Leaks and bursts in water supply networks can cause significant infrastructure damage and pose contamination risks. Even utilities with robust rehabilitation strategies are not immune to the costly consequences of major bursts. A key question is whether such events can be prevented by detecting and localizing them while they are still small (i.e., leakage flows below 3 L/s). The model-based algorithm Dual Model has demonstrated both simplicity and precision, securing first place among 18 algorithms in the Battle of the Leakage Detection and Isolations Methods. However, mismatches of around 10% between the hydraulic model and the real network can hinder its performance, particularly in detecting and locating small leaks. In this work, we enhance the Dual Model by incorporating source inflows, allowing discrepancies between the real and simulated networks to be expressed as residual virtual flows. These residuals are integrated into the model as demand patterns, enabling the detection of leaks as small as 2–3 L/s even under perturbations of roughness and base demand exceeding 35%. Additionally, this approach calibrates nodal pressures without requiring manual adjustments to roughness or demand values.

Climate change and industrialization necessitate a reassessment of water management strategies, particularly in agriculture, where reclaimed water supply often fails to meet irrigation needs. Storage can bridge supply gaps but raises concerns about water quality deterioration due to microbial changes and pathogen regrowth. This study examined microbial dynamics and regrowth during reclaimed water storage from a municipal wastewater treatment plant in Germany. The treatment train included ozonation, filtration and UV disinfection, and samples were analyzed using traditional culture methods for indicator organisms (e.g., Escherichia coli,  Clostridium perfringens spores, and somatic coliphages) and 16S rRNA gene amplicon sequencing. Samples were collected throughout the treatment train and stored at 22 °C in the dark for up to 15 days. Results showed effective microbial reduction by treatment, with storage alone achieving similar declines in many cases. While treatment reduced bacterial diversity, storage gradually restored it, forming distinct microbial profiles from the original water quality. Bacterial communities converged during storage, suggesting a succession-like stabilization process. The findings highlight the dynamic nature of reclaimed water microbiomes and the importance of stimulating stable microbial communities to preserve water quality during storage. Advanced treatment should remove contaminants while supporting microbiomes that protect public health and the environment.

Agricultural water reuse is one approach to mitigate water stress. In addition to the minimum requirements, the European water-reuse regulation 2020/741 mandates a risk management approach for agricultural water reuse. In contrast to the microbiological monitoring, the extent of the chemical risk assessment and monitoring is not clearly defined. The resulting complexity of a typical agricultural water-reuse scheme was analyzed. Potentially relevant parameters were identified based on European and German regulatory frameworks, concerning key subjects of protection. An interdisciplinary assessment of efforts and challenges, regarding required analyses, was accomplished, using expertise from recent research investigating agricultural water reuse in Germany. Suggestions were provided for disinfection validation, microbiological monitoring parameters and analytical methods. Additionally, chemical indicator parameters were suggested to address relevant processes during monitoring. Both microbiological and chemical parameters presented analytical challenges, which were described with future needs to support water-reuse implementation. Costs for analyses were estimated using available price information, highlighting the high costs of certain analyses, especially for organic micropollutants. Therefore, analyses need to be further facilitated by the application of process indicators and the implementation of cost-effective, multi-target methods tailored to the requirements of risk management for agricultural water reuse.