Quantifying the change in soil moisture modeling uncertainty from remote sensing observations using Bayesian inference techniques

Water Resources Research

Volumn 48, Issue 11, 2012, Pages

  Harrison, Kenneth W ^a,b   Kumar, Sujay V ^b,c   Peters Lidard, Christa D ^b   Santanello, Joseph A ^b  

^a UNIVERSITY OF MARYLAND   (United States)

^b NASA GODDARD SPACE FLIGHT CENTER   (United States)

^c SAIC FREDERICK INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALTERNATIVE APPROACH; BAYESIAN ANALYSIS; BAYESIAN INFERENCE; BEST FIT; DIFFERENTIAL EVOLUTION; EXPERIMENTAL WATERSHEDS; IN-SITU DATA; LAND INFORMATION SYSTEMS; LAND SURFACE MODELS; LOCAL PARAMETERS; MARKOV CHAIN MONTE CARLO; MICROWAVE REMOTE SENSING; MODELING SCALE; MOISTURE MODELING; MOISTURE SIMULATIONS; PARALLEL IMPLEMENTATIONS; PARAMETER UNCERTAINTY; REMOTE SENSING DATA; RESEARCH NEEDS; SOIL HYDRAULIC PROPERTIES; UNCERTAINTY ESTIMATION; WALNUT GULCH; WEATHER PREDICTION;

BAYESIAN NETWORKS; ESTIMATION; EVOLUTIONARY ALGORITHMS; INFERENCE ENGINES; MARKOV PROCESSES; NASA; PARAMETER ESTIMATION; REMOTE SENSING; RESEARCH; SOIL MOISTURE;

UNCERTAINTY ANALYSIS;

BAYESIAN ANALYSIS; HYDRAULIC PROPERTY; MARKOV CHAIN; MONTE CARLO ANALYSIS; NUMERICAL MODEL; OBSERVATIONAL METHOD; PREDICTION; REMOTE SENSING; SOIL MOISTURE; UNCERTAINTY ANALYSIS;

JUGLANS;

EID: 84863308317     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2012WR012337     Document Type: Article

References (131)

1. Arora, B., B. P. Mohanty, and J. T. McGuire (2012), Uncertainty in dual permeability model parameters for structured soils, Water Resour. Res., 48, W01524, doi:10.1029/2011WR010500.
2. Baroni, G., A. Facchi, C. Gandolfi, B. Ortuani, D. Horeschi, and J. C. van Dam (2010), Uncertainty in the determination of soil hydraulic parameters and its influence on the performance of two hydrological models of different complexity, Hydrol. Earth Syst. Sci., 14(2), 251-270.
3. Berbery, E. H., Y. Luo, K. E. Mitchell, and A. K. Betts (2003), Eta model estimated land surface processes and the hydrologic cycle of the Mississippi basin, J. Geophys. Res, 108(D22), 8852.
4. Betts, A. K. (2000), Idealized model for equilibrium boundary layer over land, J. Hydrometeorol., 1(6), 507-523. (Pubitemid 33231478)
5. Betts, A. K., J. H. Ball, M. Bosilovich, P. Viterbo, Y. C. Zhang, and W. B. Rossow (2003), Intercomparison of water and energy budgets for five Mississippi subbasins between ECMWF reanalysis (ERA-40) and NASA Data Assimilation Office fvGCM for 1990-1999, J. Geophys. Res., 108(D16), 8618, doi:10.1029/2002JD003127.
6. Bouwer, H., and R. C. Rice (1984), Hydraulic-properties of stony vadose zones, Ground Water, 22(6), 696-705.
7. Brakensiek, D. L., and W. J. Rawls (1994), Soil containing rock fragments\Effects on infiltration, Catena, 23(1-2), 99-110.
8. Brakensiek, D. L., W. J. Rawls, and G. R. Stephenson (1986), Determining the saturated hydraulic conductivity of a soil containing rock fragments, Soil Sci. Soc. Am. J., 50(3), 834-835. (Pubitemid 16586883)
9. Braun, F. J., and G. Schadler (2005), Comparison of soil hydraulic parame-terizations for mesoscale meteorological models, J. Appl. Meteorol., 44(7), 1116-1132. (Pubitemid 41269618)
10. Burke, E. J., R. J. Gurney, L. P. Simmonds, and T. J. Jackson (1997), Calibrating a soil water and energy budget model with remotely sensed data to obtain quantitative information about the soil, Water Resour. Res., 33(7), 1689-1697. (Pubitemid 27295409)
11. Campbell, G. S. (1974), Simple method for determining unsaturated conductivity from moisture retention data, Soil Sci., 117(6), 311-314.
12. Chen, F., Z. Janjic, and K. Mitchell (1997), Impact of atmospheric surface-layer parameterizations in the new land-surface scheme of the NCEP mesoscale Eta model, Boundary Layer Meteorol., 85(3), 391-421.
13. Cosby, B. J., G. M. Hornberger, R. B. Clapp, and T. R. Ginn (1984), A statistical exploration of the relationships of soil-moisture characteristics to the physical-properties of soils, Water Resour. Res., 20(6), 682-690.
14. Dai, A., K. E. Trenberth, and T. T. Qian (2004), A global dataset of Palmer Drought Severity Index for 1870-2002: Relationship with soil moisture and effects of surface warming, J. Hydrometeorol., 5(6), 1117-1130. (Pubitemid 40112597)
15. Das, N. N., B. P. Mohanty, and E. G. Njoku (2008), A Markov chain Monte Carlo algorithm for upscaled soil-vegetation-atmosphere-transfer modeling to evaluate satellite-based soil moisture measurements, Water Resour. Res., 44, W05416, doi:10.1029/2007WR006472. (Pubitemid 351897328)
16. Das, N. N., B. P. Mohanty, and Y. Efendiev (2010), Characterization of effective saturated hydraulic conductivity in an agricultural field using Karhunen-Loeve expansion with the Markov chain Monte Carlo technique, Water Resour. Res., 46, W06521, doi:10.1029/2008WR007100.
17. De Jeu, R. A. M., and M. Owe (2003), Further validation of a new methodology for surface moisture and vegetation optical depth retrieval, Int. J. Remote Sensing, 24(22), 4559-4578. (Pubitemid 38143535)
18. Deng, H. L., M. Ye, M. G. Schaap, and R. Khaleel (2009), Quantification of uncertainty in pedotransfer function-based parameter estimation for un-saturated flow modeling, Water Resour. Res., 45, W04409, doi:10.1029/ 2008WR007477.
19. Doherty, J. (2010), PEST: Model-Independent Parameter Estimation, User Manual, 5th edition, Watermark Numer. Comput., Corinda, Australia.
20. Doherty, J., and D. Welter (2010), A short exploration of structural noise, Water Resour. Res., 46, W05525, doi:10.1029/2009WR008377.
21. Dostert, P., Y. Efendiev, and B. Mohanty (2009), Efficient uncertainty quantification techniques in inverse problems for Richards' equation using coarse-scale simulation models, Adv. Water Resour., 32(3), 329-339.
22. Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley (2003), Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res., 108(D22), 8851, doi:10.1029/2002JD003296.
23. Entekhabi, D., et al. (2010), The soil moisture active passive (SMAP) mission, Proc. IEEE, 98(5), 704-716.
24. Findell, K. L., and E. A. B. Eltahir (2003), Atmospheric controls on soil moisture-boundary layer interactions. Part II: Feedbacks within the continental United States, J. Hydrometeorol., 4(3), 570-583. (Pubitemid 36906490)
25. Food and Agriculture Organization (1981), Soil map of the world, ten volumes, food and agriculture organization, 1971-1981, report, U. N. Educ. Sci. Cultural Org., Rome.
26. Garcia, M., C. D. Peters-Lidard, and D. C. Goodrich (2008), Spatial interpolation of precipitation in a dense gauge network for monsoon storm events in the southwestern United States, Water Resour. Res., 44, W05S13, doi:10.1029/2006WR005788. (Pubitemid 351897347)
27. Gelman, A., J. B. Carlin, H. S. Stern, and D. B. Rubin (2004), Bayesian Data Analysis, Chapman and Hall/CRC, Boca Raton, Fla.
28. Geyer, C. J. (2010), Introduction to Markov Chain Monte Carlo, in Handbook of Markov Chain Monte Carlo: Methods and Applications, edited by A. Gelman et al., CRC, Boca Raton, Fla.
29. Gilks, W. R., S. Richardson, and D. J. Spiegelhalter (1996), Introducing Markov Chain Monte Carlo, in Markov Chain Monte Carlo in Practice, edited by W. R. Gilks, S. Richardson and D. J. Spiegelhalter, pp. 1-19, Chapman and Hall, Boca Raton, Fla.
30. Green, P. J. (1995), Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, 82(4), 711-732.
31. Gupta, H. V., S. Sorooshian, and P. O. Yapo (1998), Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information, Water Resour. Res., 34(4), 751-763. (Pubitemid 28159454)
32. Gutmann, E. D., and E. E. Small (2005), The effect of soil hydraulic properties vs. soil texture in land surface models, Geophys. Res. Lett., 32, L02402, doi:10.1029/2004GL021843. (Pubitemid 40473025)
33. Gutmann, E. D., and E. E. Small (2007), A comparison of land surface model soil hydraulic properties estimated by inverse modeling and pedo-transfer functions, Water Resour. Res., 43, W05418, doi:10.1029/2006WR005135.
34. Gutmann, E. D., and E. E. Small (2010), A method for the determination of the hydraulic properties of soil from MODIS surface temperature for use in land-surface models, Water Resour. Res., 46, W06520, doi:10.1029/2009WR008203.
35. Haario, H., M. Laine, M. Lehtinen, E. Saksman, and J. Tamminen (2004), Markov chain Monte Carlo methods for high dimensional inversion in remote sensing, J. R. Stat. Soc. Ser. B, 66, 591-607.
36. Hald, A. (1999), On the history of maximum likelihood in relation to inverse probability and least squares, Stat. Sci., 14(2), 214-222.
37. Hansen, M. C., R. S. Defries, J. R. G. Townshend, and R. Sohlberg (2000), Global land cover classification at 1km spatial resolution using a classification tree approach, Int. J. Remote Sensing, 21(6-7), 1331-1364. (Pubitemid 30242138)
38. Harrison, K. W. (2007), Test application of Bayesian Programming: Adaptive water quality management under uncertainty, Adv. Water Resour., 30(3), 606-622. (Pubitemid 46131840)
39. Hastings, W. K. (1970), Monte-Carlo sampling methods using Markov Chains and their applications, Biometrika, 57(1), 97.
40. Hilborn, R., and M. Mangel (1997), The Ecological Detective: Confronting Models With Data, Princeton Univ., Princeton, N. J.
41. Hill, C., C. DeLuca, Balaji, M. Suarez, and A. da Silva (2004), The architecture of the Earth system modeling framework, Comput. Sci. Eng., 6(1), 18-28.
42. Hobbs, B. F. (1997), Bayesian methods for analysing climate change and water resource uncertainties, J. Environ. Manage., 49(1), 53-72.
43. Holland, J. (1975), Adaptation in Natural and Artificial Systems, edited, Univ. Mich., Ann Arbor, Mich.
44. Hollenbeck, K. J., and K. H. Jensen (1998), Maximum-likelihood estimation of unsaturated hydraulic parameters, J. Hydrol., 210(1-4), 192-205. (Pubitemid 28504057)
45. Holtan, H. N., C. B. England, G. P. Lawless, and G. A. Schumaker (1968), Moisture-tension data for selected soils on experimental watersheds, edited, Agric. Res. Serv., U.S. Dep. of Agric., Beltsville, Md.
46. Huard, D., and A. Mailhot (2006), A Bayesian perspective on input uncertainty in model calibration: Application to hydrological model "abc," Water Resour. Res., 42, W07416, doi:10.1029/2005WR004661. (Pubitemid 44300483)
47. Ines, A. V. M., and B. P. Mohanty (2008a), Parameter conditioning with a noisy Monte Carlo genetic algorithm for estimating effective soil hydraulic properties from space, Water Resour. Res., 44, W08441, doi:10.1029/2007WR006125.
48. Ines, A. V. M., and B. P. Mohanty (2008b), Near-surface soil moisture assimilation for quantifying effective soil hydraulic properties using genetic algorithm: 1. Conceptual modeling, Water Resour. Res., 44, W06422, doi:10.1029/2007WR005990.
49. Ines, A. V. M., and B. P. Mohanty (2008c), Near-surface soil moisture assimilation for quantifying effective soil hydraulic properties under different hydroclimatic conditions, Vadose Zone J., 7(1), 39-52. (Pubitemid 351362687)
50. Ines, A. V. M., and B. P. Mohanty (2009), Near-surface soil moisture assimilation for quantifying effective soil hydraulic properties using genetic algorithms: 2. Using airborne remote sensing during SGP97 and SMEX02, Water Resour. Res., 45, W01408, doi:10.1029/2008WR007022.
51. Jana, R. B., and B. P. Mohanty (2011), Enhancing PTFs with remotely sensed data for multi-scale soil water retention estimation, J. Hydrol., 399(3-4), 201-211.
52. Jana, R. B., and B. P. Mohanty (2012a), A comparative study of multiple approaches to soil hydraulic parameter scaling applied at the hillslope scale, Water Resour. Res., 48, W02520, doi:10.1029/2010WR010185.
53. Jana, R. B., and B. P. Mohanty (2012b), A topography-based scaling algorithm for soil hydraulic parameters at hillslope scales: Field testing, Water Resour. Res., 48, W02519, doi:10.1029/2011WR011205.
54. Jana, R. B., and B. P. Mohanty (2012c), On topographic controls of soil hydraulic parameter scaling at hillslope scales, Water Resour. Res., 48, W02518, doi:10.1029/2011WR011204.
55. Jana, R. B., B. P. Mohanty, and E. P. Springer (2007), Multiscale pedotrans-fer functions for soil water retention, Vadose Zone J., 6(4), 868-878. (Pubitemid 350232078)
56. Jana, R. B., B. P. Mohanty, and E. P. Springer (2008), Multiscale Bayesian neural networks for soil water content estimation, Water Resour. Res., 44, W08408, doi:10.1029/2008WR006879.
57. Jasra, A., D. A. Stephens, and C. C. Holmes (2007), On population-based simulation for static inference, Stat. Comput., 17(3), 263-279.
58. Kabat, P., R. W. A. Hutjes, and R. A. Feddes (1997), The scaling characteristics of soil parameters: From plot scale heterogeneity to subgrid parameterization, J. Hydrol., 190(3-4), 363-396.
59. Kavetski, D., G. Kuczera, and S. W. Franks (2006a), Bayesian analysis of input uncertainty in hydrological modeling: 2. Application, Water Resour. Res., 42, W03408, doi:10.1029/2005WR004376.
60. Kavetski, D., G. Kuczera, and S. W. Franks (2006b), Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory, Water Resour. Res., 42, W03407, doi:10.1029/2005WR004368.
61. Kerr, Y. H., et al. (2010), The SMOS Mission: New tool for monitoring key elements of the global water cycle, Proc. IEEE, 98(5), 666-687.
62. Koster, R. D., et al. (2004), Regions of strong coupling between soil moisture and precipitation, Science, 305(5687), 1138-1140. (Pubitemid 39100323)
63. Koster, R. D., et al. (2011), The second phase of the global land-atmosphere coupling experiment: Soil moisture contributions to subseasonal forecast skill, Ji Hydrometeoroli, 12(5), 805-822.
64. Krzysztofowicz, R. (1999), Bayesian theory of probabilistic forecasting via deterministic hydrologic model, Water Resour. Res., 35(9), 2739-2750. (Pubitemid 29412106)
65. Krzysztofowicz, R. (2002), Probabilistic flood forecast: bounds and approximations, J. Hydrol., 268(1-4), 41-55. (Pubitemid 35325966)
66. Krzysztofowicz, R. (2010), Decision criteria, data fusion and prediction calibration: A Bayesian approach, Hydrol. Sci. J., 55(6), 1033-1050.
67. Krzysztofowicz, R., and K. S. Kelly (2000), Hydrologic uncertainty processor for probabilistic river stage forecasting, Water Resour. Res., 36(11), 3265-3277.
68. Kumar, S. V., et al. (2006), Land information system: An interoperable framework for high resolution land surface modeling, Environ. Modell. Software, 21(10), 1402-1415. (Pubitemid 44038636)
69. Kumar, S. V., R. H. Reichle, C. D. Peters-Lidard, R. D. Koster, X. W. Zhan, W. T. Crow, J. B. Eylander, and P. R. Houser (2008), A land surface data assimilation framework using the land information system: Description and applications, Adv. Water Resour., 31(11), 1419-1432.
70. Kumar, S. V., R. H. Reichle, K. W. Harrison, C. D. Peters-Lidard, S. Yatheendradas, and J. A. Santanello (2012), A comparison of methods for a priori bias correction in soil moisture data assimilation, Water Resour. Res., 48, W03515, doi:10.1029/2010WR010261.
71. Kurc, S. A., and E. E. Small (2004), Dynamics of evapotranspiration in semi-arid grassland and shrubland ecosystems during the summer monsoon season, central New Mexico, Water Resour. Res., 40, W09305, doi:10.1029/2004WR003068. (Pubitemid 39385270)
72. Kustas, W. P., and D. C. Goodrich (1994), Preface [to special section on Monsoon '90 Multidisciplinary Experiment], Water Resour. Res., 30(5), 1211-1225, doi:10.1029/93WR03068. (Pubitemid 24404188)
73. (ZS) and high-performance computing, Water Resour. Res., 48, W01526, doi:10.1029/2011WR 010608.
74. Laloy, E., D. Fasbender, and C. L. Bielders (2010a), Parameter optimization and uncertainty analysis for plot-scale continuous modeling of runoff using a formal Bayesian approach, J. Hydrol., 380(1-2), 82-93.
75. Laloy, E., M. Weynants, C. L. Bielders, M. Vanclooster, and M. Javaux (2010b), How efficient are one-dimensional models to reproduce the hydrodynamic behavior of structured soils subjected to multi-step outflow experiments?, J. Hydrol., 393(1-2), 37-52.
76. Laskey, K. B., and J. W. Myers (2003), Population Markov Chain Monte Carlo, Machi Learning, 50(1-2), 175-196.
77. Mallants, D., B. P. Mohanty, A. Vervoort, and J. Feyen (1997), Spatial analysis of saturated hydraulic conductivity in a soil with macropores, Soil Technol., 10(2), 115-131.
78. Metropolis, N., A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller (1953), Equation of state calculations by fast computing machines, J. Chem. Phys., 21(6), 1087-1092.
79. Mo, K. C. (2008), Model-Based Drought Indices over the United States, J. Hydrometeorol., 9(6), 1212-1230.
80. Mohanty, B. P., and J. T. Zhu (2007), Effective hydraulic parameters in horizontally and vertically heterogeneous soils for steady-state land-atmosphere interaction, J. Hydrometeoroli, 8(4), 715-729. (Pubitemid 47491196)
81. Molders, N. (2005), Plant-and soil-parameter-caused uncertainty of predicted surface fluxes, Mon. Weather Rev., 133(12), 3498-3516. (Pubitemid 43141744)
82. Moran, M. S., C. D. Peters-Lidard, J. M. Watts, and S. McElroy (2004), Estimating soil moisture at the watershed scale with satellite-based radar and land surface models, Can. J. Remote Sensing, 30(5), 805-826. (Pubitemid 40050096)
83. Moré, G., and K. E. Hillstrom (1980), User Guide for MINPACK-1, Rep. ANL-80-74, Argonne Natl. Lab. Argonne, Ill.
84. Niemann, W. L., and C. W. Rovey (2009), A systematic field-based testing program of hydraulic conductivity and dispersivity over a range in scale, Hydrogeol. J., 17(2), 307-320.
85. Njoku, E. G., T. J. Jackson, V. Lakshmi, T. K. Chan, and S. V. Nghiem (2003), Soil moisture retrieval from AMSR-E, IEEE Trans. Geosci. Remote Sensing, 41(2), 215-229.
86. Ntelekos, A. A., K. P. Georgakakos, and W. F. Krajewski (2006), On the uncertainties of flash flood guidance: Toward probabilistic forecasting of flash floods, J. Hydrometeorol., 7(5), 896-915. (Pubitemid 44734304)
87. Owe, M., R. de Jeu, and T. Holmes (2008), Multisensor historical climatology of satellite-derived global land surface moisture, J. Geophys. Res., 113, F01002, doi:10.1029/2007JF000769.
88. Pauwels, V. R. N., A. Balenzano, G. Satalino, H. Skriver, N. E. C. Verhoest, and F. Mattia (2009), Optimization of soil hydraulic model parameters using synthetic aperture radar data: An integrated multidisciplinary approach, IEEE Trans. Geosci. Remote Sensing, 47(2), 455-467.
89. Peters-Lidard, C. D., P. R. Houser, Y. Tian, S. V. Kumar, J. Geiger, S. Olden, L. Lighty, B. Doty, P. Dirmeyer, and J. Adams (2007), High-performance Earth system modeling with NASA/GSFC's land information system, Innov. Syst. Software Eng., 3(3), 157-165. (Pubitemid 47412764)
90. Peters-Lidard, C. D., D. M. Mocko, M. Garcia, J. A. Santanello, M. A. Tischler, M. S. Moran, and Y. H. Wu (2008), Role of precipitation uncertainty in the estimation of hydrologic soil properties using remotely sensed soil moisture in a semiarid environment, Water Resour. Res., 44, W05S18, doi:10.1029/2007WR005884. (Pubitemid 351897352)
91. Rawls, W. J., P. Yates, and L. Asmussen (1976), Calibration of selected infiltration equations for the Georgia Coastal Plain, edited, p. 110, U.S. Dep. of Agric., Beltsville, MD.
92. Rawls, W. J., D. L. Brakensiek, and K. E. Saxton (1982), Estimation of soil-water properties, Trans. ASAE, 25(5), 1316-1320.
93. Reichle, R. H., M. G. Bosilovich, W. T. Crow, R. D. Koster, S. V. Kumar, S. P. P. Mahanama, and B. F. Zaitchik (2009), Recent advances in land data assimilation at the NASA Global Modeling and Assimilation Office, in Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, edited by S. K. Park and L. Xu, pp. 407-428, Springer Verlag, New York.
94. Richards, L. A. (1931), Capillary conduction of liquids through porous mediums, Physics A, 1(1), 318-333.
95. Roberts, G. O., and J. S. Rosenthal (2009), Examples of adaptive MCMC, J. Comput. Graphical Stat., 18(2), 349-367.
96. Robock, A., et al. (2003), Evaluation of the North American Land Data Assimilation System over the southern Great Plains during the warm season, J. Geophys. Res., 108(D22), 8846, doi:10.1029/2002JD003245.
97. Rosenthal, J. S. (2010), Optimal proposal distributions and adaptive MCMC, in Handbook of Markov Chain Monte Carlo: Methods and Applications, edited by A. Gelman, et al., pp. 93-112, CRC, Boca Raton, Fla.
98. Rovey, C. W. (1998), Digital simulation of the scale effect in hydraulic conductivity, Hydrogeol. J., 6(2), 216-225.
99. Salvucci, G. D., and D. Entekhabi (2011), An alternate and robust approach to calibration for the estimation of land surface model parameters based on remotely sensed observations, Geophys. Res. Lett., 38, L16404, doi:10.1029/2011GL048366.
100. Santanello, J. A., C. D. Peters-Lidard, M. E. Garcia, D. M. Mocko, M. A. Tischler, M. S. Moran, and D. P. Thoma (2007), Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed, Remote Sensing Environ., 110(1), 79-97. (Pubitemid 47077437)
101. Santanello, J. A., C. D. Peters-Lidard, and S. V. Kumar (2011), Diagnosing the sensitivity of local land-atmosphere coupling via the soil moisture-boundary layer interaction, J. Hydrometeorol., 12(5), 766-786.
102. Saxton, K. E., and W. J. Rawls (2006), Soil water characteristic estimates by texture and organic matter for hydrologic solutions, Soil Sci. Soc. Ami J., 70(5), 1569-1578. (Pubitemid 44373285)
103. Schaap, M. G., and F. J. Leij (1998), Database-related accuracy and uncertainty of pedotransfer functions, Soil Sci., 163(10), 765-779. (Pubitemid 28501717)
104. Schaap, M. G., F. J. Leij, and M. T. van Genuchten (2001), ROSETTA: A computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions, J. Hydrol., 251(3-4), 163-176. (Pubitemid 32792750)
105. Schaap, M. G., Y. Pachepsky, and W. J. Rawls (2004), Accuracy and uncertainty in PTF predictions, in Development of Pedotransfer Functions in Soil Hydrology, edited, pp. 33-43, Elsevier, New York.
106. Schaefli, B., D. B. Talamba, and A. Musy (2007), Quantifying hydrological modeling errors through a mixture of normal distributions, J. Hydrol., 332(3-4), 303-315. (Pubitemid 44969651)
107. Scharnagl, B., J. A. Vrugt, H. Vereecken, and M. Herbst (2011), Inverse modelling of in situ soil water dynamics: Investigating the effect of different prior distributions of the soil hydraulic parameters, Hydrol. Earth Syst. Sci., 15(10), 3043-3059.
108. Schmugge, T., T. J. Jackson, W. P. Kustas, R. Roberts, R. Parry, D. C. Goodrich, S. A. Amer, and M. A. Weltz (1994), Push broom microwave radiometer observations of surface soil-moisture in monsoon 90, Water Resour. Res., 30(5), 1321-1327.
109. Schoups, G., and J. A. Vrugt (2010), A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non-Gaussian errors, Water Resour. Res., 46, W10531, doi:10.1029/2009WR008933.
110. Scott, R. L., W. J. Shuttleworth, T. O. Keefer, and A. W. Warrick (2000), Modeling multiyear observations of soil moisture recharge in the semi-arid American Southwest, Water Resour. Res., 36(8), 2233-2247. (Pubitemid 30610911)
111. Shao, Y. P., and P. Irannejad (1999), On the choice of soil hydraulic models in land-surface schemes, Boundary-Layer Meteorol., 90(1), 83-115. (Pubitemid 29228996)
112. Sheffield, J., G. Goteti, F. H. Wen, and E. F. Wood (2004), A simulated soil moisture based drought analysis for the United States, J. Geophys. Res., 109, D24108, doi:10.1029/2004JD005182.
113. Stedinger, J. R., R. M. Vogel, S. U. Lee, and R. Batchelder (2008), Appraisal of the generalized likelihood uncertainty estimation (GLUE) method, Water Resour. Res., 44, W00B06, doi:10.1029/2008WR006822, [printed 45(12), 2009].
114. Steenpass, C., J. Vanderborght, M. Herbst, J. Simunek, and H. Vereecken (2010), Estimating soil hydraulic properties from infrared measurements of soil surface temperatures and TDR data, Vadose Zone J., 9(4), 910-924.
115. Talamba, D. B., E. Parent, and A. Musy (2010), Bayesian multiresponse calibration of TOPMODEL: Application to the Haute-Mentue catchment, Switzerland, Water Resour. Res., 46, W08524, doi:10.1029/2007WR006449.
116. ter Braak, C. J. F. (2006), A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces, Stat. Comput., 16(3), 239-249. (Pubitemid 43992692)
117. ter Braak, C. J. F., and J. A. Vrugt (2008), Differential evolution Markov Chain with snooker updater and fewer chains, Stat. Comput., 18(4), 435-446.
118. Teuling, A. J., R. Uijlenhoet, B. van den Hurk, and S. I. Seneviratne (2009), Parameter Sensitivity in LSMs: An analysis using stochastic soil moisture models and ELDAS soil parameters, J. Hydrometeorol., 10(3), 751-765.
119. Thoma, D. P., M. S. Moran, R. Bryant, M. Rahman, C. D. Holifield-Collins, S. Skirvin, E. E. Sano, and K. Slocum (2006), Comparison of four models to determine surface soil moisture from C-band radar imagery in a sparsely vegetated semiarid landscape, Water Resour. Res., 42, W01418, doi:10.1029/2004WR003905. (Pubitemid 43352596)
120. Tonkin, M., and J. Doherty (2009), Calibration-constrained Monte Carlo analysis of highly parameterized models using subspace techniques, Water Resour. Res., 45, W00B10, doi:10.1029/2007WR006678.
121. Troen, I., and L. Mahrt (1986), A simple-model of the atmospheric boundary-layer-sensitivity to surface evaporation, Boundary Layer Meteorol., 37(1-2), 129-148.
122. Vereecken, H., R. Kasteel, J. Vanderborght, and T. Harter (2007), Upscal-ing hydraulic properties and soil water flow processes in heterogeneous soils: A review, Vadose Zone J., 6(1), 1-28. (Pubitemid 47252325)
123. Vrugt, J. A., and W. Bouten (2002), Validity of first-order approximations to describe parameter uncertainty in soil hydrologic models, Soil Sci. Soc. Am. J., 66(6), 1740-1751. (Pubitemid 35352878)
124. Vrugt, J. A., W. Bouten, H. V. Gupta, and J. W. Hopmans (2003), Toward improved identifiability of soil hydraulic parameters: On the selection of a suitable parametric model, Vadose Zone J., 2(1), 98-113.
125. Vrugt, J. A., C. J. F. ter Braak, C. G. H. Diks, B. A. Robinson, J. M. Hyman, and D. Higdon (2009), Accelerating Markov Chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling, Int. J. Nonlin. Sci. Numer. Simul., 10(3), 273-290.
126. Wohling, T., and J. A. Vrugt (2011), Multiresponse multilayer vadose zone model calibration using Markov chain Monte Carlo simulation and field water retention data, Water Resour. Res., 47, W04510, doi:10.1029/2010WR009265.
127. Xie, H., J. W. Eheart, Y. G. Chen, and B. A. Bailey (2009), An approach for improving the sampling efficiency in the Bayesian calibration of computationally expensive simulation models, Water Resour. Res., 45, W06419, doi:10.1029/2007WR006773.
128. Yang, J., P. Reichert, K. C. Abbaspour, and H. Yang (2007), Hydrological modelling of the chaohe basin in china: Statistical model formulation and Bayesian inference, J. Hydrol., 340(3-4), 167-182. (Pubitemid 46935784)
129. Zhang, Z. F. (2010), Effective hydraulic conductivity of unsaturated iso-tropic soils with multidimensional heterogeneity, Soil Sci. Soc. Am. J., 74(3), 734-743.
130. Zhu, J. T., and B. P. Mohanty (2002), Upscaling of soil hydraulic properties for steady state evaporation and infiltration, Water Resour. Res., 38(9), 1178, doi:10.1029/2001WR000704.
131. Zhu, J. T., B. P. Mohanty, and N. N. Das (2006), On the effective averaging schemes of hydraulic properties at the landscape scale, Vadose Zone J., 5(1), 308-316. (Pubitemid 47254038)