Risk Map Research Articles

Assessment of socio-economic strategies for managing regional flood risk in an urban coastal catchment

With the cascading flooding events over the past decades, several structural and non-structural measures have been effectively implemented for improved flood management. However, the uneven impacts of the flooding events have highlighted that socio-economic inequality amongst the community needs to be addressed along with hazards to devise long term and sustainable flood management strategies. This study aims to evaluate the impact of socio-economic adaptive scenarios over an urban coastal catchment for reduction in flood risk. Flood risk is estimated utilizing socio-economic aspects (vulnerability) and flood inundation (hazard), for the highly flood-prone coastal city of Mumbai, India. A multivariate approach is used for assessing socio-economic vulnerability, starting with Principal Component Analysis followed by Data Envelopment Analysis, for various socio-economic scenarios, i.e., by reducing percentage of certain feasible socio-economic sensitivity indicators, to identify the key indicators, which contribute to significant change in vulnerability. The flood hazard is assessed through a comprehensive hydrodynamic flood modelling approach to quantify the flood inundation depth and extent. The flood risk is, thereafter, evaluated by combining socio-economic vulnerability and flood hazard using bivariate risk mapping approach. The results indicate that, with the decrease in alteration percentage of certain feasible socio-economic sensitivity indicators, the overall vulnerability and flood risk may be reduced. The areas under very high and high vulnerability classes reduce gradually for all scenarios as the percentage reduction for various indicators is varied from 5 % to 15 %, total illiteracy being the most significant indicator. This study marks the first effort to discern socio-economic vulnerability indicators that have the potential to significantly diminish flood risk, thereby constituting a viable long-term flood risk mitigation strategy. This novel framework is generic and applicable to any flood prone catchments, to devise appropriate long-term flood management measures.

Seismic Risk Mapping of Bridges Using HAZUS and ArcGIS: The Case of Surat City, India

Seismic Risk Mapping of Bridges Using HAZUS and ArcGIS: The Case of Surat City, India

5.C. Scientific session: Strengthen resilience against emerging vector-borne diseases through the ECDC Fellowship Programmes

Abstract Over the past decade, climate change has impacted the transmission of a wide range of vector-borne diseases (VBDs) in Europe, where locally transmitted outbreaks have occurred. It has been implicated in the observed expansion of competent disease vectors in Europe such as Aedes albopictus, which transmits diseases such as Zika, Dengue and Chikungunya. Climate may influence the life cycle of vectors, and the reproduction rate of viral particles inside vectors and human hosts, which means that upsurges in temperature can reduce the incubation period of these pathogens and the life cycle of vectors, thus boosting transmission risk through elevated vector populations. Moreover, globalization and international air travel contribute to pathogen and vector dispersion internationally. Risk mapping suggests further expansion of the species in northern Europe due to climate change. The above underscores the necessity for more proactive measures, such as developing an effective vector control program by monitoring the density and distribution of hosts and pathogens at urban, national, and cross-border levels. Additionally, establishing early warning systems based on integrated human and zoonotic and entomological surveillance is crucial to detect alerts promptly and comprehend where and when infections may emerge or spread. Furthermore, cross-border collaboration and harmonization of vector-borne surveillance systems across European countries may improve preparedness, control and response to mosquito-borne diseases. Preparedness and response require significant efforts from EU Member States and neighbouring countries. Only through a multidisciplinary approach, involving multiple sectors, can we truly prepare to address public health threats. To contribute to strengthening resilience against cross-border public health and emerging health threats in EU Member States and neighbouring countries, the European Centre of Disease Prevention and Control (ECDC) has established three fellowship training programmes. These programmes aim to create and maintain a network of highly qualified epidemiologists, microbiologists and public health professionals and strengthen the public health epidemiology workforce at EU and neighbor countries. This workshop will offer attendees a comprehensive overview of the ECDC’s fellowship programmes, describe how they function and explain their role in preparing for health threats. It will provide examples of three projects carried out by fellows on the implementation of preparedness, response and control measures for emerging vector-borne diseases in European countries at local, national and multinational levels. Those will set the scene for an in-depth analysis of the key challenges faced and can help promote awareness that integrated, multidisciplinary interventions are both feasible and effective in reducing the threat of vector-borne infectious diseases spreading in Europe and neighboring countries. Key messages • ECDC fellowship programmes aim to strengthen resilience against emerging public health threats in EU Member States and neighboring countries. • Fellows are engaged in national and cross-border integrated and multidisciplinary interventions to strengthen preparedness and mitigate the spread of emerging vector-borne diseases.

Wavelet-Entropy Enhanced Clustering: A Comprehensive Analysis of Drought Patterns in the Southern Plains, USA

Abstract Droughts may exhibit spatiotemporal heterogeneity at regional scale. Effective drought assessment and management necessitates identifying homogeneous areas. However, previous studies often simplified clustering analysis by focusing only on a single variable. In this study, we present a novel drought risk map for the Southern Plains (SP) region of the United States by integrating the wavelet-entropy approach with k-means clustering algorithm to capture spatio-temporal patterns of drought-related variables across various resolutions while eliminating redundant information. We considered multiple drought indicators and indices including gridded precipitation (P), potential evapotranspiration (PET), normalized difference vegetation index (NDVI), and Standardized Precipitation Evapotranspiration Index (SPEI) as well as geographical coordinates and topography map. Through evaluating five different combinations of input datasets, we selected the one demonstrating optimal results based on Davies-Bouldin and Calinski-Harabasz criteria. In addition to P, PET, and NDVI, including the coordinates and elevation as secondary variables significantly enhanced the clustering performance. Using these variables, the region was subdivided into 21 clusters. The Pearson’s correlation coefficients for the SPEI between centroid members and corresponding cells within clusters averaged between 0.84 to 0.94. Comparison with an existing cluster map (DRA) for the region revealed that our proposed cluster map showed higher variability between clusters for P, PET, and NDVI, confirming the robustness of the clustering results for drought conditions in the SP. The new clustering framework is expected to provide valuable insights for understanding and addressing drought dynamics in the SP region.

Android-based framework for condition assessment of existing RC hospital buildings

Rapid visual screening (RVS) is a practically viable tool implemented all over the world to rank structures based on their seismic vulnerability. Seismic assessment of hospital buildings is essential in seismic-prone regions to ensure their structural integrity and readiness to provide emergency care and medical treatment after significant seismic events. Therefore, in the current study, an approach is developed that is similar to the proven methodologies reported in the literature, for the seismic evaluation of structural and non-structural elements of hospital buildings in a metropolitan city like Karachi. Karachi is located in the southwest of Pakistan and is vulnerable to earthquake and tsunami hazards. To this end, an android-based RVS system has been developed for large-scale seismic risk mapping of hospital buildings. The proposed system acquires the physical status of the building features in real time and identifies the building performance obtained from the calculated seismic risk index. Moreover, three case study hospital buildings were employed and analysed through the proposed RVS system. Results illustrate the performance of case study hospital buildings at organisational, structural and non-structural levels based on seismic risk indices.

Where to start with climate-smart forest management? Climatic risk for forest-based mitigation

Abstract. Natural disturbances like windthrows or forest fires alter the provision of forest ecosystem services such as timber production, protection from natural hazards, and carbon sequestration. After a disturbance, forests release large amounts of carbon and therefore change their status from carbon sinks to carbon sources for some time. Climate-smart forest management may decrease forest vulnerability to disturbances and thus reduce carbon emissions as a consequence of future disturbances. But how can we prioritise the stands most in need of climate-smart management? In this study we adopted a risk mapping framework (hazard times vulnerability) to assess the risk to climate-related forest ecosystem services (carbon stock and sink) in forests prone to windthrow (in the Julian Alps, Italy) and forest fires (in the Apennines, Italy). We calculated hazard by using forest fire and windthrow simulation tools and examined the most important drivers of the respective hazards. We then assessed vulnerability by calculating current carbon stocks and sinks in each forest stand. We combined these values together with the calculated hazard to estimate “carbon risk” and prioritised high-risk stands for climate-smart management. Our findings demonstrate that combining disturbance simulation tools and forest carbon measurements may aid in risk-related decision-making in forests and in planning decisions for climate-smart forestry. This approach may be replicated in other mountain forests to enhance our understanding of their actual carbon vulnerability to forest disturbances.

Mapping of Risks and Firm Performance: Insights from Small Scale Garment Enterprises in Eldoret Town, Uasin Gishu County, Kenya

Small-scale garment enterprise projects are pivotal in Kenya’s economic development, contributing significantly to employment and poverty alleviation. These projects often face challenges that impede their growth and performance. The paper seeks to establish the effect of mapping risks on the performance of small-scale garment enterprise projects in Eldoret town. An explanatory research design was used and the target population consisted of 386 small-scale garment enterprise owners. A sample size of 196 small-scale garment enterprise projects in Eldoret town was selected using Yarmane formula. The respondents were selected using stratified sampling method and data was collected using questionnaires. The analysis was done using both descriptive and inferential statistics. Result showed that scheduling milestones had statistically positive effect on the performance of garment enterprise projects (β 0.947, p<0.05). The paper concludes that mapping of risks is critical processes of ensuring that garment enterprises attain performance targets. It is therefore recommended that comprehensive risk mapping practices need to be regularly undertaken by SMEs in the garment sector in Eldoret town

Mass Timber Buildings: The associated risks of rainwater exposure during construction in the Portuguese climate

The increase in the number and complexity of wooden buildings creates a series of challenges, like moisture control and management during the construction phase. The risks associated with the direct exposure of timber elements to rain during construction depend on the severity of the climate and the specific details of each project and therefore must be analysed locally. In this context, the results of 16 weeks of moisture content monitorization of Cross Laminated Timber (CLT) floor-wall connection specimens exposed to the outdoors are presented and evaluated. The monitorization demonstrated that end-grain surfaces are the most sensitive to the action and movement of water in CLT and within a few days of exposure to rain reached alarming moisture content (MC>30 %), with water entrapment at the interface between elements, preventing drying in a reasonable time for construction workflow (MC>30 % after 39 days of drying). On the other hand, zones with faster drying exhibited severe physical damage such as delamination and cracks. Additionally, the study evaluates the feasibility of using satellite-obtained meteorological data to develop a degradation risk map (Scheffer Climate Index) for mainland Portugal. The high coefficient of determination (R2 ≥ 0.81) and model efficiency (EF ~ 0.80–0.90) demonstrate the reliability of the data. Finally, two degradation risk maps for mainland Portugal are presented.

An Integrated Approach for Development of a Decision Support System for Early Surveillance of Japanese Encephalitis: a Remote Sensing-based study in Conjunction with Epidemiological Scenario in Assam, India

Abstract. Japanese Encephalitis (JE) is one of the serious mosquito borne viral diseases mostly prevalent in India and many of the South Asian countries. A comprehensive study was conducted in Dibrugarh, a JE endemic district of Assam taking into the account of the environmental and social factors that determine the outbreak of the disease. Effect of environmental and climatic factors on mosquito vector densities was investigated using geospatial tools and techniques. Wetlands were found to be the most preferred habitat type for most of the JE-transmitting mosquitoes followed by paddy-growing areas and ponds. The villages abundant with these habitats or located nearby were found to be more vulnerable to JE risk. Based on the spatial distribution of habitat types, villages in the district were divided into three categories of vector abundance and hence the risk of JE. It is found that rainfall (intensity as well as duration) is the most significant determinant in the modulation of mosquito density followed by temperature. It is also observed that the derived models could explain about 61% to 73% variation in mosquito density due to change in weather variables as indicated by R2 values of the Models. Among socio-economic determinants, pig rearing habit of the villagers was found to be most significant. JE risk map was prepared by integrating vector abundance map with the map of host abundance in GIS domain. Attempts were also made to forecast the intensity of JE cases based on a time series analysis of historical morbidity pattern, whereas forecast for onset of the disease was based on the modulation of weather variables in the study area. A web-based decision support system (DSS) developed by integrating the forecasts of JE onset, intensity and JE prone villages has been provided to the concerned health authorities to take timely intervention measures. The JE early warning system developed for Dibrugarh district was extended to two more JE prone districts viz, Sibsagar and Tinsukia as validation sites and observed that the accuracy of forecast was within the range of 69% to 77%. DSS on JE has opened up new opportunities to study similar vector-borne diseases of the JE serogroup viz., West Nile.

Modeling climate-related global risk maps of rice bacterial blight caused by Xanthomonas oryzae (Ishiyama 1922) using geographical information system (GIS).

Rice is a critical staple crop that feeds more than half of the world's population. Still, its production confronts various biotic risks, notably the severe bacterial blight disease produced by Xanthomonas oryzae. Understanding the possible effects of climate change on the geographic distribution of this virus is critical to ensuring food security. This work used ecological niche modeling and the Maxent algorithm to create future risk maps for the range of X. oryzae under several climate change scenarios between 2050 and 2070. The model was trained using 93 occurrence records of X. oryzae and five critical bioclimatic variables. It has an excellent predictive performance, with an AUC of 0.889. The results show that X. oryzae's potential geographic range and habitat suitability are expected to increase significantly under low (RCP2.6) and high (RCP8.5) emission scenarios. Key climatic drivers allowing this development include increased yearly precipitation, precipitation during the wettest quarter, and the wettest quarter's mean temperature. These findings are consistent with broader research revealing that climate change is allowing many plant diseases and other dangerous microbes to spread across the globe. Integrating these spatial predictions with data on host susceptibility, agricultural practices, and socioeconomic vulnerabilities can help to improve targeted surveillance, preventative, and management methods for reducing the growing threat of bacterial blight to rice production. Proactive, multidisciplinary efforts to manage the changing disease dynamics caused by climate change will be critical to assuring global food security in the future decades.

Geospatial analysis and AHP for flood risk mapping in Quetta, Pakistan: a tool for disaster management and mitigation

The 2022 flood events in Quetta, Pakistan, caused severe damage to the economy, properties, and lives. Therefore, flood risk mapping to identify flood-prone areas is essential for planners and decision-makers to take critical protective measures to control the effects of flooding. This study focuses on mapping flood-prone regions in the Quetta district of Pakistan using an analytical hierarchy process (AHP) and a geographic information system (GIS). The factors influencing flood used in the present study were topographic witness index (TWI), elevation, slope, land use, land cover, precipitation, stream distance, drainage density, and soil type. Weights and ranks were allocated separately to all factors through AHP and were interpreted in a GIS environment. The produced flood hazard model of the study area depicted four zones. These zones ranged from low (19.49%), moderate (43.34%), high (28.30%), to very high (8.87%). The model was further validated through previous flood events in the study area. Around 90% of flood hazard events in the past took place mainly in the produced model's very high and high zones, which is why the current model is reliable. Finally, integrating geospatial approaches with AHP in flood hazard mapping is a quick, reliable, and affordable method that may be utilized in the area.

Land cover and drought risk assessment in Türkiye's mountain regions using neutrosophic decision support system.

Earth observation (EO) provides dynamic scientific methods for tracking and defining ecological parameters in mountainous regions. Open-source platforms are frequently utilized in this context to efficiently collect and evaluate spatial data. In this study, we used Collect Earth (CE), an open-source land monitoring platform, to reveal and assess land cover, land cover change, and relevant ecological parameters such as drought risk. Mountain ecosystems were subject to an evaluation for the first time by combining remote sensing with a hybridization of Decision-Making Trial and Evaluation Laboratory (DEMATEL), analytic hierarchy process (AHP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for neutrosophic sets in risk assessment problems of several connected criteria. The high and dispersed high alpine environment of Türkiye accommodates land with relatively less human influence, making it suitable to observe climate change impacts. In the framework of the study, we evaluated more than two decades (2000-2022) of land use and land cover (LULC) changes in the mountain regions of the country. Using nine identified ecological parameters, we also evaluated drought risk. The parameters included were the LULC classes and their change, elevation, slope, aspect, precipitation, temperature, normalized difference vegetation index (NDVI), water deficit, and evapotranspiration (ET). The risk map we produced revealed a high to very high drought risk for almost throughout the Türkiye's mountainous areas. We concluded that integrating geospatial techniques with hybridization is promising for mapping drought risk, helping policymakers prepare effective drought mitigation measures to reasonably adapt to climate change impacts.

Modeling health risks using neural network ensembles.

This study aims to demonstrate that demographics combined with biometrics can be used to predict obesity related chronic disease risk and produce a health risk score that outperforms body mass index (BMI)-the most commonly used biomarker for obesity. We propose training an ensemble of small neural networks to fuse demographics and biometrics inputs. The categorical outputs of the networks are then turned into a multi-dimensional risk map, which associates diverse inputs with stratified, output health risk. Our ensemble model is optimized and validated on disjoint subsets of nationally representative data (N~100,000) from the National Health and Nutrition Examination Survey (NHANES). To broaden applicability of the proposed method, we consider only non-invasive inputs that can be easily measured through modern devices. Our results show that: (a) neural networks can predict individual conditions (e.g., diabetes, hypertension) or the union of multiple (e.g., nine) health conditions; (b) Softmax model outputs can be used to stratify individual- or any-condition risk; (c) ensembles of neural networks improve generalizability; (d) multiple-input models outperform BMI (e.g., 75.1% area under the receiver operator curve for eight-input, any-condition models compared to 64.2% for BMI); (e) small neural networks are as effective as larger ones for the inference tasks considered; the proposed models are small enough that they can be expressed as human-readable equations, and they can be adapted to clinical settings to identify high-risk, undiagnosed populations.

KONSTRUKSI MODEL PENCEGAHAN FRAUD MELALUI RED FLAG BASED SYSTEM DI PEMERINTAH KABUPATEN JEMBER (STUDI KASUS DI INSPEKTORAT KABUPATEN JEMBER)

This research aims to present a system that can prevent fraud in the form of compliance and internal control within the Jember Regency Government through a red flag-based system. The study uses a qualitative case study method. The Inspectorate currently requires indicators to prevent the occurrence of fraud. The techniques employed to collect data include observation, interviews, and documentation. The results indicate that all Regional Device Organizations (OPD) in Jember Regency analyze various risks. These risks will be further analyzed for their root causes and the impacts they may generate. Based on the risk map that has been created, there are a total of 412 risks identified across 72 Regional Device Organizations (OPD) in Jember Regency. The focus of the risks with potential fraud in this study lies within the category related to finance (finance risk). Specifically, there are 33 financial-related risks identified across all Regional Device Organizations (OPD) in Jember Regency.

Spatial analysis and mapping of malaria endemic-prone areas using environmental risk factors and spatial multi criteria decision analysis in the Northern Zone of Plateau State, Nigeria.

Compressive mapping and in-depth analysis of malaria vulnerability risk foci are important tools for strategic planning, vector surveillance, implementing necessary interventions, and managing scarce resources to combat the daunting challenges of malaria infections in Nigeria. The study utilized environmental risk factors and spatial multi-criteria decision analysis techniques to analyze and map the spatial variations in malaria-endemic prone areas within the Northern Zone of Plateau State. Analysis of the criteria's weights of environmental risk factors and the stratification of the malaria risk vulnerability revealed that high-risk areas covered a total of 4017.25 Km2, while low-risk areas covered 1294.83Km2, having a close resemblance to the corresponding ground conditions. The results showed that a significant 60% of the study areas were characterized by high risk of malaria transmission, particularly in most settlements around the Jos North, Jos South and Barkin Ladi Local Government Areas. The malaria risk map's predictive abilities demonstrated a high level of accuracy, as indicated by the area under the curve score of 0.989. Furthermore, the risk map demonstrates a high-performance rate in identifying malaria hotspots, with a sensitivity of 98%, a proficiency rate of 54%, and a statistical significance confidence level ranging from 90% to 99% for identifying malaria hotspots and coldspots. The malaria risk analysis and mapping within the endemic prone areas of the Northern Zone of Plateau State not only equips policymakers with effective tools for identifying malaria foci but also offers a deeper comprehension of the appropriate intervention measures to be implemented.

High-resolution spatio-temporal risk mapping for malaria in Namibia: a comprehensive analysis

BackgroundNamibia, a low malaria transmission country targeting elimination, has made substantial progress in reducing malaria burden through improved case management, widespread indoor residual spraying and distribution of insecticidal nets. The country's diverse landscape includes regions with varying population densities and geographical niches, with the north of the country prone to periodic outbreaks. As Namibia approaches elimination, malaria transmission has clustered into distinct foci, the identification of which is essential for deployment of targeted interventions to attain the southern Africa Elimination Eight Initiative targets by 2030. Geospatial modelling provides an effective mechanism to identify these foci, synthesizing aggregate routinely collected case counts with gridded environmental covariates to downscale case data into high-resolution risk maps.MethodsThis study introduces innovative infectious disease mapping techniques to generate high-resolution spatio-temporal risk maps for malaria in Namibia. A two-stage approach is employed to create maps using statistical Bayesian modelling to combine environmental covariates, population data, and clinical malaria case counts gathered from the routine surveillance system between 2018 and 2021.ResultsA fine-scale spatial endemicity surface was produced for annual average incidence, followed by a spatio-temporal modelling of seasonal fluctuations in weekly incidence and aggregated further to district level. A seasonal profile was inferred across most districts of the country, where cases rose from late December/early January to a peak around early April and then declined rapidly to a low level from July to December. There was a high degree of spatial heterogeneity in incidence, with much higher rates observed in the northern part and some local epidemic occurrence in specific districts sporadically.ConclusionsWhile the study acknowledges certain limitations, such as population mobility and incomplete clinical case reporting, it underscores the importance of continuously refining geostatistical techniques to provide timely and accurate support for malaria elimination efforts. The high-resolution spatial risk maps presented in this study have been instrumental in guiding the Namibian Ministry of Health and Social Services in prioritizing and targeting malaria prevention efforts. This two-stage spatio-temporal approach offers a valuable tool for identifying hotspots and monitoring malaria risk patterns, ultimately contributing to the achievement of national and sub-national elimination goals.

Perceived discrimination and refraining from seeking physician’s care in Sweden: an intersectional analysis of individual heterogeneity and discriminatory accuracy (AIHDA)

BackgroundDiscrimination may further impede access to medical care for individuals in socially disadvantaged positions. Sociodemographic information and perceived discrimination intersect and define multiple contexts or strata that condition the risk of refraining from seeking physician’s care. By applying analysis of individual heterogeneity and discriminatory accuracy (AIHDA) we aimed to improve the mapping of risk by considering both strata average risk differences and the accuracy of such strata risks for distinguishing between individuals who did or did not refrain from seeking physician’s care.MethodsWe analysed nine annual National Public Health Surveys (2004, 2007–2014) in Sweden including 73,815 participants. We investigated the risk of refraining from seeking physician’s care across 64 intersectional strata defined by sex, education, age, country of birth, and perceived discrimination. We calculated strata-specific prevalences and prevalence ratios (PR) with 95% confidence intervals (CI), and the area under the receiver operating characteristic curve (AUC) to evaluate the discriminatory accuracy (DA).ResultsDiscriminated foreign-born women aged 35–49 with a low educational level show a six times higher risk (PR = 6.07, 95% CI 5.05–7.30) than non-discriminated native men with a high educational level aged 35–49. However, the DA of the intersectional strata was small (AUC = 0.64). Overall, discrimination increased the absolute risk of refraining from seeking physician’s care, over and above age, sex, and educational level.ConclusionsAIHDA disclosed complex intersectional inequalities in the average risk of refraining from seeking physician’s care. This risk was rather high in some strata, which is relevant from an individual perspective. However, from a population perspective, the low DA of the intersectional strata suggests that potential interventions to reduce such inequalities should be universal but tailored to the specific contextual characteristics of the strata. Discrimination impairs access to healthcare.

Mapping the health impacts of transport noise in the densely populated area of the Île-de-France region

The European Noise Directive (END) requires agglomerations of more than 100,000 inhabitants to produce strategic noise maps. In the Ile-de-France region, 14 agglomerations with a total population of 10,5 million inhabitants are concerned. Bruitparif applied the health impact assessment methodology recommended by the WHO to carry out a detailed territorial diagnosis of the issues related to transport noise. For this purpose, population exposure was assessed on the basis of strategic noise maps produced according to the 4th round of the END and the associated risks were calculated using the exposure-response functions published by the WHO in October 2018. The work continued with the valuation of healthy life years lost due to transport noise. Two types of maps have been produced with a 200 m2 grid as well as at the city scale: maps of collective health impacts related to population noise exposure and maps of average individual risk corresponding to the potential impact per individual. The combination of these two types of information has made it possible to highlight significant territorial disparities and to identify the sectors most concerned by the health issues related to noise, a prerequisite for prioritizing public action.

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.

Geospatial analysis of alarmingly increasing flood vulnerability and disaster risk within the northeast Himalaya Region of India

Geoenvironmetally the eastern part of the Himalaya region is highly vulnerable to flood and other natural disasters as it consists of fragmented and tectonically active geology and geomorphology, very high monsoon rainfall (>360cm) and subsequent runoff, rugged hilly terrain with high ranges of elevation and slope, dense drainage density etc. Other hand, the unplanned developmental activities keep going and scaling up this vulnerability and risk to floods and other disasters. Addressing this burning issue, a geospatial technology-based case study of the Kohima district, Nagaland state (India), is presented here. The geospatial "technology-based" analyses employed in the study are thematic GIS mapping of flood hazard, vulnerability and risk controlling factors; and performing overlay operation using the AHP model in GIS software to generate spatio-temporal map layers of flood vulnerability and disaster risk. Results reveal that the region is under a high rate of monsoon climate change (increasing temperature, rainfall, rainy days, rainfall events and flood events with an annual rate of 0.35%, 1.12%, 0.36%, 2.67% and 4% respectively), land use degradation (increasing built-up area with 0.60%, annual rate decreasing forest, shrubs and water bodies with 0.80% accumulated annual rates respectively) and demographic changes (increasing urban as well and rural population density with 0.53-2.10% and 0.55-2.14% respectively). Accumulated impacts of climate change, land use degradation and demographic changes causing an increase in flood hazard, vulnerability and disaster risk. Flood hazard zones and vulnerability zones extending with 0.50% (4.89km2) and 0.84% (8.16km2) annual rates respectively, subsequently the food risk zones categorized as a moderate, high and very high potential risk, have been spreading out with a yearly rate of 0.07% (0.65km2), 0.13% (1.31km2) and 0.03% (0.33km2) respectively. It decreases the area under low and very low-risk zones by 0.17% (1.63km2) and 0.07% (0.65km2) annual rates respectively. Following up on these annual rates, the spatial distribution of flood hazard, vulnerability and risk zones for the next decade (2031-2040) have also been projected, revealing alarming situations, if flood disaster risk reduction (F-DRR) measures were not implemented in timely. It is strongly believed that the proposed study will be very useful for district-level planners and administrators to implement sustainable development planning, for the scientific fraternity to enhance their research work in the field of flood disaster management and for individuals for their safety in terms of life and property.