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'''Derek Nueharth''' - "Evolution of divergent and strike-slip boundaries in response to surface processes"
Plate tectonics describes the movement of rigid plates at the surface of the Earth as well as their complex deformation at three types of plate boundaries: 1) divergent boundaries such as rift zones and mid-ocean ridges, 2) strike-slip boundaries where plates grind past each other, such as the San Andreas Fault, and 3) convergent boundaries that form large mountain ranges like the Andes. The generally narrow deformation zones that bound the plates exhibit complex strain patterns that evolve through time. During this evolution, plate boundary deformation is driven by tectonic forces arising from Earth’s deep interior and from within the lithosphere, but also by surface processes, which erode topographic highs and deposit the resulting sediment into regions of low elevation. Through the combination of these factors, the surface of the Earth evolves in a highly dynamic way with several feedback mechanisms. At divergent boundaries, for example, tensional stresses thin the lithosphere, forcing uplift and subsequent erosion of rift flanks, which creates a sediment source. Meanwhile, the rift center subsides and becomes a topographic low where sediments accumulate. This mass transfer from foot-to hanging wall plays an important role during rifting, as it prolongs the activity of individual normal faults. When rifting continues, continents are eventually split apart, exhuming Earth’s mantle and creating new oceanic crust. Because of the complex interplay between deep tectonic forces that shape plate boundaries and mass redistribution at the Earth’s surface, it is vital to understand feedbacks between the two domains and how they shape our planet.
Here, we use numerical models to provide insight on how surface processes influence tectonics at divergent and strike-slip boundaries through two studies. The first study takes a detailed look at the evolution of rift systems using two-dimensional models. Specifically, we extract faults from a range of rift models and correlate them through time to examine how fault networks evolve in space and time. By implementing a two-way coupling between the geodynamic code ASPECT and landscape evolution code FastScape, we investigate how the fault network and rift evolution are influenced by the system’s erosional efficiency, which represents many factors like lithology or climate. The second study uses the two-way numerical coupling between tectonics and landscape evolution to investigate how a strike-slip boundary responds to large sediment loads, and whether this is sufficient to form an entirely new type of flexural strike-slip basin.
'''Danghan Xie''' - "Responses of mangrove forests to sea-level rise and human interventions: a bio-morphodynamic modelling study"
Co-Authors - Christian Schwarz2,3, Maarten G. Kleinhans4 and Barend van Maanen5<br>
2Hydraulics and Geotechnics, Department of Civil Engineering, KU Leuven, Belgium <br>
3Department of Earth and Environmental Sciences, KU Leuven, Belgium <br>
4Department of Physical Geography, Utrecht University, Utrecht, the Netherlands<br>
5Department of Geography, University of Exeter, Exeter, UK <br>
Corresponding author: Danghan Xie (danghan@bu.edu) <br>
Mangroves preserve valuable coastal resources and services along tropical and subtropical shorelines. However, ongoing and future sea-level rise (SLR) is threatening mangrove habitats by increasing coastal flooding. Changing sediment availability, the development of coastal structures (such as barriers), and coastal restoration strategies (such as mangrove removal) not only constrain the living space of mangrove forests but also affect coastal landscape evolution. Due to limitations in studying various temporal and spatial scales in the field under SLR and human interventions, insights thus far remain inconclusive. Results of bio-morphodynamic model predictions can fill this gap by accounting for interactions between vegetation, hydrodynamic forces, and sediment transport.
Here, we present a numerical modeling approach to studying bio-morphodynamic feedbacks within mangrove forests through a coupled model technique using Delft3d and Matlab. This approach takes into account (1) multiple colonization restrictions that control not only the initial mangrove colonization but also the subsequent response to SLR, (2) the possibility of coastal progradation and seaward mangrove expansion despite SLR under high sediment supply, (3) modulation of tidal currents based on vegetation presence and coastal profile evolution which, in turn, affect mangrove growth and even species distributions, and (4) profile reconfiguration under SLR which may contribute to the infilling of new accommodation space.
Our model results display both spatial and temporal variations in sediment delivery across mangrove forests, leading to species replacements arising from landward sediment starvation and prolonged inundation. The strength of bio-morphodynamic feedbacks depends on variations in mangrove root density, which further steers the inundation-accretion decoupling and, as a result, mangrove distribution. Moreover, an extended analysis studying mangrove behaviors is conducted under varying coastal conditions, including varying tidal range, wave action, and sediment supply. The results indicate that mangroves in micro-tidal systems are most vulnerable, even if sediment availability is ample. Ultimately, coastal restoration strategies like mangrove removal aiming to reduce local mud might not be achieved due to sediment redistribution post mangrove removal, which could enhance coastal muddification.
Further reading:
* Xie, D., Schwarz, C., Brückner, M. Z., Kleinhans, M. G., Urrego, D. H., Zhou, Z., & Van Maanen, B. (2020). Mangrove diversity loss under sea-level rise triggered by bio-morphodynamic feedbacks and anthropogenic pressures. Environmental Research Letters, 15(11), 114033. https://doi.org/10.1088/1748-9326/abc122
* Xie, D., Schwarz, C., Kleinhans, M. G., Zhou, Z., & van Maanen, B. (2022). Implications of Coastal Conditions and Sea‐Level Rise on Mangrove Vulnerability: A Bio‐Morphodynamic Modeling
(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).
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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 https://www.sz4dmcs.org/webinars). +
A range of Earth surface processes may drive rapid ice sheet retreat in the future, contributing to equally rapid global sea level rise. Though the pace of discovering these new feedback processes has accelerated in the past decade, predictions of future evolution of ice sheets are still subject to considerable uncertainty, originating from unknown future carbon emissions, and poorly understood ice sheet processes. In this talk, I explain why sea level rise projections past the next few decades are so uncertain, and how we are developing new stochastic ice sheet modeling methods to reduce uncertainty in projections, and the limits of uncertainty reduction. I also discuss the ongoing debate over whether uncertainty is important to consider at all in developing sea level projections that are usable by coastal planners. +
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. +
A wide variety of hydrological models are used by hydrologists: some differ because they were designed for different applications, some because of personal preferences of the modeller. All of them share the property that, like most scientific research code, it is rather hard to get someone elses model to run. The recently launched eWaterCycle platform takes away the headache of working with each other's models. In eWaterCycle models are run in containers and communicate with the central (Jupyter based) runtime environment through BMI. In this way a user can be talking to a Fortran model from Python without having to know anything about Fortran.
Removing this headache allows hydrologists to easily run and couple each other's models facilitating science questions like the impact of model choice on results, or coupling different (regional, processes) models together with ease. In this talk I will highlight (and demonstrate) both the technology behind the eWaterCycle platform as well as the current and future research being done using the platform. +
ANUGA is an open source software package capable of simulating small-scale hydrological processes such as dam breaks, river flooding, storm surges and tsunamis. ANUGA is a Python-language model that solves the Shallow Water Wave Equation on an unstructured triangular grid and can simulate shock waves and rapidly changing flows. It was developed by the Australian National University and Geosciences Australia and has an active developer and user community.<br><br>The package supports discontinuous elevation, or ‘jumps’ in the bed profile between neighbouring cells. This has a number of benefits. Firstly it can preserve lake-at-rest type stationary states with wet-dry fronts. It can also simulate very shallow frictionally dominated flow down sloping topography, as typically occurs in direct-rainfall flood models. A further benefit of the discontinuous-elevation approach, when combined with an unstructured mesh, is that the model can sharply resolve rapid changes in the topography associated with e.g. narrow prismatic drainage channels, or buildings, without the computational expense of a very fine mesh. The boundaries between such features can be embedded in the mesh using break-lines, and the user can optionally specify that different elevation datasets are used to set the elevation within different parts of the mesh (e.g. often it is convenient to use a raster digital elevation model in terrestrial areas, and surveyed channel bed points in rivers). The discontinuous-elevation approach also supports a simple and computationally efficient treatment of river walls. These are arbitrarily narrow walls between cells, higher than the topography on either side, where the flow is controlled by a weir equation and optionally transitions back to the shallow water solution for sufficiently submerged flows. This allows modelling of levees or lateral weirs which are much finer than the mesh size.<br><br>This clinic will provide a hands-on introduction to hydrodynamic modeling using ANUGA. We will discuss the structure and capabilities of the model as we build and run increasingly complex simulations involving channels and river walls. No previous knowledge of Python is required. Example input files will be provided and participants will be able to explore the code and outputs at their own pace.
ANUGA is an open source software package capable of simulating small-scale hydrological processes such as dam breaks, river flooding, storm surges and tsunamis. Thanks to its modular structure, we’ve incorporated additional components to ANUGA that allow it to model suspended sediment transport and vegetation drag. ANUGA is a Python-language model that solves the Shallow Water Wave Equation on an unstructured triangular grid and can simulate shock waves and rapidly changing flows. It was developed by the Australian National University and Geosciences Australia and has an active developer and user community.<br><br>This clinic will provide a hands-on introduction to hydrodynamic modeling using ANUGA. We will discuss the structure and capabilities of the model as we build and run increasingly complex simulations. No previous knowledge of Python is required. Example input files will be provided and participants will be able to explore the code and outputs at their own pace. +
Accurately characterizing the spatial and temporal variability of water and energy fluxes in many hydrologic systems requires an integrated modeling approach that captures the interactions and feedbacks between groundwater, surface water, and land- surface processes. Increasing recognition that these interactions and feedbacks play an important role in system behavior has lead to exciting new developments in coupled surface-subsurface modeling, with coupled surface-subsurface modeling becoming an increasingly useful tool for describing many hydrologic systems.<br><br>This clinic will provide a brief background on the theory of coupled surface-subsurface modeling techniques and parallel applications, followed by examples and hands-on experience using ParFlow, an open-source, object-oriented, parallel watershed flow model. ParFlow includes fully-integrated overland flow; the ability to simulate complex topography, geology and heterogeneity; and coupled land-surface processes including the land-energy budget, biogeochemistry, and snow processes. ParFlow is multi-platform and runs with a common I/O structure from laptop to supercomputer. ParFlow is the result of a long, multi-institutional development history and is now a collaborative effort between CSM, LLNL, UniBonn, and UC Berkeley. Many different configurations related to common hydrologic problems will be discussed through example problems. +
Addressing society's water and energy challenges requires sustainable use of the Earth's critical zones and subsurface environment, as well as technological innovations in treatment and other engineered systems. Reactive transport models (RTMs) provide a powerful tool to inform engineering design and provide solutions for these critical challenges. In this keynote, I will showcase the flexibility and value of RTMs using real-world applications that focus on (1) assessing groundwater quality management with respect to nitrate under agricultural managed aquifer recharge, and (2) systematically investigating the physical, chemical and biological conditions that enhance CO2 drawdown rates in agricultural settings using enhanced weathering. The keynote will conclude with a discussion of the possibilities to advance the use of reactive transport models and future research opportunities therein. +
Agent-Based Modeling (ABM) or Individual-Based Modeling is a research method rapidly increasing in popularity -- particularly among social scientists and ecologists interested in using simulation techniques to better understand the emergence of interesting system-wide patterns from simple behaviors and interactions at the individual scale. ABM researchers frequently partner with other scientists on a wide variety of topics related to coupled natural and human systems. Human societies impact (and are impacted by) various earth systems across a wide range of spatial and temporal scales, and ABM is a very useful tool for better understanding the effect of individual and social decision-making on various surface processes. The clinic will focus on introducing the basic toolkit needed to understand and pursue ABM research, and consider how ABM work differs from other computational modeling approaches. The clinic: - Will explore examples of the kinds of research questions and topics suited to ABM methods. - Will (attempt to) define some key concepts relevant to ABM research, such as emergence, social networks, social dilemmas, and complex adaptive systems. - Will provide an introduction to ABM platforms, particularly focused on NetLogo. - Discuss approaches to verification, validation, and scale dependency in the ABM world. - Introduce the Pattern-Oriented Modeling approach to ABM. - Discuss issues with reporting ABM research (ODD specification, model publishing). - Brainstorm tips and tricks for working with social scientists on ABM research. +
Agent-Based Models (ABMs) can provide important insights into the nonlinear dynamics that emerge from the interactions of individual agents. While ABMs are commonly used in the social and ecological sciences, this rules-based modeling approach has not been widely adopted in the Surface Dynamics Modeling community. In this clinic, I will show how to build mixed models that utilize ABMs for some processes (e.g., forest dynamics and soil production) and numerical solutions to partial differential equations for other processes (e.g., hillside sediment transport). Specifically, I will introduce participants to pyNetLogo, a library that enables coupling between NetLogo ABMs and Python-based Landlab components. While active developers in either the NetLogo or Landlab communities will find this clinic useful, experience in both programming languages is not needed. +
Agent-based modeling (ABM) developed as a method to simulate systems that include a number of agents – farmers, households, governments as well as biological organisms – that make decisions and interact according to certain rules. In environmental modeling, ABM is one of the best ways to explicitly account for human behavior, and to quantify cumulative actions of various actors distributed over the spatial landscape. This clinic provides an introduction to ABM and covers such topics as:<ol><li>Modeling heterogeneous agents that vary in attributes and follow different decision-strategies</li><li>Going beyond rational optimization and accommodating bounded rationality</li><li>Designing/representing agents’ interactions and learning.</ol>The clinic provides hands-on examples using the open-source modeling environment NetLogo https://ccl.northwestern.edu/netlogo. While no prior knowledge of NetLogo is required, participants are welcome to explore its super user-friendly tutorial. The clinic concludes with highlighting the current trends in ABM such as its applications in climate change research, participatory modeling and its potential to link with other types of simulations. +
Agent-based modeling (ABM) is a powerful simulation tool with applications across disciplines. ABM has also emerged as a useful tool for capturing complex processes and interactions within socio-environmental systems. This workshop will offer a brief introduction to ABM for socio-environmental systems modeling including an overview of opportunities and challenges. Participants will be introduced to NetLogo, a popular programming language and modeling environment for ABM. In groups, participants will have the hands-on opportunity to program different decision-making methods in an existing model and observe how outcomes change. We will conclude with an opportunity for participants to raise questions or challenges they are experiencing with their own ABMs and receive feedback from the group. +
An abstract was not required for this meeting +
An overview of what the interagency Working Group stands for. +
An update of what CSDMS has accomplished so far. +
An update of what CSDMS has accomplished so far. +
An update on CoMSES. +
Answers to scientific questions often involve coupled systems that lie within separate fields of study. An example of this is flexural isostasy and surface mass transport. Erosion, deposition, and moving ice masses change loads on the Earth surface, which induce a flexural isostatic response. These isostatic deflections in turn change topography, which is a large control on surface processes. We couple a landscape evolution model (CHILD) and a flexural isostasy model (Flexure) within the CSDMS framework to understand interactions between these processes. We highlight a few scenarios in which this feedback is crucial for understanding what happens on the surface of the Earth: foredeeps around mountain belts, rivers at the margins of large ice sheets, and the "old age" of decaying mountain ranges. We also show how the response changes from simple analytical solutions for flexural isostasy to numerical solutions that allow us to explore spatial variability in lithospheric strength. This work places the spotlight on the kinds of advances that can be made when members of the broader Earth surface process community design their models to be coupleable, share them, and connect them under the unified framework developed by CSDMS. We encourage Earth surface scientists to unleash their creativity in constructing, sharing, and coupling their models to better learn how these building blocks make up the wonderfully complicated Earth surface system. +
Are you confused about the best way to make your models and data accessible, reusable, and citable by others? In this clinic we will give you tools, information, and some dedicated time to help make your models and data FAIR - findable, accessible, interoperable and reusable. Models in the CSDMS ecosystem are already well on their way to being more FAIR than models that are not. But here, you will learn more about developments, guidelines, and tools from recent gatherings of publishers, repository leaders, and information technology practitioners at recent FAIR Data meetings, and translate this information into steps you can take to make your scientific models and data FAIR. +
Are you interested in expanding the reach of your scientific data or models? One way of increasing the FAIRness of your digital resources (i.e., making them more findable, accessible, interoperable, and reproducible) is by annotating them with metadata about the scientific variables they describe. In this talk, we provide a simple introduction to the Scientific Variables Ontology (SVO) and show how, with only a small number of design patterns, it can be used to neatly unpack the definitions of even quite complex scientific variables and translate them into machine-readable form. +
Are you tired of hearing about the FAIR Principles? This clinic is for you then, because after you participate you’ll never need to attend another one!*
Good science depends on the careful and meticulous management and documentation of our research process. This includes our computational models, the datasets we use, the data transformation, analysis, and visualization scripts and workflows we build to evaluate and assess our models, and the assumptions and design decisions we make while writing our software.
Join us for a Carpentries-style interactive clinic with hands-on exercises where we will provide concrete guidance and examples for how to approach, conceptualize, and transform your computational models of earth systems into FAIR contributions to the scientific record whether they are greenfield projects or legacy code with a focus on existing, open infrastructure (GitHub / GitLab / Zenodo). We’ll also cover containerization (Docker, Apptainer) as a way to transparently document system and software dependencies for your models, and how it can be used to support execution on the Open Science Grid Consortium’s Open Science Pool fair-share access compute resources. Big parallel fun! https://osg-htc.org
∗ individual results may vary, this statement is provably false +
As agreed at earlier CSDMS forums, the major
impediment in using AI for modeling the deep-ocean
seafloor is a lack of training data, the data which guides the AI -
whichever set of algorithms is chosen. This clinic will expose participants to
globally-extensive datasets which are available through CSDMS.
It will debate the scientific questions of why certain data work well,
are appropriate to the processes, and are properly scaled.
Participants are encouraged to bring their own AI challenges to the clinic. +
As global population grows and infrastructure expands, the need to understand and predict processes
at and near the Earth’s surface—including water cycling, soil erosion, landsliding, flood
hazards, permafrost thaw, and coastal change—becomes increasingly acute. Progress in understanding
and predicting these systems requires an ongoing integration of data and numerical
models. Advances are currently hampered by technical barriers that inhibit finding, accessing,
and operating modeling software and related tools and data sets. To address these challenges, we present the CSDMS@HydroShare, a cloud-based platform for accessing and running models, developing model-data workflows, and sharing reproducible results.
CSDMS@HydroShare brings together cyberinfrastructure developed by two important community facilities: HydroShare (https://www.hydroshare.org/), which is an online collaboration environment for sharing data, models, and tools, and CSDMS Workbench (https://csdms.colorado.edu/wiki/Workbench), which is the integrated system of software tools, technologies, and standards for building, interfacing, and coupling models.
This workshop presents how to use CSDMS@HydroShare to discover, access, and operate the Python Modeling Tool (PyMT). PyMT is one of the tools from the CSDMS Workbench, which allows users to interactively run and couple numerical models contributed by the community. In PyMT, there are already model components for coastal & permafrost modeling, stratigraphic and subsidence modeling, and terrestrial landscape evolution modeling. It also includes data components to access and download hydrologic and soil datasets from remote servers to feed the model components as inputs.
This workshop aims to encourage the community to use existing or develop new model or data components under the PyMT modeling framework and share them through CSDMS@HydroShare to support reproducible research. This workshop includes hands-on exercises using tutorial Jupyter Notebooks and provides general steps for how to develop new components.
At a global scale, deltas significantly concentrate people by providing diverse ecosystem services and benefits for their populations. At the same time, deltas are also recognized as one of the most vulnerable coastal environments, due to a range of adverse drivers operating at multiple scales. These include global climate change and sea-level rise, catchment changes, deltaic-scale subsidence and land cover changes, such as rice to aquaculture. These drivers threaten deltas and their ecosystem services, which often provide livelihoods for the poorest communities in these regions. Responding to these issues presents a development challenge: how to develop deltaic areas in ways that are sustainable, and benefit all residents? In response to this broad question we have developed an integrated framework to analyze ecosystem services in deltas and their linkages to human well-being. The main study area is part of the world’s most populated delta, the Ganges-Brahmaputra-Meghna Delta within Bangladesh. The framework adopts a systemic perspective to represent the principal biophysical and socio-ecological components and their interaction. A range of methods are integrated within a quantitative framework, including biophysical and socio-economic modelling, as well as analysis of governance through scenario development. The approach is iterative, with learning both within the project team and with national policy-making stakeholders. The analysis allows the exploration of biophysical and social outcomes for the delta under different scenarios and policy choices. Some example results will be presented as well as some thoughts on the next steps. +
Bed material abrasion is a key control on the partitioning of basin scale sediment fluxes between coarse and fine material. While abrasion is traditionally treated as a simple exponential function of transport distance and a rock-specific abrasion coefficient, experimental studies have demonstrated greater complexity in the abrasion process: the rate of abrasion varies with clast angularity, transport rate, and grain size. Yet, few studies have attempted to assess the importance of these complexities in the field setting. Furthermore, existing approaches generally neglect the heterogeneity in size, abrasion potential, and clast density of the source sediment.
Combining detailed field measurements and new modeling approaches, we quantify abrasion in the Suiattle River, a basin in the North Cascades of Washington State dominated by a single coarse sediment source: large, recurrent debris flows from a tributary draining Glacier Peak stratovolcano. Rapid downstream strengthening of river bar sediment and a preferential loss of weak, low-density vesicular volcanic clasts relative to non-vesicular ones suggest that abrasion is extremely effective in this system. The standard exponential model for downstream abrasion fails to reproduce observed downstream patterns in lithology and clast strength in the Suiattle, even when accounting for the heterogeneity of source material strength and the underestimate of abrasion rates by tumbler experiments. Incorporating transport-dependent abrasion into our model largely resolves this failure. These findings hint at the importance of abrasion and sediment heterogeneity in the morphodynamics of sediment pulse transport in river networks. A new modeling tool will allow us to tackle these questions: the NetworkSedimentTransporter, a Landlab component to model Lagrangian bed material transport and channel bed evolution. This tool will allow for future work on the interplay of bed material abrasion and size selective transport at the basin scale.
While a simplified approach to characterizing abrasion is tempting, our work demonstrates that sediment heterogeneity and transport-dependent abrasion are important controls on the downstream fate of coarse sediment in fluvial systems.
Biostabilizing organisms, such as saltmarsh and microphytobenthos, can play a crucial role in shaping the morphology of estuaries and coasts by locally stabilizing the sediment. However, their impact on large-scale morphology, which highly depends on the feedback between spatio-temporal changes in their abundance and physical forcing, remains highly uncertain.
We studied the effect of seasonal growth and decay of biostabilizing organisms, in response to field calibrated physical forcings, on estuarine morphology over decadal timescales using a novel eco-morphodynamic model. The code includes temporal saltmarsh an microphytobenthos growth and aging as well as spatially varying vegetation fractions determined by mortality pressures. Growth representations are empirical and literature-based to avoid prior calibration.
Novel natural patterns emerged in this model revealing that observed density gradients in vegetation are defined by the life-stages that increase vegetation resilience with age. The model revealed that the formation of seasonal and long term mud layering is governed by a ratio of flow velocity and hydroperiod altered by saltmarsh and microphytobenthos differently, showing that the type of biostabilizer determines the conditions under which mud can settle and be preserved. The results show that eco-engineering effects define emerging saltmarsh patterns from a combination of a positive effect reducing flow velocities and a negative effect enhancing hydroperiod. Consequently, saltmarsh and mud patterns emerge from their bilateral interactions that hence strongly define morphological development. +
CSDMS 3.0 updates +
CSDMS Basic Model Interface (BMI) - When equipped with a Basic Model Interface, a model is given a common set of functions for configuring and running the model (as well as getting and setting its state). Models with BMIs can communicate with each other and be coupled in a modeling framework. The coupling of models from different authors in different disciplines may open new paths to scientific discovery. In this first of a set of webinars on the CSDMS BMI, we'll provide an overview of BMI and the functions that define it. This webinar is appropriate for new users of BMI, although experienced users may also find it useful.
'''Instructor:''' Mark Piper, Research Software Engineer, University of Colorado, Boulder
'''When:''' November 13th, 12PM Eastern Time +
CSDMS develops and maintains a suite of products and services with the goal of supporting research in the Earth and planetary surface processes community. This includes products such as Landlab, the Basic Model Interface, Data Components, the Model Repository, EKT Labs, and ESPIn. Examples of services include the Help Desk, Office Hours, Roadshows, RSEaaS, and EarthscapeHub. One problem, though, is that if the community doesn't know about these products and services, then they don't get used—and, like the Old Gods in Neil Gaiman's American Gods, they fade into obscurity. Let's break the cycle! Please join us for this webinar where we will present information about all of the products and services offered by CSDMS, and explain how they can help you accelerate your research. Attendees will leave with knowledge of what CSDMS can do for them, which they can bring back to their home institutions and apply to their research and share with their colleagues.
<br> +
CSDMS has developed a Web-based Modeling Tool – the WMT. WMT allows users to select models, to edit model parameters, and run the model on the CSDMS High-Performance Computing System. The web interface makes it straightforward to configure different model components and run a coupled model simulation. Users can monitor progress of simulations and download model output.<br><br> CSDMS has developed educational labs that use the WMT to teach quantitative concepts in geomorphology, hydrology, coastal evolution. These labs are intended to be used by Teaching assistants and Faculty alike. Descriptions of 4-hr hands-on labs have been developed for HydroTrend, Plume, Sedflux, CHILD, ERODE and ROMS-Lite. These labs include instructions for students to run the models and explore dominant parameters in sets of simulations. Learning objectives are split between topical concepts, on climate change and sediment transport amongst many others, and modeling strategies, modeling philosophy and critical assessment of model results.<br><br>In this clinic, we will provide an overview of the available models and labs, and their themes and active learning objectives. We will discuss the requirements and logistics of using the WMT in your classroom. We will run some simulations hands-on, and walk through one lab in more detail as a demonstration. Finally, the workshop intends to discuss future developments for undergraduate course use with the participants. +
CSDMS has developed a Web-based Modeling Tool – the WMT. WMT allows users to select models, to edit model parameters, and run the model on the CSDMS High-Performance Computing System. The web tool makes it straightforward to configure different model components and run a coupled model simulation. Users can monitor progress of simulations and download model output.<br><br>CSDMS has designed educational labs that use the WMT to teach quantitative concepts in geomorphology, hydrology, coastal evolution and coastal sediment transport. These labs are intended for use by Teaching assistants and Faculty alike. Descriptions of 2 to 4-hr hands-on labs have been developed for HydroTrend, Plume, Sedflux, CHILD, TOPOFLOW and ROMS-Lite. These labs include instructions for students to run the models and explore dominant parameters in sets of simulations. Learning objectives are split between topical concepts, on climate change and sediment transport amongst many others, and modeling strategies, modeling philosophy and critical assessment of model results.<br><br>In this clinic, we will provide an overview of the available models and labs, and their themes and active learning objectives. We will discuss the requirements and logistics of using the WMT in your classroom. We will run some simulations hands-on, and walk through one lab in more detail as a demonstration. Finally, the workshop intends to discuss future developments for earning assessment tools with the participants. +
CSDMS has developed the Basic Model Interface (BMI) to simplify the conversion of an existing model in C, C++, Fortran, Java, or Python into a reusable, plug-and-play component. By design, the BMI functions are straightforward to implement. However, in practice, the devil is in the details.<br><br>In this hands-on clinic, we will take a model -- in this case, an implementation of the two-dimensional heat equation in Python -- and together, we will write the BMI functions to transform it into a component. As we develop, we’ll unit test our component with nose, and we’ll explore how to use the component with a Jupyter Notebook. Optionally, we can set up a GitHub repository to store and to track changes to the code we write.<br><br>To get the most out of this clinic, come prepared to code! We have a lot to write in the time allotted. We recommend that clinic attendees have a laptop with the Anaconda Python distribution installed. We also request that you skim:<br><br>⤅ BMI description (https://csdms.colorado.edu/wiki/BMI_Description)<br>⤅ BMI documentation (http://bmi-forum.readthedocs.io/en/latest)<br>⤅ BMI GitHub repo(https://github.com/csdms/bmi-live)<br><br>before participating in the clinic. +
CSDMS’s newly released Python Modeling Tool (PyMT) is an open source python package that provides convenient tools for coupling of models that use the Basic Model Interface. Historically, earth-surface process models have often been complex and difficult to work with. To help improve this situation and make the discovery process more efficient, the CSDMS Python Modeling Tool (PyMT) provides an environment in which community-built numerical models and tools can be initialized and run directly from a Python command line or Jupyter notebook. To illustrate how PyMT works and the advantages it provides, we will present a demonstration of two coupled models. By simplifying the process of learning, operating, and coupling models, PyMT frees researchers to focus on exploring ideas, testing hypotheses, and comparing models with data. +
CSDMS’s newly released Python Modeling Tool (PyMT) is an open source Python package that provides convenient tools for coupling models that use the Basic Model Interface. Historically, earth-surface process models have often been complex and difficult to work with. To help improve this situation and make the discovery process more efficient, PyMT provides an environment in which community-built numerical models and tools can be initialized and run directly from a Python command line or a Jupyter Notebook. To illustrate how PyMT works and the advantages it provides, we will present a demonstration of two coupled models. By simplifying the process of learning, operating, and coupling models, PyMT frees researchers to focus on exploring ideas, testing hypotheses, and comparing models with data. Pre-registration required.<br><br>''See also: https://pymt.readthedocs.io/en/latest/'' +
Changing depth to water table and the associated stored water volume is a crucial component of the global hydrological cycle, with impacts on climate and sea level. However, long-term changes in global water-table distribution are not well understood. Coupled ground- and surface-water models are key to understanding the hydrologic evolution of post-glacial landscapes, the significance of terrestrial water storage, and the interrelationships between freshwater and climate. Here, I present the Water Table Model (WTM), which is capable of computing changes in water table elevation at large spatial scales and over long temporal scales. The WTM comprises groundwater and dynamic lake components to incorporate lakes into water-table elevation estimates. Sample results on both artificial and real-world topographies demonstrate the two-way coupling between dynamic surface-water and groundwater levels and flow. +
Cheniers are ridges consisting of coarse-grained sediments, resting on top of muddy sediment. Along these muddy coastlines, cheniers provide shelter against wave attack, mitigating erosion or even enhancing accretion. As such, cheniers play an important role in the dynamics of the entire coastal landscape. This research focused on cheniers along mangrove-mud coasts. Therefore, chenier dynamics needed to be understood at the temporal and spatial scales of the mangrove vegetation as well. We developed a hybrid modelling approach, combining the strengths of complex process-based modelling (Delft3D), which allowed us to model the mixed-sediment dynamics at small temporal and spatial scales, with the strengths of a highly idealized profile model, providing low computational efforts for larger temporal and spatial scales. +
Climate and tectonics ultimately drive the physical and chemical surface processes that evolve landscape structure, including the connectivity of landscape portions that facilitate or impede movement of organismal populations. Connectivity controls population spatial distribution, drives speciation where populations spatially fragment, and increases extinction susceptibility of species where its habitat shrinks. Here I demonstrate the role that landscape evolution models can have in exploring these process linkages in investigations of species diversification driven by climatic and tectonic forcings. The models were built with the tool, SpeciesEvolver that constructs lineages in response to environmental change at geologic, macroevolutionary, and landscape scales. I will also suggest how future studies can use landscape evolution models and tools such as SpeciesEvolver to pursue questions regarding the mechanisms by which lineages respond to the drivers and details of landscape evolution, and taxon-specific and region-specific interactions between biotas and their environments. +
Climate-induced disturbances are expected to increase in frequency and intensity and affect coastal wetland ecosystem mainly through altering its hydrology. Investigating how wetland hydrology responds to climate disturbances is an important first step to understand the ecological response of coastal wetlands to these disturbances. In this talk, I am going to introduce my research work on improving the understanding of how the water storage of coastal wetlands at North Carolina, Delaware Bay, and the entire southeast U.S. changes under climatic disturbances. In particular, I will address the uncertainties in estimating water flow through coastal wetlands by considering 1) the regional-scale hydrologic interaction between uplands, coastal wetlands, and the ocean and 2) the impact of coastal eco-geomorphologic change on the freshwater and saltwater interaction on coastal marshlands. +
Closing of the meeting +
Cloud computing is a powerful tool for both analyzing large datasets and running models. This clinic will provide an introduction to approaches for accessing and using cloud resources for research in the Geosciences. During the hands-on portion of this clinic, participants will learn how to use Amazon Web Services (AWS) to open a terminal, analyze model output in python, and run a model, time permitting. This workshop assumes no experience with cloud computing. +
Coastal Risk is a flood and natural hazard risk assessment technology company. Our mission is to help individuals, businesses and governments in the US and around the world achieve resilience and sustainability.<br>In the past year, Coastal Risk’s Technology supported nearly $2 billion in US commercial real estate investment and development. Coastal Risk’s unique business model combines high-tech, flood, climate and natural hazards risk assessments and high-value, risk communication reports with personalized, resilience-accelerating advice for individuals, corporations and governments. Our risk modeling and reports help save lives and property in the US. In order to take our system around the world, however, we need higher resolution DEMs. The 30m resolution currently available is a big obstacle to going international. This is something that we would like to get from NASA. Also, we are interested in high-resolution, “before-and-after” satellite imagery of flooded areas to compare with our modeling and to help individuals, businesses and governments understand how to better defend against floods. +
Coastal communities facing shoreline erosion preserve their beaches both for recreation and for property protection. One approach is nourishment, the placement of externally-sourced sand to increase the beach’s width, forming an ephemeral protrusion that requires periodic re-nourishment. Nourishments add value to beachfront properties, thereby affecting re-nourishment choices for an individual community. However, the shoreline represents an alongshore-connected system, such that morphodynamics in one community are influenced by actions in neighboring communities. Prior research suggests coordinated nourishment decisions between neighbors were economically optimal, though many real-world communities have failed to coordinate, and the geomorphic consequences of which are unknown. Toward understanding this geomorphic-economic relationship, we develop a coupled model representing two neighboring communities and an adjacent non-managed shoreline. Within this framework, we examine scenarios where communities coordinate nourishment choices to maximize their joint net benefit versus scenarios where decision-making is uncoordinated such that communities aim to maximize their independent net benefits. We examine how community-scale property values affect choices produced by each management scheme and the economic importance of coordinating. The geo-economic model produces four behaviors based on nourishment frequency: seaward growth, hold the line, slow retreat, and full retreat. Under current conditions, coordination is strongly beneficial for wealth-asymmetric systems, where less wealthy communities acting alone risk nourishing more than necessary relative to their optimal frequency under coordination. For a future scenario, with increased material costs and background erosion due to sea-level rise, less wealthy communities might be unable to afford nourishing their beach independently and thus lose their beachfront properties. +
Coastal environments are complex because of the interplay between aeolian and nearshore processes. Waves, currents, tides, and winds drive significant short term (<weekly) changes to coastal landforms which augment longer term (> annual) geomorphic trends. Great strides have been made in recent years regarding our ability to model coastal geomorphic change in this range of societally relevant time scales. However, a great disparity exists in modeling coastal evolution because subaqueous and subaerial processes are typically assessed completely independent of one another. By neglecting the co-evolution of subtidal and supratidal regions within our current framework, we are precluded from fully capturing non-linear dynamics of these complex systems. This has implications for predicting coastal change during both fair weather and storm conditions, hindering our ability to answer important scientific questions related to coastal vulnerability and beach building.<br><br>Recognizing these historic limitations, here we present the outline for a coupled subaqueous (XBeach) and subaerial (Coastal Dune Model) morphodynamic modeling system that is in active development with the goal of exploring coastal co-evolution on daily to decadal timescales. Furthermore we present recently collected datasets of beach and dune morphology in the Pacific Northwest US that will be used to validate trends observed within the coupled model platform. +
Coastal flooding and related hazards have increasingly become one of the most impactful events as climate change continues to change the risk due to these events. Measuring the change in the risk of a particular flood level has therefore taken on a greater urgency, as historic measurements and statistics are no longer sufficient to measure the risk to coastal communities. Enabling our ability to compute these changes has become the focus as adaptation strategies due to the changing climate become increasingly critical. This talk will outline some of these challenges and ways we are attempting to address the problem in a multi-hazard aware way. +
Coastal morphological evolution is caused by a wide range of coupled cross-shore and alongshore sediment transport processes associated with short waves, infragravity waves, and wave-induced currents. However, the fundamental transport mechanisms occur within the thin bottom boundary layer and are dictated by turbulence-sediment interaction and inter-granular interactions. In the past decade, significant progresses have been made in modeling sediment transport using Eulerian-Eulerian or Eulerian-Lagrangian two-phase flow approach. However, most of these models are limited to one-dimensional-vertical (1DV) formulation, which is only applicable to Reynolds-averaged sheet flow condition. Consequently, complex processes such as instabilities of the transport layer, bedform dynamics and turbulence-resolving capability cannot be simulated. The main objective of my research study was to develop a multi-dimensional four-way coupled two-phase model for sediment transport that can be used for Reynolds-averaged modeling for large-scale applications or for turbulence-resolving simulations at small-scale. +
Coastal systems are an environmental sink for a wide range of materials of scientific interest, including sediments, nutrients, plastics, oils, seeds, and wood, to name only a few. Due to differences in material properties such as buoyancy, each of these materials are liable to have characteristic transport pathways which differ from the mean flow and each other, hydraulically “sorting” these materials in space. However, it remains difficult to quantify these differences in transport, due in part to the use of disparate models and approaches for each respective material. In this talk, I will advance a novel modeling framework for simulating the patterns of transport for a wide range of fluvially-transported materials using a single unified reduced-complexity approach, allowing us to compare and quantify differences in transport between materials. Using a hydrodynamic model coupled with the stochastic Lagrangian particle-routing model “dorado,” we are able to simulate at the process-level how local differences in material buoyancy lead to emergent changes in partitioning and nourishment in river deltaic systems. I will show some of the insights we have learned regarding the tendency for materials to be autogenically sorted in space, as well as progress we have made bridging between the process-level framework used in dorado and more physics-based approaches based on transport theory. +
Computer models help us explore the consequences of scientific hypotheses at a level of precision and quantification that is impossible for our unaided minds. The process of writing and debugging the necessary code is often time-consuming, however, and this cost can inhibit progress. The code-development barrier can be especially problematic when a field is rapidly unearthing new data and new ideas, as is presently the case in surface dynamics.<br/><br/>To help meet the need for rapid, flexible model development, we have written a prototype software framework for two-dimensional numerical modeling of planetary surface processes. The Landlab software can be used to develop new models from scratch, to create models from existing components, or a combination of the two. Landlab provides a gridding module that allows you to create and configure a model grid in just a few lines of code. Grids can be regular or unstructured, and can readily be used to implement staggered-grid numerical solutions to equations for various types of geophysical flow. The gridding module provides built-in functions for common numerical operations, such as calculating gradients and integrating fluxes around the perimeter of cells. Landlab is written in Python, a high-level language that enables rapid code development and takes advantage of a wealth of libraries for scientific computing and graphical output. Landlab also provides a framework for assembling new models from combinations of pre-built components.<br/><br/>In this clinic we introduce Landlab and its capabilities. We emphasize in particular its flexibility, and the speed with which new models can be developed under its framework. In particular, we will introduce the many tools available within Landlab that make development of new functionality and new descriptions of physical processes both easy and fast. Participants will finish the clinic with all the knowledge necessary to build, run and visualize 2D models of various types of earth surface systems using Landlab.
D-Claw is an extension of the software package GeoClaw (www.clawpack.org) for simulating flows of granular-fluid mixtures with evolving volume fractions. It was developed primarily for landslides, debris flows and related phenomena by incorporating principles of solid, fluid and soil mechanics. However, because the two-phase model accommodates variable phase concentrations, it can also be used to model fluid problems in the absence of solid content (the model equations reduce to the shallow water equations as the solid phase vanishes). We therefore use D-Claw to seamlessly simulate multifaceted problems that involve the interaction of granular-fluid mixtures and bodies of water. This includes a large number of cascading natural hazards, such as debris-avalanches and lahars that enter rivers and lakes, landslide-generated tsunamis, landslide dams and outburst floods that entrain debris, and debris-laden tsunami inundation. I will describe the basis of D-Claw's model equations and highlight some recent applications, including the 2015 Tyndall Glacier landslide and tsunami, potential lahars on Mt. Rainier that displace dammed reservoirs, and a hypothetical landslide-generated lake outburst flood near Sisters, Oregon. +
DES3D (Dynamic Earth Solver in Three Dimensions) is a flexible, open-source finite element solver that models momentum balance and heat transfer in elasto-visco-plastic material in the Lagrangian form using unstructured meshes. It provides a modeling platform for long-term tectonics as well as various problems in civil and geotechnical engineering. On top of the OpenMP multi-thread parallelism, DES3D has recently adopted CUDA for GPU computing. The CUDA-enabled version shows speedup of two to three orders of magnitude compared to the single-thread performance, making high-resolution 3D models affordable. This clinic will provide an introduction to DynEarthSol3D’s features and capabilities and hands-on tutorials to help beginners start using the code for simple tectonic scenarios. Impact of the two types of parallelization on performance will be demonstrated as well. +
Dakota (https://dakota.sandia.gov) is an open-source software toolkit, designed and developed at Sandia National Laboratories, that provides a library of iterative systems analysis methods, including sensitivity analysis, uncertainty quantification, optimization, and parameter estimation. Dakota can be used to answer questions such as:
* What are the important parameters in my model?
* How safe, robust, and reliable is my model?
* What parameter values best match my observational data?
Dakota has been installed on the CSDMS supercomputer, ''beach.colorado.edu'', and is available to all registered users. The full set of Dakota methods can be invoked from the command line on ''beach''; however, this requires detailed knowledge of Dakota, including how to set up a Dakota input file and how to pass parameters and responses between a model and Dakota. To make Dakota more accessible to the CSDMS community, a subset of its functionality has been configured to run through the CSDMS Web Modeling Tool (WMT; https://csdms.colorado.edu/wmt). WMT currently provides access to Dakota's vector, centered, and multidimensional parameter study methods.<br><br>In this clinic, we'll provide an overview of Dakota, then, through WMT, set up and perform a series of numerical experiments with Dakota on ''beach'', and evaluate the results.
Other material can be downloaded from: https://github.com/mdpiper/dakota-tutorial.<br> +
Dakota is a flexible toolkit with algorithms for parameter optimization, uncertainty quantification, parameter estimation, and sensitivity analysis. In this clinic we will work through examples of using Dakota to compare field observations with model output using methods of sensitivity analysis and parameter optimization. We will also examine how the choice of comparison metrics influences results. Methods will be presented in the context of the Landlab Earth-surface dynamics framework but are generalizable to other models. Participants who are not familiar with Landlab are encouraged (but not required) to sign up for the Landlab clinic, which will take place before this clinic.<br><br>Participants are encouraged to install both Landlab and Dakota on their computers prior to the clinic. Installation instructions for Landlab can be found at: http://landlab.github.io (select "Install" from the menu bar at the top of the page). Installation instructions for Dakota can be found at https://dakota.sandia.gov/content/install-dakota. +
Dakota is a flexible toolkit with algorithms for parameter optimization, uncertainty quantification, parameter estimation, and sensitivity analysis. In this clinic we will cover the basics of the Dakota framework, work through examples of using Dakota to compare field observations with model output using methods of sensitivity analysis and parameter optimization, and briefly cover the theoretical background of the Dakota methods used. If time permits, we will examine how the choice of comparison metrics influences results. Methods will be presented in the context of the Landlab Earth-surface dynamics framework but are generalizable to other models. Participants who are not familiar with Landlab are encouraged (but not required) to sign up for the Landlab clinic, which will take place before this clinic.<br>Participants do not need to install Landlab or Dakota prior to the clinic but will need to sign up for a Hydroshare account. https://www.hydroshare.org/sign-up/. <br>For those students interested in installing Landlab or Dakota: Installation instructions for Landlab can be found at: http://landlab.github.io (select "Install" from the menu bar at the top of the page). Installation instructions for Dakota can be found at https://dakota.sandia.gov/content/install-dakota. +
Dakota is an open-source toolkit with several types of algorithms, including sensitivity analysis (SA), uncertainty quantification (UQ), optimization, and parameter calibration. Dakota provides a flexible, extensible interface between computational simulation codes and iterative analysis methods such as UQ and SA methods. Dakota has been designed to run on high-performance computing platforms and handles a variety of parallelism. In this clinic, we will provide an overview of Dakota algorithms, specifically focusing on uncertainty quantification (including various types of sampling, reliability analysis, stochastic expansion, and epistemic methods), sensitivity analysis (including variance-based decomposition methods and design of experiments), and parameter calibration (including nonlinear least squares and Bayesian methods). The tutorial will provide an overview of the methods and discuss how to use them. In addition, we will briefly cover how to interface your simulation code to Dakota. +
Data component is a software tool that wraps an API for a data source with a Basic Model Interface (BMI). It is designed to provide a consistent way to access various types of datasets and subsets of them without needing to know the original data API. Each data component can also interact with numerical models that are wrapped in the pymt modeling framework. This webinar will introduce the data component concept with a demonstration of several examples for time series, raster, and multidimensional space-time data. +
Debris flows pose a substantial threat to downstream communities in mountainous regions across the world, and there is a continued need for methods to delineate hazard zones associated with debris-flow inundation. Here we present ProDF, a reduced-complexity debris-flow inundation model. We calibrated and tested ProDF against observed debris-flow inundation from eight study sites across the western United States. While the debris flows at these sites varied in initiation mechanism, volume, and flow characteristics, results show that ProDF is capable of accurately reproducing observed inundation in different settings and geographic areas. ProDF reproduced observed inundation while maintaining computational efficiency, suggesting the model may be applicable in rapid hazard assessment scenarios. +
Decision framing is a key, early step in any effective decision support engagement in which modelers aim to inform decision and policy making. In this clinic participants will work through and share the results of decision framing exercises for a variety of policy decisions. We will organize the exercise using the XLRM elicitation, commonly used in decision making under deep uncertainty (DMDU) stakeholder engagements. The XLRM framework is useful because it helps organize relevant factors into the components of a decision-centric analysis. The letters X, L, R, and M refer to four categories of factors important to RDM analysis: outcome measures (M) that reflect decision makers’ goals; policy levers (L) that decision makers use to pursue their goals; uncertainties (X) that may affect the connection between policy choices and outcomes; and relationships (R), often instantiated in mathematical simulation models, between uncertainties and levers and outcomes. +
Deep-learning emulators permit to reduce dramatically the computational times for solving physical models. Trained from a state-of-the-art high-order ice flow model, the Instructed Glacier Model (IGM, https://github.com/jouvetg/igm) is an easy-to-use python code based on the Tensorflow library that can simulate the 3D evolution of glaciers several orders of magnitude faster than the instructor model with minor loss of accuracy. Switching to Graphics Processing Unit (GPU) permits additional significant speed-ups, especially when modeling large-scale glacier networks and/or high spatial resolutions. Taking advantage of GPUs, IGM can also track a massive amount of particles moving within the ice flow, opening new perspectives for modeling debris transportation of any size (e.g., erratic boulders). Here I give an overview of IGM, illustrate its potential to simulate paleo and future glacier evolution in the Alps together with particle tracking applications, and do a quick live demo of the model. +
Delta morphology +
Deltas are highly sensitive to local human activities, land subsidence, regional water management, global sea-level rise, and climate extremes. In this talk, I’ll discuss a recently developed risk framework for estimating the sensitivity of deltas to relative sea level rise, and the expected impact on flood risk. We apply this framework to an integrated set of global environmental, geophysical, and social indicators over 48 major deltas to quantify how delta flood risk due to extreme events is changing over time. Although geophysical and relative sea-level rise derived risks are distributed across all levels of economic development, wealthy countries effectively limit their present-day threat by gross domestic product–enabled infrastructure and coastal defense investments. However, when investments do not address the long-term drivers of land subsidence and relative sea-level rise, overall risk can be very sensitive to changes in protective capability. For instance, we show how in an energy-constrained future scenario, such protections will probably prove to be unsustainable, raising relative risks by four to eight times in the Mississippi and Rhine deltas and by one-and-a-half to four times in the Chao Phraya and Yangtze deltas. This suggests that the current emphasis on short-term solutions on the world’s deltas will greatly constrain options for designing sustainable solutions in the long term. +
Developed barriers are tightly-coupled systems driven by feedbacks between natural processes and human decisions to maintain development. Coastal property markets are dynamically linked to the physical environment: large tax revenues and high-value infrastructure necessitate defensive coastal management through beach nourishment, dune development, overwash removal, and construction of hard structures. In turn, changes to environmental characteristics such as proximity to the beach, beach width, and the height of dunes influence coastal property values. In this talk I will use a new exploratory model framework – the CoAStal Community-lAnDscape Evolution (CASCADE) model – to explore the coupled evolution of coastal real estate markets and barrier landscapes. The framework couples two geomorphic models of barrier evolution (Barrier3D and BRIE) with an agent-based real estate model – the Coastal Home Ownership Model (CHOM). CHOM receives information about the coastal environment and acts on that information to cause change to the environment, including decisions about beach nourishment and dune construction and maintenance. Through this coupled model framework, I will show how the effects of dune and beach management strategies employed in the wake of extreme storms cascade through decades to alter the evolution of barriers, inadvertently inhibiting their resilience to sea level rise and storms, and ultimately unraveling coastal real estate markets. +
Developers of solvers for PDE-based models and other computationally intensive tasks are confronted with myriad complexity, from science requirements to algorithms and data structures to GPU programming models. We will share a fresh approach that has delivered order of magnitude speedups in computational mechanics workloads, minimizing incidental complexity while offering transparency and extensibility. In doing so, we'll examine the PETSc and libCEED libraries, validate performance models, and discuss sustainable architecture for community development. We'll also check out Enzyme, an LLVM-based automatic differentiation tool that can be used with legacy code and multi-language projects to provide adjoint (gradient) capabilities. +
Digital twins are increasingly important in many domains, including for understanding and managing the natural environment. Digital twins of the natural environment are fueled by the unprecedented amounts of environmental data now available from a variety of sources from remote sensing to potentially dense deployment of earth-based sensors. Because of this, data science techniques inevitably have a crucial role to play in making sense of this complex, highly heterogeneous data. This webinar will reflect on the role of data science in digital twins of the natural environment, with particular attention on how resultant data models can work alongside the rich legacy of process models that exist in this domain. We will seek to unpick the complex two-way relationship between data and process understanding. By focusing on the interactions, we will end up with a template for digital twins that incorporates a rich, highly dynamic learning process with the potential to handle the complexities and emergent behaviors of this important area. +
Does permafrost impart topographic signatures, and how does subsequent warming affect hillslope and channel form? Permafrost controls the depth to immobile soil, and tundra vegetation influences infiltration and erosion thresholds. I will use high-resolution maps of arctic landscapes to examine morphometric properties like hillslope length, curvature and drainage density as functions of climate and vegetation. I will then compare these data to existing models of climate-modulated sediment flux and channel incision in Landlab, exploring the effect of more nuanced representations of permafrost flux laws and hydrology. I will also compare modeled landscapes forced with Pleistocene-Holocene climate to mid-latitude landscape form. +
During a clinic session in the 2013 CSDMS annual meeting, the OpenFOAM®, an open source computational fluid dynamics (CFD) platform, was first introduced by Dr. Xiaofeng Liu (now at Penn State University) for modeling general earth surface dynamics. OpenFOAM® provides various libraries, solvers and toolboxes for solving various fluid physics via finite volume method. The objective of this clinic is to further discuss its recent development and applications to coastal sediment transport. The clinic will start with an overview of a range of coastal applications using OpenFOAM®. We will then focus on a recently released solver, SedFOAM, for modeling sand transport by using an Eulerian two-phase flow methodology. Specifically, we will focus on applying the model to study wave-driven sheet flows and the occurrence of momentary bed failure. The code can be downloaded via CSDMS code repository and participants will receive a hands-on training of the coding style, available numerical schemes in OpenFOAM®, computational domain setup, input/output and model result analysis. Knowledge of C++, object-oriented programming, and parallel computing is not required but will be helpful. +
During the clinic we'll introduce the new Delft3D Flexible Mesh modeling environment. We'll discuss the basic features and set up a simple 2D morphological model. The ongoing developments and the possibility to use BMI for runtime interaction will be presented as well.
The user interface runs on Windows, so make sure that you have a Windows computer or virtual machine available during the meeting. The user interface will be provided precompiled; the computational kernels you'll have to compile yourself. We'll provide instructions on how to compile the FORTRAN/C kernels before the clinic. +
Earth scientists face serious challenges when working with large datasets. Pangeo is a rapidly growing community initiative and open source software ecosystem for scalable geoscience using Python. Three of Pangeo’s core packages are 1) Jupyter, a web-based tool for interactive computing, 2) Xarray, a data-model and toolkit for working with N-dimensional labeled arrays, and 3) Dask, a flexible parallel computing library. When combined with distributed computing, these tools can help geoscientists perform interactive analysis on datasets up to petabytes in size. In this interactive tutorial we will demonstrate how to employ this platform using real science examples from hydrology, remote sensing, and oceanography. Participants will follow along using Jupyter notebooks to interact with Xarray and Dask running in Google Cloud Platform. +
Earth surface processes are modulated by fascinating interactions between climate, tectonics, and biota. These interactions are manifested over diverse temporal and spatial scales ranging from seconds to millions of years, and microns to thousands of kilometers, respectively. Investigations into Earth surface shaping by biota have gained growing attention over the last decades and are a research frontier. In this lecture, I present an integration of new observational and numerical modeling research on the influence of vegetation type and cover on the erosion of mountains. I do this through an investigation of millennial timescale catchment denudation rates measured along the extreme climate and ecologic gradient of the western margin of South America. +
Earthquakes are the most frequent source of classic tsunami waves. Other processes that generate tsunami waves include, landslides, volcanic eruption and meteorite impacts. Furthermore, atmospheric disturbances can also generate tsunami waves or at least tsunami-like waves, but we are just at the beginning of understanding their physics and frequency. Classic tsunami waves long waves with wavelength that are much longer than the water depth. For earthquake-generated tsunami waves that is true. However, landslides and meteorite impacts generate tsunami waves that are shorter which has a profound effect on the tsunami evolution, but no less dangerous.<br>Fortunately, tsunamis do not occur frequently enough in any given region to make meaningful prediction of the future tsunami hazard based only on recorded history. The geologic record has to be interrogated. The inversion of meaningful and quantitative data from the geologic record is the main goal of my research. However, there are problems with the geologic record. The most important problem is that we often have trouble to identify tsunami deposits. Second, it is very often difficult to separate the tsunami record from the storm record in regions where storms and tsunamis are competing agents of coastal change. Other problems are concerned with he completeness of the deposits, but also the fact that sedimentary environment before the tsunami hit most likely was eroded is no longer part of the record makes inversion especially tricky. In my research, I assume that the tsunami deposit is identified, but perhaps not complete and what we know about the pre-event conditions is limited.<br>My talk will cover how the geologic record is used to invert quantitative information about the causative process. We are going to look at grain sizes from sand to boulders and what we can learn from the transport of these very different grain sizes about tsunamis and their impacts along respective coastal areas. The models that are employed to invert flow characteristics from deposits are based on Monte-Carlo simulations to overcome the issue of not knowing the pre-tsunami conditions with great confidence. If time permits, we also see how sea-level change affects tsunami impact at the coast.
Earth’s surface is the living skin of our planet – it connects physical, chemical, & biological systems. Over geological time, this surface evolves with rivers fragmenting the landscape into environmentally diverse range of habitats. These rivers not only carve canyons & form valleys, but also serve as the main conveyors of sediment & nutrients from mountains to continental plains & oceans. Here we hypothesise that it is not just geodynamics or climate, but their interaction, which, by regulating topography and sedimentary flows, determines long-term evolution of biodiversity. As such, we propose that surface processes are a prime limiting factor of diversification of Life on Earth before any form of intrinsic biotic process.
To test this hypothesis, we use reconstructions of ancient climates & plate tectonics to simulate the evolution of landscape & sedimentary history over the entire Phanerozoic era, a period of 540 million years. We then compare these results with reconstructions of marine & continental biodiversity over geological times. Our findings suggest that biodiversity is strongly influenced by landscape dynamics, which at any given moment determine the carrying capacity of continental & oceanic domains, i.e., the maximum number of different species they can support at any given time.
In the oceans, diversity closely correlates with the sedimentary flow from the continents, providing the necessary nutrients for primary production. Episodes of mass extinctions in the oceans have occurred shortly after a significant decrease in sedimentary flow, suggesting that a nutrient deficit destabilizes biodiversity & makes it particularly vulnerable to catastrophic events.
On the continents, it took the gradual coverage of the surface with sedimentary basins for plants to develop & diversify, thanks to the development of more elaborate root systems. This slow expansion of terrestrial flora was further stimulated during tectonic episodes.
Ecological Network Analysis (ENA) enables quantitative study of ecosystem models by formulating system-wide organizational properties, such as how much nutrient cycling occurs within the system, or how essential a particular component is to the entire ecosystem function. EcoNet is a free online software for modeling, simulation and analysis of ecosystem network models, and compartmental flow-storage type models in general. It combines dynamic simulation with Ecological Network Analysis. EcoNet does not require an installation, and runs on any platform equipped with a standard browser. While it is designed to be easy to use, it does contain interesting features such as discrete and continuous stochastic solutions methods. +
Ecology is largely considered to have its foundations in physics, and indeed physics frames many of the constraints on ecosystem dynamics. Physics has its limitations, however, especially when dealing with strongly heterogeneous systems and with the absence of entities. Networks are convenient tools for dealing with heterogeneity and have a long history in ecology, however most research in networks is dedicated to uncovering the mechanisms that give rise to network types. Causality in complex heterogeneous systems deals more with configurations of processes than it does with objects moving according to laws. Phenomenological observation of ecosystems networks reveals regularities that the laws of physics are unequipped to determine. The ecosystem is not a machine, but rather a transaction between contingent organization and entropic disorder. +
Economic losses and casualties due to riverine flooding increased in past decades and are most likely to further increase due to global change. To plan effective mitigation and adaptation measures and since floods often affect large areas showing spatial correlation, several global flood models (GFMs) were developed. Yet, they are either based on hydrologic or on hydrodynamic model codes. This may lower the accuracy of inundation estimates as large-scale hydrologic models often lack advanced routing schemes, reducing timeliness of simulated discharge, while hydrodynamic models depend on observed discharge or synthesized flood waves, hampering the representation of intra-domain processes.<br>To overcome this, GLOFRIM was developed. Currently, it allows for coupling one global hydrologic model, producing discharge and runoff estimates, with two hydrodynamics which perform the routing of surface water. By employing the Basic Model Interface (BMI) concept, both online and spatially explicit coupling of the models is supported. This way the coupled models remained unaffected, facilitating the separate development, storage, and updating of the models and their schematizations. Additionally, the framework is developed with easy accessibility and extensibility in mind, which allows other models to be added without extensive re-structuring. <br>In this presentation, the main underlying concepts of GLOFRIM as well as its workflow will be outlined, and first results showing the benefit of model coupling will be discussed. Besides, current limitations and need for future improvements will be pointed out. Last, current developments in code development, applications, and integrations with other research fields will be presented and discussed. +
Ecosystems are in transition globally with critical societal consequences. Global warming, growing climatic extremes, land degradation, human-introduced herbivores, and climate-related disturbances (e.g., wildfires) drive rapid changes in ecosystem productivity and structure, with complex feedbacks in watershed hydrology, geomorphology, and biogeochemistry. There is a need to develop models that can represent ecosystem changes by incorporating the role of individual plant patches. We developed ecohydrologic components in Landlab that can be coupled to create models to simulate local soil moisture dynamics and plant dynamics with spatially-explicit cellular automaton plant establishment, mortality, fires, and grazing. In this talk, I will present a model developed to explore the interplay between ecosystem state, change in climate, resultant grass connectivity, fire frequency, and topography. A transition from a cool-wet climate to a warm-dry climate leads to shrub expansion due to drought-induced loss of grass connectivity. Shrubs dominate the ecosystem if dry conditions persist longer. The transition back to a tree or grass-dominated ecosystem from a shrub-dominated ecosystem can only happen when climate shifts from dry to wet. The importance of the length of dry or wet spells on ecosystem structure is highlighted. Aspect plays a critical role in providing topographical refugia for trees during dry periods and influences the rate of ecosystem transitions during climate change. +
Ecosystems present spatial patterns controlled by climate, topography, soils, plant interactions, and disturbances. Geomorphic transport processes mediated by the state of the ecosystem leave biotic imprints on erosion rates and topography. This talk will address the following questions at the watershed scale: What are emergent properties of biotic landscapes, and how do they form? How do biotic landscapes respond to perturbations in space and time? First, formation of patterns and ecologic rates of change to perturbations in semiarid ecosystems will be investigated using Landlab. Second, we will examine eco-geosphere interactions and outcomes using a landscape evolution model. The role of solar radiation on ecogeomorphic forms, and watershed ecogeomorphic response to climate change will be elaborated. Finally, reflecting on the findings of previous research, some future directions in numerical modeling for linking ecosphere and geosphere will be discussed. +
Environmental management decisions increasingly rely on quantitative integrated ecological models to forecast potential outcomes of management actions. These models are becoming increasingly complex through the integration of processes from multiple disciplines (e.g., linking physical process, engineering and ecological models). These integrated modeling suites are viewed by many decision makers as unnecessarily complex black boxes, which can lead to mistrust, misinterpretation and/or misapplication of model results. Numerical models have historically been developed without decision makers and stakeholders involved in model development, which further complicates communication as diverse project teams have differing levels of understanding of models and their uses. For example, explaining to a group of non-modelers how hydrodynamic model output was aggregated at ecologically-relevant scales can be difficult to explain to someone who was not exposed to that modeling decision. The mistrust of models and associated outputs can lead to poor decision-making, increase the risk of ineffective decisions and can lead to litigation over decisions. Improved integrated ecological model development practices are needed to increase transparency, include stakeholders and decision makers throughout the entire modeling process from conceptualization through application.
This clinic describes a suite of techniques, best practices, and tools for rapid developing applied integrated ecological models in conjunction with technical stakeholder audiences and agency practitioners. First, a workshop approach for applied ecosystem modeling problems is described that cultivates a foundational understanding of integrated ecological models through hands-on, interactive model development. In this workshop environment, interdisciplinary and interagency working groups co-develop models in real-time which demystifies technical issues and educates participants on the modeling process. Second, a Toolkit for interActive Modeling (TAM) is presented as a simple platform for rapidly developing index-based ecological models, which we have found useful for developing a strong modeling foundation for large, multidisciplinary teams involved in environmental decision making. Third, the EcoRest R package is described, which provides a library of functions for computing habitat suitability and decision support via cost-effectiveness and incremental cost analysis. Based on 10 workshops over the last 8 years, these techniques facilitated rapid, transparent development and application of integrated ecological models, informed non-technical stakeholders of the complexity facing decision-makers, created a sense of model ownership by participants, built trust among partners, and ultimately increased “buy-in” of eventual management decisions.
Established in 2005, GEO (http://www.earthobservations.org/) is a voluntary partnership of governments and organizations that envisions “a future wherein decisions and actions for the benefit of humankind are informed by coordinated, comprehensive and sustained Earth observations and information.” GEO Member governments include 96 nations and the European Commission, and 87 Participating Organizations comprised of international bodies with a mandate in Earth observations. Together, the GEO community is creating a Global Earth Observation System of Systems (GEOSS) that will link Earth observation resources world-wide across multiple Societal Benefit Areas - agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water and weather - and make those resources available for better informed decision-making. Through the GEOSS Common Infrastructure (GCI), GEOSS resources, including Earth observation data (satellite, airborne, in situ, models), information services, standards and best practices, can be searched, discovered and accessed by scientists, policy leaders, decision makers, and those who develop and provide information services across the entire spectrum of users.
The presentation will cover the GCI overall architecture and some possible future developments. +
Exchanges of sediment between marshes and estuaries affect coastal geomorphology, wetland stability and habitat, but can be difficult to predict due to the many processes that influence dynamics in these systems. This study uses a modeling approach to analyze how spatially variability in marsh-edge erosion, vegetation, and hydrodynamic conditions affect sediment fluxes between marshes and estuaries in Barnegat Bay, New Jersey. Specifically, the three-dimensional Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) numerical model was used. Model results showed that marsh-estuarine sediment fluxes varied spatially due to changes in wave thrust, currents, and sediment availability. +
Exploratory models that simulate landscape change incorporate only the most essential processes that are hypothesized to control a behavior of interest. These “rule-based” models have been used successfully to examine behaviors in natural landscapes over large spatial (many kms) and temporal scales (decades to millennia). In many geomorphic systems, the dynamics of developed landscapes differ significantly from natural landscapes. For example, humans can alter the physical landscape through the introduction of hard infrastructure and removal of vegetation. Humans can also modify the internal and external forces that naturally change landscapes, including flows of water, wind, and sediment as well as climatic factors. As with natural processes, in exploratory models human behavior must be parameterized. However, the level of detail to which human behavior can be reduced while still accurately reproducing feedbacks across the coupled human-natural landscape is a complex, user-based decision.
In this clinic, we will work in small groups and through a Jupyter Notebook to parameterize a new human behavior within a modular coastal barrier evolution model (Barrier3D, within the CASCADE modeling framework). The clinic will incorporate discussions and prompts about how to broadly identify important model “ingredients” and reduce model complexity, and will therefore be generalizable to other geomorphic landscapes. +
