Flooding is one of the most persistent and destructive natural hazards in West Virginia. However, community-scale assessments that connect social vulnerability with physical flood vulnerability are still limited. Existing floodplain management plans often focus on infrastructure and hydrology, overlooking how socioeconomic disparities shape resilience. This study assesses flood resilience in West Virginia communities by connecting socioeconomic vulnerability with physical flood vulnerability. Using data from the American Community Survey (ACS) and state floodplain maps, we developed a Socioeconomic Vulnerability Index (SEVI) and combined it with physical indicators, such as the percentage of residential buildings in the 100-year floodplain, the share of mobile homes in flood-prone areas, the presence of essential facilities and community assets within flood zones, and the proportion of roads submerged by at least one foot of water. Incorporated and unincorporated communities were analyzed separately to reflect differences in governance and service capacity. The results reveal that high flood vulnerability areas often coincide with high socioeconomic vulnerability, especially in the southern and southeastern counties, where long-term economic decline has increased risks. Communities like McDowell and Mingo face a combined challenge of social and physical vulnerability, adding pressure to populations already dealing with limited resources. These findings emphasize the importance of integrated resilience planning that combines physical protection with social support. Considering the increasing intensity of extreme precipitation events associated with climate change, these findings also highlight the importance of incorporating long-term climate considerations into flood resilience planning. Policy suggestions include expanding targeted flood insurance subsidies for low-income households, prioritizing the relocation or retrofitting of mobile homes and essential facilities, investing in green and open spaces, and encouraging community-based mitigation strategies. Together, these actions can lower long-term flood risks while addressing structural inequalities that make certain populations more vulnerable.

Surface mining has significantly altered landscapes in Appalachia, impacting forested ecosystems, natural habitats, and water quality. Building on recent regional modeling efforts in Pennsylvania, this study used high‐resolution datasets (2011, 2016, 2020) to assess land cover change and identify operationally feasible post‐mining lands for bioenergy crop development in West Virginia. It identified 9435 ha of postmining land that met suitability criteria, including slope (<15%), low vegetation cover, and at least 2 ha in size. Over the study period, forest cover increased by 8583 ha from 2011 to 2016 and 8448 ha from 2016 to 2020. Of the lands found suitable for bioenergy crop development, the results show that businesses controlled 6880 ha, private individuals 1913 ha, and government entities 518 ha of the identified land. Our analysis provides finer scale insights into where, and under what conditions, bioenergy crop establishment may be feasible. These mine lands are transitioning rapidly from herbaceous cover to shrubland and forest through natural succession, increasing the cost and complexity of bioenergy crop development. As such, the window for lower input biomass establishment is narrowing, making proactive planning and policy support essential.

Natural gas wells nearing the end of their production cycles represent both an environmental liability and an opportunity for sustainable land reuse in Appalachia. This study develops a fuzzy-logic geographic information system (GIS) model to identify well sites in West Virginia (WV) suitable for reclamation through small-ruminant agrivoltaic systems. The model integrates topographic, solar, and accessibility variables—slope, aspect, land cover, solar radiation, and distance to roads—each standardized by fuzzy membership functions and combined through fuzzy overlay to generate a continuous suitability surface. A biologically derived pairwise buffer analysis couples a 2.02 ha well-pad exclusion zone with a 1.48 ha grazing buffer representing the minimum forage area required for rotational grazing. Within a four-county case study, the model isolated 129 wells of high suitability, primarily in Tyler, Doddridge, and Harrison Counties. The framework provides a reproducible spatial decision tool for targeting post-extraction sites where solar infrastructure and managed grazing can co-locate, supporting both renewable-energy and land-restoration goals in the Appalachian region.

Erosion is frequently cited as the most significant environmental impact from trails and can require costly design and management considerations. To address this issue, this study utilized sUAS-based remote sensing and topographic derivatives to analyze and predict locations susceptible to water-based trail erosion. A trail totaling 4 km was segmented based on presence or absence of water-based erosion for analyses and then flown with sUAS technology. Three logistic regression (LR) models were generated utilizing summary statistics from hydrological terrain models of varying resolutions to determine the effects of spatial resolution on the models' predictive accuracies. Receiver operator characteristics, kappa, and overall accuracy assessments all indicated better predictive accuracy for the highest resolution sUASbased data. The LR model for the sUAS-based data identified areas with high total catchment area and low profile curvature values as having higher probability for erosion. This study offers novel approaches for novel approaches to sustainable trail design and monitoring.

Comprehensive reviews of continuously vegetated areas to determine dispersed locations of invasive species require intensive use of computational resources. Furthermore, effective mechanisms aiding identification of locations of specific invasive species require approaches relying on geospatial indicators and ancillary images. This study develops a two-stage data workflow for the invasive species Kudzu vine (Pueraria montana) often found in small areas along roadsides. The INHABIT database from the United States Geological Survey (USGS) provided geospatial data of Kudzu vines and Google Street View (GSV) a set of images. Stage one built up a set of Kudzu images to be implemented in an object detection technique, You Only Look Once (YOLO v8s), for training, validating, and testing. Stage two defined a dataset of confirmed locations of Kudzu which was followed to retrieve images from GSV and analyzed with YOLO v8s. The effectiveness of the YOLO v8s model was assessed to determine the locations of Kudzu identified from georeferenced GSV images. This data workflow demonstrated that field observations can be virtually conducted by integrating geospatial data and GSV images; however, its potential is confined to the updated periodicity of GSV images or similar services.

The Central Appalachian region contains several high-elevation endemic woodland salamanders, which are thought to be particularly vulnerable to climate change due to their restricted distributions and low vagility. In West Virginia, there is a strong management focus on protection and recovery of the federally threatened Cheat Mountain salamander Plethodon nettingi. To support this focus, there is a need for improved understanding of the occurrence-habitat relationships and spatially explicit projections of fine-scale contemporary and potential future habitat quality to inform management actions. In addition, there is concern among natural resource managers that climate change may increase habitat quality at high elevations for potential competitors of P. nettingi, particularly the eastern red-backed salamander P. cinereus, potentially resulting in increased competition pressure for P. nettingi. To address these knowledge gaps concerning future habitat availability and competition, we created ecological niche models using the random forest classification algorithm and used the estimated occurrence-habitat relationships to assess ecological niche overlap between the species and project fine-scale contemporary and potential future habitat availability and quality. We estimated that the ecological niches of P. nettingi and P. cinereus were 80.5% similar. For P. nettingi, we estimated that the amount of high-quality habitat will be reduced by mid-century and potentially lost by end-of-century, but that moderate and low-quality habitat will persist. For P. cinereus, we estimated that the amount of high-quality habitat will increase through end-of-century, and that high elevations will become more suitable for this species, indicating that potential competition pressure for P. nettingi is likely to increase.

Timber harvest is used to promote early-successional and mature forest songbird populations and diversity in forested landscapes, but we have an incomplete understanding of long-term bird responses to landscape-scale harvest intensity. Here, we used 17 years of historical bird survey data to address this knowledge gap by investigating how avian diversity, abundance, and population dynamics changed over time in two Central Appalachian forested landscapes with varying levels of timber harvest intensity. Our specific objectives were to examine the influence and effect of interactions between time and landscape-scale timber harvest intensity on breeding season songbird guild richness, focal species abundance, and focal species nest success. We found that guild richness and focal species abundance tended to be consistently higher in the actively harvested landscape, and trends in guild richness and species abundance over time were consistently positive in the actively harvested landscape and negative in the minimally harvested landscape. In particular, early-successional / edge-associated species and forest-gap species were found in higher numbers and exhibited positive temporal trends in the actively harvested landscape. However, a holistic assessment that included trends in reproductive success highlighted long-term declines in nest success for a forest-interior species of regional conservation concern within the actively harvested landscape but not the minimally harvested landscape. Thus, there are important trade-offs to consider when using landscape-scale forest management to promote songbird communities and populations in forested landscapes. While timber harvest operations can benefit particular songbird guilds and species, it is also valuable to maintain minimally harvested landscapes, and potentially manage for old-growth conditions, to support bird species that require extensive stands of mature forest.

In recent decades, urban areas have faced increasing flood risk, disproportionately affecting marginalized groups. The 1930s redlining practices in the United States have had socioeconomic and environmental impacts in multiple cities. However, there remains an incomplete understanding of the relationship between redlining and urban flood risk. This study addresses this need by investigating the spatial distribution of green infrastructure (GI) and impervious surfaces, as influential factors in mitigating and exacerbating flood risk, respectively, in the flood-prone redlined zones in Houston, Texas. Census blocks that intersected with floodplains and redlining map categories were identified, assigning redlining classes based on the overlap percentages. The proportions of impervious surfaces and GI elements were extracted from land cover data. Their median ratios were compared across redlining categories, and significant clusters of high imperviousness and low GI were mapped. The study found higher imperviousness and lower GI median ratios in redlined zones with strict housing loan restrictions categorized as "Hazardous" and "Definitely Declining." In addition, the significant clusters of high imperviousness and low GI were mainly located within these redlining zones. The spatial and temporal resolution limitations of available data are acknowledged. Higher-resolution land cover data spanning different periods and comprehensive flood hazard datasets including future climate change scenarios are recommended for more accurate analysis. This study linked prior research on flood risk disparities and historical discriminatory practices, revealing significant spatial relationships between Houston's redlined zones and the distribution of GI and imperviousness in flood-prone areas.

Over the past few decades, cities have experienced increased floods affecting property and threatening human life as a result of a warming planet. There is still an incomplete understanding of the flood risk patterns in urban communities with different socioeconomic characteristics. In this study, we produced separate flood exposure and vulnerability indices based on relevant factors, then combined them as a risk index for Houston, Texas and Charleston, West Virginia. We applied statistical methods to extract the most significant social vulnerability factors in each study area. Finally, we mapped significant hot spots or clusters of high flood risk and compared results to socioeconomically disadvantaged populations. Based on the results, high-risk or 1%-annual-chance floodplains cover 23% of the Houston and 7% of Charleston study areas. Within these floodplains, 13% of the total developed land in Houston and 9% in Charleston are situated. In the event of a 1%-annual-chance flood, an estimated 5% of the total population in Houston and 6% in Charleston may require evacuation. Statistically significant flood risk clusters could only be identified in Houston. The implications from this work help to provide an analysis framework for larger urban areas while offering suggestions for its improvement in smaller populated areas.

Energy transition from conventional centralized power plants, particularly coal-fired units, is critical for West Virginia's long-term energy and economic future. The socioeconomic challenges faced by West Virginia are closely linked to its reliance on the centralized coal industry and economy, which has declined precipitously in the past decade. Many postindustrial communities in rural areas struggle to sustain economic viability, resulting in documented outmigration and diminished energy resilience. We investigated the possibility of introducing community-sized distributed energy systems in these rural communities to improve energy resilience and support the transition toward more sustainable energy production. This study investigated the feasibility of introducing community-sized distributed energy systems in rural West Virginia to enhance energy resilience and facilitate the transition away from traditional centralized energy. Utilizing a geospatial modeling approach with Multi-Criteria Decision Analysis (MCDA) and Geographic Information System (GIS) suitability assessment, we identified optimal locations for small-scale distributed wind, solar, and hydropower energy generation. The study conducted a net value comparison analysis, assessing the levelized cost of energy (LCOE) and levelized avoided cost of energy (LACE) to determine the economic feasibility of each distributed generation type compared to traditional coal-generated electricity. Our findings revealed that wind and solar distributed generation are most suitable in southern and eastern West Virginia counties, while potential sites for small hydropower development are dispersed across the state . This study offers valuable insights into the possible future of distributed energy and its infrastructure development in rural West Virginia, thus contributing to the state's energy transition and economic revitalization efforts.

Outdoor recreation is one of the fastest-growing economic sectors in the United States and is being used by communities to support economic development, social prosperity, and environmental protection. For communities that have whitewater rivers, whitewater recreation provides a powerful economic alternative to ailing extractive and manufacturing industries that have long dominated rural communities. In order to promulgate a whitewater recreation-based economy, stakeholders need information about their whitewater resources, including how often and when they can be paddled. The overall goal of this study, therefore, was to develop an analytical framework that quantifies boatable days, that is, the number of days that streamflow exceeds the minimum boatable flow levels needed to paddle downstream. Importantly, our framework uses publicly available streamflow and minimum boatable flow information that can be used to quantify boatable days for any whitewater run in the country, irrespective of watershed size or river flashiness. We applied the framework to three world-class whitewater rivers in the central Appalachian Mountains, USA, and found abundant and stable boating opportunities throughout the year. Our results underscore the potential for strategically developing whitewater recreation as a means of economic diversification and highlight how boatable days analysis can be used for quantifying whitewater resources.

Saturated hydraulic conductivity (Ksat) is a hydrologic flux parameter commonly used to determine water movement through the saturated soil zone. Understanding the influences of land-use-specific Ksat on the model estimation error of water balance components is necessary to advance model predictive certainties and land management practices. An exploratory modeling approach was developed in the physically based Soil and Water Assessment Tool (SWAT) framework to investigate the effects of spatially distributed observed Ksat on local water balance components using three digital elevation model (DEM) resolution scenarios (30 m, 10 m, and 1 m). All three DEM scenarios showed satisfactory model performance during calibration (R2 > 0.74, NSE > 0.72, and PBIAS ≤ ±13%) and validation (R2 > 0.71, NSE > 0.70, and PBIAS ≤ ±6%). Results showed that the 1 m DEM scenario provided more realistic streamflow results (0.315 m3/s) relative to the observed streamflow (0.292 m3/s). Uncertainty analysis indicated that observed Ksat forcings and DEM resolution significantly influence predictions of lateral flow, groundwater flow, and percolation flow. Specifically, the observed Ksat has a more significant impact on model predictive confidence than DEM resolution. Results emphasize the potential uncertainty of using observed Ksat for hydrological modeling and demonstrate the importance of finer-resolution spatial data (i.e., 1 m DEM) applied in smaller watersheds.

Geospatial technologies are a critical tool within the mining and reclamation sector to monitor and analyze changes on the landscape. One of the newest ways to collect high temporal and spatial resolution data is with unmanned aerial vehicles (UAVs) or drones. Our study highlights the available benefits of using UAV imagery to map and analyze legacy issues with former mine lands. The data that can now be collected over hundreds of acres provides site-level modelling to plan for both the reclamation and future development of former mine lands. Our approach is unique in the capture of engineering-level site measurements using a fixed-wing UAV at a former mine land site in the southern coalfields of WV. The information collected and processed included an orthophoto, a digital surface model (DSM), and a digital elevation model (DEM). The ability to have this information in a timely high-resolution (4 cm) manner provided for a quick analysis of disturbed areas. Models could be built of overland drainage and topographic constraints to help in the planning of development options. We found the UAV data collection approach with a specific fixed-wing unit allowed a small crew to effectively and efficiently capture information that resulted in major time and cost savings compared to traditional field sampling and analysis. We were able to construct bare earth as well as vegetation height models to help us identify sites at the former mine land where development options could be compared with costs associated with cut/fill and other volumetric analyses needed for access and development. The detail of the surface model provided a way to track overland flow across the landscape and account for drainage paths. The high-resolution imagery overlaid on the surface models allowed for development renditions in a landscape context. These site plans remove uncertainty in the construction process for post-mining development options.

Understanding population dynamics of migratory birds requires baseline knowledge of nonbreeding distributions, migratory routes, and population-specific migratory connectivity. The Canada Warbler (Cardellina canadensis) is a declining Nearctic-Neotropical migratory songbird, but limited information exists describing its population-specific migration ecology. We aimed to understand how individuals from a central Appalachian breeding population were spatially distributed during the stationary nonbreeding period, infer the strength and pattern of migratory connectivity, and understand migratory route patterns. We deployed 32 geolocator tags on adult male birds in 2020 and 10 tags retrieved in 2021 provided usable data. Stationary nonbreeding season locations suggest moderate population connectivity, with birds clustered on the Andean slopes on each side of the Magdalena Valley in northern Colombia. Post-breeding (boreal fall) migration was largely overland through Mexico and Central America, and pre-breeding (boreal spring) migration routes were significantly farther east based on the western most longitude recorded during migration (mean difference = 232 km), consistent with a pattern of counter clockwise loop migration. The evidence of counter clockwise loop migration is a novel finding in Canada Warblers and suggests the shorter duration of pre-breeding migration may be due to a time-minimizing strategy involving trans-Gulf flights. Our data additionally provide the first insight into the migratory ecology of a population of Canada Warblers breeding near the southern extent of their range and serve as a foundation for full annual cycle demographic modeling. Received 10 June 2023. Accepted 30 December 2023.

The Appalachian region of the United States has experienced significant growth in the production of natural gas. Developing the infrastructure required to transport this resource to market creates significant disturbances across the landscape, as both well pads and transportation pipelines must be created in this mountainous terrain. Midstream infrastructure, which includes pipeline rights-of-way and associated infrastructure, can cause significant environmental degradation, especially in the form of sedimentation. The introduction of this non-point source pollutant can be detrimental to freshwater ecosystems found throughout this region. This ecological risk has necessitated the enactment of regulations related to midstream infrastructure development. Weekly, inspectors travel afoot along new pipeline rights-of-way, monitoring the re-establishment of surface vegetation and identifying failing areas for future management. The topographically challenging terrain of West Virginia makes these inspections difficult and dangerous to the hiking inspectors. We evaluated the accuracy at which unmanned aerial vehicles replicated inspector classifications to evaluate their use as a complementary tool in the pipeline inspection process. Both RGB and multispectral sensor collections were performed, and a support vector machine classification model predicting vegetation cover were made for each dataset. Using inspector defined validation plots, our research found comparable high accuracy between the two collection sensors. This technique displays the capability of augmenting the current inspection process, though it is likely that the model can be improved further. The high accuracy thus obtained suggests valuable implementation of this widely available technology in aiding these challenging inspections.

Abstract. Flood risk, encompassing hazard, exposure, and vulnerability is defined concerning potential losses. Machine learning techniques have gained traction among researchers to address the complexities of multi-variable flood risk assessment models and overcome issues associated with non-linear relationships. However, the focus has primarily been on flood hazard prediction rather than comprehensive risk assessment and damage estimations. Therefore, there is a need for experiments that combine risk elements using such methods. To address this need, this study utilized the Random Forest algorithm to analyze the correlations between the physical flood damage caused by Hurricane Harvey in 2017 in Houston, Texas and certain hazard, exposure, and vulnerability-related variables. The study identified poorly drained soils as the primary contributor to the losses, followed by population density and the ratio of developed lands with medium intensity. The study's findings also explored the reasons for the unexpectedly low importance of social vulnerability factors compared to the environmental justice concept. These findings and conclusions can provide insights to planners and stakeholders enhancing their understanding of the underlying causes contributing to flood risk. Future research can expand upon this study's methodology and findings by incorporating additional factors related to climate change.

The United States' (U.S.) land management planning and evaluation focuses on the scenic, esthetic, and visual qualities of biophysical properties of lands, and is more analytic and is often led by experts/planners. This approach lacks consideration of everyday or degraded landscapes and public preference in general. A systematic approach is sought to meet the growing land use demands, recovery from the past industrial exploitation and activities, and to achieve a sustainable goal of meeting present and future needs. For sustainable development, understanding characteristics of the land resource is imperative. This study discusses methods of landscape assessment in the U.S. and Europe, especially the Landscape Character Assessment (LCA). The LCA's focus of the "character" of landscape will offer insights and opportunities for understanding the characteristics of landscapes in the proposed Appalachian Geopark in West Virginia. We employed a Geographic Information System tool with raster and vector datasets to analyze and assess typology and spatial information of the study region. The results revealed that distinct landscape characteristics in the study region are due to the geography of the area with sandstone and limestone-rich rocks, river systems, and the historical/cultural land use practices. Gorges, rock features, valleys, rivers, waterfalls, railways, coalfields, coal towns, karsts, pastures, etc. are the representative landscape features in the study region. These attributes are the potential nature merchandizing assets of this region that support a Geopark concept and would help develop the region sustainably.

Photographs have been utilized as substitutes for on-site scenes in the assessment and evaluation of landscape's visual quality, perspective, and preference. Visual quality, perception, and preference are assessed through human eyes and their judgment. However, the human judgement is often generally categorized as expert vs. citizen. Literature searches show that the expert-based assessment dominates over the citizen level judgement. There is a lack of information on methodologies to assess public preference of landscape and landscape attributes. This paper discussed two different approaches of assessing landscape preferences of the public (local and visitors) in the proposed Appalachian Geopark Project (hereafter referred as pAGP) covering Fayette, Greenbrier, and Raleigh Counties in West Virginia (WV). A set of two questionnaire surveys were administered. There were questions for answering as a cognitive preference exercise and a set of photographs for rating as a visual stimulation exercise. Both instruments were delivered to respondents as anonymous links using Survey123 and Qualtrics software respectively. The results from both surveys revealed the highest preference was found for forested landscapes followed by water features and the associated landscapes. This study's findings revealed how multiple methods of assessing public preferences can strengthen and justify the results from different methods. Surveys were completed by 47 respondents.