Articles | Volume 28, issue 20
https://doi.org/10.5194/hess-28-4685-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-4685-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Oct 2024
Research article |
| 28 Oct 2024
| 28 Oct 2024
Simulation-based inference for parameter estimation of complex watershed simulators
Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
GeoSystems Analysis, Tucson, AZ, USA
Elena Leonarduzzi
High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA
Luis De La Fuente
Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
Hoang Viet Tran
Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
Andrew Bennett
Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
Peter Melchior
Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA
Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA
Reed M. Maxwell
High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA
Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
Integrated GroundWater Modeling Center, Princeton University, Princeton, NJ, USA
Laura E. Condon
Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
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Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-345, https://doi.org/10.5194/hess-2022-345, 2022
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As the stress on water resources from climate change grows, we need models that represent water processes at the scale of counties, states, and even countries in order to make viable predictions about things will change. While such models are powerful, they can be cumbersome to deal with because they are so large. This research explores a novel way of increasing the efficiency of large-scale hydrologic models using an approach called Simulation-Based Inference.
Chen Yang, Zitong Jia, Wenjie Xu, Zhongwang Wei, Xiaolang Zhang, Yiguang Zou, Jeffrey McDonnell, Laura Condon, Yongjiu Dai, and Reed Maxwell
Hydrol. Earth Syst. Sci., 29, 2201–2218, https://doi.org/10.5194/hess-29-2201-2025, https://doi.org/10.5194/hess-29-2201-2025, 2025
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We developed the first high-resolution, integrated surface water–groundwater hydrologic model of the entirety of continental China using ParFlow. The model shows good performance in terms of streamflow and water table depth when compared to global data products and observations. It is essential for water resources management and decision-making in China within a consistent framework in the changing world. It also has significant implications for similar modeling in other places in the world.
Max Berkelhammer, Gerald F. M. Page, Frank Zurek, Christopher Still, Mariah S. Carbone, William Talavera, Laura Hildebrand, James Byron, Kyle Inthabandith, Angellica Kucinski, Melissa Carlson, Kelsey Foss, Wendy Brown, Rosemary W. H. Carroll, Austin Simonpietri, Marshall Worsham, Ian Breckheimer, Anna Ryken, Reed Maxwell, David Gochis, Mark S. Raleigh, Eric Small, and Kenneth H. Williams
Hydrol. Earth Syst. Sci., 29, 701–718, https://doi.org/10.5194/hess-29-701-2025, https://doi.org/10.5194/hess-29-701-2025, 2025
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Warming in montane systems is affecting the snowmelt input amount. At the global scale, this will impact subalpine forests that rely on spring snowmelt to support their water demands. We use a network of sensors across a hillslope in the Upper Colorado Basin to show that the changing spring snowpack has a more pronounced impact on dense forest stands, while open stands show a higher reliance on summer rain and are less sensitive to significant changes in snow.
Benjamin D. West, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci., 29, 245–259, https://doi.org/10.5194/hess-29-245-2025, https://doi.org/10.5194/hess-29-245-2025, 2025
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This article describes the addition of reservoirs to the hydrologic model ParFlow. ParFlow is particularly good at helping us understand some of the broader drivers behind different parts of the water cycle. By having reservoirs in such a model, we hope to be able to better understand both our impacts on the environment and how to adjust our management of reservoirs to changing conditions.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
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Hurricanes may worsen 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 the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Peyman Abbaszadeh, Fadji Zaouna Maina, Chen Yang, Dan Rosen, Sujay Kumar, Matthew Rodell, and Reed Maxwell
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-280, https://doi.org/10.5194/hess-2024-280, 2024
Revised manuscript under review for HESS
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To manage Earth's water resources effectively amid climate change, it's crucial to understand both surface and groundwater processes. We developed a new modeling system that combines two advanced tools, ParFlow and LIS/Noah-MP, to better simulate both land surface and groundwater interactions. By testing this integrated model in the Upper Colorado River Basin, we found it improves predictions of hydrologic processes, especially in complex terrains.
Jennie C. Steyaert and Laura E. Condon
Hydrol. Earth Syst. Sci., 28, 1071–1088, https://doi.org/10.5194/hess-28-1071-2024, https://doi.org/10.5194/hess-28-1071-2024, 2024
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Reservoirs impact all river systems in the United States, yet their operations are difficult to quantify due to limited data. Using historical reservoir operations, we find that storage has declined over the past 40 years, with clear regional differences. We observe that active storage ranges are increasing in arid regions and decreasing in humid regions. By evaluating reservoir model assumptions, we find that they may miss out on seasonal dynamics and can underestimate storage.
Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon
Hydrol. Earth Syst. Sci., 28, 945–971, https://doi.org/10.5194/hess-28-945-2024, https://doi.org/10.5194/hess-28-945-2024, 2024
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Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but it is difficult to extract knowledge from it. We propose HydroLSTM, which represents processes like a hydrological reservoir. Models based on HydroLSTM perform similarly to LSTM while requiring fewer cell states. The learned parameters are informative about the dominant hydrology of a catchment. Our results show how parsimony and hydrological knowledge extraction can be achieved by using the new structure.
Amanda Triplett and Laura E. Condon
Hydrol. Earth Syst. Sci., 27, 2763–2785, https://doi.org/10.5194/hess-27-2763-2023, https://doi.org/10.5194/hess-27-2763-2023, 2023
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Accelerated melting in mountains is a global phenomenon. The Heihe River basin depends on upstream mountains for its water supply. We built a hydrologic model to examine how shifts in streamflow and warming will impact ground and surface water interactions. The results indicate that degrading permafrost has a larger effect than melting glaciers. Additionally, warming temperatures tend to have more impact than changes to streamflow. These results can inform other mountain–valley system studies.
Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura E. Condon
EGUsphere, https://doi.org/10.5194/egusphere-2023-666, https://doi.org/10.5194/egusphere-2023-666, 2023
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Long Short-Term Memory (LSTM) is a widely-used machine learning (ML) model in hydrology. However, it is difficult to extract knowledge from it. We propose HydroLSTM which represents processes analogous to a hydrological reservoir. Models using HydroLSTM perform similarly to LSTM but require fewer cell states. The learned parameters are informative about the dominant hydroclimatic characteristics of a catchment. Our results demonstrate how hydrological knowledge is encoded in the new structure.
Aniket Gupta, Alix Reverdy, Jean-Martial Cohard, Basile Hector, Marc Descloitres, Jean-Pierre Vandervaere, Catherine Coulaud, Romain Biron, Lucie Liger, Reed Maxwell, Jean-Gabriel Valay, and Didier Voisin
Hydrol. Earth Syst. Sci., 27, 191–212, https://doi.org/10.5194/hess-27-191-2023, https://doi.org/10.5194/hess-27-191-2023, 2023
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Patchy snow cover during spring impacts mountainous ecosystems on a large range of spatio-temporal scales. A hydrological model simulated such snow patchiness at 10 m resolution. Slope and orientation controls precipitation, radiation, and wind generate differences in snowmelt, subsurface storage, streamflow, and evapotranspiration. The snow patchiness increases the duration of the snowmelt to stream and subsurface storage, which sustains the plants and streamflow later in the summer.
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-345, https://doi.org/10.5194/hess-2022-345, 2022
Publication in HESS not foreseen
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As the stress on water resources from climate change grows, we need models that represent water processes at the scale of counties, states, and even countries in order to make viable predictions about things will change. While such models are powerful, they can be cumbersome to deal with because they are so large. This research explores a novel way of increasing the efficiency of large-scale hydrologic models using an approach called Simulation-Based Inference.
Jennie C. Steyaert and Laura E. Condon
EGUsphere, https://doi.org/10.5194/egusphere-2022-1051, https://doi.org/10.5194/egusphere-2022-1051, 2022
Preprint archived
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All river systems in the US are impacted by dams, yet analyses are limited by a lack of data. We use the first national dataset of reservoir data to analyze reservoir storage trends from 1980–2019. We show that reservoir storage has decreased over the past 40 years. The range in monthly storage has increased over time in drier regions and decreased in wetter ones. Lastly, we find that most regions have reservoir storage that takes longer to recover from and are therefore more vulnerable.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
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Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is
hiddenunderground and thus hard to measure. We suggest using multiple complementary strategies to assess the performance of a model (
model evaluation).
Mary M. F. O'Neill, Danielle T. Tijerina, Laura E. Condon, and Reed M. Maxwell
Geosci. Model Dev., 14, 7223–7254, https://doi.org/10.5194/gmd-14-7223-2021, https://doi.org/10.5194/gmd-14-7223-2021, 2021
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Modeling the hydrologic cycle at high resolution and at large spatial scales is an incredible opportunity and challenge for hydrologists. In this paper, we present the results of a high-resolution hydrologic simulation configured over the contiguous United States. We discuss simulated water fluxes through groundwater, soil, plants, and over land, and we compare model results to in situ observations and satellite products in order to build confidence and guide future model development.
Elena Leonarduzzi, Brian W. McArdell, and Peter Molnar
Hydrol. Earth Syst. Sci., 25, 5937–5950, https://doi.org/10.5194/hess-25-5937-2021, https://doi.org/10.5194/hess-25-5937-2021, 2021
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Landslides are a dangerous natural hazard affecting alpine regions, calling for effective warning systems. Here we consider different approaches for the prediction of rainfall-induced shallow landslides at the regional scale, based on open-access datasets and operational hydrological forecasting systems. We find antecedent wetness useful to improve upon the classical rainfall thresholds and the resolution of the hydrological model used for its estimate to be a critical aspect.
Jacob Hirschberg, Alexandre Badoux, Brian W. McArdell, Elena Leonarduzzi, and Peter Molnar
Nat. Hazards Earth Syst. Sci., 21, 2773–2789, https://doi.org/10.5194/nhess-21-2773-2021, https://doi.org/10.5194/nhess-21-2773-2021, 2021
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Debris-flow prediction is often based on rainfall thresholds, but uncertainty assessments are rare. We established rainfall thresholds using two approaches and find that 25 debris flows are needed for uncertainties to converge in an Alpine basin and that the suitable method differs for regional compared to local thresholds. Finally, we demonstrate the potential of a statistical learning algorithm to improve threshold performance. These findings are helpful for early warning system development.
Jun Zhang, Laura E. Condon, Hoang Tran, and Reed M. Maxwell
Earth Syst. Sci. Data, 13, 3263–3279, https://doi.org/10.5194/essd-13-3263-2021, https://doi.org/10.5194/essd-13-3263-2021, 2021
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Existing national topographic datasets for the US may not be compatible with gridded hydrologic models. A national topographic dataset developed to support physically based hydrologic models at 1 km and 250 m over the contiguous US is provided. We used a Priority Flood algorithm to ensure hydrologically consistent drainage networks and evaluated the performance with an integrated hydrologic model. Datasets and scripts are available for direct data usage or modification of processing as desired.
Elena Leonarduzzi and Peter Molnar
Nat. Hazards Earth Syst. Sci., 20, 2905–2919, https://doi.org/10.5194/nhess-20-2905-2020, https://doi.org/10.5194/nhess-20-2905-2020, 2020
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Landslides are a natural hazard that affects alpine regions. Here we focus on rainfall-induced shallow landslides and one of the most widely used approaches for their predictions: rainfall thresholds. We design several comparisons utilizing a landslide database and rainfall records in Switzerland. We find that using daily rather than hourly rainfall might be a better option in some circumstances, and mean annual precipitation and antecedent wetness can improve predictions at the regional scale.
Cited articles
Alsing, J. and Wandelt, B.: Nuisance Hardened Data Compression for Fast Likelihood-Free Inference, Mon. Not. R. Astron. Soc., 488, 5093–5103, https://doi.org/10.1093/mnras/stz1900, 2019.
Alsing, J., Charnock, T., Feeney, S., and Wandelt, B.: Fast likelihood-free cosmology with neural density estimators and active learning, Mon. Not. R. Astron. Soc., 488, 4440–458, https://doi.org/10.1093/mnras/stz1960, 2019.
Bastidas, L., Gupta, H., Sorooshian, S., Shuttleworth, W., and Yang, Z.-L.: Sensitivity analysis of a land surface scheme using multicriteria methods, J. Geophys. Res., 104, 19481–19490, https://doi.org/10.1029/1999JD900155, 1999.
Beven, K.: Parameter Estimation and Predictive Uncertainty, in: Rainfall‐Runoff Modelling, John Wiley & Sons, Ltd, 231–287, https://doi.org/10.1002/9781119951001.ch7, 2012.
Beven, K. and Binley, A.: The future of distributed models: Model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, https://doi.org/10.1002/hyp.3360060305, 1992.
Beven, K. and Binley, A.: GLUE: 20 years on, Hydrol. Process., 28, 5897–5918, https://doi.org/10.1002/hyp.10082, 2014.
Beven, K. and Westerberg, I.: On red herrings and real herrings: disinformation and information in hydrological inference, Hydrol. Process., 25, 1676–1680, https://doi.org/10.1002/hyp.7963, 2011.
Bishop, C.: Mixture Density Networks, Neural Computing Research Group Report, Department of Computer Science and Applied Mathematics: Aston University, https://www.microsoft.com/en-us/research/publication/mixture-density-networks/ (last access: 12 September 2024), 1994.
Castle, S. L., Thomas, B. F., Reager, J. T., Rodell, M., Swenson, S. C., and Famiglietti, J. S.: Groundwater depletion during drought threatens future water security of the Colorado River Catchment, Geophys. Res. Lett., 41, 5904–5911, https://doi.org/10.1002/2014GL061055, 2014.
Condon, L. E.: Scientist Spotlight: Laura Condon, University of Arizona, https://science.arizona.edu/news/2022/06/scientist-spotlight-laura-condon (last access: 11 July 2024), 2022.
Cranmer, K.: A3D3 Seminar: Accelerating Simulation-based Inference | Kyle S. Cranmer, YouTube, https://www.youtube.com/watch?v=3ULdEHaAWJQ (last access: 12 September 2024), 2022.
Cranmer, K., Brehmer, J., and Louppe, G.: The frontier of simulation-based inference, P. Natl. Acad. Sci. USA, 117, 30055–30062, https://doi.org/10.1073/pnas.1912789117, 2020.
Diggle, P. J. and Gratton, R. J.: Monte Carlo methods of inference for implicit statistical models, J. R. Stat. Soc.-B, 46, 193–212, 1984.
Draper, D.: Assessment and Propagation of Model Uncertainty, J. R. Stat. Soc.-B, 57, 45–70, https://doi.org/10.1111/j.2517-6161.1995.tb02015.x, 1995.
Duan, Q., Ajami, N. K., Gao, X., and Sorooshian, S.: Multi-model ensemble hydrologic prediction using Bayesian model averaging, Adv. Water Resour., 30, 1371–1386, https://doi.org/10.1016/j.advwatres.2006.11.014, 2007.
Fatichi, S., Vivoni, E. R., Ogden, F. L., Ivanov, V. Y., Mirus, B., Gochis, D., Downer, C. W., Camporese, M., Davison, J. H., Ebel, B., Jones, N., Kim, J., Mascaro, G., Niswonger, R., Restrepo, P., Rigon, R., Shen, C., Sulis, M., and Tarboton, D.: An overview of current applications, challenges, and future trends in distributed process-based models in hydrology, J. Hydrol., 537, 45–60, https://doi.org/10.1016/j.jhydrol.2016.03.026, 2016.
Feng, D., Fang, K., and Shen, C.: Enhancing Streamflow Forecast and Extracting Insights Using Long-Short Term Memory Networks With Data Integration at Continental Scales, Water Resour. Res., 56, e2019WR026793, https://doi.org/10.1029/2019WR026793, 2020.
Fenicia, F., Kavetski, D., Reichert, P., and Albert, C.: Signature-Domain Calibration of Hydrological Models Using Approximate Bayesian Computation: Empirical Analysis of Fundamental Properties, Water Resour. Res., 54, 3958–3987, https://doi.org/10.1002/2017WR021616, 2018.
Freund, Y. and Schapire, R. E.: A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, J. Comput. Syst. Sci., 55, 119–139, https://doi.org/10.1006/jcss.1997.1504, 1997.
Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., and Gelman, A.: Visualization in Bayesian workflow, J. R. Stat. Soc.-A Stat., 182, 389–402, https://doi.org/10.1111/rssa.12378, 2019.
Greenberg, D. S., Nonnenmacher, M., and Macke, J. H.: Automatic Posterior Transformation for Likelihood-Free Inference, arXiv [preprint], https://doi.org/10.48550/ARXIV.1905.07488, 2019.
Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information, Water Resour. Res., 34, 751–763, https://doi.org/10.1029/97WR03495, 1998.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Gupta, H. V., Clark, M. P., Vrugt, J. A., Abramowitz, G., and Ye, M.: Towards a comprehensive assessment of model structural adequacy, Water Resour. Res., 48, W08301, https://doi.org/10.1029/2011WR011044, 2012.
Hermans, J., Delaunoy, A., Rozet, F., Wehenkel, A., and Louppe, G.: A Crisis In Simulation-Based Inference? Beware, Your Posterior Approximations Can Be Unfaithful, Transactions on Machine Learning Research, https://openreview.net/pdf?id=LHAbHkt6Aq (last access: 12 September 2024), 2022.
Hochreiter, S. and Schmidhuber, J.: Long Short-Term Memory, Neural Comput., 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997.
Hoeting, J. A., Madigan, D., Raftery, A. E., and Volinsky, C. T.: Bayesian model averaging: a tutorial (with comments by: M. Clyde, David Draper and E. I. George, and a rejoinder by the authors), Stat. Sci., 14, 382–417, https://doi.org/10.1214/ss/1009212519, 1999.
Hunt, R. J., Doherty, J., and Tonkin, M. J.: Are Models Too Simple? Arguments for Increased Parameterization, Groundwater, 45, 254–262, https://doi.org/10.1111/j.1745-6584.2007.00316.x, 2007.
Jiang, S., Zheng, Y., and Solomatine, D.: Improving AI system awareness of geoscience knowledge: Symbiotic integration of physical approaches and deep learning, Geophys. Res. Lett., 47, e2020GL088229, https://doi.org/10.1029/2020GL088229, 2020.
Jones, J. E. and Woodward, C. S.: Newton-Krylov-multigrid solvers for large-scale, highly heterogenous, variably saturated flow problems, Adv. Water Resour., 24, 763–774, https://doi.org/10.1016/S0309-1708(00)00075-0, 2001.
Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., and Kumar, V.: Theory-guided data science: A new paradigm for scientific discovery from data, IEEE T. Knowl. Data Eng., 29, 2318–2331, 2017.
Kollet, S. J. and Maxwell, R. M.: Capturing the influence of groundwater dynamics on land surface processes using an integrated, distributed catchment model, Water Resour. Res., 44, W02402, https://doi.org/10.1029/2007wr006004, 2008.
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018.
Leonarduzzi, E., Tran, H., Bansal, V., Hull, R. B., De la Fuente, L., Bearup, L. A. , Melchior, P., Condon, L. E., and Maxwell, R. M.: Training Machine Learning with Physics-Based Simulations to Predict 2D Soil Moisture Fields in a Changing Climate, Frontiers in Water, 4, 92711, https://doi.org/10.3389/frwa.2022.927113, 2022.
Leamer, E. E.: Specification searches: ad hoc inference with nonexperimental data, Wiley, https://www.anderson.ucla.edu/faculty/edward.leamer/books/specification_searches/specification_searches.htm (last access: 18 September 2024), 1978.
Liu, Y. and Ker, A. P.: Rating Crop Insurance Contracts with Nonparametric Bayesian Model Averaging, J. Agr. Resour. Econ., 45,244–64, 2020.
Lueckmann, J.-M., Goncalves, P. J., Bassetto, G., Öcal, K., Nonnenmacher, M., and Macke, J. H.: Flexible statistical inference for mechanistic models of neural dynamics, arXiv [preprint], https://doi.org/10.48550/ARXIV.1711.01861, 2017.
Madigan, D. and Raftery, A. E.: Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window, J. Am. Stat. Assoc., 89, 1535–1546, https://doi.org/10.1080/01621459.1994.10476894, 1994.
Maesschalck, R. D., Jouan-Rimbaud, D., and Massart, D. L.: The Mahalanobis distance, Chemometr. Intell. Lab., 50, 1–18, https://doi.org/10.1016/S0169-7439(99)00047-7, 2000.
Margalit, D. and Rabinoff, J.: Determinants and Volumes, in: Interactive Linear Algebra, Georgia Institute of Technology, 222–235, https://textbooks.math.gatech.edu/ila/determinants-volumes.html (last access: 12 September 2024), 2017.
Maxwell, R. M. and Kollet, S. J.: Integrated surface–groundwater flow modeling: A free-surface overland flow boundary condition in a parallel groundwater flow model, Adv. Water Resour., 945–958, https://doi.org/10.1016/j.advwatres.2005.08.006, 2006.
Maxwell, R. M. and Miller, N. L.: Development of a Coupled Land Surface and Groundwater Model, J. Hydrometeorol., 233–247, https://doi.org/10.1175/JHM422.1, 2005.
Maxwell, R. M., Condon, L. E., and Kollet, S. J.: A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3, Geosci. Model Dev., 8, 923–937, https://doi.org/10.5194/gmd-8-923-2015, 2015.
Maxwell, R. M., Condon, L. E., and Melchior, P.: 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, 3633, https://doi.org/10.3390/w13243633, 2021.
Mohanty, B. P., Cosh, M. H., Lakshmi, V., and Montzka, C.: Soil moisture remote sensing: State-of-the-science, Vadose Zone J., 16, 1–9, 2017.
Nearing, G. S., Tian, Y., Gupta, H. V., Clark, M. P., Harrison, K. W., and Weijs, S. V.: A philosophical basis for hydrological uncertainty, Hydrolog. Sci. J., 61, 1666–1678, https://doi.org/10.1080/02626667.2016.1183009, 2016.
Ofterdinger, U., A. M. MacDonald, J.-C. Comte, and M. E. Young. “Groundwater in Fractured Bedrock Environments: Managing Catchment and Subsurface Resources – an Introduction.” In Groundwater in Fractured Bedrock Environments: Managing Catchment and Subsurface Resources, edited by: Ofterdinger, U., MacDonald, A. M., Comte, J.-C., and Young, M. E., 479, Geological Society of London, https://doi.org/10.1144/SP479-2018-170, 2019.
Oreskes, N., Shrader-Frechette, K., and Belitz, K.: Verification, Validation, and Confirmation of Numerical Models in the Earth Science, Science (New York, N.Y.), 263, 641–646, https://doi.org/10.1126/science.263.5147.641, 1994.
Paniconi, C. and Putti, M.: Physically based modeling in catchment hydrology at 50: Survey and outlook, Water Resour. Res., 51, 7090–7129, https://doi.org/10.1002/2015WR017780, 2015.
Papamakarios, G. and Murray, I.: Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation, arXiv [preprint], https://doi.org/10.48550/ARXIV.1605.06376, 2016.
Papamakarios, G., Sterratt, D. C., and Murray, I.: Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows, arXiv [preprint], https://doi.org/10.48550/ARXIV.1805.07226, 2018.
Petropoulos, G. P., Ireland, G., and Barrett, B.: Surface soil moisture retrievals from remote sensing: Current status, products & future trends, Phys. Chem. Earth, Parts A/B/C, 83, 36–56, 2015.
Raftery, A. E., Madigan, D., and Hoeting, J. A.: Bayesian Model Averaging for Linear Regression Models, J. Am. Stat. Assoc., 92, 179–191, https://doi.org/10.1080/01621459.1997.10473615, 1997.
Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev., 133, 1155–1174, https://doi.org/10.1175/MWR2906.1, 2005.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019.
Hull, R.: Code and resources supporting the paper. “Simulation-based Inference for Parameter Estimation of Complex Watershed Simulators”, Zenodo [code], https://doi.org/10.5281/zenodo.13899823, 2024.
Roberts, H. V.: Probabilistic Prediction, J. Am. Stat. Assoc., 60, 50–62, https://doi.org/10.1080/01621459.1965.10480774, 1965.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J.: Learning representations by back-propagating errors, Nature, 323, 533–536, https://doi.org/10.1038/323533a0, 1986.
Santos, N. and Patno, H.: Operations Plan for Colorado River Reservoirs from the Bureau of Reclamation, https://www.usbr.gov/lc/region/g4000/24mo/2022/AUG22_MIN.pdf (last access: 12 September 2024), 2022
Schoups, G., van de Giesen, N. C., and Savenije, H. H. G.: Model complexity control for hydrologic prediction, Water Resour. Res., 44, W00B03, https://doi.org/10.1029/2008WR006836, 2008.
Smith, P., Beven, K. J., and Tawn, J. A.: Informal likelihood measures in model assessment: Theoretic development and investigation, Adv. Water Resour., 31, 1087–1100, https://doi.org/10.1016/j.advwatres.2008.04.012, 2008.
Tejero-Cantero, A., Boelts, J., Deistler, M., Lueckmann, J.-M., Durkan, C., Gonçalves, P. J., Greenberg, D. S., and Macke, J. H.: sbi: A toolkit for simulation-based inference, Journal of Open Source Software, 5, 2505, https://doi.org/10.21105/joss.02505, 2020.
Tenney, W.: A Tier 2a Shortage Is Confirmed, but Uncertainty Remains, Arizona Municipal Water Users Association (blog), https://www.amwua.org/blog/a-tier-2a-shortage-is-confirmed-but-uncertainty-remains (last access: 12 September 2024), 2022.
Tran, H., Leonarduzzi, E., De la Fuente, L., Hull, R. B., Bansal, V., Chennault, C., Gentine, P., Melchior, P., Condon, L. E., and Maxwell, R. M.: Development of a Deep Learning Emulator for a Distributed Groundwater-Surface Water Model: ParFlow-ML, Water, 13, 3393, https://doi.org/10.3390/w13233393, 2021.
Tran, H., Zhang, J., O'Neill, M. M., Ryken, A., Condon, L. E., and Maxwell, R. M.: A hydrological simulation dataset of the Upper Colorado River Catchment from 1983 to 2019, Scientific Data, 9, 16, https://doi.org/10.1038/s41597-022-01123-w, 2022.
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.
Uria, B., Côté, M.-A., Gregor, K., Murray, I., and Larochelle, H.: Neural Autoregressive Distribution Estimation, J. Mach. Learn. Res., 17, 1–37, 2016.
van Fraassen, B. C.: The Scientific Image, Clarendon Library of Logic and Philosophy (Oxford), Oxford Academic, https://doi.org/10.1093/0198244274.001.0001, 1980.
Vriens, B., Plante, B., Seigneur, N., and Jamieson, H.: Mine Waste Rock: Insights for Sustainable Hydrogeochemical Management, Minerals, 10, 728, https://doi.org/10.3390/min10090728, 2021.
Vrugt, J. A. and Sadegh, M.: Toward diagnostic model calibration and evaluation: Approximate Bayesian computation, Water Resour. Res., 49, 4335–4345, https://doi.org/10.1002/wrcr.20354, 2013.
Weiss, G. and von Haeseler, A.: Inference of Population History Using a Likelihood Approach, Genetics, 149, 1539–1546, https://doi.org/10.1093/genetics/149.3.1539, 1998.
White, J. T., Hunt, R. J., Fienen, M. N., and Doherty, J. E.: Approaches to highly parameterized inversion: PEST
Wikle, C. K. and Berliner, L. M.: A Bayesian tutorial for data assimilation, Physica D, 230, 1–16, https://doi.org/10.1016/j.physd.2006.09.017, 2007.
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J. G., Groth, P., Goble, C., Grethe, J. S., Heringa, J., 't Hoen, P. A. C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B.: The FAIR Guiding Principles for scientific data management and stewardship., Sci. Data, 3, 160018, https://doi.org/10.1038/sdata.2016.18, 2016.
Williams, A. P., Cook, B. I., and Smerdon, J. E.: Rapid intensification of the emerging southwestern North American megadrought in 2020–2021, Nat. Clim. Change, 12, 232–234, https://doi.org/10.1038/s41558-022-01290-z, 2022.
Zhao, W. L., Gentine, P., Reichstein, M., Zhang, Y., Zhou, S., Wen, Y., Lin, C., Li, X., and Qiu, G. Y.: Physics-constrained machine learning of evapotranspiration, Geophys. Res. Lett., 46, 14496–14507, 2019.
Short summary
Large-scale hydrologic simulators are a needed tool to explore complex watershed processes and how they may evolve with a changing climate. However, calibrating them can be difficult because they are costly to run and have many unknown parameters. We implement a state-of-the-art approach to model calibration using neural networks with a set of experiments based on streamflow in the upper Colorado River basin.
Large-scale hydrologic simulators are a needed tool to explore complex watershed processes and...
