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Coastal development in small islands needs adapting to climate and ecosystem changes in the Anthropocene era. Understanding variability of coastal vulnerability along the entire coastline informs coastal planning and management at an island-wide scale as some coastal stretches are more appropriate for big-scale development, while others require additional coastal protection and/or ecosystem conservation. To date, few researches focused on developing macro-scale coastal vulnerability index at an island or archipelagic-scale. This paper fills a knowledge gap by developing an integrated coastal vulnerability index (ICVI) for nine small islands in the Azores archipelago. Considering that degree of vulnerability varies according to human-environment traits of each coastal stretch, this paper characterises integrated coastal vulnerability according to three broad attributes, i.e. exposure to external stressors, biophysical features and socioeconomic characteristics. Using field work, semi-quantitative analysis and GIS, ICVI is a simple and relatively quick approach that provides a broad overview of coastal vulnerability in small island context. A set of six accessible and representative parameters was employed as indicators for this vulnerability assessment, i.e. type of cliff; type of beach; coastal defences; exposure to swell/storm waves; outcrop flooded and land-use. The entire coastline of each island was divided into segments according to their geomorphic compartments and subsequently assigned with a relative ICVI value. Each segment was ranked into five classes ranging from very low to very high based on its relative degree of vulnerability. While majority of the coasts are of moderate relative vulnerability in the Azores, vulnerability varies broadly along the coast between low, moderate and high. The ICVI approach serves as a useful decision support tool to facilitate effective planning and management for the Azores small islands and the methodology has the flexibility of being scaled deep by adding more indicators where necessary and available or scaled out to other small islands.

Freshwater systems are subjected to multiple anthropogenic stressors and natural disturbances that act as debilitating agents and modifiers of river systems, causing cumulative and synergistic effects that deteriorate their health and result in watershed vulnerability. This study proposes an easy-to-apply spatial method of watershed vulnerability evaluation using Geographic Information Systems (GIS) in the Andalién River watershed, located in the Chilean mediterranean. A watershed vulnerability index (WVI) based on three sub-indices - anthropogenic stressors, environmental fragility and natural disturbances - was developed. To determine the index grouping weights, expert surveys were carried out using the Delphi method. We subsequently normalized and integrated the factors of each sub-index with relative weights. The ranges of each thematic layer were re-classified to establish vulnerability scores. The watershed was divided into three sections: headwaters zone, transfer zone and depositional zone. The watershed vulnerability index showed that 41% of the watershed had very low vulnerability and 42% had medium vulnerability, while only 1% - in the depositional zone - had high vulnerability. A one-way ANOVA was carried out to analyze the vulnerability differences among the three sections of the watershed; it showed significant differences (F (2, 16) = 8.15: p 0.05). The a posteriori test showed differences between the headwaters and depositional zones (Tukey test, p = 0.005) and between the transfer and depositional zones (Tukey test, p = 0.014). To validate the WVI, water quality was measured at 16 stations in the watershed; there was a significant correlation between vulnerability level and NO

Drought vulnerability is a complex concept that identifies the capacity to cope with drought, and reveals the susceptibility of a system to the adverse impacts of drought. In this study, a multi-dimensional modeling framework is carried out to investigate drought vulnerability at a national level across the African continent. Data from 28 factors in six different components (i.e. economy, energy and infrastructure, health, land use, society, and water resources) are collected for 46 African countries during 1960-2015, and a composite Drought Vulnerability Index (DVI) is calculated for each country. Various analyses are conducted to assess the reliability and accuracy of the proposed DVI, and the index is evaluated against historical observed drought impacts. Then, regression models are fitted to the historical time-series of DVI for each country, and the models are extrapolated for the period of 2020-2100 to provide three future scenarios of DVI projection (low, medium, and high) based on historical variations and trends. Results show that Egypt, Tunisia, and Algeria are the least drought vulnerable countries, and Chad, Niger, and Malawi are the most drought vulnerable countries in Africa. Future DVI projections indicate that the difference between low- and high-vulnerable countries will increase in future, with most of the southern and northern African countries becoming less vulnerable to drought, whereas the majority of central African countries indicate increasing drought vulnerability. The projected DVIs can be utilized for long-term drought risk analysis as well as strategic adaptation planning purposes.

Constructing accurate and reliable groundwater risk maps provide scientifically prudent and strategic measures for the protection and management of groundwater. The objectives of this paper are to design and validate machine learning based-risk maps using ensemble-based modelling with an integrative approach. We employ the extreme learning machines (ELM), multivariate regression splines (MARS), M5 Tree and support vector regression (SVR) applied in multiple aquifer systems (e.g. unconfined, semi-confined and confined) in the Marand plain, North West Iran, to encapsulate the merits of individual learning algorithms in a final committee-based ANN model. The DRASTIC Vulnerability Index (VI) ranged from 56.7 to 128.1, categorized with no risk, low and moderate vulnerability thresholds. The correlation coefficient (r) and Willmott's Index (d) between NO3 concentrations and VI were 0.64 and 0.314, respectively. To introduce improvements in the original DRASTIC method, the vulnerability indices were adjusted by NO3 concentrations, termed as the groundwater contamination risk (GCR). Seven DRASTIC parameters utilized as the model inputs and GCR values utilized as the outputs of individual machine learning models were served in the fully optimized committee-based ANN-predictive model. The correlation indicators demonstrated that the ELM and SVR models outperformed the MARS and M5 Tree models, by virtue of a larger d and r value. Subsequently, the r and d metrics for the ANN-committee based multi-model in the testing phase were 0.8889 and 0.7913, respectively; revealing the superiority of the integrated (or ensemble) machine learning models when compared with the original DRASTIC approach. The newly designed multi-model ensemble-based approach can be considered as a pragmatic step for mapping groundwater contamination risks of multiple aquifer systems with multi-model techniques, yielding the high accuracy of the ANN committee-based model.

In environmental health, vulnerability reflecting the cumulative harmful constraints and nuisances to which populations are subjected and resilience defined as the capacity of a territory to cope with health inequalities have been little extensively investigated together with the same importance. Besides the diversity of factors involved, there is no consensual framework to develop composite indices, one recognized methodology to deal with a multifaceted issue. Therefore, this research aims to establish a new transferable approach to assess the spatial heterogeneity of territorial inequalities. This new strategy relies on the simultaneous evaluation of resilience and vulnerability and the joint analysis based on the cross-interpretation of the spatialized composite indices of resilience and vulnerability. A case study was conducted to demonstrate the feasibility of this methodology, using the municipality as a spatial unit of analysis within a region in the north of France. To provide the most holistic description possible of the 3817 studied municipalities, 50 variables related to the economic, environment, policy, health, services and social dimensions were used to develop the composite indices. The vulnerability Index has a median value of 0.151 with an IQR of [0.126–0.180] and the Resilience Index has a median value of 0.341 with an IQR of [0.273–0.401]. The joint analysis was conducted to classify each municipality among four defined typologies: 1687 municipalities (44.2%) belong to the “To monitor” category, 1646 (43.1%) to the “Resilient” category, 329 (8.6%) to the “Have resources” category and 155 (4.1%) to the “Territorial blackspot” category. The methodology herein may be a diagnostic tool to identify and prioritize municipalities that could benefit from the implementation of specifically tailored public health policies.

Evaluation of aquifer vulnerability comprehends the integration of very diverse data, including soil characteristics (texture), hydrologic settings (recharge), aquifer properties (hydraulic conductivity), environmental parameters (relief), and ground water quality (nitrate contamination). It is therefore a multi-geosphere problem to be handled by a multidisciplinary team. The DRASTIC model remains the most popular technique in use for aquifer vulnerability assessments. The algorithm calculates an intrinsic vulnerability index based on a weighted addition of seven factors. In many studies, the method is subject to adjustments, especially in the factor weights, to meet the particularities of the studied regions. However, adjustments made by different techniques may lead to markedly different vulnerabilities and hence to insecurity in the selection of an appropriate technique. This paper reports the comparison of 5 weighting techniques, an enterprise not attempted before. The studied area comprises 26 aquifer systems located in Portugal. The tested approaches include: the Delphi consensus (original DRASTIC, used as reference), Sensitivity Analysis, Spearman correlations, Logistic Regression and Correspondence Analysis (used as adjustment techniques). In all cases but Sensitivity Analysis, adjustment techniques have privileged the factors representing soil characteristics, hydrologic settings, aquifer properties and environmental parameters, by leveling their weights to ≈4.4, and have subordinated the factors describing the aquifer media by downgrading their weights to ≈1.5. Logistic Regression predicts the highest and Sensitivity Analysis the lowest vulnerabilities. Overall, the vulnerability indices may be separated by a maximum value of 51 points. This represents an uncertainty of 2.5 vulnerability classes, because they are 20 points wide. Given this ambiguity, the selection of a weighting technique to integrate a vulnerability index may require additional expertise to be set up satisfactorily. Following a general criterion that weights must be proportional to the range of the ratings, Correspondence Analysis may be recommended as the best adjustment technique.

Human vulnerability represents an open field of study that allows us, through the use of indicators, to measure a territory’s weakness and potential for dealing with negative climate impacts. This kind of assessment is important within the scope of Sustainable Development Goals (SDGs), especially as a tool that is able to direct the strategies and planning in countries in the context of regional climate change (SDG 13.2). The aim of this work is to demonstrate the application of a method that uses a software created for the Brazilian context ( SISVUCLIMA ) to evaluate human vulnerability to climate change in the state of Maranhao, Brazil. Via an assessment of socioeconomics, demographics, climate, epidemiological, and environmental aspects compiled to develop a municipal vulnerability index, it was clear that the central and western mesoregions of Maranhao are more fragile regarding exposure, sensitivity, and adaptative capacities. These locations may be the most affected by rising temperatures and reduced rainfall that is projected for the next 20 years in the region. It is believed that the results obtained in this study may contribute indirectly to the adoption of effective actions directed at SDG 13 and other SDGs in the country. By considering the socio-ecological and health determinants that define the vulnerability profile of the municipalities of Maranhao, it will be possible to suggest specific actions to reduce the fragility of the local population to climate impacts. Through a spatial and multi-dimensional view of the problem, the proposed indicators can act as an effective instrument for overall environmental management from a sustainable perspective.

This study developed a new framework to quantify spatial vulnerability for sustainable water resources management. Four hydrologic vulnerability indices--potential flood damage (PFDC), potential drought damage (PDDC), potential water quality deterioration (PWQDC), and watershed evaluation index (WEIC)--were modified to quantify flood damage, drought damage, water quality deterioration, and overall watershed risk considering the impact of climate change, respectively. The concept of sustainability in the Driver-Pressure-State-Impact-Response (DPSIR) framework was applied in selecting all appropriate indicators (criteria) of climate change impacts. In the examination of climate change, future meteorological data was obtained using CGCM3 (Canadian Global Coupled Model) and SDSM (Statistical Downscaling Model), and future stream run-off and water quality were simulated using HSPF (Hydrological Simulation Program - Fortran). The four modified indices were then calculated using TOPSIS, a multi-attribute method of decision analysis. As a result, the ranking obtained can be changed in consideration of climate change impacts. This study represents a new attempt to quantify hydrologic vulnerability in a manner that takes into account both climate change impacts and the concept of sustainability.