Drought Vulnerability Index Research Articles

Water vulnerability and environmental impacts in the State of Mato Grosso

In recent decades, Brazilian tropical regions have experienced a significant reduction in water surface, driven by environmental and anthropogenic factors, and the state of Mato Grosso is no exception, as it hosts three biomes (Cerrado, Pantanal, and Amazon). Concerned about climate change and its effects on geo-hydrology, this study aimed to conduct a diagnosis of the water surface in the municipalities of the state of Mato Grosso. We used the AdaptaBrasil and MapBiomas platforms to identify the areas most vulnerable to geo-hydrological disasters and drought. The analysis showed that the geo-hydrological vulnerability index for the state is high (0.69), with 56% of the municipalities at high risk. On the other hand, the drought vulnerability index was low, with an average of 0.26, and 61% of the municipalities at low risk of drought. Although drought vulnerability is relatively low compared to geo-hydrological vulnerability, significant challenges persist, especially in municipalities with unregulated population growth and inadequate water resource management. Municipalities such as Colniza, Cotriguaçu, and Serra Nova Dourada are highly vulnerable to geo-hydrological conditions, while Várzea Grande, Nova Guarita, and Cuiabá are vulnerable to drought. This study emphasizes the urgent need to implement conservation measures and sustainable water resource management, including the restoration of degraded areas and the protection of remaining forests. Additionally, it is suggested that integrating state policies with federal initiatives could strengthen the state's resilience to climate change.

Regional agricultural drought vulnerability prediction based on interpretable Random Forest.

Drought is one of the leading natural disasters in the world. It severely restricts the normal functioning of societies and economies. The purpose of this paper is to predict the drought vulnerability index in Henan Province so that timely measures can be taken to mitigate the impact of drought and to ensure the continued stability of people's lives and social development. First, the drought vulnerability index of Henan Province for 2010-2022 was calculated based on relevant literature and the regional situation of Henan Province. In addition, the main factors of sensibility and resilience are selected, the prediction indicator system is established, and the grey correlation analysis model is applied to select the top 10 main factors of drought vulnerability. Second, the Random Forest model was trained and simulated, and the drought vulnerability index of Henan Province was predicted for 2023-2025. Finally, the SHapley Additive exPlanations model was used to explain the specific effects of the influences of the Random Forest model. The interpretable Random Forest model not only accurately predicts the drought vulnerability index in Henan Province but also alleviates concerns about the "black box" problem of indirect interpretation of the Random Forest model. The results of the study show that the Average Percentage Error of the drought vulnerability index in Henan Province from 2010 to 2022 simulated by the Random Forest model is as low as 3.0457%, and the simulation accuracy is as high as 96.9643%. Drought vulnerability prediction for Henan Province was also made for the period 2023-2025, showing an increasing trend. Relevant sectors should enhance awareness and early warning of drought, improve resilience, and take effective measures to reduce the socio-economic impact of drought.

Drought risk assessment based on hazard, vulnerability, and coping capacity concepts for hot and dry climate regions of Iran

Due to the complex and multi-dimensional nature of droughts, it is not possible to assess drought-induced damage and its consequences for various social, economic, and environmental aspects of societies by relying only on a univariate index such as precipitation-based drought indices. The present study aimed to develop a practical and scientific framework based on hazard, vulnerability (social, economic, and environmental), and coping capacity to generate a drought risk map for the hot and dry climate regions of Iran. Accordingly, the Drought Hazard Index (DHI), Drought Vulnerability Index (DVI), and Drought Coping Capacity Index (DCCI) were derived from “the Standardized Precipitation Evapotranspiration Index (SPEI)”, “16 social, economic and environmental variables” and “three social, economic variables”, respectively. The layers of all variables of the three indices in the GIS were provided, and they were combined in the form of an equation to produce a drought hazard map of central and southeastern Iran. The results indicate that the counties most and least vulnerable to drought were located in the southeast and west of the case study area, respectively. A number of large households, long distances from provincial centers, and soil erosion were the most important social, economic, and environmental factors making the southeast of the case study (including south of Sistan and Baluchestan and south of Kerman provinces) most vulnerable to drought. Due to their high drought coping capacity, counties located in the west of the case study (west of Kerman and south of Yazd provinces) were least vulnerable to drought. Extended support for low-income households by charitable organizations, tertiary education, and most importantly, a variety of jobs and career opportunities were the most important factors in reducing vulnerability in this part of Iran. Furthermore, our methodology by taking social, economic, and environmental dimensions into account as risk, vulnerability, and coping capacity indices can be far more efficient than the methods considering only risk and vulnerability factors.

Fuzzy comparison of drought vulnerability based on the analytical hierarchy process

The vulnerability represents the level to which any ecosystem experiences impairment due to natural distress. One such distress is drought, a prolonged phase of deficient rainfall that causes the depletion of water resources. The possibility of the influences of drought principally depends on the vulnerability of the affected area. The predominantly arid climate of Pakistan and the dependence of its economy on agriculture make it vulnerable to climate change. Rahim Yar Khan and Tharparkar have similar environmental conditions, but Tharparkar experiences more droughts. Vulnerability factors observed by the two districts among the regions are different even with similar climate conditions. The system vulnerability to drought was observed for two districts by using the analytical hierarchy process (AHP) and drought vulnerability index (DVI). The drought vulnerability index (DVI) was used to assess the meteorological, agricultural, hydrological, and socio-economic indicators to build the hierarchy for these inconsistence factors and evaluate the most vulnerable and affecting cause among the meteorological, agricultural, hydrological, and socio-economic indicators. The selected measures specify the hypotheses and the relativity of the indicators to the overall vulnerability of the system. The evaluated weights for each variable are derived from the analytical hierarchy process by comparing the variables of the selected indicators. Due to different meteorological, agricultural, and socio-economic conditions, the weights for the DVI were not the same in both study districts, i.e., Rahim Yar Khan and Tharparkar. The classification of vulnerability depending upon the drought vulnerability index with AHP suggests that the DV indexation is favorable in assessing the regional drought vulnerability. The drought vulnerability assessment of Tharparkar indicates that Tharparkar is highly vulnerable to drought, i.e., DVI = 0.066, as it is highly dependent on irrigation resources for its agriculture and livestock. The unavailability of hydrological resources has increased the drought vulnerability of the Tharparkar, which corresponds to a high level of agricultural damage due to droughts. However, the district of Rahim Yar Khan with developed irrigation resources has a lower drought vulnerability index (DVI = 0.011). It is necessary for Tharparkar to establish countermeasures to mitigate the drought vulnerability through the development of water resources. Future studies are needed to be focused on more indicators for assessing particular drought vulnerability issues in order to reduce the drought vulnerability of Tharparkar.

Drought risk assessment: The importance of vulnerability factors interdependencies in regional drought risk management

Recognizing the vulnerability factors is essential in assessing the drought risk of a region. Moreover, considering the interactions and interdependencies between the vulnerability factors will generate a more accurate and realistic analysis. The present study tries to depict the interdependencies between vulnerability factors and their covering effect on each other. Kashkan River basin in the west of Iran has hydrological, physical, social, and cultural diversity; hence, one of the main goals of the current research is to evaluate drought risk and detect its affecting factors in each rural district. Twelve vulnerability factors were selected and standardized based on regional risk management. To model interdependencies of vulnerability factors, the integration of the Fuzzy Analytic Network Process (FANP) and Decision-making Trial and Evaluation Laboratory (DEMATEL) method was compared with the Fuzzy Analytic Hierarchical Process (FAHP). The Drought Vulnerability Index (DVI), a weighted aggregation of factors, was multiplied by the Drought Hazard Index (DHI) to obtain a drought risk map. Based on the results, FANP-DEMATEL modifies the risk severity of 20 % of districts. Both methods introduce the western part of the basin with intense risk levels, indicating the irrefutable effect of the hazard index on drought risk maps. The scarcity of hydrological factors can cause profound risk changes in both methods. Furthermore, comparing weighting scenarios revealed that, in some cases, sophisticated weighting methods can be replaced by logical weighting strategies.

Multidimensional assessment of agricultural drought vulnerability based on socioeconomic and biophysical indicators

Agricultural drought threatens Türkiye's food security and economy, revealing a lack of multidimensional provincial vulnerability studies. Therefore, the primary objective of this study is to evaluate Türkiye's vulnerability to drought utilizing a multidimensional strategy incorporating biophysical and socio-economic indicators. In this context, an initial step involved the development of a conceptual framework for drought vulnerability, drawing upon the Intergovernmental Panel on Climate Change recommended elements of exposure, sensitivity, and adaptive capacity. Subsequently, this conceptual framework was applied at the provincial level, encompassing parameters such as temperature, precipitation, soil characteristics, water resources, agricultural earnings, demographic traits, educational levels, innovation, insurance coverage, and road density.The Aridity Index was used as an indicator for drought exposure, representing the physical dimension of drought. Under the sensitivity component, a total of 14 variables were defined under the indicators of “soil”, “water and land resources”, “economy”, and “population”, while under the adaptive capacity component, a total of five variables were identified. The weights of these indicators and variables were determined based on expert opinions using the Analytic Hierarchy Process method. As a result, five different indices were obtained by combining the indicators: Exposure Index, Sensitivity Index, Adaptation Capacity Index, Potential Drought Impact Index, and Agricultural Drought Vulnerability Index. In addition, spatial distribution models of these indices were also generated. Research results showed that the climatic regions most exposed to drought are also the most vulnerable. The Central, Southeastern, and Eastern Anatolian regions constitute the regions where the most vulnerable provinces are concentrated spatially.

Assessment of multidimensional drought vulnerability using exposure, sensitivity, and adaptive capacity components.

This study provides a method for analyzing the drought-vulnerability index (DVI) from a multidimensional perspective that includes biophysical and social aspects, considering the Intergovernmental Panel on Climate Change's (IPCC) assessment. The proposed method generates the "exposure index (EI)", "sensitivity index (SI)", and "adaptive capacity index (ACI)" components of the proposed DVI using nine sub-indicators and 29 proxy variables. By using it throughout all of Turkey's provinces, the performance of the developed index was evaluated. In this study, the decision matrices were built utilizing expert knowledge, and the weights of the indicators and variables were obtained by using the Analytical Hierarchy Process (AHP) technique. Moreover, the values of these four indices were classified as "very high, high, moderate, low, and very low," and their geographical distribution across the country was drawn, as well as relevant patterns retrieved. The study's major results show that 17 of the 81 provinces are classified as "very high," 16 as "high," 15 as "moderate," 17 as "low," and the remaining 16 as "very low" drought vulnerable. Another significant result is that the majority of people in the country's south, center, and southeast rely on agriculture and are thus more vulnerable to drought due to socioeconomic underdevelopment in those regions.

Drought and Vulnerability in Mexico’s Forest Ecosystems

In Mexico, some regions have frequent droughts, while others are beginning to experience their presence and impacts. Therefore, this work aimed to characterize drought in the last twenty-one years and evaluate the vulnerability of forests to this phenomenon. The method consisted of applying the standard precipitation index (SPI-12), calculating the drought vulnerability index (DVI), and applying it to the country’s forest areas. The results confirm that forests are vulnerable to drought for five main reasons. First, geographically, the country has large arid and semiarid areas with little natural precipitation. Second, droughts frequently occur and are present in the national territory, covering from 25% to 75% of the surface in recent years. Third, the socioeconomic characteristics of the population living in municipalities and forest territories increase the vulnerability of these areas to drought. Fourth, drought can trigger other catastrophes, such as fires or forest pests. Fifth, the combination of two or more of the above in the same territory magnifies exposure to drought for both forests and people. Temperate forest ecosystems, in particular, have been subject to the prevalence of drought in recent years and, thus, should receive more attention. Finally, technical and cartographic elements, such as those presented herein, are essential for supporting the formulation of proactive forest response plans to address drought events.

Evaluating Hydrological Drought Risk in Lithuania

Hydrological drought poses a major global challenge, exacerbated by climate change and increasing water demand, leading to water scarcity, environmental degradation, and socioeconomic impacts. Thereby, there is a need for comprehensive methods to assess and predict hydrological droughts. The methodology part was based on the calculation of hydrological drought risk components—hazard and vulnerability—according to the equal weight scale of each variable. The spatial distribution of point values was performed by the inverse distance weighting interpolation method. To calculate indices, the spatial layer overlapping of variables was performed using the Raster Calculator tool. Statistical tools were used to estimate drought risk in river catchments. As a result, three main maps were prepared: The hydrological drought hazard index, the hydrological drought vulnerability index, and the hydrological drought risk. These maps highlight regional variations in drought hazards, vulnerability, and risk. Hazard and risk index values are higher in the northern part of Lithuania and lower in the south. The central region exhibits the highest percentage of areas at high and very high risk; the western region shows less risk due to a maritime climate; and the Southeastern region demonstrates the lowest susceptibility to hydrological drought due to physical-geographical factors.

Assessment and projection of the groundwater drought vulnerability under different climate scenarios and land use changes in the Sanjiang Plain, China

Study RegionA district of the Sanjiang Plain, China. Study focusAiming at assessing the likelihood of groundwater exposure to drought in the present and future, a comprehensive evaluation framework based on the groundwater drought vulnerability index (GDVI) is proposed. Firstly, nine influencing factors are selected in terms of groundwater dependence, hydrogeology, and meteorology, and the current drought vulnerability is assessed by combining the analytic hierarchy process (AHP) and random forest (RF) models. Afterwards, by projecting land use and future climate under the shared socioeconomic pathways of SSP1–2.6, SSP2–4.5, and SSP5–8.5, the GDVI is calculated for three future periods, i.e., 2015–2039, 2040–2064, and 2065–2089. New hydrological insights for the regionMore than 30% of the groundwater experiences high and very high vulnerability during the baseline period, concentrated in areas with arable land and a thick clay cover. With rising temperatures and the rapid expansion of paddy fields, areas of high-vulnerability will continue to expand and will exceed 50% by the middle of the 21st century. If the groundwater irrigation and the agricultural land development are not restricted, the groundwater drought will be exacerbated. The proposed evaluation framework can be used in other areas with high groundwater use or where groundwater resources are at risk of depletion, and the findings provide guidance for agricultural development planning and joint surface water-groundwater scheduling.

The spatial distribution of a comprehensive drought risk index in Java, Indonesia

Drought is a common hydro-meteorological disaster in several remote regions of Java, Indonesia, due to the limitation of supporting facilities in water supply management. However, little attention has been paid to drought preparedness, especially within drought risk assessment. Since Java is the most densely populated area in Indonesia and has been identified as a national food center, an effective drought risk assessment is needed for drought disaster mitigation. Therefore, a spatial drought risk assessment for the sub-district level was conducted for Java using a framework that considers the combined roles of drought hazard and drought vulnerability. Observed rainfall data from 1294 stations across Java for the time period 1991–2020 were used to define a drought hazard index. Furthermore, a drought vulnerability index was obtained by classifying several indicators that accommodated non-climate aspects. Based on these, an overall drought risk index was obtained by integrating the drought hazard index and drought vulnerability index. The results identified several areas of Java in need of priority government attention for drought disaster mitigation. Of these, the main priorities are regions with a high risk of drought, which were spread across five provinces as follows (percentage represents the number of high drought hazard index values in the sub-district to the total number of sub-districts): Yogyakarta (13.92%), West Java (7.26%), Banten (5.77%), Central Java (6.23%), and East Java (4.32%).

Drought vulnerability of central Sahel agrosystems: a modelling-approach based on magnitudes of changes and machine learning techniques

ABSTRACT Agricultural drought is a complex phenomenon with numerous consequences and negative implications for agriculture and food systems. The Sahel is frequently affected by severe droughts, leading to significant losses in agricultural yields. Consequently, assessing vulnerability to agricultural drought is essential for strengthening early warning systems. The aim of this study is to develop a new multivariate agricultural drought vulnerability index (MADVI) that combines static and dynamic factors extracted from satellite data. First, pixel temporal regression from 1981 to 2021 was applied to climatic and biophysical covariates to determine the gradients of trend magnitudes. Second, principal component analysis was applied to groups of factors that indicate the same type of vulnerability to configure the basic equation of vulnerability to agricultural drought. Then, random forest (RF), K-nearest neighbours (KNN), support vector machine (SVM) and naïve Bayes (NB) were used to predict drought vulnerability classes using the 28 factors as inputs and 708 pts of randomly distributed class labels. The results showed statistical agreement between the predicted MADVI spatial variability and the reference model (R=0.86 for RF) and its statistical relationships with the vulnerability subcomponents, with an R=0.73 with exposure to climate risk, R=0.64 with the socioeconomic sensitivity index, R=0.6 with the biophysical sensitivity index and a relatively weak correlation (R=0.21) with the physiographic sensitivity index. The overall vulnerability situation in the watershed is 21.8% extreme, 10% very high, 16.8% high, 27.7% moderate, 22.2% low and 1.5% relatively low considering the cartographic results of the predicted vulnerability classes with SVM having the best performance (accuracy=0.96, Kappa=0.95). The study is the first approach that uses the gradients of magnitudes of satellite covariate anomaly trends in multivariate modelling of vulnerability to agricultural drought. It can be easily scaled up across the Sahel region to improve early warning measures related to the impacts of agricultural drought.

Socio-Environmental Vulnerability to Drought in the Seridó Potiguar, Brazil: Building Indicators

Imprecise in space-time and with damaging effects, drought as a historical-natural phenomenon of high complexity has been afflicting the population of the Seridó Potiguar Region, located in the state of Rio Grande do Norte, Northeast Brazil. For centuries, the region, of state relevance, is part of the Brazilian semiarid region. This reality produces socio-spatial, socio-economic, and political-institutional conflicts. Considering this, and the fact that drought in the 21st century continues to reach disastrous proportions and cause harm to human systems, this research aimed to offer a partial overview of the socio-environmental vulnerability of the Seridó region to drought, focusing on seven key municipalities: Bodó, Caicó, Currais Novos, Ipueira, Jardim de Piranhas, Jucurutu, and Parelhas. The Drought Vulnerability Index (DVI) was applied to these municipalities, composed of the sub-indices of Exposure, Susceptibility, and Adaptive Capacity, based on socio-economic and physical-environmental variables, which were calculated, classified, and cartographically represented using the RGB color composition. After analyzing the results, it was found that the municipalities ranged from low to moderate vulnerability, which can be justified by the compensation between the variables themselves and the good indicators regarding social assistance, mixed economy, hydraulic infrastructure, and means of coping with the semiarid region.

Drought vulnerability range assessment: A dynamic and impact-driven method for multiple vulnerable systems

Current approaches to identify vulnerability to drought often lack ground-truthing and transparent methodology; they are also often biased by expert subjectivity. Furthermore, most drought vulnerability assessments are seldom system-specific but of general social relevance. Consequently, resultant vulnerability maps are typically a general snapshot of case-specific decisions, lacking teeth in their statistical soundness and system-specific relevance. Thus, this study presents an impact-based method for a transparent selection of effective vulnerability factors for six vulnerable systems (social, economic, agricultural, ecological, water resources, and energy-industry) The study is based on a backward multivariate linear regression (MLR) model, which links vulnerability factors to historical drought impacts for the case of Iran. The drought vulnerability index (DVI) and the comprehensive drought vulnerability index (CDVI) are calculated by aggregating the vulnerability of the six systems. Four different weighting methods are then applied to combine driving vulnerability factors to assess the vulnerability of each system for three timesteps (2005, 2010, and 2015). The vulnerability values are presented as ranges by combining the results of four weighting methods. Furthermore, identified driving vulnerability factors are classified by their manageability. A range of 154 vulnerability factors were tested as possible drivers, from which 44 were identified as effective factors. Overall, the results especially highlight that the social and economic systems are vulnerable to drought. From a systemic perspective, socioeconomic factors, such as infrastructure development, health conditions, and industrialization, might play a crucial role in reducing vulnerability. Thus, they should be prioritized in vulnerability reduction programs.

Assessment of Agricultural Drought Vulnerability Based on Crop Growth Stages: A Case Study of Huaibei Plain, China

Climate change can lead to and intensify drought disasters. Quantifying the vulnerability of disaster-affected elements is significant for understanding the mechanisms that transform drought intensity into eventual loss. This study proposed a growth-stage-based drought vulnerability index (GDVI) of soybean using meteorological, groundwater, land use, and field experiment data and crop growth model simulation. The CROPGRO-Soybean model was used to simulate crop growth and water deficit. Four growth stages were considered since the sensitivity of soybean to drought is strictly related to the growth stage. The GDVI was applied to the Huaibei Plain, Anhui Province, China, with the goal of quantifying the spatiotemporal characteristics of soybean drought vulnerability in typical years and growth stages. The results show that: (1) The sensitivity of leaf-related parameters exceeded that of other parameters during the vegetative growth stage, whereas the top weight and grain yield showed a higher sensitivity in the reproductive growth stage; (2) A semi-logarithmic law can describe the relationship between the drought sensitivity indicators and the GDVI during the four growth stages. The pod-filling phase is the most vulnerable stage for water deficit and with the highest loss upper limit (over 70%); (3) The 2001 and 2002 seasons were the driest time during 1997−2006. Fuyang and Huainan Cities were more vulnerable to drought than other regions on the Huaibei Plain in 2001, while Huaibei and Suzhou Cities were the most susceptible areas in 2002. The results could provide effective decision support for the categorization of areas vulnerable to droughts.

Developing alternatives to adaptive silviculture: Thinning and tree growth resistance to drought in a Pinus species on an elevated gradient in Southern Spain

Forest plantations are more vulnerable to the stress induced by biotic and abiotic factors than are naturally regenerated forests. These effects can be aggravated by a lack of management in large reforestation areas, and thinning could, therefore, help trees to reduce dieback and tree mortality related to drought. We address this question using a dendrochronology and modelling approach to improve the understanding of the growth response of high-density planted pine forests to thinning in drought-prone areas of Southern Spain. An experimental trial was, therefore, carried out with three species (Pinus halepensis, P. nigra, and P. sylvestris) and three thinning treatments (unthinned, moderate, and heavy thinning), after which growth-climate relationships and drought vulnerability indices were assessed. Three separate generalized linear mixed-effects models (GLMM), one for each species and location, were fitted using BAI as the response variable, and post-thinning growth trajectories and drought vulnerability indices were also simulated. Ten-year basal area showed strong growth responses following the thinning treatment (BAI10, 72% for P. halepensis and 50% for P. sylvestris as regards heavy thinning and 51% for P. nigra as regards moderate thinning), with different responses to precipitation and temperature according to species and thinning intensity. The significant effects of thinning on drought vulnerability indices indicated that the thinning treatments had a positive effect, irrespective of the pine species, although this was more evident in the case of P. sylvestris (recovery F = 28.10, p < 0.001, and resilience F = 35.21, p < 0.001 respectively) and P. halepensis (recovery F = 10.97, p < 0.001 and resilience F = 16.91, p < 0.001). The models also showed that climatic effect was greater for P. nigra than for P. sylvestris. The simulation also provides information on the long-term effectiveness of thinning; in P. sylvestris the effect of thinning tended to be attenuated after 20 years, but this was not the case of P. nigra and P. halepensis. High values of modelled resilience were found after thinning, with a time to recovery of between two and four years after drought depending on thinning intensity. Our findings showed the advantages of thinning for growth under different climatic constraints, particularly drought. This work is a new contribution that demonstrates the urgent need for forest managers to take steps in order to help drought-vulnerable Mediterranean pine plantations to adapt to the risks posed by climate change.

Proposing an ensemble machine learning based drought vulnerability index using M5P, dagging, random sub-space and rotation forest models

Drought is one of the major barriers to the socio-economic development of a region. To manage and reduce the impact of drought, drought vulnerability modelling is important. The use of an ensemble machine learning technique i.e. M5P, M5P -Dagging, M5P-Random SubSpace (RSS) and M5P-rotation forest (RTF) to assess the drought vulnerability maps (DVMs) for the state of Odisha in India was proposed for the first time. A total of 248 drought-prone villages (samples) and 53 drought vulnerability indicators (DVIs) under exposure (28), sensitivity (15) and adaptive capacity (10) were used to produce the DVMs. Out of the total samples, 70% were used for training the models and 30% were used for validating the models. Finally, the DVMs were authenticated by the area under curve (AUC) of receiver operating characteristics, precision, mean-absolute-error, root-mean-square-error, K-index and Friedman and Wilcoxon rank test. Nearly 37.9% of the research region exhibited a very high to high vulnerability to drought. All the models had the capability to model the drought vulnerability. As per the Friedman and Wilcoxon rank test, significant differences occurred among the output of the ensemble models. The accuracy of the M5P base classifier improved after ensemble with RSS and RTF meta classifiers but reduced with Dagging. According to the validation statistics, M5P-RFT model achieved the highest accuracy in modelling the drought vulnerability with an AUC of 0.901. The prepared model would help planners and decision-makers to formulate strategies for reducing the damage of drought.

Characterizing spatial–temporal drought risk heterogeneities: A hazard, vulnerability and resilience-based modeling

Droughts are among the most socio-economically costly natural phenomenon. While most of the related research has focused on drought as a hazard, it is essential to go beyond such practice and utilize hazard information in integration with vulnerability and resilience. We put forth a multi-dimensional high-resolution assessment of drought risk in Pakistan on a national scale. To this end, we utilize a Drought Risk Index (DRI) based on integrated Drought Hazard (DHI), Drought Vulnerability (DVI), and Drought Resilience (DReI) indices. We further employ geospatial techniques to identify and pinpoint statistically significant clustering in the form of hot and cold spots (higher and lower values concentration, respectively). To the best of our knowledge, this is a preliminary effort to engage the state-of-the-art space–time pattern mining technique for the simultaneous spatial–temporal dynamics investigation of droughts. We observed varying north–south geographical distributions in DHI across the country with higher drought intensities in southern regions. While a sizeable spatial drought risk disparity is evident across Pakistan, the worst DRI results are seen in Balochistan, followed by Sindh. Notably, around one-third of the country is identified as the hot spot with the highest drought risk, which ascertains a grave situation under global warming. Our comprehensive results provide more insights to prepare for drought mitigation, adaptation measures, and systematic impact evaluation in the face of environmental changes.

Drought vulnerability assessment by employingtheGeographical Information Systemand Analytical Hierarchy Processfor the Kurnool district of Andhra Pradesh, India

Droughts are recurring events that adversely affect agricultural activities, population density, and irrigated land. The consequences vary from region to region and are called a 'creeping phenomenon'. It is necessary to map drought vulnerability comprehensively in order to develop and implement drought mitigation strategies. In this regard, remote sensing-based studies are most robust, effective, and efficient at monitoring and mapping droughts compared to conventional ground survey methods. The purpose of this study was to develop a comprehensive drought vulnerability map using geospatial techniques and remote sensing to assess the spatial extent of drought vulnerability in Kurnool district, Andhra Pradesh, India. A total of 13 drought-influencing parameters such as Slope, Elevation, Aspect, Soil texture, Geology/Lithology, Land Use &amp; Land Cover, Drainage density, Distance from water bodies, Groundwater fluctuation, Normalized Difference Vegetation Index, Rainfall, Land Surface Temperature, and Topographic Wetness Index were selected for the assessment of drought vulnerability. These indices were integrated to create drought maps of both spatial and temporal extent in ArcGIS. Using pairwise comparison matrices, the Analytical hierarchy process (AHP) calculates weights for each criterion. The Drought Vulnerability Assessment map is generated by analyzing the parameters through spatial analysis. The results revealed that the drought vulnerability index of Kurnool is 42.5. This finding has been successfully validated by historical drought records of the region. Decision-makers can use the results to develop and apply proactive drought mitigation strategies.

An index-based approach to assess the vulnerability of socio-ecological systems to aridity and drought in the Danube Delta, Romania

Delta regions are compound socio-ecological systems increasingly exposed to various multi-hazards (e.g., drought, flooding). The Danube Delta, one of the most complex wetland areas in Europe, and one of the most vulnerable regions when it comes to drought, defined by its key vulnerable categories: women, children, the elderly population, the unemployed etc. In addition to the demographic aspects, there is also the infrastructure that puts a damper on the development of certain sectors of the economy, be they technical (water, gas, electricity), or social (schools, medicine, medical facilities). The current paper aims to apply a multi-criteria regional-level aridity and drought vulnerability assessment relying on both quantitative and qualitative methods in attaining a two-fold objective: (1) developing a comprehensive socio-economic aridity and drought vulnerability index in order to (2) identifying aridity and drought-vulnerable areas based on the finest indicators available for quantifying socio-economic vulnerability. The main research findings point to a general high and very high socio-economic vulnerability in the central and northern parts of the Danube Delta (36.3% of the total population) because of the high values of the elderly population, of households not connected to the water supply and sewage network, of the economic dependency rate, the high distance from the polarizing centres and the small numbers of emergency situation units. The low and very low vulnerability is registered in the West and in Sulina town (19.4% of the total population) where the accessibility to infrastructure and services makes up for the overall high drought exposure (dry/arid and semiarid climate). The research findings will improve the general understanding of drought vulnerability in a deltaic area, thus providing valuable information to be used by policymakers for drought management.