On minimum relative entropy concepts and prior compatibility issues in vadose zone inverse and forward modeling

Water Resources Research

Volumn 41, Issue 12, 2005, Pages 1-13

  Hou, Zhangshuan ^a   Rubin, Yoram ^a  

^a Berkeley   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BOUNDARY CONDITIONS; COMPUTER SIMULATION; ENTROPY; HYDRAULICS; MOISTURE; RANDOM PROCESSES; SOILS;

FORWARD MODELING; METEOROLOGICAL MEASUREMENTS; MINIMUM RELATIVE ENTROPY (MRE); SOIL MOISTURE;

HYDROLOGY;

BOUNDARY CONDITIONS; COMPUTER SIMULATION; ENTROPY; HYDRAULICS; HYDROLOGY; MOISTURE; RANDOM PROCESSES; SOILS;

ENTROPY; INVERSE ANALYSIS; RICHARDS EQUATION; SOIL MOISTURE; VADOSE ZONE;

EID: 31444436643     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2005WR004082     Document Type: Article

References (56)

1. Allen, R. G., L. S. Pereira, D. Raes, and M. Smith (1998), Crop evapotranspiration - Guidelines for computing crop water requirements, FAO Irrig. Drain. Pap., 56.
2. Bretthorst, G. L. (1988), Bayesian Spectrum Analysis and Parameter Estimation, Lect. Notes Stat. Ser., vol. 48, Springer, New York.
3. Cooley, R. L. (1993a), Exact Scheffe-type confidence intervals for output from groundwater flow models: 1. Use of hydrogeologic information, Water Resour. Res., 29, 17-34.
4. Cooley, R. L. (1993b), Exact Scheffe-type confidence intervals for output from groundwater flow models: 2. Combined use of hydrogeologic information and calibration data, Water Resour. Res., 29, 35-50.
5. Cooley, R. L., and A. V. Vecchia (1987), Calculation of nonlinear confidence and prediction intervals for groundwater flow models, Water Resour. Bull., 23(4), 581-599.
6. Davies, J. A., and C. D. Allen (1973), Equilibrium, potential and actual evaporation from cropped surfaces in southern Ontario, J. Appl. Meteorol., 12, 649-657.
7. Federer, C. A. (1979), A soil-plant-atmosphere model for transpiration and availability of soil water, Water Resour. Res., 15, 555-562.
8. Federer, C. A. (1982), Transpirational supply and demand: Plant, soil, and atmospheric effects evaluated by simulation, Water Resour. Res., 18, 355-362.
9. Finsterle, S. (1999), iTOUGH2 user's guide, Rep. LBNL-40040, Lawrence Berkeley Natl. Lab, Berkeley, Calif.
10. Freeze, R. A., J. Massman, L. Smith, T. Sperling, and B. James (1990), Hydrogeological decision analysis: 1. A framework, Ground Water, 28(5), 738-766.
11. Grote, K., S. Hubbard, and Y. Rubin (2003), Field-scale estimation of water content using ground-penetrating radar ground wave techniques, Water Resour. Res., 39(11), 1321, doi:10.1029/2003WR002045.
12. Harter, T., and T. C. J. Yeh (1996), Stochastic analysis of solute transport in heterogeneous, variably saturated soils, Water Resour. Res., 32, 1585-1595.
13. Hollenbeck, K. J., and K. H. Jensen (1998), Maximum-likelihood estimation of unsaturated hydraulic parameters, J. Hydrol., 210, 192-205.
14. Hubbard, S. S., and Y. Rubin (1997), Saturation and permeability measurement using radar data, Water Resour. Res., 33, 971-990.
15. Hubbard, S., and Y. Rubin (2000), A review of selected estimation techniques using geophysical data, J. Contam. Hydrol., 45, 3-34.
16. Hubbard, S., J. E. Peterson, E. L. Majer, P. T. Zawislanski, J. Roberts, K. H. Williams, and F. Wobber (1997), Estimation of permeable pathways and water content using tomographic radar data, Lead. Edge Explor., 16(11), 1623-1628.
17. Hubbard, S., Y. Rubin, and E. Majer (2000), Geophysical characterization of the vadose zone, in Vadose Zone, Science and Technology Solutions, edited B. Looney and R. Falta, pp. 215-236, Batelle, Columbus, Ohio.
18. Hubbard, S., J. Chen, J. Peterson, E. Majer, K. Williams, D. Swift, B. Mailliox, and Y. Rubin (2001), Hydrogeological characterization of the South Oyster bacterial transport site using geophysical data, Water Resour. Res., 37, 2431-2456.
19. Hubbard, S., K. Grote, and Y. Rubin (2002), Estimation of near-subsurface water content using high frequency GPR ground wave, Lead. Edge Explor., 21(6), 552-559.
20. Hughson, D. L., and T.-C. J. Yeh (2000), An inverse model for three dimensional flow in variably saturated porous media, Water Resour. Res., 36, 829-839.
21. Iman, R. L., and W. J. Conover (1980), Small sample sensitivity analysis techniques for computer models, with an application to risk assessment, Commun. Stat., Part A, Theory Methods, 17, 1749-1842.
22. 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), 3499-3514.
23. Kapur, J. N. (1989), Maximum Entropy Models in Science and Engineering, 635 pp., John Wiley, Hoboken, N. J.
24. Kool, J. B., J. C. Parker, and M. T. van Genuchten (1985), ONESTEP: A nonlinear parameter estimation program for evaluating soil hydraulic properties from one-step outflow experiments, Bull. 85-3, Va. Polytech. Inst. and State Univ., Blacksburg.
25. Kowalsky, M. B., P. Dietrich, G. Teutsch, and Y. Rubin (2001), Forward modeling of ground-penetrating radar data using digitized outcrop images and multiple scenarios of water saturation, Water Resour. Res., 37, 1615-1626.
26. Kowalsky, M., S. Finsterle, and Y. Rubin (2004), Estimating flow parameter distributions using ground-penetrating radar and hydrological measurements during transient flow in the vadose zone, Adv. Water Resour., 27(6), 583-599.
27. Lambert, G., J. Kashiwagi, B. Hansen, P. Gale, and A. Endo (1978), Soil survey of Napa County, California, report, Soil Surv., U.S. Dep. of Agric., Washington, D. C.
28. Maxwell, R. M., W. E. Kastenberg, and Y. Rubin (1999), A methodology to integrate site characterization information into groundwater-driven health risk assessment, Water Resour. Res., 35, 2841-2855.
29. McLaughlin, D., and L. R. Townley (1996), A reassessment of the ground-water inverse problem, Water Resour. Res., 32, 1131-1161.
30. Mishra, A., J. C. Parker, and N. Singhul (1989), Estimation of soil hydraulic properties and their uncertainty from particle size distribution data, J. Hydrol., 108, 1-18.
31. Neuman, S. P. (1973), Calibration of distributed parameter groundwater flow models viewed as a multiple-objective decision process under uncertainty, Water Resour. Res., 9, 1006-1021.
32. Press, W. H., B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling (1992), Numerical Recipes in FORTRAN: The Art of Scientific Computing, 2nd ed., Cambridge Univ. Press, New York.
33. Pruess, K., C. Oldenburg, and G. Moridis (1999), TOUGH2 user's guide, version 2.0, Rep. LBNL-43134, Lawrence Berkeley Natl. Lab., Berkeley, Calif.
34. Rubin, Y. (2003), Applied Stochastic Hydrogeology, Oxford Univ. Press, New York.
35. Rubin, Y., and D. Or (1993), Stochastic modeling of unsaturated flow in heterogeneous soils with water uptake by plant roots: The parallel columns model, Water Resour. Res., 29, 619-632.
36. Saxton, K. W., W. J. Rawls, J. S. Romberger, and R. I. Papendick (1986), Estimating generalized soil-water characteristic from texture, Soil Sci. Soc. Am. J., 50(4), 1031-1036.
37. Schreider, Y. A. (1966), The Monte Carlo Method, Elsevier, New York.
38. Simunek, J., and M. T. van Genuchten (1996), Estimating soil hydraulic properties from tension disc infiltrometer data by numerical inversion, Water Resour. Res., 32, 2683-2696.
39. Smith, R. E., and B. Diekkruger (1992), Field scale soil water flow in heterogeneous soils: I. Modeling statistical variation and large scale constituent relations, Model. Geo Biosphere Processes, 1, 205-227.
40. Spittlehouse, D. L., and T. A. Black (1981), A growing season water balance model applied to two Douglas-fir stands, Water Resour. Res., 17, 1651-1656.
41. Sun, N. Z. (1994), Inverse Problems in Groundwater Modeling, Springer, New York.
42. Tarantola, A. (1987), Inverse Problem Theory, Elsevier, New York.
43. Ulrych, T. J., M. Sacchi, and A. D. Woodbury (2001), A Bayes tour of inversion: A tutorial, Geophysics, 66(1), 55-69.
44. van Genuchten, M. T. (1980), A closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 44, 892-898.
45. Wang, W., S. P. Neuman, T. Yao, and P. J. Wierenga (2003), Simulation of large-scale field infiltration experiments using a hierarchy of models based on public, generic and site data, Vadose Zone J., 2, 297-312.
46. Williams, L. E. (2001), Irrigation of winegrapes in California, Pract. Winery Vineyard Mag., Nov./Dec., 42-55.
47. Woodbury, A. D., and Y. Rubin (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.
48. Woodbury, A. D., and E. A. Sudicky (1992), Inversion of the Borden Tracer Experiment data: Investigation of stochastic moment models, Water Resour. Res., 28, 2387-2398.
49. Woodbury, A. D., and T. J. Ulrych (1993), Minimum relative entropy: Forward probabilistic modeling, Water Resour. Res., 29, 2847-2860.
50. Woodbury, A. D., F. W. Render, and T. J. Ulrych (1995), Practical probabilisticgroundwater modeling, Ground Water, 33(4), 532-538.
51. Yeh, T. C. J., and J. Simunek (2002), Stochastic fusion of information for characterizing and monitoring vadose zone, Vadose Zone J., 1, 207-221.
52. Yeh, T.-C. J., and J. Zhang (1996), A geostatistical inverse method for variably saturated flow in the vadose zone, Water Resour. Res., 32, 2757-2766.
53. Yeh, W. W.-G. (1986), Review of parameter identification procedures in groundwater hydrology: The inverse problem, Water Resour. Res., 22, 95-108.
54. Zhang, J., and T.-C. J. Yeh (1997), An iterative geostatistical inverse method for steady flow in the vadose zone, Water Resour. Res., 33, 63-71.
55. Zhou, Z.-Y., M. H. Young, Z. Li, and P. J. Wierenga (2001), Estimation of depth averaged unsaturated soil hydraulic properties from infiltration experiments, J. Hydrol., 242, 26-42.
56. Zijlstra, J., and J. H. Dane (1996), Identification of hydraulic parameters in layered soils based on a quasi-Newton method, J. Hydrol., 181, 233-250.