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
| 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
Jeffrey Keck, Erkan Istanbulluoglu, Benjamin Campforts, Gregory Tucker, and Alexander Horner-Devine
Earth Surf. Dynam., 12, 1165–1191, https://doi.org/10.5194/esurf-12-1165-2024, https://doi.org/10.5194/esurf-12-1165-2024, 2024
Short summary
Short summary
MassWastingRunout (MWR) is a new landslide runout model designed for sediment transport, landscape evolution, and hazard assessment applications. MWR is written in Python and includes a calibration utility that automatically determines best-fit parameters for a site and empirical probability density functions of each parameter for probabilistic model implementation. MWR and Jupyter Notebook tutorials are available as part of the Landlab package at https://github.com/landlab/landlab.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
Short summary
Short summary
This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
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.
Coline Ariagno, Philippe Steer, Pierre Valla, and Benjamin Campforts
EGUsphere, https://doi.org/10.5194/egusphere-2025-2088, https://doi.org/10.5194/egusphere-2025-2088, 2025
Short summary
Short summary
This study explored the impact of landslides on their topography using a landscape evolution model called ‘Hyland’, which enables long-term topographical analysis. Our finding reveal that landslides are concentrated at two specific elevations over time and predominantly affect the highest and steepest slopes, particularly along ridges and crests. This study is part of the large question about the origin of the erosion acceleration during the Quaternary.
Nicole M. Gasparini, Adam M. Forte, and Katherine R. Barnhart
Earth Surf. Dynam., 12, 1227–1242, https://doi.org/10.5194/esurf-12-1227-2024, https://doi.org/10.5194/esurf-12-1227-2024, 2024
Short summary
Short summary
The time it takes for a landscape to adjust to new environmental conditions is critical for understanding the impacts of past and future environmental changes. We used different computational models and methods and found that predicted times for a landscape to reach a stable condition vary greatly. Our results illustrate that reporting how timescales are measured is important. Modelers should ensure that the measurement technique addresses the question.
Jeffrey Keck, Erkan Istanbulluoglu, Benjamin Campforts, Gregory Tucker, and Alexander Horner-Devine
Earth Surf. Dynam., 12, 1165–1191, https://doi.org/10.5194/esurf-12-1165-2024, https://doi.org/10.5194/esurf-12-1165-2024, 2024
Short summary
Short summary
MassWastingRunout (MWR) is a new landslide runout model designed for sediment transport, landscape evolution, and hazard assessment applications. MWR is written in Python and includes a calibration utility that automatically determines best-fit parameters for a site and empirical probability density functions of each parameter for probabilistic model implementation. MWR and Jupyter Notebook tutorials are available as part of the Landlab package at https://github.com/landlab/landlab.
Alexander B. Prescott, Luke A. McGuire, Kwang-Sung Jun, Katherine R. Barnhart, and Nina S. Oakley
Nat. Hazards Earth Syst. Sci., 24, 2359–2374, https://doi.org/10.5194/nhess-24-2359-2024, https://doi.org/10.5194/nhess-24-2359-2024, 2024
Short summary
Short summary
Fire can dramatically increase the risk of debris flows to downstream communities with little warning, but hazard assessments have not traditionally included estimates of inundation. We unify models developed by the scientific community to create probabilistic estimates of inundation area in response to rainfall at forecast lead times (≥ 24 h) needed for decision-making. This work takes an initial step toward a near-real-time postfire debris-flow inundation hazard assessment product.
Francis K. Rengers, Samuel Bower, Andrew Knapp, Jason W. Kean, Danielle W. vonLembke, Matthew A. Thomas, Jaime Kostelnik, Katherine R. Barnhart, Matthew Bethel, Joseph E. Gartner, Madeline Hille, Dennis M. Staley, Justin K. Anderson, Elizabeth K. Roberts, Stephen B. DeLong, Belize Lane, Paxton Ridgway, and Brendan P. Murphy
Nat. Hazards Earth Syst. Sci., 24, 2093–2114, https://doi.org/10.5194/nhess-24-2093-2024, https://doi.org/10.5194/nhess-24-2093-2024, 2024
Short summary
Short summary
Every year the U.S. Geological Survey produces 50–100 postfire debris-flow hazard assessments using models for debris-flow likelihood and volume. To refine these models they must be tested with datasets that clearly document rainfall, debris-flow response, and debris-flow volume. These datasets are difficult to obtain, but this study developed and analyzed a postfire dataset with more than 100 postfire storm responses over a 2-year period. We also proposed ways to improve these models.
Daniel O'Hara, Liran Goren, Roos M. J. van Wees, Benjamin Campforts, Pablo Grosse, Pierre Lahitte, Gabor Kereszturi, and Matthieu Kervyn
Earth Surf. Dynam., 12, 709–726, https://doi.org/10.5194/esurf-12-709-2024, https://doi.org/10.5194/esurf-12-709-2024, 2024
Short summary
Short summary
Understanding how volcanic edifices develop drainage basins remains unexplored in landscape evolution. Using digital evolution models of volcanoes with varying ages, we quantify the geometries of their edifices and associated drainage basins through time. We find that these metrics correlate with edifice age and are thus useful indicators of a volcano’s history. We then develop a generalized model for how volcano basins develop and compare our results to basin evolution in other settings.
Katherine R. Barnhart, Christopher R. Miller, Francis K. Rengers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 24, 1459–1483, https://doi.org/10.5194/nhess-24-1459-2024, https://doi.org/10.5194/nhess-24-1459-2024, 2024
Short summary
Short summary
Debris flows are a type of fast-moving landslide that start from shallow landslides or during intense rain. Infrastructure located downstream of watersheds susceptible to debris flows may be damaged should a debris flow reach them. We present and evaluate an approach to forecast building damage caused by debris flows. We test three alternative models for simulating the motion of debris flows and find that only one can forecast the correct number and spatial pattern of damaged buildings.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
Short summary
Short summary
This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Matthew C. Morriss, Benjamin Lehmann, Benjamin Campforts, George Brencher, Brianna Rick, Leif S. Anderson, Alexander L. Handwerger, Irina Overeem, and Jeffrey Moore
Earth Surf. Dynam., 11, 1251–1274, https://doi.org/10.5194/esurf-11-1251-2023, https://doi.org/10.5194/esurf-11-1251-2023, 2023
Short summary
Short summary
In this paper, we investigate the 28 June 2022 collapse of the Chaos Canyon landslide in Rocky Mountain National Park, Colorado, USA. We find that the landslide was moving prior to its collapse and took place at peak spring snowmelt; temperature modeling indicates the potential presence of permafrost. We hypothesize that this landslide could be part of the broader landscape evolution changes to alpine terrain caused by a warming climate, leading to thawing alpine permafrost.
Luke A. McGuire, Scott W. McCoy, Odin Marc, William Struble, and Katherine R. Barnhart
Earth Surf. Dynam., 11, 1117–1143, https://doi.org/10.5194/esurf-11-1117-2023, https://doi.org/10.5194/esurf-11-1117-2023, 2023
Short summary
Short summary
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 to use topographic data from steep channel networks to infer how erosion rates vary across a landscape.
Francis K. Rengers, Luke A. McGuire, Katherine R. Barnhart, Ann M. Youberg, Daniel Cadol, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 23, 2075–2088, https://doi.org/10.5194/nhess-23-2075-2023, https://doi.org/10.5194/nhess-23-2075-2023, 2023
Short summary
Short summary
Debris flows often occur after wildfires. These debris flows move water, sediment, and wood. The wood can get stuck in channels, creating a dam that holds boulders, cobbles, sand, and muddy material. We investigated how the channel width and wood length influenced how much sediment is stored. We also used a series of equations to back calculate the debris flow speed using the breaking threshold of wood. These data will help improve models and provide insight into future field investigations.
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
Short summary
Short summary
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.
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,...
