679
Views
1
CrossRef citations to date
0
Altmetric
Original Articles

Hydrologic simulation approach for El Niño Southern Oscillation (ENSO)-affected watershed with limited raingauge stations

Hydrologique simulation approach pour El Niño Southern Oscillation (ENSO de) - affected watershed avec limited raingauge stations

, , &
Pages 991-1000 | Received 29 Sep 2013, Accepted 10 Jul 2014, Published online: 08 Mar 2016

References

  • Abrahart, R. and See, L., 2007. Neural network modelling of non-linear hydrological relationships. Hydrology & Earth System Sciences, 11, 5.
  • Barsugli, J.J., et al., 1999. The effect of the 1997/98 El Niño on individual large-scale weather events. Bulletin of the American Meteorological Society, 80, 1399–1411. doi:10.1175/1520-0477(1999)080<1399:TEOTEN>2.0.CO;2
  • Bosznay, M., 1989. Generalization of SCS curve number method. Journal of Irrigation and Drainage Engineering, 115, 139–144. doi:10.1061/(ASCE)0733-9437(1989)115:1(139)
  • Chiew, F., et al., 1998. El Niño/Southern Oscillation and Australian rainfall, streamflow and drought: links and potential for forecasting. Journal of Hydrology, 204 (1–4), 138–149. doi:10.1016/S0022-1694(97)00121-2
  • Chiu, S., ed., 1996. Method and software for extracting fuzzy classification rules by subtractive clustering. In: Biennial conference of the North American Fuzzy Information Processing Society (NAFIPS), 19–22 January, Berkeley, CA. IEEE.
  • Dawson, C.W., Abrahart, R.J., and See, L.M., 2007. HydroTest: a web-based toolbox of statistical measures for the standardised assessment of hydrological forecasts. Environmental Modelling & Software, 27, 1034–1052.
  • Dunn, J.C., 1973. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3, 32–57.
  • Hansen, J., et al., 2001. El Niño-Southern Oscillation impacts on crop production in the southeast United States. ASA Special Publication, 63, 55–76.
  • Jang, J.S.R., 1993. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23 (3), 665–685. doi:10.1109/21.256541
  • Jang, J.S.R. and Sun, C.T., 1995. Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83 (3), 378–406. doi:10.1109/5.364486
  • Jayawardena, A., Muttil, N., and Lee, J., 2006. Comparative analysis of data-driven and GIS-based conceptual rainfall–runoff model. Journal of Hydrologic Engineering, 11, 1–11. doi:10.1061/(ASCE)1084-0699(2006)11:1(1)
  • Keener, V., et al., 2007. Effects of El Niño/Southern Oscillation on simulated phosphorus loading in South Florida. Transactions of the ASAE, 50 (6), 2081–2089.
  • Khalil, A.F., et al., 2005. Basin scale water management and forecasting using artificial neural networks1. JAWRA Journal of the American Water Resources Association, 41 (1), 195–208. doi:10.1111/j.1752-1688.2005.tb03728.x
  • Kulkarni, J., 2000. Wavelet analysis of the association between the southern oscillation and the Indian summer monsoon. International Journal of Climatology, 20 (1), 89–104. doi:10.1002/(SICI)1097-0088(200001)20:1<89::AID-JOC458>3.0.CO;2-W
  • Lim, K.J., et al., 2005. Automated Web Gis based hydrograph analysis tool, WHAT1. JAWRA Journal of the American Water Resources Association, 41 (6), 1407–1416. doi:10.1111/j.1752-1688.2005.tb03808.x
  • Lukas, R., Hayes, S.P., and Wyrtki, K., 1984. Equatorial sea level response during the 1982–1983 El Niño. Journal of Geophysical Research: Oceans (1978–2012), 89 (C6), 10425–10430. doi:10.1029/JC089iC06p10425
  • Makkeasorn, A., Chang, N.-B., and Zhou, X., 2008. Short-term streamflow forecasting with global climate change implications – a comparative study between genetic programming and neural network models. Journal of Hydrology, 352 (3–4), 336–354. doi:10.1016/j.jhydrol.2008.01.023
  • McCabe, G.J. and Dettinger, M.D., 1999. Decadal variations in the strength of ENSO teleconnections with precipitation in the western United States. International Journal of Climatology, 19 (13), 1399–1410. doi:10.1002/(SICI)1097-0088(19991115)19:13<1399::AID-JOC457>3.0.CO;2-A
  • Moriasi, D., et al., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50 (3), 885–900.
  • Mukerji, A., Chatterjee, C., and Raghuwanshi, N.S., 2009. Flood forecasting using ANN, neuro-fuzzy, and neuro-GA models. Journal of Hydrologic Engineering, 14, 647–652. doi:10.1061/(ASCE)HE.1943-5584.0000040
  • Nash, J. and Sutcliffe, J., 1970. River flow forecasting through conceptual models part I – a discussion of principles. Journal of Hydrology, 10 (3), 282–290. doi:10.1016/0022-1694(70)90255-6
  • Nayak, P., et al., 2004. A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology, 291 (1–2), 52–66. doi:10.1016/j.jhydrol.2003.12.010
  • Pascual, M., et al., 2000. Cholera dynamics and El Niño-Southern Oscillation. Science, 289 (5485), 1766–1769. doi:10.1126/science.289.5485.1766
  • Piechota, T.C. and Dracup, J.A., 1996. Drought and regional hydrologic variation in the United States: associations with the El Niño-Southern Oscillation. Water Resources Research, 32 (5), 1359–1373. doi:10.1029/96WR00353
  • Pramanik, N. and Panda, R.K., 2009. Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction. Hydrological Sciences Journal, 54 (2), 247–260. doi:10.1623/hysj.54.2.247
  • Rajagopalan, B. and Lall, U., 1998. Interannual variability in western US precipitation. Journal of Hydrology, 210 (1–4), 51–67. doi:10.1016/S0022-1694(98)00184-X
  • Ropelewski, C. and Halpert, M., 1986. North American precipitation and temperature patterns associated with the El Niño/Southern Oscillation (ENSO). Monthly Weather Review (United States), 114, 12.
  • Roy, S.S., 2006. The impacts of ENSO, PDO, and local SSTs on winter precipitation in India. Physical Geography, 27 (5), 464–474. doi:10.2747/0272-3646.27.5.464
  • Sharma, S., 2012. Incorporating El Niño Southern Oscillation (ENSO)-induced climate variability for long-range hydrologic forecasting and stream water quality protection. Auburn University.
  • Sharma, S., et al., 2012a. Deriving spatially distributed precipitation data using the artificial neural network and multilinear regression models. Journal of Hydrologic Engineering, 18 (2), 194–205. doi:10.1061/(ASCE)HE.1943-5584.0000617
  • Sharma, S., et al., 2012b. Incorporating climate variability for point-source discharge permitting in a complex river system. Transactions of the ASABE, 55 (6), 2213–2228. doi:10.13031/2013.42507
  • Sheridan, J., Merkel, W., and Bosch, D., 2002. Peak rate factors for flatland watersheds. Applied Engineering in Agriculture, 18 (1), 65–69. doi:10.13031/2013.7712
  • Singh, V.P., 1988. Hydrologic systems. Volume I: Rainfall–runoff modeling. Englewood Cliffs, NJ: Prentice Hall, 480.
  • Trenberth, K.E. and Stepaniak, D.P., 2001. Indices of El Niño evolution. Journal of Climate, 14 (8), 1697–1701. doi:10.1175/1520-0442(2001)014<1697:LIOENO>2.0.CO;2
  • Yan, H., Zou, Z., and Wang, H., 2010. Adaptive neuro fuzzy inference system for classification of water quality status. Journal of Environmental Sciences, 22 (12), 1891–1896. doi:10.1016/S1001-0742(09)60335-1

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.