【摘要】 参考作物蒸散量（reference crop evapotranspiration, ET₀）是作物精准灌溉管理与农业高效用水的核心参数。为提高川中丘陵区气象资料缺省下的ET₀预报精度,利用不同的气象因子组合,建立15种基于多元自适应回归样条算法（multivariate adaptive regression splines, MARS）的ET₀预报模型。选取11个代表性气象站点1961—2016年逐日气象资料进行分析,将其与其他ET₀预报模型进行对比,并利用可移植性分析评价MARS模型在川中丘陵区的适用性。结果表明:基于温度和风速项输入的MARS₅（输入大气顶层辐射、最高气温、最低气温、2m处风速）、MARS₉（输入最高气温、最低气温、2 m处风速）和MARS₁₃（输入最高气温、2 m处风速）模型,以及仅基于风速项输入的MARS₁₅模型都具有良好的模拟精度;大气顶层辐射和风速是决定机器学习模型地域性适应能力的关键;引入大气顶层辐射后,MARS₆（输入大气顶层辐射、最高气温、最低气温、相对湿度）、MARS₇（输入大气顶层辐射、最高气温、最低气温、日照时长）、MARS₈（输入大气顶层辐射、最高气温、最低气温）模型均优于相同气象因子依赖下的Irmak-Allen、Irmak、Hargreaves-M4模型;通过可移植性分析发现,在训练站点和测试站点的随机交叉组合下,MARS₅模型保持了较高的精度（纳什效率系数和决定系数均大于0.985）,且输出较为稳定的模拟结果,均方根误差变化范围为0.121～0.193 mm/d,平均相对误差变化范围为2.7%～4.2%。因此,基于多元自适应回归样条算法的ET₀预报模型可作为川中丘陵区ET₀预报的推荐模型。更多还原

【Abstract】 The reference crop evapotranspiration（ET₀） is a kernel parameter for precise irrigation management of crops and agriculture efficient water use. In order to improve the accuracy of the ET₀ prediction in the hilly area of central Sichuan with missing meteorological data in some area, 15 different prediction models based on multivariate adaptive regression splines（MARS） were established by using different meteorological factors. The daily meteorological data of 11 representative meteorological stations from 1961 to 2016 were analyzed by the MARS models. These data were divided into training set and test set in a ratio of 7:3, and the simulation results of the MARS models were statistically evaluated using the calculation results of the FAO 56 Penman-Monteith model as a standard. In the statistical evaluation, 4 statistical parameters were obtained by the prediction sequence and the calculation result of the FAO 56 P-M model. They were root mean square error（RMSE）, mean relative error（MRE）, Nash efficiency coefficient（NSE）, and R². The value of the index above were used to calculate a score for evaluating the prediction accuracy of the models, and rank the models based on the scores. Then the results were compared with other ET₀ prediction models and the applicability of the models in the hilly area of central Sichuan was evaluated by the portability analysis. The results showed that the full MARS model with 6 input parameters had the highest accuracy. Decreasing 1 input of relative humidity, the model still had the higher accuracy, ranking No 1 based on comprehensive performance indicator（CPI）, which was same with the full model ranking. Reducing continually 1 input of sunshine duration still yielded the high simulation accuracy with NSE and R² higher than 0.985. Further decreasing 1-2 input, the model NSE and R² still were higher than 0.9. Among these models, the model with 2 inputs of radiation and wind speed was the most easy to use since the radiation could be calculated and only wind speed was required to measure. Radiation and wind speed were the keys to determine the regional adaptability of machine learning models. Radiation contained the geographic and temporal information of the site, which made it a key factor in the MARS models to distinguish the differences in geographical environment. On the other hand, radiation could compensate for the negative impact caused by the lack of sunshine duration on the prediction accuracy of the MARS models. The wind speed was more important than the other meteorological factors because the response of MARS models were more sensitive to it. Compared with the Irmak-Allen Model, the Irmak Model, and the Hargreaves-M4 Model, the MARS₆, MARS₇, and MARS₈ improve the accuracy. Under the same meteorological factors input, the MARS models had a stronger simulation ability for ET₀ than the existing empirical models; Through the portability analysis, the MARS model with 4 input parameters of radiation, maximum and minimum temperature and wind speed maintained high precision with NSE and R² both higher than 0.985, RMSE 0.121-0.193 mm/d and MRE 2.7%-4.1%. In sum, the MARS model realized the deletion and replacement of meteorological factors, reduced dependence of ET₀ forecasting on meteorological data, and maintained a relatively high forecasting accuracy and wide applicability. The MARS was recommended as a reliable ET₀ prediction model in the hilly area of central Sichuan.更多还原