The average thickness of continental crust is ~30–40 km far-removed from plate boundaries and in the absence of significant topography. Nonetheless, there are many examples of anomalously thick crust (>40–50 km) that have been stable over hundreds to thousands of m.y. Here, we present new estimates of crustal thickness based on seismic data from the state of Ohio in the United States. This region is deep within the interior of the North American plate and is characterized by extremely low topographic relief; however, crustal thickness is ~60 km beneath the central part of the state. Anomalously thick crust, together with narrow, positive gravity anomalies, suggest that the lower crust beneath central Ohio is characterized by a mafic crustal root. Mafic material was intruded and underplated beneath the North American lithosphere during Midcontinent rifting at ~1109 Ma. Following rifting, the region experienced significant contraction and shortening during the collisional Grenville Orogeny. We propose that the combination of neutrally buoyant mafic material in the lower crust and short wavelength Moho relief is necessary for the long-term stability of thick crust beneath Ohio. This model reconciles seismic and gravity models of the crustal structure and geologic history of the region, suggesting that part of the Midcontinent Rift was deformed and imbricated within the Grenville Front in the Proterozoic Eon.
Between 23 and 27 September 2024, antecedent rain followed by Hurricane Helene produced one of the most damaging weather events in southern Appalachia history. The back-to-back storm events resulted in a maximum cumulative rainfall of 848 mm and hurricane-force wind gusts over 170 km/h in western North Carolina, eastern Tennessee, and southwestern Virginia. The resulting regional flooding, landslides, and tree blowdown caused over 100 fatalities, damaged or destroyed critical infrastructure and thousands of structures, and severed connectivity across the region. Over the next several weeks, a multi-agency landslide response produced a rapid hazard assessment and mapped 2217 landslides, 55% of which damaged infrastructure or property. Orographic uplift enhanced rainfall, resulting in concentrated landsliding along the ~250 km swath of the Blue Ridge escarpment in western North Carolina. Landslides initiated predominantly on windward-facing (southeast-facing) slopes, and localized clustering of initiation points indicated a strong influence of hillslope-scale meteorological and geomorphic factors. Many shallow landslides mobilized into larger, highly mobile, and damaging debris flows that graded into floods. Here, we put our preliminary observations in the context of historical storm-driven landslide events and open new avenues for investigating the nature and extent of landslides and their effects in southern Appalachia and similar environments.
Earth-observing satellites have revolutionized the field of fluvial geomorphology by providing large-scale and spatially contiguous observations. The recently launched Surface Water and Ocean Topography (SWOT) satellite’s novel interferometric synthetic aperture radar (inSAR) instrument delivers global measurements of several key geomorphic parameters, such as river surface water elevation, slope, and width, and thus presents the opportunity to study fluvial processes in new ways. Here we explore the utility of the SWOT satellite for advancing understanding of fluvial geomorphology across river systems in the United States, specifically focusing on water surface elevation variations in large braided rivers, temporally dynamic shear stress in bedrock rivers, and the processes associated with knickpoints and dam failures. We also discuss other relevant potential applications of SWOT satellite data related to fluvial geomorphology beyond the scope of these early explorations. By providing global multitemporal observations of several key variables in fluvial geomorphology, SWOT represents a major advance in our ability to quantify, monitor, and understand fluvial systems and their dynamics.
