Articles | Volume 15, issue 7
https://doi.org/10.5194/gmd-15-2917-2022
© Author(s) 2022. 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-15-2917-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
07 Apr 2022
Development and technical paper |
| 07 Apr 2022
| 07 Apr 2022
Modeling of streamflow in a 30 km long reach spanning 5 years using OpenFOAM 5.x
Pacific Northwest National Laboratory, Richland, WA, USA
Jie Bao
Pacific Northwest National Laboratory, Richland, WA, USA
Yilin Fang
Pacific Northwest National Laboratory, Richland, WA, USA
William A. Perkins
Pacific Northwest National Laboratory, Richland, WA, USA
Huiying Ren
Pacific Northwest National Laboratory, Richland, WA, USA
Xuehang Song
Pacific Northwest National Laboratory, Richland, WA, USA
Zhuoran Duan
Pacific Northwest National Laboratory, Richland, WA, USA
Zhangshuan Hou
Pacific Northwest National Laboratory, Richland, WA, USA
Xiaoliang He
Pacific Northwest National Laboratory, Richland, WA, USA
Timothy D. Scheibe
Pacific Northwest National Laboratory, Richland, WA, USA
Related authors
No articles found.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-70, https://doi.org/10.5194/gmd-2024-70, 2024
Preprint under review for GMD
Short summary
Short summary
Hurricanes may worsen the water quality in the lower Mississippi River Basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate-nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in LMRB during Hurricane Ida in 2021, but less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Chang Liao, Darren Engwirda, Matthew Cooper, Mingke Li, and Yilin Fang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-398, https://doi.org/10.5194/essd-2023-398, 2024
Preprint under review for ESSD
Short summary
Short summary
Discrete Global Grid systems, or DGGs, are digital frameworks that help us organize information about our planet. Although scientists have used DGGs in areas like weather and nature, using them in the water cycle has been challenging because some core datasets are missing. We created a way to generate these datasets. We then developed the datasets in the Amazon Basin, which plays an important role in our planet's climate. These datasets may help us improve our water cycle models.
James Stegen, Amy Burgin, Michelle Busch, Joshua Fisher, Joshua Ladau, Jenna Abrahamson, Lauren Kinsman-Costello, Li Li, Xingyuan Chen, Thibault Datry, Nate McDowell, Corianne Tatariw, Anna Braswell, Jillian Deines, Julia Guimond, Peter Regier, Kenton Rod, Edward Bam, Etienne Fluet-Chouinard, Inke Forbrich, Kristin Jaeger, Teri O'Meara, Tim Scheibe, Erin Seybold, Jon Sweetman, Jianqiu Zheng, Daniel Allen, Elizabeth Herndon, Beth Middleton, Scott Painter, Kevin Roche, Julianne Scamardo, Ross Vander Vorste, Kristin Boye, Ellen Wohl, Margaret Zimmer, Kelly Hondula, Maggi Laan, Anna Marshall, and Kaizad Patel
EGUsphere, https://doi.org/10.5194/egusphere-2024-98, https://doi.org/10.5194/egusphere-2024-98, 2024
Short summary
Short summary
The loss and gain of surface water (variable inundation) is a common process across Earth. Global change shifts variable inundation dynamics, highlighting a need for unified understanding that transcends individual variably inundated ecosystems (VIEs). We review literature, highlight challenges, and emphasize opportunities to generate transferable knowledge by viewing VIEs through a common lens. We aim to inspire the emergence of a cross-VIE community based on a proposed continuum approach.
Stephanie G. Fulton, Morgan Barnes, Mikayla A. Borton, Xingyuan Chen, Yuliya Farris, Brieanne Forbes, Vanessa A. Garayburu-Caruso, Amy E. Goldman, Samantha Grieger, Robert Hall Jr., Matthew H. Kaufman, Xinming Lin, Erin McCann, Sophia A. McKever, Allison Myers-Pigg, Opal C. Otenburg, Aaron C. Pelly, Huiying Ren, Lupita Renteria, Timothy D. Scheibe, Kyongho Son, Jerry Tagestad, Joshua M. Torgeson, and James C. Stegen
EGUsphere, https://doi.org/10.5194/egusphere-2023-3038, https://doi.org/10.5194/egusphere-2023-3038, 2024
Short summary
Short summary
This research examines oxygen use in rivers, which is central to the carbon cycle and water quality. The study focused on an environmentally diverse river basin in the western United States and found that oxygen use in river water was very slow and influenced by factors like water temperature and concentrations of nutrients and carbon in the water. Results suggest that in the study system, most of the oxygen use occurs via mechanisms directly or indirectly associated with riverbed sediments.
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
Short summary
Short summary
Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Marius S. A. Lambert, Hui Tang, Kjetil S. Aas, Frode Stordal, Rosie A. Fisher, Yilin Fang, Junyan Ding, and Frans-Jan W. Parmentier
Geosci. Model Dev., 15, 8809–8829, https://doi.org/10.5194/gmd-15-8809-2022, https://doi.org/10.5194/gmd-15-8809-2022, 2022
Short summary
Short summary
In this study, we implement a hardening mortality scheme into CTSM5.0-FATES-Hydro and evaluate how it impacts plant hydraulics and vegetation growth. Our work shows that the hydraulic modifications prescribed by the hardening scheme are necessary to model realistic vegetation growth in cold climates, in contrast to the default model that simulates almost nonexistent and declining vegetation due to abnormally large water loss through the roots.
Yilin Fang, L. Ruby Leung, Charles D. Koven, Gautam Bisht, Matteo Detto, Yanyan Cheng, Nate McDowell, Helene Muller-Landau, S. Joseph Wright, and Jeffrey Q. Chambers
Geosci. Model Dev., 15, 7879–7901, https://doi.org/10.5194/gmd-15-7879-2022, https://doi.org/10.5194/gmd-15-7879-2022, 2022
Short summary
Short summary
We develop a model that integrates an Earth system model with a three-dimensional hydrology model to explicitly resolve hillslope topography and water flow underneath the land surface to understand how local-scale hydrologic processes modulate vegetation along water availability gradients. Our coupled model can be used to improve the understanding of the diverse impact of local heterogeneity and water flux on nutrient availability and plant communities.
Yilin Fang, L. Ruby Leung, Ryan Knox, Charlie Koven, and Ben Bond-Lamberty
Geosci. Model Dev., 15, 6385–6398, https://doi.org/10.5194/gmd-15-6385-2022, https://doi.org/10.5194/gmd-15-6385-2022, 2022
Short summary
Short summary
Accounting for water movement in the soil and water transport within the plant is important for plant growth in Earth system modeling. We implemented different numerical approaches for a plant hydrodynamic model and compared their impacts on the simulated aboveground biomass (AGB) at single points and globally. We found care should be taken when discretizing the number of soil layers for numerical simulations as it can significantly affect AGB if accuracy and computational costs are of concern.
Huiying Ren, Erol Cromwell, Ben Kravitz, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 1727–1743, https://doi.org/10.5194/hess-26-1727-2022, https://doi.org/10.5194/hess-26-1727-2022, 2022
Short summary
Short summary
We used a deep learning method called long short-term memory (LSTM) to fill gaps in data collected by hydrologic monitoring networks. LSTM accounted for correlations in space and time and nonlinear trends in data. Compared to a traditional regression-based time-series method, LSTM performed comparably when filling gaps in data with smooth patterns, while it better captured highly dynamic patterns in data. Capturing such dynamics is critical for understanding dynamic complex system behaviors.
Yilin Fang, Xingyuan Chen, Jesus Gomez Velez, Xuesong Zhang, Zhuoran Duan, Glenn E. Hammond, Amy E. Goldman, Vanessa A. Garayburu-Caruso, and Emily B. Graham
Geosci. Model Dev., 13, 3553–3569, https://doi.org/10.5194/gmd-13-3553-2020, https://doi.org/10.5194/gmd-13-3553-2020, 2020
Short summary
Short summary
Surface water quality along river corridors can be improved by the area of the stream bed and stream bank in which stream water mixes with shallow groundwater or hyporheic zones (HZs). These zones are ubiquitous and dominated by microorganisms that can process the dissolved nutrients exchanged at this interface of these zones. The modulation of surface water quality can be simulated by connecting the channel water and HZs through hyporheic exchanges using multirate mass transfer representation.
Y. Fang, C. Liu, and L. R. Leung
Geosci. Model Dev., 8, 781–789, https://doi.org/10.5194/gmd-8-781-2015, https://doi.org/10.5194/gmd-8-781-2015, 2015
Short summary
Short summary
1. A gradient projection method was used to reduce the computation time of carbon-nitrogen spin-up processes in CLM4.
2. Point-scale simulations showed that the cyclic stability of total carbon for some cases differs from that of the periodic atmospheric forcing, and some cases even showed instability.
3. The instability issue is resolved after the hydrology scheme in CLM4 is replaced with a flow model for variably saturated porous media.
Y. Sun, Z. Hou, M. Huang, F. Tian, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 17, 4995–5011, https://doi.org/10.5194/hess-17-4995-2013, https://doi.org/10.5194/hess-17-4995-2013, 2013
Y. Fang, M. Huang, C. Liu, H. Li, and L. R. Leung
Geosci. Model Dev., 6, 1977–1988, https://doi.org/10.5194/gmd-6-1977-2013, https://doi.org/10.5194/gmd-6-1977-2013, 2013
C. Zhao, X. Liu, Y. Qian, J. Yoon, Z. Hou, G. Lin, S. McFarlane, H. Wang, B. Yang, P.-L. Ma, H. Yan, and J. Bao
Atmos. Chem. Phys., 13, 10969–10987, https://doi.org/10.5194/acp-13-10969-2013, https://doi.org/10.5194/acp-13-10969-2013, 2013
Related subject area
Hydrology
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
Fluvial Flood Inundation and Humanitarian Impact Model Based On Open Data
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)
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
Deep Dive into Global Hydrologic Simulations: Harnessing the Power of Deep Learning and Physics-informed Differentiable Models (δHBV-globe1.0-hydroDL)
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
STORM v.2: A simple, stochastic decision-support tool for exploring the impacts of climate and climate change at, and near the land surface in gauged watersheds
RoGeR v3.0.3 – a process-based hydrological toolbox model in Python
Simulation of crop yield using the global hydrological model H08 (crp.v1)
How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model
iHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction
GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model
Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation (within CAESAR-Lisflood version 1.9j-WS)
Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics
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
Thermal modeling of three lakes within the continuous permafrost zone in Alaska using the LAKE 2.0 model
Water balance model (WBM) v.1.0.0: a scalable gridded global hydrologic model with water-tracking functionality
Coupling a large-scale hydrological model (CWatM v1.1) with a high-resolution groundwater flow model (MODFLOW 6) to assess the impact of irrigation at regional scale
RavenR v2.1.4: an open-source R package to support flexible hydrologic modelling
Developing a parsimonious canopy model (PCM v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest
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.
Lukas Riedel, Thomas Röösli, Thomas Vogt, and David N. Bresch
EGUsphere, https://doi.org/10.5194/egusphere-2024-93, https://doi.org/10.5194/egusphere-2024-93, 2024
Short summary
Short summary
River floods are among the most devastating natural disasters. We propose a flood model with 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 floods, we show that it can be used for forecasting the magnitude of flood events in terms of humanitarian impacts and for comparing these with past events.
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.
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.
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. Discuss., https://doi.org/10.5194/gmd-2023-190, https://doi.org/10.5194/gmd-2023-190, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Accurate hydrological 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 approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
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.
Manuel Felipe Rios Gaona, Katerina Michaelides, and Michael Bliss Singer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-98, https://doi.org/10.5194/gmd-2023-98, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
STORM v.2 (short for STOchastic Rainfall Model version 2.0) is an open-source, and user-friendly modeling 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.
Robin Schwemmle, Hannes Leistert, Steinbrich Andreas, and Markus Weiler
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-118, https://doi.org/10.5194/gmd-2023-118, 2023
Revised manuscript accepted for GMD
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 an effective water resources management. This paper presents the toolbox concept and provides a simple example on providing estimations to water resources management.
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.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
Short summary
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.
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.
Jason A. Clark, Elchin E. Jafarov, Ken D. Tape, Benjamin M. Jones, and Victor Stepanenko
Geosci. Model Dev., 15, 7421–7448, https://doi.org/10.5194/gmd-15-7421-2022, https://doi.org/10.5194/gmd-15-7421-2022, 2022
Short summary
Short summary
Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect Arctic lakes to warm the surrounding frozen ground. We simulate water temperatures in three Arctic lakes in northern Alaska over several years. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season and that more heat storage by lakes would enhance thawing of frozen ground.
Danielle S. Grogan, Shan Zuidema, Alex Prusevich, Wilfred M. Wollheim, Stanley Glidden, and Richard B. Lammers
Geosci. Model Dev., 15, 7287–7323, https://doi.org/10.5194/gmd-15-7287-2022, https://doi.org/10.5194/gmd-15-7287-2022, 2022
Short summary
Short summary
This paper describes the University of New Hampshire's water balance model (WBM). This model simulates the land surface components of the global water cycle and includes water extractions for use by humans for agricultural, domestic, and industrial purposes. A new feature is described that permits water source tracking through the water cycle, which has implications for water resource management. This paper was written to describe a long-used model and presents its first open-source version.
Luca Guillaumot, Mikhail Smilovic, Peter Burek, Jens de Bruijn, Peter Greve, Taher Kahil, and Yoshihide Wada
Geosci. Model Dev., 15, 7099–7120, https://doi.org/10.5194/gmd-15-7099-2022, https://doi.org/10.5194/gmd-15-7099-2022, 2022
Short summary
Short summary
We develop and test the first large-scale hydrological model at regional scale with a very high spatial resolution that includes a water management and groundwater flow model. This study infers the impact of surface and groundwater-based irrigation on groundwater recharge and on evapotranspiration in both irrigated and non-irrigated areas. We argue that water table recorded in boreholes can be used as validation data if water management is well implemented and spatial resolution is ≤ 100 m.
Robert Chlumsky, James R. Craig, Simon G. M. Lin, Sarah Grass, Leland Scantlebury, Genevieve Brown, and Rezgar Arabzadeh
Geosci. Model Dev., 15, 7017–7030, https://doi.org/10.5194/gmd-15-7017-2022, https://doi.org/10.5194/gmd-15-7017-2022, 2022
Short summary
Short summary
We introduce the open-source RavenR package, which has been built to support the use of the hydrologic modelling framework Raven. The R package contains many functions that may be useful in each step of the model-building process, including preparing model input files, running the model, and analyzing the outputs. We present six reproducible use cases of the RavenR package for the Liard River basin in Canada to demonstrate how it may be deployed.
Bahar Bahrami, Anke Hildebrandt, Stephan Thober, Corinna Rebmann, Rico Fischer, Luis Samaniego, Oldrich Rakovec, and Rohini Kumar
Geosci. Model Dev., 15, 6957–6984, https://doi.org/10.5194/gmd-15-6957-2022, https://doi.org/10.5194/gmd-15-6957-2022, 2022
Short summary
Short summary
Leaf area index (LAI) and gross primary productivity (GPP) are crucial components to carbon cycle, and are closely linked to water cycle in many ways. We develop a Parsimonious Canopy Model (PCM) to simulate GPP and LAI at stand scale, and show its applicability over a diverse range of deciduous broad-leaved forest biomes. With its modular structure, the PCM is able to adapt with existing data requirements, and run in either a stand-alone mode or as an interface linked to hydrologic models.
Cited articles
Alfieri, L., Lorini, V., Hirpa, F. A., Harrigan, S., Zsoter, E., Prudhomme, C.,
and Salamon, P.: A global streamflow reanalysis for 1980–2018, J.
Hydrol. X, 6, 100049, https://doi.org/10.1016/j.hydroa.2019.100049, 2020. a
Bao, J., Zhou, T., Huang, M., Hou, Z., Perkins, W., Harding, S., Titzler, S.,
Hammond, G., Ren, H., Thorne, P., Suffield, S., Murray, C., and Zachara, J.:
Modulating factors of hydrologic exchanges in a large‐scale river reach:
Insights from three‐dimensional computational fluid dynamics simulations,
Hydrol. Process., 32, 3446–3463, https://doi.org/10.1002/hyp.13266, 2018. a, b
Bao, J., Chen, Y., Fang, Y., Song, X., Perkins, W., Duan, Z., Shuai, P., Ren,
H., Hou, Z., Richmond, M., He, X., and Scheibe, T.: Modeling framework for
evaluating the impacts of hydrodynamic pressure on hydrologic exchange fluxes
and residence time for a large-scale river section over a long-term period,
Environ. Modell. Softw., 148, 105277,
https://doi.org/10.1016/j.envsoft.2021.105277, 2022. a, b
Bates, P. D., Anderson, M. G., and Hervouet, J. M.: Initial comparison of two
two-dimensional finite element codes for river flood simulation, P. I. Civil Eng.-Water, 112,
238–248, https://doi.org/10.1680/iwtme.1995.27886, 1995. a
Bates, P. D., Lane, S. N., and Ferguson, R. I.: Computational fluid dynamics:
applications in environmental hydraulics, edited by: Bates, P. D., Lane, S. N., and Ferguson, R. I., John Wiley & Sons, Ltd,
Chichester, UK, https://doi.org/10.1002/0470015195, 2005. a
Berenbrock, C. and Tranmer, A. W.: Simulation of flow, sediment transport, and
sediment mobility of the Lower Coeur d'Alene River, Idaho, USGS Scientific
Investigations Report, p. 164, https://doi.org/10.3133/sir20085093, 2008. a
Biddanda, B. A.: Global significance of the changing freshwater
carbon cycle, EOS, 98, 6, 15–17, https://doi.org/10.1029/2017EO069751,
2017 a
Bierkens, M. F. P., Bell, V. A., Burek, P., Chaney, N., Condon, L. E., David,
C. H., de Roo, A., Döll, P., Drost, N., Famiglietti, J. S.,
Flörke, M., Gochis, D. J., Houser, P., Hut, R., Keune, J., Kollet, S.,
Maxwell, R. M., Reager, J. T., Samaniego, L., Sudicky, E., Sutanudjaja,
E. H., van de Giesen, N., Winsemius, H., and Wood, E. F.: Hyper-resolution
global hydrological modelling: what is next?, Hydrol. Process., 29,
310–320, https://doi.org/10.1002/hyp.10391, 2015. a
Blocken, B., Stathopoulos, T., and Carmeliet, J.: CFD simulation of the
atmospheric boundary layer: wall function problems, Atmos. Environ.,
41, 238–252, https://doi.org/10.1016/j.atmosenv.2006.08.019, 2007. a, b
Blumberg, A. F. and Mellor, G. L.: Diagnostic and prognostic numerical
circulation studies of the South Atlantic Bight, J. Geophys.
Res.-Oceans, 88, 4579–4592, https://doi.org/10.1029/JC088iC08p04579, 1983. a
Booker, D. J., Sear, D. A., and Payne, A. J.: Modelling three-dimensional flow
structures and patterns of boundary shear stress in a natural pool-riffle
sequence, Earth Surf. Proc. Land., 26, 553–576,
https://doi.org/10.1002/esp.210, 2001. a
Bovee, K. D.: The incremental method of assessing habitat potential for
coolwater species, with management implications, in: American Fisheries
Society Special Publication, 1st edn., vol. 11, edited by: Kendall, R. L., Amer Fisheries Society, pp. 340–346, ISBN-10 0913235083, ISBN-13 978-0913235089,
1978. a
Broecker, T., Teuber, K., Sobhi Gollo, V., Nützmann, G., Lewandowski,
J., and Hinkelmann, R.: Integral flow modelling
approach for surface water-groundwater interactions
along a rippled streambed, Water, 11, 1517,
https://doi.org/10.3390/w11071517, 2019. a
Cao, Z., Carling, P., and Oakey, R.: Flow reversal over a natural pool-riffle
sequence: a computational study, Earth Surf. Proc. Land., 28,
689–705, https://doi.org/10.1002/esp.466, 2003. a
Cardenas, M. B. and Wilson, J. L.: Dunes, turbulent eddies, and interfacial
exchange with permeable sediments, Water Resour. Res., 43, W08412,
https://doi.org/10.1029/2006WR005787, 2007. a, b
Carling, P. A. and Wood, N.: Simulation of flow over pool-riffle topography: a
consideration of the velocity reversal hypothesis, Earth Surf. Proc.
Land., 19, 319–332, https://doi.org/10.1002/esp.3290190404, 1994. a
Carr, J. C., DiBiase, R. A., and Yeh, E. C.: Geomorphic and structural mapping
for remote bedrock river corridors in the Taiwan Central Range using paired
outcrop and kilometer scale UAV surveys, in: GSA Annual Meeting, Phoenix,
Arizona, USA, 22–25 September 2019,https://doi.org/10.1130/abs/2019AM-335826, 2019. a
Chaudhry, M. H. H. (Ed.): Open-channel flow, Springer US, New York, NY, USA, 2nd
edn., https://doi.org/10.1007/978-0-387-68648-6, 2008. a
Chen, Y., Dibiase, R. A. R. A., McCarroll, N., and Liu, X.: Quantifying flow
resistance in mountain streams using computational fluid dynamics modeling
over structure-from-motion photogrammetry-derived microtopography, Earth
Surf. Proc. Land., 44, 1973–1987, https://doi.org/10.1002/esp.4624,
2019. a, b
Chen, Y., Bao J., Fang, Y., Perkings, W. A., Ren, H., Song, X., Duan, Z., Hou, Z., He, X., Scheibe, T. D.: (2021): Model, data, and code for paper “Modeling of streamflow in a 30-kilometer-long reach spanning 5 years using OpenFOAM 5.x”, River Corridor and Watershed Biogeochemistry SFA, ESS-DIVE repository [data set, code], https://doi.org/10.15485/1819956, 2021. a
Condon, L. E., Kollet, S., Bierkens, M. F. P., Fogg, G. E., Maxwell, R. M.,
Hill, M. C., Fransen, H. H., Verhoef, A., Van Loon, A. F., Sulis, M., and
Abesser, C.: Global Groundwater Modeling and Monitoring: Opportunities and
Challenges, Water Resour. Res., 57, e2020WR029500,
https://doi.org/10.1029/2020WR029500, 2021. a
Correia, L. P., Krishnappan, B. G., and Graf, W. H.: Fully coupled unsteady
mobile boundary flow model, J. Hydraul. Eng., 118, 476–494,
https://doi.org/10.1061/(ASCE)0733-9429(1992)118:3(476), 1992. a
Crowder, D. W. and Diplas, P.: Evaluating spatially explicit metrics of stream
energy gradients using hydrodynamic model simulations, Can. J.
Fish. Aquat. Sci., 57, 1497–1507, https://doi.org/10.1139/f00-074, 2000. a
Cui, Y., Parker, G., Pizzuto, J., and Lisle, T. E.: Sediment pulses in
mountain rivers: 2. comparison between experiments and numerical
predictions, Water Resour. Res., 39, 1240, https://doi.org/10.1029/2002WR001805,
2003. a
Danhoff, B. M. and Huckins, C. J.: Modelling submerged fluvial substrates with
structure‐from‐motion photogrammetry, River Res. Appl.,
36, 128–137, https://doi.org/10.1002/rra.3532, 2020. a
Darby, S. E., Alabyan, A. M., and Van de Wiel, M. J.: Numerical simulation
of bank erosion and channel migration in meandering rivers, Water Resour.
Res., 38, 1163, https://doi.org/10.1029/2001WR000602, 2002. a
Demuren, A. O.: A numerical model for flow in meandering channels with natural
bed topography, Water Resour. Res., 29, 1269–1277,
https://doi.org/10.1029/92WR02907, 1993. a
Demuren, A. O. and Rodi, W.: Calculation of flow and pollutant dispersion in
meandering channels, J. Fluid Mech., 172, 63–92,
https://doi.org/10.1017/S0022112086001659, 1986. a
Deshpande, S. S., Anumolu, L., and Trujillo, M. F.: Evaluating the performance
of the two-phase flow solver interFoam, Computational Science &
Discovery, 5, 14016, https://doi.org/10.1088/1749-4699/5/1/014016, 2012. a, b, c
Duan, J. G., Wang, S. S. Y., and Jia, Y.: The applications of the enhanced
CCHE2D model to study the alluvial channel migration processes, J.
Hydraul. Res., 39, 469–480, https://doi.org/10.1080/00221686.2001.9628272, 2001. a
Ferguson, R. I.: Reach-Scale Flow Resistance, in: Treatise
on Geomorphology, second edn., vol. 6, edited by:
Shroder, J. F., Academic Press, 110–132, ISBN 978-0-12-818235-2, https://doi.org/10.1016/B978-0-12-409548-9.09386-6, 2022. a, b
Ferguson, R. I., Church, M., and Weatherly, H.: Fluvial aggradation in Vedder
River: testing a one-dimensional sedimentation model, Water Resour.
Res., 37, 3331–3347, https://doi.org/10.1029/2001WR000225, 2001. a
Hamrick, J. M.: A three-dimensional environmental fluid dynamics computer
code: theoretical and computational aspects, in: Special report in applied
marine science and ocean engineering, 317, p. 64, Virginia Institute of
Marine Science, College of William and Mary, https://doi.org/10.21220/V5TT6C, 1992. a, b
Hardy, R. J. J., Lane, S. N. N., Lawless, M. R. R., Best, J. L. L., Elliott,
L., and Ingham, D. B. B.: Development and testing of a numerical code for
treatment of complex river channel topography in three-dimensional CFD models
with structured grids, J. Hydraul. Res., 43, 468–480,
https://doi.org/10.1080/00221680509500145, 2005. a
Harrigan, S., Zsoter, E., Alfieri, L., Prudhomme, C., Salamon, P., Wetterhall, F., Barnard, C., Cloke, H., and Pappenberger, F.: GloFAS-ERA5 operational global river discharge reanalysis 1979–present, Earth Syst. Sci. Data, 12, 2043–2060, https://doi.org/10.5194/essd-12-2043-2020, 2020. a
Harvey, J. W.: Hydrologic Exchange Flows and Their Ecological Consequences in River Corridors, in: Stream Ecosystems in a Changing Environment, edited by: Jones, J. B. and Stanley, E. H., Academic Press, 1–83, ISBN 978-0-12-405890-3, https://doi.org/10.1016/B978-0-12-405890-3.00001-4,
2016. a, b, c
Hester, E. T., Cardenas, M. B., Haggerty, R., and Apte, S. V.: The importance
and challenge of hyporheic mixing, Water Resour. Res., 53, 3565–3575,
https://doi.org/10.1002/2016WR020005, 2017. a
Hicks, F. E. and Peacock, T.: Suitability of HEC-RAS for flood forecasting,
Can. Water Resour. J., 30, 159–174, https://doi.org/10.4296/cwrj3002159,
2005. a
Hiemstra, K. S., van Vuren, S., Vinke, F. S. R., Jorissen, R. E., and Kok, M.:
Assessment of the functional performance of lowland river systems subjected
to climate change and large-scale morphological trends, International
Journal of River Basin Management, 20, 45–56,
https://doi.org/10.1080/15715124.2020.1790580, 2020. a
Hirt, C. W. and Nichols, B. D.: Volume of fluid (VOF) method for the dynamics
of free boundaries, J. Computat. Phys., 39, 201–225,
https://doi.org/10.1016/0021-9991(81)90145-5, 1981. a, b
Hodskinson, A.: Computational fluid dynamics as a tool for investigating
separated flow in river bends, Earth Surf. Proc. Land., 21,
993–1000,
https://doi.org/10.1002/(SICI)1096-9837(199611)21:11<993::AID-ESP698>3.0.CO;2-R, 1996. a
Hodskinson, A. and Ferguson, R. I.: Numerical modelling of separated flow in
river bends: model testing and experimental investigation of geometric
controls on the extent of flow separation at the concave bank, Hydrol.
Process., 12, 1323–1338,
https://doi.org/10.1002/(SICI)1099-1085(19980630)12:8<1323::AID-HYP617>3.0.CO;2-S,
1998. a
Hoey, T. B. and Ferguson, R.: Numerical simulation of downstream fining by
selective transport in gravel bed rivers: model development and
illustration, Water Resour. Res., 30, 2251–2260,
https://doi.org/10.1029/94WR00556, 1994. a
Horritt, M. S.: Parameterisation, validation and uncertainty analysis of CFD
models of fluvial and flood hydraulics in the natural environment, in:
Computational Fluid Dynamics, edited by: Bates, P. D.,
Lane, S. N., and Ferguson, R. I., pp. 193–213, John Wiley & Sons, Ltd,
Chichester, UK, https://doi.org/10.1002/0470015195.ch9, 2005. a
Howard, A. D.: Modeling channel migration and floodplain sedimentation in
meandering streams, in: Lowland floodplain rivers : geomorphological
perspectives, edited by: Carling, P. A. and Petts, G. E., p. 302, John Wiley,
Chichester, UK, ISBN-10 0471931195,
ISBN-13 978-0471931195, 1992. a
Huang, J., Patel, V. C., Lai, Y. G., and Weber, L. J.: Simulation study of
flow through a reach of the Chattachoochee River, J. Hydraul.
Res., 42, 487–492, https://doi.org/10.1080/00221686.2004.9641218, 2004. a
Issa, R. I.: Solution of the implicitly discretised fluid flow equations by
operator-splitting, J. Comput. Phys., 62, 40–65,
https://doi.org/10.1016/0021-9991(86)90099-9, 1985. a
Ji, Z., Hu, G., Shen, J., and Wan, Y.: Three-dimensional modeling of
hydrodynamic processes in the St. Lucie Estuary, Estuarine, Coastal and
Shelf Science, 73, 188–200, https://doi.org/10.1016/j.ecss.2006.12.016, 2007. a
Johnson, B. H., Kim, K. W., Heath, R. E., Hsieh, B. B., and Butler, H. L.:
Validation of three‐dimensional hydrodynamic model of Chesapeake Bay,
J. Hydraul. Eng., 119, 2–20, https://doi.org/10.1061/(ASCE)0733-9429(1993)119:1(2), 1993. a
Kang, S. and Sotiropoulos, F.: Flow phenomena and mechanisms in a field-scale
experimental meandering channel with a pool-riffle sequence: Insights gained
via numerical simulation, J. Geophys. Res., 116, F03011,
https://doi.org/10.1029/2010JF001814, 2011. a
Kang, S. and Sotiropoulos, F.: Assessing the predictive capabilities of
isotropic, eddy viscosity Reynolds-averaged turbulence models in a
natural-like meandering channel, Water Resour. Res., 48, W06505,
https://doi.org/10.1029/2011WR011375, 2012. a
Kang, S., Lightbody, A., Hill, C., and Sotiropoulos, F.: High-resolution
numerical simulation of turbulence in natural waterways, Adv. Water
Resour., 34, 98–113, https://doi.org/10.1016/j.advwatres.2010.09.018, 2011. a
Keller, E. A. and Florsheim, J. L.: Velocity-reversal hypothesis: a model
approach, Earth Surf. Proc. Land., 18, 733–740,
https://doi.org/10.1002/esp.3290180807, 1993. a
Khosronejad, A., Le, T., DeWall, P., Bartelt, N., Woldeamlak, S., Yang, X., and
Sotiropoulos, F.: High-fidelity numerical modeling of the Upper Mississippi
River under extreme flood condition, Adv. Water Resour., 98,
97–113, https://doi.org/10.1016/j.advwatres.2016.10.018, 2016. a, b
Khosronejad, A., Flora, K., and Kang, S.: Effect of inlet turbulent boundary
conditions on scour predictions of coupled LES and morphodynamics in a
field-scale river: bankfull flow conditions, J. Hydraul.
Eng., 146, 04020020, https://doi.org/10.1061/(ASCE)HY.1943-7900.0001719, 2020. a
Kolden, E., Fox, B. D., Bledsoe, B. P., and Kondratieff, M. C.: Modelling
whitewater park hydraulics and fish habitat in Colorado, River Res.
Appl., 32, 1116–1127, https://doi.org/10.1002/rra.2931, 2016. a
Kuzmin, D., Möller, M., and Turek, S.: Multidimensional FEM-FCT schemes
for arbitrary time stepping, Int. J. Numer. Meth.
Fl., 42, 265–295, https://doi.org/10.1002/fld.493, 2003. a
Lai, Y. G.: Quantitative modeling tools for large wood debris and other
in-stream structures, Tech. Rep. ST-2016-4495-01, U.S. Department of the
Interior, Bureau of Reclamation, Denver, CO, https://www.usbr.gov/research/projects/download_product.cfm?id=938 (last access: 4 April 2022), 2016. a
Lane, S. N. and Ferguson, R. I.: Modelling reach-scale fluvial flows, in:
Computational Fluid Dynamics, edited by: Bates, P. D., Lane, S. N., and
Ferguson, R. I., pp. 215–269, John Wiley & Sons, Ltd,
Chichester, UK, https://doi.org/10.1002/0470015195.ch10, 2005. a, b, c
Lane, S. N. and Richards, K. S.: High resolution, two-dimensional spatial
modelling of flow processes in a multi-thread channel, Hydrol.
Process., 12, 1279–1298,
https://doi.org/10.1002/(SICI)1099-1085(19980630)12:8<1279::AID-HYP615>3.0.CO;2-E,
1998. a
Lane, S. N., Bradbrook, K. F., Richards, K. S., Biron, P. A., and Roy, A. G.:
The application of computational fluid dynamics to natural river channels:
three-dimensional versus two-dimensional approaches, Geomorphology, 29,
1–20, https://doi.org/10.1016/S0169-555X(99)00003-3, 1999. a, b, c
Lane, S. N., Hardy, R. J., Elliot, L., Ingham, D. B., Elliott, L., Ingham,
D. B., Elliot, L., and Ingham, D. B.: Numerical modeling of flow processes
over gravelly surfaces using structured grids and a numerical porosity
treatment, Water Resour. Res., 40, W01302,
https://doi.org/10.1029/2002WR001934, 2004. a
Lane, S. N., Hardy, R. J., Ferguson, R. I., and Parsons, D. R.: A framework
for model verification and validation of CFD schemes in natural open channel
flows, in: Computational Fluid Dynamics, edited by: Bates, P. D.,
Lane, S. N., and Ferguson, R. I., pp. 169–192, John Wiley & Sons,
Ltd, Chichester, UK, https://doi.org/10.1002/0470015195.ch8, 2005. a, b
Le, T. B., Khosronejad, A., Sotiropoulos, F., Bartelt, N., Woldeamlak, S., and
Dewall, P.: Large-eddy simulation of the Mississippi River under base-flow
condition: hydrodynamics of a natural diffluence-confluence region, J. Hydraul. Res., 57, 836–851, https://doi.org/10.1080/00221686.2018.1534282,
2019. a, b, c
Leclerc, M., Boudreault, A., Bechara, T. A., and Corfa, G.: Two-dimensional
hydrodynamic modeling: a neglected tool in the instream flow incremental
methodology, T. Am. Fish. Soc., 124, 645–662,
https://doi.org/10.1577/1548-8659(1995)124<0645:TDHMAN>2.3.CO;2, 1995. a
Leschziner, M. A. and Rodi, W.: Calculation of strongly curved open channel
flow, J. Hydraul. Eng. Div., 105, 1297–1314,
https://doi.org/10.1061/JYCEAJ.0005286, 1979. a
Lesser, G. R., Roelvink, J. A., van Kester, J. A. T. M., and Stelling, G. S.: Development and validation of a three-dimensional morphological model, Coast.
Eng., 51, 883–915, https://doi.org/10.1016/j.coastaleng.2004.07.014, 2004. a
Li, B., Liu, X., Kaufman, M. H., Turetcaia, A., Chen, X., and Cardenas, M. B.:
Flexible and modular simultaneous modeling of flow and reactive transport in
rivers and hyporheic zones, Water Resour. Res., 56, e2019WR02652,
https://doi.org/10.1029/2019WR026528, 2020. a
Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David,
C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., and Wood,
E. F.: Global Reconstruction of Naturalized River Flows at 2.94 Million
Reaches, Water Resour. Res., 55, 6499–6516,
https://doi.org/10.1029/2019WR025287, 2019. a
Liu, X., Chen, Y., and Shen, C.: Coupled two-dimensional surface flow and
three-dimensional sub-surface flow modeling for the drainage of permeable
road pavement, J. Hydrol. Eng., 21, 4016051,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0001462, 2016. a
Lorke, A. and MacIntyre, S.: The benthic boundary layer (in rivers, lakes, and
reservoirs), in: Encyclopedia of Inland Waters, edited by: Likens, G. E., pp. 505–514, Elsevier,
https://doi.org/10.1016/B978-012370626-3.00079-X, 2009. a
Ma, L., Ashworth, P. J., Best, J. L., Elliott, L., Ingham, D. B., and
Whitcombe, L. J.: Computational fluid dynamics and the physical modelling of
an upland urban river, Geomorphology, 44, 375–391,
https://doi.org/10.1016/S0169-555X(01)00184-2, 2002. a
Marjoribanks, T. I., Hardy, R. J., Lane, S. N., and Tancock, M. J.:
Patch-scale representation of vegetation within hydraulic models, Earth
Surf. Proc. Land., 42, 699–710, https://doi.org/10.1002/esp.4015, 2017. a
Maxwell, R. M., Putti, M., Meyerhoff, S., Delfs, J.-O., Ferguson, I. M.,
Ivanov, V., Kim, J., Kolditz, O., Kollet, S. J., Kumar, M., Lopez, S., Niu,
J., Paniconi, C., Park, Y.-J., Phanikumar, M. S., Shen, C., Sudicky, E. A.,
and Sulis, M.: Surface-subsurface model intercomparison: A first set of
benchmark results to diagnose integrated hydrology and feedbacks, Water
Resour. Res., 50, 1531–1549, https://doi.org/10.1002/2013WR013725, 2014. a
Menter, F. R., Kuntz, M., and Langtry, R.: Ten years of industrial experience
with the SST turbulence model turbulence heat and mass transfer, in:
Proceedings of the 4th International Symposium on Turbulence, Heat and Mass
Transfer, edited by: Hanjalic, K., Nagano, Y., and Tummers, M. J., pp.
625–632, Begell House, Antalya, Turkey, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.460.2814&rep=rep1&type=pdf (last access: 4 April 2022), 2003. a
Milhous, R. T., Wegner, D. L., and Waddle, T.: User's guide to the physical
habitat simulation system (PHABISM), in: FWS/OBS, 81/43, p. FWS/OBS, U.S.
Fish and Wildlife Service, https://pubs.er.usgs.gov/publication/fwsobs81_43 (last access: 4 April 2022), 1984. a
Miller, A. J.: Debris‐fan constrictions and flood hydraulics in river
canyons: Some implications from two‐dimensional flow modelling, Earth
Surf. Proc. Land., 19, 681–697, https://doi.org/10.1002/esp.3290190803,
1994. a
Nagata, N., Hosoda, T., and Muramoto, Y.: Numerical analysis of river channel
processes with bank erosion, J. Hydraul. Eng., 126,
243–252, https://doi.org/10.1061/(ASCE)0733-9429(2000)126:4(243), 2000. a
Nicholas, A. P. and Sambrook Smith, G. H.: Numerical simulation of
three-dimensional flow hydraulics in a braided channel, Hydrol.
Process., 13, 913–929,
https://doi.org/10.1002/(SICI)1099-1085(19990430)13:6<913::AID-HYP764>3.0.CO;2-N, 1999. a, b
Nikuradse, J.: Laws of flow in rough pipes (English translation), Tech. Rep.,
National Advisory Commission for Aeronautics, Washington, DC, USA, https://ntrs.nasa.gov/citations/19930093938 (last access: 4 April 2022), 1933. a
Olsen, N. R. B. and Stokseth, S.: Three-dimensional numerical modelling of
water flow in a river with large bed roughness, J. Hydraul.
Res., 33, 571–581, https://doi.org/10.1080/00221689509498662, 1995. a
Palmer, M. A., Hondula, K. L., and Koch, B. J.: Ecological restoration of
streams and rivers: shifting strategies and shifting goals, Annu. Rev.
Ecol. Evol. S., 45, 247–269,
https://doi.org/10.1146/annurev-ecolsys-120213-091935, 2014. a
Perkins, W. A. and Richmond, M. C.: MASS2, modular aquatic simulation system
in two dimensions, theory and numerical methods, Tech. Rep., Pacific
Northwest National Laboratory (PNNL), Richland, WA (United States),
https://doi.org/10.2172/919712, 2007. a
Podhorányi, M., Unucka, J., Bobál', P., Říhová, V.,
Podhoranyi, M., Unucka, J., Bobal, P., and Rihova, V.: Effects of LIDAR DEM
resolution in hydrodynamic modelling: model sensitivity for cross-sections,
Int. J. Digit. Earth, 6, 3–27,
https://doi.org/10.1080/17538947.2011.596578, 2013. a
Potter, C., Zhang, P., Klooster, S., Genovese, V., Shekhar, S., and Kumar, V.:
Understanding controls on historical river discharge in the world's largest
drainage basins, Earth Interact., 8, 1–21,
https://doi.org/10.1175/1087-3562(2004)008<0001:UCOHRD>2.0.CO;2, 2004. a
Powell, D. M.: Flow resistance in gravel-bed rivers: progress in research,
Earth-Sci. Rev., 136, 301–338, https://doi.org/10.1016/j.earscirev.2014.06.001,
2014. a
Richards, K. S.: Simulation of flow geometry in a riffle-pool stream, Earth
Surface Processes, 3, 345–354, https://doi.org/10.1002/esp.3290030403, 1978. a
Richmond, M. C. and Perkins, W. A.: Efficient calculation of dewatered and
entrapped areas using hydrodynamic modeling and GIS, Environ. Modell.
Softw., 24, 1447–1456, https://doi.org/10.1016/j.envsoft.2009.06.001, 2009. a, b
Richmond, M. C., Perkins, W. A., and Chien, Y.: Regional scale simulation of
water temperature and dissolved gas variations in the Columbia River basin,
in: HydroVision 2002 Technical Papers, June, HCI Publications, Portland, OR,
USA, https://www.researchgate.net/publication/278848235 (last access: 4 April 2022), 2002. a
Rickenmann, D. and Recking, A.: Evaluation of flow resistance in gravel-bed
rivers through a large field data set, Water Resour. Res., 47,
W07538, https://doi.org/10.1029/2010WR009793, 2011. a
Rodriguez, J. F., Bombardelli, F. A., García, M. H., Frothingham, K. M.,
Rhoads, B. L., and Abad, J. D.: High-resolution numerical simulation of flow
through a highly sinuous river reach, Water Resour. Manag., 18,
177–199, https://doi.org/10.1023/B:WARM.0000043137.52125.a0, 2004. a
Sinha, S., Liu, X., and Garcia, M. H.: A three-dimensional water quality model
of Chicago Area Waterway System (CAWS), Environ. Model.
Assess., 18, 567–592, https://doi.org/10.1007/s10666-013-9367-1, 2013. a
Sinha, S. K., Sotiropoulos, F., and Odgaard, A. J.: Three-dimensional
numerical model for flow through natural rivers, J. Hydraul.
Eng., 124, 13–24, https://doi.org/10.1061/(ASCE)0733-9429(1998)124:1(13), 1998. a
Smith, M. W.: Roughness in the earth sciences, Earth-Sci. Rev., 136,
202–225, https://doi.org/10.1016/j.earscirev.2014.05.016, 2014. a
Sun, T., Meakin, P., Jøssang, T., and Schwarz, K.: A simulation model for
meandering rivers, Water Resour. Res., 32, 2937–2954,
https://doi.org/10.1029/96WR00998, 1996. a
Talbot, T. and Lapointe, M.: Numerical modeling of gravel bed river response
to meander straightening: The coupling between the evolution of bed pavement
and long profile, Water Resour. Res., 38, 1074,
https://doi.org/10.1029/2001WR000330, 2002. a
Thompson, D. M., Nelson, J. M., and Wohl, E. E.: Interactions between pool
geometry and hydraulics, Water Resour. Res., 34, 3673–3681,
https://doi.org/10.1029/1998WR900004, 1998. a
Tonina, D. and Buffington, J. M.: Hyporheic exchange in gravel bed rivers with
pool-riffle morphology: Laboratory experiments and three-dimensional
modeling, Water Resour. Res., 43, W01421,
https://doi.org/10.1029/2005WR004328, 2007. a
Tonina, D., McKean, J. A., Benjankar, R. M., Wright, C. W., Goode, J. R., Chen,
Q., Reeder, W. J., Carmichael, R. A., and Edmondson, M. R.: Mapping river
bathymetries: evaluating topobathymetric LiDAR survey, Earth Surf.
Proc. Land., 44, 507–520, https://doi.org/10.1002/esp.4513, 2019. a
Tsai, W.-P., Feng, D., Pan, M., Beck, H., Lawson, K., Yang, Y., Liu, J., and
Shen, C.: From calibration to parameter learning: Harnessing the scaling
effects of big data in geoscientific modeling, Nat. Commun., 12,
5988, https://doi.org/10.1038/s41467-021-26107-z, 2021. a
van Niekerk, A., Vogel, K. R., Slingerland, R. L., and Bridge, J. S.: Routing
of heterogeneous sediments over movable bed: model development, J.
Hydraul. Eng., 118, 246–262,
https://doi.org/10.1061/(ASCE)0733-9429(1992)118:2(246), 1992. a
Veldkamp, T. I., Zhao, F., Ward, P. J., De Moel, H., Aerts, J. C., Schmied,
H. M., Portmann, F. T., Masaki, Y., Pokhrel, Y., Liu, X., Satoh, Y., Gerten,
D., Gosling, S. N., Zaherpour, J., and Wada, Y.: Human impact
parameterizations in global hydrological models improve estimates of monthly
discharges and hydrological extremes: A multi-model validation study,
Environ. Res. Lett., 13, 055008, https://doi.org/10.1088/1748-9326/aab96f, 2018.
a
Wampler, P. J.: Rivers and streams – water and sediment in motion, Nature
Education Knowledge, 3, 18, https://www.nature.com/scitable/knowledge/library/rivers-and-streams-water-and-26405398/ (last access: 4 April 2022), 2012. a
Wei, X., Cai, S., Ni, P., and Zhan, W.: Impacts of climate change and human
activities on the water discharge and sediment load of the Pearl River,
southern China, Scientific Reports, 10, 16743,
https://doi.org/10.1038/s41598-020-73939-8, 2020. a
Wilcox, D. C.: Turbulence modeling for CFD, 3rd edn., DCW Industries, La Canada, Calif, ISBN-10 1928729088,
ISBN-13 978-1928729082, 2006. a
Wilson, C., Yagci, O., Rauch, H. P., and Olsen, N.: 3D numerical modelling of
a willow vegetated river/floodplain system, J. Hydrol., 327,
13–21, https://doi.org/10.1016/j.jhydrol.2005.11.027, 2006. a
Wohl, E., Angermeier, P. L., Bledsoe, B., Kondolf, G. M., MacDonnell, L.,
Merritt, D. M., Palmer, M. A., Poff, N. L. R., and Tarboton, D.: River
restoration, Water Resour. Res., 41, W10301,
https://doi.org/10.1029/2005WR003985, 2005. a
Wohl, E., Lane, S. N., and Wilcox, A. C.: The science and practice of river
restoration, Water Resour. Res., 51, 5974–5997,
https://doi.org/10.1002/2014WR016874, 2015. a, b
Wood, E. F., Roundy, J. K., Troy, T. J., van Beek, L. P. H., Bierkens, M.
F. P., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J.,
Gochis, D., van de Giesen, N., Houser, P., Jaffé, P. R., Kollet, S.,
Lehner, B., Lettenmaier, D. P., Peters-Lidard, C., Sivapalan, M., Sheffield,
J., Wade, A., and Whitehead, P.: Hyperresolution global land surface
modeling: Meeting a grand challenge for monitoring Earth's terrestrial
water, Water Resour. Res., 47, W05301, https://doi.org/10.1029/2010WR010090,
2011. a
Xu, D., Ivanov, V. Y., Li, X., and Troy, T. J.: Peak runoff timing is linked
to global warming trajectories, Earth's Future, 9, e2021EF002083,
https://doi.org/10.1029/2021EF002083, 2021. a
Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. H.,
Lu, H., Yang, K., Hong, Y., and Wood, E. F.: Global Reach-Level 3-Hourly
River Flood Reanalysis (1980–2019), B. Am.
Meteorol. Soc., 102, E2086–E2105, https://doi.org/10.1175/BAMS-D-20-0057.1,
2021. a
Zalesak, S. T.: Fully multidimensional flux-corrected transport algorithms for
fluids, J. Comput. Phys., 31, 335–362,
https://doi.org/10.1016/0021-9991(79)90051-2, 1979. a
Zhou, T., Bao, J., Huang, M., Hou, Z., Arntzen, E., Song, X., Harding, S. F.,
Titzler, P. S., Ren, H., Murray, C. J., Perkins, W. A., Chen, X., Stegen,
J. C., Hammond, G. E., Thorne, P. D., and Zachara, J. M.: Riverbed
hydrologic exchange dynamics in a large regulated river reach, Water
Resour. Res., 54, 2715–2730, https://doi.org/10.1002/2017WR020508, 2018. a, b
Short summary
Climate change affects river discharge variations that alter streamflow. By integrating multi-type survey data with a computational fluid dynamics tool, OpenFOAM, we show a workflow that enables accurate and efficient streamflow modeling at 30 km and 5-year scales. The model accuracy for water stage and depth average velocity is −16–9 cm and 0.71–0.83 in terms of mean error and correlation coefficients. This accuracy indicates the model's reliability for evaluating climate impact on rivers.
Climate change affects river discharge variations that alter streamflow. By integrating...
