Property:CSDMS meeting abstract presentation


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(Thanks to Adam LeWinter and Tim Stanton)</i></span><br><br>Rates of coastal cliff erosion are a function of the geometry and substrate of the coast; storm frequency, duration, magnitude, and wave field; and regional sediment sources. In the Arctic, the duration of sea ice-free conditions limits the time over which coastal erosion can occur, and sea water temperature modulates erosion rates where ice content of coastal bluffs is high. Predicting how coastal erosion rates in this environment will respond to future climate change requires that we first understand modern coastal erosion rates.<br><br>Arctic coastlines are responding rapidly to climate change. Remotely sensed observations of coastline position indicate that the mean annual erosion rate along a 60-km reach of Alaska’s Beaufort Sea coast, characterized by high ice content and small grain size, doubled from 7 m yr-1 for the period 1955-1979 to 14 m yr-1 for 2002-2007. Over the last 30 years the duration of the open water season expanded from ∼45 days to ∼95 days, increasing exposure of permafrost bluffs to seawater by a factor of 2.5. Time-lapse photography indicates that coastal erosion in this environment is a halting process: most significant erosion occurs during storm events in which local water level is elevated by surge, during which instantaneous submarine erosion rates can reach 1-2 m/day. In contrast, at times of low water, or when sea ice is present, erosion rates are negligible.<br><br>We employ a 1D coastal cross-section numerical model of the erosion of ice-rich permafrost bluffs to explore the sensitivity of the system to environmental drivers. Our model captures the geometry and style of coastal erosion observed near Drew Point, Alaska, including insertion of a melt-notch, topple of ice-wedge-bounded blocks, and subsequent degradation of these blocks. Using consistent rules, we test our model against the temporal pattern of coastal erosion over two periods: the recent past (~30 years), and a short (~2 week) period in summer 2010. Environmental conditions used to drive model runs for the summer of 2010 include ground-based measurements of meteorological conditions (air temperature, wind speed, wind direction) and coastal waters (water level, wave field, water temperature), supplemented by high temporal frequency (4 frames/hour) time-lapse photography of the coast. Reconstruction of the 30-year coastal erosion history is accomplished by assembling published observations and records of meteorology and sea ice conditions, including both ground and satellite-based records, to construct histories of coastline position and environmental conditions. We model wind-driven water level set-up, the local wave field, and water temperature, and find a good match against the short-term erosion record. We then evaluate which environmental drivers are most significant in controlling the rates of coastal erosion, and which melt-erosion rule best captures the coastal history, with a series of sensitivity analyses. The understanding gained from these analyses provides a foundation for evaluating how continuing climate change may influence future coastal erosion rates in the Arctic.  
* Project Team 1: '''Exploring the effects of rainstorm sequences on a river hydrograph''', Brooke Hunter presenting (Brooke Hunter, University of Oregon, Celia Trunz, University of Arkansas, Lisa Luna, University of Potsdam, Tianyue Qu, University of Pittsburgh and Yuval Shmilovitz, The Hebrew University of Jerusalem ). * Project Team 2: '''Coupling grids with different geometries and scales: an example from fluvial geomorphology''', Rachel Bosch presenting (Rachel Bosch, University of Cincinnati, Shelby Ahrendt, University of Washington, Francois Clapuyt, Université Catholique de Louvain, Eric Barefoot, Rice University, Mohit Tunwal, Penn State University, Vinicius Perin, North Carolina State University, Edwin Saavedra Cifuentes, Northwestern University, Hima Hassenruck-Gudipati, University of Texas Austin and Josie Arcuri, Indiana University). * Project Team 3: '''Lagrangian particle transport through a tidal estuary''', Rachel Allen presenting (Rachel Allen, UC Berkeley, Ningjie Hu, Duke University, Jayaram Hariharan, University of Texas, Aleja Geiger-Ortiz, Colby College and Collin Roland, University of Wisconsin). * Project Team 4: '''Using Landlab to Model Tectonic Activities in a Landscape Evolution Model''', Gustav Pallisgaard-Olesen presenting (Gustav Pallisgaard-Olesen, Aarhus University, Xiaoni Hu, Penn State University, Eyal Mardar, Colorado State University, Liang Xue, Bowling Green State University, and Chris Sheehan, University of Cincinnati). * Project Team 5: '''Land geomorphology evolution over a continuous permafrost region by applying Ku-model and hillslope diffusion model''', Zhenming Wu presenting( Zhenming Wu, University of Reeding and Fien De Doncker, University of Lausanne).   +
11:00AM '''Introductions''' '''11:05AM Project Team 1: "Simulating Shoreline Change Using Coupled CoastSat and Coastline Evolution Model (CEM)"''', Ahmed Elghandour, TU Delft, Benton Franklin, UNC, Conner Lester, Duke U, Megan Gillen, MIT/WHOI, Meredith Leung, Oregon State U & Samuel Zapp, LSU. Sandy shorelines are areas of dynamic geomorphic change, evolving on timescales ranging from hours to centuries. As part of the CSDMS ESPIn workshop, this educational lab was designed to allow users to observe firsthand the long-term change of a sandy coast of their choosing and explore the processes driving that change. The CEM was developed by Ashton et al. (2001) as an exploratory model that uses wave climate characteristics to model the evolution of an idealized coastline. In this educational lab, we couple CoastSat (a python tool that extracts shoreline geometry from satellite imagery (Vos et al., 2019)) to the CEM by initializing the model with observed shorelines from anywhere in the world. The CEM is then further driven by an average wave climate derived from local buoy data. This allows users to visualize the evolution of any sandy beach in the world through time. Through an introductory-level coding exercise, users will learn how to extract complex datasets, run a geomorphic model, and explore the impact of different wave climates on a beach they care about. '''11:15AM Project Team 2: "Including wildfires in a landscape evolution model"''', Kevin Pierce, UBC, Laurent Roberge, Tulane U Nishani Moragoda, U Alabama. Wildfires modify sediment inputs to streams by removing vegetation and encouraging overland flow. Unfortunately our ability to calculate sediment delivery from wildfires remains limited. Here, we present a stochastic wildfire component we recently developed for Landlab. This work provides a new computational method to relate stream sediment yields to the frequency and magnitude of wildfires. '''11:25AM Project Team 3: "Landscape-Scale Modeling across a variable-slip fault "''', Emery Anderson-Merritt, U Mass, Tamara Aranguiz, UW, Katrina Gelwick, ETH Zurich, Francesco Pavano, Lehigh U, & Josh Wolpert, U Toronto. The accommodation of deformation along a strike-slip fault can result in oblique kinematics featuring along-strike gradients in horizontal and vertical components of movement. While strike-slip fault models often simplify factors such as channel sedimentation, erosion processes and channel geometry, complex rock uplift fields related to oblique faulting may significantly impact the dynamics of a drainage system. With the objective of representing these along-strike kinematic variations commonly observed in strike-slip fault settings, we modify an existing Landlab component for lateral faulting (Reitmann et al., 2019) to incorporate spatially variable rock uplift. Our simulations demonstrate landscape evolution in an oblique faulting setting, highlighting the complicated response of a landscape’s drainage network and other geomarkers. '''11:35AM Project Team 4: "Paleoclimate and Elevation Data Used to Implement the Frost Cracking Window Concept”''', Risa Madoff, U North Dakota, Jacob Hirschberg, Swiss Federal Research Inst, Allie Balter LDEO/Columbia U. Frost cracking is a key weathering process in cold environments (e.g., Hales & Roering). Concepts from previous work on frost cracking (Anderson, 1998) provide foundations for understanding regional controls on landscape evolution. Recent research applying transient climate records and the frost-cracking model to estimate weathering rates (Marshall et al., 2021) represent ways that computational approaches are being adopted in the community. To bring a frost-cracking model into the CSDMS framework, we combined elevation-scaled PMIP6 paleoclimate data with a soil thermal profile model extant in the CSDMS repository (Tucker, 2020) to estimate frost-cracking intensity at a landscape scale. Our frost-cracking model is hosted in an EKT Jupyter notebook for instructional and exploratory applications of the thermal diffusion equation and the relationship between temperature and landscape development. In the future, our model could be implemented to compare modeled frost-cracking intensities with contemporary geomorphology in regions with differing climate histories. '''11:45AM Project Team 5: "Make storms, make erosion: How do storm intensity, duration, and frequency influence river channel incision."''', Angel Monsalve, U Idaho, Sam Anderson, Tulane U, Safiya Alpheus, Penn State U, Muriel Bruckner, U Exeter, Mariel Nelson, UT, Austin, Grace Guryan, UT, Austin. Erosion in the river bed is usually associated with a representative scale of stream power or shear stress of a given flow discharge. However, on a catchment scale, assuming a constant, steady-state flow of water in channels may not be adequate to represent the erosion process because of the temporal and spatial variability in rainfall. We coupled three different landLab components (OveralndFlow, DetachmentLimitedErosion, and SpatialPrecipitationDistribution) to create a more realistic representation of the topography evolution at a basin-scale and analyze the influence of storm intensity, duration, and frequency on channel incision. '''11:55AM Project Team 6: "Simulation of sediment pulses in Landlab NetworkSedimentTransporter (NST) component"''', Se Jong Cho, USGS, Muneer Ahammad, Virginia Tech, Marius Huber, U de Lorraine, Mel Guirro, Durham U. We synthetically introduce sediment pulses to simulate erosive conditions, which may be caused by fire or landslide occurrences in the landscape, and sediment yield across river network using the Landlab NetworkSedimentTransporter (NST) component. The goal of the project is to couple existing landlab models with external drivers of sediment sources and other input conditions that drive sediment transport. '''12:05-12:15PM Team 7: "Simulating Craters on Planetary Surfaces"''', Emily Bamber, UT Austin, Gaia Stucky de Quay, Harvard Impact cratering has been and still is the main geomorphic process on many planetary bodies, and is therefore key to understanding the evolution of planetary surfaces and their habitability. There are existing numerical models of planetary surface evolution that include cratering, but they are written in Fortran. As part of the CSDMS ESPIn 2021 summer workshop, we used the concepts for simulating crater shape and frequency on the surface from Howard (2007), and wrote a python code to simulate cratering, specifically on Mars. This code is freely available on GitHub, and currently utilises the LandLab model grid, which means our model integrates easily with the numerous landscape evolution modules that already exist as part of LandLab. An educational lab detailing the approach to simulating craters has also been produced and is available on the CSDMS website.  
<i>Background</i><br>When it comes to building a general, efficient, surface process code, there are a couple of significant challenges that stand in our way. One is to address the interesting operators that appear in the mathematical formulation that are not commonly encountered in computational mechanics. The other is to cater for the many different formulations that have been put forward in the literature as no single, universal set of equations has been agreed upon by the community.<br><br><i>Computational Approach</i><br>We view Quagmire as a community toolbox and acknowledge that this means there is no one best way to formulate any of the landscape evolution models. We instead provide a collection of useful operators and examples of how to assemble various kinds of models. We do assume that:<br><ul><li>the surface is a single-valued height field described by the coordinates on a two-dimensional plane</li><li>the vertical evolution can be described by the time-derivative of the height field</li><li>the horizontal evolution can be described by an externally imposed velocity field</li><li>the formulation can be expressed through (non-linear) operators on the two dimensional plane</li><li>any sub-grid parameterisation (e.g. of stream bed geometry) is expressible at the grid scale</li><li>a parallel algorithm is desirable</li></ul>We don't make any assumptions about:<br><ul><li>the nature of the mesh: triangulation or a regular array of 'pixels'</li><li>the parallel decomposition (except that it is assumed to be general)</li><li>the specific erosion / deposition / transport model</li></ul>Quagmire is a collection of python objects that represent parallel vector and matrix operations on meshes and provide a number of useful surface-process operators in matrix-vector form. The implementation is through PETSc, numpy, and scipy. Quagmire is open source and a work in progress.<br><br><i>Mathematical Approach</i><br>Matrix-vector multiplication is the duct tape of computational science: fast, versatile, ubiquitous. Any problem that can be formulated in terms of basic linear algebra operations can usually be rendered into an abstract form that opens up multiple avenues to solve the resulting matrix equations and it is often possible to make extensive use of existing fast, parallel software libraries. Quagmire provides parallel, matrix-based operators on regular Cartesian grid and unstructured triangulations for local operations such as gradient evaluation, diffusion, smoothing but also for non-local, graph-based operations that include catchment identification, upstream summation, and flood-filling. <br>The advantage of the formulation is in the degree of abstraction that is introduced, separating the details of the discrete implementation from the solution phase. The matrix-based approach also makes it trivial to introduce concepts such as multiple-pathways in graph traversal / summation operations without altering the structure of the solution phase in any way. Although we have not yet done so, there are obvious future possibilities in developing implicit, non-linear solvers to further improve computational performance and to place the model equations in an inverse modelling framework.  
A comprehensive understanding of hydrologic processes affecting streamflow is required to effectively manage water resources to meet present and future human and environmental needs. The National Hydrologic Model (NHM), developed by the U.S. Geological Survey, can address these needs with an approach supporting coordinated, comprehensive, and consistent hydrologic modeling at multiple scales for the conterminous United States. The NHM fills knowledge gaps in ungaged areas, providing nationally consistent, locally informed, stakeholder relevant results. In this presentation, we will introduce the NHM and a publicly available Dockerized version that is currently providing daily operational results of water availability and stream temperature. We finish with a quick demonstration of a new experimental version of PRMS, the NHMs underlying hydrologic model, available through the CSDMS Python Modeling Toolkit (pymt).  +
A presentation from Phaedra and Greg, that was presented at the Modeling Collaboratory for Subduction Research Coordination Network Webinar Series, that features conversations between the leaders of successful interdisciplinary collaborations (see also  +
A recent trend in the Earth Sciences is the adaptation of so-called “Digital Twins”. In Europe multi-million and even multi-billion projects are initiated for example, the Digital Twin of the Ocean and the Digital Twin Earth. But also many smaller digital-twin projects are popping up in the fields of city management, tunnels, hydraulic structures, waterways and coastal management. But what are Digital Twins really? Why are they now trending? What makes a Digital Twin different from a serious game, a numerical model or a simulator? In this session we will look at examples of digital twins, we will compare them to more traditional platforms and together define our expectations on future digital twins.  +