Articles | Volume 16, issue 3
https://doi.org/10.5194/gmd-16-977-2023
© Author(s) 2023. 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-16-977-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
08 Feb 2023
Model description paper |
| 08 Feb 2023
| 08 Feb 2023
SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics
Daniel Caviedes-Voullième
CORRESPONDING AUTHOR
Simulation and Data Lab Terrestrial Systems, Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich, Germany
Mario Morales-Hernández
Fluid Mechanics, I3A, Universidad de Zaragoza, Zaragoza, Spain
Oak Ridge National Laboratory, Oak Ridge, USA
Matthew R. Norman
Oak Ridge National Laboratory, Oak Ridge, USA
Ilhan Özgen-Xian
Institute of Geoecology, Technische Universität Braunschweig, Braunschweig, Germany
Earth & Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, USA
Related authors
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
Short summary
Short summary
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.
Shahin Khosh Bin Ghomash, Heiko Apel, and Daniel Caviedes-Voullième
Nat. Hazards Earth Syst. Sci., 24, 2857–2874, https://doi.org/10.5194/nhess-24-2857-2024, https://doi.org/10.5194/nhess-24-2857-2024, 2024
Short summary
Short summary
Early warning is essential to minimise the impact of flash floods. We explore the use of highly detailed flood models to simulate the 2021 flood event in the lower Ahr valley (Germany). Using very high-resolution models resolving individual streets and buildings, we produce detailed, quantitative, and actionable information for early flood warning systems. Using state-of-the-art computational technology, these models can guarantee very fast forecasts which allow for sufficient time to respond.
Denise Degen, Daniel Caviedes Voullième, Susanne Buiter, Harrie-Jan Hendricks Franssen, Harry Vereecken, Ana González-Nicolás, and Florian Wellmann
Geosci. Model Dev., 16, 7375–7409, https://doi.org/10.5194/gmd-16-7375-2023, https://doi.org/10.5194/gmd-16-7375-2023, 2023
Short summary
Short summary
In geosciences, we often use simulations based on physical laws. These simulations can be computationally expensive, which is a problem if simulations must be performed many times (e.g., to add error bounds). We show how a novel machine learning method helps to reduce simulation time. In comparison to other approaches, which typically only look at the output of a simulation, the method considers physical laws in the simulation itself. The method provides reliable results faster than standard.
Zbigniew P. Piotrowski, Jaro Hokkanen, Daniel Caviedes-Voullieme, Olaf Stein, and Stefan Kollet
EGUsphere, https://doi.org/10.5194/egusphere-2023-1079, https://doi.org/10.5194/egusphere-2023-1079, 2023
Preprint withdrawn
Short summary
Short summary
The computer programs capable of simulation of Earth system components evolve, adapting new fundamental science concepts and more observational data on more and more powerful computer hardware. Adaptation of a large scientific program to a new type of hardware is costly. In this work we propose cheap and simple but effective strategy that enable computation using graphic processing units, based on automated program code modification. This results in better resolution and/or longer predictions.
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
Short summary
Short summary
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.
Shahin Khosh Bin Ghomash, Heiko Apel, and Daniel Caviedes-Voullième
Nat. Hazards Earth Syst. Sci., 24, 2857–2874, https://doi.org/10.5194/nhess-24-2857-2024, https://doi.org/10.5194/nhess-24-2857-2024, 2024
Short summary
Short summary
Early warning is essential to minimise the impact of flash floods. We explore the use of highly detailed flood models to simulate the 2021 flood event in the lower Ahr valley (Germany). Using very high-resolution models resolving individual streets and buildings, we produce detailed, quantitative, and actionable information for early flood warning systems. Using state-of-the-art computational technology, these models can guarantee very fast forecasts which allow for sufficient time to respond.
Denise Degen, Daniel Caviedes Voullième, Susanne Buiter, Harrie-Jan Hendricks Franssen, Harry Vereecken, Ana González-Nicolás, and Florian Wellmann
Geosci. Model Dev., 16, 7375–7409, https://doi.org/10.5194/gmd-16-7375-2023, https://doi.org/10.5194/gmd-16-7375-2023, 2023
Short summary
Short summary
In geosciences, we often use simulations based on physical laws. These simulations can be computationally expensive, which is a problem if simulations must be performed many times (e.g., to add error bounds). We show how a novel machine learning method helps to reduce simulation time. In comparison to other approaches, which typically only look at the output of a simulation, the method considers physical laws in the simulation itself. The method provides reliable results faster than standard.
Zbigniew P. Piotrowski, Jaro Hokkanen, Daniel Caviedes-Voullieme, Olaf Stein, and Stefan Kollet
EGUsphere, https://doi.org/10.5194/egusphere-2023-1079, https://doi.org/10.5194/egusphere-2023-1079, 2023
Preprint withdrawn
Short summary
Short summary
The computer programs capable of simulation of Earth system components evolve, adapting new fundamental science concepts and more observational data on more and more powerful computer hardware. Adaptation of a large scientific program to a new type of hardware is costly. In this work we propose cheap and simple but effective strategy that enable computation using graphic processing units, based on automated program code modification. This results in better resolution and/or longer predictions.
Marco A. Giorgetta, William Sawyer, Xavier Lapillonne, Panagiotis Adamidis, Dmitry Alexeev, Valentin Clément, Remo Dietlicher, Jan Frederik Engels, Monika Esch, Henning Franke, Claudia Frauen, Walter M. Hannah, Benjamin R. Hillman, Luis Kornblueh, Philippe Marti, Matthew R. Norman, Robert Pincus, Sebastian Rast, Daniel Reinert, Reiner Schnur, Uwe Schulzweida, and Bjorn Stevens
Geosci. Model Dev., 15, 6985–7016, https://doi.org/10.5194/gmd-15-6985-2022, https://doi.org/10.5194/gmd-15-6985-2022, 2022
Short summary
Short summary
This work presents a first version of the ICON atmosphere model that works not only on CPUs, but also on GPUs. This GPU-enabled ICON version is benchmarked on two GPU machines and a CPU machine. While the weak scaling is very good on CPUs and GPUs, the strong scaling is poor on GPUs. But the high performance of GPU machines allowed for first simulations of a short period of the quasi-biennial oscillation at very high resolution with explicit convection and gravity wave forcing.
Tigstu T. Dullo, George K. Darkwah, Sudershan Gangrade, Mario Morales-Hernández, M. Bulbul Sharif, Alfred J. Kalyanapu, Shih-Chieh Kao, Sheikh Ghafoor, and Moetasim Ashfaq
Nat. Hazards Earth Syst. Sci., 21, 1739–1757, https://doi.org/10.5194/nhess-21-1739-2021, https://doi.org/10.5194/nhess-21-1739-2021, 2021
Short summary
Short summary
We studied the effect of potential future climate change on floods, flood protection, and electricity infrastructure in the Conasauga River watershed in the US using ensemble hydrodynamic modeling. We used a GPU-accelerated Two-dimensional Runoff Inundation Toolkit for Operational Needs (TRITON) hydrodynamic model to simulate floods. Overall, this study demonstrates how a fast hydrodynamic model can enhance flood frequency maps and vulnerability assessment under changing climatic conditions.
Related subject area
Hydrology
Generalised drought index: a novel multi-scale daily approach for drought assessment
Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake
Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL)
PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration
Prediction of hysteretic matric potential dynamics using artificial intelligence: application of autoencoder neural networks
Regionalization in global hydrological models and its impact on runoff simulations: a case study using WaterGAP3 (v 1.0.0)
STORM v.2: A simple, stochastic rainfall model for exploring the impacts of climate and climate change at and near the land surface in gauged watersheds
Fluvial flood inundation and socio-economic impact model based on open data
RoGeR v3.0.5 – a process-based hydrological toolbox model in Python
Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) – towards an improved representation of mountain water resources in global assessments
An open-source refactoring of the Canadian Small Lakes Model for estimates of evaporation from medium-sized reservoirs
EvalHyd v0.1.2: a polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions
Modelling water quantity and quality for integrated water cycle management with the Water Systems Integrated Modelling framework (WSIMOD) software
HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model
Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications
Reservoir Assessment Tool version 3.0: a scalable and user-friendly software platform to mobilize the global water management community
HydroFATE (v1): a high-resolution contaminant fate model for the global river system
Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2
GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications
GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic drivers
mesas.py v1.0: a flexible Python package for modeling solute transport and transit times using StorAge Selection functions
rSHUD v2.0: advancing the Simulator for Hydrologic Unstructured Domains and unstructured hydrological modeling in the R environment
GLOBGM v1.0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model
Development of inter-grid-cell lateral unsaturated and saturated flow model in the E3SM Land Model (v2.0)
The global water resources and use model WaterGAP v2.2e: description and evaluation of modifications and new features
pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information
Representing the impact of Rhizophora mangroves on flow in a hydrodynamic model (COAWST_rh v1.0): the importance of three-dimensional root system structures
Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification
Enhancing the representation of water management in global hydrological models
NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process
Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations
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
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
Operational water forecast ability of the HRRR-iSnobal combination: an evaluation to adapt into production environments
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
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
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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. Discuss., https://doi.org/10.5194/gmd-2023-213, https://doi.org/10.5194/gmd-2023-213, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
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 resources assessments since 1996. We show the effects of new model features and model evaluations against observed streamflow and water storage anomalies as well as water abstractions statistics. The publically available model output for several variants is described.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
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
Short summary
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
Short summary
Short summary
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
Short summary
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.
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
Short summary
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
Short summary
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
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Abderrezzak, K. E. K., Paquier, A., and Mignot, E.:
Modelling flash flood propagation in urban areas using a two-dimensional numerical model, Nat. Hazards, 50, 433–460, https://doi.org/10.1007/s11069-008-9300-0, 2008. a
Alexander, F., Almgren, A., Bell, J., Bhattacharjee, A., Chen, J., Colella, P., Daniel, D., DeSlippe, J., Diachin, L., Draeger, E., Dubey, A., Dunning, T., Evans, T., Foster, I., Francois, M., Germann, T., Gordon, M., Habib, S., Halappanavar, M., Hamilton, S., Hart, W., Huang, Z. H., Hungerford, A., Kasen, D., Kent, P. R. C., Kolev, T., Kothe, D. B., Kronfeld, A., Luo, Y., Mackenzie, P., McCallen, D., Messer, B., Mniszewski, S., Oehmen, C., Perazzo, A., Perez, D., Richards, D., Rider, W. J., Rieben, R., Roche, K., Siegel, A., Sprague, M., Steefel, C., Stevens, R., Syamlal, M., Taylor, M., Turner, J., Vay, J.-L., Voter, A. F., Windus, T. L., and Yelick, K.:
Exascale applications: skin in the game, Philos. T. R. Soc. A, 378, 20190056, https://doi.org/10.1098/rsta.2019.0056, 2020. a
An, H., Yu, S., Lee, G., and Kim, Y.:
Analysis of an open source quadtree grid shallow water flow solver for flood simulation, Quatern. Int., 384, 118–128, https://doi.org/10.1016/j.quaint.2015.01.032, 2015. a
Arpaia, L. and Ricchiuto, M.:
r-adaptation for Shallow Water flows: conservation, well balancedness, efficiency, Comput. Fluids, 160, 175–203, https://doi.org/10.1016/j.compfluid.2017.10.026, 2018. a
Artigues, V., Kormann, K., Rampp, M., and Reuter, K.:
Evaluation of performance portability frameworks for the implementation of a particle-in-cell code, Concurr. Comput.-Pract. E., 32, https://doi.org/10.1002/cpe.5640, 2019. a
Aureli, F., Maranzoni, A., Mignosa, P., and Ziveri, C.:
A weighted surface-depth gradient method for the numerical integration of the 2D shallow water equations with topography, Adv. Water Resour., 31, 962–974, https://doi.org/10.1016/j.advwatres.2008.03.005, 2008. a
Aureli, F., Prost, F., Vacondio, R., Dazzi, S., and Ferrari, A.:
A GPU-Accelerated Shallow-Water Scheme for Surface Runoff Simulations, Water, 12, 637, https://doi.org/10.3390/w12030637, 2020. a
Aureli, F., Maranzoni, A., and Petaccia, G.:
Review of Historical Dam-Break Events and Laboratory Tests on Real Topography for the Validation of Numerical Models, Water, 13, 1968, https://doi.org/10.3390/w13141968, 2021. a
Ayog, J. L., Kesserwani, G., Shaw, J., Sharifian, M. K., and Bau, D.:
Second-order discontinuous Galerkin flood model: Comparison with industry-standard finite volume models, J. Hydrol., 594, 125924, https://doi.org/10.1016/j.jhydrol.2020.125924, 2021. a
Bates, P. and Roo, A. D.:
A simple raster-based model for flood inundation simulation, J. Hydrol., 236, 54–77, https://doi.org/10.1016/S0022-1694(00)00278-X, 2000. a
Bauer, P., Dueben, P. D., Hoefler, T., Quintino, T., Schulthess, T. C., and Wedi, N. P.:
The digital revolution of Earth-system science, Nature Computational Science, 1, 104–113, https://doi.org/10.1038/s43588-021-00023-0, 2021. a
Beckingsale, D. A., Burmark, J., Hornung, R., Jones, H., Killian, W., Kunen, A. J., Pearce, O., Robinson, P., Ryujin, B. S., and Scogland, T. R.:
RAJA: Portable Performance for Large-Scale Scientific Applications, in: 2019 IEEE/ACM International Workshop on Performance, Portability and Productivity in HPC (P3HPC), 71–81, https://doi.org/10.1109/p3hpc49587.2019.00012, 2019. a
Bellos, V. and Tsakiris, G.:
A hybrid method for flood simulation in small catchments combining hydrodynamic and hydrological techniques, J. Hydrol., 540, 331–339, https://doi.org/10.1016/j.jhydrol.2016.06.040, 2016. a
Berger, M. J., George, D. L., LeVeque, R. J., and Mandli, K. T.:
The GeoClaw software for depth-averaged flows with adaptive refinement, Adv. Water Resour., 34, 1195–1206, https://doi.org/10.1016/j.advwatres.2011.02.016, 2011. a
Bertagna, L., Deakin, M., Guba, O., Sunderland, D., Bradley, A. M., Tezaur, I. K., Taylor, M. A., and Salinger, A. G.:
HOMMEXX 1.0: a performance-portable atmospheric dynamical core for the Energy Exascale Earth System Model, Geosci. Model Dev., 12, 1423–1441, https://doi.org/10.5194/gmd-12-1423-2019, 2019. a
Bomers, A., Schielen, R. M. J., and Hulscher, S. J. M. H.:
The influence of grid shape and grid size on hydraulic river modelling performance, Environ. Fluid Mech., 19, 1273–1294, https://doi.org/10.1007/s10652-019-09670-4, 2019. a
Bout, B. and Jetten, V.:
The validity of flow approximations when simulating catchment-integrated flash floods, J. Hydrol., 556, 674–688, https://doi.org/10.1016/j.jhydrol.2017.11.033, 2018. a
Bradford, S. F. and Sanders, B. F.:
Finite-Volume Model for Shallow-Water Flooding of Arbitrary Topography, J. Hydraul. Eng., 128, 289–298, https://doi.org/10.1061/(asce)0733-9429(2002)128:3(289), 2002. a
Briggs, M. J., Synolakis, C. E., Harkins, G. S., and Green, D. R.:
Laboratory experiments of tsunami runup on a circular island, Pure Appl. Geophys., 144, 569–593, https://doi.org/10.1007/bf00874384, 1995. a
Brodtkorb, A. R., Sætra, M. L., and Altinakar, M.:
Efficient shallow water simulations on GPUs: Implementation, visualization, verification, and validation, Comput. Fluids, 55, 1–12, https://doi.org/10.1016/j.compfluid.2011.10.012, 2012. a, b
Brufau, P., García-Navarro, P., and Vázquez-Cendón, M. E.:
Zero mass error using unsteady wetting-drying conditions in shallow flows over dry irregular topography, Int. J. Numer. Meth. Fl., 45, 1047–1082, https://doi.org/10.1002/fld.729, 2004. a
Brunner, G.:
HEC-RAS 2D User's Manual Version 6.0, Hydrologic Engineering Center, Davis, CA, USA, https://www.hec.usace.army.mil/confluence/rasdocs/r2dum/latest (last access: 22 August 2022), 2021. a
Brunner, P. and Simmons, C. T.:
HydroGeoSphere: A Fully Integrated, Physically Based Hydrological Model, Ground Water, 50, 170–176, https://doi.org/10.1111/j.1745-6584.2011.00882.x, 2012. a
Bruwier, M., Archambeau, P., Erpicum, S., Pirotton, M., and Dewals, B.:
Discretization of the divergence formulation of the bed slope term in the shallow-water equations and consequences in terms of energy balance, Appl. Math. Model., 40, 7532–7544, https://doi.org/10.1016/j.apm.2016.01.041, 2016. a
Burguete, J., García-Navarro, P., and Murillo, J.:
Friction term discretization and limitation to preserve stability and conservation in the 1D shallow-water model: Application to unsteady irrigation and river flow, Int. J. Numer. Meth. Fl., 58, 403–425, https://doi.org/10.1002/fld.1727, 2008. a
Buttinger-Kreuzhuber, A., Horváth, Z., Noelle, S., Blöschl, G., and Waser, J.:
A fast second-order shallow water scheme on two-dimensional structured grids over abrupt topography, Adv. Water Resour., 127, 89–108, https://doi.org/10.1016/j.advwatres.2019.03.010, 2019. a
Buttinger-Kreuzhuber, A., Konev, A., Horváth, Z., Cornel, D., Schwerdorf, I., Blöschl, G., and Waser, J.:
An integrated GPU-accelerated modeling framework for high-resolution simulations of rural and urban flash floods, Environ. Modell. Softw., 156, 105480, https://doi.org/10.1016/j.envsoft.2022.105480, 2022. a
Caldas Steinstraesser, J. G., Delenne, C., Finaud-Guyot, P., Guinot, V., Kahn Casapia, J. L., and Rousseau, A.:
SW2D-LEMON: a new software for upscaled shallow water modeling, in: Simhydro 2021 – 6th International Conference Models for complex and global water issues – Practices and expectations, Sophia Antipolis, France, https://hal.inria.fr/hal-03224050 (last access: 22 August 2022), 2021. a
Carlotto, T., Chaffe, P. L. B., dos Santos, C. I., and Lee, S.:
SW2D-GPU: A two-dimensional shallow water model accelerated by GPGPU, Environ. Modell. Softw., 145, 105205, https://doi.org/10.1016/j.envsoft.2021.105205, 2021. a
Carroll, R. W. H., Bearup, L. A., Brown, W., Dong, W., Bill, M., and Willlams, K. H.:
Factors controlling seasonal groundwater and solute flux from snow-dominated basins, Hydrol. Process., 32, 2187–2202, https://doi.org/10.1002/hyp.13151, 2018. a
Caviedes-Voullième, D. and Kesserwani, G.:
Benchmarking a multiresolution discontinuous Galerkin shallow water model: Implications for computational hydraulics, Adv. Water Resour., 86, 14–31, https://doi.org/10.1016/j.advwatres.2015.09.016, 2015. a, b, c
Caviedes-Voullième, D., García-Navarro, P., and Murillo, J.:
Influence of mesh structure on 2D full shallow water equations and SCS Curve Number simulation of rainfall/runoff events, J. Hydrol., 448–449, 39–59, https://doi.org/10.1016/j.jhydrol.2012.04.006, 2012. a, b
Caviedes-Voullième, D., Fernández-Pato, J., and Hinz, C.:
Cellular Automata and Finite Volume solvers converge for 2D shallow flow modelling for hydrological modelling, J. Hydrol., 563, 411–417, https://doi.org/10.1016/j.jhydrol.2018.06.021, 2018. a, b
Caviedes Voullième, D., Morales-Hernández, M., and Özgen-Xian, I.: SERGHEI (1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7041423, 2022. a
Caviedes Voullième, D., Morales-Hernández, M., and Özgen-Xian, I.: Test cases for SERGHEI v1.0, Zenodo [data set], https://doi.org/10.5281/zenodo.7041392, 2022b. a
Cea, L. and Bladé, E.:
A simple and efficient unstructured finite volume scheme for solving the shallow water equations in overland flow applications, Water Resour. Res., 51, 5464–5486, https://doi.org/10.1002/2014WR016547, 2015. a
Cea, L., Garrido, M., Puertas, J., Jácome, A., Río, H. D., and Suárez, J.:
Overland flow computations in urban and industrial catchments from direct precipitation data using a two-dimensional shallow water model, Water Sci. Technol., 62, 1998–2008, https://doi.org/10.2166/wst.2010.746, 2010b. a, b, c
Chang, T.-J., Chang, Y.-S., and Chang, K.-H.:
Modeling rainfall-runoff processes using smoothed particle hydrodynamics with mass-varied particles, J. Hydrol., 543, 749–758, https://doi.org/10.1016/j.jhydrol.2016.10.045, 2016. a
Choi, B. H., Kim, D. C., Pelinovsky, E., and Woo, S. B.:
Three-dimensional simulation of tsunami run-up around conical island, Coast. Eng., 54, 618–629, https://doi.org/10.1016/j.coastaleng.2007.02.001, 2007. a
Clark, M. P., Bierkens, M. F. P., Samaniego, L., Woods, R. A., Uijlenhoet, R., Bennett, K. E., Pauwels, V. R. N., Cai, X., Wood, A. W., and Peters-Lidard, C. D.:
The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism, Hydrol. Earth Syst. Sci., 21, 3427–3440, https://doi.org/10.5194/hess-21-3427-2017, 2017. a
Coon, E., Svyatsky, D., Jan, A., Kikinzon, E., Berndt, M., Atchley, A., Harp, D., Manzini, G., Shelef, E., Lipnikov, K., Garimella, R., Xu, C., Moulton, D., Karra, S., Painter, S., Jafarov, E., and Molins, S.:
Advanced Terrestrial Simulator, Computer Software, USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23), https://doi.org/10.11578/DC.20190911.1, 2019. a
Costabile, P. and Costanzo, C.:
A 2D SWEs framework for efficient catchment-scale simulations: hydrodynamic scaling properties of river networks and implications for non-uniform grids generation, J. Hydrol., 599, 126306, https://doi.org/10.1016/j.jhydrol.2021.126306, 2021. a
Costabile, P., Costanzo, C., Ferraro, D., and Barca, P.:
Is HEC-RAS 2D accurate enough for storm-event hazard assessment? Lessons learnt from a benchmarking study based on rain-on-grid modelling, J. Hydrol., 603, 126962, https://doi.org/10.1016/j.jhydrol.2021.126962, 2021. a
Crompton, O., Katul, G. G., and Thompson, S.:
Resistance formulations in shallow overland flow along a hillslope covered with patchy vegetation, Water Resour. Res., 56, e2020WR027194, https://doi.org/10.1029/2020wr027194, 2020. a
David, A. and Schmalz, B.:
A Systematic Analysis of the Interaction between Rain-on-Grid-Simulations and Spatial Resolution in 2D Hydrodynamic Modeling, Water, 13, 2346, https://doi.org/10.3390/w13172346, 2021. a
Dazzi, S., Vacondio, R., Palù, A. D., and Mignosa, P.:
A local time stepping algorithm for GPU-accelerated 2D shallow water models, Adv. Water Resour., 111, 274–288, https://doi.org/10.1016/j.advwatres.2017.11.023, 2018. a
Delestre, O., Lucas, C., Ksinant, P., Darboux, F., Laguerre, C., Vo, T., James, F., and Cordier, S.:
SWASHES: a compilation of shallow water analytic solutions for hydraulic and environmental studies, Int. J. Numer. Meth. Fl., 72, 269–300, https://doi.org/10.1002/fld.3741, 2013. a, b, c, d, e, f, g, h
Delestre, O., Darboux, F., James, F., Lucas, C., Laguerre, C., and Cordier, S.:
FullSWOF: Full Shallow-Water equations for Overland Flow, Journal of Open Source Software, 2, 448, https://doi.org/10.21105/joss.00448, 2017. a
Demeshko, I., Watkins, J., Tezaur, I. K., Guba, O., Spotz, W. F., Salinger, A. G., Pawlowski, R. P., and Heroux, M. A.:
Toward performance portability of the Albany finite element analysis code using the Kokkos library, Int. J. High Perform. C., 33, 332–352, https://doi.org/10.1177/1094342017749957, 2018. a
Djemame, K. and Carr, H.:
Exascale Computing Deployment Challenges, in: Economics of Grids, Clouds, Systems, and Services, Springer International Publishing, https://doi.org/10.1007/978-3-030-63058-4_19, pp. 211–216, 2020. a
Dullo, T. T., Darkwah, G. K., Gangrade, S., Morales-Hernández, M., Sharif, M. B., Kalyanapu, A. J., Kao, S.-C., Ghafoor, S., and Ashfaq, M.:
Assessing climate-change-induced flood risk in the Conasauga River watershed: an application of ensemble hydrodynamic inundation modeling, Nat. Hazards Earth Syst. Sci., 21, 1739–1757, https://doi.org/10.5194/nhess-21-1739-2021, 2021a. a
Dullo, T. T., Gangrade, S., Morales-Hernández, M., Sharif, M. B., Kao, S.-C., Kalyanapu, A. J., Ghafoor, S., and Evans, K. J.:
Simulation of Hurricane Harvey flood event through coupled hydrologic-hydraulic models: Challenges and next steps, J. Flood Risk Manag., 14, https://doi.org/10.1111/jfr3.12716, 2021b. a
Duran, A., Liang, Q., and Marche, F.:
On the well-balanced numerical discretization of shallow water equations on unstructured meshes, J. Comput. Phys., 235, 565–586, https://doi.org/10.1016/j.jcp.2012.10.033, 2013. a
Echeverribar, I., Morales-Hernández, M., Brufau, P., and García-Navarro, P.:
2D numerical simulation of unsteady flows for large scale floods prediction in real time, Adv. Water Resour., 134, 103444, https://doi.org/10.1016/j.advwatres.2019.103444, 2019. a
Echeverribar, I., Morales-Hernández, M., Brufau, P., and García-Navarro, P.:
Analysis of the performance of a hybrid CPU/GPU 1D2D coupled model for real flood cases, J. Hydroinform., 22, 1198–1216, https://doi.org/10.2166/hydro.2020.032, 2020. a
Edwards, H. C., Trott, C. R., and Sunderland, D.:
Kokkos: Enabling manycore performance portability through polymorphic memory access patterns, J. Parallel Distr. Com., 74, 3202–3216, https://doi.org/10.1016/j.jpdc.2014.07.003, Domain-Specific Languages and High-Level Frameworks for High-Performance Computing, 2014. a
Fan, Y., Clark, M., Lawrence, D. M., Swenson, S., Band, L. E., Brantley, S. L., Brooks, P. D., Dietrich, W. E., Flores, A., Grant, G., Kirchner, J. W., Mackay, D. S., McDonnell, J. J., Milly, P. C. D., Sullivan, P. L., Tague, C., Ajami, H., Chaney, N., Hartmann, A., Hazenberg, P., McNamara, J., Pelletier, J., Perket, J., Rouholahnejad-Freund, E., Wagener, T., Zeng, X., Beighley, E., Buzan, J., Huang, M., Livneh, B., Mohanty, B. P., Nijssen, B., Safeeq, M., Shen, C., van Verseveld, W., Volk, J., and Yamazaki, D.:
Hillslope Hydrology in Global Change Research and Earth System Modeling, Water Resour. Res., 55, 1737–1772, https://doi.org/10.1029/2018wr023903, 2019. a
Fatichi, S., Vivoni, E. R., Ogden, F. L., Ivanov, V. Y., Mirus, B., Gochis, D., Downer, C. W., Camporese, M., Davison, J. H., Ebel, B., Jones, N., Kim, J., Mascaro, G., Niswonger, R., Restrepo, P., Rigon, R., Shen, C., Sulis, M., and Tarboton, D.:
An overview of current applications, challenges, and future trends in distributed process-based models in hydrology, J. Hydrol., 537, 45–60, https://doi.org/10.1016/j.jhydrol.2016.03.026, 2016. a
Fernández-Pato, J. and García-Navarro, P.:
A 2D zero-inertia model for the solution of overland flow problems in flexible meshes, J. Hydrol. Eng., 21, https://doi.org/10.1061/(asce)he.1943-5584.0001428, 2016. a
Fernández-Pato, J., Caviedes-Voullième, D., and García-Navarro, P.:
Rainfall/runoff simulation with 2D full shallow water equations: sensitivity analysis and calibration of infiltration parameters, J. Hydrol., 536, 496–513, https://doi.org/10.1016/j.jhydrol.2016.03.021, 2016. a
Fernández-Pato, J., Martínez-Aranda, S., and García-Navarro, P.:
A 2D finite volume simulation tool to enable the assessment of combined hydrological and morphodynamical processes in mountain catchments, Adv. Water Resour., 141, 103617, https://doi.org/10.1016/j.advwatres.2020.103617, 2020. a
Gan, L., Fu, H., and Yang, G.:
Translating novel HPC techniques into efficient geoscience solutions, J. Comput. Sci.-Neth., 52, 101212, https://doi.org/10.1016/j.jocs.2020.101212, 2020. a
García-Alén, G., González-Cao, J., Fernández-Nóvoa, D., Gómez-Gesteira, M., Cea, L., and Puertas, J.:
Analysis of two sources of variability of basin outflow hydrographs computed with the 2D shallow water model Iber: Digital Terrain Model and unstructured mesh size, J. Hydrol., 612, 128182, https://doi.org/10.1016/j.jhydrol.2022.128182, 2022. a
García-Feal, O., González-Cao, J., Gómez-Gesteira, M., Cea, L., Domínguez, J., and Formella, A.:
An Accelerated Tool for Flood Modelling Based on Iber, Water, 10, 1459, https://doi.org/10.3390/w10101459, 2018. a
García-Navarro, P., Murillo, J., Fernández-Pato, J., Echeverribar, I., and Morales-Hernández, M.:
The shallow water equations and their application to realistic cases, Environ. Fluid Mech., 19, 1235–1252, https://doi.org/10.1007/s10652-018-09657-7, 2019. a, b
George, D. L.:
Adaptive finite volume methods with well-balanced Riemann solvers for modeling floods in rugged terrain: Application to the Malpasset dam-break flood (France, 1959), Int. J. Numer. Meth. Fl., 66, 1000–1018, https://doi.org/10.1002/fld.2298, 2010. a
Giardino, J. R. and Houser, C.:
Introduction to the critical zone, in: Developments in Earth Surface Processes, vol. 19, chap. 1, edited by: J. R. Giardino, C. H., Elsevier B. V., Amsterdam, the Netherlands, https://doi.org/10.1016/b978-0-444-63369-9.00001-x, 2015. a
Ginting, B. M.:
Central-upwind scheme for 2D turbulent shallow flows using high-resolution meshes with scalable wall functions, Comput. Fluids, 179, 394–421, https://doi.org/10.1016/j.compfluid.2018.11.014, 2019. a
Gottardi, G. and Venutelli, M.:
An accurate time integration method for simplified overland flow models, Adv. Water Resour., 31, 173–180, https://doi.org/10.1016/j.advwatres.2007.08.004, 2008. a
Govindaraju, R. S., Kavvas, M. L., and Jones, S. E.:
Approximate Analytical Solutions for Overland Flows, Water Resour. Res., 26, 2903–2912, https://doi.org/10.1029/WR026i012p02903, 1990. a, b
Grant, R. and the Ecosys development team: The Ecosys Modelling Project, https://ecosys.ualberta.ca/, last access: 22 August 2022. a
Grant, R. F., Barr, A. G., Black, T. A., Gaumont-Guay, D., Iwashita, H., Kidson, J., McCaughey, H., Morgenstern, K., Murayama, S., Nesic, Z., Saigusa, N., Shashkov, A., and Zha, T.:
Net ecosystem productivity of boreal jack pine stands regenerating from clearcutting under current and future climates, Glob. Change Biol., 13, 1423-1440, https://doi.org/10.1111/j.1365-2486.2007.01363.x, 2007. a
Grete, P., Glines, F. W., and O'Shea, B. W.:
K-Athena: A Performance Portable Structured Grid Finite Volume Magnetohydrodynamics Code, IEEE T. Parall. Distr., 32, 85–97, https://doi.org/10.1109/tpds.2020.3010016, 2021. a
Halver, R., Meinke, J. H., and Sutmann, G.:
Kokkos implementation of an Ewald Coulomb solver and analysis of performance portability, J. Parallel Distr. Com., 138, 48–54, https://doi.org/10.1016/j.jpdc.2019.12.003, 2020. a
Hartanto, I., Beevers, L., Popescu, I., and Wright, N.:
Application of a coastal modelling code in fluvial environments, Environ. Modell. Softw., 26, 1685–1695, https://doi.org/10.1016/j.envsoft.2011.05.014, 2011. a
Hervouet, J.-M. and Petitjean, A.:
Malpasset dam-break revisited with two-dimensional computations, J. Hydraul. Res., 37, 777–788, https://doi.org/10.1080/00221689909498511, 1999. a, b
Hiver, J.:
Adverse-Slope and Slope (bump), in: Concerted Action on Dam Break Modelling: Objectives, Project Report, Test Cases, Meeting Proceedings, edited by: Soares-Frazão, S., Morris, M., and Zech, Y., vol. CD-ROM, Université Catholique de Louvain, Civil Engineering Department, Hydraulics Division, Louvain-la-Neuve, Belgium, 2000. a
Hou, J., Liang, Q., Simons, F., and Hinkelmann, R.:
A stable 2D unstructured shallow flow model for simulations of wetting and drying over rough terrains, Comput. Fluids, 82, 132–147, https://doi.org/10.1016/j.compfluid.2013.04.015, 2013a. a, b
Hou, J., Simons, F., Mahgoub, M., and Hinkelmann, R.:
A robust well-balanced model on unstructured grids for shallow water flows with wetting and drying over complex topography, Comput. Method. Appl. M., 257, 126–149, https://doi.org/10.1016/j.cma.2013.01.015, 2013b. a, b
Hou, J., Liang, Q., Zhang, H., and Hinkelmann, R.:
An efficient unstructured MUSCL scheme for solving the 2D shallow water equations, Environ. Modell. Softw., 66, 131–152, https://doi.org/10.1016/j.envsoft.2014.12.007, 2015. a, b
Hou, J., Wang, R., Liang, Q., Li, Z., Huang, M. S., and Hinkelmann, R.:
Efficient surface water flow simulation on static Cartesian grid with local refinement according to key topographic features, Comput. Fluids, 176, 117–134, https://doi.org/10.1016/j.compfluid.2018.03.024, 2018. a
Hou, J., Kang, Y., Hu, C., Tong, Y., Pan, B., and Xia, J.:
A GPU-based numerical model coupling hydrodynamical and morphological processes, Int. J. Sediment Res., 35, 386–394, https://doi.org/10.1016/j.ijsrc.2020.02.005, 2020. a, b
Hubbard, S. S., Williams, K. H., Agarwal, D., Banfield, J., Beller, H., Bouskill, N., Brodie, E., Carroll, R., Dafflon, B., Dwivedi, D., Falco, N., Faybishenko, B., Maxwell, R., Nico, P., Steefel, C., Steltzer, H., Tokunaga, T., Tran, P. A., Wainwright, H., and Varadharajan, C.:
The East River, Colorado, Watershed: A Mountainous Community Testbed for Improving Predictive Understanding of Multiscale Hydrological-Biogeochemical Dynamics, Vadose Zone J., 17, 180061, https://doi.org/10.2136/vzj2018.03.0061, 2018. a
Jain, M. K. and Kothyari, U. C.:
A GIS based distributed rainfall-runoff model, J. Hydrol., 299, 107–135, 2004. a
Jeong, W., Yoon, J.-S., and Cho, Y.-S.:
Numerical study on effects of building groups on dam-break flow in urban areas, J. Hydro-Environ. Res., 6, 91–99, https://doi.org/10.1016/j.jher.2012.01.001, 2012. a
Jodhani, K. H., Patel, D., and Madhavan, N.:
A review on analysis of flood modelling using different numerical models, Mater. Today-Proc., https://doi.org/10.1016/j.matpr.2021.07.405, 2021. a
Kesserwani, G. and Liang, Q.:
Well-balanced RKDG2 solutions to the shallow water equations over irregular domains with wetting and drying, Comput. Fluids, 39, 2040–2050, https://doi.org/10.1016/j.compfluid.2010.07.008, 2010. a
Kesserwani, G. and Liang, Q.:
Dynamically adaptive grid based discontinuous Galerkin shallow water model, Adv. Water Resour., 37, 23–39, https://doi.org/10.1016/j.advwatres.2011.11.006, 2012. a, b
Kesserwani, G. and Sharifian, M. K.:
(Multi)wavelets increase both accuracy and efficiency of standard Godunov-type hydrodynamic models: Robust 2D approaches, Adv. Water Resour., 144, 103693, https://doi.org/10.1016/j.advwatres.2020.103693, 2020. a, b, c
Kesserwani, G. and Sharifian, M. K.:
(Multi)wavelet-based Godunov-type simulators of flood inundation: static versus dynamic adaptivity, Adv. Water Resour., 171, 104357, https://doi.org/10.1016/j.advwatres.2022.104357, 2022. a
Kesserwani, G., Shaw, J., Sharifian, M. K., Bau, D., Keylock, C. J., Bates, P. D., and Ryan, J. K.:
(Multi)wavelets increase both accuracy and efficiency of standard Godunov-type hydrodynamic models, Adv. Water Resour., 129, 31–55, https://doi.org/10.1016/j.advwatres.2019.04.019, 2019. a
Kim, B., Sanders, B. F., Schubert, J. E., and Famiglietti, J. S.:
Mesh type tradeoffs in 2D hydrodynamic modeling of flooding with a Godunov-based flow solver, Adv. Water Resour., 68, 42–61, https://doi.org/10.1016/j.advwatres.2014.02.013, 2014. a
Kirstetter, G., Delestre, O., Lagrée, P.-Y., Popinet, S., and Josserand, C.:
B-flood 1.0: an open-source Saint-Venant model for flash-flood simulation using adaptive refinement, Geosci. Model Dev., 14, 7117–7132, https://doi.org/10.5194/gmd-14-7117-2021, 2021. a
Kobayashi, K., Kitamura, D., Ando, K., and Ohi, N.:
Parallel computing for high-resolution/large-scale flood simulation using the K supercomputer, Hydrological Research Letters, 9, 61–68, https://doi.org/10.3178/hrl.9.61, 2015. a
Kollet, S., Sulis, M., Maxwell, R. M., Paniconi, C., Putti, M., Bertoldi, G., Coon, E. T., Cordano, E., Endrizzi, S., Kikinzon, E., Mouche, E., Mügler, C., Park, Y.-J., Refsgaard, J. C., Stisen, S., and Sudicky, E.:
The integrated hydrologic model intercomparison project, IH-MIP2: A second set of benchmark results to diagnose integrated hydrology and feedbacks, Water Resour. Res., 53, 867–890, https://doi.org/10.1002/2016wr019191, 2017. a
Kuffour, B. N. O., Engdahl, N. B., Woodward, C. S., Condon, L. E., Kollet, S., and Maxwell, R. M.:
Simulating coupled surface–subsurface flows with ParFlow v3.5.0: capabilities, applications, and ongoing development of an open-source, massively parallel, integrated hydrologic model, Geosci. Model Dev., 13, 1373–1397, https://doi.org/10.5194/gmd-13-1373-2020, 2020. a
Lacasta, A., Morales-Hernández, M., Murillo, J., and García-Navarro, P.:
An optimized GPU implementation of a 2D free surface simulation model on unstructured meshes, Adv. Eng. Softw., 78, 1–15, https://doi.org/10.1016/j.advengsoft.2014.08.007, 2014. a, b, c
Lacasta, A., Morales-Hernández, M., Murillo, J., and García-Navarro, P.:
GPU implementation of the 2D shallow water equations for the simulation of rainfall/runoff events, Environ. Earth. Sci., 74, 7295–7305, https://doi.org/10.1007/s12665-015-4215-z, 2015. a, b
Lawrence, B. N., Rezny, M., Budich, R., Bauer, P., Behrens, J., Carter, M., Deconinck, W., Ford, R., Maynard, C., Mullerworth, S., Osuna, C., Porter, A., Serradell, K., Valcke, S., Wedi, N., and Wilson, S.:
Crossing the chasm: how to develop weather and climate models for next generation computers?, Geosci. Model Dev., 11, 1799–1821, https://doi.org/10.5194/gmd-11-1799-2018, 2018. a
Leiserson, C. E., Thompson, N. C., Emer, J. S., Kuszmaul, B. C., Lampson, B. W., Sanchez, D., and Schardl, T. B.:
There's plenty of room at the Top: What will drive computer performance after Moore's law?, Science, 368, 6495, https://doi.org/10.1126/science.aam9744, 2020. a, b
Li, Z., Özgen-Xian, I., and Maina, F. Z.:
A mass-conservative predictor-corrector solution to the 1D Richards equation with adaptive time control, J. Hydrol., 592, 125809, https://doi.org/10.1016/j.jhydrol.2020.125809, 2021. a
Liang, D., Lin, B., and Falconer, R. A.:
A boundary-fitted numerical model for flood routing with shock-capturing capability, J. Hydrol., 332, 477–486, https://doi.org/10.1016/j.jhydrol.2006.08.002, 2007. a
Liang, Q., Hou, J., and Xia, X.:
Contradiction between the C-property and mass conservation in adaptive grid based shallow flow models: cause and solution, Int. J. Numer. Meth. Fl., 78, 17–36, https://doi.org/10.1002/fld.4005, 2015. a
Liang, Q., Smith, L., and Xia, X.:
New prospects for computational hydraulics by leveraging high-performance heterogeneous computing techniques, J. Hydrodyn Ser. B, 28, 977–985, https://doi.org/10.1016/S1001-6058(16)60699-6, 2016. a, b
Lichtner, P. C., Hammond, G. E., Lu, C., Karra, S., Bisht, G., Andre, B., Mills, R., and Kumar, J.:
PFLOTRAN user manual: A massively parallel reactive flow and transport model for describing surface and subsurface processes, Tech. rep., Los Alamos National Laboratory, New Mexico, USA, 2015. a
Liu, P. L. F., Cho, Y.-S., Briggs, M. J., Kanoglu, U., and Synolakis, C. E.:
Runup of solitary waves on a circular Island, J. Fluid Mech., 302, 259–285, https://doi.org/10.1017/s0022112095004095, 1995. a
Loukili, Y. and Soulaïmani, A.:
Numerical Tracking of Shallow Water Waves by the Unstructured Finite Volume WAF Approximation, International Journal for Computational Methods in Engineering Science and Mechanics, 8, 75–88, https://doi.org/10.1080/15502280601149577, 2007. a
Lynett, P. J., Wu, T.-R., and Liu, P. L.-F.:
Modeling wave runup with depth-integrated equations, Coast. Eng., 46, 89–107, https://doi.org/10.1016/s0378-3839(02)00043-1, 2002. a
Maneta, M. P. and Silverman, N. L.:
A spatially distributed model to simulate water, energy, and vegetation dynamics using information from regional climate models, Earth Interact., 17, 11.1–11.44, 2013. a
Mann, A.:
Core Concept: Nascent exascale supercomputers offer promise, present challenges, P. Natl. Acad. Sci. USA, 117, 22623–22625, https://doi.org/10.1073/pnas.2015968117, 2020. a
Martínez-Aranda, S., Fernández-Pato, J., Caviedes-Voullième, D., García-Palacín, I., and García-Navarro, P.:
Towards transient experimental water surfaces: A new benchmark dataset for 2D shallow water solvers, Adv. Water Resour., 121, 130–149, https://doi.org/10.1016/j.advwatres.2018.08.013, 2018. a, b, c
Matsuyama, M. and Tanaka, H.:
An experimental study oh the highest run-up height in the 1993 Hokkaido Nansei-oki earthquake tsunami, ITS Proceedings, 879–889, 2001. a
Morales-Hernández, M., García-Navarro, P., and Murillo, J.:
A large time step 1D upwind explicit scheme (CFL > 1): Application to shallow water equations, J. Comput. Phys., 231, 6532–6557, https://doi.org/10.1016/j.jcp.2012.06.017, 2012. a, b
Morales-Hernández, M., Hubbard, M., and García-Navarro, P.:
A 2D extension of a Large Time Step explicit scheme (CFL > 1) for unsteady problems with wet/dry boundaries, J. Comput. Phys., 263, 303–327, https://doi.org/10.1016/j.jcp.2014.01.019, 2014. a
Morales-Hernández, M., Sharif, M. B., Gangrade, S., Dullo, T. T., Kao, S.-C., Kalyanapu, A., Ghafoor, S. K., Evans, K. J., Madadi-Kandjani, E., and Hodges, B. R.:
High-performance computing in water resources hydrodynamics, J. Hydroinform., https://doi.org/10.2166/hydro.2020.163, 2020. a, b
Morales-Hernández, M., Sharif, M. B., Kalyanapu, A., Ghafoor, S., Dullo, T., Gangrade, S., Kao, S.-C., Norman, M., and Evans, K.:
TRITON: A Multi-GPU open source 2D hydrodynamic flood model, Environ. Modell. Softw., 141, 105034, https://doi.org/10.1016/j.envsoft.2021.105034, 2021. a, b, c
Moulinec, C., Denis, C., Pham, C.-T., Rougé, D., Hervouet, J.-M., Razafindrakoto, E., Barber, R., Emerson, D., and Gu, X.-J.:
TELEMAC: An efficient hydrodynamics suite for massively parallel architectures, Comput. Fluids, 51, 30–34, https://doi.org/10.1016/j.compfluid.2011.07.003, 2011. a
Mügler, C., Planchon, O., Patin, J., Weill, S., Silvera, N., Richard, P., and Mouche, E.:
Comparison of roughness models to simulate overland flow and tracer transport experiments under simulated rainfall at plot scale, J. Hydrol., 402, 25–40, https://doi.org/10.1016/j.jhydrol.2011.02.032, 2011. a, b, c, d
Murillo, J. and García-Navarro, P.:
Weak solutions for partial differential equations with source terms: Application to the shallow water equations, J. Comput. Phys., 229, 4327–4368, https://doi.org/10.1016/j.jcp.2010.02.016, 2010. a, b, c
Murillo, J., García-Navarro, P., and Burguete, J.:
Time step restrictions for well-balanced shallow water solutions in non-zero velocity steady states, Int. J. Numer. Meth. Fl., 60, 1351–1377, https://doi.org/10.1002/fld.1939, 2009. a, b
Navas-Montilla, A. and Murillo, J.:
2D well-balanced augmented ADER schemes for the Shallow Water Equations with bed elevation and extension to the rotating frame, J. Comput. Phys., 372, 316–348, https://doi.org/10.1016/j.jcp.2018.06.039, 2018. a
Nikolos, I. and Delis, A.:
An unstructured node-centered finite volume scheme for shallow water flows with wet/dry fronts over complex topography, Comput. Method. Appl. M., 198, 3723–3750, https://doi.org/10.1016/j.cma.2009.08.006, 2009. a, b
Özgen, I., Liang, D., and Hinkelmann, R.:
Shallow water equations with depth-dependent anisotropic porosity for subgrid-scale topography, Appl. Math. Model., 40, 7447–7473, https://doi.org/10.1016/j.apm.2015.12.012, 2015a. a
Özgen, I., Teuber, K., Simons, F., Liang, D., and Hinkelmann, R.:
Upscaling the shallow water model with a novel roughness formulation, Environ. Earth. Sci., 74, 7371–7386, https://doi.org/10.1007/s12665-015-4726-7, 2015b. a
Özgen-Xian, I., Kesserwani, G., Caviedes-Voullième, D., Molins, S., Xu, Z., Dwivedi, D., Moulton, J. D., and Steefel, C. I.:
Wavelet-based local mesh refinement for rainfall–runoff simulations, J. Hydroinform., 22, 1059–1077, https://doi.org/10.2166/hydro.2020.198, 2020. a, b, c
Özgen-Xian, I., Xia, X., Liang, Q., Hinkelmann, R., Liang, D., and Hou, J.:
Innovations Towards the Next Generation of Shallow Flow Models, Adv. Water Resour., 149, 103867, https://doi.org/10.1016/j.advwatres.2021.103867, 2021. a
Paniconi, C. and Putti, M.:
Physically based modeling in catchment hydrology at 50: Survey and outlook, Water Resour. Res., 51, 7090–7129, https://doi.org/10.1002/2015WR017780, 2015. a
Park, S., Kim, B., and Kim, D. H.: 2D GPU-Accelerated High Resolution Numerical Scheme for Solving Diffusive Wave Equations, Water, 11, 1447, https://doi.org/10.3390/w11071447, 2019. a
Petaccia, G., Soares-Fraz ao, S., Savi, F., Natale, L., and Zech, Y.:
Simplified versus Detailed Two-Dimensional Approaches to Transient Flow Modeling in Urban Areas, J. Hydraul. Eng., 136, 262–266, https://doi.org/10.1061/(asce)hy.1943-7900.0000154, 2010. a
Roe, P.:
Approximate Riemann solvers, parameter vectors, and difference schemes, J. Comput. Phys., 43, 357–372, https://doi.org/10.1016/0021-9991(81)90128-5, 1981. a
Schulthess, T. C.:
Programming revisited, Nat. Phys., 11, 369–373, https://doi.org/10.1038/nphys3294, 2015. a
Schwanenberg, D. and Harms, M.:
Discontinuous Galerkin Finite-Element Method for Transcritical Two-Dimensional Shallow Water Flows, J. Hydraul. Eng., 130, 412–421, https://doi.org/10.1061/(ASCE)0733-9429(2004)130:5(412), 2004. a
Serrano-Pacheco, A., Murillo, J., and Garcia-Navarro, P.:
A finite volume method for the simulation of the waves generated by landslides, J. Hydrol., 373, 273–289, https://doi.org/10.1016/j.jhydrol.2009.05.003, 2009. a
Sharif, M. B., Ghafoor, S. K., Hines, T. M., Morales-Hernández, M., Evans, K. J., Kao, S.-C., Kalyanapu, A. J., Dullo, T. T., and Gangrade, S.:
Performance Evaluation of a Two-Dimensional Flood Model on Heterogeneous High-Performance Computing Architectures, in: Proceedings of the Platform for Advanced Scientific Computing Conference, ACM, https://doi.org/10.1145/3394277.3401852, 2020. a
Shaw, J., Kesserwani, G., Neal, J., Bates, P., and Sharifian, M. K.:
LISFLOOD-FP 8.0: the new discontinuous Galerkin shallow-water solver for multi-core CPUs and GPUs, Geosci. Model Dev., 14, 3577–3602, https://doi.org/10.5194/gmd-14-3577-2021, 2021. a, b
Simons, F., Busse, T., Hou, J., Özgen, I., and Hinkelmann, R.:
A model for overland flow and associated processes within the Hydroinformatics Modelling System, J. Hydroinform., 16, 375–391, https://doi.org/10.2166/hydro.2013.173, 2014. a, b, c, d
Singh, J., Altinakar, M. S., and Ding, Y.:
Numerical Modeling of Rainfall-Generated Overland Flow Using Nonlinear Shallow-Water Equations, J. Hydrol. Eng., 20, 04014089, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001124, 2015. a
Sivapalan, M.:
From engineering hydrology to Earth system science: milestones in the transformation of hydrologic science, Hydrol. Earth Syst. Sci., 22, 1665–1693, https://doi.org/10.5194/hess-22-1665-2018, 2018. a
Sætra, M. L., Brodtkorb, A. R., and Lie, K.-A.:
Efficient GPU-Implementation of Adaptive Mesh Refinement for the Shallow-Water Equations, J. Sci. Comput., 63, 23–48, https://doi.org/10.1007/s10915-014-9883-4, 2015. a
Smith, L. S. and Liang, Q.:
Towards a generalised GPU/CPU shallow-flow modelling tool, Comput. Fluids, 88, 334–343, https://doi.org/10.1016/j.compfluid.2013.09.018, 2013. a
Soares-Frazāo, S.:
Experiments of dam-break wave over a triangular bottom sill, J. Hydraul. Res., 45, 19–26, https://doi.org/10.1080/00221686.2007.9521829, 2007. a
Soares-Frazāo, S. and Zech, Y.:
Dam-break flow through an idealised city, J. Hydraul. Res., 46, 648–658, https://doi.org/10.3826/jhr.2008.3164, 2008. a
Steefel, C. I.:
CrunchFlow: Software for modeling multicomponent reactive flow and transport, Tech. rep., Lawrence Berkeley National Laboratory, California, USA, 2009. a
Steffen, L., Amann, F., and Hinkelmann, R.:
Concepts for performance improvements of shallow water flow simulations, in: Proceedings of the 1st IAHR Young Professionals Congress, online, ISBN 978-90-82484-6-63,
2020. a
Stoker, J.:
Water Waves: The Mathematical Theory with Applications, New York Interscience Publishers, Wiley, ISBN 978-0-471-57034-9, 1957. a
Su, B., Huang, H., and Zhu, W.:
An urban pluvial flood simulation model based on diffusive wave approximation of shallow water equations, Hydrol. Res., 50, 138–154, https://doi.org/10.2166/nh.2017.233, 2017. a
Suarez, E., Eicker, N., and Lippert, T.:
Modular Supercomputing Architecture: From Idea to Production, in: Contemporary High Performance Computing, CRC Press, blackboxPlease add the place of publication., https://doi.org/10.1201/9781351036863-9, pp. 223–255, 2019. a
Tatard, L., Planchon, O., Wainwright, J., Nord, G., Favis-Mortlock, D., Silvera, N., Ribolzi, O., Esteves, M., and Huang, C. H.:
Measurement and modelling of high-resolution flow-velocity data under simulated rainfall on a low-slope sandy soil, J. Hydrol., 348, 1–12, https://doi.org/10.1016/j.jhydrol.2007.07.016, 2008. a
The third international workshop on long-wave runup models: http://isec.nacse.org/workshop/2004_cornell/bmark2.html (last access: 22 August 2022), 2004. a
Toro, E.:
Shock-Capturing Methods for Free-Surface Shallow Flows, Wiley, ISBN 978-0-471-98766-6, 2001. a
Trott, C., Berger-Vergiat, L., Poliakoff, D., Rajamanickam, S., Lebrun-Grandie, D., Madsen, J., Awar, N. A., Gligoric, M., Shipman, G., and Womeldorff, G.:
The Kokkos EcoSystem: Comprehensive Performance Portability for High Performance Computing, Comput. Sci. Eng., 23, 10–18, https://doi.org/10.1109/mcse.2021.3098509, 2021. a, b
Turchetto, M., Palu, A. D., and Vacondio, R.:
A general design for a scalable MPI-GPU multi-resolution 2D numerical solver, IEEE T. Parall. Distr., 31, https://doi.org/10.1109/tpds.2019.2961909, 2019. a
Vacondio, R., Palù, A. D., and Mignosa, P.:
GPU-enhanced Finite Volume Shallow Water solver for fast flood simulations, Environ. Modell. Softw., 57, 60–75, https://doi.org/10.1016/j.envsoft.2014.02.003, 2014. a, b
Vacondio, R., Palù, A. D., Ferrari, A., Mignosa, P., Aureli, F., and Dazzi, S.:
A non-uniform efficient grid type for GPU-parallel Shallow Water Equations models, Environ. Modell. Softw., 88, 119–137, https://doi.org/10.1016/j.envsoft.2016.11.012, 2017. a, b
Valiani, A., Caleffi, V., and Zanni, A.:
Case Study: Malpasset Dam-Break Simulation using a Two-Dimensional Finite Volume Method, J. Hydraul. Eng., 128, 460–472, https://doi.org/10.1061/(ASCE)0733-9429(2002)128:5(460), 2002. a
Vanderbauwhede, W.:
Making legacy Fortran code type safe through automated program transformation, J. Supercomput., 78, 2988–3028, 2021. a
Vanderbauwhede, W. and Davidson, G.:
Domain-specific acceleration and auto-parallelization of legacy scientific code in FORTRAN 77 using source-to-source compilation, Comput. Fluids, 173, 1–5, 2018. a
Vanderbauwhede, W. and Takemi, T.:
An investigation into the feasibility and benefits of GPU/multicore acceleration of the weather research and forecasting model, in: 2013 International Conference on High Performance Computing and Simulation (HPCS), Helsinki, Finland, IEEE, https://doi.org/10.1109/hpcsim.2013.6641457, 2013. a
Vater, S., Beisiegel, N., and Behrens, J.:
A limiter-based well-balanced discontinuous Galerkin method for shallow-water flows with wetting and drying: Triangular grids, Int. J. Numer. Meth. Fl., 91,
395–418, https://doi.org/10.1002/fld.4762, 2019. a
Wang, Y., Liang, Q., Kesserwani, G., and Hall, J. W.:
A 2D shallow flow model for practical dam-break simulations, J. Hydraul. Res., 49, 307–316, https://doi.org/10.1080/00221686.2011.566248, 2011. a
Wang, Z., Walsh, K., and Verma, B.:
On-Tree Mango Fruit Size Estimation Using RGB-D Images, Sensors, 17, 2738, https://doi.org/10.3390/s17122738, 2017. a
Watkins, J., Tezaur, I., and Demeshko, I.:
A Study on the Performance Portability of the Finite Element Assembly Process Within the Albany Land Ice Solver, Springer International Publishing, Cham, 177–188, https://doi.org/10.1007/978-3-030-30705-9_16, 2020. a
Weill, S.:
Modélisation des échanges surface/subsurface à l'échelle de la parcelle par une approche darcéenne multidomaine, PhD thesis, Ecole des Mines de Paris, 2007. a
Wittmann, R., Bungartz, H.-J., and Neumann, P.:
High performance shallow water kernels for parallel overland flow simulations based on FullSWOF2D, Comput. Math. Appl., 74, 110–125, https://doi.org/10.1016/j.camwa.2017.01.005, 2017. a
Xia, J., Falconer, R. A., Lin, B., and Tan, G.:
Numerical assessment of flood hazard risk to people and vehicles in flash floods, Environ. Modell. Softw., 26, 987–998, https://doi.org/10.1016/j.envsoft.2011.02.017, 2011. a
Xia, X. and Liang, Q.:
A new efficient implicit scheme for discretising the stiff friction terms in the shallow water equations, Adv. Water Resour., 117, 87–97, https://doi.org/10.1016/j.advwatres.2018.05.004, 2018. a
Xia, X., Liang, Q., Ming, X., and Hou, J.:
An efficient and stable hydrodynamic model with novel source term discretization schemes for overland flow and flood simulations, Water Resour. Res., 53, 3730–3759, https://doi.org/10.1002/2016WR020055, 2017. a, b
Xia, X., Liang, Q., and Ming, X.:
A full-scale fluvial flood modelling framework based on a High-Performance Integrated hydrodynamic Modelling System (HiPIMS), Adv. Water Resour., 132, 103392, https://doi.org/10.1016/j.advwatres.2019.103392, 2019. a
Yu, C. and Duan, J.:
Two-dimensional depth-averaged finite volume model for unsteady turbulent flow, J. Hydraul. Res., 50, 599–611, https://doi.org/10.1080/00221686.2012.730556, 2012.
a
Yu, C. and Duan, J.:
Simulation of Surface Runoff Using Hydrodynamic Model, J. Hydrol. Eng., 22, 04017006, https://doi.org/10.1061/(asce)he.1943-5584.0001497, 2017. a, b, c
Zhao, J., Özgen Xian, I., Liang, D., Wang, T., and Hinkelmann, R.:
An improved multislope MUSCL scheme for solving shallow water equations on unstructured grids, Comput. Math. Appl., 77, 576–596, https://doi.org/10.1016/j.camwa.2018.09.059, 2019. a, b
Zhou, F., Chen, G., Huang, Y., Yang, J. Z., and Feng, H.:
An adaptive moving finite volume scheme for modeling flood inundation over dry and complex topography, Water Resour. Res., 49, 1914–1928, https://doi.org/10.1002/wrcr.20179, 2013. a, b
Short summary
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.
This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such...
