Articles | Volume 28, issue 1
https://doi.org/10.5194/hess-28-21-2024
© 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-28-21-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jan 2024
Research article |
| 02 Jan 2024
| 02 Jan 2024
A framework for parameter estimation, sensitivity analysis, and uncertainty analysis for holistic hydrologic modeling using SWAT+
Salam A. Abbas
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO 80521, USA
Ryan T. Bailey
Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO 80521, USA
Jeremy T. White
INTERA, Inc., Perth, Australia
Jeffrey G. Arnold
Grassland Soil and Water Research Laboratory, USDA–ARS, Temple, TX 76502, USA
Michael J. White
Grassland Soil and Water Research Laboratory, USDA–ARS, Temple, TX 76502, USA
Natalja Čerkasova
Blackland Research & Extension Center, Texas A&M AgriLife, Temple, TX 76502, USA
Jungang Gao
Blackland Research & Extension Center, Texas A&M AgriLife, Temple, TX 76502, USA
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In coastal areas, groundwater managers require information on the risk of well salinization associated with various pumping scenarios. We developed a modeling approach to identify the optimal tradeoff between groundwater pumping and probability of salinization, considering model parameter and historical observation uncertainty as well as uncertainty in sea level and recharge projections. The workflow can be implemented in a wide range of coastal settings.
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The SWAT model can simulate the transport of water-soluble chemicals through the landscape but neglects the transport through groundwater or agricultural tile drains. These transport pathways are, however, important to assess the amount of chemicals in streams. We added this capability to the model, which significantly improved the simulation. The representation of all transport pathways in the model enables watershed managers to develop robust strategies for reducing chemicals in streams.
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Manuscript not accepted for further review
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A recently developed groundwater module (gwflow) coupled with the soil water assessment tool (SWAT+) is used to simulate the streamflow of the Dijle catchment, Belgium. The standalone model (SWAT+) resulted in unsatisfactory streamflow simulations while SWAT+gwflow produced streamflow that considerably mimics the measured river discharge. Furthermore, modifications to the gwflow module are made to account for the vital hydrological process (groundwater-soil profile interactions).
Cited articles
Abbas, S., Xuan, Y., and Bailey, R.: Assessing Climate Change Impact on Water Resources in Water Demand Scenarios Using SWAT-MODFLOW-WEAP, Hydrology, 9, 164, https://doi.org/10.3390/hydrology9100164, 2022.
Arnold, J., Srinivasan, R., Muttiah, R., and Williams, J.: Large area hydrologic modeling and assessment part I: Model development, J. Am. Water Resour. Assoc., 34, 73–89, https://doi.org/10.1111/j.1752-1688.1998.tb05961.x, 1998.
Arnold, J., Kiniry, J., Srinivasan, R., Williams, J., Haney, E., and Neitsch, S.: Soil & Water Assessment Tool: Input/output documentation, version 2012, 2013 TR-439, Texas Water Resources Institute, https://swat.tamu.edu/media/69296/swat-io-documentation-2012.pdf (last access: 16 June 2023), 2013.
Arnold, J., White, M., Allen, P., Gassman, P., and Bieger, K.: Conceptual Framework of connectivity for a national agroecosystem model based on transport processes and management practices. J. Am. Water Resour. Assoc., 57, 154–169, https://doi.org/10.1111/1752-1688.12890, 2020.
Bahremand, A. and De Smedt, F.: Predictive Analysis and Simulation Uncertainty of a Distributed Hydrological Model, Water Resour. Manage., 24, 2869–2880, https://doi.org/10.1007/s11269-010-9584-1, 2010.
Bailey, R. and Alderfer, C.: Groundwater Data in Unconfined Aquifers – conterminous United States, figshare [data set], https://doi.org/10.6084/m9.figshare.c.5918738.v2, 2022.
Bailey, R., Wible, T., Arabi, M., Records, R., and Ditty, J.: Assessing regional-scale spatio-temporal patterns of groundwater-surface water interactions using a coupled SWAT-MODFLOW model, Hydrol. Process., 30, 4420–4433, https://doi.org/10.1002/hyp.10933, 2016.
Bailey, R., Bieger, K., Arnold, J., and Bosch, D.: A new physically-based spatially-distributed groundwater flow module for SWAT+, Hydrology, 7, 75, https://doi.org/10.3390/hydrology7040075, 2020.
Bailey, R., Abbas, S., Arnold, J., White, M., Gao, J., and Čerkasova, N.: Augmenting the national agroecosystem model with physically based spatially distributed groundwater modeling, Environ. Model. Softw., 160, 105589, https://doi.org/10.1016/j.envsoft.2022.105589, 2023.
Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton, S. H., Jakeman, A. J., Marsili-Libelli, S., Newham, L. T. H., Norton, J. P., Perrin, C., Pierce, S. A., Robson, B., Seppelt, R., Voinov, A. A., Fath, B. D., and Andreassian, V.: Characterising performance of environmental models, Environ. Model. Softw., 40, 1–20, https://doi.org/10.1016/j.envsoft.2012.09.011, 2013.
Bhatta, B., Shrestha, S., Shrestha, P., and Talchabhadel, R.: Evaluation and application of a SWAT model to assess the climate change impact on the hydrology of the Himalayan River Basin, Catena, 181, 104082, https://doi.org/10.1016/j.catena.2019.104082, 2019.
Bieger, K., Hörmann, G., and Fohrer, N.: Detailed spatial analysis of SWAT-simulated surface runoff and sediment yield in a mountainous watershed in China, Hydrolog. Sci. J., 60, 784–800, https://doi.org/10.1080/02626667.2014.965172, 2015.
Bieger, K., Arnold, J. G., Rathjens, H., White, M. J., Bosch, D. D., Allen, P. M., Volk, M., and Srinivasan, R.: Introduction to SWAT+, a completely restructured version of the soil and water assessment tool, J. Am. Water Resour. Assoc., 53, 115–130, https://doi.org/10.1111/1752-1688.12482, 2017.
Bocquet, M. and Sakov, P.: An iterative ensemble Kalman smoother, Q. J. Roy. Meteorol. Soc., 140, 1521–1535, https://doi.org/10.1002/qj.2236, 2014.
Brunner, P., Therrien, R., Renard, P., Simmons, C., and Franssen, H.: Advances in understanding river-groundwater interactions, Rev. Geophys., 55, 818–854, https://doi.org/10.1002/2017RG000556, 2017.
Campolongo, F., Cariboni, J., and Saltelli, A.: An effective screening design for sensitivity analysis of large models, Environ. Model. Softw., 22, 1509–1518, https://doi.org/10.1016/j.envsoft.2006.10.004, 2007.
Čerkasova, N., Umgiesser, G., and Ertürk, A.: Modelling framework for flow, sediments and nutrient loads in a large transboundary river watershed: A climate change impact assessment of the Nemunas river watershed, J. Hydrol., 598, 126422, https://doi.org/10.1016/j.jhydrol.2021.126422, 2021.
Chen, Y. and Oliver, D.: Ensemble randomized maximum likelihood method as an iterative ensemble smoother, Math. Geosci., 44, 1–26, https://doi.org/10.1007/s11004-011-9376-z, 2012.
Chen, Y. and Oliver, D.: Levenberg–Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification, Comput. Geosci., 17, 689–703, https://doi.org/10.1007/s10596-013-9351-5, 2013.
Crestani, E., Camporese, M., Baú, D., and Salandin, P.: Ensemble Kalman filter versus ensemble smoother for assessing hydraulic conductivity via tracer test data assimilation, Hydrol. Earth Sys.t Sci., 17, 1517–1531, https://doi.org/10.5194/hess-17-1517-2013, 2013.
Crook, N., Binley, A., Knight, R., Robinson, D., Zarnetske, J., and Haggerty, R.: Electrical resistivity imaging of the architecture of substream sediments, Water Resour. Res., 44, W00D13, https://doi.org/10.1029/2008WR006968, 2008.
Devak, M. and Dhanya, C.: Sensitivity analysis of hydrological models: Review and way forward, J. Water Clim., 8, 557–575, https://doi.org/10.2166/wcc.2017.149, 2017.
Dieter, C., Maupin, M., Caldwell, R., Harris, M., Ivahnenko, T., Lovelace, J., Barber, N., and Linsey, K.: Water availability and use science program: Estimated use of water in the United States in 2015 (Circular 1441), US Geological Survey, https://doi.org/10.3133/cir1441, 2018.
Doherty, J.: PEST model-independent parameter estimation user manual, Watermark Numerical Computing, Brisbane, Australia, 3338–3349, https://www.epa.gov/sites/default/files/documents/PESTMAN.PDF (last access: 16 June 2023), 2004.
Doherty, J.: PEST, Model-independent Parameter Estimation: User Manual, 7th Edn., Watermark Numerical Computing, Brisbane, Australia, 3338–3349, https://pesthomepage.org/documentation (last access: 16 June 2023), 2020.
Doherty, J. and Hunt, R.: Two statistics for evaluating parameter identifiability and error reduction, J. Hydrol., 366, 119–127, https://doi.org/10.1016/j.jhydrol.2008.12.018, 2009.
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res.-Oceans, 99, 10143–10162, https://doi.org/10.1029/94JC00572, 1994.
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.
Fox, G.: Estimating streambed conductivity: guidelines for stream-aquifer analysis tests, T. ASABE, 50, 107–113, https://doi.org/10.13031/2013.22416, 2007.
Fox, G. and Durnford, D.: Unsaturated hyporheic zone flow in stream/aquifer conjunctive systems, Adv. Water Resour., 26, 989–1000, https://doi.org/10.1016/S0309-1708(03)00087-3, 2003.
Gesch, D., Evans, G., Oimoen, M., and Arundel, S.: The national elevation dataset, American Society for Photogrammetry and Remote Sensing, 83–110, https://pubs.usgs.gov/publication/70201572 (last access: 16 June 2023), 2018.
Ghaffari, G., Keesstra, S., Ghodousi, J., and Ahmadi, H.: SWAT-simulated hydrological impact of land-use change in the Zanjanrood basin, Northwest Iran, Hydrol. Process., 24, 892–903, https://doi.org/10.1002/hyp.7530, 2010.
Helton, J.: Uncertainty and sensitivity analysis techniques for use in performance assessment for Radioactive Waste Disposal, Reliab. Eng. Syst. Safe., 42, 327–367, https://doi.org/10.1016/0951-8320(93)90097-i, 1993.
Herzog, A., Hector, B., Cohard, J., Vouillamoz, J., Lawson, F. M., Peugeot, C., and De Graaf, I.: A parametric sensitivity analysis for prioritizing regolith knowledge needs for modeling water transfers in the West African critical zone, Vadose Zone J., 20, e20163, https://doi.org/10.1002/vzj2.20163, 2021.
Horton, J., San Juan, C., and Stoeser, D.: The state geologic map compilation (SGMC) geodatabase of the conterminous United States, ver. 1.1, August 2017 (Data Series 1052), USGS, https://doi.org/10.3133/ds1052, 2017.
Izady, A., Joodavi, A., Ansarian, M., Shafiei, M., Majidi, M., Davary, K., Ziaei, A. N., Ansari, H., Nikoo, M. R., Al-Maktoumi, A., Chen, M., and Abdalla, O.: A scenario-based coupled SWAT-MODFLOW decision support system for Advanced Water Resource Management, J. Hydroinform., 24, 56–77, https://doi.org/10.2166/hydro.2021.081, 2022.
Jiang, S., Jomaa, S., and Rode, M.: Modelling inorganic nitrogen leaching in nested mesoscale catchments in central Germany. Ecohydrology, 7, 1345–1362, https://doi.org/10.1002/eco.1462, 2014.
Kalbus, E., Schmidt, C., Molson, J. W., Reinstorf, F., and Schirmer, M.: Influence of aquifer and streambed heterogeneity on the distribution of groundwater discharge, Hydrol. Earth Syst. Sci., 13, 69–77., https://doi.org/10.5194/hess-13-69-2009, 2009.
Koo, H., Chen, M., Jakeman, A., and Zhang, F.: A global sensitivity analysis approach for identifying critical sources of uncertainty in non-identifiable, spatially distributed environmental models: A holistic analysis applied to SWAT for input datasets and model parameters, Environ. Model. Softw., 127, 104676, https://doi.org/10.1016/j.envsoft.2020.104676, 2020.
Leta, O., Nossent, J., Velez, C., Shrestha, N., Van Griensven, A., and Bauwens, W.: Assessment of the different sources of uncertainty in a SWAT model of the River Senne (Belgium), Environ. Model. Softw., 68, 129–146, https://doi.org/10.1016/j.envsoft.2015.02.010, 2015.
Liu, H., Jia, Y., Niu, C., Su, H., Wang, J., Du, J., Khaki, M., Hu, P., and Liu, J.: Development and validation of a physically-based, national-scale hydrological model in China, J. Hydrol., 590, 125431, https://doi.org/10.1016/j.jhydrol.2020.125431, 2020.
Moore, R. and Dewald, T.: The road to nhdp lus – advancements in digital stream networks and associated catchments, J. Am. Water Resour. Assoc., 52, 890–900, https://doi.org/10.1111/1752-1688.12389, 2016.
Morris, M.: Factorial sampling plans for preliminary computational experiments, Technometrics, 33, 161–174, https://doi.org/10.2307/1269043, 1991.
Neitsch, S., Arnold, J., Kiniry, J., and Williams, J.: Soil and Water Assessment Tool Theoretical Documentation version 2009, Texas Water Resources Institute, https://swat.tamu.edu/media/99192/swat2009-theory.pdf (last access: 16 June 2023), 2011.
Nossent, J., Elsen, P., and Bauwens, W.: Sobol' sensitivity analysis of a complex environmental model, Environ. Model. Softw., 26, 1515–1525, https://doi.org/10.1016/j.envsoft.2011.08.010, 2011.
Olaya-Abril, A., Parras-Alcántara, L., Lozano-García, B., and Obregón-Romero, R.: Soil Organic Carbon Distribution in Mediterranean areas under a climate change scenario via multiple linear regression analysis, Sci. Total Environ., 592, 134–143, https://doi.org/10.1016/j.scitotenv.2017.03.021, 2017.
Partington, D., Therrien, R., Simmons, C., and Brunner, P.: Blueprint for a coupled model of sedimentology, hydrology, and hydrogeology in streambeds, Rev. Geophys., 55, 287–309, https://doi.org/10.1002/2016RG000530, 2017.
Pianosi, F., Iwema, J., Rosolem, R., and Wagener, T.: A multimethod global sensitivity analysis approach to support the calibration and evaluation of Land Surface Models, Sens. Anal. Earth Obs. Model., 2017, 125–144, https://doi.org/10.1016/b978-0-12-803011-0.00007-0, 2017.
Plischke, E., Borgonovo, E., and Smith, C.: Global sensitivity measures from given data. European Eur, J. Oper. Res., 226, 536–550, https://doi.org/10.1016/j.ejor.2012.11.047, 2013.
Pokhrel, Y., Felfelani, F., Satoh, Y., Boulange, J., Burek, P., Gädeke, A., Gerten, D., Gosling, S. N., Grillakis, M., Gudmundsson, L., Hanasaki, N., Kim, H., Koutroulis, A., Liu, J., Papadimitriou, L., Schewe, J., Müller Schmied, H., Stacke, T., Telteu, C.-E., Thiery, W., Veldkamp, T., Zhao, F., and Wada, Y.: Global terrestrial water storage and drought severity under climate change, Nat. Clim. Change, 11, 226–233, https://doi.org/10.1038/s41558-020-00972-w, 2021.
Qiu, J., Yang, Q., Zhang, X., Huang, M., Adam, J. C., and Malek, K.: Implications of water management representations for watershed hydrologic modeling in the Yakima River Basin, Hydrol Earth Syst. Sci., 23, 35–49, https://doi.org/10.5194/hess-23-35-2019, 2019.
Rode, M., Suhr, U., and Wriedt, G.: Multi-objective calibration of a river water quality model—Information content of calibration data, Ecol. Model., 204, 129–142, https://doi.org/10.1016/j.ecolmodel.2006.12.037, 2007.
Ryken, A., Bearup, L., Jefferson, J., Constantine, P., and Maxwell, R.: Sensitivity and model reduction of simulated snow processes: Contrasting observational and parameter uncertainty to improve prediction, Adv. Water Resour., 135, 103473, https://doi.org/10.1016/j.advwatres.2019.103473, 2020.
Santos, L., Andersson, J., and Arheimer, B.: Evaluation of parameter sensitivity of a rainfall-runoff model over a global catchment set, Hydrolog. Sci. J., 67, 342–357, https://doi.org/10.1080/02626667.2022.2035388, 2022.
Shangguan, W., Hengl, T., Mendes de Jesus, J., Yuan, H., and Dai, Y.: Mapping the global depth to bedrock for land surface modeling, J. Adv. Model. Earth Syst., 9, 65–88, https://doi.org/10.1002/2016ms000686, 2017.
Shi, W. and Wang, Q.: An Analytical Model of Multi-layered Heat Transport to Estimate Vertical Streambed Fluxes and Sediment Thermal Properties, J. Hydrol., 625, 129963, https://doi.org/10.1016/j.jhydrol.2023.129963, 2023.
Sith, R., Watanabe, A., Nakamura, T., Yamamoto, T., and Nadaoka, K.: Assessment of water quality and evaluation of best management practices in a small agricultural watershed adjacent to Coral Reef area in Japan, Agr. Water Manage., 213, 659–673, https://doi.org/10.1016/j.agwat.2018.11.014, 2019.
Skinner, K. and Maupin, M.: Point-source nutrient loads to streams of the conterminous United States, 2012 (No. 1101), US Geological Survey, https://doi.org/10.3133/ds1101, 2019.
Soil Survey Staff: Gridded soil survey geographic (gSSURGO) database for the conterminous United States, https://data.nal.usda.gov/dataset/gridded-soil-survey-geographic-database-gssurgo (last access: 1 July 2020), 2014.
SWAT – Soil & Water Assessment Tool: gwflow module for SWAT+, https://swat.tamu.edu/software/plus/gwflow/ (last access: 22 December 2023), 2023.
Valayamkunnath, P., Barlage, M., Chen, F., Gochis, D., and Franz, K.: Mapping of 30-meter resolution tile-drained croplands using a geospatial modeling approach, Sci. Data, 7, 257, https://doi.org/10.1038/s41597-020-00596-x, 2020.
Van Leeuwen, P. and Evensen, G.: Data assimilation and inverse methods in terms of a probabilistic formulation, Mon. Weather Rev., 124, 2898–2913, https://doi.org/10.1175/1520-0493(1996)124<2898:DAAIMI>2.0.CO;2, 1996.
Wang, Y., Bian, J., Zhao, Y., Tang, J., and Jia, Z.: Assessment of future climate change impacts on nonpoint source pollution in snowmelt period for a cold area using SWAT, Sci. Rep., 8, 1–13, https://doi.org/10.1038/s41598-018-20818-y, 2018.
Wei, X., Bailey, R., and Tasdighi, A.: Using the SWAT model in intensively managed irrigated watersheds: Model modification and Application, J. Hydrol. Eng., 23, 04018044-1–04018044-17, https://doi.org/10.1061/(asce)he.1943-5584.0001696, 2018.
White, J.: A model-independent iterative ensemble smoother for efficient history-matching and uncertainty quantification in very high dimensions, Environ. Model. Softw., 109, 191–201, https://doi.org/10.1016/j.envsoft.2018.06.009, 2018.
White, J., Hunt, R., Fienen, M., and Doherty, J.: Approaches to Highly Parameterized Inversion: PEST
White, M. J., Arnold, J. G., Bieger, K., Allen, P. M., Gao, J., Čerkasova, N., Gambone, M., Park, S., Bosch, D. D., Yen, H., and Osorio, J. M.: Development of a field scale SWAT+ Modeling Framework for the contiguous U.S., J. Am. Water Resour. Assoc., 58, 1545–1560, https://doi.org/10.1111/1752-1688.13056, 2022.
Wojnar, A., Mutiti, S., and Levy, J.: Assessment of geophysical surveys as a tool to estimate riverbed hydraulic conductivity, J. Hydrol., 482, 40–56, https://doi.org/10.1016/j.jhydrol.2012.12.018, 2013.
Wolock, D. M.: Base-flow index grid for the conterminous United States (No. 2003-263), USGS, https://doi.org/10.3133/ofr03263, 2003.
Wu, B., Zheng, Y., Tian, Y., Wu, X., Yao, Y., Han, F., Liu, J., and Zheng, C.: Systematic assessment of the uncertainty in integrated surface water-groundwater modeling based on the probabilistic collocation method, Water Resour. Res., 50, 5848–5865, https://doi.org/10.1002/2014WR015366, 2014.
Yan, L. and Roy, D.: Conterminous United States crop field size quantification from multi-temporal Landsat Data, Remote Sen. Environ., 172, 67–86, https://doi.org/10.1016/j.rse.2015.10.034, 2016.
Zhang, H., Wang, B., Li Liu, D., Zhang, M., Leslie, L. M., and Yu, Q.: Using an improved SWAT model to simulate hydrological responses to land use change: A case study of a catchment in tropical Australia, J. Hydrol., 585, 124822, https://doi.org/10.1016/j.jhydrol.2020.124822, 2020.
Short summary
Research highlights.
1. Implemented groundwater module (gwflow) into SWAT+ for four watersheds with different unique hydrologic features across the United States.
2. Presented methods for sensitivity analysis, uncertainty analysis and parameter estimation for coupled models.
3. Sensitivity analysis for streamflow and groundwater head conducted using Morris method.
4. Uncertainty analysis and parameter estimation performed using an iterative ensemble smoother within the PEST framework.
Research highlights.
1. Implemented groundwater module (gwflow) into SWAT+ for four watersheds...
