Drought Forecasting in a Semi-arid Watershed Using Climate Signals:a Neuro-fuzzy Modeling Approach

作     者：Bahram CHOUBIN Shahram KHALIGHI-SIGAROODI Arash MALEKIAN Sajjad AHMAD Pedram ATTAROD 

作者机构：Department of Watershed Management Sari University of Agriculture Sciences and Natural ResourcesSari 48181-68984Iran University of Tehran Faculty of Natural ResourcesKaraj 31585-3314 Iran Department of Civil and Environmental Engineering University of NevadaLas Vegas NV 89154-4015 USA 

出 版 物：《Journal of Mountain Science》 (山地科学学报（英文）)

年 卷 期：2014年第11卷第6期

页      面：1593-1605页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

主　　题：Annual Rainfall Large-scale Climate Signals Neuro-Fuzzy Cross-Correlation Principal Components Analysis Drought 

摘      要：Large-scale annual climate indices were used to forecast annual drought conditions in the Maharlu-Bakhtegan watershed,located in Iran,using a neuro-fuzzy *** Standardized Precipitation Index(SPI) was used as a proxy for drought *** the 45 climate indices considered,eight identified as most relevant were the Atlantic Multidecadal Oscillation(AMO),Atlantic Meridional Mode(AMM),the Bivariate ENSO Time series(BEST),the East Central Tropical Pacific Surface Temperature(NINO 3.4),the Central Tropical Pacific Surface Temperature(NINO 4),the North Tropical Atlantic Index(NTA),the Southern Oscillation Index(SOI),and the Tropical Northern Atlantic Index(TNA).These indices accounted for 81% of the variance in the Principal Components Analysis(PCA) *** Atlantic surface temperature(SST:Atlantic) had an inverse relationship with SPI,and the AMM index had the highest *** forecasts of neuro-fuzzy model demonstrate better prediction at a two-year lag compared to a stepwise regression model.