Form:Annualmeeting2020

CSDMS 2020: Linking Ecosphere and Geosphere

May 20 -21^st 2020, Boulder Colorado, USA

Introduction

Due to the current COVID-19 physical distancing restrictions, CSDMS will provide the CSDMS2020 – Linking Ecosphere and Geosphere meeting in a no-cost virtual format now open to all community members! This year, the meeting is co-convened with the International Society for Ecological Modeling, and an important aim of the meeting is to explore connections between ecosystems and earth-surface processes such as erosion, sedimentation, soil dynamics, and landscape/seascape evolution.

Topics that we hope to address at the meeting include (but are not necessarily limited to):

• Life on the surface: biosphere-hydrosphere-lithosphere interactions
• Feedbacks between solid earth, fluid earth and biosphere
• Changing paradigms and challenges in linking ecosystem and earth surface research
• Dynamics of the Critical Zone
• Crossing climate threshold

This year's meeting aims to:

1. Identify new frontiers in fundamental process understanding of linkages between the biosphere and geosphere. New algorithms, cyberinfrastructure development and new model couplings appear paramount to explore important process dynamics and linkages.
2. Identify critical missing components in our ability to overcome model and process boundaries.
3. Build researcher-to-researcher connections. Better connect earth surface process modelers with ecological modelers, social scientists and engineers to facilitate interdisciplinary exploration of ecosystem dynamics and the human dimensions in earth surface processes.

Agenda

Click here to view a draft of the agenda.

May 20

Virtual Keynote Presentations - Session 1: 9 - 11:10am MST

Registration is required to access the Keynote Presentations for Session 1, register at: https://cuboulder.zoom.us/meeting/register/tJYudeCqrTIvHNx973AdZeLcxxmOYvXxt9eG

Michael Dietze
Boston University

21st Century Science For 21st Century Environmental Decision Making: The Challenges And Opportunities Of Near-Term Iterative Environmental Forecasting 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.

Nathan Lyons
Tulane University

Life in Landscape Evolution Models: Investigations of Climate and Tectonics as Drivers of Biological Evolution 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.

Ian Reeves
University of North Carolina at Chapel Hill

Impacts of Seagrass Dynamics on the Coupled Long‐Term Evolution of Barrier‐Marsh‐Bay Systems 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.

Pamela Sullivan
Oregon State University

Are rate changes in biotic processes altering subsurface hydrologic partitioning in the Anthropocene? The degree to which subsurface architecture – pores space and connectivity—fluctuates and/or evolves is largely ignored in predictions of how Earth’s critical zone can respond to changes in biotic processes (direct and indirect) in the Anthropocene. Specifically, changes in microbial carbon decomposition rates and root growth can influence the generation of macropores, whose porosity accounts for only ~2% of the subsurface but accounts for ~70% of water transmitted to depth. We argue that the community needs to consider that changes in the subsurface structure throttles the partitioning of water, and thus the fluxes of carbon, nutrients, and weathering products. Using empirical data and modeling we explore this connectivity between biotic processes (e.g., root growth, carbon turn over) and subsurface pore structure from the pedon to the continental scale, quantifying the impact of this interaction on stocks and fluxes of water and nutrients. We then examine how over longer time periods, this change in hydrologic partitioning can influence the depth to which reaction fronts propagate into the subsurface and the role in which these changes could influence the trajectory of landscape evolution.

Robert Ulanowicz
Biology Dept., Univ. Florida Univ. MD Ctr for Env. Science

Process Ecology: A Step beyond Physics 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.

Virtual Clinic Presentations: 1 - 3pm MST

Register for a clinic of interest (only 1 each day as these are given in parallel - if you select a 2-part clinic, you must register separately for each day)

Daniel Buscombe & Evan Goldstein
Marda Science & The Univ. of North Carolina

Part I: Landcover and landform classification using deep neural networks A fatal error occurred in the #info parser function

Daniel Buscombe & Evan Goldstein
Marda Science & The Univ. of North Carolina

Part II: Landcover and landform classification using deep neural networks A fatal error occurred in the #info parser function

Eric Hutton & Greg Tucker
CSDMS IF

Part I: Exploring Surface Processes using CSDMS Tools: How to Build Coupled Models 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. </br></br>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.</br></br>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. </br></br>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.</br></br>Materials for this clinic can be found at: https://github.com/csdms/csdms-2020

Eric Hutton & Greg Tucker
CSDMS IF

Part II: Exploring Surface Processes using CSDMS Tools: How to Build Coupled Models 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. </br></br>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.</br></br>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. </br></br>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.</br></br>Materials for this clinic can be found at: https://github.com/csdms/csdms-2020

Caner Kazanci
University of Georgia

Ecological Network Analysis/EcoNet 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.
Sold out
(Max capacity 40 participants)

John Swartz
University of Texas, Austin

Exploring surface processes and landscape connectivity through high-resolution topography: integration of high resolution data in numerical modeling High-resolution topographic (HRT) data is becoming more easily accessible and prevalent, and is rapidly advancing our understanding of myriad surface and ecological processes. Landscape connectivity is the framework that describes the routing of fluids, sediments, and solutes across a landscape and is a primary control on geomorphology and ecology. Connectivity is not a static parameter, but rather a continuum that dynamically evolves on a range of temporal and spatial scales, and the observation of which is highly dependent on the available methodology. In this clinic we showcase the utility of HRT for the observation and characterization of landscapes and compare results with those of coarser spatial resolution data-sets. We highlight the potential for integrating HRT observations and parameters such as vegetation density, surface relief, and local slope variability with numerical surface process models. Participants will gain an understanding of the basics of HRT, data availability and basic analysis, and the use of HRT parameters in modeling.
Sold out
(Max capacity 20 participants)

May 21

Virtual Keynote Presentations - Session 2: 9 - 10:30am MST

Registration is required to access the Keynote Presentations for Session 2, register at: https://cuboulder.zoom.us/meeting/register/tJEtdu6uqj0pGtO1BEMCPYOPzPoVK9CwwRIK

Gordon Bonan
National Center for Atmospheric Research

Reinventing Nature: Environmental Stewardship in the Age of Earth System Models Global models of Earth’s climate have expanded beyond their geophysical heritage to include terrestrial ecosystems, biogeochemical cycles, vegetation dynamics, and anthropogenic uses of the biosphere. Ecological forcings and feedbacks are now recognized as important for climate change simulation, and the models are becoming models of the entire Earth system. This talk introduces Earth system models, how they are used to understand the connections between climate and ecology, and how they provide insight to environmental stewardship for a healthy and sustainable planet. Two prominent examples discussed in the talk are anthropogenic land use and land-cover change and the global carbon cycle. However, there is considerable uncertainty in how to represent ecological processes at the large spatial scale and long temporal scale of Earth system models. Further scientific advances are straining under the ever-growing burden of multidisciplinary breadth, countered by disciplinary chauvinism and the extensive conceptual gap between observationalists developing process knowledge at specific sites and global scale modelers. The theoretical basis for Earth system models, their development and verification, and experimentation with these models requires a new generation of scientists, adept at bridging the disparate fields of science and using a variety of research methodologies including theory, numerical modeling, observations, and data analysis. The science requires a firm grasp of models, their theoretical foundations, their strengths and weaknesses, and how to appropriately use them to test hypotheses of the atmosphere-biosphere system. It requires a reinvention of how we learn about and study nature.

Muriel Brückner
Utrecht University

Modelling the effects of dynamic saltmarsh and microphytobenthos growth on the large-scale morphology of estuaries 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. </br>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.</br>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.

Erkan Istanbulluoglu
University of Washington

Ecosystem processes and landscape evolution 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.

Suzanne Pierce
University of Texas, TACC

Recent lessons from data- and model-driven DSS in the wicked, wild world Major societal and environmental challenges require forecasting how natural processes and human activities affect one another. There are many areas of the globe where climate affects water resources and therefore food availability, with major economic and social implications. Today, such analyses require significant effort to integrate highly heterogeneous models from separate disciplines, including geosciences, agriculture, economics, and social sciences. Model integration requires resolving semantic, spatio-temporal, and execution mismatches, which are largely done by hand today and may take more than two years. The Model INTegration (MINT) project will develop a modeling environment which will significantly reduce the time needed to develop new integrated models, while ensuring their utility and accuracy. Research topics to be addressed include: 1) New principle-based semiautomatic ontology generation tools for modeling variables, to ground analytic graphs to describe models and data; 2) A novel workflow compiler using abductive reasoning to hypothesize new models and data transformation steps; 3) A new data discovery and integration framework that finds new sources of data, learns to extract information from both online sources and remote sensing data, and transforms the data into the format required by the models; 4) A new methodology for spatio-temporal scale selection; 5) New knowledge-guided machine learning algorithms for model parameterization to improve accuracy; 6) A novel framework for multi-modal scalable workflow execution; and 7) Novel composable agroeconomic models.

Virtual Clinic Presentations: 1 - 3pm MST

Register for a clinic of interest (only 1 each day as these are given in parallel - if you select a 2-part clinic, you must register separately for each day)

Daniel Buscombe
Marda Science

Part 2: Part I: Landcover and landform classification using deep neural networks A fatal error occurred in the #info parser function
Sold out (If you registered for part 1, than you are automatically registered the 2nd part)

Daniel Buscombe
Marda Science

Part 2: Part II: Landcover and landform classification using deep neural networks A fatal error occurred in the #info parser function
Sold out (If you registered for part 1, than you are automatically registered the 2nd part)

Eric Hutton
CSDMS IF

Part 2: Part I: Exploring Surface Processes using CSDMS Tools: How to Build Coupled Models 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. </br></br>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.</br></br>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. </br></br>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.</br></br>Materials for this clinic can be found at: https://github.com/csdms/csdms-2020
Sold out (If you registered for part 1, than you are automatically registered the 2nd part)

Eric Hutton
CSDMS IF

Part 2: Part II: Exploring Surface Processes using CSDMS Tools: How to Build Coupled Models 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. </br></br>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.</br></br>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. </br></br>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.</br></br>Materials for this clinic can be found at: https://github.com/csdms/csdms-2020
Sold out (If you registered for part 1, than you are automatically registered the 2nd part)

Kim de Mutsert
George Mason University

Introduction to Ecopath with Ecosim This clinic will offer you an introduction to developing food web models using Ecopath with Ecosim software. Ecopath with Ecosim (EwE) is an ecological modeling software suite for personal computers that has been built and extended on for over thirty-five years. EwE is the first ecosystem level simulation model to be widely and freely accessible. EwE is the most applied tool for modeling marine and aquatic ecosystems globally, with over 400 models published to date, making EwE an important modeling approach to explore ecosystem related questions in marine science. In addition, Ecopath software was recognized as one of NOAA’s top ten scientific breakthroughs in the last 200 years. In this clinic, we will start with a brief introduction, then download the freeware and start setting up some simple models which we will use in example exercises. Note: the software works in a Windows environment; Mac computers can be used if they are set up with Parallels Desktop or a similar application to run programs in a Windows environment on a Mac.
Sold out
(Max capacity 25 participants)

Post-Conference Event

The GeoClaw Software for Tsunamis, Storm Surge, and Overland Flooding

This one-day post-conference workshop provided by Professor Randall LeVeque, University of Washington, on Friday, May 22^nd, 2020 will cover GeoClaw. GeoClaw (http://www.geoclaw.org), part of the open source software package Clawpack, has been extensively used for modeling tsunamis, storm surge, and overland flooding from dam breaks or glacial outburst floods. Adaptive mesh refinement allows tracking waves across the ocean, or water advancing down a valley, and also zooming in with much higher resolution in particular regions of interest. This workshop will consist of an overview of some of the capabilities and basic usage, followed by time to explore some sample problems. Several GeoClaw developers will be present and current users of GeoClaw are also welcome to come with questions about using more advanced features or to give feedback on present and desired capabilities.

Syvitski Student Modeler Award 2020

Submissions are closed now.

Student Scholarships

Submissions are closed now.

Important dates

• May 15: Registration deadline for Clinics and Post-conference event
• May 19: Registration deadline for Keynote Sessions 1 & 2
• May 20-21: CSDMS annual meeting
• May 22: Post-conference workshop
• May 22: CSDMS Executive and Steering committees meetings (by invitation only)