Multimodel Bayesian analysis of groundwater data worth

Water Resources Research

Volumn 50, Issue 11, 2014, Pages 8481-8496

  Xue, Liang ^a,c   Zhang, Dongxiao ^a   Guadagnini, Alberto ^b,c   Neuman, Shlomo P ^b  

^a PEKING UNIVERSITY   (China)

^b UNIVERSITY OF ARIZONA   (United States)

^c POLITECNICO DI MILANO   (Italy)

Author keywords

data worth; inverse stochastic moment solution; maximum likelihood Bayesian model averaging; model uncertainty

Indexed keywords

AQUIFERS; BAYESIAN NETWORKS; GROUNDWATER FLOW; GROUNDWATER RESOURCES; HYDRAULIC CONDUCTIVITY; INTELLIGENT SYSTEMS; MAXIMUM LIKELIHOOD ESTIMATION; MODEL STRUCTURES; MONTE CARLO METHODS; NUMERICAL METHODS; PARAMETER ESTIMATION; STOCHASTIC SYSTEMS; UNCERTAINTY ANALYSIS;

AQUIFER HETEROGENEITY; BAYESIAN MODEL AVERAGING; DATA WORTH; GEOSTATISTICAL INVERSION; MODEL UNCERTAINTIES; STOCHASTIC FLOW MODEL; STOCHASTIC MOMENTS; UNCERTAIN MEASUREMENTS;

STOCHASTIC MODELS;

AQUIFER; BAYESIAN ANALYSIS; DATA SET; FLOW MODELING; GEOSTATISTICS; GROUNDWATER; HETEROGENEITY; HYDRAULIC CONDUCTIVITY; MONTE CARLO ANALYSIS; NUMERICAL MODEL; STEADY-STATE EQUILIBRIUM; STOCHASTICITY; UNCERTAINTY ANALYSIS;

EID: 84919681031     PISSN: 00431397     EISSN: 19447973     Source Type: Journal    
DOI: 10.1002/2014WR015503     Document Type: Article

References (34)

1. Carrera, J., and, S. P. Neuman, (1986), Estimation of aquifer parameters under transient and steady state conditions: 1. Maximum likelihood method incorporating prior information, Water Resour. Res., 22 (2), 199-210, doi: 10.1029/WR022i002p00199.
2. De Barros, F. P. J., S. Ezzedine, and, Y. Rubin, (2012), Impact of hydrogeological data on measures of uncertainty, site characterization and environmental performance metrics, Adv. Water Resour., 36, 51-63, doi: 10.1016/j.advwatres.2011.05.004.
3. De Marsily, G., C. Lavedan, M. Bouchere, and, G. Fasanino, (1984), Interpretation of interference tests in a well field using geostatistical techniques to fit the permeability distribution in a reservoir model, in Geostatistics for Natural Resources Characterization, Part 2, edited by, G. Verly, et al., pp. 831-849, D. Reidel, Dordrecht, Holland.
4. Deutsch, C. V., and, A. G. Journel, (1998), GSLIB: Geostatistical Software Library and User's Guide, 2nd ed., Oxford Univ. Press, N. Y.
5. Di Federico, V., and, S. P. Neuman, (1997), Scaling of random fields by means of truncated power variograms and associated spectra, Water Resour. Res., 33 (5), 1075-1085, doi: 10.1029/97WR00299.
6. Draper, D., (1995), Assessment and propagation of model uncertainty, J. R. Stat. Soc. Ser. B, 57 (1), 45-90.
7. Fang, Z., Z. Hou, G. Lin, D. Engel, Y. Fang, and, P. Eslinger, (2014), Exploring the effects of data quality, data worth, and redundancy of CO2 gas pressure and saturation data on reservoir characterization through PEST inversion, Environ. Earth Sci., 71 (7), 3025-3037, doi: 10.1007/s12665-013-2680-9.
8. Guadagnini, A., and, S. P. Neuman, (1999), Nonlocal and localized analyses of conditional mean steady state flow in bounded, randomly nonuniform domains: 1. Theory and computational approach, Water Resour. Res., 35 (10), 2999-3018, doi: 10.1029/1999WR900160.
9. Guadagnini, A., S. P. Neuman, M. G. Schaap, and, M. Riva, (2014), Frequency distributions and scaling of soil texture and hydraulic properties in a stratified deep vadose zone near Maricopa, Arizona, in Mathematics of Planet Earth, pp. 189-192, Springer, Berlin.
10. Hendricks Franssen, H. J., A. Alcolea, M. Riva, M. Bakr, N. van der Wiel, F. Stauffer, and, A. Guadagnini, (2009), A comparison of seven methods for the inverse modelling of groundwater flow. Application to the characterisation of well catchments, Adv. Water Resour., 32, 851-872, doi: 10.1016/j.advwatres.2009.02.011.
11. Hernandez, A.F., S. P. Neuman, A. Guadagnini, and, J. Carrera, (2003), Conditioning mean steady state flow on hydraulic head and conductivity through geostatistical inversion, Stoch. Environ. Res. Risk Assess., 17 (5), 329-338.
12. Hernandez, A. F., S. P. Neuman, A. Guadagnini, and, J. Carrera, (2006), Inverse stochastic moment analysis of steady state flow in randomly heterogeneous media, Water Resour. Res., 42, W05425, doi: 10.1029/2005WR004449.
13. Hoeting, J. A., D. Madigan, A. E. Raftery, and, C. T. Volinsky, (1999), Bayesian model averaging: A tutorial, Stat. Sci., 14 (4), 382-417.
14. James, B. R., and, S. M. Gorelick, (1994), When enough is enough: The worth of monitoring data in aquifer remediation design, Water Resour. Res., 30 (12), 3499-3513, doi: 10.1029/94WR01972.
15. Kashyap, R. L., (1982), Optimal choice of AR and MA parts in autoregressive moving average models, IEEE Trans. Pattern Anal. Mach. Intell., 4 (2), 99-104.
16. Leube, P. C., A. Geiges, and, W. Nowak, (2012), Bayesian assessment of the expected data impact on prediction confidence in optimal sampling design, Water Resour. Res., 48, W02501, doi: 10.1029/2010WR010137.
17. Liu, X., J. Lee, P. K. Kitanidis, J. Parker, and, U. Kim, (2012), Value of information as a context-specific measure of uncertainty in groundwater remediation, Water Resour. Manage., 26 (6), 1513-1535.
18. Lu, D., M. Ye, and, S. P. Neuman, (2011), Dependence of Bayesian model selection criteria and Fisher information matrix on sample size, Math. Geosci., 43 (8), 971-993.
19. Lu, D., M. Ye, S. P. Neuman, and, L. Xue, (2012), Multimodel Bayesian analysis of data-worth applied to unsaturated fractured tuffs, Adv. Water Resour., 35, 69-82.
20. Neuman, S. P., (1994), Generalized scaling of permeabilities: Validation and effect of support scale, Geophys. Res. Lett., 21 (5), 349-352, doi: 10.1029/94GL00308.
21. Neuman, S. P., (2003), Maximum likelihood Bayesian averaging of uncertain model predictions, Stoch. Environ. Res. Risk Assess., 17 (5), 291-305.
22. Neuman, S. P., and, V. Di Federico, (2003), Multifaceted nature of hydrogeologic scaling and its interpretation, Rev. Geophys., 41, 1014, doi: 10.1029/2003RG000130.
23. Neuman, S. P., M. Riva, and, A. Guadagnini, (2008), On the geostatistical characterization of hierarchical media, Water Resour. Res., 44, W02403, doi: 10.1029/2007WR006228.
24. Neuman, S. P., L. Xue, M. Ye, and, Lu, D., (2012), Bayesian analysis of data-worth considering model and parameter uncertainties, Adv. Water Resour., 36, 75-85.
25. Nowak, W., (2010), Measures of parameter uncertainty in geostatistical estimation and geostatistical optimal design, Math. Geosci., 42 (2), 199-221, doi: 10.1007/s11004-009-9245-1.
26. Nowak, W., F. P. J. de Barros, and, Y. Rubin, (2010), Bayesian geostatistical design: Task-driven optimal site investigation when the geostatistical model is uncertain, Water Resour. Res., 46, W03535, doi: 10.1029/2009WR008312.
27. Nowak, W., Y. Rubin, and, F. P. J. de Barros, (2012), A hypothesis-driven approach to optimize field campaigns, Water Resour. Res., 48, W06509, doi: 10.1029/2011WR011016.
28. Panzeri, M., M. Riva, A. Guadagnini, and, S. P. Neuman, (2013), Data assimilation and parameter estimation via ensemble Kalman filter coupled with stochastic moment equations of transient groundwater flow, Water Resour. Res., 49, 1334-1344, doi: 10.1002/wrcr.20113.
29. Riva, M., A. Guadagnini, S. P. Neuman, E. Bianchi Janetti, B. Malama, (2009), Inverse analysis of stochastic moment equations for transient flow in randomly heterogeneous media, Adv. Water Res., 32, 1495-1507, doi: 10.1016/j.advwatres.2009.07.003.
30. Riva, M., A. Guadagnini, F. De Gaspari, and, A. Alcolea, (2010), Exact sensitivity matrix and influence of the number of pilot points in the geostatistical inversion of moment equations of groundwater flow, Water Resour. Res., 46, W11513, doi: 10.1029/2009WR008476.
31. Riva, M., M. Panzeri, A. Guadagnini, and, S. P. Neuman, (2011), Role of model selection criteria in geostatistical inverse estimation of statistical data- and model-parameters, Water Resour. Res., 47, W07502, doi: 10.1029/2011WR010480.
32. Trainor-Guitton, W. J., J. K. Caers, and, T. Mukerji, (2011), A methodology for establishing a data reliability measure for value of spatial information problems, Math. Geosci., 43 (8), 929-949, doi: 10.1007/s11004-011-9367-0.
33. Trainor-Guitton, W. J., T. Mukerji, and, R. Knight, (2013), A methodology for quantifying the value of spatial information for dynamic Earth problems, Stoch. Environ. Res. Risk Assess., 27 (4), 969-983, doi: 10.1007/s00477-012-0619-4.
34. Ye, M., P. D. Meyer, and, S. P. Neuman, (2008), On model selection criteria in multimodel analysis, Water Resour. Res., 44, W03428, doi: 10.1029/2008WR006803.