Prediction of Drought Thresholds Triggering Winter Wheat Yield Losses in the Future Based on the CNN-LSTM Model and Copula Theory: A Case Study of Henan Province

Agronomy, Journal Year: 2025, Volume and Issue: 15(4), P. 954 - 954

Published: April 14, 2025

As global warming progresses, quantifying drought thresholds for crop yield losses is crucial food security and sustainable agriculture. Based on the CNN-LSTM model Copula function, this study constructs a conditional probability framework under future climate change. It analyzes relationship between Standardized Precipitation–Evapotranspiration Index (SPEI) winter wheat yield, assesses vulnerability of in various regions to stress, quantifies The results showed that (1) SPEI Zhoukou, Sanmenxia, Nanyang was significantly correlated with yield; (2) southern eastern higher than center, western, northern past (2000–2023) (2024–2047); (3) there were significant differences thresholds. loss below 30, 50, 70 percentiles (past/future) −1.86/−2.47, −0.85/−1.39, 0.60/0.35 (Xinyang); −1.45/−2.16, −0.75/−1.34, −0.17/−0.43 (Nanyang); −1.47/−2.24, −0.97/−1.61, 0.69/0.28 (Zhoukou); −2.18/−2.86, −1.80/−2.36, −0.75/−1.08 (Kaifeng), indicating threshold will reduce future. This mainly due different soil conditions Henan Province. In context change, droughts be more frequent. Hence, research provide valuable reference efficient utilization agricultural water resources prevention control risk change

Language: Английский

An Approach for Future Droughts in Northwest Türkiye: SPI and LSTM Methods

Sustainability, Journal Year: 2024, Volume and Issue: 16(16), P. 6905 - 6905

Published: Aug. 12, 2024

Predetermining the risk of possible future droughts enables proactive measures to be taken in key areas such as agriculture, water management, and food security. Through these predictions, governments, non-governmental organizations, farmers can develop water-saving strategies, encourage more efficient use water, minimize economic losses that may occur due drought. Thus, drought forecasts stand out a strategic planning tool for protection natural resources. To achieve this aim, forecasted conditions next decade (2024–2034) at nine meteorological stations Sakarya basin, located northwest Türkiye, are examined, using historical monthly precipitation data from 1991 2023. This study uses Standardized Precipitation Index (SPI) Long Short-Term Memory (LSTM) deep learning methods investigate droughts. The research confirms compatibility reliability LSTM method forecasting by comparing SPI values’ correlograms trends. In addition, maps created visually represent spatial distribution most severe expected coming years, Basin determined. contributes limited literature on forward-looking provides valuable information long-term resource management region.

Language: Английский