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中国农学通报

中国农学通报 ›› 2025, Vol. 41 ›› Issue (34): 106-111.doi: 10.11924/j.issn.1000-6850.casb2024-0696

• 资源·环境·生态·土壤·气象 • 上一篇    下一篇

基于热量积累驱动的高寒草原牧草返青期预测模型研究

张海春1(), 刘文兵1, 黄杰2, 李金红1   

  1. 1 青海省海南州气象局，青海共和 813099
    2 青海省贵南县气象局，青海贵南 813201
  • 收稿日期:2025-05-15 修回日期:2025-09-27 出版日期:2025-12-04 发布日期:2025-12-04
  • 通讯作者:
    李金红，女，1978年出生，青海湟中人，副高级工程师，学士，主要从事气象业务方面的工作。通信地址：813099 青海省共和县恰卜恰镇城北新区政和大街97号 海南州气象局。
  • 作者简介:

    张海春，男，1975年出生，工程师，本科，主要从事气象业务方面的工作。通信地址：813099 青海省共和县恰卜恰镇城北新区政和大街97号 海南州气象局，E-mail：。

Prediction Model Research on Green-Up Period of Alpine Grassland Grass Based on Heat Accumulation-Driven

ZHANG Haichun1(), LIU Wenbing1, HUANG Jie2, LI Jinhong1   

  1. 1 Hainan Prefecture Meteorological Bureau, Gonghe, Qinghai 813099
    2 Guinan County Meteorological Bureau, Guinan, Qinghai 813201
  • Received:2025-05-15 Revised:2025-09-27 Published:2025-12-04 Online:2025-12-04

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摘要：

为探究气象因子对高寒草原牧草返青期的影响机制，并提高预测精度，基于青海省同德县气象局2005—2023年牧草物候观测与气象观测数据，采用线性趋势分析、Pearson相关分析和逐步回归方法，分析返青期的年际变化特征，识别关键气象因子，并建立多元线性回归预测模型。结果表明：（1）牧草返青期多年平均为5月1日（儒略日121 d），年际波动显著（标准差5.8 d，极差26 d），但长期变化趋势不显著（气候倾向率0.4 d/10 a，P>0.10），反映出其对短期气候变异的高度敏感性。（2）≥0℃积温与返青期呈极显著正相关(r=0.83，P<0.001)，日照时数亦显著相关(r=0.61，P<0.01)，而降水因子的影响不显著。（3）基于逐步回归构建的预测模型以≥3℃初日日序和≥0℃积温为自变量，模型决定系数R2=0.869(F=52.92，P<0.001)，回代检验平均误差为2.0 d，准确率达98.6%。研究表明，春季热量积累是驱动返青期变化的主导因素，水分条件影响较弱。该模型可为高寒草地物候监测与放牧管理提供科学依据。

关键词: 高寒草原, 牧草返青期, ≥0℃积温, 气候响应, 物候预测, 逐步回归

Abstract:

To elucidate the influence mechanism of meteorological factors on the green-up period of forage grass in alpine grassland and improve prediction accuracy, this study integrated 2005-2023 phenological observations and meteorological data from the Tongde County Meteorological Bureau (Qinghai Province). Using linear trend analysis, Pearson correlation, and stepwise regression, we characterized interannual variation of the green-up date, identified key climatic drivers, and established a multiple linear regression prediction model. The results demonstrated that: (1) the mean green-up date was 1 May (Julian day 121 d), exhibited pronounced interannual variability (SD=5.8 d; range: 26 d), with no significant long-term trend (0.4 d/10 a, P>0.10), indicating high sensitivity to short-term climate fluctuations. (2) Accumulated temperature ≥0℃ (AT) showed a highly significant positive correlation with the green-up date (r=0.83, P<0.001), while sunshine hours also exhibited a significant correlation (r=0.61, P<0.01), precipitation had no discernible effect. (3) The stepwise regression model retained the ordinal date of the first AT≥3℃ and AT as predictors, achieving an R2=0.869 (F=52.92, P<0.001); Backward substitution validation yielded a mean absolute error of 2.0 d (98.6% accuracy, with 95% of predictions within ±2.0 d). This study confirms that that spring heat accumulation is the dominant driver of green-up timing, whereas water availability plays a secondary role. The proposed model provides a robust framework for phonological monitoring and sustainable grazing management in alpine ecosystems.

Key words: alpine steppe, forage grass green-up date, accumulated temperature ≥0℃, climatic response, phenological prediction, stepwise regression