Vulnerability Map Research Articles

Flash-flood susceptibility modelling in a data-scarce region using a novel hybrid approach and trend analysis of precipitation

ABSTRACTRecently, the incidence of heavy rainfall events and associated flash floods have encouraged us to investigate long-term trends in extreme rainfall and flash flood vulnerability mapping. Thus, in this study, a hybrid model was designed by integrating weight of evidence and Naïve Bayes (WOE-NB) to identify areas in Uttarakhand prone to flash floods, and we compared its ability with that of AdaBoost. Furthermore, the significance of long-term rainfall trends was evaluated using Mann–Kendall, modified Mann-Kendall, and innovative trend analysis (ITA), and extreme rainfall events were examined for 51 years (1970–2020). Results showed the WOE-NB and AdaBoost had acceptable goodness of fit (area under the curve = 0.969 and 0.973, respectively). Moreover, ITA can identify some important patterns based on on-trend results that other tests cannot. The return period revealed about 97.54% of the flash floods were caused by normal rainfall, with 2.45% being caused by severely abnormal rainfall.

Assessing vulnerability of coastal aquifer to seawater intrusion using Convolutional Neural Network

This study examined coastal aquifer vulnerability to seawater intrusion (SWI) in the Shiramin area in northwest Iran. Here, six types of hydrogeological data layers existing in the traditional GALDIT framework (TGF) were used to build one vulnerability map. Moreover, a modified traditional GALDIT framework (mod-TGF) was prepared by eliminating the data layer of aquifer type from the GALDIT model and adding the data layers of aquifer media and well density. To the best of our knowledge, there is a research gap to improve the TGF using deep learning algorithms. Therefore, this research adopted the Convolutional Neural Network (CNN) as a new deep learning algorithm to improve the mod-TGF framework for assessing the coastal aquifer vulnerability. Based on the findings, the CNN model could increase the performance of the mod-TGF by >30 %. This research can be a reference for further aquifer vulnerability studies.

Groundwater vulnerability assessment in central Iran: Integration of GIS-based DRASTIC model and a machine learning approach

The study try to evaluate the susceptibility of groundwater. The DRASTIC model was implemented through GIS. Various input variables, such as water table depth, net recharge, aquifer and soil media, topography, vadose zone impact, and hydraulic conductivity, were evaluated within the model to generate a groundwater vulnerability map. Subsequently, machine-learning algorithms (SVM, RF, and GLM) employed using the SDM package in R software to optimize the DRASTIC method. To assess the performance of groundwater pollution risk models, training and validation datasets were evaluated using the ROC curve. The results revealed that approximately 40% of the study area fell within the high vulnerability range, while around 30% exhibited moderate pollution risk. Evaluation of the machine learning models indicated their effectiveness in model development. The RF model demonstrated the highest predictive power, achieving an AUC of 0.98. Additionally, the GLM and SVM algorithms achieved AUC values of approximately 76%. These algorithms can serve as efficient techniques for evaluating and managing groundwater resources. The findings underscored relatively poor groundwater quality in the study area, with excessive aquifer exploitation by the agricultural sector and infiltration of urban sewage and industrial waste identified as the primary causes of groundwater pollution. The implications of these findings are crucial for devising strategies and implementing preventive measures to mitigate water resource vulnerability and associated health risks in central Iran.

Mapping Vulnerability to Flood in Urbanizing Watershed: The Case of Surakarta Urban Region

The vulnerability of urban areas to flooding increased along with the rapid urbanization process. The study selected the Subosukawonosraten (Surakarta, Boyolali, Sukoharjo, Karanganyar, Wonogiri, Sragen, and Klaten) Region as a case study to assess the vulnerability. The ESA framework: exposure, sensitivity, and adaptability—is applied. Exposure is examined using the built-up index in 2020. Sensitivity is assessed based on population density in 2020. Additionally, precipitation and topography are also discussed. The study uses hot spot analysis to pinpoint the most populated and developed areas of the region, which are Surakarta and its peri-urban. Following that, the study investigates to what extent urban villages situated in this hot spot can adapt in light of the floods that often occur in the region. The findings imply that, primarily because of rapid urbanization, the strategy to deal with vulnerability should be considered an important regional-scale strategy that should integrate technical and community-based development approaches to establish a more sustainable strategy for resiliency.

Identification of Aquifer Vulnerability to Pollution Based on GIS in Jekan Raya and Sabangau Districts Palangka Raya City, Central Kalimantan Province

Groundwater is the main source of needs for living things. Groundwater is stored in aquifer systems contained in the soil. Under certain conditions, aquifers will be very vulnerable to pollution that can be caused by various sources of pollution depending on existing geological conditions. Therefore, it is necessary to identify the vulnerability of aquifers to polluting sources so that they can assist the government in planning and formulating policies related to regional spatial planning. Identification of aquifer vulnerability can be done using the DRASTIC method which is a weighting method of various parameters such as groundwater table depth (D), Rainfall (R), aquifer media (A), soil media (S), Topography (T), Unsaturated zone (I), and hydraulics conductivity (C) and combined with HAI (Human Activity Impact). The weighting results will produce a Static and Dynamic Aquifer Vulnerability Map. The two maps will be stacked with the Spatial Plans of Palangka Raya City so that it is expected to be a consideration for the Palangka Raya City government in the preparation of the next Regional Spatial Planning policy. The results of weighting with the DRASTIC Index and HAI whole Jekan Raya District and Sabangau District, Palangka Raya City are included in a high level of vulnerability which at this level is an area that is very easy to be polluted by all pollutants, so proper handling is needed so that the impact that will be caused is not too high risk for the condition of aquifers in the area.

Assessing the soil salinity vulnerability and groundwater quality variations due to drying up of the lake.

Shrinkage of lakes is considered a serious ecological challenge. The impact of this phenomenon on the environment can be devastating. Monitoring the spatiotemporal variations of lake's water surface and its salinization is essential for planning and adopting mitigation measures. In this study, a new multiscale-kernel-based method was used to assess the spatiotemporal variations of the shrinkage of the Urmia Lake and develop soil salinity vulnerability map for its basin. For this aim, remote sensing, empirical wavelet transform (EWT), differential symbolic entropy (DSE), and Gaussian regression process (GPR) techniques were used. Vulnerable areas were identified using geo-environmental parameters extracted from the in situ observations and satellite datasets. In the next step, considering three time periods including the lake normal period, lake drying period, and lake restoration period, the variations in the quality of groundwater were investigated. Results showed that the east and south sections of the lake were more prone to severe salinization. Saline lands caused negative impacts on air quality and agricultural activities in these areas. It was found that both climate change and human activities had contributed to the shrinking of the lake. Results showed that the quality of groundwater in the area around the lake has been affected by the excessive salinity of the lake water and the encroachment process of saline water. The water quality index has increased during the drying period of the lake and caused negative effects on the quality of water used for drinking and agricultural activities. In the lake restoration period, a slight increase in water quality was observed.

Geophysics reveals forest vulnerability to drought

AbstractDrought and heat‐induced forest die‐off are being increasingly reported across the planet. As vulnerable areas tend to have thin soils and poor water holding capacities, quantification of soil depth thresholds, relative to drought intensity, has global implications for identifying forest areas at risk. Measuring soil depth at forest stand or regional scales is, however, difficult. Our aim was to quantify soil thickness across drought impacted forest stands using geophysics. We asked whether impacted sites had shallow soils and whether soil thickness was associated with drought effects and forest structure. Electrical resistivity measurements were conducted at three sites in the Northern Jarrah Forest, Western Australia, which experienced die‐off during a 2010 drought and subsequent 2011 heatwave. Multispectral imaging quantified stand structure and vegetation cover. Geophysics identified shallow bedrock in the centre of all drought sites. Soil thicknesses correlated well with stand structure and cover, consistent with increasing water limitation in thinner soils. Smaller cover by trees and shrubs, more ground cover and shorter canopies were observed in soils <20 m thick and were more likely in soils <12 m thick, while treeless areas had thin soils, <2 m thick. The apparent resilience of this forest to long‐term drying looks to be due to the region's deep soils. With predicted future drying it is expected die‐off patches will expand outwards and new die‐off patches will emerge over thin soils. Geophysics can identify areas of forest vulnerable to future drought events, suggesting the potential for landscape‐scale mapping of drought vulnerability via airborne methods.

Groundwater Availability and Suitability Evaluation for Irrigated Agriculture in Mbale District

The purpose of this study was to evaluate the availability and suitability of groundwater in Mbale district basing on potential, quality and vulnerability to support irrigated agriculture. Groundwater potential was assessed using geospatial technique through AHP model. Thematic layers were prepared namely: land use and land cover, slope, soil, rainfall, lineament density, lithology, geomorphology and drainage density. All layers were integrated using the MLC technique. Ranking of each parameter was performed using WOIA. Weights were assigned to each subject class basing on AHP results. The consistency of the outputs was tested by computation of CR and was at a reasonable acceptable level (0.029 < 0.1). Groundwater potential was delineated basing on the values of groundwater potential index. Groundwater quality was determined using IWQI method. Thirty-three water samples were collected. Five chemical parameters were tested in the laboratory: chloride, electrical conductivity, bicarbonate, sodium and SAR to generate the water quality database using Kriging interpolation technique. Computation of IWQI values for each source was made and was used to generate the IWQI map using the weighted summation. DRATIC method was used to delineate vulnerability of groundwater. Layers were generated using the following parameters: depth to groundwater, net recharge, aquifer media, topography, vadose zone impacts, and hydraulic conductivity. The aquifer vulnerability map was prepared by overlaying layers. Three different vulnerability zones were determined according to DRASTIC scores low (<100), medium (100-140) and high (>140). Integration of maps of quality, vulnerability and potential of groundwater was made using an unsupervised MLC classification method. Groundwater potential was in three zones: very good zone was 26.99 km2 (12.98%), the good zone was 126.22 km2 (60.71%) and the poor zone was 54.69 km2 (26.31%). The average annual exploitable groundwater reserves are estimated at 0.026 (MCM/km2) in the zone of “very good”, 0.024 MCM/km2 in the zone of “good” and 0.018 MCM/km2 in the zone of “poor”. Groundwater quality was in two use restrictions: High Restriction of 90.90% with the IWQI value from 40 to 55 on the area of 188.98 km2 of the study area and Severe Restriction of 9.10% with IWQI values from 32 to 40 covering of 18.92 km2. The study area had a high probability of contamination. Low vulnerability covers an area of 11.23km2 (5.40%), 77.83km2 (37.44%) for medium and 118.83km2 (57.16%) for high. The resulting clustered map was classified into five categories with their respective regions: 17.58% very poor (36.56km2), 13.84% poor (28.77km2), 12.69% good (26.39km2), 31.46% very good (65.39km2) and 24.43 % excellent (50.78km2). The sub counties in Mbale district that have inadequate and unsuitable groundwater for irrigated agriculture are Budwale, Wanale, Busano, Bubyangu, Bufumbo and Nyondo since they belong to the zones of very poor and poor. The remaining sub counties have available and suitable groundwater for irrigated agriculture since they belong to the zones of good, very good and excellent.

Recharge and vulnerability assessment of groundwater resources in North west India: Insights from isotope-geospatial modelling approach

Recent studies indicate dwindling groundwater quantity and quality of the largest regional aquifer system in North West India, raising concern over freshwater availability to about 182 million population residing in this region. Widespread agricultural activities have resulted severe groundwater pollution in this area, demanding a systematic vulnerability assessment for proactive measures. Conventional vulnerability assessment models encounter drawbacks due to subjectivity, complexity, data-prerequisites, and spatial–temporal constraints. This study incorporates isotopic information into a weighted-overlay framework to overcome the above-mentioned limitations and proposes a novel vulnerability assessment model. The isotope methodology provides crucial insights on groundwater recharge mechanisms (18O and 2H) and dynamics (3H) - often ignored in vulnerability assessment. Isotopic characterisation of precipitation helped in establishing Local Meteoric Water Line (LMWL) as well as inferring contrasting recharge mechanisms operating in different aquifers. Shallow aquifer (depth < 60 m) showed significant evaporative signature with evaporation loss accounting up to 18.04% based on Rayleigh distillation equations. Inter-aquifer connections were apparent from Kernel Density Estimate (KDE) and isotope correlations. A weighted overlay isotope-geospatial model was developed combining 18O, 3H, aquifer permeability, and water level data. The central and northern parts of study area fall under least (0.29%) and extremely (1.79%) vulnerable zones respectively, while majority of the study area fall under moderate (42.71%) and highly vulnerable zones (55.20%). Model validation was performed using groundwater NO3– concentration, which showed an overall accuracy up to 82%. Monte Carlo Simulation (MCS) was performed for sensitivity analysis and permeability was found to be the most sensitive input parameter, followed by 3H, 18O, and water level. Comparing the vulnerability map with Land Use Land Cover (LULC) and population density maps helped in precisely identifying the high-risk sites, warranting a prompt attention. The model developed in this study integrates isotopic information with vulnerability assessment and resulted in model output with good accuracy, scientific basis, and widespread relevance, which highlights its crucial role in formulating proactive water resource management plans, especially in less explored data-scarce locations.

Risk factors, logistic model, and vulnerability mapping of lumpy skin disease in livestock at the farm level in Indragiri Hulu District, Riau Province, Indonesia, in 2022.

Lumpy skin disease (LSD) is an emerging epidemic in livestock in Indonesia. It was first reported in the Indragiri Hulu Regency of Riau Province, which has more cases than the surrounding regencies. This study aimed to identify the risk factors and generate a logistic regression model and vulnerability map of LSD in the Indragiri Hulu Regency. We used a structured questionnaire to interview the case and control farm owners to evaluate the risk factors. We evaluated 244 samples, consisting of 122 case and control farm samples each. At the cattle farm level, the risk factor data related to LSD were analyzed using descriptive statistics, bivariate analysis with Chi-square, and odds ratio, while the logistic model was derived using multivariate logistic regression analysis. Using variables, such as the number of cases and risk factor variables included in the model logistic, and the temperature, humidity, and rainfall data from the Meteorology, Climatology, and Geophysical Agency, we analyzed the vulnerability map of LSD in the regency using scoring, weighting, and overlay methods. Ten significant risk factors were associated with LSD occurrence. The LSD model obtained from the logistic regression analysis was LSD (Y) = -3.92095 + 1.13107 (number of cattle >3) + 1.50070 (grazing cattle together with other farmers' cattle) + 1.03500 (poor management of farm waste/dirt) + 2.49242 (presence of livestock collectors/traders near the farm location) + 1.40543 (introduction of new livestock) + 2.15196 (lack of vector control measures on the farm). The LSD vulnerability map indicated that the villages with high vulnerability levels were Rantau Bakung, Kuantan Babu, and Sungai Lala in the Rengat Barat, Rengat, and Sungai Lala subdistricts, respectively. We found 10 significant risk factors associated with LSD occurrence. The LSD model included the number of cattle (>3), cograzing with other farmers' cattle, poor management of farm waste/dirt, the presence of livestock collectors/traders near the farm, introduction of new livestock, and lack of vector control measures on the farm. The LSD vulnerability map indicated that villages with high vulnerability levels included Rantau Bakung in the Rengat Barat subdistrict, Kuantan Babu in the Rengat subdistrict, and Sungai Lala in the Sungai Lala subdistrict.

Urban Earthquake Vulnerability Assessment and Mapping at the Microscale Based on the Catastrophe Progression Method

Vulnerability assessment and mapping play a crucial role in disaster risk reduction and planning for adaptation to a future earthquake. Turkey is one of the most at-risk countries for earthquake disasters worldwide. Therefore, it is imperative to develop effective earthquake vulnerability assessment and mapping at practically relevant scales. In this study, a holistic earthquake vulnerability index that addresses the multidimensional nature of earthquake vulnerability was constructed. With the aim of representing the vulnerability as a continuum across space, buildings were set as the smallest unit of analysis. The study area is in İzmit City of Turkey, with the exposed human and structural elements falling inside the most hazardous zone of seismicity. The index was represented by the building vulnerability, socioeconomic vulnerability, and vulnerability of the built environment. To minimize the subjectivity and uncertainty that the vulnerability indices based on expert knowledge are suffering from, an extension of the catastrophe progression method for the objective weighing of indicators was proposed. Earthquake vulnerability index and components were mapped, a local spatial autocorrelation metric was employed where the hotspot maps demarcated the earthquake vulnerability, and the study quantitatively revealed an estimate of people at risk. With its objectivity and straightforward implementation, the method can aid decision support for disaster risk reduction and emergency management.

Sea level rise implication on small islands in Kepulauan Seribu, Jakarta

The phenomenon of sea level rise is caused by global warming, which impacts inundation in coastal areas, especially on small islands. The coastal area of small islands is very vulnerable to the impact of sea level rise, including Lancang Island (covers Lancang Besar and Lancang Kecil Island), Tidung Kecil Island, and Pramuka Island, which is part of small islands in Kepulauan Seribu, Jakarta. Sea level rise will become a problem if the seawater reaches land and causes environmental damage in coastal areas. Therefore, this study aims to create a vulnerability map for all those small islands due to coastal inundation using GIS technology. Analysis of areas affected by coastal inundation is carried out by processing DTM data derived from drone data. Based on 29 years of sea level rise data with the highest tide model, coastal inundation in 2050 is predicted to occur on all those small islands in Kepulauan Seribu and can be appropriately modelled. The results show that the affected areas on Tidung Kecil Island, Lancang Island, and Pramuka Island sequentially were 6.06 ha, 7.41 ha, and 3.47 ha. Some areas affected by coastal inundation on the three islands generally include dock and resident areas, empty land, vegetation, mangroves, forest, and road access. Mitigation efforts that can be carried out to reduce the impact of sea level rise in coastal areas include building embankments on the shoreline, dams, drainage systems, and mechanical wetlands, which must be optimized by increasing public awareness of coastal flooding.

Assessing social vulnerability to flood hazards: A case study of Sarawak's divisions

Social vulnerability assessment to flood hazard depends upon multiple factors that can vary across the different indicators. However, there is limited knowledge on specific indicators suitable for assessing social vulnerability on Sarawak. This study systematically analyzed important components of vulnerability and mapped them by weight for the 12 different divisions. Indices focusing on components of the two different dimensions of vulnerability (physical exposure and resistances) were identified based on the literature. Data on these different indices were then collected through relevant government agencies. Components of vulnerability dimensions were assessed and significantly contributing components were identified by Principal Component Analysis (PCA). An entropy method was used to weight the dimensions of vulnerability for the different divisions. Vulnerability was estimated based on the Iyengar and Sudarshan methodology and the data were used to produce a vulnerability map based on a proposed Social Vulnerability Index (SVI). The results indicated that divisions of Kuching, Miri, Sibu and Bintulu were more vulnerable (score over than 0.81) than those in other divisions. Greater vulnerability was mainly due to high exposure to extreme events and less adaptive capacity of resistance, which can affect agricultural production negatively, in combination with high population density in communities. The map clearly shows which areas are more susceptible, indicating that the government's adaptation measures should vary depending on the available resources, urgency, and means of survival needed to achieve resilience against climate change.

Mapping of the Vulnerability to Water Contamination of the Kharroub River Dam (Northern Morocco) by Application of the SW Method

Pollution and contamination pose a permanent risk of limiting water resources. Urbanization and economic development contribute to the degradation of water quality. The preservation of this natural resource is a major necessity, especially in arid and semi-arid areas where surface water is very scarce. This work constitutes part of this environmental concern. It aims at mapping the vulnerability to contamination of the surface water of the Kharroub River dam (Morocco) and protecting its water resources. The method applied is the SW (Surface Water) method, which consists in elaborating thematic maps of three parameters: the hydric functioning of soils, the density of the hydrographic network and the importance of agricultural drainage. The realization and superposition of these maps allowed us to obtain the vulnerability map to surface water contamination of the Kharroub river watershed. The results obtained show that the low and medium vulnerability classes, representing 41.91% and 32.49% respectively, occupy most of the study area and are distributed throughout the basin. The areas presenting a high and a very high degree of vulnerability occupy respectively 24.65% and 0.95% of the surface of the watershed; they are located principally in the northern and the southern parts of the basin.

Geospatial Evaluation of Service Areas: Empowering Maternal Health for Ensuring Healthy Lives and Well-Being in the Wenchi Municipality of Ghana

This geospatial study in Wenchi Municipality sheds light on the intricate dynamics of maternal healthcare access. Leveraging Geographic Information System (GIS) mapping, network analysis, and Thiessen polygons, the service areas around nine health facilities were delineated, revealing a linear catchment and highlighting regions with maternal healthcare accessibility gaps. Of the 85 communities analyzed, only 34 enjoy easy access, emphasizing a critical need for targeted interventions. The vulnerability map, derived from Thiessen polygons, visually pinpoints areas at risk, fostering a nuanced understanding of localized disparities. Key recommendations include targeted infrastructure development, community engagement programs, and the integration of technology to enhance maternal healthcare accessibility. This study contributes valuable insights for evidence-based decision-making, emphasizing the importance of context-specific approaches in maternal healthcare planning. As maternal health disparities persist, this research provides a foundation for policymakers, healthcare practitioners, and local communities to collaborate in addressing the unique challenges faced by vulnerable populations, ensuring equitable maternal well-being across Wenchi Municipality.

Intrinsic Vulnerability Assessment of the Qingduo Karst System, Henan Province

Groundwater vulnerability assessments are vital for protecting valuable resources by revealing susceptibility to contamination. This study developed an enhanced index model to assess the intrinsic vulnerability of a supplied karst aquifer in Qingduo, Henan Province. The model considered the 3-D geological structure and modified indices to account for Northern China’s mild karstification. Emphasizing the absolute infiltration capacity of surface contaminants, the model also integrated the groundwater sources and sinks (SS) index. The vulnerability map revealed that over 60% of the aquifers, including the Qingduo wellfield, exhibited very low to low vulnerability. Conversely, only small areas (&lt;5%) along the Kejing (KJ) fault’s southern wall were classified as highly vulnerable. These findings highlighted the significant role of groundwater flow alongside aquifer conditions. The upward groundwater flow through the Fengmenkou (FMK) faults slowed the downward infiltration of surface contaminants into the lower karst aquifer, effectively reducing vulnerabilities. Lower levels of dissolved lead (Pb) and nitrate (NO3−) in Qingduo groundwater aligned with PISSR vulnerability mapping. Sensitivity analysis assessed the results’ sensitivity to index weight assignment. The inclusion of the sources and sinks (SS) index holds implications for semi-quantitatively assessing dynamic groundwater vulnerability by delineating flow patterns.

Co-creating a Transdisciplinary Map of Technology-mediated Harms, Risks and Vulnerabilities: Challenges, Ambivalences and Opportunities

The phrase "online harms'' has emerged in recent years out of a growing political willingness to address the ethical and social issues associated with the use of the Internet and digital technology at large. The broad landscape that surrounds online harms gathers a multitude of disciplinary, sectoral and organizational efforts while raising myriad challenges and opportunities for the crossing entrenched boundaries. In this paper we draw lessons from a journey of co-creating a transdisciplinary knowledge infrastructure within a large research initiative animated by the online harms agenda. We begin with a reflection of the implications of mapping, taxonomizing and constructing knowledge infrastructures and a brief review of how online harm and adjacent themes have been theorized and classified in the literature to date. Grounded on our own experience of co-creating a map of online harms, we then argue that the map---and the process of mapping---perform three mutually constitutive functions, acting simultaneously as method, medium and provocation. We draw lessons from how an open-ended approach to mapping, despite not guaranteeing consensus, can foster productive debate and collaboration in ethically and politically fraught areas of research. We end with a call for CSCW research to surface and engage with the multiple temporalities, social lives and political sensibilities of knowledge infrastructures.

Vulnerability and risk assessment mapping of Bhitarkanika national park, Odisha, India using machine-based embedded decision support system

The vulnerability and flood risk assessment of Bhitarkanika National Park in Odisha, India, was conducted using a data-driven approach and a machine-based embedded decision support system. The park, located in the estuaries of the Brahmani, Baitarani, Dharma, and Mahanadi river systems, is home to India’s second-largest mangrove environment and the world’s most active and diverse saline wetland. To evaluate its vulnerability and risk, various threats were considered, with a focus on floods. Satellite imageries, such as Landsat 8 OLI, SRTM digital elevation model, open street map, Google pro image, reference map, field survey, and other ancillary data, were utilized to develop vulnerability and risk indicators. These indicators were then reclassified into ‘Cost’ and ‘Benefit’ categories for better understanding. The factors were standardized using the max-min standardization method before being fed into the vulnerability and risk model. Initially, an analytical hierarchy approach was used to develop the model, which was later compared with machine learning algorithms (e.g., SVM) and uncertainty analysis indices (e.g., overall accuracy, kappa, map quality, etc.). The results showed that the SVM-RBF machine learning algorithm outperformed the traditional geostatistical model (AHP), with an overall accuracy of 99.54% for flood risk mapping compared to AHP’s 91.12%. The final output reveals that a large area of Bhitarkanika National park falls under high flood risk zone. The Eastern coastal regions of Govindapur, Kanhupur, Chinchri, Gobardhanpur and Barunei fall under high risk zone of tidal floods, The Northern and western regions of Ramachandrapur, Jaganathpur, Kamalpur, Subarnapur, Paramanandapur, etc., Fall under high risk region of riverine floods. The study also revealed that the areas covered with mangroves have a higher elevation and hence are repellent to any kind of flood. In the event of a flood high priority conservation measures should be taken along all high flood risk areas. This study is helpful for decision-making and carrying out programs for the conservation of natural resources and flood management in the national park and reserve forest for ecological sustainability to support sustainable development goals (e.g., SDGs-14, 15).

Assessment of groundwater vulnerability for seawater intrusion using DRASTIC model in coastal area at Patuakhali District, Bangladesh.

Seawater intrusion is a growing concern for coastal regions in Bangladesh. Overexploitation of groundwater, low rainfall, a high rate of evaporation, rising sea level, cyclones, storm surges, and shrimp cultivation are the causes of seawater intrusion that polluted the groundwater near the coastal area. High values of EC, TDS, Na+, and Cl- ions in groundwater indicate the seawater intrusion. Piper's Trilinear and Durov diagrams of groundwater indicated that most gathered groundwater show predominantly Na-Cl-type of water. Based on the USSL diagram, it is observed that the salinity of the study area is medium to high and the alkalinity is medium to very high. Also, in view of the Wilcox outline, most of the samples in the area of interest are permissible to doubtful, and doubtful to unsuitable. Chloride is moderately correlated with Na+ (R2 = 0.590) and poorly correlated with (HCO3- + CO32-) (R2 = 0.128), which indicates seawater intrusion. According to a comparison of the levels of Fe and Mn in the groundwater of the study area with those recommended by the World Health Organization (WHO) and Department of the Environment (DOE), twenty-two out of thirty-one samples exceed the acceptable limit for Fe and thirty out of thirty-one samples fall within the acceptable limit for Mn. The calculated water quality index reveals that most samples are of low drinking water quality. The research area's poor water type is found in the north-central and southern parts. The WQI geographic variation map reveals that the study's water has a WQI of more than 100, making it unsuitable for consumption. The final seawater intrusion vulnerability map clearly identifies the moderate to highly vulnerable zone of the study area using DRASTIC index parameters. DRASTIC index parameters revealed that some portions of the northwestern part of the sample area show a moderately vulnerable zone. And the rest of the sampling area indicates the highly vulnerable zone.

Horizontal-to-Vertical Spectral Ratios and Refraction Microtremor Analyses for Seismic Site Effects and Soil Classification in the City of David, Western Panama

The City of David constitutes one of the most important commercial centers of the Republic of Panama. However, it is located on a coastal plane, close to an area with high seismic activity and has been affected by significant earthquakes (18 July 1934, Mw = 7.4 and 12 March 1962, Mw = 6.7). The goal of this study is to estimate the seismic effects and to classify the soil in the City of David. The experimental work entailed the measurement of environmental noise for H/V spectral ratio (HVSR) analyzed at 22 stations. A series of microtremor refraction studies (ReMi) at six stations distributed from north to south of the city was also performed. The stations were distributed around urban areas of the city, which are characterized by the presence of water supply, sewerage, buildings, roads, etc. The spectral analysis of environmental noise allowed the generation of three different types of maps: First, predominant frequency maps (f0) with zones composed mainly of rigid and semi-rigid soils in the southern end of the city and rigid soils in the central and northern regions. Secondly, maximum H/V amplitude maps (A0) which evidence a low range of HVSR amplitude in the city, ranging from 1.1 to 3.8. Finally, liquefaction vulnerability (Kg) maps, with values less than 2 Hz−1, representative of a low liquefaction risk. Soil classification using ReMi and calculation of the corresponding Vs30 reveal type D soils, which correlate well with results obtained using HVSR analyses. A comparison between HVSR and ReMi shows that HVSR curves that exhibit clear peaks tend to be related to ReMi stations that presented relatively large shear-wave velocity contrasts at some depth. The results from this research are intended to aid the decision-making process related to the future development of the city, as well as government level maintenance and mitigation plans.