Compound Flood Research Articles

Modeling compound flood risk and risk reduction using a globally applicable framework: a pilot in the Sofala province of Mozambique

Abstract. In low-lying coastal areas floods occur from (combinations of) fluvial, pluvial, and coastal drivers. If these flood drivers are statistically dependent, their joint probability might be misrepresented if dependence is not accounted for. However, few studies have examined flood risk and risk reduction measures while accounting for so-called compound flooding. We present a globally applicable framework for compound flood risk assessments using combined hydrodynamic, impact, and statistical modeling and apply it to a case study in the Sofala province of Mozambique. The framework broadly consists of three steps. First, a large stochastic event set is derived from reanalysis data, taking into account co-occurrence of and dependence between all annual maximum flood drivers. Then, both flood hazard and impact are simulated for different combinations of drivers at non-flood and flood conditions. Finally, the impact of each stochastic event is interpolated from the simulated events to derive a complete flood risk profile. Our case study results show that from all drivers, coastal flooding causes the largest risk in the region despite a more widespread fluvial and pluvial flood hazard. Events with return periods longer than 25 years are more damaging when considering the observed statistical dependence compared to independence, e.g., 12 % for the 100-year return period. However, the total compound flood risk in terms of expected annual damage is only 0.55 % larger. This is explained by the fact that for frequent events, which contribute most to the risk, limited physical interaction between flood drivers is simulated. We also assess the effectiveness of three measures in terms of risk reduction. For our case, zoning based on the 2-year return period flood plain is as effective as levees with a 10-year return period protection level, while dry proofing up to 1 m does not reach the same effectiveness. As the framework is based on global datasets and is largely automated, it can easily be repeated for other regions for first-order assessments of compound flood risk. While the quality of the assessment will depend on the accuracy of the global models and data, it can readily include higher-quality (local) datasets where available to further improve the assessment.

Probabilistic compound flood hazard analysis for coastal risk assessment: A case study in Charleston, South Carolina

Coastal communities are susceptible to flooding due to flood drivers such as high tides, surge, waves, rainfall, and river discharges. Recent hurricanes such as Harvey, Florence, and Ian brought devastating impacts from combinations of high rainfall and storm surge, highlighting the need for resilience and adaptation planning to consider compound flood events when evaluating options to reduce future flood risk. Flood risk assessments often focus on a single flood driver (e.g. storm surge) due to the complexity of accounting for compound flood drivers. However, neglecting these compound flood effects can grossly underestimate the total flood risk. A probabilistic compound flood hazard analysis considers all compound events that lead to flooding, estimates their joint probabilities, simulates the flood response, and applies a probabilistic computation technique to translate flood responses and probabilities into probabilistic flood maps (such as the 100-year flood map). Probabilistic flood maps based on compound events can be used to assess risk more accurately for current and future conditions, with and without additional adaptation measures. In this paper we present an example of a probabilistic compound flood hazard analysis for the city of Charleston, South Carolina, considering tide, surge, and rainfall, for both hurricane and non-hurricane events. Charleston is regularly confronted with compound flood events, which are expected to worsen with sea level rise and more frequent tropical storms. Starting with an initial set of over 1,000 synthetic compound events, selection techniques described in the paper led to a final set of 207 compound events. The fast compound flood model SFINCS simulated the flood response for each event and, using numerical integration, compound flood return-period maps were created for Charleston, under current and future sea level rise conditions.

Co-occurrence of pluvial and fluvial floods exacerbates inundation and economic losses: evidence from a scenario-based analysis in Longyan, China

Hydrodynamics and socio-economic impacts of compound floods as the result of co-occurring pluvial and fluvial floods, have not been well studied, which challenges conventional urban flood risk management that treats different types of floods separately. This study generates a high-resolution land surface digital surface model based on images from unmanned aerial vehicles, and incorporates it into a combined flooding model that integrates the 1D river hydrodynamic model, 1D urban drainage hydrodynamic model and 2D overland flow model, in order to simulate the inundation characteristics of extreme floods when local pluvial floods coincide with upstream fluvial floods, and in a further step to quantify the direct economic losses on urban physical systems. As demonstrated by an empirical study of Longyan, China, the combination of pluvial and fluvial floods leads to substantial exacerbation in inundation extent and depth. Consequently, the joining of pluvial and fluvial floods greatly amplifies economic losses to urban physical systems, indicating that ignoring co-occurrence of different types of floods will potentially lead to underestimation of flood risk and insufficient adaptation. Our study emphasizes the need to account for the co-occurrence of multiple types of floods in flood risk assessment and management, to avoid the shortcomings of fragmented responses.

Compound flood events: analysing the joint occurrence of extreme river discharge events and storm surges in northern and central Europe

Abstract. The simultaneous occurrence of extreme events gained more and more attention from scientific research in the last couple of years. Compared to the occurrence of single extreme events, co-occurring or compound extremes may substantially increase risks. To adequately address such risks, improving our understanding of compound flood events in Europe is necessary and requires reliable estimates of their probability of occurrence together with potential future changes. In this study compound flood events in northern and central Europe were studied using a Monte Carlo-based approach that avoids the use of copulas. Second, we investigate if the number of observed compound extreme events is within the expected range of 2 standard deviations of randomly occurring compound events. This includes variations of several parameters to test the stability of the identified patterns. Finally, we analyse if the observed compound extreme events had a common large-scale meteorological driver. The results of our investigation show that rivers along the west-facing coasts of Europe experienced a higher amount of compound flood events than expected by pure chance. In these regions, the vast majority of the observed compound flood events seem to be related to the cyclonic westerly general weather pattern (Großwetterlage).

Modelling compound flooding: a case study from Jakarta, Indonesia

The paper investigates compound flooding from waves, sea surge and river flow in northern Jakarta, Indonesia, which is a global hotspot of flooding, by combining process-based coastal and river models. The coastal hydrodynamic modelling of Jakarta Bay in Indonesia shows that coastal storms can lead to a substantial increase in sea water level due to wind and wave setup in the nearshore areas, including Muara Angke river inlet. The compound flood hazard from a range of flood scenarios was simulated and analysed. The results reveal that low-lying areas around the river inlet are prone to flooding even during regular, low-intensity storm events, while rarer storms caused extensive floods. Floods were not caused by direct overwashing of sea defences but by overspill of the banks of the river inlet due to high sea water level caused by wind set up, wave setup, and sea surge obstructing the drainage of the river and elevating its water level during storms. We also found that the sea level rise combined with rapid land subsidence will inundate the existing coastal flood defences during storms in future. The majority of the city will be below mean sea level by 2100. The overflow of existing coastal defences will lead to extensive flooding in northern, western, and eastern Jakarta unless the defences are upgraded to keep up with future sea level rise.

Estimating the combined risks of sea level rise and storm surges using a numerical model: Application to Macao

The storm surges caused by tropical cyclones may lead to overtopping floods in low-lying coastal communities, which will be further exacerbated by sea level rise. In this study, a numerical model is developed that can simulate the two-dimensional surface flow and one-dimensional drainpipe flow simultaneously. Then this model is validated and applied to Macao Peninsula. The compound flood scenarios of storm surge and sea level rise are therefore forecasted, with storm surge return periods of 5-, 10-, 20-, 50-, 100-, and 200-years, and sea level rises by 0 m, 0.5 m and 1.0 m. The overtopping floods caused by storm surges are mainly distributed in the west and northwest of Macao Peninsula. When the sea level rises by 1.0 m, the backflow occurs 2 h earlier than that does under the current sea level. Adding pumping station can reduce backflow and improve drainage efficiency significantly. The effectiveness of the drainage system during floods can be quantified using backflow coefficient and drainage efficiency coefficient, which both decrease as the storm tide level increases. In the worst-case scenario of 200-year-return-period storm surge and 1-m sea level rise, the flooding area accounts for 41.1% of the land area, and the population at risk reaches 68.4% of the total population. The storm-surge-prone area overlaps with densely populated area in western Macao Peninsula, where the effects of storm surge and sea level rise are intensified.

Perspectives on Compound Flooding in Chinese Estuary Regions

Extreme surges and rainfall represent major driving factors for compound flooding in estuary regions along the Chinese coast. The combined effect of extreme surges and rainfall (that is, compound floods) might lead to greater impacts than if the drivers occurred in isolation. Hence, understanding the frequency and severity of compound flooding is important for improving flood hazard assessment and compound flood resilience in coastal cities. In this study, we examined the dependence between extreme surges and corresponding rainfall events in 26 catchments along the Chinese coastline during typhoon and non-typhoon seasons using copula functions, to identify where the two drivers more often occur together and the implication for flood management in these locations. We found that the interaction between flood drivers is statistically significant in 10 catchments located around Hainan Island (south) and Shanghai, where surge peaks occur mainly during the typhoon season and around the Bohai Sea (north), where surge peaks occur mainly during the non-typhoon season. We further applied the copula-based framework to model the dependence between surge peaks and associated rainfall and estimate their joint and conditional probability in two specific locations—Hainan Island and the Bohai Sea, where the correlation between flood drivers is statistically significant. We observed that in Hainan Island where most of the surge peaks occur during the typhoon season, extreme rainfall events during the typhoon season are generally more intense compared to annual maxima rainfall. In contrast, around the Bohai Sea where surge peaks occur mainly outside the typhoon season, rainfall is less intense than annual maxima rainfall. These results show that the interaction between extreme surges and rainfall can provide valuable insight when designing coastal and urban infrastructure, especially in highly populated urban areas prone to both coastal and pluvial flooding, such as many Chinese coastal cities.

Combined statistical and hydrodynamic modelling of compound flooding in coastal areas - Methodology and application

This paper presents a robust cost-effective framework for assessment of coastal-fluvial flooding due to compound action of multivariate dependent drivers. The methodology is an 8-step process that links statistical and hydrodynamic models to determine probabilities of multiple-driver flood events and associated hazards. The method involves individual and combined extreme value analysis, assessment of dependencies and interactions between flood drivers, multivariate joint probability determination accounting for dependencies, high-resolution hydrodynamic modelling of flood scenarios derived from multivariate statistical analysis, and ultimately mapping of inundation.Using Cork City, on the south coast of Ireland, as a study case, the research shows that the interactions and dependencies between tides, surges and river flows affect flood severity when they occur jointly. Tide-surge interactions have a damping effect on the total water level, while dependence between the surge residual and river flow amplifies the risk of flooding. The multivariate joint exceedance probability occurrence of high discharges and water levels represents a more realistic representation of the spatially variable water surface profiles then the combined univariate marginal scenarios. Multivariate analysis allows also considering multiple combinations of joint probability solutions along RP iso-curves. The results show that the quantification of compound flood impacts must be performed along the entire RP probability curve. This is because the physical/hydrological impacts of multiple-driver same-RP flood events can be very different leading to substantially different characteristics of flooding. The multi-scale nested flood model (MSN_Flood) was used to simulate flood wave propagation over urban floodplains for an ensemble of statistically derived flood scenarios. The hydrodynamic runs provide inundation maps that can be used to draw inferences about flood mechanisms and impacts.

A globally applicable framework for compound flood hazard modeling

Abstract. Coastal river deltas are susceptible to flooding from pluvial, fluvial, and coastal flood drivers. Compound floods, which result from the co-occurrence of two or more of these drivers, typically exacerbate impacts compared to floods from a single driver. While several global flood models have been developed, these do not account for compound flooding. Local-scale compound flood models provide state-of-the-art analyses but are hard to scale to other regions as these typically are based on local datasets. Hence, there is a need for globally applicable compound flood hazard modeling. We develop, validate, and apply a framework for compound flood hazard modeling that accounts for interactions between all drivers. It consists of the high-resolution 2D hydrodynamic Super-Fast INundation of CoastS (SFINCS) model, which is automatically set up from global datasets and coupled with a global hydrodynamic river routing model and a global surge and tide model. To test the framework, we simulate two historical compound flood events, Tropical Cyclone Idai and Tropical Cyclone Eloise in the Sofala province of Mozambique, and compare the simulated flood extents to satellite-derived extents on multiple days for both events. Compared to the global CaMa-Flood model, the globally applicable model generally performs better in terms of the critical success index (−0.01–0.09) and hit rate (0.11–0.22) but worse in terms of the false-alarm ratio (0.04–0.14). Furthermore, the simulated flood depth maps are more realistic due to better floodplain connectivity and provide a more comprehensive picture as direct coastal flooding and pluvial flooding are simulated. Using the new framework, we determine the dominant flood drivers and transition zones between flood drivers. These vary significantly between both events because of differences in the magnitude of and time lag between the flood drivers. We argue that a wide range of plausible events should be investigated to obtain a robust understanding of compound flood interactions, which is important to understand for flood adaptation, preparedness, and response. As the model setup and coupling is automated, reproducible, and globally applicable, the presented framework is a promising step forward towards large-scale compound flood hazard modeling.

Compound flood hazard assessment and analysis due to tropical cyclone-induced storm surges, waves and precipitation: a case study for coastal lowlands of Kelani river basin in Sri Lanka

Compound flood hazard assessment and analysis due to tropical cyclone-induced storm surges, waves and precipitation: a case study for coastal lowlands of Kelani river basin in Sri Lanka

Impact assessment of climate change on compound flooding in a coastal city

Flood disasters in coastal cities are generally caused by the combined effect of typhoon rainfall, storm surge and other factors. Coastal cities will face more severe compound flooding under climate change. Although some studies have analyzed compound flooding under climate change, there is a lack of research considering the combined effect of sea level rise (SLR) and future rainfall changes, especially lack of the jacking effect of SLR on the volume and duration of compound flooding. This paper investigates the impact of future rainfall changes and SLR on compound flood risk, via a case study of a coastal city, Haikou, China. Future rainfall in Haikou is obtained by bias correction of rainfall from five global climate models (GCMs) in CMIP5. The SLR in 2050 and 2090 is predicted according to IPCC. Then, a 1D/2D hydrological-hydrodynamic model for dynamic simulation of surface runoff is applied to simulate compound flooding. Results show that both future rainfall changes and SLR will lead to the increase of flood extent, flood depth and flood duration. But future rainfall changes will have a greater impact on flood, and the combined effect of future rainfall changes and SLR on flood is greater than the sum of their individual effects. The individual effects of SLR and future rainfall changes would increase flood depth in 10.6 % and 32.1 % of the study area, respectively, while their combined effect would increase this proportion to 34.9 %. Also, the jacking effect of SLR on compound flooding will lead to an increase in flood volume, but it presents a nonlinear relationship that gradually slows down with the increase of SLR. A SLR of 0.3 m and 0.63 m will lead to a 2.34 % and 3.22 % increase in flood volume, respectively. And the jacking effect of SLR is more obvious when future rainfall changes are considered, which will lead to a sharp increase in the duration of compound flooding. Moreover, water storage facilities and tidal gates are effective ways to reduce the impact of climate change on compound flooding.

Comparison of estimated flood exposure and consequences generated by different event-based inland flood inundation maps

Abstract. The flooding brought about by compound coastal flooding events can be devastating. Before, during, and immediately following these events, flood inundation maps (FIMs) can provide essential information to emergency management. However, there are a number of frameworks capable of estimating FIMs during flood events. In this article, we evaluate FIMs derived from three such frameworks in the context of Hurricane Harvey. Our analysis reveals that each of the three FIM frameworks provides different FIMs that differ in their level of accuracy. Each of the three FIMs also produces different exposure and consequence estimates because of their spatial differences. This investigation highlights the need for a centralized means of vetting and adjudicating multiple FIMs during compound flood events empowered by the ability to distribute FIMs as geographic information system (GIS) services and coalesce FIMs into a common operating picture. Furthermore, we provide evidence that the ability to produce multi-model estimates of FIMs to create probabilistic FIMs may provide a better product than the use of a lone FIM.

Compound floods in Hong Kong: Hazards, triggers, and socio-economic consequences

Study regionHong Kong is a megalopolis located on the southeast coast of China. Study focusMegalopolis located in low-lying coastal areas such as Hong Kong is especially vulnerable to compound flood risks from multiple flood drivers (e.g., heavy rainstorms and extreme sea levels). Using long-term hydrological observations and socio-economic data, this study aims to reveal the risks, causes and socio-economic impacts of compound floods from precipitation and sea level in Hong Kong. New hydrological insights for the regionWe find that the most extreme sea levels are mostly accompanied by heavy precipitation and thus lead to compound floods, while the most extreme precipitation events are less likely to coincide with extreme sea levels. TCs are the major trigger of compound floods with fractional contributions ranging from 48.8% to 83.8% in the gauges. Typhoon Wanda (1962) triggered the most severe compound flood with a joint return period of 178 years which is much shorter than the return period (>10,000 years) without the consideration of the dependence between the extremes. During the past decades, Hong Kong has experienced an increase in compound flood hazards, which is mainly attributed to the mean sea level rise. Socio-economic data reveals that TCs associated with compound floods are more destructive than TCs that induced single-type floods, highlighting the urgent need to establish a multi-hazard flood risk management strategy.

Compounding joint impact of rainfall, storm surge and river discharge on coastal flood risk: an approach based on 3D fully nested Archimedean copulas

Compound flooding is a multidimensional consequence of the joint impact of multiple intercorrelated drivers, such as oceanographic, hydrologic, and meteorological. These individual drivers exhibit interdependence due to common forcing mechanisms. If they occur simultaneously or successively, the probability of their joint occurrence will be higher than expected if considered separately. The copula-based multivariate joint analysis can effectively measure hydrologic risk associated with compound events. Because of the involvement of multiple drivers, it is necessary to switch from bivariate (2D) to trivariate (3D) analyses. This study presents an original trivariate probabilistic framework by incorporating multivariate hierarchal models called asymmetric or fully nested Archimedean (or FNA) copula in the joint analysis of compound flood risk. The efficacy of the derived FNA copulas model, together with symmetric Archimedean and Elliptical class copulas, are tested by compounding the joint impact of rainfall, storm surge, and river discharge observations through a case study at the west coast of Canada. The obtained copula-based joint analysis is employed in multivariate analysis of flood risks in trivariate and bivariate primary joint and conditional joint return periods. The estimated joint return periods are further employed in estimating failure probability statistics for assessing the trivariate (and bivariate) hydrologic risk associated with compound events. The statistical tests found the fully nested Frank copula outperforms symmetric 3D copulas. Our work confirms that for practical compound flood risk analysis together with bivariate or univariate return periods, it is essential to account for the trivariate joint return periods to assess the expected compound flood risk and strength of influence of different variables if they occur simultaneously or successively. The bivariate (also univariate) events produce a lower failure probability than trivariate analysis for the OR-joint cases. Thus, ignoring the compounding impacts via trivariate joint analysis can significantly underestimate failure probability and joint return period.

Compound flood from storm surge and runoff in the Pearl River Delta

Compound flood from storm surge and runoff in the Pearl River Delta

Compound flood risk of rainfall and storm surges during typhoon events

Compound flood risk of rainfall and storm surges during typhoon events

Coastal Flood Risk and Smart Resilience Evaluation under a Changing Climate

Coastal areas are highly vulnerable to flood risks, which are exacerbated by the changing climate. This paper provides a comprehensive review of the literature on coastal flood risk assessment and resilience evaluation and proposes a smart-resilient city framework based on pre-disaster, mid-disaster, and post-disaster evaluations. First, this paper systematically reviews the origin of the resilience concept and the development of flood resilience. Next, it introduces the social-acceptable risk criteria and the flood resilience level for different flood phases. Then, a coastal flood resilience management system for smart cities is proposed, covering 3 phases of flood disasters (before, during, and after). Risk assessment is essential in pre-disaster scenarios because it provides a comprehensive understanding of the potential hazards and vulnerabilities of an area or system. Big data monitoring during disasters is an essential component of effective emergency management and response that can allow for more informed decisions and thus quicker, more effective responses to disasters, ultimately saving lives and minimizing damage. Data-informed loss assessments are crucial in providing a rapid, accurate understanding of post-disaster impact. This understanding, in turn, is instrumental in expediting recovery and reconstruction efforts by aiding decision-making processes and resource allocation. Finally, the impacts of climate change on the 3 phases of flood disasters are summarized. The development of more resilient coastal communities that are better equipped to withstand the impacts of climate change and adapt to changing environmental conditions is crucial. To address coastal compound floods, researchers should focus on understanding trigging factor interactions, assessing economic and social risks, improving monitoring and emergency response systems, and promoting interdisciplinary research with data openness. These strategies will enable comprehensive and holistic management of coastal flood risks in the context of climate change.

Study of sediment transport distribution pattern after normalization in the Majalaya Area, Citarum River

Majalaya area in West Java, Indonesia, is prone to extreme rainfall events that cause flooding of the Citarum River. The river flows through the area, naturally containing sediments from erosion upstream that disturb the river balance. Some flat areas of the river retain sediments and cause excessive accumulation, resulting in changing the storage capacity of the river. The accumulation disrupts the flow of water and causes flooding in the area. To mitigate this problem, the River Basin Authority has implemented sediment dredging and river normalization with a total length of 5.4 km in 2020, successfully preventing flooding in the first year. However, subsequent floods that affected more than 7,000 people return to occur due to changes in land use, human activities, and unmanaged erosion upstream. This study examines the sediment accumulation pre-2020, immediately after the normalization efforts 2020, and the current sediment profile 2022. The result provides an overview of the river's morphology, sediment distribution patterns, and potential future profiles. The research results can be used to evaluate flood management risk in the area, including the compound flood risk caused by continuous sedimentation and climate change, e.g., heavier and longer rainfall.

Flood risk assessment of loss of life for a coastal city under the compound effect of storm surge and rainfall

Many coastal cities face the risk of compound floods induced by multiple factors such as storm surge and rainfall. Flood risk assessment helps mitigate and manage flood disasters in coastal cities. In current literature, study about compound flood risk assessment of loss of life is limited. This study assesses the risk of loss of life in compound floods under storm surge ad rainfall in the Macao Peninsula which is a coastal city. The joint probability for storm surge and rainfall occurrence was analyzed by copula function. The coupled hydrodynamic model of drainage and overland flow was established to simulate the compound flooding process. The risk of loss of life is quantitatively assessed based on the mortality function for flood scenarios with different probabilities. The western coastal area of Macao Peninsula is the most vulnerable area to flooding with higher mortality. Although the most area of Macao Peninsula has low mortality, there is still a serious loss of life in extreme floods due to high population density. This study provides guidance for quantitative compound flood risk assessment and risk management of loss of life in coastal cities.

Flood risk analysis and mapping under compound hazards: A copula approach for tropical coastal district of Alappuzha, India

This study investigates the determination of compound risk under the co-existence of heavy rainfall and water level rise at Alappuzha, a coastal district in Southern Kerala, India using Copulas. In the case of Alappuzha, when the combined action of rainfall and water level rise occurs, the chances of compound flooding is more in and around Vembanad Lake and lower Kuttanad regions. So, the water level and rainfall of three different locations viz. Punnmada, Cherthala, Arookutty are considered for compound flood risk analysis. A joint probability model based on Copula is used to determine the combined risk of flooding. First the marginal distributions of daily rainfall and water level data are developed for each locations and the best fit distribution is used for finding the joint probability. The three most common Archimedean copulas Gumbel–Hougaard (GH), Clayton and Frank are used to find the joint probability of rainfall and water level and the best copula for each location is also identified. Subsequently, the joint and conditional return periods of rainfall and water level are also obtained in an exercise of risk modeling. By using the digital elevation model (DEM) in a Geographic Information System (GIS) platform, the flood prone areas are calculated and represented them graphically, for specific cases of joint return period-water level combinations. This helps as an aid for administrators or policy makers to effectively perform the disaster management at Alappuzha.