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Budyko框架下黄河流域蒸散发时空变化影响因素研究

王亚琴 杨巍 邢博 罗毅

王亚琴,杨巍,邢博,等. Budyko框架下黄河流域蒸散发时空变化影响因素研究[J]. 水文地质工程地质,2022,50(0): 1-10 doi:  10.16030/j.cnki.issn.1000-3665.202205066
引用本文: 王亚琴,杨巍,邢博,等. Budyko框架下黄河流域蒸散发时空变化影响因素研究[J]. 水文地质工程地质,2022,50(0): 1-10 doi:  10.16030/j.cnki.issn.1000-3665.202205066
WANG Yaqin, YANG Wei, XING Bo, et al. A study of influencing factors of spatio-temporal evapotranspiration variation across the Yellow River Basin under the Budyko framework[J]. Hydrogeology & Engineering Geology, 2022, 50(0): 1-10 doi:  10.16030/j.cnki.issn.1000-3665.202205066
Citation: WANG Yaqin, YANG Wei, XING Bo, et al. A study of influencing factors of spatio-temporal evapotranspiration variation across the Yellow River Basin under the Budyko framework[J]. Hydrogeology & Engineering Geology, 2022, 50(0): 1-10 doi:  10.16030/j.cnki.issn.1000-3665.202205066

Budyko框架下黄河流域蒸散发时空变化影响因素研究

doi: 10.16030/j.cnki.issn.1000-3665.202205066
基金项目: 中国科学院战略重点研究计划项目(XDA20060301);国家重点研发计划项目(2016YFC0501603)
详细信息
    作者简介:

    王亚琴(1987-),女,博士,助理研究员,主要从事生态水文和遥感监测工作。E-mail:wangyq.14b@igsnrr.ac.cn

    通讯作者:

    罗毅(1966-),男,博士生导师,研究员,主要从事生态水文研究和教学工作。E-mail:luoyi@igsnrr.ac.cn

  • 中图分类号: P641.69

A study of influencing factors of spatio-temporal evapotranspiration variation across the Yellow River Basin under the Budyko framework

  • 摘要: 蒸散发是水循环过程中的重要环节,研究蒸散发时空变化影响因素,有利于认识区域水资源的时空分异规律。黄河流域地处干旱半干旱地区,水资源短缺且时空分布不均,水问题突出。在黄河流域分析蒸散发对变化环境的响应,揭示气候变化、植被季节性和物候变化的水文水资源效应,对地区水资源可持续发展和规划管理等具有重要的理论意义和现实意义。基于MARS非参数模型,采用黄河流域内30个子流域的GIMMS3g NDVI数据集、气象数据、土壤数据、土地利用/覆盖数据以及地形地貌数据,在Budyko框架下分析了水热耦合控制参数ϖ与环境变量因子的关联性,探讨了变化环境对流域蒸散发的影响机制。结果表明:(1)流域水平衡关系的空间变异与流域水热耦合季节性、地形地貌空间变异性、降水的季节性特征(平均暴雨深度和降水变异系数)显著相关。(2)年际尺度上:流域水热的不同步性是影响流域水平衡年际分异最重要的气候季节性指数,水热的不同步性增大,流域的蒸散比减小,产流增加;降水越集中、年内变异程度越高、降水的季节性越明显,流域蒸散比越小;植被的季节性特征是影响流域水平衡的重要因素,植被生长越强,生长季长度越长,流域的蒸散比越大,产流系数越小。(3)环境变量之间存在较强的自相关性,协同演化并作用于流域蒸散发。
  • 图  1  研究区位置及植被类型示意图(2010年)

    Figure  1.  Location of the study area and vegetation types (2010)

    图  2  研究方法和流程

    Figure  2.  Methods and the flow chart

    图  3  流域水热耦合季节性指数与流域植被覆盖度的关系

    Figure  3.  Relationship between the water-energy coupling control parameter and vegetation coverage fraction in the watershed

    图  4  ϖ变化率与植被和气候季节性变化率关系

    Figure  4.  Relationship between the slope of ϖ and that of vegetation seasonality and climate seasonality

    图  5  植被-气候相互作用对ϖ年际变异的影响

    Figure  5.  Influence of vegetation-climate interaction on inter-annual variation of ϖ

    表  2  30个子流域的MARS最终模型和精度评价

    Table  2.   MARS final models and accuracy evaluation for 30 sub-catchments

    水文站面积/km2ϖMARS最终模型及节点MAERMSER2
    民和153422.560.521×(0.861−x11)+−0.053×(x13-16.9)+−103×(x20-0.452)+0.080.10.65
    武山80802.530.026×(x12−109)+−95.1×(0.391−x24)+0.230.30.60
    靖远52073.55−2.57×(x14−0.969)+−0.018×(92.9−x12)+0.150.180.65
    郭城驿54703.36−0.282×(x11−0.962)++0.514×(0.962−x11)+−1.96×(x14−1.03)+0.220.30.38
    秦安98053.45−80.4×(0.415−x24)++1.38×(0.653−x11)+0.220.270.62
    社棠18463.70.019×(140−x12)+−21.5×(x22−3.54)+−13.6×(3.54−x22)+0.310.390.64
    千阳35052.860.016×(x12−123)+−21.6×(x22−4.57)+−8.92×(4.57−x22)+0.450.540.54
    景村402813.253.25+0.008×(x12−132)+−0.015×(132−x12)+−2.76×(x14−1.02)+0.180.220.72
    张河15062.960.017×(x12−135)+−0.565×(x19−0.573)+0.280.350.45
    柳林7972.050.016×(x12−135)++8.11×(0.87−x14)+0.490.620.41
    雨落坪190193.350.020×(x12−124)+−3.89×(x14−0.982)+0.380.530.51
    庆阳106032.84−0.588×(x11−0.914)++2.2×(0.902−x15)+0.190.250.53
    郭家桥52161.8127×(3.15−$x_{{11} }^*$)+×($x_{{20}}^*$−0.14)++284×(3.15−$x_{{11}}^*$)+×(0.14−$x_{{20}}^*$)+0.120.150.7
    横山24152.74−0.191×(x11−0.892)++0.775×(0.892−x11)+−51.5×(0.327−x19)+0.150.190.71
    绥德38932.140.446×(1.37−x11)++10.7×(x19−0.36)++0.064×(18.4−x13)+0.10.120.83
    韩家峁24521.930.154×(2.24−x11)++0.004×(x12−93.5)+−0.009×(93.5−x12)++0.89×(1.24−x14)+0.070.080.85
    申家湾11212.386.81×(x22−1.35)+−0.181×(x11−1.02)+0.160.210.56
    神木72982.4817.5×(x19−0.325)+−46.2×(0.325−x19)+−0.262×(x11−1.04)+0.170.220.65
    龙头拐11451.9699.7×(x18−0.127)++66.3×(0.127−x18)+0.190.240.36
    新庙15272.015.36×(x23−3.04)+0.20.250.77
    上静游11902.590.755×(1.02−x11)−0.096(x13−18.7)+0.20.250.48
    林家坪18732.761.32×(0.792-x11)+−14.7×($x_{{13} }^* $−18)+×($x_{{18} }^* $0.175)+0.220.260.55
    高家川32531.64−0.201×(x11−1.83)++33.7×(x20−0.152)++0.004×(x12−121)+0.090.110.76
    后大成41022.190.655×(0.966−x11)++38.9×(x24−0.421)+−16.6×(x18−0.219)+0.160.190.59
    裴沟10232.591.04×(1−x11)+0.310.390.45
    大宁39923.071.95×(x22−3.52)+−0.371×(x11−0.55)+0.190.260.48
    新市河16623.690.011×(x12−130)+−0.018×(130-x12)+−3.78×(x14−1.01)+0.20.240.74
    大村21423.28−106×(x20−0.456)+0.240.320.31
    吉县4362.631.11×(0.797−x11)++116×(x24−0.456)++0.006×(x12−119)+0.30.370.62
    柴庄338003.09−5.72×(x15−0.795)++0.019×(x12−124)+−2.87×($x_{{12} }^* $−124)+×(0.342−$x_{{20} }^* $)+0.360.50.49
    注:变量$x_n^* $表示两个相互作用的变量;(x-t)+指括号内值恒正,如果为负值,则括号内该项为0。
    下载: 导出CSV

    表  1  变化环境因子指标体系

    Table  1.   List of indicators associated with the changed environment

    静态变量动态变量
    影响因素初选表征因子计算方法影响因素初选表征因子计算方法
    地形地貌高程(x1
    相对高程(x2
    高程变异(x3
    坡度(x4
    DEM数据
    空间统计
    气候条件水热耦合季节性指数(x11
    平均暴雨深度(x12
    降水集中指数(x13
    降水变异系数(x14
    降水季节性指数(x15
    气象数据[14,18]
    土壤条件相对土壤入渗能力(x5
    植被-土壤相对蓄水能力(x6
    土壤性质
    气候条件[33]
    植被条件植被覆盖度(x16
    年平均NDVI(x17
    非生长季NDVI(x18
    生长季最大值(x19
    生长季振幅(x20
    生长季合成植被指数(x21
    生长季活跃累积量(x22
    生长季总累积量(x23
    植被指数
    时间序列[21]
    土地利用/覆盖结构林地比例(x7
    灌丛比例(x8
    草地比例(x9
    农田比例(x10
    土地利用
    空间统计
    物候信息相对生长季长度(x24
    下载: 导出CSV
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  • 收稿日期:  2022-05-05
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