Ready for the flood? Assessing the applicability of pluvial flood mapping based on the worst urban flood in Sweden

Pluvial flooding in urban areas is one of the most significant drivers of disaster loss in Canada. Damages during pluvial flood events are associated with overwhelmed urban drainage (stormwater and wastewater) systems. During the period from 2013 to 2021, Canadian property and casualty insurers reported approximately CAD 2 billion in personal property (residential) pluvial sewer backup claims during flood catastrophes. There has been growing interest in managing pluvial urban flood risk, notably through newly funded national programs focused on climate change adaptation. These programs have included the development of new guidelines and standards focused on managing the underlying factors contributing to urban and basement flooding. Inflow and infiltration (I/I) has received limited attention in the pluvial flood literature, however. Informed by significant engagement with practitioners in Canada, this paper provides a review of the issue of I/I into wastewater systems and its relation to pluvial flooding. The paper will address concerns related to private property engagement in I/I and urban pluvial flood reduction programs. Both improved technical standards and administrative support are needed to ensure that wastewater infrastructure is less susceptible to I/I over its lifecycle.

Impact of urban drainage system malfunctions on pluvial flooding – Peri-urban study site in Austria

Urban pluvial flooding now occurs more frequently than it has in past decades, mainly due to an increasing number of extreme precipitation events occurring in the context of a changing climate. To limit the evolving risks of urban pluvial flooding in a more environmentally friendly manner, the research community has recently paid increasing attention to Nature‐Based Solutions (NBS), which are based on new green technologies. To meet the urgent demand for a comprehensive review of the most recent literature, this review conducts a systematic survey of the literature to characterize various NBS adopted in different regions of the world and to elaborate on the benefits and limitations of such NBS. The review highlights the role of NBS in urban flood risk management under ongoing climate change and rapid urbanization. It shows that NBS could effectively mitigate urban flooding caused by high‐frequency precipitation events, with additional economic, ecological, and social benefits. However, NBS are less effective at helping cope with pluvial flooding caused by extreme precipitation events over a short period of time, while gray infrastructures also have limitations as a mitigation measure against extreme pluvial flooding. We thus recommend identifying flood risk management strategies by evaluating the performance of alternative combinations of NBS with gray infrastructures in preventing pluvial flooding in the cities. Finally, recent advances made in the applications of NBS are presented with suggestions (e.g., long‐term monitoring) to improve urban flood adaptive management.This article is categorized under: Engineering Water > Planning Water Engineering Water > Sustainable Engineering of Water Science of Water > Water Extremes

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

Abstract. Fast urban pluvial flood models are necessary for a range of applications, such as near real-time flood nowcasting or processing large rainfall ensembles for uncertainty analysis. Data-driven models can help overcome the long computational time of traditional flood simulation models, and the state-of-the-art models have shown promising accuracy. Yet the lack of generalizability of data-driven urban pluvial flood models to both unseen rainfall and distinctively different terrain, at the fine resolution required for urban flood mapping, still limits their application. These models usually adopt a patch-based framework to overcome multiple bottlenecks, such as data availability and computational and memory constraints. However, this approach does not incorporate contextual information of the terrain surrounding the small image patch (typically 256 m×256 m). We propose a new deep-learning model that maintains the high-resolution information of the local patch and incorporates a larger surrounding area to increase the visual field of the model with the aim of enhancing the generalizability of data-driven urban pluvial flood models. We trained and tested the model in the city of Zurich (Switzerland), at a spatial resolution of 1 m, for 1 h rainfall events at 5 min temporal resolution. We demonstrate that our model can faithfully represent flood depths for a wide range of rainfall events, with peak rainfall intensities ranging from 42.5 to 161.4 mm h−1. Then, we assessed the model's terrain generalizability in distinct urban settings, namely, Lucerne (Switzerland) and Singapore. The model accurately identifies locations of water accumulation, which constitutes an improvement compared to other deep-learning models. Using transfer learning, the model was successfully retrained in the new cities, requiring only a single rainfall event to adapt the model to new terrains while preserving adaptability across diverse rainfall conditions. Our results indicate that by incorporating contextual terrain information into the local patches, our proposed model effectively simulates high-resolution urban pluvial flood maps, demonstrating applicability across varied terrains and rainfall events.

<p>Unmanned Aerial Vehicles (UAVs) are a very effective means to map river beds and flood extent accurately across a wide area, even while the flood is happening or shortly thereafter. Flood mapping information is also very valuable in a long-term context, for drainage infrastructure planning and management. Here, we will present three applications: UAV-based information for hydrologic modelling of the urban drainage system, for flood extent mapping and for identification of bottlenecks in the system that can cause urban flooding.</p><p>UAV flights were conducted in Kumasi, the second biggest and fastest growing city in Ghana, where urban flooding has become more frequent due to changes in the climate and have a more negative impact due to rapid urbanization and population growth. Not only are the natural flood plains increasingly being used for anthropogenic purposes, the increased population growth also brings along more solid (plastic) waste on the streets and into the riverbeds and riverways. This creates blockages in drains and riverways, which reduces its drainage capacity and adds to the flooding problems. UAVs were used to collect elevation information (DEM), river bed dimensions and land-use. This information was used to construct a hydrological model to predict river flows and flooding. In addition, using thermal imagery from UAV flights over partially flooded agricultural fields near the town of Kianjai, Kenya, we will demonstrate that UAV imagery can identify flooded areas even when cross-cut by vegetation or other obstacles.</p><p>We will present the three applications and discuss the promises and challenges of deploying UAVs for the purpose of urban hydrological modelling and flood mapping.</p>

<p>With the acceleration of urbanization, urban pluvial flooding seriously threatens urban sustainable development and human life. It is widely accepted that various landscape elements contribute to the magnitude of urban pluvial flooding. Considerable efforts investigated the universal mechanism of urban pluvial flooding by regarding the whole study area as spatial homogeneous while ignoring its local specific mechanism. The spatially heterogeneous effects of landscape elements on urban pluvial flooding remain poorly understood. Additionally, it is still unclear how the interactive effects of landscape elements affect urban pluvial flooding. In most practical situations, urban pluvial flooding is affected by multiple factors, rather than by a single factor alone. These shortcomings make it impossible to formulate urban pluvial flooding mitigation measures based on the relative contribution of various landscape elements on urban pluvial flooding. To shed some light on this topic, an innovative method that integrated the all subsect regression model, cubist regression tree, and geographical detector model is presented to spatially explicit the heterogeneous forces driving urban pluvial flooding variation and identify the pluvial flooding dominant driving forces with different local conditions. By comparing with two other commonly used regression methods (global regression model, spatial lag model), the proposed method can fully quantify this spatial non-stationarity mechanism and spatially explicit the local driving forces. Urban pluvial flooding dominant driving factors and their contribution vary with the local site conditions. Even for the same dominant factor, its contribution to pluvial flooding varies considerably in different watersheds. Based on this, local authorities can develop site-specific urban pluvial flooding mitigation strategies according to the dominant factors in different areas. The results of this study extend our scientific understanding of the site-specific mechanism of urban pluvial flooding, providing useful information for formulating more targeted and effective urban pluvial flooding mitigation strategies with different local conditions, rather than a “one-size-fits-all” policy.</p>

Mapping the capacity of concave green land in mitigating urban pluvial floods and its beneficiaries

Developing a framework for attribution analysis of urban pluvial flooding to human-induced climate impacts

Pluvial floods are and will remain an important driver of flood risk, especially in an urban context. Recently, several floods triggered by extreme rainfall made the news and led to many casualties, such as those in Valencia and Nepal in 2024. To better prepare for such disasters, urban planners may use pluvial flood maps to assess flood risk and plan accordingly. Typically, such maps are produced by distributing rainfall over topography using a hydraulic model which solves some variation of the shallow water equations. While the decision for a specific hydraulic model may impact pluvial flood maps, here we will focus on the role of pluvial input data.Typically, intensity-duration-frequency (IDF) data is used to drive these models, yet these data are highly uncertain due to, for instance, the absence of accurate rainfall observations or the application of extreme value statistics.Here, we present results of a sensitivity analysis in which we employed a range of global and national IDF data sets, such as NOAA Atlas 14, KOSTRA-DWD, BURGER, GPEX, PPDIST and PXR. Each data set is unique in the amount of data it was produced with, the spatial extent, the spatial regionalization of point-based estimates, the extreme value distribution used, and so forth. All IDF datasets were fed into a hydraulic model (LISFLOOD-FP) using the Chicago Design Storm (CDS) method to produce consistent and comparable maps of pluvial flood hazard for several test cases. Subsequently, the (dis-)agreement of the flood maps obtained is assessed.To convert flood maps into impact, they are intersected with exposure data to obtain an estimate of average annual exposure (AAE) to pluvial floods, which is a better measure for assessing the impact of these floods.While we expect that intensities extracted from the different IDF data sets will differ markedly, this study will shed light on the impact these differences may have on flood hazard and flood exposure estimates.

As part of the low countries and with one of the highest population densities worldwide, the Flemish region has experienced a long history of flooding causing tens of millions euro damage each year. In response to this, water managers invested over the past decade in flood modelling and mapping with a fluvial origin. In recent years, pluvial flooding has also occurred numerous times in Flanders, but a region-wide map describing these processes more in detail in terms of extent, depth and probability was lacking. Following a pilot-study in 2016, the VMM undertook in 2017 the VLAGG1- project to develop a region-wide, high-resolution pluvial flood map for Flanders. Via a combination of state-of-the art methodologies and web technologies, a draft flood map was presented to a broad reviewing community across Flanders, who were then able to improve it further by adding local knowledge on known flooding and more detailed data on key hydraulic structures. In a three month period, over 7000 additions were made by 370 delegates from 165 organizations that have been incorporated into, and significantly improved the quality of the final flood maps which are due to be published in 2019.

<p>High-intensity and short-duration storms can generate pluvial floods in urban areas. Currently, 2D hydrodynamic models are recognised to be the best tool to simulate pluvial floods. The T-year synthetic design storm is usually assumed to generate the T-year pluvial flood. However, synthetic design storms cannot represent the variability in duration, precipitation and intensity temporal distribution of real storms that should be considered to account for their influence on water depths in pluvial floods. A more sound approach consists in estimating the T-year water depth in a given location from the frequency curve of water depths generated by a long series of possible rainfall events similar to the real storms.</p><p>However, 2D hydrodynamic models require high computation times that are not well suited with stochastic simulations. The Safer_RAIN tool is a rapid hydrostatic flood model based on a filling-and-spilling technique that has been developed within the SAFERPLACES project funded by the EIT Climate-KIC (Samela et al., 2020). Depressions and links between them are identified from a digital terrain model. The continuity equation is used to simulate how depressions are filled and spill to downstream depressions. Infiltration is simulated by using a distributed implementation of the Green and Ampt model that accounts for ponding time.</p><p>In this study, a stochastic methodology to delineate pluvial flood hazards is proposed in the Pamplona metropolitan area in Spain. First, the Safer_RAIN tool has been benchmarked by using spatially distributed high-resolution quantitative precipitation estimates (QPE) at time steps of 10 minutes for three real pluvial flood events. QPEs were obtained merging the data recorded at a set of automatic weather stations from the Spanish State Meteorological Agency (AEMET), the Regional Government of Navarre and crowdsourced networks, with continuous fields of radar observations. The Safer_RAIN tool has been benchmarked with the 2D hydrodynamic IBER model. In Barañáin, the results show a bias of -0.17–0.18 m and a RMSE of 0.22–0.49 m between water depths, as well as an accuracy correlation coefficient (ACC) of 0.87–0.99. In Zizur Mayor, the bias is -0.19–0.20 m, the RMSE is 0.29–0.55 m and the ACC is between 0.88 and 0.98.</p><p>Second, a long set of 10 000 synthetic storms has been generated by using a stochastic rainfall generator based on a bivariate copula approach fitted to data recorded at four rainfall-gauging stations located close to the case study. The 10 000 synthetic storms generated with a Gumbel copula fitted to the real rainfall events have been used as input data of the Safer_RAIN tool. Safer_RAIN preprocessing was done in 112 seconds and each simulation lasted around 45 seconds. A Generalized Pareto distribution function was fitted to the 10 000 water depth values in each cell of the grid. Pluvial flood hazard maps were obtained by estimating the T-year water depth in each cell of the grid.</p><p><strong> </strong></p><p>Samela et al. (2020). Safer_RAIN: A DEM-Based Hierarchical Filling-&-Spilling Algorithm for Pluvial Flood Hazard Assessment and Mapping across Large Urban Areas, Water, 12, 1514.</p>

Urban flooding caused by heavy rainfall confronts cities worldwide with new challenges. Urban flash floods lead to considerable dangers and risks. In cities and urban areas, the vulnerability to pluvial flooding is particularly high. In order to be able to respond to heavy rainfall events with adaptation strategies and measures in the course of urban development, the spatial hazards, vulnerabilities and risks must first be determined and evaluated. This article shows a new, universally applicable methodical approach of a municipal pluvial flood risk assessment for small and medium-sized cities. We follow the common approaches to risk and vulnerability analyses and take into account current research approaches to heavy rainfall and urban pluvial flooding. Based on the intersection of the hazard with the vulnerability, the pluvial flood risk is determined. The aim of the present pluvial flood risk assessment was to identify particularly affected areas in the event of heavy rainfall in the small German city of Olfen. The research procedure and the results have been coordinated with the city’s administration within the framework of a real laboratory. In the course of the science–policy cooperation, it was ensured that the results could be applied appropriately in urban developments.

Pluvial flood risk is increasing in urban and rural areas due to changes in precipitation patterns and urbanization. Pluvial flooding is often associated with insufficient capacities of the sewer system or low surface drainage efficiency of urban areas. In hilly areas, hillside runoff additionally affects the risk of pluvial flooding. This article introduces a methodical approach and related evaluation criteria for a systematic analysis of potential causes of urban pluvial flooding. In the presented case study, the cause of pluvial flooding at two selected sites in a hillside settlement is investigated based on a coupled 1D/2D model of the whole hydrological catchment. The results show that even though bottlenecks in the sewer system are important, the effect of low surface drainage efficiency and hillside runoff greatly influence pluvial flooding. The knowledge of different causes of flooding can be further used for selecting and positioning appropriate adaption measures. The presented approach proved its practicability and can thus serve as a guidance and template for other applications to gain better understanding and knowledge of local specific pluvial flooding events.

Spatial heterogeneity of controlling factors’ impact on urban pluvial flooding in Cincinnati, US