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近40年三江源区高寒草地气候资源利用率及载畜量

王春雨 王军邦 张法伟 李英年 李红琴 杨永胜 罗方林

引用本文: 王春雨,王军邦,张法伟,李英年,李红琴,杨永胜,罗方林. 近40年三江源区高寒草地气候资源利用率及载畜量. 草业科学, 2022, 39(4): 672-687 doi: shu
Citation:  WANG C Y, WANG J B, ZhANG F W, LI Y N, LI H Q, YANG Y S, LUO F L. Climate resource utilization rate and livestock-carrying capacity of grasslands in the Three River Headwaters region over the past 40 years. Pratacultural Science, 2022, 39(4): 672-687 doi: shu

近40年三江源区高寒草地气候资源利用率及载畜量

    作者简介: 王春雨(1993-),女,山东德州人,在读博士生,研究方向为全球变化生态学。E-mail: wangchunyu@nwipb.cas.cn
    通讯作者: 李英年(1962-),男,青海西宁人,研究员,本科,研究方向为全球变化生态学。E-mail: ynli@nwipb.cas.cn
  • 基金项目: 中国科学院青海省人民政府三江源国家公园联合研究专项(LHZX-2020-07);国家重点研发计划项目(2017YFA0604801);国家自然科学基金项目(41877547)

摘要: 科学评估三江源区草地的气候资源利用率及载畜能力,是有效开展草地资源利用和实施生态保护的基础和前提,对促进草地畜牧业可持续发展和区域生态文明建设具有重要意义。三江源国家公园位于青藏高原高寒生态脆弱区和敏感区,其核心区是重要生物多样性保护区,国家公园以外的传统利用区是当地牧民维持生计的重要支撑区。本研究基于GLOPEM-CEVSA模型,模拟了1981–2018年三江源区草地现实产草量和气候产草量,分析了草地的气候资源利用率及载畜能力。结果表明,近40年三江源区平均现实产草量和气候产草量分别为852.56和1357.14 kg·hm−2,草地的平均气候资源利用率为62.82%,且呈西北部较高东南部较低的分布特点,国家公园3个园区草地气候资源利用率在61.92%~66.42%。除国家公园所在县域及气候资源利用率较高的唐古拉山乡外,东、南部各县仍有约35%的气候潜力,即505.53 kg·hm−2的草料潜力和每公顷0.44 标准羊单位(SU·hm−2)的载畜潜力。因此,建议在东、南部水热条件较好地区,合理开展退化草地修复工作,提高草地气候资源利用率及草地生产力,承接分担国家公园区域畜牧生产压力,进而在保护国家公园脆弱生态系统和生物多样性基础上,促进整个区域牧民生计、畜牧生产和生态系统稳定的协调可持续发展。

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  • 凯时66

    图 1  三江源地理位置及草地类型分布

    Figure 1.  Location and grassland types of the Three River Headwaters region

    图 2  三江源高寒草甸地上生物量地面观测值与模型模拟值相关性

    Figure 2.  The correlations between the model simulation and field observations of aboveground biomass (AGB) in the Three River Headwater region

    图 3   1981–2018年3个时段三江源区平均现实产草量(a, b, c)及其变化趋势(d, e, f)空间格局

    Figure 3.  Spatial pattern of average GYA (a, b, c) and changing trends (d, e, f) of inter-annual GYA in Three River Headwaters region from 1981 to 2000, 2001 to 2018, and 1981 to 2018

    GYA:现实产草量;图3 (d, e, f)仅展示趋势分析中P < 0.05区域的趋势值。

    GYA: actual grassland yield; Figure 3 (d, e, f) shows only the values in the area where P < 0.05 in the trend analysis.

    图 4  1981–2018年三江源区草地平均产草量和理论载畜量

    Figure 4.   The average grassland yield and theoretical livestock-carrying capacity of the Three River Headwaters region from 1981 to 2018

    * :异常值;GYA:现实产草量;GYC:气候产草量;CLA:现实产草量下的理论载畜量;CLC:气候产草量下的理论载畜量;下图同。

    * : outliers; GYA: actual grassland yield; GYC: climate grassland yield; CLA: theoretical livestock-carrying capacity based on actual grassland yield; CLC: theoretical livestock-carrying capacity based on climate grassland yield; this is applicable for the following figures as well.

    图 5  1981–2018年三江源区产草量及理论载畜量年际变化趋势

    Figure 5.  The changing trends of inter-annual grassland yield and theoretical livestock-carrying capacity in Three River Headwaters region from 1981 to 2018

    图 6  1981–2018年3个时段三江源区平均气候产草量(a, b, c)及其变化趋势(d, e, f)格局

    Figure 6.  Spatial pattern of average GYC and changing trends of inter-annual GYC in Three River Headwaters region from 1981 to 2000, 2001 to 2018, and 1981 to 2018

    图6 (d, e, f) 仅展示趋势分析中P < 0.05区域的趋势值。

    Figure 6 (d, e, f) shows only the values in the area where P < 0.05.

    图 7   1981–2018年3个时段三江源区现实产草量和气候产草量下的平均理论载畜量空间格局

    Figure 7.  Spatial pattern of average CLA (a, b, c) and CLC (d, e, f) in Three River Headwaters region from 1981 to 2000, 2001 to 2018, and 1981 to 2018

    图 8  1981–2018年3个时段三江源区平均气候资源利用率(a, b, c)及其变化趋势(d, e, f)空间格局

    Figure 8.   Spatial pattern of average climate resource utilization rate and trends in the Three River Headwaters region from 1981 to 2000, 2001 to 2018, and 1981 to 2018

    C:气候资源利用率;图8 (d, e, f)内嵌小图表示趋势P < 0.05区域。

    C: climate resource utilization rate; The embedded small figures in Figure 8 (d, e, f) show the area where P < 0.05 in the trend analysis.

    图 9  1981–2018年3个时段三江源区年均温 (a, b, c) 和年降水 (d, e, f ) 与现实产草量的标准回归系数

    Figure 9.  The standard regression coefficient between GYA and MAT (a, b, c) and MAP (d, e, f) in the Three River Headwaters region from 1981 to 2000, 2001 to 2018, and 1981 to 2018

    MAT:年均温;MAP:年降水量;图中仅展示回归分析中P < 0.05区域的回归系数值。下图同。

    MAT: mean annual temperature; MAP: mean annual precipitation. Figure 9 shows only the regression coefficient values in the area where P < 0.05 in the regression analysis; this is applicable for the following figures as well.

    图 10  1981–2018年3个时段三江源区年均温(a, b, c)和年降水量(d, e, f)变化趋势

    Figure 10.  Trends of MAT (a, b, c) and MAP (d, e, f) in the Three River Headwaters region from 1981 to 2000, 2001 to 2018, and 1981 to 2018

    图10内嵌小图表示趋势分析中P < 0.05区域。

    The embedded small figures show the area where P < 0.05 in the trend analysis.

    图 11  1981–2018年3个时段三江源区年均温(a, b, c)和年降水量(d, e, f)与气候产草量的标准回归系数

    Figure 11.  The standard regression coefficient between GYC and MAT (a, b, c) and MAP (d, e, f) in the Three River Headwaters region from 1981 to 2000, 2001 to 2018, and 1981 to 2018

    表 1  不同研究模拟的三江源区草地产草量结果

    Table 1.  Grassland yield of the Three River Headwaters region in different studies

    方法分类
    Method
    模型方法
    Model
    平均产草量
    Grassland yield/(kg·hm−2)
    研究时段
    Study period
    参考文献
    Reference
    遥感模型
    Remote sensing model
    CASA 529.87 2015 [1]
    GLO–PEM 442.50 1982–2005 [8]
    经验模型
    Empirical model
    回归模型
    Regression model
    490.39 2006–2013 [10]
    机器学习
    Machine learning
    1425.75 2001–2016 [35]
    机器学习
    Machine learning
    1582.90 2000–2018 [36]
    遥感–过程耦合模型
    Remote sensing–process coupling model
    MOD17A3 465.70 2010 [11]
    GLOPEM–CEVSA 852.56 1981–2018 本研究 This study
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  • 通讯作者:  李英年, ynli@nwipb.cas.cn
  • 收稿日期:  2021-09-29
  • 接受日期:  2021-12-27
  • 网络出版日期:  2022-03-29
  • 刊出日期:  2022-04-15
通讯作者: 陈斌, bchen63@163.com
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