ParFlow hydrologic model

Modelling surface and subsurface flow on high-performance computers


ParFlow 3.12.0 has been released on GitHub

This release contains some bug fixes and documentation updates, as well as a new introduced surface pressure threshold key. The full release notes are available on github as well as source downloads and a Docker image.


ParFlow is a numerical model that simulates the hydrologic cycle from the bedrock to the top of the plant canopy. It integrates three-dimensional groundwater flow with overland flow and plant processes using physically-based equations to rigorously simulate fluxes of water and energy in complex real-world systems. ParFlow is a computationally advanced model that can run on laptops and supercomputers and has been used in hundreds of studies evaluating hydrologic processes from the hillslope to the continental scale. Our code is open source and we promote a community of active users and developers interested in advancing computational hydrology and improving hydrologic understanding. Details about the model, example applications and links for downloading and getting started with the code are provided below.

ParFlow is used extensively for water cycle research in idealized and real domains as part of process studies, forecasting analysis, data assimilation frameworks, hind-casting tools and climate change projections. The model has been extensively benchmarked and has more than 130 publications describing its development and application to diverse systems around the world. ParFlow applications have been built for the continental US (CONUS) and Continental Europe in addition to more than a dozen watersheds around the world including the Big Thompson, CO; Klamath, OR; Little Washita, OK; San Joaquin, CA; Sante Fe, FL; Chesapeake, MD; Rur as well as several headwater catchments, Germany.

ParFlow is a parallel, integrated hydrology model that simulates spatially distributed surface and subsurface flow, as well as land surface processes including evapotranspiration and snow. It solves saturated and variably saturated flow in three dimensions using either an orthogonal or terrain-following, semi-structured mesh that enables fine vertical resolution near the land surface and deep (~1 km) confined and unconfined aquifers. ParFlow models dynamic surface and subsurface flow solving the simplified shallow water equations implicitly coupled to Richards’ equation; this allows for dynamic two-way groundwater surface water interactions and intermittency in streamflow. The model uses robust linear and nonlinear solution techniques and exhibits efficient parallel scaling to large processor counts, more than 100K cores, enabling very large extent simulations with fine spatial resolution. ParFlow has been coupled to various land surface and atmospheric models such as CLM, WRF, and TerrSysMP.


  • Richards' equation for variably saturated 3D subsurface flow
  • Shallow water equations for surface flow
  • Modular, coupled land model that represents full energy budget, vegetative and snow processes
  • Robust nonlinear solvers (using the Kinsol Newton-Krylov package) and efficient multigrid linear solver (using the Hypre package)
  • Parallel implementation using multiple approaches and architectures
  • Excellent parallel scalability with production runs of more than 30k processors
  • Support for OpenMP and CUDA for use on accelerator architectures such as GPUs
  • Data formats such as SILO and NetCDF4
  • Implementation on different architectures and operating systems from "Laptop to Supercomputer" (single CPU, Linux clusters, highly scalable systems such as IBM Blue Gene) with the same source code and input on all platforms
  • Widespread use on many institutional computer systems including many of the fastest supercomputers in the world (e.g. Edison, Cori, Yellowstone, JUQUEEN)
  • Application to a wide range of hydrology problems and basins from small headwaters catchment to the continent
  • Broad community development and use
  • Extensive automated testing framework that follows best software practices
  • Implementation as a Docker instance for easy and efficient deployments
  • Large set of utilities for pre- and postprocessing and diagnistics calculations
  • Flexible and portable cmake built environment
ParFlow system schematic

Under development

  • Lagrangian particle tracking to simulate water residence time, surface subsurface exchange and contaminant transport
  • Adaptive mesh refinement unsing p4est
  • Extreme scaling capability up to more than 400k processors
  • Geochemical reactive transport with higher order advection schemes and reaction kinetics using CRUNCH
  • NetCDF CF meta-data convention for portability, provenance and worflow integration
  • In-situ visualisation and processing using VisIt
  • JUBE provenance-enabled work flow engine implementation for efficient development, benchmarking and run-control
  • Integrated water management including pumping, irrigation and diversions
ParFlow water management

Some example applications

ParFlow has been widely used to simulate flow and transport systems worldwide; here we highlight some recent examples linked to their corresponding publications. (Clicking on the figures opens a link to the publication's website.)

Soil moisture monitoring and forecasting for agriculture over central Europe In the context of more frequent and consecutive droughts during the vegetation period over the last years (e.g. 2018, 2019, and 2020) in central Europe, we use ParFlow/CLM at ~600m resolution to monitor and forecast the soil water budget over Germany and the neighboring regions.
The deterministic medium-range forecast over 10 days is extended by a 50 member ensemble to account for the uncertainty of the forecast, and especially precipitation, and its impact on the soil water budget.
In addition, subseasonal probabilistic ensemble forecasts (3 months lead time) are calculated a few times per year to help farmers and their advisors to manage the water resources over a longer time span. Several parameters that are relevant for the stakeholders from the agricultural sector are derived from the standard ParFlow outputs, providing information on various aspects of the soil water budget for different root depths, e.g. plant available water, seepage water, subsurface water storage change, groundwater recharge, water table depth, and saturation.

Transient, integrated simulation of groundwater and surface water over the Continental US A representation of pre-development groundwater, surface water, and surface energy processes, which are driven by hourly forcing by NLDAS-II from the 1985 water year. At 1km lateral resolution, with 12TB of model output and 3TB of input, this is the first large-scale, high resolution simulation of its kind, capable of resolving complex interactions between climate, water and topography.

Groundwater-surface water interactions in the San Joaquin River Basin As one of the most productive agricultural regions of the United States and a major water resource for a growing population, the San Joaquin River basin in the Central Valley in California, is a case study in the water-food-energy nexus and sensitivity to a changing climate. Here, we seek to better understand the physical hydrology of the basin by simulating groundwater-surface water dynamics with ParFlow-CLM. Results suggest that mountain block hydraulic conductivity could account for 7-23% of total recharge in the Central Valley, an important finding for water resource management in California.

Groundwater-land surface-atmosphere feedbacks during the European 2003 heat wave By coupling ParFlow to land and meteorological models, the fully coupled water cycle from groundwater to atmosphere can be simulated, a novel exploration in that atmospheric models rarely incorporate a dynamic water table and three dimensional subsurface flow. This study couples ParFlow to the meteorological model over Europe during the 2003 heat wave in order to investigate the effects of various lower boundary conditions and configurations on land-atmosphere moisture exchange and thermal energy.

Continental water residence times Ever wonder how long water spends in the subsurface? Residence time and groundwater age are vital to ecosystem development and human consumption, but measuring residence time distributions is difficult at large scales. ParFlow may be used to estimate water residence time when used in conjunction with a Lagrangian particle tracking approach. A simulation of groundwater age across the continental United States allows unique insight into the relationship between geography, climate, and water residence time in major basins.

The effects of insect-induced tree mortality on water and energy in mountain headwaters The mountain pine beetle (MPB) has decimated the high elevation lodgepole and ponderosa pine forests of Western United States and Canada over the past two decades. ParFlow-CLM was used to diagnose feedbacks that land disturbance at this scale has on water and energy fluxes in the Big Thompson watershed in Colorado. Results show that insect-induced reductions in canopy interception, transpiration, and snow pack are largely mitigated by heightened ground evaporation and ablation, adding to the growing number of studies citing damping of MPB hydrologic signal at large scales.

Scale dependent parameterization in integrated hydrologic modeling The highly instrumented Wüstebach catchment in Germany allows the unique opportunity to explore the application of the information entropy concept in subsurface parameterization of three dimensional hydrological models. Results suggest that amplifying soil hydraulic conductivity in regions where aggregation of observations at model scale leads to loss of topographic information content may increase model performance, an important finding for high-resolution, large-scale physically based modeling that requires detailed subsurface parameterization.

Moisture dependent irrigation and its feedbacks with integrated hydrology In this example, ParFlow was used in conjunction with a novel linear optimization water allocation module to evaluate the impact of groundwater-surface water interactions on moisture dependent irrigation in the Little Washita River Basin, Oklahoma.

Modeling the Critical Zone in an ephemeral West-African Monsoon system West-African hydrosystems are driven by seasonal monsoon dynamics with high variability. While the extreme drought in the 70's and 80's was surprisingly associated with runoff and water table recharge increase and (due to simultaneous land use change) in the Sahel, consequences in the southern, more humid Sudanian zone were a major drop in streamflow. Streamflow generation processes in this area involve subsurface processes (as opposed to Hortonian-dominated Sahelian processes) including temporary connexion of perched and permanent water table. Water table drawdown during the dry season is controlled by land cover distribution and dominated by tree transpiration. This highly connected critical zone system (see e.g. Hector et al., 2015) requires integrated modeling to assess sensitivities to global changes and guide policymakers in this part of the world. The fully coupled ParFlow-CLM simulation of a small watershed in northern Benin (the Ara catchment) shows the intermittent streamflow generation (surface saturation as blue patches), saturation variations in the surface and unsaturated zone, and both permanent and perched water table changes (saturated, deep blue zones in the bottom of the domain) as a response of the interplay between precipitation (spatially uniform, temporally variable) and evapotranspiration (different vegetation classes across the domain). This simulation was successfully compared to a complete dataset (streamflow, water table, soil moisture, evapotranspiration, water storage...) produced by the AMMA-CATCH observatory ( It allowed to assess the importance in vegetation spatial distribution in partitionning the different terms of the water budget and thus calls to enhance the inclusion of land cover.


ParFlow is an open-source, community integrated hydrology model that is freely available on GitHub. It has well over 100 active users, and development is a callaborative effort between several institutions: Juelich Research Centre, Princeton University, Lawrence Livermore National Laboratory, Colorado School of Mines, Bonn University, Washington State University, Syracuse University and the Universty Grenoble Alpes. The Centre for High-Performance Scientific Computing in Terrestrial Systems and the Integrated GroundWater Modeling Center help anchor the development team.


Getting Started

For new ParFlow users, we recommend reading the Parflow user's manual. It contains lots of useful information on getting started, a complete table of published studies that have used ParFlow for a range of applications (which are listed below), some very helpful annotated examples and a complete library of the keys for running simulations and tools for postprocessing output. If you are building a model of a real domain there are helpful blogposts on a workflow for setting up and spinning up models on the ParFlow Blog. Also the blog contains posts on trouble shooting slow model performance and common errors when starting a simulation that will be useful to new users. Finally, the blog contains a lot of useful advice on getting ParFlow compiled on many different platforms.

Getting ParFlow

ParFlow is released on GitHub. The stable release version, older versions, and the latest ParFlow developer versions are available on GitHub under Please refer to the GitHub documentation and the blog on how to obtain and build the code. If you are using ParFlow, please subscribe to the ParFlow users mailing list; we try to keep track of active users.

The latest stable release is v3.12.0 (2023-01-06), which also contains many test cases including data to get started.


ParFlow is released under the GNU LPGL license agreement.

Citing ParFlow

If you use ParFlow in a publication, please cite the these papers that describe model physics:

If you use ParFlow coupled to CLM in a publication, please also cite two additional papers that describe the coupled model physics:

Bug reports and feature requests

To report bugs or request features, please use the ParFlow Issue Tracker on GitHub. Please note that ParFlow is a community supported research code and while we will attempt to answer questions posted to this list your patience is appreciated.



List of publications that use ParFlow (last update: 2023-01-10):

  1. Schreiner-McGraw, A.P., and Ajami, H. (2022). Combined impacts of uncertainty in precipitation and air temperature on simulated mountain system recharge from an integrated hydrologic model. Hydrol. Earth Syst. Sci., 26(4), 1145-1164, doi:10.5194/hess-26-1145-2022.
  2. Soltani, S.S., Fahs, M., Al Bitar,, and Ataie-Ashtiani, B. (2022). Improvement of soil moisture and groundwater level estimations using a scale-consistent river parameterization for the coupled ParFlow-CLM hydrological model: A case study of the Upper Rhine Basin. J. Hydrol., 610, doi:10.1016/j.jhydrol.2022.127991.
  3. Friedemann, S., and Raffin, B. (2022). An elastic framework for ensemble-based large-scale data assimilation. Int. J. High Perform. Comput. Appl., 36(4), 543-563, doi:10.1177/10943420221110507.
  4. Gallagher, L.K., Farley, A.J., Chennault, C., Cerasoli, S., Jourdain, S., O'Leary, P., Condon, L.E., and Maxwell, R.M. (2022). The ParFlow Sandtank: An interactive educational tool making invisible groundwater visible. Front. Water, 4, doi:10.3389/frwa.2022.909918.
  5. Leonarduzzi, E., Tran, H., Bansal, V., Hull, R.B., de la, Bearup, L.A., Melchior, P., Condon, L.E., and Maxwell, R.M. (2022). Training machine learning with physics-based simulations to predict 2D soil moisture fields in a changing climate. Front. Water, 4, doi:10.3389/frwa.2022.927113.
  6. Fang, Y.L., Leung, L.R., Koven, C.D., Bisht, G., Detto, M., Cheng, Y.Y., McDowell, N., Muller-Landau, H., Wright, S.J., and Chambers, J.Q. (2022). Modeling the topographic influence on aboveground biomass using a coupled model of hillslope hydrology and ecosystem dynamics. Geosci. Model Dev., 15(20), 7879-7901, doi:10.5194/gmd-15-7879-2022.
  7. Lim, T., and Wang, K.D. (2022). Comparison of machine learning algorithms for emulation of a gridded hydrological model given spatially explicit inputs. Comput. Geosci., 159, doi:10.1016/j.cageo.2021.105025.
  8. Tran, H., Zhang, J., O'Neill, M.M., Ryken, A., Condon, L.E., and Maxwell, R.M. (2022). A hydrological simulation dataset of the Upper Colorado River Basin from 1983 to 2019. Sci. Data, 9(1), doi:10.1038/s41597-022-01123-w.
  9. Maxwell, R.M., Condon, L.E., and Melchior, P. (2021). 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. Water, 13(24), doi:10.3390/w13243633.
  10. Tran, H., Leonarduzzi, E., de la, Hull, R.B., Bansal, V., Chennault, C., Gentine, P., Melchior, P., Condon, L.E., and Maxwell, R.M. (2021). Development of a Deep Learning Emulator for a Distributed Groundwater-Surface Water Model: ParFlow-ML. Water, 13(23), doi:10.3390/w13233393.
  11. Talebpour, M., Welty, C., and Bou-Zeid, E. (2021). Development and testing of a fully-coupled subsurface-land surface-atmosphere hydrometeorological model: High-resolution application in urban terrains. Urban CLim., 40, doi:10.1016/j.uclim.2021.100985.
  12. O'Neill, M.M.F., Tijerina, D.T., Condon, L.E., and Maxwell, R.M. (2021). Assessment of the ParFlow-CLM CONUS 1.0 integrated hydrologic model: evaluation of hyper-resolution water balance components across the contiguous United States. Geosci. Model Dev., 14(12), 7223-7254, doi:10.5194/gmd-14-7223-2021.
  13. Herzog, A., Hector, B., Cohard, J.M., Vouillamoz, J.M., Lawson, F.M.A., Peugeot, C., and de Graaf, (2021). A parametric sensitivity analysis for prioritizing regolith knowledge needs for modeling water transfers in the West African critical zone. Vadose Zone J., 20(6), doi:10.1002/vzj2.20163.
  14. Schalge, B., Baroni, G., Haese, B., Erdal, D., Geppert, G., Saavedra, P., Haefliger, V., Vereecken, H., Attinger, S., Kunstmann, H., Cirpka, O.A., Ament, F., Kollet, S., Neuweiler, I., Franssen, H.J.H., and Simmer, C. (2021). Presentation and discussion of the high-resolution atmosphere-land-surface-subsurface simulation dataset of the simulated Neckar catchment for the period 2007-2015. Earth Syst. Sci. Data, 13(9), 4437-4464, doi:10.5194/essd-13-4437-2021.
  15. Zhao, H.J., Montzka, C., Baatz, R., Vereecken, H., and Franssen, H.J.H. (2021). The Importance of Subsurface Processes in Land Surface Modeling over a Temperate Region: An Analysis with SMAP, Cosmic Ray Neutron Sensing and Triple Collocation Analysis. Remote Sens., 13(16), doi:10.3390/rs13163068.
  16. Tijerina, D., Condon, L., FitzGerald, K., Dugger, A., O'Neill, M.M., Sampson, K., Gochis, D., and Maxwell, R. (2021). Continental Hydrologic Intercomparison Project, Phase 1: A Large-Scale Hydrologic Model Comparison Over the Continental United States. Water Resour. Res., 57(7), doi:10.1029/2020WR028931.
  17. Jiang, A.L., Lee, M.C., Zhou, G.F., Zhong, D.B., Hawaria, D., Kibret, S., Yewhalaw, D., Sanders, B.F., Yan, G.Y., and Hsu, K.L. (2021). Predicting distribution of malaria vector larval habitats in Ethiopia by integrating distributed hydrologic modeling with remotely sensed data. Sci Rep, 11(1), doi:10.1038/s41598-021-89576-8.
  18. Kumar, P., Debele, S.E., Sahani, J., Rawat, N., Marti-Cardona, B., Alfieri, S.M., Basu, B., Basu, A.S., Bowyer, P., Charizopoulos, N., Gallotti, G., Jaakko, J., Leo, L.S., Loupis, M., Menenti, M., Mickovski, S.B., Mun, S.J., Gonzalez-Ollauri, A., Pfeiffer, J., Pilla, F., Proll, J., Rutzinger, M., Santo, M.A., Sannigrahi, S., Spyrou, C., Tuomenvirta, H., and Zieher, T. (2021). Nature-based solutions efficiency evaluation against natural hazards: Modelling methods, advantages and limitations. Sci. Total Environ., 784, doi:10.1016/j.scitotenv.2021.147058.
  19. Yang, C., Zhang, Y.K., Liang, X.Y., Olschanowsky, C., Yang, X.F., and Maxwell, R. (2021). Accelerating the Lagrangian particle tracking of residence time distributions and source water mixing towards large scales. Comput. Geosci., 151, doi:10.1016/j.cageo.2021.104760.
  20. Hokkanen, J., Kollet, S., Kraus, J., Herten, A., Hrywniak, M., and Pleiter, D.. Leveraging HPC accelerator architectures with modern techniques - hydrologic modeling on GPUs with ParFlow. Comput. Geosci., doi:10.1007/s10596-021-10051-4.
  21. Schreiner-McGraw, A.P., and Ajami, H. (2020). Impact of Uncertainty in Precipitation Forcing Data Sets on the Hydrologic Budget of an Integrated Hydrologic Model in Mountainous Terrain. Water Resour. Res., 56(12), doi:10.1029/2020WR027639.
  22. Lapides, D.A., David, C., Sytsma, A., Dralle, D., and Thompson, S. (2020). Analytical solutions to runoff on hillslopes with curvature: numerical and laboratory verification. Hydrol. Process., 34(24), 4640-4659, doi:10.1002/hyp.13879.
  23. Pomeon, T., Wagner, N., Furusho, C., Kollet, S., and Reinoso-Rondinel, R. (2020). Performance of a PDE-Based Hydrologic Model in a Flash Flood Modeling Framework in Sparsely-Gauged Catchments. Water, 12(8), doi:10.3390/w12082157.
  24. Forrester, M.M., and Maxwell, R.M. (2020). Impact of Lateral Groundwater Flow and Subsurface Lower Boundary Conditions on Atmospheric Boundary Layer Development over Complex Terrain. J. Hydrometeorol., 21(6), 1133-1160, doi:10.1175/JHM-D-19-0029.1.
  25. Yang, C., Li, H.Y., Fang, Y.L., Cui, C.X., Wang, T.Y., Zheng, C.M., Leung, L.R., Maxwell, R.M., Zhang, Y.K., and Yang, X.F. (2020). Effects of Groundwater Pumping on Ground Surface Temperature: A Regional Modeling Study in the North China Plain. J. Geophys. Res.-Atmos., 125(9), doi:10.1029/2019JD031764.
  26. Maples, S.R., Foglia, L., Fogg, G.E., and Maxwell, R.M. (2020). Sensitivity of hydrologic and geologic parameters on recharge processes in a highly heterogeneous, semi-confined aquifer system. Hydrol. Earth Syst. Sci., 24(5), 2437-2456, doi:10.5194/hess-24-2437-2020.
  27. de Graaf,, Condon, L., and Maxwell, R. (2020). Hyper-Resolution Continental-Scale 3-D Aquifer Parameterization for Groundwater Modeling. Water Resour. Res., 56(5), doi:10.1029/2019WR026004.
  28. Trevisan, A., Venema, V., Kollet, S., and Rahman, M. (2020). The topographic control on land surface energy fluxes: A statistical approach to bias correction. J. Hydrol., 584, doi:10.1016/j.jhydrol.2020.124669.
  29. Reyes, B., Hogue, T.S., and Maxwell, R.M. (2020). Urban irrigation in the modeling of a semi-arid urban environment: Ballona Creek watershed, Los Angeles, California. Hydrol. Sci. J.-J. Sci. Hydrol., 65(8), 1344-1357, doi:10.1080/02626667.2020.1751846.
  30. Batelis, S.C., Rahman, M., Kollet, S., Woods, R., and Rosolem, R. (2020). Towards the representation of groundwater in the Joint UK Land Environment Simulator. Hydrol. Process., 34(13), 2843-2863, doi:10.1002/hyp.13767.
  31. Hinderer, J., Hector, B., Riccardi, U., Rosat, S., Boy, J.P., Calvo, M., Littel, F., and Bernard, J.D. (2020). A study of the monsoonal hydrology contribution using a 8-yr record (2010-2018) from superconducting gravimeter OSG-060 at Djougou (Benin, West Africa). Geophys. J. Int., 221(1), 431-439, doi:10.1093/gji/ggaa027.
  32. Kuffour, B.N.O., Engdahl, N.B., Woodward, C.S., Condon, L.E., Kollet, S., and Maxwell, R.M. (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. Geosci. Model Dev., 13(3), 1373-1397, doi:10.5194/gmd-13-1373-2020.
  33. Ryken, A., Bearup, L.A., Jefferson, J.L., Constantine, P., and Maxwell, R.M. (2020). Sensitivity and model reduction of simulated snow processes: Contrasting observational and parameter uncertainty to improve prediction. Adv. Water Resour., 135, doi:10.1016/j.advwatres.2019.103473.
  34. Schalge, B., Haefliger, V., Kollet, S., and Simmer, C. (2019). Improvement of surface run-off in the hydrological model ParFlow by a scale-consistent river parameterization. Hydrol. Process., 33(14), 2006-2019, doi:10.1002/hyp.13448.
  35. Barnes, M.L., and Welty, C. (2019). Quantifying Water Balance Components at a Permeable Pavement Site Using a Coupled Groundwater-Surface Water Model. J. Hydrol. Eng., 24(7), doi:10.1061/(ASCE)HE.1943-5584.0001789.
  36. Erdal, D., Baroni, G., Sanchez-Leon, E., and Cirpka, O.A. (2019). The value of simplified models for spin up of complex models with an application to subsurface hydrology. Comput. Geosci., 126, 62-72, doi:10.1016/j.cageo.2019.01.014.
  37. Hein, A., Condon, L., and Maxwell, R. (2019). Evaluating the relative importance of precipitation, temperature and land-cover change in the hydrologic response to extreme meteorological drought conditions over the North American High Plains. Hydrol. Earth Syst. Sci., 23(4), 1931-1950, doi:10.5194/hess-23-1931-2019.
  38. Baroni, G., Schalge, B., Rakovec, O., Kumar, R., Schuler, L., Samaniego, L., Simmer, C., and Attinger, S. (2019). A Comprehensive Distributed Hydrological Modeling Intercomparison to Support Process Representation and Data Collection Strategies. Water Resour. Res., 55(2), 990-1010, doi:10.1029/2018WR023941.
  39. Lim, T.C., and Welty, C. (2018). Assessing Variability and Uncertainty in Green Infrastructure Planning Using a High-Resolution Surface-Subsurface Hydrological Model and Site-Monitored Flow Data. Front. Built Environ., 4, doi:10.3389/fbuil.2018.00071.
  40. Hector, B., Cohard, J.M., Seguis, L., Galle, S., and Peugeot, C. (2018). Hydrological functioning of western African inland valleys explored with a critical zone model. Hydrol. Earth Syst. Sci., 22(11), 5867-5900, doi:10.5194/hess-22-5867-2018.
  41. Ajami, H., and Sharma, A. (2018). Disaggregating Soil Moisture to Finer Spatial Resolutions: A Comparison of Alternatives. Water Resour. Res., 54(11), 9456-9483, doi:10.1029/2018WR022575.
  42. Gou, S., Miller, G.R., Saville, C., Maxwell, R.M., and Ferguson, I.M. (2018). Simulating groundwater uptake and hydraulic redistribution by phreatophytes in a high-resolution, coupled subsurface-land surface model. Adv. Water Resour., 121, 245-262, doi:10.1016/j.advwatres.2018.08.008.
  43. Barnes, M.L., Welty, C., and Miller, A.J. (2018). Impacts of Development Pattern on Urban Groundwater Flow Regime. Water Resour. Res., 54(8), 5198-5212, doi:10.1029/2017WR022146.
  44. Sharples, W., Zhukov, I., Geimer, M., Goergen, K., Luehrs, S., Breuer, T., Naz, B., Kulkarni, K., Brdar, S., and Kollet, S. (2018). A run control framework to streamline profiling, porting, and tuning simulation runs and provenance tracking of geoscientific applications. Geosci. Model Dev., 11(7), 2875-2895, doi:10.5194/gmd-11-2875-2018.
  45. Gilbert, J.M., and Maxwell, R.M. (2018). Contrasting warming and drought in snowmelt-dominated agricultural basins: revealing the role of elevation gradients in regional response to temperature change. Environ. Res. Lett., 13(7), doi:10.1088/1748-9326/aacb38.
  46. Danesh-Yazdi, M., Klaus, J., Condon, L.E., and Maxwell, R.M. (2018). Bridging the gap between numerical solutions of travel time distributions and analytical storage selection functions. Hydrol. Process., 32(8), 1063-1076, doi:10.1002/hyp.11481.
  47. Burstedde, C., Fonseca, J.A., and Kollet, S. (2018). Enhancing speed and scalability of the ParFlow simulation code. Comput. Geosci., 22(1), 347-361, doi:10.1007/s10596-017-9696-2.
  48. Lim, T.C., and Welty, C. (2017). Effects of spatial configuration of imperviousness and green infrastructure networks on hydrologic response in a residential sewershed. Water Resour. Res., 53(9), 8084-8104, doi:10.1002/2017WR020631.
  49. Fang, Y.L., Leung, L.R., Duan, Z.R., Wigmosta, M.S., Maxwell, R.M., Chambers, J.Q., and Tomasella, J. (2017). Influence of landscape heterogeneity on water available to tropical forests in an Amazonian catchment and implications for modeling drought response. J. Geophys. Res.-Atmos., 122(16), 8410-8426, doi:10.1002/2017JD027066.
  50. Gilbert, J.M., Maxwell, R.M., and Gochis, D.J. (2017). Effects of Water-Table Configuration on the Planetary Boundary Layer over the San Joaquin River Watershed, California. J. Hydrometeorol., 18(5), 1471-1488, doi:10.1175/JHM-D-16-0134.1.
  51. Sulis, M., Williams, J.L., Shrestha, P., Diederich, M., Simmer, C., Kollet, S.J., and Maxwell, R.M. (2017). Coupling Groundwater, Vegetation, and Atmospheric Processes: A Comparison of Two Integrated Models. J. Hydrometeorol., 18(5), 1489-1511, doi:10.1175/JHM-D-16-0159.1.
  52. Sweetenham, M.G., Maxwell, R.M., and Santi, P.M. (2017). Assessing the timing and magnitude of precipitation-induced seepage into tunnels bored through fractured rock. Tunn. Undergr. Space Technol., 65, 62-75, doi:10.1016/j.tust.2017.02.003.
  53. Gebler, S., Franssen, H.J.H., Kollet, S.J., Qu, W., and Vereecken, H. (2017). High resolution modelling of soil moisture patterns with TerrSysMP: A comparison with sensor network data. J. Hydrol., 547, 309-331, doi:10.1016/j.jhydrol.2017.01.048.
  54. Jefferson, J.L., Maxwell, R.M., and Constantine, P.G. (2017). Exploring the Sensitivity of Photosynthesis and Stomatal Resistance Parameters in a Land Surface Model. J. Hydrometeorol., 18(3), 897-915, doi:10.1175/JHM-D-16-0053.1.
  55. Gilbert, J.M., and Maxwell, R.M. (2017). Examining regional groundwater-surface water dynamics using an integrated hydrologic model of the San Joaquin River basin. Hydrol. Earth Syst. Sci., 21(2), 923-947, doi:10.5194/hess-21-923-2017.
  56. Kollet, S., Sulis, M., Maxwell, R.M., Paniconi, C., Putti, M., Bertoldi, G., Coon, E.T., Cordano, E., Endrizzi, S., Kikinzon, E., Mouche, E., Mugler, C., Park, Y.J., Refsgaard, J.C., Stisen, S., and Sudicky, E. (2017). The integrated hydrologic model intercomparison project, IH-MIP2: A second set of benchmark results to diagnose integrated hydrology and feedbacks. Water Resour. Res., 53(1), 867-890, doi:10.1002/2016WR019191.
  57. Atchley, A.L., Kinoshita, A.M., Lopez, S.R., Trader, L. and Middleton, R. (2017). Simulating Surface and Subsurface Water Balance Changes Due to Burn Severity. Vadoze Zone Journal, 17, doi:10.2136/vzj2018.05.0099.
  58. Gilbert, J.M., Maxwell, R.M. and Gochis, D.J. (2017). Effects of water table configuration on the planetary boundary layer over the San Joaquin River watershed, California. Journal of Hydrometeorology, 18, 1471-1488, doi:10.1175/JHM-D-16-0134.1.
  59. Gebler, S., Hendricks-Franssen, H.J., Kollet, S.J., Qu, W., Vereecken, H. (2017). High resolution modeling of soil moisture patterns with TerrSysMP: A comparison with sensor network data. in press. doi:10.1016/j.jhydrol.2017.01.048.
  60. Sweetenham M.G.,Maxwell, R.M. (2017). and Santi, P.M. Assessing the timing and magnitude of precipitation-induced seepage into tunnels bored through fractured rock. Tunnelling and Underground Space Technology, 65, 62-75, doi:10.1016/j.tust.2017.02.003.
  61. Sulis, M., Williams, J.L., Shrestha, P., Diederich, M., Simmer, C., Kollet, S.J., and Maxwell, R.M. (2017). Coupling groundwater, vegetation, and atmospheric processes: a comparison of two integrated models. Journal of Hydrometeorology, 18, 1489-1511, doi:10.1175/JHM-D-16-0159.1.
  62. Condon, L.E. and Maxwell, R.M. (2017). Systematic shifts in Budyko relationships caused by groundwater storage changes. Hydrology and Earth System Sciences, 21, 1117-1135, doi:10.5194/hess-21-1117-2017.
  63. Gilbert, J.M. and Maxwell, R.M. (2017). Examining regional groundwater-surface water dynamics using an integrated hydrologic model of the San Joaquin River basin. Hydrology and Earth System Sciences, 21, 923-947, doi:10.5194/hess-2aw31-923-2017, 2017.
  64. Jefferson, J.L., Maxwell, R.M. and Constantine, P.G. (2017). Exploring the sensitivity of photosynthesis and stomatal resistance parameters in a land surface model. Journal of Hydrometeorology, 18(3), 897-915, doi:10.1175/JHM-D-16-0053.1
  65. Kollet, S.J., Sulis, M., Maxwell, R.M., Paniconi, C., Putti, M., Bertoldi, G., Coon, E.T., Cordano, E., Endrizzi, S., Kikinzon, E., Mouche, E., Mügler, C., Park, Y-J, Stisen, S., Sudicky, E., (2017). The Integrated Hydrologic Model Intercomparison Project, IH-MIP2: A second set of benchmark results to diagnose integrated hydrology and feedbacks. Water Resources Research, 53(1), 867-890, doi:10.1002/2016WR019191.
  66. Keune, J., F. Gasper, K. Goergen, A. Hense, P. Shrestha, M. Sulis and S. Kollet (2016). Sudying the influence of groundwater representations on land surface-atmosphere feedbacks during the European heat wave in 2003. J. of Geophys. Res. Atmos.. 121(13), 301-13,325. doi:10.1002/2016JD025426.
  67. Rahman, M., M. Sulis, and S.J. Kollet (2016). Evaluating the dual-boundary forcing concept in subsurface-land surface interactions of the hydrological cycle. Hydrological Processes . 30, 1563-1573.
  68. Kollet, S.J. (2016). Technical note: Inference in hydrology from entropy balance considerations. Hydrol. Earth Syst. Sci. , 20, 2801-2809, doi:10.5194/hess-20-2801-2016.
  69. Fang, Z., H. Bogena, S. Kollet, and H. Vereecken (2016). Scale dependent parameterization of soil hydraulic conductivity in 3D simulation of hydrological processes in a forested headwater catchment. Journal of Hydrology, 536, 365-375, doi:10.1016/j.jhydrol.2016.03.020.
  70. Koch, J., Cornelissen, T., Fang, Z., Bogen, H., Diekkrüger, B., Doller, S., and S. Stisen (2016). Inter-comparison of three distributed hydrolgical models with respect to seasonal variability of soil moisture patterns at a small forested catchment. Journal of Hydrology. 533, 234-249, doi:10.1016/j.jhydrol.2015.12.002.
  71. Pribulick, C.E., Foster, L.M., Bearup, L.A., Navarre-Sitchler, A.K., Williams, K.H., Carroll, R.W.H, and Maxwell, R.M. (2016). Contrasting the hydrologic response due to land cover and climate change in a mountain headwaters system. Ecohydrology, 9(8), 1431-1438, doi:10.1002/eco.1779.
  72. Maxwell, R.M. and Condon, L.E. (2016). Connections between groundwater flow and transpiration partitioning. Science, 353(6297), 377-380. doi:10.1126/science.aaf7891.
  73. Kurtz, W., He, G., Kollet, S.J., Maxwell, R.M., Vereecken, H. and Hendricks Franssen,H.-J. (2016). TerrSysMP-PDAF (version 1.0): A modular high-performance data assimilation framework for an integrated land surface-subsurface model. Geoscientific Model Development, 9, 1341-1360, doi:10.5194/gmd-9-1341-2016.
  74. Gilbert, J.M., Jefferson, J.L., Constantine, P.G. and Maxwell, R.M. (2016). Global spatial sensitivity of runoff to subsurface permeability using the active subspace method. Advances in Water Resources, 92, 30-42, doi:10.1016/j.advwatres.2016.03.020.
  75. Penn, C.A., Bearup, L.A., Maxwell, R.M. and Clow, D.W. (2016). Numerical experiments to explain multi-scale hydrological responses to mountain pine beetle tree mortality in a headwater watershed. Water Resources Research, 52:4, 3143-3161, doi:10.1002/2015WR018300.
  76. Markovich, K.H., Maxwell, R.M. and Fogg, G.E. (2016). Hydrogeological response to climate change in alpine hillslopes. Hydrological Processes, 30, 3126-3138, doi:10.1002/hyp.10851.
  77. Lopez, S.R. and Maxwell, R.M. (2016). Identifying Urban Features from LiDAR for a High-Resolution Urban Hydrologic Model. Journal of the American Water Resources Association, 52:3, 756-768, doi:10.1111/1752-1688.12425.
  78. Foster, L.M., Bearup, L.A., Molotch, N.P., Brooks, P.D. and Maxwell, R.M. (2016). Energy Budget Increases Reduce Mean Streamflow More Than Snow-Rain Transitions: Using integrated modeling to isolate climate change impacts on Rocky Mountain hydrology. Environmental Research Letters, 11(40), doi:10.1088/1748-9326/11/4/044015.
  79. Bearup, L.A., Maxwell, R.M. and McCray, J.E. (2016). Hillslope response to insect-induced land-cover change: an integrated model of end-member mixing. Ecohydrology, 9, 195-203, doi:10.1002/eco.1729.
  80. Maxwell, R.M., Condon, L.E., Kollet, S.J., Maher, K., Haggerty, R., and Forrester, M.M. (2016) The imprint of climate and geology on the residence times of groundwater. Geophysical Research Letters, 43(2), 701-708, doi:10.1002/2015GL066916.
  81. Ferguson, I.M., Jefferson, J.L., Maxwell, R.M. and Kollet, S.J. (2016). Effects of Root Water Uptake Formulation on Simulated Water and Energy Budgets at Local and Basin Scales. Environmental Earth Sciences, 75(316), doi:10.1007/s12665-015-5041-z,.
  82. Reyes, B., Maxwell, R.M. and Hogue, T.S. (2015). Impact of lateral flow and spatial scaling on the simulation of semi-arid urban land surfaces in an integrated hydrologic and land surface model. Hydrological Processes, doi:10.1002/hyp.10683, 2015.
  83. Condon, L.E. and Maxwell, R.M. (2015). Evaluating the relationship between topography and groundwater using outputs from a continental scale integrated hydrology model. Water Resources Research, 51, doi:10.1002/2014WR016774.
  84. Jefferson, J.L., Gilbert, J.M., Constantine, P.G. and Maxwell, R.M. (2015). Active subspaces for sensitivity analysis and dimension reduction of an integrated hydrologic model. Computers and Geosciences, 83, 127-138, doi:10.1016/j.cageo.2015.07.001.
  85. Jefferson, J.L. and Maxwell, R.M. (2015). Evaluation of simple to complex parameterizations of bare ground evaporation. Journal of Advances in Modeling Earth Systems, 7, 1-15, doi:10.1002/2014MS000398.
  86. Rihani, J., Chow, F.K., and Maxwell, R.M. (2015). Isolating Effects of Terrain and Soil Moisture Heterogeneity on the Atmospheric Boundary Layer: Idealized simulations to diagnose land-atmosphere feedbacks. Journal of Advances in Modeling Earth Systems, 7(2), 915-937, doi:10.1002/2014MS000371.
  87. Condon, L.E., Hering, A.S. and Maxwell, R.M. (2015). Quantitative assessment of groundwater controls across major US river basins using a multi-model regression algorithm. Advances in Water Resources, 82, 106-123, doi:10.1016/j.advwatres.2015.04.008.
  88. Maxwell, R.M., Condon, L.E., and Kollet, S.J. (2015). A high resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3. Geoscientific Model Development, 8, 923-937, doi:10.5194/gmd-8-1-2015.
  89. Beisman, J.J., Maxwell, R.M., Navarre-Sitchler, A.K., Steefel, C.I., and Molins Rafa, S. (2015). ParCrunchFlow: An Efficient, Parallel Reactive Transport Simulation Tool for Physically and Chemically Heterogeneous Saturated Subsurface Environments. Computational Geosciences, 19(2), 403-422, doi:10.1007/s10596-015-9475-x.
  90. Seck, A. Welty, C., and Maxwell, R.M. (2015). Spin-up behavior and effects of initial conditions for an integrated hydrologic model. Water Resources Research, 51, doi:10.1002/2014WR016371.
  91. Bhaskar, A.S. Welty, C., Maxwell, R.M. and Miller, A.J. (2015). Untangling the effects of urban development on subsurface storage in Baltimore. Water Resources Research, 51, 2, 1158-1181, doi:10.1002/2014WR016039.
  92. Engdahl, N.B. and Maxwell, R.M. (2015). Quantifying changes in age distributions and the hydrologic balance of a high- mountain watershed from climate induced variations in recharge. Journal of Hydrology, 522, 152-162, doi:10.1016/j.jhydrol.2014.12.032.
  93. Kollet, S.J. (2015). Modeling nitrogen transport and transformation in aquifers using a particle-tracking approach. Computers and Geosciences, 70, 1-14, doi:10.1016/j.cageo.2014.05.005.
  94. Ajami, H., McCabe, M.F., Evans, J.P. and Stisen, S. (2014). Assessing the impact of model spin-up on surface water-groundwater interactions using an integrated hydrologic model. Water Resources Research, 50(3), 2636-2656, doi:10.1002/2013WR014258.
  95. Shrestha, P., Sulis, M., Masbou, M., Dollet, S. And Simmer, C. (2014). A scale consistent terrestrial systems modeling platform based on COSMO, CLM, and ParFlow. Monthly Weather Review, 142, 3466-3483, doi:10.1175/MWR-D-14-00029.1.
  96. Rahman, M. M. Sulis, and S.J. Kollet (2015). The subsurface–land surface–atmosphere connection under convective conditions. Advances in Water Resources, 83, 240-249, doi:10.1016/j.advwatres.2015.06.003.
  97. Fang, Z., Bogena, H., Kollet, S. , Koch, J., and Vereecken, H. (2015). Spatio-temporal validation of long-term 3D hydrological simulations of a forested catchment using empirical orthogonal functions and wavelet coherence analysis. Journal of Hydrology, 529(3), 1754-1767, doi:10.1016/j.jhydrol.2015.08.011.
  98. Shrestha,P., M. Sulis, C. Simmer, and S. Kollet (2015). Impacts of grid resolution on surface energy fluxes simulated with an integrated surface-groundwater flow model. Hydrol. Earth Syst. Sci., 19, 4317-4326, doi:10.5194/hess-19-4317-2015.
  99. Rahman, M., M. Sulis, and S.J. Kollet (2015). Evaluating the dual-boundary forcing concept in subsurface–land surface interactions of the hydrological cycle. Hydrological Processes, 30(10), 1563-1573, doi:10.1002/hyp.10702.
  100. Koch, J., Cornelissen, T., Fang, Z., Bogen, H., Diekkrüger, B.H., Kollet, S., and S. Stisen (2015). Inter-comparison of three distributed hydrological models with respect to seasonal variability of soil moisture patterns at a small forested catchment. Journal of Hydrology, 533, 234-249, doi:10.1016/j.jhydrol.2015.12.002.
  101. Ajami,H., M.F. McCabe, and J.P. Evans (2015). Impacts of model initialization on an integrated surface water–groundwater model. Hydrological Processes, 29(17), 3790-3801, doi:10.1002/hyp.10478.
  102. Barnes, M. C. Welty, and A. Miller (2015). Global Topographic Slope Enforcement to Ensure Connectivity and Drainage in an Urban Terrain. Journal of Hydrologic Engineering, 21(4), doi:10.1061/(ASCE)HE.1943-5584.0001306.
  103. Ajami, H., Evans, J. P., McCabe M. F., and Stisen, S. (2014). Technical Note: Reducing the spin-up time of integrated surface water–groundwater models. Hydrol. Earth Syst. Sci.18, 5169-5179, doi:10.5194/hess-18-5169-2014.
  104. Srivastava, V., Graham, W.D., Muñoz-Carpena, R. and Maxwell, R.M. (2014). Insights on geologic and vegetative controls over hydrologic behavior of a large complex basin: Global Sensitivity Analysis of an Integrated Parallel Hydrologic Model. Journal of Hydrology, 519b, 2238-2257, doi:10.1016/j.jhydrol.2014.10.020.
  105. Cui, Z., Welty, C. and Maxwell, R.M. (2014). Modeling Nitrogen Transport and Transformation in Aquifers Using a Particle-Tracking Approach. Computers and Geosciences, 70, 1-14, doi:10.1016/j.cageo.2014.05.005.
  106. Condon, L.E. and Maxwell, R.M. (2014). Feedbacks between managed irrigation and water availability: Diagnosing temporal and spatial patterns using an integrated hydrologic model. Water Resources Research, 50(3), 2600-2616, doi:10.1002/2013WR014868.
  107. Meyerhoff, S.B., Maxwell, R.M., Revil, A., Martin, J.B., Karaoulis, M. and Graham, W.D. (2014). Characterization of groundwater and surface water mixing in a semi-confined karst aquifer using time-lapse electrical resistivity tomography. Water Resources Research, 50(3) 2566-2585, doi:10.1002/2013WR013991.
  108. Condon, L.E. and Maxwell, R.M. (2014). Groundwater-fed irrigation impacts spatially distributed temporal scaling behavior of the natural system: A spatio-temporal framework for understanding water management impacts. Environmental Research Letters, 9(3), 034009, doi:10.1088/1748-9326/9/3/034009.
  109. Maxwell, R.M., Putti, M., Meyerhoff, S.B., 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. (2014). Surface-subsurface model intercomparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks. Water Resources Research, 50(2) 1531-1549, doi:10.1002/2013WR013725.
  110. Meyerhoff, S.B., Maxwell, R.M., Graham, W.D. and Williams, J.L. III (2014). Improved Hydrograph Prediction Through Subsurface Characterization: Conditional Stochastic Hillslope Simulations. Hydrogeolgy Journal, doi:10.1007/s10040-014-1112-6.
  111. Williams, J.L. III, Maxwell, R.M. and Delle Monache, L. (2013). Development and verification of a new wind speed forecasting system using an Ensemble Kalman Filter data assimilation technique in a fully coupled hydrologic and atmospheric model. Journal of Advances in Modeling Earth Systems, 5(4) 785-800, doi:10.1002/jame.20051.
  112. Condon, L.E., Maxwell, R.M. and Gangopadhyay, S. The impact of subsurface conceptualization on land energy fluxes. Advances in Water Resources, 60, 188-203, doi:10.1016/j.advwatres.2013.08.001.
  113. Condon, L.E. and Maxwell, R.M. (2013). Implementation of a linear optimization water allocation algorithm into a fully integrated physical hydrology model. Advances in Water Resources, 60, 135-147, doi:10.1016/j.advwatres.2013.07.012.
  114. Atchley, A.L., Maxwell, R.M. and Navarre-Sitchler, A.K. (2013). Human health risk assessment of CO2 leakage into overlying aquifers using a stochastic, geochemical reactive transport approach. Environmental Science and Technology, 47(11), 5954-5962, doi:10.1021/es400316c.
  115. Mikkelson, K.M., Maxwell, R.M., Ferguson, I.M., McCray, J.E., Stednick, J.D., Sharp, J.O. (2013). Mountain pine beetle infestation impacts: Modeling water and energy budgets at the hill-slope scale. Ecohydrology, 6(1), 64-72, doi: 10.1002/eco.278.
  116. D. E Keyes, L. C. McInnes, C. Woodward, W. Gropp, E. Myra, M. Pernice, J. Bell, J. Brown, A. Clo, J. Connors, E. Constantinescu, D. Estep, K. Evans, C. Farhat, A. Hakim, G. Hammond, G. Hansen, J. Hill, T. Isaac, X. Jiao, K. Jordan, D. Kaushik, E. Kaxiras, A. Koniges, K. Lee, A. Lott, Q. Lu, J. Magerlein, Maxwell, R.M. M., McCourt, M. Mehl, R. Pawlowski, A.P. Randles, D. Reynolds, B. Riviere, U. Ruede, T. Scheibe, J. Shadid, B. Sheehan, M. Shephard, A. Siegel, B. Smith, X. Tang, C. Wilson and B. Wohlmuth (2013). Multiphysics simulations: Challenges and opportunities. International Journal of High Performance Computing Applications, 27(1):4-83, doi: 10.1177/1094342012468181.
  117. Maxwell, R.M. (2013). A terrain-following grid transform and preconditioner for parallel, large-scale, integrated hydrologic modeling. Advances in Water Resources, 53:109-117, doi:10.1016/j.advwatres.2012.10.001.
  118. Bürger, C.M., Kollet, S., Schumacher, J. and Bösel, D. (2012). Introduction of a web service for cloud computing with the integrated hydrologic simulation platform ParFlow. Computers and Geosciences, 48, 334-336, doi:10.1016/j.cageo.2012.01.007.
  119. Atchley, A.L., Maxwell, R.M. and Navarre-Sitchler, A.K. (2013). Using streamlines to simulate stochastic reactive transport in heterogeneous aquifers: Kinetic metal release and transport in CO2 impacted drinking water aquifers. Advances in Water Resources, 52:93-106, doi:10.1016/j.advwatres.2012.09.005.
  120. de Rooij, R., Graham, W., Maxwell, R.M. (2013). A particle-tracking scheme for simulating pathlines in coupled surface-subsurface flows. Advances in Water Resources, 53:7-18, doi:10.1016/j.advwatres.2012.07.022.
  121. Ferguson, I.M. and Maxwell, R.M. (2012). Human impacts on terrestrial hydrology: Climate change versus pumping and irrigation. Environmental Research Letters, 7 044022, doi:10.1088/1748-9326/7/4/044022.
  122. Siirila, E.R. and Maxwell, R.M. (2012). A new perspective on human health risk assessment: Development of a time dependent methodology and the effect of varying exposure durations. Science of the Total Environment 431, 221-232, doi:10.1016/j.scitotenv.2012.05.030.
  123. Siirila, E.R. and Maxwell, R.M. (2012). Evaluating effective reaction rates of kinetically driven solutes in large-scale, statistically anisotropic media: Human health risk implications. Water Resources Research 48, W04527, 23pp, doi:10.1029/2011WR011516.
  124. Siirila, E.R., Navarre-Sitchler, A.K., Maxwell, R.M. and McCray, J.E. (2012). A quantitative methodology to assess the risks to human health from CO2 leakage into groundwater. Advances in Water Resources,36(2), 146-164, doi:10.1016/j.advwatres.2010.11.005.
  125. Major, E., Benson, D.A., Revielle, J., Ibrahim, H., Dean, A., Maxwell, R.M., Poeter, E. and Dogan, M. (2011). Comparison of Fickian and temporally non-local transport theories over many scales in an exhaustively sampled sandstone slab. Water Resources Research, 47, W10519, 14pp, doi:10.1029/2011WR010857.
  126. Meyerhoff, S.B. and Maxwell, R.M.(2011). Quantifying the effects of subsurface heterogeneity on hillslope runoff using a stochastic approach. Hydrogeology Journal 19:1515-1530, doi:10.1007/s10040-011-0753-y.
  127. Williams, J.L. III and Maxwell, R.M. (2011). Propagating subsurface uncertainty to the atmosphere using fully-coupled, stochastic simulations. Journal of Hydrometeorology, 12, 690-701, doi:10.1175/2011JHM1363.1.
  128. Ferguson, I.M. and Maxwell, R.M. (2011). Hydrologic and land-energy feedbacks of agricultural water management practices. Environmental Research Letters, 6, 014006, doi:10.1088/1748-9326/6/1/014006.
  129. Kollat, J.B., Reed P.M. and Maxwell, R.M. (2011). Many-Objective Groundwater Monitoring Network Design Using Bias-Aware Ensemble Kalman Filtering, Evolutionary Optimization, and Visual Analytics. Water Resources Research, 47, W02529, doi:10.1029/2010WR009194.
  130. Daniels, M.H., Maxwell, R.M., Chow, F.K. (2011). An algorithm for flow direction enforcement using subgrid-scale stream location data, Journal of Hydrologic Engineering 16, 677, doi:10.1061/(ASCE)HE.1943-5584.0000340.
  131. Atchley, A.L. and Maxwell, R.M. (2011). Influences of subsurface heterogeneity and vegetation cover on soil moisture, surface temperature, and evapotranspiration at hillslope scales. Hydrogeology Journal 91(2), 289-305, doi:10.1007/s10040-010-0690-1.
  132. Maxwell, R.M., Lundquist, J.K., Mirocha, J.D., Smith, S.G., Woodward, C.S. and Tompson, A.F.B. (2011). Development of a coupled groundwater-atmospheric model. Monthly Weather Review 139(1), 96-116, doi:10.1175/2010MWR3392.
  133. Rihani, J., Maxwell, R.M., and Chow, F.K. (2010). Coupling groundwater and land-surface processes: Idealized simulations to identify effects of terrain and subsurface heterogeneity on land surface energy fluxes. Water Resources Research 46, W12523, doi:10.1029/2010WR009111.
  134. Maxwell, R.M. (2010). Infiltration in arid environments: Spatial patterns between subsurface heterogeneity and water-energy balances. Vadose Zone Journal 9, 970-983, doi:10.2136/vzj2010.0014.
  135. Ferguson, I.M. and Maxwell, R.M. (2010). The role of groundwater in watershed response and land surface feedbacks under climate change. Water Resources Research 46, W00F02, doi:10.1029/2009WR008616.
  136. Kollet, S.J., Maxwell, R.M., Woodward, C.S., Smith, S.G., Vanderborght, J., Vereecken, H., and Simmer, C. (2010). Proof-of-concept of regional scale hydrologic simulations at hydrologic resolution utilizing massively parallel computer resources. Water Resources Research, 46, W04201, doi:10.1029/2009WR008730.
  137. Sulis, M., Meyerhoff, S.B., Paniconi, C., Maxwell, R.M., Putti, M. and Kollet, S.J. (2010). A comparison of two physics-based numerical models for simulating surface water-groundwater interactions. Advances in Water Resources, 33(4), 456-467, doi:10.1016/j.advwatres.2010.01.010.
  138. Kollet, S.J. (2009). Influence of soil heterogeneity on evapotranspiration under shallow water table conditions: transient, stochastic simulations Environmental Research Letters, 4(3), doi:10.1088/1748-9326/4/3/035007.
  139. Kollet, S.J., Cvijanovic, I., Schüttemeyer, D., Maxwell, R.M., Moene, A.F. and Bayer P. (2009) The Influence of Rain Sensible Heat and Subsurface Energy Transport on the Energy Balance at the Land Surface. Vadose Zone Journal, doi:10.2136/vzj2009.0005.
  140. Frei, S., Fleckenstein, J.H., Kollet, S.J. and Maxwell, R.M. (2009). Patterns and dynamics of river-aquifer exchange with variably-saturated flow using a fully-coupled model. Journal of Hydrology 375(3-4), 383-393, doi:10.1016/j.jhydrol.2009.06.038.
  141. Maxwell, R.M., Tompson, A.F.B. and Kollet, S.J. (2009). A Serendipitous, Long-Term Infiltration Experiment: Water and Tritium Circulation Beneath the CAMBRIC Trench at the Nevada Test Site. Journal of Contaminant Hydrology 108(1-2) 12-28, doi:10.1016/j.jconhyd.2009.05.002.
  142. de Barros, F.P.J., Rubin, Y. and Maxwell, R.M.(2009). The concept of comparative information yield curves and their application to risk-based site characterization. Water Resources Research 45, W06401, doi:10.1029/2008WR007324.
  143. Maxwell, R.M. and Kollet, S.J. (2008). Interdependence of groundwater dynamics and land-energy feedbacks under climate change. Nature Geoscience 1(10) 665-669, doi:10.1038/ngeo315.
  144. Kollet, S.J. and Maxwell, R.M. (2008). Demonstrating fractal scaling of baseflow residence time distributions using a fully-coupled groundwater and land surface model. Geophysical Research Letters 35, L07402, doi:10.1029/2008GL033215.
  145. Maxwell, R.M. and Kollet, S.J., (2008). Quantifying the effects of three-dimensional subsurface heterogeneity on Hortonian runoff processes using a coupled numerical, stochastic approach. Advances in Water Resources 31(5), 807-817.
  146. Kollet, S.J. and Maxwell, R.M. (2008). Capturing the influence of groundwater dynamics on land surface processes using an integrated, distributed watershed model. Water Resources Research 44: W02402, doi:10.1029/2007WR006004.
  147. Maxwell, R.M., Carle, S.F. and Tompson, A.F.B., (2008). Contamination, Risk, and Heterogeneity: On the Effectiveness of Aquifer Remediation. Environmental Geology 54:1771-1786.
  148. Maxwell, R.M., Chow, F.K. and Kollet, S.J., (2007). The groundwater-land-surface-atmosphere connection: soil moisture effects on the atmospheric boundary layer in fully-coupled simulations. Advances in Water Resources 30(12), doi:10.1016/j.advwatres.2007.05.018.
  149. Maxwell, R.M., Welty, C. and R.W. Harvey, R.W. (2007). Revisiting the Cape Cod Bacteria Injection Experiment Using a Stochastic Modeling Approach. Environmental Science and Technology 41(15), 5548-5558, doi:10.1021/es062693a.
  150. Kollet, S.J. and Maxwell, R.M. (2006). Integrated surface-groundwater flow modeling: A free-surface overland flow boundary condition in a parallel groundwater flow model. Advances in Water Resources, 29(7), 945-958, doi:10.1016/j.advwatres.2005.08.006.
  151. Tompson, A.F.B., Maxwell, R.M., Carle, S.F., Zavarin, M., Pawloski, G.A. and Shumaker, D.E. (2005). Evaluation of the Non-Transient Hydrologic Source Term from the CAMBRIC Underground Nuclear Test in Frenchman Flat, Nevada Test Site Lawrence Livermore National Laboratory, Livermore, CA, UCRL-TR-217191.
  152. Maxwell, R.M. and N.L. Miller. (2003). Development of a coupled land surface and groundwater model. Journal of Hydrometeorology, 6(3), 233-247, doi:10.1175/JHM422.1, 2005.
  153. Maxwell, R.M., C. Welty, and A.F.B. Tompson (2003). Streamline-based simulation of virus transport resulting from long term artificial recharge in a heterogeneous aquifer. Advances in Water Resources, 25(10),1075-1096, doi:10.1016/S0309-1708(03)00074-5.
  154. Tompson, A.F.B., Bruton,C.J. and Pawloski, G.A. eds. (1999). Evaluation of the hydrologic source term from underground nuclear tests in Frenchman Flat at the Nevada Test Site: The Cambric test. Lawrence Livermore National Laboratory, Livermore, CA (UCRL-ID-132300), 360pp.
  155. Tompson, A.F.B., R.D. Falgout, S.G. Smith, W.J. Bosl and S.F. Ashby (1998). Analysis of subsurface contaminant migration and remediation using high performance computing. Advances in Water Resources, 22(3):203–221, doi:10.1016/S0309-1708(98)00013-X.