Articles | Volume 12, issue 4
https://doi.org/10.5194/gmd-12-1267-2019
© Author(s) 2019. 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-12-1267-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Terrainbento 1.0: a Python package for multi-model analysis in long-term drainage basin evolution
Katherine R. Barnhart
CORRESPONDING AUTHOR
Cooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder, Boulder, CO, USA
Department of Geological Sciences, University of Colorado at Boulder, Boulder, CO, USA
Rachel C. Glade
Department of Geological Sciences, University of Colorado at Boulder, Boulder, CO, USA
Institute for Arctic and Alpine Research, University of Colorado at Boulder, Boulder, CO, USA
Charles M. Shobe
Cooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder, Boulder, CO, USA
Department of Geological Sciences, University of Colorado at Boulder, Boulder, CO, USA
Gregory E. Tucker
Cooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder, Boulder, CO, USA
Department of Geological Sciences, University of Colorado at Boulder, Boulder, CO, USA
Related authors
Francis K. Rengers, Samuel Bower, Andrew Knapp, Jason W. Kean, Danielle W. vonLembke, Matthew A. Thomas, Jaime Kostelnik, Katherine R. Barnhart, Matthew Bethel, Joseph E. Gartner, Madeline Hille, Dennis M. Staley, Justin Anderson, Elizabeth K. Roberts, Stephen B. DeLong, Belize Lane, Paxton Ridgway, and Brendan P. Murphy
EGUsphere, https://doi.org/10.5194/egusphere-2023-2063, https://doi.org/10.5194/egusphere-2023-2063, 2023
Short summary
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Every year the U.S. Geological Survey produces 50–100 postfire debris flow hazard assessments using models for debris flow likelihood and volume. To refine these models they must be tested with datasets that clearly document rainfall, debris flow response, and debris flow volume. These datasets are difficult to obtain, but this study developed and analyzed a postfire dataset with more than 100 postfire storm responses over a two year period. We also proposed ways to improve these models.
Katherine R. Barnhart, Christopher R. Miller, Francis K. Rengers, and Jason W. Kean
EGUsphere, https://doi.org/10.5194/egusphere-2023-1892, https://doi.org/10.5194/egusphere-2023-1892, 2023
Short summary
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Debris flows are a type of fast-moving landslide that start from shallow landslides or during intense rain. Infrastructure located downstream of watersheds susceptible to debris flows may be damaged should a debris flow reach them. We present and evaluate an approach to forecast building damage caused by debris flows. We test three alternative models for simulating the motion of debris flows and find that only one can forecast the correct number and spatial pattern of damaged buildings.
Alexander B. Prescott, Luke A. McGuire, Kwang-Sung Jun, Katherine R. Barnhart, and Nina S. Oakley
EGUsphere, https://doi.org/10.5194/egusphere-2023-1931, https://doi.org/10.5194/egusphere-2023-1931, 2023
Short summary
Short summary
Fire can dramatically increase the risk of debris flows to downstream communities with little warning, but hazard assessments have not traditionally included estimates of inundation. We unify models developed by the scientific community to create probabilistic estimates of inundation area in response to rainfall at forecast lead times (≥ 24 hours) needed for decision-making. This work takes an initial step towards an operational postfire debris-flow inundation hazard assessment product.
Francis K. Rengers, Luke A. McGuire, Katherine R. Barnhart, Ann M. Youberg, Daniel Cadol, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 23, 2075–2088, https://doi.org/10.5194/nhess-23-2075-2023, https://doi.org/10.5194/nhess-23-2075-2023, 2023
Short summary
Short summary
Debris flows often occur after wildfires. These debris flows move water, sediment, and wood. The wood can get stuck in channels, creating a dam that holds boulders, cobbles, sand, and muddy material. We investigated how the channel width and wood length influenced how much sediment is stored. We also used a series of equations to back calculate the debris flow speed using the breaking threshold of wood. These data will help improve models and provide insight into future field investigations.
Nicole M. Gasparini, Katherine R. Barnhart, and Adam M. Forte
Earth Surf. Dynam. Discuss., https://doi.org/10.5194/esurf-2023-17, https://doi.org/10.5194/esurf-2023-17, 2023
Preprint under review for ESurf
Short summary
Short summary
Computational landscape evolution models (LEMs) show how landscapes change through time. There are many LEMs in the scientific community, but there are no standards for testing whether LEMs produce correct solutions or comparing output among LEMs. We present a comparison of three LEMs, illustrating both strengths and weaknesses. We hope our examples will motivate the LEM community to develop methods for inter-model comparison, which could help to avoid current and future modeling pitfalls.
Luke A. McGuire, Scott W. McCoy, Odin Marc, William Struble, and Katherine R. Barnhart
Earth Surf. Dynam. Discuss., https://doi.org/10.5194/esurf-2022-47, https://doi.org/10.5194/esurf-2022-47, 2022
Preprint under review for ESurf
Short summary
Short summary
Debris flows are mixtures of mud and rocks that can travel at high speeds across steep landscapes. Here, we propose a new model to describe how landscapes are shaped by debris flow erosion over long timescales. Model results demonstrate that the shapes of channel profiles are sensitive to uplift rate, meaning that it may be possible use topographic data from steep channel networks to infer how erosion rates vary in space across a landscape.
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.
Katherine R. Barnhart, Eric W. H. Hutton, Gregory E. Tucker, Nicole M. Gasparini, Erkan Istanbulluoglu, Daniel E. J. Hobley, Nathan J. Lyons, Margaux Mouchene, Sai Siddhartha Nudurupati, Jordan M. Adams, and Christina Bandaragoda
Earth Surf. Dynam., 8, 379–397, https://doi.org/10.5194/esurf-8-379-2020, https://doi.org/10.5194/esurf-8-379-2020, 2020
Short summary
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Landlab is a Python package to support the creation of numerical models in Earth surface dynamics. Since the release of the 1.0 version in 2017, Landlab has grown and evolved: it contains 31 new process components, a refactored model grid, and additional utilities. This contribution describes the new elements of Landlab, discusses why certain backward-compatiblity-breaking changes were made, and reflects on the process of community open-source software development.
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.
K. R. Barnhart, I. Overeem, and R. S. Anderson
The Cryosphere, 8, 1777–1799, https://doi.org/10.5194/tc-8-1777-2014, https://doi.org/10.5194/tc-8-1777-2014, 2014
Francis K. Rengers, Samuel Bower, Andrew Knapp, Jason W. Kean, Danielle W. vonLembke, Matthew A. Thomas, Jaime Kostelnik, Katherine R. Barnhart, Matthew Bethel, Joseph E. Gartner, Madeline Hille, Dennis M. Staley, Justin Anderson, Elizabeth K. Roberts, Stephen B. DeLong, Belize Lane, Paxton Ridgway, and Brendan P. Murphy
EGUsphere, https://doi.org/10.5194/egusphere-2023-2063, https://doi.org/10.5194/egusphere-2023-2063, 2023
Short summary
Short summary
Every year the U.S. Geological Survey produces 50–100 postfire debris flow hazard assessments using models for debris flow likelihood and volume. To refine these models they must be tested with datasets that clearly document rainfall, debris flow response, and debris flow volume. These datasets are difficult to obtain, but this study developed and analyzed a postfire dataset with more than 100 postfire storm responses over a two year period. We also proposed ways to improve these models.
Katherine R. Barnhart, Christopher R. Miller, Francis K. Rengers, and Jason W. Kean
EGUsphere, https://doi.org/10.5194/egusphere-2023-1892, https://doi.org/10.5194/egusphere-2023-1892, 2023
Short summary
Short summary
Debris flows are a type of fast-moving landslide that start from shallow landslides or during intense rain. Infrastructure located downstream of watersheds susceptible to debris flows may be damaged should a debris flow reach them. We present and evaluate an approach to forecast building damage caused by debris flows. We test three alternative models for simulating the motion of debris flows and find that only one can forecast the correct number and spatial pattern of damaged buildings.
Alexander B. Prescott, Luke A. McGuire, Kwang-Sung Jun, Katherine R. Barnhart, and Nina S. Oakley
EGUsphere, https://doi.org/10.5194/egusphere-2023-1931, https://doi.org/10.5194/egusphere-2023-1931, 2023
Short summary
Short summary
Fire can dramatically increase the risk of debris flows to downstream communities with little warning, but hazard assessments have not traditionally included estimates of inundation. We unify models developed by the scientific community to create probabilistic estimates of inundation area in response to rainfall at forecast lead times (≥ 24 hours) needed for decision-making. This work takes an initial step towards an operational postfire debris-flow inundation hazard assessment product.
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
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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.
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. Discuss., https://doi.org/10.5194/gmd-2023-127, https://doi.org/10.5194/gmd-2023-127, 2023
Preprint under review for GMD
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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 processes modeling. The Data Components support the creation of open data-model integration workflows to improve the research transparency and reproducibility.
Francis K. Rengers, Luke A. McGuire, Katherine R. Barnhart, Ann M. Youberg, Daniel Cadol, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 23, 2075–2088, https://doi.org/10.5194/nhess-23-2075-2023, https://doi.org/10.5194/nhess-23-2075-2023, 2023
Short summary
Short summary
Debris flows often occur after wildfires. These debris flows move water, sediment, and wood. The wood can get stuck in channels, creating a dam that holds boulders, cobbles, sand, and muddy material. We investigated how the channel width and wood length influenced how much sediment is stored. We also used a series of equations to back calculate the debris flow speed using the breaking threshold of wood. These data will help improve models and provide insight into future field investigations.
Nicole M. Gasparini, Katherine R. Barnhart, and Adam M. Forte
Earth Surf. Dynam. Discuss., https://doi.org/10.5194/esurf-2023-17, https://doi.org/10.5194/esurf-2023-17, 2023
Preprint under review for ESurf
Short summary
Short summary
Computational landscape evolution models (LEMs) show how landscapes change through time. There are many LEMs in the scientific community, but there are no standards for testing whether LEMs produce correct solutions or comparing output among LEMs. We present a comparison of three LEMs, illustrating both strengths and weaknesses. We hope our examples will motivate the LEM community to develop methods for inter-model comparison, which could help to avoid current and future modeling pitfalls.
Luke A. McGuire, Scott W. McCoy, Odin Marc, William Struble, and Katherine R. Barnhart
Earth Surf. Dynam. Discuss., https://doi.org/10.5194/esurf-2022-47, https://doi.org/10.5194/esurf-2022-47, 2022
Preprint under review for ESurf
Short summary
Short summary
Debris flows are mixtures of mud and rocks that can travel at high speeds across steep landscapes. Here, we propose a new model to describe how landscapes are shaped by debris flow erosion over long timescales. Model results demonstrate that the shapes of channel profiles are sensitive to uplift rate, meaning that it may be possible use topographic data from steep channel networks to infer how erosion rates vary in space across a landscape.
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.
Kelly Kochanski, Gregory Tucker, and Robert Anderson
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-205, https://doi.org/10.5194/tc-2021-205, 2021
Manuscript not accepted for further review
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Falling snow does not life flat. When blown by the wind, it forms elaborate structures, like dunes. Where these dunes form, they change the way heat flows through the snow. This can accelerate sea ice melt and climate change. Here, we use both field observations obtained during blizzards in Colorado and simulations performed with a state-of-the-art model, to quantify the impact of snow dunes on Arctic heat flows.
Benjamin Campforts, Charles M. Shobe, Philippe Steer, Matthias Vanmaercke, Dimitri Lague, and Jean Braun
Geosci. Model Dev., 13, 3863–3886, https://doi.org/10.5194/gmd-13-3863-2020, https://doi.org/10.5194/gmd-13-3863-2020, 2020
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Landslides shape the Earth’s surface and are a dominant source of terrestrial sediment. Rivers, then, act as conveyor belts evacuating landslide-produced sediment. Understanding the interaction among rivers and landslides is important to predict the Earth’s surface response to past and future environmental changes and for mitigating natural hazards. We develop HyLands, a new numerical model that provides a toolbox to explore how landslides and rivers interact over several timescales.
Katherine R. Barnhart, Eric W. H. Hutton, Gregory E. Tucker, Nicole M. Gasparini, Erkan Istanbulluoglu, Daniel E. J. Hobley, Nathan J. Lyons, Margaux Mouchene, Sai Siddhartha Nudurupati, Jordan M. Adams, and Christina Bandaragoda
Earth Surf. Dynam., 8, 379–397, https://doi.org/10.5194/esurf-8-379-2020, https://doi.org/10.5194/esurf-8-379-2020, 2020
Short summary
Short summary
Landlab is a Python package to support the creation of numerical models in Earth surface dynamics. Since the release of the 1.0 version in 2017, Landlab has grown and evolved: it contains 31 new process components, a refactored model grid, and additional utilities. This contribution describes the new elements of Landlab, discusses why certain backward-compatiblity-breaking changes were made, and reflects on the process of community open-source software development.
Alison R. Duvall, Sarah A. Harbert, Phaedra Upton, Gregory E. Tucker, Rebecca M. Flowers, and Camille Collett
Earth Surf. Dynam., 8, 177–194, https://doi.org/10.5194/esurf-8-177-2020, https://doi.org/10.5194/esurf-8-177-2020, 2020
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In this study, we examine river patterns and the evolution of the landscape within the Marlborough Fault System, South Island, New Zealand, where the Australian and Pacific tectonic plates collide. We find that faulting, uplift, river capture and the long-lived nature of the drainage network all dictate river patterns at this site. Based on these results and a wealth of previous geologic studies, we propose two broad stages of landscape evolution over the last 25 million years of orogenesis.
Kelly Kochanski, Robert S. Anderson, and Gregory E. Tucker
The Cryosphere, 13, 1267–1281, https://doi.org/10.5194/tc-13-1267-2019, https://doi.org/10.5194/tc-13-1267-2019, 2019
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Wind-blown snow does not lie flat. It forms dunes, ripples, and anvil-shaped sastrugi. These features ornament much of the snow on Earth and change the snow's effects on polar climates, but they have rarely been studied. We spent three winters watching snow move through the Colorado Front Range and present our findings here, including the first time-lapse videos of snow dune and sastrugi growth.
Gregory E. Tucker, Scott W. McCoy, and Daniel E. J. Hobley
Earth Surf. Dynam., 6, 563–582, https://doi.org/10.5194/esurf-6-563-2018, https://doi.org/10.5194/esurf-6-563-2018, 2018
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This article presents a new technique for computer simulation of slope forms. The method provides a way to study how events that disturb soil or turn rock into soil add up over time to produce landforms. The model represents a cross section of a hypothetical landform as a lattice of cells, each of which may represent air, soil, or rock. Despite its simplicity, the model does a good job of simulating a range of common of natural slope forms.
Ronda Strauch, Erkan Istanbulluoglu, Sai Siddhartha Nudurupati, Christina Bandaragoda, Nicole M. Gasparini, and Gregory E. Tucker
Earth Surf. Dynam., 6, 49–75, https://doi.org/10.5194/esurf-6-49-2018, https://doi.org/10.5194/esurf-6-49-2018, 2018
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We develop a model of annual probability of shallow landslide initiation triggered by soil water from a hydrologic model. Our physically based model accommodates data uncertainty using a Monte Carlo approach. We found elevation-dependent patterns in probability related to the stabilizing effect of forests and soil and slope limitation at high elevations. We demonstrate our model in Washington, USA, but it is designed to run elsewhere with available data for risk planning using the Landlab.
Abigail L. Langston and Gregory E. Tucker
Earth Surf. Dynam., 6, 1–27, https://doi.org/10.5194/esurf-6-1-2018, https://doi.org/10.5194/esurf-6-1-2018, 2018
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While vertical incision in bedrock rivers is widely implemented in landscape evolution models, lateral erosion is largely ignored. This makes current models unfit to explain the formation of wide bedrock valleys and strath terraces. In this study we present a fundamental advance in the representation of lateral erosion of bedrock rivers in a landscape evolution model. The model results show a scaling relationship between valley width and drainage area similar to that found in natural systems.
Charles M. Shobe, Gregory E. Tucker, and Katherine R. Barnhart
Geosci. Model Dev., 10, 4577–4604, https://doi.org/10.5194/gmd-10-4577-2017, https://doi.org/10.5194/gmd-10-4577-2017, 2017
Short summary
Short summary
Rivers control the movement of sediment and nutrients across Earth's surface. Understanding how rivers change through time is important for mitigating natural hazards and predicting Earth's response to climate change. We develop a new computer model for predicting how rivers cut through sediment and rock. Our model is designed to be joined with models of flooding, landslides, vegetation change, and other factors to provide a comprehensive toolbox for predicting changes to the landscape.
Jordan M. Adams, Nicole M. Gasparini, Daniel E. J. Hobley, Gregory E. Tucker, Eric W. H. Hutton, Sai S. Nudurupati, and Erkan Istanbulluoglu
Geosci. Model Dev., 10, 1645–1663, https://doi.org/10.5194/gmd-10-1645-2017, https://doi.org/10.5194/gmd-10-1645-2017, 2017
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OverlandFlow is a 2-dimensional hydrology component contained within the Landlab modeling framework. It can be applied in both hydrology and geomorphology applications across real and synthetic landscape grids, for both short- and long-term events. This paper finds that this non-steady hydrology regime produces different landscape characteristics when compared to more traditional steady-state hydrology and geomorphology models, suggesting that hydrology regime can impact resulting morphologies.
Daniel E. J. Hobley, Jordan M. Adams, Sai Siddhartha Nudurupati, Eric W. H. Hutton, Nicole M. Gasparini, Erkan Istanbulluoglu, and Gregory E. Tucker
Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017, https://doi.org/10.5194/esurf-5-21-2017, 2017
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Many geoscientists use computer models to understand changes in the Earth's system. However, typically each scientist will build their own model from scratch. This paper describes Landlab, a new piece of open-source software designed to simplify creation and use of models of the Earth's surface. It provides off-the-shelf tools to work with models more efficiently, with less duplication of effort. The paper explains and justifies how Landlab works, and describes some models built with it.
Gregory E. Tucker, Daniel E. J. Hobley, Eric Hutton, Nicole M. Gasparini, Erkan Istanbulluoglu, Jordan M. Adams, and Sai Siddartha Nudurupati
Geosci. Model Dev., 9, 823–839, https://doi.org/10.5194/gmd-9-823-2016, https://doi.org/10.5194/gmd-9-823-2016, 2016
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This paper presents a new Python-language software library, called CellLab-CTS, that enables rapid creation of continuous-time stochastic (CTS) cellular automata models. These models are quite useful for simulating the behavior of natural systems, but can be time-consuming to program. CellLab-CTS allows users to set up models with a minimum of effort, and thereby focus on the science rather than the software.
K. R. Barnhart, I. Overeem, and R. S. Anderson
The Cryosphere, 8, 1777–1799, https://doi.org/10.5194/tc-8-1777-2014, https://doi.org/10.5194/tc-8-1777-2014, 2014
Related subject area
Hydrology
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Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0
DynQual v1.0: a high-resolution global surface water quality model
Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model
Simulation of crop yield using the global hydrological model H08 (crp.v1)
How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model
iHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction
GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model
Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation (within CAESAR-Lisflood version 1.9j-WS)
Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics
Enhancing the representation of water management in global hydrological models
A simple, efficient, mass-conservative approach to solving Richards' equation (openRE, v1.0)
Customized deep learning for precipitation bias correction and downscaling
Implementation and sensitivity analysis of the Dam-Reservoir OPeration model (DROP v1.0) over Spain
Regional coupled surface–subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency domain discharge data
Representing the impact of Rhizophora mangroves on flow and sediment transport in a hydrodynamic model (COAWST_rh v1.0): the importance of three-dimensional root system structures
Operational water forecast ability of the HRRR-iSnobal combination: an evaluation to adapt into production environments
Dynamic weighted ensemble of geoscientific models via automated machine learning-based classification
Prediction of algal blooms via data-driven machine learning models: an evaluation using data from a well-monitored mesotrophic lake
UniFHy v0.1.1: a community modelling framework for the terrestrial water cycle in Python
mesas.py v1.0: A flexible Python package for modeling solute transport and transit times using StorAge Selection functions
Basin-scale gyres and mesoscale eddies in large lakes: a novel procedure for their detection and characterization, assessed in Lake Geneva
SIMO v1.0: simplified model of the vertical temperature profile in a small, warm, monomictic lake
Thermal modeling of three lakes within the continuous permafrost zone in Alaska using the LAKE 2.0 model
Water balance model (WBM) v.1.0.0: a scalable gridded global hydrologic model with water-tracking functionality
Coupling a large-scale hydrological model (CWatM v1.1) with a high-resolution groundwater flow model (MODFLOW 6) to assess the impact of irrigation at regional scale
RavenR v2.1.4: an open-source R package to support flexible hydrologic modelling
Developing a parsimonious canopy model (PCM v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest
Synergy between satellite observations of soil moisture and water storage anomalies for runoff estimation
A physically based distributed karst hydrological model (QMG model-V1.0) for flood simulations
Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability
CREST-VEC: a framework towards more accurate and realistic flood simulation across scales
Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains
The eWaterCycle platform for open and FAIR hydrological collaboration
Evaluating the Atibaia River hydrology using JULES6.1
A framework for ensemble modelling of climate change impacts on lakes worldwide: the ISIMIP Lake Sector
CLIMFILL v0.9: a framework for intelligently gap filling Earth observations
Modeling subgrid lake energy balance in ORCHIDEE terrestrial scheme using the FLake lake model
Evaluating a reservoir parametrization in the vector-based global routing model mizuRoute (v2.0.1) for Earth system model coupling
Improved runoff simulations for a highly varying soil depth and complex terrain watershed in the Loess Plateau with the Community Land Model version 5
GSTools v1.3: a toolbox for geostatistical modelling in Python
AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods
Modeling of streamflow in a 30 km long reach spanning 5 years using OpenFOAM 5.x
Tree hydrodynamic modelling of the soil–plant–atmosphere continuum using FETCH3
Effects of dimensionality on the performance of hydrodynamic models for stratified lakes and reservoirs
Computation of backwater effects in surface waters of lowland catchments including control structures – an efficient and re-usable method implemented in the hydrological open-source model Kalypso-NA (4.0)
Inishell 2.0: semantically driven automatic GUI generation for scientific models
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, https://doi.org/10.5194/gmd-16-5035-2023, 2023
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NEOPRENE is an open-source, freely available library allowing scientists and practitioners to generate synthetic time series and maps of rainfall. These outputs will help to explore plausible events that were never observed in the past but may occur in the near future and to generate possible future events under climate change conditions. The paper shows how to use the library to downscale daily precipitation and how to use synthetic generation to improve our characterization of extreme events.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
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We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
Po-Wei Huang, Bernd Flemisch, Chao-Zhong Qin, Martin O. Saar, and Anozie Ebigbo
Geosci. Model Dev., 16, 4767–4791, https://doi.org/10.5194/gmd-16-4767-2023, https://doi.org/10.5194/gmd-16-4767-2023, 2023
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Water in natural environments consists of many ions. Ions are electrically charged and exert electric forces on each other. We discuss whether the electric forces are relevant in describing mixing and reaction processes in natural environments. By comparing our computer simulations to lab experiments in literature, we show that the electric interactions between ions can play an essential role in mixing and reaction processes, in which case they should not be neglected in numerical modeling.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
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DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
Hugo Delottier, John Doherty, and Philip Brunner
Geosci. Model Dev., 16, 4213–4231, https://doi.org/10.5194/gmd-16-4213-2023, https://doi.org/10.5194/gmd-16-4213-2023, 2023
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Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.
Zhipin Ai and Naota Hanasaki
Geosci. Model Dev., 16, 3275–3290, https://doi.org/10.5194/gmd-16-3275-2023, https://doi.org/10.5194/gmd-16-3275-2023, 2023
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Simultaneously simulating food production and the requirements and availability of water resources in a spatially explicit manner within a single framework remains challenging on a global scale. Here, we successfully enhanced the global hydrological model H08 that considers human water use and management to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. Our improved model will be beneficial for advancing global food–water nexus studies in the future.
Emilie Rouzies, Claire Lauvernet, Bruno Sudret, and Arthur Vidard
Geosci. Model Dev., 16, 3137–3163, https://doi.org/10.5194/gmd-16-3137-2023, https://doi.org/10.5194/gmd-16-3137-2023, 2023
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Water and pesticide transfer models are complex and should be simplified to be used in decision support. Indeed, these models simulate many spatial processes in interaction, involving a large number of parameters. Sensitivity analysis allows us to select the most influential input parameters, but it has to be adapted to spatial modelling. This study will identify relevant methods that can be transposed to any hydrological and water quality model and improve the fate of pesticide knowledge.
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
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In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts
Geosci. Model Dev., 16, 2437–2454, https://doi.org/10.5194/gmd-16-2437-2023, https://doi.org/10.5194/gmd-16-2437-2023, 2023
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We present a computer simulation model of the hydrological system and human system, which can simulate the behaviour of individual farmers and their interactions with the water system at basin scale to assess how the systems have evolved and are projected to evolve in the future. For example, we can simulate the effect of subsidies provided on investment in adaptation measures and subsequent effects in the hydrological system, such as a lowering of the groundwater table or reservoir level.
Matthew D. Wilson and Thomas J. Coulthard
Geosci. Model Dev., 16, 2415–2436, https://doi.org/10.5194/gmd-16-2415-2023, https://doi.org/10.5194/gmd-16-2415-2023, 2023
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During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
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It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
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Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
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This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such models, often used for surface flow and flood modelling, are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing surface flow simulation to be enabled on the newest and upcoming consumer hardware and supercomputers very efficiently.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A F M Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-12, https://doi.org/10.5194/gmd-2023-12, 2023
Revised manuscript accepted for GMD
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Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
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Richards' equation (RE) is used to describe the movement and storage of water in a soil profile and is a component of many hydrological and earth-system models. Solving RE numerically is challenging due to the non-linearities in the properties. Here, we present a simple but effective and mass-conservative solution to solving RE, which is ideal for teaching/learning purposes but also useful in prototype models that are used to explore alternative process representations.
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023, https://doi.org/10.5194/gmd-16-535-2023, 2023
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Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a customized deep learning (DL) model to bias-correct and downscale gridded precipitation data using radar observations. The results showed that the customized DL model can generate improved precipitation at fine resolutions where regular DL and statistical methods experience challenges. The new model can be used to improve precipitation estimates, especially for capturing extremes at smaller scales.
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023, https://doi.org/10.5194/gmd-16-427-2023, 2023
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Predicting water resource evolution is a key challenge for the coming century.
Anthropogenic impacts on water resources, and particularly the effects of dams and reservoirs on river flows, are still poorly known and generally neglected in global hydrological studies. A parameterized reservoir model is reproduced to compute monthly releases in Spanish anthropized river basins. For global application, an exhaustive sensitivity analysis of the model parameters is performed on flows and volumes.
Nicolas Flipo, Nicolas Gallois, and Jonathan Schuite
Geosci. Model Dev., 16, 353–381, https://doi.org/10.5194/gmd-16-353-2023, https://doi.org/10.5194/gmd-16-353-2023, 2023
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A new approach is proposed to fit hydrological or land surface models, which suffer from large uncertainties in terms of water partitioning between fast runoff and slow infiltration from small watersheds to regional or continental river basins. It is based on the analysis of hydrosystem behavior in the frequency domain, which serves as a basis for estimating water flows in the time domain with a physically based model. It opens the way to significant breakthroughs in hydrological modeling.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
EGUsphere, https://doi.org/10.5194/egusphere-2022-1350, https://doi.org/10.5194/egusphere-2022-1350, 2023
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Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow and sediment transport in hydrodynamic models has been challenging. This study presents a new drag and turbulence model that leverages an empirical model for root systems. The model can be applied without rigorous measurements of root structures and showed high performance in flow simulations, which may provide a better understanding of sedimentary processes in Rhizophora mangrove forests.
Joachim Meyer, John Horel, Patrick Kormos, Andrew Hedrick, Ernesto Trujillo, and S. McKenzie Skiles
Geosci. Model Dev., 16, 233–250, https://doi.org/10.5194/gmd-16-233-2023, https://doi.org/10.5194/gmd-16-233-2023, 2023
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Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
EGUsphere, https://doi.org/10.5194/egusphere-2022-1326, https://doi.org/10.5194/egusphere-2022-1326, 2023
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Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here proposed an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrated the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman
Geosci. Model Dev., 16, 35–46, https://doi.org/10.5194/gmd-16-35-2023, https://doi.org/10.5194/gmd-16-35-2023, 2023
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The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
Thibault Hallouin, Richard J. Ellis, Douglas B. Clark, Simon J. Dadson, Andrew G. Hughes, Bryan N. Lawrence, Grenville M. S. Lister, and Jan Polcher
Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, https://doi.org/10.5194/gmd-15-9177-2022, 2022
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A new framework for modelling the water cycle in the land system has been implemented. It considers the hydrological cycle as three interconnected components, bringing flexibility in the choice of the physical processes and their spatio-temporal resolutions. It is designed to foster collaborations between land surface, hydrological, and groundwater modelling communities to develop the next-generation of land system models for integration in Earth system models.
Ciaran Harman and Esther Xu Fei
EGUsphere, https://doi.org/10.5194/egusphere-2022-1262, https://doi.org/10.5194/egusphere-2022-1262, 2022
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Over the last 10 years scientists have developed a new way of modeling how material is transported through complex systems, called StorAge Selection. Here we present some new code implementing this method that is easy to use, but also flexible and very accurate. We show that for cases where we know exactly what the answer should be, our code gets the right answer. We also show that our code is closer than some other people's code to the right answer in an important way: it conserves mass.
Seyed Mahmood Hamze-Ziabari, Ulrich Lemmin, Frédéric Soulignac, Mehrshad Foroughan, and David Andrew Barry
Geosci. Model Dev., 15, 8785–8807, https://doi.org/10.5194/gmd-15-8785-2022, https://doi.org/10.5194/gmd-15-8785-2022, 2022
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A procedure combining numerical simulations, remote sensing, and statistical analyses is developed to detect large-scale current systems in large lakes. By applying this novel procedure in Lake Geneva, strategies for detailed transect field studies of the gyres and eddies were developed. Unambiguous field evidence of 3D gyre/eddy structures in full agreement with predictions confirmed the robustness of the proposed procedure.
Kristina Šarović, Melita Burić, and Zvjezdana B. Klaić
Geosci. Model Dev., 15, 8349–8375, https://doi.org/10.5194/gmd-15-8349-2022, https://doi.org/10.5194/gmd-15-8349-2022, 2022
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We develop a simple 1-D model for the prediction of the vertical temperature profiles in small, warm lakes. The model uses routinely measured meteorological variables as well as UVB radiation and yearly mean temperature data. It can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.
Jason A. Clark, Elchin E. Jafarov, Ken D. Tape, Benjamin M. Jones, and Victor Stepanenko
Geosci. Model Dev., 15, 7421–7448, https://doi.org/10.5194/gmd-15-7421-2022, https://doi.org/10.5194/gmd-15-7421-2022, 2022
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Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect Arctic lakes to warm the surrounding frozen ground. We simulate water temperatures in three Arctic lakes in northern Alaska over several years. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season and that more heat storage by lakes would enhance thawing of frozen ground.
Danielle S. Grogan, Shan Zuidema, Alex Prusevich, Wilfred M. Wollheim, Stanley Glidden, and Richard B. Lammers
Geosci. Model Dev., 15, 7287–7323, https://doi.org/10.5194/gmd-15-7287-2022, https://doi.org/10.5194/gmd-15-7287-2022, 2022
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This paper describes the University of New Hampshire's water balance model (WBM). This model simulates the land surface components of the global water cycle and includes water extractions for use by humans for agricultural, domestic, and industrial purposes. A new feature is described that permits water source tracking through the water cycle, which has implications for water resource management. This paper was written to describe a long-used model and presents its first open-source version.
Luca Guillaumot, Mikhail Smilovic, Peter Burek, Jens de Bruijn, Peter Greve, Taher Kahil, and Yoshihide Wada
Geosci. Model Dev., 15, 7099–7120, https://doi.org/10.5194/gmd-15-7099-2022, https://doi.org/10.5194/gmd-15-7099-2022, 2022
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We develop and test the first large-scale hydrological model at regional scale with a very high spatial resolution that includes a water management and groundwater flow model. This study infers the impact of surface and groundwater-based irrigation on groundwater recharge and on evapotranspiration in both irrigated and non-irrigated areas. We argue that water table recorded in boreholes can be used as validation data if water management is well implemented and spatial resolution is ≤ 100 m.
Robert Chlumsky, James R. Craig, Simon G. M. Lin, Sarah Grass, Leland Scantlebury, Genevieve Brown, and Rezgar Arabzadeh
Geosci. Model Dev., 15, 7017–7030, https://doi.org/10.5194/gmd-15-7017-2022, https://doi.org/10.5194/gmd-15-7017-2022, 2022
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We introduce the open-source RavenR package, which has been built to support the use of the hydrologic modelling framework Raven. The R package contains many functions that may be useful in each step of the model-building process, including preparing model input files, running the model, and analyzing the outputs. We present six reproducible use cases of the RavenR package for the Liard River basin in Canada to demonstrate how it may be deployed.
Bahar Bahrami, Anke Hildebrandt, Stephan Thober, Corinna Rebmann, Rico Fischer, Luis Samaniego, Oldrich Rakovec, and Rohini Kumar
Geosci. Model Dev., 15, 6957–6984, https://doi.org/10.5194/gmd-15-6957-2022, https://doi.org/10.5194/gmd-15-6957-2022, 2022
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Leaf area index (LAI) and gross primary productivity (GPP) are crucial components to carbon cycle, and are closely linked to water cycle in many ways. We develop a Parsimonious Canopy Model (PCM) to simulate GPP and LAI at stand scale, and show its applicability over a diverse range of deciduous broad-leaved forest biomes. With its modular structure, the PCM is able to adapt with existing data requirements, and run in either a stand-alone mode or as an interface linked to hydrologic models.
Stefania Camici, Gabriele Giuliani, Luca Brocca, Christian Massari, Angelica Tarpanelli, Hassan Hashemi Farahani, Nico Sneeuw, Marco Restano, and Jérôme Benveniste
Geosci. Model Dev., 15, 6935–6956, https://doi.org/10.5194/gmd-15-6935-2022, https://doi.org/10.5194/gmd-15-6935-2022, 2022
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This paper presents an innovative approach, STREAM (SaTellite-based Runoff Evaluation And Mapping), to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and terrestrial total water storage anomalies. Potentially useful for multiple operational and scientific applications, the added value of the STREAM approach is the ability to increase knowledge on the natural processes, human activities, and their interactions on the land.
Ji Li, Daoxian Yuan, Fuxi Zhang, Jiao Liu, and Mingguo Ma
Geosci. Model Dev., 15, 6581–6600, https://doi.org/10.5194/gmd-15-6581-2022, https://doi.org/10.5194/gmd-15-6581-2022, 2022
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A new karst hydrological model (the QMG model) is developed to simulate and predict the floods in karst trough valley basins. Unlike the complex structure and parameters of current karst groundwater models, this model has a simple double-layered structure with few parameters and decreases the demand for modeling data in karst areas. The flood simulation results based on the QMG model of the Qingmuguan karst trough valley basin are satisfactory, indicating the suitability of the model simulation.
Luca Trotter, Wouter J. M. Knoben, Keirnan J. A. Fowler, Margarita Saft, and Murray C. Peel
Geosci. Model Dev., 15, 6359–6369, https://doi.org/10.5194/gmd-15-6359-2022, https://doi.org/10.5194/gmd-15-6359-2022, 2022
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MARRMoT is a piece of software that emulates 47 common models for hydrological simulations. It can be used to run and calibrate these models within a common environment as well as to easily modify them. We restructured and recoded MARRMoT in order to make the models run faster and to simplify their use, while also providing some new features. This new MARRMoT version runs models on average 3.6 times faster while maintaining very strong consistency in their outputs to the previous version.
Zhi Li, Shang Gao, Mengye Chen, Jonathan Gourley, Naoki Mizukami, and Yang Hong
Geosci. Model Dev., 15, 6181–6196, https://doi.org/10.5194/gmd-15-6181-2022, https://doi.org/10.5194/gmd-15-6181-2022, 2022
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Operational streamflow prediction at a continental scale is critical for national water resources management. However, limited computational resources often impede such processes, with streamflow routing being one of the most time-consuming parts. This study presents a recent development of a hydrologic system that incorporates a vector-based routing scheme with a lake module that markedly speeds up streamflow prediction. Moreover, accuracy is improved and flood false alarms are mitigated.
Suyeon Choi and Yeonjoo Kim
Geosci. Model Dev., 15, 5967–5985, https://doi.org/10.5194/gmd-15-5967-2022, https://doi.org/10.5194/gmd-15-5967-2022, 2022
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Here we present the cGAN-based precipitation nowcasting model, named Rad-cGAN, trained to predict a radar reflectivity map with a lead time of 10 min. Rad-cGAN showed superior performance at a lead time of up to 90 min compared with the reference models. Furthermore, we demonstrate the successful implementation of the transfer learning strategies using pre-trained Rad-cGAN to develop the models for different dam domains.
Rolf Hut, Niels Drost, Nick van de Giesen, Ben van Werkhoven, Banafsheh Abdollahi, Jerom Aerts, Thomas Albers, Fakhereh Alidoost, Bouwe Andela, Jaro Camphuijsen, Yifat Dzigan, Ronald van Haren, Eric Hutton, Peter Kalverla, Maarten van Meersbergen, Gijs van den Oord, Inti Pelupessy, Stef Smeets, Stefan Verhoeven, Martine de Vos, and Berend Weel
Geosci. Model Dev., 15, 5371–5390, https://doi.org/10.5194/gmd-15-5371-2022, https://doi.org/10.5194/gmd-15-5371-2022, 2022
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With the eWaterCycle platform, we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both open science and FAIR science. The eWatercyle platform gives easy access to well-known hydrological models, big datasets and example experiments. Using eWaterCycle hydrologists can easily compare the results from different models, couple models and do more complex hydrological computational research.
Hsi-Kai Chou, Ana Maria Heuminski de Avila, and Michaela Bray
Geosci. Model Dev., 15, 5233–5240, https://doi.org/10.5194/gmd-15-5233-2022, https://doi.org/10.5194/gmd-15-5233-2022, 2022
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Land surface models allow us to understand and investigate the cause and effect of environmental process changes. Therefore, this type of model is increasingly used for hydrological assessments. Here we explore the possibility of this approach using a case study in the Atibaia River basin, which serves as a major water supply for the metropolitan regions of Campinas and São Paulo, Brazil. We evaluated the model performance and use the model to simulate the basin hydrology.
Malgorzata Golub, Wim Thiery, Rafael Marcé, Don Pierson, Inne Vanderkelen, Daniel Mercado-Bettin, R. Iestyn Woolway, Luke Grant, Eleanor Jennings, Benjamin M. Kraemer, Jacob Schewe, Fang Zhao, Katja Frieler, Matthias Mengel, Vasiliy Y. Bogomolov, Damien Bouffard, Marianne Côté, Raoul-Marie Couture, Andrey V. Debolskiy, Bram Droppers, Gideon Gal, Mingyang Guo, Annette B. G. Janssen, Georgiy Kirillin, Robert Ladwig, Madeline Magee, Tadhg Moore, Marjorie Perroud, Sebastiano Piccolroaz, Love Raaman Vinnaa, Martin Schmid, Tom Shatwell, Victor M. Stepanenko, Zeli Tan, Bronwyn Woodward, Huaxia Yao, Rita Adrian, Mathew Allan, Orlane Anneville, Lauri Arvola, Karen Atkins, Leon Boegman, Cayelan Carey, Kyle Christianson, Elvira de Eyto, Curtis DeGasperi, Maria Grechushnikova, Josef Hejzlar, Klaus Joehnk, Ian D. Jones, Alo Laas, Eleanor B. Mackay, Ivan Mammarella, Hampus Markensten, Chris McBride, Deniz Özkundakci, Miguel Potes, Karsten Rinke, Dale Robertson, James A. Rusak, Rui Salgado, Leon van der Linden, Piet Verburg, Danielle Wain, Nicole K. Ward, Sabine Wollrab, and Galina Zdorovennova
Geosci. Model Dev., 15, 4597–4623, https://doi.org/10.5194/gmd-15-4597-2022, https://doi.org/10.5194/gmd-15-4597-2022, 2022
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Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
Verena Bessenbacher, Sonia Isabelle Seneviratne, and Lukas Gudmundsson
Geosci. Model Dev., 15, 4569–4596, https://doi.org/10.5194/gmd-15-4569-2022, https://doi.org/10.5194/gmd-15-4569-2022, 2022
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Earth observations have many missing values. They are often filled using information from spatial and temporal contexts that mostly ignore information from related observed variables. We propose the gap-filling method CLIMFILL that additionally uses information from related variables. We test CLIMFILL using gap-free reanalysis data of variables related to soil–moisture climate interactions. CLIMFILL creates estimates for the missing values that recover the original dependence structure.
Anthony Bernus and Catherine Ottlé
Geosci. Model Dev., 15, 4275–4295, https://doi.org/10.5194/gmd-15-4275-2022, https://doi.org/10.5194/gmd-15-4275-2022, 2022
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The lake model FLake was coupled to the ORCHIDEE land surface model to simulate lake energy balance at global scale with a multi-tile approach. Several simulations were performed with various atmospheric reanalyses and different lake depth parameterizations. The simulated lake surface temperature showed good agreement with observations (RMSEs of the order of 3 °C). We showed the large impact of the atmospheric forcing on lake temperature. We highlighted systematic errors on ice cover phenology.
Inne Vanderkelen, Shervan Gharari, Naoki Mizukami, Martyn P. Clark, David M. Lawrence, Sean Swenson, Yadu Pokhrel, Naota Hanasaki, Ann van Griensven, and Wim Thiery
Geosci. Model Dev., 15, 4163–4192, https://doi.org/10.5194/gmd-15-4163-2022, https://doi.org/10.5194/gmd-15-4163-2022, 2022
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Human-controlled reservoirs have a large influence on the global water cycle. However, dam operations are rarely represented in Earth system models. We implement and evaluate a widely used reservoir parametrization in a global river-routing model. Using observations of individual reservoirs, the reservoir scheme outperforms the natural lake scheme. However, both schemes show a similar performance due to biases in runoff timing and magnitude when using simulated runoff.
Jiming Jin, Lei Wang, Jie Yang, Bingcheng Si, and Guo-Yue Niu
Geosci. Model Dev., 15, 3405–3416, https://doi.org/10.5194/gmd-15-3405-2022, https://doi.org/10.5194/gmd-15-3405-2022, 2022
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This study aimed to improve runoff simulations and explore deep soil hydrological processes for a highly varying soil depth and complex terrain watershed in the Loess Plateau, China. The actual soil depths and river channels were incorporated into the model to better simulate the runoff in this watershed. The soil evaporation scheme was modified to better describe the evaporation processes. Our results showed that the model significantly improved the runoff simulations.
Sebastian Müller, Lennart Schüler, Alraune Zech, and Falk Heße
Geosci. Model Dev., 15, 3161–3182, https://doi.org/10.5194/gmd-15-3161-2022, https://doi.org/10.5194/gmd-15-3161-2022, 2022
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The GSTools package provides a Python-based platform for geoostatistical applications. Salient features of GSTools are its random field generation, its kriging capabilities and its versatile covariance model. It is furthermore integrated with other Python packages, like PyKrige, ogs5py or scikit-gstat, and provides interfaces to meshio and PyVista. Four presented workflows showcase the abilities of GSTools.
Ather Abbas, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun, and Kyung Hwa Cho
Geosci. Model Dev., 15, 3021–3039, https://doi.org/10.5194/gmd-15-3021-2022, https://doi.org/10.5194/gmd-15-3021-2022, 2022
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The field of artificial intelligence has shown promising results in a wide variety of fields including hydrological modeling. However, developing and testing hydrological models with artificial intelligence techniques require expertise from diverse fields. In this study, we developed an open-source framework based upon the python programming language to simplify the process of the development of hydrological models of time series data using machine learning.
Yunxiang Chen, Jie Bao, Yilin Fang, William A. Perkins, Huiying Ren, Xuehang Song, Zhuoran Duan, Zhangshuan Hou, Xiaoliang He, and Timothy D. Scheibe
Geosci. Model Dev., 15, 2917–2947, https://doi.org/10.5194/gmd-15-2917-2022, https://doi.org/10.5194/gmd-15-2917-2022, 2022
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Climate change affects river discharge variations that alter streamflow. By integrating multi-type survey data with a computational fluid dynamics tool, OpenFOAM, we show a workflow that enables accurate and efficient streamflow modeling at 30 km and 5-year scales. The model accuracy for water stage and depth average velocity is −16–9 cm and 0.71–0.83 in terms of mean error and correlation coefficients. This accuracy indicates the model's reliability for evaluating climate impact on rivers.
Marcela Silva, Ashley M. Matheny, Valentijn R. N. Pauwels, Dimetre Triadis, Justine E. Missik, Gil Bohrer, and Edoardo Daly
Geosci. Model Dev., 15, 2619–2634, https://doi.org/10.5194/gmd-15-2619-2022, https://doi.org/10.5194/gmd-15-2619-2022, 2022
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Our study introduces FETCH3, a ready-to-use, open-access model that simulates the water fluxes across the soil, roots, and stem. To test the model capabilities, we tested it against exact solutions and a case study. The model presented considerably small errors when compared to the exact solutions and was able to correctly represent transpiration patterns when compared to experimental data. The results show that FETCH3 can correctly simulate above- and below-ground water transport.
Mayra Ishikawa, Wendy Gonzalez, Orides Golyjeswski, Gabriela Sales, J. Andreza Rigotti, Tobias Bleninger, Michael Mannich, and Andreas Lorke
Geosci. Model Dev., 15, 2197–2220, https://doi.org/10.5194/gmd-15-2197-2022, https://doi.org/10.5194/gmd-15-2197-2022, 2022
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Reservoir hydrodynamics is often described in numerical models differing in dimensionality. 1D and 2D models assume homogeneity along the unresolved dimension. We compare the performance of models with 1 to 3 dimensions. All models presented reasonable results for seasonal temperature dynamics. Neglecting longitudinal transport resulted in the largest deviations in temperature. Flow velocity could only be reproduced by the 3D model. Results support the selection of models and their assessment.
Sandra Hellmers and Peter Fröhle
Geosci. Model Dev., 15, 1061–1077, https://doi.org/10.5194/gmd-15-1061-2022, https://doi.org/10.5194/gmd-15-1061-2022, 2022
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A hydrological method to compute backwater effects in surface water streams and on adjacent lowlands caused by mostly complex flow control systems is presented. It enables transfer of discharges to water levels and calculation of backwater volume routing along streams and lowland areas by balancing water level slopes. The developed, implemented and evaluated method extends the application range of hydrological models significantly for flood-routing simulation in backwater-affected catchments.
Mathias Bavay, Michael Reisecker, Thomas Egger, and Daniela Korhammer
Geosci. Model Dev., 15, 365–378, https://doi.org/10.5194/gmd-15-365-2022, https://doi.org/10.5194/gmd-15-365-2022, 2022
Short summary
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Most users struggle with the configuration of numerical models. This can be improved by relying on a GUI, but this requires a significant investment and a specific skill set and does not fit with the daily duties of model developers, leading to major maintenance burdens. Inishell generates a GUI on the fly based on an XML description of the required configuration elements, making maintenance very simple. This concept has been shown to work very well in our context.
Cited articles
Ahnert, F.: Brief description of a comprehensive three-dimensional
process-response model of landform development, Z. Geomorfol.,
Supplementband, 25, 29–49, 1976.
Andrews, D. J. and Bucknam, R. C.: Fitting degradation of shoreline scarps by
a nonlinear diffusion model, J. Geophys. Res., 92, 12857–12867,
https://doi.org/10.1029/JB092iB12p12857, 1987.
Andrews, D. J. and Hanks, T. C.: Scarp degraded by linear diffusion: Inverse
solution for age, J. Geophys. Res., 90, 10193–10208,
https://doi.org/10.1029/JB090iB12p10193, 1985.
Attal, M., Tucker, G. E., Whittaker, A. C., Cowie, P. A., and Roberts, G. P.:
Modeling fluvial incision and transient landscape evolution: Influence of
dynamic channel adjustment, J. Geophys. Res., 113, F03013,
https://doi.org/10.1029/2007JF000893, 2008.
Attal, M., Cowie, P., Whittaker, A., Hobley, D., Tucker, G., and Roberts, G.:
Testing fluvial erosion models using the transient response of bedrock
rivers to tectonic forcing in the Apennines, Italy, J. Geophys. Res., 116,
F02005, https://doi.org/10.1029/2010JF001875, 2011.
Barnhart, K. R., Shobe, C. M., Glade, R. C., Tucker, G. E., and Hutton, E.:
TerrainBento/terrainbento: Soba (Version v1.0.0), Zenodo,
https://doi.org/10.5281/zenodo.2566501, 2019a.
Barnhart, K. R., Shobe, C. M., Glade, R. C., and Tucker, G. E.:
TerrainBento/examples_tests_and_tutorials: Soba (Version v1.0.0), Zenodo,
https://doi.org/10.5281/zenodo.2566608, 2019b.
Beven, K. and Kirkby, M.: A physically based, variable contributing area
model of basin hydrology, Hydrolog. Sci. J., 24, 43–69, 1979.
Bishop, P.: Long-term landscape evolution: linking tectonics and surface
processes, Earth Surf. Proc. Landf., 32, 329–365, 2007.
Carretier, S. and Lucazeau, F.: How does alluvial sedimentation at range
fronts modify the erosional dynamics of mountain catchments?, Basin Res.,
17, 361–381, 2005.
Carretier, S., Martinod, P., Reich, M., and Godderis, Y.: Modelling sediment
clasts transport during landscape evolution, Earth Surf. Dynam., 4, 237–251,
https://doi.org/10.5194/esurf-4-237-2016, 2016.
Chen, A., Darbon, J., and Morel, J.-M.: Landscape evolution models: A review
of their fundamental equations, Geomorphology, 219, 68–86,
https://doi.org/10.1016/j.geomorph.2014.04.037, 2014.
Codilean, A. T., Bishop, P., and Hoey, T. B.: Surface process models and the
links between tectonics and topography, Prog. Phys. Geog., 30, 307–333,
2006.
Cohen, S., Willgoose, G., and Hancock, G.: The mARM3D spatially distributed
soil evolution model: Three-dimensional model framework and analysis of
hillslope and landform responses, J. Geophys. Res., 115, F04013,
https://doi.org/10.1029/2009JF001536, 2010.
Coulthard, T. J.: Landscape evolution models: a software review, Hydrol.
Process., 15, 165–173, 2001.
Culling, W. E. H.: Soil Creep and the Development of Hillside Slopes, J.
Geol., 71, 127–161, 1963.
Davy, P. and Lague, D.: Fluvial erosion/transport equation of landscape
evolution models revisited, J. Geophys. Res., 114, F03007,
https://doi.org/10.1029/2008JF001146, 2009.
DiBiase, R. and Whipple, K.: 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.
Dietrich, W., Wilson, C., Montgomery, D., and McKean, J.: Analysis of erosion
thresholds, channel networks, and landscape morphology using a digital
terrain model, J. Geol., 101, 259–278, 1993.
Dietrich, W. E., Bellugi, D. G., Sklar, L. S., Stock, J. D., Heimsath, A. M.,
and Roering, J. J.: Geomorphic transport laws for predicting landscape form
and dynamics, in: Prediction in geomorphology, edited by: Wilcock, P. and
Iverson, R., Geophysical Monograph Series, AGU, Washington, D.C.,
https://doi.org/10.1029/135GM09, 2003.
Doane, T. H., Furbish, D. J., Roering, J. J., Schumer, R., and Morgan, D. J.:
Nonlocal Sediment Transport on Steep Lateral Moraines, Eastern Sierra Nevada,
California, USA, J. Geophys. Res.-Earth, 123, 187–208,
https://doi.org/10.1002/2017JF004325, 2018.
Dunne, T. and Black, R.: Partial area contributions to storm runoff in a
small New England watershed, Water Resour. Res., 6, 1296–1311, 1970.
Duvall, A., Kirby, E., and Burbank, D.: Tectonic and lithologic controls on
bedrock channel profiles and processes in coastal California, J. Geophys.
Res., 109, F03002, https://doi.org/10.1029/2003JF000086, 2004.
Duvall, A. R. and Tucker, G. E.: Dynamic Ridges and Valleys in a Strike-Slip
Environment, J. Geophys. Res.-Earth, 120, 2016–2026, 2015.
Eagleson, P.: Climate, soil, and vegetation, 2, The distribution of annual
precipitation derived from observed storm sequences, Water Resour. Res., 14,
713–721, 1978.
Freeman, T. G.: Calculating catchment area with divergent flow based on a
regular grid, Comput. Geosci., 17, 413–422,
https://doi.org/10.1016/0098-3004(91)90048-I, 1991.
Ganti, V., Passalacqua, P., and Foufoula-Georgiou, E.: A sub-grid scale
closure for nonlinear hillslope sediment transport models, J. Geophys. Res.,
117, F02012, https://doi.org/10.1029/2011JF002181, 2012.
Gasparini, N., Tucker, G., and Bras, R.: Network-scale dynamics of grain-size
sorting: Implications for downstream fining, stream-profile concavity, and
drainage basin morphology, Earth Surf. Proc. Land., 29, 401–421,
https://doi.org/10.1002/esp.1031, 2004.
Gran, K. B., Finnegan, N., Johnson, A. L., Belmont, P., Wittkop, C., and
Rittenour, T.: Landscape evolution, valley excavation, and terrace
development following abrupt postglacial base-level fall, Geol. Soc. Am.
Bull., 125, 1851–1864, 2013.
Gray, H. J., Shobe, C. M., Hobley, D. E. J., Tucker, G. E., Duvall, A. R.,
Harbert, S. A., and Owen, L. A.: Off-fault deformation rate along the
southern San Andreas fault at Mecca Hills, southern California, inferred from
landscape modeling of curved drainages, Geology, 46, 59–62,
https://doi.org/10.1130/G39820.1, 2018.
Hancock, G. and Willgoose, G.: Use of a landscape simulator in the validation
of the SIBERIA catchment evolution model: Declining equilibrium landforms,
Water Resour. Res., 37, 1981–1992, 2001.
Hancock, G., Lowry, J., Coulthard, T., Evans, K., and Moliere, D.: A
catchment scale evaluation of the SIBERIA and CAESAR landscape evolution
models, Earth Surf. Proc. Land., 35, 863–875, 2010.
Hanks, T. C.: The Age of Scarplike Landforms From Diffusion-Equation
Analysis, in: Quaternary Geochronology, edited by: Noller, J. S., Sowers, J.
M., and Lettis, W. R., https://doi.org/doi:10.1029/RF004p0313, 2013.
Heimsath, A., Dietrich, W., Nishiizumi, K., and Finkel, R.: The soil
production function and landscape equilibrium, Nature, 388, 358–361, 1997.
Herman, F. and Braun, J.: A parametric study of soil transport mechanisms,
in: Special Paper: Tectonics, Climate, and Landscape Evolution, Geological
Society of America, 398, 191–200, https://doi.org/10.1130/2006.2398(11), 2006.
Hill, M. C., Kavetski, D., Clark, M., Ye, M., Arabi, M., Lu, D., Foglia, L.,
and Mehl, S.: Practical use of computationally frugal model analysis methods,
Groundwater, 54, 159–170, 2016.
Hobley, D. E., Sinclair, H. D., Mudd, S. M., and Cowie, P. A.: Field
calibration of sediment flux dependent river incision, J. Geophys. Res., 116,
F04017, https://doi.org/10.1029/2010JF001935, 2011.
Hobley, D. E. J., Adams, J. M., Nudurupati, S. S., Hutton, E. W. H.,
Gasparini, N. M., Istanbulluoglu, E., and Tucker, G. E.: Creative computing
with Landlab: an open-source toolkit for building, coupling, and exploring
two-dimensional numerical models of Earth-surface dynamics, Earth Surf.
Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017, 2017.
Howard, A. D.: A detachment-limited model of drainage basin evolution, Water
Resour. Res., 30, 2261–2285, 1994.
Howard, A. D. and Kerby, G.: Channel changes in badlands, Geol. Soc. Am.
Bull., 94, 739–752, 1983.
Howard, A. D., Dietrich, W. E., and Seidl, M. A.: Modeling fluvial erosion on
regional to continental scales, J. Geophys. Res., 99, 13971–13986, 1994.
Huang, X. and Niemann, J.: An evaluation of the geomorphically effective
event for fluvial processes over long periods, J. Geophys. Res., 111, F03015,
https://doi.org/10.1029/2006JF000477, 2006.
Ijjasz-Vasquez, E. J., Bras, R. L., and Rodriguez-Iturbe, I.: Hack's relation
and optimal channel networks: The elongation of river basins as a consequence
of energy minimization, Water Resour. Res., 20, 1583–1586, 1993.
Ivanov, V. Y., Bras, R. L., and Curtis, D. C.: A weather generator for
hydrological, ecological, and agricultural applications, Water Resour. Res.,
43, W10406, https://doi.org/10.1029/2006WR005364, 2007.
Johnstone, S. A. and Hilley, G. E.: Lithologic control on the form of
soil-mantled hillslopes, Geology, 43, 83–86, 2015.
Julien, P.: Erosion and sedimentation, 2nd edn., Cambridge University Press,
Cambridge, 2010.
Kavetski, D. and Kuczera, G.: Model smoothing strategies to remove microscale
discontinuities and spurious secondary optima in objective functions in
hydrological calibration, Water Resour. Res., 43, W03411,
https://doi.org/10.1029/2006WR005195, 2007.
Kirby, E. and Whipple, K.: Quantifying differential rock-uplift rates via
stream profile analysis, Geology, 29, 415–418, 2001.
Kirchner, J. W., Dietrich, W. E., Iseya, F., and Ikeda, H.: The variability
of critical shear stress, friction angle, and grain protrusion in
water-worked sediments, Sedimentology, 37, 647–672,
https://doi.org/10.1111/j.1365-3091.1990.tb00627.x, 1990.
Kooi, H. and Beaumont, C.: Escarpment evolution on high-elevation rifted
margins: Insights derived from a surface processes model that combines
diffusion, advection, and reaction, J. Geophys. Res., 99, 12191–12209, 1994.
Lague, D.: Reduction of long-term bedrock incision efficiency by short-term
alluvial cover intermittency, J. Geophys. Res., 115, F02011,
https://doi.org/10.1029/2008JF001210, 2010.
Lague, D., Hovius, N., and Davy, P.: Discharge, discharge variability, and
the bedrock channel profile, J. Geophys. Res, 110, F04006,
https://doi.org/10.1029/2004JF000259, 2005.
Lavé, J. and Avouac, J.: Fluvial incision and tectonic uplift across the
Himalayas of central Nepal, J. Geophys. Res., 106, 26561–26591,
https://doi.org/10.1029/2001JB000359, 2001.
Loget, N., Davy, P., and Van Den Driessche, J.: Mesoscale fluvial erosion
parameters deduced from modeling the Mediterranean sea level drop during the
Messinian (late Miocene), J. Geophys. Res., 111, F03005,
https://doi.org/10.1029/2005JF000387, 2006.
Martin, Y. and Church, M.: Numerical modelling of landscape evolution:
Geomorphological perspectives, Prog. Phys. Geog., 28, 317–339, 2004.
McEwan, I. and Heald, J.: Discrete particle modeling of entrainment from flat
uniformly sized sediment beds, J. Hydraul. Eng., 127, 588–597, 2001.
Miller, S. R. and Slingerland, R. L.: Topographic advection on fault-bend
folds: Inheritance of valley positions and the formation of wind gaps,
Geology, 34, 769–772, 2006.
Miller, S. R., Slingerland, R. L., and Kirby, E.: Characteristics of steady
state fluvial topography above fault-bend folds, J. Geophys. Res., 112,
F04004, https://doi.org/10.1029/2007JF000772, 2007.
O'Callaghan, J. F. and Mark, D. M.: The extraction of drainage networks from
digital elevation data, Comput. Vision Graph., 28, 323–344,
https://doi.org/10.1016/S0734-189X(84)80011-0, 1984.
O'Loughlin, E.: Prediction of surface saturation zones in natural catchments
by topographic analysis, Water Resour. Res., 22, 794–804, 1986.
Pazzaglia, F. J.: Landscape evolution models, Developments in Quaternary
Sciences, 1, 247–274, 2003.
Peckham, S. D., Hutton, E. W., and Norris, B.: A component-based approach to
integrated modeling in the geosciences: The design of CSDMS, Comput. Geosci.,
53, 3–12, 2013.
Pelletier, J.: 2.3 Fundamental Principles and Techniques of Landscape
Evolution Modeling, in: Treatise on Geomorphology, edited by: Shroder, J. F.,
Academic Press, San Diego, 29–43, https://doi.org/10.1016/B978-0-12-374739-6.00025-7,
2013.
Pelletier, J. D.: How do pediments form?: A numerical modeling investigation
with comparison to pediments in southern Arizona, USA, Geol. Soc. Am. Bull.,
122, 1815–1829, 2010.
Pelletier, J. D., DeLong, S. B., Al-Suwaidi, A. H., Cline, M., Lewis, Y.,
Psillas, J. L., and Yanites, B.: Evolution of the Bonneville shoreline scarp
in west-central Utah: Comparison of scarp-analysis methods and implications
for the diffusion model of hillslope evolution, Geomorphology, 74, 257–270,
https://doi.org/10.1016/j.geomorph.2005.08.008, 2006.
Pelletier, J. D., McGuire, L. A., Ash, J. L., Engelder, T. M., Hill, L. E.,
Leroy, K. W., Orem, C. A., Rosenthal, W. S., Trees, M. A., Rasmussen, C., and
Chorover, J.: Calibration and testing of upland hillslope evolution models in
a dated landscape: Banco Bonito, New Mexico, J. Geophys. Res., 116, F04004,
https://doi.org/10.1029/2011JF001976, 2011.
Perron, J. T., Kirchner, J. W., and Dietrich, W. E.: Formation of evenly
spaced ridges and valleys, Nature, 460, 502–505, 2009.
Petit, C., Gunnell, Y., Saholiariliva, N. G., Meyer, B., and Séguinot,
J.: Faceted spurs at normal fault scarps: Insights from numerical modeling,
J. Geophys. Res., 114, B05403, https://doi.org/10.1029/2008JB005955, 2009.
Roering, J.: How well can hillslope evolution models “explain” topography?
Simulating soil transport and production with high-resolution topographic
data, Geol. Soc. Am. Bull., 120, 1248–1262, 2008.
Roering, J., Kirchner, J., and Dietrich, W.: Evidence for nonlinear,
diffusive sediment transport on hillslopes and implications for landscape
morphology, Water Resour. Res., 35, 853–870, 1999.
Roering, J., Kirchner, J., Sklar, L., and Dietrich, W.: Hillslope evolution
by nonlinear creep and landsliding: An experimental study, Geology, 29,
143–146, 2001.
Rossi, M. W., Whipple, K. X., and Vivoni, E. R.: Precipitation and
evapotranspiration controls on daily runoff variability in the contiguous
United States and Puerto Rico, J. Geophys. Res.-Earth, 121, 128–145, 2016.
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.
Small, E., Anderson, R., and Hancock, G.: Estimates of the rate of regolith
production using 10Be and 26Al from an alpine hillslope, Geomorphology, 27,
131–150, 1999.
Snyder, N. P., Whipple, K. X., Tucker, G. E., and Merritts, D. J.: Landscape
response to tectonic forcing: Digital elevation model analysis of stream
profiles in the Mendocino triple junction region, northern California, Bull.
Geol. Soc. Am., 112, 1250–1263,
https://doi.org/10.1130/0016-7606(2000)112<1250:LRTTFD>2.0.CO;2, 2000.
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., 108, 2117,
https://doi.org/10.1029/2001JB001655, 2003a.
Snyder, N. P., Whipple, K. X., Tucker, G. E., and Merritts, D. J.: Channel
response to tectonic forcing: analysis of stream morphology and hydrology in
the Mendocino triple junction region, northern California, Geomorphology, 53
97–127, 2003b.
Soil Survey Staff: Soil taxonomy: A basic system of soil classification for
making and interpreting soil surveys, 2nd edn., Natural Resources
Conservation Service, U.S. Department of Agriculture Handbook, 436 pp., 1999.
Stock, J. D. and Montgomery, D. R.: Geologic constraints on bedrock river
incision using the stream power law, J. Geophys. Res., 104, 4983–4993, 1999.
Tarboton, D. G.: A new method for the determination of flow directions and
upslope areas in grid digital elevation models, Water Resour. Res., 33,
309–319, https://doi.org/10.1029/96WR03137, 1997.
Temme, A., Schoorl, J., Claessens, L., and Veldkamp, A.: 2.13 Quantitative
modeling of landscape evolution, in: Treatise on Geomorphology, edited by:
Shroder, J. F., Academic Press, San Diego, 180–200,
https://doi.org/10.1016/B978-0-12-374739-6.00039-7, 2013.
Tomkin, J. H., Brandon, M. T., Pazzaglia, F. J., Barbour, J. R., and Willett,
S. D.: Quantitative testing of bedrock incision models for the Clearwater
River, NW Washington State, J. Geophys. Res., 108, 2308,
https://doi.org/10.1029/2001JB000862, 2003.
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.
Tucker, G. E. and Bras, R. L.: Hillslope processes, drainage density, and
landscape morphology, Water Resour. Res., 36, 1953–1964, 1998.
Tucker, G. E. and Bras, R. L.: A stochastic approach to modeling the role of
rainfall variability in drainage basin evolution, Water Resour. Res., 36,
1953–1964, 2000.
Tucker, G. E. and Hancock, G. R.: Modelling Landscape Evolution, Earth Surf.
Proc. Land., 46, 28–50, 2010.
Tucker, G. E. and Slingerland, R. L.: Drainage basin response to climate
change, Water Resour. Res., 33, 2031–2047, 1997.
Tucker, G. E. and Whipple, K. X.: Topographic outcomes predicted by stream
erosion models: Sensitivity analysis and intermodel comparison, J. Geophys.
Res., 107, 2179, https://doi.org/10.1029/2001JB000162, 2002.
Tucker, G. E., Lancaster, S. T., Gasparini, N. M., Bras, R. L., and
Rybarczyk, S. M.: An object-oriented framework for hydrologic and geomorphic
modeling using triangular irregular networks, Comput. Geosci., 27, 959–973,
2001.
Valla, P., van der Beek, P., and Lague, D.: Fluvial incision into bedrock:
Insights from morphometric analysis and numerical modeling of gorges incising
glacial hanging valleys (Western Alps, France), J. Geophys. Res., 115,
F02010, https://doi.org/10.1029/2008JF001079, 2010.
Valters, D.: Modelling Geomorphic Systems: Landscape Evolution, in:
Geomorphological Techniques, British Society for Geomorphology, UK, 12,
1–24, 2016.
van der Beek, P. and Bishhop, P.: Cenozoic river profile development in the
Upper Lachlan catchment (SE Australia) as a test quantitative fluvial
incision models, J. Geophys. Res., 108, 2309, https://doi.org/10.1029/2002JB002125,
2003.
Vanwalleghem, T., Stockmann, U., Minasny, B., and McBratney, A. B.: A
quantitative model for integrating landscape evolution and soil formation,
J. Geophys. Res.-Earth, 118, 331–347, https://doi.org/10.1029/2011JF002296, 2013.
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., 104, 17661–17674, 1999.
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, 2000a.
Whipple, K. X., Snyder, N. P., and Dollenmayer, K.: Rates and processes of
bedrock incision by the Upper Ukak River since the 1912 Novarupta ash flow in
the Valley of Ten Thousand Smokes, Alaska, Geology, 28, 835–838, 2000b.
Whittaker, A., Cowie, P., Attal, M., Tucker, G., and Roberts, G.: Contrasting
transient and steady-state rivers crossing active normal faults: new field
observations from the Central Apennines, Italy, Basin Res., 19, 529–556,
2007.
Wilcock, P. R. and McArdell, B. W.: Partial transport of a sand/gravel
sediment, Water Resour. Res., 33, 235–245, https://doi.org/10.1029/96WR02672, 1997.
Willgoose, G.: Mathematical modeling of whole landscape evolution, Annu. Rev.
Earth Pl. Sc., 33, 14.1–14.17, 2005.
Willgoose, G., Bras, R. L., and Rodriguez-Iturbe, I.: A physical explanation
of an observed link area-slope relationship, Water Resour. Res., 27,
1697–1702, 1991.
Willgoose, G. R. and Hancock, G. R.: Applications of long-term erosion and
landscape evolution models, in: Handbook of Erosion Modelling, John Wiley &
Sons, Ltd, 18, 339–359, 2011.
Willgoose, G. R., Hancock, G. R., and Kuczera, G.: A Framework for the
Quantitative Testing of Landform Evolution Models, in: Prediction in
Geomorphology, edited by: Wilcock, P. R. and Iverson, R. M.,, American
Geophysical Union (AGU), Washington, D.C., 195–216, https://doi.org/10.1029/135GM14,
2003.
Wilson, P. and Toumi, R.: A fundamental probability distribution for heavy
rainfall, Geophys. Res. Lett., 32, L14812, https://doi.org/10.1029/2005GL022465, 2005.
Wohl, E. and David, G. C. L.: Consistency of scaling relations among bedrock
and alluvial channels, J. Geophys. Res., 113, F04013,
https://doi.org/10.1029/2008JF000989, 2008.
Yanites, B., Tucker, G., Mueller, K., Chen, Y., Wilcox, T., Huang, S., and
Shi, K.: Incision and channel morphology across active structures along the
Peikang River, central Taiwan: Implications for the importance of channel
width, Geol. Soc. Am. Bull., 122, 1192–1208, https://doi.org/10.1130/B30035.1, 2010.
Zhang, L., Parker, G., Stark, C. P., Inoue, T., Viparelli, E., Fu, X., and
Izumi, N.: Macro-roughness model of bedrock-alluvial river morphodynamics,
Earth Surf. Dynam., 3, 113–138, https://doi.org/10.5194/esurf-3-113-2015, 2015.
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.
Terrainbento 1.0 is a Python package for modeling the evolution of the surface of the Earth over...