基于条件植被温度指数的夏玉米生长季干旱预测研究

Drought Forecasting during Maize Growing Season Based on Vegetation Temperature Condition Index

为验证条件植被温度指数(VTCI)在夏玉米生长季干旱预测中的适用性,以河北中部平原为研究区,应用求和自回归移动平均(ARIMA)模型及季节性求和自回归移动平均(SARIMA)模型,对该地区VTCI时间序列数据进行分析建模预测。首先基于49个气象站点所在像素的VTCI时间序列数据,选取不同长度时间序列建立ARIMA模型,并分析时间序列长度与预测精度间关系,以期为时间序列长度选择提供依据;然后选择理想长度的VTCI时间序列数据,分别建立ARIMA模型和SARIMA模型,用于研究区域2017年夏玉米生长季VTCI预测,并分析评价两模型预测精度;最后采用性能较好的ARIMA模型逐像素建模预测,得到2016-2018年9月上旬至下旬VTCI预测结果。结果表明:基于ARIMA模型的VTCI预测精度与时间序列长度未呈现明显的相关关系,但随时间序列长度增加,模型预测精度逐渐趋于稳定;ARIMA模型对干旱的预测精度高于基于SARIMA模型,其1步、2步、3步VTCI预测结果均方根误差较SARIMA模型分别降低0. 06、0. 07、0. 09;ARIMA模型在不同年份夏玉米生长季VTCI1~3步的预测精度稳定性较好,2016-2018年1步、2步和3步VTCI预测结果绝对误差绝对值大于0. 20的像素平均百分比分别为5. 84%、6. 38%、8. 72%。

Drought was an important factor restricting agricultural production and economic development.It was of great significance for promoting economic development and ensuring food security to study the law of occurrence and development of drought and effectively predict the local future drought situation.The purpose was to verify the applicability of vegetation temperature condition index( VTCI) in the drought prediction during summer maize growing season. Taking the central plain of Hebei as the research area and the time series of drought monitoring results of vegetation temperature condition index as the data source,and autoregressive integrated moving average( ARIMA) model and seasonal autoregressive integrated moving average( SARIMA) model were used to forecast agricultural drought. First of all,based on the time series of vegetation temperature condition index of 49 meteorological stations,the VTCI data of different lengths were used to build ARIMA prediction models,and the variation characteristics of ARIMA model prediction accuracy with the increase of VTCI time series length were analyzed. The results showed that there existed no clear dependence between the performance of the model and the training lengths corresponding to the historical datasets of VTCI,but the prediction accuracy of the model tended to be stable with the increase of time series length. Then,the VTCI time series data from early July 2010 to late August 2017 was used as modeling data,the ARIMA model and SARIMA model were applied to predict VTCI in September 2017, and the prediction accuracy of the two models was evaluated. The results showed that the prediction accuracy of the ARIMA model was higher than that of the SARIMA model. The root mean square error of the 1-step VTCI prediction of the ARIMA model was0. 06 lower than that of the SARIMA model,and the 2-step prediction was 0. 07 lower,and the 3-step prediction was 0. 09 lower. Therefore,the ARIMA model was more suitable for the drought prediction during the summer maize growing season in the study area. Finally,the ARIMA model with better performance was modeled pixel by pixel to obtain the VTCI prediction results from early September to late September,2016-2018. The results showed that the ARIMA model had a good prediction accuracy for1-step,2-step and 3-step of VTCI during summer maize growth season in different years. The average percentage of pixels with absolute error larger than 0. 20 in 1-step,2-step and 3-step in 2016-2018 was only 5. 84%,6. 38% and 8. 72%,respectively.