An efficient integrated approach for global sensitivity analysis of hydrological model parameters

Environmental Modelling and Software

Volumn 41, Issue , 2013, Pages 39-52

  Zhan, Che sheng ^a   Song, Xiao meng ^b   Xia, Jun ^a   Tong, Charles ^c  

^a INSTITUTE OF GEOGRAPHIC SCIENCES AND NATURAL RESOURCES RESEARCH   (China)

^b NANJING HYDRAULIC RESEARCH INSTITUTE   (China)

^c LAWRENCE LIVERMORE NATIONAL LABORATORY   (United States)

Author keywords

DTVGM; Global sensitivity analysis; Response surface model; RSMSobol' method; Statistical emulator

Indexed keywords

DTVGM; GLOBAL SENSITIVITY ANALYSIS; RESPONSE SURFACE MODELS; RSMSOBOL' METHOD; STATISTICAL EMULATOR;

HYDROLOGY; INTEGRATED CONTROL; SOIL MOISTURE; STRESS INTENSITY FACTORS; SURFACE PROPERTIES;

SENSITIVITY ANALYSIS;

CORRELATION; HYDROLOGICAL MODELING; INTEGRATED APPROACH; QUALITATIVE ANALYSIS; RESPONSE ANALYSIS; SENSITIVITY ANALYSIS; SOIL MOISTURE;

CHINA; HUAI BASIN;

EID: 84871038799     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2012.10.009     Document Type: Article

References (86)

1. Ascough J.C., Green T.R., Ma L., Ahjua L.R. Key criteria and selection of sensitivity analysis methods applied to natural resource models. Modsim 2005 International Congress on Modeling and Simulation 2005, 2463-2469. Modeling and Simulation Society of Australia and New Zealand, Melbourne, Australia.
2. Bahremand A., De Smedt F. Distributed hydrological modeling and sensitivity analysis in Torysa watershed, Slovakia. Water Resources Management 2008, 22:393-408.
3. Balshi M.S., McGuire A.D., Duffy P., Flannigan M., Walsh J., Melillo J. Assessing the response of area burned to changing climate in western boreal North America using a multivariate adaptive regression splines (MARS) approach. Global Change Biology 2009, 15(3):578-600.
4. Campolongo F., Braddock R. The use of graph theory in the sensitivity analysis of the model output: a new screening method. Reliability Engineering & System Safety 1999, 64(1):1-12.
5. Campolongo F., Cariboni J., Saltelli A. An effective screening design for sensitivity analysis of large models. Environmental Modelling & Software 2007, 22(10):1509-1518.
6. Campolongo F., Saltelli A., Cariboni J. From screening to quantitative sensitivity analysis. A unified approach. Computer Physics Communications 2011, 182:928-988.
7. Campolongo F., Tarantola S., Saltelli A. Tackling quantitatively large dimensionality problems. Computer Physics Communications 1999, 117:75-85.
8. Cariboni J., Gatelli D., Liska R., Saltelli A. The role of sensitivity analysis in ecologic modeling. Ecological Modelling 2007, 203:167-182.
9. Cibin R., Sudheer K.P., Chaubey I. Sensitivity and identifiability of stream flow generation parameters of the SWAT model. Hydrological Processes 2010, 24:1133-1148.
10. Cloke H.L., Pappenberger F., Renaud J.P. Multi-method global sensitivity analysis (MMGSA) for modelling floodplain. Hydrological Processes 2008, 22(11):1660-1674.
11. Confalonieri R. Monte Carlo based sensitivity analysis of two crop simulators and considerations on model balance. European Journal of Agronomy 2010, 33(2):89-93.
12. Confalonieri R., Bellocchi G., Bregaglio S., Donatelli M., Acutis M. Comparison of sensitivity analysis techniques: a case study with the rice model WARM. Ecological Modelling 2010, 221(16):1897-1906.
13. Crosetto M., Tarantola S. Uncertainty and sensitivity analysis: tools for GIS-based model implementation. International Journal of Geographical Information Science 2001, 15(5):415-438.
14. Cukier R.I., Levine H.B., Shuler K.E. Nonlinear sensitivity analysis of multiparameter model systems. Journal of Computational Physics 1978, 26:1-42.
15. Elsawwaf M., Willems P., Feyen J. Assessment of the sensitivity and prediction uncertainty of evaporation models applied to Nasser Lake, Egypt. Journal of Hydrology 2010, 395:10-22.
16. Francos A., Elorza F.J., Bouraoui F., Bidoglio G., Galbiati L. Sensitivity analysis of distributed environmental simulation models: understanding the model behavior in hydrological studies at the catchment scale. Reliability Engineering & System Safety 2003, 79(2):205-218.
17. Freer J., Beven K.J., Ambroise B. Bayesian estimation of uncertainty in runoff prediction and the value of data: an application of the GLUE approach. Water Resources Research 1996, 32(7):2162-2173.
18. Friedman J.H. Multivariate adaptive regression splines. The Annals of Statistics 1991, 19(1):1-67.
19. Hamby D.M. A review of techniques for parameter sensitivity analysis of environmental models. Environmental Monitoring and Assessment 1994, 32:135-154.
20. Helton J.C., Johnson J.D., Sallaberry C.J., Storlie C.B. Survey of sampling-based methods for uncertainty and sensitivity analysis. Reliability Engineering & System Safety 2006, 91(10-11):1175-1209.
21. Holvoet K., van Griensven A., Seuntjens P., Vanrolleghem P.A. Sensitivity analysis for hydrology and pesticide supply towards the river in SWAT. Physics and Chemistry of the Earth 2005, 30(8-10):518-526.
22. Homma T., Saltelli A. Importance measures in global sensitivity analysis of model output. Reliability Engineering & System Safety 1996, 52(1):1-17.
23. Hsieh, H., 2007. Application of the PSUADE Tool for Sensitivity Analysis of an Engineering Simulation. Lawrence Livermore National Laboratory Report, UCRL-TR-237205.
24. Jakeman A.J., Letcher R.A., Norton J.P. Ten iterative steps in development and evaluation of environmental models. Environmental Modelling & Software 2006, 21(5):602-614.
25. Lee D.H. Testing a conceptual hillslope recession model based on the storage-discharge relationship with the Richards equation. Hydrological Processes 2007, 21:3155-3161.
26. Lenhart T., Eckhardt K., Fohrer N., Frede H.G. Comparison of two different approaches of sensitivity analysis. Physics and Chemistry of the Earth 2002, 27:645-654.
27. Li L., Xia J., Xu C.Y., Chu J.T., Wang R. Analyse the sources of equifinality in hydrological model using GLUE methodology. Hydroinformatics in Hydrology, Hydrogeology and Water Resources, Proceedings of Symposium JS.4 at the Joint IAHS & IAH Convention, Hyderabad, India, September 2009 2009, vol. 331:130-138. IAHS Publ.
28. Li L., Xia J., Xu C.Y., Singh V.P. 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 2010, 390(3-4):210-221.
29. Lilburne L., Tarantola S. Sensitivity analysis of spatial models. International Journal of Geographical Information Science 2009, 23(2):151-168.
30. Makler-Pick V., Gal G., Gorfine M., Hipsey M.R., Carmel Y. Sensitivity analysis for complex ecological models - a new approach. Environmental Modelling & Software 2011, 26:124-134.
31. Manache G., Melching C.S. Identification of reliable regression- and correlation-based sensitivity measures for importance ranking of water-quality model parameters. Environmental Modelling & Software 2008, 23:549-562.
32. Marrel A., Looss B., Laurent B., Roustant O. Calculations of Sobol indices for the Gaussian process metamodel. Reliability Engineering & System Safety 2009, 94:742-751.
33. McKay, M.D., 1995. Evaluating Prediction Uncertainty. Los Alamos National Laboratory Technical Report NUREG/CR-6311, LA-12915-MS.
34. Meyers R.H., Montgomery D.C. Response Surface Methodology: Process and Product Optimization Using Designed Experiments 2002, Wiley-Interscience.
35. Moore L.M., Ray B.K. Statistical methods for sensitivity and performance analysis in computer experiments. Proceedings of the 1999 Winter Simulation Conference, Arizona, United States 1999, 486-491.
36. Morris M.D. Factorial sampling plans for preliminary computational experiments. Technometrics 1991, 33(2):161-174.
37. Muleta M.K., Nicklow J.W. Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model. Journal of Hydrology 2005, 306(1-4):127-145.
38. Oakley J.E., O'Hagan A. Probabilistic sensitivity analysis of complex models: a Bayesian approach. Journal of the Royal Statistical Society Series B 2004, 66:751-769.
39. Pappenberger F., Iorgulescu I., Beven K.J. Sensitivity analysis based on regional splits and regression trees (SARS-RT). Environmental Modelling & Software 2006, 21(7):976-990.
40. Pappenberger F., Beven K.J., Ratto M., Matgen P. Multi-method global sensitivity analysis of flood inundation models. Advances in Water Resources 2008, 31(1):1-14.
41. Ratto M., Tarantola S., Saltelli A. Sensitivity analysis in model calibration: GSA-GLUE approach. Computer Physics Communications 2001, 136:212-224.
42. Ren Q.W., Chen Y.P., Shu X.J. Global sensitivity analysis of Xinanjiang model parameters based on extend FAST method. Acta Scientiarum Naturalium Unversitatis Sunyatseni 2010, 49(3):127-134. (in Chinese).
43. Saltelli A. Sensitivity analysis: could better methods be used?. Journal of Geophysical Research - Atmospheres 1999, 104(D3):3789-3793.
44. Saltelli A., Annoni P. How to avoid a perfunctory sensitivity analysis. Environmental Modelling & Software 2010, 25:1508-1517.
45. Saltelli A., Bolado R. An alternative way to compute Fourier amplitude sensitivity test (FAST). Computational Statistics & Data Analysis 1998, 26(4):445-460.
46. Saltelli A., Tarantola S. On the relative importance of input factors in mathematical models: safety assessment for nuclear waste disposal. Journal of American Statistical Association 2002, 97:702-709.
47. Saltelli A., Annoni P., Azzini I., Campolongo F., Ratto M., Tarantola S. Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Computer Physics Communication 2010, 181(2):259-270.
48. Saltelli A., Campolongo F., Cariboni J. Screening important inputs in models with strong interaction properties. Reliability Engineering & System Safety 2009, 94(7):1149-1155.
49. Saltelli A., Chan K., Scott M. Sensitivity Analysis. Probability and Statistics Series 2000, John Wiley & Sons, Ltd., West Sussex, England.
50. Saltelli A., Ratto M., Andres T., Campolongo F., Cariboni J., Gatelli D., Saisana M., Tarantola S. Global Sensitivity Analysis: the Primer 2008, John Wiley & Sons, Ltd., West Sussex, England.
51. Saltelli A., Ratto M., Tarantola S., Campolongo F. Sensitivity analysis for chemical models. Chemical Reviews 2005, 105(7):2811-2828.
52. Saltelli A., Ratto M., Tarantola S., Campolongo F. Sensitivity analysis practices: strategies for model-based inference. Reliability Engineering & System Safety 2006, 91(10-11):1109-1125.
53. Saltelli A., Tarantola S., Chan K. Quantitative model-independent method for global sensitivity analysis of model output. Technometrics 1999, 41(1):39-56.
54. Saltelli A., Tarantola S., Campolongo F., Ratto M. Sensitivity Analysis in Practice: a Guide to Assessing Scientific Models 2004, John Wiley & Sons, Ltd., England.
55. Sieber A., Uhlenbrook S. Sensitivity analysis of a distributed catchment model to verify the model structure. Journal of Hydrology 2005, 310:216-235.
56. Sobol' I.M. On the distribution of points in a cube and the approximate evaluation of integrals. USSR Computational Mathematics and Mathematical Physics 1967, 7:86-112.
57. Sobol' I.M. Sensitivity estimates for nonlinear mathematical models. Mathematical Modeling and Computational Experiment 1993, 1:407-414.
58. Sobol' I.M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation 2001, 55(1-3):271-280.
59. Sobol' I.M., Shukhman B.V. Integration with quasi-random sequences: numerical experience. International Journal of Modern Physics C 1995, 6(2):263-275.
60. Song X.M., Kong F.Z., Zhan C.S., Han J.W. A hybrid optimization rainfall-runoff simulation based on Xinanjiang model and artificial neural network. Journal of Hydrologic Engineering 2012, 17(9):1033-1041. 10.1061/(ASCE)HE.1943-5584.0000548.
61. Song X.M., Zhan C.S., Xia J. Integration of statistical emulator approach with SCE-UA method in parameter optimization of hydrological model. Chinese Science Bulletin 2012, 57(26):3393-3407. 10.1007/s11434-012-5305-x.
62. Song X.M., Zhan C.S., Xia J., Kong F.Z. An efficient global sensitivity analysis approach for distributed hydrological model. Journal of Geographical Sciences 2012, 22(2):209-222.
63. Song X.M., Zhan C.S., Kong F.Z., Xia J. Advances in the study of uncertainty quantification of large-scale hydrological modeling system. Journal of Geographical Sciences 2011, 21(5):801-819.
64. Spruill C.A., Workman S.R., Taraba J.L. Simulation of daily and monthly stream discharge from small watersheds using the SWAT model. Transactions of the ASAE 2000, 43(6):1431-1439.
65. Statnikow R.B., Matusov J.B. Multicriteria Analysis in Engineering 2002, Kluwer Academic Publishers, Netherlands.
66. Tang Y., Reed P.M., Wagener T., van Werkhoven K. Advancing the identification and evaluation of distributed rainfall-runoff models using global sensitivity analysis. Water Resources Research 2007, 43(6):W06415.
67. Tang Y., Reed P.M., van Werkhoven K., Wagener T. Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation. Hydrology and Earth System Sciences 2007, 11:793-817.
68. Tarantola S., Saltelli A. SAMO 2001: methodological advances and innovative applications of sensitivity analysis. Reliability Engineering & System Safety 2003, 79(2):121-122.
69. Tong C. Refinement strategies for stratified sampling methods. Reliability Engineering & System Safety 2006, 91(10-11):1257-1265.
70. Tong, C., 2007a. The PSUADE Software Package Version 1.0. LLNL code release UCRL-CODE-235523.
71. Tong, C., 2007b. Toward a More Robust Variance-based Global Sensitivity Analysis of Model Outputs. Lawrence Livermore National Laboratory Report, UCRL-TR-235561.
72. Tong, C., 2008. Quantifying Uncertainty of a Soil-foundation Structure-interaction System Under Seismic Excitation. Lawrence Livermore National Laboratory Report, UCRL-TR-402883.
73. 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 Engineering, vol. 62, pp. 277-299.
74. Tong C. Self-validated variance-based methods for sensitivity analysis of model outputs. Reliability Engineering & System Safety 2010, 95(3):301-309.
75. van Griensven A., Meixner T., Grunwwald S., Bishop T., Diluzio M., Srinivasan R. A global sensitivity tool for the parameters of multi-variable catchment models. Journal of Hydrology 2006, 324(1-4):10-23.
76. van Werkhoven K., Wagener T., Reed P., Tang Y. Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models. Advances in Water Resources 2009, 32:1154-1169.
77. Wang, G.S., 2005. Theory and method of distributed time-variant gain model. Ph.D. thesis, China Academic Science, Beijing (in Chinese).
78. Wang G.S., Xia J., Chen J. Quantification of effects of climate variations and human activities on runoff by a monthly water balance model: a case study of the Chaobai River basin in northern China. Water Resources Research 2009, 45:W00A11. 10.1029/2007WR006768.
79. Wang G.S., Xia J., Chen J.F. A multi-parameter sensitivity and uncertainty analysis method to evaluate relative importance of parameters and model performance. Geographical Research 2010, 29(2):263-270. (in Chinese).
80. Xia J. A system approach to real time hydrological forecasts in watersheds. Water International 2002, 27(1):87-97.
81. Xia J., O'Connor K.M., Kachroo R.H., Liang G.C. A non-linear perturbation model considering catchment wetness and its application in river flow forecasting. Journal of Hydrology 1997, 200(1-4):164-178.
82. Xia J., Wang G.S., Tan G., Ye A.Z., Huang G.H. Development of distributed time-variant gain model for nonlinear hydrological systems. Science in China Series D, Earth Sciences 2005, 48(6):713-723.
83. Xu C.G., Gertner G. Understanding and comparisons of different sampling approaches for the Fourier amplitudes sensitivity test (FAST). Computational Statistics & Data Analysis 2011, 55(1):184-198.
84. Yang J. Convergence and uncertainty analysis in Monte-Carlo based sensitivity analysis. Environmental Modelling & Software 2011, 26(4):444-457.
85. Ye, A.Z., 2007. Study on catchment water cycle simulation in changing environments. Ph.D. thesis, Wuhan University, Wuhan (in Chinese).
86. Ye A.Z., Duan Q.Y., Zeng H.J., Li L., Wang C.Y. A distributed time-variant gain hydrological model based on remote sensing. Journal of Resources and Ecology 2010, 1(3):222-230.