Smart rainwater harvesting (RWH) systems can automatically release stormwater prior to rainfall events to increase detention capacity on a household level. However, impacts and benefits of a widespread implementation of these systems are often unknown. This works aims to investigate the effect of a large-scale implementation of smart RWH systems on urban resilience by hypothetically retrofitting an Alpine municipality with smart rain barrels. Smart RWH systems represent dynamic systems, and therefore, the interaction between the coupled systems RWH units, an urban drainage network (UDN) and digital infrastructure is critical for evaluating resilience against system failures. In particular, digital parameters (e.g., accuracy of weather forecasts, or reliability of data communication) can differ from an ideal performance. Therefore, different digital parameters are varied to determine the range of uncertainties associated with smart RWH systems. As the results demonstrate, smart RWH systems can further increase integrated system resilience but require a coordinated integration into the overall system. Additionally, sufficient consideration of digital uncertainties is of great importance for smart water systems, as uncertainties can reduce/eliminate gained performance improvements. Moreover, a long-term simulation should be applied to investigate resilience with digital applications to reduce dependence on boundary conditions and rainfall patterns.

Enhancing resilience of drainage networks is a crucial practice to protect both humans and nature. One way to enhance resilience is to identify critical parts of drainage networks for targeted management and maintenance strategies. While hydrodynamic modelling approaches for identification are computationally intensive, in this study, a novel method based on complex network analysis is used to determine the most critical pipes in a benchmark and a real network of an Alpine municipality. For evaluation, the results of the proposed graph method are compared with hydrodynamic simulations in terms of accuracy and computational time. Results show that the proposed method is very accurate (R2 = 0.98) for branched benchmark network while the accuracy reduces slightly for the more complex real network (R2 = 0.96). Furthermore, the accuracy of the proposed method decreases with increasing loop degree and when the system is pressured with higher return period rainfall. Although the outcomes of the proposed method show slight differences to hydrodynamic modelling, it is still very useful because the computational time and data required are much less than a hydrodynamic model.

The first step in real-time management, optimal design, and retrofitting of urban drainage networks (UDNs) involves developing a model capable of rapidly and accurately assessing resilience under various failure scenarios. This study introduces a novel physics-guided approach based on graph theory as a surrogate model for resilience assessment, offering a computationally highly efficient alternative to traditional hydrodynamic models such as the Storm Water Management Model (SWMM). Given the study's focus on resilience evaluation, only the maximum flow of subcatchments was used as input for the physics-guided model, which employs a nonlinear method derived from SWMM outputs to ensure a fair comparison with SWMM's resilience computations. The graph-based resilience assessment incorporates the weighted shortest path method and introduces modified hydraulically informed graph metrics for flow routing in UDN pipes. This approach enables resilience assessment under structural (i.e., single-pipe failure) and functional failures (i.e., high-intensity rainfalls) significantly faster than the SWMM model. The methodology was validated using two real-world case studies under different rainfall scenarios, demonstrating that the graph-based resilience assessment achieved high consistency with the resilience calculations based on the SWMM hydrodynamic model.

Water distribution networks (WDNs) are critical infrastructures prone to vulnerabilities which lead to failures. Identifying vulnerable components, especially multiple pipe failure combinations, is crucial for effective management and ensuring high reliability. Hydraulic simulations are commonly used for analysing the criticality of WDN, but are time-consuming and highly data-reliant, limiting the number of testable combinations. To address these limitations and constraints, a graph-based method is proposed to quantify the impact magnitude of multiple pipe failure scenarios on performance, enabling the identification of critical combinations. The proposed graph-based approach utilizes structural and topological characteristics of WDNs as well as spatial demand distribution to replicate hydraulic behaviour. The accuracy of the approach is assessed by testing it on three case studies with various pipe failure combinations, and the results are compared with hydraulic analyses. The results demonstrate a strong correlation (Spearman coefficient > 0.75) between graph-based ranking and state-of-the-art hydraulic-based ranking. Additionally, the method exhibits a significant computational gain factor of greater than 30 compared with the hydraulic-based method, rendering it valuable for actively exploring a wide range of critical pipe failure combinations and devising countermeasures. Furthermore, a hybrid-based method that integrates both the graph and hydraulic-based methods is proposed for enhanced accuracy and robust assessments.

One way to enhance the resilience of urban stormwater networks is to identify critical components of the network for targeted management and maintenance strategies. However, tackling multiple-pipe failures poses a significant hurdle due to the computational burden of conventional methods, making it impractical to analyze all potential combinations of pipe failures. To address this research gap, this study proposes a novel method based on complex network analysis to determine critical pipe failure combinations. The method incorporates network topology and estimates flooding impacts based on graph measures instead of solving complex hydraulic equations. The method is tested on two case studies with varying loop degrees and for different failure levels and compared with the state-of-the-art hydrodynamic modeling method (HMM) in terms of accuracy and computational time. Results show that the proposed graph theory method (GTM) can be used to identify the most critical pipe failure combinations. However, the accuracy of the GTM decreases slightly with increasing failure level and with increasing loop degree of the network. A hybrid graph-hydrodynamic model (GHM) is also developed as part of the study which combines advantages from both GTM and HMM.