Flood-prone Regions Research Articles

Flood impact assessment in remote areas using machine learning, SAR, and GIS: a case study of Ngabang District, Indonesia

ABSTRACT Flooding in remote regions presents significant challenges due to data scarcity, complicating impact assessment and mitigation efforts. This research delineates an integrated methodology for quantifying flood impacts in such contexts. By leveraging machine-learning algorithms, Sentinel-1 synthetic aperture radar (SAR) imagery was combined with digital elevation model data and river proximity metrics to predict and accurately demarcate flood extents. Geographic information systems overlay techniques were then employed for spatial analysis of the floods’ impacts on population and infrastructural assets. The methodology was applied in a case study in Ngabang District, Indonesia, demonstrating its utility. Analysis using decision tree, random forest (RF), and gradient boosting machine models provided critical insights into flood prediction factors. The RF model was chosen as the best, successfully identified flood-prone regions, achieving an accuracy of 0.94 and a Kappa of 0.87 on the testing data, demonstrating its robustness. The flood map showed significant impacts, affecting 373.81 hectares, 10,706 people, 1,500 buildings, and 15 km of roads. This study highlights the importance of proximity, elevation, SAR imagery, and iterative model improvements in flood prediction, offering valuable insights for flood management and mitigation efforts in data-scarce regions.

Assessment of the extent of community preparedness to flood risk in Jigawa State, Nigeria

Jigawa State faces annual flood disasters that have resulted in significant loss of life and property, putting many communities at risk. This study aim to evaluate the preparedness for flood risk in Jigawa State, Nigeria. A sample of 666 respondents was selected for a self-administered questionnaire, with 601 fully completed. The collected data were analyzed descriptively to determine the preparedness index. (PI). From the result of the findings, a majority (77.7%) of the respondents agreed that the community is aware of flood risks. There was also a provision of a neighborhood directory (97.8%), local shelter (75.9%) and emergency contacts (55.4%) during floods. The PI (92.6%, 98.6% 84.1% and 85.1% respectively), are above the 65% threshold and hence considered ‘prepared’ in this regard; but, ‘somewhat prepared’ (PI of 61.7%) regarding awareness of flood disaster risk management strategies; ‘and unprepared’ (PI of 55.5%, 50.5%, 50.5%, 50.8% and 54.3% respectively) in terms of community organization of public education and training of flood risk reduction (66.6%); engagement on rehearsals/emergency drills (51.6%); good community-based warning system (51.4%); and a standalone system for property protection measures during floods (52.4%); as well as an active recovery plan (62.9%). It was recommended that proper embankment protection and water regulation outlets be established. Additionally, real-time flood forecasting and effective early warning systems should be developed. Communities should be assessed to identify flood risk zones before construction activities begin, or alternatively, resettlement to safer areas should be considered for those living in flood-prone regions.

Comparative analysis of cloud properties over drought- and flood-prone regions of western India using machine learning techniques

ABSTRACT Cloud properties are pivotal in analyzing rainfall patterns globally, especially in monsoon-dependent countries such as India. The impact of climate change becomes more important in regions susceptible to hydrometeorological events due to different monsoon regimes. To examine regional heterogeneity of cloud properties, this study investigates long-term trends and predictive capabilities for cloud properties in drought-prone and flood-prone regions of western India, utilizing satellite data and employing various machine learning (ML) models to comprehend intricate data patterns and enhance predictive accuracy. The results show higher mean and variability in cloud parameters over the flood-prone area due to favorable rain conditions, reflecting higher cloud microphysical and optical properties. These parameters negatively correlate with some cloud macrophysical properties along with the aerosol property in the drought-prone area. Additionally, a moderate correlation exists between certain cloud characteristics of one region and another. Employing ML algorithms for regression analysis and comparing them for cloud effective radius across regions shows promising results, with random forest (RF) demonstrating high coefficient of determination (0.86, 0.93) and low root mean squared error (0.76, 1.15) due to its robustness and high accuracy. This research enhances the understanding of regional heterogeneity in India and shows that ML can be helpful in predicting future cloud dynamics and climate variables identifying the most suitable model.

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.

Performance of different rice genotypes cultivated under submergence and normal growing conditions

Abstract Submergence negatively affects the plant growth and yield of rice genotypes, which in turn results in huge financial loss. Thus, identification of submergence tolerance rice genotypes is needed for sustainable rice cultivation in flood-prone areas. Keeping this in view, a two-year (2019 and 2020) field study was undertaken at Rice Research Farm of Bihar Agricultural College, Bihar Agricultural University, Sabour, Bhagalpur to investigate and compare the yield responses of rice genotypes under submergence and normal growing conditions. Twenty-five days old seedlings of 25 rice genotypes were transplanted in a randomized complete block design in two replicates, which were allowed to grow under normal practice for the next 25 days. Thereafter, the 50-day old plants were completely submerged with turbid water of 1.5 m height for the next 18 days. After de-submergence, all the recommended agronomic practices were adopted during the rest of the crop period. Under submergence, higher grain yield was recorded under all the genotypes during 2020 as compared to 2019 mainly due to higher temperature and lesser rainfall during 2020. Under submergence conditions, Jalmagna, Nona Bokra and Swarna Sub 1 were the best performers having produced average yields of 3,167, 2,786 and 2,741 kg/ha, respectively. Whereas under normal growing practice, Swarna Sub 1, Jalmagna and Sudha were the first three best performing genotypes producing average yields of 5,063, 4,565 and 4,456 kg/ha, respectively. Therefore, Jalmagna should be preferred for cultivation in the flood-prone regions of the state, however in non-flooded areas; Swarna Sub 1 may be cultivated.

Enhancing the durability of earth construction in flood-prone regions through clay stabilization

Enhancing the durability of earth construction in flood-prone regions through clay stabilization

Development and validation of a novel tool: The family resilience assessment scale for flood-affected families (FRAS-FAF)

Family resilience is critical in coping with disasters such as floods, yet there is a lack of tools specifically designed to assess family resilience in flood-prone regions. This study aimed to develop and validate the Family Resilience Assessment Scale for Flood-Affected Families (FRAS-FAF), a culturally sensitive tool tailored to families impacted by recurrent flooding in Pakistan. A mixed-methods study design was employed in two phases. In Phase I, qualitative data were collected through in-depth interviews with heads of flood-affected families in the Nowshera district of Pakistan, revealing key themes related to family resilience. These insights informed the creation of 54 initial items for the FRAS-FAF. In Phase II, the psychometric properties of the tool were assessed using factor analysis with 400 participants, allocated for Exploratory Factor Analysis (EFA) 200 and the remaining 200 for Confirmatory Factor Analysis (CFA). The final version of the scale retained 52 items across five factors: Family Culture, Family Structure, Family Spirituality, Family Resources, and Environment. EFA and CFA confirmed a strong five-factor structure, with model fit indices demonstrating adequate construct validity (χ2 ​= ​1551/df ​= ​936, CFI ​= ​.91, TLI ​= ​.89, SRMR ​= ​.075, RMSEA ​= ​.057). Internal consistency was high for all factors, with Cronbach's alpha values ranging from .85 to .96. FRAS-FAF is a reliable and valid tool for assessing family resilience in flood-affected families. It provides a framework for practitioners and policymakers to identify areas of strength and vulnerability, informing the development of targeted interventions aimed at enhancing family resilience in flood-prone regions. The tool's culturally sensitive design ensures its relevance for flood-affected families in Pakistan and offers the potential for adaptation in other disaster-prone areas.

Integrating HEC-RAS, GIS, and LISREL for assessing and enhancing urban building resilience against flood threats: Comprehensive model and analysis

Floods pose significant threats to urban areas, resulting in substantial human and financial losses annually. The vulnerability of key urban centers to these risks diminishes their efficiency, leading to public dissatisfaction and service deficiencies. Recognizing and enhancing the resilience of essential buildings becomes crucial in mitigating these challenges. This study employs a comprehensive approach to achieve a resilience model for critical buildings facing floods. The research process involves the identification of city assets based on leveling criteria, utilizing GIS technology for spatial mapping. HECRAS software aids in river flow modeling, identifying areas lacking flood-carrying capacity. By overlaying vulnerable gravity centers with flood-prone regions, building resilience components are computed through structural factor analysis and LISREL modeling. The study identifies ten key criteria. Further analysis includes resilience modeling using TOPSIS and AHP methods. The positive ideal and negative ideal solutions are determined, resulting in the grading of building resilience. Notably, the balance redundancy index with cascading potential effects attains the highest positive ideal value at 0.257, while the resistance to a level of stress index achieves the lowest negative ideal value at 0.02. This comprehensive approach and modeling contribute to the understanding and enhancement of urban building resilience in the face of flood threats.

Identification of Flood-Prone Areas Using Geo-Informatics: A Case Study of Erbil City, Kurdistan Region, Iraq

Global flood events, intensified by an intricate interplay of natural dynamics and human activities, pose severe environmental and safety threats, driving the demand for advanced vulnerability assessments. This research innovates with a holistic geo-informatics methodology, pinpointing flood-prone regions in Erbil City, Kurdistan Region, Iraq. It harnesses the Analytic Hierarchy Process and Geographic Information System synergies, enriched with insights from multispectral satellite observations, to conduct an exhaustive analysis of key flood susceptibility indicators. These encompass geological, hydrological, meteorological, and anthropogenic parameters, integrated into a robust Geographic Information System analytical matrix. Through strategic factor analysis and prioritization, the study distills a detailed flood susceptibility cartogram, classifying regional vulnerabilities into distinct risk echelons: high (encompassing 25%), moderate (38.1%), low (28.4%), and very low (8.5%). The final flood map highlights an acute susceptibility epicenter in the northeast, contrasting with the minimal threat southwest. This demarcation reaffirms the Analytic Hierarchy Process-Geographic Information System fusion's precision in translating multifaceted data into strategic intelligence, proving instrumental for scholars, administrative frameworks, and developmental strategists. The insights garnered are pivotal in sculpting informed urban development and flood counteractive blueprints, instrumental in mitigating flood repercussions and preserving ecological and human integrities.

Impacts of submergence stress on rice plants and its adaptation: A review

The main aim of this review is to convey information in summarized form by compiling and interpreting the major findings of recent studies on the impacts of submergence stress on rice and tolerance mechanisms. Published research papers available in Google Scholar, Web of Science, and Pub Med, mainly by Elsevier and SpringerLink, were critically analyzed and summarized for the preparation of the manuscript. In rice, plant survival rates, growth, and development are adversely affected by submergence. Major findings documented that submergence alters the soil aeration and creates hypoxic and anoxic conditions, which results in low photosynthetic efficiency and sugar status in rice plants. Compared to a tolerant cultivar, a sensitive cultivar produces more ethylene and causes injury to the plant. Controlled underwater shoot elongation, higher conserved non-structural carbohydrates, and better hormonal regulation, especially ethylene and gibberellin, and abscisic acid, are the primary adaptive mechanisms of tolerant plants in submergence, which helps better recovery at the post-submergence stage, too. The Sub1 gene and the associated QTLs are crucial for the superior performance of tolerant cultivars in submergence. Any agronomic management practices that can reduce ethylene production and enhance the nutrient status of plants can alleviate the severity of submergence. Understanding the intricate relationship between submergence and rice plant response is essential, mainly how submergence affects the rice plant and its tolerance mechanism to develop resilient rice cultivars that can grow in flood-prone regions.

Enhancing streamflow simulations with gated recurrent units deep learning models in the flood prone region with low-convergence streamflow data

Highly accurate streamflow simulations are essential for water resources and river management. However, there are longstanding challenges for streamflow modeling in the hydrology field. As a result, deep learning methods have been introduced as novel tools and are being used in simulation. This paper proposes a novel approach that utilizes unconventional data preprocessing techniques to improve the accuracy of daily streamflow predictions in flood-prone regions. We applied this approach to a flood-prone area in western Iran and simulated daily streamflow using a deep learning Gated Recurrent Unit (GRU) method under various data selection scenarios. These scenarios focused on identifying the optimal combination of input variables, time steps, and outlier removal techniques. The outlier removal methods investigated in this study include Mahalanobis distance, critical section removal, Z-score, and no removal. The average rainfall of the area, data driven precipitation, Normalized Difference Vegetation Index (NDVI), surface soil moisture, groundwater baseflows, and streamflow in the hydrometric station were evaluated using correlation control method, and inputs with the lowest correlation were removed. Based on the results obtained from the deep learning models produced in the research, it was found that the GRU model, with various modified inputs using Z-score removal, had the best performance. The model had an average of Root Mean Squared Error (RMSE) at 5.24 mm and R2 (Coefficient of Determination) at 0.91 during training, while during validation, it had an RMSE of 7.74 mm and R2 of 0.83. Considering using relatively low convergence data in streamflow simulation in this study, it can be said that the listed scenarios showed an appropriate result in dealing with the data and recognizing the complex pattern of the daily streamflow, and the future studies will show the improvement of the GRU models if higher convergence data will be used.

HydroSAR: A Cloud-Based Service for the Monitoring of Inundation Events in the Hindu Kush Himalaya

The Hindu Kush Himalaya (HKH) is one of the most flood-prone regions in the world, yet heavy cloud cover and limited in situ observations have hampered efforts to monitor the impact of heavy rainfall, flooding, and inundation during severe weather events. This paper introduces HydroSAR, a Sentinel-1 SAR-based hazard monitoring service which was co-developed with in-region partners to provide year-round, low-latency weather hazard information across the HKH. This paper describes the end user-focused concept and overall design of the HydroSAR service. It introduces the main processing algorithms behind HydroSAR’s broad product portfolio, which includes qualitative visual layers as well as quantitative products measuring the surface water extent and water depth. We summarize the cloud-based implementation of the developed service, which provides the capability to scale automatically with the event size. A performance assessment of our quantitative algorithms is described, demonstrating the capabilities to map the flood extent and water depth with an accuracy of >90% and <1 m, respectively. An application of the HydroSAR service to the 2023 South Asia monsoon seasons showed that monsoon floods peaked near 6 August 2023 and covered 11.6% of Bangladesh in water. At the peak of the flood season, nearly 13.5% of Bangladesh’s agriculture areas were affected.

A systematic framework for assessing social vulnerability to flood for integrated flood risk management: A case study in Metro Manila, Philippines

Assessing social vulnerability to floods (SVF) is a crucial component of integrated flood risk management, especially in flood-prone regions such as Metro Manila in the Philippines. This study presents a holistic framework to assess SVF, based on susceptibility, exposure, and resilience factors while recognizing its multi-dimensional, multi-attribute, and context-dependent nature. The assessment was applied to three cities of Metro Manila: Marikina, Parañaque, and Manila. Indicators were systematically identified using deductive and hierarchical approaches, and factor indices were derived using a combined Simple Additive Weighting and Analytic Hierarchy Process, while the SVF index was estimated using the vulnerability relationship of the factors. The results exhibit a strong positive correlation with historical social flood damages and robustness across weight and normalization variations, with Marikina being the most vulnerable, followed by Parañaque and Manila. The city-level vulnerability profiles highlight salient determinants such as high population exposure in Parañaque, a relatively high population of susceptible social groups in Marikina, and significant disparity in flood control projects among the cities. Scenario analyses underscore the efficacy of flood countermeasures that reduce exposure and increase resilience. This study offers valuable insights and practical guidance for developing effective disaster reduction plans and policies in the study areas and similar regions by identifying disparities in vulnerability and understanding local conditions and drivers.

Mechanistic Insights and Genetic Approaches for Enhancing Submergence Tolerance in Rice

Rice, a pivotal staple crop, serves as a dietary foundation for approximately half of the global population. Its cultivation, traditionally reliant on submerged conditions, faces significant challenges due to sudden flooding events. The majority of rice varieties are highly sensitive to prolonged submersion beyond a mere seven days, resulting in substantial yield losses. As global climate patterns shift, the occurrence and intensity of flooding are predictable to increase, exacerbating this issue. Therefore, development of rice varieties having enhanced submergence tolerance is critical to sustaining productivity and ensuring food security in flood-prone regions. Understanding the physiological and biochemical mechanisms underlying submergence tolerance is crucial in this context. Key physiological mechanisms include the regulation of carbohydrate reserves for energy during low oxygen availability, ethylene-responsive factors, especially the Sub1A gene to, regulate growth and stress responses, curbing excessive elongation to conserve energy and enhance survival. Additionally, specialized tissues such as aerenchyma and adventitious roots improve oxygen transport and nutrient uptake under flooded conditions. Genetic approaches, including Marker-Assisted Selection (MAS), have been instrumental in the development of submergence-tolerant rice varieties. The integration of the Sub1 QTL, especially the Sub1A gene, into high-yielding rice varieties has led to significant advancements in breeding submergence-tolerant cultivars. These genetic improvements provide a promising solution to mitigate the detrimental effects of flooding on rice production, ensuring the stability and sustainability of this essential crop in the face of changing climate conditions. This review discusses various mechanisms adapted by rice and genetic advancements aimed at enhancing submergence tolerance in rice to secure food production for future generations.

Flood detection in the Upper Krishna Basin through integrated geospatial analysis: leveraging decision frameworks and statistical measures

ABSTRACT Floods threaten the environment and human settlements across river basins globally, including the Upper Krishna Basin in India. This research delves into evaluating flood hazard areas within the Upper Krishna Basin utilizing the Analytical Hierarchy Process (AHP), Frequency Ratio (FR), and Statistical Index (SI). These methodologies prioritize and classify flood-prone regions by integrating spatial and non-spatial criteria. The findings reveal significant variations in flood risk classification across the Upper Krishna Basin based on the three models. The AHP model identifies 3.37% of the region as low flood risk, with 22.90% classified as moderate risk, and 68.27% as high risk. In contrast, the FR model designates 3.76% as low risk, 10.50% as moderate risk, and 42.21% as high risk. Meanwhile, the SI model identifies 1.04% of areas with low risk, 35.38% with under-high risk, and 57.87% with very high risk. Validation using Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) values demonstrates the superior reliability of the SI model. These findings offer valuable insights for decision-makers to allocate resources and implement effective flood mitigation measures.

Coconut Shells, Water Hyacinth and Rice Husks are Very Effective in Improving the Quality of Physical and Chemical Standards of Dug Well Water in Flood Areas

Introduction: Ensuring access to clean and safe drinking water is crucial, especially in flood-prone regions where the water quality in dug wells can deteriorate due to various physicochemical factors. This research was aimed to measure the effectiveness of natural materials in improving water quality based on physicochemical parameters and to compare water quality before and after treatment in Astambul Subdistrict of Banjar Regency, South Kalimantan Province. Methods: The study involved a survey of 192 private boreholes and dug wells across five villages in the Astambul Subdistrict, with 30 samples selected for detailed analysis. Key physicochemical parameters analyzed included total dissolved solids (TDS), dissolved oxygen (DO), turbidity, and total suspended solids (TSS). TDS, with a standard limit of 50 mg/L, served as a primary water quality indicator. DO, essential for gauging water oxygenation, and was measured as it results from photosynthesis and atmospheric absorption. Turbidity was assessed using a turbidimeter to determine water clarity, and a UV-vis Spectrophotometer 2008 was utilized to measure levels of iron (Fe) and mangasene (Mn). Results and Discussion: The findings demonstrated that coconut shells, husks, and water hyacinths improved water quality across physical and chemical parameters. Statistical analysis using t-tests showed improvements in these parameters after treatment. For example, DO levels increased in multiple villages, with the highest percentage in Kaliukan Village (59.2%) and decreases in turbidity and TSS. Conclusion: The study concluded that coconut shells, husks, and water hyacinths have potential to enhance physicochemical quality of water.

Determining factors influencing flood preparedness among citizens in Jakarta: A protection motivation theory approach

The Jakarta Metropolitan Area is one of the cities heavily affected by floods in Asia. Effective disaster risk reduction strategies require deep understanding of what motivates its citizens towards preparedness and mitigation actions. This study aimed to discern these motivating factors among Jakarta's residents through the Protection Motivation Theory (PMT) approach. Through an online survey, 304 participants provided insights into how the extended PMT factors shape their Protection Motivation (PM) towards flood preparedness. A total of nine (9) variables and 41 constructs were considered in this study. PMT factors were comprised of PM, Perceived Severity (PS), Perceived Vulnerability (PV), Response Efficacy (RE), and Perceived Self-Efficacy (SE) along with additional factors such as Media (M), Government Preventive Actions (GA), Flood Experience (FE), and Geographical Perspectives (GP). Partial Least Square Structural Equation Modeling (PLS-SEM) revealed that SE had the most substantial impact on influencing PM (β = 0.522) followed by PV, PS, and RE. The results also showed the significance of M and GA to both SE and RE. Meanwhile, FE illustrates positive impact to both PV and PS. Interestingly, GP was found to be only significant to PV and not to PS. External factors M and GA demonstrated a significant indirect effect on PM through SE, whereas GP exhibited a significant indirect effect on PM through PV. These findings provide actionable insights for policymakers and disaster management practitioners, emphasizing the power of media and the need for comprehensive strategies that address both the psychological and practical aspects of flood disaster readiness in Jakarta and potentially other flood-prone regions.

Phylotranscriptomics provides a treasure trove of flood-tolerance mechanisms in the Cardamineae tribe.

Flooding events are highly detrimental to most terrestrial plant species. However, there is an impressive diversity of plant species that thrive in flood-prone regions and represent a treasure trove of unexplored flood-resilience mechanisms. Here we surveyed a panel of four species from the Cardamineae tribe representing a broad tolerance range. This included the flood-tolerant Cardamine pratensis, Rorippa sylvestris and Rorippa palustris and the flood-sensitive species Cardamine hirsuta. All four species displayed a quiescent strategy, evidenced by the repression of shoot growth underwater. Comparative transcriptomics analyses between the four species and the sensitive model species Arabidopsis thaliana were facilitated via de novo transcriptome assembly and identification of 16 902 universal orthogroups at a high resolution. Our results suggest that tolerance likely evolved separately in the Cardamine and Rorippa species. While the Rorippa response was marked by a strong downregulation of cell-cycle genes, Cardamine minimized overall transcriptional regulation. However, a weak starvation response was a universal trait of tolerant species, potentially achieved in multiple ways. It could result from a strong decline in cell-cycle activity, but is also intertwined with autophagy, senescence, day-time photosynthesis and night-time fermentation capacity. Our data set provides a rich source to study adaptational mechanisms of flooding tolerance.

Urban flash flood prediction modelling using probabilistic and statistical approaches

The development of a detailed strategy to mitigate the negative consequences of any natural calamities depends on accurately identifying sensitive zones where natural hazards frequently happen. In the present investigations, three widely utilized probabilistic approaches viz., frequency ratio (FR), statistical index (SI), and weighting factor (WF) have been utilized for prediction of flsh flood susceptibility zones in the Coochbehar urban and peri-urban area (CUPUA) (area = 26.22 km2). Ten flash flood conditioning factors have been used in this assessment based on previous literatures and experts' opinions. In the FR model, 29.40 % area is observed in the high and very high flood zones, whereas 36.27 % and 31.16 % area is identified in SI and WF model, respectively. The FR model demonstrates that five conditioning factors, viz., topographic position index (TPI), land use and land cover (LULC), normalized difference vegetation index (NDVI), distance to drainage (DtD) and rainfall were highly impacted in flash flood prediction (FFP) analysis; in SI model, LULC is the major influencing parameter, and in WF model LULC, rainfall, NDVI, and distance to road (DtR) are the effective parameters. The success rate curve of the FR, SI and WF models manifest SI model has highest training (AUC=0.903) and prediction (AUC=0.925) accuracy, and FR and WF also have very good accuracy as their AUC values are 0.899 and 0.877 (in success rate curve) and 0.900 and 0.881 (in prediction rate curve). Therefore, the application of probabilistic approaches in this active flash flood-prone region is excellently performed, and the results of this study will help hydrologists, engineers, and water management administrators to control the areas that are extremely susceptible to flash floods and reduce possible damages.

Barriers to long-term disaster recovery in rural Appalachia: A retrospective analysis of the 2016 West Virginia flood

The research presented in this paper explores the barriers to long-term disaster recovery in understudied rural communities in the eastern United States, along the Appalachian Mountains. This region, referred to as Appalachia, faces many of the same barriers to long-term disaster recovery as urban communities but also faces unique circumstances due to depressed socioeconomics, reduced access to public services, and the nominal capabilities of small, rural town governments. Devasting floods occurred throughout the eastern and central regions of Appalachia in 2016. Interviews were recently conducted with local, state, and federal government officials, nonprofit groups, and industry professionals about the long-term recovery efforts from this event. The findings highlight a multifaceted set of barriers. A total of twenty-two barriers are described, including limited economic resources, lack of flood insurance, slow allocation of funding, skilled labor shortages, unclear roles and responsibilities among stakeholders, a lack of long-term planning, regulatory delays, complex reimbursement processes, uncertain design standards, an attachment to flood-prone regions, and a lack of knowledge of building codes. Many of these barriers compound each other. Future research is needed to develop solutions that address these barriers for small, rural communities still straining to recover from a disaster that occurred years ago.