Assessing compound flood drivers in Peninsular India: Multivariate copula-based approach.

Floods are one of India’s most catastrophic natural disasters, causing extensive loss of life and property. Recent research highlights that compound floods—arising from the interplay of multiple drivers—pose greater risks than individual flood events. Although compound flood drivers like precipitation and storm surge, precipitation and runoff, and others have been the focus of recent research globally, very limited research has been done on these flood drivers in India. To address this gap, we conducted a comprehensive compound flood analysis of Peninsular India river basins from 1980 to 2023, utilizing precipitation, runoff, and soil moisture data. Extreme events were identified using a certain percentile threshold (95th and 99th percentiles) for all the parameters and each parameter was initially subjected to a univariate analysis. The preliminary results indicate that individual drivers provide limited insights of these flood drivers. To address this, we employed a bivariate copula-based approach to estimate joint distributions at varying percentiles (25th, 50th, 75th, 90th, and 95th percentile). The analysis using copula was focused to determine of exceedance probability, conditional probability, joint return period, and conditional return period for the paired variables: precipitation-runoff, precipitation-soil moisture, and runoff-soil moisture pairs, respectively. Our results illustrate that, especially in instances where there are multiple contributing components, bivariate analyses provide deeper insights into comprehending the complexity of flood dynamics. Additionally, it has been observed that some regions in our research region had shorter return durations and higher exceedance probabilities, suggesting that compound flood events of lower severity occur frequently. Identical patterns were noted for conditional return durations and conditional probabilities. These results underscore the critical importance of understanding the interconnections among flood drivers for effective flood risk estimation. Our study provides valuable insights for enhancing India’s flood management strategies by identifying disaster-prone regions and informing policymakers in the development of targeted mitigation measures.

Over half of the global population and the majority of the cities in coastal zones are at risk of coastal flooding. Changes in the occurrence of individual extremes and the interactions between hydrological and coastal variables can exacerbate flood risks. While extensive research has been conducted to understand and predict different types of flood hazards in isolation, spatial and temporal trends and variability of compound flooding, that is, flooding caused by multiple drivers, remain an open question. This study investigates the individual and joint temporal variations of multiple drivers that can cause compound flooding in Canada's coasts including total water level, storm surge, precipitation, and streamflow. Long‐term changes in the frequency and intensity of extremes are analyzed over the Atlantic, Pacific, and the Great Lakes regions. Univariate and multivariate trend tests including Mann Kendall, Covariance Inversion Test, Covariance Sum Test, and Covariance Eigenvalue Test are applied. In addition, a new multivariate index based on the contributing flood drivers transformed into a probability space is proposed, and its application to study compound flooding is investigated. Overall, results show increased risks of individual and compound flooding over the Atlantic coast and varying trends in the Pacific and Great Lakes regions. The multivariate trend indices show consistent results in most scenarios. The proposed index provides a simple and flexible measure to analyze the spatial and temporal variation of compound flooding risks at different thresholds. The results highlight the importance of considering nonstationary compound flood events to develop resilience strategies in coastal environments.

Estuarine areas are currently at risk of compound flooding, the frequency and intensity of which is expected to increase with climate change. Even though efforts are made to adapt against single flood drivers using hard protection, potential subsequent changes in flood risk due to compound flooding are often overlooked in flood risk assessments. This is because risk assessments mostly focus on individual flood drivers and do not account for changes in risk from adaptation measures. We address this question and use hydrodynamic modeling to simulate compound flooding for two adaptation scenarios. We consider adaptation in terms of storm surge barriers, in two locations along the Trave estuary, namely Schlutup and Trave, at Lübeck, Germany. We assess the effectiveness of both storm surge barriers in reducing flooding by simulating individual‐driver, as well as low‐ and high‐magnitude compound flood scenarios. We find that while during low‐magnitude compound flooding both barriers reduce the overall flood extent by 25%–86%, high‐magnitude compound flooding leads to an increase of up to 100%, depending on the location of the barrier. Our results suggest that the river contribution is amplified by 52%–100% by the Schlutup Barrier. The Trave Barrier, however, only amplifies flood extents in the high‐magnitude compound flood scenarios. Our findings highlight the need to consider compound flooding in adaptation planning to avoid defense failure and unexpected increases in risk. However, as this study considers only two (low probability) extreme events, a more comprehensive approach is necessary to fully understand the overall impact on risk.

It is widely recognized that climate change is altering the likelihood and intensity of extreme weather events globally, including hydrological extremes such as floods. Compound flooding is driven by fluvial, pluvial and coastal flooding occurring simultaneously, resulting in a potentially larger impact when co-occurring than the sum of the univariate drivers happening separately. Identifying and communicating the effect of climate change on compound flooding remains challenging. A method to quantify the effect of climate change on these events is through climate attribution assessments.  This research assesses how existing climate attribution methods can be applied to compound events instead of univariate events. An event-based storyline attribution approach for compound flooding from historical tropical cyclones (TCs) in Mozambique is used to examine the effect of climate change on multiple flood drivers propagated to impact. TC Idai hit Mozambique in 2019 and caused over 600 fatalities, affected over 1.8 million people, resulting in $3 billion in damages. Idai is used as a case study, representing a highly destructive compound flood event.  Compound flooding is modelled using a state-of-the-art hydrodynamic modelling chain that combines the Super-Fast INundation for coastS (SFINCS) model with the hydrodynamic model Delft3D Flexible Mesh and hydrological model wflow, linked to a fast impact assessment tool Delft-FIAT to calculate the flood impact, here the direct economic damages. The drivers of compound flooding from TCs that are known to be affected by climate change, such as precipitation, wind and sea-level rise, are adjusted to create counterfactual scenarios. The compound flooding is modelled for the multiple factual and counterfactual scenarios, adjusting the separate drivers individually and simultaneously.   This approach enables the attribution of climate change effects on compound flooding from TCs while identifying potential changes in the contributions of individual flood drivers. Next steps include attribution uncertainty partitioning, comparing multiple climate attribution approaches for these events, assessing regional differences with relation to climate change effects on compound flood impact and comparing this methodology for multiple TCs in the same region, which may have different driver contributions.

Climate change impact on the compound flood risk in a coastal city

Flood risk influenced by the compound effect of storm surge and rainfall under climate change for low-lying coastal areas

Abstract. Floods are consistently identified as the most serious global natural hazard, causing devastating loss of life and economic damage that runs into multiple billions of US dollars each year. At the coastline, many flood disasters are in fact compound flood events, with two or more flood drivers occurring concurrently or in quick succession. In coastal regions the combined effect of fluvial (river) and coastal (storm tides – storm surges and high astronomical tides) floods has a greater impact than if each occurred separately. Deltas in south-east Asia are particularly exposed to coastal compound floods as they are low-lying, densely populated regions subject to the intense rainfall storm surges frequently associated with tropical cyclone (TC) activity. For our study we used a sophisticated 1D river model, combined with 2D storm tide levels, to analyse past–present and future compound flood hazard and exposure for the Mekong River delta, one of the most flood-vulnerable deltas in the world. We found that with compound flooding, a greater area of the delta will be inundated, and some parts will flood to greater flood depth. Central areas around An Giang and the Dong Thap provinces are particularly impacted in our plausible scenario, where a TC makes landfall near the mouth of one Mekong River distributary. In the future delta, the impact of compound flooding is potentially more significant, as the same compound flood scenario inundates a greater area relative to the present case and to greater depth in many locations, and floods last longer. Compound flooding therefore has clear implications for flood managers of the future delta, who will need to ensure that existing and future flood defences are to the right standard and in the right locations to offer effective protection against this future risk.

Coastal areas are becoming increasingly vulnerable due to climate change. These regions are exposed to various sources of flooding, such as high sea levels, river discharge and heavy rainfall. Our study focuses on understanding compound flooding from storm surges and river discharge in Croatia. This is the first study on compound floods in this country. For this purpose, we analysed the time series of water levels and discharges from hydrological stations located along ten major coastal rivers. Since there are only a limited number of tide gauges in Croatia, we combined measured data with numerical reanalyses. The sea level data for the entire Adriatic Sea were obtained from the Copernicus Marine Service (Mediterranean Sea Physics Reanalysis) and were then corrected using machine learning and measured data.Previous studies have shown that neglecting seasonal variations in river discharge and storm surges could lead to a significant underestimation of the expected annual damage from compound floods. Different seasons bring distinct weather and river discharge patterns that influence the probability and severity of compound floods. To address this, our study investigated seasonal correlation and co-occurrence by analysing the monthly maximum values. By examining each season in detail, we uncovered the variations in the compound flood potential index.This analysis provides a more comprehensive understanding of compound floods in Croatia, which is crucial for risk assessment and risk management. Finally, we mapped the correlation coefficients, the number of co-occurrences and the compound flood potential index along the Croatian coast and organised the results in a GIS database. These maps will improve our ability to systematically select the most vulnerable areas where the risk of compound flooding should be analysed at the local level.

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

Compound flooding, which is driven by both inland and coastal forcing, poses an increasing threat to coastal regions. However, the current preliminary flood risk assessments under the EU Floods Directive (FD) focus primarily on single-driver events, often overlooking compound flooding risks. Here we introduce a cost-effective framework to identify and rank areas of potential significant flood risk (APSFRs) on the basis of their compound flooding potential. The framework uses a composite index that considers intensity, correlation and probability, along with a cluster analysis, to categorize the APSFRs by dominant flood mechanisms. Using the 1,600 km Spanish Western Mediterranean coastline as a case study, the framework is used herein to identify three key regions with high compound flood potential, with 11% of the 214 analyzed APSFRs falling into the most severe category. The results demonstrate the ability of the proposed methodology to pinpoint areas where high-resolution flood risk assessments are needed and support the efficient integration of compound flooding analysis into the third implementation cycle of the FD.

Abstract. Compound flooding is a type of flood event caused by multiple flood drivers. The associated risk has usually been assessed using statistics-based analyses or hydrodynamics-based numerical models. This study proposes a compound flood (CF) risk assessment (CFRA) framework for coastal regions in the contiguous United States (CONUS). In this framework, a large-scale river model is coupled with a global ocean reanalysis dataset to (a) evaluate the CF exposure related to the coastal backwater effects on river basins, and (b) generate spatially distributed data for analyzing the CF hazard using a bivariate statistical model of river discharge and storm surge. The two kinds of risk are also combined to achieve a holistic understanding of the continental-scale CF risk. The estimated CF risk shows remarkable inter- and intra-basin variabilities along the CONUS coast with more variabilities in the CF hazard over the US west and Gulf coastal basins. Different risk assessment methods present significantly different patterns in a few key regions such as the San Francisco Bay area, the lower Mississippi River, and Puget Sound. Our results highlight the need to weigh different CF risk measures and avoid using single statistics-based or hydrodynamics-based CFRAs. Uncertainty sources in these CFRAs include the use of gauge observations, which cannot account for the flow physics or resolve the spatial variability of risks, and underestimations of the flood extremes and the dependence of CF drivers in large-scale models, highlighting the importance of understanding the CF risks for developing a more robust CFRA.

Climate change and anthropogenic influences alter the primary flood drivers, such as sea surge, rainfall, and river flow, leading to shifts in flood risk patterns. The traditional assumption of stationarity in flood risk assessments is increasingly inadequate, as it fails to account for the dynamic interactions between these drivers. This study presents a framework to evaluate the potential for compound floods under non-stationary conditions, which considers the changing dependencies and risks between sea surge, river flow, and rainfall. The framework employs dynamic copulas to capture time-varying relationships and assess the compounded risk of multiple flood drivers.The proposed model is applied to Indian estuaries, focusing on east- and west-flowing rivers contributing to the Arabian Sea and the Bay of Bengal. By examining flood events in these regions, the study demonstrates how the potential for compound flooding is amplified under non-stationary conditions compared to traditional stationary assumptions. The results reveal that the compound flood potential increases by 11% to 18% across Indian estuaries, indicating heightened vulnerability to extreme flooding events. This finding underscores the need for updated risk assessments that incorporate non-stationarity, particularly for coastal regions, where the interplay of climatic and hydrological variables is increasingly complex.The study highlights the importance of adopting non-stationary models for flood risk evaluation in light of changing environmental conditions. By integrating dynamic copula-based approaches, this research offers a more accurate and practical framework for understanding and mitigating compound flood risks in the context of climate change.

Abstract. Compound flooding, where the combination or successive occurrence of two or more flood drivers leads to a greater impact, can exacerbate the adverse consequences of flooding, particularly in coastal–estuarine regions. This paper reviews the practices and trends in coastal–estuarine compound flood research and synthesizes regional to global findings. A systematic review is employed to construct a literature database of 279 studies relevant to compound flooding in a coastal–estuarine context. This review explores the types of compound flood events and their mechanistic processes, and it synthesizes terminology throughout the literature. Considered in the review are six flood drivers (fluvial, pluvial, coastal, groundwater, damming/dam failure, and tsunami) and five precursor events and environmental conditions (soil moisture, snow, temp/heat, fire, and drought). Furthermore, this review summarizes research methodology and study application trends, as well as considers the influences of climate change and urban environments. Finally, this review highlights knowledge gaps in compound flood research and discusses the implications on future practices. Our five recommendations for compound flood research are (1) adopt consistent terminology and approaches, (2) expand the geographic coverage of research, (3) pursue more inter-comparison projects, (4) develop modelling frameworks that better couple dynamic Earth systems, and (5) design urban and coastal infrastructure with compounding in mind.

Compound flooding is a physical phenomenon that has become more destructive in recent years. Moreover, compound flooding is a broad term that envelops many different physical processes that can range from preconditioned, to multivariate, to temporally compounding, or spatially compounding. This research aims to analyze a specific case of compound flooding related to tropical cyclones where the compounding effect is on coastal flooding due to a combination of storm surge and river discharge. In recent years, such compound flood events have increased in frequency and magnitude, due to a number of factors such as sea-level rise from warming oceans. Therefore, the ability to model such events is of increasing urgency. At present, there is no holistic, integrated modeling system capable of simulating or forecasting compound flooding on a large regional or global scale, leading to the need to couple various existing models. More specifically, two more challenges in such a modeling effort are determining the primary model and accounting for the effect of adjacent watersheds that discharge to the same receiving water body in amplifying the impact of compound flooding from riverine discharge with storm surge when the scale of the model includes an entire coastal line. In this study, we investigated the possibility of using the Advanced Circulation (ADCIRC) model as the primary model to simulate the compounding effects of fluvial flooding and storm surge via loose one-way coupling with gage data through internal time-dependent flux boundary conditions. The performance of the ADCIRC model was compared with the Hydrologic Engineering Center- River Analysis System (HEC-RAS) model both at the watershed and global scales. Furthermore, the importance of including riverine discharges and the interactions among adjacent watersheds were quantified. Results showed that the ADCIRC model could reliably be used to model compound flooding on both a watershed scale and a regional scale. Moreover, accounting for the interaction of river discharge from multiple watersheds is critical in accurately predicting flood patterns when high amounts of riverine flow occur in conjunction with storm surge. Particularly, with storms such as Hurricane Harvey (2017), where river flows were near record levels, inundation patterns and water surface elevations were highly dependent on the incorporation of the discharge input from multiple watersheds. Such an effect caused extra and longer inundations in some areas during Hurricane Harvey. Comparisons with real gauge data show that adding internal flow boundary conditions into ADCIRC to account for river discharge from multiple watersheds significantly improves accuracy in predictions of water surface elevations during coastal flooding events.

<p>Recent studies on compound flooding have considered the interaction of storm surge and fluvial or pluvial flood drivers, whereas the contribution of waves to compound flooding has so far been neglected. In this study, we assess compound flooding from waves, tides and river discharge at Breede Estuary, South Africa, using a hydrodynamic model. We estimate the contribution of extreme waves to compound flooding by analysing the driver interactions and by quantifying changes in flood characteristics. We further consider the effect of waves on flood timing and compare results of compound flood scenarios to scenarios in which single drivers are omitted. We find that flood characteristics are more sensitive to river discharge than to waves, particularly when the latter only coincide with high spring tides. When interacting with river discharge, however, the contribution of waves is high, causing larger flood extents and higher water depths. With more extreme waves, flooding can begin up to 12 hours earlier. Our findings provide insights on the magnitude and timing of compound flooding in an open South African estuary and demonstrate the need to account for the effects of waves during compound flooding in future flood impact assessments of similar coastal settings with similar wave climates.</p>