Bulletin--KGS
Reconnaissance of Landslides and Debris Flows Associated with the July 2022 Flooding in Eastern Kentucky
Suggested Citation: Crawford, C., Zhenming, W., Carpenter, N.S., Schmidt, J., Koch H., Dortch, J., 2023, Reconnissance of Landslides and Debris Flows Associated with the July 2022 Flooding in Eastern Kentucky: Kentucky Geological Survey, ser. 13, Report of Investigations 13, 14p. DOI: https://doi.org/10/13023/kgs.ri56.13.
Abstract
Between July 25 and July 30, 2022, a series of convective storms generated approximately 14–16 inches of rainfall across parts of eastern Kentucky, predominately in Clay, Leslie, Perry, Breathitt, Knott, and Letcher Counties. The peak rainfall occurred on the evening of July 27 and the morning of July 28, with the hardest-hit areas experiencing more than 10 inches in a 24-hour period. The historic rainfall led to catastrophic flooding along many rivers and streams, but also triggered widespread landslides and debris flows that damaged roads, homes, property, and other infrastructure. Once initial relief and recovery efforts were established, the Kentucky Geological Survey (KGS) geohazard section conducted a preliminary field reconnaissance that observed and documented landslides and debris flows triggered by the July storm event. We documented landslides from late August to early November 2022 using (1) visual field inspection methods and (2) a remote sensing technique called normalized differencing vegetation index (NDVI). Visual field inspection occurred primarily along roads through documentation of landslide type and location. The NDVI technique allowed identification of larger landslides and debris flows not easily accessible in a vehicle. We identified more than 1,000 new landslides and debris flows triggered by the July event. The majority of landslides the team identified were shallow translational slides, supplemented by some rotational slides (slumps), and debris flows. Documenting landslides in the field before they perish is important for future hazard assessment modeling. Landslide inventories associated with large storm events, and large impact areas, will improve our understanding of landslide occurrence and rainfall rates, and potentially our ability to forecast landslides. The data is intended for use by both scientists and non-scientists, such as emergency managers and public safety decision-makers.
