2026 CSDMS meeting-028
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Hydrological Resilience in a Reshaped World: Modelling Infiltration Shifts in Appalachian Mountaintop Mining and Valley Fill Landscapes
Kennedy Ochieng,
(Ir.),West Virginia University Morgantown West Virginia, United States. koo00002@mix.wvu.edu
Aaron Maxwell, West Virginia University Morgantown West Virginia, United States. Aaron.maxwell@mail.wvu.edu
Surface mining practices, particularly mountaintop removal and valley fill operations, substantially alter terrain structure, vegetation cover, and soil properties, thereby modifying key hydrological processes and runoff generation patterns. Infiltration process is one of the key hydrological processes affected in these landscapes. Infiltration rate estimation methods using remotely sensed data have been explored in basins impacted by agriculture, urbanization, and mining; however, studies specifically focused on MTR/VF-impacted basins are limited. This study aims at estimating infiltration rates in MTR/VF-impacted basins within the Appalachian Mountains region, an area characterized by extensive coal extraction and significant landscape disturbance. This mining method substantially alters terrain structure, vegetation cover, and soil properties, thereby modifying key hydrological processes and runoff generation patterns. To address these changes, this research integrates remote sensing and terrain analysis to model infiltration variability across mining basins. The study utilizes Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 imagery alongside terrain variables including slope, curvature, Topographic Position Index (TPI), and Topographic Wetness Index (TWI) generated from digital elevation models at a 10-meter spatial resolution. These datasets provide high spatial and temporal resolution, enabling detailed assessment of post-mining landscape conditions. Field-based infiltration measurements across representative land-cover types will be conducted in the Coal River Valley of southern West Virginia to support calibration and validation of remote sensing–based estimates. Statistical regression techniques will then be applied to identify relationships among vegetation condition, terrain characteristics, and infiltration rates. The results are expected to improve understanding of hydrological responses in reclaimed mining landscapes and enhance runoff estimation, with implications for flood mitigation, watershed management, and infrastructure planning in downstream communities.
