Drought Vulnerability in the United States: An Integrated Assessment

Geospatial assessment of agricultural drought vulnerability using integrated three-dimensional model in the upper Dwarakeshwar river basin in West Bengal, India

The amount of agricultural drought vulnerability in an underdeveloped rain-fed agro-based economy at the local, regional, and national level is most prominent factor for measurement. The desiccation of rain in agricultural sector becomes apprehensive to intercontinental food supply chain. So, adequate investigation and development of sustainable agricultural methodology are key factors to sustain the food security of a territory. In this research, delineation of agricultural drought vulnerability (ADV) status has been carried out by multidimensional mixed-method index approach using remote sensing and geographic information system. An integrated three-dimensional model is utilized to enrich this study. The three indices of this model include exposure index (EI), sensitivity index (SI), and adaptive capacity index (ACI). The ACI has been constructed by combining the environmental adaptive capacity (EAC), social adaptive capacity (SAC), and economic adaptive capacity (EcAC) index. The 40 parameters for ADV modeling are picked up by analyzing meteorological, geo-environmental, social, and remote sensing data. There are six exposure parameters, seven sensitivity parameters, twelve environmental adaptive capacity parameters, six social adaptive capacity parameters, and nine economic adaptive capacity parameters. Each index has been computed by assigning the weights based on their relative importance by using the analytic hierarchy process (AHP) approach. Final results were classified into five vulnerability zones, e.g., very low, low, moderate, high, and very high covering an area 362.32 km2, 186.68 km2, 568.69 km2, 547.05 km2, and 266.89 km2 respectively. Results have been validated with long-term Aman paddy yield data (2004 to 2014) through the yield anomaly index (YAI). Finally, the model ADV is a good model fit (R square = 0.894) and all the relationships were found significant, when SI, EI, and ACI are considered its predictors. While SI (B = 0.391, p < 0.001) and EI (B = 0.223, p < 0.001) are positively associated with ADV, ACI is negatively associated with ADV (B =  - 0.721, p < 0.001). This regional agricultural drought vulnerability model can be useful to identify drought-responsive areas and improve drought mitigation measures.

Interaction of drought-influencing factors for drought mitigation strategies in Lam Ta Kong Watershed, Khorat Plateau

Generally, drought is influenced by both spatial characteristics and anthropogenic activities within an area. Drought vulnerability assessment is a critical tool that can be effectively used to develop proper drought mitigation strategies to prevent avoidable losses. To develop suitable drought mitigation strategies, the overall drought vulnerability must be assessed, and the interaction among drought-influencing factors in the area should be considered. Consequently, this study aimed to investigate the interactions among critical drought-influencing factors and drought vulnerability in the Lam Ta Kong Watershed via spatial analysis with the analytical hierarchy process (AHP) and geographical information system (GIS) technology. Ten drought-influencing factors were considered in the vulnerability assessment: slope, elevation, soil texture, soil fertility, stream density, precipitation, temperature, precipitation days, evaporation, and land use. The results indicated that the critical drought-influencing factors were precipitation, precipitation days, and land use, resulting in most of the watershed experiencing high drought vulnerability (35.1% of the watershed or 1810.83 km2). Moreover, this research highlighted the interactions among the critical drought-influencing factors. Precipitation interacted with precipitation days to cause drought vulnerability across the watershed, with a p-value <0.05. Similarly, the interactions between precipitation and land use and between precipitation days and land use, with p-values <0.05, showed that they were associated with and influenced by drought in the Lam Ta Kong Watershed. This study further indicated that appropriate drought mitigation strategies for this watershed must consider the interactions among these drought-influencing factors, as well as their specific interactions across the watershed.

Social vulnerability and climate risk assessment for agricultural communities in the United States

Floods and droughts significantly affect agricultural activities and pose a threat to food security by subsequently reducing agricultural production. The impact of flood events is distributed disproportionately among agricultural communities based on their socio-economic fabric. Understanding climate-related hazards is critical for planning mitigation measures to secure vulnerable communities. This study introduces a holistic approach for evaluating the combined risks associated with drought and flood hazards for agricultural communities in the United States. It accomplishes this by merging social vulnerability indicators with data on drought and flood exposure, enabling the identification of the most susceptible agricultural communities. The research seeks to offer valuable insights into the vulnerability of agricultural communities across the United States. It fills a vital research gap by conducting a comprehensive nationwide assessment of social vulnerability, considering expected annual losses related to both flood and drought hazards, and amalgamating social vulnerability with these expected annual losses. The analyses were conducted by adapting datasets and methodologies that are developed by federal institutions such as FEMA, USACE, and USDA. The study identified the 30 most socially vulnerable counties and assessed their exposure to drought and flooding, finding that Mendocino, Sonoma, Humboldt, El Dorado, Fresno, and Kern counties in California had the highest drought exposure and expected annual losses, with Humboldt (CA) and Montgomery (TX) having the highest combined risk. The study estimated over $1 billion in crop damage, with California experiencing the greatest losses, primarily affecting a diverse range of crops, while the Midwest was primarily impacted in terms of major crop types. The findings of this study can serve as supportive information for policymakers to better understand climate risks in agricultural communities and identify where risk mitigation activities should be allocated.

Block-level vulnerability assessment reveals disproportionate impacts of natural hazards across the conterminous United States

The global increase in the frequency, intensity, and adverse impacts of natural hazards on societies and economies necessitates comprehensive vulnerability assessments at regional to national scales. Despite considerable research conducted on this subject, current vulnerability and risk assessments are implemented at relatively coarse resolution, and they are subject to significant uncertainty. Here, we develop a block-level Socio-Economic-Infrastructure Vulnerability (SEIV) index that helps characterize the spatial variation of vulnerability across the conterminous United States. The SEIV index provides vulnerability information at the block level, takes building count and the distance to emergency facilities into consideration in addition to common socioeconomic vulnerability measures and uses a machine-learning algorithm to calculate the relative weight of contributors to improve upon existing vulnerability indices in spatial resolution, comprehensiveness, and subjectivity reduction. Based on such fine resolution data of approximately 11 million blocks, we are able to analyze inequality within smaller political boundaries and find significant differences even between neighboring blocks.

Integrated assessment of drought vulnerability for water resources management of Bina basin in Central India

Drought is an extreme event and its frequency is expected to increase in future under the imminent threats of climate change. The areas vulnerable to drought are increasing due to increase in the spatial extent and severity of droughts. This necessitates the need for development of an integrated framework for assessment of drought vulnerability, which will be vital for water resources management policies focused towards such vulnerable areas. An integrated drought vulnerability assessment framework has been developed considering the physical indicators that vary spatially, social indicators that vary spatially but their temporal variation may be at longer time-frames, and spatio-temporal drought indicators that vary spatially and temporally during various months during drought years. This framework has been tested for Bina basin located in the drought prone Bundelkhand region of Madhya Pradesh. The drought indicators used in the study include (i) Standardized Precipitation Index (SPI) for evaluating meteorological drought characteristics, (ii) Surface water Drought Index (SDI) for evaluating streamflow drought characteristics, and (iii) Groundwater Drought Index (GDI) for evaluating groundwater drought characteristics. Groundwater levels are being observed at quarterly (3 monthly) time step only. So the relationships between GDI and 3-m SPI, 6-m SPI, and 12-m SPI have been investigated. Based on the best correlation, the 12-m SPI can be used to represent the groundwater drought in Bina basin and has therefore been used to assess the monthly variability in the groundwater drought characteristics. The spatially varying physical indicators including basin reach (elevation band), land use pattern and soil type; the spatio-temporal drought indicators including soil moisture drought, surface water drought and groundwater drought, rainfall departure and number of consecutive dry days; and the spatially varying social indicators including infants and young children, illiterate population, marginal workers and rural population have been used for the development of a Drought Vulnerability Index (DVI). The integrated drought vulnerability assessment framework has been conceptualized on the basis of DVI. Four vulnerability classes have been defined and the study area falls in mild to moderate vulnerable class, based on the analysis carried out for the various drought years in the basin. Appropriate drought management plans and mitigation strategies need to be developed to target these vulnerable areas in Bina basin.

How does precipitation data influence the land surface data assimilation for drought monitoring?

Droughts are among the costliest natural hazards that occur annually worldwide. Their socioeconomic impacts are significant and widespread, affecting the sustainable development of human societies. This study investigates the influence of different forcing precipitation data in driving Land Surface Models (LSMs) and characterizing drought conditions. Here, we utilize our recently developed LSM data assimilation system for probabilistically monitoring drought over the Contiguous United States (CONUS). The Noah-MP LSM model is forced with two widely used precipitation data including IMERG (Integrated Multi-satellitE Retrievals for GPM) and NLDAS (North American Land Data Assimilation System). Soil moisture and evapotranspiration are known to have a strong relationship in the land-atmospheric interaction processes. Unlike other studies that attempted the individual assimilation of these variables, here we propose a multivariate data assimilation framework. Therefore, in both modeling scenarios, the data assimilation approach is used to integrate remotely sensed MODIS (Moderate Resolution Imaging Spectroradiometer) evapotranspiration and SMAP (Soil Moisture Active Passive) soil moisture observations into the Noah-MP LSM. The results of this study indicate that the source of precipitation data has a significant impact on the performance of LSM data assimilation system for drought monitoring. The findings revealed that NLDAS and IMERG precipitation can result in a significant difference in identifying drought severity depending on the region and time of the year. Furthermore, our analysis indicates that regardless of the precipitation forcing data product used in the land surface data assimilation system, our modeling framework can effectively detect the drought impacts on crop yield. Additionally, we calculated the drought probability based on the ensemble of soil moisture percentiles and found that there exist temporal and spatial discrepancies in drought probability maps generated from the NLDAS and IMERG precipitation forcings.

Integrated socio-environmental vulnerability assessment of coastal hazards using data-driven and multi-criteria analysis approaches

Coastal hazard vulnerability assessment has been centered around the multi-variate analysis of geo-physical and hydroclimate data. The representation of coupled socio-environmental factors has often been ignored in vulnerability assessment. This study develops an integrated socio-environmental Coastal Vulnerability Index (CVI), which simultaneously combines information from five vulnerability groups: biophysical, hydroclimate, socio-economic, ecological, and shoreline. Using the Multi-Criteria Decision Making (MCDM) approach, two CVI (CVI-50 and CVI-90) have been developed based on average and extreme conditions of the factors. Each CVI is then compared to a data-driven CVI, which is formed based on Probabilistic Principal Component Analysis (PPCA). Both MCDM and PPCA have been tied into geospatial analysis to assess the natural hazard vulnerability of six coastal counties in South Carolina. Despite traditional MCDM-based vulnerability assessments, where the final index is estimated based on subjective weighting methods or equal weights, this study employs an entropy weighting technique to reduce the individuals’ biases in weight assignment. Considering the multivariate nature of the coastal vulnerability, the validation results show both CVI-90 and PPCA preserve the vulnerability results from biophysical and socio-economic factors reasonably, while the CVI-50 methods underestimate the biophysical vulnerability of coastal hazards. Sensitivity analysis of CVIs shows that Charleston County is more sensitive to socio-economic factors, whereas in Horry County the physical factors contribute to a higher degree of vulnerability. Findings from this study suggest that the PPCA technique facilitates the high-dimensional vulnerability assessment, while the MCDM approach accounts more for decision-makers' opinions.

Drought vulnerability and risk assessment in India: Sensitivity analysis and comparison of aggregation techniques

Long term drought management requires proper assessment and characterization of drought hazard, vulnerability and risk. This is particularly important for an agriculture-dependent, highly-populated, developing country such as India. However, the regulation of drought vulnerability and drought risk assessment in the country is mostly region-specific and ad-hoc, considering only a limited number of vulnerability indicators. In this study, a comprehensive, fine-resolution, country-wide drought risk assessment is carried out considering drought hazard in a multivariate framework, and using reliable drought vulnerability indicators that account for exposure, sensitivity and adaptive capacity. Further, multiple aggregation techniques including subjective, objective and comprehensive methods are employed for vulnerability assessment, and their performance assessed and compared. The Analytic Hierarchy Process (AHP)+Entropy and TOPSIS methods, which are comprehensive aggregation techniques are found to be better performing, TOPSIS being the most robust method. A bivariate choropleth map based on the TOPSIS-derived drought vulnerability shows regions of Punjab, Haryana, Uttar Pradesh and Tamil Nadu subjected to drought hazard-driven risk, while risk in other regions such as Rajasthan, parts of Central India, Orissa and parts of Maharashtra are driven more by drought vulnerability. Parts of Western Rajasthan, Vidharbha, North-East India, Chattisgarh, Tamil Nadu and Karnataka are under severe drought risk resulting from an interplay of hazard and vulnerability. Irrigation index, water body fraction, and groundwater availability are found to be the most significant indicators for assessing drought vulnerability in India. The above findings can aid decision makers and government bodies to plan region-specific line of action for building drought resilience.

Development of a Flash Drought Intensity Index

Flash droughts are characterized by a period of rapid intensification over sub-seasonal time scales that culminates in the rapid emergence of new or worsening drought impacts. This study presents a new flash drought intensity index (FDII) that accounts for both the unusually rapid rate of drought intensification and its resultant severity. The FDII framework advances our ability to characterize flash drought because it provides a more complete measure of flash drought intensity than existing classification methods that only consider the rate of intensification. The FDII is computed using two terms measuring the maximum rate of intensification (FD_INT) and average drought severity (DRO_SEV). A climatological analysis using soil moisture data from the Noah land surface model from 1979–2017 revealed large regional and interannual variability in the spatial extent and intensity of soil moisture flash drought across the US. Overall, DRO_SEV is slightly larger over the western and central US where droughts tend to last longer and FD_INT is ~75% larger across the eastern US where soil moisture variability is greater. Comparison of the FD_INT and DRO_SEV terms showed that they are strongly correlated (r = 0.82 to 0.90) at regional scales, which indicates that the subsequent drought severity is closely related to the magnitude of the rapid intensification preceding it. Analysis of the 2012 US flash drought showed that the FDII depiction of severe drought conditions aligned more closely with regions containing poor crop conditions and large yield losses than that captured by the intensification rate component (FD_INT) alone.

Socio-economic and environmental vulnerability to heat-related phenomena in Bucharest metropolitan area

In the recent years, the effects of extreme climate phenomena (mainly heat-related) on agricultural crops, infrastructure and human health have become increasingly severe as a result of their complex interactions with the particularities of the urban/rural habitat, as well as the social and economic factors. In Romania, heat-related phenomena (e.g. drought, heat waves) are affecting wide areas in the southern half of the territory where the study area (Bucharest Metropolitan Area) lies. The paper aims to develop a multi-criteria vulnerability assessment using both quantitative and qualitative methods. 23 indicators were selected and processed in order to assess various components of socio-economic and environmental vulnerability to heat-related phenomena using the statistical data available at local administrative units (LAU). The indicators were grouped into the three key components of vulnerability (potential exposure, sensitivity and adaptive capacity) on two dimensions (socio-economic and environmental) resulting two indexes: Socio-Economic Vulnerability Index (SEVI) and Environmental Vulnerability Index (EVI). Finally, an integrated Heat Vulnerability Index (HVI) (using Hull score, average 50 and standard deviation 14) was computed.