| 黄河流域（青海段）碳汇时空格局及影响机制 |
| 摘要点击 252 全文点击 13 投稿时间：2025-04-03 修订日期：2025-06-05 |
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| 中文关键词 黄河流域（青海段） 净生态系统生产力（NEP） 未来变化趋势 参数最优地理探测器（OPGD） 时间滞后效应 |
| 英文关键词 Yellow River Basin （Qinghai Section） net ecosystem productivity （NEP） future change trend optimal parameter Geodetector （OPGD） time lag effect |
| DOI 10.13227/j.hjkx.202504043 |
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| 中文摘要 |
| 通过卡内基-艾姆斯-斯坦福方法（CASA）与经验估算模型探究黄河流域（青海段）2001~2020年净生态系统生产力（NEP）变化趋势、趋势分析和Hurst指数，利用参数最优地理探测器（OPGD）模型分析人类活动、气候、地形和植被等因素对NEP的影响，结合逐像元时滞偏相关分析阐明气候因素对NEP的时滞效应. 结果表明：①从2001~2020年，黄河流域（青海段）的NEP持续上升. 2001年平均NEP（以C计）最低，为258.755~278.086 g·（m2·a）-1，年均增加1.34 g·（m2·a）-1. ②未来易退化地区占土地面积的60.83%，主要集中在西部和中部地区（即曲麻莱县至同仁县一带）. 相反，有改善迹象的地区主要集中在玛多县和共和县. ③FVC和GPP对NEP的解释里占主导地位. 在气候因子中，温度的解释力更强. 此外，交互效应比单一因子对NEP的解释更为显著. ④在0~3个月滞后期情况下NEP对温度和降水量变化的响应表现出显著的时滞性，其中NEP对降水和气温的平均滞后月数分别为2个月和1.62个月，证明NEP对温度波动的响应时间相较于降水更短且更敏感. |
| 英文摘要 |
| The change trends， trend analyses， and Hurst index of net ecosystem productivity （NEP） in the Yellow River Basin （Qinghai section） from 2001 to 2020 were explored via the Carnegie-Ames-Stanford Approach （CASA） and empirical estimation models. The Optimal Parameter Geodetector （OPGD） model was employed to analyze the influences of factors such as human activities， climate， topography， and vegetation on NEP and to discuss the time-lag effect of climate factors on NEP. The results indicated that： ① From 2001 to 2020， the NEP of the Yellow River Basin （Qinghai Section） kept rising. The average NEP （in terms of C） in 2001 was the lowest， ranging from 258.755 to 278.086 g·（m2·a）-1， with an average annual increase of 1.34 g·（m2·a）-1. ② The area prone to degradation in the future accounted for 60.83% of the land area， mainly concentrated in the western and central regions （i.e.， from Qumalai County to Tongren County）. In contrast， the areas showing improvement signs were mainly concentrated in Maduo County and Gonghe County. ③ FVC and GPP were dominant in the explanation of NEP. Among the climatic factors， temperature had a stronger explanatory capacity. Furthermore， the interaction effect was more remarkable in explaining NEP than a single factor. ④ Under a lag period ranging from 0 to 3 months， the responses of NEP to variations in temperature and rainfall exhibited pronounced time lags. Specifically， the average lag months of NEP to rainfall and temperature were 2 months and 1.62 months， respectively， demonstrating that the response time of NEP to temperature fluctuations was shorter and more sensitive in contrast to that of rainfall. |
