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

Q. Zhang, Y.P. Li, G.H. Huang, H. Wang, Y.F. Li and Z.Y. Shen

Agricultural Water Management, 2024, vol. 292, issue C

Abstract: 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.

Keywords: Agricultural drought; Drought prediction; CNN; Aral Sea Basin; Global warming (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378377424000180
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:292:y:2024:i:c:s0378377424000180

DOI: 10.1016/j.agwat.2024.108683

Access Statistics for this article

Agricultural Water Management is currently edited by B.E. Clothier, W. Dierickx, J. Oster and D. Wichelns

More articles in Agricultural Water Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().