A synergistic approach towards understanding flood risks over coastal multi-hazard environments: Appraisal of bivariate flood risk mapping through flood hazard, and socio-economic-cum-physical vulnerability dimensions

Multivariate indicator-based flood hazard mapping using primary drivers of coastal flood for India

Coastal regions are densely populated and economically vital but highly exposed to multiple hazard drivers, including cyclones, storm surges, high tides, and intense rainfall. The IPCC AR6 (2021) emphasizes the need to assess coastal hazards as multi-driver compound hazard events. India, with its extensive coastline of ∼11,098 km, is particularly prone to these drivers, necessitating a comprehensive multi-driver coastal flood hazard assessment. This study adopts an indicator-based approach employing Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with three objective weight estimation methods: equal weighting, statistical entropy-based weighting, and Principal Component Analysis (PCA)-based weighting, to comprehensively assess coastal flood hazard across India's coastal districts. The key indicators of coastal flood hazard include the total number of cyclones, probable maximum storm surge, probable maximum wind speed, maximum tidal range, and extreme precipitation exceedance probability. Methodological comparisons reveal that entropy-based weighting emphasizes cyclone frequency due to high data dispersion, while PCA-based weighting provides a balanced assessment by capturing overall variance across indicators. The entropy-weighted TOPSIS reflects a more optimistic hazard scenario, whereas the PCA-weighted TOPSIS offers a more conservative perspective. Our findings indicate that North-eastern and North-western coastal districts are highly hazard-prone. Coastal districts in Odisha and West Bengal consistently exhibit high hazard levels across all decades studied, while Kerala and Tamil Nadu generally show low hazard levels. Extreme rainfall and high tides predominantly drive high hazard levels in Gujarat and Maharashtra, whereas frequent cyclones are the primary hazard drivers along the Bay of Bengal coast. These findings provide a scientific basis for region-specific flood mitigation strategies, guiding policymakers in optimizing resource allocation for enhancing coastal resilience.

Identification of foam susceptible locations in the Delhi Reach of the Yamuna River

Recently, the occurrence of foam formation across many rivers has been a cause of concern for the global scientific community. The primary reasons behind foam formation include anionic surfactants, nutrients, organic and inorganic substances, and pathogens, which have been widely studied in the past. However, the issue of foam formation on water surfaces and identifying foam-susceptible locations has not been addressed comprehensively in the past literature. To address this, the present study, for the first time in river management literature, proposes a unified framework to investigate the foam formation issue and identify foam-susceptible locations over Delhi's reach of the Yamuna River, a stretch known for witnessing extensive pollution and excessive foam formation. The foam-related parameters were initially identified, and efficiency scores for four locations-Wazirabad Barrage (u/s), ITO Bridge, Nizamuddin Bridge, and Okhla Barrage (d/s)-were evaluated using the data envelopment analysis model. It was observed that three locations demonstrated low-efficiency scores in comparison to Wazirabad (u/s), indicating a high susceptibility to foam formation, which is critical from an environmental perspective, characterized by elevated levels of nutrients, surfactants, and organic pollutants. The reduced freshwater availability, lack of dissolved oxygen, discharge of untreated or partially treated effluents from multiple drains, and high concentrations of surfactants were noticed, which necessitate focused interventions in this area. In response, the study recommends remedial measures, including ensuring adequate environmental flow, pollutant oxidation, phytoremediation, stringent regulations, and public awareness to address foam formation issues in the Yamuna River.

Compound risks of floods and droughts over multi-hazard catchments: Revealing association through hydrodynamic-cum-statistical modelling and novel bivariate risk classifier

Floods and droughts are signature phases of the same hydrological cycle. Despite their profound impacts on the socio-economic structures and population globally, limited research efforts have encapsulated the resultant compound risks from them. Comprehending both water extremes presents a formidable challenge due to the relatively short-term occurrence of floods versus the prolonged impacts of droughts. The present study, for the first time in the context of global disaster risk reduction, develops a comprehensive framework driven by a novel concept of Bivariate Risk Classifier (BRC) that can readily integrate the marginal and compound impacts/hazards of floods and droughts. The study employs an exhaustive hydrodynamic-driven approach through LISFLOOD-FP and statistical modelling in terms of the Standardized Precipitation Evapotranspiration Index (SPEI), to derive compound flood and drought hazards. A series of extensive calibration and validation tests ensures minimal false alarms and inaccuracies in the simulated outputs. In the latter phase, populations and regions that are vulnerable to compound risks are identified. This study notices a high degree of synchronization of the exposed population to compound hazards that are otherwise associated with either floods or droughts. The efficient demonstration of the proposed framework over a highly disaster-sensitive catchment in India promises its further application to global multi-hazard catchments. The findings from this study call for an integrated approach directed towards targeted, adaptive disaster risk management and resilient infrastructure planning for regions facing concurrent flood and drought impacts.

A novel multi-hazard risk assessment framework for coastal cities under climate change

Coastal cities, as centres of human habitation, economic activity and biodiversity, are confronting the ever-escalating challenges posed by climate change. In this work, a novel Multi-Hazard Risk Assessment framework is presented with the focus on Coastal City Living Labs. The methodology provides a comprehensive assessment of climate-related hazards, including sea-level rise, coastal flooding, coastal erosion, land flooding, heavy precipitation, extreme temperatures, heatwaves, cold spells, landslides and strong winds. Its application is illustrated through a case study: the Coastal City Living Lab of Benidorm, Spain. The methodology incorporates remote sensing data from various satellite sources, such as ERA5, Urban Atlas and MERIT DEM, to evaluate multiple hazards through a systematic and standardized indicator-based approach, offering a holistic risk profile that allows for comparison with other European coastal cities. The integration of remote sensing data enhances the accuracy and resolution of hazard indicators, providing detailed insights into the spatiotemporal dynamics of climate risks. The incorporation of local expertise through the Coastal City Living Lab concept enriches data collection and ensures context-specific adequacy. The integration of local studies and historical extreme climate events enhances the validity and context of the risk indicators. The findings align with regional trends and reveal specific vulnerabilities, particularly related to heatwaves, heavy rainfall, and coastal flooding. Despite its strengths, the MHRA methodology faces limitations, including reliance on outdated datasets and the complexity of integrating multiple hazards. Continuous updates and adaptive management strategies are essential to maintain the accuracy and relevance of risk assessments. The broader implications of the methodology for global coastal cities highlight its potential as a model for developing targeted adaptation strategies.

Scientometric review on multiple climate-related hazards indices

As the spectre of climate change looms large, there is an increasing imperative to develop comprehensive risk assessment tools. The purpose of this work is to evaluate the evolution and current state of research on multi-hazard indices associated with climate-related hazards, highlighting their crucial role in effective risk assessment amidst the growing challenges of climate change. A notable gap in cross-regional comparative studies persists, presenting an opportunity for future research to enhance global understanding and foster universal resilience strategies. However, a significant surge in research output is apparent, following key global milestones related to climate change action. The research landscape is shown to be highly responsive to international policy developments, increasingly adopting interdisciplinary approaches that integrate physical, social, and technological dimensions. Findings reveal a robust emphasis on geospatial analysis and the development of various indices that transform abstract climate risks into actionable data, underscoring a trend towards localized, context-specific vulnerability assessments. Based on dataset systematically curated under the PRISMA guidelines, the review explores how prevailing research themes are reflected in influential journals and author networks, mapping out a dynamic and expanding academic community. Moreover, this work provides critical insights into the underlying literature by conducting a thematic analysis on the typology of studies, the focus on coastal areas, the inclusion of climate change scenarios, the geographical coverage, and the types of climate-related hazards. The practical implications of this review are profound, providing policymakers and practitioners with meaningful insights to enhance climate change mitigation and adaptation efforts through the application of index-based methodologies. By charting a course for future scholarly endeavours, this article aims to strengthen the scientific foundations supporting resilient and adaptive strategies for regions worldwide facing the multifaceted impacts of climate change.

Can geomorphic flood descriptors coupled with machine learning models enhance in quantifying flood risks over data-scarce catchments? Development of a hybrid framework for Ganga basin (India)

Quantifying flood risks through a cascade of hydraulic-cum-hydrodynamic modelling is data-intensive and computationally demanding- a major constraint for economically struggling and data-scarce low and middle-income nations. Under such circumstances, geomorphic flood descriptors (GFDs), that encompass the hidden characteristics of flood propensity may assist in developing a nuanced understanding of flood risk management. In line with this, the present study proposes a novel framework for estimating flood hazard and population exposure by leveraging GFDs and Machine Learning (ML) models over severely flood-prone Ganga basin. The study incorporates SHapley Additive exPlanations (SHAP) values in flood hazard modeling to justify the degree of influence of each GFD on the simulated floodplain maps. A set of 15 relevant GFDs derived from high-resolution CartoDEM are forced to five state-of-the-art ML models; AdaBoost, Random Forest, GBDT, XGBoost, and CatBoost, for predicting flood extents and depths. To enumerate the performance of ML models, a set of twelve statistical metrics are considered. Our result indicates a superior performance of XGBoost (κ = 0.72 and KGE = 82%) over other ML models in flood extent and flood depth prediction, resulting in about 47% of the population exposure to high-flood risks. The SHAP summary plots reveal a pre-dominance of Height Above Nearest Drainage during flood depth prediction. The study contributes significantly in comprehending our understanding of catchment characteristics and its influence in the process of sustainable disaster risk reduction. The results obtained from the study provide valuable recommendations for efficient flood management and mitigation strategies, especially over global data-scarce flood-prone basins.

Exploring the fidelity of satellite precipitation products in capturing flood risks: A novel framework incorporating hazard and vulnerability dimensions over a sensitive coastal multi-hazard catchment

Complexities involved in flood risks over global coastal multi-hazard catchments are a severe concern for vulnerable communities, infrastructure, and the environment. Data scarcity in these regions often hinders our holistic understanding of flood risks, especially when socio-economic and physical vulnerabilities are involved. The extent to which Satellite Precipitation Products (SPPs), which are looked upon as alternatives to ground-based observations, can influence flood risk dynamics remains unexplored. In an attempt to answer the most riveted questions in flood management literature, this study, for the first time, explores the suitability of two competent SPPs, i.e., CHIRPS v2.0 and PERSIANN-CDR, in multi-hazard flood risk mapping. The proposed framework is demonstrated over the sensitive flood-prone deltaic stretches of the Lower Mahanadi River Basin (India). A computationally efficient MIKE+ 1D2D hydrodynamic model is developed to account for the wave propagation of concurrent flood drivers and generate high-resolution flood hazard maps for three disastrous historical flood events (July 2019, September 2020, and August 2022). To understand the hidden characteristics of vulnerability, a comprehensive set of 24 physical and socio-economic indicators is considered in a Shannon-entropy and TOPSIS framework. The variations in flood risk from both SPPs at the finest administrative scale are represented using the novel concept of Bivariate Choropleth, which portrays the marginal and compound contributions of hazard and vulnerability. A superlative performance of CHIRPS v2.0 over PERSIANN-CDR was observed in capturing hydro-climatological behaviors. CHIRPS v2.0-derived flood hazards were found analogous to the SAR-derived maps for all the three events. >70 % of villages display large disparities in flood risk, thereby affirming the role of appropriate SPPs towards efficient flood management. The observations from the study add vital information to the existing flood management policies, especially over resource-constrained regions in low and middle-income nations.

Multi-hazard assessment of climate-related hazards for European coastal cities

The assessment of risk posed by climate change in coastal cities encompasses multiple climate-related hazards. Sea-level rise, coastal flooding and coastal erosion are important hazards, but they are not the only ones. The varying availability and quality of data across cities hinders the ability to conduct holistic and standardized multi-hazard assessments. Indeed, there are far fewer studies on multiple hazards than on single hazards. Also, the comparability of existing methodologies becomes challenging, making it difficult to establish a cohesive understanding of the overall vulnerability and resilience of coastal cities. The use of indicators allows for a standardized and systematic evaluation of baseline hazards across different cities. The methodology developed in this work establishes a framework to assess a wide variety of climate-related hazards across diverse coastal cities, including sea-level rise, coastal flooding, coastal erosion, heavy rainfall, land flooding, droughts, extreme temperatures, heatwaves, cold spells, strong winds and landslides. Indicators are produced and results are compared and mapped for ten European coastal cities. The indicators are meticulously designed to be applicable across different geographical contexts in Europe. In this manner, the proposed approach allows interventions to be prioritized based on the severity and urgency of the specific risks faced by each city.