Pluvial Flooding Research Articles

Urban drainage efficiency evaluation and flood simulation using integrated SWMM and terrain structural analysis.

A method for evaluating urban drainage system efficiency and simulating pluvial flooding is presented, incorporating rainwater inlet limitations through an integrated SWMM and terrain structural analysis (the corrected model). The corrected model is calibrated using data from two flood events and compared to an overflow model (non-corrected, without considering inlet restrictions) under the same conditions to assess its performance. The results show that the relative error of the flood peak in simulations ranges from 0.27 % to 2.80 %, while the Nash efficiency coefficient ranges from 0.3184 to 0.9504, indicating reasonable accuracy and significant comparability to the overflow model. Drainage efficiency evaluation reveals significant variation among drainage units, decreasing with increasing rainfall until surface depressions fill, after which surface drainage improves notably. This highlights the critical role of surface drainage in overall system efficiency and flood dynamics. Furthermore, two types of urban storm-induced floods are identified: infiltration excess and saturation excess floods. Modeling all floods as saturation excess may lead to substantial misestimations, stressing the need for accurate modeling that accounts for drainage unit characteristics. Understanding urban flood mechanisms is essential for improving modeling accuracy and optimizing drainage system design and flood management strategies.

Climate adaptation strategies for active transportation: Barriers and facilitators in U.S. cities

Despite efforts to mitigate climate change by promoting active travel, limited research has focused on climate adaptation for active transportation (CAAT) initiatives. To address this gap, we conducted a qualitative study based on interviews with 30 planning professionals to uncover what CAAT projects U.S. cities are implementing, their barriers, and their facilitators. We found that U.S. cities are increasingly implementing CAAT projects such as street trees and green stormwater infrastructure to address threats like extreme heat and pluvial flooding. Importantly, CAAT projects require collaborations between city departments (e.g., transportation and forestry). We also identified a complex network of barriers and facilitators shaping CAAT project implementation. Funding, politics, laws, and cross-department collaborations can be barriers and facilitators, and supportive (or unsupportive) politics and laws are strongly connected. Additionally, underserved communities face unique barriers to implementing CAAT projects, but recent facilitators such as dedicated funding have contributed to equitable investment.

Shifting cold regions streamflow regimes in North America affect flood frequency analysis

ABSTRACT Over threshold flood events in seventy years of data from 202 reference hydrometric stations in Canada and the United States were separated into nival, mixed, and pluvial flood types. While Mann-Kendall trend tests showed few significant trends in flood magnitude, significant changes in flood type fraction were found over time, with annual mean temperature, and with annual precipitation. Nival events decreased in frequency over the seventy-year period in 16% of sites, while mixed and pluvial events increased (5%, 12%). These changes indicate a shift from nival events towards more pluvial dominated systems. Fewer significant changes in flood type fraction were found with analysis against four climate indices. Flood frequency analysis using a combined distribution approach with the three flood types resulted in larger magnitude design flow estimates (median increase of 20 – 30 %) in comparison with the results from considering the data to be a single population.

A Systematic Literature Review on the Operationalization of Usability in Urban Pluvial Flooding Climate Services and the Influence of Contextual Factors

ABSTRACTExtreme weather impacts due to climate change, such as urban pluvial flooding, necessitate adaptive measures. To support decision making in climate adaptation, climate services provide information and guidance to policy makers and other stakeholders. Effective use of climate services is prohibited by usability gaps. Some causes for these gaps originate from contextual factors. However, these factors are not well understood within academic literature. Therefore, our systematic review has two research questions: (1) What dimensions of climate service usability are considered in the design and evaluation of their use and how are these operationalized?; and (2) What contextual influences may affect these identified dimensions? To this end, we analyzed 51 articles that cover one or more urban pluvial flooding climate services. Our results show several overarching categories of usability dimensions (functionality, ease of use, reducing time and effort, reliability, matching user needs, and other). However, these dimensions are not always fully operationalized by authors. Furthermore, our results show that most contextual factors originate from the meso level (within or between organizations), and in lesser extent the macro (e.g., national policies) and micro level (individual users). Here especially organizational (e.g., Does the climate service fit organizational work approaches?); and process factors (e.g., Was the climate service co‐produced?) have been shown to affect usability. We conclude that more attention should be given to usability as a concept within the design and evaluation of climate services well as to the influence of contextual influences.

A Coupled CA Urban Development Model (UDM) and Urban Fabric Generator (UFG) for the Assessment of Climate Hazards on Future Urban Development

Abstract. Cellular Automata (CA) spatial models have become a de facto means by which to simulate future scenarios of land use activity, particularly urban development. Increasingly, such models are being employed within large scale climate impact, adaption and resilience studies to provide future scenarios of land use change and urban development, facilitating the calculation of the economic impact of future climate hazards and development land use adaption options. However, many spatial CA models only provide information on whether land has undergone a transition from one use/activity to a new one. In the case of urban development, they are unable to provide information on the spatial pattern of new development at an intra-cell level of spatial fidelity. However, in many cases such information is important as the spatial impacts of hazards and the adaption and resilience of new urban development will be dependent on the precise spatial configuration of buildings, roads and urban green space. In this paper an Urban Fabric Generator (UFG) is presented that takes the outputs of an Urban Development Model (UDM) and simulates plausible spatial configurations of buildings, roads and urban green space. The utility of the UFG is demonstrated via two flooding case studies, where generated spatial patterns of urban form are used to parameterise a hydrodynamic pluvial flood model that evaluates the impact of future climate driven rainfall on property damage and the utility of infrastructure investment with regards to improving surface water flood resilience.

Private sensors and crowdsourced rainfall data: Accuracy and potential for modelling pluvial flooding in urban areas of Oslo, Norway

Cloudbursts and extreme rainstorms pose a growing threat to urban areas. Accurate rainfall data is essential for predicting inundations and urban flooding. Private weather stations are becoming increasingly common, and their spatial distribution roughly follows population density. This makes them a valuable source of crowdsourced data for high-resolution rainfall fields in urban areas. We evaluated the performance of private rain gauges in two recent pluvial flood events in Oslo. We also explored the potential use of private rain gauge data in inundation models. Our results indicate that private sensors have excellent rain detection capabilities, but they tend to underestimate the reference value on average by approximately 25%. However, bias-corrected crowdsourced rainfall data can produce significantly more accurate inundation maps than those generated from official rain gauges, if compared with maps resulting from bias-corrected weather radar. Overall, our study highlights the potential of utilizing crowdsourced rainfall data from private sensors for accurately representing pluvial flooding in urban areas. These findings have significant implications for improving flood prediction and mitigation strategies in vulnerable urban settings.

Enhancing transparency in data-driven urban pluvial flood prediction using an explainable CNN model

Mitigating severe losses caused by pluvial floods in urban areas with dense population and property requires effective flood prediction for emergency measures. Physics-based models face issues with low computational efficiency for real‐time flood prediction. An alternative approach for rapid prediction instead of physics-based models is to predict from a data-driven perspective. However, data-driven approaches for urban flood prediction are often perceived as “black box” and might raise concerns. In this study, we propose an explainable deep learning (DL) approach for rapid urban pluvial flood prediction with enhanced transparency using a convolutional neural network (CNN) and the explainable artificial intelligence (AI) framework Shapley additive explanation (SHAP). We process a systematic stepwise feature selection process and establish a CNN model considering topography, drainage networks and rainfall to predict maximum inundation depths. Then, SHAP is applied to provide trustworthy explanations for the decision making in model results. The results show that: 1) Forward selection can offer insights into selecting effective input variables for improved predictions and promote understanding of DL modelling. The spatial pattern of inundation depths predicted by the proposed CNN model shows good agreement with those predicted by the physics-based model, demonstrated by average correlation coefficient (CC) and mean absolute error (MAE) values of 0.982 and 0.021 m, respectively. 2) The CNN model substantially outperforms the physics-based model in computational speed when using the same hardware, achieving speedups of 210 times on GPU and 51 times on CPU in the case study (575167 grid cells, 14.38 km2). Particularly, it can still achieve good performance on a CPU-only standard laptop without high-performance hardware, with only a modest increase in computational time. 3) The SHAP explainable analysis confirms that the CNN model correctly captures the relationships between input variables and water depth, revealing a reasonable decision-making process, enhancing its transparency. The explainable DL approach incorporating SHAP for rapid urban pluvial flood prediction is promising to build trust among stakeholders and provide a trustworthy reference for prompt measures aiming at saving lives and protecting assets during flood emergencies. Additionally, the proposed DL approach can potentially be further expanded to analyze the causes of urban flooding events and serve as a foundation for exploring the transferability of data-driven urban flood prediction, providing benefits for better urban flood risk management.

Enhancing climate adaptation design in urban areas: a workflow for climate integrated impact assessment

Abstract Nowadays, urban settlements face the simultaneous occurrence of environmental, natural, and anthropogenic risks. In this scenario, the contribution proposes the definition of an operational workflow for climate-integrated impact assessment, to support climate adaptation design at the urban and building scale. The methodological approach focuses on the evaluation of heatwave and pluvial flooding integrated impacts to identify urban hotspots. A testing case in Naples is proposed. The workflow represents an innovative tool, employing a multi-hazard analysis at the local scale to assess climate impacts considering both vulnerability and exposure components of the built environment. Grounded in an evidence-based approach, the proposed workflow acts as an effective support tool for decision-makers in the preliminary stages of projects, considering the interaction between impact analysis and climate adaptation design at the local scale. The contribution is developed within the research Partenariato Esteso PE3, RETURN project (multi-Risk SciEnce for resilient commUnities undeR a changiNg climate) - Spoke TS1 “Urban and metropolitan settlements” activities.

A Convolutional Neural Network-Weighted Cellular Automaton Model for the Fast Prediction of Urban Pluvial Flooding Processes

Abstract Deep learning models demonstrate impressive performance in rapidly predicting urban floods, but there are still limitations in enhancing physical connectivity and interpretability. This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton (CNN-WCA) to achieve the precise and rapid prediction of urban pluvial flooding processes and enhance the physical connectivity and reliability of modeling results. The study began by generating a rainfall-inundation dataset using WCA and LISFLOOD-FP, and the CNN-WCA model was trained using outputs from LISFLOOD-FP and WCA. Subsequently, the pre-trained model was applied to simulate the flood caused by the 20 July 2021 rainstorm in Zhengzhou City. The predicted inundation spatial distribution and depth by CNN-WCA closely aligned with those of LISFLOOD-FP, with the mean absolute error concentrated within 5 mm, and the prediction time of CNN-WCA was only 0.8% that of LISFLOOD-FP. The CNN-WCA model displays a strong capacity for accurately predicting changes in inundation depths within the study area and at susceptible points for urban flooding, with the Nash-Sutcliffe efficiency values of most flood-prone points exceeding 0.97. Furthermore, the physical connectivity of the inundation distribution predicted by CNN-WCA is better than that of the distribution obtained with a CNN. The CNN-WCA model with additional physical constraints exhibits a reduction of around 34% in instances of physical discontinuity compared to CNN. Our results prove that the CNN model with multiple physical constraints has significant potential to rapidly and accurately simulate urban flooding processes and improve the reliability of prediction.

Comment on “Analysis of extreme rainfall trend and mapping of the Wadi pluvial flood in the Gaza coastal plain of Palestine”

Comment on “Analysis of extreme rainfall trend and mapping of the Wadi pluvial flood in the Gaza coastal plain of Palestine”

Ecological Flood Resilience Index (EFRI) to Assess the Urban Pluvial Flood Resilience of Blue-Green infrastructure: A case from a southwestern coastal city of India

Urban pluvial flood resilience planning must account for the ecological capacity to absorb, adapt, and transform with floods. Research on flood resilience indices highlights resilience as a normative condition, characterized by the return to equilibrium or the recovery time. However, the socio-ecological approach, including adaptive, absorptive, and transformative capacities is often overlooked, and the determinants of these capacities are underexplored. The present study addresses this gap by proposing an impact-based composite index to measure the flood resilience of urban Blue-Green Infrastructure (BGI). An eight-step procedure was used for the composite index construction. Statistical correlation and regression analysis established indicator appropriateness, and Principal Component Analysis (PCA) was used for weighing and aggregation. The Ecological Flood Resilience Index (EFRI) was developed and applied to the city of Kochi, India to assess the ability of existing urban BGI to temporarily store, infiltrate, and delay runoff, indicating the capacity of the system to withstand urban flooding. The index was validated using crowd-based data followed by sensitivity analysis. The EFRI Map outlines that the highly urbanized low resilient central areas, marked by poor vegetation and disrupted natural drainage contribute to flooding in various parts of the city. A comprehensive analysis of the 37 % regions of the city that falls within ‘very low’ to ‘extremely low’ resilience is essential to understand the critical factors in flood resilient spatial planning. The proposed composite index is significant for its novelty and theoretical clarity in resilience evaluations, promoting a shift from risk-based to resilience approaches in urban flood management.

DEM-based pluvial flood inundation modeling at a metropolitan scale

The global increase in urban flooding presents a substantial challenge that affects communities across the globe. This study introduces a post-flood inundation modeling framework tailored to pluvial floods on a metropolitan scale. We employ a dual drainage modeling approach for enhanced accuracy. The framework comprises two primary components: a Storm Water Management Model (SWMM) for simulating water movement within the storm drain system, and an advanced DEM-based flood inundation module that leverages SWMM results to predict flood inundation areas. Compared to the conventional flood spreading models commonly used in dual drainage modeling, this module incorporates high-resolution DEM cells into the surface flow routing processes, allowing to capture intricate surface terrain complexities. Additionally, the proposed module considers direct rainfall impacts on pluvial flood inundation mapping, resulting in enhanced accuracy. To evaluate the proposed model's accuracy, a two-step validation approach has been implemented. This includes comparing the proposed model results with both the high water marks measured in the aftermath of Hurricane Harvey and the flood depth map generated by a hydrodynamic model. The findings affirm the effectiveness of our proposed framework for post-flood inundation mapping within metropolitan settings. This method provides a dependable approach to urban flood modeling and represents an advancement in addressing the challenges associated with urban flooding.

Exploring the network structure of coupled green-grey infrastructure to enhance urban pluvial flood resilience: A scenario-based approach focusing on ‘centralized’ and ‘decentralized’ structures

Urban pluvial floods pose a significant risk to cities, occurring when precipitation exceeds the carrying capacity of the urban drainage network. Coupled green-grey infrastructure has emerged as a sustainable solution for mitigating urban pluvial floods. This study aims to explore best practices in the network configuration of urban drainage systems coupled with low-impact development (LID) to enhance flow distribution and stormwater infiltration. To do so, we focused on two competing key concepts in network analysis: (1) Centralization and (2) Decentralization. We integrated a one-dimensional stormwater model with a rapid flood spreading model to assess the flood mitigation performance of various centralized and decentralized network configurations in the Gangnam region of Seoul, South Korea. To further assess the combined effects of green and grey infrastructure, we compared the performance of each drainage network configuration with and without identical mixed LID practices. Here we show that the centralized drainage network scenario performed best in reducing flood volume by 40.3%, the decentralized drainage network scenario performed best in shortening flood duration by 47.8%, and the LID practices scenario performed best in mitigating peak flooding rates by 4.2%, each as independent scenarios. When all three scenarios were coupled together, flood volume could be reduced by 73.5%, flood duration by 54.7%, and peak flooding rates by 19.8% in the study area. This exploratory study underscores the potential of network analysis in urban flood research, particularly the effectiveness of loosely-connected network topology. Our findings contribute to the development of best practices for coupled green-grey infrastructure, facilitating sustainable stormwater management and urban flood resilience.

Investigating the influence of urban morphology on pluvial flooding: Insights from urban catchments in England (UK)

As climate change intensifies, cities globally are experiencing more severe rainfall and frequent pluvial floods. Urban expansion is altering the permeability of the land, thus increasing the risk of flooding. This study investigates the impact of urban morphology on pluvial floodwater distribution in 15 urban catchments across England, UK, to provide an analysis of how urban morphology influences flood magnitude. Using a cellular automata-based model, pluvial flood simulations were conducted for catchments characterized by diverse urban morphologies. Then a series of machine learning models were adopted to reveal the relationships between the morphological characteristics of urban configurations (e.g., building footprints, impervious surfaces, street network, topography) and pluvial flooding. These models were used to identify and quantify the effects of key urban morphological indicators on pluvial flooding. The results indicate that, although the total area of impervious surfaces plays the most significant role in floodwater distribution, the edge density (ED) of building footprints and impervious surfaces also influences this process. Synthetic experiments with an exemplary urban fabric show that decreasing “ED of building footprint” and increasing “ED of impervious surface” can mitigate flood volume by up to 6.3 % at 100 % drainage efficiency and 7.8 % at 50 % efficiency. The results of this study are anticipated to aid urban planners and policymakers in developing strategies for implementing flood-resilient cities.

A Coupled Human and Natural Systems (CHANS) framework integrated with reinforcement learning for urban flood mitigation

To address the challenge of escalating urban flood risk and the deficiency in effective flood emergency management, this study introduces a novel Coupled Human and Natural Systems (CHANS) modelling framework that employs hierarchical reinforcement learning to optimise mobile pump scheduling and placement for urban flood risk mitigation. The CHANS framework integrates hydrodynamic and agent-based models within a multi-GPU computing environment for high-resolution, real-time flood inundation modelling and risk assessment to enrich Reinforcement Learning (RL) training. In the application to Ninh Kieu District in Can Tho City, Vietnam, the new RL-enabled modelling framework is used to evaluate optimal mobile pumping strategies for concurrent pluvial flooding and post-flooding events against the traditional deployment approaches. Results demonstrate that RL-based strategies can significantly enhance flood risk reduction, outperforming traditional methods by achieving 2× and 4× improvements in the concurrent and post-flooding periods/events, respectively. Incorporating human factors and adapting to local conditions, the RL agent provides valuable insights into mobile pump scheduling and deployment strategies. Sensitivity analysis confirms the robustness of the CHANS modelling framework and underscores the role of RL in optimising mobile pump scheduling and placement where traditional rule-based strategies are challenging.

Synergistic assessment of multi-scenario urban waterlogging through data-driven decoupling analysis in high-density urban areas: A case study in Shenzhen, China

Extreme meteorological events and rapid urbanization have led to serious urban flooding problems. Characterizing spatial variations in flooding susceptibility and elucidating its driving factors are essential for preventing damages from urban pluvial flooding. However, conventional methods, limited by spatial heterogeneity and the intricate mechanisms of urban flooding, frequently demonstrated a deficiency in precision when assessing flooding susceptibility in dense urban areas. Therefore, this study proposed a novel framework for an integrated assessment of urban flood susceptibility, based on a comprehensive cascade modeling chain consisting of XGBoost, SHapley Additive exPlanations (SHAP), and Partial Dependence Plots (PDP) in combination with K-means. It aimed to recognize the specific influence of urban morphology and the spatial patterns of flooding risk agglomeration under different rainfall scenarios in high-density urban areas. The XGBoost model demonstrated enhanced accuracy and robustness relative to other three benchmark models: RF, SVR, and BPDNN. This superiority was effectively validated during both training and independent testing in Shenzhen. The results indicated that urban 3D morphology characteristics were the dominant factors for waterlogging magnitude, which occupied 46.02 % of relative contribution. Through PDP analysis, multi-staged trends highlighted critical thresholds and interactions between significant indicators like building congestion degree (BCD) and floor area ratio (FAR). Specifically, optimal intervals like BCD between 0 and 0.075 coupled with FAR values between 0.5 and 1 have the potential to substantially mitigate flooding risks. These findings emphasize the need for strategic building configuration within urban planning frameworks. In terms of the spatial-temporal assessment, a significant aggregation effect of high-risk areas that prone to prolonged duration or high-intensity rainfall scenarios emerged in the old urban districts. The approach in the present study provides quantitative insights into waterlogging adaptation strategies for sustainable urban planning and design.

Urban pluvial flood susceptibility mapping based on a novel explainable machine learning model with synchronous enhancement of fitting capability and explainability

Urban pluvial flood susceptibility mapping aims to identify flood-prone areas in cities. Machine learning methods are increasingly used, but typically face a trade-off between accurate identification of flood-susceptible areas and development of effective local mitigation strategies. To synchronously enhance fitting capability and explainability, this study introduced a novel machine learning model, Residual-Logistic Regression (RES-LR). RES-LR is developed by integrating Artificial Neural Network (ANN) and Logistic Regression (LR). The model adopts ANN to form its explainability module, which increases model complexity to ensuring the model’s fitting ability. Meanwhile, the explainability module was incorporated into the LR structure through residual connections to provide parametric expressions for local feature contributions, enabling the identification of key flood conditioning factor by site. The model was applied to the Beijing Metropolitan Area, achieving an accuracy rate exceeding 80% in identifying flooded sites. Based on its local explainability, the model unveiled the diverse flood formation mechanism in the study area and the complex impact of drainage-related factors. These results were informative for formulating site-specific flood mitigation measure. This study represents a significant advancement in the application of explainable machine learning for systematic flood management.

Multifunctional Porous Pavement Prototype for Urban Pluvial Flood Protection: Preliminarily Findings on Contribution of Attitudes to Acceptance Willingness Toward Proposed Scientific and Engineering Solutions

Rapid urbanization and climate change correlate with an increase in frequency of flood events, globally. For many cities worldwide pluvial floods bring significant risk. Permeable/pervious paving (PePav), as an essential sustainable urban drainage technique, is one of the key environmental solutions for urban flooding. Waste and recycled materials in the construction industry for PePav production comply with the principles of circular economy and sustainable development. The influence of the human factor is well recognized in efforts to apply those solutions and make them self-sustainable. The preliminary results show that younger female students and students with lower family monthly income are prone to express more positive attitudes toward PePav generally. Students from different study groups (Departments of Psychology, Hydraulic and Environmental Engineering, and Construction Project Management) show significantly different attitudes toward PePav. Statistical models indicate that the PePav acceptance willingness can be predicted with moderate accuracy by knowing attitudes toward PePav and personal experience in construction. The contributions of the fact that someone's close person has been affected by a flood may vary depending on the type of PePav scientific and engineering solutions.

Pluvial flood modeling for coastal areas under future climate change – A case study for Prince Edward Island, Canada

It has been increasingly understood that pluvial flooding poses a substantial risk to numerous communities across the globe. This is especially relevant for the Canadian province of Prince Edward Island (PEI), which is susceptible to a compound flood consisting of both inland and coastal flooding. Despite various studies, a comprehensive pluvial flood model still lacks that addresses the complex interplay of compound floods. Therefore, this research aims to bridge the gap in flood mitigation by developing a pluvial flood model for PEI’s coastal communities. The Intensity-Duration-Frequency (IDF) curves under current and future climatic conditions were used to portray rainfall intensity over the study area corresponding to 10-year, 25-year, 50-year, and 100-year return periods. In addition, a hydraulic model (HEC-RAS 2D) was used to drive pluvial flood maps based on two configurations, including detailed flood maps for major municipalities and an island-wide level. The validated results showed consistency in model simulation when compared to observations. The high-resolution flood maps produced by this study can support the development of flood mitigation and adaptation strategies in PEI and other parts of the world.

The role of citizen science in assessing the spatiotemporal pattern of rainfall events in urban areas: a case study in the city of Genoa, Italy

Abstract. Climate change in the Mediterranean region is manifesting itself as an increase in average air temperature and a change in the rainfall regime: the value of cumulative annual rainfall generally appears to be constant, but the intensity of annual rainfall maxima, between 1 and 24 h, is increasing, especially in the period between late summer and early autumn. The associated ground effects in urban areas consist of flash floods and pluvial floods, often in very small areas, depending on the physical-geographical layout of the region. In the context of global warming, it is therefore important to have an adequate monitoring network for rain events that are highly concentrated in space and time. This research analyses the meteo-hydrological features of the 27 and 28 August 2023 event that occurred in the city of Genoa, Italy, just 4 d after the record maximum air temperature was recorded: between 19:00 and 02:00 UTC almost 400 mm of rainfall was recorded in the eastern sector of the historic centre of Genoa, with significant ground effects such as flooding episodes and the overflowing of pressurised culverts. Rainfall observations and estimates were made using both official or “authoritative” networks (rain gauges and meteorological radar) and rain gauge networks inspired by citizen science principles. The combined analysis of observations from authoritative and citizen science networks reveals, for the event analysed, a spatial variability of the precipitation field at an hourly and a sub-hourly timescale that cannot be captured by the current spatial density of the authoritative measurement stations (which have one of the highest densities in Italy). Monthly total rainfall and short-duration annual maximum time series recorded by the authoritative rain gauge network of the Genoa area are then analysed. The results show significant variation even at distances of less than 2 km in the average rainfall depth accumulated over sub-hourly duration. Extreme weather monitoring activity is confirmed as one of the most important aspects in terms of flood prevention and protection in urban areas. The integration between authoritative and citizen science networks can prove to be a valid contribution to the monitoring of extreme events.