Estimating reference crop evapotranspiration using improved convolutional bidirectional long short-term memory network by multi-head attention mechanism in the four climatic zones of China

Dong, Juan; Xing, Liwen; Cui, Ningbo; Zhao, Lu; Guo, Li; Wang, Zhihui; Du, Taisheng; Tan, Mingdong; Gong, Daozhi

doi:10.1016/j.agwat.2023.108665

Cited by 10 publications

(1 citation statement)

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“…At present, there are various methods for calculating ET, and many scholars at home and abroad have also made certain discussions on the applicability of different estimation methods in the study area [4][5][6][7]. Based on the Food and Agriculture Organization of the United Nations (FAO)'s Penman-Monteith model [8], the mainstream model was developed, which comprehensively considers the changes in crop physiological characteristics and meteorological factors and therefore has high calculation accuracy and a wide application range.…”

Section: Introductionmentioning

confidence: 99%

The Relationship between Reference Crop Evapotranspiration Change Characteristics and Meteorological Factors in Typical Areas of the Middle of the Dry-Hot Valley of Jinsha River

Luo,

Sun,

Tang

et al. 2024

Water

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Reference crop evapotranspiration (ET0) is a key factor in ecohydrological processes. Studying the variation trend of ET0 in arid river valleys and its influencing factors is not only helpful to understanding the response of dry and hot river valleys to hydrological processes under the background of climate change but also has important guiding significance for the efficient allocation of soil and water resources and the stable maintenance of the ecosystem in this area. Based on the daily meteorological data of three representative meteorological stations in the middle Dry-hot Valley of the Jinsha River from 1988 to 2019, the ET0 variation and its influencing factors in the middle Dry-hot Valley of the Jinsha River are analyzed by quantitative and qualitative methods. The results showed that (1) the ET0 in the middle and middle of the Dry-hot Valley of Jinsha River showed a significant fluctuating trend (Z > 1.98), and the linear change rates were examined in Huaping, Yuanmou, and Panzhihua. (2) Grey correlation analysis and principal component analysis mutually verify that daily mean temperature is the most influential meteorological factor. (3) The sensitivity of ET0 to the change in meteorological factors in the middle section and its sub-sections is as follows: daily average temperature, daily relative humidity, daily average wind speed, and sunshine hours. ET0 is the most sensitive to the change in daily average temperature, followed by the strengthening of daily average wind speed and the reduction in daily relative humidity, and the sensitivity of ET0 to the change in sunshine hours is the least. (4) Among the regions, the meteorological factors that contributed the most to the increase in ET0 in Huaping, Panzhihua, and Yuanmou were daily average wind speed (6.086%), daily average wind speed (8.468%) and daily average temperature (3.869%), respectively. The meteorological factors that contributed the least were sunshine hours.

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