Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-3863-2020
© Author(s) 2020. 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-13-3863-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| Highlight paper
| 
31 Aug 2020
Model description paper | Highlight paper |
| 31 Aug 2020
| 31 Aug 2020
HyLands 1.0: a hybrid landscape evolution model to simulate the impact of landslides and landslide-derived sediment on landscape evolution
Benjamin Campforts
CORRESPONDING AUTHOR
Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, Germany
Institute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, CO, USA
Charles M. Shobe
Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, Germany
Philippe Steer
Géosciences Rennes – UMR 6118, CNRS, Université de Rennes, Rennes, France
Matthias Vanmaercke
Département de Géographie, UR SPHERES, Université de Liège, Liège, Belgium
Dimitri Lague
Géosciences Rennes – UMR 6118, CNRS, Université de Rennes, Rennes, France
Jean Braun
Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, Germany
Institute of Earth and Environmental Science, Universität Potsdam, Potsdam, Germany
Related authors
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.
Daniel O'Hara, Liran Goren, Roos M. J. van Wees, Benjamin Campforts, Pablo Grosse, Pierre Lahitte, Gabor Kereszturi, and Matthieu Kervyn
EGUsphere, https://doi.org/10.5194/egusphere-2023-1921, https://doi.org/10.5194/egusphere-2023-1921, 2023
Short summary
Short summary
Understanding how volcanic edifices develop drainage basins remains an unexplored aspect of 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 volcano basin develop and compare our results to basin evolution in other settings.
Jeffrey Keck, Erkan Istanbulluoglu, Benjamin Campforts, Gregory Tucker, and Alexander Horner-Devine
EGUsphere, https://doi.org/10.5194/egusphere-2023-1623, https://doi.org/10.5194/egusphere-2023-1623, 2023
Short summary
Short summary
Landslide hazards include both direct impacts associated with the runout extent of the landslide as well as secondary impacts associated with the sediment delivered to downslope channels. Numerous landslide runout and debris flow models exist but few can be used to track sediment or topographic change caused by the landslide. This paper introduces a new landslide runout model, called MassWastingRunout, that is specifically designed to meet these needs.
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.
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.
Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Benjamin Campforts, Tian Gan, Katherine R. Barnhart, Albert J. Kettner, Irina Overeem, Scott D. Peckham, Lynn McCready, and Jaia Syvitski
Geosci. Model Dev., 15, 1413–1439, https://doi.org/10.5194/gmd-15-1413-2022, https://doi.org/10.5194/gmd-15-1413-2022, 2022
Short summary
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.
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).
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
Short summary
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.
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
Short summary
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.
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
Short summary
Short summary
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.
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.
Chuanqi He, Ci-Jian Yang, Jens M. Turowski, Richard F. Ott, Jean Braun, Hui Tang, Shadi Ghantous, Xiaoping Yuan, and Gaia Stucky de Quay
Earth Syst. Sci. Data, 16, 1151–1166, https://doi.org/10.5194/essd-16-1151-2024, https://doi.org/10.5194/essd-16-1151-2024, 2024
Short summary
Short summary
The shape of drainage basins and rivers holds significant implications for landscape evolution processes and dynamics. We used a global 90 m resolution topography to obtain ~0.7 million drainage basins with sizes over 50 km2. Our dataset contains the spatial distribution of drainage systems and their morphological parameters, supporting fields such as geomorphology, climatology, biology, ecology, hydrology, and natural hazards.
Amanda Lily Wild, Jean Braun, Alexander C. Whittaker, and Sebastien Castelltort
EGUsphere, https://doi.org/10.5194/egusphere-2024-351, https://doi.org/10.5194/egusphere-2024-351, 2024
Short summary
Short summary
Sediments deposited within river channels form the stratigraphic record, which has been used to interpret tectonic events, basin subsidence, and changes in precipitation long after ancient mountain chains have eroded away. Our work combines methods for estimating gravel fining with a landscape evolution model in order to analyze the grain size preserved within the stratigraphic record with greater complexity (e.g. considering topography and channel dynamics) than past approaches.
Caroline Fenske, Jean Braun, François Guillocheau, and Cécile Robin
EGUsphere, https://doi.org/10.5194/egusphere-2024-160, https://doi.org/10.5194/egusphere-2024-160, 2024
Short summary
Short summary
We have developed a new numerical model to represent the formation of ferricretes which are iron-rich, hard layers found in soils and at the surface of the Earth. We assume that the formation mechanism implies variations in the height of the water table and that the hardening rate is proportional to precipitation. The model allows us to quantify the potential feedbacks they generate on the surface topography and the thickness of the regolith/soil layer.
Boris Gailleton, Luca C. Malatesta, Guillaume Cordonnier, and Jean Braun
Geosci. Model Dev., 17, 71–90, https://doi.org/10.5194/gmd-17-71-2024, https://doi.org/10.5194/gmd-17-71-2024, 2024
Short summary
Short summary
This contribution presents a new method to numerically explore the evolution of mountain ranges and surrounding areas. The method helps in monitoring with details on the timing and travel path of material eroded from the mountain ranges. It is particularly well suited to studies juxtaposing different domains – lakes or multiple rock types, for example – and enables the combination of different processes.
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.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
Short summary
Short summary
The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Ruohong Jiao, Shengze Cai, and Jean Braun
Geochronology Discuss., https://doi.org/10.5194/gchron-2023-24, https://doi.org/10.5194/gchron-2023-24, 2023
Preprint under review for GChron
Short summary
Short summary
We demonstrate a machine learning method to estimate the temperature changes of the earth's crust over time. The method respects physical laws and conditions imposed by users. By using observed rock cooling ages as constraints, the method can be used to estimate the tectonic and landscape evolution of the earth. We show the applications of the method using a synthetic rock uplift model in 1D and an evolution model of a real mountain range in 3D.
Daniel O'Hara, Liran Goren, Roos M. J. van Wees, Benjamin Campforts, Pablo Grosse, Pierre Lahitte, Gabor Kereszturi, and Matthieu Kervyn
EGUsphere, https://doi.org/10.5194/egusphere-2023-1921, https://doi.org/10.5194/egusphere-2023-1921, 2023
Short summary
Short summary
Understanding how volcanic edifices develop drainage basins remains an unexplored aspect of 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 volcano basin develop and compare our results to basin evolution in other settings.
Jeffrey Keck, Erkan Istanbulluoglu, Benjamin Campforts, Gregory Tucker, and Alexander Horner-Devine
EGUsphere, https://doi.org/10.5194/egusphere-2023-1623, https://doi.org/10.5194/egusphere-2023-1623, 2023
Short summary
Short summary
Landslide hazards include both direct impacts associated with the runout extent of the landslide as well as secondary impacts associated with the sediment delivered to downslope channels. Numerous landslide runout and debris flow models exist but few can be used to track sediment or topographic change caused by the landslide. This paper introduces a new landslide runout model, called MassWastingRunout, that is specifically designed to meet these needs.
Ngai-Ham Chan, Moritz Langer, Bennet Juhls, Tabea Rettelbach, Paul Overduin, Kimberly Huppert, and Jean Braun
Earth Surf. Dynam., 11, 259–285, https://doi.org/10.5194/esurf-11-259-2023, https://doi.org/10.5194/esurf-11-259-2023, 2023
Short summary
Short summary
Arctic river deltas influence how nutrients and soil organic carbon, carried by sediments from the Arctic landscape, are retained or released into the Arctic Ocean. Under climate change, the deltas themselves and their ecosystems are becoming more vulnerable. We build upon previous models to reproduce for the first time an important feature ubiquitous to Arctic deltas and simulate its future under climate warming. This can impact the future of Arctic deltas and the carbon release they moderate.
Philippe Steer, Laure Guerit, Dimitri Lague, Alain Crave, and Aurélie Gourdon
Earth Surf. Dynam., 10, 1211–1232, https://doi.org/10.5194/esurf-10-1211-2022, https://doi.org/10.5194/esurf-10-1211-2022, 2022
Short summary
Short summary
The morphology and size of sediments influence erosion efficiency, sediment transport and the quality of aquatic ecosystem. In turn, the spatial evolution of sediment size provides information on the past dynamics of erosion and sediment transport. We have developed a new software which semi-automatically identifies and measures sediments based on 3D point clouds. This software is fast and efficient, offering a new avenue to measure the geometrical properties of large numbers of sediment grains.
Lucas Pelascini, Philippe Steer, Maxime Mouyen, and Laurent Longuevergne
Nat. Hazards Earth Syst. Sci., 22, 3125–3141, https://doi.org/10.5194/nhess-22-3125-2022, https://doi.org/10.5194/nhess-22-3125-2022, 2022
Short summary
Short summary
Landslides represent a major natural hazard and are often triggered by typhoons. We present a new 2D model computing the respective role of rainfall infiltration, atmospheric depression and groundwater in slope stability during typhoons. The results show rainfall is the strongest factor of destabilisation. However, if the slope is fully saturated, near the toe of the slope or during the wet season, rainfall infiltration is limited and atmospheric pressure change can become the dominant factor.
Anne Felsberg, Jean Poesen, Michel Bechtold, Matthias Vanmaercke, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 22, 3063–3082, https://doi.org/10.5194/nhess-22-3063-2022, https://doi.org/10.5194/nhess-22-3063-2022, 2022
Short summary
Short summary
In this study we assessed global landslide susceptibility at the coarse 36 km spatial resolution of global satellite soil moisture observations to prepare for a subsequent combination of the two. Specifically, we focus therefore on the susceptibility of hydrologically triggered landslides. We introduce ensemble techniques, common in, for example, meteorology but not yet in the landslide community, to retrieve reliable estimates of the total prediction uncertainty.
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.
Clément Desormeaux, Vincent Godard, Dimitri Lague, Guillaume Duclaux, Jules Fleury, Lucilla Benedetti, Olivier Bellier, and the ASTER Team
Earth Surf. Dynam., 10, 473–492, https://doi.org/10.5194/esurf-10-473-2022, https://doi.org/10.5194/esurf-10-473-2022, 2022
Short summary
Short summary
Landscape evolution is highly dependent on climatic parameters, and the occurrence of intense precipitation events is considered to be an important driver of river incision. We compare the rate of erosion with the variability of river discharge in a mountainous landscape of SE France where high-magnitude floods regularly occur. Our study highlights the importance of the hypotheses made regarding the threshold that river discharge needs to exceed in order to effectively cut down into the bedrock.
M. Letard, A. Collin, D. Lague, T. Corpetti, Y. Pastol, and A. Ekelund
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 463–470, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-463-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-463-2022, 2022
Jean Braun
Earth Surf. Dynam., 10, 301–327, https://doi.org/10.5194/esurf-10-301-2022, https://doi.org/10.5194/esurf-10-301-2022, 2022
Short summary
Short summary
By comparing two models for the transport of sediment, we find that they share a similar steady-state solution that adequately predicts the shape of most depositional systems made of a fan and an alluvial plain. The length of the fan is controlled by the size of the mountain drainage area feeding the sedimentary system and its slope by the incoming sedimentary flux. We show that the models differ in their transient behavior to external forcing and are characterized by different response times.
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.
Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Benjamin Campforts, Tian Gan, Katherine R. Barnhart, Albert J. Kettner, Irina Overeem, Scott D. Peckham, Lynn McCready, and Jaia Syvitski
Geosci. Model Dev., 15, 1413–1439, https://doi.org/10.5194/gmd-15-1413-2022, https://doi.org/10.5194/gmd-15-1413-2022, 2022
Short summary
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.
Maxime Mouyen, Romain Plateaux, Alexander Kunz, Philippe Steer, and Laurent Longuevergne
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-233, https://doi.org/10.5194/gmd-2021-233, 2021
Preprint withdrawn
Short summary
Short summary
LAPS is an easy to use Matlab code that allows simulating the transport of particles in the ocean without any programming requirement. The simulation is based on publicly available ocean current velocity fields and allows to output particles spatial distribution and trajectories at time intervals defined by the user. After explaining how LAPS is working, we show a few examples of applications for studying sediment transport or plastic littering. The code is available on Github.
Philippe Steer
Earth Surf. Dynam., 9, 1239–1250, https://doi.org/10.5194/esurf-9-1239-2021, https://doi.org/10.5194/esurf-9-1239-2021, 2021
Short summary
Short summary
How landscapes respond to tectonic and climatic changes is a major issue in Earth sciences. I have developed a new model that solves for landscape evolution in two dimensions using analytical solutions. Compared to numerical models, this new model is quicker and more accurate. It can compute in a single time step the topography at equilibrium of a landscape or be used to describe its evolution through time, e.g. during changes in tectonic or climatic conditions.
Thomas G. Bernard, Dimitri Lague, and Philippe Steer
Earth Surf. Dynam., 9, 1013–1044, https://doi.org/10.5194/esurf-9-1013-2021, https://doi.org/10.5194/esurf-9-1013-2021, 2021
Short summary
Short summary
Both landslide mapping and volume estimation accuracies are crucial to quantify landscape evolution and manage such a natural hazard. We developed a method to robustly detect landslides and measure their volume from repeat 3D point cloud lidar data. This method detects more landslides than classical 2D inventories and resolves known issues of indirect volume measurement. Our results also suggest that the number of small landslides classically detected from 2D imagery is underestimated.
Thomas Croissant, Robert G. Hilton, Gen K. Li, Jamie Howarth, Jin Wang, Erin L. Harvey, Philippe Steer, and Alexander L. Densmore
Earth Surf. Dynam., 9, 823–844, https://doi.org/10.5194/esurf-9-823-2021, https://doi.org/10.5194/esurf-9-823-2021, 2021
Short summary
Short summary
In mountain ranges, earthquake-derived landslides mobilize large amounts of organic carbon (OC) by eroding soil from hillslopes. We propose a model to explore the role of different parameters in the post-seismic redistribution of soil OC controlled by fluvial export and heterotrophic respiration. Applied to the Southern Alps, our results suggest that efficient OC fluvial export during the first decade after an earthquake promotes carbon sequestration.
Nabil Hocini, Olivier Payrastre, François Bourgin, Eric Gaume, Philippe Davy, Dimitri Lague, Lea Poinsignon, and Frederic Pons
Hydrol. Earth Syst. Sci., 25, 2979–2995, https://doi.org/10.5194/hess-25-2979-2021, https://doi.org/10.5194/hess-25-2979-2021, 2021
Short summary
Short summary
Efficient flood mapping methods are needed for large-scale, comprehensive identification of flash flood inundation hazards caused by small upstream rivers. An evaluation of three automated mapping approaches of increasing complexity, i.e., a digital terrain model (DTM) filling and two 1D–2D hydrodynamic approaches, is presented based on three major flash floods in southeastern France. The results illustrate some limits of the DTM filling method and the value of using a 2D hydrodynamic approach.
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).
Maxime Bernard, Philippe Steer, Kerry Gallagher, and David Lundbek Egholm
Earth Surf. Dynam., 8, 931–953, https://doi.org/10.5194/esurf-8-931-2020, https://doi.org/10.5194/esurf-8-931-2020, 2020
Short summary
Short summary
Detrital thermochronometric age distributions of frontal moraines have the potential to retrieve ice erosion patterns. However, modelling erosion and sediment transport by the Tiedemann Glacier ice shows that ice velocity, the source of sediment, and ice flow patterns affect age distribution shape by delaying sediment transfer. Local sampling of frontal moraine can represent only a limited part of the catchment area and thus lead to a biased estimation of the spatial distribution of erosion.
Maxime Mouyen, Philippe Steer, Kuo-Jen Chang, Nicolas Le Moigne, Cheinway Hwang, Wen-Chi Hsieh, Louise Jeandet, Laurent Longuevergne, Ching-Chung Cheng, Jean-Paul Boy, and Frédéric Masson
Earth Surf. Dynam., 8, 555–577, https://doi.org/10.5194/esurf-8-555-2020, https://doi.org/10.5194/esurf-8-555-2020, 2020
Short summary
Short summary
Land erosion creates sediment particles that are redistributed from mountains to oceans through climatic, tectonic and human activities, but measuring the mass of redistributed sediment is difficult. Here we describe a new method combining gravity and photogrammetry measurements, which make it possible to weigh the mass of sediment redistributed by a landslide and a river in Taiwan from 2015 to 2017. Trying this method in other regions will help us to better understand the erosion process.
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
Short summary
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.
Anatoly Tsyplenkov, Matthias Vanmaercke, and Valentin Golosov
Proc. IAHS, 381, 87–93, https://doi.org/10.5194/piahs-381-87-2019, https://doi.org/10.5194/piahs-381-87-2019, 2019
Short summary
Short summary
Processes linked to climate change and intensified anthropogenic pressure influence the environment, the hydrology. Quantitative assessments of sediment fluxes and their temporal evolution in this mountain region are required for various environmental and engineering purposes, including the planning and maintenance of water reservoirs and other structures. we present a first analysis of the hitherto largest suspended sediment yield (SSY) database for the Caucasus region.
Philippe Steer, Thomas Croissant, Edwin Baynes, and Dimitri Lague
Earth Surf. Dynam., 7, 681–706, https://doi.org/10.5194/esurf-7-681-2019, https://doi.org/10.5194/esurf-7-681-2019, 2019
Short summary
Short summary
We use a statistical earthquake generator to investigate the influence of fault activity on river profile development and on the formation of co-seismic knickpoints. We find that the magnitude distribution of knickpoints resulting from a purely seismic fault is homogeneous. Shallow aseismic slip favours knickpoints generated by large-magnitude earthquakes nucleating at depth. Accounting for fault burial by alluvial cover can modulate the topographic expression of earthquakes and fault activity.
Guillaume Cordonnier, Benoît Bovy, and Jean Braun
Earth Surf. Dynam., 7, 549–562, https://doi.org/10.5194/esurf-7-549-2019, https://doi.org/10.5194/esurf-7-549-2019, 2019
Short summary
Short summary
We propose a new algorithm to solve the problem of flow routing across local depressions in the topography, one of the main computational bottlenecks in landscape evolution models. Our solution is more efficient than the state-of-the-art algorithms, with an optimal linear asymptotic complexity. The algorithm has been designed specifically to be used within landscape evolution models, and also suits more generally the efficient treatment of large digital elevation models.
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.
Jean Braun, Lorenzo Gemignani, and Peter van der Beek
Earth Surf. Dynam., 6, 257–270, https://doi.org/10.5194/esurf-6-257-2018, https://doi.org/10.5194/esurf-6-257-2018, 2018
Short summary
Short summary
We present a new method to interpret a type of data that geologists obtained by dating minerals in river sand samples. We show that such data contain information about the spatial distribution of the erosion rate (wear of surface rocks by natural processes such as river incision, land sliding or weathering) in the regions neighboring the river. This is important to understand the nature and efficiency of the processes responsible for surface erosion in mountain belts.
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
Short summary
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.
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.
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
Short summary
Short summary
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.
J. Braun, C. Voisin, A. T. Gourlan, and C. Chauvel
Earth Surf. Dynam., 3, 1–14, https://doi.org/10.5194/esurf-3-1-2015, https://doi.org/10.5194/esurf-3-1-2015, 2015
Short summary
Short summary
We have derived a simple solution to the stream power law equation governing the erosion of rapidly uplifting tectonic areas assuming that rainfall varies as a periodic function of time. We show that the erosional response of this forcing is characterized by an amplification of the resulting erosional flux variations as well as a time lag. We show how this time lag can be important in interpreting several geological observations.
T. Croissant and J. Braun
Earth Surf. Dynam., 2, 155–166, https://doi.org/10.5194/esurf-2-155-2014, https://doi.org/10.5194/esurf-2-155-2014, 2014
Related subject area
Earth and space science informatics
Tomofast-x 2.0: an open-source parallel code for inversion of potential field data with topography using wavelet compression
Functional analysis of variance (ANOVA) for carbon flux estimates from remote sensing data
The 4D reconstruction of dynamic geological evolution processes for renowned geological features
Machine learning for numerical weather and climate modelling: a review
Focal-TSMP: Deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation
Overcoming barriers to enable convergence research by integrating ecological and climate sciences: the NCAR–NEON system Version 1
Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale
Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China
A generalized spatial autoregressive neural network method for three-dimensional spatial interpolation
The Common Community Physics Package (CCPP) Framework v6
Causal deep learning models for studying the Earth system
A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA
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
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
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
CSDMS: a community platform for numerical modeling of Earth surface processes
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)
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
Vitaliy Ogarko, Kim Frankcombe, Taige Liu, Jeremie Giraud, Roland Martin, and Mark Jessell
Geosci. Model Dev., 17, 2325–2345, https://doi.org/10.5194/gmd-17-2325-2024, https://doi.org/10.5194/gmd-17-2325-2024, 2024
Short summary
Short summary
We present a major release of the Tomofast-x open-source gravity and magnetic inversion code that is enhancing its performance and applicability for both industrial and academic studies. We focus on real-world mineral exploration scenarios, while offering flexibility for applications at regional scale or for crustal studies. The optimisation work described in this paper is fundamental to allowing more complete descriptions of the controls on magnetisation, including remanence.
Jonathan Hobbs, Matthias Katzfuss, Hai Nguyen, Vineet Yadav, and Junjie Liu
Geosci. Model Dev., 17, 1133–1151, https://doi.org/10.5194/gmd-17-1133-2024, https://doi.org/10.5194/gmd-17-1133-2024, 2024
Short summary
Short summary
The cycling of carbon among the land, oceans, and atmosphere is a closely monitored process in the global climate system. These exchanges between the atmosphere and the surface can be quantified using a combination of atmospheric carbon dioxide observations and computer models. This study presents a statistical method for investigating the similarities and differences in the estimated surface–atmosphere carbon exchange when different computer model assumptions are invoked.
Jiateng Guo, Zhibin Liu, Xulei Wang, Lixin Wu, Shanjun Liu, and Yunqiang Li
Geosci. Model Dev., 17, 847–864, https://doi.org/10.5194/gmd-17-847-2024, https://doi.org/10.5194/gmd-17-847-2024, 2024
Short summary
Short summary
This study proposes a 3D and temporally dynamic (4D) geological modeling method. Several simulation and actual cases show that the 4D spatial and temporal evolution of regional geological formations can be modeled easily using this method with smooth boundaries. The 4D modeling system can dynamically present the regional geological evolution process under the timeline, which will be helpful to the research and teaching on the formation of typical and complex geological features.
Catherine O. de Burgh-Day and Tennessee Leeuwenburg
Geosci. Model Dev., 16, 6433–6477, https://doi.org/10.5194/gmd-16-6433-2023, https://doi.org/10.5194/gmd-16-6433-2023, 2023
Short summary
Short summary
Machine learning (ML) is an increasingly popular tool in the field of weather and climate modelling. While ML has been used in this space for a long time, it is only recently that ML approaches have become competitive with more traditional methods. In this review, we have summarized the use of ML in weather and climate modelling over time; provided an overview of key ML concepts, methodologies, and terms; and suggested promising avenues for further research.
Mohamad Hakam Shams Eddin and Juergen Gall
EGUsphere, https://doi.org/10.5194/egusphere-2023-2422, https://doi.org/10.5194/egusphere-2023-2422, 2023
Short summary
Short summary
Scientists usually rely on climate projections to analyze agricultural drought events in the future. In this study, we use deep learning and a climate simulation to forecast the vegetation health as it would be observed from satellites in the future. We found that the developed model can help to identify regions with a high risk of agricultural droughts. The main application of this study is to predict the future vegetation response to climate change based on climate scenarios.
Danica L. Lombardozzi, William R. Wieder, Negin Sobhani, Gordon B. Bonan, David Durden, Dawn Lenz, Michael SanClements, Samantha Weintraub-Leff, Edward Ayres, Christopher R. Florian, Kyla Dahlin, Sanjiv Kumar, Abigail L. S. Swann, Claire M. Zarakas, Charles Vardeman, and Valerio Pascucci
Geosci. Model Dev., 16, 5979–6000, https://doi.org/10.5194/gmd-16-5979-2023, https://doi.org/10.5194/gmd-16-5979-2023, 2023
Short summary
Short summary
We present a novel cyberinfrastructure system that uses National Ecological Observatory Network measurements to run Community Terrestrial System Model point simulations in a containerized system. The simple interface and tutorials expand access to data and models used in Earth system research by removing technical barriers and facilitating research, educational opportunities, and community engagement. The NCAR–NEON system enables convergence of climate and ecological sciences.
Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, and Bob Su
Geosci. Model Dev., 16, 5825–5845, https://doi.org/10.5194/gmd-16-5825-2023, https://doi.org/10.5194/gmd-16-5825-2023, 2023
Short summary
Short summary
Using machine learning, we estimated global surface soil moisture (SSM) to aid in understanding water, energy, and carbon exchange. Ensemble models outperformed individual algorithms in predicting SSM under different climates. The best-performing ensemble included K-neighbours Regressor, Random Forest Regressor, and Extreme Gradient Boosting. This is important for hydrological and climatological applications such as water cycle monitoring, irrigation management, and crop yield prediction.
Xiaoyi Shao, Siyuan Ma, and Chong Xu
Geosci. Model Dev., 16, 5113–5129, https://doi.org/10.5194/gmd-16-5113-2023, https://doi.org/10.5194/gmd-16-5113-2023, 2023
Short summary
Short summary
Scientific understandings of the distribution of coseismic landslides, followed by emergency and medium- and long-term risk assessment, can reduce landslide risk. The aim of this study is to propose an improved three-stage spatial prediction strategy and develop corresponding hazard assessment software called Mat.LShazard V1.0, which provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages.
Junda Zhan, Sensen Wu, Jin Qi, Jindi Zeng, Mengjiao Qin, Yuanyuan Wang, and Zhenhong Du
Geosci. Model Dev., 16, 2777–2794, https://doi.org/10.5194/gmd-16-2777-2023, https://doi.org/10.5194/gmd-16-2777-2023, 2023
Short summary
Short summary
We develop a generalized spatial autoregressive neural network model used for three-dimensional spatial interpolation. Taking the different changing trend of geographic elements along various directions into consideration, the model defines spatial distance in a generalized way and integrates it into the process of spatial interpolation with the theories of spatial autoregression and neural network. Compared with traditional methods, the model achieves better performance and is more adaptable.
Dominikus Heinzeller, Ligia Bernardet, Grant Firl, Man Zhang, Xia Sun, and Michael Ek
Geosci. Model Dev., 16, 2235–2259, https://doi.org/10.5194/gmd-16-2235-2023, https://doi.org/10.5194/gmd-16-2235-2023, 2023
Short summary
Short summary
The Common Community Physics Package is a collection of physical atmospheric parameterizations for use in Earth system models and a framework that couples the physics to a host model’s dynamical core. A primary goal for this effort is to facilitate research and development of physical parameterizations and physics–dynamics coupling methods while offering capabilities for numerical weather prediction operations, for example in the upcoming implementation of the Global Forecast System (GFS) v17.
Tobias Tesch, Stefan Kollet, and Jochen Garcke
Geosci. Model Dev., 16, 2149–2166, https://doi.org/10.5194/gmd-16-2149-2023, https://doi.org/10.5194/gmd-16-2149-2023, 2023
Short summary
Short summary
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.
Yao Hu, Chirantan Ghosh, and Siamak Malakpour-Estalaki
Geosci. Model Dev., 16, 1925–1936, https://doi.org/10.5194/gmd-16-1925-2023, https://doi.org/10.5194/gmd-16-1925-2023, 2023
Short summary
Short summary
Data-driven models (DDMs) gain popularity in earth and environmental systems, thanks in large part to advancements in data collection techniques and artificial intelligence (AI). The performance of these 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 and demonstrate the effectiveness of the framework using a DDM to predict edge-of-field runoff in the Maumee domain, USA.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Benjamin Campforts, Tian Gan, Katherine R. Barnhart, Albert J. Kettner, Irina Overeem, Scott D. Peckham, Lynn McCready, and Jaia Syvitski
Geosci. Model Dev., 15, 1413–1439, https://doi.org/10.5194/gmd-15-1413-2022, https://doi.org/10.5194/gmd-15-1413-2022, 2022
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
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.
Cited articles
Amos, C. B. and Burbank, D. W.: Channel width response to differential
uplift, J. Geophys. Res., 112, F02010,
https://doi.org/10.1029/2006JF000672, 2007. a
Andrews, D. J. and Hanks, T. C.: Scarp degraded by linear diffusion: Inverse
solution for age, J. Geophys. Res., 90, 10193,
https://doi.org/10.1029/JB090iB12p10193, 1985. a
Armitage, J. J., Whittaker, A. C., Zakari, M., and Campforts, B.: Numerical modelling of landscape and sediment flux response to precipitation rate change, Earth Surf. Dynam., 6, 77–99, https://doi.org/10.5194/esurf-6-77-2018, 2018. a
Attal, M., Tucker, G. E., Whittaker, A. C., Cowie, P. A., and Roberts, G. P.:
Modelling fluvial incision and transient landscape evolution: Influence of
dynamic Channel adjustment, J. Geophys. Res.-Earth,
113, 1–16, https://doi.org/10.1029/2007JF000893, 2008. a
Baum, R. L., Godt, J. W., and Savage, W. Z.: Estimating the timing and
location of shallow rainfall-induced landslides using a model for transient,
unsaturated infiltration, J. Geophys. Res., 115, F03013,
https://doi.org/10.1029/2009JF001321, 2010. a
Beaumont, C., Fullsack, P., and Hamilton, J.: Erosional control of active
compressional orogens, in: Thrust Tectonics, Springer
Netherlands, Dordrecht, 1–18, https://doi.org/10.1007/978-94-011-3066-0_1, 1992. a, b
Beer, A. R., Turowski, J. M., and Kirchner, J. W.: Spatial patterns of erosion
in a bedrock gorge, J. Geophys. Res.-Earth, 122,
191–214, https://doi.org/10.1002/2016JF003850, 2017. a
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty
estimation in mechanistic modelling of complex environmental systems using
the GLUE methodology, J. Hydrol., 249, 11–29,
https://doi.org/10.1016/S0022-1694(01)00421-8, 2001. a
Burbank, D., Meigs, A., and Brozović, N.: Interactions of growing folds
and coeval depositional systems, Basin Res., 8, 199–223,
https://doi.org/10.1046/j.1365-2117.1996.00181.x, 1996. a
Burbank, D. W.: Rates of erosion and their implications for exhumation,
Mineral. Mag., 66, 25–52, https://doi.org/10.1180/0026461026610014, 2002. a
Burbank, D. W. and Anderson, R. S.: Tectonic Geomorphology, John Wiley &
Sons, Ltd, Chichester, UK, https://doi.org/10.1002/9781444345063, 2011. a
Campforts, B.: BCampforts/pub_hylands_campforts_etal_GMD: pub_hylands_campforts_etal_GMD (Version v1.03), Zenodo, https://doi.org/10.5281/zenodo.3714182, 2020a. a
Campforts, B.: BCampforts/topotoolbox: topotoolbox-v2.4-HyLands-v1.0 (Version v2.4-HyLands-v1.0), Zenodo, https://doi.org/10.5281/zenodo.3712439, 2020i. a
Campforts, B. and Govers, G.: Keeping the edge: A numerical method that avoids
knickpoint smearing when solving the stream power law, J. Geophys. Res.-Earth, 120, 1189–1205,
https://doi.org/10.1002/2014JF003376, 2015. a
Campforts, B., Vanacker, V., Vanderborght, J., Baken, S., Smolders, E., and
Govers, G.: Simulating the mobility of meteoric 10 Be in the landscape
through a coupled soil-hillslope model (Be2D), Earth Planet. Sc.
Lett., 439, 143–157, https://doi.org/10.1016/j.epsl.2016.01.017, 2016. a, b
Campforts, B., Schwanghart, W., and Govers, G.: Accurate simulation of transient landscape evolution by eliminating numerical diffusion: the TTLEM 1.0 model, Earth Surf. Dynam., 5, 47–66, https://doi.org/10.5194/esurf-5-47-2017, 2017. a, b
Campforts, B., Vanacker, V., Herman, F., Vanmaercke, M., Schwanghart, W., Tenorio, G. E., Willems, P., and Govers, G.: Parameterization of river incision models requires accounting for environmental heterogeneity: insights from the tropical Andes, Earth Surf. Dynam., 8, 447–470, https://doi.org/10.5194/esurf-8-447-2020, 2020. a, b, c
Carretier, S., Tolorza, V., Regard, V., Aguilar, G., Bermúdez, M. A.,
Martinod, J., Guyot, J. L., Hérail, G., and Riquelme, R.: Review of
erosion dynamics along the major N-S climatic gradient in Chile and
perspectives, Geomorphology, 300, 45–68,
https://doi.org/10.1016/j.geomorph.2017.10.016, 2018. a
Champel, B.: Growth and lateral propagation of fault-related folds in the
Siwaliks of western Nepal: Rates, mechanisms, and geomorphic signature,
J. Geophys. Res., 107, 2111, https://doi.org/10.1029/2001JB000578, 2002. a, b, c, d
Claessens, L., Schoorl, J., and Veldkamp, A.: Modelling the location of
shallow landslides and their effects on landscape dynamics in large
watersheds: An application for Northern New Zealand, Geomorphology, 87,
16–27, https://doi.org/10.1016/j.geomorph.2006.06.039, 2007. a, b, c, d
Cook, K. L., Turowski, J. M., and Hovius, N.: A demonstration of the
importance of bedload transport for fluvial bedrock erosion and knickpoint
propagation, Earth Surf. Proc. Land., 38, 683–695,
https://doi.org/10.1002/esp.3313, 2013. a
Coulthard, T. J., Neal, J. C., Bates, P. D., Ramirez, J., de Almeida, G. A. M.,
and Hancock, G. R.: Integrating the LISFLOOD-FP 2D hydrodynamic model with
the CAESAR model: implications for modelling landscape evolution, Earth
Surf. Proc. Land., 38, 1897–1906, https://doi.org/10.1002/esp.3478,
2013. a
Croissant, T., Lague, D., Steer, P., and Davy, P.: Rapid post-seismic
landslide evacuation boosted by dynamic river width, Nat. Geosci., 10,
680–684, https://doi.org/10.1038/ngeo3005, 2017. a, b
Croissant, T., Steer, P., Lague, D., Davy, P., Jeandet, L., and Hilton, R. G.:
Seismic cycles, earthquakes, landslides and sediment fluxes: Linking
tectonics to surface processes using a reduced-complexity model,
Geomorphology, 339, 87–103, https://doi.org/10.1016/j.geomorph.2019.04.017, 2019. a, b, c
Dahlquist, M. P., West, A. J., and Li, G.: Landslide-driven drainage divide
migration, Geology, 46, 403–406, https://doi.org/10.1130/G39916.1, 2018. a
Davy, P., Croissant, T., and Lague, D.: A precipiton method to calculate river
hydrodynamics, with applications to flood prediction, landscape evolution
models, and braiding instabilities, J. Geophys. Res.-Earth, 122, 1491–1512, https://doi.org/10.1002/2016JF004156, 2017. a
DiBiase, R. A. and Whipple, K. X.: The influence of erosion thresholds and
runoff variability on the relationships among topography, climate, and
erosion rate, J. Geophys. Res., 116, F04036,
https://doi.org/10.1029/2011JF002095, 2011. a, b
Dussauge, C., Grasso, J.-R., and Helmstetter, A.: Statistical analysis of
rockfall volume distributions: Implications for rockfall dynamics, J. Geophys. Res.-Sol. Ea., 108, https://doi.org/10.1029/2001JB000650,
2003. a
Fan, L., Lehmann, P., McArdell, B., and Or, D.: Linking rainfall-induced
landslides with debris flows runout patterns towards catchment scale hazard
assessment, Geomorphology, 280, 1–15, https://doi.org/10.1016/j.geomorph.2016.10.007,
2017. 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
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S.,
Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.:
The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004,
https://doi.org/10.1029/2005RG000183, 2007. a, b, c, d
Ferrier, K. L., Huppert, K. L., and Perron, J. T.: Climatic control of bedrock
river incision, Nature, 496, 206–209, https://doi.org/10.1038/nature11982, 2013. a
Finnegan, N. J., Hallet, B., Montgomery, D. R., Zeitler, P. K., Stone, J. O.,
Anders, A. M., and Yuping, L.: Coupling of rock uplift and river incision in
the Namche Barwa-Gyala Peri massif, Tibet, Geol. Soc. Am.
Bull., 120, 142–155, https://doi.org/10.1130/B26224.1, 2008. a, b
Furbish, D. J. and Roering, J. J.: Sediment disentrainment and the concept of
local versus nonlocal transport on hillslopes, J. Geophys. Res.-Earth, 118, 937–952, https://doi.org/10.1002/jgrf.20071, 2013. a
Gallen, S. F., Clark, M. K., and Godt, J. W.: Coseismic landslides reveal
near-surface rock strength in a high-relief, tectonically active setting,
Geology, 43, 11–14, https://doi.org/10.1130/G36080.1, 2015. a, b
Gasparini, N. M., Whipple, K. X., and Bras, R. L.: Predictions of steady state
and transient landscape morphology using sediment-flux-dependent river
incision models, J. Geophys. Res., 112, F03S09,
https://doi.org/10.1029/2006JF000567, 2007. a, b
George, D. L.: Adaptive finite volume methods with well-balanced Riemann
solvers for modeling floods in rugged terrain: Application to the Malpasset
dam-break flood (France, 1959), Int. J. Numer. Meth.
Fl., 66, 1000–1018, https://doi.org/10.1002/fld.2298, 2011. 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, https://doi.org/10.1130/G46219.1, 2019. a, b, c, d
Guns, M. and Vanacker, V.: Shifts in landslide frequency-area distribution
after forest conversion in the tropical Andes, Anthropocene, 6, 75–85,
https://doi.org/10.1016/j.ancene.2014.08.001, 2014. a, b
Guzzetti, F., Malamud, B. D., Turcotte, D. L., and Reichenbach, P.: Power-law
correlations of landslide areas in central Italy, Earth Planet.
Sc. Lett., 195, 169–183, https://doi.org/10.1016/S0012-821X(01)00589-1, 2002. a, b
Guzzetti, F., Reichenbach, P., Ardizzone, F., Cardinali, M., and Galli, M.:
Estimating the quality of landslide susceptibility models, Geomorphology,
81, 166–184, https://doi.org/10.1016/j.geomorph.2006.04.007, 2006. a
Hancock, G. S. and Anderson, R. S.: Numerical modeling of fluvial
strath-terrace formation in response to oscillating climate, GSA Bulletin, 114, 1131–1142,
https://doi.org/10.1130/0016-7606(2002)114<1131:NMOFST>2.0.CO;2, 2002. a
Hobley, D. E., Sinclair, H. D., Mudd, S. M., and Cowie, P. A.: Field
calibration of sediment flux dependent river incision, J. Geophys. Res.-Earth, 116, F04017,
https://doi.org/10.1029/2010JF001935, 2011. a
Horton, P., Jaboyedoff, M., Rudaz, B., and Zimmermann, M.: Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale, Nat. Hazards Earth Syst. Sci., 13, 869–885, https://doi.org/10.5194/nhess-13-869-2013, 2013. a
Hovius, N., Stark, C. P., and Allen, P. A.: Sediment flux from a mountain belt
derived by landslide mapping, Geology, 25, 231–234,
https://doi.org/10.1130/0091-7613(1997)025<0231:SFFAMB>2.3.CO;2, 1997. a, b, c
Hovius, N., Stark, C. P., Hao‐Tsu, C., and Jiun‐Chuan, L.: Supply and
Removal of Sediment in a Landslide‐Dominated Mountain Belt: Central Range,
Taiwan, J. Geol., 108, 73–89, https://doi.org/10.1086/314387, 2000. a
Hovius, N., Meunier, P., Lin, C. W., Chen, H., Chen, Y. G., Dadson, S., Horng,
M. J., and Lines, M.: Prolonged seismically induced erosion and the mass
balance of a large earthquake, Earth Planet. Sc. Lett., 304,
347–355, https://doi.org/10.1016/j.epsl.2011.02.005, 2011. a
Howard, A. D. and Kerby, G.: Channel changes in badlands, GSA Bulletin, 94, 739–752,
https://doi.org/10.1130/0016-7606(1983)94<739:CCIB>2.0.CO;2, 1983. a, b
Iverson, R. M.: Landslide triggering by rain infiltration, Water Resour.
Res., 36, 1897–1910, https://doi.org/10.1029/2000WR900090, 2000. a, b
Iverson, R. M. and George, D. L.: Modelling landslide liquefaction, mobility
bifurcation and the dynamics of the 2014 Oso disaster, Géotechnique,
66, 175–187, https://doi.org/10.1680/jgeot.15.LM.004, 2016. a
Kean, J. W. and Smith, J. D.: Flow and boundary shear stress in channels with
woody bank vegetation, Water Sci. Appl., 8,
237–252, 2004. a
Keefer, D. K.: Landslides caused by earthquakes, GSA Bulletin, 95, 406–421,
1984. a
Keefer, D. K.: Investigating landslides caused by earthquakes – A historical
review, Surv. Geophys., 23, 473–510, https://doi.org/10.1023/A:1021274710840,
2002. a
Keefer, D. K. and Larsen, M. C.: Assessing Landslide Hazards, Science, 316,
1136–1138, https://doi.org/10.1126/science.1143308, 2007. a
King, G. E., Herman, F., Lambert, R., Valla, P. G., and Guralnik, B.:
Multi-OSL-thermochronometry of feldspar, Quatern. Geochronol., 33,
76–87, https://doi.org/10.1016/j.quageo.2016.01.004, 2016. a, b
Kirby, E. and Whipple, K. X.: Expression of active tectonics in erosional
landscapes, J. Struct. Geol., 44, 54–75,
https://doi.org/10.1016/j.jsg.2012.07.009, 2012. a
Korup, O.: Large landslides and their effect on sediment flux in South
Westland, New Zealand, Earth Surf. Proc. Land., 30, 305–323,
https://doi.org/10.1002/esp.1143, 2005. a, b
Korup, O.: Rock type leaves topographic signature in landslide-dominated
mountain ranges, Geophys. Res. Lett., 35, L11402,
https://doi.org/10.1029/2008GL034157, 2008. a
Korup, O., Clague, J. J., Hermanns, R. L., Hewitt, K., Strom, A. L., and
Weidinger, J. T.: Giant landslides, topography, and erosion, Earth
Planet. Sc. Lett., 261, 578–589, https://doi.org/10.1016/j.epsl.2007.07.025,
2007. a, b, c
Korup, O., Densmore, A. L., and Schlunegger, F.: The role of landslides in
mountain range evolution, Geomorphology, 120, 77–90,
https://doi.org/10.1016/j.geomorph.2009.09.017, 2010. a
Lague, D.: Reduction of long-term bedrock incision efficiency by short-term
alluvial cover intermittency, J. Geophys. Res.-Earth, 115, F02011,
https://doi.org/10.1029/2008JF001210, 2010. a, b, c
Lague, D.: The stream power river incision model: evidence, theory and
beyond, Earth Surf. Proc. Land., 39, 38–61,
https://doi.org/10.1002/esp.3462, 2014. a
Lague, D., Hovius, N., and Davy, P.: Discharge, discharge variability, and the
bedrock channel profile, J. Geophys. Res.-Earth,
110, F04006, https://doi.org/10.1029/2004JF000259, 2005. a
Larsen, I. J., Montgomery, D. R., and Korup, O.: Landslide erosion controlled
by hillslope material, Nat. Geosci., 3, 247–251,
https://doi.org/10.1038/ngeo776, 2010. a, b
Li, G., West, A. J., Densmore, A. L., Hammond, D. E., Jin, Z., Zhang, F., Wang,
J., and Hilton, R. G.: Connectivity of earthquake-triggered landslides with
the fluvial network: Implications for landslide sediment transport after the
2008 Wenchuan earthquake, J. Geophys. Res.-Earth,
121, 703–724, https://doi.org/10.1002/2015JF003718, 2016. a
Lin, G.-W., Chen, H., Chen, Y.-H., and Horng, M.-J.: Influence of typhoons and
earthquakes on rainfall-induced landslides and suspended sediments
discharge, Eng. Geol., 97, 32–41,
https://doi.org/10.1016/j.enggeo.2007.12.001, 2008. a
Malamud, B. D. and Turcotte, D. L.: Self-organized criticality applied to
natural hazards, Nat. Hazards, 20, 93–116,
https://doi.org/10.1023/A:1008014000515, 1999. a, b
Malamud, B. D., Turcotte, D. L., Guzzetti, F., and Reichenbach, P.: Landslide
inventories and their statistical properties, Earth Surf. Proc.
Land., 29, 687–711, https://doi.org/10.1002/esp.1064, 2004. a
Marc, O., Hovius, N., Meunier, P., Uchida, T., and Hayashi, S.: Transient
changes of landslide rates after earthquakes, Geology, 43, 883–886,
https://doi.org/10.1130/G36961.1, 2015. a
Marc, O., Stumpf, A., Malet, J.-P., Gosset, M., Uchida, T., and Chiang, S.-H.: Initial insights from a global database of rainfall-induced landslide inventories: the weak influence of slope and strong influence of total storm rainfall, Earth Surf. Dynam., 6, 903–922, https://doi.org/10.5194/esurf-6-903-2018, 2018. a, b
Marc, O., Behling, R., Andermann, C., Turowski, J. M., Illien, L., Roessner, S., and Hovius, N.: Long-term erosion of the Nepal Himalayas by bedrock landsliding: the role of monsoons, earthquakes and giant landslides, Earth Surf. Dynam., 7, 107–128, https://doi.org/10.5194/esurf-7-107-2019, 2019. a
Meunier, P., Hovius, N., and Haines, A. J.: Regional patterns of
earthquake-triggered landslides and their relation to ground motion,
Geophys. Res. Lett., 34, L20408, https://doi.org/10.1029/2007GL031337, 2007. a, b
Milliman, J. D. and Meade, R. H.: World-Wide Delivery of River Sediment to the
Oceans, J. Geol., 91, 1–21, https://doi.org/10.1086/628741, 1983. a
Montgomery, D. R. and Dietrich, W. E.: A physically based model for the
topographic control on shallow landsliding, Water Resour. Res., 30,
1153–1171, https://doi.org/10.1029/93WR02979, 1994. a, b
Montgomery, D. R. and Gran, K. B.: Downstream variations in the width of
bedrock channels, Water Resources Research, 37, 1841–1846,
https://doi.org/10.1029/2000WR900393, 2001. a
Mudd, S. M.: Detection of transience in eroding landscapes, Earth Surf.
Proc. Land., 42, 24–41, https://doi.org/10.1002/esp.3923, 2017. a
Niemi, N. A., Oskin, M., Burbank, D. W., Heimsath, A. M., and Gabet, E. J.:
Effects of bedrock landslides on cosmogenically determined erosion rates,
Earth Planet. Sc. Lett., 237, 480–498,
https://doi.org/10.1016/j.epsl.2005.07.009, 2005. a
Ouimet, W. B., Whipple, K. X., Crosby, B. T., Johnson, J. P., and Schildgen,
T. F.: Epigenetic gorges in fluvial landscapes, Earth Surf. Proc.
Land., 33, 1993–2009, https://doi.org/10.1002/esp.1650, 2008. a
Page, M. J., Reid, L. M., and Lynn, I. H.: New Zealand Hydrological Society
Sediment production from Cyclone Bola landslides, Waipaoa catchment,
J. Hydrol., 38, 289–308, 1999. a
Paola, C. and Voller, V. R.: A generalized Exner equation for sediment mass
balance, J. Geophys. Res.-Earth, 110, 1–8,
https://doi.org/10.1029/2004JF000274, 2005. a
Parker, R. N., Hales, T. C., Mudd, S. M., Grieve, S. W. D., and Constantine,
J. A.: Colluvium supply in humid regions limits the frequency of
storm-triggered landslides, Sci. Rep., 6, 34438,
https://doi.org/10.1038/srep34438, 2016. a
Pelletier, J. D.: Minimizing the grid-resolution dependence of flow-routing
algorithms for geomorphic applications, Geomorphology, 122, 91–98,
https://doi.org/10.1016/j.geomorph.2010.06.001, 2010. a
Pfeiffer, A. M., Finnegan, N. J., and Willenbring, J. K.: Sediment supply
controls equilibrium channel geometry in gravel rivers, P.
Natl. Acad. Sci. USA, 114, 3346–3351, https://doi.org/10.1073/pnas.1612907114,
2017. a
Roback, K., Clark, M. K., West, A. J., Zekkos, D., Li, G., Gallen, S. F.,
Chamlagain, D., and Godt, J. W.: The size, distribution, and mobility of
landslides caused by the 2015 Mw7.8 Gorkha earthquake, Nepal, Geomorphology,
301, 121–138, https://doi.org/10.1016/j.geomorph.2017.01.030, 2018. a
Robinson, T. R., Rosser, N. J., Densmore, A. L., Williams, J. G., Kincey, M. E., Benjamin, J., and Bell, H. J. A.: Rapid post-earthquake modelling of coseismic landslide intensity and distribution for emergency response decision support, Nat. Hazards Earth Syst. Sci., 17, 1521–1540, https://doi.org/10.5194/nhess-17-1521-2017, 2017. a
Roering, J. J., Kirchner, J. W., and Dietrich, W. E.: Evidence for nonlinear,
diffusive sediment transport on hillslopes and implications for landscape
morphology, Water Resources Research, 35, 853–870,
https://doi.org/10.1029/1998WR900090, 1999. a, b
Scherler, D., DiBiase, R. A., Fisher, G. B., and Avouac, J.-P.: Testing
monsoonal controls on bedrock river incision in the Himalaya and Eastern
Tibet with a stochastic-threshold stream power model, J. Geophys.
Res.-Earth, 122, 1389–1429, https://doi.org/10.1002/2016JF004011, 2017. a
Schwanghart, W. and Scherler, D.: Short Communication: TopoToolbox 2 – MATLAB-based software for topographic analysis and modeling in Earth surface sciences, Earth Surf. Dynam., 2, 1–7, https://doi.org/10.5194/esurf-2-1-2014, 2014. a, b, c
Schwanghart, W., Bernhardt, A., Stolle, A., Hoelzmann, P., Adhikari, B. R.,
Andermann, C., Tofelde, S., Merchel, S., Rugel, G., Fort, M., and Korup, O.:
Repeated catastrophic valley infill following medieval earthquakes in the
Nepal Himalaya, Science, 351, 147–150, https://doi.org/10.1126/science.aac9865, 2016. a
Seidl, M. A. and Dietrich, W. E.: The problem of channel erosion into
bedrock, Catena Supplement, 23, 101–124, 1992. a
Shobe, C. M., Tucker, G. E., and Anderson, R. S.: Hillslope-derived blocks
retard river incision, Geophys. Res. Lett., 43, 5070–5078,
https://doi.org/10.1002/2016GL069262, 2016. a, b, c, d
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, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q
Shobe, C. M., Tucker, G. E., and Rossi, M. W.: Variable‐Threshold Behavior
in Rivers Arising From Hillslope‐Derived Blocks, J. Geophys. Res.-Earth, 123, 1931–1957, https://doi.org/10.1029/2017JF004575, 2018. a, b
Sidle, R. C. and Ochiai, H.: Landslides: Processes, Prediction, and Land Use,
Water Resources Monograph, American Geophysical Union, Washington, D. C.,
https://doi.org/10.1029/WM018, 2006. a
Sklar, L. S. and Dietrich, W. E.: A mechanistic model for river incision into
bedrock by saltating bed load, Water Resour. Res., 40, W06301,
https://doi.org/10.1029/2003WR002496, 2004. a
Snyder, N. P., Whipple, K. X., Tucker, G. E., and Merritts, D. J.: Importance
of a stochastic distribution of floods and erosion thresholds in the bedrock
river incision problem, J. Geophys. Res.-Sol. Ea., 108,
2388, https://doi.org/10.1029/2001JB001655, 2003. a
Stark, C. P. and Hovius, N.: The characterization of landslide size
distributions, Geophys. Res. Lett., 28, 1091–1094,
https://doi.org/10.1029/2000GL008527, 2001. a, b
Taylor, D.: Fundamentals of Soil Mechanics, Wiley, New York, 1948. a
Tenorio, G. E., Vanacker, V., Campforts, B., Álvarez, L., Zhiminaicela,
S., Vercruysse, K., Molina, A., and Govers, G.: Tracking spatial variation
in river load from Andean highlands to inter-Andean valleys, Geomorphology,
308, 175–189, https://doi.org/10.1016/j.geomorph.2018.02.009, 2018. a
Tofelde, S., Duesing, W., Schildgen, T. F., Wickert, A. D., Wittmann, H.,
Alonso, R. N., and Strecker, M.: Effects of deep-seated versus shallow
hillslope processes on cosmogenic
10Be concentrations
in fluvial sand and gravel, Earth Surf. Proc. Land., 43,
3086–3098, https://doi.org/10.1002/esp.4471,
2018. a
Tucker, G. E.: Drainage basin sensitivity to tectonic and climatic forcing:
Implications of a stochastic model for the role of entrainment and erosion
thresholds, Earth Surf. Proc. Land., 29, 185–205,
https://doi.org/10.1002/esp.1020, 2004. a
Tucker, G. E. and Hancock, G. R.: Modelling landscape evolution, Earth
Surf. Proc. Land., 35, 28–50, https://doi.org/10.1002/esp.1952, 2010. a
Turowski, J. M., Lague, D., and Hovius, N.: Cover effect in bedrock abrasion:
A new derivation and its implications for the modeling of bedrock channel
morphology, J. Geophys. Res., 112, F04006,
https://doi.org/10.1029/2006JF000697, 2007. a
Turowski, J. M., Lague, D., and Hovius, N.: Response of bedrock channel width
to tectonic forcing: Insights from a numerical model, theoretical
considerations, and comparison with field data, J. Geophys. Res., 114, F03016, https://doi.org/10.1029/2008JF001133, 2009. a
Van Asch, T., Buma, J., and Van Beek, L.: A view on some hydrological
triggering systems in landslides, Geomorphology, 30, 25–32,
https://doi.org/10.1016/S0169-555X(99)00042-2, 1999. a
Van Rompaey, A. J. J. and Govers, G.: Data quality and model complexity for
regional scale soil erosion prediction, Int. J.
Geogr. Inf. Sci., 16, 663–680,
https://doi.org/10.1080/13658810210148561, 2002. a
Wang, J., Jin, Z., Hilton, R. G., Zhang, F., Densmore, A. L., Li, G., and West,
A. J.: Controls on fluvial evacuation of sediment from earthquake-triggered
landslides, Geology, 43, 115–118, https://doi.org/10.1130/G36157.1, 2015. a
Wang, W., Godard, V., Liu-Zeng, J., Scherler, D., Xu, C., Zhang, J., Xie, K.,
Bellier, O., Ansberque, C., and de Sigoyer, J.: Perturbation of fluvial
sediment fluxes following the 2008 Wenchuan earthquake, Earth Surf.
Proc. Land., 42, 2611–2622, https://doi.org/10.1002/esp.4210, 2017. a
Whipple, K. X. and Tucker, G. E.: Dynamics of the stream-power river incision
model: Implications for height limits of mountain ranges, landscape response
timescales, and research needs, J. Geophys. Res.-Sol.
Ea., 104, 17661–17674, https://doi.org/10.1029/1999JB900120, 1999. a, b, c
Whipple, K. X., Hancock, G. S., and Anderson, R. S.: River incision into
bedrock: Mechanics and relative efficacy of plucking, abrasion, and
cavitation, Geol. Soc. Am. Bull., 112, 490–503,
https://doi.org/10.1130/0016-7606(2000)112<490:RIIBMA>2.0.CO;2, 2000. a
Willgoose, G., Bras, R. L., and Rodrigueziturbe, I.: Results from a New Model
of River Basin Evolution, Earth Surf. Proc. Land., 16,
237–254, https://doi.org/10.1002/esp.3290160305, 1991. a
Wobus, C., Whipple, K. X., Kirby, E., Snyder, N., Johnson, J., Spyropolou, K.,
Crosby, B., and Sheehan, D.: Tectonics from topography: Procedures, promise,
and pitfalls, in: Tectonics, Climate, and Landscape Evolution, Geological Society of America, 398,
55–74, https://doi.org/10.1130/2006.2398(04), 2006. a, b
Wyllie, D. C. and Mah, C. W.: Rock Slope Engineering, CRC Press,
https://doi.org/10.1201/9781315274980, 2017. a
Yanites, B. J.: The Dynamics of Channel Slope, Width, and Sediment in Actively
Eroding Bedrock River Systems, J. Geophys. Res.-Earth, 123, 1504–1527, https://doi.org/10.1029/2017JF004405, 2018. a
Yanites, B. J., Tucker, G. E., and Anderson, R. S.: Numerical and analytical
models of cosmogenic radionuclide dynamics in landslide-dominated drainage
basins, J. Geophys. Res., 114, F01007,
https://doi.org/10.1029/2008JF001088, 2009. a
Zhang, J., van Westen, C. J., Tanyas, H., Mavrouli, O., Ge, Y., Bajrachary, S., Gurung, D. R., Dhital, M. R., and Khanal, N. R.: How size and trigger matter: analyzing rainfall- and earthquake-triggered landslide inventories and their causal relation in the Koshi River basin, central Himalaya, Nat. Hazards Earth Syst. Sci., 19, 1789–1805, https://doi.org/10.5194/nhess-19-1789-2019, 2019.
a
Zhang, L., Stark, C., Schumer, R., Kwang, J., Li, T., Fu, X., Wang, G., and
Parker, G.: The Advective‐Diffusive Morphodynamics of Mixed
Bedrock‐Alluvial Rivers Subjected to Spatiotemporally Varying Sediment
Supply, J. Geophys. Res.-Earth, 123, 1731–1755,
https://doi.org/10.1029/2017JF004431, 2018. a
Zhou, S., Ouyang, C., An, H., Jiang, T., and Xu, Q.: Comprehensive study of
the Beijing Daanshan rockslide based on real-time videos, field
investigations, and numerical modeling, Landslides, 17, 1217–1231,
https://doi.org/10.1007/s10346-020-01345-2, 2020. a
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.
Landslides shape the Earth’s surface and are a dominant source of terrestrial sediment....
