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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 Landslides are notoriously difficult to predict because numerous spatially and temporally varying factors contribute to slope stability. Artificial neural networks (ANN) have been shown to improve prediction accuracy but are largely uninterpretable. Here we introduce an additive ANN optimization framework to assess landslide susceptibility, as well as dataset division and outcome interpretation techniques. We refer to our approach, which features full interpretability, high accuracy, high generalizability and low model complexity, as superposable neural network (SNN) optimization. We validate our approach by training models on landslide inventories from three different easternmost Himalaya regions. Our SNN outperformed physically-based and statistical models and achieved similar performance to state-of-the-art deep neural networks. The SNN models found the product of slope and precipitation and hillslope aspect to be important primary contributors to high landslide susceptibility, which highlights the importance of strong slope-climate couplings, along with microclimates, on landslide occurrences. 

The deep critical zone (CZ) has long been recognized for its importance in influencing shallow landslides but was not considered feasible to include in slope stability models at the watershed scale. Here, we demonstrate that simple approximations of the CZ in a fully coupled hydrologic and soil slope stability model can effectively capture the location, timing, and likely size of shallow landslides. To achieve this, we use coupled, process-based models that incorporate the effects of 1) deep CZ structures, 2) three-dimensional transient hydrology, and 3) multidimensional slope stability, calibrated with data from an intensively monitored field site. Our results show that the hydrologically active deep CZ guides groundwater flow, influencing where it drains from or exfiltrates to the soil mantle and producing distinct patterns of soil saturation and seepage forces at the soil–bedrock boundary. A deep conductive, weathered bedrock drains the soil mantle, reducing the likelihood of destabilizing pore pressures, while the downslope thinning of the CZ forces groundwater to the surface. This pattern creates localized instability and a tendency for similar-sized landslides across the landscape. In contrast, the absence of conductive weathered bedrock results in more widespread destabilizing pore pressures, leading to larger landslides and the likelihood of landslides earlier in a storm than in landscapes underlain by a deep CZ. Our findings suggest that first-order variations of deep CZs can provide physical explanations for variations observed in the susceptibility, magnitude, and timing of shallow landslides, and that CZ structure may be inferred from patterns and timing of landsliding. 

Although climate can strongly influence erosional efficiency (i.e., erosion rate for a given topography), demonstrating its impact in tectonically active areas has been challenging due to other confounding controlling factors, such as lithology. Here, we show that 10Be-derived erosion rates and efficiencies in the Himalayan orogen exhibit distinct relationships with climatic factors depending on lithology. We compile 173 10Be-derived, basin- averaged erosion rates across the orogen, including 12 newly measured rates from the Dibang and Lohit valleys in the easternmost Himalaya, regions characterized by high precipitation magnitudes and variability. We group basins based on lithologies separated by orogen-scale thrust faults and quantify erosional efficiency coefficients based on the relationships between erosion rates and topographic metrics. Our results show that erosion rates and erosional efficiency from sedimentary and metasedimentary rocks along the Himalayan range front display a positive, nonlinear correlation with climatic factors, such as the number of extreme rainfall events and mean annual precipitation rates. In contrast, erosion rates from crystalline lithologies in the hanging wall of the Main Central thrust show a strong correlation with fluvial topography, whereas erosional efficiency shows no statistically significant correlation with climatic factors. Rapid erosion rates and high erosional efficiencies in the eastern Himalayan range front are likely driven by extreme precipitation on tectonically active, steep slopes composed of mechanically weak metasedimentary rocks. Our findings highlight the importance of the interplay between controlling factors, which include tectonics, lithology, and climate, that drive surface erosion and influence the topographic evolution of orogenic systems. 

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 

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. 

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. 

Semi-automatic and manual landslide inventories and environmental control datasets for the N-S (Dibang), NW-SE (range front), and E-W (Lohit) regions as described in the related publication. Shapefiles contain the individual landslides mapped and their landslide ID. Mapping procedures for this inventory can be found in the related publication. Details of environmental control datasets are described in the related publication. The dataset for the E-W region is provided as a Matlab mat file which can be used with the supplemental code for the related paper. WGS 1984 UTM Zone N47. 

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.