Stochastic calibration and learning in nonstationary hydroeconomic models

Water Resources Research

Volumn 50, Issue 5, 2014, Pages 3976-3993

  Maneta, M P ^a   Howitt, R ^b  

^a UNIVERSITY OF MONTANA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

agricultural production; ensemble Kalman filter; hydroeconomics; positive mathematical programming; regularization; water management

Indexed keywords

AGRICULTURE; CALIBRATION; CLIMATE MODELS; DATA PROCESSING; KALMAN FILTERS; MATHEMATICAL PROGRAMMING; STOCHASTIC SYSTEMS; WATER MANAGEMENT;

AGRICULTURAL PRODUCTIONS; ENSEMBLE KALMAN FILTER; HYDROECONOMICS; POSITIVE MATHEMATICAL PROGRAMMING; REGULARIZATION;

STOCHASTIC MODELS;

AGRICULTURAL PRODUCTION; ALGORITHM; CALIBRATION; DATA ASSIMILATION; HYDROLOGICAL MODELING; KALMAN FILTER; PARAMETERIZATION; REMOTE SENSING; STOCHASTICITY; WATER MANAGEMENT; WATER RESOURCE;

EID: 84900934960     PISSN: 00431397     EISSN: 19447973     Source Type: Journal    
DOI: 10.1002/2013WR015196     Document Type: Article

References (57)

1. Allen, R. G., M. Tasumi, and, R. Trezza, (2007), Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-Model, J. Irrig. Drain. Eng., 133, 380-394, doi: 10.1061/(asce)0733- 9437(2007)133:4(380). (Pubitemid 47102986)
2. Anderson, B. D. O., and, J. B. Moore, (2005), Optimal Filtering, Dover, New York.
3. Aster, R. C., B. Borchers, and, C. H. Thurber, (2005), Parameter Estimation and Inverse Problems, Int. Geophys. Ser., Elsevier, Oxford, U. K.
4. Bastiaanssen, W. G. M., M. Menenti, R. A. Feddes, and, A. A. M. Holtslag, (1998), A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation, J. Hydrol., 213, 198-212, doi: 10.1016/s0022-1694(98)00253-4. (Pubitemid 29020066)
5. Becker-Reshef, I., et al. (2010), Monitoring global croplands with coarse resolution earth observations: The global agriculture monitoring (GLAM) project, Remote Sens., 2, 1589-1609, doi: 10.3390/rs2061589.
6. Blanco-Gutiérrez, I., C. Varela-Ortega, and, G. Flichman, (2011), Cost-effectiveness of groundwater conservation measures: A multi-level analysis with policy implications, Agric. Water Manage., 98, 639-652, doi: 10.1016/j.agwat.2010.10.013.
7. Brouwer, R., and, M. Hofkes, (2008), Integrated hydro-economic modelling: Approaches, key issues and future research directions, Ecol. Econ., 66, 16-22, doi: 10.1016/j.ecolecon.2008.02.009.
8. Booker, J. F,., R. E. Howitt, A. M. Michelsen, and R. A. Young (2012), Economics and the modeling of water resources and policies, Natural Resource Modeling, 25 (1), 168-218, doi: 10.1111/j.1939-7445.2011.00105.x.
9. Cai, X., and, D. L. B. Wang, (2006), Calibrating holistic water resources-economic models, J. Water Resour. Plann. Manage., 132, 414-423. (Pubitemid 44781799)
10. Cai, X. M., (2008), Implementation of holistic water resources-economic optimization models for river basin management-Reflective experiences, Environ. Modell. Software, 23, 2-18.
11. Characklis, G. W., R. C. Griffin, and, P. B. Bedient, (1999), Improving the ability of a water market to efficiently manage drought, Water Resour. Res., 35, 823-831. (Pubitemid 29111811)
12. Connell-Buck, C. R., J. Medellín-Azuara, J. R. Lund, and, K. Madani, (2011), Adapting California's water system to warm vs. dry climates, Clim. Change, 109, 133-149, doi: 10.1007/s10584-011-0302-7.
13. Cortignani, R., and, S. Severini, (2009), Modeling farm-level adoption of deficit irrigation using positive mathematical programming, Agric. Water Manage., 96, 1785-1791, doi: 10.1016/j.agwat.2009.07.016.
14. Dee, D., (2005), Bias and data assimilation, Q. J. R. Meteorol. Soc., 131, 3323-3343, doi: 10.1256/qj.05.137.
15. Department of Water Resources (2009), California Water Plan Update, Sacramento, Calif.
16. Doraiswamy, P. C., T. R. Sinclair, S. Hollinger, B. Akhmedov, A. Stern, and, J. Prueger, (2005), Application of MODIS derived parameters for regional crop yield assessment, Remote Sens. Environ., 97, 192-202, doi: 10.1016/j.rse.2005.03.015. (Pubitemid 41026635)
17. Draper, A., M. Jenkins, K. Kirby, J. Lund, and, R. Howitt, (2003), Economic-engineering optimization for California water management, J. Water Resour. Plann. Manage., 129, 155-164, doi: 10.1061/(ASCE)0733-9496(2003)129: 3(155). (Pubitemid 36568219)
18. Drécourt, J. P., H. Madsen, and, D. Rosbjerg, (2006), Bias aware Kalman filters: Comparison and improvements, Adv. Water Resour., 29, 707-718, doi: 10.1016/j.advwatres.2005.07.006.
19. Evensen, G., (2003), The ensemble Kalman filter: Theoretical formulation and practical implementation, Ocean Dyn., 53, 343-367, doi: 10.1007/s10236-003- 0036-9.
20. Frahan, B. H., De, J., Buysse, P., Polomé, B., Fernagut, O., Harmignie, L., Lauwers, and G., Van Huylenbroeck, (2007), Positive mathematical programming for agricultural and environmental policy analysis? Review and practice, in Handbook of Operations Research in Natural Resources, edited by, A. Weintraub, et al., pp. 129-154, Springer, N. Y.
21. Frisvold, G. B., and, K. Konyar, (2012), Less water: How will agriculture in Southern Mountain states adapt? Water Resour. Res., 48, W05534, doi: 10.1029/2011WR011057.
22. Golan, A., G. G. Judge, and, D. J. Miller, (1996), Maximum Entropy Econometrics: Robust Estimation with Limited Data, John Wiley, Chichester, U. K.
23. Graveline, N., and, P. Merel, (2014), Intensive and extensive margin adjustments to water scarcity in France's cereal belt, Eur. Rev. Agric. Econ., in press.
24. Harou, J., J. Medellin, T. Zhu, S. Tanaka, J. Lund, S. Stine, M. Jenkins, and, M. Olivares, (2006), Extreme drought and water supply management in California, in ASCE World Environmental and Water Resources Institute Congress 2006, edited by, G. Randall, pp. 278, Am. Soc. of Civil Eng., Omaha, Nebr.
25. Harou, J. J., M. Pulido-Velazquez, D. E. Rosenberg, J. Medellin-Azuara, J. R. Lund, R. E. Howitt, and, J. Medellín-Azuara, (2009), Hydro-economic models: Concepts, design, applications, and future prospects, J. Hydrol., 375, 627-643, doi: 10.1016/j.jhydrol.2009.06.037.
26. Heckelei, T., and, W. Britz, (2000), Positive mathematical programming with multiple data points? A cross-sectional estimation procedure multiples?, Cah. Econ. Sociol. Rural, 57, 28-50.
27. Heckelei, T., and, W. Britz, (2001), Concept and explorative application of an EU-wide regional agricultural sector model (CAPRI-Project), in Agricultural Sector Modelling and Policy, Information Systems: Proceedings of the 65th European Seminar of the European Association of Agricultural Economists, Bonn, Germany, pp. 281-290.
28. Heckelei, T., and, W. Britz, (2005), Models based on positive mathematical programming: State of the art and further extensions, in Modelling Agricultural Policies: State of the Art and New Challenges, Proceedings of the 89th European Seminar of the European Assoiciation of Agricultural Economists (EAAE), pp. 48-73, Parma, Italy.
29. Heckelei, T., W. Britz, and, Y. Zhang, (2012), Positive mathematical programming approaches-Recent developments in literature and applied modelling, Bio-Based Appl. Econ., 1, 109-124.
30. Howitt, R. E., (1995), Positive mathematical programming, Am. J. Agric. Econ., 77, 329-342.
31. Howitt, R. E., J. Medellín-Azuara, D. MacEwan, and, J. R. Lund, (2012), Calibrating disaggregate economic models of agricultural production and water management, Environ. Modell. Software, 38, 244-258, doi: 10.1016/j.envsoft.2012.06.013.
32. Janssen, S., et al. (2010), A generic bio-economic farm model for environmental and economic assessment of agricultural systems, Environ. Manage., 46, 862-877, doi: 10.1007/s00267-010-9588-x.
33. Johnson, D. M., and, R. Mueller, (2010), The 2009 cropland data layer, Photogramm. Eng. Remote Sens., 76, 1202-1205.
34. Koundouri, P., (2004), Current issues in the economics of groundwater resources management, J. Econ. Surv., 18, 703-740. (Pubitemid 39631764)
35. Liu, F., (2000), Bayesian Time Series: Analysis Methods Using Simulation Based Computation, Duke Univ., Durham, N. C.
36. Lobell, D. B., G. P. Asner, J. I. Ortiz-Monasterio, and, T. L. Benning, (2003), Remote sensing of regional crop production in the Yaqui Valley, Mexico: Estimates and uncertainties, Agric. Ecosyst. Environ., 94, 205-220, doi: 10.1016/S0167-8809(02)00021-X. (Pubitemid 36137483)
37. Louhichi, K., A. Kanellopoulos, S. Janssen, G. Flichman, M. Blanco, H. Hengsdijk, T. Heckelei, P. Berentsen, A. O. Lansink, and, M. Van Ittersum, (2010), FSSIM, a bio-economic farm model for simulating the response of EU farming systems to agricultural and environmental policies, Agric. Syst., 103, 585-597, doi: 10.1016/j.agsy.2010.06.006.
38. Maneta, M. P., M. O. Torres, W. W. Wallender, S. Vosti, R. E. Howitt, L. Rodrigues, L. H. Bassoi, and, S. Panday, (2009), A spatially distributed hydroeconomic model to assess the effects of drought on land use, farm profits, and agricultural employment, Water Resour. Res., 45, W11412, doi: 10.1029/2008WR007534.
39. Medellín-Azuara, J., J. J. Harou, M. A. Olivares, K. Madani, J. R. Lund, R. E. Howitt, S. K. Tanaka, and, M. W. Jenkins, (2008), Adaptability and adaptations of California's water supply system to dry climate warming, Clim. Change, 87, S75-S90.
40. Medellín-Azuara, J., R. E. Howitt, D. J. MacEwan, and, J. R. Lund, (2011), Economic impacts of climate-related changes to California agriculture, Clim. Change, 109, 387-405, doi: 10.1007/s10584-011-0314-3.
41. Moradkhani, H., S. Sorooshian, H. V. Gupta, and, P. R. Houser, (2005), Dual state-parameter estimation of hydrological models using ensemble Kalman filter, Adv. Water Resour., 28, 135-147, doi: 10.1016/j.advwatres.2004.09.002. (Pubitemid 40102390)
42. Muchow, R. C., T. R. Sinclair, and, J. M. Bennett, (1990), Temperature and solar radiation effects on potential maize yield across locations, Agron. J., 82, 338, doi: 10.2134/agronj1990.00021962008200020033x.
43. Muth, J. F., (1961), Rational expectations and the theory of price movements, Econometrica, 29, 315-335, doi: 10.2307/1909635.
44. Myneni, R. B., et al. (2002), Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data, Remote Sens. Environ., 83, 214-231, doi: 10.1016/S0034-4257(02)00074-3. (Pubitemid 35307470)
45. Pappenberger, F., and, K. J. Beven, (2006), Ignorance is bliss: Or seven reasons not to use uncertainty analysis, Water Resour. Res., 42, W05302, doi: 10.1029/2005WR004820.
46. Paris, Q., and, R. E. L. B. Howitt, (1998), An analysis of ill-posed production problems using maximum entropy, Am. J. Agric. Econ., 80, 124-138. (Pubitemid 128439829)
47. Ringler, C., N. V. Huy, and, S. L. B. Msangui, (2006), Water allocation policy modeling for the Dong Nai River Basin: An integrated perspective, J. Am. Water Resour. Assoc., 42, 1465-1482. (Pubitemid 46155252)
48. Sinclair, T., and, R. C. Muchow, (1999), Radiation use efficiency, Adv. Agron., 65, 215-265.
49. Teixeira, A. H., de C., W. G. M. Bastiaanssen, M. D. Ahmad, and, M. G. Bos, (2009), Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the Low-Middle São Francisco River basin, Brazil: Part B: Application to the regional scale, Agric. For. Meteorol., 149, 477-490, doi: 10.1016/j.agrformet.2008.09.014.
50. Torres, M., de O., M. Maneta, R. Howitt, S. A. Vosti, W. W. Wallender, L. H. Bassoi, and, L. N. Rodrigues, (2011), Economic impacts of regional water scarcity in the São Francisco River Basin, Brazil: An application of a linked hydro-economic model, Environ. Dev. Econ., 21, 1-22, doi: 10.1017/S1355770X11000362.
51. US Bureau of Reclamation (2011), North of Delta Offstream Storage (NODOS) Analysis and SWAP Model Application to Natinal Economic Development (NED), US Bur. of Reclam. Mid-Pac. Reg., Sacramento, Calif.
52. Varela-Ortega, C., J. M. Sumpsi, A. Garrido, M. Blanco, and, E. Iglesias, (1998), Water pricing policies, public decision making and farmers' response: Implications for water policy, Agric. Econ., 19, 193-202. (Pubitemid 28450863)
53. Varian, H. R., (1992), Microeconomic Analysis, 3rd ed., WW Norton, New York.
54. Wang, D., and, X. Cai, (2007), Optimal estimation of irrigation schedule-An example of quantifying human interferences to hydrologic processes, Adv. Water Resour., 30, 1844-1857, doi: 10.1016/j.advwatres.2007.02.006. (Pubitemid 46712125)
55. Wang, D., and, X. Cai, (2008), Robust data assimilation in hydrological modeling-A comparison of Kalman and H-infinity filters, Adv. Water Resour., 31, 455-472, doi: 10.1016/j.advwatres.2007.10.001. (Pubitemid 351218073)
56. Ward, F. A., J. F. Booker, and, A. M. Michelsen, (2006), Integrated economic, hydrologic, and institutional analysis of policy responses to mitigate drought impacts in Rio Grande Basin, J. Water Resour. Plann. Manage., 132, 488-502. (Pubitemid 44781806)
57. West, M., (1993), Mixture models, Monte Carlo, Bayesian updating and dynamic models, Comput. Sci. Stat., 25, 325-333.