Climate-change impact assessments using agro-hydrological models often assume that farmers will stick to the same planting and harvest dates in the future, even as the climate warms. This "fixed-calendar" assumption conflicts with reality, as farmers naturally adapt to shifting seasons, leading models to miscalculate crop yields and water use. To address this, we developed a workflow in the SWAT+ model that replaces static calendar dates with dynamic rules that respond to the environment. We first analyzed historical management records to identify the specific weather conditions, such as accumulated heat or dry days, that actually prompt farmers to plant or harvest. We then translated these observations into "if-then" rules (decision tables) and tuned them to ensure they accurately reproduced historical farming timing. We applied this dynamic approach to winter wheat and corn silage in a German catchment under cool-dry, cool-wet, and warm-wet late-century climates. Rule-based management successfully adapts to future warming without requiring manual adjustments. For winter wheat, the median growing season is shortened from a historical similar to 312 days to roughly 293-306 days in the future due to faster maturation. Conversely, corn silage growing seasons extended from a historical 144 days to 162-172 days in wet scenarios, driven by significantly earlier planting. Dynamic management consistently improved corn silage yields (+28.0% to + 47.9%) compared to the fixed-calendar approach. Environmentally, the dynamic rules were far more effective at mitigating pollution: nitrate runoff decreased by 48.5% to 86.6% for winter wheat relative to the baseline. In contrast, sticking to fixed dates resulted in erratic outcomes, such as a 30.6% increase in nitrate losses under cool-dry conditions. This study demonstrates that using adaptive rules instead of fixed dates reduces bias and produces more realistic climate-change impact assessments.

This study proposes a new workflow for crop growth evaluation and yield calibration in the Soil and Water Assessment Tool Plus (SWAT+) model and evaluates its impact on simulated hydrological and biogeochemical processes. The workflow was applied for ten small agricultural catchments in Europe. A detailed demonstration is provided for the German catchment, Schwarzer Schops. The workflow proved effective across all catchments, improving yield calibration from an initial R2 of 0.5-0.84. The results show that evapotranspiration and soil moisture were only moderately affected by crop calibration in three catchments (Belgium, Czech Republic and Norway) and negligibly changed in the remaining ones. Sediment and nutrient balance were affected more strongly: sediment, nitrogen and phosphorus loss change reached 82 % (Norway), 16 % and 20 % (Czech Republic), respectively. The proposed workflow is a valuable tool for improving the accuracy of SWAT + simulations and can be used to support decision-making in environmental management.

This paper introduces SWATtunR, a GitHub-hosted R package for scripted workflows designed to streamline and enhance the calibration and validation process of SWAT+ hydrological models. Addressing widespread challenges in model reproducibility, transparency, and user control, SWATtunR integrates both soft and hard calibration workflows within a fully scriptable and procedurally reproducible environment. The tool supports expert-guided and automated workflows, enabling flexible model calibration through parameter sampling, performance evaluation, and advanced visualization. A demonstration using the Little River Experimental Watershed presents its functionality and effectiveness. In this case study, the proposed workflow achieved very good streamflow simulation performance during calibration (NSE > 0.65, KGE > 0.75, |PBIAS| < 10%) and satisfactory performance during validation, markedly improving upon the uncalibrated model. By promoting transparent and reproducible calibration practices, SWATtunR provides a practical tool for hydrological modelling studies and supports reliable, policy-relevant water resource assessments.

Deliverable report D6.3 of the EU Horizon 2020 Project OPTAIN (Grant agreement No. 862756) Summary The purpose of the OPTAIN D6.3 was to explore, which NSWRM are the most efficient in the Continental, Boreal and Pannonian biogeographical regions of Europe (EBR), focusing environmental, socio-economic and policy dimensions. To reach this, the OPTAIN researchers and CS leaders identified nearly 30 key research questions. Building on these inputs, the deliverable’s shared goal was to determine whether the NSWRMs can be recommended for retaining water, sediment and nutrients in small agricultural catchments.The key methodological approach used in the OPTAIN project was the multi-actor approach, adopted to ensure a harmonised process of stakeholder engagement and modelling across case studies and governance levels. To enable meaningful and efficient stakeholder involvement, multi-actor reference groups were established (WP1). Through a series of strategically designed workshops, these groups actively shared descriptions of good practices in a globally standardised way, using the WOCAT NSWRM catalogue (WP2).The data collection and harmonisation for model-based assessment (WP3) laid the foundation for one of the project’s most significant outcomes: a major breakthrough in creating the evidence base on the environmental and economic efficiency of measures implemented in selected case studies, achieved using the SWAT+ and SWAP models (WP4). This evidence supported the development of a shared understanding of optimised spatial combinations of measures using the COMOLA tool to explore the stakeholder-preferred outcomes (WP5).Through a series of surveys, stakeholders on various levels shared their opinions on the efficiency and sufficiency of policies and approaches for promoting the NSWRM, as well as on issues and possible solutions for improving NSWRM uptake. The results capture perspectives of stakeholders ranging from supra national, through (international) river basin, and catchment, all down to local, field scale level, and provide a valuable and comprehensive policy overview to further guide NSWRM implementation (WP6). Finally, the OPTAIN findings were distilled and have been put forward to the OPTAIN Learning Environment (LE) platform where the evidence-base, presented through various carefully designed outputs, maintains accessible after the project to support multi-actor learning in real-life contexts (WP7).The harmonised modelling in OPTAIN helps understand the efficiency of (i) land management measures (LMMs), (ii) structural linear measures (SLMs), and (iii) structural areal measures (SAMs) across the EBRs. In the Continental EBR multiple case studies report significant hydrological and agronomic benefits. LMMs reduced sediment and nutrient losses while increasing soil moisture; in some cases this came with slight grain yield reductions. SAM showed strong potential to mitigate high flows, improve low-flow conditions, reduce soil loss, and decrease nitrogen loads. SLMs reduced in-stream nitrogen loads; however, a trade-off was observed in some simulations with more days below low-flow thresholds, while crop-yield effects were minor.In the Boreal EBR, the LMMs were the most effective in reducing nitrogen and phosphorus losses and increasing early-summer soil-water content. SLMs contributed to sizeable reductions in sediment loss and declines in nutrient loads. In areas prone to spring waterlogging, retention measures improved trafficability. The ability of LMMs to enhance water retention while sharply lowering phosphorus losses suggests they can address both nutrient pollution and seasonal water-management challenges in this region. In the Pannonian EBR, NSWRM implementation delivered substantial benefits for erosion control and water retention, with mixed effects on crop production. LMMs were the most effective for reducing sediment and phosphorus losses and for increasing soil-moisture storage; they also reduced nitrogen loss. Measures that remove arable land decreased total grain production, reflecting land-take. SLMs showed limited catchment-scale benefits. Yield responses varied: LMMs increased winter crop yields in some cases but had negligible effects on other crops.Based on the findings explaining environmental and partially socioeconomic dimension to NSWRMs relevance, the OPTAIN D6.3 provides indications for governance improvements that would likely lead to improved uptake and implementation of NSWRMs in the respective EBRs. Policy and implementation surveys of WP6 show that the ambitions of the European Green Deal, Farm to Fork Strategy, and Water Framework Directive, policies must go beyond incremental improvements and actively support systemic change.The most pressing changes show that future NSWMRs policies need to improve coordination of governance across scales, ensure robust and sustained financial mechanisms, simplifiy administration, work more towards effective instead overwhelming knowledge transfer, and, most importantly, focus on improved stakeholder engagement. NSWRMs are – here we put forward the synthesis of the available evidence – a strategic tool for advancing water quality and quantity goals, climate resilience, and sustainable agricultural production. However, their full potential will only be realised through integrated, well-designed, and adequately supported governance systems.Why this work matters in terms of practical application, policy integration, and sustainability? D6.3 provides robust evidence that NSWRMs can drive a shift from incremental improvements to systemic transformation in European agriculture, directly advancing the goals of the Green Deal, Farm to Fork Strategy, and Water Framework Directive. At the same time, it highlights gaps between the effectiveness of certain measures and the existing policy instruments supporting the uptake of NSWRMs, clearly pointing to where governance improvements and targeted interventions are most needed. By building this evidence base, D6.3 lays the foundation for the co-creation of the next-generation of incentives – the focus of OPTAIN D6.4.

There is strong evidence that ecosystem-based approaches, such as Natural/Small Water Retention Measures (NSWRMs) can be an important solution to problems associated with managing water quality and quantity, soil erosion, and nutrient loss. Moreover, they deliver multiple co-benefits such as increased biodiversity, climate change adaptation and mitigation, alongside aesthetic and recreational functions. However, despite their apparent advantages and significant political momentum for their expanded deployment, implementation of NSWRMs remains slow. This study asks why this is the case and employs a methodologically rigorous variant of the SWOT framework combining qualitative and quantitative (scoring and cluster analysis) elements to assess the exact barriers and potentials for increasing the NSWRMs’ implementation across Europe. The empirical analysis draws on case studies of fourteen small watersheds distributed across twelve European countries to explore the factors affecting the NSWRMs adoption, evaluate their relative importance, and identify necessary intervention areas for their better uptake. Our findings indicate that the main drivers for NSWRMs implementation are high knowledge availability through formal and informal networks, as well as support through advisory services. On the other hand, the main hindrances are inadequate financing schemes but also uncertain societal attitudes and perceptions. Financing schemes rarely account for indirect costs, and bureaucratic procedures further discourage practitioners from pursuing these measures. Negative attitudes are linked to mismatched time horizons as well as the gap between theoretical benefits and practical implementation challenges.

The implementation of agri-environmental practices (AEPs) is a key strategy to reach biodiversity and environmental objectives in agricultural landscapes, but their widespread application is often hampered by perceived trade-offs with crop production. However, the extent of these trade-offs remains poorly understood and has rarely been quantified in real-world case studies. Hence, our aim was to analyze trade-offs between crop yield, water quality and farmland biodiversity using an optimization approach for the spatial allocation of AEPs for a catchment in Eastern Germany. Potential AEPs were selected based on a co design approach with local stakeholders (stakeholder-based scenario) and were complemented by additional AEPs to reach current EU policies targets (policy-based scenarios). Consequences for crop production and environmental objectives were evaluated through spatially-explicit crop, water and biodiversity models. Contrary to common perception, we found that crop losses required to increase environmental objectives were marginal (maximum loss of 1.1% in the stakeholder based scenario). The implementation of AEPs even led to win-win outcomes for crop production and environmental objectives in over 20% of the Pareto-optimal solutions as compared to the status quo. These win-win outcomes resulted merely from biophysical effects as positive biodiversity feedbacks to agriculture were not included in our model. Spatial optimization of AEPs allocation was key to mediating trade-offs across scenarios, highlighting the large potential of spatially explicit approaches for the management of agricultural landscapes.

Multi-objective optimization (MOO) is becoming increasingly important in environmental decision making, but interpreting highly-dimensional Pareto optimal data often constitutes a cognitive overload for both scientists and stakeholders. To address this challenge, we present PyretoClustR, a modular framework for post-processing Pareto optimal solutions. This tool aims to increase accessibility and applicability of MOO results by introducing a low-lift, iterative method to reduce the Pareto front. PyretoClustR is adaptable to various environmental datasets and decision-making scenarios, automatically selecting effective parameters for principal component analysis, clustering, and outlier handling. It produces digestible visualizations of the pruned dataset for decision-makers. We demonstrate its effectiveness using MOO results from a multifunctional landscape, highlighting trade-offs between agricultural productivity, biodiversity, water quality, and ecological flow. PyretoClustR successfully reduced the Pareto front (2419 points) to 18 representative solutions with a silhouette score of 0.33 based on decision space variables, facilitating understanding of MOO for informed decision making.

Deforestation and agricultural practices, such as livestock farming, disrupt biogeochemical cycles, contribute to climate change, and can lead to serious environmental problems. Understanding the water cycle and changes in discharge patterns at the watershed scale is essential to tracking how deforestation affects the flow to downstream water bodies and the ocean. The Amazon basin, which provides about 15–20% of the freshwater flowing into the oceans, is one of the most important river systems in the world. Despite this, it is increasingly suffering from anthropogenic pressure, mainly from converting rainforests to agricultural and livestock areas, which can drive global warming and ecosystem instability. In this study, we applied a calibrated Soil and Water Assessment Tool (SWAT) model to the Jari River Watershed, a part of the Brazilian Amazon, to assess the combined effects of deforestation and climate change on water resources between 2020 and 2050. The model was calibrated and validated using observed streamflow. The results show an NS of 0.85 and 0.89, PBIAS of −9.5 and −0.6, p-factor of 0.84 and 0.93, and r-factor of 0.84 and 0.78, for periods of calibration and validation, respectively, indicating a strong model performance. We analyzed four scenarios that examined different levels of deforestation and climate change. Our results suggest that deforestation and climate change could increase surface runoff by 18 mm, while groundwater recharge could vary between declines of −20 mm and increases of 120 mm. These changes could amplify streamflow variability, affect its dynamics, intensify flood risks, and reduce water availability during dry periods, leading to significant risks for the hydrology of Amazonian watersheds and human water supply. This, in turn, could profoundly impact the region’s megadiverse flora and fauna, which directly depend on balanced streamflow in the watersheds.

In the context of climate change, large-scale vegetation restoration projects have significantly altered hydrological processes. However, existing studies have primarily focused on the impact of the "Grain for Green" programme (GGP) on hydrological dynamics in arid areas, neglecting humid regions. To help close this gap, we simulated streamflow and sediment yield in the Three Gorges Reservoir Area (TGRA), an important ecological zone in China, using the SWAT + model. We differentiated the effects of the GGP and climate change on streamflow and sediment yield in different hydrological periods. The results show that sediment yield responds more intensely to variations in vegetation composition compared to water yield. From 2000 to 2020, reforestation has significantly reduced annual sediment yield by an average of 802.6 kg/ha. Climate change was identified as the main driver of the changes in runoff and sediment yield. Furthermore, the effects of reforestation exhibit seasonality, with runoff increasing during the dry season and sediment yield decreasing during the flood season. The GGP also reduces runoff and sediment yield extremes and promotes the stability of the water-sediment relationship. In addition, projections of future climate scenarios from 2025 to 2050 indicate an upward trend in total runoff and a downward trend in soil retention. This study provides insights into the impacts of the GGP and climate change on hydrological processes in humid regions and offers guidance for future development pathways.

The Soil and Water Assessment Tool (SWAT) is applied worldwide for modeling basin-scale processes. Its latest version (SWAT+) adds new capabilities to the tool, and collectively with increasing computational power and availability of public datasets expands model complexity as well as provides pathways for serious errors in the model setup process. These models are used to run scenarios to support decision-making processes, hence undetected faults can have a substantial socio-economic and environmental impact. We propose a 5-step SWAT + model setup verification workflow assessing the soundness of processes related to weather, water balance, management, plant growth, point source and tile drain flows. We developed an R package, called SWATdoctR, which guides the user through the model setup verification process, allowing the identification of typical, but repeatedly overlooked, issues. The workflow and the functionality of the tool is demonstrated in 4 SWAT + setups in different catchments, at various stages of model setup.

Input data collection, quality assurance and preparation are central but time_consuming steps in environmental modeling. Errors due to manual processing of model input data can result in an incorrect representation of an environmental system and may consequently lead to implausible model simulations. Correct input data preparation and thorough quality check at an early stage of the model setup procedure are essential to build confidence in model simulation results. Typically, in environmental model applications, many steps in the input data preparation phase have to be repeated with the inflow of new, additional or corrected data. In this study, we selected the widely used SWAT + ecohydrological model as an illustrative example to investigate challenges related to input data preparation. To assist in these tasks, we developed an R package named SWATprepR, which provides functions for typical and repeating SWAT + model input data preparation tasks. The package supports the preparation of weather input files, atmospheric deposition, soil parameters, crop rotations, and observed (control or calibration) data, to name a few, presently with focus on European applications. The SWATprepR functions are integrated in R script workflows and can help SWAT + modelers to avoid repetitive tasks, secure reproducibility and transparently document the data processing steps. Application of the package is illustrated with a test case of a SWAT + model for a small catchment in central Poland.

Zusammenfassung Mit monatelangen Dürrephasen, Hitzesommern und Hochwasserereignissen ist die globale Klimaerwärmung auch in Deutschland in den letzten Jahren verstärkt in Erscheinung getreten. Im 2021 novellierten Klimaschutzgesetz wird daher eine Klimaneutralität bis 2045 angestrebt. Gleichzeitig sind in Agrarlandschaften trotz entsprechender europarechtlicher Verpflichtungen günstige Erhaltungszustände bei Habitaten, Arten und Gewässer weiterhin die Ausnahme. Im nachfolgenden Beitrag schätzen wir für verschiedene landschaftsgestaltende oder produktionsintegrierte Maßnahmen die potenziellen Wirkungen hinsichtlich Klimaschutz und -anpassung, günstiger Erhaltungszustände sowie für die langfristige Versorgungssicherheit und die Profitabilität von Landnutzungen ab. Anhand dieser Wirksamkeitsabschätzungen identifizieren wir anschließend prioritär zu ergreifende Maßnahmen. Die vergleichende Wirksamkeitsabschätzung soll helfen, in Anbetracht begrenzter finanzieller und personeller Ressourcen die geeignetsten Maßnahmen vorrangig zu ergreifen.

The Mediterranean region is highly vulnerable to climate change. Longer and more intense heatwaves and droughts are expected. The Gordes Dam in Turkey provides drinking water for Izmir city and irrigation water for a wide range of crops grown in the basin. Using the Soil and Water Assessment Tool (SWAT), this study examined the effects of projected climate change (RCP 4.5 and RCP 8.5) on the simulated streamflow, nitrogen loads, and crop yields in the basin for the period of 2031–2060. A hierarchical approach to define the hydrological response units (HRUs) of SWAT and the Fast Automatic Calibration Tool (FACT) were used to reduce computational time and improve model performance. The simulations showed that the average annual discharge into the reservoir is projected to increase by between 0.7 m3/s and 4 m3/s under RCP 4.5 and RCP 8.5 climate change scenarios. The steep slopes and changes in precipitation in the study area may lead to higher simulated streamflow. In addition, the rising temperatures predicted in the projections could lead to earlier spring snowmelt. This could also lead to increased streamflow. Projected nitrogen loads increased by between 8.8 and 25.1 t/year. The results for agricultural production were more variable. While the yields of poppy, tobacco, winter barley, and winter wheat will increase to some extent because of climate change, the yields of maize, cucumbers, and potatoes are all predicted to be negatively affected. Non-continuous and limited data on water quality and crop yields lead to uncertainties, so that the accuracy of the model is affected by these limitations and inconsistencies. However, the results of this study provide a basis for developing sustainable water and land management practices at the catchment scale in response to climate change. The changes in water quality and quantity and the ecological balance resulting from changes in land use and management patterns for economic benefit could not be fully demonstrated in this study. To explore the most appropriate management strategies for sustainable crop production, the SWAT model developed in this study should be further used in a multi-criteria land use optimization analysis that considers not only crop yields but also water quantity and quality targets.

To effectively guide agricultural management planning strategies and policy, it is important to simulate water quantity and quality patterns and to quantify the impact of land use and climate change on soil functions, soil health, and hydrological and other underlying processes. Environmental models that depict alterations in surface and groundwater quality and quantity at the catchment scale require substantial input, particularly concerning movement and retention in the unsaturated zone. Over the past few decades, numerous soil information sources, containing structured data on diverse basic and advanced soil parameters, alongside innovative solutions to estimate missing soil data, have become increasingly available. This study aims to (i) catalogue open-source soil datasets and pedotransfer functions (PTFs) applicable in simulation studies across European catchments; (ii) evaluate the performance of selected PTFs; and (iii) present compiled R scripts proposing estimation solutions to address soil physical, hydraulic, and chemical data needs and gaps in catchment-scale environmental modelling in Europe. Our focus encompassed basic soil properties, bulk density, porosity, albedo, soil erodibility factor, field capacity, wilting point, available water capacity, saturated hydraulic conductivity, and phosphorus content. We aim to recommend widely supported data sources and pioneering prediction methods that maintain physical consistency and present them through streamlined workflows.

Managing agricultural land to maximize the supply of natural pest control can help reduce pesticide use. Tools that are able to represent the relationship between landscape structure, field management and natural pest control can help in deciding which management practices should be used and where. However, the reliability and the predictive power of generic models of natural pest control is largely unknown. We applied an existing generic model of natural pest control potential based on landscape structure to nine sites in five European countries and tested the resulting values against field measurements of natural pest control. Subsequently, we added information on local level factors to test the possibility of improving model performance and predictive power. The results showed that there is generally little or no evidence of correlation between modeled and field -measured values of natural pest control. Moreover, we found high variability in the results, depending on the associations of crops, pests and biocontrol agents considered (e.g. Oilseed rape-Pollen beetle-Parasitoids) and on the different case studies. Factors at the local level, such as conservation tillage, had an overall positive effect on natural pest control, and their inclusion in the models typically increased their predictive power. Our results underline the importance of developing predictive models of natural pest control which are tailored towards specific associations between crops, pests and biocontrol agents, consider local level factors and are trained using field measurements. They would serve as important tools within farmers' decision making, ultimately supporting the shift toward a low-pesticide agriculture.

The largest impact of land-use change on catchment hydrology can be linked to deforestation. This change, driven by exponential population growth, intensified food and industrial production, has resulted in alterations in river flow regimes such as high peaks, reduced base flows, and silt deposition. To reverse this trend more extensive management practices are becoming increasingly important, but can also lead to severe losses in agricultural production. Land-use optimization tools can help catchment managers to explore numerous land-use configurations for the evaluation of trade-offs amongst various uses. In this study, the Soil and water assessment tool (SWAT) model was coupled with a genetic algorithm to identify land-use/management configurations with minimal trade-offs between environmental objectives (reduced sediment load, increased stream low flow) and the crop yields of maize and soybean in Nyangores catchment (Kenya). During the land-use optimization, areas under conventional agriculture could either remain as they are or change to agroforestry or conservation agriculture (CA), where the latter was represented by introducing contour farming and vegetative filter strips. From the sets of the resulting Pareto-optimal solutions we selected mid-range solutions, representing a fair compromise among all objectives, for further analysis. We found that a combined measure implementation strategy (agroforestry on certain sites and conservation agriculture on other sites within the catchment) proved to be superior over single measure implementation strategies. On the catchment scale, a 3.6% change to forests combined with a 35% change to CA resulted in highly reduced sediment loads (−78%), increased low flow (+14%) and only slightly decreased crop yields (<4%). There was a tendency of the genetic algorithm to implement more extensive management practices in the upper part of the catchment while leaving conventional agriculture in the lower part. Our study shows that a spatially targeted implementation strategy for different conservation management practices can remarkably improve environmental sustainability with only marginal trade-offs in crop production at the catchment-level. Incentive policies such as payments for ecosystem services (PES), considering upstream and downstream stakeholders, could offer a practical way to effect these changes.

Short description This repository contains the relevant data and code used for the analyses of the scientific publication: "Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity", published in the Journal of Applied Ecology. For further details please see the original article and its supplementary materials. Organization of the data The repository contains two main folders: 1. Target indicators & spatial analysis ‘target indicators.csv’: Measured variables that have been quantified at the CROSSLINK field sampling campaign in the Zwalm catchment (EPT taxa richness, diatoms functional evenness, cotton-strip assay). ‘bio-suitability segments.csv’: Biophysical suitability for food production of the arable land for each riparian segment of the Zwalm. ‘spatial analysis.xlsx’: Results of the Zwalm spatial analyses addressing land-use and physiographic properties of the (1) local riparian corridors; (2) full riparian corridors within in the upstream catchments and (3) total upstream catchment areas for each sampling site. ‘Summary model development Zwalm.pptx’: Additional information on the models that have been used in the CoMOLA optimization framework. 2. CoMOLA input & parameterisation The files in this folder can be used for the parameterisation of the Python tool CoMOLA (Strauch et al., 2019). Source for CoMOLA, including user manual: https://github.com/michstrauch/CoMOLA ‘config.ini’: Basic configuration file of CoMOLA (needs to be adjusted to local settings) ‘input’ folder: Includes the CoMOLA input files that have been used in our study. See CoMOLA manual for more details on each file. ‘models’ folder: Includes the Python code of the models that are used for the calculation of all target indicators within the optimization framework (‘Zwalm_4_Models_v1_utf8.py’). The sub-folders ‘GIS_temp_files’ and ‘Input’ contain all files that are needed and have been used to run the Python code.

Zusammenfassung Die global steigenden Treibhausgase verändern in zunehmenden Maße auch in Deutschland die klimatischen Verhältnisse. Betroffen sind insbesondere hiesige Agrarlandschaften, die weite Teile Deutschlands umfassen und schon gegenwärtig vielfältige ökologische Probleme aufweisen. Auch wenn die landwirtschaftlichen Nutzungen prägend für Agrarlandschaften sind, so hängt ihre Zukunftsfähigkeit nicht allein von einer Veränderung der Bewirtschaftungsmethoden ab. Die Gestaltung zukunftsfähiger Agrarlandschaften bedarf einer über den einzelnen Schlag hinausgehenden Betrachtung und ist eine gesamtgesellschaftliche Aufgabe, die deutlich über die Verantwortung und Möglichkeiten der einzelnen GrundstückseigentümerInnen und -bewirtschafterInnen hinausreicht. Ausgehend von den bekannten ökologischen Problemen und den im Beitrag ausführlicher dargestellten besonderen Herausforderungen des Klimawandels untersuchen wir daher, was Zukunftsfähigkeit bedeutet und welche gesellschaftlichen Ziele und Anforderungen sich hieraus für Agrarlandschaften identifizieren lassen. Der Beitrag will damit eine Grundlage für die Ausarbeitung praktischer Maßnahmenkonzepte und entsprechender staatlicher Lenkung und Förderung schaffen.

In England, the priority catchment project focuses on developing innovative solutions to ensuring a clean and plentiful supply of water and environmental protection. Understanding the impacts of climate change on streamflow and water availability will ensure resilient management solutions into the future. The latest 12-member dynamically downscaled perturbed parameter ensemble of regional climate model projections (PPE-RCM) is part of the country specific UK Climate Projections UKCP18. In this study it was applied to estimate future changes in streamflow in an application of a new, revised version of the Soil and Water Assessment Tool (SWAT+) to two contrasting priority catchments in England. Both catchments are influenced by high rates of freshwater withdrawals but differ in their natural hydrological regimes and geographies. One is a wet coastal catchment with steep slopes while the other is a dry lowland catchment. Modelled impacts on natural monthly flows and flow duration statistics until the 2080s under the 12 member PPE were compared to those from 18 members of the euro-CORDEX initiative. Both ensembles are available for emissions pathway RCP8.5. To cover a broad range of scenarios, we also modelled the impact of the lower emissions (RCP4.5 & RCP2.6) euro-CORDEX projections.SWAT+ performs well in simulating natural flows during the validation period in both catchments. The PPE estimates are consistently drier than euro-CORDEX. It projects streamflow in the coastal catchment to increase in seasonality with higher winter and lower summer flows, while streamflow in the dry lowland catchment is projected to decrease across all months apart from February. In the dry lowland catchment, the euro-CORDEX under RCP8.5 predict the strongest decreases in streamflow for June at -13%, while the PPE projects beyond -20% decrease throughout June to September. The climate change signal in the coastal catchment is less clear. The PPE projects winter streamflow to increase by between 5% to 36% while the euro-CORDEX under RCP8.5 predict increases between 13% to 23%, summer streamflow is projected to decrease by -16% to -23% and -0.5% to -4% respectively. RCP2.6 and RCP4.5 represent a mixed result with rarely beyond 10% change and more months with increasing trends than under RCP8.5. The different emissions pathways largely agree on increasing high flows and decreasing low flows in the coastal catchment. For the lowland catchment both ensembles driven by RCP8.5 project decreases across the whole flow duration curve while RCP 2.6 and 4.5 project medium to high flows to increase and low flows at Q70 and Q95 to largely stay the same.This study suggests the need to adapt environmental protection and water withdrawals to decreasing water availability across the whole year in the lowland catchment and to pronounced changes in streamflow timing in the coastal catchment. To understand a broader range of climate impacts the UKCP18 PPE-RCMs should be used with other projections. However, they represent high-end warming scenarios translating into strong hydrological response, in particular streamflow decreases, that other ensembles might not capture, providing further insights into the challenges that water management may face.