Future Reference Evapotranspiration Trends in Shandong Province, China: Based on SAO-CNN-BiGRU-Attention and CMIP6

Yudong Wang, Guibin Pang (), Tianyu Wang, Xin Cong, Weiyan Pan, Xin Fu, Xin Wang and Zhenghe Xu
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Yudong Wang: School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China
Guibin Pang: School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China
Tianyu Wang: School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China
Xin Cong: School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China
Weiyan Pan: School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China
Xin Fu: School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China
Xin Wang: Water Resources Research Institute of Shandong Province, Jinan 250022, China
Zhenghe Xu: School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China

Agriculture, 2024, vol. 14, issue 9, 1-22

Abstract: One of the primary factors in the hydrological cycle is reference evapotranspiration (ET 0 ). The prediction of ET 0 is crucial to manage irrigation water in agriculture under climate change; however, little research has been conducted on the trends of ET 0 changes in Shandong Province. In this study, to estimate ET 0 in the entire Shandong Province, 245 sites were chosen, and the monthly ET 0 values during 1901–2020 were computed using the Hargreaves–Samani formula. A deep learning model, termed SAO-CNN-BiGRU-Attention, was utilized to forecast the monthly ET 0 during 2021–2100, and the predictions were compared to two CMIP6 climate scenarios, SSP2-4.5 and SSP5-8.5. The hierarchical clustering results revealed that Shandong Province encompassed three homogeneous regions. The ET 0 values of Clusters H1 and H2, which were situated in inland regions and major agricultural areas, were the highest. The SAO-CNN-BiGRU-Attention and SSP5-8.5 forecasting results generally displayed a monotonically growing trend during the forecast period in the three regions; however, the SAO-CNN-BiGRU-Attention model displayed a declining tendency at a few points. According to the SAO-CNN-BiGRU-Attention and SSP5-8.5 results, during 2091–2100, H1, H2, and H3 will reach their peaks; the SSP2-4.5 results show that H1, H2, and H3 will peak in 2031–2040. At the end of the forecast period, for Clusters H1, H2, and H3, the prediction rate of SAO-CNN-BiGRU-Attention increased by 1.31, 1.56%, and 1.80%, respectively, whereas SSP2-4.5’s prediction rate increased by 0.31%, 0.95%, and 1.57%, respectively, and SSP5-8.5’s prediction rate increased by 10.88%, 10.76%, and 10.69%, respectively. The prediction results of SAO-CNN-BiGRU-Attention were similar to those of SSP2-4.5 (R 2 > 0.96). The SAO-CNN-BiGRU-Attention deep learning model can be used to forecast future ET 0 .

Keywords: climate change; climate scenarios; deep learning; hierarchical clustering; irrigation water management (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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