logo

SCIENTIA SINICA Terrae, Volume 51 , Issue 7 : 1080-1091(2021) https://doi.org/10.1360/SSTe-2020-0273

植被恢复对黄土高原局地降水的反馈效应研究

More info
  • ReceivedOct 12, 2020
  • AcceptedMar 2, 2021
  • PublishedApr 28, 2021

Abstract


Funded by

国家重点研发计划项目(2020YFA0608403)

国家自然科学基金项目(42022001,41877150,42041004,42001029)


References

[1] 胡春宏, 陈绪坚, 陈建国. 2008. 黄河水沙空间分布及其变化过程研究. 水利学报, 39: 518–527. Google Scholar

[2] 贾仰文, 王浩, 仇亚琴, 周祖昊. 2006. 基于流域水循环模型的广义水资源评价(II)—黄河流域应用. 水利学报, 37: 1181–1187. Google Scholar

[3] 刘纪远, 邵全琴, 延晓冬, 樊江文, 邓祥征, 战金艳, 高学杰, 黄麟, 徐新良, 胡云峰, 王军邦, 匡文慧. 2011. 土地利用变化对全球气候影响的研究进展与方法初探. 地球科学进展, 26: 1015–1022. Google Scholar

[4] 罗立辉, 张耀南, 周剑, 潘小多, 孙维君. 2013. 基于WRF驱动的CLM 模型对青藏高原地区陆面过程模拟研究. 冰川冻土, 35: 553–564. Google Scholar

[5] 朴世龙, 张新平, 陈安平, 刘强, 连旭, 王旭辉, 彭书时, 吴秀臣. 2019. 极端气候事件对陆地生态系统碳循环的影响. 中国科学: 地球科学, 49: 1321–1334. Google Scholar

[6] 任宏昌, 史学丽, 张祖强. 2014. 2003~2009年中国地区叶面积指数变化特征分析. 气象科学, 34: 171–178. Google Scholar

[7] 汤秋鸿. 2020. 全球变化水文学: 陆地水循环与全球变化. 中国科学: 地球科学, 50: 436–438. Google Scholar

[8] 王澄海, 孙超. 2013. 一个基于WRF+CLM区域气候模式(WRFC)的建立及初步试验. 高原气象, 32: 1626–1637. Google Scholar

[9] 王飞, 王宗敏, 杨海波, 赵勇. 2018. 基于SPEI的黄河流域干旱时空格局研究. 中国科学: 地球科学, 48: 1169–1183. Google Scholar

[10] 王光谦, 张长春, 刘家宏, 魏加华, 薛海, 李铁键. 2006. 黄河流域多沙粗沙区植被覆盖变化与减水减沙效益分析. 泥沙研究, 2: 10–16. Google Scholar

[11] 王媛媛, 谢正辉, 贾炳浩, 于燕. 2015. 基于陆面过程模式CLM4的中国区域植被总初级生产力模拟与评估. 气候与环境研究, 20: 97–110. Google Scholar

[12] 肖志强, 王锦地, 王森. 2008. 中国区域MODIS LAI产品及其改进. 遥感学报, 12: 993–1000. Google Scholar

[13] 熊建胜, 张宇, 王少影, 尚伦宇, 陈云刚, 沈晓燕. 2014. CLM4.0土壤水分传输方案改进在青藏高原陆面过程模拟中的效应. 高原气象, 33: 323–336. Google Scholar

[14] 杨大文, 张树磊, 徐翔宇. 2015. 基于水热耦合平衡方程的黄河流域径流变化归因分析. 中国科学: 技术科学, 45: 1024–1034. Google Scholar

[15] 杨磊, 张涵丹, 陈利顶. 2018. 黄土宽梁缓坡丘陵区次降雨对土壤水分补给效率与阈值研究. 中国科学: 地球科学, 48: 457–466. Google Scholar

[16] 杨扬, 左洪超, 杨启东, 杜冰, 王晓霞, 王明星, 武建军. 2015. CLM4.0模式对干旱区荒漠草原过渡带快速变化陆面过程的数值模拟研究. 高原气象, 34: 923–934. Google Scholar

[17] 张宝庆, 吴普特, 赵西宁. 2011. 近30 a黄土高原植被覆盖时空演变监测与分析. 农业工程学报, 27: 287–293. Google Scholar

[18] Bao J, Feng J, Wang Y. Dynamical downscaling simulation and future projection of precipitation over China. J Geophys Res Atmos, 2015, 120: 8227-8243 CrossRef ADS Google Scholar

[19] Chen Y P, Wang K B, Lin Y S, Shi W Y, Song Y, He X H. Balancing green and grain trade. Nat Geosci, 2015, 8: 739-741 CrossRef ADS Google Scholar

[20] Chen Y, Yang K, He J, Qin J, Shi J, Du J, He Q. Improving land surface temperature modeling for dry land of China. J Geophys Res, 2011, 116: D20104 CrossRef ADS Google Scholar

[21] Chen F, Mitchell K, Schaake J, Xue Y, Pan H L, Koren V, Duan Q Y, Ek M, Betts A. Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J Geophys Res, 1996, 101: 7251-7268 CrossRef ADS Google Scholar

[22] Dudhia J. A nonhydrostatic version of the Penn State-NCAR mesoscale model: Validation tests and simulation of an Atlantic cyclone and cold front. Mon Wea Rev, 1993, 121: 1493-1513 CrossRef Google Scholar

[23] Emmanouil G, Vlachogiannis D, Sfetsos A. Exploring the ability of the WRF-ARW atmospheric model to simulate different meteorological conditions in Greece. Atmos Res, 2021, 247: 105226 CrossRef ADS Google Scholar

[24] ESA. 2017. Land Cover CCI Product User Guide Version 2. Technical Report. Google Scholar

[25] Feng X M, Fu B J, Piao S L, Wang S, Ciais P, Zeng Z Z, Lü Y H, Zeng Y, Li Y, Jiang X H, Wu B F. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat Clim Change, 2016, 6: 1019-1022 CrossRef ADS Google Scholar

[26] Ganguly S, Friedl M A, Tan B, Zhang X Y, Verma M. Land surface phenology from MODIS: Characterization of the collection 5 global land cover dynamics product. Remote Sens Environ, 2010, 114: 1805-1816 CrossRef ADS Google Scholar

[27] Gao Y, Chen F, Miguez-Macho G, Li X. Understanding precipitation recycling over the Tibetan Plateau using tracer analysis with WRF. Clim Dyn, 2020, 55: 2921-2937 CrossRef ADS Google Scholar

[28] Gibbard S, Caldeira K, Bala G, Phillips T J, Wickett M. Climate effects of global land cover change. Geophys Res Lett, 2005, 32: L23705 CrossRef ADS Google Scholar

[29] He J, Yang K, Tang W, Lu H, Qin J, Chen Y, Li X. 2020. The first high-resolution meteorological forcing dataset for land process studies over China. Sci Data, 7: 1–11. Google Scholar

[30] Hersbach H, Dee D. 2016. ERA5 reanalysis is in production. ECMWF Newslett, 147: 5–6. Google Scholar

[31] Hirsch A L, Pitman A J, Kala J. The role of land cover change in modulating the soil moisture-temperature land-atmosphere coupling strength over Australia. Geophys Res Lett, 2014, 41: 5883-5890 CrossRef ADS Google Scholar

[32] Hu Y, Zhang X Z, Mao R, Gong D Y, Liu H B, Yang J. Modeled responses of summer climate to realistic land use/cover changes from the 1980s to the 2000s over eastern China. J Geophys Res Atmos, 2015, 120: 167-179 CrossRef ADS Google Scholar

[33] Jin J, Wen L. Evaluation of snowmelt simulation in the weather research and forecasting model. J Geophys Res, 2012, 117: D10110 CrossRef ADS Google Scholar

[34] Kain J S. The Kain-Fritsch convective parameterization: An update. J Appl Meteor, 2004, 43: 170-181 CrossRef Google Scholar

[35] Liang W, Bai D, Wang F Y, Fu B J, Yan J P, Wang S, Yang Y T, Long D, Feng M Q. Quantifying the impacts of climate change and ecological restoration on streamflow changes based on a Budyko hydrological model in China’s Loess Plateau. Water Resour Res, 2015, 51: 6500-6519 CrossRef ADS Google Scholar

[36] Mlawer E J, Taubman S J, Brown P D, Iacono M J, Clough S A. Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res, 1997, 102: 16663-16682 CrossRef ADS Google Scholar

[37] Nakanishi M, Niino H. An improved Mellor-Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog. Bound-Layer Meteorol, 2006, 119: 397-407 CrossRef ADS Google Scholar

[38] Neale R, Hoskins B. 2010. Description of the NCAR community atmosphere model (CAM 5.0). NCAR Technical Note. Boulder: National Center for Atmospheric Research (NCAR). Google Scholar

[39] Sen P K. Estimates of the regression coefficient based on Kendall’s Tau. J Am Statist Associat, 1968, 63: 1379-1389 CrossRef Google Scholar

[40] Su C H, Fu B J. Evolution of ecosystem services in the Chinese Loess Plateau under climatic and land use changes. glob Planet Change, 2013, 101: 119-128 CrossRef ADS Google Scholar

[41] Subin Z M, Riley W J, Jin J M, Christianson D S, Torn M S, Kueppers L M. Ecosystem feedbacks to climate change in California: Development, testing, and analysis using a coupled regional atmosphere and land surface model (WRF3-CLM3.5). Earth Interactions, 2011, 15: 1-38 CrossRef ADS Google Scholar

[42] Theil H. 1950. A rank-invariant method of linear and polynomial regression analysis. I, II, III. Proc Roy Netherlands Acad Sci, 53: 386–392, 521–525, 1397–1412. Google Scholar

[43] Wang Y Q, Shao M A, Shao H B. A preliminary investigation of the dynamic characteristics of dried soil layers on the Loess Plateau of China. J Hydrol, 2010, 381: 9-17 CrossRef ADS Google Scholar

[44] Wang Y Q, Shao M A, Zhu Y J, Liu Z P. Impacts of land use and plant characteristics on dried soil layers in different climatic regions on the Loess Plateau of China. Agric For Meteor, 2011, 151: 437-448 CrossRef ADS Google Scholar

[45] Wang S, Fu B J, Piao S L, Lü Y H, Ciais P, Feng X M, Wang Y F. Reduced sediment transport in the Yellow River due to anthropogenic changes. Nat Geosci, 2016, 9: 38-41 CrossRef ADS Google Scholar

[46] Wen X H, Lu S H, Jin J M. Integrating remote sensing data with WRF for improved simulations of Casis effects on local weather processes over an RRID region in northwestern China. J Hydrometeorol, 2012, 13: 573-587 CrossRef ADS Google Scholar

[47] Wu L Y, Zhang J Y. Role of land-atmosphere coupling in summer droughts and floods over eastern China for the 1998 and 1999 cases. Chin Sci Bull, 2013, 58: 3978-3985 CrossRef ADS Google Scholar

[48] Xiao Z Q, Liang S L, Wang J D, Yang X, Zhao X, Song J L. Long-Time-Series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Trans Geosci Remote Sens, 2016, 54: 5301-5318 CrossRef ADS Google Scholar

[49] Yang F, Lu H, Yang K, He J, Wang W, Wright J S, Li C, Han M, Li Y. Evaluation of multiple forcing data sets for precipitation and shortwave radiation over major land areas of China. Hydrol Earth Syst Sci, 2017, 21: 5805-5821 CrossRef ADS Google Scholar

[50] Yang K, He J, Tang W, Qin J, Cheng C C K. On downward shortwave and longwave radiations over high altitude regions: Observation and modeling in the Tibetan Plateau. Agric For Meteor, 2010, 150: 38-46 CrossRef ADS Google Scholar

[51] Yu E T, Wang H J, Sun J Q. A quick report on a dynamical downscaling simulation over China using the nested model. Atmos Ocean Sci Lett, 2010, 3: 325-329 CrossRef Google Scholar

[52] Zhang B Q, He C S, Burnham M, Zhang L H. Evaluating the coupling effects of climate aridity and vegetation restoration on soil erosion over the Loess Plateau in China. Sci Total Environ, 2016, 539: 436-449 CrossRef ADS Google Scholar

  • 图 1

    黄土高原2000~2015年间LAI植被盖度(vegetation fraction)和反照率的时空变化趋势

    其中(a)、(b)和(c)分别为空间变化图, 图中黑点表示变化趋势在统计上显著(95%置信水平), 色标单位为(10a)–1; (d)、(e)和(f)分别为区域平均值时间变化趋势, 图中虚线为线性趋势, 星号代表统计上显著的变化趋势(95%)

  • 图 2

    区域气候模型WRF参数化方案比选结果

    依次为云微物理方案(a)、边界层方案(b)和积云对流方案(c)敏感性试验结果

  • 图 3

    动态植被(WRF DYN)和植被不变(WRF CTL)两种情景下降水模拟结果与观测(OBS)的对比

    (a)为降水年际变化, (b)为降水的年内变化

  • 图 4

    不同情景下WRF降水模拟的多年平均值(2000~2015年)空间分布对比图

    (a)为动态植被情景, (b)为植被不变情景, (c)为两种情景模拟降水多年平均值的差值, 图中黑点表示两组试验模拟降水量差异在统计上显著(95%置信水平)

  • 图 5

    不同情景下WRF降水模拟的变化趋势(2000~2015年)空间分布对比图

    (a)为动态植被情景; (b)为植被不变情景; (c)为两种情景模拟降水变化趋势的差值

  • 图 6

    植被恢复对黄土高原局地降水反馈效应的作用机理示意图

    图中蓝线和加号“+”表示增加, 图中红线和减号“−”表示减少, 所有的值都是动态植被情景下与默认植被情景下WRF变量2000~2015年时间段气候平均态的差值, 百分数是差值对比于默认植被情景下的WRF变量值

  • 表 1   数据来源描述

    数据来源

    变量

    时间长度(年份)

    时间分辨率

    空间分辨率

    ERA-Interim再分析数据集

    不同气压层的风场、比湿、温度等

    1999~2015

    6h

    0.5°

    ESA CCI土地覆盖数据集

    土地利用

    1999~2015

    1年

    0.3km

    GLASS数据集

    LAI、植被覆盖度、地表反照率

    2000~2015

    1天

    1km

    CMFD数据集

    降水、气温

    2000~2015

    3h

    0.1°

qqqq

Contact and support