Deep hybridnet for drought prediction based on large-scale climate indices and local meteorological conditions

Aiming to bridge the gaps identified in existing research, which lacks insights from the fusion of deep learning methods and multi-source factors for drought prediction, and the interpretability of factors influencing drought levels, this research develops an innovative, interpretable, and explainable deep HybridNet model, Squeeze-and-Excitation Convolutional Neural Network Bidirectional Long Short-Term Memory with Attention (SE-CNN-BiLSTM-Attention), for standardized precipitation index (SPI) prediction utilizing large-scale climate indices and local meteorological conditions. It not only achieves high accuracy but also elucidates the influencing factors of drought levels across various events and regions, capturing both long-term trends and short-term, real-time dynamics. Key findings include: (1) The hybrid model, which blends historical SPI sequences with climatic features and atmospheric circulation factors, notably improves SPI prediction accuracy over multiple time scales compared to other models discussed in this paper and demonstrates greater sensitivity to moderate and mild droughts. (2) Shorter time scales yield less accurate predictions due to their nonlinearity and volatility. In contrast, longer time scales capture long-term patterns and trends more effectively, enhancing predictive accuracy and serving as the optimal time scale for calculating the SPI and modeling drought conditions. (3) The prediction accuracy of SPI in arid and humid regions surpasses that in semi-arid and semi-humid central areas. With longer time scales, regions with high R2 values expand from northern and eastern areas to a broader range. (4) Historical SPI and precipitation patterns are significant predictors in drought prediction models across different climatic zones, with large-scale atmospheric circulation also playing a critical role. At Mohe Station, with minimal atmospheric circulation impact, local climate primarily determines the SPI. At Turpan Station, despite having high temperatures, temperature's influence on SPI is limited. (5) During the 1967-1969 drought in Turpan, historical SPI's negative impact on predictions peaked at an extreme drought level (SPI24 = - 2.21). The shift from extreme drought in June 1968 to light drought in 1969 was mainly driven by increased daily precipitation and mean temperature. This study provides a framework for multi-scale, cross-regional drought risk assessment and addresses the previously unmet demand for more detailed and accurate forecasting methods under different climate conditions.