D4.5 Bidirectional feedbacks between adaptation actions and climate service information

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 Deliverable D4.5 Bidirectional feedbacks between adaptation actions and climate service information 28-08-2025 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 Innovating Climate services through Integrating Scientific and local Knowledge Deliverable Title: Bidirectional feedbacks between adaptation actions and climate service information Author(s): Riccardo Biella, Lotte Muller, Nikoletta Ropero, Charles N. Wamucii Lucia De Stefano, Giuliano Di Baldassarre, Maurizio Mazzoleni Date 18-08-2025 Suggested citation: Biella et al. (2025). Bidirectional feedbacks between adaptation actions and climate service information, I-CISK Deliverable 4.5 ICISK, Human Centred Climate Services. https://icisk.eu/resources/ Availability: ☒ PU: This report is public [Please select] ☐ CO: Confidential, only for members of the consortium (including the Commission Services) Document Revisions: Author Revision Date Riccardo Biella, Lotte Muller, Nikoletta Ropero, Charles N. Wamucii, Lucia De Stefano, Giuliano Di Baldassarre, Maurizio Mazzoleni First draft 31 July 2025 Riccardo Biella, Lotte Muller, Nikoletta Ropero, Charles N. Wamucii, Lucia De Stefano, Giuliano Di Baldassarre, Maurizio Mazzoleni Revised draft 22 August 2025 Riccardo Biella, Lotte Muller, Nikoletta Ropero, Charles N. Wamucii, Lucia De Stefano, Giuliano Di Baldassarre, Maurizio Mazzoleni Final version 28 August 2025 D4.5 – Bi-directional feedbacks between adaptation actions and climate service information i Executive Summary This Deliverable presents the outcomes of WP4 of the I-CISK project, which focused on developing a suite of models of varying complexity to explore the dynamic, bidirectional feedbacks between climate adaptation actions and climate service (CS) information. These models have been tested and validated across a range of living labs, each reflecting diverse temporal and spatial contexts. A key finding is that climate services are not inherently "no-regret" solutions. While intended to support effective adaptation, their design and implementation can sometimes result in unintended consequences, including maladaptive outcomes. To address these challenges, this report introduces a set of models and tools that enable stakeholders to anticipate potential risks and trade-offs, enhancing the capacity for informed, adaptive decision-making. The models presented here captures the complex interactions between human behavior, drought management, and the influence of CS on socio-ecological system dynamics. Human responses to water governance frameworks shape consumption patterns, resource allocation, and ecological pressures within intricate hydro-systems. While adaptive measures are implemented to reduce vulnerability, they can sometimes produce maladaptive outcomes, where interventions inadvertently exacerbate or redistribute risks, compromising long-term system sustainability. CSs are pivotal in guiding decision-making within these coupled socio-ecological systems. However, humanclimate interactions and feedbacks are often non-linear, producing unexpected adaptation outcomes that challenge conventional management strategies. Understanding these dynamics is essential for developing resilient and sustainable approaches to drought management and resource governance. This research also highlights the significant role of power asymmetries among stakeholders in shaping the development, accessibility, and overall impact of climate services. For instance, in the Crete living lab, the prioritization of tourism—driven by its economic influence—has the potential to increase the vulnerability of other key sectors, particularly those already marginalized in policy and resource allocation. To support sustainable and equitable adaptation, climate services must be developed through inclusive, participatory governance processes. Addressing systemic power imbalances is crucial to prevent CS initiatives from reinforcing existing inequalities. Superficial stakeholder engagement is insufficient; meaningful inclusion of underrepresented and “non-targeted” sectors is essential throughout co-design, co-production, and codelivery processes. Importantly, the effective identification and engagement of stakeholders require more than participatory frameworks. Systems thinking methodologies, such as system dynamics modelling, are necessary to navigate complex interdependencies and uncover hidden power dynamics within socio-ecological systems. In conclusion, embedding climate service development within frameworks that explicitly recognize and address power differentials is essential. Only then can climate services fulfill their promise as tools for enhancing resilience and enabling just, inclusive climate adaptation outcomes. D4.5 – Bi-directional feedbacks between adaptation actions and climate service information ii Table of Contents 1 Introduction ................................................................................................................................................. 1 2 Human behaviour and drought management ............................................................................................. 2 3 Maladaptive dynamics across living labs ..................................................................................................... 5 4 CS-based expectations and groundwater dynamics ................................................................................. 13 5 Power asymmetries ................................................................................................................................... 20 6 Conclusions ................................................................................................................................................ 25 References ......................................................................................................................................................... 26 D4.5 – Bi-directional feedbacks between adaptation actions and climate service information iii List of Figures Figure 2.1. Short-term relations between water management, climate service, and boat recreationists in Rijnland. The orange boxes represent the climate service (CS) and water authority Rijnland decision, yellow boxes are the boat recreationists' options, rounded boxes are actor decision points, dotted red lines are the hypothetical feedback. ........................................................................................................................................ 3 Figure 2.2. Long-term relations between water management, climate service, and boat recreationists in Rijnland. The orange boxes represent the climate service (CS) and decision taken by the Rijnland water authority, yellow boxes are the boat recreationists' options, rounded boxes are actor decision points, dotted red lines are the hypothetical feedback. ............................................................................................................. 4 Figure 3.1. The four archetypes of the interaction between climate services and adaptation identified in Biella et al. (2024). ......................................................................................................................................................... 7 Figure 3.2. Methodology used for the development of the synthetic model for the Italian LL. Note how it is built upon the methodology used for the paper Thinking systemically about climate services: Using archetypes to reveal maladaptation by Biella et al. (2024). .................................................................................................. 9 Figure 3.3. Causal Loop Diagram showing the functional representation of the model. ................................. 11 Figure 3.4. Outcome of the three scenarios based on the type of climate services used, namely: mostly long term, mixed (50% of each type); and mostly short term. Each scenario displays the main systemic variables of: reservoir volume (V); maximum reservoir volume (MaxV); Wealth (W); fraction of the catchment under agricultural production (UP); and fraction of the catchment under ecological restoration (EcoRest). ............ 12 Figure 4.1. Case study location and main land uses (modified from Ropero Szymañska et al., 2025). ............ 13 Figure 4.2. CLD of Scenario B (normal/wet conditions). ................................................................................... 16 Figure 4.3. CLD of Scenario B (dry conditions). ................................................................................................. 17 Figure 4.4. Effects on the groundwater system during a wet period (Scenario A). .......................................... 18 Figure 4.5. Effects on the groundwater system during a dry period (Scenario B). ........................................... 19 Figure 5.1. Water exploitation index at basin level in Crete: (a) Reference period 1983-2009, and (b) projected period (2040-2059) – adopted from (Ziogas & Tzimas, 2022). ......................................................................... 20 Figure 5.2. The conceptual framework – adopted from (Biella et al., 2024). The different colours represent the concept of multi-sectors i.e. sector A, B, C etc. ................................................................................................. 21 Figure 5.3. Simulated sectoral power influence. ............................................................................................... 22 Figure 5.4. Simulated sectoral resources. ......................................................................................................... 23 D4.5 – Bi-directional feedbacks between adaptation actions and climate service information iv Glossary Acronym Definition API Application Programming Interface C3S Copernicus Climate Change Service CDS Climate Data Store CEMS Copernicus Emergency Management Services CMIP World Climate Research Programme’s Coupled Model Intercomparison Project CORDEX Coordinated Regional Climate Downscaling Experiment CS Climate Services CSIS Climate Services Information Systems DRR Disaster Risk Reduction GEO Group on Earth Observations GEOSS Global Earth Observation System of Systems GUI Graphical User Interface IPCC Intergovernmental Panel on Climate Change LL Climate Services Living Labs NHMS National Hydro-meteorological Service MOOC Massive Open Online Course OGC Open Geospatial Consortium S2S Sub-seasonal to Seasonal TRL Technology Readiness Level UNCCD United Nations Convention to Combat Desertification UNDRR United Nations Office for Disaster Risk Reduction UNFCCC United Nations Framework Convention on Climate Change WCRP World Climate Research Programme WFD Water Framework Directive WMO World Meteorological Organization D4.5 – Bi-directional feedbacks between adaptation actions and climate service information 1 1 Introduction Effective climate adaptation hinges on equitable access to climate information and the ability of stakeholders to meaningfully participate in decision-making processes (Hewitt et al., 2020; Lemos et al., 2012; Vaughan & Dessai, 2014). Climate Services (CS), designed to provide timely and tailored climate information, are increasingly recognized as tools to enhance adaptation and resilience to climate-related risks. However, as shown in this research (as a part of Work Package 4 of the I-CISK project), bidirectional feedbacks between adaptation actions and climate service information can potentially generate unintended consequences, including the emergence of undesired risks or maladaptive dynamics as described in Biella et al. (2024). Prior work in the I-CISK project has shown that the management of climate hazards, such as drought, is not solely a technical or ecological challenge but is deeply shaped by human behaviour. Adaptive responses to water governance frameworks influence patterns of consumption, resource allocation, and ecological pressures within complex hydro-systems. However, strategies intended to enhance resilience do not always achieve their desired outcomes. In some cases, adaptation measures result in maladaptive dynamics, where efforts to reduce vulnerability inadvertently redistribute or intensify risks, threatening the long-term sustainability of socio-ecological systems. Moreover, the development, access, and application of CS products are often shaped by stakeholder power dynamics, leading to inequities in adaptation outcomes (Brouwer et al., 2013; Howarth et al., 2022; Nost, 2019; Vaughan & Dessai, 2014). As the I-CISK project emphasizes human-centred CS development that integrates scientific and local knowledge, understanding how power influences the uptake and outcomes of CS is vital to ensuring that these tools foster inclusive and effective adaptation strategies. Power asymmetries result from the unequal distribution of influence, resources, and decision-making authority among stakeholders engaged in the co-production process (Gerlak et al., 2023; Vallet et al., 2019). These asymmetries can manifest through preferential targeting of certain sectors, uneven resource allocation, or exclusion of marginalized groups, potentially leading to maladaptation and exacerbated vulnerabilities (Garcia et al., 2024; Holland, 2017; Thomas & Twyman, 2005). Despite the growing emphasis on participatory approaches such as co-creation, co-design, and co-production (Co-Co-Co), sectoral prioritization often reinforces existing inequalities by favouring dominant stakeholders (Cantone et al., 2023; Reed et al., 2019). In this report, we expand on the work done in WP4 (Biella et al., 2024; Rastogi et al., 2025) and present a set of models built by the I-CISK Work Package 4 team to capture bidirectional feedbacks between adaptation actions and climate service information across different living labs (LLs) characterized by a range of temporal and spatial scales. These models also cover different levels of complexity, ranging from conceptual and generic models of drought management and human behaviour (Section 2) to more complex, mathematical tools capturing power asymmetries in a specific LL (Section 5). D4.5 – Bi-directional feedbacks between adaptation actions and climate service information 2 2 Human behaviour and drought management Drought management and human behaviour are closely linked in a two-way feedback loop. Whenever governments or water managers introduce drought mitigation measures, such as volumetric rationing, priority allocation, dynamic tariff structures, or the temporal closure of hydraulic infrastructure, society will need to adapt. Because of this, households will change water consumption, farmers reschedule irrigation, and industries change their production practices. These adaptation dynamics reshape spatio-temporal demand, social inequalities, water availability patterns, influence return flows, and alter the salinity and ecological stress in receiving water bodies. A concrete example of these dynamics emerge in drought management practices in the Rijnland Living Lab, one of the seven Living Labs (LL) in the I-CISK project. The management of shipping lock movements is the principal strategy for counteracting salt intrusion under drought conditions. Rijnland is one of the Netherlands’ oldest regional water authorities and manages an intricate polder-andcanal system that discharges excess water to the North Sea via the Grote Sluis at Spaarndam. During dry periods, freshwater is withdrawn from the River Lek (a branch of the River Rhine) at the sluice at Gouda. This freshwater is in part used to control salinity levels in the management area of the Rijnland water authority, though during droughts and consequent low flows in the Rhine River, freshwater availability may be reduced. Besides managing water quality and quantity, the authority must keep the waterways navigable for commercial and recreational shipping. The water authority uses ensemble-based drought and salinity forecasts, together with the nationally defined hierarchy for water-use prioritisation (Verdringingsreeks), to determine its water management strategies. Recreational boating is classified as low priority, and hence, during droughts measures for recreationists are common. To inform recreationists of the drought measures outlook, they use a climate service in the form of a colour-coded message system. Each code indicates the (expected) enacted measure(s) taken by the waterauthority, and hence the available routes and opening times of locks. Changes in the available routes and lock opening timings influence waterway traffic, and hence lock water use and salt intrusion, and public acceptance of subsequent restrictions. Based on these considerations, we developed a conceptual model representing the dynamics between climate services, water availability, gate regulations, and boat owners, based on discussions and workshops with stakeholders in the LL. We developed two models based on either short-term (Figure 2.1) or long-term (Figure 2.2) adaptation and consequent impacts. In particular, we considered that boat recreationist owners can react to the weather forecast and colour code regulation provided by Rijnland by i) waiting at the lock gates; ii) taking an alternative route; or iii) not going out with their boat at all. Each of these decisions will lead to different feedbacks and implications. However, while many dynamics were theoretically identified, we could not find strong evidence that changes in boat recreationists’ behaviour would affect the environment and thus Rijnland water management (red dashed lines in Figures 2.1 and 2.2). Below are some explanations regarding the dynamics between boat recreationists, CS, Rijnland, and the environment in the short-term. Waiting at the lock gate, typically for one to three hours during code yellow restrictions, has negligible hydrological or ecological repercussions. The water authority’s discharge-reduction targets remain unchanged because the lock water volume released per cycle is fixed. For the ecosystems, we did not find academic literature or anecdotal evidence which indicated that extended queue times measurably alter turbidity, bank erosion, or disturbance to riparian fauna. Socially, recreational sailing culture in the Netherlands has long normalised waiting as an inherent component, especially among wind-powered craft that are accustomed to weather-induced delays. Recent growth in motorboat tourism is attenuating this tolerance, but systematic complaints or non-compliance remain rare and confined to peak holiday weekends. D4.5 – Bi-directional feedbacks between adaptation actions and climate service information 3 Taking an alternative route can be a complex adaptation decision. In some cases, no viable route exists and boats may be unable to leave the harbour. However, where substitute routes do exist, it can be that temporary traffic increase, leading to dangerous crowding. No statistically significant increase in environmentally deleterious events (e.g., bank collapse, bird-nest disturbance) can be attributed to crowding. Likewise, salinity intrusion remains unaffected because the alternative locks discharge to the same receiving waters and operate under equivalent salinity-trigger protocols. Finally, while not going out with boats will lead to short-term revenue losses for local businesses (e.g. mooring fees, fuel, and hospitality services), this will, however, not have a significant effect on the water authority. The Rijnland water authority is aware of these impacts but is bound by the national hierarchy for water-use prioritisation (Verdringingsreeks), which ranks recreational boating below public water supply and ecosystem protection. Consequently, economic considerations, while noted, do not modulate the water authority Rijnland lock-closure decisions. Figure 2.1. Short-term relations between water management, climate service, and boat recreationists in Rijnland. The orange boxes represent the climate service (CS) and water authority Rijnland decision, yellow boxes are the boat recreationists' options, rounded boxes are actor decision points, dotted red lines are the hypothetical feedback. Long-term behavioural adaptation of recreational boating under recurrent drought restrictions can lead to prolonged or frequent lock closures and trigger changes in user behaviour far beyond seasonal itinerary adjustments. Conversation with the Rijnland water authority representative suggested two principal adaptation pathways for recreational boat owners. (i) Vessel substitution: Owners of wind-powered boats face disproportionately high impediments to recreation when shiplock closures coincide with bridges malfunctioning due to heat (the height of masts requires bridges to be opened, especially for boats where masts cannot easily be lowered), and calm wind conditions that prevent sailing. This could lead some owners to shift toward motor-powered boats, which exhibit greater route flexibility and lower sensitivity to intermittent lock service and bridge heat malfunctions. However, motorised boating is, based on discussions in the LL, a different recreational activity and many recreationists would not find a motorised boat a substitute D4.5 – Bi-directional feedbacks between adaptation actions and climate service information 10 • FSCS (Focus on Short-term Climate Services): Reflects reliance on seasonal climate forecasts by comparing the recharge for the upcoming year with the historical average. It increases during dry years and triggers short-term infrastructural responses, such as expanding the reservoir. • FLCS (Focus on Long-term Climate Services): Encodes attention to long-term climate change signals by comparing the predicted average recharge for the upcoming 50 years with the historical average. Through its outlook on climate change, it influences transformative adaptation strategies, such as shifting land from agriculture to restoration. FSCS is highly variable and responds to recent seasonal deviations in rainfall, simulating the volatility of shortterm forecasts. FLCS evolves more smoothly, capturing decadal-scale climate signals. Together, these metrics operationalize how different types of climate information shape decisions. By explicitly linking climate information to adaptation choices, the model enables exploration of feedbacks, thresholds, and maladaptive lock-ins under varying decision-making paradigms. Its simplicity supports transparency and experimentation while retaining the core dynamics observed in the context of the Italian LL. While simplified, the synthetic case is calibrated to reflect dynamics observed in the Italian LL and is largely similar to the Italian LL in size and recharge. Key characteristics include: • A water-limited agricultural system dependent on seasonal irrigation. • The presence of a reservoir with finite capacity and maintenance costs. • Competing land uses, where restoring land reduces immediate revenue but increases long-term sustainability. • A decision-making agent (representing collectives or authorities) who chooses between expanding infrastructure or shifting land use based on climate information. • Physical dimensions, climate, and projected climate change similar to those of the Italian LL. The model replicates the Italian context by assigning higher economic returns to irrigated agriculture than to ecological restoration, reflecting real-world incentives. Seasonal rainfall variability is introduced through a stochastic recharge function, with long-term drying trends to simulate climate change. CSs influence adaptation choices via the FSCS and FLCS indicators, whose parameters are tuned to reflect available information sources in the region (e.g., seasonal forecasts vs. regional climate scenarios). This setup enables us to abstract core dynamics without replicating all real-world complexities, thereby preserving analytical clarity while maintaining relevance to the Italian LL. It serves as a “learning laboratory” for testing how different CS framings influence decisions and long-term outcomes. D4.5 – Bi-directional feedbacks between adaptation actions and climate service information 11 Figure 3.3. Causal Loop Diagram showing the functional representation of the model. The synthetic model provides a versatile tool for examining the implications of maladaptive decision-making in agricultural water systems. Key experiments and applications include: • Policy Scenario Exploration: The model can simulate the impact of interventions, such as subsidies for restoration, penalties for over extraction, or enhanced long-term forecasting, helping to evaluate their influence on system trajectories. These scenarios can be an application of the as ex-ante assessment of maladaptation risk discussed in the previous section. By simulating future pathways under different decision rules and climate inputs, the model enables foresight into potential maladaptive outcomes. • Sensitivity Analyses: Parameters such as irrigation efficiency, recharge variability, or maintenance costs can be varied to assess tipping points or nonlinear effects. This can identify thresholds beyond which the system flips into undesirable regimes. • Learning and Education: The simplicity and transparency of the model make it ideal for stakeholder workshops, allowing users to explore consequences of decision-making in a safe, controlled environment. These tests are valuable not only for researchers but also for practitioners and decision-makers seeking to understand how institutional preferences, climate information, and resource constraints interact to shape long-term sustainability. Preliminary simulations using the synthetic model suggest that strongly short-term oriented adaptation strategies—characterized by a high reliance on seasonal forecasts—can lead to rapid economic gains in the initial years but result in long-term instability (Fig. 3.4a and Fig. 3.4b). These trajectories often exhibit cyclical crises, reservoir depletion, and ultimately, system collapse due to over-expansion of irrigated land and mounting infrastructure costs. By contrast, scenarios guided by long-term climate services favour slower economic growth but yield more stable and resilient outcomes (Fig. 4c). These include sustained reservoir levels, fewer drought-induced shocks, and higher shares of restored land. D4.5 – Bi-directional feedbacks between adaptation actions and climate service information 12 The most maladaptive outcomes occur when institutional preferences are heavily biased toward immediate action and short-term rewards. Under such conditions, repeated short-term responses create reinforcing feedbacks that amplify vulnerability and degrade long-term adaptive capacity. The model also illustrates potential regime shifts: after prolonged collapse, systems may transition into more sustainable but less productive configurations dominated by ecological restoration (Fig. 3.4a). Taken together, these findings highlight the crucial role of information framing and decision-making horizons in shaping adaptation outcomes. They demonstrate that maladaptation can emerge not only from poor design but also from excessive reliance on short-term information, even when that information is accurate and timely. This underscores the need for CSs that align with long-term resilience goals. Maladaptation is inherently difficult to study because it unfolds over long timescales and often emerges from well-intentioned actions. Traditional monitoring tools may miss slow-building risks, and ex post evaluations are limited by path dependency. Models like the one presented here fill this gap by offering foresight into how today's decisions affect tomorrow’s vulnerabilities. By combining system dynamics with insights from climate service studies, the model captures essential features of socio-ecological feedbacks. It formalizes intuitive archetypes like Band-Aid Solutions and makes them testable. Moreover, it highlights the importance of information framing: the same physical system can evolve differently depending on which signals are emphasized and how decision-makers interpret them. In summary, this modelling approach offers a flexible, robust framework for investigating long-term maladaptation. It helps bridge theory and practice, supports ex-ante risk assessment, and informs the design of climate services that foster—not hinder—transformative adaptation. As climate variability increases and adaptation becomes more urgent, such tools are indispensable for exploring the potential for climate adaptation actions leading to unintended consequences. Figure 3.4. Outcome of the three scenarios based on the type of climate services used, namely: mostly long term, mixed (50% of each type); and mostly short term. Each scenario displays the main systemic variables of: reservoir volume (V); maximum reservoir volume (MaxV); Wealth (W); fraction of the catchment under agricultural production (UP); and fraction of the catchment under ecological restoration (EcoRest). D4.5 – Bi-directional feedbacks between adaptation actions and climate service information 13 4 Climate Services based expectations and groundwater dynamics System Dynamics (SD) methods enable us to understand feedbacks between socioeconomic and physical systems and the effects that external factors can have on those systems. Causal loop diagrams (CLDs) are commonly used diagramming tools in SD and are often employed to develop a preliminary dynamic hypothesis (Elsawah et al., 2017). In the Los Pedroches LL in Andalusia, Spain, we developed two CLDs to explore the effect of CS information on livestock production decisions and their effect on the physical system. The design of the CLDs was based on extensive interactions with local stakeholders throughout the project duration. Los Pedroches-Andalusia LL The Los Pedroches-Andalucia LL covers a rural region in the Northern part of Córdoba (Spain) (Figure 4.1). It is home to around 52,000 residents. One of the main landscapes in the area is the dehesa, which is a landscape defined by gently rolling hills and shallow soils that lie atop granite bedrock. In this area, water sources for both livestock and wildlife primarily come from groundwater-fed seasonal streams, traditional wells and, more recently, deep boreholes. The granite-based terrain underlying the dehesa contains a shallow, fractured, and varied aquifer system, which is replenished by rainfall. Figure 4.1. Case study location and main land uses (modified from Ropero Szymañska et al., 2025). To assess bi-directional feedbacks between adaptation actions and climate service information, we focused on how local livestock sector interacts with the local groundwater system and how the provision of (reliable) seasonal (6-month lead time) forecast for temperature and rainfall can impact farmers’ decisions. The goal of the assessment is to explore how water table levels respond to changes in farmer decisions resulting from decreased uncertainty about temperature and precipitation in the following months because of improved CS. D4.5 – Bi-directional feedbacks between adaptation actions and climate service information 14 Livestock-farming Production Systems The Los Pedroches region represents the largest livestock farming area in Andalusia (Broekman et al., 2022), where most farms are family-owned (ADROCHES, 2021). Livestock use is related to two main sectors, milk and Iberian pig meat production. As a primarily rainfed production system, its sustainability is particularly vulnerable to droughts and heat waves, and is threatened by the increasing intensity and duration of these climatic hazards. Iberian pigs are reared based on a combination of acorns and pasture or other complementary feeds. The percentage of acorns in the pigs’ diet is closely linked to the final market value of their meat, with products from Iberian pigs that are mainly acorn-fed being especially prized. The season when acorns fall from the oaks and are consumed by the pigs—usually October and November—is referred to as the montanera. The number of piglets bred or acquired by the farmers every year in the fall depends on the expectations of acorn production and water availability in the following year. This, in turn, depends mainly on spring rainfall and temperatures. Milk production, by contrast, is more controlled, as dairy cows primarily eat fodder and can breed any time of the year. Milk cows consume 100-150 litres of water per day, with total volume varying in response to the reproductive cycle, external temperatures and type of feed. The aim of farmers is to have stable milk production throughout the year, managing heat waves or prolonged high temperatures that negatively affect milk production. On a standard dairy-only farm, farmers' decision-making focuses on how to balance food provision from local winter cereal production and purchase of corn-based fodder from external sources and ensure sufficient water supply (Iglesias et al., 2016). Rainfall is the main water resource for livestock farming and is harvested directly through livestock ponds, or indirectly through shallow wells or boreholes. In an average hydrological year, the rainy season matches the period of the lowest water demand, summer being the highest water demand period. As intermittent rivers common to the region dry up naturally during summer and rainfall is reduced, groundwater becomes the main water source during the dry season. During drought periods, groundwater-dependence increases, extending into spring and autumn, sometimes becoming the only water source throughout the year in severe drought years. In extremes cases, famers must buy water that is brought in by water tanker from external sources to complement local water resources (Ropero Szymañska et al., 2025). Decision making context The CLDs developed for Los Pedroches LL builds directly upon the data collected through two workshops, two focus groups and seventeen in-depth interviews with livestock farmers (see Ropero Szymañska et al., 2025), held between October 2022 and November 2023. To reflect the sequencing of livestock farmers’ decisions, their production activities were summarised along two decision timelines, one for Iberian pigs and one for dairy cows (for more details, see van den Homberg et al., 2023). Livestock farmers are constantly making management decisions on their farms but there are times of the year when decisions are particularly critical. For both types of livestock, early autumn (October) is a key moment for planning the production for the following year based on the farmer’s expectations of the forthcoming months in terms of precipitation and temperature, among other considerations. In absence of CS, farmers will make decisions based on experience and their own intuition about the climatic conditions in the forthcoming months. With sufficiently reliable CS information, decisions will be made for a less uncertain future. In this context, we modelled two possible scenarios for CS predictions provided in October of a given year: - Scenario A: Average/wet conditions in the forthcoming 6 months (winter-spring). D4.5 – Bi-directional feedbacks between adaptation actions and climate service information 15 - Scenario B: Dry conditions in the forthcoming 6 months (winter-spring). Under these two scenarios, we modelled the potential impact of the farmers’ actions in response to the information provided by the CS on the physical system, more specifically on the local water table levels and their recovery time at the end of the summer season each year. The model comprises three types of dynamic variables: - Climate related expectations (grey in Fig. 4.2 and 4.3), i.e. how farmers expect the incoming 6 months to be in terms of accumulated precipitation and evolution of temperatures. - Production-related decisions (orange), these refer to: a) number of hectares to be seeded with winter cereals to be used as fodder for milk cows; b) number of tons of maize-based fodder to be purchased to complement winter cereals (milk cows) c) number of Iberian piglets that farmers plan to have in the following montanera (acorn grazing for pig fattening); d) number of cows that will be inseminated, as a function of milk production needs (cows have the peak of milk production approximately two months after giving birth). - Physical variables (blue), these refer to: a) Total water demand b) Duration of groundwater abstraction c) Duration of surface water supply d) Volume of abstracted groundwater e) Water table decrease f) Long-term health of oak trees Causal Loops Diagram for Scenario A (normal/wet conditions) In Scenario A (Figure 4.2), the model hypothesis that in October of a given year, farmers have the following climate-related expectations, based on their observations and confirmed by CS information for the following 6 months: a) All the rainfed arable land that will be seeded with winter cereals in autumn will yield a good harvest in spring/early summer of the subsequent year. Acorn production for the montanera will be good in the following autumn, as acorn production is positively impacted by a wet spring. b) With sufficient winter and spring rainfall to recharge the aquifer and the groundwater-dependent streams, surface water supply for livestock farming will be sufficient to last until the end of spring. Groundwater pumping is expected to be necessary only during the summer, when streams and ponds dry up. The production-related decisions associated to those expectations will be: a) Farmers will plant the maximum available arable land with (rainfed) winter cereals for animal feeding, to be harvested in spring/summer. This option is preferable to farmers to using maize based fodder. b) Farmers will minimize the purchase of maize-based complementary fodder as they count on an abundant cereal harvest for the incoming year. c) Farmers will not store groundwater in tanks in winter and spring as they expect to have sufficient surface water for the supply until end of spring. d) Farmers will maximize the number of livestock animals in anticipation of abundant cereal harvest (milk cows) and acorn (Iberian pigs), limited or no heatwaves (milk cows) and a favourable hydrological year. D4.5 – Bi-directional feedbacks between adaptation actions and climate service information 16 The physical variables will respond as follows to those decisions: a) Total water demand will increase because of the maximization of livestock load. b) As a result of a), surface water supply may last less than usual. c) As a consequence of a) and b), groundwater pumping will be more intense in time, causing sharper decreases in the water table. d) As a result of c), in the groundwater levels will take longer to recover in autumn, and even more so in case of a dry winter/spring in the following hydrological year. This, in turn, will negatively affect the surface water supply, as local streams are rainand groundwater-dependent. Figure 4.2. CLD of Scenario B (normal/wet conditions). Scenario B (dry conditions) Under this Scenario (Figure 4.3), the model hypothesizes that in October of a given year, farmers have the following climate-related expectations, based on their observations and confirmed by CS information for the following 6 months: a) Rainfed production of winter cereals will fail due to insufficient rains during the following winter and spring; and acorn production for montanera in the following autumn will be scarce due to insufficient spring rains. b) Rainfall recharge of the aquifer and streams will be limited, thus reducing surface water availability for livestock farming in winter/spring and limiting the recovery of water table levels during the winter. The production-related decisions associated to those expectations will be: a) Farmers will plant little or no (rainfed) winter cereals for animal feed. Instead, they will place orders to purchase maize-based fodder produced outside the region. This will increase their operational costs. b) Farmers will need to rely on groundwater for their operations since early spring (or when streams run dry) and will implement measures to ensure on-farm water access during the summer, e.g. by increasing groundwater abstraction points, by pumping water all year round and storing it in local tanks and ponds. This will increase their operational costs due to construction of wells/boreholes D4.5 – Bi-directional feedbacks between adaptation actions and climate service information 17 and increased energy costs. Moreover, they may need to buy water brough in by water trucks at the end of the dry season c) Farmers will maintain or reduce the number of livestock heads in order to maximize meat and milk production while limiting extra costs due to unfavourable weather conditions (low rainfall and high temperatures). The physical variables will respond as follows: a) Total water demand will decrease because of the reduction in livestock load. b) Because of the dry conditions surface water supply will last less than usual. c) As a result of b), groundwater pumping will be more intense and distributed in time, causing sharper decreases in the water table levels. d) As a result of c), groundwater levels will recover slowly and even more so in case of a dry winter/spring in the following hydrological year. This, in turn, will negatively affect the surface water supply, as local streams are rainand groundwater-dependent. Figure 4.3. CLD of Scenario B (dry conditions). D4.5 – Bi-directional feedbacks between adaptation actions and climate service information 18 Impact of CS-informed decisions on groundwater dynamics Figure 4.4. and 4.5 depict a synthetic evolution of water table levels under Scenarios A and B during two consecutive years, to illustrate the effects of decisions in a context of potential multiannual droughts, which is typical of the LL climate. The evolution of the water table is shown for a context with (dotted lines) and without (solid lines) enhanced CS to inform farmer’s decisions. In Scenario A (wet period), we observe that higher certainty about water availability for the forthcoming months due to reliable CS information is likely to initially produce a positive effect on the aquifer because farmers will avoid unnecessary and expensive groundwater pumping and storage in winter. However, the knowledge of favourable climate conditions is also likely to generate an increase in total water demand associated with the breeding of a higher number of livestock animals. As a result, groundwater levels will start to decline in late spring – earlier than without improved CS, when no additional livestock would be added to the system – and the decline will be more pronounced due to higher total water demand. This may locally generate risk of wells temporarily drying up and the need for some farmers to purchase (expensive) water trucks to ensuring the viability of their famers’ operations. If the aquifer recharge generated by the autumn rains of the following year is also average or high (line c), the water table recovery will be slower than without CS (line a) but will enable the system to recover by mid-winter. Nevertheless, if the subsequent year is dry (line d), this could cause the system to start a dry period with unfavourable conditions, generating negative consequences, as there will be less water availability for farming operations, and because abnormally long periods of low groundwater levels negatively affect the long-term health of holm oaks and the associated acorn production. Figure 4.4. Effects on the groundwater system during a wet period (Scenario A). In Scenario B (a in Figure 4.5), farmers will prepare for a dry and hot year by reducing the livestock load and by pumping groundwater. While the first decision will have a positive effect on the water table levels because it implies decreasing the total water demand, the increased pumping and longer pumping periods will result in lower water table levels for a longer period of time and in a larger portion of the aquifer. This will reduce even more the time surface water will be available in groundwater–dependent streams and will increase the time needed for the aquifer to recover at the end of the summer. D4.5 – Bi-directional feedbacks between adaptation actions and climate service information 19 The effects of the dry year will be felt in the aquifer both in the case of a wet/average year (line b) or a second dry year (line a), and water table levels may not be able to recover sufficiently as to feed temporary streams in late winter and early spring. This, in turn may trigger a new cycle of groundwater pumping sustained in time even if hydrological conditions are more favourable than during the previous year, with the subsequent negative consequences for the natural system and the viability of livestock farms due to increased operations costs. Figure 4.5. Effects on the groundwater system during a dry period (Scenario B). Short-term impacts from the use of climate services depend on the weather conditions in the subsequent years. In this sense, climate services can potentially increase future vulnerability when they predict a favourable (wet) conditions, as this could trigger actions such as increased livestock stocking, which increases total water demand. Predictions of dry conditions can help groundwater systems to recover more quickly as it triggers contention of groundwater use in absolute terms. D4.5 – Bi-directional feedbacks between adaptation actions and climate service information 26 References AQUAMAN project. (2017). Integrated drought management study for the Region of Crete, Innovative Water Resources Management Methodologies regarding the Governance and Adaptation to Climate Change of the Region of Crete, Oct. 2016 (in Greek). AQUAMAN Project . https://aquaman.tuc.gr/images/users/sotiria/%CE%A0%CE%B1%CF%81%CE%B1%CE%B4%CE%BF%CF%84%C E%AD%CE%BF_1.pdf ADROCHES. (2021). 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Living Lab Report: Crete, Greece. EMVIS SA. D4.5 – Bi-directional feedbacks between adaptation actions and climate service information 28 Colophon: This report has been prepared by the H2020 Research Project “Innovating Climate services through Integrating Scientific and local Knowledge (I-CISK)”. This research project is a part of the European Union’s Horizon 2020 Framework Programme call, “Building a low-carbon, climate resilient future: Research and innovation in support of the European Green Deal (H2020-LC-GD-2020)”, and has been developed in response to the call topic “Developing end-user products and services for all stakeholders and citizens supporting climate adaptation and mitigation (LC-GD-9-2-2020)”. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293. This four-year project started November 1 st 2021 and is coordinated by IHE Delft Institute for Water Education. For additional information, please contact: Micha Werner ([email protected]) or visit the project website at www.icisk.eu