Virtual Experiments Guide Calibration Strategies for a Real-World Watershed Application of Coupled Surface-Subsurface Modeling
Publication: Journal of Hydrologic Engineering
Volume 21, Issue 11
Abstract
Virtual experiments have been designed for the development and validation of coupled surface-subsurface modeling. Potentially, virtual experiments can guide model calibration as well. To address the role of virtual experiments in model calibration, a novel approach was described for a real watershed calibration of Penn State Integrated Hydrologic Model (PIHM) guided by the V-shaped catchment simulation. First, a benchmarking experiment of coupled surface-subsurface modeling was developed and documented on the V-shaped catchment. Then, the performance of hydrologic predictions for the V-shaped catchment was calculated and demonstrated different levels of correlations. The correlations were found stable, which had the potential to be used as the weights of multiobjective calibration. Therefore, a weighted multiobjective calibration was developed for a real-world watershed by transferring the correlations obtained from the virtual experiments. Expectedly, the parameters calibrated using the weighted approach indicated improvement of the model performance in simulating water-table depths and evapotranspiration with little sacrifice of model performance in streamflow. In addition, this study also compares the weighted average calibration and unweighted calibration. The results demonstrate the weighted objective optimization achieved satisfactory compromise for each calibration objective. Overall, the virtual experiment is proved to be an efficient tool to facilitate calibration of complex models. The proposed weighted objective approach provides an effective calibration strategy for the multiple observation constraints, which can be applied for the calibration of coupled environmental process models with multiple observations.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors thank the editor and reviewers for their valuable input, which helped to improve the manuscript. This research was supported by the grant from the National Science Foundation, EAR-0725019, EAR-1239285, and EAR-1331726 Shale Hills-Susquehanna Critical Zone Observatory. The computing facility was supported in part through instrumentation funded by the National Science Foundation through Grant OCI–0821527.
References
Bhatt, G., Kumar, M., and Duffy, C. J. (2014). “A tightly coupled GIS and distributed hydrologic modeling framework.” Environ. Modell. Software, 62, 70–84.
Butts, M. B., Payne, J. T., Kristensen, M., and Madsen, H. (2004). “An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulation.” J. Hydrol., 298(1–4), 242–266.
Chen, F., and Dudhia, J. (2001). “Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity.” Mon. Weather Rev., 129(4), 569–585.
Davis, K. (2010). “CZO dataset: Shale Hills—Flux tower (2009–2010).” 〈http://criticalzone.org/shale-hills/data/dataset/2558/〉 (Feb. 23, 2012).
Di Giammarco, P., Todini, E., and Lamberti, P. (1996). “A conservative finite elements approach to overland flow: The control volume finite element formulation.” J. Hydrol., 175(1–4), 267–291.
Dingman, S. L. (2002). Physical hydrology, 2nd Ed., Prentice Hall, Upper Saddle River, NJ.
Duffy, C. J. (2010a). “CZO dataset: Shale Hills—Groundwater depth data (2009–2012).” 〈http://cataract.cee.psu.edu/czo/rth2/index.php?dir=Groundwater〉 (Feb. 24, 2012).
Duffy, C. J. (2010b). “CZO dataset: Shale Hills—Streamflow data (2006–2012).” 〈http://cataract.cee.psu.edu/czo/rth2/index.php?dir=Streamflow〉 (Feb. 24, 2012).
Duffy, C. J. (2012). “CZO dataset: Shale Hills—Precipitation (2006–2012).” 〈http://criticalzone.org/shale-hills/data/dataset/2556/〉 (Feb. 24, 2012).
Dung, N. V., Merz, B., Bárdossy, A., Thang, T. D., and Apel, H. (2011). “Multi-objective automatic calibration of hydrodynamic models utilizing inundation maps and gauge data.” Hydrol. Earth Syst. Sci., 15(4), 1339–1354.
Efstratiadis, A., and Koutsoyiannis, D. (2010). “One decade of multi-objective calibration approaches in hydrological modelling: A review.” Hydrol. Sci. J., 55(1), 58–78.
Eissenstat, D. (2008). “CZO dataset: Shale Hills—Vegetation—Tree survey.” 〈http://criticalzone.org/shale-hills/data/dataset/2648/〉 (Feb. 24, 2012).
Guo, Q. (2010). “Susquehanna Shale Hills critical zone observatory: Leaf off survey.” San Diego Supercomputer Center, University of California San Diego, La Jolla, CA.
Gupta, H. V., Sorooshian, S., and Yapo, P. O. (1998). “Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information.” Water Resour. Res., 34(4), 751–763.
Hansen, N., Muller, S. D., and Koumoutsakos, P. (2003). “Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES).” Evol. Comput., 11(1), 1–18.
Henry, H. R. (1964). “Effects of dispersion on salt encroachment in coastal aquifers.” U.S. Geological Survey, 70–84.
Hsie, M., Yan, S., and Pan, N. (2014). “Improvement of rainfall-runoff simulations using the runoff-scale weighting method.” J. Hydrol. Eng., 1330–1339.
Juston, J., Seibert, J., and Johansson, P.-O. (2009). “Temporal sampling strategies and uncertainty in calibrating a conceptual hydrological model for a small boreal catchment.” Hydrol. Processes, 23(21), 3093–3109.
Khu, S. T., and Madsen, H. (2005). “Multiobjective calibration with Pareto preference ordering: An application to rainfall-runoff model calibration.” Water Resour. Res., 41(3), W03004.
Kim, J., Warnock, A., Ivanov, V. Y., and Katopodes, N. D. (2012). “Coupled modeling of hydrologic and hydrodynamic processes including overland and channel flow.” Adv. Water Resour., 37(0), 104–126.
Kumar, M. (2009). “Toward a hydrologic modeling system.” Ph.D. dissertation, Pennsylvania State Univ., University Park, PA.
Kumar, M., and Duffy, C. (2015). “Exploring the role of domain partitioning on efficiency of parallel distributed hydrologic model simulations.” J. Hydrogeol. Hydrol. Eng., 4(1), 1–12.
Leij, F. J. (1996). “The UNSODA unsaturated soil hydraulic database: User’s manual.” U.S. Environmental Protection Agency, Ada, Oklahoma.
Li, X., Weller, D. E., and Jordan, T. E. (2010). “Watershed model calibration using multi-objective optimization and multi-site averaging.” J. Hydrol., 380(3), 277–288.
Lin, H. (2006). “Temporal stability of soil moisture spatial pattern and subsurface preferential flow pathways in the Shale Hills catchment.” Vadose Zone J., 5(1), 317–340.
Lin, H. (2010). “CZO dataset: Shale Hills—Hydropedologic properties (2007–2010)—Water table.” 〈http://criticalzone.org/shale-hills/data/dataset/2586/〉 (Feb. 23, 2012).
Lynch, J. A. (1976). “Effects of antecedent soil moisture on storm hydrographs.” Ph.D. thesis, Pennsylvania State Univ., University Park, PA.
Mallard, J., McGlynn, B., and Covino, T. (2014). “Lateral inflows, stream groundwater exchange, and network geometry influence stream water composition.” Water Resour. Res., 50(6), 4603–4623.
Maxwell, R. M., et al. (2014). “Surface-subsurface model intercomparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks.” Water Resour. Res., 50(2), 1531–1549.
McMillan, H., Krueger, T., and Freer, J. (2012). “Benchmarking observational uncertainties for hydrology: Rainfall, river discharge and water quality.” Hydrol. Processes, 26(26), 4078–4111.
Miller, N. L. (1995). “Sensitivity of surface heat and moisture fluxes due to topographic slope and azimuth.” J. Geophys. Res. Atmos., 100(D9), 18669–18685.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., and Veith, T. L. (2007). “Model evaluation guidelines for systematic quantification of accuracy in watershed simulations.” Trans. ASABE, 50(3), 885–900.
Nicklow, J., et al. (2010). “State of the art for genetic algorithms and beyond in water resources planning and management.” J. Water Resour. Plann. Manage., 412–432.
NLDAS (National Land Data Assimilation Systems). (1999). “Mapped monthly vegetation parameters.” 〈http://ldas.gsfc.nasa.gov/nldas/NLDASmapveg.php〉 (Mar. 10, 2012).
Nutter, W. L. (1964). “Determination of the head-discharge relationship for a sharp-crested compound weir and a sharp-crested parabolic weir.” Ph.D. thesis, Pennsylvania State Univ., University Park, PA.
Osei-Kuffuor, D., Maxwell, R. M., and Woodward, C. S. (2014). “Improved numerical solvers for implicit coupling of subsurface and overland flow.” Adv. Water Resour., 74, 185–195.
Persson, P.-O., and Strang, G. (2004). “A simple mesh generator in MATLAB.” SIAM Rev., 46(2), 329–345.
PIHM (Penn State Integrated Hydrologic Modeling System). (2016). “Overview.” 〈http://www.pihm.psu.edu/〉 (Jun. 15, 2016).
Qu, Y. (2005). “An integrated hydrologic model for multi-process simulation using semi-discrete finite volume approach.” Pennsylvania State Univ., University Park, PA.
Qu, Y., and Duffy, C. J. (2007). “A semidiscrete finite volume formulation for multiprocess watershed simulation.” Water Resour. Res., 43(8), W08419.
Razavi, S., and Tolson, B. A. (2013). “An efficient framework for hydrologic model calibration on long data periods.” Water Resour. Res., 49(12), 8418–8431.
Rientjes, T. H. M., Muthuwatta, L. P., Bos, M. G., Booij, M. J., and Bhatti, H. A. (2013). “Multi-variable calibration of a semi-distributed hydrological model using streamflow data and satellite-based evapotranspiration.” J. Hydrol., 505(15), 276–290.
Rozos, E., Efstratiadis, A., Nalbantis, I., and Koutsoyiannis, D. (2004). “Calibration of a semi-distributed model for conjunctive simulation of surface and groundwater flows.” Hydrol. Sci. J., 49(5), 819–842.
Seo, Y., and Schmidt, A. R. (2013). “Network configuration and hydrograph sensitivity to storm kinematics.” Water Resour. Res., 49(4), 1812–1827.
Shale Hills Datasets. (2012). 〈http://criticalzone.org/shale-hills/data/datasets/〉 (Feb. 24, 2012).
Shi, Y., Davis, K. J., Duffy, C. J., and Yu, X. (2013). “Development of a coupled land surface hydrologic model and evaluation at a critical zone observatory.” J. Hydrometeorol., 14(5), 1401–1420.
Shi, Y., Davis, K. J., Zhang, F., Duffy, C. J., and Yu, X. (2014). “Parameter estimation of a physically based land surface hydrologic model using the ensemble Kalman filter: A synthetic experiment.” Water Resour. Res., 50(1), 706–724.
Simpson, M. J., and Clement, T. P. (2003). “Theoretical analysis of the worthiness of the Henry and Elder problems as benchmarks of density-dependent groundwater flow models.” Adv. Water Resour., 26, 17–31.
Singh, S. K., and Bárdossy, A. (2012). “Calibration of hydrological models on hydrologically unusual events.” Adv. Water Resour., 38, 81–91.
Stisen, S., McCabe, M. F., Refsgaard, J. C., Lerer, S., and Butts, M. B. (2011). “Model parameter analysis using remotely sensed pattern information in a multi-constraint framework.” J. Hydrol., 409(1), 337–349.
van Genuchten, M. T. (1980). “A closed-form equation for predicting the hydraulic conductivity of unsaturated soils.” Soil Sci. Soc. Am. J., 44(5), 892–898.
Vrugt, J. A., Gupta, H. V., Dekker, S. C., Sorooshian, S., Wagener, T., and Bouten, W. (2006). “Application of stochastic parameter optimization to the Sacramento soil moisture accounting model.” J. Hydrol., 325(1–4), 288–307.
Wainwright, J., and Mulligan, M. (2004). Environmental modelling: Finding simplicity in complexity, Wiley, West Sussex, U.K.
Weiler, M., and McDonnell, J. (2004). “Virtual experiments: A new approach for improving process conceptualization in hillslope hydrology.” J. Hydrol., 285(1–4), 3–18.
Yu, X. (2015). “PIHM input to benchmarking V-catchment.” 〈https://doi.org/10.6084/m9.figshare.1328521〉 (Apr. 11, 2015).
Yu, X., Bhatt, G., Duffy, C., and Shi, Y. (2013). “Parameterization for distributed watershed modeling using national data and evolutionary algorithm.” Comput. Geosci., 58, 80–90.
Zidane, A., Younes, A., Huggenberger, P., and Zechner, E. (2012). “The Henry semianalytical solution for saltwater intrusion with reduced dispersion.” Water Resour. Res., 48, W06533.
Information & Authors
Information
Published In
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Aug 18, 2015
Accepted: Apr 29, 2016
Published online: Jul 19, 2016
Published in print: Nov 1, 2016
Discussion open until: Dec 19, 2016
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.