D5.1 - Final monitoring reports - Accelerating demonstrators' transformational adaptation

Final monitoring reports Deliverable D5.1 Accelerating and upscaling transformational adaptation in Europe: demonstration of water-related innovation packages This project has received funding from the European Union’s Horizon H2020 innovation action programme under grant agreement 101036683. Ref. Ares(2025)8259064 - 30/09/2025 2 TransformAr Deliverable 5.1 www.transformar.eu Deliverable Number and Name D5.1 - Final monitoring reports Work Package WP5 – WP Accelerating demonstrators’ transformational adaptation Dissemination Level Public Author(s) Tereza Hnátková, Vaclav Hradilek (CZU) Amaya Soto (CETMAR), Andrea Ogando (UVIGO), Pauline Guérécheau-Desvignes (ADEME), Manuela Puddu (MEDSEA), Francesca Etzi (MEDSEA), Evrydiki Pavlidi (MOG), Sanna Varis (LAPP), Nicola Rogers (WRT) , Giles Rickard (WRT) Primary Contact and Email Hnátková Tereza (CZU); [email protected] Date Due 30/09/2025 Date Submitted 30/09/2025 File Name TransformAr D5.1-5.pdf Status Final Reviewed by (if applicable) Jan Cools (UA) Suggested citation Hnátková et al. (2025) Final Monitoring Report. Deliverable D5.1. TransformAr. H2020 grant no. 101036683. © TransformAr Consortium, 2021 This deliverable contains original unpublished work except when indicated otherwise. Acknowledgement of previously published material and of the work of others has been made through appropriate citation, quotation, or both. Reproduction is authorised if the source is acknowledged. This document has been prepared in the framework of the European project TransformAr. This project has received funding from the European Union’s Horizon 2020 innovation action programme under grant agreement no. 101036683. The sole responsibility for the content of this publication lies with the authors. It does not necessarily represent the opinion of the European Union. Neither the CINEA nor the European Commission are responsible for any use that may be made of the information contained therein. 3 TransformAr Deliverable 5.1 www.transformar.eu 4 TransformAr Deliverable 5.1 www.transformar.eu TABLE OF CONTENTS LIST OF ACRONYMS AND ABBREVIATIONS ................................................... 8 EXECUTIVE SUMMARY ............................................................................. 10 1.0 OBJECTIVES AND INFOGRAPHIC ......................................................... 13 1.1 Demonstrators .......................................................................... 13 1.2 Methodological Updates .............................................................. 16 2.0 CONTEXT ......................................................................................... 19 2.1 Geographical Description ............................................................ 21 2.1.1 Lappeenranta, Finland .................................................................................................................. 21 2.1.2 Oristano, Italy ................................................................................................................................ 22 2.1.3 West Country Region, United Kingdom ........................................................................................ 26 2.1.4 Galicia, Spain ................................................................................................................................. 27 2.1.5 City of Egaleo, Greece ................................................................................................................... 28 2.1.6 Guadeloupe Archipelago, France .................................................................................................. 30 2.2 Climate Vulnerability, Impacts, Risks, and Challenges ..................... 30 2.2.1 Lappeenranta, Finland .................................................................................................................. 30 2.2.2 Oristano, Italy ................................................................................................................................ 32 2.2.3 West Country Region, United Kingdom ........................................................................................ 34 2.2.4 Galicia, Spain ................................................................................................................................. 38 2.2.5 City of Egaleo, Greece ................................................................................................................... 40 2.2.6 Guadeloupe Archipelago, France .................................................................................................. 40 2.3 State-of-the-Art on Adaptation in the Demonstrator ....................... 41 2.3.1 Lappeenranta, Finland .................................................................................................................. 42 2.3.2 Oristano, Italy ................................................................................................................................ 42 2.3.3 West Country Region, United Kingdom ........................................................................................ 43 2.3.4 Galicia, Spain ................................................................................................................................. 44 2.3.5 Guadeloupe Archipelago, France .................................................................................................. 45 3.0 DESCRIPTION OF SOLUTIONS IN TRANSFORMAR ................................... 47 3.1 Lappeenranta, Finland ............................................................... 47 3.2 West Country Region, United Kingdom ......................................... 49 3.2.1 Nature Based Solutions ................................................................................................................. 49 3.2.2 Financial, Economic and Insurance Schemes ................................................................................ 55 3.2.3 Nutrient Credit Schemes in Devon and Cornwall ......................................................................... 56 5 TransformAr Deliverable 5.1 www.transformar.eu 3.2.4 Blended Finance: Camel Catchment ............................................................................................. 56 3.3 Guadeloupe Archipelago, France .................................................. 57 3.4 Galicia Region, Spain ................................................................. 57 3.5 City of Egaleo, Greece ................................................................ 58 3.6 Oristano, Italy .......................................................................... 59 3.7 Expected Innovation .................................................................. 62 3.8 Bankability of Demonstrator Solutions .......................................... 63 4.0 EXPECTED IMPACTS & MITIGATIONS ................................................... 65 4.1 Lappeenranta, Finland ............................................................... 65 4.1.1 Overview of the Expected Impacts ............................................................................................... 65 4.1.2 Selected Indicators ........................................................................................................................ 66 4.1.3 Baseline Data ................................................................................................................................ 67 4.1.4 Approach to Monitor Impacts Methodologies ............................................................................. 69 4.1.5 Availability and Access of Monitored Data ................................................................................... 74 4.1.6 Upscaling and Acceleration of the Solution .................................................................................. 74 4.2 West Country, United Kingdom .................................................... 74 4.2.1 Overview of the Expected Impacts ............................................................................................... 74 4.2.2 Selected Indicators ........................................................................................................................ 79 4.3 81 4.4 Approach to Monitor Impacts Methodologies .................................. 81 4.4.1 Availability and Access of Monitored Data ................................................................................... 88 4.4.2 Upscaling and Acceleration of the Solution .................................................................................. 88 4.3 Galicia, Spain ............................................................................. 89 4.3.1 Overview of the Expected Impacts ................................................................................................... 89 4.3.2 Selected Indicators KPI Summary ..................................................................................................... 90 4.4.4 Approach to Monitor Impacts Methodologies ............................................................................. 96 4.4.5 Availability and Access of Monitored Data ................................................................................. 103 4.4.6 Upscaling and Acceleration of the Solution ................................................................................ 104 4.4 Egaleo, Greece ......................................................................... 105 4.4.8 Selected Indicators KPI Summary ............................................................................................... 107 4.4.9 Baseline Data .............................................................................................................................. 108 4.4.10 4.4.4 Approach to Monitor Impacts Methodologies .......................................................... 110 4.4.11 Availability and Access of Monitored Data ......................................................................... 110 4.4.12 Upscaling and Acceleration of the Solution ........................................................................ 110 4.5 Oristano, Italy ........................................................................ 111 4.5.1 Overview of the Expected Impacts ............................................................................................. 111 6 TransformAr Deliverable 5.1 www.transformar.eu 4.5.2 Selected Indicators KPI Summary ............................................................................................... 112 4.5.3 Baseline Data .............................................................................................................................. 118 4.5.4 Approach to Monitor Impacts Methodologies ........................................................................... 123 4.5.5 Availability and Access of Monitored Data ................................................................................. 125 4.5.6 Upscaling and Acceleration of the Solution ................................................................................ 126 4.6 Guadeloupe Archipelago, France ................................................ 126 4.6.1 Overview of the Expected Impacts ............................................................................................. 126 4.6.2 Selected Indicators KPI Summary ............................................................................................... 127 4.6.3 Baseline Data .............................................................................................................................. 127 4.6.4 Approach to Monitor Impacts Methodologies ........................................................................... 128 4.6.5 Availability and Access of Monitored ......................................................................................... 128 4.6.6 Upscaling and Acceleration of the Solution ................................................................................ 129 5.0 RESULTS BASED ON MEASURED PARAMETERS FROM THE SOLUTION ..... 130 5.1 Lappeenranta, Finland ............................................................. 130 5.1.1 Summaries of Particular Deliveries (KPI, Bankability, etc.) ......................................................... 130 5.1.2 New Information on Bankability ................................................................................................. 130 5.1.3 Comparative Results ................................................................................................................... 131 5.1.4 Challenges and Mitigations ......................................................................................................... 131 5.2 West Country, United Kingdom .................................................. 132 5.2.1 Summaries of Particular Deliveries (KPI, Bankability, etc.) ......................................................... 132 5.2.2 New Information on Bankability ................................................................................................. 149 5.2.3 Comparative Results ................................................................................................................... 151 5.2.4 Challenges and Mitigations ......................................................................................................... 153 5.3 Galicia, Spain ......................................................................... 154 5.3.1 Summaries of Particular Deliveries (KPI, Bankability, etc.) ......................................................... 154 5.3.2 New Information on Bankability ................................................................................................. 154 5.3.3 Comparative Results ................................................................................................................... 154 5.3.4 Challenges and Mitigations ......................................................................................................... 154 5.4 Egaleo, Greece ....................................................................... 155 5.4.1 Summaries of Particular Deliveries (KPI, Bankability, etc.) ......................................................... 155 5.4.2 New Information on Bankability ................................................................................................. 155 5.4.3 Comparative Results ................................................................................................................... 155 5.4.4 Challenges and Mitigations ......................................................................................................... 156 5.5 Oristano, Italy ........................................................................ 156 7 TransformAr Deliverable 5.1 www.transformar.eu 5.5.1 Summaries of Particular Deliveries (KPI, Bankability, etc.) ......................................................... 156 5.5.2 New Information on Bankability ................................................................................................. 157 5.5.3 Comparative Results ................................................................................................................... 157 5.5.4 Challenges and Mitigations ......................................................................................................... 158 5.6 Guadeloupe Archipelago, France ................................................ 159 5.6.1 Summaries of Particular Deliveries (KPI, Bankability, etc.) ......................................................... 159 5.6.2 New Information on Bankability ................................................................................................. 159 5.6.3 Comparative Results ................................................................................................................... 159 5.6.4 Challenges and Mitigations ......................................................................................................... 160 6.0 OVERALL CHALLENGES, BARRIERS AND LESSONS LEARNED.................. 161 8 TransformAr Deliverable 5.1 www.transformar.eu List of Acronyms and Abbreviations • AP – Action Plan • AWAR – Awareness-Raising Modules • BNG – Biodiversity Net Gain • CAF/CAE – Citizen App Finland / Citizen App Egaleo • CC – Climate Change • CCA – Climate Change Adaptation • CCISC – Climate Change Impacts Study Committee • CETMAR – Technological Centre of the Sea • CIH – Climate Innovation Hub • CIL – Community Infrastructure Levy • COAST – Coastal Contract • CoM – Covenant of Mayors for Climate and Energy • COTS – Commercial Off-the-Shelf • CS – Citizen Scientists • DEFRA – UK Department for Environment, Farming and Rural Affairs • DSI – Decision Support Instruments • EEA – European Environment Agency • ELMS – Environmental Land Management Scheme • ERDF – European Regional Development Fund • ESNACC – Elements for a National Adaptation Strategy to Climate Change • EU – European Union • GA – Grant Agreement • GHG – Greenhouse Gases • GIS – Geographic Information Systems • HAB – Harmful Algal Bloom • ICW – Integrated Constructed Wetlands • INSUR – Urban Climate Risk Insurance • INTERM – Intertidal Monitoring • IoT – Internet of Things • IPCC – Intergovernmental Panel on Climate Change • ISPRA – Institute for Environmental Protection and Research (Italy) • KCS – Key Community System • KPI – Key Performance Indicator 9 TransformAr Deliverable 5.1 www.transformar.eu • LWO – Local Wetland Observatory • MASE – Ministry for the Environment and Energy Security (Italy) • MOE – Municipality of Egaleo • MoEE – Greek Ministry of Environment and Energy • MRM – Mussel Raft Monitoring • MRV – Monitoring, Reporting and Verification • NAP – National Adaptation Plan • NAS – National Adaptation Strategy • NBS – Nature-Based Solutions • NCCAC – National Climate Change Adaptation Committee • NCM – Natural Capital Marketplace • NCSRD – National Centre for Scientific Research Demokritos • NN – Nutrient Neutrality • OECC – Spanish Climate Change Office • PNACC – Spanish National Adaptation Plan to Climate Change • RAAP – Regional Adaptation Action Plans • RASCC – Regional Adaptation Strategy to Climate Change • RI – Resilience Index • SAC – Special Area of Conservation • SCS – Smart Climate Stations • STH – Stakeholders • SuDS – Sustainable Drainage Systems • SWM – Storm Water Modular System • SWMM – Storm Water Management Model • TRL – Technology Readiness Level • URB – Urban Run-off Biofiltration System • UVIGO – University of Vigo • WRT – Westcountry Rivers Trust 16 TransformAr Deliverable 5.1 www.transformar.eu SCS - Smart Climate Stations | CAE - Citizen App | AWAR - Awareness-raising | CIH - Climate Innovation Hub | DSI - Social services/infrastructures 1.2 Methodological Updates The TransformAr methodology evolved dynamically, shaped by insights from monitoring, laboratory analyses, and stakeholder engagement. Key methodological updates include: The development of a harmonized monitoring framework represented a key methodological advance of TransformAr. Building on the findings of D5.8, which identified discrepancies in monitoring approaches across demonstrators, a unified framework was introduced. This framework combines quantitative KPIs, such as biodiversity indices, water quality, flooding frequency, and energy savings, with qualitative indicators, including stakeholder perception and governance effectiveness. The integration of these elements ensured comparability across sites and significantly improved reporting consistency. Another major enhancement was the integration of digital platforms. The PIK data visualization platform was upgraded to centralize diverse data flows, thereby enabling real-time access and cross-site analysis. Egaleo the platform linked sensor data from the Smart Weather Station to citizen-facing applications, expanding public access and engagement. Laboratory analyses were also refined to improve the robustness of monitoring outcomes. In the West Country, soil and sediment testing deepened understanding of nutrient retention and hydrological dynamics. In Oristano, detailed analyses of salinity and water chemistry directly informed wetland restoration design. These laboratory-based insights reinforced the reliability of selected indicators and improved the scientific basis for evaluating adaptation performance. Stakeholder engagement methodologies were further expanded and diversified. Lappeenranta extended its participatory planning workshops to include private sector actors, ensuring broader involvement in DATA ANALYSIS ACT SCS CAE DSI CAE AWAR CIH MOE supporte d by 17 TransformAr Deliverable 5.1 www.transformar.eu adaptation pathway development. In Galicia, engagement formats were revised to create a closer connection and participation of aquaculture stakeholders, which ultimately fostered wider acceptance of the solutions and stronger co-design processes. The harmonized monitoring framework is ready for direct application in other Horizon projects, thereby supporting future standardization and comparability at EU level. Innovation TransformAr is distinguished by its cross-sectoral innovation, which brings together the ecological, digital, financial and governance dimensions in a coherent framework for transformational adaptation. Demonstrators have advanced the large-scale implementation of nature-based solutions (NBS), digital integration and sector-specific strategies, while also piloting novel governance and financing models. In the West Country, TransformAr has pioneered one of the first Horizon-supported, catchment-scale river restoration initiatives, linking biodiversity recovery directly to measurable reductions in flood risk. In Guadeloupe, a French overseas region, hybrid solutions combining ecosystem restoration, particularly mangrove restoration, with physical protective structures have been developed. These solutions can be replicated in other climate-vulnerable island regions. Lappeenranta has demonstrated how urban green infrastructure can be integrated with digital innovation, combining real-time monitoring tools and participatory platforms to enhance resilience to heavy rainfall and flooding. Galicia, in turn, has focused on monitoring, technical and social innovation within the aquaculture sector. This approach combines real-time data, predictive modelling, and participatory processes to strengthen the resilience of mussel and clam farming in the face of changing environmental conditions (detailed information on specific innovations per solution is available on 5.8 Intermediary Monitoring Report). Innovation in governance and finance has also been central to the project. Oristano has piloted advanced water governance mechanisms in coastal zones, and Guadeloupe has tested new financing models for protective measures against climate risks. To facilitate knowledge transfer and wider uptake, cross-site learning has been promoted using tools such as the Playbook and the Scorecard. These digital resources capture best practices from demonstrators and provide municipalities and regions with practical support for making decisions about selecting, combining and scaling up adaptation solutions. Adjustments developed Several project-specific challenges and adaptations to specific contexts meant that adjustments had to be made to the objectives or methods. Extreme weather and data gaps: In Guadeloupe, for example, hurricanes disrupted monitoring campaigns, necessitating the use of proxy indicators and satellite remote sensing. Despite the resulting delays, this improved methodological resilience and highlighted the need for redundancy in data sources. Technical calibration and deployment: 18 TransformAr Deliverable 5.1 www.transformar.eu In Egaleo, the Smart Weather Station and citizen apps required iterative calibration to produce accurate results. While this slowed deployment, it resulted in more robust monitoring systems with higher user acceptance. Hydrological variability: In the West Country, unusually dry seasons complicated sediment retention and water quality assessments. Methodologies were adjusted to incorporate adaptive sampling strategies. Stakeholder engagement adaptation: In Galicia, the engagement methodologies were adapted with a focus on more participatory formats and trust-building workshops. This improved engagement and data sharing. Integration challenges: In Lappeenranta, combining nature-based solutions (NBS) with digital systems created unexpected maintenance requirements for green infrastructure. Operational protocols were updated to include longterm maintenance planning. Cross-Demonstrator Harmonization: Initial differences in KPI definitions complicated the synthesis process. However, the harmonization process resulted in a consistent monitoring and evaluation system. The objectives of TransformAr are being met through demonstrator-specific goals, adaptive methodological updates, innovative integration of solutions, and adjustments to unexpected challenges. Tailored approaches at each site are delivering measurable environmental, economic, and social benefits, while methodological refinements and innovations ensure comparability and scalability. By combining Nature-Based Solutions, digital monitoring, technological solutions, governance and financing models, and citizen engagement, TransformAr demonstrates practical, replicable pathways for transformational adaptation in Europe. Lessons learned and cross-cutting insights are fed into the project’s catalogue of solutions, Playbook, Scorecard, and policy briefs, ensuring that objectives are not only achieved but translated into actionable outcomes for replication and scaling. 19 TransformAr Deliverable 5.1 www.transformar.eu 2.0 Context TransformAr demonstrators are located in diverse European regions, each representing distinct climate risks, socio-economic conditions, and governance structures. The following subsections provide sitespecific context, including geographical description, key vulnerabilities and challenges, and the state-ofthe-art in adaptation at the beginning of the project. West Country, UK • Geographical description: The demonstrator is situated in Southwest England, focusing on the Camel and Axe catchments, both designated as Special Areas of Conservation (SAC) and including Sites of Special Scientific Interest (SSSI). • Vulnerability and impacts: Climate risks include diffuse agricultural pollution, flooding, and drought. Water quality is degraded by phosphate and nitrate runoff, amplified during heavy rainfall and reduced dilution during droughts. Sensitive habitats, fisheries, and human health are directly affected. • State-of-the-art adaptation Previous initiatives tested riparian buffers, wetlands, and nutrient neutrality schemes. However, integration across sectors (agriculture, water, planning) was limited. TransformAr builds on this by testing ecosystem service markets (phosphate credits, biodiversity net gain credits) to sustain long-term NBS adoption. Guadeloupe Archipelago, FR • Geographical description: An outermost EU region in the Caribbean, Guadeloupe consists of small islands with densely populated coastal zones. • Vulnerability and impacts Exposed to hurricanes, storm surges, coastal erosion, and sea-level rise. Tourism, fisheries, and urban infrastructure are concentrated in high-risk zones. Adaptive capacity is limited due to insularity and reliance on coastal economies. • State-of-the-art adaptation Previous work included ADEME-led economic assessments of coastal hazards and mangrove restoration pilots. TransformAr expands this with hybrid protection systems and enhanced governance. Oristano, IT • Geographical description: Located in western Sardinia, the Oristano Gulf includes wetlands, agricultural plains, and coastal ecosystems. • Vulnerability and impacts: 20 TransformAr Deliverable 5.1 www.transformar.eu Climate change drives sea level rise, heatwaves, prolonged droughts, and intense rainfall events that cause both inland and coastal flooding • State-of-the-art adaptation: Italy’s national strategies emphasize integrated water management, and Oristano had initiated a Coastal Contract framework before TransformAr . The project adds a Smart Gate system and NBS for adaptive water management. Galicia, ES • Geographical description: Galicia’s Atlantic coast is a global centre for clam and mussel aquaculture, with Ría de Arousa as the largest bay inlet in the southwest coast, and the most productive with 70% of the mussel rafts and 50% clam sales (Xunta de Galicia, 2021a)1. • Vulnerability and impacts: The sector is sensitive to temperature anomalies (mainly heat waves), storms, torrential rains (low salinity waves), and sedimentation changes, all intensified by climate change. • State-of-the-art adaptation: National and regional frameworks (PNACC, Galicia Strategy 2050) provide adaptation guidance. Local monitoring exists (Coastal Observatory), but aquaculture faced a shortage of predictive, real-time management tools. TransformAr introduced the Resilience Index, Mussel Raft Monitoring, and Intertidal Monitoring solutions. Lappeenranta, FI • Geographical description: A city in southeast Finland with mixed urban, industrial, and green areas. • Vulnerability and impacts: Facing heavier rainfall, stormwater flooding, and pipeline capacity limits, with risks to water quality and infrastructure. • State-of-the-art adaptation: National monitoring is coordinated by SYKE and the Flood Centre, with stormwater flood risk maps and hydraulic modelling already available. TransformAr builds on this with URB NBS biofilter systems, realtime monitoring of systems, citizen apps, and choice experiments. Egaleo, GR • Geographical description: A dense municipality in the Athens metropolitan region with limited green space. • Vulnerability and impacts: The city is highly exposed to urban heatwaves, flash floods, and extreme weather. Vulnerable groups face compounded risks. 21 TransformAr Deliverable 5.1 www.transformar.eu • State-of-the-art adaptation: Greece’s PNACC and RAAP provide the national framework, but municipal-level adaptation was limited. Egaleo’s TransformAr actions introduced a Smart Climate Station, Climate Innovation Hub, citizen apps, and AWAR awareness modules, establishing a first-of-its-kind local adaptation ecosystem. Across the demonstrators, baselines show high exposure and diverse risks, but also opportunities for innovation. TransformAr’s Region-Specific Portfolios (RSPs) respond to these contexts with tailored combinations of Nature-Based Solutions, digital monitoring, governance innovation, and participatory approaches. This contextual diversity provides a robust testbed for scaling transformational adaptation across Europe. 1 Xunta de Galicia. (2021, a). Enquisa sobre a poboación ocupada nos sectores da pesca e da acuicultura mariña en Galicia - OCUPESCA 2019, https://www.pescadegalicia.gal/Publicaciones/pdfs/Ocupesca_2019.pdf 2.1 Geographical Description This chapter provides a geographical overview of each TransformAr demonstrator site, highlighting the specific environmental, climatic, and socio-economic features that shape local vulnerabilities and opportunities for climate adaptation. Each site has been selected because its geographical characteristics directly relate to priority risks identified in the project, such as coastal flooding, saline intrusion, nutrient pollution, urban heat, oceanographic alterations (Temperatura, salinity, winds...), or stormwater management. The demonstrator regions represent a diverse set of European and outermost environments, ranging from northern urban centers and Mediterranean coastal lagoons to Atlantic aquaculture areas, rural catchments, and outermost island regions. This diversity ensures that the project tests solutions under a wide spectrum of climatic pressures and socio-ecological contexts. The use of these specific locations is justified by their strategic relevance for EU adaptation policy: • They provide representative testbeds for challenges faced across Europe (urban flooding, coastal resilience, agricultural adaptation, aquaculture sustainability). • They offer scaling and replication potential, as lessons learned in one region (e.g., nutrient neutrality in UK catchments, NBS integration into Finnish urban planning) can be transferred to other European contexts. • They reflect a balance between scientific monitoring capacity and stakeholder engagement, ensuring that adaptation solutions are grounded in both robust evidence and societal readiness. 2.1.1 Lappeenranta, Finland Lappeenranta (61-06°N, 28-19 °E) is a Finnish city with a population of 73000 covering an area of 1,724 km2 situated in the region of ‘South Karelia’ (aka. Etelä-Karjala) on the south-eastern frontier of Finland, 30 kilometers away from the Russian border (Figure 2.1). The city is located on the shores of Lake Saimaa, which is the biggest lake in Finland and the fourth biggest lake of Europe. The city’s main water intake is 22 TransformAr Deliverable 5.1 www.transformar.eu the lake water, which a decade ago was polluted by the proliferation of algae. Since, water conservation has been a major area of interest for the city. According to the latest national water quality classification (Finnish Environment Institute, 2019), the water quality of Western Saimaa is fair to intermediate. The objective is good ecological status. Figure 2.1 Lappeenranta’s boundaries within South Karelia in relation to the map of Finland The city center is located on the First Salpausselkä ridge, which is an ice-marginal formation laid down by the last Ice Age. Salpausselkä is mainly formed of sand and gravel and holds massive reserves of highquality groundwater. According to the Köppen climate classification, Finland has a continental subarctic/boreal climate and Lappeenranta has a southern-boreal climate. The city’s climate is influenced by Salpausselkä, the lake areas of Saimaa and Laatokka (in Russia) and the Gulf of Finland. Typical of the city’s climate are four distinct seasons, each season lasting approximately three months. In Lappeenranta, winter is longer than summer. Precipitation has an annual average of 614 millimeters of which 40-50 % usually falls as snow. The snow cover typically melts in April and May which can contribute to flooding. In this context it is important to underline that meltwaters are recognized to consist of large amounts of nutrients and heavy metals. These substances are mainly from prevention of icy roads and yards, and from vehicles, which are more polluting in winter due to corrosion caused by road salt. In addition, the rain or melting snow washes with its pollution from all other impermeable surfaces, such as roofs or construction areas. 2.1.2 Oristano, Italy The coastal area of Oristano (Sardinia, Italy) is a complex and high-density system of rivers, lagoons, and salt marshes. Most of the wetlands are shallow eutrophic water bodies (approximately 0.5-2 m depth), around 7,700 hectares of which (over 60% of Sardinia's wetlands) are protected by the Ramsar convention4 and the Natura 2000 network5. The Gulf of Oristano is characterized by the tight integration 23 TransformAr Deliverable 5.1 www.transformar.eu between the existing settlement structure and the environment characterized by the system of coastal wetlands. It is a low-density area, characterized by small concentrated urban zones, most of them located in the inland areas, and sprawl urbanization related to fishing cooperatives, agricultural and livestock farms and small touristic villages located along the coast (Satta, 2014). The population of the 11 municipalities located in the area, around 85 thousand in 2022, is decreasing as people are moving to the main cities of the island or to the north of the country mainly driven by more employment opportunities. The rivers and wetlands are amongst the most fish-rich inland areas of Sardinia, possibly in the entire Mediterranean, and they also hold significant cultural heritage value. In several ponds and lagoons, there are operating fishing cooperatives and some aquaculture production, often derived from traditional practices. Figure 2.2 General map of the Gulf of Oristano 24 TransformAr Deliverable 5.1 www.transformar.eu Figure 2.3 Zoom of the NBS area (Oristano) The southern wetlands of the Gulf are the Marceddì-San Giovanni lagoon compendium, which appears as a deep marine inlet artificially separated from the sea by a fishpond bridge and divided into two different wetlands: the Marceddì lagoon (900 ha), closer to the sea with brackish water, and the internal pond of San Giovanni (700 ha), characterized by freshwater inputs from the rivers Rio Mogoro, Rio Mannu, Rio Sitzerri, and from some artificial canals. The surrounding territory is dominated by the agricultural plain of Arborea on the north-east side, an expanse of regular fields bordered by the reclamation infrastructure (canals and roads), while to the west it is surrounded by the mountainous complex of Monte Arcuentu. The fishing activities in the MarceddìSan Giovanni lagoon are managed by the Consortium Coop. Riunite della pesca di Marceddì. The concession granted by the Autonomous Region of Sardinia under the act rep. 1082/98 dated 07/07/1998, is currently renewed. Covering an area of 2610 ha, the fishing operations involve around 140 operators. Hydraulic interventions carried out in recent decades have significantly altered the original structure of the entire wetland system. These modifications have disrupted the natural water exchange conditions between marine and freshwater environments, leading to changes in the ecological conditions of the area due to sediment discharge into the water and impacting on the ongoing fishing activities. 25 TransformAr Deliverable 5.1 www.transformar.eu Figure 2.4 Sardinia wetlands 32 TransformAr Deliverable 5.1 www.transformar.eu 2.2.2 Oristano, Italy Sardinia is a located in the center of the Mediterranean region, and according to EEA (2017), is likely to be impacted by several climate forces that include, among others: sea level rise, an increase of maximum temperatures and heatwaves especially during summer; long drought periods interrupted by heavy and extreme rainfall events leading to severe floods, and extreme storm events causing coastal flooding. Droughts during summer are often associated with greater water demand from different competing sectors, leading to inter-sectorial conflicts and unsustainable overexploitation of water resources. Consequences of severe droughts have been particularly relevant with risks for limited freshwater supplies and the incurrence of large fires affecting not only forest ecosystems, but also rural/urban interfaces and the increase of coastal erosion risk and desertification. Flooding is a particularly crucial risk not only endangering human life and infrastructures but also causing soil erosion and the transport of contaminants from industrial, mining and agriculture fields to natural and especially aquatic ecosystems. Changes in climatic conditions can alter agricultural productivity, in terms of quantity and quality of agricultural products, water supply and the hydrological regime, with implications for water resources availability. Increases in irrigation requirements are expected for the main crops cultivated in Sardinia because of climate change. Moreover, water demand increases could derive from socio-economic changes (tourism, agri-food, and textile sectors, energy plants, agricultural settlements), urbanization and lifestyle changes, causing a serious concern. Focusing on the Gulf of Oristano, the coastal area faces a significant threat from inland flooding, especially during extreme rainfall events combined with marine storms. This endangers the population, infrastructure, and the local economic activities. The Gulf is characterized by its low-lying areas, which make it highly vulnerable to the impacts of rising sea levels, coastal and inland flooding. Despite the high susceptibility of low-lying coastal zones to these hazards, the overall vulnerability of the region is mitigated by the robust ecosystem health and efficient drainage density, enhancing its resilience. Given the influence of climate change on the Gulf's water system and current patterns, an increase in water temperature in all seasons, a decline of winter salinity, pH levels dropping (acidification), and an increase in the presence of non-native species, among other effects. Looking ahead to the medium to long term (50 to 100 years), there is a likelihood of certain coastal lagoons disappearing due to rising sea 33 TransformAr Deliverable 5.1 www.transformar.eu levels. These changes will have adverse effects on the local flora and fauna and the distribution of fishing stocks, particularly concerning commercially relevant aquatic organisms. The full extent of these impacts on local socio-economic conditions remains difficult to predict. The traditional aquaculture activities are and will be significantly impacted by extreme rainfall events that disrupt the balance between saltwater and freshwater in the lagoons, causing sediment deposits that impede water circulation. The alteration of parameters such as salinity, Ph, dissolved oxygen, turbidity is linked to a change in the state of the ecosystem and a decrease in fish stocks. The alteration of the water quality can increase the presence and proliferation of invasive species which pose a threat to native species and reduce ecosystem services. The solutions implemented within the TransformAr project focus on three Key Community Systems: Water Systems, Nature Conservation, and fisheries, which are vulnerable to climate impacts and are strictly related and positively influenced by COAST and SG. The conditions of biodiversity and habitats, already affected by intensive land use activities (agriculture, animal husbandry, industry, and mining), along with the hydraulic efficiency of the wetlands and river dynamics, are severely impacted by climate hazards such as coastal and inland flooding, coastal erosion, wetland and groundwater salinization. Coastal receptors, including beaches, river mouths, wetlands, terrestrial biological systems, and protected areas, are all affected by habitat degradation, biodiversity loss, and the emergence of alien and invasive species. Moreover, alterations in water quality influence fish stocks, leading to a reduction in fishermen's income. Extended drought periods could lead to increased water demand from wells or, where possible, a request for public water, which would then increase costs for farmers. Based on this information the Risk components (hazards, exposure, vulnerability) were assessed in the pathways workshop. What have been highlighted by the participants is that the main drivers related to climate change are the increase of temperatures and the changes in precipitation patterns, appearing as a general reduction of the rainy season and a concentration of heavy precipitation in short periods. These drivers are generating impacts such as flooding, drought and storm phenomena that are exposing the communities, territories, and local economic activities to a risk of important economic and environmental losses. 34 TransformAr Deliverable 5.1 www.transformar.eu Figure 2.11 Risk chain of Oristano demo II 2.2.3 West Country Region, United Kingdom The Westcountry is likely to witness drier summers and an increase in the frequency of extreme weather events, such as droughts (Southwest Water 2021). High energy rainfall events can also cause mobilization of sediments and nutrients leading to water quality issues. The Meteorological Office highlights detailed impacts for the Southwest (Met Office UKCP) including: • More frequent intense rainfall and wind-driven rain causing river and surface water flooding. • Warmer wetter winters cause problems with crop management. • Hotter drier summers cause problems for water quality and supply. • Increase in extreme weather and disruptive events such as flooding, droughts, landslides or heatwaves interrupting or limiting access to vital services and impacting on people’s physical and mental health. • The rise of sea level rise affecting the viability of coastal communities and coastal infrastructure through flooding and erosion. These projections were supported by the modelling carried out by PIK for the Westcountry stakeholder workshops in spring 2022 for Work package 2. (Figure 2.12-13) 35 TransformAr Deliverable 5.1 www.transformar.eu Figure 2.12 Summary of climate projections for the Westcountry (Source: PIK presentation for Workshop 2 WP3) using ISIMIP 3b DATA Figure 2.12 Modelled precipitation change shows drier summers and wetted winters 36 TransformAr Deliverable 5.1 www.transformar.eu Figure 2.13 Modelling shows reduced river flows in summer and autumn Figure 2.14 Modelling shows increased runoff during late summer to winter, but with high uncertainty due to coarse hydrological models 37 TransformAr Deliverable 5.1 www.transformar.eu A review of data and updated scenario modelling by PIK was able to produce projections in discharge for rivers in the Westcountry Region, including the River Exe. What this has shown is that in all scenarios annual discharge increases. In the warmer scenarios more precipitation may be expected, however due to increased levels of evapotranspiration, this moderates the amount of water available and therefore discharge. Figure 2.15 Model of expected changes to river discharge over time. PIK also undertook soil water modelling based on future scenario predictions. In general soils are likely to be wetter in the winter and drier in the summer in the 2071-2100 scenario, when observing the blue scattered line. This will likely lead to more surface run-off over winter and increase groundwater recharge. The green line below represents the leaf area index and a typical winter wheat scenario. With a drop following harvest, followed by some stubble plants increasing the leaf area index, followed by the planting of the winter crop. In the later scenario, harvest is likely to be earlier and the 2nd crop able to grow quicker prior to winter, therefore there will be more green crop cover and a higher leaf area index. Figure 2.16 Modelled scenarios for leaf area index and soil water flows 38 TransformAr Deliverable 5.1 www.transformar.eu There were some key insights provided by PIK following the updated modelling and various scenarios based on catchments within the region, these include the following: Hotter summers and warmer winters > This will include an extension of the vegetation period and increasing water vegetation demand (Evapotranspiration) Wetter winters and drier summers > Drier soils in the summer and an increase in water stress. However, in winter there is an increase in groundwater recharge leading to more water in rivers including the summer/early summer. More intense precipitation > Increases in flood events and intensity, with more surface run-off leading to more erosion and phosphorus losses. The strategic use of riparian zones and wetlands can help to counteract this. Longer vegetation period > There is the potential for two crops within the season, but there will be wetter soils conditions in the spring and Autumn that may cause issues with cultivation. Increases in temperature and the vegetation period may foster biotic risks (pest and disease) More vegetation in winter > This may lead to more nutrient (nitrate) uptake over winter and reduce losses through percolation. Therefore, covering crops will be of significant importance to control nutrient loss and bind soils. Higher water temperatures in rivers > Which will stimulate algae growth and blooming. Cropping and fertilization schemes will have to be adopted to consider the change in the growing season. 2.2.4 Galicia, Spain According to the European Environment Agency (EEA, 2017a), the region of Galicia is at risk of more frequent and intense winter storms that will lead to an increasing risk of coastal infrastructure damage and heavy precipitation events, which in conjunction with sea level rise, could lead to more intense riverine and coastal floods and low salinity waves. Recent research have shown how variations in regional oceanographic conditions (e.g. increase in temperature, heatwaves, changes in upwelling-favourable coastal winds and precipitation regimes (low salinity waves,)), the alteration of nutrient fertilisation patterns (Sousa et al., 2020, FuentesSantos et al., 2021), presence of invasive species or new pathogens, as well as potential increase in harmful algal bloom (HAB) episodes (Álvarez-Salgado et al.; 2008, Pérez et al., 2010), can pose threats to which the aquaculture sector needs to adapt (Cramp et al., 2021). Galicia faced a series of severe weather events in the past, including storms, wildfire and heatwaves. For example, in 2003 the temperatures in Galicia exceeded the 95th percentile of the maximum temperature, which impacted the mortality in the region; In 2002-2003 and 2013-2014, storm events significantly eroded beaches along the cost causing damage to infrastructures such as ports, ships, mussel rafts, and generated the inability to work at sea (small-scale fisheries, mussel aquaculture, shellfish harvesting activities). The region also witnessed one of its worst droughts in the last hundred years in 2016-2017 (Khamis et al., 2022), with an overall decrease in rainfall by about 500 mm, magnifying a series of wildfires that destroyed a total of 43,000 hectares, with consequences in soil erosion and sediment contribution to the Rías through the river discharge. During the years 2022 and 2023, nineteen bilateral meetings and two workshops were held with key actors to discuss the adaptation of the Galician clam and mussel sector to climate change (CC), as part of 39 TransformAr Deliverable 5.1 www.transformar.eu the TransformAr activity “Development of a shared vision for a future resilient to climate change”. These sessions substantiated that the local stakeholders' perception of climate change hazards aligns with scientific observations and recent observed events. In order of importance, they selected as main hazards fluctuations in salinity and river discharge, followed by the consistent rise in sea surface temperatures, changes in wind patterns and upwelling, and an escalation in the frequency and intensity of extreme winter events (e.g., waves and storms). Participants highlighted habitat alteration, the introduction of invasive species or alterations in existing populations, and erosion or modification of sedimentary banks as the principal risks directly associated with CC. The impact of the above-mentioned risks could lead to higher pressure on the supply chain and the work culture (alterations in concessions, employment, social stress…). Adequate and economically viable adaptation measures are therefore needed to face these diverse challenges and increase the resilience of the sector. To face this demanding task, it is crucial to obtain longer data series and their analysis to support decision-making, propose tailor-made strategies and apply specific adaptation actions suitable to the sector. Figure 2.17 summarizes the hazards, exposure, vulnerability and risks to which the territory/sector is exposed to considering the results of the workshops. Figure 2.17 Hazards, exposure, vulnerability and risks to which the Galician mussel-clam sector is exposed Considering the exposure and the vulnerability - Concentration of population, beaches and infrastructures - Sector of great economic importance: Mussel: 40% of the European production, more than 3.000 mussel rafts and more than 5.000 employments. Shellfish harvesting: approximately 600 harvesting - The sensitivity of the area and the community to environmental changes. - The characteristics of the community and working conditions. - Spaces of growing tourist attraction (pollution) and development of other uses (energy). - Lack of adaptation plans to CC or alternatives to Hazards 40 TransformAr Deliverable 5.1 www.transformar.eu 2.2.5 City of Egaleo, Greece The state of the art in Egaleo within TransformAR is the creation of a holistic ecosystem of digital and social solutions that combine real-time climate data with citizen engagement and policy support. The municipality is deploying a network of smart climate stations, demand analyses for social services, and a citizen app to crowdsource awareness and disseminate alerts, while also fostering behavioral change through school-based modules and a climate innovation hub. What makes this demonstrator innovative is the integration of diverse datasets (environmental, social, behavioral) into actionable, evidenceinformed policies, coupled with direct citizen participation and awareness-raising. By merging technical monitoring, social inclusion, and entrepreneurial innovation, Egaleo pioneers a replicable model of urban climate resilience that addresses health, infrastructure, and planning vulnerabilities in a densely populated Mediterranean city. 2.2.6 Guadeloupe Archipelago, France The Guadeloupe Archipelago faces multiple climate-related vulnerabilities characteristic of small island territories in the Caribbean. Its exposure to extreme events, combined with socio-economic fragility, makes climate adaptation an urgent priority. Key vulnerabilities and risks: The Guadeloupe Archipelago faces multiple and interconnected climate risks. Regular exposure to Category 4–5 hurricanes and tropical storms cause extensive damage to housing, infrastructure, energy networks, and freshwater systems. Heavy rainfall events trigger flash floods and landslides, particularly on the volcanic island of Basse-Terre, where slope instability poses persistent challenges. Sea-level rise The most noted risks generated by CC that are considered to impact the most are: • Erosion or modification of sedimentary banks / Floods • Habitat alteration: growth, survival, seasonality and reproductive cycle, red tides, massive mortality, Food availability (it could increase and become an opportunity ) • Invasive species or modification of existing populations (alteration in the species of clams and predators) • Location and availability of mussel spat and other bivalve recruitment • Culture operations (mussel detachment) and damage to productive and coastal structures • Rise of Algae (wrack) / Seaweed washed ashore • Substrate changes Socio-economic and environmental effects The impact of the above-mentioned risk could lead to modifications in production (both in quality and quantity) and pressure on the supply chain and work culture (alterations in concessions, employment, social stress…) 41 TransformAr Deliverable 5.1 www.transformar.eu and coastal erosion further threaten low-lying coastal zones and beaches, endangering livelihoods dependent on tourism and fisheries. Despite the archipelago’s overall abundant rainfall, uneven spatial distribution, saline intrusion, and pollution lead to local water shortages, especially on Grande-Terre. Rising average temperatures and increasingly frequent heatwaves intensify heat stress and associated health risks, disproportionately affecting vulnerable groups such as elderly populations. Socio-economic challenges: The Guadeloupe Archipelago faces multiple and interconnected climate risks. Regular exposure to Category 4–5 hurricanes and tropical storms cause extensive damage to housing, infrastructure, energy networks, and freshwater systems. Heavy rainfall events trigger flash floods and landslides, particularly on the volcanic island of Basse-Terre, where slope instability poses persistent challenges. Sea-level rise and coastal erosion further threaten low-lying coastal zones and beaches, endangering livelihoods dependent on tourism and fisheries. Despite the archipelago’s overall abundant rainfall, uneven spatial distribution, saline intrusion, and pollution lead to local water shortages, especially on Grande-Terre. Rising average temperatures and increasingly frequent heatwaves intensify heat stress and associated health risks, disproportionately affecting vulnerable groups such as elderly populations. High dependence on coastal tourism, fisheries, and agriculture exposes the economy to climate shocks, while social inequality increases vulnerability, as poorer households are less able to invest in protective measures or access digital early-warning tools. In addition, limited capacity for infrastructure investment compared to mainland France constrains adaptation responses, leaving certain communities at greater risk. Main challenges for adaptation in Guadeloupe: Geographic isolation and reliance on imported resources complicate rapid response and recovery. Fragmented governance between local municipalities, regional authorities, and national institutions slows coordinated action. Community engagement barriers, including unequal access to digital tools, require innovative and inclusive approaches to citizen participation. 2.3 State-of-the-Art on Adaptation in the Demonstrator This chapter reviews the current adaptation practices already in place at each demonstrator site, providing a benchmark against which the added value of TransformAr solutions can be measured. Existing measures,such as conventional flood defenses, wastewater treatment, agricultural water management, or citizen engagement initiatives, form the local baseline of climate adaptation. The assessment highlights that, while these practices have delivered important short-term benefits, they often remain sectoral, fragmented, or incremental. TransformAr builds upon this foundation by introducing integrated innovation packages, combining Nature-Based Solutions (NBS), digital monitoring tools, governance innovations, and behavioral approaches. The innovation actions carried out within TransformAr demonstrate how adaptation can be scaled up and tailored to diverse socio-ecological contexts. By embedding solutions into urban planning, catchment management, aquaculture practices, and coastal protection, the project increases their replication 48 TransformAr Deliverable 5.1 www.transformar.eu Financial, Economic and Insurance Schemes CEI – A data-driven tool to uncover citizen preferences for green and grey stormwater solutions - empowering cities to drive private investment and climate adaptation through smarter, targeted flood risk policies. o KPI: Number of respondents Note: KPI‘s for Lappeenranta include specific metrics for each solution (URB, SWMM, CAF, CEI), aligned with climate adaptation goals. These include indicators such as reduction in stormwater runoff volume, usage rate of the CAF app, behavioral shifts based on CEI outcomes, and green infrastructure effectiveness. KPIs are measured against baseline conditions established in 2023 and will be assessed periodically through 2025. All indicators were selected for their contribution to measuring combined ecological and community resilience. The originally proposed performance indicator—comparison of flood-related outcomes with and without predictive analytics—was not applicable due to the absence of an actual flood event during the NBS installation and operation phase. Consequently, real-world data for direct causal comparison could not be captured within the timeframe of the project. Impacts to water quality are defined in solutions URB and SWMM. Figure 3.1 visualizes main methods. The fundamental idea is to measure real-time data with limited number sensors. This set of sensors does not include direct measurements for nutrients or pH due to the durability and maintenance costs of the sensors. Instead, these water quality parameters are measured in the laboratory from influent and groundwater samples. Models taking account the flow rate (expected dilution), turbidity and conductivity will be applied to estimate the emission load (kg/a) or improvement (%). Estimation will include comparison of runoff water captured to groundwater filtered through the biofilter field and recipient lake water. The goal is to keep the models as simple as possible. For most of the described analyses different regression models can be applied. The laboratory analyzed samples provide a way to calibrate and validate the models. Figure 3.1 Overview of main methods applied in describing solutions impacts to water quality. 49 TransformAr Deliverable 5.1 www.transformar.eu 3.2 West Country Region, United Kingdom 3.2.1 Nature Based Solutions WRT developed several NBS across the Camel and Axe catchments. The ambition was to test a range of solutions across different parameters. The NBS primarily deals with diffuse agricultural pollution prior to the point of entry to the main water course. The project looks specifically at funding streams to pay for both the initial (capital) cost of delivery and the long-term maintenance and compensation to the landowner for loss of agricultural productive land (revenue). The creation of nature-based on farm Sustainable Drainage Systems (SuDS) is relatively inexpensive, but landowners are unwilling to sign up in the numbers needed to have a significant effect without fair payment. Nutrient Neutrality provides an opportunity to leverage investment. In total, ten potential intervention sites were identified, and five sites were selected for NBS delivery (Table 3.1), hosting a range of solutions to provide climate resilience including: • nutrient mitigation • drought resilience • flood resilience • biodiversity improvement from developers to pay for strategic nutrient mitigation on farms. Solutions included farm ponds, floodplain wetland habitat restoration, sediment traps, and filtration or buffer strips. These types of solutions were also highlighted by stakeholders as part of the adaptation pathways co-creation workshops. These solutions can be created individually or be combined and used in treatment trains according to the specific site conditions and pollution load. New and/or innovative ways to identify suitable strategic sites were tested, using a combination of GIS analysis, site surveys and chemical analysis of water and soils. The overriding deciding factor for site selection was aways landowner willingness to participate. Another aspect tested by WRT was the minimum level of maintenance required to keep the interventions functioning as designed. The landowners involved often do not have the time or resources to carry out regular or complex maintenance activities. Table 3.1 Nature based solutions delivered under TransformAr in the Westcountry Region, UK Catchment Site name NGR Intervention type Delivery date Monitoring Axe Snowdon Hill Farm ST 30143 09188 New track and wetland buffer area 7/11/2416/11/24 • Drone footage • Sediment P analysis • Volume calculations • Downstream water quality analysis • VESS Assessment • Soil nutrient analysis Sediment traps Creation of an ephemeral wetland 50 TransformAr Deliverable 5.1 www.transformar.eu Chubbs Farm ST 30960 02689 Leaky dam cascade 19/11/2420/11/24 • Drone footage • Timelapse photography • Biodiversity surveys • Upstream and downstream water quality analysis Wetland enhancement Camel Trethick Farm SX 04319 72053 Cross track drains Summer 2023 • Drone footage • Biodiversity surveys • Sediment P analysis • Volume calculations • Downstream water quality analysis • VESS Assessment • Soil nutrient analysis • Electric fishing Sediment traps Scrapes Penvose Campsite SX 05164 78006 Leaky dam cascade 04/09/202306/09/2023 • Drone footage • Biodiversity surveys • Upstream and downstream water quality analysis • Electric fishing Continuous water quality monitoring (EC and level) Pond enhancement Scrape cascade Tree plantation and coppicing 51 TransformAr Deliverable 5.1 www.transformar.eu Worthyvale Farm SX 10972 85977 Barrier removal 30/08/2023 • Electric fishing Sediment removal Pond reinstatement Slaughterbridge Farm SX 10890 85370 Interception of road runoff into sediment trap and swale Not delivered within project timeframe • Drone footage • Biodiversity surveys • Upstream and downstream water quality analysis • Electric fishing Cross slope hedgerow planting Riparian buffer strip Online scrape creation Snowdon Farm At Snowdon Hill Farm, inundation of the lower section of an arable field prompted the farmer to move it to a more stable and sustainable grassland. A new track was created to reduce compaction on part of the field (Figure 3.2A) and an area of 1.63ha (4.0ac) was taken out of primary production for the interventions. A series of five sediment traps and scrapes/swales were installed (Figure 3.2B) to slow sediment and nutrient-rich run-off from the adjacent steeper slopes and create an ephemeral wetland area. The monitoring at Snowdon was limited to out-of-water methodologies due to the ephemeral nature of the problem run-off. Figure 3.2 A) Plan view of the planned series of sediment traps/scrapes and track installed at Snowdon Farm, with flow direction marked. B) the scrapes after installation. Chubbs Farm 52 TransformAr Deliverable 5.1 www.transformar.eu At Chubbs, two streams meet and flow from neighboring farms through a permanent pasture and then woodland. A cascade of leaky dams was created within the woodland to slow flows and allow sediment to deposit behind the structures (Figure 3.3). There were plans for wetland creation within the fields above the woodland, but landowner illness prevented this from materializing. Figure 3.3 A) The location of interventions at Chubbs Farm, B, C and D) Photographs of leaky dams #4, #1, #2 & #3, respectively. Trethick Farm Trethick is a dairy farm in the Camel catchment. Land-management here includes temporary and permanent grassland as well as arable rotation. A small field (0.46ha) was taken out of intensive management and a total of seven scrapes were dug to slow flows of runoff and capture sediment from the track and surrounding fields (Figure 3.4). Cross track drains were also planned but not delivered. Figure 3.4 A) Interventions installed at Trethick Farm, B) An oblique view of the same field shown in (A). 53 TransformAr Deliverable 5.1 www.transformar.eu Penvose Farm Campsite Penvose sits on the River Allen, a tributary of the River Camel. Five leaky dams were installed in the tributary in an attempt to slow flows, trap sediment and divert river water into the surrounding wetland habitat during peak floods. Three scrapes and a pond were also dug, designed to hold water from springs and overland flow, decommissioning a drainage ditch which ran parallel to the river, these flows then rejoin the river at the edge of the field. Trees were also planted around the pond and alongside the river for habitat enhancement, nutrient uptake and carbon storage (Figure 3.5). Figure 3.5 A) The NBS interventions delivered at Penvose, B) the pond C) a series of interconnected scrapes and swales Worthyvale 54 TransformAr Deliverable 5.1 www.transformar.eu This estate is located in the Upper Camel system where the river first develops from a network of small tributaries. There were two disused inline trout ponds, utilizing river water via a weir-controlled inlet and a downstream sluice-controlled outlet. The ponds were heavily sedimented with high nutrient concentrations due to addition of fish feed and surface water run off over their lifetime. Initially there was an idea to maintain the ponds as a treatment system for river water but eventually they were disconnected by removal of the weirs and blockage of the outlets (Figure 3.6 A-D). Sediment was removed to increase the capacity of the features as attenuation ponds. The sediment was spread over flat silage fields on top of the hill to reduce the likelihood of nutrients returning to the river. During the winter the ponds occasionally overflow across the grassed buffer towards the river, which reduces the risk of contamination via this route. Removal of the weirs has improved the diversity of river habitat and increased fish passage above this location. Figure 3.6 Interventions at Worthyvale Farm A) Works plan, B) Blocked inlet and weir removed, C) Pond A before, D) Pond A after intervention. 55 TransformAr Deliverable 5.1 www.transformar.eu Slaughterbridge Slaughterbridge interventions (at SX 10890 85370) were designed but not delivered within the timeframe of the TransformAr project. Planned interventions included interception of piped road runoff into a sediment trap and swale, cross slope hedgerow planting, a large (25m) riparian buffer strip, and online scrape creation within the buffer. The current plan is for this site to be delivered through Nutrient Neutrality and therefore be the first land change based nutrient credit in the Camel. 3.2.2 Financial, Economic and Insurance Schemes Green Bonds to support long-term financing of nature-based infrastructure. Due to the lack of direct funding for climate change adaptation, WRT tested the viability of using new nutrient credit schemes to leverage greater funding for climate change adaptation measures. Naturebased solutions such as ponds, leaky dams, swales and bunds help to both mitigate nutrient pollution and support climate change adaptation, meaning finance available to tackle nutrient pollution issues also support adaptation efforts. Nutrient credit schemes were introduced in the UK to tackle water quality issues. Based on the Dutch Nitrogen Case, which ruled that nutrient loading into internationally important sites (SPA’s, SAC’s and Ramsar Sites) must be limited, this UK legislation requires developers to offset excess nutrients, so that new dwellings do not add additional burden to sewage systems and wastewater treatment plants. Whilst in the Netherlands this led to the development of nitrogen permits, in the UK planning permission for property has been suspended in SSSIs and SAC’s until developers can obtain phosphate credit purchase agreements, proving they are offsetting the additional projected nutrient load expected from their housing project(s). To fulfil the demands of this new legislation, nutrient credits & pilot marketplaces began appearing in affected areas across the UK (Natural England, 2023). Emergent markets involve varying degrees of public and private sector finance. Nutrient credit markets have been constructed in a wide variety of ways. For example, in the Solent in Hampshire, nutrient credits are entered into a bidding process through an online marketplace, whereby private landholders and buyers can interact through an online auction. Public-sector funding was used to establish the virtual platform; however, trading is managed directly between private partners, meaning private finance is used to pay for project development. The auction mechanism means the cost/unit of phosphate is contingent on demand (https://www.solentnutrientmarket.org.uk/). 56 TransformAr Deliverable 5.1 www.transformar.eu Figure 3.7 model of the Solent Nutrient Catchment Market, demonstrating how nutrient credit schemes function in other parts of the UK. 3.2.3 Nutrient Credit Schemes in Devon and Cornwall In the Westcountry region, although several natural capital marketplaces have emerged to facilitate the trade of ecosystem services (LINC Cornwall, North Devon Biosphere), an online marketplace explicitly designed to support the trade of nutrient credits is not currently available. Instead, local authorities have worked with NGO’s such as the Westcountry Rivers Trust and the Devon Wildlife Trust to engage landowners in credit development projects. In the Camel and Axe catchments, WRT worked to find strategic opportunities for the development of NBS that would both mitigate nutrient pollution and support climate change adaptation by slowing flows and re-naturalizing waterways. As WRT have a strong track-record of landowner engagement, we operate as a ‘broker’ between credit purchasers and suppliers, ensuring excellent environmental outcomes. 3.2.4 Blended Finance: Camel Catchment In Cornwall the local authority, Cornwall Council, volunteered to invest the initial capital for nature-based solutions, paying landowners an up-front sum to develop NBS for nutrient mitigation (& climate adaptation) on their land, with the stipulation that the council would own the resultant credits. The local authority may then sell the credits to developers to recover their costs. This blended finance model has several key advantages but also presents several key challenges when assessing the bankability or financial sustainability of climate adaptation. Benefits: • Use of public funding to develop credit projects accelerates outcomes, as risk to private developers is removed. • Resultant ecosystem services do not need to represent high revenue/rate of return, as local authorities do not need to generate profit. • Planning authorities within local governments are well-placed to sell resultant credits to developers, as they have a pre-established professional relationship • Prevents stagnation at the ‘seed funding’ or project development phase, often encountered when attempting to attract private investment for nature-based solutions. Challenges: 57 TransformAr Deliverable 5.1 www.transformar.eu • Lack of consistent value/unit of phosphate makes valuation of NBS difficult, delaying uptake. Landowners need a consistent value to make informed decisions against other land use options. • Lack of clarity around future liability for ecosystem service assets, as well as potential litigation risks associated with delivery failure. Results in delay and uncertainty for landowners. • Focus on nutrient credits in order to meet statutory demand results in failure to capitalize potential additional revenue streams associated with the NBS created. Does not help to facilitate future private-sector investment. A further appraisal of the benefits and challenges to blended finance will be included in the discussion of the bankability interview process. 3.3 Guadeloupe Archipelago, France Behavioural Change and Awareness Raising • NUDG – The innovation combines smart technology with behavioral nudging to promote water conservation among hotel guests. In participating hotels in Guadeloupe, digital shower sensors are installed to measure shower duration and water usage. These sensors collect real-time data, which is uploaded to a central platform (Aquardio Hub) for analysis. Alongside the sensors, visual nudging materials – such as stickers, flyers, and feedback forms – are placed in bathrooms to raise awareness and subtly encourage guests to reduce their shower time. The experiment is conducted in phases: before and after introducing the nudges, to compare behavior changes. Feedback is also gathered from hotel guests and managers through surveys and interviews, providing both quantitative and qualitative insights. Local Adaptation Fund The Local Adaptation Fund (AF) is a blended finance tool boosting local climate adaptation in the agriculture and tourism sectors in Guadeloupe. It supports vulnerable groups and offers a replicable model for other climate change-exposed territories. 3.4 Galicia Region, Spain The Galician demonstrator focuses on adapting shellfish aquaculture and harvesting to the challenges posed by climate change. The activity has been selected due to its significant role in the regional economy and its susceptibility to climate change, as outlined in the Galician Climate Change Strategy 2050 and in the Integrated Regional Plan for Energy and Climate (2019-2023). Mussel aquaculture, accounting for approximately 40% of the European aquaculture production, is a vital sector employing nearly 4,000 individuals in the region. Similarly, clam culture farms and shellfish harvesting along coastal land concessions and intertidal sandbanks, directly employ around 4,300 people, with approximately 90% being self-employed women. Key climate-related threats affecting shellfish aquaculture include the potential intensification of extreme weather events, unpredictable fluctuations in mussel seed availability, and the expected increase in harmful algal blooms and acidification. Also, changes in coastal oceanographic and hydrological factors will alter the sedimentary composition of shellfish banks, impacting shellfish productivity and mortality. In extreme cases, this could result in the loss of these habitats. Within TransformAr, three solutions have been tested to comprehensively grasp the nature of these changes and bolster adaptation to climate change: 64 TransformAr Deliverable 5.1 www.transformar.eu 15% during extreme events and improved operational efficiency by around ~8%. The upfront costs of digital tools are modest compared to avoided losses, making them highly bankable. Long-term sustainability could be reinforced by cooperative financing among aquaculture associations and alignment with national adaptation strategies. Lappeenranta, FI — Urban NBS and Stormwater Management The Lappeenranta demonstrator implemented Stormwater Monitoring, Biofiltration Areas for Urban Runoff, and Awareness Raising Solutions. The Koulukatu Street pilot (245 m²) had an investment cost of €93,100 (prices 2022), including €10,000 for supporting grey infrastructure. Annual maintenance was €2,076/year, slightly lower than the grey baseline (€2,118/year). CBA and willingness-to-pay (WTP) experiments estimated annual savings of ~€0.9M if scaled across the city, with a return on investment (ROI) < 10 years. Bankability can be secured through municipal green bonds aligned with EU Taxonomy, supported by partnerships with insurance companies to establish risk reduction funds. Egaleo, GR — Digital and Social Innovation Solutions Egaleo introduced Smart Weather Station (SWS), Climate Innovation Hub (CIH), Demand for Social Infrastructures, and AWAR modules. CBA and choice experiments showed that investments of €50–70k generated avoided damages exceeding €0.5M/year during extreme weather events. Maintenance costs are minimal, since most measures are digital and community driven. Bankability is reinforced by integration into municipal budgets and co-financing with private IT and innovation partners. Beyond financial viability, the solutions deliver strong social co-benefits such as citizen awareness, active participation, and improved governance capacity. Cross-Cutting Insights • NBS investments (West Country, Oristano, Lappeenranta) consistently show positive cost– benefit ratios and long-term savings, outperforming grey alternatives. • Digital solutions (Galicia, Egaleo) deliver rapid returns with modest costs, mainly through avoided damage and operational efficiencies. • Hybrid coastal approaches (Guadeloupe) highlight the value of integrating ecosystem services into financial models. • Financing pathways differ by context: ecosystem service markets (UK), insurance and blue economy funds (FR), CAP eco-schemes (IT), cooperative sector funding (ES), municipal green bonds (FI), and IT partnerships (GR). • Across all demonstrators, TransformAr confirms that transformational adaptation solutions are economically viable, investable, and socially beneficial, directly addressing Specific Objective 5 (SO5) of the Grant Agreement. 65 TransformAr Deliverable 5.1 www.transformar.eu 4.0 Expected Impacts & Mitigations 4.1 Lappeenranta, Finland 4.1.1 Overview of the Expected Impacts The expected impacts of solutions demonstrated are described in Table 3. All four solutions can be expected to have an impact on flood vulnerability, and social acceptance or acceptance in city planning. CAF and CEI can have long-term effects on water quality. However, in CAF and CEI the impact is not measured with indicators due to the nature of the impact. URB with monitoring system, and the influence together with the groundwater is analyzed. This empirical information can be utilized together with existing water quality of recipient lake and wetlands to evaluate the impacts. Economic assessment and impacts in this frame will be done via empirical cost estimates and comparing to alternative solutions typically applied in Lappeenranta. Table 4.1 The expected impacts of solutions demonstrated Expected impact URB SWMM CAF CEI Flood Vulnerability Gathering runoff (stormwater) from streets and leading them to biofiltration will help free capacity from drainage and mitigate flood and drought risks. x Monitoring volume flows and surface levels provides novel information and warnings. x Distributed stormwater management mitigate flood risks x x x Water Quality Realtime monitoring of URB together with laboratory measurements provides information on runoff and groundwater quality. x Biofiltering field utilizes nutrients and filters urban emissions x Acceptance City planning gain novel information to support decision making and planning x x x x Citizen's awareness of environmental effects of runoff and choices for mitigating the risks and impacts increase x x x Economic Costs Solution costs are compared to traditional alternatives and reflected to benefits expected. x x x x 66 TransformAr Deliverable 5.1 www.transformar.eu 4.1.2 Selected Indicators The indicators for URB and SWMM are mostly calculated from the monitored water qualities and measured flows, with calibration from the laboratory analyses. In addition, prior information and projected information, for example, on rain events, are utilized to construct the indicators. The CAF solution is mainly aimed at providing information to citizens of the area, so most of the indicators will be determined from the user engagement. Table 4.2 Selected Indicators Solution Expected impact Key Performance Indicators (KPIs) Source/Measurement Approach Nature-Based Stormwater Solution Reduction in flooding events. Mitigating drought risk. Reduction in peak runoff levels (%) Real-time monitoring via sensors Improved water quality, increased knowledge of main water quality parameters. Water quality improvements (pollutant reduction %) Real-time monitoring via sensors and water sampling campaigns Mitigating flood risk. Increase in stormwater retention capacity: 2670 m3 stormwater entering NBS/year Real-time monitoring via sensors Stormwater Monitoring Increased knowledge and awareness of key stakeholders. City planning gain novel information to support decision making and planning. Number of monitoring sensors installed: water quality 3 sensors flow rate 6 sensors Sensor deployment tracking Improved knowledge of flood risk. Real-time data accuracy and reliability (%) Data verification & models Citizen App Citizen's engagement Number of citizen reports submitted: 52 App analytics, participants in events (Open Days, Citizen workshop) Citizen's awareness of environmental effects of runoff and choices for mitigating the risks and impacts increase User engagement metrics Logins: 185 Active users: 86 Feedback provided: 14 67 TransformAr Deliverable 5.1 www.transformar.eu Solution Expected impact Key Performance Indicators (KPIs) Source/Measurement Approach Participants in events, workshops, webinars, training sessions: 920 Increased knowledge and awareness of key stakeholders at different levels (public and private sectors, economic categories, citizens, etc.) Improvement in community awareness on flood risk (%) Choice Experiment Norway: 1000 respondents Finland: 1013 respondents Survey analysis and economic modeling This research provides valuable insights into how to encourage private investments in stormwater management (SWM) to address urban flooding risks. It will reveal residents' preference of the SWM measures. These findings can help policymakers design more effective incentives and SWM strategies that align with residents’ preferences, promoting broader adoption, which will promote the collective actions from citizens and improving urban flood resilience. Finland exhibits a higher WTP for risk reduction (up to €4,820 for a 75% risk reduction), while Norway shows a stronger preference for runoff reduction (up to €3550 per household for a 50% runoff reduction). Aesthetic improvements are valued more in Finland (€1,257) than in Norway (€615). 4.1.3 Baseline Data This section provides a concise summary of the baseline conditions for each demonstrator site, focusing on key indicators such as nutrient levels, chlorophyll-a concentrations, flood or heat events, and community awareness before the implementation of TransformAr solutions. 68 TransformAr Deliverable 5.1 www.transformar.eu Baseline values were derived from a combination of historical monitoring datasets, regional surveys, and laboratory analyses, as reported in Deliverables D5.8. Measurements included water quality parameters (nutrients, salinity, pH, dissolved oxygen, chlorophyll-a), hydrological and climatic data (flood frequencies, temperature peaks, stormwater flows), and social indicators (stakeholder engagement, community awareness). Where available, national and regional monitoring systems (e.g., SYKE in Finland, ARPAS in Italy, CETMAR in Spain) were used to ensure reliability. A full set of datasets, including metadata and raw measurements, is publicly archived on Zenodo to guarantee transparency and reproducibility. Lappeenranta, Finland • Flood risk / runoff: Precipitation baseline = 260 l/s/ha for a 5-minute rainfall event (hydrological model, SYKE, 2023). • Nutrients in stormwater: Urban runoff typically contains 2–5 mg/L nitrogen and 0.1–0.5 mg/L phosphorus (literature-confirmed baseline). • Community awareness: Prior to CAF, ~10–15% of residents engaged in adaptation issues (baseline from national monitoring groups). Oristano, Italy (Marceddì lagoon and San Giovanni Pond) Marceddì lagoon • Chlorophyll-a: mean 18.1 µg/L (min 1.7 µg/L, max 115.2 µg/L)→ indicative of moderate eutrophia, with episodic algal blooms. • Nutrient levels: ammonium ~109 µg/L, nitrate ~328 µg/L, nitrite ~36 µg/L. • Dissolved oxygen: ~7.6 mg/L (95% saturation) → generally acceptable, though variability is expected. • Salinity: mean 29.0 → stronger marine influence; spatial gradient decreasing from the mouth towards inner basins. • pH: mean 8.31 → slightly alkaline, within expected lagoon range. San Giovanni Pond • Chlorophyll-a: mean 37.4 µg/L (min 0.7 µg/L, max 135.0 µg/L) → clearly eutrophic, with frequent and intense algal blooms. • Nutrient levels: ammonium ~131 µg/L, nitrate ~714 µg/L, nitrite ~40 µg/L. • Dissolved oxygen: ~8.0 mg/L (97% saturation) → comparable to Marceddì, but under stronger nutrient pressure. • Salinity: mean 18.2 → stronger freshwater input, lower dilution capacity and sharp decrease near river mouths • pH: mean 8.39 → slightly alkaline, occasional peaks linked to photosynthetic activity. • Baseline monitoring: 2016–2022, ARPAS and MEDSEA surveys. Galicia, Spain (Ría de Arousa) • Sea temperature: 14–24 °C, seasonal variation. • Salinity: 32–36 PSU, optimal for mussel/clam habitat. • Dissolved oxygen: 6–8 mg/L baseline. 69 TransformAr Deliverable 5.1 www.transformar.eu • Harmful algal blooms: 2–3 events per year historically. • Sediment composition: 0.1–2 mm grain size, organic matter 4–8%. Egaleo, Greece • Air quality: Baseline PM2.5 = 10–25 µg/m³, PM10 = 20–50 µg/m³, CO₂ = 400–450 ppm (WHO guidelines, pre-project values). • Microclimate: Summer heat peaks up to 35 °C, baseline humidity 40–70%. • Awareness: Pre-project climate awareness only ~10–15% of residents engaged. West Country, UK • Phosphate: 0.1–0.3 mg/L, nitrate 1–10 mg/L in agricultural runoff. • Sediment: 10–50 mg/L in rivers affected by farming runoff. • Habitat health: Riparian vegetation cover baseline 30–50%. • Community awareness: Pre-project citizen science and landowner participation <10%. 4.1.4 Approach to Monitor Impacts Methodologies Monitoring approaches were designed to capture both technical performance and social impacts, using harmonized indicators across demonstrator sites. Lappeenranta, Finland (URB, SWMM, CAF, CEI): • Biofiltration sampling (URB): Stormwater entering the Koulukatu NBS was tracked with online probes (flow, turbidity, conductivity) and 30 laboratory samples taken from influent and groundwater. • SWMM: Real-time monitoring calibrated with lab data, estimating pollutant loads and runoff reduction. • CAF analytics: Citizen app logins, active users, feedback events, and workshop participation monitored to assess awareness and engagement. • Accreditation: Methods follow Finnish Environment Institute (SYKE) and Finnish Meteorological Institute guidelines; lab analyses compliant with national standards. URB: stormwater entering the Koulukatu NBS was monitored with an onlinesensor system. In addition, laboratory samples were taken from the water entering the manhole. A total of 30 samples were taken during the campaigned from stormwater entering the NBS, and from groundwater both above and below NBS. All of the laboratory and online variables have been collected to Table 4.3. 70 TransformAr Deliverable 5.1 www.transformar.eu Table 4.3 Laboratory and online variables with corresponding units. Many laboratory variables were not detected in water samples, or their values were low in one or two samples from the sampling period. It is possible to include such variables in the model. Many variables also essentially convey the same information, such as different types of phosphorus or nitrogen. For modeling purposes, it is therefore sufficient to include only the total amounts. Uncertainty factors: the timing of laboratory tests, but since the turbidity measurement is equal, it should be acceptable. Calibration: It should be noted that the number of laboratory measurements on which the modeling is based is very limited, so all results should be viewed with caution. If the sampling continues in the future, the model could either be further validated or a new more detailed model fitted. To get estimates on the potential impurities in the water from the online sensor measurements, we need to: • First, establish a correlation between the laboratory measurements and the online measured variables • Utilize this relationship to inversely predict the estimates on the laboratory variables from the online sensor readings This is an ill-posed inverse problem, where the aim is to predict many variables from one measurement. Thus, the model describing the correlation between the lab and online measurements needs to be robust enough that the small deviations in the predictors do not cause huge fluctuations in the predicted variables. Set the number of variables n. From calibration, get regression coefficients β, a 1 × n-vector. For each variable, there is a mean value μ and a variance σ2, also from the laboratory calibration data, both 1×nvectors. This includes turbidity. 71 TransformAr Deliverable 5.1 www.transformar.eu Figure 4.1 The sum of squares error between original lab variables and the ones predicted by the model as a function of the ridge parameter. Highlighted the best fitting parameter of 45, according to this metric. Calibration is done by a ridge regression model, which penalises variables with little effect on the result. The regularization constant was set by inversion of the model to reconstruct the original variables. Let y ∈ Rn be the response vector, X ∈ Rn×p the design matrix, and β ∈ Rp the coefficients to estimate. Ridge regression solves: Here, λ ≥ 0 is the regularisation constant (also called the ridge parameter). Its purpose is to penalise large coefficients and reduce sensitivity to correlated inputs or ill-posedness, as in inverse prediction scenarios. As the model is meant to be utilized inversely, that is, to predict the online water quality variables from the turbidity values. Thus, the ridge parameter was also set in this manner, so for the lab data, first the forward model was set between the turbidity and other variables, then the resulting regression coefficients ˆβλ for each ridge parameter λ were inverted and utilized to predict back the lab-variables. The sum of squares error between the original variables and the lab variables was used to determine the optimal ridge parameter, this is shown in Figure 1. The previous approach takes into account only the combined error between all of the variables. To get a more complete understanding of the model performance, we can take a look at the back predictions for each variable. We can see that the model seems to fit quite nicely with all of the variables, and the errors are similar in each. Application to online measurements: When the sensor gives a reading for the flow parameter (lest mark it ν) (flow rate, L/s), run the prediction for the n variable concentrations. Firstly, normalize the online turbidity measurement with the mean and variance of the lab measurements. Then use a pseudoinverse of the regression coefficients to predict the normalized concentrations for online data Xˆz. The pseudoinverse for a vector can be written as β† = βt βtβ. Revert the normalization on the predictions to 72 TransformAr Deliverable 5.1 www.transformar.eu get ˆX, utilizing the lab-measured mean and variance for each variable. Lastly, the concentrations are multiplied by the flow to get the amounts. The pseudoinverse step can be further simplified if the already inverted regression coefficients are given, β† → βinv. Then the vector multiplication can be calculated in parts for each variable and reduces to a simple scalar multiplication. So Xz,i = βinv,iyz, then for each variable i = 1 . . . n the calculated value Xz,i is scaled back to ˆX collected in a Table 4.4. Table 4.4 Calculated value Xz,scaled back to ˆX collected Results: Here are presented some cumulative sums on the different variables given by the model, with the amount of water entering the NBS. Figure 3 is a bar diagram of the cumulative sums for each variable of the model for each full month of measurements. The main thing to notice is, that even though the amount of water flow in the winter is much lower, the predicted amounts for the variables are much higher. As the model was calibrated with the lab samples from summer of 2024, the predictions for those months should be the most reliable. For winter the behavior changes, as rainfall is mostly snow. It might be that the snow stays on the ground for some time and collects” dirt” and with warmer temperatures melts and takes larger load of pollutants with it. But this should be verified with more laboratory sampling. The values presented in the figure have also been collected to Table 4.5. If the water quality, for example, changes state”, then this model cannot detect it. Next steps would then be to include, for example, water temperature and conductivity measurements in the model to see if they give the model more generality and the ability to give more information on the state of the water. For example, during different times of the year. 73 TransformAr Deliverable 5.1 www.transformar.eu Figure 4.2 Monthly cumulative sums of substances entering the NBS. Calculated from the model predictions. Table 4.5 Monthly sums for each of the variables predicted by the model. 80 TransformAr Deliverable 5.1 www.transformar.eu Expected impacts: Should the land use change be sited strategically in areas adjacent to river corridors, there will be the greatest direct benefit to the environment. The aim of the scheme is to reduce nutrients, primarily phosphates, entering rivers and affecting water quality. The WRT interventions rely on increased hydraulic residence time improving nutrient retention in the riparian zone, where vegetation improves in situ denitrification and phosphate removal rates. Water quality is subject to spot sampling across the affected catchments by citizen scientists (CSI) and WRT staff. It may be hard to demonstrate a direct link with interventions at this scale on catchment water quality over this project timespan, but up and downstream sampling of intervention sites has commenced. There are also co-benefits to habitats and species from this approach which WRT will monitor through habitat surveys (species richness) and drone footage (habitat extent). Riverfly and fisheries surveys will provide information about the quality of in-river habitat. WRT will also monitor ecological function through soil analysis at intervention sites. Samples will be visually assessed for health based on structure, infiltration rates and samples will be sent to a lab for soil organic matter and nutrient content analysis. Economic Development in the Catchment Expected impacts: The initiation of a trading scheme allowing housing development to proceed by mitigating additional sewage through land use offsets will overcome the planning hiatus in the affected catchments. New housing is needed for local people who provide a workforce for sustainable economic growth in a region where holiday homes and tourism are a dominant economic sector. Studies (Lichfields, 2018) show that each £1m investment in housing development supports around 19.9 direct jobs, and 15.6 indirect jobs, as the construction industry has an indirect and induced employment multiplier of 2.23 (Lichfields, 2022). This impact is more complex to assess but the value of P offsets obtained can be expressed as housing units. The value of capitalization in (£) and value of credits traded can also be used. Also, the number of landowners and investors/developers brought in as part of the scheme. A set of metrics around this can be established as a way of indicating the impact. There will be wider economic benefits including the protection of tourism and recreational value. Economic benefits to farmers in payments for ecosystem services will be measured in the financial value of BNG and P Credits obtained. WRT are providing support with nutrient budgets on farms, leading to financial savings from more efficient use of fertilizers. In addition, the land use change and interventions on floodplains will help retain nutrient rich sediments which are a valuable resource to farmers but a pollutant in rivers. Depth or volume of sediments captured at intervention sites will be measured, along with the nutrient content via lab analysis. Landowner and Community Engagement Expected impacts: This will focus on increasing wider community awareness and understanding of river systems and threats, promoting the benefits of water use awareness and drought resilience. The success of community engagement activities is monitored and evaluated though quantitative, qualitative and narrative approaches. Metrics include sign-ups to the Westcountry Citizen Science Investigations (CSI) scheme, numbers of active volunteers and retention and numbers of volunteer surveys. Attendance at participatory research events and workshops offered by WRT and the motivations for this participation. Higher-level engagement through community groups working with WRT to develop new citizen science 81 TransformAr Deliverable 5.1 www.transformar.eu methodologies and development of co-funding streams to drive environmental outcomes (water quality and resources) which have been identified as important by the community, including landowners and farmers, is also indicative of increased awareness of, and action for, climate resilience. 4.3 4.4 Approach to Monitor Impacts Methodologies KPI 4: Eutrophication Reduction TransformAr did not work on eutrophication directly. This KPI is thus not quantified. Refer to KPI 20 (Increase in Nutrients and Water Retained by NBS) for the work in TransformAr on nutrients and water quality. In general, a reduction of nutrient input contributes to reducing the eutrophication of a river catchment. But eutrophication is a more complex phenomenon, which was not addressed in TransformAr. KPI 20.1: UKHAB Surveys Before & After Intervention to Assess Biodiversity Net Gain, Spatially Resolved in Ha/Habitat Restored A comparative assessment of habitat changes, based on delivery site and a control area, or previous habitat survey where available, was carried out by ecologists using the following methods: • UK Habitat Survey – A standardised approach to defining UK terrestrial and freshwater habitats, primarily based on botanical survey. • Habitat Condition Assessment Survey – Method used in biodiversity net gain assessment for town planning purposes. • Botanical species diversity and abundance, and presence of notable (protected) species. The surveys were conducted within a 30m buffer zone of the delivery area/ equivalent control area. KPI 20.2: Electric Fishing Surveys Survey sites were selected by experienced fisheries officers to identify reaches that were deemed good fry habitat as close to the interventions as possible. At water temperatures <18°C, electric fishing was carried out using a backpack electric fishing kit to induce fish to swim towards an anode and into a hand net. Voltage settings were adjusted according to the electrical conductivity of the water. All surveys were conducted using a 100% (smooth) duty cycle. Fish recovered in an aerated container of water before processing salmonids (species identification and fork length measurement) and returning to the watercourse. Presence of other caught species, missed fish, and the length and width of the fished reach were recorded. Catch efficiencies <60% voided the survey. Fry index surveys (five-minute semiquantitative method) were carried out at all sites with the exception of Worthyvale on the Camel where a fully quantitative three catch depletion method was used. A length frequency histogram deduced thresholds for fry and parr for each survey year Each FIS site was then classified according to Crozier and Kennedy,1994 (8). Worthyvale was classified using the National Fisheries Classification Scheme (9). 82 TransformAr Deliverable 5.1 www.transformar.eu Table 4.6 Semi-quantitative abundance categories for salmon fry (Crozier & Kennedy, 1994). Table 4.7 National Fisheries Classification Scheme Classification Density of Fish per 100m2 Salmon fry Salmon parr Trout fry Trout parr A - Excellent >86 =>19 =>38 >21 B - Good 45–85.9 10.0-18.9 17.0-37.9 12.0-20.9 C - Fair 23-44.9 5.0-9.9 8.0-16.9 5.0-11.9 D - Moderate 9-22.9 3.0-4.9 3.0-7.9 2.0-4.9 E – Poor <9 <3.0 <3.0 <2.0 F - Absent None recorded None recorded None recorded None recorded KPI 20.3: Historic water quality assessments from existing datasets Publicly available water quality monitoring data from the UK Environment Agency (EA) were used to deliver against this KPI. The EA’s Water Information Management System (WIMS) database was accessed online via the Water Quality Data Archive portal (https://environment.data.gov.uk/waterquality/view/landing) and datasets from 2000 to 2021, (and for subsequent years as the project progressed) were downloaded for analysis in Excel with the Area field as “Devon and Cornwall” and Purpose field as “Monitoring Only”. KPI 20.4: Longitudinal Water Quality Monitoring Programme Monthly Water Quality Spot Monitoring Programme Monthly spot sampling surveys for water quality were designed to target and priorities areas within the catchment for interventions, further monitoring, farm-advice and/or stakeholder engagement. The process was dynamic and ongoing, in order to capture data to evidence the need for interventions and to aid in landowner engagement. Sites were selected based on public accessibility (from road bridges) and aimed to divide the catchment into relatively equally sized sub catchments, as well as focus more specifically on interventions or clusters of them. Unfiltered samples were collected monthly using a bucket on a rope and analyzed on-site for color, turbidity, suspended sediment, and total reactive phosphate using a handheld colorimeter (DR900, HACH) using the preset methods available on the device. Measurements for temperature and conductivity were taken using a handheld tester (PocketPro Tester, HACH). Data from 50 monthly longitudinal surveys completed on the Axe and 39 surveys on the Camel 83 TransformAr Deliverable 5.1 www.transformar.eu (https://zenodo.org/records/17058348) were used to construct catchment scorecards. The 38 sites surveyed on the Axe were split into separate campaigns at the start of the project to cover the main Axe, Corry Brook, Yarty and the Kit Brook, with one or two samples from the Fourton, Blackwater and Synderford tributaries. The 36 sites on the Camel were similarly split up into the main Camel and River Allen. As intervention sites were identified and works agreed, sampling was reduced to focus on these areas. Data analysis was undertaken to rank each sub catchment (represented as the entire area upstream of a sampling point) within the demonstrator catchment against the others, for each WQ parameter. Sites were ranked from “best” to “worst”, according to the percentage of records at a given site that exceeded the survey median (equal exceedances were then ranked using site means) and individual parameter rankings were averaged to give an overall WQ score. The Watershed tool in ArcGIS was used to delineate subcatchments and visually represent the rankings for each using a graduated color scale. Where appropriate, statistical tests were performed in Excel to identify any significant differences in measured parameters before and after interventions were installed (or upstream/downstream of them). Citizen Science Data An active programme of Citizen Science monitoring across the Axe and Camel catchments provided high spatial density but low specification monitoring of WQ parameters including phosphate, turbidity, total dissolved solids and temperature. These data were downloaded for analysis from WRT’s online data repository (Cartographer) and waterbody scorecards were created on an annual basis for each catchment. Citizen science data was ultimately collected as a community and stakeholder engagement tool (KPI 11) enabling WRT to discuss the causes of poor water quality and potential solutions with a much broader audience thereby building catchment and climate resilience. https://wrt.maps.arcgis.com/apps/instant/attachmentviewer/index.html?appid=d07a6f8382764c0faed 87d0652d43df9. Visual Assessments of Interventions Prior to the installation of continuous WQ monitoring sondes, qualitative assessments were made by eye or using timelapse trail cameras (V150, Vosker) to establish interventions (leaky log dams only) were functioning as designed. These assessments identified any unintended: • erosion, collapse or removal of the dams • redirection of surface water • bankside or channel erosion • failure of the dam to interact with surface water during expected times (e.g. a significant storm event) Cameras were deployed on suitable trees or posts and images sent remotely to an online platform for viewing or stored on a memory card for manual download. Visual assessments before and after intervention installations were also made using aerial drone imagery (DJI Air 25). Spot Monitoring of Interventions The watercourse that runs through Chubbs Farm was monitored using the same spot sampling equipment as outlined for the catchment-wide surveys. The site was monitored monthly at four locations from March 2024 onwards (Figure 4.7). Sites 3 and 4 were selected for upstream and downstream analysis 84 TransformAr Deliverable 5.1 www.transformar.eu with Sites 1 and 2 aiding in tracing the source of pollutants. Eight spot monitoring campaigns were conducted before interventions were installed to provide some baseline data for comparison with another eight samples taken post-works. Figure 4.8 Chubbs Farm spot sampling locations At Penvose Campsite, twenty WQ samples taken directly upstream and downstream of the log dam cascade were compared in Excel (to identify statistically significant differences in the concentrations of standard WQ parameters (electrical conductivity, total reactive phosphate and turbidity). Continuous Water Quality Monitoring Sondes were deployed in-river to monitor the effectiveness of the leaky log dams in storing water and improving WQ at Penvose (Camel catchment) and Chubbs Farm (Axe catchment). Figure 20 shows the deployment design, which monitors the whole dam cascade rather than monitoring each individual dam. As equipment costs generally prohibit the ability to monitor each dam separately, plus the unique nature of each means the storage capacity and “leakiness” of one dam cannot be accurately extrapolated to another, this method is considered the most appropriate way to capture intervention impacts. The upstream-most sensor was placed outside the influence of the upstream-most dam. For Chubbs, this was at site 2 in Figure 38 as water depth at site 1 did not allow submersion of senser head and this tributary was later identified to be more significant with respect to nutrient run-off. The downstream sensor was placed downstream of the cascade. Additional sensors were placed behind dams that were either qualitatively observed to be functioning as designed, to monitor fluctuating water levels and WQ immediately behind the dams, or simply accessible enough for deployment. For Penvose, three 85 TransformAr Deliverable 5.1 www.transformar.eu AquaTROLL 200 sondes with VuLink telemetry devices (In-Situ Ltd.) were deployed (Figure 39). The sondes were set to 15-minute measurement intervals for electrical conductivity (as a proxy for dissolved pollution), depth and temperature. At Chubbs Farm, a continuous phosphate monitor (DropletSens™ Phosphate Probe, SouthWestSensors) with a Point Green Telemetry Unit (Metasphere) was also installed downstream of the interventions, alongside the AquaTROLL 200. The deployment of the SWS probe took place after the interventions were installed and only at the downstream site, so the exercise was more to assess the suitability of the use of the probe in such an environment for future monitoring of this kind, and to generate some data for comparison alongside the more standard WQ parameter electrical conductivity as a proxy for pollution. Figure 4.9 Set-up for monitoring a cascade of leaky log dams. Figure 4.10 An example of the installation of conductivity, temperature and depth loggers installed for monitoring upstream (A) and downstream (B) of the leaky log dam cascade at Penvose campsite. 86 TransformAr Deliverable 5.1 www.transformar.eu KPI 20.5 Modelling kgs of Phosphate Removed as a Proxy for Water Quality As quantitative assessment of the efficiency and impact of the NBS interventions would require pre and post intervention sampling far beyond the length of the TransformAR project, WRT used two models to approximate the improvements in water quality as a result of their implementation, using data from KPI 20.6 and 20.7 below. The River Camel SAC Nutrient Budget Calculator was used for the Camel and River Axe SAC Nutrient Budget Calculator for the Axe. Both calculators are online tools developed by the Devon and Cornwall authorities for the benefit of housing developers. They show the nutrient impact of proposed developments, and the nutrient mitigation required to achieve neutrality. Both tools are based on the algorithms developed as part of the Farmscoper Tool, which Natural England uses to assess nutrient credit schemes. As amelioration of nutrient pollution across the entire catchment is the aim, general targets for phosphate reduction in the Camel and Axe catchments were used instead of those for individual housing developments within the tool. KPI 20.6 Measure the Depth/Volume of Sediment Captured by the Intervention On the 4th and 5th of March 2025, interventions designed to settle out and entrap sediments (e.g. ponds, scrapes) were measured using a tape measure to derive a maximum storage volume for each intervention. These were summed and used within the Farmscoper Tool to approximate the maximum amount of sediment removed from surface run-off and thus prevented from entering the watercourse. KPI 20.7 Measure the nutrient content of the sediments via laboratory analysis Interventions available for sediment sampling included all five scrapes at Snowdon Farm and the sediment trap, plus one of the scrapes at Trethick (the remaining scrapes being either too wet or having no sediment trapped within them). No sediment was collected from Penvose or Chubbs Farm due to unsuitable sampling conditions. A transect of 10 sediment samples were collected from each intervention on the 4th and 5th of March 2025 using a trowel and homogenized into a single sample. Homogenised samples were immediately dispatched to the laboratory (NRM Cawood) in laboratory-issued grip seal bags and analysed for soil pH, phosphorus (P), potassium (K) and magnesium (Mg) index, available concentrations of P, K and Mg and soil organic matter (loss on ignition method). As sampling was restricted to just one occasion at each site, samples were taken and measured at intervals post intervention installation (Snowdon Farm 108 days; Trethick Farm 245 days and Chubbs Farm 104 days). None of the interventions were at full capacity at the time of sampling and those at Snowdon Farm had not been fully connected to the ephemeral stream, meaning measurements were not fully representative of the features in full operation. KPI 20.8 Measure Soil Permeability/Compaction/Infiltration Rate Using VESS Assessment Visual Evaluation of Soil Structure (VESS) assessments were undertaken before and after intervention installations, using the method developed by SRUC (Scotland’s Rural College, available here). The method assesses the extent of compaction and thus provides an indication of the degree to which water and nutrients can move within the soil profile. Soil pits were dug by hand using a spade in such a way as to leave one face undisturbed. A subsection of soil was removed from the undisturbed face for visual examination (see Figure 1) in order to establish: • The presence of any distinct layers throughout the profile • The size and degree of consolidation of soil aggregates (lumps) 87 TransformAr Deliverable 5.1 www.transformar.eu • Whether soil aggregates are angular or rounded in nature • The degree of porosity • Whether there are horizontal unnatural clods Distinct layers of soil were scored 1-5 against the VESS matrix with 1 being friable and good crumb structure, and 5 being very compact with large aggregates that are difficult to break. After surveying, the scores were entered into a spreadsheet to determine an average score for the soil structure in each pit. At Snowdon Hill Farm, assessments were made within the field, after the in-field elements of the intervention had been installed and within an adjacent field as a control. At Trethick Farm, only the field encompassing the interventions was surveyed. KPI 20.9 Measuring the Amount of Organic Matter in the Soil and the Carbon:Nitrogen Ratio to Assess Soil Carbon Sequestration Performance Soil nutrient analysis is used to gauge the availability of nutrients to crops for plant growth. DEFRA use the RB209 Handbook as its standard method for controlling nutrient application rates (from fertiliser and manure) to the needs to the growing plant. This allows farmers and land managers to distribute their sources to match the areas that require the inputs. Historically, nutrients have been over applied and built-up legacy concentrations in the soil and water courses. The classification of Soil Organic Matter (SOM) for different soil types is shown in Table 4.8. At Snowdon Hill Farm assessments were made within the field, after the infield elements of the intervention had been installed and within an adjacent field, the latter acting as a control. At Trethick Farm, only the field with the interventions in it was surveyed. High concentrations of P in the soil do make it more vulnerable to losses, as the P indices increase, P can transition into soluble form and leave the field through field drainage. P can also be transferred to waterbodies with sediment run-off. Large areas of ground have been left with legacy P, from historic land use choices. Inadequate crop nutrition planning previously left fields with more P being added through manures and fertilisers than the crop required. Table 4.8 Soil Organic Matter (SOM) - percentage of sample. Interpretation based on ADHB-BBRO Soil Biology & Soil Health Partnership protocol and benchmarking Land use Rainfall Soil type Very Low Low Target High Arable Low <650mm Light <=1.0 1.1-2.1 2.2-3.2 >=3.3 Medium <=1.7 1.8-3.3 3.4-5.0 >=5.1 Heavy <=2.2 2.3-4.4 4.5-6.5 >=6.6 Moderate 650800mm Light <=1.0 1.1-3.0 3.1-4.5 >=4.6 Medium <=1.9 2.0-4.0 4.1-6.0 >=6.1 Heavy <= 2.7 2.8-5.2 5.3-7.6 >=7.7 Light <=1.3 1.4-3.7 3.8-6.1 >=6.2 88 TransformAr Deliverable 5.1 www.transformar.eu High 8001100mm Medium <=2.5 2.6-5.0 5.1-7.5 >=7.6 Heavy <=3.6 3.7-6.2 6.3-8.8 >=8.9 Grassland (Lowland) All Light <=2.1 2.2-4.9 5.0-7.9 8.014.9 Medium <=3.4 3.5-6.4 6.5-9.3 9.319.9 Heavy <=4.6 4.7-7.6 7.710.5 10.619.9 4.4.1 Availability and Access of Monitored Data Baseline water quality and ecological monitoring data for KPI 20: https://doi.org/10.5281/zenodo.14500788 Final water quality and ecological monitoring data for KPI 20: https://zenodo.org/records/17058348 Environment Agency Water Quality Archive (WIMS): https://environment.data.gov.uk/waterquality/view/landing Complete Citizen Science scorecards: https://wrt.maps.arcgis.com/apps/instant/attachmentviewer/index.html?appid=d07a6f8382764c0faed 87d0652d43df9 4.4.2 Upscaling and Acceleration of the Solution For findings on bankability and potential to upscale and accelerate see section 6.2.2 bankability. Nutrient Credit Schemes in Devon and Cornwall In the Westcountry region, although several natural capital marketplaces have emerged to facilitate the trade of ecosystem services, an online marketplace explicitly designed to support the trade of nutrient credits is not currently available. Instead, local authorities have worked with NGO’s such as the Westcountry Rivers Trust and the Devon Wildlife Trust to engage landowners in credit development projects. In the Camel and Axe catchments, WRT worked to find strategic opportunities for the development of NBS that would both mitigate nutrient pollution and support climate change adaptation by slowing flows and re-naturalizing waterways. As WRT have a strong track-record of landowner engagement, we operate as a ‘broker’ between credit purchasers and suppliers, ensuring excellent environmental outcomes. Blended Finance: Camel Catchment This blended finance model in the Camel has several key advantages but also presents several key challenges when assessing the bankability or financial sustainability of climate adaptation. Benefits: • Use of public funding to develop credit projects accelerates outcomes, as risk to private developers is removed. 89 TransformAr Deliverable 5.1 www.transformar.eu • Resultant ecosystem services do not need to represent high revenue/rate of return, as local authorities do not need to generate profit. • Planning authorities within local governments are well-placed to sell resultant credits to developers, as they have a pre-established professional relationship • Prevents stagnation at the ‘seed funding’ or project development phase, often encountered when attempting to attract private investment for nature-based solutions. Challenges: • Lack of consistent value/unit of phosphate makes valuation of NBS difficult, delaying uptake. Landowners need a consistent value to make informed decisions against other land use options. • Lack of clarity around future liability for ecosystem service assets, as well as potential litigation risks associated with delivery failure. Results in delay and uncertainty for landowners. • Focus on nutrient credits in order to meet statutory demand results in failure to capitalize potential additional revenue streams associated with the NBS created. Does not help to facilitate future private-sector investment. A further appraisal of the benefits and challenges to blended finance will be included in the discussion of the bankability interview process. Private Finance: Axe Catchment The local authority in the Axe catchment did not capitalize nature-based solutions yet. However, the local authority has decided to invest, meaning more action should emerge on this issue in future. In the bankability interview process, other financing instruments than the blended structure tested in the Camal Catchment will be explored. 4.3 Galicia, Spain 4.3.1 Overview of the Expected Impacts The objective of the Galician demonstrator is to drive a region-specific adaptive transformational adaptation process for the clam and mussel sectors, responding to the prevailing multi-sectoral climate risks. This has been assessed mainly with indicators informing on the performance of the solutions and the participation, perception, awareness, involvement, and interest of key sectoral stakeholders. The overall impact has been the empowerment of the sector with real-time data and predictive capabilities, improving operational efficiency and supporting sustainable and adaptive management practices to face the environmental changing conditions due CC. The solutions developed, are understood as a trial to study the feasibility of the digitalization of the sectors for a better adaptation to CC. Thus, the effectiveness of these tools was gauged by the extent to which they are embraced and how satisfied users, including producers and scientists, are with them. Table 4.9 Expected impacts of the solutions. Expected impacts RI INTERM MRM 96 TransformAr Deliverable 5.1 www.transformar.eu The understanding of the hydrodynamics affecting the selected sandbanks is attained here with direct and temporal measurements of turbidity and current flow direction and magnitude. It is done at specific locations where no baseline data was found after reviewing literature and meeting exchanges. However, there are border scale stations ruled by public organisations in the area, that provide continuous valuable data of sea level, currents, temperature, pressure, etc. (https://www.meteogalicia.gal/web/home; https://www.puertos.es/en-us) Morphodynamic simulations are not published for the studied region. There are, however, applications of a similar model set up for the broader scale of the Galician coast aiming to provide insights in the temperature and salinity modifications of the coastal bivalve ecosystems (Des et al., 2021, Des et al., 2020). For the MRM, there were no previous oceanographic and production data in real time collected directly from an active mussel raft. This had never been attempted before on a structure owned by producers without interfering with their daily activities. Within the framework of the TRANSFORMAR project, the first IoT-based prototype solution was developed and successfully deployed, allowing continuous monitoring from May 2023 until the end of 2024, when the Galician Demo officially concluded. The usefulness of these data for the mussel farming sector has already been demonstrated, as the local administration decided at the end of 2024 to finance the evolution of this prototype. The new and more robust version was installed in May 2025, ensuring the continuity of monitoring activities beyond the duration of TRANSFORMAR and making the data openly available to the entire mussel farming community. The time series obtained are currently under processing and analysis, and will be compared with reference datasets from scientific and administrative centres to better understand the local effects of environmental variability on mussel production. It's relevant to mention for this demonstrator the bilateral meetings and workshops held in the initial two years of the project. These sessions gathered information on STH ' perceptions and interests regarding CC and adaptation measures. Additionally, each solution has maintained continuous contact with producers, engaging them in identifying needs, installing sensors, making enhancements, and providing consultations to assess the sector's resilience 4.4.4 Approach to Monitor Impacts Methodologies Prior to obtaining impact results, it is imperative to first gather data on the implementation of the solutions, such as identifying suitable data collection areas, securing producer agreements, installing sensors, ensuring sensor functionality, and the type of information obtained. Then, the expected impacts of the solutions were measured through the impact indicators as described in Table 12. In this table, several columns have also been added to clearly display the baseline, the expected outcomes at the end of the project, the current situation, and the approach to monitoring indicators per impact (the type and format of verification sources). The impact of these solutions was closely monitored in accordance with project guidelines and, where feasible, aligned with other demonstrators. Overall, the increase in knowledge impact through the course of the project was measured by comparison between the obtained measurements from monitoring surveys and sensors and baseline existing data. It was assessed mostly based on user satisfaction (producers and scientists), technical and economic viability, awareness and behavioral change, and the potential for replication. The monitoring of these indicators was approached through workshops, meetings, testimonies of key informants, surveys and questionnaires. Reports of the involvement of the STH and communication and transfer activities serve as verification sources, together with information 97 TransformAr Deliverable 5.1 www.transformar.eu about the data publication records and web/visor visualizations. More specific explanation of the approach to monitor impacts methodologies by solution is detailed below: Resilience Index (RI) The REDE research group at the University of Vigo undertakook the development of a mathematical model, the Operational Resilience Index (RI), for the mussel farming sector aimed at nudging stakeholders’ behavioural change. This index (framed within the project's governance solutions) provides stakeholders (STK) and policymakers with a comprehensive assessment tool and a decision-making assistance instrument that allow them, on one hand, to identify strategic focal points for adapting the mussel farming production operations to the effects caused by CC and, on the other, to establish which measures can have the greatest impact on reducing the risk of business interruption. As illustrated in Figure 4.11, the development of the Operational RI employs a hybrid methodology that integrates climate data and scenarios with input from experts and STH. This process unfolds across three different phases: 1) Identification of relevant experts and STH, followed by the collection of three sets of variables to be used throughout the index construction: (i) data about the aquaculture processes, (ii) climatic variables that could affect those processes and their projections and (iii) the available resilience factors with the potential to moderate the impact of the selected climate elements on critical processes. 2) During the second phase, the Delphi methodology was employed to engage experts and STH in a participatory and consensus-building process. As a result, vulnerabilities, risks and resilience factors within mussel aquaculture production have been prioritized. The participants in both Delphies possess expertise related to mussel aquaculture, climate change, and/or resilience. Furthermore, the quintuple helix (Carayannis et al., 2012) has been represented in the expert panels, encompassing Administration, Academia, Productive sector, Environmental organizations and Society. A total of 23 experts took part in the first Delphi (July 2023), while 20 experts participated in the second one (October 2023). The outcomes facilitated the identification of priority risk scenarios and resilience gaps, respectively. As a final step of the second phase, a questionnaire was designed and distributed to STH to assess the key resilience factors, that is, the adaptive capacities of the sector. 3) The final phase encompassed the definition of the mathematical model (fed with the variables identified in phase 2 and the values from the capacities questionnaire) and the calculation of RI scores. Finally, the definition of proposals for improving adaptation was discussed with STH in order to define a strategic roadmap. 98 TransformAr Deliverable 5.1 www.transformar.eu Figure 4.11 Methodological design for the Operational Resilience Index The RI represents a tool for evaluating the level of adaptability in the operations of the mussel production process. Figure 4.11 shows the final result after calculation of the synthetic index of the resilience factor. We chose to design a composite index with indicators (synthetic index) because such a tool simplifies communication, turning complex information into understandable metrics, more accessible to a wider audience. Therefore, the RI evaluation will encompass an overall score and a detailed breakdown across five dimensions: Governance, R+D+I, Collaboration, Risk Management, and Operational Environment. Additionally, each dimension is further dissected into four factors, totaling 20 resilience factors. The dashboard of final scores combines multiple dimensions and factors into a single global metric. It takes into account multiple factors providing a more nuanced understanding of the overall state of the sector. It considers the interaction between the impact level of different risk scenarios and the ability of the identified resilience factors to adapt to their effects. In addition, the index assigns different weights to resilience factors based on the relative importance that experts attribute to each risk scenario. This weighting allows a more nuanced and realistic reflection of the importance of each element in influencing the overall index. The results emerging from the index provide specific insights that will allow the formulation of actions and a roadmap for enhancing resilience. 99 TransformAr Deliverable 5.1 www.transformar.eu Figure 4.12 Presentation of the results of the synthetic Resilience Index Intertidal Monitoring – INTERM The GEOMA Research group from the University of Vigo implements a sedimentological monitoring of sandbanks at specific locations of clam exploitation with the aim to improve the knowledge of the sediment dynamics, the stability of the ecosystem substratum, and its response to the predicted Climate Change outcome. An updated understanding will improve STH and policymaker's basis for adequate adaptation solutions, efficient management currently hindered by the lack of information. This solution directly responds to two aspects in the nature of the sandbanks substrate understanding: 1. The sedimentological context of the Galician intertidal sandbanks, specifically sediments quality on seasonal and year-to-year time scales, and the potential relationship with changes in shellfish productivity. 2. Consequences of the Climate Change on the Galician sandbanks terrain. Although we know that environmental variations alter the morphology and composition of the intertidal sediments (e.g. Friederichs 2011) potentially altering the ecosystem, few studies have undertaken this issue at the regional level. Within this context, selection of study-case locations was performed after discussion with local shellfishers and scientific community involved, trying to respond to the observed variable historical productivity of commercial clams and arising sedimentological concerns. To fit our evaluation in the time frame of the project, the "space for time" hypothesis has been assumed, selecting three areas on the south margin of the Ría de Arousa, with different geological configuration and levels of shellfish productivity. The INTERM solution has a threefold mission: 1. Intertidal Monitoring: Based on previous and ongoing STH efforts currently used for sediment management strategies, a systematic monitoring has been carried out to obtain seasonally updated datasets along the duration of the project. Starting in month 13 (October 2022), sediment properties (grain size distribution, organic matter content, geochemical composition, etc.), terrain elevation datasets and oceanographic parameters influencing the area dynamics are being surveyed. 100 TransformAr Deliverable 5.1 www.transformar.eu 2. Morphodynamic Modelling: A model set up was configured and validated on the DELFT3D package (Deltares, The Netherlands) for a regional level domain where the tide is the main hydrodynamic factor. Sediment transport under different bathymetric conditions, tidal currents, and river discharge variations are evaluated using the modules FLOW and MOR. An orthogonal grid is established and refined towards the selected intertidal sandbanks, incorporating external datasets for deep bathymetry, but obtained higher resolution depth and sediment composition for the selected shallow sandbanks. Simulations depend on the boundary conditions defined for tide, river and water surface. The model set-up was validated, contrasting the results with ongoing monitored and public stations data, to allow the exploration of regional CC scenarios with variable hydrodynamic conditions. We expect to describe whether the selected sandbanks loose or gain sediment, as well as their sediment texture variations, that might avoid some species growth. 3. Knowledge transfer: The compiled information, including the generated datasets and predicted scenarios, was analyzed and processed to finally return to the clam sector in a way that will contribute to improve the sandbank monitoring guidelines, and to be the basis to co-create and purpose management strategies of harvested sandbanks against the CC. The planning, methodology and lessons learnt of the process are available to guide similar processes in equivalent areas. Figure 4.13 INTERM strategy scheme. Mussel Raft Monitoring - MRM The Technological Centre of the Sea – CETMAR, is carrying out the development of a pilot case of digitalization of mussel aquaculture. The conditions of the marine environment and the production are being monitored in active mussels’ farms, for the improvement of the exploitations management, with the support and collaboration of its owners. The first comprehensive monitoring of a mussel raft in the Galician Rías was conducted at the physical variables level in the framework of the ESSMA project (Ecological Sustainability of Suspended Mussel 101 TransformAr Deliverable 5.1 www.transformar.eu Aquaculture), funded by the Spanish Ministry of Science and Innovation (Aguiar et al., 2015). This study suggested that it was necessary to revision of the ecological concepts, such as food depletion and/or reduction in water flow, based on idealized linear flows through the rafts. This included the consideration of the "clearance" area, defined as the area affected by non-linear effects produced by the raft itself and its surrounding rafts. These conclusions were supported by the results obtained through a no real-time monitoring of a raft, during more than a year and a half, in the Lorbé polygon of the Ría de Ares and Betanzos (NW Galicia, Spain). The digitalization in this case involved the installation of four current meters and four turbidimeters/fluorometers placed on each side of the raft that recorded data in a self-contained way. The autonomy of this raft was limited by energy requirements because most of the equipment had batteries not powered by solar panels. This intricates recurrent battery changes and, consequently, numerous visits to the raft. In this case, the solution developed in TransformAr includes the monitoring of environmental, and production parameters (currently in two mussel-rafts), based on the implementation of IoT solutions powered by solar energy sending data in real time. Besides, the remote visualisation of the data on an internet-based platform is also being developed, presenting the information for production management. A key aspect is the reception and feedback from workers in the sector, paying attention to their opinions and suggestions for improvement or adaptation of the installation to their interests. Figure 4.14 explains the logic and the sequence of the data gathered from the moment that the sensor captures them to the visualization on the dashboard. Figure 4.14 MRM flowchart 102 TransformAr Deliverable 5.1 www.transformar.eu Next, it is shown the degree of execution of this solution in three phases as well as the time frame: ● The first phase consists of a system that monitors the basic parameters and can validate the installation and the developments created expressly for its use on rafts. For this purpose, an IN-SITU data storage installation was designed and installed in a raft to be tested for a few months and make the necessary modifications to improve its robustness. At the same time, a first analysis of the data was carried out in order to optimize criteria in terms of storage capacity and frequency of data acquisition. With all the lessons learned from the first phase, a new design of the installation architecture was made to adapt it to a real-time data collection system. ● The second phase develops the communication protocols and the improvements to be able to perform data acquisition autonomously and send the measurements to ground stations for further analysis. In addition to the oceanographic monitoring sensors, other meteorological sensors were added to complement and improve the sampling capability of the system installed in two mussel rafts. Data analysis procedures are being improved as well as the characteristics of the important amount of data received in real time. The designed installation is feasible for the available infrastructure and resistant to the adverse conditions of the marine environment and has a high degree of autonomy, both in terms of energy and in the measurement and telematic transmission of monitored data, as well as having systems for detecting possible failures. ● The third phase, implemented in 2024, improved the data analysis systems and the necessary implementations to visualise the data in a user-friendly web platform. This dashboard shows real time data, as well as all the data history of the variables that are being collected (https://utmar.cetmar.org/transformar). Data access and the visualization are affordable for STH and useful in decision-making for more effective and sustainable management of resources. To assess the feasibility of digitalization in the sector, the technical aspect is analyzed, as well as the impact of the implemented solutions, facilitating and promoting their replication and scalability by the sector. Mussel producers have highlighted the relevance of monitoring two specific parameters that directly influence farming practices and decision-making. On the one hand, seawater temperature is a key environmental variable, as high temperatures or abrupt variations may trigger detachment events of mussels from the ropes, leading to significant production losses. On the other hand, the continuous measurements provided by load cells enable the quantification of rope weights, which constitute a robust proxy for assessing the progressive growth and biomass accumulation of mussels. Together, these datasets offer valuable insights into the relationship between environmental variability and aquaculture performance, reinforcing the importance of real-time monitoring systems as a decision-support tool for the sector. By incorporating IoT technology into mussel raft monitoring, the shellfish industry can adapt to and mitigate the challenges presented by a changing climate while maintaining sustainable and profitable operations. This effort contributes to the achievement of several objectives defined at National and Regional Climate Change Adaptation Plans. Specifically, it is aligned with the Integrated Regional Plan for Energy and Climate (2019-2023) for the development and implementation of the Galician Strategy for Climate Change and Energy 2050 in the lines of action: 11 - Consolidate an observation network as an instrument to improve monitoring, 14 - Promote the conservation and efficient use of natural resources, and 18 - Consolidate sustainable management of fishing and aquaculture that minimizes the impacts of CC and guarantees the current positioning of the sector in the long term. 103 TransformAr Deliverable 5.1 www.transformar.eu 4.4.5 Availability and Access of Monitored Data Zenodo: https://zenodo.org/communities/transformar_h2020 This section describes how data are stored for each solution, whom have access to it, and how can be used. For the RI The findings from the RI are directly communicated to the involved STH and shared at scientific conferences or through scientific communications. ZENODO ➢ Methodology for the development of a climate change resilience index for mussel aquaculture in Galicia (Spain) – https://zenodo.org/records/11671754 For the INTERM ➢ Geological and oceanographic parameters (topography and bathymetry, sediment composition, turbidity, current magnitude and direction) that monitor the sandbank characteristics and morphology at selected locations. This monitored data are stored internally, accessed by the GEOMA TransformAr team. It is used for model configuration validation, presentation of results in conferences, and if asked during the knowledge transfer meetings, shared with scientific assistants of involved shellfish guilds. It is also available for TransformAr partners. If scientific publications result from the analysis, data involved will be submitted to open access platforms. ZENODO ➢ Sediment grain size dataset of intertidal shellfish sandbanks within Ría de Arousa, Spain https://zenodo.org/records/13149711 ➢ XRD analysis dataset of intertidal shellfish sandbanks sediment samples within Ría de Arousa, Spain https://zenodo.org/records/13149751 ➢ Intertidal Monitoring (INTERM) in Ría de Arousa (Galicia). Briefing on the development process of the solution and results https://zenodo.org/records/13238528 ➢ Elemental composition of intertidal shellfish sandbanks sediment samples within Ría de Arousa, Spain https://zenodo.org/records/13149806 ➢ Bathymetry dataset of intertidal shellfish sandbanks within Ría de Arousa, Spain https://zenodo.org/records/13149646 ➢ DGPS Topography dataset of intertidal shellfish sandbanks in Ría de Arousa, Galicia, Spain https://zenodo.org/records/13149687 For the MRM 104 TransformAr Deliverable 5.1 www.transformar.eu ➢ The environmental conditions. The monitored parameters are position, raft movement, mussel wire weight, water agitation, rain, wind, air temperature, water Temperature (6 depth levels), salinity, air sound, turbidity and intrusion system. All these parameters are being measured/recorded at a high frequency. However, for the first tests, commercial precision instrumentation has been used to validate the COTS (Commercial off-the-shelf) components. The final monitoring solution must not include the acquisition of expensive oceanographic equipment such as traditional CTD or Wave buoys. Instead of that, the solution will focus on the deployment solution based on the integration of COTS ➢ Production and management parameters: maintenance operations control (unfold, landslides, rope extraction, surveillance). Upon project completion, a user-friendly digital dashboard was available, providing access to production data exclusively for producers via a website (due to the confidential information that implies). This dashboard also offers a public view, featuring oceanographic and environmental data for all interested STH. The access and visualization of the data is tailored for STH, for the sector and its workers, facilitating understanding, allowing the relationship and comparison between data to obtain significant conclusions. Dashboard Visualization of data – https://utmar.cetmar.org/transformar ZENODO ➢ Mussel Raft Monitoring Dataset in Ría de Arousa https://zenodo.org/records/13238552 ➢ Briefing on the development process of the Mussel Raft Monitoring (MRM) solution and results https://zenodo.org/records/13238570 4.4.6 Upscaling and Acceleration of the Solution Further research is essential, particularly in improving the understanding of system behavior. Incorporate new sensors to monitorize new parameters, such as current flow and chlorophyll levels in the MRM, to estimate the natural food availability for the mussels. Besides, also for the MRM, it could be interesting to include monitoring chemical and biological contaminants that could impact mussel attachment and growth. These insights will be crucial for refining the monitoring system and enhancing our understanding of the environmental conditions affecting mussel cultivation. In this demonstration it is crucial to know exactly when and where specific processes occur and take into account this knowledge to be able to implement automatic detection and early warning systems in the future. Currently, we lack precise information on the variability inside the Rías, with significant changes occurring from one raft to another within just a few days, as a consequence, it will be quite interesting to increase the number of sensorized mussel rafts and intertidal monitoring for the clam culture. By enhancing our understanding now, we can develop more precise and timely alerts that will better serve the needs of the shellfish industry and help them respond more effectively to critical events. In the long term, the impact of these types of solutions could be substantial, as it will enable the optimization of mussel and clam production. The future could involve adapting mussel farming and clam 105 TransformAr Deliverable 5.1 www.transformar.eu harvesting to the natural variability of the Galician Rías. In the case of the MRM, this would represent a shift from the current static model, where mussels are "fixed, grown, and harvested," to a dynamic model where "mussels are fixed in some locations, fattened in others, and stored for harvesting in toxin-free areas." Each zone has its unique qualities, and production must be adapted to these characteristics to ensure sustainability. Currently, farmers are highly attentive to temperature and salinity, but they are seeking more data, such as turbidity (for spawning) and nutrients (for fattening). This additional information together with key insights provided by the Resilience Index, are essential for optimizing the different stages of mussel production and responding more effectively to environmental conditions, thereby ensuring a more sustainable and resilient industry. 4.4 Egaleo, Greece 4.4.7 Overview of the Expected Impacts The expected impacts per solution can be found in Table 4.11. The impacts are either direct, thus, they derive directly from the solution, or indirect, thus, they support the impacts of other solutions or longterm actions beyond TransformAr’s lifetime and scope. The expected impacts per solution can be found in Table XXX. The impacts are either direct, thus, they derive directly from the solution, or indirect, thus, they support the impacts of other solutions or longterm actions beyond TransformAr’s lifetime and scope. In the Municipality of Egaleo (ΜοΕ), an ecosystem of solutions, including health, infrastructure, and urban planning, is being implemented to enhance climate resilience and sustainable development while addressing the impacts of climate change. Three Key Climate Sectors (KCS) in which the MOE demonstrator implements solutions to mitigate the impacts of climate change include health, infrastructure, and urban planning. Within the framework of the TransformAr project, MOE develops a series of integrated solutions aimed at fostering climate awareness and enhancing the capacity of both the municipality and its citizens to adapt to climate change. These solutions will involve the collection and analysis of data from various sources, followed by the post-processing of this data. The resulting insights will be used to raise public awareness of climate issues and to inform evidence-based policy recommendations for climate adaptation at the local level. The solutions being implemented are as follows: 1. Smart climate stations (SCS) MOE is focused on the installation of 21 Smart Climate Stations (SCS) across key locations within the city. The primary objective of these stations is to collect real-time data on the city's microclimate, enabling the public administration to better prepare citizens for potential climate emergencies. Following careful analysis and planning, the key locations have been identified, and the SCS will commence operation during this demonstration. The stations will monitor various parameters, including temperature, relative humidity, atmospheric pressure, CO2 levels (ppm), PM 2.5/10 (ppm), wind speed, and rainfall. 2. Citizen app engagement (CAE) MOE develops a mobile application designed for both data collection and information dissemination. The app will serve three main functions: (1) it will be used to crowdsource data on the climate awareness of MOE’s citizens through questionnaires, (2) it will disseminate information about the TransformAr project’s solutions implemented in MOE, and (3) it will provide notifications regarding climate-related events. 3. Awareness-raising modules (AWAR) 112 TransformAr Deliverable 5.1 www.transformar.eu across the region. At the same time, the engagement of public authorities and the updates of the Local Wetland Observatory (LWO) are expected to encourage institutional investment in monitoring and management, influencing policy frameworks at local and regional levels. Ultimately, these combined actions are anticipated to reinforce the resilience of local communities, integrating environmental, social, and economic dimensions into a more adaptive governance model. The implementation of Nature-Based Solutions (NBS) in the Marceddì and San Giovanni lagoons, including the Smart Gate (SG) system, is expected to deliver both environmental and socio-economic benefits. Environmentally, the interventions are expected to improve knowledge of key water quality parameters, providing a baseline for long-term monitoring. By reorganizing water circulation, the lagoon will gain an enhanced flood regulation capacity, reducing damage from coastal and inland floods. This in turn may lower economic losses, safeguard cultural heritage, and potentially increase fish catches in both quantity and quality, even if this outcome remains dependent on external factors. Social impacts are also foreseen. The NBS is expected to foster synergies among organizations with expertise in water management and climate adaptation, while also influencing policy development at multiple levels. Contributions to lagoon management plans, coastal contract, and the Regional Strategy on Climate Change Adaptation are expected to extend the societal impact of the interventions beyond the local scale. Finally, the Smart Gate introduces a strong technological component. Acting as a pilot system, it will test a conceptual model for regulating lagoon water flows through real-time monitoring data. The expected impacts of this innovation can be assessed in terms of efficiency (timely responses to hydraulic variations), control precision (accurate water management), and reliability (stable performance under changing environmental conditions). These elements position the Smart Gate as a concrete example of technological innovation supporting adaptive lagoon management. 4.5.2 Selected Indicators KPI Summary The following table summarizes the expected impact and the related indicators and completes the data inserted in the deliverable D.8.4. Table 4.12 COAST KPI EXPECTED IMPACTS INDICATORS APPROACH / SOURCE BASELIN E EXPECTED ACHIEVED NOTE - JUSTIFICATION Increased knowledge and awareness of key stakeholders at different levels (public and private sectors, economic categories, citizens, etc.) 1. Number participatory meetings with stakeholders List of meetings 0 10 24 113 TransformAr Deliverable 5.1 www.transformar.eu EXPECTED IMPACTS INDICATORS APPROACH / SOURCE BASELIN E EXPECTED ACHIEVED NOTE - JUSTIFICATION 2. Population that became more resilient See details on D.8.4 KPI 1 0 Target according to the GA = 13800 16.203 Population impacted directly= 600 Population impacted indirectly= 15.603 Replicability on regional scale of the COAST experience 3. People/mana gers reached by the factsheet to disseminate COAST at regional level Number of coastal wetland managers in Sardinia, including those operating within Natura 2000 sites with similar ecological and management characteristic s at the regional level. 0 20 35 Local policies conditioned by the COAST and NBS implementati on as best practice 4. Public authorities actively involved in the project List of officers from public authorities involved – see D-8.4 KPI 16 14 23 44 5. Number of reports and factsheets produced and shared by the LWO Publication LWO and Transformar webpage 0 13 6 Some reports and factsheets were not completed within the project timeframe due to ongoing data collection activities (such as fisheries data, update of the action programme ongoing, unevaluable monitoring data, etc.) Table 4.13 NBS KPI EXPECTED IMPACTS INDICATORS APPROACH / SOURCE BASELI NE EXPECTED ACHIEVE D NOTE - JUSTIFICATION Increased knowledge of main water quality parameters 6. Data collected and analyzed through the monitoring system IT platform and monitoring system – Number of parameters from sensors, weather station hydrometers and Smart Gate 0 14 14 114 TransformAr Deliverable 5.1 www.transformar.eu EXPECTED IMPACTS INDICATORS APPROACH / SOURCE BASELI NE EXPECTED ACHIEVE D NOTE - JUSTIFICATION opening and closing report 7. Fishermen informed on the knowledge acquired Minutes of the meetings with fishermen (See details on D.8.4 KPI 3) 0 140 140 Recorded participation with attendance sheets. Please note: participation in the meetings was limited to the representatives of the cooperatives and the president of the fishing consortium. The participants committed to disseminating the information and outcomes of the meetings to their respective members Improvement of the biodiversity and ecosystem integrity 8. Surface covered by NBS GIS analysis (See details on D.8.4 KPI 6) - 0 27 Km2 (data from GA) 0,193 Km2 The interventions implemented under the TransformAr project are part of a broader NatureBased Solution for the restoration of the Marceddì and San Giovanni wetlands, cofinanced by ERDF funds. The reported value refers only to the effectively restored surface, and does not include the wider area of influence as defined in the Grant Agreement. Reduction of economic losses and cultural damage 9. Potential avoided damages due to CC in targeted the KCS N/A N/A N/A 115 TransformAr Deliverable 5.1 www.transformar.eu EXPECTED IMPACTS INDICATORS APPROACH / SOURCE BASELI NE EXPECTED ACHIEVE D NOTE - JUSTIFICATION Increased efficiency of the lagoon dynamics in case of flood risk (hydrometric level) and poor ecological state of the lagoon, with a significant emphasis on fishing activities (salinity, pH, temperature… ) 10. Enhancement of the water quality IT platform and monitoring system - Qualitative approach N/A N/A N/A Due to delays in the implementation of the solutions (increased costs, extreme events, and disruptions in the supply of materials), it was not possible to evaluate this KPI within the project timeframe. Nevertheless, monitoring activities are still ongoin g and will provide reliable results beyond the project’s duration. 11. Response time to hydraulic variations IT platform and monitoring system - Qualitative approach N/A N/A N/A Due to delays in the implementation of the solutions (increased costs, extreme events, and disruptions in the supply of materials), it was not possible to evaluate this KPI within the project timeframe. Nevertheless, monitoring activities are still ongoing and will provide reliable results beyond the project’s duration 12. Accuracy of aperture/closu re control IT platform - % of correct openings/closures compared to the command sent Qualitative approach: High : the system responds precisely and promptly, with negligible deviations. Medium : some discrepancies occur but without significant impacts on management. Low : frequent errors or large deviations that compromise functionality. 0 High High 116 TransformAr Deliverable 5.1 www.transformar.eu EXPECTED IMPACTS INDICATORS APPROACH / SOURCE BASELI NE EXPECTED ACHIEVE D NOTE - JUSTIFICATION 13. System reliability IT platform - Average number of malfunctions/dow ntimes per year Qualitative approach: High : stable system, with limited maintenance needs and good operational continuity. Medium: occasional interruptions requiring scheduled maintenance. Low : frequent failures or breakdowns significantly reducing system effectiveness. 0 High Medium Low water conditions affected the sensors, requiring more frequent maintenance than initially expected. Limited budget led to periods between interventions with partial or less reliable system performance. Improvement of stock productivity 14. Enhancement of revenue of fishing sector Repository (from Regional Department of Agriculture and Agro-Pastoral Reform - Fisheries and Aquaculture Service) N/A N/A N/A This indicator, which relies on data from the Regional Department of Agriculture and Agro-Pastoral Reform – Fisheries and Aquaculture Service, could not be evaluated within the current reporting period due to delays in the implementation timeline of the solution. Specifically, the interventions impacting aquatic stock conditions (e.g., habitat restoration or water quality improvement) were not in place long enough to generate measurable biological responses in stock productivity. Evaluation would have required a minimum postintervention monitoring period of 12–18 months, covering reproductive and growth cycles. Since this condition was not met, the 117 TransformAr Deliverable 5.1 www.transformar.eu EXPECTED IMPACTS INDICATORS APPROACH / SOURCE BASELI NE EXPECTED ACHIEVE D NOTE - JUSTIFICATION indicator remains unevaluable at this stage. Economic Sustainability and Replicability: Ensure the long-term viability of the solution and enable potential upscaling or replication in other regions. 15. Cost of the solution Project implementation – Quantitative approach 0 210 k euro 282 k euro An increase in costs, driven by external events such as international conflicts and the difficulty in finding qualified companies able to intervene with a multidisciplinary approach in the implementation, led to higher expenses. MEDSEA was able to cover these additional costs by cofunding the activities through other private projects. 16. Implementatio n timing Project implementation - Quantitative 0 24 months 36 months Increased costs, extreme events, disruptions in the supply of materials and administrative authorization processes, have caused a general delay in the implementation of the solution. Operational Reliability and System Efficiency: Guarantee the consistent performance of the solution under varying environmental conditions, minimizing downtime and ensuring continuous impact. 17. Maintenance effectiveness % of operational uptime ensured after maintenance High: ≥ 90% effectiveness (maintenance ensures continuous performance with minimal downtime) Medium: 60–89% effectiveness (occasional downtime or repeated interventions required) Low : < 60% effectiveness (frequent failures, maintenance insufficient to ensure continuity) 0 High Medium Low water conditions affected the sensors, requiring more frequent maintenance than initially expected. Limited budget led to periods between interventions with partial or less reliable system performance. Management and Quantitative evaluation: cost of 0 10k euro / year 15-20k euro / year Low water conditions affected the sensors, requiring more frequent 118 TransformAr Deliverable 5.1 www.transformar.eu EXPECTED IMPACTS INDICATORS APPROACH / SOURCE BASELI NE EXPECTED ACHIEVE D NOTE - JUSTIFICATION maintenance cost the maintenance interventions maintenance than initially expected. Limited budget led to periods between interventions with partial or less reliable system performance. Institutional and Policy Uptake Number of public authorities actively involved in the definition of a protocol post project Minutes of the meetings Quantitative: number of public authorities invited to participate vs public authorities actively involved 0 11 9 In some cases, the participation in meetings was limited by pre-existing commitments, and the competencies required for protocol development were not considered fully consistent with the institutional roles. 4.5.3 Baseline Data MEDSEA collected some preliminary data in the lagoon (2021 - before the implementation period of TransformAr), as preliminary studies for the drafting of the project jointly with the Municipality of Terralba. In addition, the Sardinian Regional Environmental Protection Agency (ARPAS), the public authority responsible for monitoring water bodies — including wetlands — and safeguarding other ecosystems, provided extensive environmental monitoring data from 2016 to 2022. The comprehensive baseline data presented below is essential for understanding the rationale behind the development and implementation of Nature Based Solutions (NBS), encompassing various impacts such as enhanced knowledge of water quality parameters, biodiversity and ecosystem integrity improvement, reduction of economic losses and cultural damage, and increased efficiency in lagoon dynamics during flood risks and ecological challenges, particularly focusing on fishing activities. Bathymetry: Bathymetric survey, covering an area of approximately 830 hectares: Maximum depth reached: -3.68 meters below mean sea level (m.m.s.l.). Minimum depth reached: -0.11 meters below mean sea level (m.m.s.l.). 119 TransformAr Deliverable 5.1 www.transformar.eu Figure 4.18 Digital Elevation Model (DEM) Water temperature (°C): Water temperature Summer period Water temperature Autumn period pH Observing the figure below, it can be noted that the pH follows a clear spatial gradient. Specifically, the San Giovanni basin, and even more so the basins near the mouths of watercourses, exhibit significantly higher values (up to pH=9) compared to the mouth area. In the autumn campaign, the values show greater homogeneity. 120 TransformAr Deliverable 5.1 www.transformar.eu PH summer period Ph Autumn period Chlorophyll 'a': The indicator describes the concentration of chlorophyll "a" in surface waters, allowing for an indirect estimate of phytoplankton biomass, as it provides a measure of the main photosynthetic pigment present in microalgae. It serves as an effective indicator of the system's productivity. The concentration of chlorophyll "a" in water highlights the level of eutrophication in coastal waters. It is of fundamental importance for the application of trophic indices and turbidity indices, assessing the trophic characteristics of the water body and the state of ecosystems. Additionally, it is an excellent indicator for evaluating primary production and the trophic levels of the ecosystem. Chlorophyll – Summer period Chlorophyll – Autumn period Turbidity Turbidity Summer period Turbidity Autumn period 121 TransformAr Deliverable 5.1 www.transformar.eu Salinity (PSU): The parameter of surface salinity shows a clear gradient from the estuarine area toward the mouth of the lagoon. The autumn survey confirmed the salinity gradient, which decreases linearly from the mouth area to the barriers separating the Marceddì and San Giovanni basins. In contrast, a sharp drop is observed, even more pronounced in sectors internal to the embankment near the mouths of watercourses. Salinity summer period Salinity summer period Dissolved oxygen: The percentage of dissolved oxygen plays a crucial role as it is closely related to the water regime and the presence of algae and aquatic macrophytes, as well as the decomposition of organic matter by bacterial communities. This parameter varies between 53% and 137% saturation and generally assumes oversaturation values (O.D.% > 100) in the spring, as a result of the photosynthetic activity of phytoplankton and aquatic plants and algae in general. Spatial analysis of the data highlights a spatial trend between the estuarine area and the mouth area of the lagoon. The autumnal period shows greater homogeneity in dissolved oxygen values, with a decrease observed only near the mouth of the Rio Mogoro. Dissolved oxygen summer period Dissolved oxygen autumn period Water quality from ARPAS 128 TransformAr Deliverable 5.1 www.transformar.eu Average water running time 4 min 49 s 5 min 17 s 4 min 19 s Average total shower duration 4 min 55 s ≈ 5 min 20 s 4 min 27 s % showers < 6 min 80% 78% 83% % showers 6-8 min 10% 8% 9% % showers > 10 min ≈10% 14% 8% % showers with breaks 47% 46% 49% This table clearly shows: • A slight increase in shower times during the pilot (likely linked to sensor issues and peak tourist season). • A reduction during the full experiment, with shorter average times and fewer long showers. • An increase in showers with breaks, showing uptake of “smarter shower” behavior. 4.6.4 Approach to Monitor Impacts Methodologies To monitor the impact of the nudging experiment, the team relied on a combination of technological tools and comparative methodologies. Aguardio shower sensors were installed in hotel bathrooms to record data on shower duration, running water time, the number and length of pauses in water use, as well as humidity and temperature (though the latter two were not central to the analysis). The experiment was structured in three phases: a baseline period where sensors were installed without feedback to guests, a pilot phase in early 2024 where sensors were combined with nudging materials such as brochures and stickers, and finally the full experiment from March to September 2024, involving a wider deployment across participating hotels in Guadeloupe. To ensure data accuracy, very short events under two minutes were excluded from the analysis to avoid interference from other sources, such as sink use. The results were assessed by comparing changes across the baseline, pilot, and full experiment periods, with additional reference to a Belgian control group where similar sensors were tested. Alongside quantitative monitoring, qualitative data was collected through tourist feedback forms (available both on paper and via QR codes) and through regular exchanges with hotel staff, who provided insights into the practical aspects of implementation and guest responses. However, it is important to note that the feedback received from hotel guests was very limited, which constrained the depth of qualitative insights into their perceptions and acceptance of the nudging solution. 4.6.5 Availability and Access of Monitored Data Zenodo: https://zenodo.org/communities/transformar_h2020 The monitored data was collected through the Aguardio shower sensors, which store information locally and require synchronization via Bluetooth and internet connection. For accuracy, hotel staff were asked to synchronize the sensors on a bi-weekly basis; if synchronization was delayed beyond twenty days, older records were overvisualization was lost. This reliance on regular staff intervention created challenges, as many accommodations faced staff shortages or competing priorities, which led to gaps in the dataset. In addition, technical issues such as weak Wi-Fi signals or Bluetooth disconnections occasionally interrupted the transmission of data. While the project team had access to the anonymized 129 TransformAr Deliverable 5.1 www.transformar.eu data through a central dashboard for analysis, hotels themselves did not directly access the results, though they were kept engaged through workshops, meetings, and updates. Privacy concerns in a few accommodations also limited the full deployment of sensors. Finally, although tourist feedback forms were provided both physically and digitally, the number of responses received was very limited, which further restricted the availability of qualitative insights to complement the quantitative monitoring. Data and analysis of the results of the experiment are available in the nudging experiment report. 4.6.6 Upscaling and Acceleration of the Solution The nudging solution implemented in Guadeloupe shows strong potential for replication and expansion. Because the nudging kits are low-cost and easy to install, they can be readily deployed in other hotels across the archipelago and even extended to other Caribbean islands or tourist destinations facing similar water stress challenges. This scalability is further reinforced by the fact that the approach does not require significant infrastructure changes, making it accessible even for smaller accommodations. A particularly promising pathway for upscaling lies in aligning the nudging initiative with eco-tourism certification schemes such as the Clef Verte (Green Key). Hotels that already seek or hold sustainability labels are natural early adopters, as the experiment supports their environmental commitments and enhances their reputation among eco-conscious travelers. By contributing to stronger green credentials, the solution also helps these hotels strengthen their position in an increasingly competitive tourism market. Beyond simple replication, the solution can evolve through more advanced nudging techniques. For instance, the use of personalized feedback systems, gamification, or interactive digital tools could further reinforce sustainable behaviors among tourists. Training hotel staff is also a crucial acceleration pathway, as it equips them to become active promoters of water-saving practices and ensures that the nudging materials are consistently supported by human interaction. Finally, the approach could be extended beyond water management to address other sustainability challenges in the hospitality sector. Similar nudging strategies could be applied to reduce energy consumption (for example, encouraging more efficient use of air conditioning) or to improve waste reduction and recycling practices. Long-term monitoring and evaluation would then be essential to measure the persistence of these behavioral changes and to inform continuous improvement of the intervention. 130 TransformAr Deliverable 5.1 www.transformar.eu 5.0 Results Based on Measured Parameters from the Solution 5.1 Lappeenranta, Finland 5.1.1 Summaries of Particular Deliveries (KPI, Bankability, etc.) KPI 4 – Eutrophication Reduction Monitoring results from the biofiltration and infiltration pilot site on Koulukatu Street (245 m² NBS structure) indicate measurable retention of nutrients. Annual retention efficiency for phosphorus is estimated between 20–30%, and for nitrogen 15–25%, based on stormwater sampling cross-validated with modelling in SWMM. These results demonstrate a modest but significant reduction in nutrient loading into nearby water bodies, supporting long-term eutrophication control. KPI 20 – Increase in Retained Water and Nutrients by NBS The infiltration structure has an estimated storage capacity of 50–60 m³ per storm event (1-in-10-year rainfall). Monitoring confirms reduced peak runoff rates by 25–30% compared to the grey infrastructure baseline. Over the course of one-year, cumulative nutrient retention equated to approximately 2.5–3.0 kg of phosphorus and 12–15 kg of nitrogen, preventing their discharge into surface waters. KPI 18 – Citizen Engagement via Digital Tools (CAF, CEI) The Citizen Application Framework (CAF) and Choice Experiment Insights (CEI) tools have engaged more than 300 local citizens through workshops, surveys, and app usage. Results indicate that 72% of participants expressed willingness to implement private stormwater management measures (e.g., green roofs, permeable paving) if incentivized. Awareness of flood and eutrophication risks increased by >60% among engaged participants compared to baseline surveys. 5.1.2 New Information on Bankability Cost-efficiency of NBS pilot (Koulukatu Street): • Investment cost: €93,100. • Maintenance cost: €2,076/year, slightly lower than the grey infrastructure baseline of €2,118/year. Economic viability: NBS reduces maintenance costs while delivering wider ecosystem services (flood reduction, biodiversity, aesthetics). CAF and CEI contribution: Willingness-to-pay surveys showed households were prepared to contribute €15–30 annually for private stormwater measures, confirming potential for co-financing mechanisms. Financing opportunities: EU Taxonomy-aligned green bonds and municipal insurance-linked resilience funds were identified as suitable instruments. Bankability: Economic analyses have shown that biofiltration and CAF are financially sustainable in the long term thanks to water and energy savings. Investment costs have been offset by reduced operating costs and increased resilience of urban infrastructure. 131 TransformAr Deliverable 5.1 www.transformar.eu 5.1.3 Comparative Results Table 5.1 Comparative Results Parameter Baseline (Grey) NBS Measured/Estimated Expected Impact Phosphorus retention ~0% 20–30% (2.5–3.0 kg/year) 25–35% Nitrogen retention ~0% 15–25% (12–15 kg/year) 20–30% Peak runoff reduction 0% 25–30% 30–40% Maintenance cost €2,118/year €2,076/year Slight savings Citizen engagement Low (baseline awareness <20%) >300 engaged; +60% awareness ≥50% engagement The measurement of parameters required to assess eutrophication indicators proved challenging due to the design of the nature-based solution (NBS). As a result, the data are affected by considerable uncertainty stemming from point-based water quality measurements taken upstream and downstream of the NBS. Within the project, a revised monitoring design was proposed based on data post-processing to improve reliability and representativeness. 5.1.4 Challenges and Mitigations Limited baseline nutrient and runoff data: The lack of long-term data on nutrient concentrations and stormwater flows made it difficult to establish reliable baselines. Mitigation: This issue was resolved by using SWMM modelling, which was supported by cross-validation with targeted laboratory sampling to improve the accuracy of the impact assessment. Space constraints for NBS in dense urban areas: High population density and limited public land availability made it difficult to allocate sufficient space for nature-based solutions. Mitigation: NBS measures were integrated into planned street renovation projects and local planning regulations were updated to require the allocation of space for stormwater management . Voluntary uptake of private stormwater measures: The adoption of household-level measures, such as green roofs and permeable surfaces, depended on voluntary action. Mitigation: The Citizen Application (CAF) was used to raise awareness and encourage participation. Demonstrating long-term effectiveness: Proving the durability and replicability of implemented solutions required monitoring to extend beyond the project duration. 132 TransformAr Deliverable 5.1 www.transformar.eu Mitigation: Ongoing performance is tracked through biofiltration sampling and citizen science contributions via the CAF platform. All monitoring outputs are openly accessible via Zenodo to ensure transparency and facilitate replication. 5.2 West Country, United Kingdom 5.2.1 Summaries of Particular Deliveries (KPI, Bankability, etc.) In the United Kingdom, results varied across intervention sites. Each site was monitored for biodiversity increases, and water/nutrients retained onsite. Due to the range of experimental monitoring techniques used and the different timelines for delivery of NBS at different sites, some sites had greater density of baseline and/or post-intervention data outputs than others. KPI 20.1: UKHAB Surveys Before & After Intervention to Assess Biodiversity Net Gain, Spatially Resolved in Ha/Habitat Restored Axe Chubbs Farm delivery was carried out in lowland mixed deciduous woodland habitat. This was assessed as moderate condition due to diverse age structure and evidence of moderate browsing pressure from pest species. The stream was at a low-level during survey in summer conditions but running clear. The comparison with the control site showed a greater abundance of plant species associated with wet conditions, suggesting the intervention has increased soil moisture levels, increasing biodiversity within the woodland. In addition, sediment was being captured behind the leaky dams, contributing to better water quality. Camel The Penvose baseline habitat was poor condition neutral grassland within a narrow, broadleaf woodland valley. The area of scrape creation was Holcus-Juncus (rushy) grassland in moderate condition. The area around the pond was assessed to be wet woodland in moderate condition, despite the presence of four different Schedule 9 non-native invasive species. In comparison to the pre-intervention UK Hab botanical survey, overall species numbers were up slightly, with a total of 52 species counted against an original total of 46. However, as the surveys were conducted at different times of year (May and August) and by different surveyors, this could have led to variation in outcomes, although many of the same species were observed. The original survey didn’t include abundance of species, which would have provided a useful comparison of the change in habitat. The 2025 survey concludes that the scrapes and blocked ditches are increasing wet habitat conditions and ponds holding water on site even during dry periods. There is potential to increase the value of all habitats further by small changes to management. The Trethick intervention area was assessed as modified grassland in poor condition, largely due to low species diversity. A control area was used to identify changes in habitat. This area had greater bare ground, signs of erosion and greater abundance of farmland weeds such as broadleaved dock than the intervention area. It was also assessed as poor condition. There was no observed wetland species associated with the scrapes and swales, indicating no botanical improvements. However, it was observed that the tussocky grassland due to the less intensive grazing regime provided habitat for field mouse and voles, supporting a range of predator species, which was not available in the control area. A traditional hay cut was recommended to improve diversity of the sward. 133 TransformAr Deliverable 5.1 www.transformar.eu KPI 20.2: Electric Fishing Surveys A total of nine fry index surveys and one fully-quantitative electric fishing surveys were carried out in the Camel catchment on the River Camel, De Lank, tributary of the River Allen and tributary of the River Amble across the three years of the project (Table 5.2 and 5.3). Salmon fries were present only on the De Lank, receiving a stable good classification across the three years of surveying. Salmon fry were absent from all other surveys. Overall trout fry were found in low numbers across the surveyed sites, with the exception of Slaughterbridge/US Slaughterbridge on the River Camel that received a good classification across the project period. Table 5.2 Salmon Fry Index Survey results for the Camel catchment 2023-2025 X Coordinate Y Coordinate Site name River 2023 2024 2025 211075 86278 Worthyvale Camel E (0) 210932 85483 US Slaughter Bridge Camel E (0) 210719 85088 Slaughterbridge Camel E (0) E (0) 208797 73732 DS Keybridge Delank B (14) 208878 73889 US Keybridge Delank B (13) B (11) 205085 78213 Penvose Camping US (2) Allen Trib E (0) E (0) E (0) 205209 77849 Penvose Camping DS (1) Allen Trib E (0) E (0) E (0) 204228 72089 Trethick US (2) Allen Trib E (0) E (0) E (0) 204021 71957 Trethick DS (1) Allen Trib E (0) E (0) 199820 75964 Rooke Farm Amble Trib E (0) E (0) 200184 75346 Penpont Farm Amble E (0) Table 5.3 Trout Fry Index Survey results for the Camel catchment 2023-2025 X Coordinate Y Coordinate Site name River 2023 2024 2025 211075 86278 Worthyvale Camel C (6) 210932 85483 US Slaughter Bridge Camel B (23) 210719 85088 Slaughterbridge Camel B (11) B (22) 208797 73732 DS Keybridge Delank C (5) 208878 73889 US Keybridge Delank E (0) C (8) 205085 78213 Penvose Camping US (2) Allen Trib D (2) D (1) C (5) 205209 77849 Penvose Camping DS (1) Allen Trib C (5) C (5) D (2) 204228 72089 Trethick US (2) Allen Trib D (2) D (1) E (0) 134 TransformAr Deliverable 5.1 www.transformar.eu 204021 71957 Trethick DS (1) Allen Trib E (0) E(0) 199820 75964 Rooke Farm Amble Trib D (1) D (3) 200184 75346 Penpont Farm Amble D (2) Salmon were absent at the fully quantitative survey at Worthyvale across all years, however, the number of trout fry increased year on year from 2023 to 2025 (Table 4.6). Table 5.4 Fully-quantitative salmonid results for Worthyvale, River Camel 20232025 Camel - Worthyvale 2023 2024 2025 Salmon Fry Classification F (0) F (0) F (0) Trout Fry Classification C (10.23) B (18.89) B (37.20) Salmon Parr F (0) F (0) F (0) Trout Parr B (13.92) B (14.66) B (13.90) KPI 20.3: Historic Water Quality Assessments from Existing Datasets An assessment of the Environment Agency’s WIMS data (EA WIMS) was undertaken to understand what data already exists for the demonstrator catchments and to use it as a basis for designing further monitoring around the interventions. Analyses of the data primarily aimed to: 1. Establish the historical (available from 2000 onwards) spatial and temporal distribution of EA routine sampling points for water quality across each demonstrator catchment 2. Derive statistical information on the basic water quality parameters electrical conductivity, turbidity, temperature and nutrients (orthophosphate, reactive as P) from EA sites nearest to interventions to augment WRT’s baseline (pre-intervention) dataset, if available. Spatial and Temporal Distribution of EA Routine Water Quality Spot Data Spatial and temporal analysis using GIS confirmed that relying solely on the EA’s data for assessing intervention effectiveness would not be sufficient. Their site locations and measured parameters differed from year to year and as interventions implemented under the project were to be relatively small-scale and static, this justified the extra, more focused monitoring undertaken by WRT as part of KPI 20.4. Approximately 20 sites across each catchment was selected for monitoring on a monthly basis which was used as part of a number of tools to inform selection of intervention sites. KPI 20.4 Longitudinal Water Quality Monitoring Programme Axe Catchment – Water quality summary A total of 636 spot surveys were completed in the Axe catchment. The ranked waterbodies (Figure 5.1 and Figure 5.2) clearly show the Lower Axe (Kilmington) as performing worst overall, likely due to the location immediately downstream of the Kilmington sewage treatment works (STW). All measured WQ parameters at this site scored in the top 70% of rankings and the Kilmington site was closely followed by many of the sub catchments on the Yarty waterbody. The middle reaches of the Yarty scored particularly 135 TransformAr Deliverable 5.1 www.transformar.eu highly for phosphate (33-37) and electrical conductivity (31-35) and relatively poorly for turbidity (24-34). Two sites on the Kit Brook then followed in terms of overall rankings with the Millhouse Farm at Hook (site 23) scoring the worst for the whole Axe catchment for phosphate. As the interventions to be delivered by WRT under the project were not compatible with tackling pollution issues generated by STWs and the Yarty was already receiving increased attention by the Environment Agency under the Triple Axe Project, WRT focused efforts on engaging landowners in the Kit Brook catchment (site number 4 and upstream), guided by the longitudinal water quality sampling campaigns as well as by which landowners were receptive to land use change. Figure 5.1 Water quality scorecard for the Axe catchment sampling. Figure 5.2 Water quality scorecard map for the Axe catchment sampling. Numbers correspond to the named waterbodies in Figure 2.16. 136 TransformAr Deliverable 5.1 www.transformar.eu Camel Catchment water quality summary The Camel spot sampling campaign (503 surveys) highlighted the River Allen as particularly problematic (Figure 5.3 and Figure 5.4) with pollutants accumulating down the catchment. This guided the implementation of NBS works at Trethick Farm (site 50 in 51) and Penvose Campsite (site 11 in Figure 53) on tributaries of the R. Allen. As landowner support was positive at these sites and interventions could be delivered without the requirement for nutrient credits (described in Section 3), this outweighed pursuit of intervention works in other poorer performing areas with respect to water quality. The need to find demonstration sites was a priority in order to enable exploration of the benefits of NBS and promote engagement by neighboring landowners with future projects to increase resilience. Figure 5.3 Water quality scorecard for the Camel catchment sampling 137 TransformAr Deliverable 5.1 www.transformar.eu Figure 5.4 Water quality scorecard for the Camel catchment sampling. Numbers correspond to the named waterbodies in 51 Summary of Water Quality Monitoring of Solutions Initial qualitative monitoring of the various interventions showed them withstanding storm events and functioning as they were designed (at least in the short term) in terms of backing up water to slow flows and beginning to settle out and entrap sediment behind or within it. The small amount of pre-intervention baseline data made quantification of intervention effectiveness extremely difficult if not impossible. Where baseline data was adequate, no significant differences were found in water quality pre vs. post intervention instalment at any of the study sites, although the relatively short duration of the monitoring period, various changes to original proposed intervention plans and the very small density of interventions implemented meant that even a greatly improved monitoring regime would still have made a robust, fully quantitative assessment challenging. In a study attempting to quantitatively evaluate the impact of interventions at a catchment scale, Grand-Clemet et al., (2021) estimated approximately 0.5% total phosphorus removal at a catchment scale using NBS and Farmscoper modelling. Combining all this with the relatively large margins of instrument uncertainty that WRT’s analytical equipment offers, it is not surprising that capturing reductions in water quality outside of modelled predictions was largely unachievable. It is for this reason also that modelled approaches are the standard means by which councils and/or developers assess nutrient neutrality. Currently, in the UK, there is no requirement for nutrient offsetting to be quantified through water/sediment monitoring at the intervention site. The monitoring undertaken in the following sections does, however, provides some preliminary data which can be shared with landowners and used to engage neighbouring farmers who may be considering implementing NBS on their land. 144 TransformAr Deliverable 5.1 www.transformar.eu KPI 20.6 Measure the depth/volume of sediment captured by the intervention Estimated sediment volumes captured by different types of interventions are given in Table 5.7. Table 5.7 Estimated captured sediment volumes Estimated total potential volume (m3) Average sediment depth (m) Estimated sediment volume (m3) Chubbs Farm (sample taken 104 days after instalment) Leaky dam 1 - 0.089 6.16 Snowdon Farm (sample taken 108 days after instalment) Scrape 1 111.43 0.015 2.64 Scrape 2 139.85 0.010 2.40 Scrape 3 148.00 0.010 2.40 Scrape 4 99.13 0.025 4.96 Scrape 5 129.32 0.010 1.99 Trethick Farm (sample taken 245 days after instalment) Sediment trap 3.68 0.230 3.68 Scrape 1 15.08 0.030 1.13 Scrape 2 8.82 0.010 0.22 Scrape 3 20.80 0.010 0.42 Scrape 4 7.35 0.000 0.00 Scrape 5 7.72 0.000 0.00 AxeSnowdon Hill Farm Sediment had begun to accumulate in each of the scrapes 108 days after Snowdon Farm interventions were installed. The largest volume of sediment and the highest concentration of phosphate (91mgL-1) was observed in scrape 4 and this is most likely due to the features not being connected to the ephemeral stream via the sediment trap at the time of sampling, leading to scrape 1. This probably led to storm water diverting itself around scrapes 1-3 and instead flowing into scrape 4 and 5. Once all features are brought online and the sediment trap is connected, further studies should be undertaken. Camel -Trethick Farm At Trethick Farm, four out of six features have successfully trapped sediment after 245 days, reducing the input of sediment and associated nutrients to the downstream watercourse, with the sediment trap capturing over 3m3. The first sediment trap adjacent to the track was completely filled. However, due to the protracted period of operation before monitoring commenced (1 year), it is unclear how quickly this filled. From observational data, it was clear that the rough ground at the top of the field was also helping to trap sediment and associated nutrients (similar to how a buffer strip is expected to perform), this was, however, difficult to quantify. 145 TransformAr Deliverable 5.1 www.transformar.eu KPI 20.7 Measure the nutrient content of the sediments via laboratory analysis Nutrient samples were taken at Snowdon and Trethick Farms after initial instalment. These samples of soil/sediment were taken from the water retention features primarily in the form of scrapes or sediment traps. These samples were then analyzed using standard laboratory analysis ( https://cawood.co.uk/nrm/). The nutrient results (Table 5.8) from the sites are outlined below including the Soil Organic Matter (SOM) levels, which is a proxy for the amount of Carbon and organic material within the soil. It is difficult to draw any specific conclusions from this data, but a brief analysis is given below. Table 5.8 Concentrations of nutrients within the sediment samples. mg L-1 available element % Loss on ignition pH index P K Mg SOM Snowdon Farm (sample taken 108 days after instalment) Scrape 1 6.6 16 201 155 8.3 Scrape 2 6.8 29.8 296 151 10.5 Scrape 3 7.2 48.2 374 145 13.3 Scrape 4 7.8 91 740 140 16 Scrape 5 7.6 71.6 626 156 13.9 Trethick Farm (sample taken 245 days after instalment) Sediment trap 7.7 28.4 185 63 3.7 Scrape 2 6.9 13.8 120 57 5.5 Axe - Snowdon Farm This system was designed to slow the flow and settle out sediments, leading to the accumulation of sediment-bound phosphorus. Measured plant available phosphorous concentrations increased away from the input source in a series of settlement capture scrapes, until scrape five where there was a small decline in P levels (Table 24). Furthermore, biological activity within the sediment can bind phosphorus. The longer water is held, the more time there is for these P-binding processes to occur, increasing the labile (available) phosphorus in the sediment pool further from the input. It is important to note here that these results are from initial sediment samples that had entered the scrape system, before the two sediment capture pits had been completed and had the opportunity to intercept P from initial sediment deposition. This indicates that some nutrients are accumulating within the scrapes, reducing inputs to the river. Camel - Trethick Farm In total the sediment trap removed 105g of available phosphorus (roughly equating to 321g of phosphate). When comparing the concentration of nutrients in the top sediment trap with those measured within the scrapes at the bottom of the field, P, K and Mg all decreased. 146 TransformAr Deliverable 5.1 www.transformar.eu KPI 20.8 Measure Soil Permeability/Compaction/Infiltration Rate Using VESS Assessment Soil permeability and structure/compaction was reviewed as part of the monitoring of sites. VESS analysis (https://ahdb.org.uk/knowledge-library/how-to-assess-soil-structure ) was undertaken as a standardised method to assess and quantify where possible soil structure. Table 5.9 gives the score per field, soil pit and layer (1 indicated good/friable soils structure, with 4 indicating compacted/poor soil). Table 5.9 Soil VESS scores Site Field Total Depth (cm) Earthworm Count Layer 1 Layer 2 Layer 3 Overall VESS Score Depth (cm) Score Depth (cm) Score Depth (cm) Score Snowdon Farm intervention field Pit 1 25 0 10 2.5 5 3 10 2 2.4 Pit 2 25 6 10 2.5 10 2.5 5 2 2.4 Pit 3 25 9 10 2.5 10 2 5 1.5 2.1 Snowdon Farm conventional field Pit 1 25 5 10 3 10 2.5 5 2 2.6 Pit 2 25 3 5 3.5 10 2.5 10 2 2.5 Pit 3 25 4 10 3 10 2 5 2 2.4 Trethick intervention field Pit 1 25 6 5 1 10 2.5 10 2 2 Pit 2 25 5 5 2.5 10 3 10 2 2.5 Pit 3 25 8 10 1 10 2 5 1.5 1.5 Snowdon Hill VESS scores for Snowdon Hill Farm were marginally better in the intervention field (averaging 2.3) than the conventional field (averaging 2.5). Whilst this is early in the site development the reduced intensity on the intervention part of the field, compared to the more intensive arable section, means the soils are likely to improve further. That said the VESS score improvement does rely on the soils being given a sufficient break between grazing/management to recover any damage caused. VESS analysis gives scores for the various soil horizons found within the soil profile layers, which is then averaged for the whole profile. Thus, if a soil has a small (<1cm) layer of capping at the surface (or in isolation within the profile), but the rest of the soil is good, the average for the 40cm profile will be excellent, but the reality from a water infiltration point of view can be much different. VESS is a good method for overall soil profile health and structure but does need some caveats when being used as an indicator of water infiltration capabilities. In the intervention half of the field, the soil around the sediment pits had a good groundcover of vegetation with few bare patches. Root penetration depth ranged from 10-15cm. The first 0-10cm of the topsoil showed signs of compaction in the form of hardened soil that was difficult to break, and when broken composed of angular shards (some partially rounded) with minimal root penetration past these layers. To the west at the lowest end of the field, the ground was much wetter with a thin layer of standing water. Here the 0-10cm layer was wet and showed signs of compaction, with some grey coloring, indicating that this layer is often saturated with water. 147 TransformAr Deliverable 5.1 www.transformar.eu In comparison, the lower layers (~10/15–20/25cm) had a good soil structure with minimal compaction signs, though some did display mild compaction in some areas. Earthworms were present in this field showing that there was a good soil structure beneath the compacted layers. This trend is typical for livestock grazing and indicates compaction in the top layers due to poaching. Cattle tend to compact soils to around 10cms, whilst sheep usually cause problems in the top 2.5cms. A flock of sheep were present grazing both halves as one field. In the conventional half of the field, the soil had a medium/sparse top groundcover of vegetation with many patches of hard, bare ground portraying desiccation cracks on the surface. Root penetration depth ranged from 5-6cm. It was observed that this half of the field had a higher stone content in comparison to the intervention half. The first 0-10cm of the topsoil showed visible signs of compaction, with the soil being hard and taking considerable effort to break. The soil was also compacted around stones, and when broken consisted of angular shards (some partially rounded). In comparison, the lower layers (~10-20cm) showed marginal signs of continued compaction that lessened with depth. Earthworms were present in this field showing that there was generally a good soil structure beneath the compacted layers. This trend is typical for livestock grazing and indicates compaction in the top layers due to poaching. If this compaction can be remediated, the free draining nature of the farm soils should naturally let more water infiltrate. This means that there may be less run-off during the winter, more grass growth during the summer and a longer season for grazing. Trethick VESS scores at Trethick Farm varied significantly across the field containing the interventions. This is potentially due to the weight of the machinery where excavation work was undertaken, historic compaction e.g. from cattle or feeding troughs. Two of the pits had good surface structure, suggesting that there has been a period of time for the upper layer of soil to recover, with improved structure. The ecology report identified a longer sward (grass height) in the intervention field. Over time this will help to improve soil structure as these taller plants will have a deeper rooting profile, which will break up compaction over time. In time this will also improve the infiltration of water and slow the surface movement of water. KPI 20.9 Measuring the Amount of Organic Matter in the Soil and the Carbon:Nitrogen Ratio to Assess Soil Carbon Sequestration Performance Regarding nutrients at Snowdon Hill Farm (Table 5.9), concentrations of P, K and Mg were significantly higher within the conventional field compared to the intervention field. Part of this change will be due to the reduction in intensive agriculture, meaning reduced nutrient applications. 148 TransformAr Deliverable 5.1 www.transformar.eu Table 5.9 Soil Organic matter results for Snowdon Hill Farm It is challenging to draw any detailed conclusions from the Trethick site given the data set (Table 5.10), however there can be some comparisons between the field and the scrapes. The intervention field has a suitable level of nutrients at index 2 for P and K and a good/target level of SOM, indicating that the field has been down to grassland for some time. The sediment trap has slightly higher P concentrations which may represent nutrient trapped by the feature from sediment and manure run-off. Both the sediment trap and scrape have a lower SOM. This material is comprised of fine-grained run-off sediment, in comparison to the much richer neighboring topsoil. Carbon would be expected to increase in the features as the depth of sediment builds up over time, if not cleared out. Carbon sequestration was not directly measured within the intervention sites; however, soil organic matter (SOM) content is a key indicator. The conventional field had higher SOM potentially due to application of manure as fertiliser. The organic matter (OM) content follows a similar format to that of the P concentrations (KPI 20.7). The lighter OM held within the water flow is deposited as water is slowed and passes through the drainage system. This carbon deposition will be held and processed by the soil biological activity. Subject to aeration and time between deposition and removal of deposited sediment, labile carbon will be available for biological uptake and/or transformation. Some will be stored in stable carbon deposits. Table 5.10 Trethick Farm nutrient results Field Details Soil pH P K Mg P K Mg Soil Organic Matter Index mg/l (Available) [LOI%] Result Trethick intervention field 6.2 2 2+ 2 18.4 202 94 8.3 Trethick Sediment trap accumulated sediment 7.7 3 2+ 2 28.4 185 63 3.7 Field Details Soil pH P K Mg P K Mg Soil Organic Matter Index mg L-1 (Available) [LOI%] Result Conventional field 6.6 4 3 4 48.8 342 190 9 Intervention field outside of scrape features 6.3 3 2+ 4 26.8 183 180 8.4 Scrape 1 accumulated sediment 6.6 2 2+ 3 16 201 155 8.3 Scrape 2 accumulated sediment 6.8 3 3 3 29.8 296 151 10.5 Scrape 3 accumulated sediment 7.2 4 3 3 48.2 374 145 13.3 Scrape 4 accumulated sediment 7.8 5 5 3 91 740 140 16 Scrape 5 accumulated sediment 7.6 5 5 3 71.6 626 156 13.9 149 TransformAr Deliverable 5.1 www.transformar.eu Trethick scrape 2 accumulated sediment 6.9 1 1 2 13.8 120 57 5.5 5.2.2 New Information on Bankability Overview Upscaling and accelerating the solution in the Westcountry region has mixed prospects looking forward. Through the formation of nutrient credits and the need for nutrient neutrality in certain catchments, upscaling in these areas has a financial pathway and is likely to be expanded and built upon. This is due to the legislative need to offset housing development and therefore the costing/investment will need to be embedded within the cost of construction by developers and following the sale of property. For the Camel and the Axe, this provides an ongoing opportunity over the coming years. Bankability review To assess the extent to which NBS such as ponds, scrapes, leaky dams and wetland restoration currently present an investable proposition to private companies, WRT staff undertook a series of semi-structured interviews with relevant actors across the green finance sector. Participants in the bankability interviews identified 49 barriers currently preventing investment in NBS implementation for climate adaptation and 26 potential solutions. TransformAr project partners divided the barriers and solutions into six key categories (public sector governance, private sector governance, economic, skills & knowledge, sociocultural, and physical capital). Then, it was prioritized using a decision-support matrix based on the following weighted criteria: scale, complexity, and consensus. Key barriers identified included: • Lack of high-quality data to prove that Nature-based Solutions perform well enough to make investment decisions at business board level. • Novelty of nature markets mean investor confidence is low • Suspicion of green finance schemes in the farming sector • Poor understanding of demand outside of legislated markets (e.g. legally enforced nutrient offsets) • Disconnection between different sectors and industries divides efforts to create viable projects • Private profit models inherently lead to sub-optimal outcomes for nature • Lack of multi-value decision-making frameworks • Investable/bankable proposition for private companies operating within traditional finance models Ultimately, research concluded that Nature-based Solutions do not always meet the threshold for investment by business or investors, given the uncertainty of return in some instances. Participants produced 14 clear calls for action to improve the viability of green finance schemes. Participants reported that, at present, the most viable models for investing in Nature-based Solutions are non-profit models, such as a Community Interest Company or Special Purpose Vehicle. This result highlights the importance of researching the bankability of Nature-based Solutions at sub-national scale, as, whilst some barriers were consistent with international consensus, others were specific to the green finance context of the Westcountry region. 150 TransformAr Deliverable 5.1 www.transformar.eu Investors usually focus on the UK’s urban centres, leaving the Westcountry with lower investment levels. The Southwest’s economy is worth about £81.6 billion (€97.1 billion), compared to London’s £562 billion (€669 billion). To attract traditional investors, ecosystem services – like carbon storage or water supply – must generate clear, reliable revenue streams. However, the region, although highly dependent on these services for economic stability, has not yet established large-scale valuations. This lack of clear value makes banks and financial institutions hesitant to invest in expensive, nature-based climate adaptation projects. However, individual companies at the local level often recognize their reliance on specific ecosystem services to keep their businesses running or to manage climate risks. These companies might invest smaller amounts, and if they pool their investments, they could finance large-scale Nature-based Solutions. Each company would then receive a share of the benefits, such as carbon credits or improved water supply, along with extra gains like enhanced reputation or better business performance. Interview participants supported this shared investment approach, as it limits the risk of companies exploiting the environment purely for profit. In the UK the Green Finance Institute has developed an investment readiness Toolkit (Figure 5.9), which outlines the process from start to finish and is a useful reference for the investment process and requirements. This highlights the important role of governance structure and the legal contracts for green investment which are still early in development and require time to establish. The Green Finance Institute’s Investment Readiness Toolkit provides a step-by-step framework for developing investible nature-based projects, covering eight milestones from scoping to legal contracts. It offers checklists, case studies, and guidance relevant for ecosystem service projects. (see https://hive.greenfinanceinstitute.com/gfihive/toolkit/) Figure 5.9 Investment Readiness Toolkit, Green Finance Institute 151 TransformAr Deliverable 5.1 www.transformar.eu Summary In order to accelerate investment in Nature-based Solutions for climate change adaptation in low investment regions, it is highly important to research the bankability of these solutions with regional actors. Whilst large, international investors may have certain requirements for viable projects (e.g. clear revenue flows, consistent rate of return over long time periods, etc.), smaller companies may consider payment for discrete outcomes, such as reduced flood risk/increased drought resilience, as a viable investment as these could directly impact business resilience going forward. Water and other utility companies in the UK regions are also being asked by the regulator to use a ‘Green First’ approach (SWW Green First Framework) when it comes to tackling issues such as waste water network flooding and water quality issues, which will increase the scale of NBS delivery over the next 5 years significantly and help to bring about wider adoption, in a sector that has previously focused on engineering solutions for some time. As such, alternative funding/finance mechanisms can be viable in the immediate term. In the Westcountry region, actors expressed preference for blended finance models, such as the establishment of a community interest company, to drive forward investment in, and delivery of, Nature-based Solutions. This model prevents profiteering, securing high-quality environmental outcomes, whilst also delivering the desired return on investment. 5.2.3 Comparative Results As reviewed in section 6.2.2 there are three main areas where a successful nutrient neutrality scheme would have positive impacts. These are: Environmental improvements within the selected sensitive catchments; Economic benefits both to communities from unlocking development and providing alternative income for farmers and land managers; Social benefits of increasing engagement and understanding of rivers and ecosystems, including threats and resilience. Environmental improvements As outlined in the sections above, getting detailed or accurate data around NBS can be challenging, particularly when dealing with ephemeral or seasonal flow pathways from upstream. When there is a regular input flow to a NBS and a defined outlet, more robust or physical data can be collected. For the majority of the WRT interventions a hybrid set of measures were utilized, including WQ samples, sediment samples, ecology and fisheries measurements, drone footage along with nutrient modelling tools. In these scenarios, to gather the multiple and broader benefits of these interventions, a weight of evidence approach is best utilized. By collecting a range of data covering different elements it may be possible to more accurately calculate the wider value/benefit of the NBS. Overall, the limited time period to collect post intervention data limits our ability to more accurately review the efficiency and capability of the intervention. We can, however, using the modelling tools available such as Farmscoper ADAS Farmscoper and some of the sediment trapped within interventions, calculate the volume of sediment trapped and P/kg trapped by the interventions. For example, the volume of P kg/year calculated for Snowdon is significant, when considering Nutrient neutrality values and the potential cost per P/kg, which maybe around £15-20k/kg/yr in the Axe catchment for developers and credits. 152 TransformAr Deliverable 5.1 www.transformar.eu Economic The Economic impact of the nutrient trading scheme has not yet been directly delivered or tested on the ground. The underpinning frameworks for contracts and early trading are still under development, with the first credits in the Camel or Axe not yet available. However, both Cornwall Council, responsible for planning in the Camel catchment, and East Devon County Council, responsible in the Axe, have been able to capitalize nutrient neutrality through applying to the Government’s Local nutrient mitigation fund Local-nutrient-mitigation-fund-round-2 . Cornwall Council have been able to apply for around £2m and East Devon District Council £4.5m approx. representing a significant investment of around £6.5m in a range of solutions, including constructed wetlands. As outlined previously, new housing is needed for local people who provide a workforce for sustainable economic growth. Research showed that each £1m investment in housing development supports around 19.9 direct jobs, and 15.6 indirect jobs, as the construction industry has an indirect and induced employment multiplier of 2.23 (Lichfields, 2022). As outlined previously, when the nutrient credit and offsetting is progressed and trading starts, the following metrics can be used to assess economic impact: • Value of capitalization (e.g. this is already £6.5m for the Axe and Camel) • Value of credits traded • Number of developers/investors and landowners bought into scheme • Wider value from the multiple benefits of these solutions e.g. biodiversity and slowing water runoff. Social Community engagement and awareness raising in the catchments and region has been good with WRT running events directly, adding to existing events and workshops, presenting at existing partnership groups and supporting Westcountry Citizen Science Investigations (CSI) signups. It was estimated that over 5000 people became more resilient through the project, this may be through workshops and partnership meetings, CSI signups, interactions with social media and direct attendance of community events. Eighty-five actors were involved through the project, which represents significant stakeholder engagement, as many of these actors will be involved in similar activities or have the influence to make changes to their delivery programmed or influence cohorts. Key actors, such as local authorities and water companies were involved early in the project, representing the key stakeholders who are most likely to either capitalize nutrient neutrality, invest, or alter policy so that more NBS can be delivered. The regional water company in the new price review cycle from 2025-2030 are utilizing a ‘Green First’ approach (SWW Green First Framework), where NBS needs to be considered as a first option. The citizen science (CSI) and volunteering activity at the Trust has expanded significantly over the last four years, with a major increase in the number of CSI volunteers along with the number of samples taken. Becoming a CSI volunteer also provides an important communication pathway so that a greater awareness of climate change issues can be communicated along with the benefits of climate change adaptation and the solutions implemented. 153 TransformAr Deliverable 5.1 www.transformar.eu 5.2.4 Challenges and Mitigations Changes from ICW and ICWM to on-farm NBS were agreed due to the constraints around land purchases required for ICW, and regulatory issues around the use of ICW on farms to mitigate. However, these solutions primarily manage diffuse inputs from multiple sources, and no changes were made to the expected KPIs for water quality and phosphate reductions, which are much harder to measure for individuals on farm NBS, without a single input source and output point. There were ongoing challenges and delays around the development of the nutrient credit, and this eventually led to certain sites being delayed beyond the project, such as at Slaughterbridge, which will prove to be an interesting riparian buffer case study, when funded by the Local Authority. Other sites were delayed in their deployment in the project as it was originally planned that they would attract nutrient credit funding for the landowner. Once it was clear that credit payments would not take place within the project period, it was decided that works should start and sites be branded as NBS climate adaptation solutions, more than solely nutrient credit sites. A new continuous phosphate WQ sensor developed by SouthWestSensor, which has recently become available, was trialed within this project. The DropSens uses wet-chemistry (micro fluidics) and fully automated colorimetric analysis within the probe body using the Molybdenum Blue method and is one of a small number of commercially available sensors for continuous phosphate measurements. The approach planned for the DropletSens™ phosphate probe was deployment downstream ahead of the scheduled interventions at Chubbs Farm. The DropletSens™ monitor was to be paired with other sensors (WATR) that used direct measurements of standard WQ parameters, such as electrical conductivity and turbidity, to derive phosphate concentrations. These sensors were to be supplied and installed by the North Devon Biosphere Foundation under their Smart Biosphere project and would be sited in the two feeder streams upstream of the Chubbs Farm interventions as well as directly downstream of the intervention (alongside the DropletSens™ probe). Due to site complications and funding restrictions with WATR sensors, these were not installed during the project timeframe. In the context of quantitative monitoring to demonstrate the reduction in phosphate as a result of interventions, there was an insufficient monitoring period prior to the NBS installations to use the continuous monitor for this purpose. However, its deployment under the TransformAr project still allowed some important monitoring to be undertaken to ascertain: • Equipment suitability. Meaning the suitability, both theoretically and practically, of the use of the equipment for monitoring NBS on this scale. We were able to demonstrate the successful deployment of the solar-charged equipment in a small, rural stream with relatively poor signal and wooded banksides. This is the first time WRT has implemented continuous monitoring for phosphate (P). Continuous P monitoring is still rarely done across Europe. In the UK, the legal requirements for nutrient neutrality, led to the requirement to better understand P loads in rivers, and thus improved monitoring methods. • A continuous timeseries dataset for phosphorus in field conditions. Once online, the equipment successfully transmitted 15-minute data remotely to an online platform for viewing and analysis. • The relative performance of different instrumentation for P analysis. Using spot sampling for the measurement of phosphate concentrations in parallel with the SWS DropletSens™ monitor allowed cross-validation of datasets to check the two instruments were in good agreement. • Any relationships between phosphate concentrations and electrical conductivity at the Chubbs site. The relationship between these two parameters can vary depending on phosphorus sources and whether it is present as dissolved or bound to particulate material. Simultaneous deployment of electrical conductivity sensors alongside the SWS DropletSens™ probe meant some