Estimating annual precipitation for the Colorado River Basin using oceanic-atmospheric oscillations

Water Resources Research

Volumn 48, Issue 6, 2012, Pages

  Kalra, Ajay ^a,b   Ahmad, Sajjad ^a  

^a University of Nevada Las Vegas   (United States)

^b DESERT RESEARCH INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANNUAL PRECIPITATION; ATLANTIC MULTIDECADAL OSCILLATIONS; CLIMATE PATTERNS; CLIMATIC VARIABILITY; COLORADO RIVER BASIN; HYDROLOGIC RESPONSE; LEADTIME; MODELING FRAMEWORKS; MODIFIED SVM; MULTIVARIATE LINEAR REGRESSION MODEL; NORTH ATLANTIC OSCILLATIONS; PACIFIC DECADAL OSCILLATION; SVM MODEL; WATER MANAGERS; WESTERN UNITED STATES;

ATMOSPHERIC PRESSURE; CLIMATE MODELS; ESTIMATION; FORECASTING; NEURAL NETWORKS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES;

WATERSHEDS;

ARTIFICIAL NEURAL NETWORK; ATLANTIC MULTIDECADAL OSCILLATION; ATMOSPHERE-OCEAN COUPLING; CLIMATE MODELING; EL NINO-SOUTHERN OSCILLATION; HYDROLOGICAL RESPONSE; NORTH ATLANTIC OSCILLATION; PACIFIC DECADAL OSCILLATION; PRECIPITATION (CLIMATOLOGY); REGRESSION ANALYSIS; RIVER BASIN;

COLORADO BASIN [NORTH AMERICA];

EID: 84862883497     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2011WR010667     Document Type: Article

References (142)

1. Ahmad, S., and D. Prashar (2010), Evaluating municipal water conservation policies using a dynamic simulation model, Water Resour. Manage., 24(13), 3371-3395.
2. Ahmad, S., and S. P. Simonovic (2000), System dynamics modeling of reservoir operations for flood management, ASCE J. Comput. Civ. Eng., 14(3), 190-198.
3. Ahmad, S., and S. P. Simonovic (2001), Integration of heuristic knowledge with analytical tools for selection of flood control measures, Can. J. Civ. Eng., 28(2), 208-221. (Pubitemid 33082969)
4. Ahmad, S., and S. P. Simonovic (2004), Spatial system dynamics: A new approach for simulation of water resources systems, ASCE J. Comput. Civ. Eng., 18(4), 331-340. (Pubitemid 40475407)
5. Ahmad, S., and S. P. Simonovic (2005), An artificial neural network model for generating hydrograph from hydro-meteorological parameters, J. Hydrol., 315(1-4), 236-251. (Pubitemid 41719075)
6. Ahmad, S., and S. P. Simonovic (2006), An intelligent decision support system for management of floods, Water Resour. Manage., 20(3), 391-410.
7. Ahmad, S., A. Kalra, and H. Stephen (2010), Estimating soil moisture using remote sensing data: A machine learning approach, Adv. Water Resour., 33(1), 69-80.
8. ASCE Task Committee (2000a), Artificial neural networks in hydrology I: Preliminary concepts, J. Hydrol. Eng., 5(2), 115-123.
9. ASCE Task Committee (2000b), Artificial neural networks in hydrology II: Hydrologic applications, J. Hydrol. Eng., 5(2), 124-137.
10. Asefa, T., M. Kemblowski, M. McKee, and A. Khalil (2006), Multi-time scale stream flow predictions: The support vector machines approach, J. Hydrol., 318, 7-16. (Pubitemid 43120784)
11. Ashok, K., Z. Guan, and T. Yamagata (2001), Impact of the Indian Ocean dipole on the relationship between the Indian monsoon rainfall and ENSO, Geophys. Res. Lett., 28(23), 4499-4502, doi:10.1029/2001GL013294. (Pubitemid 34046092)
12. Ashok, K., Z. Guan, and T. Yamagata (2003), Influence of the Indian Ocean Dipole on the Australian winter rainfall, Geophys. Res. Lett., 30(15), 1821, doi:10.1029/2003GL017926.
13. Ashok, K., S. K. Behera, S. A. Rao, H. Weng, and T. Yamagata (2007), El Niño Modoki and its possible teleconnection, J. Geophys. Res., 112, C11007, doi:10.1029/2006JC003798.
14. Barlow, M., H. Cullen, and B. Lyon (2002), Drought in central and southwest Asia: La Nina, the warm pool, and Indian Ocean precipitation, J. Clim., 15(7), 697-700. (Pubitemid 34476086)
15. Bell, T. L. (1987), A space-time stochastic model of rainfall for satellite remote-sensing studies, Geophys. Res. Lett., 92(D8), 9631-9643.
16. Bjerknes, J. (1966), A possible response of the atmospheric Hadley circulation to equatorial anomalies of ocean temperature, Tellus, 18, 820-829.
17. Boser, B., I. Guyon, and V. Vapnik (1992), A training algorithm for optimal margin classifiers, in Annual Conference on Computational Learning Theory, pp. 144-152, ACM Press, Pittsburgh, Pa. (Pubitemid 23615470)
18. Brito-Castillo, L., A. Leyva-Contreras, A. V. Douglas, and D. Lluch-Belda (2002), Pacific decadal oscillation and the filled capacity of dams on the rivers of the Gulf of California continental watershed, Atmosfera, 15, 121-138. (Pubitemid 34490095)
19. California Dep. of Water Resources (2005), Colorado River Basin Climate: Paleo, Present, Future, Spec. Publ. of Assoc. Calif. Water Agencies and Colorado River Water Users Assoc. Conf., Calif. Dep. Water Res., Sacramento, Calif., 80 pp.
20. Cancelliere, A., D. Mauro, B. Bonaccorso, and G. Rossi (2007), Investigating the potential of NAO index to forecast droughts in Sicily, in Hydrology Days, edited by J. A. Ramirez, pp. 75-86, Colorado State Univ., Fort Collins, Colo.
21. Canon, J., J. Gonzalez, and J. Valdes (2007), Precipitation in the Colorado River Basin and its low frequency associations with PDO and ENSO signals, J. Hydrol., 333, 252-264. (Pubitemid 46053958)
22. Carrier, C., A. Kalra, and S. Ahmad (2011), Using proxy reconstructions for streamflow forecasting, in World Environmental and Water Resources Congress: Bearing Knowledge for Sustainability, edited by R. E. Beighley II and M. W. Killgore, pp. 3124-3133, Palm Springs, Calif.
23. Cayan, D. R., M. D. Dettinger, H. F. Diaz, and N. E. Graham (1998), Decadal climate variability of precipitation over western North America, J. Clim., 11(12), 3148-3166. (Pubitemid 29223894)
24. Cayan, D. R., K. T. Redmond, and L. G. Riddle (1999), ENSO and hydro-logic extremes in the western United States, J. Clim., 12, 2881-2893. (Pubitemid 30039782)
25. Chowdhury, S., and A. Sharma (2009), Multisite seasonal forecast of arid river flows using a dynamic model combination approach, Water Resour. Res., 45, W10428, doi:10.1029/2008WR007510.
26. Colle, B. A. (2004), Sensitivity of Orographic precipitation to changing ambient conditions and terrain geometries: An idealized modeling perspective, J. Atmos. Sci., 61, 588-606. (Pubitemid 38504578)
27. Coulibaly, P. (2006), Spatial and temporal variability of Canadian seasonal precipitation (1900-2000), Adv. Water Resour., 29(12), 1846-1865. (Pubitemid 44567357)
28. Coulibaly, P., F. Anctil, P. Rasmussen, and B. Bobee (2000), A recurrent neural networks approach using indices of low-frequency climatic variability to forecast regional annual runoff, Hydrol. Processes, 14, 2755-2777.
29. Dawadi, S., and S. Ahmad (2012), Changing Climatic Conditions in the Colorado River Basin: Implications for Water Resources Management, J. Hydrol., 430-431, 127-141, doi:10.1016/j.jhydrol.2012.02.010.
30. Diaz, H. F., and G. N. Kiladis (1992), Atmospheric teleconnection associated with the extreme phase of the Southern Oscillation, in El Nino: Historical and Paleoclimatic Aspects of the Southern Oscillation, edited by H. F. D. A. V. Markgraf, pp. 7-28, Cambridge Univ. Press, New York. (Pubitemid 23379647)
31. Diaz, H. F., M. P. Hoerling, and J. K. Eischeid (2001), ENSO variability, teleconnections and climate change, Int. J. Climatol., 21, 1845-1862. (Pubitemid 34161909)
32. Dibike, Y. B., D. Solomatine, and M. B. Abbott (1999), On the encapsulation of numerical hydraulic models in artificial neural network, J. Hydraul. Res., 37(2), 147-161.
33. Dibike, Y. B., S. Velickov, D. Solomatine, and M. B. Abbott (2001), Model induction with support vector machines: Introduction and application, J. Comput. Civ. Eng., 15(3), 208-216. (Pubitemid 32583199)
34. Dickson, R. R., T. J. Osborn, J. W. Hurrell, J. Meincke, J. Blindheim, B. Adlandsvik, T. Vinje, G. Alekseev, and W. Maslowski (2000), The Arctic Ocean response to the North Atlantic oscillation, J. Clim., 13, 2671-2696.
35. Dracup, J. A., and E. Kahya (1994), The relationship between US stream-flow and La Nina, Water Resour. Res., 30(7), 2133-2141.
36. Efron, B. (1979), Bootstrap methods: Another look at the jacknife, Ann. Stat., 7, 1-26.
37. Ellis, A. W., G. B. Goodrich, and G. M. Garfin (2010), A hydroclimatic index for examining patterns of drought in the Colorado River Basin, Int. J. Climatol., 30, 236-255.
38. Enfield, D. B., A. M. Mestas-Nunez, and P. J. Trimble (2001), The Atlantic multidecadal oscillation and its relation to rainfall and river flows in the continental U.S., Geophys. Res. Lett., 28, 2077-2080. (Pubitemid 34039420)
39. Feng, S., and Q. Hu (2007), Changes in winter snowfall/precipitation ratio in the contiguous United States, J. Geophys. Res., 112, D15109, doi:10.1029/2007JD008397. (Pubitemid 47534404)
40. Forsee, W. J., and S. Ahmad (2011), Evaluating urban stormwater infrastructure design in response to projected climate change, J. Hydrol. Eng., 16(11), 865-873, doi:10.1061/ASCE-HE.1943-5584.0000383.
41. Geisser, S. (1975), The predictive sample reuse method with applications, J. Am. Stat. Assoc., 70, 320-328.
42. Gershunov, A., and T. P. Barnett (1998), Interdecadal modulation of ENSO telecommunications, Bull. Am. Meteorol. Soc., 79, 2715-2726.
43. Giannini, A., M. A. Cane, and Y. Kushnir (2001), Interdecadal changes in the ENSO teleconnection to the Caribbean region and the North Atlantic oscillation, J. Clim., 14, 2867-2879. (Pubitemid 33045288)
44. Gill, M. K., T. Asefa, M. Kemblowski, and M. McKee (2006), Soil moisture prediction using support vector machines, J. Am. Water Resour. Assoc., 42(4), 1033-1046. (Pubitemid 44870396)
45. Grantz, K., B. Rajagopalan, M. P. Clark, and E. A. Zagona (2005), A technique for incorporating large-scale climate information in basin-scale ensemble streamflow forecasts, Water Resour. Res., 41, W10410, doi:10.1029/2004WR003467. (Pubitemid 41705695)
46. Guenni, L., and A. Bardossy (2002), A two steps disaggregation method for highly seasonal rainfall, Stoch. Environ. Res. Risk Assess., 16, 188-206. (Pubitemid 34922428)
47. Gupta, H. V., S. Sorooshian, and P. O. Yapo (1999), Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration, J. Hydrol. Eng., 4(2), 135-143. (Pubitemid 29340857)
48. Guttman, N. B., and R. G. Quayle (1996), A historical perspective of U.S. climate divisions, Bull. Am. Meteorol. Soc., 77, 293-303.
49. Gutzler, D. S., D. M. Kann, and C. Thornbrugh (2002), Modulation of ENSO-based long-lead outlooks of Southwestern U.S. winter precipitation by the pacific decadal oscillation, Bull. Am. Meteorol. Soc., 17, 1163-1172. (Pubitemid 36302904)
50. Guyon, I., B. Boser, and V. Vapnik (1993), Automatic capacity tuning of very large VC-dimension classifiers, in Advances in Neural Information Processing Systems, edited by S. J. Hanson, J. D. Cowan, and C. L. Giles, pp. 147-155, Morgan Kaufmann Publ., San Francisco, Calif.
51. Hamlet, A. F., P. W. Mote, M. P. Clark, and D. P. Lettenmaier (2005), Effects of temperature and precipitation variability on snowpack trends in the western United States, Am. Meteorol. Soc., 18, 4545-4561. (Pubitemid 41805488)
52. Haykin, S. (2003), Neural Networks: A comprehensive foundation, 4th edition, Pearson Educ., Singapore.
53. Hidalgo, H. G., and J. A. Dracup (2003), ENSO and PDO effects on hydro-climatic variations of the Upper Colorado River Basin, J. Hydrome-teorol., 4, 5-23. (Pubitemid 36400558)
54. Higgins, R. W., W. A. Leetmaa, Y. Xue, and A. Barnston (2000), Dominant factors influencing the seasonal predictability of U.S. precipitation and surface air temperature, J. Clim., 13, 3994-4017.
55. Hsu, K.-L., H. V. Gupta, and S. Sorooshian (1995), Artificial neural network modeling of the rainfall-runoff process, Water Resour. Res., 31(10), 2517-2530. (Pubitemid 26475080)
56. Hsu, K. L., X. Gao, S. Sorooshian, and H. V. Gupta (1997), Precipitation estimation from remotely sensed information using artificial neural networks, J. Appl. Meteorol., 36, 1176-1190.
57. Hu, Q., and S. Feng (2001), Variations of teleconnection of ENSO and interannual variation in summer rainfall in the central United States, J. Clim., 14, 2469-2480. (Pubitemid 33045328)
58. Hurrell, J. W. (1995), Decadal trends in the North Atlantic Oscillation: Regional temperatures and precipitation, Science, 269(5224), 676-679. (Pubitemid 26439360)
59. Kahya, E., and J. A. Dracup (1993), U.S. streamflow patterns in relation to the El Nino/southern oscillation, Water Resour. Res., 29(8), 2491-2503, doi:10.1029/93WR00744. (Pubitemid 24397574)
60. Kalra, A. (2012), Association of oceanic-atmospheric oscillations and hydroclimatic variables in the Colorado River Basin, Ph.D. dissertation, 220 pp., Univ. of Nev., Las Vegas, Nev.
61. Kalra, A., and S. Ahmad (2009), Using oceanic-atmospheric oscillations for long lead time streamflow forecasting, Water Resour. Res., 45, W03413, doi:10.1029/2008WR006855.
62. Kalra, A., and S. Ahmad (2011), Evaluating changes and estimating seasonal precipitation for Colorado River Basin using stochastic nonpara-metric disaggregation technique, Water Resour. Res., 47, W05555, doi:10.1029/ 2010WR009118.
63. Kalra, A., W. P. Miller, K. W. Lamb, S. Ahmad, and T. Piechota (2012), Using large-scale climatic patterns for improving long lead time stream-flow forecasts for Gunnison and San Juan River Basins, Hydrol. Processes, doi:10.1002/hyp.9236, inpress.
64. Kane, R. P. (1999), El Nino timings and rainfall extremes in India, Southeast Asia and China, Int. J. Climatol., 19, 653-672. (Pubitemid 29375669)
65. Karl, T. R., and R. W. Knight (1997), Secular trends of precipitation amount, frequency, and intensity in the United States, Bull. Am. Mete-orol. Soc., 79(2), 231-241. (Pubitemid 128485437)
66. Karl, T. R., C. N. Williams Jr., and P. J. Young (1986), A model to estimate the time of observation bias associated with monthly mean maximum, minimum, and mean temperatures for United States, J. Clim. Appl. Mete-orol., 25, 145-160.
67. Khalil, A., M. N. Almasri, M. McKee, and J. J. Kaluarachchi (2005), Applicability of statistical learning algorithms in groundwater quality modeling, Water Resour. Res., 41, W05010, doi:10.1029/2004WR003608. (Pubitemid 40856460)
68. Kiem, A. S., and S. W. Franks (2004), Multi-decadal variability of drought risk, eastern Australia, Hydrol. Processes, 18(11), 2039-2050. (Pubitemid 39282512)
69. Kim, T.-W., J. B. Valdes, B. Nijssen, and D. Roncayolo (2006), Quantification of linkages between large-scale climate patterns and precipitation in the Colorado River Basin, J. Hydrol., 321, 173-186.
70. Kim, T.-W., C. Yao, and J.-H. Ahn (2008), Influence of climate variation on seasonal precipitation in the Colorado River Basin, Stoch. Environ. Res. Risk Assess., 22, 411-420. (Pubitemid 351260281)
71. Kuligowski, R. J., and A. P. Barros (1998), Experiments in short-term precipitation forecasting using artificial neural networks, Mon. Weather Rev., 126, 470-482. (Pubitemid 128557303)
72. Kushnir, Y., V. J. Cardone, J. G. Greenwood, and M. A. Cane (1997), The recent increase in the North Atlantic wave heights, J. Clim., 10, 2107-2113.
73. Lall, U., and A. Sharma (1996), A nearest neighbor bootstrap for resampling hydrologic time series, Water Resour. Res., 32(3), 679-693.
74. Lall, U., B. Rajagopalan, and D. G. Tarboton (1996), A nonparametric wet/dry spell for resampling daily precipitation, Water Resour. Res., 32(9), 2803-2823.
75. Lau, K. M., and H. T. Wu (2001), Principal modes of rainfall-SST variability of the Asian summer monsoon: A reassessment of the Monsoon-ENSO relationship, J. Clim., 14, 2480-2895.
76. Legates, D. R., and G. J. McCabe (1999), Evaluating the use of "goodness-of-fit" measures in hydrologic and hydroclimatic model validation, Water Resour. Res., 35(1), 233-241. (Pubitemid 29054163)
77. Li, P. H., H. H. Kwon, L. Sun, U. Lall, and J. J. Kao (2010), A modified support vector machine based prediction model on streamflow at the Shih-men Reservoir, Taiwan, Int. J. Climatol., 30, 1256-1268.
78. Lin, G.-F., G.-R. Chen, M.-C. Wu, and Y.-C. Chou (2009), Effective forecasting of hourly typhoon rainfall using support vector machines, Water Resour. Res., 45, W08440, doi:10.1029/2009WR007911.
79. Lin, G.-F., G.-R. Chen, and P.-Y. Huang (2010), Effective typhoon characteristics and their effects on hourly reservoir inflow forecasting, Adv. Water Resour., 33, 887-898.
80. Liong, S.-Y., and C. Sivapragasam (2002), Flood stage forecasting with support vector machines, J. Am. Water Resour. Assoc., 38(1), 173-186. (Pubitemid 34253505)
81. Mantua, N. J., Y. Z. Hare, J. M. Wallace, and R. C. Francis (1997), A pacific interdecadal climate oscillation with impacts on salmon production, Bull. Am. Meteorol. Soc., 78, 1069-1079.
82. McCabe, G. J., and M. D. Dettinger (1999), Decadal variations in the strength of ENSO teleconnections with precipitation in the western United States, Int. J. Climatol., 19, 1399-1410. (Pubitemid 30054519)
83. McCabe, G. J., M. A. Palecki, and J. L. Betancourt (2004), Pacific and Atlantic Ocean influences on multidecadal drought frequency in the United States, Proc. Natl. Acad. Sci., 101(12), 4136-4141. (Pubitemid 38405895)
84. McCabe, G. J., J. L. Betancourt, and H. G. Hidalgo (2007), Associations of decadal to multidecadal sea-surface temperature variability with Upper Colorado river flow, J. Am. Water Resour. Assoc., 43(1), 183-192.
85. Melesse, A. M., S. Ahmad, M. E. McClain, X. Wang, and Y. H. Lim (2011), Suspended sediment load prediction of river systems: An artificial neural network approach, Agric. Water Manage., 98(5), 855-866.
86. Merideth, R. (2000), A primer on climatic variability and change in the southwest, 28 pp., Udall Cent. for Stud. in Public Policy and the Inst. of the Study of Plant Earth, Univ. of Ariz., Tucson, Ariz.
87. Moriasi, D. N., J. G. Arnold, M. W. Van Liew, R. L. Bingner, R. D. Harmel, and T. L. Veith (2007), Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, Trans. Am. Soc. Agric. Biol. Eng., 50(3), 885-900. (Pubitemid 47074809)
88. Mosquera-Machado, S., and S. Ahmad (2007), Flood hazard assessment of Atrato river in Colombia, Water Resour. Manage., 21(3), 591-609. (Pubitemid 46486950)
89. Nash, J. E., and J. V. Sutcliffe (1970), River flow forecasting through conceptual models: Part 1. A discussion of principles, J. Hydrol., 10(3), 282-290.
90. Olsson, J. (1998), Evaluation of a scaling cascade model for temporal rainfall disaggregation, Hydrol. Earth Syst. Sci., 2(1), 19-30. (Pubitemid 29129038)
91. Piechota, T. C., and J. A. Dracup (1996), Drought and regional hydrologic variation in the United States: Association with the El Nino-Southern Oscillation, Water Resour. Res., 32(5), 1359-1373.
92. Potts, J. M., C. K. Folland, I. T. Jolliffe, and D. Sexton (1996), Revised "LEPS" scores for assessing climate model simulations and long-range forecasts, J. Clim., 9, 34-53.
93. Prairie, J., and R. Callejo (2005), Natural Flow and Salt Computation Methods, Calendar Years 1971-1995, U.S. Dep. Interior, Bur. of Reclam., Boulder City, Nev., 112 pp.
94. Pui, A., A. Lal, and A. Sharma (2011), How does the Interdecadal Pacific Oscillation affect design floods in Australia?, Water Resour. Res., 47, W05554, doi:10.1029/2010WR009420.
95. Pulwarty, R. S., and T. S. Melis (2001), Climate extremes and adaptive management on the Colorado River: Lessons from the 1997-1998 ENSO event, J. Environ. Manage., 63, 207-324.
96. Puri, S., H. Stephen, and S. Ahmad (2011), Relating TRMM precipitation radar land surface backscatter response to soil moisture in the southern United States, J. Hydrol., 402(1-2), 115-125.
97. Qaiser, K., S. Ahmad, W. Johnson, and J. Batista (2011), Evaluating the impact of water conservation on fate of outdoor water use: A study in an arid region, J. Environ. Manage., 92(8), 2061-2068.
98. Qian, B., J. Corte-real, and H. Xu (2000), Is the North Atlantic Oscillation the most important atmospheric pattern for precipitation in Europe, Geo-phys. Res. Lett., 105, 11,901-11,910.
99. Rajagopalan, B., and U. Lall (1998), Interannual variability in western US precipitation, J. Hydrol., 210, 51-67. (Pubitemid 28504048)
100. Rajagopalan, B., and U. Lall (1999), A k-nearest neighbor simulator for daily precipitation and other weather variables, Water Resour. Res., 35(10), 3089-3101. (Pubitemid 29461972)
101. Raman, H., and N. Sunilkumar (1995), Multivariate modelling of water resources time series using artificial neural networks, J. Hydrol. Sci., 40(2), 145-163. (Pubitemid 26436381)
102. Redmond, K. T., and R. W. Koch (1991), Surface climate and streamflow variability in the western United States and their relationship to large scale circulation indices, Water Resour. Res., 27, 2381-2399.
103. Regonda, S. K., B. Rajagopalan, M. Clark, and E. Zagona (2006), A multimodel ensemble forecast framework: Application to spring seasonal flows in the Gunnison River Basin, Water Resour. Res., 42, W09404, doi:10.1029/ 2005WR004653. (Pubitemid 44619765)
104. Ropelewski, C. F., and M. S. Halpert (1986), North American precipitation and temperature patterns associated with El-Nino-Southern Oscillation (ENSO), Mon. Weather Rev., 114, 2165-2352.
105. Ropelewski, C. F., and P. D. Jones (1987), An extension of the TahitiDarwin Southern Oscillation Index, Am. Meteorol. Soc., 115(9), 2161-2165.
106. Sax, J. L., B. H. Thompson, J. D. Leshy, and R. H. Abrams (2000), Legal Control of Water Resources: Cases and Materials, West Group, St. Paul, Minn., 956 pp.
107. Schölkopf, B., K.-K. Sung, C. J. C. Burges, F. Girosi, P. Niyogi, T. Poggio, and V. Vapnik (1997), Comparing support vector machines with gaussian kernels to radial basis function classifiers, IEEE Trans. Signal Process., 45(11), 2758-2765. (Pubitemid 127766345)
108. Sharma, A., D. G. Tarboton, and U. Lall (1997), Streamflow simulation: A nonparametric approach, Water Resour. Res., 33(2), 291-308.
109. Shrestha, E., S. Ahmad, W. Johnson, P. Shrestha, and J. Batista (2011), Carbon footprint of water conveyance versus desalination as alternatives to expand water supply, Desalination, 27(1-3), 120-127.
110. Shrestha, E., S. Ahmad, W. Johnson, and J. Batista (2012), The carbon footprint of water management policy options, Energy Policy, 42, 201-212, doi:10.1016/j.enpol.2011.11.074.
111. Simonovic, S. P., and S. Ahmad (2005), Computer-based model for flood evacuation emergency planning, Nat. Hazards, 34(1), 25-51. (Pubitemid 40077323)
112. Singh, J., H. V. Knapp, and M. Demissie (2004), Hydrologic modeling of the Iroquosis River watershed using HSPF and SWAT, Rep. ISWS CR 2004-08, Ill. Water Surv., Champaign, Ill.
113. Singh, J., H. V. Knapp, J. G. Arnold, and M. Demissie (2005), Hydrologic modeling of the Iroquois River watershed using HSPF and SWAT, J. Am. Water Resour. Assoc., 41(2), 361-375.
114. Singhrattna, N., B. Rajagopalan, M. P. Clark, and K. K. Kumar (2005), Seasonal forecasting of Thailand summer monsoon rainfall, Int. J. Climatol., 25, 649-664. (Pubitemid 40595196)
115. Smola, A. J., and B. Schölkopf (2004), A tutorial on support vector regression, Stat. Comput., 14, 199-222. (Pubitemid 39063488)
116. Smola, A. J., B. Schölkopf, and K. R. Muller (1998), The connection between regularization operators and support vector kernels, Neural Networks, 11, 637-649. (Pubitemid 28400264)
117. Stephen, H., S. Ahmad, T. C. Piechota, and C. Tang (2010), Relating surface backscatter response from TRMM precipitation radar to soil moisture: Results over a semi-arid region, Hydrol. Earth Syst. Sci., 14(2), 193-204.
118. Stone, M. (1974), Cross-validatory choice and assessment of statistical predictions, J. R. Stat. Soc., 36, 111-147.
119. Suykens, J. A. K. (2001), Nonlinear modeling and support vector machines, in Proceedings of IEEE Instrumentation and Measurement Technology Conference, vol. 1, pp. 287-294, Budapest, Hungary, doi:10.1109/IMTC.2001.928828. (Pubitemid 32884027)
120. Thomas, B. E. (2007), Climatic fluctuations and forecasting of streamflow in the Lower Colorado River Basin, J. Am. Water Resour. Assoc., 46(6), 1550-1569. (Pubitemid 350197108)
121. Tokar, A. S., and P. A. Johnson (1999), Rainfall-runoff modeling using artificial neural networks, J. Hydrol. Eng., 4(3), 232-239. (Pubitemid 29437829)
122. Tokar, A. S., and M. Markus (2000), Precipitation-Runoff modeling using artificial neural networks and conceptual models, J. Hydrol. Eng., 5(2), 156-161. (Pubitemid 30458087)
123. Tripathi, S., V. V. Srinivas, and R. S. Nanjundiah (2006), Downscaling of precipitation for climate change scenarios: A support vector machine approach, J. Hydrol., 330, 621-640. (Pubitemid 44573197)
124. Twarakavi, N. K. C., D. Mishra, and S. Bandopadhyay (2006), Prediction of arsenic in bedrock derived stream sediments at a gold mine site under conditions of sparse data, Nat. Resour. Res., 15(1), 15-26.
125. Twarakavi, N. K. C., J. Simunek, and M. G. Schaap (2009), Development of pedotransfer functions for estimation of soil hydraulic parameters using support vector machines, Soil Sci. Soc. Am. J., 73(5), 1443-1452.
126. U.S. Environmental Protection Agency (2002), Guidance for quality assurance project plans for modeling, EPA QA/G-5M, Rep. EPA/240/R-02/007, Off. of Environ. Inf., U.S. Environ. Protect. Agency, Washington, D.C.
127. Vapnik, V. (1995), The Nature of Statistical Learning Theory, Springer, New York.
128. Vapnik, V. (1998), Statistical Learning Theory, Wiley, New York.
129. Vedwan, N., S. Ahmad, F. Miralles-Wilhelm, K. Broad, D. Letson, and G. Podesta (2008), Institutional evolution in Lake Okeechobee management in Florida: Characteristics, impacts, and limitations, Water Resour. Manage., 22(6), 699-718. (Pubitemid 351486331)
130. Venkatesan, A. K., S. Ahmad, W. Johnson, and J. Batista (2011a), Salinity reduction and energy conservation in direct and indirect portable water reuse, Desalination, 272(1-3), 120-127.
131. Venkatesan, A. K., S. Ahmad, W. Johnson, and J. Batista (2011b), System dynamics model to forecast salinity load to the Colorado River due to urbanization within the Las Vegas valley, Sci. Total Environ., 409(13), 2616-2625.
132. Viles, H. A., and A. S. Goudie (2003), Interannual, decadal and multideca-dal scale climate variability and geomorphology, Earth Sci. Rev., 61, 105-131. (Pubitemid 36261277)
133. Walsh, J. E., W. L. Chapman, and T. Shy (1996), Recent decrease of sea level pressure in the Central Arctic, J. Clim., 9, 480-486.
134. Wang, B., R. G. Wu, and X. H. Fu (2000), Pacific-East Asia teleconnec-tions: How does ENSO affect East Asian climate?, J. Clim., 13, 1517-1536. (Pubitemid 30416750)
135. Wang, X. L., and V. R. Swail (2001), Changes of extreme wave heights in Northern Hemisphere oceans and related atmospheric circulation regimes, J. Clim., 14, 2204-2221. (Pubitemid 33038317)
136. Webb, R. H., and J. L. Betancourt (1992), Climate variability and flood frequency of the Santa Cruz River, Pima County, Arizona, U.S. Geol. Surv. Water Suppl. Pap., 2379, pp. 40.
137. Webb, R. H., G. J. McCabe, R. Hereford, and C. Wilkowske (2004), Climatic fluctuations, drought, and flow in the Colorado River Basin, U.S. Geol. Surv. Fact Sheet, 3062-04, 4 pp.
138. Wedgbrow, C. S., R. L. Wilby, H. R. Fox, and G. O'Hare (2002), Prospects for seasonal forecasting of summer drought and low river flow anomalies in England and Wales, Int. J. Climatol., 22(2), 219-236. (Pubitemid 34276454)
139. Weng, H., K. Ashok, S. K. Behera, S. A. Rao, and T. Yamagata (2007), Impacts of recent El Niño Modoki on dry/wet conditions in the Pacific rim during boreal summer, Clim. Dyn., 29(2-3), 113-129, doi:10.1007/s00382-00007- 00234-00380. (Pubitemid 46882363)
140. Wilson, L. (1973), Variations in mean annual sediment yield as a function of mean annual precipitation, Am. J. Sci., 273, 335-349.
141. Xu, Z. X., K. Takeuchi, and H. Ishidaira (2004), Correlation between El Nino-Southern Oscillation (ENSO) and precipitation in South-east Asia and the Pacific region, Hydrol. Process., 18, 107-123. (Pubitemid 38179235)
142. Yu, X., and S.-Y. Liong (2007), Forecasting of hydrologic time series with ridge regression in feature space, J. Hydrol., 332, 290-302. (Pubitemid 44969650)