Forecasting Streamflows in the San Juan River Basin in Argentina

被引:2
作者
Carlos Gimenez, Juan [1 ]
Juan Lentini, Emilio [1 ]
Fernandez Cirelli, Alicia [1 ]
机构
[1] Univ Buenos Aires, Fac Ciencias Vet, Ctr Estudios Transdisciplinarios Agua, RA-1053 Buenos Aires, DF, Argentina
来源
WATER AND SUSTAINABILITY IN ARID REGIONS: WATER AND SUSTAINABILITY IN ARID REGIONS | 2010年
关键词
Arid basin; Backpropagation neural model; Forecast; Irrigation; Streamflow;
D O I
10.1007/978-90-481-2776-4_16
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
San Juan province, located in western Argentina, presents great climate variability with arid characteristics. Mean annual rainfall averages less than 100 mm for the whole province, and snowmelt in the Andean upper basin provides the San Juan River Basin with seasonal streamflow during summer, the period of highest water demand for irrigation. Traditional streamflow forecasts for the San Juan River are based on statistical regression models that are strongly dependent on values of snowpack in winter months (July, August, and September) and streamflow values in the spring months. However, producing forecasts for San Juan River summer streamflow using the Multivariate El Nino Southern Oscillation Index (MEI) data in the preceding June of the water year as an explicative variable call improve reservoir operating system performance for irrigation. To demonstrate this. climate predictors such as the MEI were used to forecast San Juan River streamflows to provide predictability at a six-month lead time. A backpropagation neural model, based on coupled data of snowpack and a climate predictor during the winter period, proved successful in forecasting San Juan River flows during the following summer period.
引用
收藏
页码:261 / 274
页数:14
相关论文
共 20 条
[1]  
*ASCE TASK COMM AP, 2000, J HYDROLOGICAL ENG, V5, P127
[2]  
Box G.E.P., 1976, Time Series Analysis: Forecasting and Control
[3]  
Bras R.L., 1993, RANDOM FUNCTIONS HYD
[4]  
*CFI, 2007, BAS DAT CFI PROD BRU
[5]  
*DEP HIDR, 2007, REL AGR PROV SAN JUA
[6]  
*DIR NACL PROGR EC, 2002, PAN EC PROV
[7]   Economic value of long-lead streamflow forecasts for Columbia River hydropower [J].
Hamlet, AF ;
Huppert, D ;
Lettenmaier, DP .
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 2002, 128 (02) :91-101
[8]  
Haykin S., 1999, Neural Networks: A Comprehensive Foundation, DOI DOI 10.1017/S0269888998214044
[9]  
Hertz J., 1991, Introduction to the theory of neural computation
[10]  
Hurst HE., 1965, Long-term storage: an experimental study