Physical Geography

Advances in the study of uncertainty quantification of large-scale hydrological modeling system

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  • 1. School of Resource and Earth Science, China University of Mining &Technology, Xuzhou 221008, Jiangsu, China;
    2. Key Laboratory of Water Cycle &Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
Song Xiaomeng (1987-), Master Candidate, specialized in hydrology. E-mail: wenqingsxm@126.com

Received date: 2010-12-07

  Revised date: 2011-04-29

  Online published: 2011-10-03

Supported by

National Key Basic Research Program of China, No.2010CB428403; National Grand Science and Technology Special Project of Water Pollution Control and Improvement, No.2009ZX07210-006

Abstract

The regional hydrological system is extremely complex because it is affected not only by physical factors but also by human dimensions. And the hydrological models play a very important role in simulating the complex system. However, there have not been effective methods for the model reliability and uncertainty analysis due to its complexity and difficulty. The uncertainties in hydrological modeling come from four important aspects: uncertainties in input data and parameters, uncertainties in model structure, uncertainties in analysis method and the initial and boundary conditions. This paper systematically reviewed the recent advances in the study of the uncertainty analysis approaches in the large-scale complex hydrological model on the basis of uncertainty sources. Also, the shortcomings and insufficiencies in the uncertainty analysis for complex hydrological models are pointed out. And then a new uncertainty quantification platform PSUADE and its uncertainty quantification methods were introduced, which will be a powerful tool and platform for uncertainty analysis of large-scale complex hydrological models. Finally, some future perspectives on uncertainty quantification are put forward.

Cite this article

SONG Xiaomeng, ZHAN Chesheng, KONG Fanzhe, XIA Jun . Advances in the study of uncertainty quantification of large-scale hydrological modeling system[J]. Journal of Geographical Sciences, 2011 , 21(5) : 801 -819 . DOI: 10.1007/s11442-011-0881-2

References

Ajami N K, Duan Q Y, Sorooshian S, 2007. An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction. Water Resources Research, 43, W01403, doi: 10.1029/2005WR004745.
Bastidas L A, Gupta H V, Sorooshian S et al., 1999. Sensitivity analysis of a land surface scheme using multicriteria methods. Journal of Geophysical Research, 104: 19481-19490.
Bastidas L A, Hogue T S, Sorooshian S et al., 2006. Parameter sensitivity analysis for different complexity land surface models using multicriteria methods. Journal of Geophysical Research, 111, D20101, doi: 10.1029/2005JD006377.
Beven K J, Binley A M, 1992. The future of distributed hydrological models: Model calibration and uncertainty prediction. Hydrological Processes, 6: 279-298.
Beven K J, Freer J, 2001. Equifinality, data assimilation, and uncertainty estimation in mechanistic modeling of complex environmental systems. Journal of Hydrology, 249: 11-29.
Bijlsma R M, Groenendijk P, Blind M W et al., 2007. Uncertainty analysis at large scales: Limitations and subjectivity of current practices: A water quality case study. Water Science & Technology, 56(6): 1-9.
Blasone R S, Vrugt J A, 2008. Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling. Advances in Water Resources, 31: 630-648.
Butts M B, Payne J T, Kristensen M et al., 2004. An evaluation of the impact of model structure on hydrological modeling uncertainty for streamflow simulation. Journal of Hydrology, 298: 242-266.
Chen F, Dudhia J, 2001. Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Monthly Weather Review, 129: 569-585.
Chen T H, Henderson S A, Milly P, 1997. Cabauw experimental results from the project or intercomparison of land-surface parameterization scheme. Journal of Climate, 10: 1194-1215.
Cheng Chuntian, Li Xiangyang, 2007. A parallel adaptive metropolis algorithm for uncertainty assessment of Xinanjiang model parameters. Engineering Sciences, 9(9): 47-51. (in Chinese)
Duan Q, Ye A, Dai Y, 2009. Quantification of parameter uncertainty of the common land model (CoLM). American Geophysical Union, Fall Meeting, abstract #H41F-0968.
Engeland K, Xu C Y, Gottschalk L et al., 2005. Assessment uncertainties in a conceptual water balance model using Basyesian methodology. Hydrological Sciences Journal, 50(1): 45-63.
Fernando T M K G, Maier H R, Dandy G C et al., 2007. Assessing prediction uncertainty in the BIGMOD model: A shuffled complex evolution metropolis algorithm approach. In: Oxley L, Kulasiri D. MODSIM 2007 International Congress on Modeling and Simulation. Modeling and Simulation Society of Australia and New Zealand, 1499-1505.
Gallagher M, Doherty J, 2007. Parameter estimation and uncertainty analysis for a watershed model. Environmental Modelling & Software, 22: 1000-1020.
Gao Yanhong, Chen Guodong, Cui Wenrui et al., 2006. Coupling of enhanced land surface hydrology with atmospheric mesoscale model and its implement in Heihe river basin. Advances in Earth Science, 21(12): 1283-1293. (in Chinese)
Ghent D, Kaduk J, Remedios J et al., 2010. Assimilation of land surface temperature into the land surface model JULES with an ensemble Kalman filter. Journal of Geophysical Research, 115, D19112, doi: 10.1029/2010JD014392.
Gupta H V, Bastidas L A, Sorooshian S et al., 1999. Parameter estimation of a land surface scheme using multicriteria methods. Journal of Geophysical Research, 104: 19491-19503.
Hoeting J A, Madigan D, Raftery A E et al., 1999. Bayesian model averaging: A tutorial. Statistical Science, 14(4): 382-417.
Hossain F, Anagnostou E N, 2005. Assessment of a stochastic interpolation based parameter sampling scheme for efficient uncertainty analysis of hydrologic models. Computers & Geosciences, 31: 497-512.
Hou D, Mitchell K, Toth Z et al., 2009. The effect of large-scale atmospheric uncertainty on streamflow predictability. Journal of Hydrometeorology, 10(3): 717-733.
Hsieh H, 2007. Application of the PSUADE tool for sensitivity analysis of an engineering simulation. UCRL-TR-237205 https://e-reports-ext.llnl.gov/pdf/355680.pdf.
Huang Y, Chen X, Li Y P et al., 2010. A fuzzy-based simulation method for modeling hydrological processes under uncertainty. Hydrological Processes, 24(25): 3718-3732.
Jin X L, Xu C Y, Zhang Q et al., 2010. Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model. Journal of Hydrology, 383: 147-155.
Kavetski D, Kuczera G, Frank S W et al., 2006a. Bayesian analysis of input uncertainty in hydrological modeling: I. Theory. Water Resources Research, 42, W03407, doi: 10.1029/2005WR004368.
Kavetski D, Kuczera G, Frank S W et al., 2006b. Bayesian analysis of input uncertainty in hydrological modeling: II. Application. Water Resources Research, 42, W03408, doi: 10.1029/2005WR004376.
Khu S T K, Werner M G F, 2003. Reduction of monte carlo simulation runs for uncertainty estimation in hydrological modeling. Hydrology and Earth System Sciences, 7(5): 680-692.
Koren V, Lee H, Seo D, 2009. Reducing uncertainties in model initial conditions via variational assimilation of hydrologic and hydrometeorological data into distributed hydrologic models. American Geophysical Union, Fall Meeting, abstract #H41F-0961.
Kotlarski S, Block A, Böhm U et al., 2005. Regional climate model simulations as input for hydrological applications: Evaluation of uncertainties. Advances in Geosciences, 5: 119-125.
Krzysztofowicz R, 1999. Bayesian theory of probabilistic forecasting via deterministic hydrologic model. Water Resources Research, 35(9): 2739-2750.
Kuczera G, Kavetski D, Franks S et al., 2006. Towards a Bayesian total error analysis of conceptual rainfall- runoff models: Characterising model error using torm-dependent parameters. Journal of Hydrology, 331: 167-177.
Kuczera G, Parent E, 1998. Monte Carlo assessment of parameter uncertainty in conceptual catchment models: The Metropolis algorithm. Journal of Hydrology, 221(1-4): 69-85.
Lakhtakia M N, Yarnal B, Johnson D L et al., 1998. A simulation of river-basin response to mesoscale meterological forcing: The Susquehanna river basin experiment (SRBEX). Journal of American Water Resources Association, 43: 921-937.
Li L, Xia J, Xu C Y et al., 2010 Evaluation of the subjective factors of the GLUE method and comparison with the formal Bayesian method in uncertainty assessment of hydrological models. Journal of Hydrology, 390(3/4): 210-221.
Liang Xiao, 2008. Application of Gaussian error propagation principles for assessment of uncertainty in the common land model
[D]. Beijing: Beijing Normal University. (in Chinese)
Liang Zhongmin, Dai Rong, Li Binquan, 2010. A review of hydrological uncertainty analysis based on Bayesian theory. Advances in Water Science, 21(2): 274-281. (in Chinese)
Lin Z, Beck B, 2009. Error and uncertainty in the structure of a model: Propagating into forecasts. American Geophysical Union, Fall Meeting, abstract #H23L-07.
Lohmann D, Lettenmaier D P, Liang X, 1998. The project for intercomparison of land-surface parameterization schemes (PLIPS) phase Red-Arkansas River basin experiment: 3. Spatial and temporal analysis of water fluxes. Global and Planetary Change, 19: 161-179.
Montanari A, 2005. Large sample behaviors of the generalized likelihood uncertainty estimate (GLUE) in assessing the uncertainty of the rainfall-runoff simulations. Water Resources Research, 41(8): W08406.
Montanari A, Brath A, 2004. A stochastic approach for assessing the uncertainty of rainfall-runoff simulations. Water Resources Research, 40, W01106, doi: 10.1029/2003WR002540.
Moradkhani H, Hsu K, Gupta H V et al., 2005. Uncertainty assessment of hydrologic model states and parameters: sequential data assimilation using the particle filter. Water Resources Research, 41: W05012.
Najafi M R, Moradkhani H, Jung I W, 2011. Assessing the uncertainties of hydrologic model selection in climate change impact studies. Hydrological Processes, doi: 10.1002/hyp.8043. (in press)
Neuman S P, 2003. Maximum likelihood Bayesian averaging of uncertain model predictions. Stochastic Environmental Research and Risk Assessment, 17(5): 291-305.
Pappenberger F, Cloke H L, Balsamo G et al., 2010. Global runoff routing with the hydrological component of the ECMWF NWP system. International Journal of Climatology, 30(14): 2155-2174.
Refsgaard J C, van der Sluijs J P, Brown J et al., 2006. A framework for dealing with uncertainty due to model structure error. Advances in Water Resources, 29: 1586-1597.
Reichle R H, Entekhabi D, McLaughlin D B, 2001. Downscaling of radiobrightness measurements for soil moisture estimation: A four-dimensional variational data assimilation approach. Water Resources Research, 37: 2353-2364.
Reichle R H, Mclaughlin D B, Entekhabi D, 2002. Hydrologic data assimilation with the ensemble kalman filter. Monthly Weather Review, 130: 103-114.
Renard B, Kavetski D, Kuczera G et al., 2010. Understanding predictive uncertainty in hydrologic modeling: the challenge of identifying input and structural errors. Water Resources Research, 46, W05521, 22PP, doi: 10.1029/2009WR008328.
Salamon P, Feyen L, 2010. Distentangling uncertainties in distributed hydrological modeling using multiplicative error models and sequential data assimilation. Water Resources Research, 46, W12501, doi: 10.1029/2009WR009022.
Schröter K, Llort X, Velasco-Forero C et al., 2011. Implications of radar rainfall estimates uncertainty on distributed hydrological model predictions. Atmospheric Research, 100(2/3): 237-245.
Shao Y, Henderson S A, 1996. Validation of soil moisture simulation in land surface parameterization schemes with HAPEX data. Global and Planetary Change, 13: 11-46.
Shu Chang, Liu Suxia, Mo Xingguo et al., 2008. Uncertainty analysis of Xin’anjiang model parameter. Geographical Research, 27(2): 343-352. (in Chinese)
Su Fengge, 2001. Study on the macro-scale hydrological model and its coupling with the land surface processes model
[D]. Nanjing: Hohai University. (in Chinese)
Thiemann M, Trosset M, Gupta H et al., 2001. Bayesian recursive parameter estimation for hydrologic models. Water Resources Research, 37(10): 2521-2535.
Thyer M, Renard B, Kavetski D et al., 2009. Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis. Water Resources Research, 45, W00B14, doi: 10.1029/2008WR006825.
Tong C, 2008. Quantifying uncertainty of a soil-foundation structure-interaction system under seismic excitation. UCRL-TR-402883 https://e-reports-ext.llnl.gov/pdf/359763.pdf.
Tong C, Graziani F, 2008. A practical global sensitivity analysis methodology for multi-physics applications. Computational Methods in Transport: Verification and Validation. Lecture Notes in Computational Science and Engineer, 62: 277-299.
Vrugt J A, Gupta H V, Bouten W et al., 2003a. A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resources Research, 39(8): 1201, doi: 10.1029/2002WR001642.
Vrugt J A, Gupta H V, Bouten W et al., 2003b. Effective and efficient algorithm for multiobjective optimization of hydrologic models. Water Resources Research, 39(8): 1214, doi: 10.1029/2002WR001746.
Vrugt J A, Robinson B A, 2007. Treatment of uncertainty using ensemble methods: comparison of sequential data assimilation and Bayesian model averaging. Water Resources Research, 43(1): W01411, doi: 10.1029/2005WR004838.
Wei Xiaojing, Xiong Lihua, Wan Min et al., 2009. Application of Markov Chain Monte Carlo method based modified generalized likelihood uncertainty estimation to hydrological models. Journal of Hydraulic Engineering, 40(4): 464-473. (in Chinese)
Wemhoff A P, Hsieh H, 2007. TNT prout-tompkins kinetics calibration with PSUADE. UCRL-TR-230194 https://e-reports-ext.llnl.gov/pdf/346278.pdf.
Williams J, Maxwell R M, 2011. Propagating subsurface uncertainty to the atmosphere using fully-coupled, stochastic simulations. Journal of Hydrometeorology. (in press)
Wood E F, Rodriguez-Iturbe I, 1975. Bayesian inference and decision making for extreme hydrologic events. Water Resources Research, 11(4): 533-542.
Yapo P O, Gupta H V, Sorooshian S, 1998. Multi-objective global optimization for hydrologic models. Journal of Hydrology, 204: 83-97.
Ye Shouze, Xia Jun, 2002. Century’s retrospect and looking into the future of hydrological science. Advances in Water Sciences, 13(1): 93-104. (in Chinese)
Yin Xiongrui, Xia Jun, Zhang Xiang et al., 2006. Recent progress and prospect of the study on uncertainties in hydrological modeling and forecasting. Water Power, 32(10): 27-31. (in Chinese)
Yong Bin, 2007. Development of a land-surface hydrological model TOPX and its coupling study with regional climate model RIEMS
[D]. Nanjing: Nanjing University. (in Chinese)
Yong Bin, Ren Liliang, Chen Xi et al., 2009. Development of a large-scale hydrological model TOPX and its coupling with regional intergrated environment modeling system RIEMS. Chinese Journal of Geophysics, 52(8): 1954-1965. (in Chinese)
Yong Bin, Zhang Wanchang, Liu Chuansheng, 2006. Advances in the coupling study of hydrological models and land-surface models. Journal of Glaciology and Geocryology, 28(6): 961-970. (in Chinese)
Yu Zhongbo, 2008. Principle and Application of Watershed Distributed Hydrological Model. Beijing: China Science Press. (in Chinese)

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