Linking Water Planning and Native Forest Conservation in Central Chile: A Sociohydrological Perspective

Linking Water Planning and Native Forest Conservation in Central Chile: A Sociohydrological Perspective | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Linking Water Planning and Native Forest Conservation in Central Chile: A Sociohydrological Perspective Pilar Barría, Anahí Ocampo-Melgar, Alejandro Venegas-González, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9323806/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mediterranean-type dryland socio-ecological systems worldwide face compounding pressures from intensifying drought, biodiversity loss, and the water governance fragmentation. A critical but largely unresolved challenge is how to integrate native forest conservation into basin-scale water planning frameworks that are both ecologically grounded and transferable across regions. Here we develop and apply a sociohydrological framework to quantify long-term changes in stakeholder-prioritized ecosystem functions (EF) and ecosystem services (ES) in the Aculeo Lake Basin (33°50′ S, 70°54′ W), a dryland Mediterranean watershed in central Chile. Building on prior hydrological characterization of this basin, the framework integrates stakeholder valuation, short-term field observations, satellite remote sensing, and an adapted WEAP model forced by CMIP6 climate and land-management scenarios (SSP5–8.5), and is designed to be replicable across data-scarce dryland basins. Results indicate that forest-related soil-moisture regulation approaches ~ 0.9 m³ s⁻¹ after the 2060s; winter contributions rise from ~ 1.0 to ~ 1.5 m³ s⁻¹ but weaken under declining precipitation. Despite uncertainty, simulations consistently point to declining ecosystem functioning across all forest communities. Ensemble-average projections show basin-scale reductions in carbon sequestration of ~ 20–40% by mid-century relative to 1999–2019. Biodiversity responses are non-linear and disproportionate to area loss, with scenario analyses indicating potential declines of up to 66% in basin-scale diversity under hygrophilous and xerophytic forest decline. These findings demonstrate that multiple ecosystem functions can be embedded quantitatively in operational water-planning models, supporting instruments such as Payments for Ecosystem, and provide a transferable methodological pathway for integrating forest conservation into water governance in climate-vulnerable dryland basins. water-planning native forest climate change ecosystem functions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Across dryland and Mediterranean regions, climate change and land-use change are reshaping water availability (Liu et al., 2024 ; Lindsay et al., 2021), vegetation dynamics, and the governance challenges faced by regional planning systems (Zhang et al., 2023 ). These regions are socio-ecological systems in which shifts in forest condition, biodiversity, and hydrological functioning interact with livelihoods, institutional capacity, and adaptation choices (Gauquelin et al., 2018 ; Zango-Palau et al., 2024 ). Yet regional planning tools still tend to treat water allocation, ecosystem condition, and adaptation governance as separate domains, limiting their ability to capture cross-sectoral trade-offs and to support robust responses under climate uncertainty (Waldick et al., 2017 ; Ziervogel et al., 2019 ; Ronchi and Brambilla, 2025 ). Central Chile is one of these dryland forest regions (30–38°S; Fig. 1 A). This Mediterranean-climate area, where climate change and human disturbance threaten both forest and aquatic ecosystems (Armesto et al., 1995 ; Alaniz et al., 2019 ; Bambach et al., 2013 ; Hidalgo-Corrotea et al., 2023 ), contains the only Mediterranean-type forest biome in South America (Donoso, 1982 ) and supports more than 40% of Chile’s population (INE, 2017). Climate- and land-use-driven change has already reduced provisioning, regulating, and cultural ecosystem services that are essential for regional sustainability (Smith-Ramírez et al., 2023 ). Despite this vulnerability, few studies have systematically evaluated how native forests in arid Mediterranean-type ecosystems outside the Mediterranean Basin contribute to ecosystem functions (EF) and services (Smith-Ramírez et al., 2023 ). This limits the ability to generate actionable, context-specific knowledge for conservation and restoration. A prolonged and unprecedented climatic event, the “Megadrought” (MD), beginning in 2010, has intensified water scarcity in central Chile (Garreaud et al., 2017 ; 2020 ; 2025 ). The MD has contributed to deplet surface and groundwater (Taucare et al., 2024 ) and accelerated glacier loss, increasing pressure on overextracted basins (Barría et al., 2021 ). Rising aridity has also caused widespread tree browning and canopy dieback, even in drought-adapted species (Miranda et al., 2023 ; 2024 ). Tree-ring studies document long-term growth declines in relict species such as Nothofagus macrocarpa and Austrocedrus chilensis , associated with decreasing precipitation and rising temperatures since the late 20th century (Le Quesne et al., 2006 ; Venegas-González et al., 2018 , 2022 ; Matskovski et al., 2021). Climate projections indicate a 20–40% reduction in rainfall by 2080–2099 under a high-emissions scenario (SSP5-8.5), underscoring the need for integrated planning frameworks that explicitly consider dryland EF and ES (Smith-Ramírez et al., 2023 ). Although substantial work has been done on incorporating ES into basin planning (Brauman, 2015 ; Anzaldua et al., 2018 ; Bubb et al., 2017; Chen et al., 2023 ), integrating EF into hydrological models (Nedkov and Burkhard, 2012 ; Grêt-Regamey et al., 2017 ), embedding ecological suitability together with social feasibility analyses of restauration projects in landscape planning (Zamorano-Elgueta et al., 2025 ) and developing indicators linking ecological and social outcomes (Olander et al., 2018 ; García-Díaz et al., 2021), only a small portion of this knowledge informs policy decisions (Inostroza et al., 2017 ; Olander et al., 2017 ; Shaad et al., 2022 ). Indeed, in Chile, forest ecosystem functions remain largely absent from water-planning models (Balocchi et al., 2023 ). In this context, Chile’s Strategic Water Resources Plans (in Spanish Planes Estratégicos de Recursos Hídricos, PERH) provide an opportunity to examine a challenge that extends well beyond the country: how can basin planning tools incorporate ecological change and stakeholder priorities, rather than only simulating water supply and demand? This question is central to regional adaptation planning, where cross-scale coordination, multi-level learning, and better fit between ecological and governance scales are increasingly recognized as critical (Ziervogel et al., 2019 ; Wiegant et al., 2022; Gonzales-Iwanciw et al., 2025). The Aculeo watershed, in Chile’s Metropolitan Region, illustrates the socio-ecological consequences of unmanaged water stress. After nearly a decade of drought and persistent over-demand, Aculeo Lake-formerly 12 km²-dried completely, triggering environmental degradation and social conflict (Barría et al., 2021 ). The 14,900-ha watershed is dominated by native forests and shrublands (~ 10,000 ha), which are critical in a water-scarce landscape. Vegetation has experienced severe productivity declines since the 1980s, with an average 56% reduction during the Megadrought relative to the prior six decades (Gibson-Carpintero et al., 2024 ). Understanding forest responses to climatic and anthropogenic stressors is therefore essential, given the strong dependence of local communities on the ecosystem services these forests provide (Ocampo-Melgar et al., 2021 ). This article uses the Aculeo Lake Basin as a demonstration site to examine how stakeholder-prioritized native-forest ecosystem functions can be incorporated into basin water-planning models under climate and land-management change. It builds on prior work in the basin on stakeholder valuation (Ocampo-Melgar et al., 2021 ), forest ecological characterization (Gibson-Carpintero et al., 2024 ), and a calibrated WEAP model (Barría et al., 2021 ). The original contribution is to integrate these components within a single sociohydrological framework and to advance three new steps: deriving climate-forest relationships to represent non-stationary ecosystem functioning, modifying WEAP to incorporate native-forest dynamics through time-varying vegetation parameters, and applying the framework under climate and land-management scenarios to assess implications for water availability, carbon sequestration, and biodiversity. The Aculeo case thus serves as a demonstration of a transferable approach for dryland basin planning. 2. Study area and previous work on ecosystem services quantification This study focuses on the Mediterranean forests of the Cordón Cantillana, located within the Aculeo Lake watershed (Fig. 1 ), an ecosystem characterized by high-Andean vegetation and predominantly mountainous terrain in the semi-arid region of central Chile (CONAMA, 2005). Spanning approximately 205,000 hectares, the Cordón's diverse topography and altitudinal variation (with Cerro Cantillana reaching 2,281 meters above sea level) create an ideal setting for the development of various forest woody communities. Deciduous forests dominate the higher elevations, while sclerophyllous forests and xerophytic shrublands are prevalent in the medium elevations (< 1,500 msnm), lower areas and valleys (Gibson-Carpintero et al., 2024 ). The integrated framework presented in this paper builds on previously generated social, ecological, and hydrological information from the Aculeo basin. This section briefly summarizes that prior analytical basis in order to distinguish it from the new framework development, model integration, and scenario analysis presented in Sections 3 and 4. 2.1 Progress on S1. Indentification of ecosystem functions/services valued by stakeholders The complete desiccation of Aculeo Lake turned the watershed into a focal case in Chilean water-governance debates, highlighting the severity of the regional water crisis (Barría et al., 2021 ). As shown by Ocampo-Melgar et al. ( 2021 ), unequal access to resources and differing adaptive capacities make it essential to consider diverse stakeholder interests when designing adaptation measures. Stakeholder perceptions of ecosystem services in the basin (Supplementary Table 1) were assessed by Ocampo-Melgar et al. ( 2024 ) using a mixed qualitative-quantitative questionnaire administered between June and August 2021. The one-hour survey included 24 participants representing local government, public schools, irrigation farmers, grazing organizations, export agriculture, tourism operators, rural residents, the Altos de Cantillana Reserve, neighborhood associations, cultural groups, and environmental education organizations. It produced a ranked list of 29 ecosystem services and provided information on their perceived location, management, accessibility, and vulnerability to climate change (see Ocampo-Melgar et al., 2024 for details). The ecosystem services most valued by stakeholders are presented in Supplementary Table 1 and Supplementary Fig. 1, showing that biodiversity, water provision, and clean air received the highest rankings in terms of importance and perceived climate-change vulnerability. 2.2 Progress on S2. Characterizing the Ecosystem Functions of Native Forests in Response to Climate Previous research in the Cantillana Range has documented 249 native vascular plant species and 12 naturalized non-native species, highlighting its ecological importance, high endemism, and relevance for regional tourism (Romero et al., 2014). However, the effects of the Megadrought and projected climate-change impacts on this ecosystem remain insufficiently understood (Venegas-González et al., 2023 ). To characterize native forests and generate information relevant to the ecosystem functions and services identified by stakeholders, Gibson-Carpintero et al. ( 2024 ) established six plots along an altitudinal gradient within the basin (Fig. 1 B): hygrophilous forest, sclerophyll forest under humid conditions (south-facing slopes), xerophytic shrubland, sclerophyll forest under dry conditions (north-facing slopes), high-altitude sclerophyll forest, and deciduous forest. Based on structure and composition, and following Chilean forest ecosystem classifications (Luebert & Pliscoff, 2006 ; CONAF, 1999 ), these communities were grouped into four categories: xerophytic shrubland, sclerophyllous (sclerophyll forests at mid- and high elevations), hygrophilous (hygrophilous forests at the bottom of the ravine), and deciduous forests. Gibson-Carpintero et al. ( 2024 ) further characterized species richness and abundance, along with forest growth, productivity, and water-use efficiency, information that is key for understanding EF under S2. 2.2.1 Forest richness and abundance Gibson-Carpintero et al. ( 2024 ) conducted a community-level assessment of established trees and seedling regeneration in the Cantillana Range. Their analysis showed a strong negative relationship between elevation and tree species richness (r = -0.76, pvalue = 0.004), consistent across both forest inventory plots and regeneration plots. Using Gini–Simpson, Shannon, and Simpson diversity indices (see Supplementary Table 2), Gibson-Carpintero et al. ( 2024 ) found that hygrophilous forests and sclerophyll forests under humid conditions exhibit the highest richness and entropy. These are followed by xerophytic shrubland and dry-condition sclerophyll forests, while deciduous forests show the lowest diversity. Overall, their results indicate that hygrophilous forests support the greatest tree-species diversity among the communities assessed. 2.2.2 Native Forest growth, productivity and Water Use Efficiency (iWUE) dynamics Gibson-Carpintero et al. ( 2024 ) also conducted a retrospective population-level analysis to study trends and annual variability in radial growth, carbon sequestration and water use efficiency (iWUE) in the dominant species, using dendrochronological records, x-ray densitometry and stable isotopes. Their results show consistently low radial growth rates (ring width) across all populations during the common period 1950–2019, with mean values below 2 mm per year. Growth varied between 0.7 and 1.9 mm/year, with deciduous forests exhibiting the highest rates and xerophytic communities the lowest. All populations experienced significant growth declines during the Megadrought, with reductions of around least 30%. These patterns are consistent with previous studies (Venegas-González et al., 2018 , 2023 ) and are further illustrated in Fig. 3 C and Fig. 3 D, which show marked increases in intrinsic water use efficiency (iWUE) and decreases in carbon sequestration (productivity) derived from the radial growth measured by Gibson Carpintero et al. (2024) of the four forest communities here studied, particularly during the Megadrought period. Building on these findings, the present study advances S2 by deriving quantitative relationships between forest dynamics and climate-information required to integrate native-forest ecosystem functions into water-planning models for the 2000–2065 period (near future). 2.3 Progress on S3. Adjusting the hydrological models of water planning activities within the context of a sociohydrological evaluation of native forest ecosystem functions (services) A semidistributed surface-water model based on the Water Evaluation and Planning system (WEAP; Yates et al., 2005 a, 2005 b) was previously calibrated for the Aculeo Basin by Barría et al. ( 2021 ), and later adapted to assess climate-change impacts on the coupled water-soil-forest system. To represent the spatial heterogeneity of the watershed, eight Hydrological Response Units (HRUs) were defined for water-balance estimation (Fig. 2 C). As detailed in Barría et al. ( 2021 ), model calibration used observed lake-level records (Fig. 3 A), while observed flows at the Pintué station (January 2003–December 2010) were used for validation (Fig. 3 B). Building on this earlier modeling work, the next step toward completing S3 is to adjust WEAP parameters to incorporate quantitative climate-forest-water relationships, enabling the integration of native-forest ecosystem functions into water-planning simulations. 3 Methodology and data To support basin-level planning, this study presents a socio-eco-hydrological approach to quantify key ecosystem functions provided by native forests in arid regions within water-planning activities. The methodology follows four main steps (Fig. 2 ) and builds on prior work in stakeholder valuation, ecological characterization, and hydrological model development in the basin (Ocampo-Melgar et al., 2024 ; Gibson-Carpintero et al., 2024 ; Barría et al., 2021 ). The original contribution of this study is to advance S2–S4 by deriving climate–forest relationships, incorporating dynamic forest parameters into WEAP, and applying scenario-based simulations to assess changes in water availability, carbon sequestration, and biodiversity. Accordingly, the analysis focuses on the ecosystem functions ranked highest in stakeholder valuation and climate-change vulnerability, using metrics identified through the literature and expert input (Table 1 ). Table 1 Methods for quantifying changes in ecosystem functions associated with the ecosystem services most valued by local stakeholders ES EF Water balance variable Proposed metric Water provision Volume of surface and groundwater available for potable and non-potable uses Soil moisture and aquifer recharge responses to changes in forest cover and vigor Increase in Soil Moisture ( ISM ) and Aquifer Volume (m3). Changes in ISM under Scenarios With and Without Native Forest Conservation Measures \(\:{\varDelta\:\varvec{I}\varvec{S}\varvec{M}}_{\varvec{s}\varvec{e}\varvec{a}{\:\varvec{k}}_{\:\varvec{p}\varvec{e}\varvec{r}\:\varvec{j}}}\left(\frac{{\varvec{m}}^{3}}{\varvec{y}\varvec{e}\varvec{a}\varvec{r}}\right)={\varvec{I}\varvec{S}\varvec{M}\:\varvec{i}}_{\:\varvec{s}{\varvec{e}\varvec{a}\:\varvec{k}}_{\varvec{p}\varvec{e}\varvec{r}\:\varvec{j}}}\left({\varvec{m}}^{3}\right)-{\varvec{I}\varvec{S}\varvec{M}\:\varvec{r}\varvec{e}\varvec{f}}_{\varvec{s}{\varvec{e}\varvec{a}\:\varvec{k}}_{\varvec{p}\varvec{e}\varvec{r}\:\varvec{j}}}\left({\varvec{m}}^{3}\right)\) Where: \(\:{\varDelta\:\varvec{I}\varvec{S}\varvec{M}}_{\varvec{s}{\varvec{e}\varvec{a}\:\varvec{k}}_{\varvec{p}\varvec{e}\varvec{r}\:\varvec{j}}}\:\left(\frac{\varvec{m}3}{\varvec{y}\varvec{e}\varvec{a}\varvec{r}}\right)\) : Volume of water accumulated in the soil related to native forest, during hydrological season k (rainy, snowmelt, or annual) and climatic period j (historical, near future 2030–2059, or distant future 2070–2099). \(\:{\varvec{I}\varvec{S}\varvec{M}\:\varvec{i}}_{\:\varvec{s}{\varvec{e}\varvec{a}\:\varvec{k}}_{\varvec{p}\varvec{e}\varvec{r}\:\varvec{j}}}\left(\frac{\varvec{m}3}{\varvec{y}\varvec{e}\varvec{a}\varvec{r}}\right)\) : Increase in soil moisture \(\:{\varvec{I}\varvec{S}\varvec{M}\:\varvec{r}\varvec{e}\varvec{f}}_{\varvec{s}\varvec{e}\varvec{a}{\:\varvec{k}}_{\varvec{p}\varvec{e}\varvec{r}\:\varvec{j}}}\left(\frac{\varvec{m}3}{\varvec{y}\varvec{e}\varvec{a}\varvec{r}}\right)\) : Increase in soil moisture under the reference scenario. Changes in Groundwater recharge under Scenarios With and Without Native Forest Conservation Measures \(\:{\varDelta\:\:\varvec{s}\varvec{u}\varvec{b}}_{\varvec{s}\varvec{e}\varvec{a}{\:\varvec{k}}_{\varvec{p}\varvec{e}\varvec{r}\:\varvec{j}}}\left(\frac{\varvec{m}3}{\varvec{y}\varvec{e}\varvec{a}\varvec{r}}\right)={\varDelta\:\:\varvec{s}\varvec{u}\varvec{b}\varvec{t}\:\varvec{i}}_{\:\varvec{s}{\varvec{t}\:\varvec{k}}_{\varvec{p}\varvec{e}\varvec{r}\:\varvec{j}}}\left(\varvec{m}3\right)-{\varDelta\:\:\varvec{s}\varvec{u}\varvec{b}\:\varvec{r}\varvec{e}\varvec{f}}_{\:\varvec{s}{\varvec{t}\:\varvec{k}}_{\varvec{p}\varvec{e}\varvec{r}\:\varvec{j}}}\left(\varvec{m}3\right)\) Where: \(\:{\varDelta\:\:\varvec{s}\varvec{u}\varvec{b}\varvec{t}\:\varvec{i}}_{\varvec{s}\varvec{e}\varvec{a}{\:\varvec{k}}_{\varvec{p}\varvec{e}\varvec{r}\:\varvec{j}}}\left(\frac{\varvec{m}3}{\varvec{y}\varvec{e}\varvec{a}\varvec{r}}\right)\) : Groundwater volume stored in the aquifer under scenario i, during hydrological season k and climatic period j \(\:{\varDelta\:\:\varvec{s}\varvec{u}\varvec{b}\varvec{t}\:\varvec{i}}_{\:\varvec{s}\varvec{e}\varvec{a}{\:\varvec{k}}_{\varvec{p}\varvec{e}\varvec{r}\:\varvec{j}}}\left(\frac{\varvec{m}3}{\varvec{y}\varvec{e}\varvec{a}\varvec{r}}\right)\) : Groundwater volume stored in the aquifer under the implementation of scenario i, during hydrological season k and climatic period j. \(\:{\varDelta\:\:\varvec{s}\varvec{u}\varvec{b}\varvec{t}\:\varvec{r}\varvec{e}\varvec{f}}_{\:\varvec{s}\varvec{e}\varvec{a}{\:\varvec{k}}_{\varvec{p}\varvec{e}\varvec{r}\:\varvec{j}}}\left(\frac{\varvec{m}3}{\varvec{y}\varvec{e}\varvec{a}\varvec{r}}\right)\) : Groundwater volume under the reference scenario, during hydrological season k, and climatic period j. Clean air Refers to the benefits received by stakeholders from the carbon sequestration capacity of forest cover within the watershed Carbon sequestration in relation to forest community productivity Evapotranspiration (Ton m3) Changes in Carbon sequestration under Forest Management and climate change scenarios \(\:{\varvec{C}\varvec{s}\varvec{e}\varvec{q}}_{\varvec{p}\varvec{e}\varvec{r}\:\varvec{t}}=\int\:{\varvec{C}\varvec{s}\varvec{e}\varvec{q}}_{\varvec{j},\:{\varvec{s}\varvec{p}\varvec{e}}_{\varvec{e}}}\:\varvec{d}\varvec{A}\) \(\:{\varvec{C}\varvec{s}\varvec{e}\varvec{q}}_{\varvec{p}\varvec{e}\varvec{r}\:\varvec{t}}\left(\frac{\varvec{T}\varvec{o}\varvec{n}}{\varvec{y}\varvec{e}\varvec{a}\varvec{r}}\right)\) = Carbon sequestration by the forest and shrubland cover, analyzed during period t \(\:{\varvec{C}\varvec{s}\varvec{e}\varvec{q}}_{\varvec{j},\:\varvec{s}{\varvec{p}\varvec{e}}_{\varvec{e}}}\left(\frac{\varvec{T}\varvec{o}\varvec{n}}{\varvec{h}\varvec{a}\:\varvec{y}\varvec{e}\varvec{a}\varvec{r}}\right)={\varvec{S}\varvec{C}}_{\varvec{j},\varvec{i},\:{\varvec{s}\varvec{p}\varvec{e}}_{\varvec{e}}}*{\varvec{n}}_{{\varvec{s}\varvec{p}\varvec{e}}_{\varvec{e}}}\) \(\:{\varvec{S}\varvec{C}}_{\varvec{j},\varvec{i},{\varvec{s}\varvec{p}\varvec{e}}_{\varvec{e}}}={0.5*\varvec{B}\varvec{i}\varvec{o}\varvec{m}\varvec{a}\varvec{s}\varvec{s}}_{\varvec{j},\varvec{i},\:{\varvec{s}\varvec{p}\varvec{e}}_{\varvec{e}}}\) \(\:{\varvec{S}\varvec{C}}_{\varvec{j},\varvec{i},{\varvec{s}\varvec{p}\varvec{e}}_{\varvec{e}}}\) ( \(\:\frac{\varvec{T}\varvec{o}\varvec{n}}{\varvec{y}\varvec{e}\varvec{a}\varvec{r}}\) ) = Carbon sequestration during year j by individual i of species e . \(\:\varvec{A}\varvec{b}\varvec{o}\varvec{v}\varvec{e}\varvec{g}\varvec{r}\varvec{o}\varvec{u}\varvec{n}\varvec{d}\:{\varvec{B}\varvec{i}\varvec{o}\varvec{m}\varvec{a}\varvec{s}\varvec{s}}_{\varvec{j},\varvec{i},\:\varvec{s}\varvec{e}}\) = Aboveground Biomass accumulated by individual i , specie e, year j (Gibson-Carpintero et al, 2024 ). \(\:{\varvec{n}}_{{\varvec{s}\varvec{p}\varvec{e}}_{\varvec{e}}}\) = Number of individuals of species e per hectare. \(\:\varvec{d}\varvec{A}\) = Surface (ha) Biodiversity Native forests underpin biodiversity by maintaining the structural and functional integrity of ecosystems that support diverse plant and animal species Species richness and abundance in relation to forest community productivity Indirect relationship Changes in Diversity and Abundance Under Forest Management and Climate Change Scenarios Species richness and abundance were characterized by Gibson-Carpintero et al. ( 2024 ), using Gini-Simpson, Simpson (S) and Shannon (H) indices (Gini, 1912; Shannon, 1948). Adding the probability of species presence or vegetation vigor decline, it enables projections of potential changes in ecosystem services (SSEE), calculated as: \(\:{\varDelta\:}_{\varvec{b}\varvec{i}\varvec{o},\:\varvec{s}\varvec{p}\varvec{e}\:\varvec{l}}=\frac{\sum\:_{\varvec{l}=1}^{\varvec{m}}\sum\:_{\varvec{i}=1}^{\varvec{n}}{\varvec{I}\varvec{n}\varvec{d}}_{\varvec{i}}\varvec{*}{\varvec{S}}_{\varvec{s}\varvec{p}\varvec{e}\:\varvec{l}}}{{\varvec{S}\varvec{u}\varvec{r}\varvec{f}}_{\varvec{f}\varvec{o}\varvec{r}\varvec{e}\varvec{s}\varvec{t}}}\) Where: \(\:{\varDelta\:}_{\varvec{b}\varvec{i}\varvec{o},\:\varvec{s}\varvec{p}\varvec{e}\:\varvec{l}}\) : Biodiversity proxy for woody specie l, within the basin. \(\:{\varvec{I}\varvec{n}\varvec{d}}_{\varvec{i}}\) : Diversity value based on the different indices considered. \(\:{\varvec{S}}_{\varvec{s}\varvec{p}\varvec{e}\:\varvec{l}}\:\left(\varvec{h}\varvec{a}\right)\) : Surface of woody specie l \(\:{\varvec{S}\varvec{u}\varvec{r}\varvec{f}}_{\varvec{f}\varvec{o}\varvec{r}\varvec{e}\varvec{s}\varvec{t}}\) (ha) : Total area of native forest in the basin. 3.1 Step 2- Quantitative relationships between forest growth, productivity and climatic variables. To quantify the relationships between forest dynamics and climate (S2), we developed statistical functions linking carbon sequestration, and intrinsic water-use efficiency (iWUE) to seasonal precipitation and temperature. Monthly precipitation and temperature fields were extracted from the CR2met gridded dataset (Boisier et al., 2018 ), at the sampling locations. These relationships provide the basis for estimating tree-ring growth and aboveground biomass production under climate-change scenarios. 3.1.1 Historical hydrometeorological data Precipitation and temperature data from the Aculeo Lake meteorological station (33.89° S and 71.45° W, 358 m.a.s.l.), administered by the Chilean Water Directorate (DGA), were used to evaluate the goodness of fit of the CR2met gridded dataset version 2 ( https://www.cr2.cl/datos-productos-grillados/ ) (Boisier et al., 2018 ). CR2met, widely used and validated in Chile, provides the advantage of characterizing not only the temporal variability but also the spatial variability of the climate. These characteristics are especially relevant for the watershed under study due to its significant topographic gradient, which ranges from 350 to 2250 meters above sea level. Then, for climate characterization, calibration of the WEAP model (Example for the Pintué 1 HRU presented in Supplementary Table 3), and scaling of climate change data, the gridded CR2met data was considered. Accurate spatialization of climate change impacts requires climate and topographic data that describe the country’s and the basin’s characteristic topographic and latitudinal gradients. In this case, temperature (°C) and monthly precipitation (mm) data were used for the period between 1979 and 2020. As presented in Fig. 3 B, annual precipitation happens mostly during winter months, with mean values around 100 to 150 mm/month. However, the last 14 years have been marked by the occurrence of the MD, which is characterized by precipitation reductions of about 38%, and increases of temperature of about 0.4°C (Barría et al., 2021 ). 3.1.2 Hydrometeorological projections under climate change For this study, monthly precipitation (pr) and mean temperature (tas) data, covering the period from 1979 to 2015 (historical) and from 2016 to 2100 (future simulations), were downloaded from 37 climate change models from the CMIP6 ensemble (O’Neill et al., 2016; Eyring et al., 2016 ) corresponding to a high greenhouse gas emissions scenario, SSP585 (Riahi et al., 2017 ; Kriegler et al., 2014 ), available on the CMIP6 ESGF website. Corrections for biases in the monthly precipitation and temperature simulations from the GCMs due to differing scales of atmospheric and oceanic physical processes in the models were applied using the Quantile Delta Mapping (DQM) method (Cannon et al., 2015 ). Supplementary Fig. 2 shows the annual precipitation series (hydrological year) for 37 models across the four Hydrological Response Units (HRUs) of the Pintué stream (Fig. 1 C). The different HRUs of Pintué stream represent the range of altitudes within the sub-basin. The projections indicate that despite the large dispersion across models, there are coherence regarding the significant reductions in precipitation simulated by the end of the century. The figure also includes the 5%, 50%, and 95% percentiles, along with the observed annual precipitation series (green line), indicating that after the downscaling process, the mean and variability of the observed data are well preserved in the modeled time series during the historical period. Although the GCM provide continuous simulations for the whole 1979–2100 period, the planning exercise conducted in this work considered projections of native forest ecosystem functions for the near future: 2030–2065. 3.2 Step 3- Hydrological model adjustment within the context of a sociohydrological evaluation of native forest ecosystem functions/processes To transform the existing hydrological model into a socio-eco-hydrological framework, several adjustments were implemented to incorporate native-forest dynamics into water-balance processes. 3.2.1 Linking forest productivity with water balance To relate water-balance components to observed tree-growth patterns, we adapted the crop-coefficient (Kc) approach traditionally used to estimate evapotranspiration. Following Pôças et al. ( 2015 ), monthly crop coefficients were derived for each native-forest community using MODIS-based Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI) for 2000–2019. These time-varying Kc curves capture seasonal vegetation dynamics and were validated against tree-ring growth patterns (not shown), confirming that satellite-based metrics provide a robust proxy in basins without detailed in situ information. The vegetation-index-based crop coefficient (KcVI) was estimated as: $$\:{\varvec{K}}_{\varvec{c}{\varvec{V}\varvec{I}}_{\varvec{j}\varvec{i}}}=\:{\varvec{K}}_{\varvec{m}\varvec{i}\varvec{n}}+\:{\varvec{K}}_{{\varvec{d}}_{{\varvec{j}}_{\varvec{i}}}}\:\left(\frac{{\varvec{V}\varvec{I}}_{\varvec{j}\varvec{i}}-{\varvec{V}\varvec{I}}_{\varvec{j}\:\varvec{m}\varvec{i}\varvec{n}}}{{\varvec{V}\varvec{I}}_{\varvec{j}\:\varvec{m}\varvec{a}\varvec{x}}-{\varvec{V}\varvec{I}}_{\varvec{j}\:\varvec{m}\varvec{i}\varvec{n}}}\right)$$ Where: \(\:{K}_{cVI}\) Crop coefficient estimated for a given land use (or native forest community) \(\:{K}_{min}\) Baseline crop coefficient, related to the soil (typically 0.1) \(\:{K}_{d}\) Canopy cover density coefficient \(\:{VI}_{ji}\) Vegetation Index (NDVI) for community j within month i \(\:{VI}_{j\:min}\) Minimum monthly NDVI value observed in the analyzed patch or polygon during the study period \(\:{VI}_{j\:max}\) Maximum monthly NDVI value observed in the analyzed patch or polygon during the study period The canopy density coefficient (Kd) was computed using the LAI-based relationship of Pereira et al. (2020): $$\:{\varvec{K}}_{\varvec{d}{\varvec{j}}_{\varvec{i}}}=(1-\:{\varvec{e}}^{-\text{0,7}\:{\varvec{L}\varvec{A}\varvec{I}}_{\varvec{j}\varvec{i}}})$$ Where: LAIji: Leaf area index for native forest community j within month i 3.2.2 Incorporating seasonal forest vigor into water-balance parameters In addition to the Kc adjustments, model parameters were optimized for each HRU; the calibrated parameters for the Pintué 1 HRU are reported in Supplementary Table 3. To represent vegetation dynamics, we incorporated the seasonal variability of the Runoff Resistance Factor (RRF), which captures the influence of forest vigor and canopy phenology on water availability. Monthly RRF values were parameterized for each HRU using the forest-productivity metrics described above (Supplementary Table 3), allowing seasonal changes in forest roughness, water-uptake capacity, and canopy development to be explicitly represented in the model water balance. Following Barría et al. ( 2021 ), observed lake levels were used for model calibration (Fig. 3 A), while observed Pintué streamflow data from January 2003 to December 2010 were used for validation (Fig. 3 B). 3.3. Step 4- Integration and modeling of social-forest-climate relations for ecosystem services quantification As the aim of the framework is to maximize synergies between water planning activities and conservation objectives, the quantification of ecosystem functions must also consider plausible land management scenarios for the medium and long term, based on existing territorial planning instruments. For the Laguna de Aculeo watershed, two legal instruments regulate land use: the Metropolitan Regulatory Plan of Santiago (PRMS) and the Municipal Regulatory Plan (PRC) of Paine. The PRMS defines areas as either Urbanized or Urbanizable Zones and Restricted or Excluded from Urban Development. According to the PRMS, most of the Aculeo Lake watershed falls under the latter category, except for the lower part of Pintué sub-basin (HRU Pintué 1), which was designated as urban under the Paine PRC in 2015. Within the restricted zones, the PRMS identifies Ecological Protection Areas, which are designated for their natural or scenic interest, including vegetation, wildlife, and natural water sources. In the Aculeo watershed, there are two Controlled Development Ecological Protection Areas (PEDCs) allow for limited urban activities alongside forestry and agricultural activities, to prioritize conservation of the natural environment: Valley Sector (below 400 m.a.s.l.) and Foothill Sector (400–600 m.a.s.l.). This classification indicates that both agricultural activities and real estate development could potentially expand up to 600 m.a.s.l. with few restrictions (Universidad de Chile, 2020). Then, we designed three scenarios: Reference Scenario: Assumes that the 2018 land use remains constant through 2100. Changes in the water balance are driven exclusively by climate change, based on SSP585 projections from a 37-model ensemble of CMIP6. Conservation Scenario: Assumes no land-use changes occurred between 2006 and 2018, maintaining forest and shrubland cover constant from 2006 to 2100. This scenario includes an additional 74 ha of forest and 103 ha of shrubland compared to the reference scenario. Climate change projections are based on SSP585 from the CMIP6 ensemble. Dystopian Scenario: Assumes significant land-use changes between 2018 and 2100, with a 37% reduction in forest cover and a 36% reduction in shrubland cover, reflecting full development of the PEDC zones. Climate change projections are based on SSP585 from the CMIP6 ensemble. 4 Results- Quantification of ecosystem functions for Central Chile dryland forest in a Water Planning Context The activities for S2-S4 of the four-step framework (Fig. 2 ) were applied to the Aculeo Lake Basin, and the resulting analyses are presented below. 4.1. Climate-driven responses of native forest ecosystem functions (S2) 4.1.1. Climate impacts on native forest growth and water-use efficiency To complete S2, we conducted correlation analyses between yearly carbon sequestration derived from ring-width chronologies and climate variables (not shown). As illustrated in Fig. 4 , the two forest types with taller trees, hygrophilous and deciduous, show strong positive correlations between carbon sequestration and precipitation during the rainy season (April-October; Fig. 4 A, B), with no significant associations with temperature. In contrast, sclerophyllous and xerophytic communities show positive correlations with annual precipitation (April-March) and summer temperatures (January-March; Fig. 4 C, D). The observed decline in precipitation during the MD within the study area has coincided with an increasing frequency of heatwaves, particularly in spring and summer (González-Reyes et al., 2023 ), intensifying climatic stress on vegetation, explaining the tree ring reduction described in Section 2.2.2. In addition, the drought conditions has increased in intrinsic water-use efficiency (iWUE; Fig. 3 C) for all forest communities, which align with global patterns in which warmer temperatures and elevated atmospheric CO₂ reduce stomatal conductance and transpiration. As shown in Fig. 3 C, all forest types exhibited similar iWUE levels between 2010 and 2012, coinciding with the onset of the MD. In response to declining precipitation, hygrophilous, xerophytic, and sclerophyllous forests initially increased iWUE; however, this response was not sustained, showing a decline after 2019, one of the driest years of the MD. Notably, this decrease in efficiency coincided with substantial biomass (and carbon sequestration) losses in sclerophyllous and hygrophilous forests (43% and 28% respectively), concordant with previous studies (Gibson-Carpintero, 2024; Santini Jr. et al., 2024) which described that extreme droughts induce shifts in wood anatomical and physiological traits in high-elevation communities, potentially affecting water-use efficiency across the elevational gradient of the study area. These results suggest that the initial increase in iWUE in hygrophilous and sclerophyllous forests likely reflects reduced productivity rather than enhanced adaptive performance, whereas in xerophytic forests, where productivity losses were less pronounced (24%), the increase in iWUE may indicate greater adaptive capacity. Taken together, these results indicate that forest growth and water-use strategies are highly sensitive to changing climatic conditions. Therefore, assuming stationary forest ecosystem functions in basin-planning processes is inappropriate under current and projected climate change. 4.1.2. Climate impacts on forest productivity According to Supplementary Table 1, one of the most prioritized ecosystem services of native forests by stakeholders in the Aculeo lake basin is clean air. Within the sociohydrological framework presented in this article, the corresponding forest ecosystem function to be estimated using the methods outlined in Table 1 , are carbon sequestration by the various native forest communities of the arid lands of Central Chile. Following the procedures described in Section 3.1, we analyzed linear correlations between seasonal precipitation and temperature (both of the concurrent and previous year) and the carbon sequestration of different forest types during 1999 and 2019. The results, presented in Fig. 4 , show that, with the exception of xerophytic shrublands, all forest formations exhibited significant linear correlations with precipitation. The strongest association was found for hygrophilous forests, whose carbon sequestration was positively correlated with precipitation from April to October (winter–spring), with a coefficient of determination (R² = 0.61, p < 0.05). Deciduous forests followed, with carbon sequestration positively correlated (R² = 0.43) with precipitation from May to November. In xerophytic and sclerophyllous communities, model performance improved when both growing-season temperature and precipitation were considered. In particular, sclerophyllous forests were correlated with January-March temperature combined with April-March precipitation (hydrological year). Xerophytic shrublands showed the weakest correlation, with R²=0.29 (not significative, pvalue = 0.054) when considering the same variables (Fig. 4 C). According to the results, sclerophyllous and hygrophilus forest presented the largest reductions within the studied period (36% and 29% for the 2010–2019 versus 1999–2009 period respectively), most likely due to reduced precipitation and increased temperature during the MD period, while the xerophytic and deciduous forest, although also had a reduction in carbon sequestration during the MD, its magnitude is smaller compared to the other two formations (24% and 20% for the for the 2010–2019 versus 2000–2009 period respectively). 4.2. Modeling social–forest–climate interactions for characterizing ecosystem functions under climate change (S3 and S4) As described in Section 3.2, selected WEAP model parameters were modified to better represent forest dynamics. Incorporating native forest seasonality into the Kc and Runoff Resistance Factor (RRF) parameters for each forest community substantially improved model performance. The calibrated model showed good agreement with observations, with a Nash–Sutcliffe Efficiency (NSE) of 0.41 for lake-level simulations and 0.78 for Pintué runoff validation, and a Kling–Gupta Efficiency (KGE) of 0.74 (validation KGE = 0.87), indicating strong correlation and realistic representation of variability (Fig. 3 B) and improving predictability relative to the Barría et al. ( 2021 ) model. This improvement is particularly relevant given that, by 2018, native forests covered approximately 69% of the basin (Fig. 1 ), comprising 1,977 ha of hygrophilous forest, 2,157 ha of xerophytic shrubland, 5,218 ha of sclerophyllous forest, and 1,011 ha of deciduous forest. This spatial delineation, combined with HRU boundaries, enabled a detailed assessment of forest biophysical characteristics and their climatic and hydrological controls. The analysis of native forest ecosystem functions under historical climate conditions was extended to future decades using CMIP6 general circulation model ensembles under a high-emissions scenario (SSP585). As shown in Supplementary Fig. 2, annual precipitation exhibits strong spatial variability across HRUs, largely driven by basin orography, with the highest values occurring in the higher-elevation Pintué 4 and Pintué 3 HRUs, followed by Pintué 2 and Pintué 1. Despite uncertainties in climate projections, all models consistently indicate sustained declines in annual precipitation toward the end of the century. In these arid and semi-arid systems, such reductions are likely to alter forest ecosystem functions and associated ecosystem service provision, with important implications for long-term planning and decision-making. 4.2.1 Water provision responses to changes in forest cover and vigor According to the methods explained in Table 1 , to estimate the impact of native forests on water availability in the watershed, the differences between two variables were analyzed: the volume of water stored in the aquifer and the volume stored as soil moisture. These variables were derived from water balances generated using WEAP for the period 1979–2065 under two scenarios: Conservation and Dystopian. This approach was chosen because, based on the configuration of the WEAP soil moisture model, the variable “increase in soil moisture” (ISM) represents the amount of water entering the upper bucket of the HRUs (Hydrological Response Units). From this bucket, water is made available for runoff generation, irrigation, evapotranspiration, and infiltration, allowing an estimation of surface water availability. Figure 5 presents the projected differences in soil moisture availability (ISM) between the dystopian and conservation management scenarios, as simulated by the WEAP model for the near future, 2030 to 2065. Importantly, because the figure shows differences between two management approaches under the same climate change projection, the overall effect of reduced precipitation in future decades is effectively controlled for. This allows the analysis to isolate and highlight the influence of land and water management strategies, rather than climatic forcing alone, on soil water availability. To estimate the total contribution of native forests to surface water availability, we calculated the weighted average ISM contributions from the HRUs of Las Cabras, ASC Alto, and Pintué, weighted by the forested areas of each HRU. This value was then scaled to the total forested area to derive the equivalent surface water contribution in m 3 /s under the SSP585 climate change scenario. Figure 5 shows that the contribution of native forests to the annual regulation of soil moisture increases during the first three decades, and from the 2060s onward stabilizes at approximately 0.9 m³/s. As illustrated in Fig. 5 b, the role of native forests is particularly relevant during the winter months, with values rising from around 1 m³/s to about 1.5 m³/s in the 2060 decade. These results suggest that, under initial climate change conditions, the interception capacity and increased surface roughness associated with native forests substantially enhance water retention and soil moisture regulation. However, as precipitation declines more sharply in later decades, this effect weakens, reducing vegetation vigor and, consequently, lowering crop coefficients (Kc) and runoff reduction factors (RRF). Subsequently, the contribution of native forests to groundwater availability was assessed through an annual comparison of aquifer storage under dystopian and conservation scenarios. To isolate the effect of forest cover, the analysis was corrected by subtracting the difference in groundwater extraction between scenarios, thereby removing the influence of increased irrigation demand in the dystopian case. As described by Barría et al. ( 2021 ), the 324 Mm³ aquifer connected to the Aculeo Lake basin comprises three main hydrogeological units: (i) shallow colluvial and fluvial sediments with low permeability, (ii) a silt-dominated aquitard with very low permeability, and (iii) a confined aquifer composed of coarse-to-medium sands with higher permeability. As shown in Fig. 5 C, native forests increase groundwater availability by an average of ~ 9.7 × 10⁻⁶ m³ s⁻¹ ha⁻¹, reaching a maximum of 8.13 × 10⁻⁵ m³ s⁻¹ ha⁻¹ around mid-century, followed by a gradual decline. This decrease reflects reduced irrigation demand simulated in the dystopian scenario over time due to declining groundwater availability. When scaled to the total forested area, the contribution ranges from approximately 5 Mm³ in the 2030s to 25 Mm³ in the 2060s, values comparable in magnitude to total basin-wide groundwater withdrawals for drinking water, agriculture, and recreational irrigation (~ 14.2 Mm³; Meneses et al., 2019). 4.2.2 Carbon sequestration (clean air) in relation to forest community productivity To assess the variability of the clean air ecosystem service under increasing aridity (Section 3.2.2), decadal carbon sequestration was estimated using the polynomial regressions shown in Fig. 4 . Decadal projections by forest type were derived from 37 CMIP6 climate simulations under the SSP5-8.5 scenario for the study watershed. These estimates rely exclusively on empirically derived relationships between climate and carbon sequestration inferred from individual tree growth measured in the Reserve and assume a constant tree density over time (Table 1 ). Figure 6 presents historical carbon sequestration based on field measurements (blue line), together with the 15th, 50th, and 95th percentiles of the climate simulations for each decade (green lines). To adopt a conservative estimate of ecosystem services under climate change, the 15th percentile was selected as the reference value. Comparison with observed values shows that, in several cases, carbon sequestration during the Megadrought (MD; dotted blue line) is lower than projected future values. This reflects the extreme nature of the MD event (Garreaud et al., 2025 ), which is not reproduced by the models during the historical period but emerges in later decades under higher greenhouse gas forcing. Overall, climate-driven polynomial regression models indicate substantial reductions in carbon sequestration across all four forest communities in the coming decades. These projections should be interpreted individually, as the regression models differ in both structure and statistical significance (Fig. 4 ). Xerophytic and sclerophyllous forests were modeled using precipitation and temperature, whereas hygrophilous and deciduous forests were modeled using precipitation only; additionally, the xerophytic model did not reach statistical significance. Under the climate change scenario (Fig. 6 ), xerophytic and sclerophyllous forests exhibit sharp declines in carbon sequestration of approximately 41% and 37%, respectively, during 2040–2060 relative to 1999–2019, driven by reduced precipitation and increasing temperatures. Hygrophilous forests show sustained declines beyond MD levels, reaching ~ 600 kg C ha⁻¹ yr⁻¹ by the 2060s, corresponding to a 9% reduction relative to 1999–2019. Deciduous forests also show persistent decreases (6% by 2040–2060), tracking declining precipitation and reaching ~ 950 kg C ha⁻¹ yr⁻¹ by the 2060s, approaching values observed in low-elevation hygrophilous communities. Finally, this assessment focuses on aboveground carbon stocks. Future studies should incorporate soil carbon dynamics, given the critical role of belowground microbial communities and root systems in carbon storage and ecosystem functioning (Fierer et al., 2009 ; Bastida et al., 2021 ). 4.2.3. Species richness and abundance in relation to forest community productivity As reported by Gibson-Carpintero et al. ( 2024 ) and based on the biodiversity indices shown in Fig. 7 A and Supplementary Table 2, hygrophilous and xerophytic forests exhibit the highest species richness and diversity in the basin. At the tree level, hygrophilous communities show the highest diversity, even after accounting for surface area, contributing 37%, 38%, and 40% of basin-wide diversity according to the Gini, Simpson, and Shannon indices, respectively (Gini = 0.76, S = 4.28, H = 1.64). These are followed by xerophytic, sclerophyllous, and deciduous communities. In contrast, at the regeneration level, sclerophyllous forests are the most diverse, contributing 49%, 44%, and 46% of basin-wide diversity based on the Gini, Simpson, and Shannon indices, respectively (Gini = 0.51, S = 2.1, H = 0.87), followed by xerophytic, hygrophilous, and deciduous communities. Deciduous forests consistently show the lowest diversity across all indices (Fig. 7 A; Supplementary Table 2). Despite their high biodiversity value, high-elevation hygrophilous and sclerophyllous forests are among the most vulnerable to drought in the drylands of central Chile (Section 4.1.1). Moreover, climate projections indicate that drought conditions are likely to substantially reduce forest cover across the basin (Fig. 6 ). To derive a conservative estimate of future biodiversity, a scenario-based analysis was conducted using an area-weighted average of diversity and richness indices, applying a leave-one-community-out approach (Fig. 7 B). This analysis identifies hygrophilous and xerophytic forests as the main contributors to basin-wide biodiversity. Consistent with the declining carbon sequestration trends shown in Fig. 6 , reductions in hygrophilous, xerophytic, and sclerophyllous forests are likely to result in substantial biodiversity losses. For example, using surface-weighted Simpson indices at the tree level (Fig. 7 B), a reduction in hygrophilous and xerophytic forests would lead to an estimated 66% decrease in basin-wide biodiversity value. 5 Discussion and Conclusions This study provides quantitative evidence that native forests exert a measurable but non-stationary influence on key basin-scale ecosystem functions, water regulation, carbon sequestration, and biodiversity, in the drylands of central Chile, with distinct responses to climate forcing and land-management trajectories. Crucially, these functions can be explicitly quantified and dynamically represented within water-planning models, rather than treated as static or qualitative co-benefits, with direct implications for the integration of conservation objectives into instruments such as Chile’s Strategic Water Resources Plans. Rather than generating entirely new baseline social or ecological datasets, the main contribution of this study is to integrate previously separate lines of evidence into an operational sociohydrological framework for basin planning under climate change. From a water-planning perspective, the results show that native forest cover enhances both soil moisture (key processes for surface water availability) and groundwater availability under current and near-future climate conditions (2030–2065). Under the SSP5-8.5 scenario, forest-related soil moisture regulation reaches approximately 0.9 m³ s⁻¹ after the 2060s, with winter contributions increasing from ~ 1.0 to ~ 1.5 m³ s⁻¹. Groundwater contributions range from 5 Mm³ in the 2030s to up to 25 Mm³ by the 2060s, values comparable to total basin-wide groundwater withdrawals (~ 14.2 Mm³). However, these benefits weaken over time as declining precipitation reduces vegetation vigor, lowering crop coefficients (Kc) and runoff resistance factors (RRF). This demonstrates that forest-related hydrological benefits are climate-dependent and cannot be treated as constant inputs in long-term planning. Carbon sequestration, the ecosystem functions associated with “clean air”, shows a consistently negative trajectory across all forest communities under climate change. Decadal projections based on climate-driven polynomial regressions indicate reductions of approximately 41% in xerophytic shrublands (non significant linear model) and 37% in sclerophyllous forests during 2040–2060 relative to 1999–2019. Hygrophilous forests decline more gradually but reach ~ 600 kg C ha⁻¹ yr⁻¹ by the 2060s (≈ 9% reduction), while deciduous forests decrease by ~ 6% to ~ 950 kg C ha⁻¹ yr⁻¹. Importantly, carbon sequestration during the Megadrought was, in several cases, lower than the conservative (15th percentile) future projections, highlighting that extreme events already exceed the range of historical variability represented in climate models. These results indicate that climate change will substantially erode the capacity of native forests to provide this ecosystem service, even under conservation-oriented land-use scenarios. Biodiversity responses further reinforce this non-stationarity. Hygrophilous forests currently contribute 37–40% of basin-wide tree-level diversity (depending on index), while sclerophyllous forests dominate regeneration-level diversity (44–49%). Yet these high-diversity communities coincide with those most vulnerable to drought. Scenario-based analyses show that reductions in hygrophilous and xerophytic forests would lead to an estimated 66% decline in basin-wide biodiversity value when surface-weighted Simpson indices are considered. This implies that biodiversity losses under climate change are likely to be disproportionate relative to area loss, with strong implications for conservation prioritization. Methodologically, this study demonstrates that integrating forest dynamics into hydrological models used for water planning is feasible and improves model performance. Incorporating forest seasonality into Kc and RRF parameters increased WEAP performance (NSE = 0.41 for lake levels; NSE = 0.78 and KGE = 0.87 for streamflow validation), relative to previous model versions developed by Barría et al. ( 2021 ). However, important limitations remain. Carbon sequestration estimates are restricted to aboveground tree biomass and assume constant tree density, excluding soil carbon pools, regeneration dynamics, and mortality processes. In addition, not all climate–growth relationships were statistically significant, particularly for xerophytic communities (R² = 0.29, p = 0.054), reflecting both ecological complexity and limited in situ data. These limitations underscore the need for expanded field monitoring, especially in xerophytic and high-elevation communities, and for incorporating soil carbon, regeneration, and mortality processes into future modeling efforts. Without such data, linear and polynomial models risk oversimplifying ecosystem responses under increasing aridity. Accordingly, the hydrological responses reported here should be interpreted as the most robust component of the framework, carbon responses as climate-sensitive but structurally simplified estimates, and biodiversity projections as exploratory scenario-based proxies rather than direct forecasts. Beyond the Aculeo case, this study offers a transferable sociohydrological framework for linking ecosystem conservation and water planning in climate-vulnerable basins. By integrating participatory valuation, ecological observations, remote sensing, and hydrological modelling, the approach quantifies forest contributions to water availability, carbon sequestration, and biodiversity. It also shows that these contributions decline under climate change and vary strongly among forest types, underscoring the risks of treating forest ecosystem functions as stationary in regional planning. Declarations The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions: CRediT Pilar Barría: Writing original draft, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. Anahí Ocampo-Melgar: Writing review & editing, Conceptualization, Investigation. Alejandro Venegas-González: Writing review & editing, Methodology, Formal analysis, Investigation, Data curation. Ariel Muñoz: Writing review & editing, Investigation, Resources. Funding sources This work was partially supported by the following grants: Initiation Fondecyt grant number 11200854, of the Chilean Agency for Research and Development (ANID), and by the grant N°022/2020 of the National Forest Corporation of Chile (CONAF). References Alaniz, A. J., Carvajal, M. A., Núñez-Hidalgo, I., and Vergara, P. M. (2019). Chronicle of an environmental disaster: Aculeo Lake, the collapse of the largest natural freshwater ecosystem in central Chile. Environ. Conserv., 46(3), 201-204. https://doi.org/10.1017/S0376892919000122 Anzaldua, G., Gerner, N. V., Lago, M., Abhold, K., Hinzmann, M., Beyer, S., ... & Birk, S. (2018). 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Water-related ecosystem services interactions and their natural-human activity drivers: Implications for ecological protection and restoration. J. Environ. Manag., 352, 120101. https://doi.org/10.1016/j.jenvman.2024.120101 Luebert, F., & Pliscoff, P. (2006). Sinopsis bioclimática y vegetacional de Chile. Editorial universitaria. Miranda, A., Syphard, A. D., Berdugo, M., Carrasco, J., Gómez-González, S., Ovalle, J.F., et al. (2023). Widespread synchronous decline of Mediterranean-type forest driven by accelerated aridity. Nat. Plants. 9, 1810–1817. https://doi.org/10.1038/s41477-023-01541-7 Miranda, M. D., Vergara, B., Dobbs, C., and Becerra, P. (2024). Losses in primary productivity within Mediterranean sclerophyllous forests in Chile are influenced by both vegetation structure and physiographic conditions in the context of severe droughts and heatwaves. Available at SSRN 4744795. doi: https://dx.doi.org/10.2139/ssrn.4744795 Nedkov, S., & Burkhard, B. (2012). Flood regulating ecosystem services-Mapping supply and demand, in the Etropole municipality, Bulgaria. Ecol. Indic., 21, 67-79. https://doi.org/10.1016/j.ecolind.2011.06.022 O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., et al. (2016). The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461-3482. https://doi.org/10.5194/gmd-9-3461-2016 Ocampo-Melgar, A., Barría, P., Cerda, C., Venegas-González, A., Fernández, J., Díaz-Vasconcellos, R., and Zamora, J. (2024). Payment for Ecosystem Services: institutional arrangements for a changing climate in the Chilean Mediterranean Region. npj Clim. Action 3(1), 52. doi: https://doi.org/10.1038/s44168-024-00132-2 Ocampo-Melgar, A., Barría, P., Chadwick, C., and Villoch, P. (2021). Restoration perceptions and collaboration challenges under severe water scarcity: The Aculeo lake process. Restor. Ecol. 29 (2): e13337. https://doi.org/10.1111/rec.13337 Olander, L., Polasky, S., Kagan, J. S., Johnston, R. J., Wainger, L., Saah, D., ... & Yoskowitz, D. (2017). So you want your research to be relevant? Building the bridge between ecosystem services research and practice. Ecosyst. Serv., 26, 170-182 Olander, L. P., Johnston, R. J., Tallis, H., Kagan, J., Maguire, L. A., Polasky, S., ... & Palmer, M. (2018). Benefit relevant indicators: Ecosystem services measures that link ecological and social outcomes. Ecological Indicators, 85, 1262-1272. Pôças, I., Paço, T. A., Paredes, P., Cunha, M., & Pereira, L. S. (2015). Estimation of actual crop coefficients using remotely sensed vegetation indices and soil water balance modelled data. Remote Sens. 7(3), 2373-2400. Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., et al. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Change 42, 153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009 Romero, F. and Teillier, S. (2014). Flora vascular de los Altos del Cantillana, Región Metropolitana, Chile: pisos de vegetación subandino y andino. Chloris Chilensis, 17(1): 5. Ronchi, S., & Brambilla, M. (2025). Scarce consideration of climate and land use changes impacts on ecosystem services and biodiversity in the Apennines Mountain system, Italy. Regional Environmental Change, 25, 58. https://doi.org/10.1007/s10113-025-02392-y Santini Jr, L., Craven, D., Rodriguez, D. R. O., Quintilhan, M. T., Gibson-Carpintero, S., Torres, C. A., ... & Venegas-Gonzalez, A. (2024). Extreme drought triggers parallel shifts in wood anatomical and physiological traits in upper treeline of the Mediterranean Andes. Ecol Process., 13(1), 10. Shaad, K., Souter, N. J., Vollmer, D., Regan, H. M., & Bezerra, M. O. (2022). Integrating ecosystem services into water resource management: an indicator-based approach. Environ. Manag., 69(4), 752-767 Smith-Ramírez, C., Grez, A., Galleguillos, M., Cerda, C., Ocampo-Melgar, A., Miranda, M. D., et al. (2023). Ecosystem services of Chilean sclerophyllous forests and shrublands on the verge of collapse: A review. J. Arid Environ. 211. https://doi.org/10.1016/j.jaridenv.2022.104927 Taucare, M., Viguier, B., Figueroa, R., and Daniele, L. (2024). The alarming state of Central Chile's groundwater resources: A paradigmatic case of a lasting overexploitation. Sci. Total Environ. 906, https://doi.org/10.1016/j.scitotenv.2023.167723 Venegas-González, A., Gibson-Carpintero, S., Anholetto-Junior, C., Mathiasen, P., Premoli, A. C., and Fresia, P. (2022). Tree-Ring Analysis and Genetic Associations Help to Understand Drought Sensitivity in the Chilean Endemic Forest of Nothofagus macrocarpa. Front. For. Glob. Change 5:762347. https://doi.org/10.3389/ffgc.2022.762347 Venegas-González, A., Muñoz, A. A., Carpintero-Gibson, S., González-Reyes, A., Schneider, I., Gipolou-Zuñiga, T., et al. (2023). Sclerophyllous forest tree growth under the influence of a historic megadrought in the Mediterranean Ecoregion of Chile. Ecosystems 26(2), 344-361. https://doi.org/10.1007/s10021-022-00760-x Venegas-González, A., Roig Juñent, F., Gutiérrez, A. G., Peña-Rojas, K., and Tomazello Filho, M. (2018). Efecto de la variabilidad climática sobre los patrones de crecimiento y establecimiento de Nothofagus macrocarpa en Chile central. Bosque. 39(1), 81-93. https://repositorio.uchile.cl/handle/2250/150155 (accessed 26 December 2025) Venegas-González, A., Roig, F. A., Peña-Rojas, K., Hadad, M. A., Aguilera-Betti, I., and Muñoz, A. A. (2019). Recent Consequences of Climate Change Have Affected Tree Growth in Distinct Nothofagus macrocarpa (DC.) FM Vaz & Rodr Age Classes in Central Chile. Forests, 10(8). https://doi.org/10.3390/f10080653 Waldick, R., Bizikova, L., White, D. et al. (2017). An integrated decision-support process for adaptation planning: climate change as impetus for scenario planning in an agricultural region of Canada. Regional Environmental Change, 17, 187–200. https://doi.org/10.1007/s10113-016-0992-5 Yates, D., Sieber, J., Purkey, D., & Huber-Lee, A. (2005). WEAP21—A Demand-, Priority-, and Preference-Driven Water Planning Model: Part 1: Model Characteristics. Water Int. 30(4), 487–500. https://doi.org/10.1080/02508060508691893 Yates, D., Sieber, J., Purkey, D., & Huber-Lee, A. (2005). WEAP21—A demand-, priority-, and preference-driven water planning model: part 1: model characteristics. Water Int., 30(4), 487-500. Zhang, Y., Tariq, A., Hughes, A.C., Hong, D., Wei, F., Sun, H., et al. (2023). Challenges and solutions to biodiversity conservation in arid lands. Sci. Total Environ. 857(3): 159695. https://doi.org/10.1016/j.scitotenv.2022.159695 Zamorano-Elgueta, C., Orsi, F., Geneletti, D., Cayuela, L., Hamer, R., Lara, A., & Benayas, J. M. R. (2025). Integrating Ecological Suitability and Socioeconomic Feasibility at Landscape Scale to Restore Biodiversity and Ecosystem Services in Southern Chile. Environmental Management, 75(3), 588-605. https://doi.org/10.1007/s00267-024-02103-z Zango-Palau, A., Jolivet, A., Lurgi, M. et al. (2024). A quantitative approach to the understanding of social-ecological systems: a case study from the Pyrenees. Regional Environmental Change, 24, 9. https://doi.org/10.1007/s10113-023-02177-1 Ziervogel, G., Satyal, P., Basu, R. et al. (2019). Vertical integration for climate change adaptation in the water sector: lessons from decentralisation in Africa and India. Regional Environmental Change, 19, 2729–2743. https://doi.org/10.1007/s10113-019-01571-y Additional Declarations No competing interests reported. Supplementary Files SF1.pdf 26SupplementaryMateriallast.docx SupplementaryFigure2.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9323806","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625793077,"identity":"c5478a6c-b30e-4643-b10a-e4bc3c29c2f3","order_by":0,"name":"Pilar Barría","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYDACCRBhwCDHwMDDcOABkM1HrBZjsJYEIJuNOC0MDIkNQC0MRGkxl25+9rmiwC59w/GzBw8kVDDIE9RiOeeY8cwzBsm5G87kJRxIOMNg2EZIi8GNBGPGBgPm3A03eAwOJLYxJBC0xeBG+meglvp0A7CWf0RpyQHZcjgBoqWBCC2Wc84UA7UcN5x5JsfgQMIxCcJ+MZdu38zY8Kdanu/4GeMPH2ps5PkJOgyNL0FIA6aWUTAKRsEoGAWYAABhjT2TNoFcTgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Chile","correspondingAuthor":true,"prefix":"","firstName":"Pilar","middleName":"","lastName":"Barría","suffix":""},{"id":625793094,"identity":"4a4d2d25-d5f5-4133-bfc7-60ae0c61be01","order_by":1,"name":"Anahí Ocampo-Melgar","email":"","orcid":"","institution":"University of Chile","correspondingAuthor":false,"prefix":"","firstName":"Anahí","middleName":"","lastName":"Ocampo-Melgar","suffix":""},{"id":625793095,"identity":"bf9ab31c-b474-43ff-803d-33424c07a38b","order_by":2,"name":"Alejandro Venegas-González","email":"","orcid":"","institution":"O’Higgins University","correspondingAuthor":false,"prefix":"","firstName":"Alejandro","middleName":"","lastName":"Venegas-González","suffix":""},{"id":625793105,"identity":"c07c70db-3042-44ae-88ab-583c73126ae0","order_by":3,"name":"Ariel Muñoz","email":"","orcid":"","institution":"Pontificia Universidad Católica de Valparaíso","correspondingAuthor":false,"prefix":"","firstName":"Ariel","middleName":"","lastName":"Muñoz","suffix":""}],"badges":[],"createdAt":"2026-04-05 03:24:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9323806/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9323806/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107922202,"identity":"1a2699b9-9e98-4ae9-a899-d9e3584d4722","added_by":"auto","created_at":"2026-04-27 14:59:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":953844,"visible":true,"origin":"","legend":"\u003cp\u003eAculeo lake basin. A) Location of the basin. B) Land use and land cover of the basin, and location of the in-situ field measurements. C1: hygrophilous forest community, C2: moist sclerophyllous forest, C3: ecotone shrub-trees forest community, C4: dry sclerophyllous forest community, C5: montane sclerophyllous forest community; C6: deciduous forest community. C) The 8 Hydrological Response Units (HRU) used to represent the basin within the WEAP model: Aculeo sin las Cabras Alto (ASC Alto) 4759,7 ha, Aculeo sin las Cabras Bajo (ASC 1321 Bajo) (ha) 1780,4 ha, Las Cabras (ha) 1633,4 ha, Aculeo lake (ha) 1206,2 ha, Pintué 1 (ha) 1976,9ha, Pintué 2 (ha) 1396,4 ha, Pintué 3 (ha) 1100,8 ha and Pintué 4 (ha) 1035,2 ha\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9323806/v1/e5f76bd77b2d147c0f50aafc.png"},{"id":108007283,"identity":"4bce1039-3a00-49e7-83fb-ab02a63c1681","added_by":"auto","created_at":"2026-04-28 12:59:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":126753,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual socio-hydrological framework proposed in this study\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9323806/v1/f3c160cb19746a519aa98e93.png"},{"id":107922205,"identity":"eb73039a-5329-4beb-b5d2-a0f2b6e15d02","added_by":"auto","created_at":"2026-04-27 14:59:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70340,"visible":true,"origin":"","legend":"\u003cp\u003eData used to implement the socio-hydrological approach for quantifying EF of native forests. (A) Observed and WEAP-simulated monthly water volumes of Lake Aculeo. (B) Seasonal variability of observed and WEAP-simulated runoff in the Pintué Stream, together with observed basin-scale precipitation. (C) Triannual water-use efficiency (iWUE) values for each forest community sampled within the basin. (D) Triannual forest productivity values (carbon sequestration) for each forest community, derived from field measurements.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9323806/v1/e3f73278ab72c9893acbd737.png"},{"id":107922230,"identity":"eb005c4b-2ba1-45e4-a259-c43d9efd8a48","added_by":"auto","created_at":"2026-04-27 14:59:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":67672,"visible":true,"origin":"","legend":"\u003cp\u003ePolynomic regressions to assess relationship between carbon sequestration of the differente forest communities and observed climate variables from the 2000-2021 period. A) Polynomic regression between hygrophilous communities and April to October precipitation; B) Polynomic regression between deciduous communities and April to October precipitation; C) Polynomic regression between xerophytic communities and April to March precipitation and January to March temperature; and D) Polynomic regression between sclerophyllous communities and April to March precipitation and January to March temperature\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9323806/v1/4420a7b95c9b8388ff39e5a8.png"},{"id":107922207,"identity":"568404ca-2e89-4e2a-afb7-a12291230148","added_by":"auto","created_at":"2026-04-27 14:59:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":44279,"visible":true,"origin":"","legend":"\u003cp\u003eEstimation of the native forests regulation of soil moisture for the 2030-2065 period, based on the difference in storage between the dystopian and conservation scenarios per unit area of forest. The results correspond to annual soil moisture, the pluvial season (April–September, blue areas) soil moisture, and the snowmelt period (October–March, orange points) soil moisture. C) Estimation of the native forest contribution to groundwater storage, based on the difference in storage between the dystopian and conservation scenarios, per unit of forest area\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9323806/v1/289f4146dfc5b8c6733b8d98.png"},{"id":108006143,"identity":"8dfc173a-25af-4829-9da9-e2eb6901ccc3","added_by":"auto","created_at":"2026-04-28 12:53:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":161038,"visible":true,"origin":"","legend":"\u003cp\u003eProjections of decadal carbon sequestration in Aculeo Basin for the 1988 and 2065 period for: A) Deciduous forest community, B) Hygrophilous forest community, C) Xerophytic forest community and D) sclerophyllous forest community\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9323806/v1/6b76e54d0a4b5aa0d2b7e26b.png"},{"id":108007360,"identity":"c35a48f5-355b-442e-9583-3301e863914e","added_by":"auto","created_at":"2026-04-28 12:59:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":31513,"visible":true,"origin":"","legend":"\u003cp\u003eDiversity indices for native forest communities in the Aculeo Basin. (A) Community-level diversity indices derived from field measurements. (B) Surface-weighted diversity indices calculated using a leave-one-community-out approach.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-9323806/v1/11c8eb07c47c8c51c6e78630.png"},{"id":108181082,"identity":"906fe531-85ae-40df-8c23-3031ab490bca","added_by":"auto","created_at":"2026-04-30 08:57:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1756506,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9323806/v1/87b54255-1ad9-41e5-abcf-8b6fee920fb9.pdf"},{"id":107922203,"identity":"d3989654-28e4-4457-ac47-c3c5c0f83a9a","added_by":"auto","created_at":"2026-04-27 14:59:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28584,"visible":true,"origin":"","legend":"","description":"","filename":"SF1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9323806/v1/9e42b0acedfcb0d9c300c600.pdf"},{"id":107922210,"identity":"5215daf0-0751-4326-b8eb-8180af3859ff","added_by":"auto","created_at":"2026-04-27 14:59:48","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":407737,"visible":true,"origin":"","legend":"","description":"","filename":"26SupplementaryMateriallast.docx","url":"https://assets-eu.researchsquare.com/files/rs-9323806/v1/372269c112b737e3c2c48102.docx"},{"id":108007570,"identity":"182c2f0b-0022-4ea0-9280-b80dd51a819f","added_by":"auto","created_at":"2026-04-28 13:00:34","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":63380,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9323806/v1/59e45daec04a468655ae70d0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Linking Water Planning and Native Forest Conservation in Central Chile: A Sociohydrological Perspective","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAcross dryland and Mediterranean regions, climate change and land-use change are reshaping water availability (Liu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lindsay et al., 2021), vegetation dynamics, and the governance challenges faced by regional planning systems (Zhang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These regions are socio-ecological systems in which shifts in forest condition, biodiversity, and hydrological functioning interact with livelihoods, institutional capacity, and adaptation choices (Gauquelin et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zango-Palau et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Yet regional planning tools still tend to treat water allocation, ecosystem condition, and adaptation governance as separate domains, limiting their ability to capture cross-sectoral trade-offs and to support robust responses under climate uncertainty (Waldick et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ziervogel et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ronchi and Brambilla, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCentral Chile is one of these dryland forest regions (30\u0026ndash;38\u0026deg;S; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). This Mediterranean-climate area, where climate change and human disturbance threaten both forest and aquatic ecosystems (Armesto et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Alaniz et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bambach et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hidalgo-Corrotea et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), contains the only Mediterranean-type forest biome in South America (Donoso, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) and supports more than 40% of Chile\u0026rsquo;s population (INE, 2017). Climate- and land-use-driven change has already reduced provisioning, regulating, and cultural ecosystem services that are essential for regional sustainability (Smith-Ram\u0026iacute;rez et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite this vulnerability, few studies have systematically evaluated how native forests in arid Mediterranean-type ecosystems outside the Mediterranean Basin contribute to ecosystem functions (EF) and services (Smith-Ram\u0026iacute;rez et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This limits the ability to generate actionable, context-specific knowledge for conservation and restoration.\u003c/p\u003e \u003cp\u003eA prolonged and unprecedented climatic event, the \u0026ldquo;Megadrought\u0026rdquo; (MD), beginning in 2010, has intensified water scarcity in central Chile (Garreaud et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The MD has contributed to deplet surface and groundwater (Taucare et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and accelerated glacier loss, increasing pressure on overextracted basins (Barr\u0026iacute;a et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Rising aridity has also caused widespread tree browning and canopy dieback, even in drought-adapted species (Miranda et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Tree-ring studies document long-term growth declines in relict species such as \u003cem\u003eNothofagus macrocarpa\u003c/em\u003e and \u003cem\u003eAustrocedrus chilensis\u003c/em\u003e, associated with decreasing precipitation and rising temperatures since the late 20th century (Le Quesne et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Venegas-Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Matskovski et al., 2021).\u003c/p\u003e \u003cp\u003eClimate projections indicate a 20\u0026ndash;40% reduction in rainfall by 2080\u0026ndash;2099 under a high-emissions scenario (SSP5-8.5), underscoring the need for integrated planning frameworks that explicitly consider dryland EF and ES (Smith-Ram\u0026iacute;rez et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although substantial work has been done on incorporating ES into basin planning (Brauman, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Anzaldua et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bubb et al., 2017; Chen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), integrating EF into hydrological models (Nedkov and Burkhard, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gr\u0026ecirc;t-Regamey et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), embedding ecological suitability together with social feasibility analyses of restauration projects in landscape planning (Zamorano-Elgueta et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and developing indicators linking ecological and social outcomes (Olander et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Garc\u0026iacute;a-D\u0026iacute;az et al., 2021), only a small portion of this knowledge informs policy decisions (Inostroza et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Olander et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shaad et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Indeed, in Chile, forest ecosystem functions remain largely absent from water-planning models (Balocchi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, Chile\u0026rsquo;s Strategic Water Resources Plans (in Spanish Planes Estrat\u0026eacute;gicos de Recursos H\u0026iacute;dricos, PERH) provide an opportunity to examine a challenge that extends well beyond the country: how can basin planning tools incorporate ecological change and stakeholder priorities, rather than only simulating water supply and demand? This question is central to regional adaptation planning, where cross-scale coordination, multi-level learning, and better fit between ecological and governance scales are increasingly recognized as critical (Ziervogel et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wiegant et al., 2022; Gonzales-Iwanciw et al., 2025).\u003c/p\u003e \u003cp\u003eThe Aculeo watershed, in Chile\u0026rsquo;s Metropolitan Region, illustrates the socio-ecological consequences of unmanaged water stress. After nearly a decade of drought and persistent over-demand, Aculeo Lake-formerly 12 km\u0026sup2;-dried completely, triggering environmental degradation and social conflict (Barr\u0026iacute;a et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The 14,900-ha watershed is dominated by native forests and shrublands (~\u0026thinsp;10,000 ha), which are critical in a water-scarce landscape. Vegetation has experienced severe productivity declines since the 1980s, with an average 56% reduction during the Megadrought relative to the prior six decades (Gibson-Carpintero et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Understanding forest responses to climatic and anthropogenic stressors is therefore essential, given the strong dependence of local communities on the ecosystem services these forests provide (Ocampo-Melgar et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis article uses the Aculeo Lake Basin as a demonstration site to examine how stakeholder-prioritized native-forest ecosystem functions can be incorporated into basin water-planning models under climate and land-management change. It builds on prior work in the basin on stakeholder valuation (Ocampo-Melgar et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), forest ecological characterization (Gibson-Carpintero et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and a calibrated WEAP model (Barr\u0026iacute;a et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The original contribution is to integrate these components within a single sociohydrological framework and to advance three new steps: deriving climate-forest relationships to represent non-stationary ecosystem functioning, modifying WEAP to incorporate native-forest dynamics through time-varying vegetation parameters, and applying the framework under climate and land-management scenarios to assess implications for water availability, carbon sequestration, and biodiversity. The Aculeo case thus serves as a demonstration of a transferable approach for dryland basin planning.\u003c/p\u003e"},{"header":"2. Study area and previous work on ecosystem services quantification","content":"\u003cp\u003eThis study focuses on the Mediterranean forests of the Cord\u0026oacute;n Cantillana, located within the Aculeo Lake watershed (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), an ecosystem characterized by high-Andean vegetation and predominantly mountainous terrain in the semi-arid region of central Chile (CONAMA, 2005).\u003c/p\u003e\n\u003cp\u003eSpanning approximately 205,000 hectares, the Cord\u0026oacute;n\u0026apos;s diverse topography and altitudinal variation (with Cerro Cantillana reaching 2,281 meters above sea level) create an ideal setting for the development of various forest woody communities. Deciduous forests dominate the higher elevations, while sclerophyllous forests and xerophytic shrublands are prevalent in the medium elevations (\u0026lt;\u0026thinsp;1,500 msnm), lower areas and valleys (Gibson-Carpintero et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe integrated framework presented in this paper builds on previously generated social, ecological, and hydrological information from the Aculeo basin. This section briefly summarizes that prior analytical basis in order to distinguish it from the new framework development, model integration, and scenario analysis presented in Sections 3 and 4.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Progress on S1. Indentification of ecosystem functions/services valued by stakeholders\u003c/h2\u003e\n \u003cp\u003eThe complete desiccation of Aculeo Lake turned the watershed into a focal case in Chilean water-governance debates, highlighting the severity of the regional water crisis (Barr\u0026iacute;a et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As shown by Ocampo-Melgar et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), unequal access to resources and differing adaptive capacities make it essential to consider diverse stakeholder interests when designing adaptation measures.\u003c/p\u003e\n \u003cp\u003eStakeholder perceptions of ecosystem services in the basin (Supplementary Table\u0026nbsp;1) were assessed by Ocampo-Melgar et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) using a mixed qualitative-quantitative questionnaire administered between June and August 2021. The one-hour survey included 24 participants representing local government, public schools, irrigation farmers, grazing organizations, export agriculture, tourism operators, rural residents, the Altos de Cantillana Reserve, neighborhood associations, cultural groups, and environmental education organizations. It produced a ranked list of 29 ecosystem services and provided information on their perceived location, management, accessibility, and vulnerability to climate change (see Ocampo-Melgar et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e for details).\u003c/p\u003e\n \u003cp\u003eThe ecosystem services most valued by stakeholders are presented in Supplementary Table\u0026nbsp;1 and Supplementary Fig.\u0026nbsp;1, showing that biodiversity, water provision, and clean air received the highest rankings in terms of importance and perceived climate-change vulnerability.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Progress on S2. Characterizing the Ecosystem Functions of Native Forests in Response to Climate\u003c/h2\u003e\n \u003cp\u003ePrevious research in the Cantillana Range has documented 249 native vascular plant species and 12 naturalized non-native species, highlighting its ecological importance, high endemism, and relevance for regional tourism (Romero et al., 2014). However, the effects of the Megadrought and projected climate-change impacts on this ecosystem remain insufficiently understood (Venegas-Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTo characterize native forests and generate information relevant to the ecosystem functions and services identified by stakeholders, Gibson-Carpintero et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) established six plots along an altitudinal gradient within the basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB): hygrophilous forest, sclerophyll forest under humid conditions (south-facing slopes), xerophytic shrubland, sclerophyll forest under dry conditions (north-facing slopes), high-altitude sclerophyll forest, and deciduous forest. Based on structure and composition, and following Chilean forest ecosystem classifications (Luebert \u0026amp; Pliscoff, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; CONAF, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), these communities were grouped into four categories: xerophytic shrubland, sclerophyllous (sclerophyll forests at mid- and high elevations), hygrophilous (hygrophilous forests at the bottom of the ravine), and deciduous forests.\u003c/p\u003e\n \u003cp\u003eGibson-Carpintero et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) further characterized species richness and abundance, along with forest growth, productivity, and water-use efficiency, information that is key for understanding EF under S2.\u003c/p\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1 Forest richness and abundance\u003c/h2\u003e\n \u003cp\u003eGibson-Carpintero et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) conducted a community-level assessment of established trees and seedling regeneration in the Cantillana Range. Their analysis showed a strong negative relationship between elevation and tree species richness (r = -0.76, pvalue\u0026thinsp;=\u0026thinsp;0.004), consistent across both forest inventory plots and regeneration plots.\u003c/p\u003e\n \u003cp\u003eUsing Gini\u0026ndash;Simpson, Shannon, and Simpson diversity indices (see Supplementary Table\u0026nbsp;2), Gibson-Carpintero et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that hygrophilous forests and sclerophyll forests under humid conditions exhibit the highest richness and entropy. These are followed by xerophytic shrubland and dry-condition sclerophyll forests, while deciduous forests show the lowest diversity. Overall, their results indicate that hygrophilous forests support the greatest tree-species diversity among the communities assessed.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2 Native Forest growth, productivity and Water Use Efficiency (iWUE) dynamics\u003c/h2\u003e\n \u003cp\u003eGibson-Carpintero et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) also conducted a retrospective population-level analysis to study trends and annual variability in radial growth, carbon sequestration and water use efficiency (iWUE) in the dominant species, using dendrochronological records, x-ray densitometry and stable isotopes. Their results show consistently low radial growth rates (ring width) across all populations during the common period 1950\u0026ndash;2019, with mean values below 2 mm per year. Growth varied between 0.7 and 1.9 mm/year, with deciduous forests exhibiting the highest rates and xerophytic communities the lowest. All populations experienced significant growth declines during the Megadrought, with reductions of around least 30%.\u003c/p\u003e\n \u003cp\u003eThese patterns are consistent with previous studies (Venegas-Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and are further illustrated in Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, which show marked increases in intrinsic water use efficiency (iWUE) and decreases in carbon sequestration (productivity) derived from the radial growth measured by Gibson Carpintero et al. (2024) of the four forest communities here studied, particularly during the Megadrought period.\u003c/p\u003e\n \u003cp\u003eBuilding on these findings, the present study advances S2 by deriving quantitative relationships between forest dynamics and climate-information required to integrate native-forest ecosystem functions into water-planning models for the 2000\u0026ndash;2065 period (near future).\u003c/p\u003e\u003cspan\u003e\n \u003ch2\u003e2.3 Progress on S3. Adjusting the hydrological models of water planning activities within the context of a sociohydrological evaluation of native forest ecosystem functions (services)\u003c/h2\u003e\n \u003c/span\u003e\n \u003cp\u003eA semidistributed surface-water model based on the Water Evaluation and Planning system (WEAP; Yates et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2005\u003c/span\u003ea, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2005\u003c/span\u003eb) was previously calibrated for the Aculeo Basin by Barr\u0026iacute;a et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and later adapted to assess climate-change impacts on the coupled water-soil-forest system. To represent the spatial heterogeneity of the watershed, eight Hydrological Response Units (HRUs) were defined for water-balance estimation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\n \u003cp\u003eAs detailed in Barr\u0026iacute;a et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), model calibration used observed lake-level records (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), while observed flows at the Pintu\u0026eacute; station (January 2003\u0026ndash;December 2010) were used for validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003eBuilding on this earlier modeling work, the next step toward completing S3 is to adjust WEAP parameters to incorporate quantitative climate-forest-water relationships, enabling the integration of native-forest ecosystem functions into water-planning simulations.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3 Methodology and data","content":"\u003cp\u003eTo support basin-level planning, this study presents a socio-eco-hydrological approach to quantify key ecosystem functions provided by native forests in arid regions within water-planning activities. The methodology follows four main steps (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and builds on prior work in stakeholder valuation, ecological characterization, and hydrological model development in the basin (Ocampo-Melgar et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gibson-Carpintero et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Barr\u0026iacute;a et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The original contribution of this study is to advance S2\u0026ndash;S4 by deriving climate\u0026ndash;forest relationships, incorporating dynamic forest parameters into WEAP, and applying scenario-based simulations to assess changes in water availability, carbon sequestration, and biodiversity. Accordingly, the analysis focuses on the ecosystem functions ranked highest in stakeholder valuation and climate-change vulnerability, using metrics identified through the literature and expert input (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMethods for quantifying changes in ecosystem functions associated with the ecosystem services most valued by local stakeholders\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eES\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eEF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eWater balance variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eProposed metric\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWater provision\u003c/p\u003e\n \u003cp\u003eVolume of surface and groundwater available for potable and non-potable uses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSoil moisture and aquifer recharge responses to changes in forest cover and vigor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eIncrease in Soil Moisture (\u003cem\u003eISM\u003c/em\u003e) and \u003cem\u003eAquifer Volume\u003c/em\u003e (m3).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003eChanges in ISM under Scenarios With and Without Native Forest Conservation Measures\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:\\varvec{I}\\varvec{S}\\varvec{M}}_{\\varvec{s}\\varvec{e}\\varvec{a}{\\:\\varvec{k}}_{\\:\\varvec{p}\\varvec{e}\\varvec{r}\\:\\varvec{j}}}\\left(\\frac{{\\varvec{m}}^{3}}{\\varvec{y}\\varvec{e}\\varvec{a}\\varvec{r}}\\right)={\\varvec{I}\\varvec{S}\\varvec{M}\\:\\varvec{i}}_{\\:\\varvec{s}{\\varvec{e}\\varvec{a}\\:\\varvec{k}}_{\\varvec{p}\\varvec{e}\\varvec{r}\\:\\varvec{j}}}\\left({\\varvec{m}}^{3}\\right)-{\\varvec{I}\\varvec{S}\\varvec{M}\\:\\varvec{r}\\varvec{e}\\varvec{f}}_{\\varvec{s}{\\varvec{e}\\varvec{a}\\:\\varvec{k}}_{\\varvec{p}\\varvec{e}\\varvec{r}\\:\\varvec{j}}}\\left({\\varvec{m}}^{3}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eWhere:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:\\varvec{I}\\varvec{S}\\varvec{M}}_{\\varvec{s}{\\varvec{e}\\varvec{a}\\:\\varvec{k}}_{\\varvec{p}\\varvec{e}\\varvec{r}\\:\\varvec{j}}}\\:\\left(\\frac{\\varvec{m}3}{\\varvec{y}\\varvec{e}\\varvec{a}\\varvec{r}}\\right)\\)\u003c/span\u003e\u003c/span\u003e: Volume of water accumulated in the soil related to native forest, during hydrological season\u0026nbsp;k\u0026nbsp;(rainy, snowmelt, or annual) and climatic period\u0026nbsp;j\u0026nbsp;(historical, near future 2030\u0026ndash;2059, or distant future 2070\u0026ndash;2099).\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{I}\\varvec{S}\\varvec{M}\\:\\varvec{i}}_{\\:\\varvec{s}{\\varvec{e}\\varvec{a}\\:\\varvec{k}}_{\\varvec{p}\\varvec{e}\\varvec{r}\\:\\varvec{j}}}\\left(\\frac{\\varvec{m}3}{\\varvec{y}\\varvec{e}\\varvec{a}\\varvec{r}}\\right)\\)\u003c/span\u003e\u003c/span\u003e: Increase in soil moisture\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{I}\\varvec{S}\\varvec{M}\\:\\varvec{r}\\varvec{e}\\varvec{f}}_{\\varvec{s}\\varvec{e}\\varvec{a}{\\:\\varvec{k}}_{\\varvec{p}\\varvec{e}\\varvec{r}\\:\\varvec{j}}}\\left(\\frac{\\varvec{m}3}{\\varvec{y}\\varvec{e}\\varvec{a}\\varvec{r}}\\right)\\)\u003c/span\u003e\u003c/span\u003e: Increase in soil moisture under the reference scenario.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eChanges in Groundwater recharge under Scenarios With and Without Native Forest Conservation Measures\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:\\:\\varvec{s}\\varvec{u}\\varvec{b}}_{\\varvec{s}\\varvec{e}\\varvec{a}{\\:\\varvec{k}}_{\\varvec{p}\\varvec{e}\\varvec{r}\\:\\varvec{j}}}\\left(\\frac{\\varvec{m}3}{\\varvec{y}\\varvec{e}\\varvec{a}\\varvec{r}}\\right)={\\varDelta\\:\\:\\varvec{s}\\varvec{u}\\varvec{b}\\varvec{t}\\:\\varvec{i}}_{\\:\\varvec{s}{\\varvec{t}\\:\\varvec{k}}_{\\varvec{p}\\varvec{e}\\varvec{r}\\:\\varvec{j}}}\\left(\\varvec{m}3\\right)-{\\varDelta\\:\\:\\varvec{s}\\varvec{u}\\varvec{b}\\:\\varvec{r}\\varvec{e}\\varvec{f}}_{\\:\\varvec{s}{\\varvec{t}\\:\\varvec{k}}_{\\varvec{p}\\varvec{e}\\varvec{r}\\:\\varvec{j}}}\\left(\\varvec{m}3\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eWhere:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:\\:\\varvec{s}\\varvec{u}\\varvec{b}\\varvec{t}\\:\\varvec{i}}_{\\varvec{s}\\varvec{e}\\varvec{a}{\\:\\varvec{k}}_{\\varvec{p}\\varvec{e}\\varvec{r}\\:\\varvec{j}}}\\left(\\frac{\\varvec{m}3}{\\varvec{y}\\varvec{e}\\varvec{a}\\varvec{r}}\\right)\\)\u003c/span\u003e\u003c/span\u003e: Groundwater volume stored in the aquifer under scenario i, during hydrological season k and climatic period j\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:\\:\\varvec{s}\\varvec{u}\\varvec{b}\\varvec{t}\\:\\varvec{i}}_{\\:\\varvec{s}\\varvec{e}\\varvec{a}{\\:\\varvec{k}}_{\\varvec{p}\\varvec{e}\\varvec{r}\\:\\varvec{j}}}\\left(\\frac{\\varvec{m}3}{\\varvec{y}\\varvec{e}\\varvec{a}\\varvec{r}}\\right)\\)\u003c/span\u003e\u003c/span\u003e: Groundwater volume stored in the aquifer under the implementation of scenario\u0026nbsp;i, during hydrological season\u0026nbsp;k and climatic period\u0026nbsp;j.\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:\\:\\varvec{s}\\varvec{u}\\varvec{b}\\varvec{t}\\:\\varvec{r}\\varvec{e}\\varvec{f}}_{\\:\\varvec{s}\\varvec{e}\\varvec{a}{\\:\\varvec{k}}_{\\varvec{p}\\varvec{e}\\varvec{r}\\:\\varvec{j}}}\\left(\\frac{\\varvec{m}3}{\\varvec{y}\\varvec{e}\\varvec{a}\\varvec{r}}\\right)\\)\u003c/span\u003e\u003c/span\u003e: Groundwater volume under the reference scenario, during hydrological season\u0026nbsp;k,\u0026nbsp;and climatic period\u0026nbsp;j.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eClean air\u003c/p\u003e\n \u003cp\u003eRefers to the benefits received by stakeholders from the carbon sequestration capacity of forest cover within the watershed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCarbon sequestration in relation to forest community productivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cem\u003eEvapotranspiration (Ton m3)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003eChanges in Carbon sequestration under Forest Management and climate change scenarios\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{C}\\varvec{s}\\varvec{e}\\varvec{q}}_{\\varvec{p}\\varvec{e}\\varvec{r}\\:\\varvec{t}}=\\int\\:{\\varvec{C}\\varvec{s}\\varvec{e}\\varvec{q}}_{\\varvec{j},\\:{\\varvec{s}\\varvec{p}\\varvec{e}}_{\\varvec{e}}}\\:\\varvec{d}\\varvec{A}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{C}\\varvec{s}\\varvec{e}\\varvec{q}}_{\\varvec{p}\\varvec{e}\\varvec{r}\\:\\varvec{t}}\\left(\\frac{\\varvec{T}\\varvec{o}\\varvec{n}}{\\varvec{y}\\varvec{e}\\varvec{a}\\varvec{r}}\\right)\\)\u003c/span\u003e\u003c/span\u003e= Carbon sequestration by the forest and shrubland cover, analyzed during period \u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{C}\\varvec{s}\\varvec{e}\\varvec{q}}_{\\varvec{j},\\:\\varvec{s}{\\varvec{p}\\varvec{e}}_{\\varvec{e}}}\\left(\\frac{\\varvec{T}\\varvec{o}\\varvec{n}}{\\varvec{h}\\varvec{a}\\:\\varvec{y}\\varvec{e}\\varvec{a}\\varvec{r}}\\right)={\\varvec{S}\\varvec{C}}_{\\varvec{j},\\varvec{i},\\:{\\varvec{s}\\varvec{p}\\varvec{e}}_{\\varvec{e}}}*{\\varvec{n}}_{{\\varvec{s}\\varvec{p}\\varvec{e}}_{\\varvec{e}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{S}\\varvec{C}}_{\\varvec{j},\\varvec{i},{\\varvec{s}\\varvec{p}\\varvec{e}}_{\\varvec{e}}}={0.5*\\varvec{B}\\varvec{i}\\varvec{o}\\varvec{m}\\varvec{a}\\varvec{s}\\varvec{s}}_{\\varvec{j},\\varvec{i},\\:{\\varvec{s}\\varvec{p}\\varvec{e}}_{\\varvec{e}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{S}\\varvec{C}}_{\\varvec{j},\\varvec{i},{\\varvec{s}\\varvec{p}\\varvec{e}}_{\\varvec{e}}}\\)\u003c/span\u003e\u003c/span\u003e \u003cstrong\u003e(\u003c/strong\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\varvec{T}\\varvec{o}\\varvec{n}}{\\varvec{y}\\varvec{e}\\varvec{a}\\varvec{r}}\\)\u003c/span\u003e\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e= Carbon sequestration during year \u003cem\u003ej\u003c/em\u003e by individual \u003cem\u003ei\u003c/em\u003e of species \u003cem\u003ee\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{A}\\varvec{b}\\varvec{o}\\varvec{v}\\varvec{e}\\varvec{g}\\varvec{r}\\varvec{o}\\varvec{u}\\varvec{n}\\varvec{d}\\:{\\varvec{B}\\varvec{i}\\varvec{o}\\varvec{m}\\varvec{a}\\varvec{s}\\varvec{s}}_{\\varvec{j},\\varvec{i},\\:\\varvec{s}\\varvec{e}}\\)\u003c/span\u003e\u003c/span\u003e\u003cstrong\u003e=\u003c/strong\u003e Aboveground Biomass accumulated by individual \u003cem\u003ei\u003c/em\u003e, specie e, year \u003cem\u003ej\u003c/em\u003e (Gibson-Carpintero et al, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{n}}_{{\\varvec{s}\\varvec{p}\\varvec{e}}_{\\varvec{e}}}\\)\u003c/span\u003e\u003c/span\u003e\u003cstrong\u003e=\u003c/strong\u003e Number of individuals of species \u003cem\u003ee\u003c/em\u003e per hectare.\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{d}\\varvec{A}\\)\u003c/span\u003e\u003c/span\u003e\u003cstrong\u003e= Surface (ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBiodiversity\u003c/p\u003e\n \u003cp\u003eNative forests underpin biodiversity by maintaining the structural and functional integrity of ecosystems that support diverse plant and animal species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSpecies richness and abundance in relation to forest community productivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cem\u003eIndirect relationship\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003eChanges in Diversity and Abundance Under Forest Management and Climate Change Scenarios\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSpecies richness and abundance were characterized by Gibson-Carpintero et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), using Gini-Simpson, Simpson (S) and Shannon (H) indices (Gini, 1912; Shannon, 1948).\u003c/p\u003e\n \u003cp\u003eAdding the probability of species presence or vegetation vigor decline, it enables projections of potential changes in ecosystem services (SSEE), calculated as:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:}_{\\varvec{b}\\varvec{i}\\varvec{o},\\:\\varvec{s}\\varvec{p}\\varvec{e}\\:\\varvec{l}}=\\frac{\\sum\\:_{\\varvec{l}=1}^{\\varvec{m}}\\sum\\:_{\\varvec{i}=1}^{\\varvec{n}}{\\varvec{I}\\varvec{n}\\varvec{d}}_{\\varvec{i}}\\varvec{*}{\\varvec{S}}_{\\varvec{s}\\varvec{p}\\varvec{e}\\:\\varvec{l}}}{{\\varvec{S}\\varvec{u}\\varvec{r}\\varvec{f}}_{\\varvec{f}\\varvec{o}\\varvec{r}\\varvec{e}\\varvec{s}\\varvec{t}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eWhere:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:}_{\\varvec{b}\\varvec{i}\\varvec{o},\\:\\varvec{s}\\varvec{p}\\varvec{e}\\:\\varvec{l}}\\)\u003c/span\u003e\u003c/span\u003e: Biodiversity proxy for woody specie l, within the basin.\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{I}\\varvec{n}\\varvec{d}}_{\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e: Diversity value based on the different indices considered.\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{S}}_{\\varvec{s}\\varvec{p}\\varvec{e}\\:\\varvec{l}}\\:\\left(\\varvec{h}\\varvec{a}\\right)\\)\u003c/span\u003e\u003c/span\u003e: Surface of woody specie l\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{S}\\varvec{u}\\varvec{r}\\varvec{f}}_{\\varvec{f}\\varvec{o}\\varvec{r}\\varvec{e}\\varvec{s}\\varvec{t}}\\)\u003c/span\u003e\u003c/span\u003e \u003cstrong\u003e(ha)\u003c/strong\u003e: Total area of native forest in the basin.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Step 2- Quantitative relationships between forest growth, productivity and climatic variables.\u003c/h2\u003e\n \u003cp\u003eTo quantify the relationships between forest dynamics and climate (S2), we developed statistical functions linking carbon sequestration, and intrinsic water-use efficiency (iWUE) to seasonal precipitation and temperature. Monthly precipitation and temperature fields were extracted from the CR2met gridded dataset (Boisier et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), at the sampling locations. These relationships provide the basis for estimating tree-ring growth and aboveground biomass production under climate-change scenarios.\u003c/p\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1 Historical hydrometeorological data\u003c/h2\u003e\n \u003cp\u003ePrecipitation and temperature data from the Aculeo Lake meteorological station (33.89\u0026deg; S and 71.45\u0026deg; W, 358 m.a.s.l.), administered by the Chilean Water Directorate (DGA), were used to evaluate the goodness of fit of the CR2met gridded dataset version 2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cr2.cl/datos-productos-grillados/\u003c/span\u003e\u003c/span\u003e) (Boisier et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). CR2met, widely used and validated in Chile, provides the advantage of characterizing not only the temporal variability but also the spatial variability of the climate. These characteristics are especially relevant for the watershed under study due to its significant topographic gradient, which ranges from 350 to 2250 meters above sea level.\u003c/p\u003e\n \u003cp\u003eThen, for climate characterization, calibration of the WEAP model (Example for the Pintu\u0026eacute; 1 HRU presented in Supplementary Table\u0026nbsp;3), and scaling of climate change data, the gridded CR2met data was considered. Accurate spatialization of climate change impacts requires climate and topographic data that describe the country\u0026rsquo;s and the basin\u0026rsquo;s characteristic topographic and latitudinal gradients. In this case, temperature (\u0026deg;C) and monthly precipitation (mm) data were used for the period between 1979 and 2020.\u003c/p\u003e\n \u003cp\u003eAs presented in Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, annual precipitation happens mostly during winter months, with mean values around 100 to 150 mm/month. However, the last 14 years have been marked by the occurrence of the MD, which is characterized by precipitation reductions of about 38%, and increases of temperature of about 0.4\u0026deg;C (Barr\u0026iacute;a et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.2 Hydrometeorological projections under climate change\u003c/h2\u003e\n \u003cp\u003eFor this study, monthly precipitation (pr) and mean temperature (tas) data, covering the period from 1979 to 2015 (historical) and from 2016 to 2100 (future simulations), were downloaded from 37 climate change models from the CMIP6 ensemble (O\u0026rsquo;Neill et al., 2016; Eyring et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) corresponding to a high greenhouse gas emissions scenario, SSP585 (Riahi et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kriegler et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), available on the CMIP6 ESGF website. Corrections for biases in the monthly precipitation and temperature simulations from the GCMs due to differing scales of atmospheric and oceanic physical processes in the models were applied using the Quantile Delta Mapping (DQM) method (Cannon et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eSupplementary Fig. 2 shows the annual precipitation series (hydrological year) for 37 models across the four Hydrological Response Units (HRUs) of the Pintu\u0026eacute; stream (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The different HRUs of Pintu\u0026eacute; stream represent the range of altitudes within the sub-basin. The projections indicate that despite the large dispersion across models, there are coherence regarding the significant reductions in precipitation simulated by the end of the century. The figure also includes the 5%, 50%, and 95% percentiles, along with the observed annual precipitation series (green line), indicating that after the downscaling process, the mean and variability of the observed data are well preserved in the modeled time series during the historical period.\u003c/p\u003e\n \u003cp\u003eAlthough the GCM provide continuous simulations for the whole 1979\u0026ndash;2100 period, the planning exercise conducted in this work considered projections of native forest ecosystem functions for the near future: 2030\u0026ndash;2065.\u003c/p\u003e\u003cspan\u003e\n \u003ch2\u003e\u003cstrong\u003e3.2 Step 3- Hydrological model adjustment within the context of a sociohydrological evaluation of native forest ecosystem functions/processes\u003c/strong\u003e\u003c/h2\u003e\n \u003c/span\u003e\n \u003cp\u003eTo transform the existing hydrological model into a socio-eco-hydrological framework, several adjustments were implemented to incorporate native-forest dynamics into water-balance processes.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Linking forest productivity with water balance\u003c/h2\u003e\n \u003cp\u003eTo relate water-balance components to observed tree-growth patterns, we adapted the crop-coefficient (Kc) approach traditionally used to estimate evapotranspiration. Following P\u0026ocirc;\u0026ccedil;as et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), monthly crop coefficients were derived for each native-forest community using MODIS-based Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI) for 2000\u0026ndash;2019. These time-varying Kc curves capture seasonal vegetation dynamics and were validated against tree-ring growth patterns (not shown), confirming that satellite-based metrics provide a robust proxy in basins without detailed in situ information.\u003c/p\u003e\n \u003cp\u003eThe vegetation-index-based crop coefficient (KcVI) was estimated as:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:{\\varvec{K}}_{\\varvec{c}{\\varvec{V}\\varvec{I}}_{\\varvec{j}\\varvec{i}}}=\\:{\\varvec{K}}_{\\varvec{m}\\varvec{i}\\varvec{n}}+\\:{\\varvec{K}}_{{\\varvec{d}}_{{\\varvec{j}}_{\\varvec{i}}}}\\:\\left(\\frac{{\\varvec{V}\\varvec{I}}_{\\varvec{j}\\varvec{i}}-{\\varvec{V}\\varvec{I}}_{\\varvec{j}\\:\\varvec{m}\\varvec{i}\\varvec{n}}}{{\\varvec{V}\\varvec{I}}_{\\varvec{j}\\:\\varvec{m}\\varvec{a}\\varvec{x}}-{\\varvec{V}\\varvec{I}}_{\\varvec{j}\\:\\varvec{m}\\varvec{i}\\varvec{n}}}\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{K}_{cVI}\\)\u003c/span\u003e\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCrop coefficient estimated for a given land use (or native forest community)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{K}_{min}\\)\u003c/span\u003e\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBaseline crop coefficient, related to the soil (typically 0.1)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{K}_{d}\\)\u003c/span\u003e\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCanopy cover density coefficient\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{VI}_{ji}\\)\u003c/span\u003e\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eVegetation Index (NDVI) for community j within month i\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{VI}_{j\\:min}\\)\u003c/span\u003e\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMinimum monthly NDVI value observed in the analyzed patch or polygon during the study period\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{VI}_{j\\:max}\\)\u003c/span\u003e\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMaximum monthly NDVI value observed in the analyzed patch or polygon during the study period\u003c/p\u003e\n \u003cp\u003eThe canopy density coefficient (Kd) was computed using the LAI-based relationship of Pereira et al. (2020):\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:{\\varvec{K}}_{\\varvec{d}{\\varvec{j}}_{\\varvec{i}}}=(1-\\:{\\varvec{e}}^{-\\text{0,7}\\:{\\varvec{L}\\varvec{A}\\varvec{I}}_{\\varvec{j}\\varvec{i}}})$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere:\u003c/p\u003e\n \u003cp\u003eLAIji: Leaf area index for native forest community j within month i\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2 Incorporating seasonal forest vigor into water-balance parameters\u003c/h2\u003e\n \u003cp\u003eIn addition to the Kc adjustments, model parameters were optimized for each HRU; the calibrated parameters for the Pintu\u0026eacute; 1 HRU are reported in Supplementary Table\u0026nbsp;3. To represent vegetation dynamics, we incorporated the seasonal variability of the Runoff Resistance Factor (RRF), which captures the influence of forest vigor and canopy phenology on water availability. Monthly RRF values were parameterized for each HRU using the forest-productivity metrics described above (Supplementary Table\u0026nbsp;3), allowing seasonal changes in forest roughness, water-uptake capacity, and canopy development to be explicitly represented in the model water balance. Following Barr\u0026iacute;a et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), observed lake levels were used for model calibration (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), while observed Pintu\u0026eacute; streamflow data from January 2003 to December 2010 were used for validation (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Step 4- Integration and modeling of social-forest-climate relations for ecosystem services quantification\u003c/h2\u003e\n \u003cp\u003eAs the aim of the framework is to maximize synergies between water planning activities and conservation objectives, the quantification of ecosystem functions must also consider plausible land management scenarios for the medium and long term, based on existing territorial planning instruments. For the Laguna de Aculeo watershed, two legal instruments regulate land use: the Metropolitan Regulatory Plan of Santiago (PRMS) and the Municipal Regulatory Plan (PRC) of Paine.\u003c/p\u003e\n \u003cp\u003eThe PRMS defines areas as either Urbanized or Urbanizable Zones and Restricted or Excluded from Urban Development. According to the PRMS, most of the Aculeo Lake watershed falls under the latter category, except for the lower part of Pintu\u0026eacute; sub-basin (HRU Pintu\u0026eacute; 1), which was designated as urban under the Paine PRC in 2015. Within the restricted zones, the PRMS identifies Ecological Protection Areas, which are designated for their natural or scenic interest, including vegetation, wildlife, and natural water sources.\u003c/p\u003e\n \u003cp\u003eIn the Aculeo watershed, there are two Controlled Development Ecological Protection Areas (PEDCs) allow for limited urban activities alongside forestry and agricultural activities, to prioritize conservation of the natural environment: Valley Sector (below 400 m.a.s.l.) and Foothill Sector (400\u0026ndash;600 m.a.s.l.).\u003c/p\u003e\n \u003cp\u003eThis classification indicates that both agricultural activities and real estate development could potentially expand up to 600 m.a.s.l. with few restrictions (Universidad de Chile, 2020). Then, we designed three scenarios:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eReference Scenario:\u003c/strong\u003e Assumes that the 2018 land use remains constant through 2100. Changes in the water balance are driven exclusively by climate change, based on SSP585 projections from a 37-model ensemble of CMIP6.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eConservation Scenario:\u003c/strong\u003e Assumes no land-use changes occurred between 2006 and 2018, maintaining forest and shrubland cover constant from 2006 to 2100. This scenario includes an additional 74 ha of forest and 103 ha of shrubland compared to the reference scenario. Climate change projections are based on SSP585 from the CMIP6 ensemble.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDystopian Scenario:\u003c/strong\u003e Assumes significant land-use changes between 2018 and 2100, with a 37% reduction in forest cover and a 36% reduction in shrubland cover, reflecting full development of the PEDC zones. Climate change projections are based on SSP585 from the CMIP6 ensemble.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Results- Quantification of ecosystem functions for Central Chile dryland forest in a Water Planning Context","content":"\u003cp\u003eThe activities for S2-S4 of the four-step framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were applied to the Aculeo Lake Basin, and the resulting analyses are presented below.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Climate-driven responses of native forest ecosystem functions (S2)\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1. Climate impacts on native forest growth and water-use efficiency\u003c/h2\u003e \u003cp\u003eTo complete S2, we conducted correlation analyses between yearly carbon sequestration derived from ring-width chronologies and climate variables (not shown). As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the two forest types with taller trees, hygrophilous and deciduous, show strong positive correlations between carbon sequestration and precipitation during the rainy season (April-October; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B), with no significant associations with temperature. In contrast, sclerophyllous and xerophytic communities show positive correlations with annual precipitation (April-March) and summer temperatures (January-March; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003eThe observed decline in precipitation during the MD within the study area has coincided with an increasing frequency of heatwaves, particularly in spring and summer (Gonz\u0026aacute;lez-Reyes et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), intensifying climatic stress on vegetation, explaining the tree ring reduction described in Section 2.2.2. In addition, the drought conditions has increased in intrinsic water-use efficiency (iWUE; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) for all forest communities, which align with global patterns in which warmer temperatures and elevated atmospheric CO₂ reduce stomatal conductance and transpiration.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, all forest types exhibited similar iWUE levels between 2010 and 2012, coinciding with the onset of the MD. In response to declining precipitation, hygrophilous, xerophytic, and sclerophyllous forests initially increased iWUE; however, this response was not sustained, showing a decline after 2019, one of the driest years of the MD. Notably, this decrease in efficiency coincided with substantial biomass (and carbon sequestration) losses in sclerophyllous and hygrophilous forests (43% and 28% respectively), concordant with previous studies (Gibson-Carpintero, 2024; Santini Jr. et al., 2024) which described that extreme droughts induce shifts in wood anatomical and physiological traits in high-elevation communities, potentially affecting water-use efficiency across the elevational gradient of the study area. These results suggest that the initial increase in iWUE in hygrophilous and sclerophyllous forests likely reflects reduced productivity rather than enhanced adaptive performance, whereas in xerophytic forests, where productivity losses were less pronounced (24%), the increase in iWUE may indicate greater adaptive capacity.\u003c/p\u003e \u003cp\u003eTaken together, these results indicate that forest growth and water-use strategies are highly sensitive to changing climatic conditions. Therefore, assuming stationary forest ecosystem functions in basin-planning processes is inappropriate under current and projected climate change.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2. Climate impacts on forest productivity\u003c/h2\u003e \u003cp\u003eAccording to Supplementary Table\u0026nbsp;1, one of the most prioritized ecosystem services of native forests by stakeholders in the Aculeo lake basin is clean air. Within the sociohydrological framework presented in this article, the corresponding forest ecosystem function to be estimated using the methods outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, are carbon sequestration by the various native forest communities of the arid lands of Central Chile.\u003c/p\u003e \u003cp\u003eFollowing the procedures described in Section 3.1, we analyzed linear correlations between seasonal precipitation and temperature (both of the concurrent and previous year) and the carbon sequestration of different forest types during 1999 and 2019. The results, presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, show that, with the exception of xerophytic shrublands, all forest formations exhibited significant linear correlations with precipitation. The strongest association was found for hygrophilous forests, whose carbon sequestration was positively correlated with precipitation from April to October (winter\u0026ndash;spring), with a coefficient of determination (R\u0026sup2; = 0.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Deciduous forests followed, with carbon sequestration positively correlated (R\u0026sup2; = 0.43) with precipitation from May to November. In xerophytic and sclerophyllous communities, model performance improved when both growing-season temperature and precipitation were considered. In particular, sclerophyllous forests were correlated with January-March temperature combined with April-March precipitation (hydrological year). Xerophytic shrublands showed the weakest correlation, with R\u0026sup2;=0.29 (not significative, pvalue\u0026thinsp;=\u0026thinsp;0.054) when considering the same variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the results, sclerophyllous and hygrophilus forest presented the largest reductions within the studied period (36% and 29% for the 2010\u0026ndash;2019 versus 1999\u0026ndash;2009 period respectively), most likely due to reduced precipitation and increased temperature during the MD period, while the xerophytic and deciduous forest, although also had a reduction in carbon sequestration during the MD, its magnitude is smaller compared to the other two formations (24% and 20% for the for the 2010\u0026ndash;2019 versus 2000\u0026ndash;2009 period respectively).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Modeling social\u0026ndash;forest\u0026ndash;climate interactions for characterizing ecosystem functions under climate change (S3 and S4)\u003c/h2\u003e \u003cp\u003eAs described in Section 3.2, selected WEAP model parameters were modified to better represent forest dynamics. Incorporating native forest seasonality into the Kc and Runoff Resistance Factor (RRF) parameters for each forest community substantially improved model performance. The calibrated model showed good agreement with observations, with a Nash\u0026ndash;Sutcliffe Efficiency (NSE) of 0.41 for lake-level simulations and 0.78 for Pintu\u0026eacute; runoff validation, and a Kling\u0026ndash;Gupta Efficiency (KGE) of 0.74 (validation KGE\u0026thinsp;=\u0026thinsp;0.87), indicating strong correlation and realistic representation of variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) and improving predictability relative to the Barr\u0026iacute;a et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) model.\u003c/p\u003e \u003cp\u003eThis improvement is particularly relevant given that, by 2018, native forests covered approximately 69% of the basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), comprising 1,977 ha of hygrophilous forest, 2,157 ha of xerophytic shrubland, 5,218 ha of sclerophyllous forest, and 1,011 ha of deciduous forest. This spatial delineation, combined with HRU boundaries, enabled a detailed assessment of forest biophysical characteristics and their climatic and hydrological controls.\u003c/p\u003e \u003cp\u003eThe analysis of native forest ecosystem functions under historical climate conditions was extended to future decades using CMIP6 general circulation model ensembles under a high-emissions scenario (SSP585). As shown in Supplementary Fig.\u0026nbsp;2, annual precipitation exhibits strong spatial variability across HRUs, largely driven by basin orography, with the highest values occurring in the higher-elevation Pintu\u0026eacute; 4 and Pintu\u0026eacute; 3 HRUs, followed by Pintu\u0026eacute; 2 and Pintu\u0026eacute; 1. Despite uncertainties in climate projections, all models consistently indicate sustained declines in annual precipitation toward the end of the century. In these arid and semi-arid systems, such reductions are likely to alter forest ecosystem functions and associated ecosystem service provision, with important implications for long-term planning and decision-making.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Water provision responses to changes in forest cover and vigor\u003c/h2\u003e \u003cp\u003eAccording to the methods explained in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, to estimate the impact of native forests on water availability in the watershed, the differences between two variables were analyzed: the volume of water stored in the aquifer and the volume stored as soil moisture. These variables were derived from water balances generated using WEAP for the period 1979\u0026ndash;2065 under two scenarios: Conservation and Dystopian. This approach was chosen because, based on the configuration of the WEAP soil moisture model, the variable \u0026ldquo;increase in soil moisture\u0026rdquo; (ISM) represents the amount of water entering the upper bucket of the HRUs (Hydrological Response Units). From this bucket, water is made available for runoff generation, irrigation, evapotranspiration, and infiltration, allowing an estimation of surface water availability.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the projected differences in soil moisture availability (ISM) between the dystopian and conservation management scenarios, as simulated by the WEAP model for the near future, 2030 to 2065. Importantly, because the figure shows differences between two management approaches under the same climate change projection, the overall effect of reduced precipitation in future decades is effectively controlled for. This allows the analysis to isolate and highlight the influence of land and water management strategies, rather than climatic forcing alone, on soil water availability.\u003c/p\u003e \u003cp\u003eTo estimate the total contribution of native forests to surface water availability, we calculated the weighted average ISM contributions from the HRUs of Las Cabras, ASC Alto, and Pintu\u0026eacute;, weighted by the forested areas of each HRU. This value was then scaled to the total forested area to derive the equivalent surface water contribution in m\u003csup\u003e3\u003c/sup\u003e/s under the SSP585 climate change scenario. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that the contribution of native forests to the annual regulation of soil moisture increases during the first three decades, and from the 2060s onward stabilizes at approximately 0.9 m\u0026sup3;/s. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, the role of native forests is particularly relevant during the winter months, with values rising from around 1 m\u0026sup3;/s to about 1.5 m\u0026sup3;/s in the 2060 decade.\u003c/p\u003e \u003cp\u003eThese results suggest that, under initial climate change conditions, the interception capacity and increased surface roughness associated with native forests substantially enhance water retention and soil moisture regulation. However, as precipitation declines more sharply in later decades, this effect weakens, reducing vegetation vigor and, consequently, lowering crop coefficients (Kc) and runoff reduction factors (RRF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, the contribution of native forests to groundwater availability was assessed through an annual comparison of aquifer storage under dystopian and conservation scenarios. To isolate the effect of forest cover, the analysis was corrected by subtracting the difference in groundwater extraction between scenarios, thereby removing the influence of increased irrigation demand in the dystopian case. As described by Barr\u0026iacute;a et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the 324 Mm\u0026sup3; aquifer connected to the Aculeo Lake basin comprises three main hydrogeological units: (i) shallow colluvial and fluvial sediments with low permeability, (ii) a silt-dominated aquitard with very low permeability, and (iii) a confined aquifer composed of coarse-to-medium sands with higher permeability.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, native forests increase groundwater availability by an average of ~\u0026thinsp;9.7 \u0026times; 10⁻⁶ m\u0026sup3; s⁻\u0026sup1; ha⁻\u0026sup1;, reaching a maximum of 8.13 \u0026times; 10⁻⁵ m\u0026sup3; s⁻\u0026sup1; ha⁻\u0026sup1; around mid-century, followed by a gradual decline. This decrease reflects reduced irrigation demand simulated in the dystopian scenario over time due to declining groundwater availability. When scaled to the total forested area, the contribution ranges from approximately 5 Mm\u0026sup3; in the 2030s to 25 Mm\u0026sup3; in the 2060s, values comparable in magnitude to total basin-wide groundwater withdrawals for drinking water, agriculture, and recreational irrigation (~\u0026thinsp;14.2 Mm\u0026sup3;; Meneses et al., 2019).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Carbon sequestration (clean air) in relation to forest community productivity\u003c/h2\u003e \u003cp\u003eTo assess the variability of the clean air ecosystem service under increasing aridity (Section 3.2.2), decadal carbon sequestration was estimated using the polynomial regressions shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Decadal projections by forest type were derived from 37 CMIP6 climate simulations under the SSP5-8.5 scenario for the study watershed. These estimates rely exclusively on empirically derived relationships between climate and carbon sequestration inferred from individual tree growth measured in the Reserve and assume a constant tree density over time (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents historical carbon sequestration based on field measurements (blue line), together with the 15th, 50th, and 95th percentiles of the climate simulations for each decade (green lines). To adopt a conservative estimate of ecosystem services under climate change, the 15th percentile was selected as the reference value. Comparison with observed values shows that, in several cases, carbon sequestration during the Megadrought (MD; dotted blue line) is lower than projected future values. This reflects the extreme nature of the MD event (Garreaud et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which is not reproduced by the models during the historical period but emerges in later decades under higher greenhouse gas forcing.\u003c/p\u003e \u003cp\u003eOverall, climate-driven polynomial regression models indicate substantial reductions in carbon sequestration across all four forest communities in the coming decades. These projections should be interpreted individually, as the regression models differ in both structure and statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Xerophytic and sclerophyllous forests were modeled using precipitation and temperature, whereas hygrophilous and deciduous forests were modeled using precipitation only; additionally, the xerophytic model did not reach statistical significance.\u003c/p\u003e \u003cp\u003eUnder the climate change scenario (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), xerophytic and sclerophyllous forests exhibit sharp declines in carbon sequestration of approximately 41% and 37%, respectively, during 2040\u0026ndash;2060 relative to 1999\u0026ndash;2019, driven by reduced precipitation and increasing temperatures. Hygrophilous forests show sustained declines beyond MD levels, reaching\u0026thinsp;~\u0026thinsp;600 kg C ha⁻\u0026sup1; yr⁻\u0026sup1; by the 2060s, corresponding to a 9% reduction relative to 1999\u0026ndash;2019. Deciduous forests also show persistent decreases (6% by 2040\u0026ndash;2060), tracking declining precipitation and reaching\u0026thinsp;~\u0026thinsp;950 kg C ha⁻\u0026sup1; yr⁻\u0026sup1; by the 2060s, approaching values observed in low-elevation hygrophilous communities.\u003c/p\u003e \u003cp\u003eFinally, this assessment focuses on aboveground carbon stocks. Future studies should incorporate soil carbon dynamics, given the critical role of belowground microbial communities and root systems in carbon storage and ecosystem functioning (Fierer et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Bastida et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3. Species richness and abundance in relation to forest community productivity\u003c/h2\u003e \u003cp\u003eAs reported by Gibson-Carpintero et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and based on the biodiversity indices shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and Supplementary Table\u0026nbsp;2, hygrophilous and xerophytic forests exhibit the highest species richness and diversity in the basin.\u003c/p\u003e \u003cp\u003eAt the tree level, hygrophilous communities show the highest diversity, even after accounting for surface area, contributing 37%, 38%, and 40% of basin-wide diversity according to the Gini, Simpson, and Shannon indices, respectively (Gini\u0026thinsp;=\u0026thinsp;0.76, S\u0026thinsp;=\u0026thinsp;4.28, H\u0026thinsp;=\u0026thinsp;1.64). These are followed by xerophytic, sclerophyllous, and deciduous communities. In contrast, at the regeneration level, sclerophyllous forests are the most diverse, contributing 49%, 44%, and 46% of basin-wide diversity based on the Gini, Simpson, and Shannon indices, respectively (Gini\u0026thinsp;=\u0026thinsp;0.51, S\u0026thinsp;=\u0026thinsp;2.1, H\u0026thinsp;=\u0026thinsp;0.87), followed by xerophytic, hygrophilous, and deciduous communities. Deciduous forests consistently show the lowest diversity across all indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA; Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eDespite their high biodiversity value, high-elevation hygrophilous and sclerophyllous forests are among the most vulnerable to drought in the drylands of central Chile (Section 4.1.1). Moreover, climate projections indicate that drought conditions are likely to substantially reduce forest cover across the basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo derive a conservative estimate of future biodiversity, a scenario-based analysis was conducted using an area-weighted average of diversity and richness indices, applying a leave-one-community-out approach (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). This analysis identifies hygrophilous and xerophytic forests as the main contributors to basin-wide biodiversity.\u003c/p\u003e \u003cp\u003eConsistent with the declining carbon sequestration trends shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, reductions in hygrophilous, xerophytic, and sclerophyllous forests are likely to result in substantial biodiversity losses. For example, using surface-weighted Simpson indices at the tree level (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB), a reduction in hygrophilous and xerophytic forests would lead to an estimated 66% decrease in basin-wide biodiversity value.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5 Discussion and Conclusions","content":"\u003cp\u003eThis study provides quantitative evidence that native forests exert a measurable but non-stationary influence on key basin-scale ecosystem functions, water regulation, carbon sequestration, and biodiversity, in the drylands of central Chile, with distinct responses to climate forcing and land-management trajectories. Crucially, these functions can be explicitly quantified and dynamically represented within water-planning models, rather than treated as static or qualitative co-benefits, with direct implications for the integration of conservation objectives into instruments such as Chile\u0026rsquo;s Strategic Water Resources Plans. Rather than generating entirely new baseline social or ecological datasets, the main contribution of this study is to integrate previously separate lines of evidence into an operational sociohydrological framework for basin planning under climate change.\u003c/p\u003e \u003cp\u003eFrom a water-planning perspective, the results show that native forest cover enhances both soil moisture (key processes for surface water availability) and groundwater availability under current and near-future climate conditions (2030\u0026ndash;2065). Under the SSP5-8.5 scenario, forest-related soil moisture regulation reaches approximately 0.9 m\u0026sup3; s⁻\u0026sup1; after the 2060s, with winter contributions increasing from ~\u0026thinsp;1.0 to ~\u0026thinsp;1.5 m\u0026sup3; s⁻\u0026sup1;. Groundwater contributions range from 5 Mm\u0026sup3; in the 2030s to up to 25 Mm\u0026sup3; by the 2060s, values comparable to total basin-wide groundwater withdrawals (~\u0026thinsp;14.2 Mm\u0026sup3;). However, these benefits weaken over time as declining precipitation reduces vegetation vigor, lowering crop coefficients (Kc) and runoff resistance factors (RRF). This demonstrates that forest-related hydrological benefits are climate-dependent and cannot be treated as constant inputs in long-term planning.\u003c/p\u003e \u003cp\u003eCarbon sequestration, the ecosystem functions associated with \u0026ldquo;clean air\u0026rdquo;, shows a consistently negative trajectory across all forest communities under climate change. Decadal projections based on climate-driven polynomial regressions indicate reductions of approximately 41% in xerophytic shrublands (non significant linear model) and 37% in sclerophyllous forests during 2040\u0026ndash;2060 relative to 1999\u0026ndash;2019. Hygrophilous forests decline more gradually but reach\u0026thinsp;~\u0026thinsp;600 kg C ha⁻\u0026sup1; yr⁻\u0026sup1; by the 2060s (\u0026asymp;\u0026thinsp;9% reduction), while deciduous forests decrease by ~\u0026thinsp;6% to ~\u0026thinsp;950 kg C ha⁻\u0026sup1; yr⁻\u0026sup1;. Importantly, carbon sequestration during the Megadrought was, in several cases, lower than the conservative (15th percentile) future projections, highlighting that extreme events already exceed the range of historical variability represented in climate models. These results indicate that climate change will substantially erode the capacity of native forests to provide this ecosystem service, even under conservation-oriented land-use scenarios.\u003c/p\u003e \u003cp\u003eBiodiversity responses further reinforce this non-stationarity. Hygrophilous forests currently contribute 37\u0026ndash;40% of basin-wide tree-level diversity (depending on index), while sclerophyllous forests dominate regeneration-level diversity (44\u0026ndash;49%). Yet these high-diversity communities coincide with those most vulnerable to drought. Scenario-based analyses show that reductions in hygrophilous and xerophytic forests would lead to an estimated 66% decline in basin-wide biodiversity value when surface-weighted Simpson indices are considered. This implies that biodiversity losses under climate change are likely to be disproportionate relative to area loss, with strong implications for conservation prioritization.\u003c/p\u003e \u003cp\u003eMethodologically, this study demonstrates that integrating forest dynamics into hydrological models used for water planning is feasible and improves model performance. Incorporating forest seasonality into Kc and RRF parameters increased WEAP performance (NSE\u0026thinsp;=\u0026thinsp;0.41 for lake levels; NSE\u0026thinsp;=\u0026thinsp;0.78 and KGE\u0026thinsp;=\u0026thinsp;0.87 for streamflow validation), relative to previous model versions developed by Barr\u0026iacute;a et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, important limitations remain. Carbon sequestration estimates are restricted to aboveground tree biomass and assume constant tree density, excluding soil carbon pools, regeneration dynamics, and mortality processes. In addition, not all climate\u0026ndash;growth relationships were statistically significant, particularly for xerophytic communities (R\u0026sup2; = 0.29, p\u0026thinsp;=\u0026thinsp;0.054), reflecting both ecological complexity and limited in situ data.\u003c/p\u003e \u003cp\u003eThese limitations underscore the need for expanded field monitoring, especially in xerophytic and high-elevation communities, and for incorporating soil carbon, regeneration, and mortality processes into future modeling efforts. Without such data, linear and polynomial models risk oversimplifying ecosystem responses under increasing aridity. Accordingly, the hydrological responses reported here should be interpreted as the most robust component of the framework, carbon responses as climate-sensitive but structurally simplified estimates, and biodiversity projections as exploratory scenario-based proxies rather than direct forecasts.\u003c/p\u003e \u003cp\u003eBeyond the Aculeo case, this study offers a transferable sociohydrological framework for linking ecosystem conservation and water planning in climate-vulnerable basins. By integrating participatory valuation, ecological observations, remote sensing, and hydrological modelling, the approach quantifies forest contributions to water availability, carbon sequestration, and biodiversity. It also shows that these contributions decline under climate change and vary strongly among forest types, underscoring the risks of treating forest ecosystem functions as stationary in regional planning.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions: CRediT\u003c/p\u003e\n\u003cp\u003ePilar Barr\u0026iacute;a: Writing original draft, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. Anah\u0026iacute; Ocampo-Melgar: Writing review \u0026amp; editing, Conceptualization, Investigation. Alejandro Venegas-Gonz\u0026aacute;lez: Writing review \u0026amp; editing, Methodology, Formal analysis, Investigation, Data curation. Ariel Mu\u0026ntilde;oz: Writing review \u0026amp; editing, Investigation, Resources.\u003c/p\u003e\n\u003cp\u003eFunding sources\u003c/p\u003e\n\u003cp\u003eThis work was partially supported by the following grants: Initiation Fondecyt grant number 11200854, of the Chilean Agency for Research and Development (ANID), and by the grant N\u0026deg;022/2020 of the National Forest Corporation of Chile (CONAF).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlaniz, A. J., Carvajal, M. A., N\u0026uacute;\u0026ntilde;ez-Hidalgo, I., and Vergara, P. M. (2019). Chronicle of an environmental disaster: Aculeo Lake, the collapse of the largest natural freshwater ecosystem in central Chile. Environ. Conserv., 46(3), 201-204. https://doi.org/10.1017/S0376892919000122\u003c/li\u003e\n\u003cli\u003eAnzaldua, G., Gerner, N. V., Lago, M., Abhold, K., Hinzmann, M., Beyer, S., ... \u0026amp; Birk, S. (2018). 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Regional Environmental Change, 19, 2729\u0026ndash;2743. https://doi.org/10.1007/s10113-019-01571-y \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"water-planning, native forest, climate change, ecosystem functions","lastPublishedDoi":"10.21203/rs.3.rs-9323806/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9323806/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMediterranean-type dryland socio-ecological systems worldwide face compounding pressures from intensifying drought, biodiversity loss, and the water governance fragmentation. A critical but largely unresolved challenge is how to integrate native forest conservation into basin-scale water planning frameworks that are both ecologically grounded and transferable across regions. Here we develop and apply a sociohydrological framework to quantify long-term changes in stakeholder-prioritized ecosystem functions (EF) and ecosystem services (ES) in the Aculeo Lake Basin (33\u0026deg;50\u0026prime; S, 70\u0026deg;54\u0026prime; W), a dryland Mediterranean watershed in central Chile. Building on prior hydrological characterization of this basin, the framework integrates stakeholder valuation, short-term field observations, satellite remote sensing, and an adapted WEAP model forced by CMIP6 climate and land-management scenarios (SSP5\u0026ndash;8.5), and is designed to be replicable across data-scarce dryland basins. Results indicate that forest-related soil-moisture regulation approaches\u0026thinsp;~\u0026thinsp;0.9 m\u0026sup3; s⁻\u0026sup1; after the 2060s; winter contributions rise from ~\u0026thinsp;1.0 to ~\u0026thinsp;1.5 m\u0026sup3; s⁻\u0026sup1; but weaken under declining precipitation. Despite uncertainty, simulations consistently point to declining ecosystem functioning across all forest communities. Ensemble-average projections show basin-scale reductions in carbon sequestration of ~\u0026thinsp;20\u0026ndash;40% by mid-century relative to 1999\u0026ndash;2019. Biodiversity responses are non-linear and disproportionate to area loss, with scenario analyses indicating potential declines of up to 66% in basin-scale diversity under hygrophilous and xerophytic forest decline. These findings demonstrate that multiple ecosystem functions can be embedded quantitatively in operational water-planning models, supporting instruments such as Payments for Ecosystem, and provide a transferable methodological pathway for integrating forest conservation into water governance in climate-vulnerable dryland basins.\u003c/p\u003e","manuscriptTitle":"Linking Water Planning and Native Forest Conservation in Central Chile: A Sociohydrological Perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-27 14:59:39","doi":"10.21203/rs.3.rs-9323806/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb504a38-ea75-45f4-98a0-7481b7e1793e","owner":[],"postedDate":"April 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T14:59:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-27 14:59:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9323806","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9323806","identity":"rs-9323806","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}