Property:CSDMS meeting abstract
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It has been hypothesized that large, rare flooding events in semi-arid to arid climate regimes may do more erosive work than the frequent storm events that occur in humid or temperate climates. Previous work has demonstrated that added variability in modeled climate or water discharges may be linked to changes in landscape form or channel characteristics. Many landscape evolution models do not capture hydrograph dynamics, so they may miss critical aspects linking flood events and erosion. To explore how different climates shape landscapes, this work uses a hydrodynamic model to simulate flooding and erosion processes. Precipitation time series, based on observed event frequency data from NOAA, are used to differentiate modeled wet and dry regimes. The drier regime is characterized by a heavy-tailed flood probability distribution, where the rarest events have a greater magnitude than storms of a similar recurrence in wetter regions. Hydrographs driven by these precipitation time series are used to erode the topography of a synthetic watershed. Simulations are run with and without an incision threshold. After 10^4 modeled years, landscape characteristics such as relief and channel concavity can be compared. Total eroded depths are evaluated for the different storm frequencies to explore how individual floods and the cumulative work of all floods sculpt landscapes. We propose when an incision threshold is considered, the higher magnitude events in arid regimes will be more effective at shaping watersheds than events of the same frequency in temperate climates. These results inform the discussion of how fluvial erosion may change if anthropogenic climate change leads to the aridification of presently temperate regimes. Additionally, this study will illustrate how hydrograph shape and duration impact modeled landforms, processes not captured in traditional landscape evolution models. +
It has been well documented that climate warming was greater in the Arctic than elsewhere. However, it is still poorly understood how climate changed over different permafrost zones and its potential impacts on permafrost thermal dynamics. In this study, we investigated changes in air temperatures, especially seasonal air temperatures, over different permafrost regions in the Northern Hemisphere using the Climate Research Unit (CRU) gridded datasets from 1976-2016. The primary results indicated that permafrost regions as a whole experienced a warming at 0.36, 0.41, and 0.46 °C/decade in mean annual maximum, mean, and minimum air temperature, respectively, which are 16%, 32%, and 44% higher than the corresponding trend in non-permafrost regions. More importantly, strong increases occurred in cold months and nighttime over continuous permafrost zone, exceeding 0.72 °C/decade in Spring and Autumn; while summer air temperature had a relatively small increase or no statistically significant trends. As a result, the decrease of air freezing index by 529 °C-day would result in permafrost temperature increase by 1.43 °C in continuous permafrost zone over the past four decades. This may explain the observed evidence that increase of cold permafrost temperature was greater than that of warm permafrost, while active layer thickness had little or no change during the past several decades. These results suggest that predicted reduction of permafrost area by previous studies might be overestimated. +
Lago Cachet Dos (LC2) is a glacially-dammed lake adjacent to the Northern Patagonian Ice Field (NPIF), formed by the blockage of Cachet Basin (CB) by the Colonia Glacier. This glacier has experienced rapid (~1-2 km) retreat of its terminus as well as ~1-2 m/yr of thinning, documented over the past several decades. Furthermore, the glacier has exhibited a change in hydrologic regime and the frequency of high energy glacial lake outburst flood (GLOF) events since 2008. These historical changes appear to be coupled with regional climate change; summer mean maximum and minimum temperatures in nearby Cochrane show a steady increase since 1971, whereas winter mean maximum temperatures show cooling in the 1970s and 1980s, followed by gradual warming with rapid acceleration in the 2000s-present. Preliminary correlations with a recently installed weather station at Sol de Mayo (~12 km downstream of the Colonia Glacier terminus) show a strong positive correlation with the Cochrane data, indicating these climate changes are regional and not local and thereby have implications for the evolution of other alpine basins of the NPIF and perhaps glaciers on a global scale. Recent observations from unmanned aerial vehicle (UAV) flights, satellite imagery, and geologic mapping suggest unprecedented glacier deterioration near the southern limit of CB. An UAV flight in January 2016 revealed that during GLOF events, the lake drained through a large hole at the base of the glacier. Upon entering this chasm, the water made a sharp east turn (towards the bedrock abutting the glacier’s eastern margin) and appeared to flow beneath the ice at this point. Subsequently, a large (~2km long x 100 m wide) supra-glacial channel has opened directly above the drainage hole, effectively separating the glacier from bedrock. Ice elevation data reveal that healing of this channel may not be possible under the current climate regime, suggesting the basin could be experiencing a long-term (over human timescales) shift to fluvial deposition from a dominantly lacustrine environment, corresponding to an inability to impound water associated with the glacier's retreat. Basin stratigraphy indicates these oscillations between lacustrine and fluvial conditions have occurred repeatedly throughout the Holocene, but the timing of these changes are poorly constrained. Optically stimulated luminescence (OSL) dating of CB sediments will be applied to identify the timing and periodicity of these depositional shifts, with the broader goal of linking these oscillations with local and regional climate and stability of the Colonia Glacier.
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Lake records provide a long-term record of climate events and transitions, earthquakes in tectonically active regions, landscape response during and following deglaciation and recent human influenced land use changes. In order to unravel the story preserved in lake sediments, it is necessary to understand the dynamics of the lake system and the source of the sediment coming into the lake. Our study focuses on Lake Ohau, New Zealand, which occupies a fault controlled glacial valley and contains a high resolution sedimentary record of the last ~17 ka. It is presently the focus of a multi-disciplinary studied which aims to recover a long core encompassing the whole ~17 ka record in the next several years.
We use two CSDMS codes: HydroTrend, a climate-driven hydrological model, and Sedflux, a basin filling model, to model sediment flux into Lake Ohau. Using measured climate parameters from the last 60 years, we model water and sediment discharge into the lake and the distribution of sediment through the lake basin. Using a simple conceptual model of the lake dynamics, we produce a series of simulations to examine sediment accumulation at different positions across the lake basin. We then compare these modelled accumulation records to short cores from a number of locations within the lake basin. +
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Landlab is python software framework for the creation of surface dynamics and process models. It provides grid structures, stable and intercompatible process components, and utilities for data input, output and visualization. Here we present two new types of components within the Landlab framework: the FlowAccumulator and the FlowDirectors. These components have been designed to implement one of the basic functions of surface dynamics modeling, the routing and accumulation of water over a surface. These components split up the functionality of the previously implemented FlowRouter component in order to make it easier for the addition of new algorithms for flow direction to Landlab. As part of these components, we include a new algorithm for efficient flow accumulation when flow is routed to multiple neighboring nodes.
Routing of water over a surface can be split into two steps: direction and accumulation. Before outlining these steps, it is useful to state the terminology used to describe the grid. In Landlab the physical processes operate on a model grid which stores information about spatial location and properties that may vary in space (e.g. soil thickness, surface water discharge). The model grid is a dual plane graph in which quantities such as topographic elevation are defined at node points. Between neighboring node points are lines called links on which water, sediment, or other quantities can flow.
To route flow over the surface, flow directions at a given node must first be assigned to indicate which, if any, of the neighboring nodes will receive any flow that arrives in that node. This is typically done using the relative elevations of a node’s neighbors. Previously Landlab supported the steepest descent (or D4) algorithm for both rectilinear and non-rectilinear grids and the D8 algorithms for rectilinear grids. As part of the presented improvement, Landlab includes the Multiple Flow Direction and D infinity algorithms. Each algorithm for directing flow is its own component, but all share core functionality of the FlowDirector class. This shared functionality includes attributes necessary for interacting with other Landlab components, including the FlowAccumulator. This design permits easy addition of new flow direction algorithms while maintaining interoperability with other Landlab components.
Once flow directions have been assigned, surface water discharge and drainage area can be calculated through flow accumulation. This functionality is provided by the FlowAccumulator component which is compatible with all FlowDirector components. Depending on the algorithm chosen, flow accumulation can be computationally inefficient, scaling at a rate greater than O(N). We present a new algorithm for accumulating flow for in the case where flow is directed to more than one receiver that scales with the number of links that flow is directed over.
Landscape evolution is driven by tectonic processes that build up topography and erosional processes that work to tear down that topography and move material out of the landscape. Climate or more specifically water accumulated in rivers as discharge, is an important driver of these erosional processes. Despite its influence in shaping landscapes, there remains much to be learned concerning the relationships between climate, topography and discharge variability in forcing erosion. One reason for this is that climate itself, as well as the relationship between climate and erosion, is difficult to measure. Climate data typically consists of temporally averaged modern measurements of rainfall, which often miss important variability; or spatially inconsistent discharge data from stream gauges. Further, fluvial incision occurs during flood events that overcome a threshold for erosion, the frequency of exceeding this threshold is influenced by both discharge magnitude and variability. In this work we employ a numerical modeling approach to build upon previous research concerning the importance of discharge variability in driving erosion. We couple a landscape evolution model, Landlab, with a high-resolution atmospheric model, WRF (Weather Research and Forecasting), in order to generate high resolution discharge data. In these experiments, we run the WRF model over two artificial topographies with low (100m) and high (4km) elevations and extract discharge data from five drainage basins across the study domain at 10 to 40S. With this experimental setup we are able to analyze how discharge distributions change with topography and across several climate regimes. We find that the presence of high topography results in higher mean discharge across the five domains, particularly at 10, 20, and 30S. Additionally, discharge variability is greatest at 20 and 30S and less variable at 0, 10, and 40S for both the low- and high-elevation experiments. We then use these discharge distributions to drive a 1D river incision model to explore the relationship between discharge variability and erosion between high and low elevation domains and among different climate regimes, particularly in the presence of erosion thresholds. With this modeling approach we aim to gain insight into the influence of discharge variability and erosion thresholds on the relationships between topography, climate and erosion.
Landscape evolution models (LEMs) are a virtual representation of geomorphic processes as observed in the field or in experimental settings. LEMs offer the flexibility to evaluate a range of interactions between surface processes at timescales which cannot be observed. Notwithstanding the added value of LEMs in unravelling the tectonic-climate-erosion enigma at geological timescales, the use of LEMs to explain real-world earth surface processes remains challenging. For a LEM to be representative for a specific area, field data should be used to calibrate and validate the simulated processes. Notwithstanding the continuously growing database on erosion rates at different spatial and temporal scales, the number of datasets and the area they cover is inversely correlated with the timescale considered. Although more data is thus available at shorter timescales, including them into LEMs is not straightforward as short-term observations are known to reflect the stochasticity of earth surface processes.
In this contribution, we focus on the role landslides, a stochastic hillslope processes in steep mountainous mostly not included in long term LEMs, but strongly reflected in short term field data. We first integrate the formation of landslides and the transport of the thereby generated sediments in a previously developed LEM (TTLEM). Landslide initiation is implemented as a stochastic process depending on a landslide failure index whereas landslide size depends on the slope stability calculated using the Cullman index. The updated model (TTLEM_Sed) is thereafter applied to the New Zealand-Alps where long term erosion measurements and landslide inventories allow to calibrate model parameters. Landslide inventories are traditionally analyzed using statistical relationships between slope stability and conditioning factors such as distance to rivers and distance to active faults. However, the use of conditioning factors in landslide hazard maps is based on empirical observations and lacks physical grounding. Indeed, hillslopes closer to rivers should automatically become more prone to landslides as rivers incise and undercut hillslope foots. The integration of landslides in a LEM now allows to simulate this dynamic interplay over different timescales. By varying the simulated timescale over which the model is run, we identify critical timescales at which a-priori imposed statistical relations between landscape characteristics and landslide occurrence are no longer required and represented by the internal dynamics of the evolving landscape.
With TTLEM_Sed, we present an open-source model tool that allows to simulate landslides and sediment propagation. A modelling approach to study landslides is different from classical landslide hazard mapping approaches as it allows to simulate landscapes over longer timescales therefore allowing to identify physical drivers of landslide formation and landslide initiation. Moreover, the explicit integration of landslides in TTLEM_Sed potentially allows for the integration of widely available short-term field data in future model applications.
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Landscape evolution models use mass transport rules to simulate the development of topography over timescales too long for humans to observe. The ability of models to reproduce various attributes of real landscapes must be tested against natural systems in which driving forces, boundary conditions, and timescales of landscape evolution can be well constrained over millennia. We test and calibrate a landscape evolution model by comparing it with a well-constrained natural experiment using a formal inversion method to obtain best-fitting parameter values.
Our case study is the Dragon's Back Pressure Ridge, a region of elevated topography parallel to the south central San Andreas Fault that serves as a natural laboratory for studying how the timing and spatial distribution of uplift affects topography. We apply an optimization procedure to identify the parameter ranges and combinations that best account for the observed topography. Direct-search inversion models can be used to convert observations from such natural systems into inferences of the processes that governed their
formation through the use of repeat forward modeling. Simple inversion techniques have been used before in landscape evolution modeling, but these are imprecise and computationally expensive. We present the application of a more efficient inversion technique, the Neighborhood Algorithm (NA), to optimize the search for the model parameters values that are most consistent with the formation of the Dragon's Back Pressure Ridge through repeat forward modeling using CHILD.
Inversion techniques require the comparison of model results with direct observations to evaluate misfit. For our target landscape, this is done through a series of topographic metrics that include hypsometry, slope-area curves, and channel concavity. NA uses an initial Monte Carlo simulation for which misfits have been calculated to guide a second iteration of forward models. At each iteration, NA uses n-dimensional Voronoi cells to explore the parameter space and find the zones of best-fit, from which it selects new parameter values for the forward models. As it proceeds, the algorithm concentrates sampling around the cells with the best-fit models. The resulting distribution of forward models and misfits in multi-parameter space can then be analyzed to obtain probability density distributions for each parameter.
Preliminary results suggest that, when combined with robust algorithms for the calculation of the misfit, NA quickly centers the parameter search around values that capture the key features of the observed topography. The ability of NA to provide probability distributions for parameter values gives an indication of uncertainty in each, and can be used to guide field measurements for model testing. This application of advanced inversion techniques for landscape evolution modeling is a significant step towards the use of more formal mathematical methods in geomorphology that are already applied by other disciplines in the geosciences.
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Landscape evolution studies typically simulate long-term soil development by varying production rates as a function of the local thickness of soil. Whether this function monotonically decreases with increasing soil depth remains an active source of debate. In modest relief landscapes, the presence of isolated bedrock outcrops called tors are often used to argue for the so-called ‘humped’ soil production function, which hypothesizes that there is an optimal, non-zero soil depth for soil production. Furthermore, in many steep landscapes, the fraction of bedrock exposed at the surface can be very high where cliffs bands form in response to high erosion rates. Yet, numerical models of hillslope evolution struggle to reproduce the continuous transition from fully soil-mantled to bedrock-dominated hillsides within a single framework. To address this, we present a new Agent-Based Model (ABM) of forest dynamics and soil production that is coupled to continuum-based models of hillslope sediment transport (i.e., linear and nonlinear creep). In this model, tors and bedrock cliffs are emergent features that manifest even when maximum possible soil production rates occur at zero soil depth. The intermittency of seed germination and sapling recruitment on bare bedrock surfaces combined with the rapid downslope transport of newly developed soil facilitates the persistence of exposed bedrock. By linking soil development to plant functional type, this work also shows how soil depths and bedrock exposure patterns that evolve over millennia may be mechanistically linked to forest properties that stabilize over shorter timescales. To illustrate model behavior, we use plant parameters inspired by Pinus ponderosa, the dominant species observed at low to intermediate montane forests in the Colorado Front Range. Using this as a baseline, we then test model sensitivity to a variety of tree functional parameters including seed fecundity, seed dispersal distance, maximum rooting depth, and maximum individual lifespans. Our work is the first to couple a NetLogo ABM to Python-based Landlab via the pyNetLogo library. As such, this use-case serves as a template for other landscape evolution studies targeting feedbacks among biological agents, substrate properties, and sediment transport.
Landscape morphology is the fingerprint of natural processes and human activities and is commonly represented by Digital Elevation Models (DEMs). The spatial structure of a landscape encodes essential information, making it fundamental for analyzing and understanding their responses to various influencing factors. In this study, we utilize the hierarchical relations of topographic depressions—features once dismissed as artifacts or errors in DEMs—to characterize landscape structure. We extract the hierarchical relationships using the Depression Hierarchy algorithm applied to the DEMs.
In this study, we extract depression hierarchies from the SRTM DEM at 30-meter resolution for the continental United States, initially representing them as binary trees. To enhance real-world applicability, we convert these binary trees into general trees. In the depression hierarchy general tree, the top-level depressions are the depressions traditionally defined by DEM filling algorithms, while the bottom-level depressions generally are the small and independent pits in the DEM. We extract the top three and bottom three depression levels and analyze the relationship between depression parameters, such as median depression volume, and various environmental variables, including land use, lithology, soil type, climate classification, glacier zones, aridity index, and mean annual precipitation at a HUC-8 watershed scale.
We use ridge regression to analyze the correlation between median depression volumes and environmental variables. The strongest correlation occurs at the bottom hierarchical level (leaf depression). The three variables most strongly correlated with depression volume are land use, climate classification, and aridity index. The R² values range from 0.30 to 0.45. When all variables, which include land use, lithology, soil type, climate classification, glacier zones, aridity index, and mean annual precipitation, are used in the ridge regression, the model has an R² of approximately 0.6. These results highlight the significance of depression structures in shaping the landscape and reveal the connections between depressions and both natural processes and human activities.
Landscapes in a transient state can be described as areas that are undergoing topographical and geomorphological adjustments. In tectonically active areas, it is straightforward to interpret and reconstruct topography using transient geomorphic signals based on stream power theory. However, areas without tectonic activity are also rife with topographic transience but lack a clear trigger. Landscapes with heterogeneous lithological substrates present geomorphic signals of an ongoing transient state even when their geological context implies tectonic and climatic quiescence. Here, I propose to investigate the transient landscape response to the exhumation of heterogenous lithology. The main objective of this project is to quantify the influence of lithology on landscape evolution processes in the absence of tectonic activity. I hypothesize that divide asymmetries are created by the differential incision power each basin has at the lithologic transition. The differences are a function of the drainage area and the distance crossed over the resistant rocks. Our work is based on a LandLab simulation in which the synthetic DEM has a rock in its lower (southern) portion that is more resistant to erosion. The boundary conditions were configured so that only the southern part was open to the river system, which resulted in drainages flowing from north to south. The drainage basins was then analyzed from the same base level, controlled by the exhumation of the most resistant rock. Preliminary results indicate that basins with a smaller drainage area are subject to shrinkage processes after the exhumation of resistant rock in their outlet (base level). While the basins that had the largest drainage area gained area via river capture and divide migration, expanding their total area over time and thus shrinking the adjacent basins. +
Landslides are hazardous phenomena affecting mountainous regions worldwide. Our objective is to develop a Warning System for rainfall-induced shallow landslides in Switzerland, where such a dedicated tool is currently missing. Initially, we focused on empirical rainfall thresholds for landslide triggering based on long-term precipitation data and historical landslide inventories. The results showed that, although precipitation is the main triggering factor, event magnitude (intensity) by itself is not sufficient to explain the occurrence of many landslides. To improve the performance, the antecedent soil moisture prior to the rainfall event has to be taken into account, which we explore using a distributed hydrological model. The overall aim is to understand and quantify (a) the geological and hydrologic conditions critical for landslide initiation and (b) the optimal resolutions for hydrological modelling geared towards landslide prediction.
In fact, while slope stability assessment with a geotechnical model requires high resolution topography, the optimal spatial resolution for the computationally expensive hydrological modeling component remains unknown. To address this question, we conducted numerical experiments for a synthetic digital elevation model (DEM) used to simulate a simplified valley. The DEM is an inclined V-shaped domain and simulations are carried out with the coupled hydrologic-land surface model, ParFlow-CLM, in combination with the infinite slope stability model. We tested different slopes, valley convergence angles, soil layering, and permeability contrasts to assess the effect of spatial resolution on the estimation of antecedent soil moisture and the corresponding Factor of Safety.
These numerical experiments will help inform comparative simulations for real catchments in Switzerland and Colorado, as we explore the potential of using topographic methods for downscaling of the estimated antecedent soil wetness from coarser to fine scales. In particular, we will explore whether or not the soil-topographic index can provide a viable alternative to running the hydrological model at the very high spatial resolutions needed for the geotechnical model.
Landslides are key agents of sediment production and transport. Ongoing efforts to map and simulate landslides continuously improve our knowledge of landslide mechanisms. However, understanding sediment dynamics following landslide events is equally crucial for developing hazard mitigation strategies. An outstanding research challenge is to better constrain the dynamic feedbacks between landslides and fluvial processes. Fluvial processes simultaneously (i) act as conveyor belts evacuating landslide-derived sediment and (ii) lower the hillslope’s base level triggering further landsliding. Landslides in turn can choke river channels with sediment, thereby critically altering fluvial responses to external tectonic or climatic perturbations.
Here, we present HYLANDS, a hybrid landscape evolution model, which is designed to numerically simulate both landslide activity and sediment dynamics following mass failure. The hybrid nature of the model is in its capacity to simulate both erosion and deposition at any place in the landscape. This is achieved by coupling the existing SPACE (Stream Power with Alluvium Conservation and Entrainment) model for channel incision with a new module simulating rapid, stochastic mass wasting (landsliding).
In this contribution, we first illustrate the functionality of HYLANDS to capture river dynamics ranging from detachment-limited to transport-limited configurations. Subsequently, we apply the model to a portion of the Namch-Barwa massive in Eastern Tibet and compare simulated and observed landslide magnitude-frequency and area-volume scaling relationships. Finally, we illustrate the relevance of explicitly simulating stochastic landsliding and sediment dynamics over longer timescales on landscape evolution in general and river dynamics in particular under varying climatologic and tectonic configurations.
With HYLANDS we provide a hybrid tool to understand both the long and short-term coupling between stochastic hillslope processes, river incision and source-to-sink sediment dynamics. We further highlight the unique potential of bridging those timescales to generate better assessments of both on-site and downstream landslide risks.
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Landslides are often assumed to exhibit self-similarity in their failure geometry, and thus a linear scaling between slip depth and rupture length. This assumption has important implications for the prediction of large landslide volumes and for the estimation of erosion budgets by mass-wasting. Nevertheless, some field data indicate a break from self-similarity and imply that, in some circumstances, landslide depth may scale non-linearly with length. Here we test the simple scaling hypothesis by numerical experiment. Modeling is performed with SNAC (StGermaiN Analysis of Continua), a 3D community code originally designed to model viscoelastoplastic deformation on a crustal scale and newly adapted to treating hillslope failure. SNAC employs a parallelized, Lagrangian, explicit finite-difference scheme and dynamic relaxation to solve static, quasi-static and steady-state problems. Landslide rupture is treated as emergent shear localization under strain-weakening Mohr-Coulomb plasticity. We model only the initial slip and early motion of a landslide; granular flow during the runout phase is not considered. A set of 2+1D simulations of failures spanning lengths of 50–400m suffice to vindicate cross-sectional self-similarity—absent any dominant depth scale or trend in the variation of cohesion—and a depth-length ratio of 11–15% is recorded. An interesting by-product of the choice of experimental geometry is some unanticipated complexity in the evolution of the slip plane. Failure initiates at the toe, propagates upslope, and asymptotically parallels the planar upper boundary. However, a connected failure surface is only achieved once a secondary rupture has propagated downwards into this slip plane from the upper breakaway zone. The broader outcome of our numerical experiments is a demonstration of how 3D continuum modeling of soil and rock-slope failure, and the study of their rich behavior, is now feasible using non-commercial code on supercomputing platforms.
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Landslides are responsible for mobilizing enormous volumes of sediment from hillslopes into channels. At short timescales, these landslide deposits can change channel geometries and impact a system’s ability to efficiently export sediment. Mountain channels must mobilize this sediment before they can incise into their bedrock. There is evidence that mass failures like landsliding set an upper limit to relief and thus shape topography. However, few studies have investigated how landslides impact the landscape- and geologic-scale shape of landscapes. We aim to use topographic metrics to characterize how varying degrees of landsliding at geologic timescales shape landscapes. To address the long-term interplay between landslides, sediment dynamics, and the topographic evolution of Earth’s surface, we use landscape volution modeling which incorporates landslide triggering combined with river bedrock erosion, gravel transport that includes abrasion of both sediment load and bedrock, and dynamic channel adjustment consistent with that of near-threshold alluvial channels. We find that the inclusion of sediment attrition is necessary to reduce the flux of gravel and is consistent with inferences from field studies. We compare our model results to landscapes with constrained landslide histories among a spectrum of tectonic and climatic environments to assess our model’s ability to accurately capture realistic landscape features. This comparison enables us to explore the impact of future, rapid environmental climate change on landslide occurrence and risk, especially the influence of sediment supply on the cascade of downstream hazards +
Landslides represent a significant natural hazard that often cascade off earthquakes and other major disaster events, further impacting landscapes and human infrastructure globally. Accurate modelling of landslide-prone areas is crucial for risk assessment and mitigation strategies, especially in seismically active regions. This study introduces a novel simulation component designed to predict the spatial distribution of seismically-triggered landslides.
Seamlessly integrating within the broader Landlab modelling framework, the new component leverages high-resolution topographic data and incorporates key factors such as slope stability, soil thickness, and various hydrological conditions to predict locations that are particularly sensitive to failure in the event of an earthquake, mapping their probable extents.
A key aspect of this component is the incorporation of the Newmark method, a classical mechanical model used in seismic landslide hazard analysis. Proposed by Newmark in 1965, it models a landslide as a rigid block sliding on an inclined plane. It calculates the critical acceleration needed to overcome the friction of the sliding surface, allowing the block to move when seismic intensity exceeds the slope’s stability.
The model is currently being tested and validated against a detailed landslide inventory from central Nepal, with further plans to validate it against additional inventories from Papua New Guinea and New Zealand. Preliminary results demonstrate the component’s capability to accurately replicate observed shallow landslide patterns, and its potential for application in real-time hazard forecasting.
Here, we present the methodological framework, the challenges encountered during development, and the validation process. Additionally, we will showcase case studies highlighting the component’s practical applications, especially in seismically active regions, and discuss future enhancements to improve its predictive accuracy and computational efficiency.
Large blocks of rock are common in steep landscapes developed in the presence of a resistant lithology. However, the influence of blocks on landscape evolution is not well known. We developed a hybrid discrete-continuum coupled numerical model of hillslope and channel evolution in the presence of blocks (BlockLab). The model consists of a horizontal resistant layer of rock overlying softer, more erodible rock. A channel reach, driven by an external base level forcing, incises through the middle of the domain and provides the base level for the hillslopes. The hillslope model uses a continuum approach to treat depth-dependent production and transport of soil. Retreat of the resistant layer, however, is treated discretely. A relief threshold determines release of discrete blocks from the resistant layer onto the hillslope, where each model cell is either filled with blocks or contains no blocks. These blocks are allowed to weather at a constant rate, and are tracked as they move downslope according to a relief threshold related to their diameter. Once blocks enter the channel, they influence channel evolution by covering the bed and reducing available bed shear stress. A force balance on in-channel blocks determines whether they can move downstream. Blocks in the channel are reduced in size by abrasion according to the shear stresses exerted on them. The presence of blocks affects channel incision rates, in turn influencing evolution of the hillslopes. This is the first model to account for the role of blocks in channel hillslope evolution feedbacks (CHEFs), allowing us to better model the evolution of real landscapes. +
Large sediment pulses deposited in river channels can alter channel morphology and amplify downstream flood hazards. In the Suiattle River of Washington State, abrasion controls the downstream impact of sediment supply from Glacier Peak, a stratovolcano that regularly supplies the channel with large sediment pulses. This phenomenon is evident by the persistence of strong volcanic grains on the streambed and the rapid downstream loss of weak volcanic grains to fine sediment. Although cobbles and boulders dominate pulses in the channel, the Suiattle has an unusually high supply of fine sediment and contributes to fine sediment impacts downstream. Despite the evidence that the abrasion of coarse sediment during downstream transport drastically impacts the balance of fine and coarse sediment in the channel and new studies on the variability of abrasion, no work – in the Suiattle or elsewhere – has been done to determine the importance of abrasion on sediment pulse evolution in a highly abrasion-prone setting. Here, we test the extent to which abrasion acts as a control on sediment pulse transport from large mass wasting deposits of heterogeneous sediment characteristics. We employ the use of the Network Sediment Transporter, a 1-D river morphodynamics and Lagrangian modeling component in Landlab, to explore channel response to a large sediment pulse in the Suiattle River basin, tracking bed elevation and grain size changes and compare model results of variable sediment abrasion scenarios. We simulate abrasion as the mass of the grain loss per distance traveled and the scenarios include: no abrasion, a distribution of Schmidt Hammer Rock Strength (SHRS) measurements (a proxy for tumbler-derived abrasion rate), and a doubling of the SHRS proxy to account for the underprediction of the tumbler and a lack of in-place abrasion. We drive the model with a 2-year exceedance flow from discharge data from a distributed hydrology model to account for the complicated rain/snow hydrology of this mountainous region. Our initial results indicate negligible variances in bed elevation solely attributable to abrasion. Instead, our primary findings highlight the importance of the canyon reaches in modulating the coherence of the pulse as it transports downstream. Before the canyon reaches, a coherent wave of bed elevation change is apparent in all model runs, with modest differences between abrasion scenarios. Downstream of this canyon reach, this coherence dissipates.
Large-scale Earth system and land surface models often lack an adequate representation of subgrid-scale processes in permafrost landscapes. Small-scale processes such as thermokarst formation might, however, considerably impact the energy and carbon budgets in way which is not resolved within large-scale models. Since a spatially high-resolved simulation of such processes is not feasible, novel techniques for up-scaling subgrid processes are demanded.
Within this work a one-dimensional model of the ground thermal regime of land surfaces, CryoGrid 3, is employed to conceptually represent small-scale features of permafrost landscapes, particularly those related to thermokarst. For example, the model has been shown to adequately describe the degradation of permafrost underneath waterbodies in a warming climate. Using tiling approaches such point-wise realizations can be up-scaled in a statistical way in order to represent larger land surface units.
The model development is closely linked to field campaigns to the Lena River Delta in Siberia which offers very diverse land surface features such as polygonal tundra and thermos-erosional valleys. These features are related to the region’s diverse soil stratigraphies, in particular the occurrence of ice-rich ground. Combining field measurements with modelling ultimately allows an improvement in the qualitative and quantitative understanding of the typical geomorphological processes in permafrost landscapes and their representation in large-scale land surface models. +
Large-scale flow-routing algorithms efficiently route water to the ocean, neglecting both inland basins that may be able to form lakes and changing groundwater storage. We add these elements of reality in a simplified and computationally-efficient way, combining groundwater and surface-water routing to simulate changing groundwater levels, surface-water flow pathways, and lake locations and extents through time. The groundwater component is based upon a linear-diffusive model for an unconfined aquifer developed by Reinfelder et al (2013), and surface water is routed through a simple downslope-flow algorithm that differs from most flow-routing algorithms in that it takes into account the elevation of the water surface, and not just the land surface. Our model requires as inputs topography, climatic data (P-ET and winter temperature), and an approximation of hydraulic conductivity based on topographic slope and mapped soils. The model outputs grids of depth to water table and thickness of surface water; the latter depicts any lakes that would form under the topographic and climatic conditions. The model can be run to equilibrium, or, if a starting depth to water table input is provided, for any user-selected length of time. Such solutions are transient only with respect to groundwater movement: surface-water flow is significantly faster, so it is always run to equilibrium. The model allows infiltration when surface water flows across cells that are not fully saturated in the groundwater, and it allows exfiltration and the formation of groundwater-fed lakes where convergent groundwater flow raises the water table above the land surface.<br>We show sample results from this model on a test area. Future work using this model will include global runs since the Last Glacial Maximum, with ground truthing possible using past lake shoreline data. Changing depth to water table plus the surface water storage computed using this model allows computation of changing terrestrial water storage volume through time.
