Loading...

Long Term Seasonal Rainfall and Stramflow Prediction using Ocean- Atmospheric Climate Variables (case study: Bukan Dam)

Taraghi Delgarm, Razieh | 2016

680 Viewed
  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 48536 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Tajrishy, Masoud
  7. Abstract:
  8. Due to water resources restriction and increasing water demand, the optimal utilization of water resources in the country is more and more necessary. Optimal utilization of these resources highly require more precise prediction of the river's stream entering the dam with anticipated intervals of one to some months. In recent decades, identification of large-scale signals as predictors of hydrological climate changes has created great expectations and a lot of studies have been done in this area.One of the most important principles of planning and management of water resources in each country from the point of view of hydrological, the rainfall is forecast. In this study,using principal components regression modeling and comparison with Artificial Neural Network and consider precipitation as meteorological variables, large-scale climate signals, sea surface temperatures of adjacent seas and 500 mbar geopotential height, streamflow of Boukan dam above three periods (Feb-Jun), (March-Jun) and (April-June) is forecasted. Also relationship between seasonal rainfall and climate indices and territorial rainfall heat sources has been considered and seasonal rainfall has been predicted. The results help to predict the long-term situation of Buchan Dam catchment rainfall and subsequent runoff input to the dam and they also planning and management of water resources.The results show that changes in sea surface temperature of the Red Sea and the Persian Gulf in the winter and spring precipitation are effective and exist meaningful relationship between sea surface temperature changes in the western Mediterranean with fall precipitaion in Alasghol, Darrepanbedan and Baghchemishe station, As well as changes in sea surface temperatures in the eastern Mediterranean basin modeling and spring precipitation has been effective. The use of predictive equations proposed, can be considered Finally four variables and parameters in the absence of snow up to 83 percent of the variance in the first period (Feb - June), 70 percent of the variance in the second period (March - June) and 78 percent of the variance in the third period (April - June) was forecast. The performances in La Niña years are less than other years. The neural network method taught by genetic algorithms and neural network regression method for comparing the precision was used. The results showed that neural networks can be an effective way to predict and seasonal precipitation in Bokan Dam basin
  9. Keywords:
  10. Seasonal Rainfall Prediction ; Artificial Neural Network ; Genetic Algorithm ; Ocean-Atmospheric Variables ; Bookan Dam ; Long-Lead Streamflow Forecasting ; Principal Component Regression

 Digital Object List

 Bookmark

No TOC