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Earth's drainage networks encode clues that can be used to decipher geologic and geomorphic history. Dendritic drainage patterns, the most common, typically form on approximately homogeneous bedrock. Variations in rock properties or lithologic structure can disrupt dendritic patterns and form, e.g., trellis or rectangular networks. Although textbooks include such lithological–drainage links, the mechanisms driving drainage reorganization via lithologic variability remain poorly understood. To cast light on this mystery, we study drainage patterns in post-glacial landscapes of the Upper Mississippi River Valley (UMRV). Pleistocene glaciers deposited till across parts of this region, burying a landscape of fluvially dissected sedimentary rock whose buried valley network differs from modern-day drainage patterns. As the current river network erodes and exhumes the bedrock, it comes to a geomorphic "decision point": Does it reorganize to recreate the paleodrainage network, or does it maintain its new drainage pattern? To understand this decision-making, we conducted idealized landscape evolution modeling experiments. Modeled landscapes that reintegrated more of the paleodrainage network exhibited higher tortuosity, measured by dividing the real flowpath length by shortest path-length to the outlet, and obtuse tributary-junction angles. We apply this metric to two adjacent landscape types in the UMRV: (1) never glaciated (Driftless Area, DA) and (2) formerly till-mantled (Driftless-style Area, DSA), and measure the basin-averaged tortuosity for sub-basins draining streams of order 1 through 7. Across the UMRV, tortuosity increases as the maximum stream order of the sub-basin increases. For each order, tortuosity is statistically higher in areas that had been previously buried and re-exhumed (DSA) than the DA, indicating that the rivers in the DSA have reintegrated the paleodrainage network since deglaciation. For the 1st and 2nd order sub-basins, the mean basin-averaged tortuosity in the DSA is ~1-2% higher than the DA (p-value < 0.01) and ~10-14% higher (p-value < 0.01) in the 6th and 7th order sub-basins. Our analysis suggests that a drainage-based metric, tortuosity, can identify landscapes where lithological heterogeneity or structure plays a dominant role in drainage organization. 

For landscapes to achieve a topographic steady state, they require steady tectonic uplift and climate, and a bedrock that is uniformly erodible in the vertical direction. Basic landscape evolution models predict that incising drainage networks will eventually reach a static geometric equilibrium – that is, the map-view channel pattern will remain constant. In contrast, natural rivers typically incise through heterogeneous bedrock, which can force reorganization of the drainage structure. To investigate how lithological variability can force landscape reorganization, we draw inspiration from formerly glaciated portions of the upper Mississippi Valley. In this region, depth-to-bedrock maps reveal buried dendritic river networks dissecting paleozoic sedimentary rock. During the Pleistocene, ice advance buried the bedrock topography with glacial till, resurfacing the landscape and resetting the landscape evolution clock. As newly formed drainage networks develop and incise into the till-covered surface, they exhume the buried bedrock topography. This then leads to a geomorphic "decision point": Will the rivers follow the course of the bedrock paleodrainage network? Or will they maintain their new pattern? Using a numerical landscape evolution model, we find that two parameters determine this decision: (1) the contrast between the rock erodibility of the glacial till (more erodible) and of the buried sedimentary rock (less erodible) and (2) the orientation of the surface drainage network with respect to the buried network. We find that as the erodibility contrast increases, the drainage pattern is more likely to reorganize to follow the buried bedrock valleys. Additionally, as the alignment of the two networks increases, the surface drainage network also tends to restructure itself to follow the paleodrainage network. However, when there is less contrast and/or alignment, the surface drainage pattern becomes superimposed on the bedrock topography, with streams cutting across buried bedrock ridges. Our results agree with field studies demonstrating that variability in erodibility exerts a first-order control on landscape evolution and morphology. Our findings can provide insight into how lithologic variation affects surface processes, drives drainage reorganization, and creates geopatterns. 

Abstract Erosion degrades soils and undermines agricultural productivity. For agriculture to be sustainable, soil erosion rates must be low enough to maintain fertile soil. Hence, quantifying both pre-agricultural and agricultural erosion rates is vital for determining whether farming practices are sustainable. However, there have been few measurements of pre-agricultural erosion rates in major farming areas where soils form from Pleistocene deposits. We quantified pre-agricultural erosion rates in the midwestern United States, one of the world's most productive agricultural regions. We sampled soil profiles from 14 native prairies and used in situ–produced 10Be and geochemical mass balance to calculate physical erosion rates. The median pre-agricultural erosion rate of 0.04 mm yr–1 is orders of magnitude lower than agricultural values previously measured in adjacent fields, as is a site-averaged diffusion coefficient (0.005 m2 yr–1) calculated from erosion rate and topographic curvature data. The long-term erosion rates are also one to four orders of magnitude lower than the assumed 1 mm yr–1 soil loss tolerance value assigned to these locations by the U.S. Department of Agriculture. Hence, quantifying long-term erosion rates using cosmogenic nuclides provides a means for more robustly defining rates of tolerable erosion and for developing management guidelines that promote soil sustainability. 

Dendritic, i.e., tree-like, river networks are ubiquitous features on Earth’s landscapes; however, how and why river networks organize themselves into this form are incompletely understood. A branching pattern has been argued to be an optimal state. Therefore, we should expect models of river evolution to drastically reorganize (suboptimal) purely nondendritic networks into (more optimal) dendritic networks. To date, current physically based models of river basin evolution are incapable of achieving this result without substantial allogenic forcing. Here, we present a model that does indeed accomplish massive drainage reorganization. The key feature in our model is basin-wide lateral incision of bedrock channels. The addition of this submodel allows for channels to laterally migrate, which generates river capture events and drainage migration. An important factor in the model that dictates the rate and frequency of drainage network reorganization is the ratio of two parameters, the lateral and vertical rock erodibility constants. In addition, our model is unique from others because its simulations approach a dynamic steady state. At a dynamic steady state, drainage networks persistently reorganize instead of approaching a stable configuration. Our model results suggest that lateral bedrock incision processes can drive major drainage reorganization and explain apparent long-lived transience in landscapes on Earth. 

Abstract Soil erosion diminishes agricultural productivity by driving the loss of soil organic carbon (SOC). The ability to predict SOC redistribution is important for guiding sustainable agricultural practices and determining the influence of soil erosion on the carbon cycle. Here, we develop a landscape evolution model that couples soil mixing and transport to predict soil loss and SOC patterns within agricultural fields. Our reduced complexity numerical model requires the specification of only two physical parameters: a plow mixing depth,L_p, and a hillslope diffusion coefficient,D. Using topography as an input, the model predicts spatial patterns of surficial SOC concentrations and complex 3D SOC pedostratigraphy. We use soil cores from native prairies to determine initial SOC‐depth relations and the spatial pattern of remote sensing‐derived SOC in adjacent agricultural fields to evaluate the model predictions. The model reproduces spatial patterns of soil loss comparable to those observed in satellite images. Our results indicate that the distribution of soil erosion and SOC in agricultural fields can be predicted using a simple geomorphic model where hillslope diffusion plays a dominant role. Such predictions can aid estimates of carbon burial and evaluate the potential for future soil loss in agricultural landscapes.