Multi-hazards vulnerability assessment of southern coasts of Iran

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.

A conceptual framework to help choose appropriate blue nature-based solutions

Biodiversity loss and climate change have severely impacted ecosystems and livelihoods worldwide, compromising access to food and water, increasing disaster risk, and affecting human health globally. Nature-based Solutions (NbS) have gained interest in addressing these global societal challenges. Although much effort has been directed to NbS in urban and terrestrial environments, the implementation of NbS in marine and coastal environments (blue NbS) lags. The lack of a framework to guide decision-makers and practitioners through the initial planning stages appears to be one of the main obstacles to the slow implementation of blue NbS. To address this, we propose an integrated conceptual framework, built from expert knowledge, to inform the selection of the most appropriate blue NbS based on desired intervention objectives and social-ecological context. Our conceptual framework follows a four incremental steps structure: Step 1 aims to identify the societal challenge(s) to address; Step 2 highlights ecosystem services and the underlying biodiversity and ecological functions that could contribute to confronting the societal challenge(s); Step 3 identify the specific environmental context the intervention needs to be set within (e.g. the spatial scale the intervention will operate within, the ecosystem's vulnerability to stressors, and its ecological condition); and Step 4 provides a selection of potential blue NbS interventions that would help address the targeted societal challenge(s) considering the context defined through Step 3. Designed to maintain, enhance, recover, rehabilitate, or create ecosystem services by supporting biodiversity, the blue NbS intervention portfolio includes marine protection (i.e., fully, highly, lightly, and minimally protected areas), restorative activities (i.e., active, passive, and partial restoration; rehabilitation of ecological function and ecosystem creation), and other management measures (i.e., implementation and enforcement of regulation). Ultimately, our conceptual framework guides decision-makers toward a versatile portfolio of interventions that cater to the specific needs of each ecosystem rather than imposing a rigid, one-size-fits-all model. In the future, this framework needs to integrate socio-economic considerations more comprehensively and be kept up-to-date by including the latest scientific information.

Vulnerability assessment of strait/canals in maritime transportation using fuzzy evidential reasoning approach

The safety and security of straits and canals have been playing an important role in maritime transportation. The disruption of a strait or canal will lead to increased transportation costs and world trade problems. Therefore, an advanced approach incorporating fuzzy logic and an evidential reasoning (ER) algorithm is developed to conduct the vulnerability assessment of straits or canals in this paper. A hierarchical structure is first developed taking into account both qualitative and quantitative factors. The fuzzy rule-based transformation technique is applied to convert quantitative factors into qualitative ones, which enables the application of a fuzzy ER method to synthesize all the information from the bottom to the top along the developed hierarchical structure. The software of intelligent decision system (IDS) is used to facilitate the process of vulnerability assessment. The developed framework then is validated and demonstrated in a case study for vulnerability prioritization which can be used as a reference to ensure the safety and security of straits and canals for decision-makers.

Coastal vulnerability assessment for the coast of Tamil Nadu, India-a geospatial approach

A coastal region is a section of land that borders a significant body of water, often the sea or ocean. Despite their productivity, they are sensitive to even little alterations in the outside environment. This study aims to develop a spatial coastal vulnerability index (CVI) map for the Tamil Nadu coast of India, which has diverse coastal and marine environments that are ecologically fragile zones. Climate change is expected to increase the intensity and frequency of severe coastal hazards, such as rising sea levels, cyclones, storm surges, tsunamis, erosion, and accretion, severely impacting local environmental and socio-economic conditions. This research employed expert knowledge, weights, and scores from the analytical hierarchy process (AHP) to create vulnerability maps. The process includes the integration of various parameters such as geomorphology, Land use and land cover (LULC), significant wave height (SWH), rate of sea level rise (SLR), shoreline change (SLC), bathymetry, elevation, and coastal inundation. Based on the results, the very low, low, and moderate vulnerability regions comprise 17.26%, 30.77%, and 23.46%, respectively, whereas the high and very high vulnerability regions comprise 18.20% and 10.28%, respectively. The several locations tend to be high and very high due to land-use patterns and coastal structures, but very few are contributed by geomorphological features. The results are validated by conducting a field survey in a few locations along the coast. Thus, this study establishes a framework for decision-makers to implement climate change adaptation and mitigation actions in coastal zones.

Observing Spatiotemporal Inconsistency of Erosion and Accretion in the Barak River Using Remote Sensing and GIS Techniques

Alluvial rivers all over the world have one common problem, which is their meandering pattern. This meander formation is because of natural and anthropogenic processes. Barak River is dynamic, and due to this, it is exposed to regular shifting and creates many problems for the people who reside near the river. The livelihood of many people depends on agriculture, which they conduct on the nearby sides of the river. However, the regular shifting of riverbanks makes their life miserable and leads to severe economic losses. Further, roadways and railways run along the banks of the Barak River, and during monsoon, Assam (Silchar), along with three states, Mizoram, Manipur, and Tripura, become disconnected from the rest of India because the road and rail connections fail due to riverbank erosion. Therefore, considering the catchment area and the importance of this river, we have tried to understand the spatiotemporal changes (erosion, deposition, and unchanged area) in the Barak River. From our analysis, we found that the maximum and minimum amount of erosion occurred from 2012–2017 and 2002–2012 and were 727.56 ha and 332.69 ha, respectively. While the highest amount of deposition that occurred during 1984–2017 was 1054.21 ha, the minimum amount of deposition that occurred during 2012–2017 was 351.32. Overall, it was identified that the area under the deposition was more dynamic than the erosion from 1984–2017. Moreover, from the temporal analysis of land use/land cover from 1984–2017, it was found that the area that comes under the settlement and arable land has increased by 10.47% and 5.05%, respectively. The dynamic factors, such as the nature of channel gradient, land use/land cover, and riparian vegetative cover, could be the probable driving forces that cause changes in the erosional and depositional areas. This study will help us understand the dynamics of the Barak River and other rivers of this type worldwide. This study shall help implement strategies that will help manage bank erosion by adapting scientific bank protection measures.

Rapid migration of mainland China's coastal erosion vulnerability due to anthropogenic changes

With the global rise in sea levels caused by climate change and frequent extreme weather processes, high-density population aggregation and human development activities to enhance coastal areas vulnerability, populations, resources, and the ecological environment are facing huge pressure. Natural coastlines are being destroyed, and increasingly serious problems, such as coastal erosion and ecological fragility, have become disasters in coastal zones. The coastal vulnerability changed by climatic variables has created a major concern at regional, national and global scales. By comparing the data of two periods in the past 40 years, coastline vulnerability of coastal erosion in mainland China were evaluated by use of reverse cloud model and AHP with 10 indicators, including natural, anthropogenic, social and economic factors, etc. The main factors controlling coastal erosion included the proportion of Quaternary strata, the gradual reclamation of marine areas as land areas (in kilometres) and the percentage decrease in coastal sediment entering the sea. The secondary impact factors included the high proportion of artificial coastlines and the impacts of waves and storm surges under the influence of relative sea level changes. Human activities could further influence coastal vulnerability, making the erosion risk a considerable concern. Legislation, coordinated management system and technology are proposed to improve the quality of the marine ecological environment.

A comparative analysis of three multi-criteria decision-making methods for land suitability assessment

Natural resource management relies on identifying the ecological constraints, assessing land suitability, and considering the socio-economic demands in the region. However, in many developing countries, natural resources are extensively overused in favor of economic growth. This is due to the fact that conservation and natural constraints are not always taken into consideration during the planning phase, especially when the decision-making process is mainly influenced by political or economical views. To avoid these subjective plannings, environmental planners are encouraged to consider quantitative planning approaches that can integrate environmental, social, economic, and political matters through a non-bias procedure. The present study, therefore, examines the application of three multi-criteria decision-making methods (MCDM), namely, analytic hierarchical process (AHP), fuzzy analytic hierarchical process (fuzzy AHP), and technique for order of preference by similarity to ideal solution (TOPSIS), for the assessment of land suitability afforestation. Siahpoosh Watershed, in Iran, is used as a case study to compare three MCDM methods. To achieve this, a set of land suitability criteria (i.e., slope, elevation, aspect, soil texture, soil depth, drainage, erosion, temperature, rainfall, and vegetation type and cover) was defined and weighted using the AHP and fuzzy AHP methods. TOPSIS was then used to prioritize and rank the suitability of different sections of the study area for afforestation. The study demonstrates that the fuzzy AHP method combined with TOPSIS generates more reliable outcomes than the AHP method. The results could be useful for making more informed decisions about afforestation in the region.

Seasonal heavy metal accumulations in the bivalve Barbatia decussate and their relationships with water quality and the metal-induced biochemical biomarkers

Seasonal tissue concentrations of heavy metals, antioxidant enzymes, immunological components, and water quality parameters were investigated during 1 year in the ark clam, Barbatia decussate, from the coast of Lengeh port, located in the north of the Persian Gulf, Iran. The tissue accumulation of the heavy metals (Cd, Pb, Hg) significantly increased accumulations in late autumn and winter (P < 0.01). Theconcentrations of Ni and Cr nearly remained unchanged throughout the 1 year sampling period (P > 0.01). Seasonal changes were also observed in metal-induced biochemical components. In this regard, the malondialdehyde (MDA) levels and the activity of superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) elevated throughout spring and summer and then declined during autumn and winter (P <  0.01). Phagocytosis activity significantly decreased from December to February and then increased from March to September (P < 0.01). Total hemocyte counts decreased from October to March and then elevated until April (P < 0.01). Significant relationships were found between tissue heavy metal concentrations, water quality parameters, and biochemical components (P < 0.01). The negative correlations were O2 vs. antioxidant enzymes, phagocytosis, and total counts of the hemocytes (THCs); pH vs. SOD; salinity vs. Cr; and temperature vs. GPx and Ni. Positive correlations were O2 vs. Cd, Pb, Hg, and Ni; temperature vs. phagocytosis and THCs; and turbidity vs. phagocytosis, THCs, CAT, and GPx. The results of the present study showed a seasonal pattern in the accumulation of heavy metals, with maximum levels in winter for the ark clam, B. decussate . Furthermore, antioxidant defense and immunity of B. decussate are reduced during winter, which may make B. decussate susceptible to diseases.

Fuzzy-based vulnerability assessment of coupled social-ecological systems to multiple environmental hazards and climate change

Climate change and combining related parameters of environmental hazards have left a considerable challenge in assessing social-ecological vulnerability. Here we integrated a fuzzy-based approach in the vulnerability assessment of mangrove social-ecological systems combining environmental parameters, socio-economic, and vegetative components from exposure dimensions, sensitivity and adaptive capacity along the northern coasts of the Persian Gulf and the Gulf of Oman for the first time. This study aims to provide critical information for habitat-scale management strategies and adaptation plans by assessing the vulnerability of mangrove social-ecological systems. This study provides a methodology framework that consists of five steps. Step 1: We combined the fuzzy weighted maps of seven environmental hazards, including tidal range, maximum wind speeds, drought magnitude, maximum temperatures, extreme storm surge, sea-level rise, significant wave height, and social vulnerability. This map combination determined that the computed exposure index is from 1.07 to 4.32 across the study areas, with an increasing trend from the coasts of the Persian Gulf to the Gulf of Oman. Step 2: We integrated the fuzzy weighted maps of four sensitivity variables, including area change, health change, seaward edge retreat, and production potential change. The findings show that the sensitivity index is from 1.40 to 2.64 across the study areas, increasing the trend from the Persian Gulf coast to the Gulf of Oman. Step 3: Besides, we combined the fuzzy weighted maps of three adaptive capacity variables, including the availability of migration areas, recruitment, and local communities' participation in restoration projects and education programs. The result showed that the index value across the study areas varies between 0.087 and 2.38, decreasing the trend from the Persian Gulf coast to the Gulf of Oman. Step 4: Implementing fuzzy hierarchical analysis process to determine the relative weight of variables corresponding to exposure, sensitivity and adaptive capacity. Step 5: The integration of exposure, sensitivity and adaptive capacity and the vulnerability index maps in the study areas showed variation from 0.25 to 5.92, with the vulnerability of mangroves from the west coast of the Persian Gulf (Nayband) decreasing towards Khamir, then increasing to the eastern coasts of the Gulf of Oman (Jask and Gwadar). Overall, the results indicate the importance of the proposed approach to the vulnerability of mangroves at the habitat scale along a coastal area and across environmental gradients of climatic, maritime and socio-economic variables. This study validated the findings based on the ground truth measurements, and high-resolution satellite data incorporated the Consistency Rate (CR) in the Fuzzy Analytic Hierarchy Process (FAHP). The overall accuracy of all classified remote sensing images and maps consistently exceeded 90%, and the CR of the 25 completed questionnaires was <0.1. Finally, this study indicates differences in vulnerability of various habitats, leading to focus conservation completion and rehabilitation and climate change adaptation planning to support the Sustainable Development Goal (SDG)-13 implementation.

Risk mapping of Indian coastal districts using IPCC-AR5 framework and multi-attribute decision-making approach

Strategic location of coastal areas across the world causes them to be prone to disaster risks. In the global south, the Indian coast is one of the most susceptible to oceanic extreme events, such as cyclones, storm surge and high tides. This study provides an understanding of the risk experienced (currently as well as back in 2001) by the districts along the Indian coastline by developing a quantitative risk index. In the process, it attempts to make a novel contribution to the risk literature by following the definition of risk as a function of hazard, exposure and vulnerability as stated in the most recent (Fifth) assessment report of the Intergovernmental Panel on Climate Change (IPCC). Indicators of bio-physical hazards (such as cyclones, storm surge, tides and precipitation), and socio-economic contributors of vulnerability (such as infrastructure, technology, finance and social nets) and exposure (space), are combined to develop an overall risk index at a fine administrative scale of district-level over the entire coastline. Further, the study employs a multi-attribute decision-making (MADM) method, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), to combine the contributing indicators and generate indices on hazard, exposure and vulnerability. The product of these three components is thereafter defined as risk. The results suggest that most districts of the eastern coast have higher risk indices compared to those in the west, and the risk has increased since 2001. The higher risk can be attributed to the higher hazard indices in the eastern districts which are aggravated by their higher vulnerability index values. This study is the first effort made to map risk for the entire coastline of India - which in turn has resulted in a new cartographic product at a district-scale. Such assessments and maps have implications for environmental and risk-managers as they can help identify the regions needing adaptive interventions.

Vulnerability of coastal communities to climate change: Thirty-year trend analysis and prospective prediction for the coastal regions of the Persian Gulf and Gulf of Oman

This study relates changes in social vulnerability of 20 counties on the northern coasts of the Persian Gulf (PG) and the Gulf of Oman (GO) over a 30-year period (1988-2017) to changing socio-economic conditions and environmental (climate) hazard. Social vulnerability in 2030, 2040 and 2050 is predicted based on the RCP8.5 climate change scenario that projects drought intensities and rising sea levels. Social vulnerability was based on the three dimensions of sensitivity, exposure, and adaptive capacity using 18 socio-economic and five climate indicators identified by experts. All but one indicator related very strongly to the dimension it sought to represent. Despite improvements in adaptive capacity over time, social vulnerability increased between 1988 and 2017 and rates of change accelerated after change point years that occurred between 1998 and 2002 in most counties. Extrapolating past changes of each indicator over time enabled forecasts of social vulnerability in the future. While social variability decreased between 2017 and 2030, it increased again between 2030 and 2050. The lowest future social vulnerability is expected along the eastern PG coast, the greatest along the western PG and the GO. The worsening of socio-economic indicators contributed to increased sensitivity, and increased drought intensities plus the expected rise in sea levels will lead to social vulnerabilities in 2050 comparable to present levels. Between 1.4 and 1.7 M people will live in areas that are likely submerged by water in the future. About 80% of these people live in six counties with variable social vulnerabilities. While counties with lower social variabilities might be better able to cope with the challenges posed by climate change, adaptation programs to enhance the resilience of the residents in these and the remaining counties along the PG and the GO need to be implemented soon to avoid uncontrolled mass migration of millions of people from the region.

Spatial modeling of exposure of mangrove ecosystems to multiple environmental hazards

Determining the level of ecosystems exposure to multiple environmental hazards or risk factors is of paramount importance for developing, adopting, and planning management strategies to minimize the harmful effects of these hazards. We quantified the level of exposure of mangroves on the northern coasts of the Persian Gulf (PG) and the Gulf of Oman (GO) between 1986 and 2019 to eight environmental hazards, i.e., drought, maximum temperatures, rising sea levels, change of freshwater inflows to coasts, extreme storm surges, significant wave height (SWH), seaward edge retreat in the mangroves, and fishery intensity. Based on expert opinion, fuzzy weights were used to integrate these exposures into a single index (EI) for the region. Experts gave the greatest weight/importance to the risks posed by sea-level rise and seaward retreat of mangroves and the lowest risk to significant wave height and fishery intensity in coastal waters. The overall EI and six of eight individual variables (except fishery intensity and maximum temperatures) pointed to exposure levels of mangroves that increased from the coasts of the PG (EI 0.69) to the GO (EI 6.69). Since these hazards are expected to continue in the future, local/regional management responses should focus on minimizing regional anthropogenic threats and halt conversion of natural areas to agricultural and open areas to maintain freshwater inputs to coastal areas, particularly on the GO. Further, uplands that may serve as future refugia into which mangroves may expand over time as sea levels continue to rise should be protected from development. This was the first study that used an analytic framework to compute a mangrove exposure index to a suite of physical and socio-economic hazards across a region. This framework may provide insights into cost-effective resilience-based design and management of socio-ecologically coupled ecosystems in an era of increasing types and intensities of environmental hazards.

Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction

Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with ~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility maps provide a basis for developing more efficient fire-fighting strategies and reorganizing policies in favor of sustainable management of forest resources.

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

Groundwater potential maps are one of the most important tools for the management of groundwater storage resources. In this study, we proposed four ensemble soft computing models based on logistic regression (LR) combined with the dagging (DLR), bagging (BLR), random subspace (RSSLR), and cascade generalization (CGLR) ensemble techniques for groundwater potential mapping in Dak Lak Province, Vietnam. A suite of well yield data and twelve geo-environmental factors (aspect, elevation, slope, curvature, Sediment Transport Index, Topographic Wetness Index, flow direction, rainfall, river density, soil, land use, and geology) were used for generating the training and validation datasets required for the building and validation of the models. Based on the area under the receiver operating characteristic curve (AUC) and several other validation methods (negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, and Kappa), it was revealed that all four ensemble learning techniques were successful in enhancing the validation performance of the base LR model. The ensemble DLR model (AUC = 0.77) was the most successful model in identifying the groundwater potential zones in the study area, followed by the RSSLR (AUC = 0.744), BLR (AUC = 0.735), CGLR (AUC = 0.715), and single LR model (AUC = 0.71), respectively. The models developed in this study and the resulting potential maps can assist decision-makers in the development of effective adaptive groundwater management plans.

Use of Mamdani Fuzzy Algorithm for Multi-Hazard Susceptibility Assessment in a Developing Urban Settlement (Mamak, Ankara, Turkey)

Urban areas may be affected by multiple hazards, and integrated hazard susceptibility maps are needed for suitable site selection and planning. Furthermore, geological–geotechnical parameters, construction costs, and the spatial distribution of existing infrastructure should be taken into account for this purpose. Up-to-date land-use and land-cover (LULC) maps, as well as natural hazard susceptibility maps, can be frequently obtained from high-resolution satellite sensors. In this study, an integrated hazard susceptibility assessment was performed for a developing urban settlement (Mamak District of Ankara City, Turkey) considering landslide and flood potential. The flood susceptibility map of Ankara City was produced in a previous study using modified analytical hierarchical process (M-AHP) approach. The landslide susceptibility map was produced using the logistic regression technique in this study. Sentinel-2 images were employed for generating LULC data with the random forest classification method. Topographical derivatives obtained from a high-resolution digital elevation model and lithological parameters were employed for the production of landslide susceptibility maps. For the integrated hazard susceptibility assessment, the Mamdani fuzzy algorithm was considered, and the results are discussed in the present study. The results demonstrate that multi-hazard susceptibility assessment maps for urban planning can be obtained by combining a set of expert-based and ensemble learning methods.