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| DOI：10.13522/j.cnki.ggps.2025284 |
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| A proposed model for simulating daily reference crop evapotranspiration |
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LI Runtong, XING Liwen, CUI Ningbo, JIANG Shouzheng, WANG Zhihui,
ZHU Guoyu, LIU Jincheng, HE Qingyan
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1. State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resources and HydroPower,
Sichuan University, Chengdu 610065, China; 2. Daying County Bureau of Agriculture and Rural Affairs, Suining 629399, China;
3. Sichuan Academy of Agricultural Machinery Sciences, Chengdu 610066, China
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| Abstract: |
| 【Objective】The reference crop evapotranspiration (ET0) is an important parameter for irrigation scheduling and water resource management, but its estimation is often constrained by limited meteorological data. This paper proposes a method to bridge this technological gap.【Method】The study was conducted for the arid regions in Northwestern China. We divided the regions into four climatic zones: temperate continental arid zone, temperate continental high-temperature arid zone, plateau continental semi-arid zone, and temperate monsoon semi-arid zone. Daily meteorological data from 1961 to 2019 at eight representative weather stations in the region were used as baseline data for the model, and the ET0 calculated using the FAO-56 Penman-Monteith model served as the benchmark ET0. Three deep learning models, LSTM, GA-LSTM and PSO-LSTM, optimized the long short-term memory (LSTM) network hyperparameters using a genetic algorithm (GA) and particle swarm optimization (PSO), were developed to estimate ET0 by using air temperature, sunshine duration and relative humidity, either separately or in their combination, as the independent variables. We also compared the proposed models with three other empirical models: Priestley-Taylor, Hargreaves-Samani, and Romanenko model.【Result】The PSO-LSTM model was the most accurate, with its coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE) being 0.831-0.923 and 0.801-0.922, and RMSE, RRMSE, MAE and GPI being 0.476-0.866 mm/d, 0.190-0.382, 0.299-0.627 mm/d and 0.208-0.598, respectively. For areas with only temperature and radiation data, the PSO-LSTM1 model was the most accurate for all four climatic zones, with its R2 varying from 0.893 to 0.923. Intelligent optimization significantly improved the accuracy of LSTM, especially with PSO.【Conclusion】The PSO-LSTM model using temperature and radiation was found to be the most effective and reliable for estimating ET0 in arid regions in Northwestern China. It can be used to estimate ET0 in regions where meteorological data are limited. |
| Key words: neural networks; genetic algorithm; particle swarm optimization; ET0 simulation; arid regions in Northwestern China |
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