Risk of extreme events in delta environment: A case study of the Mahanadi delta

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.

Climate change as a veiled driver of migration in Bangladesh and Ghana

People living in deltaic areas in developing countries are especially prone to suffer the effects from natural disasters due to their geographical and economic structure. Climate change is contributing to an increase in the frequency and intensity of extreme events affecting the environmental conditions of deltas, threatening the socioeconomic development of people and, eventually, triggering migration as an adaptation strategy. Climate change will likely contribute to worsening environmental stress in deltas, and understanding the relations between climate change, environmental impacts, socioeconomic conditions, and migration is emerging as a key element for planning climate adaptation. In this study, we use data from migration surveys and econometric techniques to analyse the extent to which environmental impacts affect individual migration decision-making in two delta regions in Bangladesh and Ghana. The results show that, in both deltas, climatic shocks that negatively affect economic security are significant drivers of migration, although the surveyed households do not identify environmental pressures as the root cause of the displacement. Furthermore, environmental impacts affecting food security and crop and livestock production are also significant as events inducing people to migrate, but only in Ghana. We also find that suffering from environmental stress can intensify or reduce the effects of socioeconomic drivers. In this sense, adverse climatic shocks may not only have a direct impact on migration but may also condition migration decisions indirectly through the occupation, the education, or the marital status of the person. We conclude that although climate change and related environmental pressures are not perceived as key drivers of migration, they affect migration decisions through indirect channels (e.g., reducing economic security or reinforcing the effect of socioeconomic drivers).

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

The dynamics of flood risk over Coastal Multi-hazard Catchments (CMC) exhibit bizarre characteristics. In these regions, flood hazards are governed by a complex interaction of multiple flood-inducing sources; varying in magnitudes, origin, and direction of propagation. Our conventional understanding of vulnerability may be obscure within these catchments. This can be attributable to the heterogeneous nature of various physical and socio-economic entities. The study proposes a comprehensive framework to quantify bivariate flood risks over a severely flood-prone region in India. The study considers flood hazards, along with vulnerabilities transpiring from (a) physical, (b) socio-economic, and (c) composite (combination of both) groups of indicators. To overcome data scarcity prevalent in CMCs, CHIRPS v2.0, a high-resolution Satellite Precipitation Product, along with other ancillary datasets, are forced to 1D2D coupled MIKE+ hydrodynamic model to simulate flood hazards. A set of 24 indicators are considered within the Shannon Entropy-cum-TOPSIS framework to derive three types of vulnerability. The marginal and compound contributions of hazard and each vulnerability type are represented through a novel concept of bivariate flood risk classifier at the village scale. We notice high and very-high flood hazards over the coastline and floodplains. An equitable influence of socio-economic vulnerability and hazards is reflected, as they cover 41 % of villages together under varied degrees of flood risks. The impacts of hazards are underscored in the presence of physical vulnerability, as the latter contributes to risks in about 72 % of villages. Composite vulnerability prevails its impact over 53 % of villages, dominating its influence on flood risks over hazards. The study delivers vital information to the global flood management community on the prudent selection of indicators, as their influence is markedly noticed on the overall flood risks. The diversified characteristics of flood risk inspire a rationalized implementation of structural and non-structural options in resource-constrained conditions.

Heavy metal(loid)s in agricultural soil from main grain production regions of China: Bioaccessibility and health risks to humans

Unintentional ingestion of metal-contaminated soils may pose a great threat to human health. To accurately evaluate the health risks of heavy metal(loid)s in soils, their bioaccessibility has been widely determined by in vitro assays and increasingly employed to optimize the assessment parameters. Given that, using meta-analysis, we analyzed the literature on farmland heavy metal(loid)s (As, Cd, Cr, Cu, Hg, Pb, Ni, and Zn) in Chinese main grain production regions, and collected their total and bioaccessibility data to accurately assess their human health risks. Monte Carlo simulation was used to reduce the uncertainty in metal concentration, intake rate, toxicity coefficient, and body weight. We found that the mean concentration (0.47 mg/kg) and geological accumulation index (I_geo, 0-5.24) of Cd were the priority position of controlling metals. Moreover, children are more vulnerable to carcinogenic risks than adults. Soil mineralogy, physicochemical properties, Fe, and the types of in vitro assays are the influencing factors of bioaccessibility discrepancy. Furthermore, appropriate bioaccessibility determination methods can be adapted according to the differences in ecological receptors for the risk assessment, like developing a "personalized assessment" scheme for polluted farmland soil management. Collectively, bioaccessibility-based models may provide an accurate and effective approach to human health risk assessment.

A novel flood risk mapping approach with machine learning considering geomorphic and socio-economic vulnerability dimensions

Quantifying flood hazards by employing hydraulic/hydrodynamic models for flood risk mapping is a widely implemented non-structural flood management strategy. However, the unavailability of multi-domain and multi-dimensional input data and expensive computational resources limit its application in resource-constrained regions. The fifth and sixth IPCC assessment reports recommend including vulnerability and exposure components along with hazards for capturing risk on human-environment systems from natural and anthropogenic sources. In this context, the present study showcases a novel flood risk mapping approach that considers a combination of geomorphic flood descriptor (GFD)-based flood susceptibility and often neglected socio-economic vulnerability components. Three popular Machine Learning (ML) models, namely Decision Tree (DT), Random Forest (RF), and Gradient-boosted Decision Trees (GBDT), are evaluated for their abilities to combine digital terrain model-derived GFDs for quantifying flood susceptibility in a flood-prone district, Jagatsinghpur, located in the lower Mahanadi River basin, India. The area under receiver operating characteristics curve (AUC) along with Cohen's kappa are used to identify the best ML model. It is observed that the RF model performs better compared to the other two models on both training and testing datasets, with AUC score of 0.88 on each. The socio-economic vulnerability assessment follows an indicator-based approach by employing the Charnes-Cooper-Rhodes (CCR) model of Data Envelopment Analysis (DEA), an efficient non-parametric ranking method. It combines the district's relevant socio-economic sensitivity and adaptive capacity indicators. The flood risk classes at the most refined administrative scale, i.e., village level, are determined with the Jenks natural breaks algorithm using flood susceptibility and socio-economic vulnerability scores estimated by the RF and CCR-DEA models, respectively. It was observed that >40 % of the villages spread over Jagatsinghpur face high and very high flood risk. The proposed novel framework is generic and can be used to derive a wide variety of flood susceptibility, vulnerability, and subsequently risk maps under a data-constrained scenario. Furthermore, since this approach is relatively data and computationally parsimonious, it can be easily implemented over large regions. The exhaustive flood maps will facilitate effective flood control and floodplain planning.

Earth observation and geospatial data can predict the relative distribution of village level poverty in the Sundarban Biosphere Reserve, India

There is increasing interest in leveraging Earth Observation (EO) and geospatial data to predict and map aspects of socioeconomic conditions to support survey and census activities. This is particularly relevant for the frequent monitoring required to assess progress towards the UNs' Sustainable Development Goals (SDGs). The Sundarban Biosphere Reserve (SBR) is a region of international ecological importance, containing the Indian portion of the world's largest mangrove forest. The region is densely populated and home to over 4.4 million people, many living in chronic poverty with a strong dependence on nature-based rural livelihoods. Such livelihoods are vulnerable to frequent natural hazards including cyclone landfall and storm surges. In this study we examine associations between environmental variables derived from EO and geospatial data with a village level multidimensional poverty metric using random forest machine learning, to provide evidence in support of policy formulation in the field of poverty reduction. We find that environmental variables can predict up to 78% of the relative distribution of the poorest villages within the SBR. Exposure to cyclone hazard was the most important variable for prediction of poverty. The poorest villages were associated with relatively small areas of rural settlement (<∼30%), large areas of agricultural land (>∼50%) and moderate to high cyclone hazard. The poorest villages were also associated with less productive agricultural land than the wealthiest. Analysis suggests villages with access to more diverse livelihood options, and a smaller dependence on agriculture may be more resilient to cyclone hazard. This study contributes to the understanding of poverty-environment dynamics within Low-and middle-income countries and the associations found can inform policy linked to socio-environmental scenarios within the SBR and potentially support monitoring of work towards SDG1 (No Poverty) across the region.

Predominant factors of disaster caused by tropical cyclones in South China coast and implications for early warning systems

Predicting disastrous wind and rainfall associated with tropical cyclones (TCs) is critical to prevent and mitigate the casualties and damage of TCs. The studied warning area was chosen with a radius of 800 km centered on Hong Kong in which the tracks of TCs making landfall in China are concentrated. In general, the number of TCs making landfall decreased but landfall locations and intensities of TCs increased since 1990. Our results suggested minimum sea level pressure (MSLP) in TC affected areas was the predominant disaster-warning factor and indicator for the resulting risks and damages of TCs in 1975-2017. The MSLP of 990 hPa monitored in a TC affected area was a threshold for severe impacts and prediction of strong wind and heavy rainfall. Early warning using a combination of MSLP and the nearest approach distance of TCs (MSLP of 990 hPa for distance of 100 km) outperformed the current warning system based on wind speed, often providing more timely warning and reducing the false warnings.