Spatio-temporal drought forecasting within Bayesian networks

Journal of Hydrology

Volumn 512, Issue , 2014, Pages 134-146

  Madadgar, Shahrbanou ^a   Moradkhani, Hamid ^a  

^a Portland State University   (United States)

Author keywords

Bayesian networks; Conditional probability; Copula; Drought forecast; Standardized Runoff Index

Indexed keywords

BAYESIAN NETWORKS; DISTRIBUTION FUNCTIONS; RUNOFF; WEATHER FORECASTING;

CONDITIONAL PROBABILITIES; COPULA; DISTRIBUTED-PARAMETER; MULTIVARIATE DISTRIBUTIONS; PHYSICALLY-BASED HYDROLOGIC MODELS; PROBABILISTIC FORECASTS; RUNOFF INDICES; SPATIOTEMPORAL CHARACTERISTICS;

DROUGHT;

BAYESIAN ANALYSIS; DROUGHT; HYDROLOGICAL MODELING; PROBABILITY; RUNOFF; SPATIOTEMPORAL ANALYSIS; UNCERTAINTY ANALYSIS; WEATHER FORECASTING;

COLORADO; GUNNISON RIVER; UNITED STATES;

EID: 84896131892     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2014.02.039     Document Type: Article

References (62)

1. Akaike H. A new look at the statistical model identification. IEEE Trans. Autom. Control 1974, 19(6):716-723.
2. Andreadis K.M., Lettenmaier D.P. Trends in 20th century over the continental United States. Geophys. Res. Lett. 2006, 33:L10403. 10.1029/2006GL025711.
3. Bárdossy A. Copula-based geostatistical models for groundwater quality parameters. Water Resour. Res. 2006, 42:W11416. 10.1029/2005WR004754.
4. Barros A.P., Bowden G.J. Toward long-lead operational forecasts of drought: an experimental study in the Murray-Darling River Basin. J. Hydrol. 2008, 357:349-367.
5. Burke E.J., Perry R.H.J., Brown S.J. An extreme value analysis of UK drought and projections of change in the future. J. Hydrol. 2010, 388:131-143.
6. Cancelliere A., Mauro G.D., Bonaccorso B., Rossi G. Drought forecasting using the Standardized Precipitation Index. Water Resour. Manage. 2007, 21:801-819.
7. Carbone G.J., Dow K. Water resource management and drought forecasts in South Carolina. J. Am. Water Resour. As. (JAWRA) 2005, 41(1):145-155.
8. Dai A. Drought under global warming: a review. WIREs Clim. Change 2011, 2:45-65. 10.1002/wcc.81.
9. DeChant C., Moradkhani H. Radiance data assimilation for operational snow and streamflow forecasting. Adv. Water Resour. 2011, 34:351-364.
10. DeChant C., Moradkhani H. Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation. Hydrol. Earth Syst. Sci. 2011, 15:3399-3410. 10.5194/hess-15-3399.
11. Duan Q., Sorooshian S., Gupta V.K. Optimal use of the SCE-UA global optimization method for calibrating watershed models. J. Hydrol. 1994, 158:265-284.
12. Dupuis D.J. Using copulas in hydrology: benefits, cautions, and issues. J. Hydrol. Eng. 2007, 12(4):381-393.
13. Embrechts P., Lindskog F., McNeil A.J. Modelling dependence with copulas and applications to risk management. Handbook of Heavy Tailed Distributions in Finance 2003, Elsevier Science, Amsterdam, The Netherlands. S.T. Rachev (Ed.).
14. Favre A.C., Adlouni S.E., Perreault L., Thiéonge N., Bobée B. Multivariate hydrological frequency analysis using copulas. Water Resour. Res. 2004, 40:W01101.
15. Genest C., Rémillard B. Validity of the parametric bootstrap for goodness-of-fit testing in semiparametric models. Annales de I'Institut Henri Poincaŕe. Probabilit́es et Statistiques 2008, 44(6):1096-1127.
16. Genest C., Rémillard B., Beaudoin D. Goodness-of-fit tests for copulas: a review and a power study. Ins.: Math. Econom. 2009, 44:199-213.
17. Ghosh S., Mujumdar P.P. Nonparametric methods for modeling GCM and scenario uncertainty in drought assessment. Water Resour. Res. 2007, 43(7). W07405-W07405.
18. Huang W.C., Chou C.C. Risk-based drought early warning system in reservoir operation. Adv. Water Resour. 2008, 31:649-660.
19. Hwang Y., Carbone G.J. Ensemble forecasts of drought indices using a conditional residual resampling technique. J. Appl. Meteor. Climatol. 2009, 48:1289-1301.
20. Joe H. Multivariate Models and Dependence Concepts 1997, Chapman & Hall, London.
21. Jung I., Chang H., Moradkhani H. Quantifying uncertainty in urban flooding analysis by combined effect of climate and land use change scenarios. Hydrol. Earth Syst. Sci. 2011, 15:617-633.
22. Jung I., Moradkhani H., Chang H. Uncertainty assessment of climate change impact for hydrologically distinct river basins. J. Hydrol. 2012, 466-467:73-87. http://dx.doi.org/10.1016/j.jhydrol.2012.08.002.
23. Kao S., Govindaraju R.S. Trivariate statistical analysis of extreme rainfall events via Plackett family of copulas. Water Resour. Res. 2008, 44:W02415. 10.1029/2007WR006261.
24. Kao S., Govindaraju R.S. A copula-based joint deficit index for droughts. J. Hydrol. 2010, 380:121-134.
25. Karl T., Quinlan F., Ezell D.S. Drought termination and amelioration: its climatological probability. J. Clim. Appl. Meteor. 1987, 26(9):1198-1209.
26. Kendall J.R., Dracup D.A. On the generation of drought events using an alternating renewal-reward model. Stoch. Hydrol. Hydraul. 1992, 6:55-68.
27. Leavesley, G.H., Lichty, R.W., Troutman, B.M., Saindon, L.G., 1983. Precipitation-Runoff Modeling System: User's Manual. U.S. Geological Survey Water-Resources Investigations 83-4238, 207 p.
28. Loaiciga H.A., Leipnik R.B. Stochastic renewal model of low-flow streamflow sequences. Stoch. Hydrol. Hydraul. 1996, 10:65-85.
29. Lohani V.K., Loganathan G.V. An early warning system for drought management using the palmer drought index. J. Am. Water Resour. As. 1997, 33(6):1375-1386.
30. Madadgar S., Moradkhani H. A Bayesian framework for probabilistic seasonal drought forecasting. J. Hydrometeorol. 2013, 14:1685-1705. special issue of Advances in Drought Monitoring, http://dx.doi.org/10.1175/JHM-D-13-010.1.
31. Madadgar S., Moradkhani H. Drought analysis under climate change using copula. J. Hydrol. Eng. 2011, http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000532.
32. Madadgar S., Moradkhani H., Garen D. Towards improved post-processing of hydrologic forecast ensembles. Hydrol. Process. 2012, http://dx.doi.org/10.1002/hyp.9562.
33. Moradkhani H., Meier M. Long-lead water supply forecast using large-scale climate predictors and independent component analysis. J. Hydrol. Eng. 2010, 15(10):744-762.
34. Moradkhani H., DeChant C.M., Sorooshian S. Evolution of ensemble data assimilation for uncertainty quantification using the particle filter-markov chain monte carlo method. Water Resour. Res. 2012, 48:W12520. 10.1029/2012WR012144.
35. Moradkhani H. Hydrologic remote sensing and land surface data assimilation. Sensors 2008, 8:2986-3004. 10.3390/s8052986.
36. McKee, T.B., Doesken, N.J., Kleist, J., 1993. The relationship of drought frequency and duration to time scales. In: 8th Conference on Applied Climatology, Am. Meteorol. Soc., Anaheim, Calif.
37. Mishra A.K., Desai V.R. Drought forecasting using stochastic models. Stoch. Env. Res. Risk A 2005, 19(5):326-339.
38. Mishra A.K., Desai V.R. Drought forecasting using feed-forward recursive neural network. Ecol. Modell. 2006, 198:127-138.
39. Mishra A.K., Singh V.P. A review of drought concepts. J. Hydrol. 2010, 391:202-216.
40. Moradkhani H., Baird R.G., Wherry S. Impact of climate change on floodplain mapping and hydrologic ecotones. J. Hydrol. 2010, 395:264-278.
41. Najafi M.R., Moradkhani H., Jung I. Assessing the uncertainties of hydrologic model selection in climate change impact studies. Hydrol. Process. 2011, 25(18):2814-2826.
42. Najafi M.R., Moradkhani H., Piechota T. Ensemble streamflow prediction: climate signal weighting vs. climate forecast system reanalysis. J. Hydrol. 2012, 442-443:105-116. http://dx.doi.org/10.1016/j.jhydrol.2012.04.003.
43. Nelsen R.B. An Introduction to Copulas 1999, Springer, New York.
44. Niemeyer, S., 2008. New drought indices, in: Drought Management: Scientific and Technological Innovations. In: Proceedings of the 1st International Conference "Drought management: Scientific and technological innovations, pp. 267-274.
45. NOAA, 2013. 2012-13 U.S. Winter Recap: Mixed Messages on Drought. ClimateWatch Magazine.
46. Palmer, W.C., 1965. Meteorologic Drought, U.S. Department of Commerce. Weather Bureau, Research Paper No. 45, 58 pp.
47. Pearl, J., 1985. Bayesian Networks: A Model of Self-Activated Memory for Evidential Reasoning (UCLA Technical Report CSD-850017). In: Proceedings of the 7th Conference of the Cognitive Science Society. University of California, Irvine, CA, pp. 329-334.
48. Peterson T.C., Stott P.A., Herring S. Explaining extreme events of 2011 from a climate perspective. Bull. Amer. Meteor. Soc. 2012, 93:1041-1067.
49. Risley J., Moradkhani H., Hay L., Markstrom S. Statistical comparisons of watershed-scale response to climate change in selected basins across the United States. Earth Interact. 2011, 15:1-26. 10.1175/2010EI364.1.
50. Salvadori G., De Michele C. Multivariate multiparameter extreme value models and return periods: a copula approach. Water Resour. Res. 2010, 46:W10501. 10.1029/2009WR009040.
51. Sheffield J., Wood E.F. Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations. Clim. Dynam. 2008, 31(1):79-105.
52. Shiau J.T. Fitting drought duration and severity with two-dimensional copulas. Water Resour. Manage. 2006, 20(5):795-815.
53. Shukla S., Steinemann A.C., Lettenmaier D.P. Drought monitoring for washington state: indicators and applications. J. Hydrometeorol. 2011, 12:66-83.
54. Shukla S., Wood A.W. Use of a standardized runoff index for characterizing hydrologic drought. Geophys. Res. Lett. 2008, 35:L02405. 10.1029/2007GL032487.3.
55. Sklar K. Fonctions de repartition 'a n Dimensions et Leura Marges. Publ. Inst. Stat. Univ. Paris 1959, 8. 229-231.3.
56. Steinemann A. Drought indicators and triggers: a stochastic approach to evaluation. J. Am. Water Resour. As. 2003, 39(5):1217-1233.
57. Steinemann A. Using climate forecasts for drought management. J. Appl. Meteor. Climatol. 2006, 45:1353-1361.
58. Thulasiraman K., Swamy M.N.S. Graphs: Theory and Algorithms 1992, John Wiley and Son.
59. Trenberth K.E. Framing the way to relate climate extremes to climate change. Clim. Change 2011, 115(2):283-290.
60. Wong G., Lambert M.F., Leonard M., Metcalfe A.V. Drought analysis using trivariate copulas conditional on climatic states. J. Hydrol. Eng. 2010, 15(2):129-141.
61. WWA and NIDIS, 2012. The 2012 Drought in Colorado, Utah and Wyoming: A July 2012 update from the Western Water Assessment and the National Integrated Drought Information System.
62. Zhang L., Singh V.P. Gumbel-hougaard copula for trivariate rainfall frequency analysis. J. Hydrol. Eng. 2007, 12(4):409-419.