The Role of Prior Model Calibration on Predictions with Ensemble Kalman Filter

Ground Water

Volumn 49, Issue 6, 2011, Pages 845-858

  Huber, E ^a   Hendricks Franssen, H J ^a   Kaiser, H P ^b   Stauffer, F ^a  

^a ETH ZURICH   (Switzerland)

^b Water Supply of Zurich (WVZ)   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ASSIMILATION; ENSEMBLE KALMAN FILTER; ERROR REDUCTION; GROUNDWATER FLOW MODEL; GROUNDWATER FLOW SYSTEMS; HEAD DATA; HEAD LOCATION; HISTORICAL INFORMATION; HYDRAULIC HEAD PREDICTIONS; MEAN ABSOLUTE ERROR; MEASUREMENT DATA; MODEL CALIBRATION; MODEL PREDICTION; MONITORING LOCATIONS; ON-LINE SENSOR; PREDICTION ERRORS; REAL TIME; STEADY STATE; SWITZERLAND; TRANSIENT STATE;

AQUIFERS; CALIBRATION; DATA PROCESSING; GROUNDWATER FLOW; GROUNDWATER RESOURCES; KALMAN FILTERS; MONITORING;

FORECASTING;

GROUND WATER;

CALIBRATION; DATA ASSIMILATION; ERROR ANALYSIS; FLOW MODELING; GROUNDWATER FLOW; HYDROLOGICAL MODELING; KALMAN FILTER; MONITORING SYSTEM; NUMERICAL MODEL; PREDICTION; REAL TIME; WATER LEVEL;

ARTICLE; ENVIRONMENTAL MONITORING; THEORETICAL MODEL;

ENVIRONMENTAL MONITORING; GROUNDWATER; MODELS, THEORETICAL;

SWITZERLAND;

EID: 79952206028     PISSN: 0017467X     EISSN: 17456584     Source Type: Journal    
DOI: 10.1111/j.1745-6584.2010.00784.x     Document Type: Article

References (40)

1. Alcolea, A., J. Carrera, and A. Medina 2006. Pilot points method incorporating prior information for solving the groundwater flow inverse problem. Advances in Water Resources 29, no. 11: 1678-1689.
2. Allen, R.G., L.S. Pereira, D. Raes, and M. Smith 1998. Crop evapotranspiration-Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56 (accessed August 12, 2010).
3. Barnhart, K., I. Urteaga, Q. Han, A. Jayasumana, and T. Illangasekare 2010. On integrating groundwater transport models with wireless sensor networks. Ground Water 48, no. 5: 771-780.
4. Burgers, G., P.J. van Leeuwen, and G. Evensen 1998. Analysis scheme in the Ensemble Kalman filter. Monthly Weather Review 126: 1719-1724.
5. 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 Resources Research 22, no. 2: 199-210.
6. Carrera, J., A. Alcolea, A. Medina, J. Hidalgo, and L.J. Slooten 2005. Inverse problem in hydrogeology. Hydrogeology Journal 13, no. 1: 206-222.
7. Chen, Y., and D. Zhang 2006. Data assimilation for transient flow in geologic formations via ensemble Kalman filter. Advances in Water Resources 29, no. 8: 1107-1122.
8. Delta h, Ingenieurgesellschaft GmbH. 2006. Spring 3.31.
9. Doppler, T., H.-J. Hendricks-Franssen, H.-P. Kaiser, U. Kuhlmann, and F. Stauffer 2007. Field evidence of a dynamic leakage coefficient for modelling river aquifer interactions. Journal of Hydrology 347, no. 1-2: 177-187.
10. Engeler, I., H.-J. Hendricks-Franssen, R. Müller, and F. Stauffer 2011. The importance of coupled modelling of variable saturated groundwater flow-heat transport for assessing river-aquifer interactions. Journal of Hydrology. DOI: 10.1016/j.hydrol.2010.12.007.
11. Evensen, G. 1992. Using the extended Kalman filter with a multilayer quasi-geostrophic ocean model. Journal of Geophysical Research 97, no. C11: 17905-17924.
12. Evensen, G. 1994. Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research 99, no. C5: 10143-10162.
13. Freyberg, D.L. 1988. An exercise in ground-water model calibration and prediction. Ground Water 26, no. 3: 350-360.
14. Gómez-Hernández, J.J., and A.G. Journel 1993. Joint sequential simulation of multi-Gaussian fields. In Geostatistics Tróia '92, vol. 1, ed. A. Soares, 85-94. Dordrecht (the Netherlands): Kluwer Academic Publishers.
15. Gómez-Hernández, J.J., A. Sahuquillo, and J.E. Capilla 1997. Stochastic simulation of transmissivity fields conditional to both transmissivity and piezometric data. 1. Theory. Journal of Hydrology 1-4, no. 203: 162-174.
16. Hendricks-Franssen, H.-J., and W. Kinzelbach 2008. Real-time groundwater flow modeling with the ensemble Kalman filter: Joint estimation of states and parameters and the filter inbreeding problem. Water Resources Research 44: W09408. DOI: 10.1029/2007WR006505.
17. Hendricks-Franssen, H.-J., and W. Kinzelbach 2009. Ensemble Kalman filtering versus sequential self-calibration for inverse modelling of dynamic groundwater flow systems. Journal of Hydrology 365, no. 3-5: 261-274.
18. Hendricks-Franssen, H.-J., J.J. Gómez-Hernández, A. Sahuquillo, and J.E. Capilla 1999. Joint simulation of transmissivity and storativity fields conditional to hydraulic head data. Advances in Water Resources 23, no. 1: 1-13.
19. Hendricks-Franssen, H.-J., A. Alcolea, M. Riva, M. Bakr, N. van de 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. Advances in Water Resources 32, no. 6: 851-872.
20. Hendricks-Franssen, H.-J., H.P. Kaiser, U. Kuhlmann, G. Bauser, R. Müller, F. Stauffer, and W. Kinzelbach 2011. Operational real-time modeling with EnKF of variably saturated subsurface flow including stream-aquifer interaction and parameter updating. Water Resources Research. In press.
21. Kempf, T., M. Freimoser, P. Haldimann, V. Longo, E. Müller, C. Schindler, G. Styger, and L. Wyssling 1986. Die Grundwasservorkommen im Kanton Zürich. Beiträge zur Geologie der Schweiz. Geotechnische Serie. Bern: Kümmerly & Frey.
22. Kitanidis, P.K., and E.G. Vomvoris 1983. A geostatistical approach to the inverse problem in groundwater modeling (steady state) and one-dimensional simulations. Water Resources Research 19, no. 3: 677-690.
23. Koorevaar, P., G. Menelik, and C. Dirksen 1983. Elements of Soil Physics. Amsterdam, The Netherlands: Elsevier Science Publishers.
24. Kowalsky, M.B., S. Finsterle, and Y. Rubin 2004. Estimating flow parameter distributions using ground-penetrating radar and hydrological measurements during transient flow in the vadose zone. Advances in Water Resources 27, no. 6: 583-599.
25. La Venue, A.M., B.S. RamaRao, G. De Marsily, and M.G. Marietta 1995. Pilot point methodology for automated calibration of an ensemble of conditionally simulated transmissivity fields: 2. Application. Water Resources Research 31, no. 3: 495-516.
26. Liu, G., Y. Chen, and D. Zhang 2008. Investigation of flow and transport processes at the MADE site using ensemble Kalman filter. Advances in Water Resources 31, no. 7: 975-986.
27. de Marsily, G. 1978. De l'identification des systèmes hydrogéologiques (tome 1), Université Pierre et Marie Curie-Paris VI, Ph.D. thesis, 58-130.
28. MeteoSchweiz. 2009. Bundesamt für Meteorologie und Klimatologie MeteoSchweiz. (accessed August 12, 2010).
29. Nash, J.E., and J.V. Sutcliffe 1970. River flow forecasting through conceptual models, Part I-A discussion of principles. Journal of Hydrology 10, no. 3: 282-290.
30. Nowak, W. 2009. Best unbiased ensemble linearization and the quasi-linear Kalman ensemble generator. Water Resources Research 45, no. 4. DOI: 10.1029/2008WR007328.
31. Renard, Ph., G. Le Loc'h, E. Ledoux, G. de Marsily, and R. Mackay 2000. A fast algorithm for the estimation of the equivalent hydraulic conductivity of heterogeneous porous media. Water Resources Research 36, no. 12: 3567-3580.
32. Reichel, G., and R. Fäh 1995. Programmpaket FLORIS-Grundlagen und praktische Anwendungen, Seminar Mathematische Modelle offener Gerinne, November 21, 1995, Wien, 33-52.
33. Rings, J., J.A. Huisman, and H. Vereecken 2010. Coupled hydrogeophysical parameter estimation using a sequential Bayesian approach. Hydrology and Earth System Sciences 14, no. 3: 545-556.
34. Rubin, Y., and S.S. Hubbard, ed. 2005. Hydrogeophysics. Dordrecht, The Netherlands: Springer.
35. Schultz, G.A. 1967. Die Anwendung von Computer-Programmen für das Unit Hydrograph Verfahren am Beispiel eines Donauzubringers. Paper for the Fifth Conference of the Danube River Countries, Bratislava, CSSR.
36. Swiss Federal Office for Environment. 2008. Hydrologisches Jahrbuch der Schweiz 2007. No. 0824 in Umwelt-Wissen, Bundesamt für Umwelt, Bern, 576.
37. Tonkin, M.J., and J. Doherty 2005. A hybrid regularized inversion methodology for highly parameterized environmental models. Water Resources Research 41, no. 10: W10412. DOI: 10.1029/2005WR003995.
38. Van Geer, F.C., C.B.M. Te Stroet, and Z. Yangxiao 1991. Using Kalman filtering to improve and quantify the uncertainty of numerical groundwater simulations: 1. The role of system noise and its calibration. Water Resources Research 27, no. 8: 1987-1994.
39. Yeh, T.-C.J., and S. Liu 2000. Hydraulic tomography: Development of a new aquifer test method. Water Resources Research 36, no. 8: 2095-2105.
40. Yeh, T.-C.J., J. Xiang, R.M. Suribhatla, K.-C. Hsu, C.-H. Lee, and J.-C. Wen 2009. River stage tomography: A new approach for characterizing groundwater basins. Water Resources Research 45, no. 5: W05409. DOI: 10.1029/2008WR007233.