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.
Model description paper
| Highlight paper
| 
03 Apr 2019
Model description paper | Highlight paper |
| 03 Apr 2019
| 03 Apr 2019
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
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Debris flows are mixtures of mud and rocks that can travel at high speeds across steep landscapes. Here, we propose a new model to describe how landscapes are shaped by debris flow erosion over long timescales. Model results demonstrate that the shapes of channel profiles are sensitive to uplift rate, meaning that it may be possible to use topographic data from steep channel networks to infer how erosion rates vary across a landscape.
Francis K. Rengers, Luke A. McGuire, Katherine R. Barnhart, Ann M. Youberg, Daniel Cadol, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Jason W. Kean
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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.
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Manuscript not accepted for further review
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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
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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
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Customized deep learning for precipitation bias correction and downscaling
Implementation and sensitivity analysis of the Dam-Reservoir OPeration model (DROP v1.0) over Spain
Hannes Müller Schmied, Tim Trautmann, Sebastian Ackermann, Denise Cáceres, Martina Flörke, Helena Gerdener, Ellen Kynast, Thedini Asali Peiris, Leonie Schiebener, Maike Schumacher, and Petra Döll
Geosci. Model Dev., 17, 8817–8852, https://doi.org/10.5194/gmd-17-8817-2024, https://doi.org/10.5194/gmd-17-8817-2024, 2024
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Assessing water availability and water use at the global scale is challenging but essential for a range of purposes. We describe the newest version of the global hydrological model WaterGAP, which has been used for numerous water resource assessments since 1996. We show the effects of new model features, as well as model evaluations, against water abstraction statistics and observed streamflow and water storage anomalies. The publicly available model output for several variants is described.
João António Martins Careto, Rita Margarida Cardoso, Ana Russo, Daniela Catarina André Lima, and Pedro Miguel Matos Soares
Geosci. Model Dev., 17, 8115–8139, https://doi.org/10.5194/gmd-17-8115-2024, https://doi.org/10.5194/gmd-17-8115-2024, 2024
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This study proposes a new daily drought index, the generalised drought index (GDI). The GDI not only identifies the same events as established indices but is also capable of improving their results. The index is empirically based and easy to compute, not requiring fitting the data to a probability distribution. The GDI can detect flash droughts and longer-term events, making it a versatile tool for drought monitoring.
Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, and Shiliang Shan
Geosci. Model Dev., 17, 7751–7766, https://doi.org/10.5194/gmd-17-7751-2024, https://doi.org/10.5194/gmd-17-7751-2024, 2024
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We develop an operational forecast system, Coastlines-LO, that can simulate water levels and surface waves in Lake Ontario driven by forecasts of wind speeds and pressure fields from an atmospheric model. The model has relatively low computational requirements, and results compare well with near-real-time observations, as well as with results from other existing forecast systems. Results show that with shorter forecast lengths, storm surge and wave predictions can improve in accuracy.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024, https://doi.org/10.5194/gmd-17-7181-2024, 2024
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Accurate hydrologic modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall–runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional modeling approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Matevž Vremec, Raoul A. Collenteur, and Steffen Birk
Geosci. Model Dev., 17, 7083–7103, https://doi.org/10.5194/gmd-17-7083-2024, https://doi.org/10.5194/gmd-17-7083-2024, 2024
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Geoscientists commonly use various potential evapotranpiration (PET) formulas for environmental studies, which can be prone to errors and sensitive to climate change. PyEt, a tested and open-source Python package, simplifies the application of 20 PET methods for both time series and gridded data, ensuring accurate and consistent PET estimations suitable for a wide range of environmental applications.
Nedal Aqel, Lea Reusser, Stephan Margreth, Andrea Carminati, and Peter Lehmann
Geosci. Model Dev., 17, 6949–6966, https://doi.org/10.5194/gmd-17-6949-2024, https://doi.org/10.5194/gmd-17-6949-2024, 2024
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The soil water potential (SWP) determines various soil water processes. Since remote sensing techniques cannot measure it directly, it is often deduced from volumetric water content (VWC) information. However, under dynamic field conditions, the relationship between SWP and VWC is highly ambiguous due to different factors that cannot be modeled with the classical approach. Applying a deep neural network with an autoencoder enables the prediction of the dynamic SWP.
Jenny Kupzig, Nina Kupzig, and Martina Flörke
Geosci. Model Dev., 17, 6819–6846, https://doi.org/10.5194/gmd-17-6819-2024, https://doi.org/10.5194/gmd-17-6819-2024, 2024
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Valid simulation results from global hydrological models (GHMs) are essential, e.g., to studying climate change impacts. Adapting GHMs to ungauged basins requires regionalization, enabling valid simulations. In this study, we highlight the impact of regionalization of GHMs on runoff simulations using an ensemble of regionalization methods for WaterGAP3. We have found that regionalization leads to temporally and spatially varying uncertainty, potentially reaching up to inter-model differences.
Zhi Li, Gregor Rickert, Na Zheng, Zhibo Zhang, Ilhan Özgen-Xian, and Daniel Caviedes-Voullième
EGUsphere, https://doi.org/10.5194/egusphere-2024-2588, https://doi.org/10.5194/egusphere-2024-2588, 2024
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We introduce SERGHEI-RE, a 3D subsurface flow simulator with performance-portable parallel computing capabilities. SERGHEI-RE performs effectively on various computational devices, from personal computers to advanced clusters. It allows users to solve flow equations with multiple numerical schemes, making it adaptable to various hydrological scenarios. Testing results show its accuracy and performance, confirming that SERGHEI-RE is a powerful tool for hydrological research.
Till Francke, Cosimo Brogi, Alby Duarte Rocha, Michael Förster, Maik Heistermann, Markus Köhli, Daniel Rasche, Marvin Reich, Paul Schattan, Lena Scheiffele, and Martin Schrön
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-106, https://doi.org/10.5194/gmd-2024-106, 2024
Revised manuscript accepted for GMD
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Multiple methods for measuring soil moisture beyond the point scale exist. Their validation generally hindered by lack of knowing the truth. We propose a virtual framework, in which this truth is fully known and the sensor observations for Cosmic Ray Neutron Sensing, Remote Sensing, and Hydrogravimetry are simulated. This allows the rigourous testing of these virtual sensors to understand their effectiveness and limitations.
Chi-Ling Wei, Pei-Chun Chen, Chien-Yu Tseng, Ting-Yu Dai, Yun-Ting Ho, Ching-Chun Chou, Christian Onof, and Li-Pen Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-1918, https://doi.org/10.5194/egusphere-2024-1918, 2024
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pyBL is an open-source package for generating realistic rainfall time series based on the Bartlett-Lewis (BL) model. It can preserve not only standard but also extreme rainfall statistics across various timescales. Notably, compared to traditional frequency analysis methods, the BL model requires only half the record length (or even shorter) to achieve similar consistency in estimating sub-hourly rainfall extremes. This makes it a valuable tool for modelling rainfall extremes with short records.
Kerry L. Callaghan, Andrew D. Wickert, Richard Barnes, and Jacqueline Austermann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-131, https://doi.org/10.5194/gmd-2024-131, 2024
Revised manuscript accepted for GMD
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We present the Water Table Model (WTM), which simulates groundwater and lake levels at continental scales over millennia. Our simulations show that North America held more ground- and lake-water at the Last Glacial Maximum than in the present day – enough to lower sea level by 6 cm. We also simulate the changing water table from 21,000 to 16,000 years ago, finding that groundwater storage decreased following reduced precipitation in the model inputs. Open-source WTM code is available on Github.
Manuel F. Rios Gaona, Katerina Michaelides, and Michael Bliss Singer
Geosci. Model Dev., 17, 5387–5412, https://doi.org/10.5194/gmd-17-5387-2024, https://doi.org/10.5194/gmd-17-5387-2024, 2024
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STORM v.2 (short for STOchastic Rainfall Model version 2.0) is an open-source and user-friendly modelling framework for simulating rainfall fields over a basin. It also allows simulating the impact of plausible climate change either on the total seasonal rainfall or the storm’s maximum intensity.
Lukas Riedel, Thomas Röösli, Thomas Vogt, and David N. Bresch
Geosci. Model Dev., 17, 5291–5308, https://doi.org/10.5194/gmd-17-5291-2024, https://doi.org/10.5194/gmd-17-5291-2024, 2024
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River floods are among the most devastating natural hazards. We propose a flood model with a statistical approach based on openly available data. The model is integrated in a framework for estimating impacts of physical hazards. Although the model only agrees moderately with satellite-detected flood extents, we show that it can be used for forecasting the magnitude of flood events in terms of socio-economic impacts and for comparing these with past events.
Robin Schwemmle, Hannes Leistert, Andreas Steinbrich, and Markus Weiler
Geosci. Model Dev., 17, 5249–5262, https://doi.org/10.5194/gmd-17-5249-2024, https://doi.org/10.5194/gmd-17-5249-2024, 2024
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The new process-based hydrological toolbox model, RoGeR (https://roger.readthedocs.io/), can be used to estimate the components of the hydrological cycle and the related travel times of pollutants through parts of the hydrological cycle. These estimations may contribute to effective water resources management. This paper presents the toolbox concept and provides a simple example of providing estimations to water resources management.
Sarah Hanus, Lilian Schuster, Peter Burek, Fabien Maussion, Yoshihide Wada, and Daniel Viviroli
Geosci. Model Dev., 17, 5123–5144, https://doi.org/10.5194/gmd-17-5123-2024, https://doi.org/10.5194/gmd-17-5123-2024, 2024
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This study presents a coupling of the large-scale glacier model OGGM and the hydrological model CWatM. Projected future increase in discharge is less strong while future decrease in discharge is stronger when glacier runoff is explicitly included in the large-scale hydrological model. This is because glacier runoff is projected to decrease in nearly all basins. We conclude that an improved glacier representation can prevent underestimating future discharge changes in large river basins.
M. Graham Clark and Sean K. Carey
Geosci. Model Dev., 17, 4911–4922, https://doi.org/10.5194/gmd-17-4911-2024, https://doi.org/10.5194/gmd-17-4911-2024, 2024
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This paper provides validation of the Canadian Small Lakes Model (CSLM) for estimating evaporation rates from reservoirs and a refactoring of the original FORTRAN code into MATLAB and Python, which are now stored in GitHub repositories. Here we provide direct observations of the surface energy exchange obtained with an eddy covariance system to validate the CSLM. There was good agreement between observations and estimations except under specific atmospheric conditions when evaporation is low.
Thibault Hallouin, François Bourgin, Charles Perrin, Maria-Helena Ramos, and Vazken Andréassian
Geosci. Model Dev., 17, 4561–4578, https://doi.org/10.5194/gmd-17-4561-2024, https://doi.org/10.5194/gmd-17-4561-2024, 2024
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The evaluation of the quality of hydrological model outputs against streamflow observations is widespread in the hydrological literature. In order to improve on the reproducibility of published studies, a new evaluation tool dedicated to hydrological applications is presented. It is open source and usable in a variety of programming languages to make it as accessible as possible to the community. Thus, authors and readers alike can use the same tool to produce and reproduce the results.
Barnaby Dobson, Leyang Liu, and Ana Mijic
Geosci. Model Dev., 17, 4495–4513, https://doi.org/10.5194/gmd-17-4495-2024, https://doi.org/10.5194/gmd-17-4495-2024, 2024
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Water management is challenging when models don't capture the entire water cycle. We propose that using integrated models facilitates management and improves understanding. We introduce a software tool designed for this task. We discuss its foundation, how it simulates water system components and their interactions, and its customisation. We provide a flexible way to represent water systems, and we hope it will inspire more research and practical applications for sustainable water management.
Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, https://doi.org/10.5194/gmd-17-3559-2024, 2024
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We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
Willem J. van Verseveld, Albrecht H. Weerts, Martijn Visser, Joost Buitink, Ruben O. Imhoff, Hélène Boisgontier, Laurène Bouaziz, Dirk Eilander, Mark Hegnauer, Corine ten Velden, and Bobby Russell
Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, https://doi.org/10.5194/gmd-17-3199-2024, 2024
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We present the wflow_sbm distributed hydrological model, recently released by Deltares, as part of the Wflow.jl open-source modelling framework in the programming language Julia. Wflow_sbm has a fast runtime, making it suitable for large-scale modelling. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets, which results in satisfactory to good performance (without much tuning). We show this for a number of specific cases.
Sanchit Minocha, Faisal Hossain, Pritam Das, Sarath Suresh, Shahzaib Khan, George Darkwah, Hyongki Lee, Stefano Galelli, Konstantinos Andreadis, and Perry Oddo
Geosci. Model Dev., 17, 3137–3156, https://doi.org/10.5194/gmd-17-3137-2024, https://doi.org/10.5194/gmd-17-3137-2024, 2024
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The Reservoir Assessment Tool (RAT) merges satellite data with hydrological models, enabling robust estimation of reservoir parameters like inflow, outflow, surface area, and storage changes around the world. Version 3.0 of RAT lowers the barrier of entry for new users and achieves scalability and computational efficiency. RAT 3.0 also facilitates open-source development of functions for continuous improvement to mobilize and empower the global water management community.
Heloisa Ehalt Macedo, Bernhard Lehner, Jim Nicell, and Günther Grill
Geosci. Model Dev., 17, 2877–2899, https://doi.org/10.5194/gmd-17-2877-2024, https://doi.org/10.5194/gmd-17-2877-2024, 2024
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Treated and untreated wastewaters are sources of contaminants of emerging concern. HydroFATE, a new global model, estimates their concentrations in surface waters, identifying streams that are most at risk and guiding monitoring/mitigation efforts to safeguard aquatic ecosystems and human health. Model predictions were validated against field measurements of the antibiotic sulfamethoxazole, with predicted concentrations exceeding ecological thresholds in more than 400 000 km of rivers worldwide.
Pedro Felipe Arboleda-Obando, Agnès Ducharne, Zun Yin, and Philippe Ciais
Geosci. Model Dev., 17, 2141–2164, https://doi.org/10.5194/gmd-17-2141-2024, https://doi.org/10.5194/gmd-17-2141-2024, 2024
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We show a new irrigation scheme included in the ORCHIDEE land surface model. The new irrigation scheme restrains irrigation due to water shortage, includes water adduction, and represents environmental limits and facilities to access water, due to representing infrastructure in a simple way. Our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, even if there are difficulties due to shortcomings and limited information.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
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Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
Atabek Umirbekov, Richard Essery, and Daniel Müller
Geosci. Model Dev., 17, 911–929, https://doi.org/10.5194/gmd-17-911-2024, https://doi.org/10.5194/gmd-17-911-2024, 2024
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We present a parsimonious snow model which simulates snow mass without the need for extensive calibration. The model is based on a machine learning algorithm that has been trained on diverse set of daily observations of snow accumulation or melt, along with corresponding climate and topography data. We validated the model using in situ data from numerous new locations. The model provides a promising solution for accurate snow mass estimation across regions where in situ data are limited.
Ciaran J. Harman and Esther Xu Fei
Geosci. Model Dev., 17, 477–495, https://doi.org/10.5194/gmd-17-477-2024, https://doi.org/10.5194/gmd-17-477-2024, 2024
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Over the last 10 years, scientists have developed StorAge Selection: a new way of modeling how material is transported through complex systems. Here, we present some new, easy-to-use, flexible, and very accurate code for implementing this method. We show that, in 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 codes to the right answer in an important way: it conserves mass.
Lele Shu, Paul Ullrich, Xianhong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev., 17, 497–527, https://doi.org/10.5194/gmd-17-497-2024, https://doi.org/10.5194/gmd-17-497-2024, 2024
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Our team developed rSHUD v2.0, a toolkit that simplifies the use of the SHUD, a model simulating water movement in the environment. We demonstrated its effectiveness in two watersheds, one in the USA and one in China. The toolkit also facilitated the creation of the Global Hydrological Data Cloud, a platform for automatic data processing and model deployment, marking a significant advancement in hydrological research.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, https://doi.org/10.5194/gmd-17-275-2024, 2024
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This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1 km at the Equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for a higher-resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For a long transient simulation using a parallel prototype of MODFLOW 6, we show that our implementation is efficient for a relatively low number of processor cores.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
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We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Damian N. Mingo, Remko Nijzink, Christophe Ley, and Jack S. Hale
EGUsphere, https://doi.org/10.5194/egusphere-2023-2865, https://doi.org/10.5194/egusphere-2023-2865, 2024
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Hydrologists are often faced with selecting amongst a set of competing models with different numbers of parameters and ability to fit available data. The Bayes’ factor is a tool that can be used to compare models, however it is very difficult to compute the Bayes’ factor numerically. In our paper we explore and develop highly efficient algorithms for computing the Bayes’ factor of hydrological systems, which will bring this useful tool for selecting models to everyday hydrological practice.
Daniel Boateng and Sebastian G. Mutz
Geosci. Model Dev., 16, 6479–6514, https://doi.org/10.5194/gmd-16-6479-2023, https://doi.org/10.5194/gmd-16-6479-2023, 2023
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We present an open-source Python framework for performing empirical-statistical downscaling of climate information, such as precipitation. The user-friendly package comprises all the downscaling cycles including data preparation, model selection, training, and evaluation, designed in an efficient and flexible manner, allowing for quick and reproducible downscaling products. The framework would contribute to climate change impact assessments by generating accurate high-resolution climate data.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023, https://doi.org/10.5194/gmd-16-5847-2023, 2023
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Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow 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; this may provide a better understanding of hydrodynamics and related transport processes in Rhizophora mangrove forests.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023, https://doi.org/10.5194/gmd-16-5685-2023, 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 propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate 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.
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., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
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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.
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
Short summary
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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
Short summary
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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
Short summary
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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.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
Short summary
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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
Short summary
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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
Short summary
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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.
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...
