Articles | Volume 6, issue 1
https://doi.org/10.5194/esurf-6-77-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/esurf-6-77-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
15 Feb 2018
Research article |
| 15 Feb 2018
Numerical modelling of landscape and sediment flux response to precipitation rate change
Institut de Physique du Globe de Paris, Université Sorbonne Paris Cité, Paris, France
Alexander C. Whittaker
Department of Earth Science and Engineering, Imperial College London, London, UK
Mustapha Zakari
Institut de Physique du Globe de Paris, Université Sorbonne Paris Cité, Paris, France
Benjamin Campforts
Division Geography, Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
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Cited
21 citations as recorded by crossref.
- Global sensitivity analysis of parameter uncertainty in landscape evolution models C. Skinner et al. 10.5194/gmd-11-4873-2018
- Continental‐Scale Landscape Evolution: A History of North American Topography V. Fernandes et al. 10.1029/2018JF004979
- Oceanward rift migration during formation of Santos–Benguela ultra-wide rifted margins M. Araujo et al. 10.1144/SP524-2021-123
- HyLands 1.0: a hybrid landscape evolution model to simulate the impact of landslides and landslide-derived sediment on landscape evolution B. Campforts et al. 10.5194/gmd-13-3863-2020
- Lithium isotope evidence for enhanced weathering and erosion during the Paleocene-Eocene Thermal Maximum P. Pogge von Strandmann et al. 10.1126/sciadv.abh4224
- Orbital global change drove fluvial aggradation and incision in Tibetan upper Mekong river: Chronological perspectives Y. Zhou et al. 10.1016/j.quageo.2024.101546
- Assessing the hydrological and geomorphic behaviour of a landscape evolution model within a limits‐of‐acceptability uncertainty analysis framework J. Wong et al. 10.1002/esp.5140
- Deep-Water Syn-rift Stratigraphy as Archives of Early-Mid Pleistocene Palaeoenvironmental Signals and Controls on Sediment Delivery T. Cullen et al. 10.3389/feart.2021.715304
- Lithospheric Strength and Rift Migration Controls on Synrift Stratigraphy and Breakup Unconformities at Rifted Margins: Examples From Numerical Models, the Atlantic and South China Sea Margins M. Pérez‐Gussinyé et al. 10.1029/2020TC006255
- Sediment generation and sediment routing systems from a quantitative provenance analysis perspective: Review, application and future development L. Caracciolo 10.1016/j.earscirev.2020.103226
- Evaluating alluvial stratigraphic response to cyclic and non‐cyclic upstream forcing through process‐based alluvial architecture modelling Y. Wang et al. 10.1111/bre.12454
- Sediment Generation and Sediment Routing Systems L. Caracciolo et al. 10.1016/j.earscirev.2020.103221
- Sedapp v2021: a nonlinear diffusion-based forward stratigraphic model for shallow marine environments J. Li et al. 10.5194/gmd-14-4925-2021
- Parameterization of river incision models requires accounting for environmental heterogeneity: insights from the tropical Andes B. Campforts et al. 10.5194/esurf-8-447-2020
- Insights into mineralogical distribution mechanism and environmental significance from geochemical behavior of sediments in the Yellow River Basin, China W. Li et al. 10.1016/j.scitotenv.2023.166278
- Fluvial landscape evolution controlled by the sediment deposition coefficient: Estimation from experimental and natural landscapes L. Guerit et al. 10.1130/G46356.1
- Short communication: flow as distributed lines within the landscape J. Armitage 10.5194/esurf-7-67-2019
- On the main components of landscape evolution modelling of river systems M. Nones 10.1007/s11600-020-00401-8
- Linking continental erosion to marine sediment transport and deposition: A new implicit and O(N) method for inverse analysis X. Yuan et al. 10.1016/j.epsl.2019.115728
- Palaeocene–Eocene Thermal Maximum prolonged by fossil carbon oxidation S. Lyons et al. 10.1038/s41561-018-0277-3
- Times Associated With Source-to-Sink Propagation of Environmental Signals During Landscape Transience S. Tofelde et al. 10.3389/feart.2021.628315
21 citations as recorded by crossref.
- Global sensitivity analysis of parameter uncertainty in landscape evolution models C. Skinner et al. 10.5194/gmd-11-4873-2018
- Continental‐Scale Landscape Evolution: A History of North American Topography V. Fernandes et al. 10.1029/2018JF004979
- Oceanward rift migration during formation of Santos–Benguela ultra-wide rifted margins M. Araujo et al. 10.1144/SP524-2021-123
- HyLands 1.0: a hybrid landscape evolution model to simulate the impact of landslides and landslide-derived sediment on landscape evolution B. Campforts et al. 10.5194/gmd-13-3863-2020
- Lithium isotope evidence for enhanced weathering and erosion during the Paleocene-Eocene Thermal Maximum P. Pogge von Strandmann et al. 10.1126/sciadv.abh4224
- Orbital global change drove fluvial aggradation and incision in Tibetan upper Mekong river: Chronological perspectives Y. Zhou et al. 10.1016/j.quageo.2024.101546
- Assessing the hydrological and geomorphic behaviour of a landscape evolution model within a limits‐of‐acceptability uncertainty analysis framework J. Wong et al. 10.1002/esp.5140
- Deep-Water Syn-rift Stratigraphy as Archives of Early-Mid Pleistocene Palaeoenvironmental Signals and Controls on Sediment Delivery T. Cullen et al. 10.3389/feart.2021.715304
- Lithospheric Strength and Rift Migration Controls on Synrift Stratigraphy and Breakup Unconformities at Rifted Margins: Examples From Numerical Models, the Atlantic and South China Sea Margins M. Pérez‐Gussinyé et al. 10.1029/2020TC006255
- Sediment generation and sediment routing systems from a quantitative provenance analysis perspective: Review, application and future development L. Caracciolo 10.1016/j.earscirev.2020.103226
- Evaluating alluvial stratigraphic response to cyclic and non‐cyclic upstream forcing through process‐based alluvial architecture modelling Y. Wang et al. 10.1111/bre.12454
- Sediment Generation and Sediment Routing Systems L. Caracciolo et al. 10.1016/j.earscirev.2020.103221
- Sedapp v2021: a nonlinear diffusion-based forward stratigraphic model for shallow marine environments J. Li et al. 10.5194/gmd-14-4925-2021
- Parameterization of river incision models requires accounting for environmental heterogeneity: insights from the tropical Andes B. Campforts et al. 10.5194/esurf-8-447-2020
- Insights into mineralogical distribution mechanism and environmental significance from geochemical behavior of sediments in the Yellow River Basin, China W. Li et al. 10.1016/j.scitotenv.2023.166278
- Fluvial landscape evolution controlled by the sediment deposition coefficient: Estimation from experimental and natural landscapes L. Guerit et al. 10.1130/G46356.1
- Short communication: flow as distributed lines within the landscape J. Armitage 10.5194/esurf-7-67-2019
- On the main components of landscape evolution modelling of river systems M. Nones 10.1007/s11600-020-00401-8
- Linking continental erosion to marine sediment transport and deposition: A new implicit and O(N) method for inverse analysis X. Yuan et al. 10.1016/j.epsl.2019.115728
- Palaeocene–Eocene Thermal Maximum prolonged by fossil carbon oxidation S. Lyons et al. 10.1038/s41561-018-0277-3
- Times Associated With Source-to-Sink Propagation of Environmental Signals During Landscape Transience S. Tofelde et al. 10.3389/feart.2021.628315
Latest update: 29 Jun 2024
Short summary
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.
We explore how two landscape evolution models respond to a change in climate. The two models are...
