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D., White, J. T., Starn, J. J., & Fienen, M. N. +(2016). Scripting MODFLOW model development using python and flopy. +Groundwater, 54, 733–739. +https://doi.org/10.1111/gwat.12413 + + + FloPy v3.3.3 + Bakker + 10.5066/F7BK19FH + 2021 + Bakker, M., Post, V., Langevin, C. +D., Hughes, J. D., White, J. T., Leaf, A. T., Paulinski, S. R., Larsen, +J. D., Towes, M., Morway, E. D., Bellino, J. C., Starn, J. J., & +Fienen, M. N. (2021). FloPy v3.3.3. U.S. Geological Survey Software +Release. https://doi.org/10.5066/F7BK19FH + + + Optimal pump and recharge management model +for nitrite removal in the warren groundwater basin, +california + Chiu + Journal of Water Resources Planning and +Management + 3 + 136 + 10.1061/(ASCE)WR.1943-5452.0000034 + 2010 + Chiu, Y., Nishikawa, T., & Yeh, +W.-G. (2010). Optimal pump and recharge management model for nitrite +removal in the warren groundwater basin, california. Journal of Water +Resources Planning and Management, 136(3). +https://doi.org/10.1061/(ASCE)WR.1943-5452.0000034 + + + Fundamentals of hydrology + Davie + 10.4324/9780203798942 + 2019 + Davie, T., & Quinn, N. W. (2019). +Fundamentals of hydrology. Routledge. +https://doi.org/10.4324/9780203798942 + + + An overview of current applications, +challenges, and future trends in distributed process-based models in +hydrology + Fatichi + 537 + 10.1016/j.jhydrol.2016.03.026 + 2016 + 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., & Tarboton, D. +(2016). An overview of current applications, challenges, and future +trends in distributed process-based models in hydrology. 537, 45–60. +https://doi.org/10.1016/j.jhydrol.2016.03.026 + + + Input data processing tools for the +integrated hydrologic model GSFLOW. + Gardner + Environmental modelling & +software + 109 + 10.1016/j.envsoft.2018.07.020 + 2018 + Gardner, M. A., Morton, C. G., +Huntington, J. L., Niswonger, R. G., & Henson, W. R. (2018). Input +data processing tools for the integrated hydrologic model GSFLOW. +Environmental Modelling & Software, 109, 41–53. +https://doi.org/10.1016/j.envsoft.2018.07.020 + + + Managing water resources to adapt to climate +change: Facing uncertainty and scarcity in a changing +context + Garrote + Water Resources Management + 31 + 10.1007/s11269-017-1714-6 + 2017 + Garrote, L. (2017). Managing water +resources to adapt to climate change: Facing uncertainty and scarcity in +a changing context. Water Resources Management, 31, 2951–2963. +https://doi.org/10.1007/s11269-017-1714-6 + + + MODFLOW-2005, the U.S. Geological Survey +modular ground-water model – the ground-water flow +process + Harbaugh + 10.3133/tm6A16 + 2005 + Harbaugh, A. W. (2005). MODFLOW-2005, +the U.S. Geological Survey modular ground-water model – the ground-water +flow process. U.S. Geological Survey. +https://doi.org/10.3133/tm6A16 + + + Matplotlib: A 2D graphics +environment + Hunter + Computing in Science & +Engineering + 3 + 9 + 10.1109/MCSE.2007.55 + 2007 + Hunter, J. D. (2007). Matplotlib: A +2D graphics environment. Computing in Science & Engineering, 9(3), +90–95. https://doi.org/10.1109/MCSE.2007.55 + + + Jupyter notebooks – a publishing format for +reproducible computational workflows + Kluyver + 10.3233/978-1-61499-649-1-87 + 2016 + Kluyver, T., Ragan-Kelley, B., Pérez, +F., Granger, B., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J., +Grout, J., Corlay, S., Ivanov, P., Avila, D., Abdalla, S., & +Willing, C. (2016). Jupyter notebooks – a publishing format for +reproducible computational workflows (F. Loizides & B. Schmidt, +Eds.; pp. 87–90). IOS Press. +https://doi.org/10.3233/978-1-61499-649-1-87 + + + MODSIM: River basin management decision +support system, user manual + Labadie + 2006 + Labadie, J., & Larson, R. (2006). +MODSIM: River basin management decision support system, user manual. +Department of Civil Engineering, Colorado State University, Ft. Collins, +CO. http://modsim.engr.colostate.edu/modsim.php + + + pyGSFLOW v1.0.0 + Larsen + 10.5066/P9NPZ5AD + 2021 + Larsen, J. D., Alzraiee, A., & +Niswonger, R. G. (2021). pyGSFLOW v1.0.0. U.S. Geological Survey +Software Release. +https://doi.org/10.5066/P9NPZ5AD + + + Documentation for the MODFLOW 6 groundwater +flow model + Langevin + 10.3133/tm6A55 + 2017 + Langevin, C. D., Hughes, J. D., +Banta, E. R., Niswonger, R. G., Panday, S., & Provost, A. M. (2017). +Documentation for the MODFLOW 6 groundwater flow model. U.S Geological +Survey. https://doi.org/10.3133/tm6A55 + + + Geohydrology, simulation of regional +groundwater flow, and assessment of water management strategies, +twentynine palms area, california + Li + 10.3133/sir20105249 + 2011 + Li, Z., & Martin, P. (2011). +Geohydrology, simulation of regional groundwater flow, and assessment of +water management strategies, twentynine palms area, california. U.S. +Geological Survey Scientific Investigations Report. +https://doi.org/10.3133/sir20105249 + + + Global food demand, productivity growth, and +the scarcity of land and water resources: A spatially explicit +mathematical programming approach + Lotze-Campen + Agricultural Economics + 39 + 10.1111/j.1574-0862.2008.00336.x + 2006 + Lotze-Campen, H., Muller, C., +Bondeau, A., Stefanie, R., Popp, A., & Lucht, W. (2006). Global food +demand, productivity growth, and the scarcity of land and water +resources: A spatially explicit mathematical programming approach. +Agricultural Economics, 39, 325–338. +https://doi.org/10.1111/j.1574-0862.2008.00336.x + + + GSFLOW-coupled ground-water and surface-water +FLOW model based on the integration of the precipitation-runoff modeling +system (PRMS) and the modular ground-water flow model +(MODFLOW-2005) + Markstrom + 10.13140/2.1.2741.9202 + 2008 + Markstrom, S. L., Niswonger, R. G., +Regan, R. S., Prudic, D. E., & Barlow, P. M. (2008). GSFLOW-coupled +ground-water and surface-water FLOW model based on the integration of +the precipitation-runoff modeling system (PRMS) and the modular +ground-water flow model (MODFLOW-2005). U.S Geological Survey. +https://doi.org/10.13140/2.1.2741.9202 + + + PRMS-IV, the precipitation-runoff modeling +system, version 4 + Markstrom + 10.3133/tm6B7 + 2015 + Markstrom, S. L., Regan, R. S., Hay, +L. E., Viger, R. J., Webb, R. M. T., Payn, R. A., & H., L. J. +(2015). PRMS-IV, the precipitation-runoff modeling system, version 4. +U.S Geological Survey. +https://doi.org/10.3133/tm6B7 + + + Four billion people facing severe water +scarcity + Mekonnen + Science Advances + 2 + 2 + 10.1126/sciadv.1500323 + 2016 + Mekonnen, M. M., & Hoekstra, A. +Y. (2016). Four billion people facing severe water scarcity. Science +Advances, 2(2). +https://doi.org/10.1126/sciadv.1500323 + + + GSFLOW–GRASS v1.0.0: GIS-enabled hydrologic +modeling of coupled groundwater–surface-water systems + Ng + Geoscientific Model +Development + 11 + 10.5194/gmd-11-4755-2018 + 2018 + Ng, G. H. C., Wickert, A. D., Somers, +L. D., Saberi, L., Cronkite-Ratcliff, C., Niswonger, R. G., & +McKenzie, J. M. (2018). GSFLOW–GRASS v1.0.0: GIS-enabled hydrologic +modeling of coupled groundwater–surface-water systems. Geoscientific +Model Development, 11, 4755–4777. +https://doi.org/10.5194/gmd-11-4755-2018 + + + MODFLOW-NWT, a newton formulation for +MODFLOW-2005 + Niswonger + 10.3133/tm6A37 + 2011 + Niswonger, R. G., Panday, S., & +Ibaraki, M. (2011). MODFLOW-NWT, a newton formulation for MODFLOW-2005. +U.S Geological Survey. +https://doi.org/10.3133/tm6A37 + + + MODFLOW-USG version 1: An unstructured grid +version of MODFLOW for simulating groundwater flow and tightly coupled +processes using a control volume finite-difference +formulation + Panday + 10.3133/tm6A45 + 2013 + Panday, S., Langevin, C. D., +Niswonger, R. G., Ibaraki, M., & Hughes, J. D. (2013). MODFLOW-USG +version 1: An unstructured grid version of MODFLOW for simulating +groundwater flow and tightly coupled processes using a control volume +finite-difference formulation. U.S Geological Survey. +https://doi.org/10.3133/tm6A45 + + + The visualization toolkit: An object-oriented +approach to 3D graphics. ISBN 978-1-930934-19-1. + Schroeder + 2006 + Schroeder, W., Martin, K., & +Lorensen, B. (2006). The visualization toolkit: An object-oriented +approach to 3D graphics. ISBN 978-1-930934-19-1. Kitware. +https://books.google.com/books?id=rx4vPwAACAAJ + + + USGS water data for the nation: U.S. +Geological Survey national water information system +database + U.S. Geological Survey + 10.5066/F7P55KJN + 2022 + U.S. Geological Survey. (2022). USGS +water data for the nation: U.S. Geological Survey national water +information system database. +https://doi.org/10.5066/F7P55KJN + + + Groundwater-flow and land-subsidence model of +antelope valley, california + Siade + 10.3133/sir20145166 + 2014 + Siade, A. J., Nishikawa, T., Rewis, +D. L., Martin, P., & Phillips, S. P. (2014). Groundwater-flow and +land-subsidence model of antelope valley, california. U.S. Geological +Survey, Scientific Investigations Report 2014–5166. +https://doi.org/10.3133/sir20145166 + + + PRMS-python: A python framework for +programmatic PRMS modeling and access to its data +structures + Volk + Environmental Modelling & +Software + 114 + 10.1016/J.ENVSOFT.2019.01.006 + 2019 + Volk, J. M., & Turner, M. A. +(2019). PRMS-python: A python framework for programmatic PRMS modeling +and access to its data structures. Environmental Modelling & +Software, 114, 152–165. +https://doi.org/10.1016/J.ENVSOFT.2019.01.006 + + + Simulation of groundwater and surface-water +resources of the santa rosa plain watershed, sonoma county, +california + Woolfenden + 10.3133/sir20145052 + 2014 + Woolfenden, L. R., & Nishikawa, +T. (2014). Simulation of groundwater and surface-water resources of the +santa rosa plain watershed, sonoma county, california. U.S. Geological +Survey Scientific Investigations Report. +https://doi.org/10.3133/sir20145052 + + + + + + From d073bef5a00f99a48d2561026ddf28cb2eba8eae Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Wed, 20 Apr 2022 21:30:39 +0100 Subject: [PATCH 3/3] Creating 10.21105.joss.03852.jats --- joss.03852/10.21105.joss.03852.jats | 810 ++++++++++++++++++++++++++++ 1 file changed, 810 insertions(+) create mode 100644 joss.03852/10.21105.joss.03852.jats diff --git a/joss.03852/10.21105.joss.03852.jats b/joss.03852/10.21105.joss.03852.jats new file mode 100644 index 0000000000..08b68d90b9 --- /dev/null +++ b/joss.03852/10.21105.joss.03852.jats @@ -0,0 +1,810 @@ + + +

+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +3852 +10.21105/joss.03852 + +Integrated hydrologic model development and +postprocessing for GSFLOW using pyGSFLOW + + + + +Larsen +Joshua D. + + +* + + + +Alzraiee +Ayman + + + + + +Niswonger +Richard G. + + + + + +U.S. Geological Survey, California Water Science Center, +Sacramento, CA + + + + +U.S. Geological Survey, Integrated Modeling and Prediction +Division, Water Mission Area, Menlo Park, CA + + + + +* E-mail: + + +18 +2 +2022 + +7 +72 +3852 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +hydrology +integrated hydrologic modeling +groundwater +surfacewater +GSFLOW +MODFLOW +PRMS + + + + + + Overview +

pyGSFLOW is a Python package designed to create new GSFLOW + integrated hydrologic models, read existing models, edit model input + data, run GSFLOW models, process output, and visualize model data.

+
+ + Introduction +

Effective management of limited water resources is challenging + because of competing interests and rapid development have resulted in + increased demands for water + (Garrote, + 2017; + Lotze-Campen + et al., 2006; + Mekonnen + & Hoekstra, 2016). Understanding interactions among the + different components of a hydrologic system, often supported by + physics-based models, is important information for water resources + planning and management. For example, groundwater models often serve + as a tool for exploring the impacts of different water management and + conservation scenarios + (Davie + & Quinn, 2019). The traditional groundwater modeling + approach has mainly focused on simulating flow and storage in aquifers + that have simplified boundary conditions to represent exchanges with + surface water + (Chiu + et al., 2010; + Li + & Martin, 2011; + Siade + et al., 2014). Ignoring spatiotemporal changes in the + hydraulically connected surface water and groundwater systems by + applying simple and loosely coupled boundary conditions within + hydrologic models is no longer adequate to address water management + issues + (Fatichi + et al., 2016). Instead, integrated hydrologic models (IHMs) + that couple governing equations for surface-water and groundwater flow + are necessary to represent water resources that are impacted by + changes in climate and increases in withdrawals and consumption of + water.

+

GSFLOW is an IHM + (Markstrom + et al., 2008) that simulates surface and subsurface hydrologic + processes by integrating the Precipitation Runoff Modeling System + (PRMS) + (Markstrom + et al., 2015) and MODFLOW + (Harbaugh, + 2005; + Niswonger + et al., 2011) into a single code that simulates feedbacks + between the two systems. Because modelers are moving toward simulating + the hydrologic cycle in greater detail, larger datasets from multiple + sources are used to parameterize these models. Beyond the scope of + example problems, most applied problems require custom workflows and + code to process large datasets related to model inputs and outputs. + Scripting languages like Python make it easier to process large data + sets and provide standard methods that can be used for developing, + editing, and properly formatting model input files and for analyzing + model output data. These developments have led to major advancements + in model reproducibility and improvements in model applicability + (Bakker + et al., 2016; + Gardner + et al., 2018; + Ng + et al., 2018).

+
+ + Statement of need +

GSFLOW model development has previously been a piecemeal approach. + Arcpy-GSFLOW scripts + (Gardner + et al., 2018) or GSFLOW-GRASS packages have been used to + process surface-water input data into model files. PRMS-Python + (Volk + & Turner, 2019) could be used to edit most of the PRMS + inputs to GSFLOW. Finally, FloPy + (Bakker + et al., 2016, + 2021) + could be used to edit most of the MODFLOW inputs to GSFLOW. This + approach is not tightly coupled and still requires manual edits and + additional external scripts to edit, run models, and process output + data. Because of the complexity of IHMs and the need for model + reproducibility, a single integrated scripting package will help + standardize and streamline model development and calibration.

+
+ + pyGSFLOW +

pyGSFLOW + (Larsen + et al., 2021) is a Python package for creating new GSFLOW + models, importing existing models, running GSFLOW models, processing + model outputs, and visualizing model data. Instead of working directly + with formatted model input files, the pyGSFLOW Application Programming + Interface (API) allows the user to work with class-based methods to + create GSFLOW + (Markstrom + et al., 2008) and PRMS + (Markstrom + et al., 2015) input files, MODSIM + (Labadie + & Larson, 2006) vectorized surface-water operations + networks, and MODFLOW + (Harbaugh, + 2005) model packages and binds them into a single integrated + model instance. pyGSFLOW leverages features from FloPy, an existing + Python package for the MODFLOW suite of groundwater modeling software + (Harbaugh, + 2005; + Langevin + et al., 2017; + Niswonger + et al., 2011; + Panday + et al., 2013) and extends the capabilities for IHMs. pyGSFLOW + relies on FloPy model and package objects and interfaces with these + features to provide FloPy users with familiar code syntax and to + ensure the long-term maintainability of the code base.

+

The pyGSFLOW package was developed for hydrologic modelers and + researchers who are developing, calibrating, or applying models as + part of their scientific investigation (e.g., prediction scenarios, + stream capture) with GSFLOW. The code base currently is being used for + the development of several river-basin scale hydrologic models in + western basins, including the example shown below highlighting + application to the Russian River basin and the Santa Rosa Plain + Watershed (figure 1) + (Gardner + et al., 2018; + Woolfenden + & Nishikawa, 2014).

+

The Santa Rosa Plain (SRP) model + (Woolfenden + & Nishikawa, 2014) is an IHM that was developed as a tool + to provide scientific information to water managers about future + climate change scenarios. The SRP model applied four global-climate + models and simulated relative change in groundwater storage and + availability under each scenario. Prior to simulating future change, + the model was calibrated to historical groundwater and surface-water + conditions. Part of the calibration process involves identifying + sensitive and insensitive parameters. Calibration and sensitivity + analysis experiments on model parameters can provide insight into + reducing model error when predicting results such as simulated + streamflow (figure 1). Daily streamflow data from National Water + Information System (NWIS) site 11466800 + (U.S. + Geological Survey, 2022) provided observations for this + experiment. Twenty-one iterations of the SRP model were run in series + with pyGSFLOW to test the sensitivity of the snarea_curve (snow + depletion curve), ssr2gw_rate (gravity reservoir to groundwater + reservior routing coeficient), and gwflow_coef (linear groundwater + discharge equation coeficient) with a simple “for loop.” The + ssr2gw_rate was identified as a much more sensitive parameter to model + calibration than the snarea_curve and gwflow_coef parameters (figure + 1). Insights like these allow researchers to focus their calibration + efforts on the most sensitive parameters and fix insensitive + parameters, thus reducing the time and complexity of the calibration + process. Although actual calibration generally is not done directly + with pyGSFLOW, it provides an easy to use interface to update + parameters based on grid cell location or parameter zone that can be + used for complex operations in conjuction with external calibration + software.

+ +

Root mean squared error in streamflow predictions at U.S + Geological Survey streamgage 11466800 + (U.S. + Geological Survey, 2022) for three PRMS parameters + (gwflow_coef, snarea_curve, and ssr2gw_rate) during calibration + experiments on the Santa Rosa Plain Integrated Hydrologic Model, + Santa Rosa, California.

+ +
+

The pyGSFLOW package also includes features to visualize input and + output data spatially using Matplotlib + (Hunter, + 2007) plots and by exporting datasets to shapefile or the + visualization toolkit (VTK) format + (Schroeder + et al., 2006). By providing pyGSFLOW a shapefile or list of + hydrologic response unit (HRU) geometries, the code is able to plot + arrays and contour arrays of unique parameter values and is fully + compatible with the FloPy plotting routines for MODFLOW. PRMS input + parameter values can be layered over MODFLOW output and can + potentially help identify trends and sensitive parameters controlling + those trends in streamflow, recharge, and groundwater levels + throughout the model. The ssr2gw_rate parameter, which scales the + exchange between the PRMS gravity reservoir and the MODFLOW + groundwater reservior in GSFLOW, can then be overlain on top of + recharge arrays to inspect the input and output for correlated trends + (figure 2). Figure 2 shows that in the western part of the basin, both + the ssr2gw_rate and the relative amount of areal recharge is slightly + greater than the eastern part of the basin. The simulated recharge + data also show the highest volume of recharge occurs along a few short + losing stream sections. These insights can help the researcher adjust + input parameters for both streamflow and groundwater level + calibration.

+ +

Sagehen Creek GSFLOW model, Truckee, California. Mean + recharge for the entire simulation from MODFLOW is overlain with a + spatial contour plot of PRMS parameter ssr2gw_rate which is a + multiplier that scales the volume of recharge from PRMS to MODFLOW. + Black fill indicates inactive model cells.

+ +
+

The online documentation for pyGSFLOW + (Larsen + et al., 2021) contains API information for all major classes + and methods and is updated with each new major release. In addition to + the online documentation, sample Jupyter notebooks + (Kluyver + et al., 2016) are included in the repository to help users + become familiar with the code.

+
+ + Package architecture +

The pyGSFLOW package includes the gsflow module and 5 sub-packages + (figure 3):

+ + +

gsflow: the gsflow module contains the integrated modeling + object GsflowModel, which allows the user to build new GSFLOW + models and import existing models. This module calls classes and + methods from the following 5 sub-packages within pyGSFLOW.

+
+ +

prms: the prms sub-package contains classes and methods to + build new PRMS models, import existing PRMS models, edit model + input data, and write PRMS input data to file.

+
+ +

modflow: the modflow package contains modules and classes that + interface with FloPy and allow the user to create new MODFLOW + packages, edit existing packages, and write MODFLOW input data to + file.

+
+ +

modsim: the modsim sub-package contains classes that translate + MODFLOW model stream and lake networks into vectorized shapefile + representations that can be used to define surface-water operation + networks in MODSIM.

+
+ +

output: the output sub-package contains modules that allow the + user to define their surface-water model discretization and + visualize output data via matplotlib plots.

+
+ +

utils: includes general use utilities that are integrated into + built in functions in the gsflow module, and prms, modflow, and + modsim sub-packages.

+
+
+ +

Hierarchical representation of the pyGSFLOW package. + Each sub-package lists the model building classes within each + package. The GsflowModel class interacts with each of these listed + modules and the FloPy package.

+ +
+
+ + Conclusion +

GSFLOW IHMs simulate complex processes and interactions between + surface-water and groundwater flow systems. Parameterizing these model + processes requires large datasets from multiple sources to represent + the hydrologic cycle. Previous approaches involved multiple + disconnected scripts and packages, some of which rely on proprietary + code, which makes reproducibility difficult. pyGSFLOW is a tightly + coupled software package, that allows the user to import all parts of + their model in one script, which helps to standardize and streamline + model development, calibration, and output analysis.

+
+ + Acknowledgments +

The authors welcome additions, suggestions, and assistance from the + scientific community, and thank all past contributors for their work. + Any use of trade, product, or firm names is for descriptive purposes + only and does not imply endorsement by the U.S. Government.

+
+ + Funding +

This work was funded by the United States Geological Survey’s Water + Availability and Use Program.

+
+ + + + + + + BakkerM. + PostV. + LangevinC. D. + HughesJ. D. + WhiteJ. T. + StarnJ. J. + FienenM. N + + Scripting MODFLOW model development using python and flopy + Groundwater + 2016 + 54 + 10.1111/gwat.12413 + 733 + 739 + + + + + + BakkerM. + PostV. + LangevinC. D. + HughesJ. D. + WhiteJ. T. + LeafA. T. + PaulinskiS. R. + LarsenJ. D. + TowesM. + MorwayE. D. + BellinoJ. C. + StarnJ. J. + FienenM. N. + + FloPy v3.3.3 + U.S. Geological Survey Software Release + 2021 + 10.5066/F7BK19FH + + + + + + ChiuY. + NishikawaT. + YehW. -G. + + Optimal pump and recharge management model for nitrite removal in the warren groundwater basin, california + Journal of Water Resources Planning and Management + 2010 + 136 + 3 + 10.1061/(ASCE)WR.1943-5452.0000034 + + + + + + DavieT. + QuinnN. W. + + Fundamentals of hydrology + Routledge + 2019 + 10.4324/9780203798942 + + + + + + FatichiS. + VivoniE. R. + OgdenF. L. + IvanovV. Y. + MirusB. + GochisD. + DownerC. W. + CamporeseM. + DavisonJ. H. + EbelB. + JonesN. + KimJ. + MascaroG. + NiswongerR. + RestrepoP. + RigonR. + ShenC. + SulisM. + TarbotonD. + + An overview of current applications, challenges, and future trends in distributed process-based models in hydrology + 2016 + 537 + 10.1016/j.jhydrol.2016.03.026 + 45 + 60 + + + + + + GardnerM. A. + MortonC. G. + HuntingtonJ. L. + NiswongerR. G. + HensonW. R. + + Input data processing tools for the integrated hydrologic model GSFLOW. + Environmental modelling & software + 2018 + 109 + 10.1016/j.envsoft.2018.07.020 + 41 + 53 + + + + + + GarroteL. + + Managing water resources to adapt to climate change: Facing uncertainty and scarcity in a changing context + Water Resources Management + 2017 + 31 + 10.1007/s11269-017-1714-6 + 2951 + 2963 + + + + + + HarbaughA. W. + + MODFLOW-2005, the U.S. Geological Survey modular ground-water model – the ground-water flow process + U.S. Geological Survey + 2005 + 10.3133/tm6A16 + + + + + + HunterJ. 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