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Drought forecasting in a semi-arid watershed using climate signals: a neuro-fuzzy modeling approach

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Abstract

Large-scale annual climate indices were used to forecast annual drought conditions in the Maharlu-Bakhtegan watershed, located in Iran, using a neuro-fuzzy model. The Standardized Precipitation Index (SPI) was used as a proxy for drought conditions. Among 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) method. The Atlantic surface temperature (SST: Atlantic) had an inverse relationship with SPI, and the AMM index had the highest correlation. Drought forecasts of neuro-fuzzy model demonstrate better prediction at a two-year lag compared to a stepwise regression model.

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Authors and Affiliations

  1. Department of Watershed Management, Sari University of Agriculture Sciences and Natural Resources, Sari, 48181-68984, Iran

    Bahram Choubin

  2. Faculty of Natural Resources, University of Tehran, Karaj, 31585-3314, Iran

    Shahram Khalighi-Sigaroodi, Arash Malekian & Pedram Attarod

  3. Department of Civil and Environmental Engineering, University of Nevada, Las Vegas, NV, 89154-4015, USA

    Sajjad Ahmad

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  1. Bahram Choubin
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  2. Shahram Khalighi-Sigaroodi
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  3. Arash Malekian
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  4. Sajjad Ahmad
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  5. Pedram Attarod
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Correspondence to Sajjad Ahmad.

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Choubin, B., Khalighi-Sigaroodi, S., Malekian, A. et al. Drought forecasting in a semi-arid watershed using climate signals: a neuro-fuzzy modeling approach. J. Mt. Sci. 11, 1593–1605 (2014). https://doi.org/10.1007/s11629-014-3020-6

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