Articles | Volume 15, issue 4
https://doi.org/10.5194/gmd-15-1413-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-1413-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| Highlight paper
| 
17 Feb 2022
Model description paper | Highlight paper |
| 17 Feb 2022
CSDMS: a community platform for numerical modeling of Earth surface processes
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, CO, USA
Department of Geological Sciences, University of Colorado Boulder, Boulder, CO, USA
Eric W. H. Hutton
Institute for Arctic and Alpine Research (INSTAAR), University of Colorado Boulder, Boulder, CO, USA
Mark D. Piper
Institute for Arctic and Alpine Research (INSTAAR), University of Colorado Boulder, Boulder, CO, USA
Benjamin Campforts
Institute for Arctic and Alpine Research (INSTAAR), University of Colorado Boulder, Boulder, CO, USA
Tian Gan
Institute for Arctic and Alpine Research (INSTAAR), University of Colorado Boulder, Boulder, CO, USA
Katherine R. Barnhart
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, CO, USA
Department of Geological Sciences, University of Colorado Boulder, Boulder, CO, USA
current address: Geologic Hazards Science Center, U.S. Geological Survey, Golden, CO, USA
Albert J. Kettner
Institute for Arctic and Alpine Research (INSTAAR), University of Colorado Boulder, Boulder, CO, USA
Irina Overeem
Department of Geological Sciences, University of Colorado Boulder, Boulder, CO, USA
Institute for Arctic and Alpine Research (INSTAAR), University of Colorado Boulder, Boulder, CO, USA
Scott D. Peckham
Institute for Arctic and Alpine Research (INSTAAR), University of Colorado Boulder, Boulder, CO, USA
Lynn McCready
Institute for Arctic and Alpine Research (INSTAAR), University of Colorado Boulder, Boulder, CO, USA
Jaia Syvitski
Institute for Arctic and Alpine Research (INSTAAR), University of Colorado Boulder, Boulder, CO, USA
Related authors
Kelly Kochanski, Gregory Tucker, and Robert Anderson
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-205, https://doi.org/10.5194/tc-2021-205, 2021
Manuscript not accepted for further review
Short summary
Short summary
Falling snow does not life flat. When blown by the wind, it forms elaborate structures, like dunes. Where these dunes form, they change the way heat flows through the snow. This can accelerate sea ice melt and climate change. Here, we use both field observations obtained during blizzards in Colorado and simulations performed with a state-of-the-art model, to quantify the impact of snow dunes on Arctic heat flows.
Katherine R. Barnhart, Eric W. H. Hutton, Gregory E. Tucker, Nicole M. Gasparini, Erkan Istanbulluoglu, Daniel E. J. Hobley, Nathan J. Lyons, Margaux Mouchene, Sai Siddhartha Nudurupati, Jordan M. Adams, and Christina Bandaragoda
Earth Surf. Dynam., 8, 379–397, https://doi.org/10.5194/esurf-8-379-2020, https://doi.org/10.5194/esurf-8-379-2020, 2020
Short summary
Short summary
Landlab is a Python package to support the creation of numerical models in Earth surface dynamics. Since the release of the 1.0 version in 2017, Landlab has grown and evolved: it contains 31 new process components, a refactored model grid, and additional utilities. This contribution describes the new elements of Landlab, discusses why certain backward-compatiblity-breaking changes were made, and reflects on the process of community open-source software development.
Alison R. Duvall, Sarah A. Harbert, Phaedra Upton, Gregory E. Tucker, Rebecca M. Flowers, and Camille Collett
Earth Surf. Dynam., 8, 177–194, https://doi.org/10.5194/esurf-8-177-2020, https://doi.org/10.5194/esurf-8-177-2020, 2020
Short summary
Short summary
In this study, we examine river patterns and the evolution of the landscape within the Marlborough Fault System, South Island, New Zealand, where the Australian and Pacific tectonic plates collide. We find that faulting, uplift, river capture and the long-lived nature of the drainage network all dictate river patterns at this site. Based on these results and a wealth of previous geologic studies, we propose two broad stages of landscape evolution over the last 25 million years of orogenesis.
Kelly Kochanski, Robert S. Anderson, and Gregory E. Tucker
The Cryosphere, 13, 1267–1281, https://doi.org/10.5194/tc-13-1267-2019, https://doi.org/10.5194/tc-13-1267-2019, 2019
Short summary
Short summary
Wind-blown snow does not lie flat. It forms dunes, ripples, and anvil-shaped sastrugi. These features ornament much of the snow on Earth and change the snow's effects on polar climates, but they have rarely been studied. We spent three winters watching snow move through the Colorado Front Range and present our findings here, including the first time-lapse videos of snow dune and sastrugi growth.
Katherine R. Barnhart, Rachel C. Glade, Charles M. Shobe, and Gregory E. Tucker
Geosci. Model Dev., 12, 1267–1297, https://doi.org/10.5194/gmd-12-1267-2019, https://doi.org/10.5194/gmd-12-1267-2019, 2019
Short summary
Short summary
Terrainbento 1.0 is a Python package for modeling the evolution of the surface of the Earth over geologic time (e.g., thousands to millions of years). Despite many decades of effort by the geomorphology community, there is no one established governing equation for the evolution of topography. Terrainbento 1.0 thus provides 28 alternative models that support hypothesis testing and multi-model analysis in landscape evolution.
Gregory E. Tucker, Scott W. McCoy, and Daniel E. J. Hobley
Earth Surf. Dynam., 6, 563–582, https://doi.org/10.5194/esurf-6-563-2018, https://doi.org/10.5194/esurf-6-563-2018, 2018
Short summary
Short summary
This article presents a new technique for computer simulation of slope forms. The method provides a way to study how events that disturb soil or turn rock into soil add up over time to produce landforms. The model represents a cross section of a hypothetical landform as a lattice of cells, each of which may represent air, soil, or rock. Despite its simplicity, the model does a good job of simulating a range of common of natural slope forms.
Ronda Strauch, Erkan Istanbulluoglu, Sai Siddhartha Nudurupati, Christina Bandaragoda, Nicole M. Gasparini, and Gregory E. Tucker
Earth Surf. Dynam., 6, 49–75, https://doi.org/10.5194/esurf-6-49-2018, https://doi.org/10.5194/esurf-6-49-2018, 2018
Short summary
Short summary
We develop a model of annual probability of shallow landslide initiation triggered by soil water from a hydrologic model. Our physically based model accommodates data uncertainty using a Monte Carlo approach. We found elevation-dependent patterns in probability related to the stabilizing effect of forests and soil and slope limitation at high elevations. We demonstrate our model in Washington, USA, but it is designed to run elsewhere with available data for risk planning using the Landlab.
Abigail L. Langston and Gregory E. Tucker
Earth Surf. Dynam., 6, 1–27, https://doi.org/10.5194/esurf-6-1-2018, https://doi.org/10.5194/esurf-6-1-2018, 2018
Short summary
Short summary
While vertical incision in bedrock rivers is widely implemented in landscape evolution models, lateral erosion is largely ignored. This makes current models unfit to explain the formation of wide bedrock valleys and strath terraces. In this study we present a fundamental advance in the representation of lateral erosion of bedrock rivers in a landscape evolution model. The model results show a scaling relationship between valley width and drainage area similar to that found in natural systems.
Charles M. Shobe, Gregory E. Tucker, and Katherine R. Barnhart
Geosci. Model Dev., 10, 4577–4604, https://doi.org/10.5194/gmd-10-4577-2017, https://doi.org/10.5194/gmd-10-4577-2017, 2017
Short summary
Short summary
Rivers control the movement of sediment and nutrients across Earth's surface. Understanding how rivers change through time is important for mitigating natural hazards and predicting Earth's response to climate change. We develop a new computer model for predicting how rivers cut through sediment and rock. Our model is designed to be joined with models of flooding, landslides, vegetation change, and other factors to provide a comprehensive toolbox for predicting changes to the landscape.
Jordan M. Adams, Nicole M. Gasparini, Daniel E. J. Hobley, Gregory E. Tucker, Eric W. H. Hutton, Sai S. Nudurupati, and Erkan Istanbulluoglu
Geosci. Model Dev., 10, 1645–1663, https://doi.org/10.5194/gmd-10-1645-2017, https://doi.org/10.5194/gmd-10-1645-2017, 2017
Short summary
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OverlandFlow is a 2-dimensional hydrology component contained within the Landlab modeling framework. It can be applied in both hydrology and geomorphology applications across real and synthetic landscape grids, for both short- and long-term events. This paper finds that this non-steady hydrology regime produces different landscape characteristics when compared to more traditional steady-state hydrology and geomorphology models, suggesting that hydrology regime can impact resulting morphologies.
Daniel E. J. Hobley, Jordan M. Adams, Sai Siddhartha Nudurupati, Eric W. H. Hutton, Nicole M. Gasparini, Erkan Istanbulluoglu, and Gregory E. Tucker
Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017, https://doi.org/10.5194/esurf-5-21-2017, 2017
Short summary
Short summary
Many geoscientists use computer models to understand changes in the Earth's system. However, typically each scientist will build their own model from scratch. This paper describes Landlab, a new piece of open-source software designed to simplify creation and use of models of the Earth's surface. It provides off-the-shelf tools to work with models more efficiently, with less duplication of effort. The paper explains and justifies how Landlab works, and describes some models built with it.
Gregory E. Tucker, Daniel E. J. Hobley, Eric Hutton, Nicole M. Gasparini, Erkan Istanbulluoglu, Jordan M. Adams, and Sai Siddartha Nudurupati
Geosci. Model Dev., 9, 823–839, https://doi.org/10.5194/gmd-9-823-2016, https://doi.org/10.5194/gmd-9-823-2016, 2016
Short summary
Short summary
This paper presents a new Python-language software library, called CellLab-CTS, that enables rapid creation of continuous-time stochastic (CTS) cellular automata models. These models are quite useful for simulating the behavior of natural systems, but can be time-consuming to program. CellLab-CTS allows users to set up models with a minimum of effort, and thereby focus on the science rather than the software.
Francis K. Rengers, Luke A. McGuire, Katherine R. Barnhart, Ann M. Youberg, Daniel Cadol, Alexander N. Gorr, Olivia Hoch, Rebecca Beers, and Jason W. Kean
EGUsphere, https://doi.org/10.5194/egusphere-2022-1398, https://doi.org/10.5194/egusphere-2022-1398, 2022
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Debris flows often occur after wildfires. These debris flows move water, sediment, and wood. The wood can get stuck in channels and create piles of sediment within channels. We investigated how the channel and wood size/shape influence how much sediment is stored. We also used a series of equations to estimate how the wood size/shape would reflect the velocity needed to break the wood. These data will help improve models and insight from future field investigations.
Luke A. McGuire, Scott W. McCoy, Odin Marc, William Struble, and Katherine R. Barnhart
Earth Surf. Dynam. Discuss., https://doi.org/10.5194/esurf-2022-47, https://doi.org/10.5194/esurf-2022-47, 2022
Revised manuscript under review for ESurf
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Debris flows are mixtures of mud and rocks that can travel at high speeds across steep landscapes. Here, we propose a new model to describe how landscapes are shaped by debris flow erosion over long timescales. Model results demonstrate that the shapes of channel profiles are sensitive to uplift rate, meaning that it may be possible use topographic data from steep channel networks to infer how erosion rates vary in space across a landscape.
Vao Fenotiana Razanamahandry, Marjolein Dewaele, Gerard Govers, Liesa Brosens, Benjamin Campforts, Liesbet Jacobs, Tantely Razafimbelo, Tovonarivo Rafolisy, and Steven Bouillon
Biogeosciences, 19, 3825–3841, https://doi.org/10.5194/bg-19-3825-2022, https://doi.org/10.5194/bg-19-3825-2022, 2022
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In order to shed light on possible past vegetation shifts in the Central Highlands of Madagascar, we measured stable isotope ratios of organic carbon in soil profiles along both forested and grassland hillslope transects in the Lake Alaotra region. Our results show that the landscape of this region was more forested in the past: soils in the C4-dominated grasslands contained a substantial fraction of C3-derived carbon, increasing with depth.
Rolf Hut, Niels Drost, Nick van de Giesen, Ben van Werkhoven, Banafsheh Abdollahi, Jerom Aerts, Thomas Albers, Fakhereh Alidoost, Bouwe Andela, Jaro Camphuijsen, Yifat Dzigan, Ronald van Haren, Eric Hutton, Peter Kalverla, Maarten van Meersbergen, Gijs van den Oord, Inti Pelupessy, Stef Smeets, Stefan Verhoeven, Martine de Vos, and Berend Weel
Geosci. Model Dev., 15, 5371–5390, https://doi.org/10.5194/gmd-15-5371-2022, https://doi.org/10.5194/gmd-15-5371-2022, 2022
Short summary
Short summary
With the eWaterCycle platform, we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both open science and FAIR science. The eWatercyle platform gives easy access to well-known hydrological models, big datasets and example experiments. Using eWaterCycle hydrologists can easily compare the results from different models, couple models and do more complex hydrological computational research.
Liesa Brosens, Benjamin Campforts, Gerard Govers, Emilien Aldana-Jague, Vao Fenotiana Razanamahandry, Tantely Razafimbelo, Tovonarivo Rafolisy, and Liesbet Jacobs
Earth Surf. Dynam., 10, 209–227, https://doi.org/10.5194/esurf-10-209-2022, https://doi.org/10.5194/esurf-10-209-2022, 2022
Short summary
Short summary
Obtaining accurate information on the volume of geomorphic features typically requires high-resolution topographic data, which are often not available. Here, we show that the globally available 12 m TanDEM-X DEM can be used to accurately estimate gully volumes and establish an area–volume relationship after applying a correction. This allowed us to get a first estimate of the amount of sediment that has been mobilized by large gullies (lavaka) in central Madagascar over the past 70 years.
Kelly Kochanski, Gregory Tucker, and Robert Anderson
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-205, https://doi.org/10.5194/tc-2021-205, 2021
Manuscript not accepted for further review
Short summary
Short summary
Falling snow does not life flat. When blown by the wind, it forms elaborate structures, like dunes. Where these dunes form, they change the way heat flows through the snow. This can accelerate sea ice melt and climate change. Here, we use both field observations obtained during blizzards in Colorado and simulations performed with a state-of-the-art model, to quantify the impact of snow dunes on Arctic heat flows.
Arthur Depicker, Gerard Govers, Liesbet Jacobs, Benjamin Campforts, Judith Uwihirwe, and Olivier Dewitte
Earth Surf. Dynam., 9, 445–462, https://doi.org/10.5194/esurf-9-445-2021, https://doi.org/10.5194/esurf-9-445-2021, 2021
Short summary
Short summary
We investigated how shallow landslide occurrence is impacted by deforestation and rifting in the North Tanganyika–Kivu rift region (Africa). We developed a new approach to calculate landslide erosion rates based on an inventory compiled in biased © Google Earth imagery. We find that deforestation increases landslide erosion by a factor of 2–8 and for a period of roughly 15 years. However, the exact impact of deforestation depends on the geomorphic context of the landscape (rejuvenated/relict).
Mariela Perignon, Jordan Adams, Irina Overeem, and Paola Passalacqua
Earth Surf. Dynam., 8, 809–824, https://doi.org/10.5194/esurf-8-809-2020, https://doi.org/10.5194/esurf-8-809-2020, 2020
Short summary
Short summary
We propose a machine learning approach for the classification and analysis of large delta systems. The approach uses remotely sensed data, channel network extraction, and the analysis of 10 metrics to identify clusters of islands with similar characteristics. The 12 clusters are grouped in six main classes related to morphological processes acting on the system. The approach allows us to identify spatial patterns in large river deltas to inform modeling and the collection of field observations.
Benjamin Campforts, Charles M. Shobe, Philippe Steer, Matthias Vanmaercke, Dimitri Lague, and Jean Braun
Geosci. Model Dev., 13, 3863–3886, https://doi.org/10.5194/gmd-13-3863-2020, https://doi.org/10.5194/gmd-13-3863-2020, 2020
Short summary
Short summary
Landslides shape the Earth’s surface and are a dominant source of terrestrial sediment. Rivers, then, act as conveyor belts evacuating landslide-produced sediment. Understanding the interaction among rivers and landslides is important to predict the Earth’s surface response to past and future environmental changes and for mitigating natural hazards. We develop HyLands, a new numerical model that provides a toolbox to explore how landslides and rivers interact over several timescales.
Benjamin Campforts, Veerle Vanacker, Frédéric Herman, Matthias Vanmaercke, Wolfgang Schwanghart, Gustavo E. Tenorio, Patrick Willems, and Gerard Govers
Earth Surf. Dynam., 8, 447–470, https://doi.org/10.5194/esurf-8-447-2020, https://doi.org/10.5194/esurf-8-447-2020, 2020
Short summary
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In this contribution, we explore the spatial determinants of bedrock river incision in the tropical Andes. The model results illustrate the problem of confounding between climatic and lithological variables, such as rock strength. Incorporating rock strength explicitly into river incision models strongly improves the explanatory power of all tested models and enables us to clarify the role of rainfall variability in controlling river incision rates.
Katherine R. Barnhart, Eric W. H. Hutton, Gregory E. Tucker, Nicole M. Gasparini, Erkan Istanbulluoglu, Daniel E. J. Hobley, Nathan J. Lyons, Margaux Mouchene, Sai Siddhartha Nudurupati, Jordan M. Adams, and Christina Bandaragoda
Earth Surf. Dynam., 8, 379–397, https://doi.org/10.5194/esurf-8-379-2020, https://doi.org/10.5194/esurf-8-379-2020, 2020
Short summary
Short summary
Landlab is a Python package to support the creation of numerical models in Earth surface dynamics. Since the release of the 1.0 version in 2017, Landlab has grown and evolved: it contains 31 new process components, a refactored model grid, and additional utilities. This contribution describes the new elements of Landlab, discusses why certain backward-compatiblity-breaking changes were made, and reflects on the process of community open-source software development.
Alison R. Duvall, Sarah A. Harbert, Phaedra Upton, Gregory E. Tucker, Rebecca M. Flowers, and Camille Collett
Earth Surf. Dynam., 8, 177–194, https://doi.org/10.5194/esurf-8-177-2020, https://doi.org/10.5194/esurf-8-177-2020, 2020
Short summary
Short summary
In this study, we examine river patterns and the evolution of the landscape within the Marlborough Fault System, South Island, New Zealand, where the Australian and Pacific tectonic plates collide. We find that faulting, uplift, river capture and the long-lived nature of the drainage network all dictate river patterns at this site. Based on these results and a wealth of previous geologic studies, we propose two broad stages of landscape evolution over the last 25 million years of orogenesis.
Kelly Kochanski, Robert S. Anderson, and Gregory E. Tucker
The Cryosphere, 13, 1267–1281, https://doi.org/10.5194/tc-13-1267-2019, https://doi.org/10.5194/tc-13-1267-2019, 2019
Short summary
Short summary
Wind-blown snow does not lie flat. It forms dunes, ripples, and anvil-shaped sastrugi. These features ornament much of the snow on Earth and change the snow's effects on polar climates, but they have rarely been studied. We spent three winters watching snow move through the Colorado Front Range and present our findings here, including the first time-lapse videos of snow dune and sastrugi growth.
Katherine R. Barnhart, Rachel C. Glade, Charles M. Shobe, and Gregory E. Tucker
Geosci. Model Dev., 12, 1267–1297, https://doi.org/10.5194/gmd-12-1267-2019, https://doi.org/10.5194/gmd-12-1267-2019, 2019
Short summary
Short summary
Terrainbento 1.0 is a Python package for modeling the evolution of the surface of the Earth over geologic time (e.g., thousands to millions of years). Despite many decades of effort by the geomorphology community, there is no one established governing equation for the evolution of topography. Terrainbento 1.0 thus provides 28 alternative models that support hypothesis testing and multi-model analysis in landscape evolution.
Kang Wang, Elchin Jafarov, Irina Overeem, Vladimir Romanovsky, Kevin Schaefer, Gary Clow, Frank Urban, William Cable, Mark Piper, Christopher Schwalm, Tingjun Zhang, Alexander Kholodov, Pamela Sousanes, Michael Loso, and Kenneth Hill
Earth Syst. Sci. Data, 10, 2311–2328, https://doi.org/10.5194/essd-10-2311-2018, https://doi.org/10.5194/essd-10-2311-2018, 2018
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Ground thermal and moisture data are important indicators of the rapid permafrost changes in the Arctic. To better understand the changes, we need a comprehensive dataset across various sites. We synthesize permafrost-related data in the state of Alaska. It should be a valuable permafrost dataset that is worth maintaining in the future. On a wider level, it also provides a prototype of basic data collection and management for permafrost regions in general.
Gregory E. Tucker, Scott W. McCoy, and Daniel E. J. Hobley
Earth Surf. Dynam., 6, 563–582, https://doi.org/10.5194/esurf-6-563-2018, https://doi.org/10.5194/esurf-6-563-2018, 2018
Short summary
Short summary
This article presents a new technique for computer simulation of slope forms. The method provides a way to study how events that disturb soil or turn rock into soil add up over time to produce landforms. The model represents a cross section of a hypothetical landform as a lattice of cells, each of which may represent air, soil, or rock. Despite its simplicity, the model does a good job of simulating a range of common of natural slope forms.
Derek T. Robinson, Alan Di Vittorio, Peter Alexander, Almut Arneth, C. Michael Barton, Daniel G. Brown, Albert Kettner, Carsten Lemmen, Brian C. O'Neill, Marco Janssen, Thomas A. M. Pugh, Sam S. Rabin, Mark Rounsevell, James P. Syvitski, Isaac Ullah, and Peter H. Verburg
Earth Syst. Dynam., 9, 895–914, https://doi.org/10.5194/esd-9-895-2018, https://doi.org/10.5194/esd-9-895-2018, 2018
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Understanding the complexity behind the rapid use of Earth’s resources requires modelling approaches that couple human and natural systems. We propose a framework that comprises the configuration, frequency of interaction, and coordination of communication between models along with eight lessons as guidelines to increase the success of coupled human–natural systems modelling initiatives. We also suggest a way to expedite model coupling and increase the longevity and interoperability of models.
John J. Armitage, Alexander C. Whittaker, Mustapha Zakari, and Benjamin Campforts
Earth Surf. Dynam., 6, 77–99, https://doi.org/10.5194/esurf-6-77-2018, https://doi.org/10.5194/esurf-6-77-2018, 2018
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We explore how two landscape evolution models respond to a change in climate. The two models are developed from a divergent assumption on the efficiency of sediment transport. Despite the different resulting mathematics, both numerical models display a similar functional response to a change in precipitation. However, if we model sediment transport rather than assume it is instantaneously removed, the model responds more rapidly, with a response time similar to that observed in nature.
Ronda Strauch, Erkan Istanbulluoglu, Sai Siddhartha Nudurupati, Christina Bandaragoda, Nicole M. Gasparini, and Gregory E. Tucker
Earth Surf. Dynam., 6, 49–75, https://doi.org/10.5194/esurf-6-49-2018, https://doi.org/10.5194/esurf-6-49-2018, 2018
Short summary
Short summary
We develop a model of annual probability of shallow landslide initiation triggered by soil water from a hydrologic model. Our physically based model accommodates data uncertainty using a Monte Carlo approach. We found elevation-dependent patterns in probability related to the stabilizing effect of forests and soil and slope limitation at high elevations. We demonstrate our model in Washington, USA, but it is designed to run elsewhere with available data for risk planning using the Landlab.
Abigail L. Langston and Gregory E. Tucker
Earth Surf. Dynam., 6, 1–27, https://doi.org/10.5194/esurf-6-1-2018, https://doi.org/10.5194/esurf-6-1-2018, 2018
Short summary
Short summary
While vertical incision in bedrock rivers is widely implemented in landscape evolution models, lateral erosion is largely ignored. This makes current models unfit to explain the formation of wide bedrock valleys and strath terraces. In this study we present a fundamental advance in the representation of lateral erosion of bedrock rivers in a landscape evolution model. The model results show a scaling relationship between valley width and drainage area similar to that found in natural systems.
Charles M. Shobe, Gregory E. Tucker, and Katherine R. Barnhart
Geosci. Model Dev., 10, 4577–4604, https://doi.org/10.5194/gmd-10-4577-2017, https://doi.org/10.5194/gmd-10-4577-2017, 2017
Short summary
Short summary
Rivers control the movement of sediment and nutrients across Earth's surface. Understanding how rivers change through time is important for mitigating natural hazards and predicting Earth's response to climate change. We develop a new computer model for predicting how rivers cut through sediment and rock. Our model is designed to be joined with models of flooding, landslides, vegetation change, and other factors to provide a comprehensive toolbox for predicting changes to the landscape.
Jordan M. Adams, Nicole M. Gasparini, Daniel E. J. Hobley, Gregory E. Tucker, Eric W. H. Hutton, Sai S. Nudurupati, and Erkan Istanbulluoglu
Geosci. Model Dev., 10, 1645–1663, https://doi.org/10.5194/gmd-10-1645-2017, https://doi.org/10.5194/gmd-10-1645-2017, 2017
Short summary
Short summary
OverlandFlow is a 2-dimensional hydrology component contained within the Landlab modeling framework. It can be applied in both hydrology and geomorphology applications across real and synthetic landscape grids, for both short- and long-term events. This paper finds that this non-steady hydrology regime produces different landscape characteristics when compared to more traditional steady-state hydrology and geomorphology models, suggesting that hydrology regime can impact resulting morphologies.
Benjamin Campforts, Wolfgang Schwanghart, and Gerard Govers
Earth Surf. Dynam., 5, 47–66, https://doi.org/10.5194/esurf-5-47-2017, https://doi.org/10.5194/esurf-5-47-2017, 2017
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Despite a growing interest in landscape evolution models, accuracy assessment of the numerical methods they are based on has received little attention. We test a higher-order flux-limiting finite-volume method to simulate river incision and tectonic displacement. We show that this scheme significantly influences the evolution of simulated landscapes and the spatial and temporal variability of erosion rates. Moreover, it allows for the simulation of lateral tectonic displacement on a fixed grid.
Daniel E. J. Hobley, Jordan M. Adams, Sai Siddhartha Nudurupati, Eric W. H. Hutton, Nicole M. Gasparini, Erkan Istanbulluoglu, and Gregory E. Tucker
Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017, https://doi.org/10.5194/esurf-5-21-2017, 2017
Short summary
Short summary
Many geoscientists use computer models to understand changes in the Earth's system. However, typically each scientist will build their own model from scratch. This paper describes Landlab, a new piece of open-source software designed to simplify creation and use of models of the Earth's surface. It provides off-the-shelf tools to work with models more efficiently, with less duplication of effort. The paper explains and justifies how Landlab works, and describes some models built with it.
Gregory E. Tucker, Daniel E. J. Hobley, Eric Hutton, Nicole M. Gasparini, Erkan Istanbulluoglu, Jordan M. Adams, and Sai Siddartha Nudurupati
Geosci. Model Dev., 9, 823–839, https://doi.org/10.5194/gmd-9-823-2016, https://doi.org/10.5194/gmd-9-823-2016, 2016
Short summary
Short summary
This paper presents a new Python-language software library, called CellLab-CTS, that enables rapid creation of continuous-time stochastic (CTS) cellular automata models. These models are quite useful for simulating the behavior of natural systems, but can be time-consuming to program. CellLab-CTS allows users to set up models with a minimum of effort, and thereby focus on the science rather than the software.
K. R. Barnhart, I. Overeem, and R. S. Anderson
The Cryosphere, 8, 1777–1799, https://doi.org/10.5194/tc-8-1777-2014, https://doi.org/10.5194/tc-8-1777-2014, 2014
Related subject area
Earth and space science informatics
SHAFTS (v2022.3): a deep-learning-based Python package for simultaneous extraction of building height and footprint from sentinel imagery
Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI
A methodological framework for improving the performance of data-driven models, a case study for daily runoff prediction in the Maumee domain, U.S.
Twenty-five years of the IPCC Data Distribution Centre at the DKRZ and the Reference Data Archive for CMIP data
Effectiveness and computational efficiency of absorbing boundary conditions for full-waveform inversion
LAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation
Causal deep learning models for studying the Earth system: soil moisture-precipitation coupling in ERA5 data across Europe
Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5
Fast infrared radiative transfer calculations using graphics processing units: JURASSIC-GPU v2.0
A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes
Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator
Turbidity maximum zone index: a novel model for remote extraction of the turbidity maximum zone in different estuaries
dh2loop 1.0: an open-source Python library for automated processing and classification of geological logs
Copula-based synthetic data augmentation for machine-learning emulators
Automated geological map deconstruction for 3D model construction using map2loop 1.0 and map2model 1.0
A spatially explicit approach to simulate urban heat mitigation with InVEST (v3.8.0)
S-SOM v1.0: a structural self-organizing map algorithm for weather typing
Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China
Current status on the need for improved accessibility to climate models code
ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather
A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time
A new end-to-end workflow for the Community Earth System Model (version 2.0) for the Coupled Model Intercomparison Project Phase 6 (CMIP6)
HyLands 1.0: a hybrid landscape evolution model to simulate the impact of landslides and landslide-derived sediment on landscape evolution
Comparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0)
Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model
VISIR-1.b: ocean surface gravity waves and currents for energy-efficient navigation
Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets
Global hydro-climatic biomes identified via multitask learning
A run control framework to streamline profiling, porting, and tuning simulation runs and provenance tracking of geoscientific applications
An improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mapping
High-performance software framework for the calculation of satellite-to-satellite data matchups (MMS version 1.2)
A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1)
Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming
A high-fidelity multiresolution digital elevation model for Earth systems
CPMIP: measurements of real computational performance of Earth system models in CMIP6
Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling
Community Intercomparison Suite (CIS) v1.4.0: a tool for intercomparing models and observations
Asynchronous communication in spectral-element and discontinuous Galerkin methods for atmospheric dynamics – a case study using the High-Order Methods Modeling Environment (HOMME-homme_dg_branch)
GO2OGS 1.0: a versatile workflow to integrate complex geological information with fault data into numerical simulation models
An open and extensible framework for spatially explicit land use change modelling: the lulcc R package
Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative
Non-singular spherical harmonic expressions of geomagnetic vector and gradient tensor fields in the local north-oriented reference frame
An approach to enhance pnetCDF performance in environmental modeling applications
A strategy for GIS-based 3-D slope stability modelling over large areas
An approach to computing direction relations between separated object groups
Improving computational efficiency in large linear inverse problems: an example from carbon dioxide flux estimation
Coupling technologies for Earth System Modelling
Quality assessment concept of the World Data Center for Climate and its application to CMIP5 data
A web service based tool to plan atmospheric research flights
Automated continuous verification for numerical simulation
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev., 16, 751–778, https://doi.org/10.5194/gmd-16-751-2023, https://doi.org/10.5194/gmd-16-751-2023, 2023
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We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery), a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.
Feng Yin, Philip E. Lewis, and Jose L. Gómez-Dans
Geosci. Model Dev., 15, 7933–7976, https://doi.org/10.5194/gmd-15-7933-2022, https://doi.org/10.5194/gmd-15-7933-2022, 2022
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The proposed SIAC atmospheric correction method provides consistent surface reflectance estimations from medium spatial-resolution satellites (Sentinel 2 and Landsat 8) with per-pixel uncertainty information. The outputs from SIAC have been validated against a wide range of ground measurements, and it shows that SIAC can provide accurate estimations of both surface reflectance and atmospheric parameters, with meaningful uncertainty information.
Yao Hu, Chirantan Ghosh, and Siamak Malakpour-Estalaki
EGUsphere, https://doi.org/10.5194/egusphere-2022-815, https://doi.org/10.5194/egusphere-2022-815, 2022
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Data-driven models (DDMs) gain popularity in earth and environmental systems due mainly to the advancements in data collection techniques and artificial intelligence (AI). The performance of such models is determined by the underlying machine learning (ML) algorithms. In this study, we develop a framework to improve the model performance by optimizing ML algorithms. We demonstrate the effectiveness of the framework using a DDM to predict edge-of-field runoff in the Maumee domain, U.S.
Martina Stockhause and Michael Lautenschlager
Geosci. Model Dev., 15, 6047–6058, https://doi.org/10.5194/gmd-15-6047-2022, https://doi.org/10.5194/gmd-15-6047-2022, 2022
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The Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC) celebrates its 25th anniversary in 2022. DDC Partner DKRZ has supported the IPCC Assessments and preserved the quality-assured, citable climate model data underpinning the Assessment Reports over these years over the long term. With the introduction of the IPCC FAIR Guidelines into the current AR6, the value of DDC services has been recognized. However, DDC sustainability remains unresolved.
Daiane Iglesia Dolci, Felipe A. G. Silva, Pedro S. Peixoto, and Ernani V. Volpe
Geosci. Model Dev., 15, 5857–5881, https://doi.org/10.5194/gmd-15-5857-2022, https://doi.org/10.5194/gmd-15-5857-2022, 2022
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We investigate and compare the theoretical and computational characteristics of several absorbing boundary conditions (ABCs) for the full-waveform inversion (FWI) problem. The different ABCs are implemented in an optimized computational framework called Devito. The computational efficiency and memory requirements of the ABC methods are evaluated in the forward and adjoint wave propagators, from simple to realistic velocity models.
Mauro Rossi, Txomin Bornaetxea, and Paola Reichenbach
Geosci. Model Dev., 15, 5651–5666, https://doi.org/10.5194/gmd-15-5651-2022, https://doi.org/10.5194/gmd-15-5651-2022, 2022
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LAND-SUITE is a software package designed to support landslide susceptibility zonation. The software integrates, extends, and completes LAND-SE (Rossi et al., 2010; Rossi and Reichenbach, 2016). The software is implemented in R, a free software environment for statistical computing and graphics, and gives expert users the possibility to perform easier, more flexible, and more informed statistically based landslide susceptibility applications and zonations.
Tobias Tesch, Stefan Kollet, and Jochen Garcke
EGUsphere, https://doi.org/10.5194/egusphere-2022-105, https://doi.org/10.5194/egusphere-2022-105, 2022
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A recent statistical approach for studying relations in the Earth system is to train deep learning (DL) models to predict Earth system variables given one or several others, and use interpretable DL to analyze the relations learned by the models. Here, we propose to combine the approach with a theorem from causality research to ensure that the deep learning model learns causal rather than spurious relations. As an example, we apply the method to study soil moisture-precipitation coupling.
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, and Karthik Kashinath
Geosci. Model Dev., 15, 2221–2237, https://doi.org/10.5194/gmd-15-2221-2022, https://doi.org/10.5194/gmd-15-2221-2022, 2022
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There is growing interest in data-driven weather forecasting, i.e., to predict the weather by using a deep neural network that learns from the evolution of past atmospheric patterns. Here, we propose three components to add to the current data-driven weather forecast models to improve their performance. These components involve a feature that incorporates physics into the neural network, a method to add data assimilation, and an algorithm to use several different time intervals in the forecast.
Paul F. Baumeister and Lars Hoffmann
Geosci. Model Dev., 15, 1855–1874, https://doi.org/10.5194/gmd-15-1855-2022, https://doi.org/10.5194/gmd-15-1855-2022, 2022
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The efficiency of the numerical simulation of radiative transport is shown on modern server-class graphics cards (GPUs). The low-cost prefactor on GPUs compared to general-purpose processors (CPUs) enables future large retrieval campaigns for multi-channel data from infrared sounders aboard low-orbit satellites. The validated research software JURASSIC is available in the public domain.
Danilo César de Mello, Gustavo Vieira Veloso, Marcos Guedes de Lana, Fellipe Alcantara de Oliveira Mello, Raul Roberto Poppiel, Diego Ribeiro Oquendo Cabrero, Luis Augusto Di Loreto Di Raimo, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes Filho, Emilson Pereira Leite, and José Alexandre Melo Demattê
Geosci. Model Dev., 15, 1219–1246, https://doi.org/10.5194/gmd-15-1219-2022, https://doi.org/10.5194/gmd-15-1219-2022, 2022
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We used soil parent material, terrain attributes, and geophysical data from the soil surface to test and compare different and unprecedented geophysical sensor combination, as well as different machine learning algorithms to model and predict several soil attributes. Also, we analyzed the importance of pedoenvironmental variables. The soil attributes were modeled throughout different machine learning algorithms and related to different geophysical sensor combinations.
Duncan Watson-Parris, Andrew Williams, Lucia Deaconu, and Philip Stier
Geosci. Model Dev., 14, 7659–7672, https://doi.org/10.5194/gmd-14-7659-2021, https://doi.org/10.5194/gmd-14-7659-2021, 2021
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The Earth System Emulator (ESEm) provides a fast and flexible framework for emulating a wide variety of Earth science datasets and tools for constraining (or tuning) models of any complexity. Three distinct use cases are presented that demonstrate the utility of ESEm and provide some insight into the use of machine learning for emulation in these different settings. The open-source Python package is freely available so that it might become a valuable tool for the community.
Chongyang Wang, Li Wang, Danni Wang, Dan Li, Chenghu Zhou, Hao Jiang, Qiong Zheng, Shuisen Chen, Kai Jia, Yangxiaoyue Liu, Ji Yang, Xia Zhou, and Yong Li
Geosci. Model Dev., 14, 6833–6846, https://doi.org/10.5194/gmd-14-6833-2021, https://doi.org/10.5194/gmd-14-6833-2021, 2021
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The turbidity maximum zone (TMZ) is a special phenomenon in estuaries worldwide. However, the extraction methods and criteria used to describe the TMZ vary significantly both spatially and temporally. This study proposes an new index, the turbidity maximum zone index, based on the corresponding relationship of total suspended solid concentration and Chl a concentration, which could better extract TMZs in different estuaries and on different dates.
Ranee Joshi, Kavitha Madaiah, Mark Jessell, Mark Lindsay, and Guillaume Pirot
Geosci. Model Dev., 14, 6711–6740, https://doi.org/10.5194/gmd-14-6711-2021, https://doi.org/10.5194/gmd-14-6711-2021, 2021
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We have developed a software that allows the user to extract and standardize drill hole information from legacy datasets and/or different drilling campaigns. It also provides functionality to upscale the lithological information. These functionalities were possible by developing thesauri to identify and group geological terminologies together.
David Meyer, Thomas Nagler, and Robin J. Hogan
Geosci. Model Dev., 14, 5205–5215, https://doi.org/10.5194/gmd-14-5205-2021, https://doi.org/10.5194/gmd-14-5205-2021, 2021
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A major limitation in training machine-learning emulators is often caused by the lack of data. This paper presents a cheap way to increase the size of training datasets using statistical techniques and thereby improve the performance of machine-learning emulators.
Mark Jessell, Vitaliy Ogarko, Yohan de Rose, Mark Lindsay, Ranee Joshi, Agnieszka Piechocka, Lachlan Grose, Miguel de la Varga, Laurent Ailleres, and Guillaume Pirot
Geosci. Model Dev., 14, 5063–5092, https://doi.org/10.5194/gmd-14-5063-2021, https://doi.org/10.5194/gmd-14-5063-2021, 2021
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We have developed software that allows the user to extract sufficient information from unmodified digital maps and associated datasets that we are able to use to automatically build 3D geological models. By automating the process we are able to remove human bias from the procedure, which makes the workflow reproducible.
Martí Bosch, Maxence Locatelli, Perrine Hamel, Roy P. Remme, Jérôme Chenal, and Stéphane Joost
Geosci. Model Dev., 14, 3521–3537, https://doi.org/10.5194/gmd-14-3521-2021, https://doi.org/10.5194/gmd-14-3521-2021, 2021
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The article presents a novel approach to simulate urban heat mitigation from land use/land cover data based on three biophysical mechanisms: tree shade, evapotranspiration and albedo. An automated procedure is proposed to calibrate the model parameters to best fit temperature observations from monitoring stations. A case study in Lausanne, Switzerland, shows that the approach outperforms regressions based on satellite data and provides valuable insights into design heat mitigation policies.
Quang-Van Doan, Hiroyuki Kusaka, Takuto Sato, and Fei Chen
Geosci. Model Dev., 14, 2097–2111, https://doi.org/10.5194/gmd-14-2097-2021, https://doi.org/10.5194/gmd-14-2097-2021, 2021
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This study proposes a novel structural self-organizing map (S-SOM) algorithm. The superiority of S-SOM is that it can better recognize the difference (or similarity) among spatial (or temporal) data used for training and thus improve the clustering quality compared to traditional SOM algorithms.
Batunacun, Ralf Wieland, Tobia Lakes, and Claas Nendel
Geosci. Model Dev., 14, 1493–1510, https://doi.org/10.5194/gmd-14-1493-2021, https://doi.org/10.5194/gmd-14-1493-2021, 2021
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Extreme gradient boosting (XGBoost) can provide alternative insights that conventional land-use models are unable to generate. Shapley additive explanations (SHAP) can interpret the results of the purely data-driven approach. XGBoost achieved similar and robust simulation results. SHAP values were useful for analysing the complex relationship between the different drivers of grassland degradation.
Juan A. Añel, Michael García-Rodríguez, and Javier Rodeiro
Geosci. Model Dev., 14, 923–934, https://doi.org/10.5194/gmd-14-923-2021, https://doi.org/10.5194/gmd-14-923-2021, 2021
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This work shows that it continues to be hard, if not impossible, to obtain some of the most used climate models worldwide. We reach this conclusion through a systematic study and encourage all development teams and research centres to make public the models they use to produce scientific results.
Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, Ankur Mahesh, Kevin Yang, Colby Lewis, Jiayi Chen, Andrew Lou, Sathyavat Chandran, Ben Toms, Will Chapman, Katherine Dagon, Christine A. Shields, Travis O'Brien, Michael Wehner, and William Collins
Geosci. Model Dev., 14, 107–124, https://doi.org/10.5194/gmd-14-107-2021, https://doi.org/10.5194/gmd-14-107-2021, 2021
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Detecting extreme weather events is a crucial step in understanding how they change due to climate change. Deep learning (DL) is remarkable at pattern recognition; however, it works best only when labeled datasets are available. We create
ClimateNet– an expert-labeled curated dataset – to train a DL model for detecting weather events and predicting changes in extreme precipitation. This work paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data.
Xiang Que, Xiaogang Ma, Chao Ma, and Qiyu Chen
Geosci. Model Dev., 13, 6149–6164, https://doi.org/10.5194/gmd-13-6149-2020, https://doi.org/10.5194/gmd-13-6149-2020, 2020
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This paper presents a spatiotemporal weighted regression (STWR) model for exploring nonstationary spatiotemporal processes in nature and socioeconomics. A value change rate is introduced in the temporal kernel, which presents significant model fitting and accuracy in both simulated and real-world data. STWR fully incorporates observed data in the past and outperforms geographic temporal weighted regression (GTWR) and geographic weighted regression (GWR) models in several experiments.
Sheri Mickelson, Alice Bertini, Gary Strand, Kevin Paul, Eric Nienhouse, John Dennis, and Mariana Vertenstein
Geosci. Model Dev., 13, 5567–5581, https://doi.org/10.5194/gmd-13-5567-2020, https://doi.org/10.5194/gmd-13-5567-2020, 2020
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Every generation of MIP exercises introduces new layers of complexity and an exponential growth in the amount of data requested. CMIP6 required us to develop a new tool chain and forced us to change our methodologies. The new methods discussed in this paper provided us with an 18 times faster speedup over our existing methods. This allowed us to meet our deadlines and we were able to publish more than half a million data sets on the Earth System Grid Federation (ESGF) for the CMIP6 project.
Benjamin Campforts, Charles M. Shobe, Philippe Steer, Matthias Vanmaercke, Dimitri Lague, and Jean Braun
Geosci. Model Dev., 13, 3863–3886, https://doi.org/10.5194/gmd-13-3863-2020, https://doi.org/10.5194/gmd-13-3863-2020, 2020
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Landslides shape the Earth’s surface and are a dominant source of terrestrial sediment. Rivers, then, act as conveyor belts evacuating landslide-produced sediment. Understanding the interaction among rivers and landslides is important to predict the Earth’s surface response to past and future environmental changes and for mitigating natural hazards. We develop HyLands, a new numerical model that provides a toolbox to explore how landslides and rivers interact over several timescales.
Jorge Vicent, Jochem Verrelst, Neus Sabater, Luis Alonso, Juan Pablo Rivera-Caicedo, Luca Martino, Jordi Muñoz-Marí, and José Moreno
Geosci. Model Dev., 13, 1945–1957, https://doi.org/10.5194/gmd-13-1945-2020, https://doi.org/10.5194/gmd-13-1945-2020, 2020
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The modeling of light propagation through the atmosphere is key to process satellite images and to understand atmospheric processes. However, existing atmospheric models can be complex to use in practical applications. Here we aim at providing a new software tool to facilitate using advanced models and to generate large databases of simulated data. As a test case, we use this tool to analyze differences between several atmospheric models, showing the capabilities of this open-source tool.
Jiali Wang, Prasanna Balaprakash, and Rao Kotamarthi
Geosci. Model Dev., 12, 4261–4274, https://doi.org/10.5194/gmd-12-4261-2019, https://doi.org/10.5194/gmd-12-4261-2019, 2019
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Parameterizations are frequently used in models representing physical phenomena and are often the computationally expensive portions of the code. Using model output from simulations performed using a weather model, we train deep neural networks to provide an accurate alternative to a physics-based parameterization. We demonstrate that a domain-aware deep neural network can successfully simulate the entire diurnal cycle of the boundary layer physics and the results are transferable.
Gianandrea Mannarini and Lorenzo Carelli
Geosci. Model Dev., 12, 3449–3480, https://doi.org/10.5194/gmd-12-3449-2019, https://doi.org/10.5194/gmd-12-3449-2019, 2019
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The VISIR ship-routing model is updated in order to deal with ocean currents.
The optimal tracks we computed through VISIR in the Atlantic ocean show great seasonal and regional variability, following a variable influence of surface gravity waves and currents. We assess how these tracks contribute to voyage energy-efficiency gains through a standard indicator (EEOI) of the International Maritime Organization. Also, the new model features are validated against an exact analytical benchmark.
Grzegorz Muszynski, Karthik Kashinath, Vitaliy Kurlin, Michael Wehner, and Prabhat
Geosci. Model Dev., 12, 613–628, https://doi.org/10.5194/gmd-12-613-2019, https://doi.org/10.5194/gmd-12-613-2019, 2019
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We present the automated method for recognizing atmospheric rivers in climate data, i.e., climate model output and reanalysis product. The method is based on topological data analysis and machine learning, both of which are powerful tools that the climate science community often does not use. An advantage of the proposed method is that it is free of selection of subjective threshold conditions on a physical variable. This method is also suitable for rapidly analyzing large amounts of data.
Christina Papagiannopoulou, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, and Willem Waegeman
Geosci. Model Dev., 11, 4139–4153, https://doi.org/10.5194/gmd-11-4139-2018, https://doi.org/10.5194/gmd-11-4139-2018, 2018
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Common global land cover and climate classifications are based on vegetation–climatic characteristics derived from observational data, ignoring the interaction between the local climate and biome. Here, we model the interplay between vegetation and local climate by discovering spatial relationships among different locations. The resulting global
hydro-climatic biomescorrespond to regions of coherent climate–vegetation interactions that agree well with traditional global land cover maps.
Wendy Sharples, Ilya Zhukov, Markus Geimer, Klaus Goergen, Sebastian Luehrs, Thomas Breuer, Bibi Naz, Ketan Kulkarni, Slavko Brdar, and Stefan Kollet
Geosci. Model Dev., 11, 2875–2895, https://doi.org/10.5194/gmd-11-2875-2018, https://doi.org/10.5194/gmd-11-2875-2018, 2018
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Next-generation geoscientific models are based on complex model implementations and workflows. Next-generation HPC systems require new programming paradigms and code optimization. In order to meet the challenge of running complex simulations on new massively parallel HPC systems, we developed a run control framework that facilitates code portability, code profiling, and provenance tracking to reduce both the duration and the cost of code migration and development, while ensuring reproducibility.
Daojun Zhang, Na Ren, and Xianhui Hou
Geosci. Model Dev., 11, 2525–2539, https://doi.org/10.5194/gmd-11-2525-2018, https://doi.org/10.5194/gmd-11-2525-2018, 2018
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Geographically weighted regression is a widely used method to deal with spatial heterogeneity, which is common in geostatistics. However, most existing software does not support logistic regression and cannot deal with missing data, which exist extensively in mineral prospectivity mapping. This work generalized logistic regression to spatial statistics based on a spatially weighted technique. The new model also supports an anisotropic local window, which is another innovative point.
Thomas Block, Sabine Embacher, Christopher J. Merchant, and Craig Donlon
Geosci. Model Dev., 11, 2419–2427, https://doi.org/10.5194/gmd-11-2419-2018, https://doi.org/10.5194/gmd-11-2419-2018, 2018
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For calibration and validation purposes it is necessary to detect simultaneous data acquisitions from different spaceborne platforms. We present an algorithm and a software system which implements a general approach to resolve this problem. The multisensor matchup system (MMS) can detect simultaneous acquisitions in a large dataset (> 100 TB) and extract data for matching locations for further analysis. The MMS implements a flexible software infrastructure and allows for high parallelization.
David Hassell, Jonathan Gregory, Jon Blower, Bryan N. Lawrence, and Karl E. Taylor
Geosci. Model Dev., 10, 4619–4646, https://doi.org/10.5194/gmd-10-4619-2017, https://doi.org/10.5194/gmd-10-4619-2017, 2017
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We present a formal data model for version 1.6 of the CF (Climate and Forecast) metadata conventions that provide a description of the physical meaning of geoscientific data and their spatial and temporal properties. We describe the CF conventions and how they lead to our CF data model, and compare it other data models for storing data and metadata. We present cf-python version 2.1: a software implementation of the CF data model capable of manipulating any CF-compliant dataset.
Iulia Ilie, Peter Dittrich, Nuno Carvalhais, Martin Jung, Andreas Heinemeyer, Mirco Migliavacca, James I. L. Morison, Sebastian Sippel, Jens-Arne Subke, Matthew Wilkinson, and Miguel D. Mahecha
Geosci. Model Dev., 10, 3519–3545, https://doi.org/10.5194/gmd-10-3519-2017, https://doi.org/10.5194/gmd-10-3519-2017, 2017
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Accurate representation of land-atmosphere carbon fluxes is essential for future climate projections, although some of the responses of CO2 fluxes to climate often remain uncertain. The increase in available data allows for new approaches in their modelling. We automatically developed models for ecosystem and soil carbon respiration using a machine learning approach. When compared with established respiration models, we found that they are better in prediction as well as offering new insights.
Xinqiao Duan, Lin Li, Haihong Zhu, and Shen Ying
Geosci. Model Dev., 10, 239–253, https://doi.org/10.5194/gmd-10-239-2017, https://doi.org/10.5194/gmd-10-239-2017, 2017
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This article proposes an optimized transformation for topographic datasets. The resulting topographic grid exhibits good surface approximation and quasi-uniform high-quality. Both features of the processed topography build a concrete base from which improved endogenous or exogenous parameters can be derived, and makes it suitable for Earth and environmental simulations.
Venkatramani Balaji, Eric Maisonnave, Niki Zadeh, Bryan N. Lawrence, Joachim Biercamp, Uwe Fladrich, Giovanni Aloisio, Rusty Benson, Arnaud Caubel, Jeffrey Durachta, Marie-Alice Foujols, Grenville Lister, Silvia Mocavero, Seth Underwood, and Garrett Wright
Geosci. Model Dev., 10, 19–34, https://doi.org/10.5194/gmd-10-19-2017, https://doi.org/10.5194/gmd-10-19-2017, 2017
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Climate models are among the most computationally expensive scientific applications in the world. We present a set of measures of computational performance that can be used to compare models that are independent of underlying hardware and the model formulation. They are easy to collect and reflect performance actually achieved in practice. We are preparing a systematic effort to collect these metrics for the world's climate models during CMIP6, the next Climate Model Intercomparison Project.
Massimiliano Alvioli, Ivan Marchesini, Paola Reichenbach, Mauro Rossi, Francesca Ardizzone, Federica Fiorucci, and Fausto Guzzetti
Geosci. Model Dev., 9, 3975–3991, https://doi.org/10.5194/gmd-9-3975-2016, https://doi.org/10.5194/gmd-9-3975-2016, 2016
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Slope units are morphological mapping units bounded by drainage and divide lines that maximize within-unit homogeneity and between-unit heterogeneity. We use r.slopeunits, a software for the automatic delination of slope units. We outline an objective procedure to optimize the software input parameters for landslide susceptibility (LS) zonation. Optimization is achieved by maximizing an objective function that simultaneously evaluates terrain aspect segmentation quality and LS model performance.
Duncan Watson-Parris, Nick Schutgens, Nicholas Cook, Zak Kipling, Philip Kershaw, Edward Gryspeerdt, Bryan Lawrence, and Philip Stier
Geosci. Model Dev., 9, 3093–3110, https://doi.org/10.5194/gmd-9-3093-2016, https://doi.org/10.5194/gmd-9-3093-2016, 2016
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In this paper we describe CIS, a new command line tool for the easy visualization, analysis and comparison of a wide variety of gridded and ungridded data sets used in Earth sciences. Users can now use a single tool to not only view plots of satellite, aircraft, station or model data, but also bring them onto the same spatio-temporal sampling. This allows robust, quantitative comparisons to be made easily. CIS is an open-source project and welcomes input from the community.
Benjamin F. Jamroz and Robert Klöfkorn
Geosci. Model Dev., 9, 2881–2892, https://doi.org/10.5194/gmd-9-2881-2016, https://doi.org/10.5194/gmd-9-2881-2016, 2016
Short summary
Short summary
The scalability of computational applications on current and next-generation supercomputers is increasingly limited by the cost of inter-process communication. We implement communication hiding data exchange in the High-Order Methods Modeling Environment (HOMME) for the time integration of the hydrostatic fluid equations using both the spectral-element and discontinuous Galerkin methods. The presented approach produces significant performance and scalability gains in large-scale simulations.
T. Fischer, D. Naumov, S. Sattler, O. Kolditz, and M. Walther
Geosci. Model Dev., 8, 3681–3694, https://doi.org/10.5194/gmd-8-3681-2015, https://doi.org/10.5194/gmd-8-3681-2015, 2015
Short summary
Short summary
We present a workflow to convert geological models into the open-source VTU format for usage in numerical simulation models. Tackling relevant scientific questions or engineering tasks often involves multidisciplinary approaches. Conversion workflows are needed between the diverse tools of the various disciplines. Our approach offers an open-source, platform-independent, robust, and comprehensible method that is potentially useful for a multitude of similar environmental studies.
S. Moulds, W. Buytaert, and A. Mijic
Geosci. Model Dev., 8, 3215–3229, https://doi.org/10.5194/gmd-8-3215-2015, https://doi.org/10.5194/gmd-8-3215-2015, 2015
Short summary
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The contribution of lulcc is to provide a free and open-source framework for land use change modelling. The software, which is provided as an R package, addresses problems associated with the current paradigm of closed-source, specialised land use change modelling software which disrupt the scientific process. It is an attempt to move the discipline towards open and transparent science and to ensure land use change models are accessible to scientists working across the geosciences.
B. Poulter, N. MacBean, A. Hartley, I. Khlystova, O. Arino, R. Betts, S. Bontemps, M. Boettcher, C. Brockmann, P. Defourny, S. Hagemann, M. Herold, G. Kirches, C. Lamarche, D. Lederer, C. Ottlé, M. Peters, and P. Peylin
Geosci. Model Dev., 8, 2315–2328, https://doi.org/10.5194/gmd-8-2315-2015, https://doi.org/10.5194/gmd-8-2315-2015, 2015
Short summary
Short summary
Land cover is an essential variable in earth system models and determines conditions driving biogeochemical, energy and water exchange between ecosystems and the atmosphere. A methodology is presented for mapping plant functional types used in global vegetation models from a updated land cover classification system and open-source conversion tool, resulting from a consultative process among map producers and modelers engaged in the European Space Agency’s Land Cover Climate Change Initiative.
J. Du, C. Chen, V. Lesur, and L. Wang
Geosci. Model Dev., 8, 1979–1990, https://doi.org/10.5194/gmd-8-1979-2015, https://doi.org/10.5194/gmd-8-1979-2015, 2015
D. C. Wong, C. E. Yang, J. S. Fu, K. Wong, and Y. Gao
Geosci. Model Dev., 8, 1033–1046, https://doi.org/10.5194/gmd-8-1033-2015, https://doi.org/10.5194/gmd-8-1033-2015, 2015
M. Mergili, I. Marchesini, M. Alvioli, M. Metz, B. Schneider-Muntau, M. Rossi, and F. Guzzetti
Geosci. Model Dev., 7, 2969–2982, https://doi.org/10.5194/gmd-7-2969-2014, https://doi.org/10.5194/gmd-7-2969-2014, 2014
Short summary
Short summary
The article deals with strategies to (i) reduce computation time and to (ii) appropriately account for uncertain input parameters when applying an open source GIS sliding surface model to estimate landslide susceptibility for a 90km² study area in central Italy. For (i), the area is split into a large number of tiles, enabling the exploitation of multi-processor computing environments. For (ii), the model is run with various parameter combinations to compute the slope failure probability.
H. Yan, Z. Wang, and J. Li
Geosci. Model Dev., 6, 1591–1599, https://doi.org/10.5194/gmd-6-1591-2013, https://doi.org/10.5194/gmd-6-1591-2013, 2013
V. Yadav and A. M. Michalak
Geosci. Model Dev., 6, 583–590, https://doi.org/10.5194/gmd-6-583-2013, https://doi.org/10.5194/gmd-6-583-2013, 2013
S. Valcke, V. Balaji, A. Craig, C. DeLuca, R. Dunlap, R. W. Ford, R. Jacob, J. Larson, R. O'Kuinghttons, G. D. Riley, and M. Vertenstein
Geosci. Model Dev., 5, 1589–1596, https://doi.org/10.5194/gmd-5-1589-2012, https://doi.org/10.5194/gmd-5-1589-2012, 2012
M. Stockhause, H. Höck, F. Toussaint, and M. Lautenschlager
Geosci. Model Dev., 5, 1023–1032, https://doi.org/10.5194/gmd-5-1023-2012, https://doi.org/10.5194/gmd-5-1023-2012, 2012
M. Rautenhaus, G. Bauer, and A. Dörnbrack
Geosci. Model Dev., 5, 55–71, https://doi.org/10.5194/gmd-5-55-2012, https://doi.org/10.5194/gmd-5-55-2012, 2012
P. E. Farrell, M. D. Piggott, G. J. Gorman, D. A. Ham, C. R. Wilson, and T. M. Bond
Geosci. Model Dev., 4, 435–449, https://doi.org/10.5194/gmd-4-435-2011, https://doi.org/10.5194/gmd-4-435-2011, 2011
Cited articles
Adams, J. M., Gasparini, N. M., Hobley, D. E. J., Tucker, G. E., Hutton, E. W. H., Nudurupati, S. S., and Istanbulluoglu, E.: The Landlab v1.0 OverlandFlow component: a Python tool for computing shallow-water flow across watersheds, Geosci. Model Dev., 10, 1645–1663, https://doi.org/10.5194/gmd-10-1645-2017, 2017. a, b
Addor, N. and Melsen, L.: Legacy, rather than adequacy, drives the selection of
hydrological models, Water Resour. Res., 55, 378–390,
https://doi.org/10.1029/2018WR022958, 2019. a
Adorf, C. S., Ramasubramani, V., Anderson, J. A., and Glotzer, S. C.: How to
professionally develop reusable scientific software – And when not to,
Comput. Sci. Eng., 21, 66–79, 2018. a
Ahalt, S., Band, L., Christopherson, L., Idaszak, R., Lenhardt, C., Minsker,
B., Palmer, M., Shelley, M., Tiemann, M., and Zimmerman, A.: Water Science
Software Institute: Agile and open source scientific software development,
Comput. Sci. Eng., 16, 18–26, 2014. a
AlNoamany, Y. and Borghi, J. A.: Towards computational reproducibility:
researcher perspectives on the use and sharing of software, PeerJ Comput.
Sci., 4, e163, https://doi.org/10.7717/peerj-cs.163, 2018. a, b
Anderson, R. S., Dietrich, W. E., Furbish, D., Hanes, D., Howard, A., Paola,
C., Pelletier, J., Slingerland, R., Stallard, B., Syvitski, J., Vorosmarty,
C., and Wiberg, P.: Community Surface Dynamics Modeling System Science
Plan, Tech. rep., CSDMS Working Group, https://csdms.colorado.edu/wiki/CSDMS_docs (last access: 11 February 2022), 2004. a
Arndt, D., Bangerth, W., Blais, B., Fehling, M., Gassmöller, R., Heister,
T., Heltai, L., Köcher, U., Kronbichler, M., Maier, M., Munch, P.,
Pelteret, J.-P., Proell, S., Simon, K., Turcksin, B., Wells, D., and Zhang,
J.: The
deal.II Library, Version 9.3, J. Numer.
Math., 29, 171–186, https://doi.org/10.1515/jnma-2021-0081, 2021a. a
Arndt, D., Bangerth, W., Davydov, D., Heister, T., Heltai, L., Kronbichler, M.,
Maier, M., Pelteret, J.-P., Turcksin, B., and Wells, D.: The deal.II finite
element library: Design, features, and insights, Comput. Math.
Appl., 81, 407–422, https://doi.org/10.1016/j.camwa.2020.02.022,
2021b. a
Balay, S., Gropp, W. D., McInnes, L. C., and Smith, B. F.: Efficient Management
of Parallelism in Object Oriented Numerical Software Libraries, in: Modern
Software Tools in Scientific Computing, edited by: Arge, E., Bruaset, A. M.,
and Langtangen, H. P., Birkhäuser Press, 163–202, 1997. a
Balay, S., Abhyankar, S., Adams, M. F., Brown, J., Brune, P., Buschelman, K.,
Dalcin, L., Eijkhout, V., Gropp, W. D., Kaushik, D., Knepley, M. G., McInnes,
L. C., Rupp, K., Smith, B. F., Zampini, S., and Zhang, H.: PETSc Users
Manual, Tech. Rep. ANL-95/11 – Revision 3.6, Argonne National Laboratory,
http://www.mcs.anl.gov/petsc (last access: 11 February 2022), 2015a. a
Balay, S., Abhyankar, S., Adams, M. F., Brown, J., Brune, P., Buschelman, K.,
Dalcin, L., Eijkhout, V., Gropp, W. D., Kaushik, D., Knepley, M. G., McInnes,
L. C., Rupp, K., Smith, B. F., Zampini, S., and Zhang, H.: PETSc Web
page, http://www.mcs.anl.gov/petsc (last access: 11 February 2022),
2015b. a
Bangerth, W. and Heister, T.: What makes computational open source software
libraries successful?, Comput. Sci. Discovery, 6, 015010, https://doi.org/10.1088/1749-4699/6/1/015010, 2013. a, b
Barba, L. A.: The hard road to reproducibility, Science, 354, 142–142, 2016. a
Barnes, R.: RichDEM: Terrain Analysis Software,
http://github.com/r-barnes/richdem (last access: 11 February 2022), 2016. a
Barnhart, K. R., Glade, R. C., Shobe, C. M., and Tucker, G. E.: Terrainbento 1.0: a Python package for multi-model analysis in long-term drainage basin evolution, Geosci. Model Dev., 12, 1267–1297, https://doi.org/10.5194/gmd-12-1267-2019, 2019. a, b, c
Barnhart, K. R., Hutton, E. W. H., Tucker, G. E., Gasparini, N. M., Istanbulluoglu, E., Hobley, D. E. J., Lyons, N. J., Mouchene, M., Nudurupati, S. S., Adams, J. M., and Bandaragoda, C.: Short communication: Landlab v2.0: a software package for Earth surface dynamics, Earth Surf. Dynam., 8, 379–397, https://doi.org/10.5194/esurf-8-379-2020, 2020. a, b
Barnhart, K. R., Tucker, G. E., Doty, S., Shobe, C. M., Glade, R. C., Rossi,
M. W., and Hill, M. C.: Inverting topography for landscape evolution model
process representation: Part 1, conceptualization and sensitivity analysis,
J. Geophys. Res.-Earth, 125, e2018JF004961, https://doi.org/10.1029/2018JF004961
2020b. a
Barnhart, K. R., Tucker, G. E., Doty, S., Shobe, C. M., Glade, R. C., Rossi,
M. W., and Hill, M. C.: Inverting topography for landscape evolution model
process representation: Part 2, calibration and validation, J.
Geophys. Res.-Earth, 125, e2018JF004963, https://doi.org/10.1029/2018JF004963, 2020c. a
Basili, V. R., Carver, J. C., Cruzes, D., Hochstein, L. M., Hollingsworth,
J. K., Shull, F., and Zelkowitz, M. V.: Understanding the
high-performance-computing community: A software engineer's perspective, IEEE
Software, 25, 29–36, 2008. a
Baxter, R., Hong, N. C., Gorissen, D., Hetherington, J., and Todorov, I.: The
research software engineer, in: Digital Research Conference, Oxford,
2012. a
Benureau, F. C. and Rougier, N. P.: Re-run, repeat, reproduce, reuse,
replicate: transforming code into scientific contributions, Front.
Neuroinf., 11, 69, https://doi.org/10.3389/fninf.2017.00069, 2018. a
Bras, R., Tucker, G., and Teles, V.: Six myths about mathematical modeling in
geomorphology, in: Prediction in Geomorphology, edited by: Wilcock, P. and
Iverson, R., American Geophysical Union, 63–79, https://doi.org/10.1029/135GM06,
2003. a
Bryan, J.: Excuse me, do you have a moment to talk about version control?,
Am. Stat., 72, 20–27, 2018. a
Campforts, B., Shobe, C. M., Steer, P., Vanmaercke, M., Lague, D., and Braun, J.: HyLands 1.0: a hybrid landscape evolution model to simulate the impact of landslides and landslide-derived sediment on landscape evolution, Geosci. Model Dev., 13, 3863–3886, https://doi.org/10.5194/gmd-13-3863-2020, 2020. a, b
Chen, X., Dallmeier-Tiessen, S., Dasler, R., Feger, S., Fokianos, P., Gonzalez,
J. B., Hirvonsalo, H., Kousidis, D., Lavasa, A., Mele, S., Rodriguez, D. R.,
Šimko, T., Smith, T., Trisovic, A., Trzcinska, A., Tsanaktsidis, I., Zimmermann, M., Cranmer, K., Heinrich, L., Watts, G., Hildreth, M., Lloret Iglesias, L., Lassila-Perini, K., and Neubert, S.: Open is
not enough, Nature Phys., 15, 113–119, 2019. a
Chue Hong, N. P., Katz, D. S., Barker, M., Lamprecht, A.-L., Martinez, C., Psomopoulos, F.E., Harrow, J., Castro, L.J., Gruenpeter, M., Martinez, P. A., Honeyman, T., Struck, A., Lee, A., Loewe, A., van Werkhove, B., Jones, C., Garijo, D., Plomp, E., Genova, F., Shanahan, H., Leng, J., Hellström, M., Sandström, M., Sinha, M., Kuzak, M., Herterich, P., Zhang, Q., Islam, S., Sansone, S.-A., Pollard, T., Atmojo, U.D., Williams, A., Czerniak, A., Niehues, A., Fouilloux, A.C., Desinghu, B., Goble, C., Richard, C., Gray, C., Erdmann, C., Nüst, D., Tartarini, D., Ranguelova, E., Anzt, H., Todorov, I., McNally, J., Moldon, J., Burnett, J., Garrido-Sánchez, J., Belhajjame, K., Sesink, L., Hwang, L., Tovani-Palone, M. R., Wilkinson, M.D., Servillat, M., Liffers, M., Fox, M., Miljković, N., Lynch, N., Martinez Lavanchy, P., Gesing, S., Stevens, S., Martinez Cuesta, S., Peroni, S., Soiland-Reyes, S., Bakker, T., Rabemanantsoa, T., Sochat, V., and Yehudi, Y.: FAIR principles for research software (FAIR4RS principles),
Research Data Alliance, https://doi.org/10.15497/RDA00065, 2021. a, b
Eghbal, N.: Roads and Bridges: The Unseen labor behind our digital
infrastructure, Tech. rep., Ford Foundation, 143 pp., 2016. a
Epperly, T. G., Kumfert, G., Dahlgren, T., Ebner, D., Leek, J., Prantl, A., and
Kohn, S.: High-performance language interoperability for scientific computing
through Babel, Int. J. High Perform. C., 26, 260–274, 2012. a
ESMF Joint Specification Team: Earth System Modeling Framework ESMF
Reference Manual for Fortran, Version 8.2.0, Earth System Modeling
Framework, https://earthsystemmodeling.org/docs/release/latest/ESMF_refdoc/ (last access: 11 February 2022), 2021. a
Fan, X., Scaringi, G., Korup, O., West, A. J., Westen, C. J., Tanyas, H.,
Hovius, N., Hales, T. C., Jibson, R. W., Allstadt, K. E., Zhang, L., Evans,
S. G., Xu, C., Li, G., Pei, X., Xu, Q., and Huang, R.: Earthquake‐Induced
Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts, Rev.
Geophys., 57, 421–503, https://doi.org/10.1029/2018RG000626, 2019. a
Fomel, S. and Claerbout, J. F.: Reproducible research, Comput. Sci.
Eng., 11, 5–7, 2009. a
Fox, T. A., Gao, M., Barchyn, T. E., Jamin, Y. L., and Hugenholtz, C. H.: An
agent-based model for estimating emissions reduction equivalence among leak
https://doi.org/10.1016/j.jclepro.2020.125237, 2020. a
Glade, R. C., Shobe, C. M., Anderson, R. S., and Tucker, G. E.: Canyon shape
and erosion dynamics governed by channel-hillslope feedbacks, Geology, 47,
650–654, 2019. a
Gray, H. J., Shobe, C. M., Hobley, D. E., Tucker, G. E., Duvall, A. R.,
Harbert, S. A., and Owen, L. A.: Off-fault deformation rate along the
southern San Andreas fault at Mecca Hills, southern California, inferred from
landscape modeling of curved drainages, Geology, 46, 59–62, 2017. a
Groenenberg, R. M., Hodgson, D. M., Prelat, A., Luthi, S. M., and Flint, S. S.:
Flow–deposit interaction in submarine lobes: Insights from outcrop
observations and realizations of a process-based numerical model, J.
Sediment. Res., 80, 252–267, 2010. a
Guest, O. and Martin, A. E.: How computational modeling can force theory
building in psychological science, Perspect. Psychol. Sci., 16, 789–802,
https://doi.org/10.1177/1745691620970585, 2020. a
Harpham, Q., Hughes, A., and Moore, R.: Introductory overview: The OpenMI 2.0
standard for integrating numerical models, Environ. Modell.
Softw., 122, 104549, https://doi.org/10.1016/j.envsoft.2019.104549, 2019. a
Hastings, J., Haug, K., and Steinbeck, C.: Ten recommendations for software
engineering in research, GigaScience, 3, 31, https://doi.org/10.1186/2047-217X-3-31, 2014. a
Hatton, L.: The T-experiments: errors in scientific software, in: Quality of
Numerical Software, edited by: Boisvert, R. F., IFIP Advances in Information and Communication Technology. Springer, Boston, MA, 12–31, https://doi.org/10.1007/978-1-5041-2940-4_2, 1997. a
Hatton, L.: The chimera of software quality, Computer, 40, 104–103, 2007. a
Heaton, D. and Carver, J. C.: Claims about the use of software engineering
practices in science: A systematic literature review, Comm. Com. Inf. Sc., 67, 207–219, 2015. a
Hestenes, D.: Modeling Methodology for Physics Teachers: Proceedings of the
International Conference on Undergraduate Physics Education, College Park, August 1996. a
Hobley, D. E. J., Adams, J. M., Nudurupati, S. S., Hutton, E. W. H., Gasparini, N. M., Istanbulluoglu, E., and Tucker, G. E.: Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics, Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017, 2017. a, b
Hoch, J. M. and Trigg, M. A.: Advancing global flood hazard simulations by
improving comparability, benchmarking, and integration of global flood
models, Environ. Res. Lett., 14, 034001, https://doi.org/10.1088/1748-9326/aaf3d3, 2019. a
Hoch, J. M., Eilander, D., Ikeuchi, H., Baart, F., and Winsemius, H. C.: Evaluating the impact of model complexity on flood wave propagation and inundation extent with a hydrologic–hydrodynamic model coupling framework, Nat. Hazards Earth Syst. Sci., 19, 1723–1735, https://doi.org/10.5194/nhess-19-1723-2019, 2019. a, b, c, d
Hsu, L., Martin, R. L., McElroy, B., Litwin-Miller, K., and Kim, W.: Data
management, sharing, and reuse in experimental geomorphology: Challenges,
strategies, and scientific opportunities, Geomorphology, 244, 180–189, 2015. a
Hut, R., Drost, N., van de Giesen, N., van Werkhoven, B., Abdollahi, B., Aerts, J., Albers, T., Alidoost, F., Andela, B., Camphuijsen, J., Dzigan, Y., van Haren, R., Hutton, E., Kalverla, P., van Meersbergen, M., van den Oord, G., Pelupessy, I., Smeets, S., Verhoeven, S., de Vos, M., and Weel, B.: The eWaterCycle platform for Open and FAIR Hydrological collaboration, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2021-344, in review, 2021. a
Hutton, E. and Piper, M.: csdms/babelizer: (v0.3.8), Zenodo [code], https://doi.org/10.5281/zenodo.4985181, 2021. a
Hutton, E. W. and Syvitski, J. P.: Sedflux 2.0: An advanced process-response
model that generates three-dimensional stratigraphy, Comput.
Geosci., 34, 1319–1337, 2008. a
Hutton, E., Barnhart, K., Hobley, D., Tucker, G., Nudurupati, S. S., Adams, J., Gasparini, N. M., Shobe, C., Strauch, R., Knuth, J., Mouchene, M., Lyons, N., Litwin, D., Glade, R., Cipolla, G., Manaster, A., alangston, Thyng, K., and Rengers, F.: landlab/landlab: Mrs. Weasley (v2.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.3776837, 2020b. a
Hutton, E., Piper, M., Gan, T., Kettner, A. J., and Drost, N.: csdms/pymt: (v1.3.1), Zenodo [code], https://doi.org/10.5281/zenodo.4985222, 2021. a
Istanbulluoglu, E. and Bras, R. L.: Vegetation-modulated landscape evolution:
Effects of vegetation on landscape processes, drainage density, and
topography, J. Geophys. Res., 110, F02012, https://doi.org/10.1029/2004JF000249, 2005. a
Jacobs, C. T., Gorman, G. J., Rees, H. E., and Craig, L. E.: Experiences with
efficient methodologies for teaching computer programming to geoscientists,
J. Geosci. Educ., 64, 183–198, 2016. a
Johanson, A. and Hasselbring, W.: Software engineering for computational
science: Past, present, future, Comput. Sci. Eng., 20, 90–109,
https://doi.org/10.1109/MCSE.2018.021651343, 2018. a
Katz, D. S., Gruenpeter, M., and Honeyman, T.: Taking a fresh look at FAIR for
research software, Patterns, 2, 3, https://doi.org/10.1016/j.patter.2021.100222, 2021. a, b
Kellogg, L. H., Hwang, L. J., Gassmöller, R., Bangerth, W., and Heister,
T.: The role of scientific communities in creating reusable software: Lessons
from geophysics, Comput. Sci. Eng., 21, 25–35, 2018. a
Kelly, D. F.: A software chasm: Software engineering and scientific computing,
IEEE Software, 24, 120–119, 2007. a
Kettner, A. J.: CSDMS by the numbers, https://csdms.colorado.edu/wiki/CSDMS_in_numbers, last access: 14 February 2022. a
King, J. and South, J.: Reimagining the role of technology in higher education:
A supplement to the national education technology plan, US Department of
Education, Office of Educational Technology, 2017. a
Krafczyk, M., Shi, A., Bhaskar, A., Marinov, D., and Stodden, V.: Scientific
Tests and Continuous Integration Strategies to Enhance Reproducibility in the
Scientific Software Context, in: Proceedings of the 2nd International
Workshop on Practical Reproducible Evaluation of Computer Systems,
23–28, 2019. a
Kuehl, S. A., Alexander, C. R., Blair, N. E., Harris, C. K., Marsaglia, K. M.,
Ogston, A. S., Orpin, A. R., Roering, J. J., Bever, A. J., Bilderback, E. L.,
Carter, L., Cerovski-Darriau, C., Childress, L. B., Corbett, D. R., Hale, R. P., Leithold, E. L., Litchfield, N., Moriarty, J. M., Page, M. J., Pierce, L. E. R., Upton, P., and Walsh, J. P.: A source-to-sink perspective of the Waipaoa River margin,
Earth-Sci. Rev., 153, 301–334, 2016. a
Lai, J. and Anders, A. M.: Modeled postglacial landscape evolution at the
southern margin of the Laurentide Ice Sheet: hydrological connection of
uplands controls the pace and style of fluvial network expansion, J.
Geophys. Res.-Earth, 123, 967–984, 2018. a
Lamprecht, A.-L., Garcia, L., Kuzak, M., Martinez, C., Arcila, R., Martin
Del Pico, E., Dominguez Del Angel, V., Van De Sandt, S., Ison, J., Martinez,
P. A., McQuilton, P., Valencia, A., Harrow, J., Psomopoulos, F., Gelpi, J. L., Chue Hong, N., Goble, C., and Capella-Gutierrez, S.: Towards FAIR principles for research software, Data Science,
3, 37–59, 2020. a, b
Langston, A. L. and Tucker, G. E.: Developing and exploring a theory for the lateral erosion of bedrock channels for use in landscape evolution models, Earth Surf. Dynam., 6, 1–27, https://doi.org/10.5194/esurf-6-1-2018, 2018. a
Lathrop, S., Folk, M., Katz, D. S., McInnes, L. C., and Terrel, A.:
Introduction to Accelerating Scientific Discovery With Reusable Software,
Comput. Sci. Eng., 21, 5–7, 2019. a
Leavesley, G., Lichty, R., Troutman, B., and Saindon, L.: Precipitation-runoff
modeling system: User's manual, Vol. 83, U.S. Department of the Interior,
1983. a
Leavesley, G., Restrepo, P. J., Markstrom, S., Dixon, M., and Stannard, L.:
The modular modeling system (MMS): User's manual, US Geological Survey
Open-File Report, 96, 1996. a
LeVeque, R. J.: Top ten reasons to not share your code (and why you should
anyway), Siam News, 46, 2013. a
Litwin, D. G., Tucker, G. E., Barnhart, K. R., and Harman, C. J.:
GroundwaterDupuitPercolator: A Landlab component for groundwater flow,
J. Open Source Softw., 5, 1935, https://doi.org/10.21105/joss.01935, 2020. a, b
Luettich, R. A., Westerink, J. J., Scheffner, N. W.: ADCIRC: an
advanced three-dimensional circulation model for shelves, coasts, and
estuaries, Report 1, Theory and methodology of ADCIRC-2DD1 and ADCIRC-3DL, Technical Report, Coastal Engineering and Research Center and Engineer Research and Development Center, US Army Corps of Engineers,
1992. a
Lyons, N., Albert, J., and Gasparini, N.: SpeciesEvolver: A Landlab component
to evolve life in simulated landscapes, J. Open Source Softw., 5,
2066, https://doi.org/10.21105/joss.02066, 2020. a, b
Manduca, C. A., Baer, E., Hancock, G., Macdonald, R. H., Patterson, S., Savina,
M., and Wenner, J.: Making undergraduate geoscience quantitative, Eos
T. Am. Geophys. Un., 89, 149–150, 2008. a
Mariotti, G.: Marsh channel morphological response to sea level rise and
sediment supply, Estuar. Coast. Shelf S., 209, 89–101, 2018. a
Markstrom, S. L., Regan, R. S., Hay, L. E., Viger, R. J., Webb, R. M., Payn,
R. A., and LaFontaine, J. H.: PRMS-IV, the precipitation-runoff modeling
system, version 4, US Geological Survey Techniques and Methods, book 6, chap. B7, 158 pp., https://doi.org/10.3133/tm6B7,
ISSN 2328-7055,
2015. a
Miller, G.: A scientist's nightmare: Software problem leads to five
retractions, Science, 314, 1856–1857, https://doi.org/10.1126/science.314.5807.1856,
2006. a
Nanthaamornphong, A. and Carver, J. C.: Test-Driven Development in HPC Science:
A Case Study, Comput. Sci. Eng., 20, 98–113, 2018. a
Nasr-Azadani, M., Hall, B., and Meiburg, E.: Polydisperse turbidity currents
propagating over complex topography: comparison of experimental and
depth-resolved simulation results, Comput. Geosci., 53, 141–153,
2013. a
Nguyen-Hoan, L., Flint, S., and Sankaranarayana, R.: A survey of scientific
software development, in: Proceedings of the 2010 ACM-IEEE International
Symposium on Empirical Software Engineering and Measurement, 1–10, 2010. a
NRC: A Visioin for NSF Earth Sciences 2020–2030: Earth in Time, The National
Academies Press, https://doi.org/10.17226/25761, 2020. a
Overeem, I., Berlin, M. M., and Syvitski, J. P.: Strategies for integrated
modeling: The Community Surface Dynamics Modeling System example,
Environ. Modell. Softw., 39, 314–321, 2013. a
Pelletier, J. D., Barron-Gafford, G. A., Guttierez-Jurado, H., Hinckley,
E.-L. S., Istanbulluoglu, E., McGuire, L. A., Niu, G.-Y., Poulos, M. J.,
Rasmussen, C., Richardson, P., Swetnam, T. L., and Tucker, G. E.: Which way do you lean? Using slope
aspect variations to understand Critical Zone processes and feedbacks, Earth
Surf. Proc. Land., 43, 1133–1154, https://doi.org/10.1002/esp.4306, 2017. a
Peng, R. D.: Reproducible research in computational science, Science, 334,
1226–1227, 2011. a
Pfeiffer, A. M., Barnhart, K. R., Czuba, J. A., and Hutton, E. W. H.:
NetworkSedimentTransporter: A Landlab component for bed material transport
through river networks, J. Open Source Softw., 5, 2341, https://doi.org/10.21105/joss.02341, 2020. a
Pipitone, J. and Easterbrook, S.: Assessing climate model software quality: a defect density analysis of three models, Geosci. Model Dev., 5, 1009–1022, https://doi.org/10.5194/gmd-5-1009-2012, 2012. a, b
Poisot, T.: Best publishing practices to improve user confidence in scientific
software, Ideas in Ecology and Evolution, 8, 50–54,https://doi.org/10.4033/iee.2015.8.8.f, 2015. a, b
Post, D.: The changing face of scientific and engineering computing, Comput. Sci. Eng., 15, 4–6, 2013. a
Prabhu, P., Kim, H., Oh, T., Jablin, T. B., Johnson, N. P., Zoufaly, M., Raman,
A., Liu, F., Walker, D., Zhang, Y., Ghosh, S., August, D. I., Huang, J., and Beard, S.: A survey of the practice of
computational science, in: SC'11: Proceedings of 2011 International
Conference for High Performance Computing, Networking, Storage and Analysis,
1–12, 2011. a, b, c, d
Reed, D. A., Bajcsy, R., Fernandez, M. A., Griffiths, J.-M., Mott, R. D., Dongarra, J., Johnson, C. R., Inouye, A. S., Miner, W., Matzke, M. K., and Ponick, T. L.: Computational Science: Ensuring America's Competitiveness, President's
Information Technology Advisory Committee, National Coordination Office for
Information Technology Research & Development, US government technical report, 2005. a
Regan, R. S., Markstrom, S. L., Hay, L. E., Viger, R. J., Norton, P. A.,
Driscoll, J. M., and LaFontaine, J. H.: Description of the national
hydrologic model for use with the precipitation-runoff modeling system
(PRMS), Tech. rep., US Geological Survey, 2018. a
Regan, R. S., Juracek, K. E., Hay, L. E., Markstrom, S., Viger, R. J.,
Driscoll, J. M., LaFontaine, J., and Norton, P. A.: The US Geological Survey
National Hydrologic Model infrastructure: Rationale, description, and
application of a watershed-scale model for the conterminous United States,
Environ. Modell. Softw., 111, 192–203, 2019. a
Reitman, N. G., Mueller, K. J., Tucker, G. E., Gold, R. D., Briggs, R. W., and
Barnhart, K. R.: Offset Channels May Not Accurately Record Strike-Slip Fault
Displacement: Evidence From Landscape Evolution Models, J.
Geophys. Res.-Sol. Ea., 124, 13427–13451, 2019. a
Robinson, D. T., Di Vittorio, A., Alexander, P., Arneth, A., Barton, C. M., Brown, D. G., Kettner, A., Lemmen, C., O'Neill, B. C., Janssen, M., Pugh, T. A. M., Rabin, S. S., Rounsevell, M., Syvitski, J. P., Ullah, I., and Verburg, P. H.: Modelling feedbacks between human and natural processes in the land system, Earth Syst. Dynam., 9, 895–914, https://doi.org/10.5194/esd-9-895-2018, 2018. a
Roy, S., Koons, P., Upton, P., and Tucker, G.: Dynamic links among rock damage,
erosion, and strain during orogenesis, Geology, 44, 583–586, 2016. a
Schmid, M., Ehlers, T. A., Werner, C., Hickler, T., and Fuentes-Espoz, J.-P.: Effect of changing vegetation and precipitation on denudation – Part 2: Predicted landscape response to transient climate and vegetation cover over millennial to million-year timescales, Earth Surf. Dynam., 6, 859–881, https://doi.org/10.5194/esurf-6-859-2018, 2018. a
Schwab, M., Karrenbach, N., and Claerbout, J.: Making scientific computations
reproducible, Comput. Sci. Eng., 2, 61–67, 2000. a
Scott, S.: ESIP Software Assessment Guidelines, Earth Science Information Partners, 53 pp., 2017. a
Shchepetkin, A. F. and McWilliams, J. C.: The regional oceanic modeling system
(ROMS): a split-explicit, free-surface, topography-following-coordinate
oceanic model, Ocean Modell., 9, 347–404, 2005. a
Shobe, C. M., Tucker, G. E., and Barnhart, K. R.: The SPACE 1.0 model: a Landlab component for 2-D calculation of sediment transport, bedrock erosion, and landscape evolution, Geosci. Model Dev., 10, 4577–4604, https://doi.org/10.5194/gmd-10-4577-2017, 2017. a, b
Singer, S. R., Nielsen, N. R., and Schweingruber, H. A. (Eds.): Discipline-based education research: Understanding and
improving learning in undergraduate science and engineering, National
Academies Press, 2012. a
Singh Chawla, D.: The unsung heroes of scientific software, Nature News, 529, 115–116,
2016. a
Smith, A. M., Niemeyer, K. E., Katz, D. S., Barba, L. A., Githinji, G., Gymrek,
M., Huff, K. D., Madan, C. R., Mayes, A. C., Moerman, K. M., Prins, P., Ram, K., Rokem, A., Teal, T. K., Valls Guimera, R., and Vanderplas, J. T.: Journal
of Open Source Software (JOSS): design and first-year review, PeerJ Comput.
Sci., 4, e147, https://doi.org/10.7717/peerj-cs.147, 2018. a
Steckler, M. S., Hutton, E., Ologan, D., Tucker, G. E., Grall, C., and Gurcay,
S.: Developing Sequence Stratigraphic Modeling in Landlab to improve
understanding of the tectonics in the Gulf of Kusadasi, Turkey, AGUFM, 2019,
EP21D–2227, 2019. a
Stodden, V., Borwein, J., and Bailey, D. H.: Setting the default to
reproducible, computational science research, SIAM News, 46, 4–6, 2013. a
Stodden, V., Krafczyk, M. S., and Bhaskar, A.: Enabling the verification of
computational results: An empirical evaluation of computational
reproducibility, in: Proceedings of the First International Workshop on
Practical Reproducible Evaluation of Computer Systems, 1–5, 2018. a
Stoica, M.: Scientific Variables Ontology and Associated Tools,
https://github.com/mariutzica/ScientificVariablesOntology (last access: 11 February 2022), 2020. a
Stoica, M. and Peckham, S. D.: An Ontology Blueprint for Constructing
Qualitative and Quantitative Scientific Variables, in: International
Semantic Web Conference (P&D/Industry/BlueSky), 2018. a
Stoica, M. and Peckham, S.: Incorporating New Concepts Into the Scientific
Variables Ontology, in: 2019 15th International Conference on eScience
(eScience), 539–540, 2019a. a
Stoica, M. and Peckham, S.: The Scientific Variables Ontology: A blueprint
for custom manual and automated creation and alignment of
machine-interpretable qualitative and quantitative variable concepts,
http://pittmodelingconference.sci.pitt.edu (last access: 11 February 2022),
2019b. a
Strauch, R., Istanbulluoglu, E., Nudurupati, S. S., Bandaragoda, C., Gasparini, N. M., and Tucker, G. E.: A hydroclimatological approach to predicting regional landslide probability using Landlab, Earth Surf. Dynam., 6, 49–75, https://doi.org/10.5194/esurf-6-49-2018, 2018. a, b
SVO: Scientific Variables Ontology, http://geoscienceontology.org,
last access: 26 October 2020. a
Taschuk, M. and Wilson, G.: Ten simple rules for making research software more
robust, PLoS Comput. Biol., 13, e1005412, https://doi.org/10.1371/journal.pcbi.1005412.
2017. a
Thyng, K. M., Greene, C. A., Zimmerle, H. M., and DiMarco, S. F.: True Colors
of Oceanography: Guidelines for Effective and Accurate Colormap Selection,
Oceanography, 29, 9–13,
https://doi.org/10.5670/oceanog.2016.66, 2016. a
Tucker, G. E.: Python code and documentation for island simulation example, Zenodo [code], https://doi.org/10.5281/zenodo.6049847, 2022. a
Tucker, G. E., Lancaster, S. T., Gasparini, N. M., and Bras, R. L.: The
Channel-Hillslope Integrated Landscape Development Model (CHILD), in:
Landscape Erosion and Evolution Modeling, edited by: Harmon, R. S. and Doe,
W. W., Kluwer Press, Dordrecht, 349–388, 2001. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright,
J., van der Walt, S. J., Brett, M., Wilson, J., Jarrod Millman, K.,
Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E.,
Carey, C., Polat, İ., Feng, Y., Moore, E. W., Vand erPlas, J.,
Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero,
E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa,
F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: Fundamental
Algorithms for Scientific Computing in Python, Nat. Methods, 17, 261–272,
https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Voinov, A., Fitz, C., Boumans, R., and Costanza, R.: Modular ecosystem
modeling, Environ. Modell. Softw., 19, 285–304, 2004. a
W3C Working Group: Best Practice Recipes for Publishing RDF Vocabularies,
https://www.w3.org/TR/swbp-vocab-pub/ (last access:
26 October 2020), 2008. a
Wiese, I. S., Polato, I., and Pinto, G.: Naming the Pain in Developing
Scientific Software, IEEE Software, 37, 75–82, 2019. a
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M.,
Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, Alasdair, J. G., Groth, P., Goble, C., Grethe, J. S., Heringa, J., ’t Hoen, P. A. C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A., Thompson, M., van der Lei, J.,
van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B.: The FAIR Guiding Principles for scientific data management and
stewardship, Sci. Data, 3, 160018, https://doi.org/10.1038/sdata.2016.18, 2016. a, b
Wilson, G., Aruliah, D. A., Brown, C. T., Hong, N. P. C., Davis, M., Guy,
R. T., Haddock, S. H., Huff, K. D., Mitchell, I. M., Plumbley, M. D., Waugh, B., White, E. P., and Wilson, P.:
Best practices for scientific computing, PLoS biology, 12, e1001745, https://doi.org/10.1371/journal.pbio.1001745,
2014.
a, b
Wilson, G., Bryan, J., Cranston, K., Kitzes, J., Nederbragt, L., and Teal,
T. K.: Good enough practices in scientific computing, PLoS Comput.
Biol., 13, e1005510, https://doi.org/10.1371/journal.pcbi.1005510, 2017. a
Short summary
Scientists use computer simulation models to understand how Earth surface processes work, including floods, landslides, soil erosion, river channel migration, ocean sedimentation, and coastal change. Research benefits when the software for simulation modeling is open, shared, and coordinated. The Community Surface Dynamics Modeling System (CSDMS) is a US-based facility that supports research by providing community support, computing tools and guidelines, and educational resources.
Scientists use computer simulation models to understand how Earth surface processes work,...
