Property:CSDMS meeting abstract presentation
From CSDMS
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Accurate projections of future coastal change require a tight integration of coastal geomorphological models with data. For long-term projections, beyond the year 2100 for example, one particular difficulty is that our typical validation timeseries are short (often <30 years) compared to our projection horizon.
In this talk I will discuss two model-data integration methods that we have used to circumvent this issue. The first method is to apply generic simple models to many different coastal systems, and do space-for-time substitution. For example, we test the effect of sea-level rise on wetland change by comparing wetland change between rapidly subsiding and rapidly uplifting coasts. The second method also employs simple models but tests them using long timeseries extracted from sediment core data. For example, we test and apply our barrier island models using paleogeographic reconstructions going back ~5000 years. In this case it is key to use simple models to not overdimensionalize the model fitting.
For both methods there is an added benefit: we learn something about coastal geomorphology along the way. +
Accurately characterizing the spatial and temporal variability of water and energy fluxes in many hydrologic systems requires an integrated modeling approach that captures the interactions and feedbacks between groundwater, surface water, and land- surface processes. Increasing recognition that these interactions and feedbacks play an important role in system behavior has lead to exciting new developments in coupled surface-subsurface modeling, with coupled surface-subsurface modeling becoming an increasingly useful tool for describing many hydrologic systems.<br><br>This clinic will provide a brief background on the theory of coupled surface-subsurface modeling techniques and parallel applications, followed by examples and hands-on experience using ParFlow, an open-source, object-oriented, parallel watershed flow model. ParFlow includes fully-integrated overland flow; the ability to simulate complex topography, geology and heterogeneity; and coupled land-surface processes including the land-energy budget, biogeochemistry, and snow processes. ParFlow is multi-platform and runs with a common I/O structure from laptop to supercomputer. ParFlow is the result of a long, multi-institutional development history and is now a collaborative effort between CSM, LLNL, UniBonn, and UC Berkeley. Many different configurations related to common hydrologic problems will be discussed through example problems. +
Addressing environmental challenges requires models that are not only scientifically robust but also accessible to diverse stakeholders, including non-technical users. However, for many, these models remain "black boxes," creating barriers to understanding and trust. These barriers hinder effective collaboration between modellers and decision-makers, ultimately limiting the impact of scientific insights.
This webinar introduces an innovative approach to modelling that emphasizes accessibility and understanding through learning-by-doing. We will explore an interactive toolbox (impact-erosion.github.io) built using Jupyter Notebooks, designed to guide users through essential modelling concepts and processes—from basic initial tasks such as data pre-processing to advanced techniques like uncertainty and sensitivity analysis.
By integrating interactive elements and visualization, iMPACT-erosion promotes an easier and more fluent user-model conversation, making models approachable for students, professors, researchers, and decision-makers. Beyond showcasing the toolbox, the webinar also empowers attendees to create their own accessible and interactive tools by integrating Jupyter Notebooks, interactive visualizations, and basic Python programming. Participants will learn how to deploy these tools on the cloud at no cost, making them easily shareable and usable by anyone with a web browser.
While the webinar focuses on hydrology and soil erosion, the underlying philosophy of interactive, educational and exploratory modelling is highly transferable across disciplines, offering a pathway to democratize modelling in various scientific domains. +
Addressing society's water and energy challenges requires sustainable use of the Earth's critical zones and subsurface environment, as well as technological innovations in treatment and other engineered systems. Reactive transport models (RTMs) provide a powerful tool to inform engineering design and provide solutions for these critical challenges. In this keynote, I will showcase the flexibility and value of RTMs using real-world applications that focus on (1) assessing groundwater quality management with respect to nitrate under agricultural managed aquifer recharge, and (2) systematically investigating the physical, chemical and biological conditions that enhance CO2 drawdown rates in agricultural settings using enhanced weathering. The keynote will conclude with a discussion of the possibilities to advance the use of reactive transport models and future research opportunities therein. +
Agent-Based Modeling (ABM) or Individual-Based Modeling is a research method rapidly increasing in popularity -- particularly among social scientists and ecologists interested in using simulation techniques to better understand the emergence of interesting system-wide patterns from simple behaviors and interactions at the individual scale. ABM researchers frequently partner with other scientists on a wide variety of topics related to coupled natural and human systems. Human societies impact (and are impacted by) various earth systems across a wide range of spatial and temporal scales, and ABM is a very useful tool for better understanding the effect of individual and social decision-making on various surface processes. The clinic will focus on introducing the basic toolkit needed to understand and pursue ABM research, and consider how ABM work differs from other computational modeling approaches. The clinic: - Will explore examples of the kinds of research questions and topics suited to ABM methods. - Will (attempt to) define some key concepts relevant to ABM research, such as emergence, social networks, social dilemmas, and complex adaptive systems. - Will provide an introduction to ABM platforms, particularly focused on NetLogo. - Discuss approaches to verification, validation, and scale dependency in the ABM world. - Introduce the Pattern-Oriented Modeling approach to ABM. - Discuss issues with reporting ABM research (ODD specification, model publishing). - Brainstorm tips and tricks for working with social scientists on ABM research. +
Agent-Based Models (ABMs) can provide important insights into the nonlinear dynamics that emerge from the interactions of individual agents. While ABMs are commonly used in the social and ecological sciences, this rules-based modeling approach has not been widely adopted in the Surface Dynamics Modeling community. In this clinic, I will show how to build mixed models that utilize ABMs for some processes (e.g., forest dynamics and soil production) and numerical solutions to partial differential equations for other processes (e.g., hillside sediment transport). Specifically, I will introduce participants to pyNetLogo, a library that enables coupling between NetLogo ABMs and Python-based Landlab components. While active developers in either the NetLogo or Landlab communities will find this clinic useful, experience in both programming languages is not needed. +
Agent-based modeling (ABM) developed as a method to simulate systems that include a number of agents – farmers, households, governments as well as biological organisms – that make decisions and interact according to certain rules. In environmental modeling, ABM is one of the best ways to explicitly account for human behavior, and to quantify cumulative actions of various actors distributed over the spatial landscape. This clinic provides an introduction to ABM and covers such topics as:<ol><li>Modeling heterogeneous agents that vary in attributes and follow different decision-strategies</li><li>Going beyond rational optimization and accommodating bounded rationality</li><li>Designing/representing agents’ interactions and learning.</ol>The clinic provides hands-on examples using the open-source modeling environment NetLogo https://ccl.northwestern.edu/netlogo. While no prior knowledge of NetLogo is required, participants are welcome to explore its super user-friendly tutorial. The clinic concludes with highlighting the current trends in ABM such as its applications in climate change research, participatory modeling and its potential to link with other types of simulations. +
Agent-based modeling (ABM) is a powerful simulation tool with applications across disciplines. ABM has also emerged as a useful tool for capturing complex processes and interactions within socio-environmental systems. This workshop will offer a brief introduction to ABM for socio-environmental systems modeling including an overview of opportunities and challenges. Participants will be introduced to NetLogo, a popular programming language and modeling environment for ABM. In groups, participants will have the hands-on opportunity to program different decision-making methods in an existing model and observe how outcomes change. We will conclude with an opportunity for participants to raise questions or challenges they are experiencing with their own ABMs and receive feedback from the group. +
Alluvial rivers transform themselves as the environment changes. Excess sediment input may fill their beds and cause them to steepen. Additional water discharge can cause rivers to incise, widen, and/or reduce their slopes. When rivers flood above their banks, they deposit sediments across their floodplains. Such geomorphic changes produce stabilizing feedbacks: Floodplain deposition heightens the channel banks, decreasing the probability of a follow-up overbank flood. Likewise, channels widen in response to increasing streamflow or narrow through lateral deposition as streamflow wanes. To simulate how these interactions drive river-system change, we build theoretical and numerical approaches to solving coupled river-system dynamics while simultaneously assembling historical and geological data sets for model validation and improvement. The resultant tools can help to teach us the stories that past landscapes are sharing, and to forecast the impacts of intentional and incidental human impacts on rivers into the future. +
An abstract was not required for this meeting +
An overview of what the interagency Working Group stands for. +
An update of what CSDMS has accomplished so far. +
An update of what CSDMS has accomplished so far. +
An update on CoMSES. +
Ancient Mars maintained an active water cycle. Low-lying impact craters gathered water from precipitation, thereby forming lakes. When water levels surpassed the crater rim topography, these lakes could breach and trigger floods that released large volumes of high-energy water, sculpting vast canyons that are ubiquitous on Mars. In fact, past work has shown that these floods eroded a quarter of the erosional volumes of the Martian surface. However, the persistence of flow from these lakes after their breaching floods remains uncertain. Here, we use a semi-automated workflow to estimate the flood volumes released for 200 lake systems. We then developed an Earth-based theoretical erosion framework to estimate the amount of water that continued to flow from each lake after the floods occurred, using simple topographic metrics such as channel volume, slope, and length. Results from this work provide new quantitative constraints on the volume and persistence of subsequent water flow after flood events, deepening our understanding of Mars' early climate, hydrology, and past habitability. +
Answers to scientific questions often involve coupled systems that lie within separate fields of study. An example of this is flexural isostasy and surface mass transport. Erosion, deposition, and moving ice masses change loads on the Earth surface, which induce a flexural isostatic response. These isostatic deflections in turn change topography, which is a large control on surface processes. We couple a landscape evolution model (CHILD) and a flexural isostasy model (Flexure) within the CSDMS framework to understand interactions between these processes. We highlight a few scenarios in which this feedback is crucial for understanding what happens on the surface of the Earth: foredeeps around mountain belts, rivers at the margins of large ice sheets, and the "old age" of decaying mountain ranges. We also show how the response changes from simple analytical solutions for flexural isostasy to numerical solutions that allow us to explore spatial variability in lithospheric strength. This work places the spotlight on the kinds of advances that can be made when members of the broader Earth surface process community design their models to be coupleable, share them, and connect them under the unified framework developed by CSDMS. We encourage Earth surface scientists to unleash their creativity in constructing, sharing, and coupling their models to better learn how these building blocks make up the wonderfully complicated Earth surface system. +
Are you confused about the best way to make your models and data accessible, reusable, and citable by others? In this clinic we will give you tools, information, and some dedicated time to help make your models and data FAIR - findable, accessible, interoperable and reusable. Models in the CSDMS ecosystem are already well on their way to being more FAIR than models that are not. But here, you will learn more about developments, guidelines, and tools from recent gatherings of publishers, repository leaders, and information technology practitioners at recent FAIR Data meetings, and translate this information into steps you can take to make your scientific models and data FAIR. +
Are you interested in expanding the reach of your scientific data or models? One way of increasing the FAIRness of your digital resources (i.e., making them more findable, accessible, interoperable, and reproducible) is by annotating them with metadata about the scientific variables they describe. In this talk, we provide a simple introduction to the Scientific Variables Ontology (SVO) and show how, with only a small number of design patterns, it can be used to neatly unpack the definitions of even quite complex scientific variables and translate them into machine-readable form. +
Are you tired of hearing about the FAIR Principles? This clinic is for you then, because after you participate you’ll never need to attend another one!*
Good science depends on the careful and meticulous management and documentation of our research process. This includes our computational models, the datasets we use, the data transformation, analysis, and visualization scripts and workflows we build to evaluate and assess our models, and the assumptions and design decisions we make while writing our software.
Join us for a Carpentries-style interactive clinic with hands-on exercises where we will provide concrete guidance and examples for how to approach, conceptualize, and transform your computational models of earth systems into FAIR contributions to the scientific record whether they are greenfield projects or legacy code with a focus on existing, open infrastructure (GitHub / GitLab / Zenodo). We’ll also cover containerization (Docker, Apptainer) as a way to transparently document system and software dependencies for your models, and how it can be used to support execution on the Open Science Grid Consortium’s Open Science Pool fair-share access compute resources. Big parallel fun! https://osg-htc.org
∗ individual results may vary, this statement is provably false +
As agreed at earlier CSDMS forums, the major
impediment in using AI for modeling the deep-ocean
seafloor is a lack of training data, the data which guides the AI -
whichever set of algorithms is chosen. This clinic will expose participants to
globally-extensive datasets which are available through CSDMS.
It will debate the scientific questions of why certain data work well,
are appropriate to the processes, and are properly scaled.
Participants are encouraged to bring their own AI challenges to the clinic. +
