Model description paper
23 Mar 2020
Model description paper
| 23 Mar 2020
Simulating coupled surface–subsurface flows with ParFlow v3.5.0: capabilities, applications, and ongoing development of an open-source, massively parallel, integrated hydrologic model
Benjamin N. O. Kuffour et al.
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Cited
23 citations as recorded by crossref.
- Leveraging HPC accelerator architectures with modern techniques — hydrologic modeling on GPUs with ParFlow J. Hokkanen et al. 10.1007/s10596-021-10051-4
- A parametric sensitivity analysis for prioritizing regolith knowledge needs for modeling water transfers in the West African critical zone A. Herzog et al. 10.1002/vzj2.20163
- Resolution matters when modeling climate change in headwaters of the Colorado River L. Foster et al. 10.1088/1748-9326/aba77f
- A hydrological simulation dataset of the Upper Colorado River Basin from 1983 to 2019 H. Tran et al. 10.1038/s41597-022-01123-w
- Parallel Hydrological Model Parameter Uncertainty Analysis Based on Message-Passing Interface Z. Yin et al. 10.3390/w12102667
- Parameter estimation and uncertainty analysis in hydrological modeling P. Herrera et al. 10.1002/wat2.1569
- Multiscale simulations for multi-continuum Richards equations J. Park et al. 10.1016/j.cam.2021.113648
- Strong hydroclimatic controls on vulnerability to subsurface nitrate contamination across Europe R. Kumar et al. 10.1038/s41467-020-19955-8
- Numerical Modeling of Surface Water and Groundwater Interactions Induced by Complex Fluvial Landforms and Human Activities in the Pingtung Plain Groundwater Basin, Taiwan Q. Tran et al. 10.3390/app10207152
- Sandtank-ML: An Educational Tool at the Interface of Hydrology and Machine Learning L. Gallagher et al. 10.3390/w13233328
- Continental Hydrologic Intercomparison Project, Phase 1: A Large‐Scale Hydrologic Model Comparison Over the Continental United States D. Tijerina et al. 10.1029/2020WR028931
- Automatic Recognition of Financial Instruments Based on Anisotropic Partial Differential Equations W. Pan et al. 10.1155/2021/6529859
- PHEV! The PHysically-based Extreme Value distribution of river flows S. Basso et al. 10.1088/1748-9326/ac3d59
- Transmissivity and groundwater flow exert a strong influence on drainage density E. Luijendijk 10.5194/esurf-10-1-2022
- A national topographic dataset for hydrological modeling over the contiguous United States J. Zhang et al. 10.5194/essd-13-3263-2021
- Integrated Hydrologic Modelling of Groundwater-Surface Water Interactions in Cold Regions X. Yang et al. 10.3389/feart.2021.721009
- Modeling geogenic and atmospheric nitrogen through the East River Watershed, Colorado Rocky Mountains T. Maavara et al. 10.1371/journal.pone.0247907
- Development of a Deep Learning Emulator for a Distributed Groundwater–Surface Water Model: ParFlow-ML H. Tran et al. 10.3390/w13233393
- A Physics-Informed, Machine Learning Emulator of a 2D Surface Water Model: What Temporal Networks and Simulation-Based Inference Can Help Us Learn about Hydrologic Processes R. Maxwell et al. 10.3390/w13243633
- Routing Nutrient Concentrations in a River Reach Using an Object-Oriented Modeling Based on the Concepts of System Dynamics M. Ghashghaie et al. 10.1007/s41101-020-00092-8
- Accelerating the Lagrangian particle tracking of residence time distributions and source water mixing towards large scales C. Yang et al. 10.1016/j.cageo.2021.104760
- Identification of management options to mitigate seawater intrusion in an overexploited multi-layered coastal aquifer by integrated rainfall–runoff, surface water and density-dependent groundwater flow modeling S. Rajaveni et al. 10.1007/s12665-021-09836-8
- SW2D-GPU: A two-dimensional shallow water model accelerated by GPGPU T. Carlotto et al. 10.1016/j.envsoft.2021.105205
23 citations as recorded by crossref.
- Leveraging HPC accelerator architectures with modern techniques — hydrologic modeling on GPUs with ParFlow J. Hokkanen et al. 10.1007/s10596-021-10051-4
- A parametric sensitivity analysis for prioritizing regolith knowledge needs for modeling water transfers in the West African critical zone A. Herzog et al. 10.1002/vzj2.20163
- Resolution matters when modeling climate change in headwaters of the Colorado River L. Foster et al. 10.1088/1748-9326/aba77f
- A hydrological simulation dataset of the Upper Colorado River Basin from 1983 to 2019 H. Tran et al. 10.1038/s41597-022-01123-w
- Parallel Hydrological Model Parameter Uncertainty Analysis Based on Message-Passing Interface Z. Yin et al. 10.3390/w12102667
- Parameter estimation and uncertainty analysis in hydrological modeling P. Herrera et al. 10.1002/wat2.1569
- Multiscale simulations for multi-continuum Richards equations J. Park et al. 10.1016/j.cam.2021.113648
- Strong hydroclimatic controls on vulnerability to subsurface nitrate contamination across Europe R. Kumar et al. 10.1038/s41467-020-19955-8
- Numerical Modeling of Surface Water and Groundwater Interactions Induced by Complex Fluvial Landforms and Human Activities in the Pingtung Plain Groundwater Basin, Taiwan Q. Tran et al. 10.3390/app10207152
- Sandtank-ML: An Educational Tool at the Interface of Hydrology and Machine Learning L. Gallagher et al. 10.3390/w13233328
- Continental Hydrologic Intercomparison Project, Phase 1: A Large‐Scale Hydrologic Model Comparison Over the Continental United States D. Tijerina et al. 10.1029/2020WR028931
- Automatic Recognition of Financial Instruments Based on Anisotropic Partial Differential Equations W. Pan et al. 10.1155/2021/6529859
- PHEV! The PHysically-based Extreme Value distribution of river flows S. Basso et al. 10.1088/1748-9326/ac3d59
- Transmissivity and groundwater flow exert a strong influence on drainage density E. Luijendijk 10.5194/esurf-10-1-2022
- A national topographic dataset for hydrological modeling over the contiguous United States J. Zhang et al. 10.5194/essd-13-3263-2021
- Integrated Hydrologic Modelling of Groundwater-Surface Water Interactions in Cold Regions X. Yang et al. 10.3389/feart.2021.721009
- Modeling geogenic and atmospheric nitrogen through the East River Watershed, Colorado Rocky Mountains T. Maavara et al. 10.1371/journal.pone.0247907
- Development of a Deep Learning Emulator for a Distributed Groundwater–Surface Water Model: ParFlow-ML H. Tran et al. 10.3390/w13233393
- A Physics-Informed, Machine Learning Emulator of a 2D Surface Water Model: What Temporal Networks and Simulation-Based Inference Can Help Us Learn about Hydrologic Processes R. Maxwell et al. 10.3390/w13243633
- Routing Nutrient Concentrations in a River Reach Using an Object-Oriented Modeling Based on the Concepts of System Dynamics M. Ghashghaie et al. 10.1007/s41101-020-00092-8
- Accelerating the Lagrangian particle tracking of residence time distributions and source water mixing towards large scales C. Yang et al. 10.1016/j.cageo.2021.104760
- Identification of management options to mitigate seawater intrusion in an overexploited multi-layered coastal aquifer by integrated rainfall–runoff, surface water and density-dependent groundwater flow modeling S. Rajaveni et al. 10.1007/s12665-021-09836-8
- SW2D-GPU: A two-dimensional shallow water model accelerated by GPGPU T. Carlotto et al. 10.1016/j.envsoft.2021.105205
Latest update: 26 Jun 2022
Short summary
Integrated hydrologic models (IHMs) were developed in order to allow for more accurate simulations of real-world ecohydrologic conditions. Many IHMs exist, and the literature can be dense, so it is often difficult to understand what a specific model can and cannot do. We provide a review of the current core capabilities, solution techniques, communication structure with other models, some limitations, and potential future improvements of one such open-source integrated model called ParFlow.
Integrated hydrologic models (IHMs) were developed in order to allow for more accurate...