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Woli, P., Jones, J., Ingram, K., & Paz, J. (2013). Forecasting Drought Using the Agricultural Reference Index for Drought (ARID): A Case Study.
Drought forecasting can aid in developing mitigation strategies and minimizing losses. Drought may be forecast using a drought index, an indicator of drought. Using Agricultural Reference Index for Drought (ARID), the possibility of using climate indices (CIs) as predictors to improve the current level of forecasting, which is El Niño-Southern Oscillation (ENSO)-based, was investigated; and the performance of linear regression (LR), artificial neural network (ANN), adaptive neuron-fuzzy inference system (ANFIS), and autoregressive moving average (ARMA) models were compared with the ENSO approach. Monthly values of ARID spanning 56 years were computed for five locations in the southeast USA and those of the CIs having significant connections with weather in this region obtained. For ENSO approach, the ARID values were separated into three ENSO phases and averaged by phase. For ARMA models, monthly time series of ARID were used. For ANFIS, ANN, and LR models, ARID was predicted 1, 2, and 3 months ahead using the past values of the first principal component of CIs. Model performances were assessed using the Nash-Sutcliffe index. Results indicated that drought forecasting could be improved for the southern part of the region using ANN models and CIs. The ANN outperformed the other models for most locations in the region. The CI-based models and the ENSO approach performed better during the winter, whereas the efficiency of ARMA models depended on precipitation periodicities. All models performed better for southern locations. The CIs showed good potentials for use in forecasting drought, especially for southern locations in the winter.
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