Property:CSDMS meeting abstract presentation
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Numerical modeling is at the core of prediction in coastal settings. Observational data is used in tandem with models for a variety of modeling tasks, but the perhaps the coupling could be tighter? I will discuss a range of Machine Learning tools that co-workers and I have integrated with coastal morphodynamic models that allow for a tight coupling of models and data, and provide morphodynamic insight. +
Numerical models describe the world around us mathematically, allowing us to visualize changes to physical systems through both space and time. These models are essential tools for geoscientists, but writing your own model can be a daunting task.
In this clinic, we’ll develop an understanding of what numerical models are, and then we’ll delve into the math that functions as the basis for many models. Participants will learn how to apply basic conservation principles to developing equations that describe a physical system that changes through time. This workshop will expose participants to deriving differential equations, and using basic Python programming to visualize their solutions. Prior experience is not necessary. +
Numerical stratigraphic modelling of the impact of paleoclimate changes on earthscape evolution and sedimentary basin stratigraphy is of great value to better understand and predict the impact of global warming and increasingly frequent extreme events on the environment. To illustrate the contribution of stratigraphic modelling, we propose a modular model, ArcaDES (a.k.a. Dionisos), able to simulate geological processes in 3D on large scales of space and times (tens to hundreds of kilometres, and thousands to tens of millions of years).
ArcaDES is a 3D software written in C++ and implemented within the Arcane object-oriented high-performance computing platform co-developed by the CEA and IFPEN. This modular code includes three main components to handle hydrology, accommodation space and sediment transport. Taking into account precipitation, evaporation and soil infiltration capacity, the first component calculates steady-state runoff, surface and ground water flows, and water table elevation. The second component considers tectonic subsidence and uplift, flexure, sea level variations and sediment compaction to define the accommodation space. The third component deals with time-averaged physical laws describing erosion, transport by fluvial and marine currents, and deposition of sediments from fluvial to deep-marine systems to calculate sediment distribution and stratigraphic architecture.
This stratigraphic forward model is applied to two case studies: the Congo basin and the Alboran sea, to illustrate the impact of the last Holocene glaciations on the deep-sea fan of the Congo and the contouritic systems in the Alboran Sea. +
Observations in coastal environments show that seabed resuspension can impact water quality and biogeochemical dynamics by vertically mixing sediment and water, and by redistributing material that has been entrained into the water column. Yet, ocean models that incorporate both sediment transport and biogeochemical processes are rare. The scientific community frequently utilizes hydrodynamic-sediment transport numerical models, but hydrodynamic-biogeochemical models ignore or simplify sediment processes, and have not directly accounted for the effect of resuspension on oxygen and nutrient dynamics.<br><br>This presentation focuses on development and implementation of HydroBioSed, a coupled hydrodynamic-sediment transport-biogeochemistry model that was developed within the open-source Regional Ocean Modeling System (ROMS) framework. HydroBioSed can account for processes including advection, resuspension, diffusion within the seabed and at the sediment-water interface, organic matter remineralization, and oxidation of reduced chemical species. Implementation of the coupled HydroBioSed model for different locations, including the Rhone River subaqueous delta and the northern Gulf of Mexico, have helped to quantify the effects of both sediment transport and biogeochemical processes. Results indicate that resuspension-induced exposure of anoxic, ammonium-rich portions of the seabed to the more oxic, ammonium-poor water column can significantly affect seabed-water column fluxes of dissolved oxygen and nitrogen. Also, entrainment of seabed organic matter into the water column may significantly draw down oxygen concentrations in some environments. Ongoing work focuses on how resuspension and redistribution of organic matter and sediment may influence oxygen dynamics in the Chesapeake Bay. +
One of the challenges for modelers is to get their results into the hands of potential users. We do this by creating informative and relevant maps, charts, and indicators. Sometimes we try to go further. We want end-users to 'feel' the model, using techniques like haptic interactions, extended reality. We do this to help the user to get a better understanding (exploration, interaction) or to develop a shared concept (by socializing around the model), or to provide the user with an immersive experience (using photorealistic rendering).
Using the BMI interface, which we also use for model coupling, we have changed several models from passive to interactive. We integrated these interactive models into different environments, such as the recently developed Virtual River Game, the Coastal Sandbox. Here we present recent developments, technical considerations and the results of the user studies that helped shape our vision towards more effective scientific communication and interaction. +
One of the most intriguing issues in fine sediment transport, including turbidity currents, current-driven transport and wave-driven transport, is that the presence of sediments may significantly attenuate flow turbulence. Depending on the level of turbulence suppression, it may lead to the formation of lutocline (a sharp negative gradient of sediment concentration) which further encourages offshore-directed gravity flow; or it may cause catastrophic collapse of turbulence and sediment deposition. Through idealized 3D turbulence-resolving simulations of fine sediment (mud) transport in wave bottom boundary layer based on a pseudo-spectral scheme, our recent studies show that the transition of these flow modes can be caused by various degree of sediment-induced stable density stratification. This effort demonstrates the success of using a turbulence-resolving simulation tool to diagnose complex fine sediment transport processes. This talk further reports our recent development of this turbulence-resolving numerical model with a goal to provide a predictive tool for more realistic fine sediment transport applications.<br/><br/>Assuming a small Stokes number (St<0.3), which is appropriate for typical fine sediment, the Equilibrium approximation to the Eulerian two-phase flow equations is applied. The resulting simplified equations are solved with a high-accuracy hybrid spectral-compact finite difference scheme. The numerical approach extends the earlier pseudo-spectral model with a sixth-order compact finite difference scheme in the bed-normal direction. The compact finite difference scheme allows easy implementation of flow-dependent sediment properties and complex bottom boundary conditions. Hence, several new capabilities are included in the numerical simulation, such as rheological stress (enhance viscosity in high sediment concentration), hindered settling, erodible/depositional bottom boundary, and higher order inertia terms critical for fine sand fraction.<br/><br/>In the past decade, the role of wave bottom boundary layer in delivering fine sediment offshore via wave-supported gravity current (WSGC) has been well-recognized. We hypothesize that the generation, transport and termination of WSGC is directly associated with the flow modes discussed previously. In addition to the well-known Richardson number control (i.e., associated with sediment-induced density stratification), in this talk we will discuss how enhanced viscosity via rheological stress and high erodibility of the mud bed (e.g., low critical shear stress for unconsolidated mud bed) can trigger catastrophic collapse of turbulence and sediment deposition. The significance of bed erodibility in determining the resulting flow modes motivates future study regarding the effect of sand fraction on fine sediment transport via armoring.
OpenFoamÒ is an open-source computational fluid dynamic platform, built upon a finite-volume framework with Messaging Passing Interface (MPI). In the past decade, OpenFoamÒ has become increasingly popular among researchers who are interested in fluvial and coastal processes. In this clinic, recent progress in developing OpenFoamÒ for several coastal applications will be discussed. In particular, we will focus on three subjects: (1) wave-induced seabed dynamics (pore-pressure response), (2) stratified flow application, particularly laboratory scale river plume modeling, and (3) 3D large-eddy simulation of wave-breaking and suspended sediment transport processes.<br>In particular, hand-on exercise will be given for 3D large-eddy simulation of wave-breaking processes to illustrate several important insights on how to use OpenFoamÒ to carry out high quality large-eddy simulations. Some cautionary notes and limitations will also be discussed. +
Opening of the CSDMS 2023 annual meeting +
Opening of the CSDMS annual meeting +
Opening of the meeting +
Opening of the meeting +
Opening of the meeting +
Our extensive transdisciplinary efforts since 2010 in the northern Gulf of Mexico (Mississippi, Alabama, and the Florida panhandle) have resulted in an advanced capability to model and assess hydrodynamic and ecological impacts of climate change at the coastal land margin (visit http://agupubs.onlinelibrary.wiley.com/hub/issue/10.1002/(ISSN)2328-4277.GULFSEARISE1/). The concerted efforts of natural and social scientists as well as engineers have contributed to a paradigm shift that goes well beyond “bathtub” approaches. Potential deleterious effects to barrier islands, shorelines, dunes, marshes, etc., are now better understood. This is because the methodology enables assessment of not just eustatic sea level rise (SLR), but gets to the basis of projections of climate change and the associated impacts, i.e., carbon emission scenarios. The paradigm shift, input from coastal resource managers, and future expected conditions now provides a rationale to evaluate and quantify the ability of natural and nature-based feature (NNBF) approaches to mitigate the present and future effects of surge and nuisance flooding.<br>Over the majority of the 20th century, the largely linear rate of eustatic SLR was realized by thermal expansion of seawater as a function of a gradual increase in the average annual global temperature. Global satellite altimetry indicates that the rate of global mean SLR has accelerated from approximately 1.6 to 3.4 mm/year. While the year-by-year acceleration of the rate of rise cannot be measured adequately, it is reasonable to assume that it was relatively stable throughout the 20th century. For the 21st century, general circulation models project that posed atmospheric carbon emission scenarios will result in higher global average temperatures. A warmer global system will introduce new mechanisms (e.g., land ice loss, isotatic adjustments, and changes in land water storage) that will contribute to relatively abrupt changes in sea state levels. The additions to thermal expansion will drive higher sea levels and the increases in sea level will be attained by further accelerations in the rate of the rise. Because of the nature of the new mechanisms that will govern sea levels, it is unlikely that future accelerations in the rate of rise will be smooth.<br>To further address the complications associated with relatively abrupt changes in SLR and related impacts of climate change at the coastal land margin we intend to: (1) refine, enhance, and extend the coupled dynamic, bio-geo-physical models of coastal morphology, tide, marsh, and surge; (2) advance the paradigm shift for climate change assessments by linking economic impact analysis and ecosystem services valuation directly to these coastal dynamics; (3) pursue transdisciplinary outcomes by engaging a management transition advisory group throughout the entire project process; and (4) deliver our results via a flexible, multi-platform mechanism that allows for region-wide or place-based assessment of NNBFs. This presentation will share examples of our recent efforts and discuss progress to-date.
Our understanding of human systems has been synthesized and advanced by computationally representing human decision-making in agent-based models. Whether representing individuals, households, firms, or larger organization, agent-based modelling approaches are often used to model processes (e.g., urban growth, agricultural land management) that directly effect and are affected by natural systems. Contemporary efforts coupling models of human and natural systems have demonstrated that results significantly differ from isolated representations of either system. However, coupling models of human and natural systems is conceptually and computationally challenging. In addition to discussing these challenges and approaches to overcoming them, this talk will also suggest that research quantifying natural processes at the decision-making scale of the land user is needed. Using structure-from-motion and unmanned aerial vehicle (UAV) imagery, we can accurately quantify natural processes like soil erosion to a high level of accuracy and that frequently modelled processes (e.g., flow accumulation) typically differ from reality. Novel data from the field or parcel scale are needed to calibrate and validate our representation of natural processes if we are to advance our representation of feedbacks between natural processes and human decision-making. By improving our representation of both natural processes and human decision-making at the scale of the decision-maker, we add confidence in our ability to scale out to larger spatial extents that are reflective of natural processes (e.g., watershed) or policy driving human decisions from municipal, state, or national governments. +
Overview and Update of CSDMS accomplishments +
Panel discussion +
Panel discussion on AI/ML +
Parametric insurance represents a major breakthrough in the accessibility of risk financing for natural disasters. Instead of compensating for actual assessed loss, parametric (or index-based) insurance instead uses measurement of the hazard itself as a proxy for loss, paying out a pre-agreed amount for an event with certain intensity, location and, sometimes, duration. This allows for rapid settlement and reduced costs – of claims adjustment / processing and in the margin added by risk takers for uncertainty in projected outcomes. The quantitative, independent and objective nature of EO data, and also its availability in real time, makes it ideal as a basis for parametric insurance, particularly in the developing world where claims data for policy pricing is non-existent. Examples of parametric products based on EO data already in the market include protection against high and low rainfall, use of vegetation greenness indices, and footprint mapping as a basis for flood protection. +
Part 1 will focus on the use of Doodler (https://github.com/Doodleverse/dash_doodler), a 'human-in-the-loop' labeling tool for image segmentation (described in this paper: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021EA002085). We'll cover the two primary uses of Doodler; a) for relatively rapid image segmentation of a small set of images, and b) for making libraries of labeled imagery for training Machine Learning models to automate the process of image segmentation on larger datasets. We'd ideally like participants to label the same imagery in-class so we can discuss image interpretation and label agreement. This may even result in a publishable dataset; participants would receive co-authorship and could opt-in/out.
We will provide example datasets and models, but participants will also be encouraged to bring their own imagery sets. That way, participants will have time to familiarize themselves with the burgeoning Doodleverse tools (https://github.com/Doodleverse) in between classes on their own data. +
Part 2 will focus on the use of Segmentation Gym (https://github.com/Doodleverse/segmentation_gym), for training and implementing deep-learning-based image segmentation models. Participants will be given datasets and models to use for their own model building and implementation, or optionally they may use their own data, for example label images they made in Part 1. Hardware needs, and common problems and their workarounds will be discussed. +
Participants in this clinic will learn how to compile and run a Regional Ocean Modeling (ROMS) test case for an idealized continental shelf. The hydrodynamic model that we will use includes wave forcing and suspended sediment transport.<br/><br/>ROMS is an open source, three dimensional primitive equation hydrodynamic ocean model that uses a structured curvilinear horizontal grid and a stretched terrain following vertical grid. For more information see https://www.myroms.org. It currently has more than 4,000 registered users, includes modules for sediment transport and biogeochemistry, and has several options for turbulence closures and numerical schemes. Model input is specified using a combination of ASCII text files and NetCDF (Network Common Data Form) files. Output is written to NetCDF files. In part because ROMS was designed to provide flexibility for the choice of model parameterizations and processes, and to run in parallel, implementing the code can seem daunting, but in this clinic we will present an idealized ROMS model that can be run on the CSDMS cluster.<br/><br/>As a group, we will compile and run an idealized ROMS model on the CSDMS computer, Beach. The group will choose a modification to the standard model. While the modified model runs, we will explore methods for visualizing model output. Participants who have an account on Beach can try to run the model themselves. Clinic participants who have Matlab set up to visualize NetCDF files will be able to browse model output files during the clinic.<br/><br/>Following the clinic, participants should have access to tools for looking at ROMS output, an example ROMS model run, and experience with ROMS input and output files. +
Participants in this clinic will learn how to run a Regional Ocean Modeling System (ROMS) test case for an idealized continental shelf model domain within the CSDMS Web Modeling Toolkit (WMT). The model implementation that we will use includes wave forcing, a riverine source, suspended sediment transport.<br><br>ROMS is an open source, three-dimensional primitive equation hydrodynamic ocean model that uses a structured curvilinear horizontal grid and a stretched terrain following vertical grid. For more information see https://www.myroms.org. It currently has more than 4,000 registered users, and the full model includes modules for sediment transport and biogeochemistry, and several options for turbulence closures and numerical schemes. In part because ROMS was designed to provide flexibility for the choice of model parameterizations and processes, and to run in parallel, implementing the code can seem daunting, but in this clinic, we will present an idealized ROMS model that can be run on the CSDMS cluster via the WMT. One goal is to provide a relatively easy introduction to the numerical modeling process that can be used within upper level undergraduate and graduate classes to explore sediment transport on continental shelves.<br><br>As a group, we will run an idealized ROMS model on the CSDMS computer, Beach. The group will choose a modification to the standard model. While the modified model runs, we will explore methods for visualizing model output. Participants who have access to WMT can run the model themselves. Clinic participants who have access to Matlab and/or Panoply will be able to browse model output files during the clinic.<br><br>Following the clinic, participants should have access to an example ROMS model run, experience running ROMS within the WMT and with ROMS input and output files, and. ROMS lesson plans. +
Participatory modeling (PM) is a collaborative approach to formalize shared representations of a problem and design and test solutions through a joint modeling process. PM is well-suited for addressing complex social and environmental problems like climate change, social and economic injustice, and sustainable resource management. This workshop will introduce and test a prototype version of Fora.ai, a new PM platform developed at Northeastern University. Fora.ai is a simple digital environment that enables groups to collaboratively understand real world problems and create novel solutions. Stakeholders interact through this digital representation with input from other stakeholders, then iteratively revise and test solutions until diverse needs are addressed. Fora.ai provides quick simulation results for data-driven proof of concepts that are ready to be presented, designed, and implemented in the real world, giving everyone in a team the power to share their unique perspective and build the world they want to live in together. +
Participatory modeling (PM) is a collaborative approach to formalize shared representations of a problem and design and test solutions through a joint modeling process. PM is well-suited for addressing complex social and environmental problems like climate change, social and economic injustice, and sustainable resource management. This workshop will introduce and test a prototype version of Fora.ai, a new PM platform developed at Northeastern University. Fora.ai is a simple digital environment that enables groups to collaboratively understand real world problems and create novel solutions. Stakeholders interact through this digital representation with input from other stakeholders, then iteratively revise and test solutions until diverse needs are addressed. Fora.ai provides quick simulation results for data-driven proof of concepts that are ready to be presented, designed, and implemented in the real world, giving everyone in a team the power to share their unique perspective and build the world they want to live in together. +
Permafrost is one of the Arctic climate indicators, and feedback of thawing permafrost to the global climate system through the impacts on the carbon cycle remains an important research topic. Observations can assess the current state of permafrost, but models are eventually essential to make predictions of future permafrost state.<br>In this 2hr clinic, we will present a new, easy-to-access and comprehensive cyberinfrastructure for permafrost modeling. The ‘PermaModel Integrated Modeling Toolbox’ includes three permafrost models of increasing complexity. The IMT is embedded within the Community Surface Dynamics Modeling System Web Modeling Tool (WMT). We include multiple sets of sample inputs, representing a variety of climate and soil conditions and locations, to enable immediate use of the IMT.<br>The hands-on clinic teaches students and researchers how to run and use several permafrost models. The presented models are envisioned to be the suitable for quick exploration of hypotheses and for teaching purposes. +
Permafrost is one of the Arctic climate indicators, and feedback of thawing permafrost to earth surface processes and vice versa is a research frontier. Observations can assess the current state of permafrost, but models are eventually essential to make predictions of future permafrost state and impacts on surface processes. In this 2hr clinic, we will present a new, easy-to-access and comprehensive cyberinfrastructure for permafrost modeling. The ‘Permafrost Modeling Toolbox’ includes three permafrost models of increasing complexity. The tools are embedded within the Community Surface Dynamics Modeling System Web Modeling Tool. We include multiple sets of sample inputs, representing a variety of climate and soil conditions and locations, to enable immediate use of the tools.<br>The hands-on clinic teaches students and researchers how to run and use several permafrost models with associated datasets. The presented models are envisioned to be the suitable for quick exploration of hypotheses and for teaching purposes. We will also explore options for model coupling, demonstrating an example of a model of coastal/delta sedimentation in permafrost environments. +
Plastic pollution is a ubiquitous issue impacting the health of marine ecosystems worldwide. Yet, critical knowledge gaps surrounding the fate and transport of plastic once it enters the ocean impede remediation and prevention efforts. Predicting transport is difficult for any particle in the ocean, but microplastics present a particular challenge because their size and density fall outside the regimes of traditionally studied environmental particles such as low-density bubbles and high-density sediment. In this talk I will discuss recent work addressing these challenges with both modelling and experiments. +
Plate tectonics is the primary process controlling the Earth’s surface topography. In recent years, geodynamicists have emphasised the role that deep mantle flow may play in directly creating long wavelength, low amplitude topography (a so-called “dynamic” contribution to surface topography). In parallel, geomorphologists have investigated how surface processes (erosion, transport and sedimentation) may affect dynamic topography, with the aim of better understanding its signature in the geological record. To achieve this, we have developed a new class of surface processes models that represent the combined effects of physical erosion and chemical alteration within continental interiors. In developing these models, we have paid much attention to maintaining high efficiency and stability such that they could be used to model large continental areas with sufficient spatial resolution to represent the processes at the appropriate scale. I will briefly present these algorithms as well as the results of two separate studies in which we explain the anomalously rapid erosion of surface material during the passage of a continent over a fixed source of dynamic topography driven by upward flow in the mantle. I will also comment on how these models are strongly dependent on precipitation patterns and, ultimately, will need to be fully coupled to climate models to provide more meaningful constraints on the past evolution of surface topography. +
Predicting long-term Earth surface change, the impacts of short-term natural hazards and biosphere/geosphere dynamics requires computational models. Many existing numerical models quantitatively describe sediment transport processes, predicting terrestrial and coastal change at a great variety of scales. However, these models often address a single process or component of the earth surface system.
The Community Surface Dynamics Modeling System is an NSF-funded initiative that supports the open software efforts of the surface processes community. CSDMS distributes >200 models and tools, and provides cyberinfrastructure to simulate lithosphere, hydrosphere, atmosphere, or cryosphere dynamics. Many of the most exciting problems in these fields arise at the interfaces of different environments and through complex interactions of processes.
This workshop presents recent cyberinfrastructure tools for hypothesis-driven modeling— the Python Modeling Tool (PyMT) and LandLab. PyMT allows users to interactively run and couple numerical models contributed by the community. There are already tools for coastal & permafrost modeling, stratigraphic and subsidence modeling, and terrestrial landscape evolution modeling (including hillslope, overflow, landslide processes, and a suite of erosion processes with vegetation interactions), and these are easy to run and further develop in a Python environment.
This 2-part tutorial aims to provide a short overview of the PyMT and Landlab, a demonstration of running a coupled model, and hands-on exercises using Jupyter notebooks in small groups of attendees. The organizers will facilitate break-out groups for discussion of pressing research needs and then have a plenary discussion with reports of each of the breakouts on future frontier applications of coupled landscape/bioscape process modeling.
Materials for this clinic can be found at: https://github.com/csdms/csdms-2020 +
Predicting long-term Earth surface change, the impacts of short-term natural hazards and biosphere/geosphere dynamics requires computational models. Many existing numerical models quantitatively describe sediment transport processes, predicting terrestrial and coastal change at a great variety of scales. However, these models often address a single process or component of the earth surface system.
The Community Surface Dynamics Modeling System is an NSF-funded initiative that supports the open software efforts of the surface processes community. CSDMS distributes >200 models and tools, and provides cyberinfrastructure to simulate lithosphere, hydrosphere, atmosphere, or cryosphere dynamics. Many of the most exciting problems in these fields arise at the interfaces of different environments and through complex interactions of processes.
This workshop presents recent cyberinfrastructure tools for hypothesis-driven modeling— the Python Modeling Tool (PyMT) and LandLab. PyMT allows users to interactively run and couple numerical models contributed by the community. There are already tools for coastal & permafrost modeling, stratigraphic and subsidence modeling, and terrestrial landscape evolution modeling (including hillslope, overflow, landslide processes, and a suite of erosion processes with vegetation interactions), and these are easy to run and further develop in a Python environment.
This 2-part tutorial aims to provide a short overview of the PyMT and Landlab, a demonstration of running a coupled model, and hands-on exercises using Jupyter notebooks in small groups of attendees. The organizers will facilitate break-out groups for discussion of pressing research needs and then have a plenary discussion with reports of each of the breakouts on future frontier applications of coupled landscape/bioscape process modeling.
Materials for this clinic can be found at: https://github.com/csdms/csdms-2020 +
Process-based modeling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are not easily interpretable and are unable to answer specific scientific questions. A recently proposed genre of physics-informed machine learning, called “differentiable” modeling (DM, https://t.co/qyuAzYPA6Y), trains neural networks (NNs) with process-based equations (priors) together in one stage (so-called “end-to-end”) to benefit from the best of both NNs and process-based paradigms. The NNs do not need target variables for training but can be indirectly supervised by observations matching the outputs of the combined model, and differentiability critically supports learning from big data. We propose that differentiable models are especially suitable as global- or continental-scale geoscientific models because they can harvest information from big earth observations to produce state-of-the-art predictions (https://mhpi.github.io/benchmarks/), enable physical interpretation naturally, extrapolate well (due to physical constraints) in space and time, enforce known physical laws and sensitivities, and leverage progress in modern AI computing architecture and infrastructure. Differentiable models can also synergize with existing process-based models in terms of providing to them parameters or identifying optimal processes, learning from the lessons of the community. Differentiable models can answer pressing societal questions on water resources availability, climate change impact assessment, water management, and disaster risk mitigation, among others. We demonstrate the power of differentiable modeling using computational examples in rainfall-runoff modeling, river routing, ecosystem and water quality modeling, and forcing fusion. We discuss how to address potential challenges such as implementing gradient tracking for implicit numerical schemes and addressing process tradeoffs. Furthermore, we show how differentiable modeling can enable us to ask fundamental questions in hydrologic sciences and get robust answers from big global data.
Process-based models are able to predict velocity fields, sediment transport and associated morphodynamic developments over time. These models can generate realistic morphological patterns and stable morphodynamic developments over time scales of millennia under schematized model settings. However, more realistic case studies raise questions on model skill and confidence levels. Process-based models require detailed information on initial conditions (e.g. sediment characteristics, initial distribution of sediment fractions over the model domain), process descriptions (e.g. roughness and sediment transport formulations) and forcing conditions (e.g. time varying hydrodynamic and sediment forcing). The value of the model output depends to a high degree on the uncertainty associated with these model input parameters.<br/><br/>Our study explores a methodology to quantify model output uncertainty levels and to determine which parameters are responsible for largest output uncertainty. Furthermore we explore how model skill and uncertainty develop over time. We describe the San Pablo Bay (USA) case study and the Western Scheldt (Netherlands) case study in a 100 year hindcast and a more than 100 year forecast.<br/><br/>Remarkably, model skill and uncertainty levels depend on model input parameter variations only to a limited extent. Model skill is low first decades, but increases afterwards to become excellent after 70 years. The possible explanation is that the interaction of the major tidal forcing and the estuarine plan form governs morphodynamic development in confined environments to a high degree. +
Proposed in 2018, the Open Modeling Foundation (OMF) initiative aims to establish an international open science community to enable the next generation modeling of human and natural systems. The OMF is envisioned as an alliance of modeling organizations that develops and administers a community-wide open modeling standards and best practices for the social, ecological, environmental, and geophysical sciences. It will support these efforts though informational, data, and technological resources for the scientific communities it serves. This webinar reviews the history of the OMF, its current status, future plans, and how scientists can participate in this initiative. +
PyMT is the “Python Modeling Toolkit”. It is an Open Source Python package, developed by the Community Surface Dynamics Modeling System, that provides tools used to couple models that expose the Basic Model Interface (BMI). PyMT is:
* a toolbox for coupling models of disparate time and space scales,
* a collection of Earth-surface models, and
* an extensible plug-in framework for user-contributed models.
In this hands-on clinic we will use Jupyter Notebooks to explore how to run standalone models within PyMT. Since all PyMT models are based on the BMI, they all share the same user interface and so if you know how to run one model, you know how to run all PyMT models. We will then look at some of the model-coupling tools packaged with PyMT and how they can be used for more complex couplings. We will then run through examples that use these tools to couple models to data as well as to other PyMT models.
We highly recommend that clinic attendees come with a laptop with the Anaconda Python distribution installed. +
Quantitative analysis is often indispensable for making sound policy choices. But when decisionmakers confront today’s conditions of fast-paced, transformative, and even surprising change, they sometimes find that commonly used quantitative methods and tools prove counterproductive or lead them astray. Typically, quantitative analysis provides decisionmakers with information about the future by making predictions. But predictions are often wrong, and relying on them can be dangerous. Moreover, decisionmakers know that predictions are often wrong; this can cause them to discount or ignore the crucial information that quantitative analysis can provide. Fortunately, the combination of new information technology and new insights from the decision sciences now enables innovative ways to support decisions with quantitative analysis. This talk describes how one such approach—Robust Decision Making (RDM)—informs good decisions without requiring confidence in and agreement on predictions and offers examples of its increasing impact in a wide range of policy areas. +
R has been widely used by ecologists. It is a powerful language to build statistical models. However, R applications in landscape ecology are relatively limited. In this model clinic, we will introduce R programming and two recently developed packages “NLMR” and “landscapetools” in generating and visualizing neutral landscapes.
Neutral models are useful tools for testing the effect of different spatial processes on observed patterns, as they create landscape patterns in the absence of specific processes. Comparisons between a landscape model and a neutral model simulation will provide insights into how these specific processes affect landscape patterns. Different algorithms exist to generate neutral landscapes and they have been traditionally included in different programs. Now the NLMR package in R integrated all these different algorithms into one place.
In addition to providing instructions on how to use R, “NLMR and “landscapetools packages”, we will showcase real-world examples on neutral landscapes’ applications in ecology, such as predicting coastal wetland change in response to sea-level rise. +
Recent additions to Python have made it an increasingly popular language for data analysis. In particular, the pandas library provides an R-like data-fame in Python, which is data structure that resembles a spreadsheet. This provides an efficient way to load, slice, reshape, query, summarize, and visualize your data. Combining this with numpy, maplotlib, and scikit-learn creates a powerful set of tools for data analysis. In this hands-on tutorial, we will cover the basics of numpy, matplotlib, pandas, and introduce scikit-learn. +
Recent technological advances in data collection techniques have yielded opportunities to better quantify stratigraphic stacking patterns, flow processes and sedimentation from outcrops of ancient sediment transport systems. These advancements created opportunities for field geologists to reduce uncertainty in the interpretation of the stratigraphic record and have likewise created data sets from which the efficacy of numerical models and physical experiments can be evaluated. The goals of this presentation are to (1) review some combined outcrop-model based studies, (2) discuss how these integrated studies test model and field-based uncertainty, and (3) share a vision for how field geologists and modelers can leverage from each other’s perspectives.<br/><br/>Five examples of studies that bridged the gap between outcrop stratigraphy and experimental and/or numerical models include: (1) documentation of how mineralogy varies spatially in submarine fans, (2) relating flow processes to sedimentation in sinuous submarine channels, (3) evaluating compensational stacking in deltas and submarine fans, (4) relating stratigraphic architecture of deltas to inherited water depth and seafloor gradient, and (5) testing how shelf-edge deltas pipe coarse-grained sediment to submarine fans. These and similarly focused studies are important because they used common workflows and quantitative methods to evaluate similarities and differences between modeled and natural systems, resulting in a more complete view of the processes and products being studied. Whereas common workflows can provide a means to test the efficacy of physical and numerical modeling, it is critical to consider how modeling sheds insight into how one interprets the stratigraphic record from outcrop and subsurface data sets. +
Recent theoretical work suggests that autogenic processes in sediment transport systems have the capacity to shred signals of environmental and tectonic perturbations prior to transfer to the stratigraphic record. We view this theory as a major conceptual and quantitative breakthrough in long time scale Earth-surface processes and stratigraphy, but the general theory still needs to be adapted to deal with specific types of signals. Many argue that the tug of Relative Sea Level (RSL) change represents the most important boundary condition forcing affecting continental margin transport systems. However, we still lack quantitative theory to explain what properties RSL cycles must have to be stored in stratigraphy, thus limiting the usefulness of stratigraphy for defining paleo-environments. Results from our previously conducted laboratory experiments suggest that RSL cycles with amplitudes less than a channel depth and of periodicities less than the amount of time necessary to deposit, on average, one channel depth of stratigraphy over a delta-top are susceptible to signal shredding. Our hypothesis is supported using existing data sets and new numerical and physical experiments in which the surface process response and preserved record of RSL cycles of varying magnitudes and periodicities is constrained. Quantitative theory and predictions produced from this work is benchmarked against stratigraphy from the Late Miocene to Quaternary stratigraphy of the Mississippi Delta. During this time interval a significant change in the magnitude and periodicity of RSL cycles occurred. RSL cycles in the Late Miocene for the Mississippi Delta are predicted to be shredded, while more recent cycles are predicted to be preserved. +
Repeated continental glaciation of the US Central Lowlands disrupted pre-Pleistocene fluvial drainage networks by filling valleys, rerouting major rivers, and incising oversize meltwater channels. Post-glacial landscapes are characterized by large fractions of non contributing area (NCA) which does not contribute flow to external drainage networks by steepest decent algorithms. Analysis of land surfaces most recently glaciated between 130,000 and 10,000 years ago suggests that NCA is lost over time as fluvial networks are reestablished. Low surface slopes combined with significant fractions of NCA make such fluvial network growth difficult to reconcile with standard treatments of flow routing. We develop modules in Land Lab that allow for connection of NCA via filling and spilling from closed depressions on the surface and through groundwater flow across subtle surface water divides to explore the impacts of these mechanisms of flow accumulation on the pace of evolution and morphology of resulting river networks. This work highlights the more general need to consider the relationship, or lack of relationship, between topography and river discharge. +
Reporting to the community of what CSDMS has accomplished and what can be expected with CSDMS 3.0 +
Research communities and peer-review journals are increasingly requiring authors to make available the code and data behind computational results reported in published studies. The Whole Tale platform is an open-access and open source system designed to enable researchers to package and archive their code, data, computational workflow, and information about the computational environment to better enable others to assess and repeat their results. During this webinar, we will introduce participants to the concepts of computational reproducibility and transparency and demonstrate core features of the platform. +
Research in Earth-surface processes and subsurface stratal development is in a data-rich era with rapid expansion of facilities that produce tremendous digital data with time and space resolution far beyond what we can collect in the field. Despite these advances, sediment experimentalists are an example community in the “long tail”, meaning that their data are often collected in one-of-a-kind experimental set-ups and isolated from other experiments. Experimentalists also have a lot of “dark data” that are difficult or impossible to access through the Internet. The Sediment Experimentalist Network (SEN) was formed to address these challenges. Over the last three years, SEN launched a Knowledge Base website, held international workshops, and provided educational short courses. Through workshops and short courses, SEN has identified and shared experimental data best practices, developed metadata standards for data collection, and fostered data management and sharing efforts within the experimentalist community. '''Now is the time to extend this collaboration toward Earth-surface modelers to advance geoscience research and education.''' We identified three grand challenges for SEN: (1) How best to relate experiments to natural systems and theory, (2) How to ensure comparability of experimental results from disparate facilities, and (3) How to distinguish external versus intrinsic processes observed in experiments. Experimentalist-modeler collaborations are essential for achieving solutions to all of these grand challenges. Theoretical and numerical modeling based on first principles can help to extrapolate insight from experiments to field scales, to compare results from different lab facilities, and to decouple autogenic processes and allogenic forcings in geomorphology and stratigraphy. The experimentalist-modeler collaborative effort will result in tremendous opportunities for overcoming grand challenges in our communities. +
Researchers and decision makers are increasingly interested in understanding the many ways in which human and Earth systems interact with one another, at scales from local (e.g., a city) to regional to global. For example, how might changes in population, income, or technology development alter crop production, energy demand, or water withdrawals? How do changes in one region's demand for energy affect energy, water, and land in other regions? This session will focus on two models – GCAM and Demeter – that provide capabilities to address these types of questions.
GCAM is an open-source, global, market equilibrium model that represents the linkages between energy, water, land, climate, and economic systems. A strength of GCAM is that it can be used to quickly explore, and quantify the uncertainty in, a large number of alternate future scenarios while accounting for multi-sector, human-Earth system dynamics. One of GCAM’s many outputs is projected land cover/use by subregion. Subregional projections provide context and can be used to understand regional land dynamics; however, Earth System Models (ESMs) generally require gridded representations of land at finer scales. Demeter, a land use and land cover disaggregation model, was created to provide this service. Demeter directly ingests land projections from GCAM and creates gridded products that match the desired resolution and land class requirements of the user.
This clinic will introduce both GCAM and Demeter at a high-level. We will also provide a hands-on walk through for a reference case so attendees can become familiar with configuring and running these two models. Our goal will be for attendees to leave the clinic with an understanding of 1) the value of capturing a global perspective when informing subregional and local analysis, 2) possibilities to conduct scenario exploration experiments that capture multi-sector/scale dynamics, 3) and a hands-on experience with GCAM and Demeter.
Researchers and decision makers are increasingly interested in understanding the many ways in which human and Earth systems interact with one another, at scales from local (e.g., a city) to regional to global. For example, how might changes in population, income, or technology cost alter crop production, energy demand, or water withdrawals? How do changes in one region's demand for energy affect energy, water, and land in other regions? This session will focus on two models – GCAM and Demeter – that provide the capability to address these types of questions.<br><br>GCAM is an open-source, global, market equilibrium model that represents the linkages between energy, water, land, climate, and economic systems (Calvin et al. 2019). A strength of GCAM is that it runs fast and can be used to explore, and quantify the uncertainty in, a large number of alternate future scenarios while accounting for multisector, human-Earth system dynamics. One of GCAM’s many outputs is projected land cover/use by subregion. Subregional projections provide context and can be used to understand regional land dynamics; however, Earth System Models (ESMs) generally require gridded representations of land at finer scales. Demeter, a land use and land cover disaggregation model, was created to provide this service (Vernon et al. 2018). Demeter directly ingests land projections from GCAM and creates gridded products that match the desired resolution and land class requirements of the user.<br><br>This clinic will introduce both GCAM and Demeter at a high-level. We will also provide a hands-on walk through for a reference case so attendees can become familiar with setting-up and running these two models. Our goal will be for attendees to leave the clinic with an understanding of 1) the value of capturing a global perspective when informing subregional and local analysis, 2) possibilities to conduct scenario exploration experiments that capture multisector/scale dynamics, 3) a hands-on experience with GCAM and Demeter, and 4) key model assumption drivers and simulated model results available.
River deltas will likely experience significant land loss because of relative sea-level rise (RSLR), but predictions have remained elusive. Here, we use global data of RSLR and river sediment supply to build a validated model of delta response to RSLR for all ~10,000 deltas globally. Applying this model to predict future delta change, we find that all IPCC RCP sea-level scenarios lead to a net delta loss by the end of the 21st century, ranging from -52 ± 36 (1 s.d.) km2yr-1 for RCP2.6 to -808 ± 80 km2yr-1 for RCP8.5. We find that river dams, subsidence, and sea-level rise have had a comparable influence on reduced delta growth over the past decades, but that by 2100 under RCP8.5 more than 80% of delta land loss will be caused by climate-change driven sea-level rise. +
SNAC (StGermaiN Analysis of Continua) is a 3D parallel explicit finite element code for modeling long-term deformations of lithosphere. It is an open source being distributed through Computational Infrastructure for Geodynamics (http://geodynamics.org/cig/software/snac/) as well as through CSDMS web site (https://csdms.colorado.edu/wiki/Model:SNAC).<br/><br/>This clinic will provide an overview of SNAC and lead participants through a typical work procedure for producing a 3D lithospheric deformation model on a high performance cluster. Specifically, participants will take the following steps: 0) acquiring an account on the CSDMS HPC (to be done before the clinic); 1) checking out the source code through a version control system; 2) building SNAC on the cluster; 3) getting familiar with SNAC by running a cookbook example in parallel and visualizing outputs; 4) modifying the source codes to customize a model. +
Salt marshes are biogeomorphic features that are under increasing pressure from sea level rise, land use change, and other external stressors. Modeling of salt marshes has traditionally been “stovepiped” into three general disciplines: ecology, geomorphology, and engineering, resulting in contrasting approaches and relative rigor. I will highlight successes and failures across these efforts, and identify how the three disciplines can move forward using advances from each other. +
Scientific communities and peer-review journals are increasingly requiring authors to make available the code and data behind computational results reported in published research. This tutorial will introduce participants to the NSF-funded Whole Tale platform, an open-access and open-source system designed to enable authors to package and archive their code, data, computational workflow and information about the computational environment to better enable others to repeat their results. We will walk through the basic features of the platform with hands-on exercises. +
Seagrass provides a wide range of economically and ecologically valuable ecosystem services, with shoreline erosion control often listed as a key service. But seagrass can also alter the sediment dynamics and waves of back-barrier bays by reducing wave height and attenuating wave and current shear stresses acting on the sediment bed. This suggests that seagrass can play an important role in the evolution of the entire shallow coastal bay, back-barrier marsh, and barrier-island system, yet no study has previously examined these subsystems coupled together. Here we incorporate seagrass dynamics of the back-barrier bay into the existing coupled barrier-marsh model GEOMBEST+. In our new integrated model, bay depth and distance from the marsh edge determine the location of suitable seagrass habitat, and the presence or absence, size, and shoot density of seagrass meadows alters the bathymetry of the bay and wave power reaching the marsh edge. We use this model to run 3 sets of experiments to examine the coupled interactions of the back-barrier bay with both adjacent (marsh) and non-adjacent (barrier) subsystems. While seagrass reduces marsh edge erosion rates and increases progradation rates in many of our model simulations, seagrass surprisingly increases marsh edge erosion rates when sediment export from the back-barrier basin is negligible. Adding seagrass to the bay subsystem leads to increased deposition in the bay, reduced sediment available to the marsh, and enhanced marsh edge erosion until the bay reaches a new, shallower equilibrium depth. In contrast, removing seagrass liberates previously-sequestered sediment that is then delivered to the marsh, leading to enhanced marsh progradation. Lastly, we find that seagrass reduces barrier island migration rates in the absence of back-barrier marsh by filling accommodation space in the bay. These model observations suggest that seagrass meadows operate as dynamic sources and sinks of sediment that can influence the evolution of coupled marsh and barrier island landforms in unanticipated ways.
Seasonal seagrass growth and senescence exert a strong influence on shallow coastal environments. We applied a hydrodynamic and sediment transport Delft3D model that included coupled effects of seagrass on flow, waves, and sediment resuspension in a shallow coastal bay to quantify seasonal seagrass effects on bay dynamics. Simulation results show that seagrass meadows significantly attenuated flow (60%) and waves (20%) and reduced suspended sediment concentration (85%) during the growing season. Although low-densities of seagrass in winter had limited effects on flow and wave attenuation, small changes in winter seagrass density could alter the annual sediment budget of these seagrass ecosystems. +
Sediment diversions costing billions of dollars are planned on deltas globally, to mitigate land loss due to rising sea levels and subsidence. Downstream of engineered levee breaks, land building will rely on natural delta processes to disperse sediment. But, external factors known to affect natural delta processes vary between possible diversion sites (e.g., wave energy, basin substrate, marsh activity), making it difficult to quantitatively compare land-building potential between sites and optimally allocate engineering resources. We have implemented the pyDeltaRCM numerical model to provide an easily extensible platform for simulating delta evolution under arbitrary environmental factors. With the computationally efficient model, we isolate (and combine) these factors to observe effects on land building, and build a framework to quickly assess land-building potential at different sites. In this presentation, I will describe pyDeltaRCM model design, and show ongoing studies to assess land-building potential of diversions under different forcings. Model computational efficiency enables uncertainty quantification that will benefit diversion planning and resource allocation, by identifying relative impact of different external factors. +
Sediment production and transfer processes shape river basins and networks and are driven by variability in precipitation, runoff and temperature. Changes in these hydrological and geomorphological processes are especially difficult to predict in temperature-sensitive environments such as the European Alps. We used a model chain to quantify possible impacts of climate change on sediment transfer and hazard in a debris flow-prone catchment in the Swiss Alps (Illgraben). We combined a stochastic weather generator1 with downscaled and bias-corrected climate change projections2 to generate climate simulations. These climate simulations then feed the hillslope-channel sediment cascade model, SedCas3, which is calibrated against observed debris-flow magnitudes estimated from force plate measurements4, to make predictions of sediment transfer and debris flow hazard in the Illgraben over the 21st century5.
The results demonstrate the complex interplay between hydrology, sediment production and elevation in alpine catchment response to climate change. The hydrological potential to transport sediment and generate debris flows will increase, driven by increases in precipitation and air temperature. Indeed, if sediment supply to the channel by landslides were unlimited, this would result in an increase in future sediment yield of 48% by the end of the century. However, sediment transfer is also a function of sediment supply by landslides at the head of the catchment, driven by highly temperature sensitive freeze-thaw processes6. At the elevation of the Illgraben (<2000 m), freeze-thaw processes and thus sediment supply will decrease in a warming climate resulting in a decrease in sediment yield of 48% by the end of the century. This result and the competition between hydrological debris flow triggering potential and sediment supply is highly elevation dependent. As we increase mean catchment elevation, sediment production increases due to decreased snow cover and increased exposure of bedrock to freeze-thaw weathering, with implications for the application of findings to other catchments. Although uncertainties in our results are large, we show that these can mostly be attributed to irreducible internal climate variability. Our findings have important implications for the assessment of natural hazards and risks in mountain environments.
REFERENCES
1 Fatichi et al., 2011: Simulation of future climate scenarios with a weather generator
2 National Centre for Climate Services, 2018: CH2018 - Climate Scenarios for Switzerland
3 Bennett et al., 2014: A probabilistic sediment cascade model of sediment transfer in the Illgraben
4 McArdell et al., 2007: Field observations of basal forces and fluid pressure in a debris flow
5Hirshberg et al., 2021: Climate change impacts on sediment yield and debris flow activity
6 Bennett et al., 2013: Patterns and controls of sediment production, transfer and yield in the Illgraben
Sediment transport in rivers is a key parameter in landscape evolution, fluvial sedimentation, and river engineering. In particular, information on the time-averaged virtual velocity and the channel/floodplain exchange rate of sediment is extremely useful for quantifying long-term sediment transport dynamics. This data is expensive and time-consuming to obtain. A potential solution is to use luminescence, a property of matter normally used for dating. I develop a model based on conservation of energy and sediment mass to explain the patterns of luminescence in river channel sediment. The parameters from the model can then be used to estimate the time-averaged virtual velocity, characteristic transport lengthscales, storage timescales, and floodplain exchange rates of fine sand-sized sediment in a fluvial system. I show that this model can accurately reproduce the luminescence observed in previously published field measurements. I test these predictions in three rivers where the sediment transport information is well known: the South River and Difficult Run in Virginia, and Linganore Creek in Maryland. Each of these rivers tests key predictions of the model with the South River having favorable conditions, Difficult Run having large amounts of human influence, and Linganore Creek switching from alluvial to bedrock and vice versa along its course. In the South River, the model successfully reproduces the virtual-velocity and exchange rates from previously published data. In Difficult Run, we find that the influx of sediment from human development obfuscates the model-predicted pattern as expected. In Linganore Creek, the shift from alluvial covered to bedrock and back produces a change in the luminescence consistent with the predictions made by the model. From these results, I conclude that when model assumptions are upheld, luminescence can provide a useful method to obtain sediment transport information. This finding, coupled with the advent of portable luminescence technology, opens the door for rapid and inexpensive collection of long-term sediment transport data.
Segmentation, or the classification of pixels (grid cells) in imagery, is ubiquitously applied in the natural sciences. An example close to the CSDMS community might be translating images of earth surface into arrays of land cover to be used as model initial conditions, or to test model output. Manual segmentation is often prohibitively time-consuming, especially when images have significant spatial heterogeneity of colors or textures.
This Clinic is focused on demonstrating a machine learning method for image segmentation using two software tools: The first is “Doodler”, a fast, semi-automated, method for interactive segmentation of N-dimensional (x,y,N) images into two-dimensional (x,y) label images. It uses human-in-the-loop ML to achieve consensus between the labeler and a model in an iterative workflow. Second, we will demonstrate Segmentation Zoo, a python toolbox to segment imagery with a variety of deep learning models that uses output from Doodler with existing models, or train entirely new models.
Ideally the clinic will be divided into two separate days. Day 1 would be a short introductory lecture, a live code demo, and then homework — participants will doodle imagery to gain familiarity with the software and create training data for a segmentation model. Day 2 would be a short introductory lecture on machine learning, and a live code demo for how to use doodled images in Segmentation Zoo (i.e., the images that participants doodled).
There are two concrete goals for the clinic: 1) demonstrate how participants can use these two tools, and; 2) a group authored dataset of doodled images that will be placed in a Zenodo repository with all participants who contribute as coauthors.
Doodler preprint: https://doi.org/10.31223/X59K83
Doodler repository: https://github.com/dbuscombe-usgs/dash_doodler
Doodler Website: https://dbuscombe-usgs.github.io/dash_doodler/
Segmentation Zoo repository: https://github.com/dbuscombe-usgs/segmentation_zoo
Seismic observations document how substantial amounts of sediments may be transported from the onshore to the offshore during formation of extensional continental margins. Thick sedimentary packages are, for example, found on the margins of Norway, the eastern US coast, and the Gulf of Mexico. In contrast, the Goban Spur, Galicia Bank, and the Red Sea are examples of sediment-starved margins. Such variations in the amount of sediments impact not only the development of offshore sedimentary basins, but the changes in mass balance by erosion and sedimentation can also interact with extensional tectonic processes. In convergent settings, such feedback relationships between erosion and tectonic deformation have long been highlighted: Erosion reduces the elevation and width of mountain belts and in turn tectonic activity and exhumation are focused at regions of enhanced erosion. But what is the role played by surface processes during formation of extensional continental margins? In this lecture, I will discuss geodynamic experiments that explore the response of continental rifts to erosion and sedimentation from initial rifting to continental break-up. These experiments show how the interaction of extensional tectonics and surface processes can fundamentally alter the width and topography of continent-ocean boundaries. +
Seismo-acoustic techniques can provide continuous, real-time observations with high temporal resolution and broad spatial coverage for process monitoring, detection and characterization in accessible environments. These capabilities are rapidly advancing with the growing use of distributed acoustic sensing (DAS) systems, which use fiber optic cables to provide continuous records of ground motion comparable to large-N arrays of single-component accelerometers or geophones. Compared to traditional seismic arrays, DAS arrays can be tens of kilometers in length with spatial resolution of meters and sampling frequencies from millihertz to kilohertz.
In this clinic, participants will learn about the basics of DAS instrumentation and deployment in an introductory lecture, and be introduced to hands-on DAS data input, analysis and visualization concepts through Jupyter notebooks. The clinic will also provide participants with resources for further exploring and utilizing DAS, including guides to open DAS datasets, and the growing resource lists and GitHub organization managed by the NSF-funded DAS Research Coordination Network (https://www.iris.edu/hq/initiatives/das_rcn). +
Sequence is a modular 2D (i.e., profile) sequence stratigraphic model that is written in Python and implemented within the Landlab framework. Sequence represents time-averaged fluvial and marine sediment transport via differential equations. The modular code includes components to deal with sea level changes, sediment compaction, local or flexural isostasy, and tectonic subsidence and uplift. Development of the code was spurred by observations of repetitive stratigraphic sequences in western Turkey that are distorted by tectonics. +
Sinuous channels commonly migrate laterally and interact with banks of different strengths—an interplay that links geomorphology and life, and shapes diverse landscapes from the seafloor to planetary surfaces. To investigate feedbacks between meandering rivers and landscapes over geomorphic timescales, numerical models typically represent bank properties using structured or unstructured grids. Grid-based models, however, implicitly include unintended thresholds for bank migration that can control simulated landscape evolution. I will present a vector-based approach to land surface- and subsurface-material tracking that overcomes the resolution-dependence inherent in grid-based techniques by allowing high-fidelity representation of bank-material properties for curvilinear banks and low channel lateral migration rates. The vector-based technique is flexible for tracking evolving topography and stratigraphy to different environments, including aggrading floodplains and mixed bedrock-alluvial river valleys. Because of its geometric flexibility, the vector-based material tracking approach provides new opportunities for exploring the co-evolution of meandering rivers and surrounding landscapes over geologic timescales. +
Six years ago, we set out to study how complex systems simulations could support collaborative water planning. We hypothesized that, by allowing participants to see the hidden effects of land- and water-use decisions on water flow, such tools could provide a platform for collective and innovative solution-building to complex environmental problems. We first adopted a developmental and collaborative agent-based approach, where groups of stakeholders learned how to inform and use models to assess the impacts of different implementation strategies. Despite their improved understanding and enhanced exploration of solutions, participants resisted policy innovation beyond familiar strategies. We refined our approach towards facilitated interaction with complex systems models and additional interfaces to help stakeholders provide direct input to the simulations, comprehend model outputs, and negotiate tradeoffs. Participants challenged outdated and false assumptions and identified novel solutions to their water woes. Nevertheless, at times the dissonance between simulation outputs and participants’ expectations was too great to accept and own. We share three stories of the obstacles encountered and offer suggestions to overcome them: keep models and interfaces simple, make both biophysical processes and values visible and tangible, and explicitly structure the social aspects of the simulation’s use. We draw on our experiences to show what aspects of visualization can support participatory planning. +
Society is facing unprecedented environmental challenges that have pushed us into a world dominated by transients and variability. Informed decision making in this era, at scales from the individual to the globe, requires explicit predictions on management-relevant timescales, based on the best available information, and considering a wide range of uncertainties. As a research community, we are not yet meeting this need. In this talk I will introduce the Ecological Forecasting Initiative (EFI), an international grass-roots research consortium aimed at building a community of practice. I will discuss EFI’s cross-cutting efforts to tackle community-wide bottlenecks in cyberinfrastructure, community standards, methods and tools, education, diversity, knowledge transfer, decision support, and our theoretical understanding of predictability. I will highlight examples of near real-time iterative ecological forecasts across a wide range of terrestrial and aquatic systems, as well as work done by my own group developing PEcAn (a terrestrial ecosystem model-data informatics and forecasting system) and our recent efforts to generalize these approaches to other forecasts. Finally, I will also introduce EFI’s ecological forecasting competition, which relies on a wide range of continually-updated NEON (National Ecological Observatory Network) data. +
Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. With the advent of new techniques, data sources, and computational power on the one hand, and the growing sustainability challenges on the other, the expectation is that SES modeling should be more widely used to inform decision-making at multiple scales. This presentation will highlight the grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling.
These challenges include: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. The presentation will outline the steps required to surmount the underpinning barriers and priority research areas in SES modelling and propose clear directions for future generations of models and modeling, to both their developers and users. +
Software sustainability - the ability for software to continue to function - and the FAIR principles (Findable, Accessible, Interoperable and Reusable) are important features of software used in research. But how do they apply to research into environmental extremes? In this presentation, I will summarise the work of the Software Sustainability Institute, including my work on the FAIR principles for research software, and what we understand about the challenges and benefits of applying software sustainability and FAIR to this area. +
Soil science has developed as a critical discipline of the biosphere and continues to develop every day; yet state-of-the-art modeling is unable to adequately synthesize many processes in applied earth system models. If we agree that soil is a critical life-supporting compartment that supports ecosystem functions (e.g., habitat for biodiversity) and ecosystem services (e.g., water filtration, nutrient management), and that produces food, feed, fiber and energy for our societies, then our inability to integrate soil processes into the broader array of earth system models is an issue that needs solving. Integration is an achievable goal. Other research communities have collaborated intensively over the past decades—specifically the climate modeling community—but even many of their approaches overlook (or over-average) the detailed and advanced shared knowledge of the soil compartment. This represents a gap in how scientific knowledge is implemented. Over the recent decades, a new generation of soil models has been developed, based on a whole systems approach comprising all physical, mechanical, chemical, and biological processes. The processes are needed to fill these critical knowledge gaps and contribute to the preservation of ecosystem function, improve our understanding of climate-change feedback processes, bridge basic soil science research and management, and facilitate the communication between science and society. The International Soil Modeling Consortium (ISMC) was formed in 2016 as a new community effort of soil modelers to improve how soil processes are communicated to other scientific communities, from earth dynamics to biogeosciences to global climate modelers. ISMC was formed around three themes: linking data and observations to models; creating the means for soil model intercomparison studies; and connecting our soil-related knowledge between science communities. Within less than 12 months of inception, ISMC has warehoused nearly 40 soil-related models, initiated data sets and platforms for modeling studies, and facilitated collaborations with several international groups, including CSDMS. In this discussion, we will describe the motivation and genesis of ISMC, present current status of our research, and seek to create new research partnerships.
Soils control the influence of how land use and land cover (LULC) change the global water, energy, and biogeochemical cycles. However, Earth System Models often assume soil properties stay constant over time that leaves uncertainties in assessing LULC impacts. This study quantifies impacts of agriculture, pasture, grazing, vegetation harvest, and secondary vegetation cover on SOC, texture, and bulk density through meta-analyses. We showed how LULCs link to different soils and constructed a model to estimate how LULCs change soil properties and how climate and soil conditions alter the impacts. Results provide better land surface characteristics to improve Earth systems modeling. +
Spring School Student Presentations +
State of CSDMS +
