Property:CSDMS meeting abstract
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Quantitative constraints on the frequency of hazards is vital to risk assessments and appropriate mitigation strategies. The frequency of landslides, a common hazard in steep landscapes, is difficult to quantify for a number of reasons including: (1) infrequent occurrence; (2) rapid deterioration of the morphological signature of a landslide event; (3) expensive geochronological approaches are often require to obtain the age of a single event. Through the use of numerical modeling, I propose that more careful approach of using cosmogenic nuclide concentrations of alluvial sediment sourced in landslide dominated drainage basins can alleviate many of these hurdles and provide regional constraints on landslide frequency. This suggestion stems from new development of an old numerical code that quantifies the impacts of landslides on CRN concentrations in alluvial sediment. The modeling shows that quantitative insight can be obtained by measuring CRN concentrations (1) of multiple nuclides (10Be and 14C), (2) of multiple grain sizes (i.e. coarse material sourced from depth in the hillslope), and (3) over time. I will present the new model developments and results as well as discuss some strategies towards applying this in field settings. +
Rainfall intensity thresholds are used to estimate when postfire debris flows may occur in the western US. Prior research has shown that postfire debris flows are highly correlated with short-duration rainfall intensity, and that short duration rainfall thresholds (e.g., 15-minute rainfall intensity) can be estimated based on wildfire and terrain attributes. Consequently, it is possible to determine possible debris flow activity in recent burn areas in the western United States by tracking rainfall rates using publicly available rainfall data. We have developed a software (FlowAlert) and an accompanying map dashboard that monitors when and where rain gages near burn areas cross rainfall intensity thresholds. The software runs continuously on a linux server, processing more than 2500 rain gages every two hours. When rainfall rates near a burn area are higher than a rainfall threshold, symbols are updated on a map indicating possible debris flow activity. Rainfall plots are also provided on the dashboard, and via email alerts for the gages that have crossed the rainfall intensity threshold. FlowAlert can be used for situational awareness to alert authorities of potential debris flow activity in remote areas. Additionally, the data stream produced by FlowAlert can be used by managers to adjust the rainfall intensity threshold in areas following storms based on observed activity. For example, if rainfall thresholds were crossed, but no debris flows were observed, managers may choose to increase the rainfall threshold to avoid warning fatigue. This presentation will focus on the utility of the new FlowAlert software, and how it might be used for decision support in burn areas. +
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Reactive Transport Modeling (RTM) has been developed in the past decades and used extensively to understand the coupling between fluid flow, diffusive and dispersive transport, and biogeochemical processes in the natural subsurface in a wide range of applications relevant to earth and environmental sciences. Reactive transport modeling solves conservation equations of mass, momentum, and energy. Process-based reactive transport modeling allows the regeneration of spatial and temporal propagation of tightly coupled subsurface processes at spatial scales ranging from single pores (microns) to watershed scales (kilometers). RTM can keep track of evolving porous medium properties including porosity, permeability, surface area, and mineralogical composition. In this presentation I will introduce the general framework of RTM together with its advantages and challenges. The use of RTM at different spatial and temporal scales will be illustrated using two examples. A one-dimensional chemical weathering model for soil formation in Marcellus Shale will illustrate its use in Critical Zone (CZ) processes at the time scales of tens of thousands of years. A two dimensional biogeochemical transport model will exemplify its use in understanding engineered bioremediation processes in natural, heterogeneous porous media at the time scale of months to years. +
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Recent analytical and modeling studies on the Lowermost Mississippi River (LMR) have enhanced our understanding of sand flux at key locations, providing a framework for evaluating sand as a renewable resource for coastal restoration projects. This research is part of the Louisiana Sediment Management Program (LASMP) and aims to evaluate and quantify recharge rates of borrow areas within the LMR. It also seeks to determine whether channel maintenance dredging requirements are reduced downstream of borrow areas after they are dredged.
To characterize pit evolution and calculate infill rates, time-series bathymetric surveys from two recent projects with borrow areas - Spanish Pass Ridge and Marsh Creation (borrow area at Venice Anchorage), and Upper Barataria Marsh Creation (borrow areas at Alliance Bar) - were analyzed. Existing Delft3D numerical models were updated, refined, and calibrated for each reach using these time-series bathymetric data and recent sediment data.
Observations of infilling rates within a month of dredging were approximately 225,000 m3/month, declining rapidly to approximately 74,000 m3/month in February 2022 and declining further to 40,000 m3/month through March 24, 2022. During low flow conditions, infilling was minimal, but it increased to 230,000 m³/month in January 2023. For another area, initial infilling rates in the first month were around 125,000 m³/month, rapidly decreasing to approximately 75,000 m³/month by May 23, 2022. These rates fluctuated between 1,000 and 10,000 m³/month until December 27, 2022, before rising to 110,000 m³/month in January 2023.
This analysis provides a framework to forecast sediment recharge rates in LMR borrow pits, which can be incorporated into LASMP sediment resource availability estimates. It also suggests that sand extracted from the LMR for restoration can help reduce navigation channel maintenance costs. Future research opportunities include leveraging local model results and additional observed infilling rates from various locations along the Mississippi River. This research could lead to a better understanding of the relationship between the location of borrow pits, hydrograph characteristics, and corresponding infilling rates.
Furthermore, machine learning tools could be utilized to develop this correlation. Such a relationship would provide engineers and planners with an easy-to-use tool to evaluate first-order infilling rates and the time required for infilling, which is crucial for restoration project planning, including dredging operations and determining locations for borrow pits. Ultimately, this would support and promote sustainable sand extraction for restoration projects.
Recent discovery of a well-preserved drowned bald cypress forest offshore Alabama has spurred the search for analogous sites, as they provide valuable paleoclimate proxies and potential paleohuman habitats. However, drowned forests are difficult to detect when buried beneath the seabed, and degrade rapidly when exposed to the water column.
In this study, various machine learning algorithms within NRL's Global Predictive Seabed Model (GPSM) are used to geospatially predict the location of buried ancient forests offshore Mississippi. Subsurface sediment cores containing evidence of ancient forests (wood debris) are used as training and validation data, and feature layers include modern bathymetry, paleo-topographic surfaces, and seabed substrate. The resulting maps of probability of encountering wood-bearing sediments will be used to guide future data acquisition efforts. +
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Recent morphodynamic modeling of non-uniform turbulent transport and deposition of sediment in a standing body of water devoid of tides and waves shows that sediment caliber plays a major role in determining the shapes, cumulative number of distributaries, and wetland areas of river-dominated deltas. In this study we introduce metrics for quantifying delta shoreline rugosity and foreset dip (clinoform) variability, and explore their variation with sediment caliber. Delta shoreline rugosity is calculated using the isoperimetric quotient, IP = 4 pi A / P2, where a circle has a value of one. Clinoform complexity is calculated using the uniformity test in circular statistics wherein clinoform dip direction uniformity is the sum of the deviations of dip azimuths from a theoretical uniform distribution. Analysis of fifteen simulated deltas shows that IP increases from 0.1 to 0.5 as the normalized shear stress for re-erosion of cohesive sediment, τn, increases from 0.65 to 1. Clinoform dip azimuth uniformity decreases from 300 to 130 with increasing τn. Preliminary analysis of data from outcrops of the Cretaceous Ferron Delta and ground penetrating radar data of the Pleistocene Weber and Brigham City Deltas are consistent with these trends. These results imply that changes in sediment caliber delivered to a deltaic coastal system will profoundly change its wetland area, bathymetric hypsometry, ecological function, and interior stratigraphy. +
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Recent post-tsunami field surveys show that sandy tsunami deposits usually cannot cover all of the tsunami flow inundation areas. The difference between the sandy tsunami deposits inland extent and the flow inundation limit can be used to estimate tsunami magnitude. However, the relationship between tsunami deposit inland extent and inundation limit is still not fully understood. This paper focuses on studying the relationship and its control factors by using a parameter study and field measurements. Deposition ratio is a ratio between the sediment layer inland extent and the tsunami inundation limit to quantify this relationship. In the parameter study carried by a state-of-the-art sediment transport model (GeoClaw-STRICHE), we change grain size, offshore wave height, and onshore slope. The deposition ratio for tsunami deposit extent ($\xi_0$) is not sensitive to the grain size. However, the deposition ratios for observable sediment layer inland extent ($\xi_{0.5}$ and $\xi_{1}$) are affected by the grain size, offshore wave height, and onshore slope. The deposition ratios for a 0.5 cm thick sediment layer from parameter study are consistent with field measurements from the 2011 T\={o}hoku-oki tsunami on Sendai Plain. The topography, especially onshore slope, strongly influences the deposition ratio in this case. The combination of different deposition ratios can be used to estimate tsunami inundation area from tsunami deposits and improve tsunami hazard assessments. +
Recent research has highlighted the idea that long distance particle motions can be a significant component of the hillslope sediment flux. In this situation, mathematical descriptions of hillslope sediment transport must be nonlocal. That is, the flux at a position x, is a weighted function of conditions around x. This contrasts with local conditions which state that the flux is only a function of conditions at x. There are several ways to incorporate nonlocality into a mathematical description of sediment transport. Here, we focus on implementing and testing a convolution integral-like formulation. In this case, the flux is a convolution integral of a volumetric entrainment rate and a kernel that is related to the probability distribution of particle travel distance. Computation of convolution integrals is typically done by taking advantage of the convolution theorem for Fourier transforms, where a convolution integral becomes multiplication in wavenumber domain. However, in our case, the kernel is a function of position, and therefore precludes us from taking advantage of this method. Here, we apply a method that can reduce the problem back to a proper convolution integral and therefore allows for rapid computation (Gilad and von Hardenberg, 2006). We use this method to demonstrate nonlocal transport on lateral moraines on the east side of the Sierra Nevada. This method has applications in all convolution integral-like formulations including nonlinear filtering. +
Recent trends in Earth system modeling, climate data collection, and computing architecture have opened new opportunities for machine learning to improve ESMs. First, new and cheaper satellites are generating large volumes of observational data (e.g. Arctic and Antarctic DEMs), and massive climate modeling projects are generating large volumes of simulated climate data (e.g. CMIP5, CMIP6, CESM-LE). Second, machine learning applications are driving the design of next-generation computing architectures that will accelerate applications like neural nets without ameliorating the computational bottlenecks (ref: NOAA HPC position paper) that limit existing climate models. Third, the climate science community is becoming increasingly familiar with machine learning techniques. Here, I summarize opportunities for CSDMS practitioners to use machine learning techniques to improve Earth system and Earth surface models. +
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Recurved barrier spits occur in a wide variety of environments, from active delta complexes to rocky coasts, where spits extend depositionally from a shore that is otherwise eroding. Although controls on spit orientation are often presented in the literature a posteriori (i.e. after the spit has been observed), there surprisingly remains no general model that predicts spit shape and orientation in terms of external variables, such as wave climate, sediment supply, and embayment depth. We study spit shape controls using the Coastline Evolution Model (CEM), a numerical model that evolves the plan-view coast based upon the processes of alongshore sediment transport and barrier overwash maintaining a minimum critical barrier width. Model results demonstrate that the directional distribution of approaching waves serves as a first-order control on spit shape, with waves from multiple directions playing a vital role in spit extension and reshaping. Surprisingly, we find that boundary effects, namely the rate of change of the updrift coast location, play a similarly important role in spit shape. The depth of the platform upon which a spit grows plays another important role, with deeper platforms tending to accommodate more sharply curved spits. Every day, spits act as a type of messenger in disguise, revealing wave forcing, sediment supply, and local geometry. +
Reduction of nitrogen (N), phosphorus (P), and suspended sediment (SS) load has been a principal focus of Chesapeake Bay Watershed management for decades. To evaluate the progress of management actions in the Bay's largest tributary, the Susquehanna River, we analyzed the long-term seasonal trends of flow-normalized N, P, and SS load over the last two to three decades, both above and below the Lower Susquehanna River Reservoir System. Our results indicate that annual and decadal-scale trends of nutrient and sediment load generally followed similar patterns in all four seasons, implying that changes in watershed function and land use had similar impacts on nutrient and sediment load at all times of the year. Above the reservoir system, the combined loads from the Marietta and Conestoga Stations indicate general trends of N, P, and SS reduction in the Susquehanna River Basin, which can most likely be attributed to a suite of management actions on point, agricultural, and stormwater sources. In contrast, upward trends of SS and particulate-associated P and N were generally observed below the Conowingo Reservoir since the mid-1990s. Our analyses suggest that (1) the reservoirs' capacity to trap these materials has been diminishing over the past two to three decades, and especially so for SS and P since the mid-1990s, and that (2) the Conowingo Reservoir has already neared its sediment storage capacity. These changes in reservoir performance will pose significant new kinds of challenges to attainment of total maximum daily load goals for the Susquehanna River Basin, and particularly if also accompanied by increases in storm frequency and intensity due to climate change. Accordingly, the reservoir issue may need to be factored into the proper establishment of regulatory load requirements and the development of watershed implementation plans. (Published in Science of the Total Environment (2013); available at http://dx.doi.org/10.1016/j.scitotenv.2013.02.012. For a pdf pre-print, please contact Qian Zhang at qzhang19@jhu.edu.)
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Reef islands are carbonate detrital landforms perched atop shallow reef flats of atolls and barrier reef systems. Often comprising the only subaerial, inhabitable land of many island chains and island nations, these low-lying, geomorphically active landforms face considerable hazards from climate change. Sea-level rise and wave climate change will affect sediment transport and shoreline dynamics, including the possibility for wholesale reorganization of the islands themselves. Here we apply a hierarchical modeling approach to quantify the potential responses of reef island systems to future changes. Using parameterizations of sediment transport pathways and feedbacks from previously presented XBeach modeling results, we investigate how sea-level rise, change in storminess, and different carbonate production rates can affect the profile evolution of reef islands, including feedbacks with the shallow reef flat that bounds the islands offshore (and lagoonward). Model results demonstrate that during rising sea levels, the reef flat can serve as a sediment trap, starving reef islands of detrital sediment that could otherwise fortify the shore against sea-level-rise-driven erosion. On the other hand, if reef flats are currently shallow (likely due to geologic inheritance or biologic cementation processes) such that sea-level rise does not result in sediment accumulation on the flat, reef island shorelines may be more resilient to rising seas. This simplified modeling approach, focusing on boundary dynamics and mass fluxes, including carbonate sediment production, provides a quantitative tool to predict the response of reef island environments to climate change. +
Relating scientific results generated from modeling, remote sensing, or instrumental measurements of Earth’s surface to topics of social relevance often poses challenges for theoretical scientists. Theoretical science is treated as divorced from social matters, as a matter of definition, unless specific problems are solved, then it shifts into an applied realm and is viewed as spatially constrained. A paradox results: if scientific results are “merely” theoretical, yet intended to be universal, then it is undetermined whether results would follow everywhere, but if they are collected from actual measures and observations it is undetermined whether the same conditions are everywhere and apply to all spatial and temporal scales. The problem is particularly acute for Earth’s surface which is both physical and has high value for all human beings and infrastructures and all life. It is also the object of theoretical models. So, it is critical for human life and its connections to all life that models aim to model the spatial and temporal reality of Earth’s surface including regional variabilities with their constraints. Social relevance is a value that can be viewed together with physical characterizations as part of understanding landscapes.
Outlined are considerations to help integrate values that express social relevance into the scope of how theoretical science is conducted and approached to address the scale of connectedness of Earth’s surface while expanding the human reach of Earth surface modeling. Inspiration has been taken from recent NSF sponsored initiatives, such as efforts to expand and unify through diversity and inclusion the critical zone (CZRN) and convergence research in Navigating the New Arctic, as well as the open science philosophy of CSDMS, and a recent Greenland Data workshop seeking to unify data management of Greenland. I contribute my synthesis to engage with others about how the application of computational approaches and landscape data management can be used to provide basic platforms for treating and comparing earth surface data at multiple temporal and spatial scales while having “convergence” of social spheres as an underlying consideration. Therefore, the infinite potential at any point on Earth’s surface is representable, relatable, and connectable numerically, and it recognizes and includes the realm of human beings as investigators and inhabitants of any landscape.
Computational thinking may be a source for thinking about expanding who are considered the inhabitants of landscapes and who studies the landscapes, i.e. including diverse identities in research while also unifying them through shared and recognized goals. Expanding the realm of theory in Earth surface processes to include model data about people, life, geographies, climates, processes and their change through time is a new science frontier. The idea that information has many dynamic layers and dimensions and that there are many ways to connect and relate them through time with computational approaches as a starting point may serve as a guide for integrating social value into theoretical research.
River delta morphology is shaped by complex interactions between sediment supply and discharge variability, influencing their resilience to environmental changes. This study employs Delft3D-Flow to investigate how different discharge scenarios—constant discharge, a unimodal flood hydrograph, and a monthly flood hydrograph—affect the long-term evolution of a river-dominated delta modeled after the Wax Lake Delta, Louisiana. Over a 50-year simulation period, results indicate that unsteady discharge promotes a more symmetric delta morphology with broader sediment deposition, while constant discharge leads to localized deposition near the river mouth, resulting in an elongated delta with fewer channels. These findings highlight the role of discharge variability in delta formation, with implications for coastal management and restoration strategies. +
River deltas are dynamic landforms that archive the complex interplay among sediment supply, water discharge, and sea-level change. Understanding how these factors shape delta morphology over centennial to millennial time scales remains a central challenge, particularly in the context of accelerating sea-level rise. To investigate fan delta evolution under a wide range of external forcings, we couple geometric and enthalpy-based numerical models with experimental data.
We analyze 14 experimental runs conducted in a tilting flume facility that produced isolated fan deltas over a sloped, non-erodible basement. Each run maintained a fixed water-to-sediment discharge ratio and implemented a sea-level scenario—either constant, rising, or falling. These experiments span both steady base-level conditions with varying sediment and water inputs, and sea-level rise scenarios. To extract morphodynamic data, we apply a computer vision algorithm to time-lapse imagery, enabling automated reconstruction of topset and foreset geometries.
Our first modeling approach uses a geometric framework that assumes conical fan delta shapes to estimate three-dimensional volumes and sediment partitioning between the subaerial topset and subaqueous foreset. The model quantifies key metrics (slope and opening angle), reveals a consistent relationship across scenarios between plan-view opening angle at the alluvial-bedrock transition and sediment/water discharge ratios: increasing sediment supply or decreasing water discharge produces narrower opening angles, and vice versa.
The second model is a moving-boundary numerical framework based on the enthalpy method, enabling more realistic geometries and dynamic responses. Simulations under continuous sea-level rise replicate key experimental observations: foreset starvation leads to abandonment of the submarine delta front, while the topset migrates landward and narrows. This retreating geometric adjustment is effectively captured by the model. Its simplicity enables efficient exploration of a wide parameter space, providing new insights into deltaic evolution and sedimentary prism development under changing environmental conditions.
The integrated modeling framework provides a quantitative foundation for linking external forcings to delta morphology and stratigraphy. Our long-term goal is to apply this approach to constrain past sea-level histories and sediment budgets from plan view geometries, with future applications to Arctic deltas and other climate-sensitive coastal systems.
River deltas ringing the Arctic Ocean coastline are unique landforms shaped by both highly seasonal cold region hydrology and permafrost features such as thermokarst lakes. These lakes trap, store, and modulate the timing and magnitudes of riverine freshwater, sediment, and nutrients. Future climate warming is expected to thaw permafrost, modifying lake coverage and therefore riverine flux delivery to the Arctic Ocean. How and where thermokarst lake coverage on deltas will change remains highly uncertain, in part due to the difficulty in separating perennially inundated thermokarst lakes which undergo thermal expansion in response to warming, from ephemeral wetlands resulting from interannual and seasonal hydrologic variability. We present a methodology that allows us to classify waterbodies as perennial lakes or ephemeral wetlands, by examining their presence in a 20 year record of Landsat imagery. By analyzing 12 deltas laying on a gradient of temperature and ice content, we find that perennial lakes and ephemeral wetlands have universal but distinct size distributions which result from different mechanisms forming the two waterbodies. We also find that colder deltas have larger lakes on average, mechanistically attributed to thicker and colder permafrost which supports larger lakes by preventing sub-lake unfrozen zones (i.e. taliks) from connecting to the sub-permafrost groundwater table. Lastly, we explore how differences in the spatial patterns of lakes across the deltas may relate to climate variability. These findings provide the basis for quantitative predictions for the trajectory of lake and wetland coverage on arctic deltas under projected warming. +
River morphodynamics can affect overbank flooding if changes to cross-sectional area or roughness reduce the flow conveyance capacity of the channel. Correspondingly, extreme floods can also cause drastic adjustments to river morphology on relatively short timescales. These co-occurring processes raise the question: how do flood dynamics and river morphology co-evolve? The difficulty of conducting rapid field measurements of river geometry during peak flows has limited existing research on channel adjustment and recovery during a flood hydrograph. Here, we leverage a one-month Delft3D simulation to investigate flood hydraulics and river morphodynamics in a November 2021 flood event in the Nooksack River, western Washington State (WA). This flood devastated river-adjacent WA communities along the Lower Nooksack. Flood waters additionally overtopped the levees near Everson, WA and traveled over 25 km north towards the Fraser River, causing extensive and costly damages across the U.S.-Canada border. To understand the feedbacks between river morphodynamics and floods, we analyze streamwise changes in flood hydraulics (flow velocity & shear stress) and morphodynamics (bed elevation change). Within the Everson overflow region, we find that spatial gradients in flow velocity shift between in-bank- and peak-flow conditions. Bed elevation changes are commonly located where hydrodynamic gradients intensify during peakflows. Importantly, at the Everson overflow location, bed deposition co-occurs with a drastic along-channel velocity decrease and overbank flow, suggesting that the hydraulics of overbank flooding can contribute to local morphodynamic adjustment. We additionally investigate how channel adjustments during the first November 2021 flood peak affect flooding during a secondary flood peak occurring two weeks later. These results are of particular interest to the Nooksack River floodplain managers who are eager for insights on the contribution of channel morphodynamics to flooding and who are actively investigating methods to alleviate overtopping including setback levees and dredging.
River profiles are shaped by a combination of tectonic forcing, climatic history, and internal feedbacks. One example of internal dynamics is the self-formation of waterfalls in steep channels, which can cause erosion rates to both accelerate (‘fast waterfalls’) or decelerate (‘slow waterfalls) relative to waterfall-free reaches. We previously used a 1D stream power model with a waterfall rule to show that the self-formation of waterfalls above a threshold slope can alter river long profiles over km scales. In the 1D model, the formation of fast waterfalls results in a uniform-gradient zone maintained by a dynamic equilibrium, while slow waterfalls can cause autogenic knickpoints. However, it is not clear how these findings alter river profile form in a 2D setting in which hillslopes and channels are linked and adjacent basins can interact. Here, we ask the question: are long profile signatures from waterfall formation enhanced or erased by additional internal feedbacks, such as hillslope diffusion and planform channel adjustment? To address this knowledge gap, we implemented our waterfall model in a 2D setting, using Landlab and specifically the SPACE and hillslope diffusion components. Our results in 2D show agreement with the initial 1D implementation, and additional results show that self-formation of waterfalls can alter landscape-scale metrics, including drainage density and slope-area relationships. Additional exploration of natural variability, including stochastic rock strength show that increasing natural variability can expand the length of the affected profile. +
River-bed grain size distributions in fluvial systems set the initial condition for landscape change on the event to millennia scale. These distributions are used to infer characteristic flow conditions or estimate mass flux through a fluvial system, and this is often under the assumption that sediment grain-size distribution on the bed remains static over time. However, recent work has shown that grain size distributions can fluctuate over individual flow events, can be dependent on the sequence of successive flow events, and can change seasonally. This discrepancy can lead to order of magnitude differences in estimating sediment flux or characteristic hydrologic conditions. To constrain bed grain size evolution, we perform numerical simulations using a probabilistic, discrete model of a river bed in which computational cells represent grains randomly distributed across the surface of the bed. Patterns and timing of grain mobilization are determined according to distributions of entrainment thresholds for individual grains and flow rate dependent distributions of velocity fluctuations that vary over hydrographs. Entrained grains are replaced from a static distribution allowing the bed grain size distribution to evolve throughout the simulation depending on the imposed flow condition. Our preliminary model results from varying initial grain size distribution, individual event shape, and flow sequencing show a significant dependence on the range of grain sizes available for transport as well as the total duration of individual flow events, while seasonal variability has a moderate impact on bed grain size evolution. +
Rivers are key drivers of landscape evolution. Transient signals of base level fall propagate up rivers and cause increased erosion rates, which in turn increases channel steepness. In landscapes with horizontally layered rocks, erosion rates vary in both space and time as different layers are eroded and exposed at the surface, complicating how these landscapes respond to base level fall and influencing the topographic expression of different lithologies. Lithologic variations further influence river response by producing sediment, which can armor the channel bed and reduce bedrock erosion. Motivated by the lithologic variability found in the Guadalupe Mountains of Texas and New Mexico, we use the Stream Power with Alluvium Conservation and Entrainment (SPACE) model to test how sediment cover affects channel steepness and erosion rates in horizontally layered rocks. We simulate 1.2 million years of landscape evolution with an imposed uplift rate of 1.0 mm/yr in alternating layers of hard and soft rock, systematically varying the relative amount of alluvial cover. We compare the normalized channel steepness of the model output against the steepness values predicted by the stream power incision model, which is the most commonly used model for interpreting steepness variations in real landscapes. We find that in model runs with sediment cover, channel steepness is systematically higher than predicted by the stream power model in soft rock layers, and lower than predicted in hard rocks. Sediment cover also exerts a strong control on erosion rates. Sediment cover preferentially accumulates over soft rock layers, decreasing erosion rates in these layers while increasing erosion in the unarmored hard rock layers. As the degree of sediment cover is increased, the maximum erosion rate at any given point along the channel decreases. Increasing the sediment cover effectively decreases the erodibility contrast between hard and soft rocks, illustrating the importance of considering the role of sediment when interpreting channel profiles.
