Multivariate time series convolutional neural networks for long-term agricultural drought prediction under global warming

• Published in: Agricultural water management Vol. 292; p. 108683
• Main Authors: Zhang, Q., Li, Y.P., Huang, G.H., Wang, H., Li, Y.F., Shen, Z.Y.
• Format: Journal Article
• Language: English
• Published: Elsevier 01.03.2024
• Subjects: Agricultural drought; Aral Sea Basin; CNN; Drought prediction; Global warming
• ISSN: 0378-3774; 1873-2283
• DOI: 10.1016/j.agwat.2024.108683

Agricultural drought (AD) is disastrous to crop production and plant growth. The prediction of AD with sufficient lead time is helpful for developing agricultural water strategy, particularly under the context of global warming. However, the previous studies mainly focused on short lead times (1–6 months) and only used 3 or less variables to predict AD through copula models. In this study, a novel multivariate time series convolutional neural network (T-CNN) is developed to predict AD with long lead times based on multiple meteorological variables. To demonstrate its feasibility and novelty, T-CNN is used in the Aral Sea Basin (ASB) where agricultural production is dominant. Three global climate models (GCMs) and three shared socioeconomic pathways (SSPs) from CMIP6 are considered during 2026–2100. Results indicate that (1) precipitation, temperature, potential evapotranspiration, relative humidity and northward wind are significantly correlated with AD, and are selected as the predictors of AD; (2) compared with the conventional CNN and convolutional long short-term memory (ConvLSTM), T-CNN’s performance is better, taking only about 10% of the computation time of ConvLSTM; (3) T-CNN can effectively extract the spatiotemporal characteristics of meteorological predictors and reproduce AD, showing high correlation coefficients (R>0.9) for 92.5% of the grids across ASB; (4) the result of simple model averaging (SMA) is better than other GCMs, indicating that the spatial differences in AD would become more pronounced with increasing time and emission level; (5) compared with the historical period, under SSP585, the extreme drought would increase 0.20 months/year (2026–2050), 0.23 months/year (2051–2075) and 0.28 months/year (2076–2100). The results highlight the spatiotemporal variation of AD in 21st century with a high resolution (0.1°×0.1°), which can provide scientific support for agricultural water management and long-term drought prevention in ASB.