Form:Annualmeeting2026

CSDMS 2026: Modeling Landscapes in Motion

At University of Minnesota, Minneapolis MN, May 19 -22^nd 2026

Registration

Registration & abstract submission will open early 2026

Introduction

The CSDMS 2026 Annual Meeting will be broad in scope, bringing together CSDMS members to present new scientific insights in the modeling of surface dynamics and the impact of time and process scales, new advances in cyber-infrastructure, examples on coupling models, how social and ecological models can inform management, and more. Also, this is the 4^th year that you can submit Electronic Publications (Epubs); Jupyter notebooks that contain e.g. a scientific hypotheses description, a numerical solution, and some findings that are investigated by numerical algorithms or model, see also: Form:Annualmeeting2026#Electronic_publications_(Epubs). We reserved time during one of the plenary sessions for presentations of Epubs, so don't hold back and submit your Epubs! The meeting will further include:

• State-of-the art keynote presentations in earth-surface dynamics
• Hands-on clinics related to community models, tools and approaches
• Transformative software products and approaches designed to be accessible, easy to use, and relevant
• Breakout sessions
• Poster and Epubs Sessions

Agenda

A draft agenda will be posted closer to date.

Keynote presentations

Jay Dickson
UMN - Polar Geospatial Center

All of the Earth at High-Resolution in 3D: Scaling Surface Processes From Local to Continental to Global The Earth's surface is typically shaped by local processes that act within regional, continental and global contexts. Traditionally, high-resolution local data are analyzed within low-resolution regional/global context data, yielding uncertainty about the scalability of well-understood local phenomena. This gap between high-resolution local data and low-resolution global data is narrowing due to multiple decades of successful Earth Observing missions that have yielded meter-scale information about the entire Earth's three-dimensional shape and surface composition and how both have changed over the last 25 years. Meanwhile, computation has improved at a rate such that these massive volumes of data can be processed and analyzed efficiently at the global scale. This alignment of data and computation sets the stage for a new era of global analysis of the Earth's geomorphology as a function of time, including forecasts of global surface change in the future. This presentation will discuss how the Polar Geospatial Center at the University of Minnesota is making tens of petabytes of data of the Earth's surface accessible and analyzable to individual researchers and broader communities with the goal of further understanding the wholesale evolution of the Earth's crust.

Einat Lev
Lamont-Doherty Earth Observatory

Introducing VICTOR, a new cyberinfrastructure for volcanology The volcanology community faces a need for centralized and modern cyberinfrastructure and computational tools. As the community prepares for future volcanic eruptions and their potential global impacts, the availability and accessibility of reliable and sophisticated models is particularly important. The Volcanology Infrastructure for Computational Tools and Resources, VICTOR, answers this need.</br></br>The presentation will introduce VICTOR, a cloud-based cyberinfrastructure designed to modernize computational workflows and data access in volcanology. Built around a scalable JupyterHub environment, VICTOR provides users with an array of pre-installed modeling tools, remote sensing data access workflows, geochemical calculators, and the pyVICTOR utility library for geospatial and visualization tasks. The platform drives educational efforts through courses, modular teaching materials, and multilingual documentation. </br></br>VICTOR promotes open science by making tools findable, accessible, interoperable, and reproducible (FAIR) and enables innovative workflows including multi-model intercomparisons and inversion schemes. We describe its architecture, current tool suite, community engagement activities, and plans for model coupling, machine learning integration, and expanded observatory support. Similarly to CSDMS, VICTOR exemplifies a community-driven approach to infrastructure that empowers researchers, educators, and stakeholders in volcanic hazard science.

David Litwin
Temple University

Landscape Evolution as a Surface-Subsurface Problem: From Headwaters to Orogens Decades of research have shown that storm runoff in many settings is primarily composed of pre-event groundwater. This groundwater is actively flowing, sometimes against topographic gradients, and in quantities substantial enough to alter the catchment water balance. Such effects have been observed across diverse lithologies and topographic settings, including mountainous environments that fluvial landscape evolution models often intend to capture. Yet to this day, most landscape evolution models represent runoff as a simple overland flow process. To explore the effects of groundwater flow on landscape evolution, we have developed coupled models in Landlab that represent both geomorphic change and surface-subsurface flow processes, in which runoff generated by a distributed hydrological model drives stream power fluvial erosion. We examine (1) hydrological function and topography in headwaters at geomorphic steady-state, and (2) transient dynamics at orogen-scale drainage divides, grounding our work in case studies and large-sample analyses. The results suggest that interactions between the surface and subsurface are often critical to understanding landscape evolution, and that long-term coevolution of hydrological and geomorphic processes may explain certain emergent hydrological traits of watersheds today.

Giulio Mariotti
Louisiana State University

Three ingredients for numerically efficient 2D delta morphodynamic modeling Several models exist to simulate 2D deltaic morphodynamics, but most remain computationally expensive. Here I show that minor simplifications in the governing equations lead to massive increase in computational efficiency. First, the model solves the two-dimensional steady-state shallow water equations while neglecting momentum advection, a term here shown to have a small influence on large-scale and long-term morphodynamics. The resulting momentum equations are linearized with respect to velocity and water level, then solved iteratively. Second, all bed-material transport is represented as suspended load (non-equilibrium) rather than bedload (at-equilibrium). Sediment concentration is assumed to be at steady state, though not necessarily in equilibrium with local resuspension. When discretized numerically, steady-state non-equilibrium transport is mathematically equivalent to at-equilibrium transport subjected to a spatial convolution filter, which explains why the non-equilibrium formulation is orders of magnitude more stable. Third, downslope transport is represented as a bed-elevation diffusion term, with diffusivity proportional to total sediment flux and computed from both upslope and downslope cells. This formulation, equivalent to introducing a transverse sediment flux, is stable when computed with an implicit algorithm and automatically captures bank erosion. The model reproduces large-scale deltaic morphologies that are realistic and comparable to those generated by full hydrodynamic and sediment-transport models, but at a fraction of the computational cost. The model also includes tide and wave hydrodynamics, as well as mud transport and vegetation processes, using which a large range of coastal settings can be reproduced.

Angel Monsalve
University of Idaho

New Landlab Components for Modeling River Hydraulics and Morphodynamics: Applications to Event-Scale Sediment Transport We present two novel Landlab components: RiverFlowDynamics and RiverBedDynamics, that enable physically based simulation of coupled flow hydraulics and sediment transport in river systems. RiverFlowDynamics solves the 2D shallow water equations to predict spatially distributed flow depths, velocities, and shear stresses under unsteady conditions. RiverBedDynamics implements fractional sediment transport formulations to simulate bedload transport and predict changes in bed surface elevation and grain size distributions through both erosion and deposition processes.</br></br>We demonstrate the capabilities of these components through coupled and uncoupled modeling scenarios. A key application focuses on predicting storm-driven morphodynamic changes in Last Chance Canyon, New Mexico, where we simulate events of varying durations and intensities. Model outputs provide event-scale sediment budgets and spatially explicit metrics including coarse-fraction mobility windows and residence-time distributions. These results enable testing hypotheses regarding the frequency of coarse sediment mobilization and its role as channel armor.</br></br>Finally, we explore extensions coupling these morphodynamic models with vegetation colonization, growth, and mortality processes, demonstrating the potential for integrated eco-geomorphic modeling within the Landlab framework. This modular approach enables researchers to investigate complex feedbacks between flow, sediment transport, and ecological processes across multiple spatiotemporal scales.

Maya Stokes
Florida State University

Topographic and biological signatures of repeated river captures Drainage divides, the topographic boundaries of river basins, are dynamic landscape features. Asymmetric erosion rates across drainage divides can cause gradual divide migration and occasionally, the flow of water may be redirected towards a neighboring basin in via ‘river capture’. Geomorphologists often use topographic evidence to infer erosional disequilibrium across drainage divides. Similarly, freshwater biologists infer a history of river capture using biological lines of evidence including the presence of disjunct populations of freshwater organisms found on the ‘wrong’ side of the drainage divide and/or phylogenetic relationships that reflect paleo-river networks. Yet, these lines of evidence may be challenging to interpret when there is a history of multiple river capture events in the same basin that each cause transient erosional responses and repeatedly transfer freshwater organisms across the drainage divide. Here, I explore the topographic and biological signatures of repeated river captures using coupled population-genetic simulations and landscape evolution models. I also present an empirical case-study across the Eastern Continental Divide, USA. Population genetic analysis of the Saffron Shiner (Hydrophlox rubricoceus) and topographic evidence (knickpoints and windgaps) suggest that the Linville River (Atlantic-draining) has repeatedly captured area from the upper tributaries of the Tennessee River (Gulf-draining). The results highlight the challenges and promise of integrating biological and geologic datasets to investigate the history of river network reorganization.

Brian Yanites
Indiana University

The lasting imprint of sediment pulses: linking river hazards and landscape evolution Understanding mountain river evolution requires connecting event-scale sediment dynamics to their topographic consequences across timescales. I synthesize observations and models to show how sediment-rich events influence both short-term hazards and longer-term patterns of topographic evolution. Observations and hydraulic analyses show that storm-driven sediment inputs can aggrade channels, reduce conveyance, and modify flood expression well beyond the event itself, effectively conditioning future hazards on prior sediment history rather than water discharge alone. At longer timescales, sediment delivered during extreme events interacts with bedrock channel processes to influence patterns of incision, planation, and terrace formation. Numerical models show that when sediment supply is strongly event-dominated, large precipitation events may suppress rather than enhance bedrock erosion by burying the channel bed. This behavior introduces time lags between forcing and response, complicates interpretations of river incision histories, and links hazard-scale sediment dynamics to the development of persistent topographic features. Together, these results highlight sediment as a key agent linking event-scale disturbances, cascading hazards, and long-term landscape evolution. Disentangling these connections requires models that explicitly represent sediment supply, storage, and transport across scales. Such integrative modeling is essential for predicting how mountain rivers respond to future extremes and for interpreting the geomorphic record left by past events.

Clinics

Joanmarie Del Vecchio
William and Mary

PyCoGSS: Helping you (and your students!) wrangle big landscape data This workshop will introduce participants to the Python Computational Geomorphology Software System (PyCoGSS) project that aims to broaden access to computational tools for working with landscape data. Participants will first tour Python activities geared toward undergraduate geoscience students, followed by intermediate “onboarding” activities intended to train research students to collect their own data. Next, participants will get hands-on experience in using PyCoGSS software, including tools developed by undergraduates, to collect their own multispectral and topographic datasets, and perform intermediate analyses. Participants will also get a chance to explore advanced programming techniques to manipulate and visualize geospatial data, and perform specialized statistical analyses and numerical across bigger spatial and temporal scales. PyCoGSS developers will also be seeking feedback from participants on how software resources can better serve research and teaching needs of geomorphologists of all career levels and institution types. Participants will leave the workshop equipped to use, and advise their students on using, software tools and data analysis for geoscience research.

Emily Fairfax
University of Minnesota

Using Machine Learning for Landscape Feature Detection: Beaver Dams and Beyond! Image recognition is a powerful application of machine learning (ML) where computers can learn to automatically identify objects, patterns, and more. Meanwhile, there are enormous volumes of satellite imagery being collected every day with a variety of important landscape features readily visible. Though the name "image recognition" sounds like it's just based on visual data, modern ML methods allow many types of data to be included in the "image" - including full multispectral raster stacks and digital elevation models. If a data type can be converted to a raster, then ML image recognition can learn from it and recognize patterns in it. In this clinic, we will cover how to get started using ML to detect interesting landscape features in remotely sensed imagery using beaver dam identification as a case study.

Mohamed Fathi Said
Florida Gulf Coast University

Advancing Spatiotemporal Modeling with Deep Learning: CNN–LSTM Integration This workshop introduces participants to a hybrid Deep Learning (DL) framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to capture complex spatiotemporal dynamics. Such architectures are increasingly important for modeling and forecasting in Earth and Environmental Sciences, where processes evolve across both space and time. While flooding will be used as a motivating example, through predicting inundation maps and water depth in two-dimensional domains, the framework applies broadly to other domains, including geomorphological evolution, groundwater dynamics, coastal wave processes, and climate-driven hydrological responses. Participants will begin with a conceptual overview of CNNs, LSTMs, and their integration into a unified pipeline, followed by hands-on implementation using PyTorch. A small-scale two-dimensional flume dataset will serve as the training example, chosen for its simplicity, yet the same approach scales to complex, real-world systems and large spatial domains. The workshop will also emphasize the advantages of hybrid deep learning approaches compared to conventional physics-based models (e.g., hydrodynamic simulators such as HEC-RAS), particularly in terms of computational efficiency and scalability for large-scale or long-duration simulations. Finally, participants will learn best practices, recognize common challenges, and explore strategies for adapting CNN–LSTM architectures to a wide range of spatiotemporal applications. For this workshop, a basic understanding of machine learning in Python is recommended to maximize the benefits of this session.

Nicole Gasparini
Tulane University

Tips and Tricks for Submitting an NSF Proposal This clinic will review best practices for preparing and submitting an NSF grant proposal. We will discuss what to do before you start writing, how to tackle the different required documents, ideas for making your proposal stand out, follow-up once the proposal is submitted, and what to do if your proposal is or is not selected for funding.</br></br></br>Most of the content is general and could be applied to grant applications in response to calls for proposals from other agencies and foundations. This clinic is geared towards early career scientists who have never submitted a grant proposal or have only submitted a few. However, participants with all levels of experience are welcome to attend and share their personal best practices. </br></br>To get the most out of this clinic, you will receive suggestions of material to prepare before the clinic. The degree of advanced preparation is up to the attendee. All attendees are welcome, even if they are not able to prepare in advance. Preparation details will be sent out once clinic attendees are assigned. </br></br>About the clinic leader: Dr. Nicole Gasparini has submitted numerous (~25) NSF proposals and has had an ~50% success in funding rate. She has served on ~10 NSF panels; she has not kept track of the number of proposals she has reviewed. She spent 2024 as a visiting NSF program officer in the Division of Earth Sciences. She will provide insights from all sides of the proposal process.

Bert Jagers
Deltares

Morphodynamical simulations using Delft3D FM This clinic will give an overview of: the steps to build Delft3D Flexible Mesh from source (Windows, Linux), and pre- and postprocessing of Delft3D FM models using the graphical user interface or Python scripting. The focus will be some basic morphodynamic models for riverine, deltaic, and coastal landscapes. However, we will also touch on water quality, vegetation dynamics, ice cover, etc.

Jeff Keck
WA Dept. of Natural Resources

Simulating landslide sediment transport and runout hazard in Landlab using MassWastingRunout In this hands-on clinic, participants will simulate landslide runout and sediment transport patterns using the model MassWastingRunout (MWR). MWR is coded in Python and implemented as a component for the package Landlab. MWR uses runout algorithms typically found in landscape evolution and watershed sediment yield models to replicate the complex depositional and erosional behavior of actual landslides. Additional details on MWR be found here: https://esurf.copernicus.org/articles/12/1165/2024/.</br></br>By the end of the clinic, it is hoped that participants will have an understanding of how to setup and calibrate MWR to a field site. This clinic consists of a series of brief presentations followed by hands-on Jupyter notebook tutorials. It is divided into three sections: (1) MWR model conceptualization, behavior and limitations, illustrated on virtual terrains; (2) MWR performance at an actual field site and; (3) How to use MWR’s calibration utility to parameterize MWR to site specific landslide runout behavior. </br></br>At the end of the second and third sections, we will assist those who wish to set up MWR for their own field site or one of the other example field sites. Model inputs for the example field sites will be provided but can also be found at the link below. MWR can be run at most sites by preparing model inputs following the format of the example inputs. </br></br>Example model inputs: https://www.hydroshare.org/resource/55813a5e01764546b76641a7385c2236/

Jeffrey Kwang
U.S. Geological Survey

TopoRivBlender: Reproducible 3D Visualizations in Blender and Python High-quality 3D visualizations can effectively engage audiences and aid our understanding environmental data yet creating them often feels out of reach for many researchers. The initial effort to overcome the technical complexity of gathering multiple datasets and 3D visualization software can be a barrier to producing photorealistic 3D images. This workshop aims to help participants create 3D visualizations of environmental data using Blender, a free, open-source 3D software. Here, we will focus on visualizing digital elevation models (DEMs), but the skills learned can also apply to visualizing other environmental datasets, for example, modeling results, field measurements, experimental data. We will use a Python-based workflow called TopoRivBlender to fetch, process, and visualize topographic, hydrographic, and satellite imagery geospatial data. With just a single command and a few minutes, this workflow will programmatically download an area of interest, process and project multiple geospatial datasets, and render photorealistic 3D visualizations with Blender. By the end of this workshop, participants will be able to:</br></br></br>- Create their own Blender images of topography, hydrography, and satellite imagery of any location on Earth</br></br>- Modify basic Blender settings for customizing the look of the 3D images</br></br>- Describe data pipelines and how TopoRivBlender uses the workflow management tool Snakemake to fetch, process, and visualize the geospatial data

Jin-Si Over
U.S. Geological Survey

Introduction to Structure-from-Motion Photogrammetry for Mapping Landscapes This clinic will be a hands-on exercise in making quantitative 3D digital models and orthomosaics from a collection of photographs, focusing on small to medium landscape scenes (think beach ripples to a small dune system). We will provide some background theory and work through the steps of constructing and assessing the accuracy of the model using commercial software (Agisoft Metashape). Pitfalls and best practices will be explained. Students are invited to bring their own imagery (check with us for guidance on best practices for image collection) or we will provide some, and we might collect some prior to the classroom portion. Any laptop will work, but those with NVIDIA graphics cards will be faster.

David Vetsch
ETH Zurich

River morphodynamic modelling across scales using BASEMENT software River morphodynamic processes shape the landscape and are relevant for their characteristics. The modelling of such processes allows for analyzing and predicting trajectories in landscape evolution. The BASEMENT software is a versatile tool for the numerical modelling of various river morphodynamic processes from catchment to reach scale. The available 1D and 2D models are based on the Saint-Venant-Exner equations for transient flow including bed and suspended load, i.e. transport of coarse and fine sediments. Implemented numerical schemes for solving the governing equations are best choice for alpine conditions and efficiency is employed by parallelization for multi-core CPU and GPU. The software has an intuitive user interface that simplifies the setting up of models and comes along with comprehensive documentation. BASEMENT is available for free and is in transition to open source software. </br>The clinic includes a short introduction to the theoretical background of the models and demonstrates how to setup hydro and morphodynamic simulations that can be run straight away. For the case of the 1D model, a local river widening is considered where all states of gradually varied flow including flow transition may occur during bed level evolution until the situation reaches its dynamic equilibrium state. The case study follows a conceptual approach following best practice in application to ensure robust model setup and to avoid pitfalls.

Moira Zellner
Northeastern University

Fora.ai: Embedding earth systems modeling in a collaborative framework for innovative, impactful and resilient solution-building Fora.ai is an intuitive digital environment that enables groups with diverse expertise to collaboratively interact with embedded simulation models to understand real world socio-environmental problems and create novel and impactful solutions. Participants interact with this digital representation and with each other, iteratively creating, revising and testing solutions until diverse needs are addressed. Workshop participants will use Fora.ai’s interactive game-board to collectively build solutions to environmental hazards (e.g., flooding and wildfires). The virtual environment allows for participation in a facilitated process in which users: 1) input their individual priorities, 2) collaboratively run simulations to understand the complexity of the hazards, 3) co-design solutions to address these problems, 4) see how their solutions affect outputs of interest, and 5) deliberate on the tradeoffs that arise from each solution due to competing priorities. Participants will be introduced to the environmental hazard model and, with facilitator assistance, engage in multiple iterations of the process of prioritization, solution-building, and reflection on results. This process will allow them to refine their proposed solutions towards intervention strategies that they would jointly support for implementation, with an understanding of its benefits and drawbacks. The workshop will end with a focus group debrief. Laptops or tablets required.

Interested in providing a clinic during the next annual meeting? Contact CSDMS@Colorado.EDU.

Participants

Who is registered as of 01/08/2026?

• Participants meeting
• Submitted abstracts

Conference Venue

This year the conference will be held at the John T. Tate Hall at the University of Minnesota, Minneapolis, Minnesota.

Address:
University of Minnesota
John T. Tate Hall
116 Church Street SE
Minneapolis, MN. 55455

Conference Lodging suggestion

Unfortunately, this year’s budget does not allow to provide lodging support. We recommend to book your stay at the following hotel, which is within walking distance of the meeting venue.

Graduate by Hilton
615 Washington Ave.SE
Minneapolis, MN. 55414

Poster guidelines

The poster boards are configured for up to 46" wide by 60" tall (portrait orientation) posters (116 cm wide by 152 cm tall). Anything larger than these dimensions will reduce the space of your colleagues so please be respectfull of these poster dimensions.

Electronic publications (Epubs)

We're excited to announce that we will also offer Epub submissions for this year's annual meeting. Epubs are Jupyter notebooks that contain a scientific problem description and some findings that are investigated by numerical algorithms or models, that walks the reader through the science by executing the algorithms or models. The Epubs will be reviewed and can be part of a poster or oral presentation or separately submitted. Guidelines on what the notebook should include can be found here.

Travel Scholarships

Applications due by February XXX, 2026
This year CSDMS is offering a limited number of travel scholarships to attend the CSDMS annual meeting, for graduate students, post-docs, early career faculty, and faculty to maximize the breath and depth of the scientific workforce. To be eligible, applicants need to meet the following requirements:

• Attend the whole meeting (May 19-21, 2026) at the University of Minnesota, Minnesota
• Submit an abstract for and provide a poster presentation at the meeting (this requirement may be waived under limited conditions, i.e. 1st year graduate student that has not started their research, etc.)
• Submit a letter of motivation that states why you wish to participate in the meeting and explain how/if your participation would maximize the breath and depth of surface dynamics modeling.

The CSDMS travel scholarships will cover:

• Registration costs (to be reimbursed after attending the meeting)
• Travel (for US participants airfare and local transport up to $600, for international participants up to $1,200 of transportation costs will be reimbursed)
• Per diem to help reimburse the cost of meals from 19-21 May 2026 not offered in the conference schedule
• Shared lodging in the conference hotel for the evenings of May 18th, 19th and 20th.

Please submit your letter of motivation and contact information to csdms@colorado.edu by February XXX, 2026. Applicants will be notified of the decision by the end of February.

Transportation to and from Minneapolis

We encourage all who are able, to take advantage of public transportation or ride-share services between the Minneapolis–Saint Paul International Airport and your hotel.

• Metro Transit's light rail (Blue & Green Lines) for public transit: https://www.metrotransit.org/

Land Acknowledgement

We acknowledge that the land, on which we will hold our meeting in Minneapolis, Minnesota, is on the traditional homelands of the Dakota people. It is important to acknowledge the peoples on whose land we live, learn, and work as we seek to improve and strengthen our relations with our tribal nations. For more information, see the Acknowledgement.

Code of Conduct

CSDMS is committed to fostering a professional, respectful, and inclusive environment at the annual meeting, such that all participants can participate to the fullest in a welcoming, respectful, inclusive, and collaborative environment that is free of harassment and discrimination. CSDMS expects all participants and staff to comply with this code of conduct, as outlined at CSDMS code of conduct.

Important dates

• January 26: Application deadline Student Modeler Award 2026
• February XXX: Application deadline travel scholarships
• April 1: Abstract submission deadline
• April 1: Meeting registration deadline
• May 19-21: CSDMS annual meeting
• May 22, 9AM to 11AM: CSDMS Executive committee meeting (by invitation only)
• May 22, 12PM to 2PM: CSDMS Steering committee meeting (by invitation only)