Editorial Type:
Article
Article Type:
Research Article

Uncertainty Analysis of Runoff Simulations and Parameter Identifiability in the Community Land Model: Evidence from MOPEX Basins

Maoyi Huang Pacific Northwest National Laboratory, Richland, Washington

Search for other papers by Maoyi Huang in
Current site
Google Scholar
PubMed Close
,
Zhangshuan Hou Pacific Northwest National Laboratory, Richland, Washington

Search for other papers by Zhangshuan Hou in
Current site
Google Scholar
PubMed Close
,
L. Ruby Leung Pacific Northwest National Laboratory, Richland, Washington

Search for other papers by L. Ruby Leung in
Current site
Google Scholar
PubMed Close
,
Yinghai Ke Pacific Northwest National Laboratory, Richland, Washington, and Base of the State Key Laboratory of Urban Environment Process and Digital Modelling, Department of Resource Environment and Tourism, Capital Normal University, Beijing, China

Search for other papers by Yinghai Ke in
Current site
Google Scholar
PubMed Close
,
Ying Liu Pacific Northwest National Laboratory, Richland, Washington

Search for other papers by Ying Liu in
Current site
Google Scholar
PubMed Close
,
Zhufeng Fang Pacific Northwest National Laboratory, Richland, Washington

Search for other papers by Zhufeng Fang in
Current site
Google Scholar
PubMed Close
, and
Yu Sun Pacific Northwest National Laboratory, Richland, Washington, and Department of Hydraulic Engineering, State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, China

Search for other papers by Yu Sun in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Dec 2013
DOI:
https://doi.org/10.1175/JHM-D-12-0138.1
Page(s):
1754–1772
Restricted access

Abstract

In this study, the authors applied version 4 of the Community Land Model (CLM4) integrated with an uncertainty quantification (UQ) framework to 20 selected watersheds from the Model Parameter Estimation Experiment (MOPEX) spanning a wide range of climate and site conditions to investigate the sensitivity of runoff simulations to major hydrologic parameters and to assess the fidelity of CLM4, as the land component of the Community Earth System Model (CESM), in capturing realistic hydrological responses. They found that for runoff simulations, the most significant parameters are those related to the subsurface runoff parameterizations. Soil texture–related parameters and surface runoff parameters are of secondary significance. Moreover, climate and soil conditions play important roles in the parameter sensitivity. In general, water-limited hydrologic regime and finer soil texture result in stronger sensitivity of output variables, such as runoff and its surface and subsurface components, to the input parameters in CLM4. This study evaluated the parameter identifiability of hydrological parameters from streamflow observations at selected MOPEX basins and demonstrated the feasibility of parameter inversion/calibration for CLM4 to improve runoff simulations. The results suggest that in order to calibrate CLM4 hydrologic parameters, model reduction is needed to include only the identifiable parameters in the unknowns. With the reduced parameter set dimensionality, the inverse problem is less ill posed.

Corresponding author address: Maoyi Huang, Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99352. E-mail: maoyi.huang@pnnl.gov

Abstract

In this study, the authors applied version 4 of the Community Land Model (CLM4) integrated with an uncertainty quantification (UQ) framework to 20 selected watersheds from the Model Parameter Estimation Experiment (MOPEX) spanning a wide range of climate and site conditions to investigate the sensitivity of runoff simulations to major hydrologic parameters and to assess the fidelity of CLM4, as the land component of the Community Earth System Model (CESM), in capturing realistic hydrological responses. They found that for runoff simulations, the most significant parameters are those related to the subsurface runoff parameterizations. Soil texture–related parameters and surface runoff parameters are of secondary significance. Moreover, climate and soil conditions play important roles in the parameter sensitivity. In general, water-limited hydrologic regime and finer soil texture result in stronger sensitivity of output variables, such as runoff and its surface and subsurface components, to the input parameters in CLM4. This study evaluated the parameter identifiability of hydrological parameters from streamflow observations at selected MOPEX basins and demonstrated the feasibility of parameter inversion/calibration for CLM4 to improve runoff simulations. The results suggest that in order to calibrate CLM4 hydrologic parameters, model reduction is needed to include only the identifiable parameters in the unknowns. With the reduced parameter set dimensionality, the inverse problem is less ill posed.

Corresponding author address: Maoyi Huang, Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99352. E-mail: maoyi.huang@pnnl.gov
Save
Cover Journal of Hydrometeorology
Journal of Hydrometeorology

  • Akaike, H., 1974: New Look at statistical-model identification. IEEE Autom. Control, 19, 716–723.

  • Arnold, J. G., and Allen P. M. , 1999: Automated methods for estimating baseflow and ground water recharge from streamflow records. J. Amer. Water Resour. Assoc., 35, 411–424.

    • Search Google Scholar
    • Export Citation

  • Bates, B. C., Kundzewicz Z. W. , Wu S. , and Palutikof J. P. , 2008: Climate change and water. IPCC Tech. Paper VI, IPCC, 210 pp.

  • Beven, K., 1997: TOPMODEL: A critique. Hydrol. Processes, 11, 1069–1085.

  • Beven, K., and Binley A. , 1992: The future of distributed models: Model calibration and uncertainty prediction. Hydrol. Processes, 6, 279–298.

    • Search Google Scholar
    • Export Citation

  • Brooks, P. D., Troch P. A. , Durcik M. , Gallo E. , and Schlegel M. , 2011: Quantifying regional scale ecosystem response to changes in precipitation: Not all rain is created equal. Water Resour. Res., 47, W00J08, doi:10.1029/2010WR009762.

    • Search Google Scholar
    • Export Citation

  • Cosby, B. J., Hornberger G. M. , Clapp R. B. , and Ginn T. R. , 1984: A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resour. Res., 20, 682–690.

    • Search Google Scholar
    • Export Citation

  • Cosgrove, B. A., and Coauthors, 2003: Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res., 108, 8842, doi:10.1029/2002JD003118.

    • Search Google Scholar
    • Export Citation

  • Duan, Q., Sorooshian S. , and Gupta V. , 1992: Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour. Res., 28, 1015–1031.

    • Search Google Scholar
    • Export Citation

  • Duan, Q., Gupta V. K. , and Sorooshian S. , 1993: Shuffled complex evolution approach for effective and efficient global minimization. J. Optim. Theory Appl., 76, 501–521.

    • Search Google Scholar
    • Export Citation

  • Duan, Q., and Coauthors, 2006: Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops. J. Hydrol., 320, 3–17.

    • Search Google Scholar
    • Export Citation

  • Eckhardt, K., 2005: How to construct recursive digital filters for baseflow separation. Hydrol. Processes, 19, 507–515.

  • Forsythe, W. C., Rykiel E. J. Jr., Stahl R. S. , Wu H.-i. , and Schoolfield R. M. , 1995: A model comparison for daylength as a function of latitude and day of year. Ecol. Modell., 80, 87–95.

    • Search Google Scholar
    • Export Citation

  • Freer, J., Beven K. , and Ambroise B. , 1996: Bayesian estimation of uncertainty in runoff prediction and the value of data: An application of the GLUE approach. Water Resour. Res., 32, 2161–2173.

    • Search Google Scholar
    • Export Citation

  • Gent, P., Yeager S. , Neale R. , Levis S. , and Bailey D. , 2010: Improvements in a half degree atmosphere/land version of the CCSM. Climate Dyn., 34, 819–833.

    • Search Google Scholar
    • Export Citation

  • 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, 751–763.

    • Search Google Scholar
    • Export Citation

  • Hamon, W. R., 1961: Estimating the potential evapotranspiration. J. Hydraul. Div. Amer. Soc. Civ. Eng., 87, 107–120.

  • Hou, Z., and Rubin Y. , 2005: On minimum relative entropy concepts and prior compatibility issues in vadose zone inverse and forward modeling. Water Resour. Res., 41, W12425, doi:10.1029/2005WR004082.

    • Search Google Scholar
    • Export Citation

  • Hou, Z., Rubin Y. , Hoversten G. M. , Vasco D. , and Chen J. S. , 2006: Reservoir-parameter identification using minimum relative entropy-based Bayesian inversion of seismic AVA and marine CSEM data. Geophysics, 71, O77–O88.

    • Search Google Scholar
    • Export Citation

  • Hou, Z., Huang M. , Leung L. R. , Lin G. , and Ricciuto D. M. , 2012: Sensitivity of surface flux simulations to hydrologic parameters based on an uncertainty quantification framework applied to the Community Land Model. J. Geophys. Res., 117, D15108, doi:10.1029/2012JD017521.

    • Search Google Scholar
    • Export Citation

  • Iorgulescu, I., and Musy A. , 1997: Generalization of TOPMODEL for a power law transmissivity profile. Hydrol. Processes, 11, 1353–1355.

    • Search Google Scholar
    • Export Citation

  • Janetos, A. C., and Coauthors, 2009: Science challenges and future directions: Climate change integrated assessment research. Rep. PNNL-18417, PNNL, 100 pp. [Available online at http://science.energy.gov/∼/media/ber/pdf/ia_workshop_low_res_06_25_09.pdf.]

  • Jaynes, E. T., 1968: Prior probabilities. IEEE Trans. Sys. Sci. Cybern.,4, 227–241.

  • Ke, Y., Leung R. L. , Huang M. , Coleman A. M. , Li H. , and Wigmosta M. S. , 2012: Developing high resolution land surface parameters for Community Land Model. Geosci. Model Dev., 5, 1435–1481.

    • Search Google Scholar
    • Export Citation

  • Kesavan, H. K., and Kapur J. N. , 1989: The generalized maximum entropy principle. IEEE Syst. Man Cybern. Soc., 19, 1042–1052.

  • Kirkby, M. J., 1997: TOPMODEL: A personal view. Hydrol. Processes, 11, 1087–1097.

  • Klepper, O., Scholten H. , and Van Kamer J. P. G. D. , 1991: Prediction uncertainty in an ecological model of the oosterschelde estuary. J. Forecast., 10, 191–209.

    • Search Google Scholar
    • Export Citation

  • Kuczera, G., and Parent E. , 1998: Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm. J. Hydrol., 211, 69–85.

    • Search Google Scholar
    • Export Citation

  • Lawrence, D. M., and Coauthors, 2011: Parameterization improvements and functional and structural advances in version 4 of the Community Land Model. J. Adv. Model. Earth Syst.,3, M03001, doi:10.1029/2011MS000045.

  • Lawrence, D. M., Oleson K. W. , Flanner M. G. , Fletcher C. G. , Lawrence P. J. , Levis S. , Swenson S. C. , and Bonan G. B. , 2012: The CCSM4 land simulation, 1850–2005: Assessment of surface climate and new capabilities. J. Climate, 25, 2240–2260.

    • Search Google Scholar
    • Export Citation

  • Lehner, B., Verdin K. , and Jarvis A. , 2008: New global hydrography derived from spaceborne elevation data. Eos, Trans. Amer. Geophys. Union, 89, 93–94, doi:10.1029/2008EO100001.

    • Search Google Scholar
    • Export Citation

  • Li, H., Huang M. , Wigmosta M. S. , Ke Y. , Coleman A. M. , Leung L. R. , Wang A. , and Ricciuto D. M. , 2011: Evaluating runoff simulations from the Community Land Model 4.0 using observations from flux towers and a mountainous watershed. J. Geophys. Res., 116, D24120, doi:10.1029/2011JD016276.

    • Search Google Scholar
    • Export Citation

  • Lyne, V. D., and Hollick M. , 1979: Stochastic time-variable rainfall-runoff modelling. Preprints, Hydrology and Water Resources Symposium, Perth, Australia, Institution of Engineers Australia, 89–92.

  • Maurer, E. P., Stewart I. T. , Bonfils C. , Duffy P. B. , and Cayan D. , 2007: Detection, attribution, and sensitivity of trends toward earlier streamflow in the Sierra Nevada. J. Geophys. Res., 112, D11118, doi:10.1029/2006JD008088.

    • Search Google Scholar
    • Export Citation

  • Milly, P. C. D., Betancourt J. , Falkenmark M. , Hirsch R. M. , Kundzewicz Z. W. , Lettenmaier D. P. , and Stouffer R. J. , 2008: Stationarity is dead: Whither water management? Science, 319, 573–574.

    • Search Google Scholar
    • Export Citation

  • Moss, R. H., and Coauthors, 2010: The next generation of scenarios for climate change research and assessment. Nature, 463, 747–756.

  • Mu, Q., Zhao M. , and Running S. W. , 2011: Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ., 115, 1781–1800.

    • Search Google Scholar
    • Export Citation

  • Niu, G.-Y., Yang Z.-L. , Dickinson R. E. , and Gulden L. E. , 2005: A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models. J. Geophys. Res., 110, D21106, doi:10.1029/2005JD006111.

    • Search Google Scholar
    • Export Citation

  • Niu, G.-Y., Yang Z.-L. , Dickinson R. E. , Gulden L. E. , and Su H. , 2007: Development of a simple groundwater model for use in climate models and evaluation with Gravity Recovery and Climate Experiment data. J. Geophys. Res., 112, D07103, doi:10.1029/2006JD007522.

    • Search Google Scholar
    • Export Citation

  • Oleson, K. W., and Coauthors, 2008: Improvements to the Community Land Model and their impact on the hydrological cycle. J. Geophys. Res., 113, G01021, doi:10.1029/2007JG000563.

    • Search Google Scholar
    • Export Citation

  • Oleson, K. W., and Coauthors, 2010: Technical description of version 4.0 of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-478+STR, 257 pp. [Available online at http://www.cesm.ucar.edu/models/cesm1.0/clm/CLM4_Tech_Note.pdf.]

  • Rosero, E., Yang Z.-L. , Wagener T. , Gulden L. E. , Yatheendradas S. , and Niu G.-Y. , 2010: Quantifying parameter sensitivity, interaction, and transferability in hydrologically enhanced versions of the Noah land surface model over transition zones during the warm season. J. Geophys. Res., 115, D03106, doi:10.1029/2009JD012035.

    • Search Google Scholar
    • Export Citation

  • Soofi, E. S., 2000: Principal information theoretic approaches. J. Amer. Stat. Assoc., 95, 1349–1353.

  • Sorooshian, S., 1981: Parameter estimation of rainfall-runoff models with heteroscedastic streamflow errors—The noninformative data case. J. Hydrol., 52, 127–138.

    • Search Google Scholar
    • Export Citation

  • Sorooshian, S., and Dracup J. A. , 1980: Stochastic parameter estimation procedures for hydrologic rainfall-runoff models: Correlated and heteroscedastic error cases. Water Resour. Res., 16, 430–442.

    • Search Google Scholar
    • Export Citation

  • Sorooshian, S., Duan Q. Y. , and Gupta V. K. , 1993: Calibration of rainfall-runoff Models: Application of global optimization to the Sacramento Soil Moisture Accounting Model. Water Resour. Res., 29, 1185–1194.

    • Search Google Scholar
    • Export Citation

  • Stewart, I. T., Cayan D. R. , and Dettinger M. D. , 2005: Changes toward earlier streamflow timing across western North America. J. Climate, 18, 1136–1155.

    • Search Google Scholar
    • Export Citation

  • Tarantola, A., 2005: Inverse Problem Theory and Methods for Model Parameter Estimation. Society of Industrial and Applied Mathematics, 352 pp.

  • Troch, P. A., and Coauthors, 2009: Climate and vegetation water use efficiency at catchment scales. Hydrol. Processes, 23, 2409–2414.

  • Ulrych, T. J., and Sacchi M. , 2006: Information-Based Inversion and 578 Processing with Applications. Elsevier Science, 436 pp.

  • Upton, G., and Cook I. , 1996: Understanding Statistics. Oxford University Press. 659 pp.

  • Van Straten, G. T., and Keesman K. J. , 1991: Uncertainty propagation and speculation in projective forecasts of environmental change: A lake-eutrophication example. J. Forecast., 10, 163–190.

    • Search Google Scholar
    • Export Citation

  • van Werkhoven, K., Wagener T. , Reed P. , and Tang Y. , 2009: Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models. Adv. Water Resour., 32, 1154–1169.

    • Search Google Scholar
    • Export Citation

  • Voepel, H., Ruddell B. , Schumer R. , Troch P. A. , Brooks P. D. , Neal A. , Durcik M. , and Sivapalan M. , 2011: Quantifying the role of climate and landscape characteristics on hydrologic partitioning and vegetation response. Water Resour. Res., 47, W00J09, doi:10.1029/2010WR009944.

    • Search Google Scholar
    • Export Citation

  • Vrugt, J. A., Gupta H. V. , Bouten W. , and Sorooshian S. , 2003: A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resour. Res., 39, 1201, doi:10.1029/2002WR001642.

    • Search Google Scholar
    • Export Citation

  • Wolock, D. M., and McCabe G. J. , 1999: Explaining spatial variability in mean annual runoff in the conterminous United States. Climate Res., 11, 149–159.

    • Search Google Scholar
    • Export Citation

  • Woodbury, A. D., 2011: Minimum relative entropy, Bayes and Kapur. Geophys. J. Int., 185, 181–189.

  • Woodbury, A. D., and Ulrych T. J. , 1993: Minimum relative entropy: Forward probabilistic modeling. Water Resour. Res., 29, 2847–2860.

  • Woodbury, A. D., and Rubin Y. , 2000: A full-Bayesian approach to parameter inference from tracer travel time moments and investigation of scale effects at the Cape Cod experimental site. Water Resour. Res., 36, 159–171.

    • Search Google Scholar
    • Export Citation

  • Xia, Y., Ek M. , Wei H. , and Meng J. , 2012: Comparative analysis of relationships between NLDAS-2 forcings and model outputs. Hydrol. Processes, 26, 467–474.

    • Search Google Scholar
    • Export Citation

  • Yapo, P. O., Gupta H. V. , and Sorooshian S. , 1996: Automatic calibration of conceptual rainfall-runoff models: Sensitivity to calibration data. J. Hydrol., 181, 23–48.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 315 0 0
Full Text Views 364 190 16
PDF Downloads 237 77 13