Articles | Volume 12, issue 7
https://doi.org/10.5194/gmd-12-2837-2019
https://doi.org/10.5194/gmd-12-2837-2019
Model description paper
 | 
11 Jul 2019
Model description paper |  | 11 Jul 2019

r.sim.terrain 1.0: a landscape evolution model with dynamic hydrology

Brendan Alexander Harmon, Helena Mitasova, Anna Petrasova, and Vaclav Petras

Related authors

AUTOMATED LAND COVER CHANGE DETECTION THROUGH RAPID UAS UPDATES OF DIGITAL SURFACE MODELS
C. T. White, A. Petrasova, W. Reckling, and H. Mitasova
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W11, 155–159, https://doi.org/10.5194/isprs-archives-XLII-3-W11-155-2020,https://doi.org/10.5194/isprs-archives-XLII-3-W11-155-2020, 2020
PROCESSING UAV AND LIDAR POINT CLOUDS IN GRASS GIS
V. Petras, A. Petrasova, J. Jeziorska, and H. Mitasova
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 945–952, https://doi.org/10.5194/isprs-archives-XLI-B7-945-2016,https://doi.org/10.5194/isprs-archives-XLI-B7-945-2016, 2016
OPEN SOURCE APPROACH TO URBAN GROWTH SIMULATION
A. Petrasova, V. Petras, D. Van Berkel, B. A. Harmon, H. Mitasova, and R. K. Meentemeyer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 953–959, https://doi.org/10.5194/isprs-archives-XLI-B7-953-2016,https://doi.org/10.5194/isprs-archives-XLI-B7-953-2016, 2016
TANGIBLE LANDSCAPE: COGNITIVELY GRASPING THE FLOW OF WATER
B. A. Harmon, A. Petrasova, V. Petras, H. Mitasova, and R. K. Meentemeyer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 647–653, https://doi.org/10.5194/isprs-archives-XLI-B2-647-2016,https://doi.org/10.5194/isprs-archives-XLI-B2-647-2016, 2016

Related subject area

Hydrology
Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023,https://doi.org/10.5194/gmd-16-1617-2023, 2023
Short summary
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023,https://doi.org/10.5194/gmd-16-1553-2023, 2023
Short summary
SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023,https://doi.org/10.5194/gmd-16-977-2023, 2023
Short summary
A simple, efficient, mass-conservative approach to solving Richards' equation (openRE, v1.0)
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023,https://doi.org/10.5194/gmd-16-659-2023, 2023
Short summary
Customized deep learning for precipitation bias correction and downscaling
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023,https://doi.org/10.5194/gmd-16-535-2023, 2023
Short summary

Cited articles

Barton, C. M., Ullah, I., and Mitasova, H.: Computational Modeling and Neolithic Socioecological Dynamics: a Case Study from Southwest Asia, Am. Antiquity, 75, 364–386, available at: http://www.jstor.org/stable/25766199 (last access: 3 July 2019), 2010. a, b
Bechet, J., Duc, J., Loye, A., Jaboyedoff, M., Mathys, N., Malet, J.-P., Klotz, S., Le Bouteiller, C., Rudaz, B., and Travelletti, J.: Detection of seasonal cycles of erosion processes in a black marl gully from a time series of high-resolution digital elevation models (DEMs), Earth Surf. Dynam., 4, 781–798, https://doi.org/10.5194/esurf-4-781-2016, 2016. a
Braun, J. and Sambridge, M.: Modelling landscape evolution on geological time scales: a new method based on irregular spatial discretization, Basin Res., 9, 27–52, https://doi.org/10.1046/j.1365-2117.1997.00030.x, 1997. a
Brown, L. C. and Foster, G. R.: Storm Erosivity Using Idealized Intensity Distributions, Transactions of the American Society of Agricultural Engineers, 30, 0379–0386, https://doi.org/10.13031/2013.31957, 1987. a, b
Coulthard, T. J., Macklin, M. G., and Kirkby, M. J.: A cellular model of Holocene upland river basin and alluvial fan evolution, Earth Surf. Proc. Land., 27, 269–288, https://doi.org/10.1002/esp.318, 2002. a
Download
Short summary
The numerical model, r.sim.terrain, simulates how overland flows of water and sediment shape topography over short periods of time. We tested the model by comparing runs of the simulation against a time series of airborne lidar surveys for our study landscape. Through these tests, we demonstrated that the model can simulate gully evolution including processes such as channel incision, channel widening, and the development of scour pits, rills, and depositional ridges.