This deliverable presents the implementation progress and results of the innovative technologies
demonstrated across the IMPETUS demo sites during the period M1-M49 of the project (01/10/2021–
30/09/2025). It covers the full set of activities carried out under WP4 Tasks 4.5.1 to 4.12, which together
form Bundle 2: Innovative Technologies Implementation.
Bundle 2 aims to demonstrate a suite of advanced technical solutions that increase climate resilience
across diverse geographic, hydrological, and socio-economic contexts. The bundle includes
decentralised water reuse systems, digital modelling tools, pathogen monitoring technologies, sediment
transport modelling, multi-agent water balance models, decision-support systems for heat and flood risk,
and early-warning technologies for geological hazards. These solutions collectively reinforce the
broader WP4 objective of testing and validating multi-benefit adaptation innovations that can be scaled
across Europe.
The 15 tasks reported in this deliverable demonstrate substantial progress toward climate-resilient water
management, environmental protection, and risk reduction. Task 4.5.1 deployed a hybrid decentralised
fit-for-use water reclamation system in the Coastal demo site (Catalonia), producing high-quality
reclaimed water for irrigation and cleaning within a touristic complex and validating decentralised reuse
under highly variable seasonal demand. Task 4.5.2 implemented a Sewer Mining unit in East Attica
(Mediterranean demo site), integrating real-time data, energy-autonomous operation, and co-created
adaptation services. Task 4.5.3 developed a water-energy simulation and optimisation model, enabling
the operator of the East Attica system to explore climate-proof operation strategies and circulareconomy pathways.
Across several additional tasks, advanced modelling and monitoring capabilities were demonstrated.
Tasks 4.6 and 4.7.1 developed computational tools for sediment transport and regional water balance
simulation, supporting adaptation measures under hydrological and demographic pressures. Tasks
4.7.2 and 4.10.1 - 4.10.3 delivered decision-support systems that integrate multi-layer data for WEFEnexus planning, heat stress management, and flood risk visualisation, many of which are connected to
digital twin environments. Tasks 4.8.1 and 4.8.2 tackled climate-exacerbated water quality risks by
improving bathing water management during storm events and assessing drinking water resilience to
pathogens. Finally, Tasks 4.11 and 4.12 implemented technologies for urban climate proofing in coastal
settings facing sea-level rise and for geological and avalanche early-warning systems in the Arctic and
mountainous demo sites.
Together, the technologies demonstrated under Bundle 2 provide actionable, scalable, and evidencebased adaptation options. The solutions directly support regional water resilience, enable cross-sectoral
decision-making, and reduce exposure to climate-related risks. Their integration into the Resilience
Knowledge Boosters, digital twins, and participatory processes strengthens the IMPETUS vision of
empowering local stakeholders and decision-makers with robust, technology-driven adaptation
pathways.

In 2020, the European Union published ordinance EU 2020/741, establishing minimum requirements for water reuse in agriculture. The ordinance differentiates between several water quality classes. For the highest water quality class (Class A), the ordinance mandates analytical validation of the treatment performance of new water reuse treatment plants (WRTP) related to the removal of microbial indicators for viral, bacterial, and parasitic pathogens. While the ordinance clearly defines the numeric target values for the required log10-reduction values (LRV), it provides limited to no guidance on the necessary sample sizes and statistical evaluation approaches. The main requirement is that at least 90 % of the validation samples should meet the requirements. However, the interpretation of this 90 % validation target can significantly impact the required sample size, efforts necessary, and the risk of misclassifying WRTPs in practice. The present study compares different statistical evaluation approaches that might be considered applicable for LRV validation monitoring. Special emphasis is placed on the use of tolerance intervals, which combine percentile estimations with sample size-based uncertainty and confidence regions. Tolerance interval-based approaches are compared with alternative methods, including a) a binomial evaluation and b) the calculation of empirical percentiles. The latter are already used in existing European and U.S. regulations for bathing water and irrigation water quality. Our study demonstrates that using tolerance intervals allows for the reliable validation of WRTPs that achieve high LRVs relative to regulatory targets with comparatively smaller sample sizes compared to the other two approaches, while reducing the risk of misclassification. Additionally, we show that simplified approaches, such as a “9 out of 10” approach, pose a substantial risk of misclassification and should not be applied. We illustrate the behavior of these different approaches through simulation experiments and application to real data collected in 2022 and 2023 at a large WRTP in Germany.

Short-term fecal pollution events are a major challenge for managing microbial safety at recreational waters. Long turn-over times of current laboratory methods for analyzing fecal indicator bacteria (FIB) delay water quality assessments. Data-driven models have been shown to be valuable approaches to enable fast water quality assessments. However, a major barrier towards the wider use of such models is the prevalent data scarcity at existing bathing waters, which questions the representativeness and thus usefulness of such datasets for model training. The present study explores the ability of five data-driven modelling approaches to predict short-term fecal pollution episodes at recreational bathing locations under data scarce situations and imbalanced datasets. The study explicitly focuses on the potential benefits of adopting an innovative modeling and risk-based assessment approach, based on state/cluster-based Bayesian updating of FIB distributions in relation to different hydrological states. The models are benchmarked against commonly applied supervised learning approaches, particularly linear regression, and random forests, as well as to a zero-model which closely resembles the current way of classifying bathing water quality in the European Union. For model-based clustering we apply a non-parametric Bayesian approach based on a Dirichlet Process Mixture Model. The study tests and demonstrates the proposed approaches at three river bathing locations in Germany, known to be influenced by short-term pollution events. At each river two modelling experiments (“longest dry period”, “sequential model training”) are performed to explore how the different modelling approaches react and adapt to scarce and uninformative training data, i.e., datasets that do not include event pollution information in terms of elevated FIB concentrations. We demonstrate that it is especially the proposed Bayesian approaches that are able to raise correct warnings in such situations (> 90 % true positive rate). The zero-model and random forest are shown to be unable to predict contamination episodes if pollution episodes are not present in the training data. Our research shows that the investigated Bayesian approaches reduce the risk of missed pollution events, thereby improving bathing water safety management. Additionally, the approaches provide a transparent solution for setting minimum data quality requirements under various conditions. The proposed approaches open the way for developing data-driven models for bathing water quality prediction against the reality that data scarcity is common problem at existing and prospective bathing waters.