• Amarasekera, K. N., , R. F. Lee, , E. R. Williams, , and E. A. B. Eltahir. 1997. ENSO and the natural variability in the flow of tropical river. J. Hydrol. 200:2439.

    • Search Google Scholar
    • Export Citation
  • Attia, B. B., and A. B. Abulhoda. 1992. The ENSO phenomenon and its impact on the Nile's hydrology. Climatic Fluctuations and Water Management, M. A. Abu Zeid and A. K. Biswas, Eds., Butterworth-Heinemann, 71–79.

    • Search Google Scholar
    • Export Citation
  • Awadalla, A. G., and J. Rousselle. 1999. Forecasting the Nile flood using sea surface temperatures as inputs: A comparison between transfer function with noise and neural networks. Proc. 19th AGU Hydrology Days, Fort Collins, CO, American Geophysical Union, 23–36.

    • Search Google Scholar
    • Export Citation
  • Berri, G. J., and E. A. Flamenco. 1999. Seasonal volume of the Diamante River, Argentina, based on El Niño observations and predictions. Water Resour. Res. 35:38033810.

    • Search Google Scholar
    • Export Citation
  • Bhalme, H. N., , S. K. Moolet, , and B. V. Ramana Murty. 1986. Forecasting of monsoon performance over India. J. Climatol. 6:347354.

  • Bhatt, U. S. 1989. Circulation regimes of rainfall anomalies in the African–South Asian monsoon belt. J. Climate 2:11331145.

  • Brubaker, K. L., , D. Entekhabi, , and P. Eagleson. 1993. Estimation of continental precipitation recycling. J. Climate 6:10771089.

  • Camberlin, P. 1997. Rainfall anomalies in the source region of the Nile and their connection with Indian summer monsoon. J. Climate 10:13801392.

    • Search Google Scholar
    • Export Citation
  • Conway, D., and M. Hulme. 1993. Recent fluctuations in precipitation and runoff over the Nile sub-basins and their impact on main Nile discharge. Climatic Change 25:127151.

    • Search Google Scholar
    • Export Citation
  • Dracup, J., and E. Kahya. 1994. The relationship between U.S. streamflow and La Niña events. Water Resour. Res. 30:21332141.

  • Elsner, J. B., and C. P. Schmertmann. 1993. Improving extended-range seasonal predictions of intense Atlantic hurricane activity. Wea. Forecasting 8:345351.

    • Search Google Scholar
    • Export Citation
  • Eltahir, E. A. 1996. El Niño and the natural variability in the flow of the Nile River. Water Resour. Res. 32:131137.

  • Feller, W. 1951. The asymptotic distribution of the range of sums of independent random variables. Ann. Math. Stat. 22:427432.

  • Fraedrich, K., and K. Muller. 1992. Climate anomalies in Europe associated with ENSO extremes. Int. J. Climatol. 12:2531.

  • Garen, D. C. 1992. Improved techniques in regression-based streamflow volume forecasting. J. Water Resour. Plann. Manage. 118:654670.

    • Search Google Scholar
    • Export Citation
  • Garnett, E. R., , M. L. Khandekar, , and J. C. Babb. 1998. On the utility of ENSO and PNA indices for long-lead forecasting of summer weather over the crop-growing region of the Canadian Prairies. Theor. Appl. Climatol. 60:3745.

    • Search Google Scholar
    • Export Citation
  • Gray, W. M., , W. L. Christopher, , P. W. Mielke Jr., , and K. J. Berry. 1992. Predicting Atlantic seasonal hurricane activity 6–11 months in advance. Wea. Forecasting 7:440455.

    • Search Google Scholar
    • Export Citation
  • Gray, W. M., , C. W. Landsea, , P. W. Mielke Jr., , and K. J. Berry. 1993. Predicting Atlantic basin seasonal tropical cyclonic activity by 1 August. Wea. Forecasting 8:7386.

    • Search Google Scholar
    • Export Citation
  • Gray, W. M., , W. L. Christopher, , P. W. Mielke, , and K. J. Berry. 1994. Predicting Atlantic basin seasonal tropical cyclone activity by 1 June. Wea. Forecasting 9:103115.

    • Search Google Scholar
    • Export Citation
  • Guetter, A. K., and K. P. Georgakakos. 1996. Are the El Niño and La Niña predictors of the Iowa River seasonal flow? J. Appl. Meteor. 35:690705.

    • Search Google Scholar
    • Export Citation
  • Hamlet, A. F., and D. P. Lettenmaier. 1999. Columbia river streamflow forecasting based on ENSO and PDO climate signals. Water Resour. Plann. Manage. 125:333341.

    • Search Google Scholar
    • Export Citation
  • Hurst, H. E. 1951. Long term storage capacities of reservoirs. Trans. Amer. Soc. Civ. Eng. 116:776808.

  • Johnson, P. A., and P. D. Curtis. 1994. Water balance of the Blue Nile River basin in Ethiopia. J. Irrig. Drain. Div. Amer. Soc. Civ. Eng. 120:573590.

    • Search Google Scholar
    • Export Citation
  • Kung, E. C., and T. A. Sharif. 1980. Regression forecasting of the onset of the Indian summer monsoon with antecedent upper air conditions. J. Appl. Meteor. 19:370380.

    • Search Google Scholar
    • Export Citation
  • Mandelbrot, B., and J. R. Wallis. 1968. Noah, Joseph and operational hydrology. Water Resour. Res. 4:909918.

  • Marsden, M. A., and R. T. Davis. 1968. Regression on principal components as a tool in water supply forecasting. Proc. Western Snow Conf., Lake Tahoe, NV, Western Snow Conference, 33–40.

    • Search Google Scholar
    • Export Citation
  • Pan, Y. H., and A. H. Oort. 1990. Correlation between sea surface temperature anomalies in the eastern equatorial Pacific and the world ocean. Climatic Dyn 4:191205.

    • Search Google Scholar
    • Export Citation
  • Parthasarathy, B., , H. F. Diaz, , and J. K. Eischeid. 1988. Prediction of all-India summer monsoon rainfall with regional and large-scale parameters. J. Geophys. Res. 93 D5:53415350.

    • Search Google Scholar
    • Export Citation
  • Philander, S. G. 1990. El Niño, La Niña, and the Southern Oscillation. Academic Press, 289 pp.

  • Piechota, T. C., and J. A. Dracup. 1997. Long-range streamflow forecasting using El Niño–Southern Oscillation indicators. J. Hydrol. Eng. 4:144151.

    • Search Google Scholar
    • Export Citation
  • Piechota, T. C., , F. H. S. Chiew, , and J. A. Dracup. 1998. Seasonal streamflow forecasting in eastern Australia and the El Niño-Southern Oscillation. Water Resour. Res. 34:30353044.

    • Search Google Scholar
    • Export Citation
  • Quin, W. H. 1992. A study of Southern Oscillation-related climatic activity for A.D. 622–1900 incorporating Nile River flood data. El Niño: Historical and Paleoclimatic Aspects of the Southern Oscillation, H. F. Diaz and V. Markgrat, Eds., Cambridge University Press, 119–149.

    • Search Google Scholar
    • Export Citation
  • Rasmusson, E. M., and T. H. Carpenter. 1982. Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon. Wea. Rev 110:354384.

    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., , E. B. Horton, , D. E. Parker, , C. K. Folland, , and R. B. Hackett. 1996. Version 2.2 of the global sea-ice and sea surface temperature data set, 1903–1994. Hadley Centre Rep. CRTN 74.

    • Search Google Scholar
    • Export Citation
  • Salas, J. D., , N. M. Saada, , and C. H. Chung. 1995. Stochastic modeling and simulation of the Nile River system monthly flows. Computing Hydrology Laboratory, Engineering Research Center, Colorado State University, Tech. Rep. 25, 252 pp.

    • Search Google Scholar
    • Export Citation
  • Seleshi, Y. 1991. Statistical analysis of the Ethiopian droughts in the XXth century based on monthly and yearly precipitation totals. M.S. thesis, Vrije Universiteit, Brussels, Belgium.

    • Search Google Scholar
    • Export Citation
  • Simpson, H. J., , M. A. Cane, , A. L. Herczeg, , S. E. Zebiak, , and J. H. Simpson. 1993a. Annual river discharge in Southern Australia related to El Niño–Southern Oscillation forecasts of sea surface temperature. Water Resour. Res. 29:36713680.

    • Search Google Scholar
    • Export Citation
  • Simpson, H. J., , M. A. Cane, , S. K. Lin, , and S. E. Zebiak. 1993b. Forecasting annual discharge of River Murray, Australia, from a geophysical model of ENSO. J. Climate 6:386387.

    • Search Google Scholar
    • Export Citation
  • Tanco, R. A., and G. J. Berri. 1999. CLIMLAB2000. International Research Institute for Climate Prediction, Applications and Training Pilot Project, Columbia University, 61 pp. [Available online at http://iri.columbia.edu/outreach/training/climlab2000/manual.].

    • Search Google Scholar
    • Export Citation
  • Wang, B. 1995. Interdecadal changes in El Niño onset in the last four decades. J. Climate 8:267285.

  • Wang, G., and E. A. B. Eltahir. 1999. Use of ENSO information in medium- and long-range forecasting of the Nile floods. J. Climate 12:1726737.

    • Search Google Scholar
    • Export Citation
  • Wortman, R. T. 1989. Statistical forecast model for Libby Basin, Montana. Proc. Western Snow Conf., Fort Collins, CO, Western Snow Conference, 100–107.

    • Search Google Scholar
    • Export Citation
  • Yevjevich, V. 1965. The application of surplus, deficit, and range in hydrology. Colorado State University, Hydrology Paper 10, 69 pp.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 221 221 7
PDF Downloads 65 65 2

Long-Range Forecasting of the Nile River Flows Using Climatic Forcing

View More View Less
  • a Colorado State University, Fort Collins, Colorado
  • | b Department of Civil Engineering, Colorado State University, Fort Collins, Colorado
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

Correlation analysis is used to determine the linear relationship between the Nile River flows and leading climatic indicators, such as SST and precipitation, in an effort to establish a basis for quantitative long-term streamflow prediction. The analysis of the lead–lag correlations between the Blue Nile River flows during the “flood season” [July–August–September–October (JASO)] and SSTs led to the identification of a number of regions in the oceans that are significantly correlated and suggests that the SSTs may be useful for predicting the Blue Nile flows. The significant correlation regions between SST in the Pacific and Blue Nile JASO flows evolve through time in a manner that is consistent with the ENSO development; that is, the evolution of the ENSO signal in the Pacific Ocean is reflected in the evolution of the referred cross-correlation field. In addition, the Blue Nile River JASO flow is significantly correlated with the previous year August–November Guinea precipitation, which suggests that the Guinea precipitation is another potential predictor of the Blue Nile River flows with 11 months of lead time. Furthermore, models based on multiple linear regression (MLR) and principal component analysis (PCA) are used to forecast the Blue Nile flows based on SST in the three oceans and the previous year of Guinea precipitation. The models based on PCA showed significant improvement in forecast accuracy over MLR models that were developed in terms of the original variables. The predictability is shown to be the highest for forecasts made in the preceding season and decreases as the lead time increases. The coefficients of multiple determination R2 for validation based on PCA models vary in the range 84%–59% for forecast lead times of 4–16 months. Further analysis using only SST predictors for the period 1913–89 indicates that the predictability of the Blue Nile River JASO flows is more affected by the variability of SSTs in the Pacific Ocean than by those of the other oceans. The conclusion is that long-range forecasting of the Blue Nile River flows with lead times over 1 yr is possible with a high degree of explained variance by using SST in a few regions in the Pacific Ocean and the previous year of Guinea precipitation.

Corresponding author address: Dr. Jose D. Salas, Dept. of Civil Engineering, Colorado State University, Fort Collins, CO 80523. jsalas@engr.colostate.edu

Abstract

Correlation analysis is used to determine the linear relationship between the Nile River flows and leading climatic indicators, such as SST and precipitation, in an effort to establish a basis for quantitative long-term streamflow prediction. The analysis of the lead–lag correlations between the Blue Nile River flows during the “flood season” [July–August–September–October (JASO)] and SSTs led to the identification of a number of regions in the oceans that are significantly correlated and suggests that the SSTs may be useful for predicting the Blue Nile flows. The significant correlation regions between SST in the Pacific and Blue Nile JASO flows evolve through time in a manner that is consistent with the ENSO development; that is, the evolution of the ENSO signal in the Pacific Ocean is reflected in the evolution of the referred cross-correlation field. In addition, the Blue Nile River JASO flow is significantly correlated with the previous year August–November Guinea precipitation, which suggests that the Guinea precipitation is another potential predictor of the Blue Nile River flows with 11 months of lead time. Furthermore, models based on multiple linear regression (MLR) and principal component analysis (PCA) are used to forecast the Blue Nile flows based on SST in the three oceans and the previous year of Guinea precipitation. The models based on PCA showed significant improvement in forecast accuracy over MLR models that were developed in terms of the original variables. The predictability is shown to be the highest for forecasts made in the preceding season and decreases as the lead time increases. The coefficients of multiple determination R2 for validation based on PCA models vary in the range 84%–59% for forecast lead times of 4–16 months. Further analysis using only SST predictors for the period 1913–89 indicates that the predictability of the Blue Nile River JASO flows is more affected by the variability of SSTs in the Pacific Ocean than by those of the other oceans. The conclusion is that long-range forecasting of the Blue Nile River flows with lead times over 1 yr is possible with a high degree of explained variance by using SST in a few regions in the Pacific Ocean and the previous year of Guinea precipitation.

Corresponding author address: Dr. Jose D. Salas, Dept. of Civil Engineering, Colorado State University, Fort Collins, CO 80523. jsalas@engr.colostate.edu

Save