KWR Water Research Institute

Farmers face increasingly more uncertainty with regards to crop production of due to a changing climate. Temperature and precipitation patterns change, with prolonged periods of heats and droughts. This affects the crop growth season in terms of overall duration and increases the uncertainty of the crop growth conditions. Especially crop water availability is of importance to generate a good yield. Various management strategies can help to manage the water availability, such as drainage, irrigation infrastructures or a combination of both. Within the EU FARMWISE project, various water management strategies are evaluated that could help farmers mitigate future extreme weather conditions. The goal is to determine how various irrigation strategies perform under future climatic conditions.The management strategies under investigation are traditional sprinkler irrigation, subirrigation and a combination of controlled subirrigation with tile drainage. Utilising HydroGeoSphere (HGS), a 3D physics-based integrated surface-subsurface model was setup of an experimental field in the Netherlands. At the field site, an irrigation system, comprising of a controlled drainage with subirrigation is being monitored. The HGS model was calibrated using the field data to simulate the natural groundwater fluctuations, as well as the controlled drainage and subirrigation.To determine the effectiveness of the water management scenarios under various climatic scenarios, the water management scenarios were implemented into the calibrated model. The hydraulic head response and soil moisture content were the parameters of interest. To represent the future climate scenarios, precipitation and evapotranspiration from the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios over three time horizons were used. All model results in terms of hydraulic head and soil moisture response are currently being analysed to determine the effectiveness of the various management strategies under different climates.

Bioremediation is a promising strategy for sustainable management and treatment of field sites with soil and groundwater contamination. Understanding the fate of contaminants is key for identifying optimal conditions and prediction of biodegradation in the subsurface. We develop the open-source Python package mibitrans for hydrogeological reactive transport modelling for simple use of field site modelling, transport prediction and management optimization. Mibitrans is developed in the frame of the EU-funded MiBiRem project.Mibitrans is based on physics-based (semi-)analytical solutions for multidimensional solute transport in groundwater. Biodegradation can be modelled in various ways, including simple first-order decay and chemical reactions based on electron acceptor concentrations. The mibitrans package is modular, fully tested, well documented and well-structured to allow for easy adaptation and use in various field situations. We will present the use with example field site data from ongoing bioremediation projects.

This review comprehensively examines the variability and uncertainty associated with test guideline (TG)-conform genotoxicity data and explores the respective implications for the integration of non-animal-methods (NAMs) into regulatory frameworks. Historical amendments to OECD TGs are mapped to reveal the method's evolution that improves the scientific quality of the data but also explains data heterogeneity within available databases. An analysis of the major genotoxicity databases ECVAM, ISSMIC, and OASIS demonstrates substantial variability in genotoxicity calls. Using the EFSA genotoxicity database, which currently harbours the best-curated (meta-) data, we estimate that 22-77% of compounds exhibit similarity of replicate results below 85%, depending on the assay. The potentially most important variables statistically explaining variability and sensitivity were analysed. The practical limitations to identify them with high reliability and to define their optimum needs to be accepted as a qualitative baseline uncertainty. These findings underscore the necessity of contextualizing NAM performance evaluations within the intrinsic variability and uncertainty of animal and in vitro reference data. We propose that this variability is explicitly considered in the development and validation of NAM-based Integrated Approaches for Testing and Assessment. This review provides a critical foundation for regulators and scientists aiming to enhance the acceptance and utility of NAMs in genotoxicity assessment.

Legionella pneumophila is responsible for the majority of reported Legionnaires' disease cases worldwide. However, environmental monitoring of building plumbing systems often targets a broad range of Legionella species, raising the question of whether monitoring should focus exclusively on L. pneumophila or include all Legionella species. This review examines the policy and public health implications of both strategies by assessing case attribution data for Legionnaires' disease, the environmental prevalence of Legionella species, and the validity of using non-pneumophila counts as indicators for L. pneumophila. Although over 30 species can cause illness, L. pneumophila dominates culture-confirmed cases despite the frequent detection of L. non-pneumophila species in building plumbing and other known sources. Ecological differences between species, including growth temperatures and disinfection resistance, arguably limit the suitability of L. non-pneumophila species as reliable indicators for L. pneumophila. As a result, using all Legionella species counts to inform risk management may lead to excessive interventions without proportional public health benefits. We conclude that routine monitoring should prioritize L. pneumophila to ensure targeted, cost-effective, and health-relevant risk management. Broader monitoring may be warranted in high-risk settings or where local epidemiological data justify a more inclusive approach. These findings support risk-based regulatory frameworks that align monitoring targets with public health outcomes.

Ions play a fundamental role in solid-liquid interface processes, whether as essential or undesirable components, highlighting the need for precise and quantitative real-time monitoring. Electrochemical sensors are identified as promising tools, particularly for field-deployable applications. However, conventional electrochemical sensing is inherently restricted to redox-active species and is often single use, constraining its scope. This study presents electrochemical impedance spectroscopy as an alternative for ion detection, utilizing physico-chemical interactions at the electrode-electrolyte interface. We introduce a first-principles model that describes the interfacial impedance behavior and shows how ion specific processes shape the impedance response. Based on this framework, an extensive dataset is compiled, and a machine learning model is trained to predict electrolyte composition with consistent accuracy, demonstrating detection limits at the parts-per-billion level. The findings indicate that this method has considerable potential as a real-time method for ion sensing, providing a perspective on selectivity and sensitivity beyond traditional electrochemical approaches. This work could serve as a foundation for advanced models of impedance behavior, and development of impedance-based sensors with applicability in complex environments, including biological fluids and industrial liquids.

SummaryCoastal dunes form valuable ecosystems that provide flood protection, drinking water, and high biodiversity worldwide. Although their functioning hinges on habitat zonation along >km-scale sea-to-land gradients, infrastructure development progressively squeezes natural dune ecosystems into a narrow strip. Yet it remains unknown how much undisturbed coastal width is required to support the diverse suites of habitats and species assemblages found in natural dune systems. Here, we investigate plant and habitat diversity in 614 plots along 47 sea-to-land transects in the southeastern USA and the Netherlands. We discover a linear relation between habitat diversity and species richness, indicating that species-rich dunes require diverse habitat assemblages. Moreover, we find that both plant and habitat diversity nonlinearly depend on coastal width, with cumulative plant diversity reaching ∼75% of its potential at 800 and 1,800 m widths in the southeastern USA and the Netherlands, respectively. Alarmingly, dune areas are narrower than these widths along 79% and 66% of southeastern USA and Dutch coastlines, highlighting that lack of space compromises biodiversity along the majority of coastlines. Finally, analyses of management measures along the transects reveal that strategic interventions can, at least in part, mitigate biodiversity losses from infrastructure encroachment. As coastal squeeze—i.e., combined losses from infrastructure and sea level rise—is a global phenomenon, our results suggest that it threatens biodiversity in dune ecosystems worldwide. We argue that the establishment or expansion of nature reserves may be vital for conserving wide dune systems and that targeted management measures can help maintain biodiversity where squeeze cannot be alleviated.

Abstract Managed Aquifer Recharge (MAR) is widely used to enhance groundwater storage and support sustainable water use. To support site selection and planning, machine learning (ML) models are increasingly used as computationally efficient surrogates for traditional numerical models. While ML has shown promise for steady-state simulations, capturing transient responses remains a challenge, yet these are essential for understanding how recharged water is retained through dry periods. In this study, we use ML to model transient MAR effects by decomposing the groundwater storage time series after recharge ceases into two components: the MAR-response and a decay coefficient, assuming exponential storage decline. This simplified representation captures long-term storage dynamics, with U-Net and XGBoost accurately predict these components (R2 > 0.82) for the Baakse Beek catchment in the sandy, drought-sensitive soils of the Netherlands. The trained models are computationally efficient, enabling high-resolution prediction across 1.6 million sites in under 20 seconds, making large-scale scenario testing and optimization feasible. Explainable AI techniques, specifically SHAP values, were used to identify key site management decisions and surface water properties that control MAR effectiveness. Emphasizing model interpretability enhances transparency and fosters trust among hydrologists and stakeholders, improving the potential for broader application and generalizability.

Generative artificial intelligence (AI), particularly large language models, offers transformative potential for the management and operation of urban water systems. As water utilities face increasing pressures from climate change, ageing infrastructure and population growth, AI-driven tools provide new opportunities for real-time monitoring, predictive maintenance and enhanced decision support. This article explores how generative AI can revolutionise the water industry by enabling more efficient operations, improved customer engagement and advanced training mechanisms. It examines current applications, such as AI-integrated supervisory control and data acquisition systems and conversational interfaces, and evaluates their performance through emerging case studies. While highlighting the benefits, the article also addresses key challenges, including data privacy, model reliability, ethical considerations and regulatory uncertainty. Through a balanced analysis of opportunities and risks, this study outlines future directions for research and policy, offering practical recommendations for the responsible adoption of generative AI in urban water management to improve resilience, efficiency and sustainability across the sector.