Search for: All records

Abstract The tectonic stress field induces surface deformation. At long wavelengths, both lithospheric heterogeneity (changes in the thickness and density of crust and lithospheric mantle) and basal tractions from mantle convection contribute to the stress field. Here, we analyze the global alignment of principal horizontal tectonic stresses, fault traces, and river flow directions to infer whether and how deep subsurface stresses control geomorphic features. We find that fault trace orientations are consistent with predictions from Anderson's fault theory. River directions largely align with fault traces and partly with stresses. The degree of alignment depends on fault regime, the source of stress, and river order. Extensional faulting is best predicted by stresses from lithospheric structure variations, while compressive faulting is best predicted by stresses from mantle flow. We propose a metric to quantify the relative influence of mantle flow or lithospheric heterogeneity on surface features, which provides a proxy for lithospheric strength. 

Abstract Debris flows are powered by sediment supplied from steep hillslopes where soils are often patchy and interrupted by bare‐bedrock cliffs. The role of patchy soils and cliffs in supplying sediment to channels remains unclear, particularly surrounding wildfire disturbances that heighten debris‐flow hazards by increasing sediment supply to channels. Here, we examine how variation in soil cover on hillslopes affects sediment sizes in channels surrounding the 2020 El Dorado wildfire, which burned debris‐flow prone slopes in the San Bernardino Mountains, California. We focus on six headwater catchments (<0.1 km²) where hillslope sources ranged from a continuous soil mantle to 95% bare‐bedrock cliffs. At each site, we measured sediment grain size distributions at the same channel locations before and immediately following the wildfire. We compared results to a mixing model that accounts for three distinct hillslope sediment sources distinguished by local slope thresholds. We find that channel sediment in fully soil‐mantled catchments reflects hillslope soils (D₅₀ = 0.1–0.2 cm) both before and after the wildfire. In steeper catchments with cliffs, channel sediment is consistently coarse prior to fire (D₅₀ = 6–32 cm) and reflects bedrock fracture spacing, despite cliffs representing anywhere from 5% to 95% of the sediment source area. Following the fire, channel sediment size reduces most (5‐ to 20‐fold) in catchments where hillslope sources are predominantly soil covered but with patches of cliffs. The abrupt fining of channel sediment is thought to facilitate postfire debris‐flow initiation, and our results imply that this effect is greatest where bare‐bedrock cliffs are present but not dominant. A patchwork of bare‐bedrock cliffs is common in steeplands where hillslopes respond to channel incision by landsliding. We show how local slope thresholds applied to such terrain aid in estimating sediment supply conditions before two destructive debris flows that eventually nucleated in these study catchments in 2022. 

Abstract We used seismic refraction to image the P‐wave velocity structure of a shale watershed experiencing regional compression in the Valley and Ridge Province (USA). From estimates showing strong compressional stress, we expected the depth to unweathered bedrock to mirror the hill‐valley‐hill topography (“bowtie pattern”) by analogy to seismic velocity patterns in crystalline bedrock in the North American Piedmont that also experience compression. Previous researchers used failure potentials calculated for strong compression in the Piedmont to suggest fractures are open deeper under hills than valleys to explain the “bowtie” pattern. Seismic images of the shale watershed, however, show little evidence of such a “bowtie.” Instead, they are consistent with weak (not strong) compression. This contradiction could be explained by the greater importance of infiltration‐driven weathering than fracturing in determining seismic velocities in shale compared to crystalline bedrock, or to local perturbations of the regional stress field due to lithology or structures. 

Abstract To investigate how bedrock transforms to soil, we mapped the topography of the interface demarcating onset of weathering under an east‐west trending shale watershed in the Valley and Ridge province in the USA Using wave equation travel‐time tomography from a seismic array of >4,000 geophones, we obtained a 3D P‐wave velocity (Vp) model that resolves structures ∼20 m below land surface (mbls). The depth of mobile soil and the onset of dissolution of chlorite roughly match Vp = 600 m/s and Vp = 2,700 m/s, respectively. Chlorite dissolution initiates porosity growth in the shale matrix. Depth to the 2,700 m/s contour is greater under the N‐ as compared to S‐facing hillslopes and under sub‐planar as compared to concave‐up land surfaces. Broadly, the geometries of the ‘soil’ and ‘chlorite’ Vp contours are consistent with the calculated potential for shear fracture opening under weak regional compression. However, this calculated fracture potential does not consistently explain observations related to N‐ versus S‐facing aspect nor fracture density observed by borehole televiewer. Apparently, regional compression is only a secondary influence on Vp: the primary driver of P‐wave slowing in the upper layers of this catchment is topographic control of reactive water flowpaths and their integrated effects on weathering. The Vp result is best explained as the long‐term integrated effect of groundwater flow‐induced geochemical weathering of shale in response to climate‐driven patterns of micro‐ and macro‐topography. 

Abstract Weathering processes weaken and break apart rock, freeing nutrients and enhancing permeability through the subsurface. To better understand these processes, it is useful to constrain physical properties of materials derived from weathering within the critical zone. Foliated rocks exhibit permeability, strength and seismic anisotropy–the former two bear hydrological and geomorphological consequences while the latter is geophysically quantifiable. Each of these types of anisotropy are related to rock fabric (fractures and foliation); thus, characterizing weathering‐dependent changes in rock fabric with depth may have a range of implications (e.g., landslide susceptibility, groundwater modeling, and landscape evolution). To better understand how weathering effects rock fabric, we quantify seismic anisotropy in saprolite and weathered bedrock within two catchments underlain by the Precambrian Loch Raven schist, located in Oregon Ridge Park, MD. Using circular geophone arrays and perpendicular seismic refraction profiles, anisotropy versus depth functions are created for material 0–25 m below ground surface (bgs). We find that anisotropy is relatively low (0%–15%) in the deepest material sampled (12–25 m bgs) but becomes more pronounced (29%–33%) at depths corresponding with saprolite and highly weathered bedrock (5–12 m bgs). At shallow soil depths (0–5 m bgs), material is seismically isotropic, indicating that mixing processes have destroyed parent fabric. Therefore, in situ weathering and anisotropy appear to be correlated, suggesting that in‐place weathering amplifies the intrinsic anisotropy of bedrock. 

This repository stores data using for the manuscript: Unraveling the Connection between Subsurface Stress and Geomorphic Features The data file used in this study is 'Input_stress_fault_river_BK_091525.csv'. The code used to reproduce all figures in the manuscript is 'Kuhasubpasin_et_al_2025.ipynb' The file contain these following data: Column unit range description lat degree (-90, 90) Latitude lon degree (-180, 180) Longitude azi_R degree (0, 180)* Interpolated azimuth of river network (interpolate without considering river order) azi_r1 degree (0, 180)* Interpolated azimuth of 1'-order river azi_r2 degree (0, 180)* Interpolated azimuth of 2'-order river azi_r3 degree (0, 180)* Interpolated azimuth of 3'-order river azi_r4 degree (0, 180)* Interpolated azimuth of 4'-order river azi_r5 degree (0, 180)* Interpolated azimuth of 5'-order river Drainage_area cell - Drainage area river_order order (1, 7) Majority of the order river in grid cell elev km (0, 5.1375) Elevation TcstDens g/cm^3 (2.7439,2.962) Average crustal density from CRUST 1.0 TcstThk km (5.0731 73.517) Total crustal thickness from CRUST 1.0 crust_type     Crustal type from ECM1 Te km (1,200) Effective elastic thickness MI - (-1,1) Mantle influence index azi_Z degree (0, 180)* Topographic aspect azi_F degree (0, 180)* Interpolated azimuth of faults reg_F - (0, 1) Regime of F azi_SO degree (0, 180)* Interpolated azimuth of feature 𝜎𝑂 from WSM reg_SO - (0, 1) Regime of 𝜎𝑂 azi_SO_010 degree (0, 180)* Interpolated azimuth of 𝜎𝑂 measured between 0-10 km azi_SO_1020 degree (0, 180)* Interpolated azimuth of 𝜎𝑂 measured between 10-20 km azi_SO_2030 degree (0, 180)* Interpolated azimuth of 𝜎𝑂 measured between 20-30 km azi_SO_3040 degree (0, 180)* Interpolated azimuth of 𝜎𝑂 measured between 30-40 km azi_SO_nofm degree (0, 180)* Interpolated azimuth of 𝜎𝑂 measured from focal mechanism azi_SO_fm degree (0, 180)* Interpolated azimuth of 𝜎𝑂 measured from other techniques azi_SL degree (0, 180)* Interpolated azimuth of 𝜎𝐿 reg_SL - (0, 1) Regime of 𝜎𝐿 sp1_SL Pa - Magnitude of principal stress 1 for 𝜎𝐿 sp2_SL Pa - Magnitude of principal stress 2 for 𝜎𝐿 azi_SM degree (0, 180)* Interpolated azimuth of feature 𝜎𝑀 reg_SM - (0, 1) Regime of 𝜎𝑀 sp1_SM Pa - Magnitude of principal stress 1 for 𝜎𝑀 sp2_SM Pa - Magnitude of principal stress 2 for 𝜎𝑀 azi_ST degree (0, 180)* Interpolated azimuth of feature 𝜎𝑇 reg_ST - (0, 1) Regime of 𝜎𝑇 sp1_ST Pa - Magnitude of principal stress 1 for 𝜎𝑇 sp2_ST Pa - Magnitude of principal stress 2 for 𝜎𝑇 azi_SB degree (0, 180)* Interpolated azimuth of feature 𝜎𝐵 delta_SO_F degree (0, 90) Δ𝜎𝑂−𝐹 delta_SL_F degree (0, 90) Δ𝜎𝐿−𝐹 delta_SM_F degree (0, 90) Δ𝜎𝑀−𝐹 delta_ST_F degree (0, 90) Δ𝜎𝑇−𝐹 delta_SB_F degree (0, 90) Δ𝜎𝐵−𝐹 delta_SO_R1 degree (0, 90) Δ𝜎𝑂−𝑅1 :1' order river delta_SL_R1 degree (0, 90) Δ𝜎𝐿−𝑅1 delta_SM_R1 degree (0, 90) Δ𝜎𝑀−𝑅1 delta_ST_R1 degree (0, 90) Δ𝜎𝑇−𝑅1 delta_SB_R1 degree (0, 90) Δ𝜎𝐵−𝑅1 delta_F_R1 degree (0, 90) Δ𝐹−𝑅1 delta_SO_R2 degree (0, 90) Δ𝜎𝑂−𝑅2 :2' order river delta_SL_R2 degree (0, 90) Δ𝜎𝐿−𝑅2 delta_SM_R2 degree (0, 90) Δ𝜎𝑀−𝑅2 delta_ST_R2 degree (0, 90) Δ𝜎𝑇−𝑅2 delta_SB_R2 degree (0, 90) Δ𝜎𝐵−𝑅2 delta_F_R2 degree (0, 90) Δ𝐹−𝑅2 delta_SO_R3 degree (0, 90) Δ𝜎𝑂−𝑅3 :3' order river delta_SL_R3 degree (0, 90) Δ𝜎𝐿−𝑅3 delta_SM_R3 degree (0, 90) Δ𝜎𝑀−𝑅3 delta_ST_R3 degree (0, 90) Δ𝜎𝑇−𝑅3 delta_SB_R3 degree (0, 90) Δ𝜎𝐵−𝑅3 delta_F_R3 degree (0, 90) Δ𝐹−𝑅3 delta_SO_R4 degree (0, 90) Δ𝜎𝑂−𝑅4 :4' order river delta_SL_R4 degree (0, 90) Δ𝜎𝐿−𝑅4 delta_SM_R4 degree (0, 90) Δ𝜎𝑀−𝑅4 delta_ST_R4 degree (0, 90) Δ𝜎𝑇−𝑅4 delta_SB_R4 degree (0, 90) Δ𝜎𝐵−𝑅4 delta_F_R4 degree (0, 90) Δ𝐹−𝑅4 delta_SO_R5 degree (0, 90) Δ𝜎𝑂−𝑅5 :5' order river delta_SL_R5 degree (0, 90) Δ𝜎𝐿−𝑅5 delta_SM_R5 degree (0, 90) Δ𝜎𝑀−𝑅5 delta_ST_R5 degree (0, 90) Δ𝜎𝑇−𝑅5 delta_SB_R5 degree (0, 90) Δ𝜎𝐵−𝑅5 delta_F_R5 degree (0, 90) Δ𝐹−𝑅5 delta_SO_R>1 degree (0, 90) Δ𝜎𝑂−𝑅>1 :>1' order river delta_SL_R>1 degree (0, 90) Δ𝜎𝐿−𝑅>1 delta_SM_R>1 degree (0, 90) Δ𝜎𝑀−𝑅>1 delta_ST_R>1 degree (0, 90) Δ𝜎𝑇−𝑅>1 delta_SB_R>1 degree (0, 90) Δ𝜎𝐵−𝑅>1 delta_F_R>1 degree (0, 90) Δ𝐹−𝑅>1 delta_SO_Z degree (0, 90) Δ𝜎𝑂−𝑍 delta_SL_Z degree (0, 90) Δ𝜎𝐿−𝑍 delta_SM_Z degree (0, 90) Δ𝜎𝑀−𝑍 delta_ST_Z degree (0, 90) Δ𝜎𝑇−𝑍 delta_SB_Z degree (0, 90) Δ𝜎𝐵−𝑍 delta_F_Z degree (0, 90) Δ𝐹−𝑍 delta_Z_R1 degree (0, 90) Δ𝑍−𝑅1 :1' order river delta_Z_R2 degree (0, 90) Δ𝑍−𝑅2 :2' order river delta_Z_R3 degree (0, 90) Δ𝑍−𝑅3 :3' order river delta_Z_R4 degree (0, 90) Δ𝑍−𝑅4 :4' order river delta_Z_R5 degree (0, 90) Δ𝑍−𝑅5 :5' order river delta_Z_R>1 degree (0, 90) Δ𝑍−𝑅>1 :>1' order river *The range is not (0,360) because we only consider azimuth not direction 

Temporal and spatial variations of tectonic rock uplift are generally thought to be the main controls on long-term erosion rates in various landscapes. However, rivers continuously lengthen and capture drainages in strike-slip fault systems due to ongoing motion across the fault, which can induce changes in landscape forms, drainage networks, and local erosion rates. Located along the restraining bend of the San Andreas Fault, the San Bernardino Mountains provide a suitable location for assessing the influence of topographic disequilibrium from perturbations by tectonic forcing and channel reorganization on measured erosion rates. In this study, we measured 17 new basin-averaged erosion rates using cosmogenic 10Be in river sands (hereafter, 10Be-derived erosion rates) and compiled 31 10Be-derived erosion rates from previous work. We quantify the degree of topographic disequilibrium using topographic analysis by examining hillslope and channel decoupling, the areal extent of pre-uplift surface, and drainage divide asymmetry across various landscapes. Similar to previous work, we find that erosion rates generally increase from north to south across the San Bernardino Mountains, reflecting a southward increase in tectonic activity. However, a comparison between 10Be-derived erosion rates and various topographic metrics in the southern San Bernardino Mountains suggests that the presence of transient landscape features such as relict topography and drainage-divide migration may explain local variations in 10Be-derived erosion rates. Our work shows that coupled analysis of erosion rates and topographic metrics provides tools for assessing the influence of tectonic uplift and channel reorganization on landscape evolution and 10Be-derived erosion rates in an evolving strike-slip restraining bend. 

Landscapes are frequently delineated by nested watersheds and river networks ranked via stream orders. Landscapes have only recently been delineated by their interfluves and ridge networks, and ordered based on their ridge connectivity. There are, however, few studies that have quantitatively investigated the connections between interfluve networks and landscape morphology and environmental processes. Here, we ordered hillsheds using methods complementary to traditional watersheds, via a hierarchical ordering of interfluves, and we defined hillsheds to be landscape surfaces from which soil is shed by soil creep or any type of hillslope transport. With this approach, we demonstrated that hillsheds are most useful for analyses of landscape structure and processes. We ordered interfluve networks at the Calhoun Critical Zone Observatory (CZO), a North American Piedmont landscape, and demonstrated how interfluve networks and associated hillsheds are related to landscape geomorphology and processes of land management and land-use history, accelerated agricultural gully erosion, and bedrock weathering depth (i.e., regolith depth). Interfluve networks were ordered with an approach directly analogous to that first proposed for ordering streams and rivers by Robert Horton in the GSA Bulletin in 1945. At the Calhoun CZO, low-order hillsheds are numerous and dominate most of the observatory’s ∼190 km2 area. Low-order hillsheds are relatively narrow with small individual areas, they have relatively steep slopes with high curvature, and they are relatively low in elevation. In contrast, high-order hillsheds are few, large in individual area, and relatively level at high elevation. Cultivation was historically abandoned by farmers on severely eroding low-order hillsheds, and in fact agriculture continues today only on high-order hillsheds. Low-order hillsheds have an order of magnitude greater intensity of gullying across the Calhoun CZO landscape than high-order hillsheds. In addition, although modeled regolith depth appears to be similar across hillshed orders on average, both maximum modeled regolith depth and spatial depth variability decrease as hillshed order increases. Land management, geomorphology, pedology, and studies of land-use change can benefit from this new approach pairing landscape structure and analyses.