface soil moisture component (representing the top ~10 cm of the soil column), which is expressed as volumetric water content (m³ m⁻³) and converted to equivalent water depth (mm) assuming a uniform soil layer thickness (Hulsman et al., 2017). 2.3.4. Fog and low Clouds The AMARU model (Lobos-Roco et al., 2025) uses routine meteorological data of air temperature, relative humidity, air pressure, and wind speed and direction from two meteorological stations displayed in a topographic transect to represent fog influx. For our study, we used two meteorological stations located in the Bosques de Zapallar forest (32°32’ S – 71°27’ O) at 456 m ASL and in the city of Quintero (32°47’ S - 71°31’ O), at 14 m ASL. To obtain the spatial representation of the model, fog and low clouds (FLC) frequency and spatial variability were combined. These data are obtained using images from the Geostationary Operational Environmental Satellite (GOES)-16 (Schmit et al., 2017). These images were processed through the methodology suggested by del Rio et al. (2021) and Espinoza et al. (2024). In addition, a digital elevation model (SRTM) of 30 m was included in the modeling, according to the methodology suggested by Lobos-Roco et al. (2025) to obtain the fog collection spatial variability at a 30 m pixel. To complement the satellite observations, fog water collection was measured using a Standard Fog Collector (SFC) located at “Parque el Boldo” (Fig. 1b). The SFC is a 1 m² raschel mesh frame featuring a 35% shading coefficient (Schemenauer & Cereceda, 1994b) which measures at a 10-min interval in liters per square meter of mesh (L m⁻²). Finally, to measure direct water interception by the forest canopy, we deployed a network of 18 analog rain gauges across the study area (Fig. 1b). The experimental design follows the methodology described by Cuevas et al. (2023), with analog rain gauges distributed to measure throughfall across different forest microenvironments. To ensure measurement accuracy, we applied a petroleum jelly layer at the bottom of the gauges to minimize evaporation losses. Water volumes were collected and measured biweekly during a three-month period, providing a comprehensive record of temporal variations in forest water interception. 2.3.5. Normalized difference vegetation index (NDVI) The Normalized Difference Vegetation Index (NDVI) is obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13A1 product, which has been available since 2000, providing vegetation indices at a 500-meter spatial resolution with a 16-day temporal resolution. The NDVI ranges from 0 to 1, with higher values indicating dense and healthy vegetation, and lower values corresponding to sparse or stressed vegetation. This index serves as a key indicator of vegetation density and productivity, allowing for the assessment of seasonal and spatial variations in plant coverage. To classify different vegetation types within the study area, we used SPOT imagery and topographic variables. The NDVI was calculated to enhance vegetation discrimination, and a Random Forest algorithm was applied to identify categories such as closed and open shrubland, closed and open forest, Thorn scrub and grassland, and ravine forest. 2.4. Canopy Fog efficiency collection and contribution The satellite-based and modeling results of water balance elements were systematically processed to create a standardized 30-meter gridded dataset covering the entire study area (Fig. 1a). Here, coarser resolution datasets, such as ET derived from MODIS, were split into smaller 30-m pixels. Each hydrological variable was analyzed as a total monthly amount for 2023. Based on these data, we first solved Equation (3) to obtain the forest canopy collection efficiency (\(\eta_{\text{cm}}\)). Then, using this coefficient, we solved Equation (4) to obtain the forest canopy fog water collection (\(f_{\text{cm}}\)), which is quantified to estimate its contribution to the water balance. Finally, using data collected by the gauges, we solved Equation (4) to calculate the observed canopy tree collection efficiency coefficient (\(\eta_{\text{co}}\)) and the canopy tree collection (\(f_{\text{co}}\)), which was compared to the one obtained by modeling. Results The combination of satellite data, ground-based observations and modeling results enabled us to investigate the role of fog in Mediterranean climate-type coastal forests and to quantify the amount of fog water collected by the forest. In the following subsections, we first describe the water balance elements of a typical Mediterranean fog forest ecosystem, focusing on fog variability. Second, we estimate the forest fog collection efficiency (\(\eta_{\text{cm}}\)) based on its water balance, and contrast it with the forest observed efficiency (\(\eta_{\text{co}}\)), analyzing the causes of their differences. Finally, we quantify the fog water collection of Mediterranean climate-type forests across the study area, analyzing the role of fog in their water balance. 3.1. Water balance of a Mediterranean climate-type fog forest 3.1.1. Evapotranspiration ( ET ) In coastal exorheic Mediterranean basins, where runoff depends primarily on precipitation (Tuset et al., 2016), evapotranspiration is the main water output, significantly reducing the amount of water available for storage. Figure 2a shows the monthly evapotranspiration rates (mm) across the study area. These results are averaged for the south-, west-, east-, and north-facing slopes, summing up to a total annual of 404 mm. Figure 2. (a) Monthly evapotranspiration (bars) from different slopes (South: dark green; West: light green; East: yellow; North: orange) and averaged incoming short-wave radiation (red solid line). Shade and error bars indicate standard deviations in radiation and evapotranspiration, respectively. (b) Monthly precipitation (blue bars) and averaged temperature (solid line). Shaded areas indicate the standard deviation of temperature. (c) Blue bars represent monthly soil water storage values. The south-facing slope exhibits the highest evapotranspiration rates throughout the year, with peaks of approximately 70 mm during summer. Following, the west-facing slope reaches a peak of around 60 mm during the same period. In contrast, the east and north-facing slopes show lower summer peaks of approximately 45-50 mm. During the autumn-winter transition (from May to July), evapotranspiration declines across all slopes, with values dropping to ~15 mm on the south and west slopes and ~10-15 mm on the east and north slopes. Annually, total evapotranspiration is highest on the south-facing slope, followed by the west, while the east and north slopes show the lowest values. These differences reflect local variations in slope orientation, vegetation structure, and water availability. The higher evapotranspiration on south and west slopes is driven by greater water availability, particularly on the south facing slope, where coastal fog inputs might provide supplementary moisture that supports higher water loss through evapotranspiration. This is especially evident during the spring and summer months, when evapotranspiration on the south-facing slope rises sharply compared to the other slopes. Solar radiation plays a critical role in the evapotranspiration process, providing the energy for water evaporation and plant transpiration. As shown in Figure 2a, radiation increases steadily from winter to summer, peaking in December (~280 W m⁻²). During winter, when radiation drops below 100 W m⁻², evapotranspiration values are also at their lowest, with alignment between solar radiation and evapotranspiration highlights how energy availability constrains water loss through evaporation and transpiration, especially in drier conditions. Seasonal variation in evapotranspiration also differs among the slopes. The south and west slopes show greater variability during the dry season (from January to March), largely influenced by the availability of fog moisture and radiation variability. In contrast, the east and north slopes exhibit more stable evapotranspiration rates throughout the year, as their vegetation is less influenced by fog and more dependent on direct solar radiation. During winter, evapotranspiration variation across all slopes is minimal due to the stabilizing effects of cooler temperatures and higher soil moisture availability. 3.1.2. Precipitation ( \(P\) ) Figure 3b shows the seasonal variability of precipitation in the study area, reaching up to 351 mm. This pattern aligns with the characteristic precipitation regime of the Mediterranean coastal climate, where rainfall is concentrated in the winter months. As shown in Figure 2b, approximately 254 mm of precipitation falls during this season, while summer experiences minimal rainfall. Temperature follows an inverse trend relative to precipitation, as illustrated in Figure 2b. The highest temperatures are recorded in summer, with averages ranging from 17 to 18°C, while winter months present lower temperatures around 10–12°C. This seasonal variation in temperature and precipitation typifies Mediterranean climates, marked by warm, dry summers and mild, wet winters. These fluctuations play a crucial role in shaping the ecosystem dynamics of the region, where elevated summer temperatures combined with low rainfall intensify water stress, influencing plant survival strategies and limiting the vegetation’s activity. Inversely, winter rainfalls support seasonal vegetation growth and replenishes critical water resources, fostering the ecosystem’s resilience in the face of seasonal water scarcity. 3.1.3. Soil water storage ( ΔS ) Soil moisture is a key indicator of water availability in terrestrial ecosystems, closely tied to precipitation and evapotranspiration dynamics. Figure 2c shows the seasonal variability of soil moisture, revealing higher soil moisture during winter, when rainfall replenishes water stores, and lower values during the dry season, as precipitation decreases and evapotranspiration increases. The Figure shows seasonal soil moisture patterns, with values peaking at ~12 mm during winter (from June to August) and declining to ~4 mm during the dry season. This trend reflects the combined effects of precipitation, evapotranspiration, and water retention capacity across the ecosystem. Soil moisture remains relatively stable from late autumn to winter (from March to August), supported by consistent rainfall, before declining rapidly during spring and summer (from September to February), when water inputs diminish and evapotranspiration increases. Overall, soil moisture trends align closely with precipitation patterns, although showing a lag of two months (Fig. 2b). Higher values during winter reflect the replenishment of water storage through rainfall, while the sharp decline during the dry season indicates limited water availability and higher evapotranspiration rates. This seasonal variability highlights the vulnerability of the system to water deficits during periods of low precipitation and high evapotranspiration. 3.1.4. Fog and low clouds Figure 3a shows a clear seasonal trend for fog water collection, with peaks during autumn and spring, reaching a maximum of 16 L m⁻² in August. These transitional seasons provide optimal conditions for fog and low cloud formation, thus providing optimal conditions for water collection. FLC coverage mirrors these patterns, exhibiting its highest levels in early spring and autumn. During winter (from June to August), both fog collection and FLC coverage reach their annual maxima, driven by increases in cloud coverage and atmospheric moisture typically occurring during the wet season. Figure 3 . (a) Monthly trends in total fog water collection (L m⁻²) and Fog and Low Cloud (FLC) coverage (%). The blue bars represent fog water collection, while the red line indicates FLC coverage as a percentage. (b) Hourly trends in fog water collection (L m⁻²) and solar radiation (W m⁻²). The blue bars represent fog water collection, and the orange line shows solar radiation levels, with shaded areas indicating their standard deviation. (c) Diurnal patterns of wind speed (m s⁻¹) and wind direction (°). The purple line shows wind speed, with shaded areas representing its standard deviation, and the blue line represents wind direction. Diurnal variability in fog collection and solar radiation underscores the conditions that optimize fog water capture, as shown in Figure 3b. Fog collection peaks in the early morning, between 4:00 and 8:00 LT, and declines as solar radiation increases. Around midday, when radiation reaches approximately W m⁻², fog collection is at its minimum. This inverse relationship indicates that fog capture is most effective under low radiation conditions, typically in the early morning, when cooler temperatures, higher humidity, and favourable VPD promote condensation. As radiation intensifies, fog disperses, and atmospheric humidity decreases, reducing fog collection. Even as radiation diminishes in the late afternoon, fog collection remains low due to reduced humidity and dissipated fog coverage. This diurnal cycle underscores early morning as the optimal time for fog collection and highlights the limitations of fog capture under high radiation levels. Wind speed also plays a critical role in fog collection efficiency (Lobos-Roco et al., 2025). Figure 3c shows that wind speed increases through the morning, peaking at approximately 1.5 m s -1 at around 14:00 LT, while wind direction remains stable, aligned with a south-west flow from the ocean. This consistent directional flow supports efficient fog collection by transporting moisture-laden air, maximizing SFC efficiency. Although midday fog collection is minimal due to high solar radiation, the steady wind flow sets favourable conditions for renewed fog capture in the evening, when radiation decreases. During the early morning and late evening, when fog collection is most efficient, wind speeds are lower, ranging from 0.8 to 1.0 m s -1 . While reduced wind speed slightly limits fog capture rates, the stable south-west flow still facilitates fog collection, particularly under low-radiation conditions, conducive to condensation. 3.1.5. Normalized difference vegetation index (NDVI) Figure 4 shows the vegetation vigour assessed through NDVI in the four main slopes composing the study area (Fig. 1a), which spans elevations above 150 m ASL. The south slope reaches up to 690 meters ASL and covers approximately 26 km², with an average slope of 16°. This orientation intercepts coastal fog, supporting drought-resistant sclerophyllous species and presenting moderated soil moisture loss during the dry season. Here, vegetation types include open shrubland (33%) and open forest (28%), with species like Cryptocarya alba . The east slope, with a maximum elevation of 755 meters ASL, covers 8 km² with an average slope of 24°. This orientation corresponds to a different vegetation structure, including open shrubland (34%), characteristic of drier conditions, and riparian forest (20%), with representative species such as Lithraea caustica . The west slope spans elevations up to 779 meters ASL and covers 14 km², with a gentler average slope of 14°, benefiting from consistent coastal fog inputs. Here, vegetation entails a combination of open forest (37%) and open shrubland (26%), with woody species such as Beilschmiedia miersii . Finally, the north slope is the largest of the four, spanning 36 km² with a maximum elevation of 780 meters ASL and an average slope of 18°. This slope is characterized by higher solar exposure and minimal influence from coastal fog. Its vegetation is dominated by open shrubland (31%), followed by thorn scrub and grassland (28%), with species like Baccharis linearis . Figure. 4. Monthly NDVI trends for each slope orientation showing seasonal vegetation dynamics. The South slope (dark green line), North slope (orange line), East slope (yellow line) and West slope (light green line) display distinct seasonal patterns. The photographs on the bottom show typical species for each slope: on the left, Cryptocarya alba ( south-facing slope); on the middle-left, Baccharis linearis (north-facing slope); on the middle-right, Echinopsis chiloensis (east-facing slope); on the right, Maytenus boaria (west-facing slope). Figure 4 shows that all slopes follow an NDVI seasonal pattern, with values peaking during spring (from September to November). This is likely due to the combination of sufficient soil moisture provided by the winter rains (from June to August) and the spring increase in solar radiation. However, significant differences in NDVI levels and seasonal variability are observed among the slopes, due to their different microclimatic conditions and vegetation structures. The south-facing slope consistently exhibits the highest NDVI values throughout the year, ranging from 0.55 in summer (from December to February) to peaks above 0.65 in August. This stability is attributed to the slope’s interception of coastal fog and lower solar exposure, which together create a cooler and moister environment that supports denser vegetation, including sclerophyllous species. The gradual NDVI decline during summer indicates a strong capacity of moisture retention, enabling vegetation to remain vigorous even under water-limited conditions. The west-facing slope shows NDVI values closely resembling those of the south slope. This can be attributed to the influence of coastal fog, which supplies additional moisture to the vegetation, especially during summer. However, the west slope experiences greater seasonal variability, with NDVI values dropping more sharply after their August peak of approximately 0.63. In contrast, the east-facing slope displays lower overall NDVI values, from around 0.45 in summer to approximately 0.55 during spring (from September to November). Its steeper terrain, combined with higher morning solar exposure and reduced influence from coastal fog, creates drier climate conditions, limiting its vegetation vigour. This slope exhibits more pronounced seasonal variability, as vegetation is more sensitive to moisture fluctuations throughout the year. Finally, the north-facing slope consistently exhibits the lowest NDVI values, with a summer minimum near 0.40 and a spring peak of just above 0.50. These low values reflect the slope’s high solar exposure, minimal influence from coastal fog, and overall drier conditions, which constrained vegetation growth. Moreover, the rapid NDVI decline after its peak indicates higher water stress and limited soil moisture retention, characteristic of drought-tolerant vegetation. Overall, NDVI patterns’ differences across slopes underscore the influence of diverse slope orientations, coastal fog inputs, and solar exposures on vegetation vigour. The south and west slopes maintain higher and more stable NDVI values due to their greater moisture availability, while the east and north slopes show lower values and greater seasonal variability, reflecting their more arid conditions. 3.2. Forest canopy fog collection efficiency Based on Equation 1, the four distinct water balance components within the study area are spatially characterized in Figure 5, representing the annual total mm for 2023. Water balance variables show spatial heterogeneity across the study area, resulting from slope orientation, elevation, and vegetation density differences combined with different satellite product resolutions. Figure 5. Spatial distribution of water balance inputs and outputs: (a) Soil water storage (Purple), (b) precipitation (light blue), (c) fog (Blue), and (d) Evapotranspiration (Orange). \(\Delta S\) distribution (Fig. 5a) lacks spatial detail due to the coarse resolution of the GLEAM model, with only a few pixels covering the entire study area. This limits the capacity for detailed spatial analysis, as values range from 50 to 57 mm per year. The \(P\) pattern (Fig. 5b) reveals that water deficit intensity varies by slope orientation, with values in Table 1 showing that south-facing slopes receive the highest \(P\) (396 mm), while west-facing slopes show the lowest (297 mm). \(F_{\text{in}}\) (Fig. 5c) shows a highly localized distribution, concentrated mainly on south and west-facing slopes and elevated areas. This pattern results from the altitude at which the SCu cloud impacts the coastal cordillera, forming fog. Table 1 shows that west-facing slopes receive the highest \(F_{\text{in}}\) (195 mm), followed closely by the south-facing slope (186 mm) while east-facing slopes experience the lowest \(F_{\text{in}}\) values (136 mm). Finally,ET (Fig. 5d) exhibits the highest spatial variability among the water balance components, with maximum values of 561 mm concentrated on south-facing slopes, and minimum values (378mm) on north-facing slopes. Despite the coarse pixel resolution, some spatial differences in water balance values are discernible. The mismatch between inputs (\(P\ +\ f\)) and outputs (ET) underscores the key role played by fog in sustaining the ecosystem water balance in specific topographic settings. To estimate the role of fog in the forest water balance, we first need to define the forest canopy efficiency. Figure 6 shows forest canopy efficiency values resulting from water balance (Equation 3,\(\eta_{\text{cm}}\)) and observations (Equation 4,\(\eta_{\text{co}}\)). \(\eta_{\text{cm}}\) Show values between 0 and 1 with an average of 0.45 and a median around 0.40. These results indicate that maintaining the ecosystem water balance requires the forest to be highly efficient at capturing fog. The box plot analysis reveals considerable variability in modeled efficiency across the forest, with the interquartile range spanning from approximately 0.25 to 0.65. This heterogeneity varies significantly by slope orientation, as shown in Table 1, with west-facing slopes exhibiting the highest efficiency (0.64), followed by south-facing slopes (0.58), while north-facing slopes exhibit the lowest efficiency 0.37) and east-facing slopes show intermediate results (0.41). These patterns reflect the combined influence of fog availability and water demand across different topographic settings. West and south-facing slopes, which receive higher\(\ F_{\text{in}}\), require greater collection efficiency to balance their elevated ET rates. In contrast, north-facing slopes, with lower fog inputs and reduced water demands, can maintain balance with lower efficiency. The water balance reveals that the forest must have a 0.45 collection efficiency, where nearly half of the available fog water flux must be intercepted and retained by the vegetation to compensate for the water deficit created by insufficient precipitation relative to evapotranspiration demands. Figure 6. Comparison of fog collection efficiency (\(\eta\)) across three experimental conditions: modeled through water balance canopy efficiency (\(\eta_{\text{cm}}\)), observed canopy efficiency (\(\eta_{\text{co}}\)), and combining modeled and observed canopy efficiency (\(\eta_{(cm,co)}\)). Box plots display the median values (orange line) and means (red dots) for each condition. As shown in Figure 1b and described in Section 2.2.5, tree dripping was measured with analog rain gauges across forest elevations. Table 2 presents fog dripping \((f\) co ) observed through analog rain gauges, fog influx (\(\ F\) in ) estimated using the AMARU model and observed canopy efficiency (𝜂 co ) (Equation 4) across different elevations. These results reveal varied collection rates across elevations, with observed values ranging from 1 to 47 L m⁻². Notably, the point situated at the SFC at 460 m ASL recorded 23 L m⁻². Despite these differences, the average efficiency (\(\eta_{\text{co}}\)) is ~0.12, as shown in Fig. 6a, with a narrower range than \(\eta_{\text{cm}}\) 0.05 to 0.29. The water balance model estimates a substantially higher canopy efficiency (\(\eta_{\text{cm}}\) ~0.45) than field observations (\(\eta_{\text{co}}\) ~0.12), revealing a significant gap between the water balance needs and observed efficiencies (see Figure 6a). These differences might be caused by two reasons. First, intercepted water might be absorbed through foliar uptake (Bryant et al., 2021; Cuevas et al., 2023) or drained by trunk and branches (Sadeghi et al., 2020), which are not dripping towards the surface where \(\eta_{\text{co}}\) is being measured. This explanation further supported that additional water outputs, such as runoff, are included in the water balance equation. Assuming that runoff accounts for 50% of precipitation due to the soils’ limited capacity to retain rainfall, the mean \(\eta_{\text{cm}}\) increases from 0.45 to 0.60. This reinforces the idea that, if the missing water component is fog, the forest should be capturing more water than what can be estimated through dripping. Second, our estimations of fog dripping may also be underestimated, as they do not account for the full leaf area index of each tree species in the forest. The differences between modeled and observed efficiencies underscore the need for a better understanding of the canopy’s fog interception mechanisms. To address this gap, and achieve a reliable canopy efficiency to estimate the forest fog water collection, we developed a combined approach (\(\eta_{(cm,co)}\), Fig. 6). This approach acknowledges that\(\ \eta_{\text{cm}}\), due to its calculation method, yields unrealistic values (\(\infty\)) when \(F_{\text{in}}\) approaches zero. Since these values coincide with the locations of our observations, they are replaced with the average observed efficiency (0.12), which offers a more plausible representation of canopy efficiency in areas with low \(F_{\text{in}}\). 3.3. Fog contribution to the water balance To quantify the amount of fog water collected by the forest and evaluate fog’s contribution to the water balance, we solved Equation 4 using\(\eta_{(cm,\ \ co)}\), which results in \(f_{\text{cm}}\). Figure 7 shows the temporal dynamics of the water balance using two complementary approaches: Figure 7a illustrates cumulative monthly values of the individual water balance elements, while Figure 7b compares total inputs and outputs against water storage (soil moisture), showing Equation 1 yearly dynamics. Finally, Figure 7c synthesizes these approaches by illustrating the relative contributions of fog, precipitation, and evapotranspiration to the annual hydrological balance. Together, these analyses quantify the seasonal patterns and relative contributions of each component in sustaining the Mediterranean-type climate forest ecosystem. Figure 7 . Temporal distribution of water balance variables and temporal dynamics across the whole study area. (a) Monthly aggregated values of water balance components\(\ f_{\text{cm}},\ \ P,\ \ ET,\ \Delta S.\) (b) Monthly aggregated values of both sides of the water balance, with the respective inputs and outputs ((\(f_{\text{cm}}+P)-ET\), gray) and the storage changes (ΔS, purple). (c) Relative contribution of water inputs and outputs to the total water balance (\(f_{\text{cm}}+P+ET\)). \(\Delta S\) represents water storage changes throughout the year, showing a constant increase up to ~50 mm, indicating a gradual accumulation of soil moisture. ET represents the primary water output. Its curve (Figure 7a) shows the steepest increase during late spring and summer, when radiation peaks (Fig. 3a), accumulating to a final value of ~400 mm, resulting in a clear seasonal water deficit. Regarding water inputs, the contribution of each variable varies significantly to address this deficit: P concentrates most of its values during the rainy winter season, peaking annually at ~325 mm, while\(f_{\text{cm}}\) values remain relatively consistent throughout the year, with a slight increase during winter, reaching 117 mm by the end of the year, with values ranging from 101 mm on north-facing slopes to 127 mm on west-facing slopes. Such values underscore that, although\(P\) is the main driver of water input, \(f_{\text{cm}}\) plays a complementary role in closing the water balance gap created by the high evapotranspiration demands. The values reported in Figure 7a are consistent with those shown in Figure 5, Figure 2 and Table 1. Figure 7b reports a dual seasonal dynamics in water balance: negative during the first half of the year, and becomes positive in the second half, reaching equilibrium by the end of the year. From January to July, combined water inputs (\(f_{\text{cm}}+P\)) remain belowET, with the lowest value reached in March (-50 mm). These inputs indicate ET dominates the water balance from January to March, while from March to July, decreasing ET and increasing\(f_{\text{cm}}\) compensate for each other to reach water balance. From July to September, the combined inputs (\(f_{\text{cm}}+P\)) dominate the water balance, reaching a surplus of up to ~100 mm. From September to December, increases in ET compensate for water inputs, bringing the system back to balance by the end of December. Figure 7c highlights fog’s consistent contribution to the forest water balance, with \(f_{\text{cm}}\) accounting for 18% of the total annual water input. Notably, \(f_{\text{cm}}\) becomes the only water source between January and April, compensating for the absence of \(P\)during this dry period. ET dominates the water balance from January to July, peaking in summer due to the elevated solar radiation before declining toward December. The persistence of \(f\) inputs during the dry season demonstrates their role as a stabilizing mechanism for ecosystem water availability. Figure 8. (a) Monthly spatial distribution of forest fog collection \(f_{\text{cm}}\) rates across the study area. Maps display fog water collection from January to December with colours ranging from yellow (low collection rates) to purple (high collection rates). (b) Monthly average fog collection (\(f_{\text{cm}}\)) by slope orientation. The bar chart displays \(f_{\text{cm}}\) values for the four slope orientations: the north (orange), south (dark green), east (yellow), and west (light green). Figure 8a shows the monthly spatial variability of \(f_{\text{cm}}\)across the study area, whereas Figure 8b summarizes the monthly average values of \(f_{\text{cm}}\) per slope. The maps in Figure 8a show that fog concentrates in topographically favourable locations where marine air masses encounter optimal terrain conditions for their development and persistence. Since \(f_{\text{cm}}\) occurs at elevations above 250 m ASL, where the marine SCu cloud interacts with the coastal cordillera, topographic control is evident. This pattern is consistent across all months, with fog collection closely following elevation gradients and slope orientation, particularly favouring coastal-facing (west) slopes. These results suggest that topographic factors such as slope orientation and elevation have stronger control over fog collection than seasonal atmospheric conditions. Fog collection areas show different seasonal patterns, with peak values typically occurring during late spring and early summer. While autumn and winter generally exhibit lower collection rates, August stands out as an exception, displaying the highest values of the year with over 20 L m⁻², as shown in Figure 8. On average, south and west-facing slopes report higher peak values per month, as shown in Figure 8b, where both slopes exhibit pronounced seasonal peaks throughout the year, except for the winter months. North slopes report the lowest performance across all months, and east slopes display intermediate results, yet with less pronounced seasonal variation compared to their south and west counterparts. Figure 9. Fog collection (\(f_{\text{cm}}\)) ratio to precipitation (\(P\), light blue) and evapotranspiration (ET, orange) by slope orientation. Figure 9 presents the relative importance of \(f_{\text{cm}}\) compared to \(P\) and ET across different slope orientations, providing a quantitative assessment of fog’s contribution to the water balance at each topographic position. Our results reveal that fog constitutes a substantial water input across all slope orientations, with \(f_{\text{cm}}/P\ \) ratios ranging from 0.2 to 0.43. These ratios never exceed 1.0, which indicates that precipitation remains the primary water input within the ecosystem, while on average fog contributes 0.31 of the total water required to maintain the forest water balance. West-facing slopes exhibit the highest values corresponding to 0.43, followed by south-facing slopes (0.30), while east and north slopes show more moderate values (0.20 and 0.29, respectively). In contrast, the \(f_{\text{cm}}/ET\) ratios show a different pattern, ranging from 0.21 to 0.35. East-facing slopes display the highest values (0.35), indicating that fog provides the greatest proportional contribution to meeting evapotranspiration demands in this orientation. West slopes show intermediate \(f_{\text{cm}}/ET\) ratios of 0.23, with north and south slopes exhibiting similar values (0.27 and 0.21, respectively). This inverse relationship between \(f_{\text{cm}}/P\) and\(f_{\text{cm}}/ET\) ratios across slope orientations demonstrates that fog functions in a compensatory manner. West and south slopes, which receive higher fog inputs relative to \(P\), also experience proportionally higher ET demands, resulting in lower\(f_{\text{cm}}/ET\) ratios. Conversely, east slopes show the highest\(\text{\ f}_{\text{cm}}/ET\) ratios (0.35) despite receiving less coastal fog due to their inland location, indicating that limited fog availability becomes proportionally more important for meeting their water demands. This pattern reflects the water-limited nature of Mediterranean type-climate forest ecosystems, where fog plays as a complementary water source to balance spatial heterogeneity in both water availability and water demand across different topographic locations. Conclusion This study provides comprehensive insights into the role of fog as a water input in Mediterranean climate-type coastal forests of Chile through a multi-approach analysis combining satellite observations, in situ measurements, and numerical modeling. Our water balance analysis reveals that fog contributes 31% of the total ecosystem water inputs, particularly on south- and west-facing slopes, where topographic conditions favour fog´s interception. When considering the complete water balance, fog water represents ~18% of the total water available within the ecosystem, being the only water input from January to April. The AMARU model demonstrates that the fog influx across the study area is characterized by spatial variability, with west-facing slopes receiving the highest fog inputs followed by south-facing slopes. In this context, topographic conditions strongly control fog distribution patterns. These findings address our two fundamental research questions regarding the role of fog in Mediterranean climate-type coastal forest ecosystems. For the first question, our results demonstrate that the canopies of Mediterranean climate-type forests intercept and utilize approximately 117 mm of fog water annually, with substantial spatial variability, ranging from 101 mm on north-facing slopes to 127 mm on west-facing slopes. This fog contribution is essential for the persistence of the ecosystem, as it effectively bridges the ~75 mm deficit between annual precipitation (~325 mm) and evapotranspiration demands (~400 mm). Regarding the second research question, our analysis reveals that forests must operate at remarkably high collection efficiency levels to maintain their water equilibrium. Water balance calculations indicate that the forest canopy must achieve an approximate collection efficiency of 40%, while in-situ measurements using analog rain gauges show passive water collection through dripping at only 12% efficiency. This significant difference suggests the existence of more complex water absorption mechanisms that are complementary to dripping, including foliar water uptake, trunk and branch drainage, and other water retention processes beyond traditional through-fall measurements. These additional mechanisms would explain the observed NDVI levels and the forest’s ability to maintain its water balance despite apparent collection deficits. Fog provides 20–40% of precipitation inputs and helps offset 20–35% of evapotranspiration demands across all slope orientations, playing a crucial role in supporting the ecosystem persistence in water-limited Mediterranean climate-type environments. From January to July, evapotranspiration dominates the water balance, gradually decreasing toward December. This evidence further highlights the importance of fog as a stable and complementary water source throughout the year. This complexity characterizing fog–forest interactions shows that Mediterranean climate-type coastal forests have developed multiple pathways for fog water acquisition, enabling them to effectively harness this resource. Other components of the water balance, such as surface runoff and sub-surface flows, were not included in this study and may affect the accuracy of the estimates. Future research should aim to incorporate these elements for a more complete understanding of the water dynamics of Mediterranean ecosystems. This study improves our understanding of the role of fog in the water balance of Mediterranean climate-type forests, which are threatened by climate change and drought. As these regions face increasing aridity and more frequent extreme drought events, understanding fog’s contribution to the ecosystem water availability becomes increasingly critical for predicting forest resilience and ecosystem persistence. The quantification of fog as a complementary water source provided in this research offers valuable insights into the fundamental ecohydrological processes that sustain these water-limited ecosystems. These findings highlight how fog-forest interactions may function as crucial mechanisms of ecosystem resilience under future climate scenarios marked by reduced precipitation and intensified water stress. Acknowledgements This research was funded by the Chilean National Commission of Science and Technology through grants ANID/FONDECYT/1241176, ANID/FONDECYT/1210834, and ANID/FONDECYT/11250466. The authors thank the Centro UC Desierto de Atacama and its team for their valuable support throughout this research. We also gratefully acknowledge Roberto Rondanelli for his assistance with precipitation modeling. Additional thanks go to the “Corporación Bosques de Zapallar” for their logistical support in the study area. The photographs in Figure 2 were sourced from the following websites: http://www.chilebosque.cl/shrb/echinopsis_chiloensis.html and https://www.nublenaturaleza.cl/articulos/flora/arboles/peumo. Finally, we thank the anonymous reviewers for their comments, and E. Fiorin for the English language editing. 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S., et al. (2024). Impacts of agriculture and snow dynamics on catchment water balance in the U.S. and Great Britain. Communications Earth & Environment, 5, 733. https://doi.org/10.1038/s43247-024-01891-w Slope ET (mm) \(F_{\text{in}}\) (mm) \(P\) (mm) ΔS (mm) East 367 136 374 57 North 378 164 318 55 South 561 186 396 54 West 477 195 297 50 Table 1. Annual water balance components by slope orientation. Values representing average annual totals (mm) for evapotranspiration (ET), fog influx (\(F_{\text{in}}\)), precipitation (\(P\)), and soil water storage (\(\Delta S\)) averaged across each slope orientation during 2023. h (m) \(f\) co (L m - ²) \(F\) in (L m - ²) 𝜂 co 312 2 7 0.29 340 6 40 0.15 371 11 80 0.13 396 7 140 0.05 402 1 159 0.01 423 36 207 0.17 436 10 250 0.4 440 20 250 0.8 443 13 250 0.5 460 23 293 0.8 460 47 293 0.16 468 32 309 0.1 518 22 458 0.5 Table 2 : Summary of in situ measurements including altitude ( h ), observed fog collection rate (\(f_{\text{co}}\)), fog influx rate (\(F_{\text{in}}\)), and observed canopy collection efficiency (\(\eta_{\text{co}}\)). Information & Authors Information Version history V1 Version 1 08 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords cloud forest fog collection fog-harvesting model mediterranean climate-type forest remote sensing water balance Authors Affiliations Jorge Herrera-Bórquez 0009-0008-7828-3908 [email protected] Centro UC Desierto de Atacama View all articles by this author Camilo del Río Centro UC Desierto de Atacama View all articles by this author Patricio Pliscoff 0000-0002-5971-8880 Universidad de los Andes View all articles by this author Vicente Espinoza Centro UC Desierto de Atacama View all articles by this author Felipe Lobos-Roco 0000-0002-8786-0083 Centro UC Desierto de Atacama View all articles by this author Metrics & Citations Metrics Article Usage 516 views 167 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Jorge Herrera-Bórquez, Camilo del Río, Patricio Pliscoff, et al. The contribution of fog to the water balance of Mediterranean climate-type coastal forests. Authorea . 08 August 2025. 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