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遥感技术与应用  2022, Vol. 37 Issue (4): 865-877    DOI: 10.11873/j.issn.1004-0323.2022.4.0865
蒸散发遥感专栏     
2000—2018年黄河上中游地区蒸散发年际时空变化及其影响因素分析
崔泽鹏1,2(),王志慧2(),肖培青2,申震洲2,常晓格3,石永磊3,马力2
1.郑州大学 水利科学与工程学院,河南 郑州 450001
2.黄河水利科学研究院水利部黄土高原水土保持重点实验室,河南 郑州 450003
3.河南理工大学 测绘与国土信息工程学院,河南 焦作 454000
Analysis of Spatio-temporal Dynamics of Interannual Evapotranspiration and Its Influencing Factors in the Upper and Middle Reaches of the Yellow River from 2000 to 2018
Zepeng Cui1,2(),Zhihui Wang2(),Peiqing Xiao2,Zhenzhou Shen2,Xiaoge Chang3,Yonglei Shi3,Li Ma2
1.School of Water Conservancy Engineering,Zhengzhou University,Zhengzhou 450001,China
2.Key Laboratory of Soil and Water Conservation on the Loess Plateau of Ministry of Water Resources,Yellow River Institute of Hydraulic Research,Zhengzhou 450003,China
3.School of Surveying and Land Information Engineering Henan Polytechnic University,Jiaozuo,454000,China
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摘要:

黄河流域水资源匮乏且生态系统脆弱,明晰气候与下垫面变化对蒸散发(ET)时空变化的影响机制对于未来黄河流域水资源优化配置与生态建设规划均具有重要意义。基于实测降雨、径流量和GRACE产品数据,利用线性加权融合方法对5种全球ET产品进行融合。利用去趋势法、多元线性回归、全微分和残差法定量计算ET对降雨(Pre)、温度(Temp)、日照时数(SD)、饱和水汽压差(VPD)、风速(WS)和植被叶面积指数(LAI)的敏感性系数,定量分析了各气象要素、植被和其他要素(微地形变化和农业灌溉等)对ET变化趋势的贡献作用。结果表明:①与验证精度最高的GLDAS_CLSM相比,融合ET均方根误差和平均相对误差分别减小12.8 mm和2.2%。2000—2018年黄河上中游ET净增长率为3.82 mm/a,头道拐—龙门区间ET增长率最大(6 mm/a)。②植被LAI显著增加导致上中游区ET趋势增加2.49 mm/a。各气象要素的变化趋势与ET对其敏感性系数的空间异质性共同决定了气象要素对ET的影响作用空间分布,5个气象要素对ET总体趋势的净影响量均为正值,其中温度影响作用最大(0.33 mm/a)。以微地形变化和灌溉活动为主的其他要素导致ET趋势增加0.5 mm/a,相对影响率为13.1%。③气象要素主导源区和唐乃亥—青铜峡区间ET趋势,而植被LAI主导了青铜峡-花园口区间ET趋势,其中LAI对不同子流域ET趋势影响作用排序为:延河>无定河>泾河>北洛河>汾河>窟野河>伊洛河>沁河>渭河>大黑河。其他要素对唐乃亥—青铜峡和龙门—花园口区间的ET影响作用较大,表明该区域的水利水保工程措施和灌溉等人类活动更为剧烈。

关键词: 蒸散发多源产品融合时空变化影响因素分析黄河上中游    
Abstract:

Yellow River Basin (YRB) has been facing issue of severe water shortage, hence detection and attribution of spatio-temporal variation of Evapotranspiration (ET) in the YRB is very significant for optimal allocation of water resources and sustainable development of social-economy and eco-environment. Combined with measured rainfall and runoff and GRACE product, five global ET products were merged by an linear weighting method. Detrended scheme, multiple linear regression, partial differential and residual method were then employed to calculate the sensitivities of ET to Precipitation (Pre), Temperature (Temp), Vapor Pressure Deficit (VPD), Sunshine Duration (SD), Wind Speed(WS) and Leaf Area Index (LAI), and quantitative impacts of different climatic factors, LAI and residual factors (microtopography change, irrigation, impoundment of reservoir, etc.) on the ET trend. ① Compared to the GLDAS_CLSM with best performance, RMSE and MRE of the merged ET were reduced by 12.8 mm and 2.2% respectively. The growth rate of ET over the whole study area during 2000—2018 was 3.82 mm/a, and the ET trend of Toudaoguai-Longmen was highest (6 mm/a) among all subregions. ② Dramatic increasing of LAI improved the ET trend by 2.47 mm/a in the upper and middle reaches of the Yellow River Basin. Spatial heterogeneity of change trend of meteorological factors and sensitivities of ET to them determined the spatial pattern of impacts of meteorological factors on the ET trend. The net impact of all five meteorological factors on the trend of ET was positive, with Temp having the largest impact (0.33 mm/a). The impact of residual factors on ET trend mainly induced by microtopography change and irrigation should also not be neglected, with impact and relative impact rate of 0.5 mm/a and 13.1%. ③the ET trends of source area and Tangnaihai-Qingtongxia were dominated by climatic factors. Vegetation restoration is the dominant factor for ET increasing trend in the middle reaches, where the impact rates of LAI on the ET trend were ranked as follows: Yanhe Rive > Wudinghe River > Jinghe River > Beiluohe River > Fenhe River > Kuyehe River > Yiluohe River > Qinhe River > Weihe River > Daheihe River. Residual factors have higher effects on ET in the Tangnaihai-Qingtongxia and Longmen-Huayuankou subregions, indicating that human activities such as water conservation engineering measures and irrigation are more intense in this region.

Key words: Evapotranspiration    Multi-source product merge    Spatio-temporal variation    Analysis of influencing factors    Upper and Middle reaches of the Yellow River
收稿日期: 2021-08-22 出版日期: 2022-09-28
:  P333  
基金资助: 中央级公益性科研院所基本科研业务费专项(HKY?JBYW?2022?13);国家自然科学基金黄河水科学研究联合基金项目(U2243212)
通讯作者: 王志慧     E-mail: 835243561@qq.com;wzh8588@aliyun.com
作者简介: 崔泽鹏(1996-),男,河南汝州人,硕士研究生,主要从事水文遥感研究。E?mail:835243561@qq.com
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引用本文:

崔泽鹏,王志慧,肖培青,申震洲,常晓格,石永磊,马力. 2000—2018年黄河上中游地区蒸散发年际时空变化及其影响因素分析[J]. 遥感技术与应用, 2022, 37(4): 865-877.

Zepeng Cui,Zhihui Wang,Peiqing Xiao,Zhenzhou Shen,Xiaoge Chang,Yonglei Shi,Li Ma. Analysis of Spatio-temporal Dynamics of Interannual Evapotranspiration and Its Influencing Factors in the Upper and Middle Reaches of the Yellow River from 2000 to 2018. Remote Sensing Technology and Application, 2022, 37(4): 865-877.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.4.0865        http://www.rsta.ac.cn/CN/Y2022/V37/I4/865

图1  研究区与气象水文实测站点地理空间分布
产品时间范围/年空间分辨率时间分辨率产品类型
蒸散发(ET)
GLDAS_CLSM2000—2018月尺度陆面模式同化
GLDAS_NOAH2000—20180.25°月尺度陆面模式同化
GLDAS_VIC2000—2018月尺度陆面模式同化
GLEAM_v3.3a2000—20180.25°日尺度陆面模式同化
PML_V22000—2018500 m8 d卫星遥感产品
陆地水储量变化(TWSA)
CSR RL06_mascons2003—20180.25°月尺度卫星遥感产品
JPL RL06_mascons2003—20180.5°月尺度卫星遥感产品
植被叶面积指数(LAI)
GLASS2000—20181 km8 d卫星遥感产品
表1  本研究使用的生态水文参数产品数据
数据类型时间范围/年站点个数/个时间分辨率数据来源
降雨量、风速、温度、相对湿度、日照时数2000—2018295中国气象数据网
实测径流量2000—20185黄河水利委员会水文局
表2  本研究使用的气象水文站点观测数据
图2  各ET产品的权重系数
图3  5种ET产品与融合ET产品精度验证
图4  2000—2018年黄河流域多年平均ET空间分布和ET年际变化率空间分布
图5  气象要素与植被要素变化趋势的空间分布图
图6  ET对气象要素与植被要素敏感性的空间分布图
图7  气象、植被和其他因素对ET变化趋势的影响量与相对影响率
图8  黄河流域不同区间气象、植被和其他因素对ET变化趋势的影响量与相对影响率
图9  黄河流域不同子流域气象、植被和其他因素对ET变化趋势的影响量与相对影响率
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