Stochastic Subsurface Hydrology Reading List Stochastic ...

Stochastic Subsurface Hydrology Reading List Stochastic Modeling of Groundwater Flow Freeze, R.A., A Stochastic-Conceptual Analysis of One-Dimensional Groundwater Flow in Nonuniform Homogeneous Media, Water Resources Research, 11(5) 725-739, 1975. Smith, L. and R.A. Freeze, Stochastic Analysis of Steady State Groundwater Flow in a Bounded Domain, 1. One-Dimensional Simulations, Water Resources Research 15(3),1979. Smith, L. and R.A. Freeze, Stochastic Analysis of Steady State Groundwater Flow in a Bounded Domain, 1. Two-Dimensional Simulations, Water Resources Research 15(6),1979. Bakr, A. , L. Gelhar, A. Gutjhar, and J. MacMillan, Stochastic Analysis of Spatial Variability in Subsurface Flows, 1. Comparison of One- and Three-Dimensional Flows, Water Resources Research, 14(2), 263-279, 1978. Mizell, S, A. Gutjhar, and L. Gelhar, Stochastic Analysis of Spatial Variability in TwoDimensional Steady Groundwater Flow Assuming Stationary and Nonstationary Heads, Water Resources Research 18(4) 1053-1067, 1982. Gelhar, L. , Stochastic Subsurface Hydrology From Theory to Applications, Water Resources Research, Vol22, No. 9,P AGES 135S-145S, August, 1986. McLaughlin, D. and E. Wood, A Distributed parameter Approach for Evaluating the Accuracy of Groundwater Model Predictions. 1. Theory, Water Resources Research 24(7) 1037-1047, 1988. McLaughlin, D. and E. Wood, A Distributed parameter Approach for Evaluating the Accuracy of Groundwater Model Predictions. 2. Application to Groundwater Flow, Water Resources Research 24(7) 1048-1060, 1988. Graham, W.D., and C.D. Tankersley. Optimal Estimation of Spatially Variable Recharge and Transmissivity Fields Under Steady State Groundwater Flow: 1 Theory. Journal of Hydrology, Vol. 157, 247-266, 1994. Graham, W.D., and C.R. Neff. Optimal Estimation of Spatially Variable Recharge and Transmissivity Fields Under Steady State Groundwater Flow: 2 Case Study. Journal of Hydrology, Vol. 157, 267-285, 1994. Foussereau, X., W. D. Graham, and P.S.C. Rao, Stochastic analysis of Transient Flow in Unsaturated Heterogeneous Soils, Water Resources Research, 26(4), 891-910, 2000.

Stochastic Modeling of Groundwater Transport Smith, L. and F. Schwartz, Mass Transport 1. A Stochastic Analysis of Macroscopic Dispersion, Water Resources Research 16(2), 303-313, 1980. Smith, L. and F. Schwartz, Mass Transport 2. Analysis of Uncertainty in Prediction, Water Resources Research 17(2), 351-369, 1981. Smith, L. and F. Schwartz, Mass Transport 3. Role of Hydraulic Conductivity Data in Prediction, Water Resources Research 17(5), 1463-1479, 1981. Gelhar, L. and C. Axness, Three-Dimensional Stochastic Analyslis of Macrodispersion in Aqufiers, Water Resources Research, 19(1), 161-180, 1983. Vomoris, E., and L. Gelhar, Stochastic Analysis of the Concentration Variability in a Three-Dimensional Heterogeneous Aquifer Water Resources Research 26(10), 25912602, 1990. Dagan, G., Stochastic Modeling of Groundwater Flow by Unconditional and Conditional Probabilities, 1. Conditional Simulation and the Direct Problem, Water Resources Research, 18(4), 813-833, 1982. Dagan, G., Stochastic Modeling of Groundwater Flow by Unconditional and Conditional Probabilities, 1.The Solute Transprot , Water Resources Research, 18(4), 835-848, 1982. Dagan, G., Solute Transport in Heterogeneous Formations, J. Fluid Mechanics, 145, 151177, 1984. Dagan, G. Theory of Solute Transport by Groundwater, Ann. Rev. Fluid Mechanics, 19, 183-215, 1987. Shapiro, A., and V. Cvetkovic, Stochastic Analysis of Solute Arrival Time in Heterogeneous Porous Media, Water Resources Research, 24(10), 1711-1718, 1988. Dagan, G. V. Cvetkovic, and A. Shapiro, A Solute Flux Approach to Transport in Heterogeneous Formations, 1. The General Framework, Water Resources Research, 28(5), 1369-1376, 1992. Cvetkovic, V., A. Shapiro, and G. Dagan, A Solute Flux Approach to Transport in Heterogeneous Formations, 2. Uncertainty Analysis, Water Resources Research, 28(5), 1377-1388, 1992. Destouni, G., Prediction Uncertainty in Solute Flux through Heterogeneous Soil, Water Resources Research, 28(3), 793-801, 1992.

Destouni, G., The Effect of Vertical Soil Heterogeneity on Field Scale Solute Flux, Water Resources Research, 28(5), 1303-1309, 1992. Sudicky, E. A Natural Gradient Experiment on Solute Transport in a Sand Aquifer' Spatial Variability of Hydraulic Conductivity and Its Role in the Dispersion Process, Water Resources Research, Vol. 22, No. 13, Pages 2069-2082, December 1986 Graham, W.D. and D.B. McLaughlin. Stochastic Analysis of Non-Stationary Subsurface Solute Transport, 1. Unconditional Moments, Water Resources Research, 25(2):215-232, 1989. Graham, W.D. and D.B. McLaughlin. Stochastic Analysis of Non-Stationary Subsurface Solute Transport, 2. Conditional Moments, Water Resources Research, 25(11):2331-2355, 1989. Graham, W. D. and D.B. McLaughlin. A Stochastic Model of Solute Transport in Groundwater: Application to the Borden Ontario Tracer Test, Water Resources Research, 27(6):1345-1360, 1991. Foussereau, X., W. D. Graham, G.A. Akpoji, G. Destouni, and P.S.C. Rao, Stochastic Analysis of Transport in Unsaturated Heterogeneous Soils under Transient Flow Regimes, Water Resources Research , 26(4),911-922, 2000. Zhang, Yan , Graham, Wendy D. ,Partitioning tracer transport in a hydrogeochemically heterogeneous aquifer, Water Resources Research, 37(8),2037-2048, 2001. Background on Data Assimilation Theory D. McLaughlin, “An integrated approach to hydrologic data assimilation: interpolation, smoothing and filtering" Advances in Water Resources. 25: 1275-1286. 2000. Link Schlatter, T.W. , “Variational assimilation of meteorological observations in the lower atmosphere: A tutorial on how it works” Journal of Atmospheric and Solar-Terrestrial Physics. 62(12): 1057-1070. 2000. Link Eversen, G.,“The Ensemble Kalman filter: theoretical formulation and practical implementation” Ocean Dynamics. 53: 343-367. 2003. Link M. Sanjeev Arulampalam, Simon Maskell, Neil Gordon, and Tim Clapp, A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking, IEEE Transactions On Signal Processing, Vol. 50, No. 2, February 2002. Hamill, T. , “Ensemble-based atmospheric data assimilation: A tutorial “Predictability of Weather and Climate (TN Palmer, R. Hagedorn, eds). 2004. Review Paper.

Philippe Courtier , John Derber, Ron Errico, Jean-Francois Louis and Tomislava Vukićević , Important Literature on the Use of Adjoint, Variational Methods and the Kalman Filter In Meteorology, Teelus A: Volume 45 Issue 5, Pages 342 – 357, Nov 2002 Troch, P.A., Paniconi, C. and D. McLaughlin, “Catchment-scale hydrological modeling and data assimilation” Advances in Water Resources. 26(2) : 131-135. 2003. Applications of Kriging (Geostatistics) Delhomme, J.P., Spatial Variability and Uncertainty in Groundwater Flow Parameters: A Geostatistical Approach, Water Resources Research 15(2), 269-284, 1979. Kitanidis, P., and E. Vomvoris, A geostatistical approach to the inverse problem in groundwater modeling (steady state) and one-dimensional simulations, Water Resources Research, Vol. 19, No. 3, 677-690, 1983. Hoeksema, R.J. and P.K. Kitanidis, An application of the geostatistical approach to the inverse problem in two-dimensional groundwater modeling, Water Resour. Res. 20 (7) (1984), pp. 1009–1020. Hoeksema, R.J., and P.K. Kitanidis, Comparison of conditional mean and kriging estimation in the geostatistical solution of the inverse problem, Water Resour. Res. 21 (6) (1985), pp. 825–836. Graham, W.D., and C.D. Tankersley. Optimal Estimation of Spatially Variable Recharge and Transmissivity Fields Under Steady State Groundwater Flow: 1 Theory. Journal of Hydrology, Vol. 157, 247-266, 1994. Graham, W.D., and C.R. Neff. Optimal Estimation of Spatially Variable Recharge and Transmissivity Fields Under Steady State Groundwater Flow: 2 Case Study. Journal of Hydrology, Vol. 157, 267-285, 1994. Applications of Kalman Filtering Graham, W.D. and D.B. McLaughlin. Stochastic Analysis of Non-Stationary Subsurface Solute Transport, 1. Unconditional Moments, Water Resources Research, 25(2):215-232, 1989. Graham, W.D. and D.B. McLaughlin. Stochastic Analysis of Non-Stationary Subsurface Solute Transport, 2. Conditional Moments, Water Resources Research, 25(11):2331-2355, 1989. Graham, W. D. and D.B. McLaughlin. A Stochastic Model of Solute Transport in Groundwater: Application to the Borden Ontario Tracer Test, Water Resources Research, 27(6):1345-1360, 1991.

Graham, W.D. and C.D. Tankersley. Forecasting Piezometric Head Levels in the Floridan Aquifer: A Kalman Filtering Approach. Water Resources Research, 29(11), 3791-3800, 1993. James, A.I., W.D. Graham, K. Hatfield, P.S.C. Rao and M.D. Annable, Estimation of Spatially Variable Residual Non-Aqueous Phase Liquid (NAPL) Saturations in NonUniform Flow Fields Using Partitioning Tracer Data, Water Resources Research, 26(4),999-1012, 2000. Zhang, Yan , Graham, Wendy D., Spatial characterization of a hydrogeochemically heterogeneous aquifer using partitioning tracers: Optimal estimation of aquifer parameters. Water Resources Research, 37(8), 2049-2063, 2001. Reichle, R., McLaughlin, D. and Entekhabi, “Hydrologic data assimilation with the ensemble Kalman filter” D. Monthly Weather Review. 130(1): 103-114. 2002. Margulis, S., McLaughlin, D., Entekhabi, D., and S. Dunne, “Land data assimilation and estimation of soil moisture using measurements from the Southern Great Plains 1997 field experiment” Water Resources Research, 38(12), 2002. Link Schuurmans, J.M., Troch, P.A., Veldhuizen, A.A., Bastiaanssen, W.G.M., Bierkens, M.F.P. ,“Assimilation of remotely sensed latent heat flux in a distributed hydrological model” Advances in Water Resources. 26(2). 2003. Wade T. Crow , and Eric F. Wood, The assimilation of remotely sensed soil brightness temperature imagery into a land surface model using Ensemble Kalman filtering: a case study based on ESTAR measurements during SGP97, Advances in Water Resources 26 (2003) 137–149 Jones, J.W., Graham, W.D, Wallach, D. , Bostick, M. and J. Koo, Estimating Soil Carbon Levels using an Ensemble Kalman Filter, Transactions of the ASAE, 47(1):331339, 2004. Hamid Moradkhani , Soroosh Sorooshian , Hoshin V. Gupta, Paul R. Houser, Dual state– parameter estimation of hydrological models using ensemble Kalman filter, Advances in Water Resources 28 (2005) 135–147 Susan C. Dunne, and Dara Entekhabi, Impact of Multiresolution Active and Passive Microwave Measurements on Soil Moisture Estimation Using the Ensemble Kalman Smoother, IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, NO. 4, April 2007 Koo, J., W.M. Bostick, J. B. Naab J. W. Jones, W.D. Graham and A.J. Gijsman, Estimating Soil Carbon in Agricultural Systems Using Ensemble Kalman Filter and DSSAT- Century, Transacations of the ASABE, 50(5): 1851-1865, 2007.

Valentijn R. N. Pauwels, Niko E. C. Verhoest, Gabrielle J. M. De Lannoy, Vincent Guissard,Cozmin Lucau, and Pierre Defourny, Optimization of a coupled hydrology–crop growth model through the assimilation of observed soil moisture and leaf area index values using an ensemble Kalman filter, Water Resources Research, Vol. 43, W04421, doi:10.1029/2006WR004942, 2007 H.J. Hendricks Franssen , W. Kinzelbach, Ensemble Kalman filtering versus sequential self-calibration for inverse modelling of dynamic groundwater flow systems, Journal of Hydrology 365 (2009) 261–274 Monsivais, A., W. Graham, J. Judge, and D. Agrawal, Effect of simultaneous stateparameter and forcing uncertainties on root-zone soil moisture for dynamic vegetation using EnKF, Advances in Water Resources, 33: 468–484, 2010. Flores, Alejandro N.; Bras, Rafael L.; Entekhabi, Dara Hydrologic data assimilation with a hillslope-scale-resolving model and L band radar observations: Synthetic experiments with the ensemble Kalman filter Water Resour. Res., Vol. 48, No. 8, W08509 http://dx.doi.org/10.1029/2011WR011500 15 August 2012 Kollat, J.B., Reed P.M. and Maxwell, R.M. Many-Objective Groundwater Monitoring Network Design Using Bias-Aware Ensemble Kalman Filtering, Evolutionary Optimization, and Visual Analytics. Water Resources Research, 47, W02529, doi:10.1029/2010WR009194, 2011. Applications of Particle Filtering: Dara Entekhabi, Hajime Nakamura, and Eni G. Njoku, Solving the Inverse Problem for Soil Moisture and Temperature Profiles by Sequential Assimilation of Multifrequency Remotely Sensed Observations, IEEE Transactions On Geoscience And Remote Sensing, Vol. 32, No. 2, March 1994 Moradkhani, H., Hsu, K.L., Gupta, H. and S. Sorooshian, “Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter” Water Resources Research. 41(5). 2005. Link Albrecht H. Weerts and Ghada Y. H. El Serafy, Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall-runoff models, Water Resources Research, VOL. 42, W09403, doi:10.1029/2005WR004093, 2006. Cedric Naud, David Makowski, Marie-Helene Jeuffroy, Application of an interacting particle filter to improve nitrogen nutrition index predictions for winter wheat, ecological modelling 207 (2007) 251–263. Hamid Moradkhani, Hydrologic Remote Sensing and Land Surface Data Assimilation, Sensors 2008, 8, 2986-3004; DOI: 10.3390/s8052986

Gene-Hua Crystal Ng, Dennis McLaughlin, Dara Entekhabi, and Bridget Scanlon, Using data assimilation to identify diffuse recharge mechanisms from chemical and physical data in the unsaturated zone, Water Resources Research, Vol. 45, W09409, doi:10.1029/2009WR007831, 2009 Gene‐Hua Crystal Ng, Dennis McLaughlin, Dara Entekhabi, and Bridget R. Scanlon, Probabilistic analysis of the effects of climate change on groundwater recharge , Water Resources Research, Vol. 46, W07502, doi:10.1029/2009WR007904, 2010 Nagarajan, K., J. Judge, W.D. Graham, and A Monsivais-Huertero, Particle Filter-based Assimilation Algorithms for Improved Estimation of Root-Zone Soil Moisture under Dynamic Vegetation Conditions, Volume 34, Issue 4, April 2011, Pages 433-447, ISSN 0309-1708, DOI: 10.1016/j.advwatres.2010.09.019. Applications of Generalized Likelihood Uncertainty Estimation: Keith Beven and Jim Freer, Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, Journal of Hydrology, Volume 249, Issues 1-4, , 1 August 2001, Pages 11-29. F. Pappenberger, K. Beven, M. Horritt and S. Blazkova, Uncertainty in the calibration of effective roughness parameters in HEC-RAS using inundation and downstream level observations, Journal of Hydrology, Volume 302, Issues 1-4, , 1 February 2005, Pages 46-69. Keith Beven, A manifesto for the equifinality thesis, Journal of Hydrology, Volume 320, Issues 1-2, 2006, p. 18-36. He, J., M. D. Dukes, J.W. Jones, and W. D. Graham, Applying GLUE for estimating ceres-maize genetic and soil parameters for sweet corn production, Transactions of the ASABE, Vol. 52(6): 1907-1921, 2009. He, J., J.W. Jones, W. D. Graham, and M. D. Dukes, Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method, Agricultural Systems, 103:256-264, 2010 Applications of 4-D Variational Methods: Reichle, R.H., D.B. McLaughlin, and D. Entekhabi, “Variational Data Assimilation of Microwave Radio brightness Observations for Land Surface Hydrology Applications”. IEEE Transactions on Geoscience and Remote Sensing. 39(8): 1708-1718, 2001. Eric Bélanger, Alain Vincent and Alexandre Fortin, Data Assimilation (4D-VAR) for Shallow-Water Flow: The Case of the Chicoutimi River, Visual Geosciences, Volume 8, Number 1, 2003.

Eric Belanger, Alain Vincent, Data assimilation (4D-VAR) to forecast flood in shallowwaters with sediment erosion, Journal of Hydrology 300 (2005) 114–125 X. Lai , J. Monnier , Assimilation of spatially distributed water levels into a shallowwater flood model. Part I: Mathematical method and test case, Journal of Hydrology 377 (2009) 1–11