logo

SCIENCE CHINA Earth Sciences, Volume 64 , Issue 6 : 920-931(2021) https://doi.org/10.1007/s11430-020-9751-8

Feedbacks between vegetation restoration and local precipitation over the Loess Plateau in China

More info
  • ReceivedOct 12, 2020
  • AcceptedMar 2, 2021
  • PublishedMay 6, 2021

Abstract


Funded by

the National Key R&D Program of China(Grant,No.,2020YFA0608403)

the National Natural Science Foundation of China(Grant,Nos.,42022001,42041004,41877150,&,42001029)


Acknowledgment

This work was supported by the National Key R&D Program of China (Grant No. 2020YFA0608403) and the National Natural Science Foundation of China (Grant Nos. 42022001, 41877150, 42041004 & 42001029).


References

[1] 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

[2] 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

[3] 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

[4] 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

[5] 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

[6] 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

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

[8] 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

[9] 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

[10] 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

[11] 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

[12] 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

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

[14] 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

[15] Hu C H, Chen X J, Chen J G. 2008. Study on the spatial distribution of water and sediment in the Yellow River and its change process (in Chinese). J Hydraul Eng, 39: 518–527. Google Scholar

[16] 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

[17] 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

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

[19] 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

[20] Liu J Y, Shao Q Q, Yan X D, Fan W J, Deng X Zh, Zhan J Y,Gao X J, Huang L, Xu X L, Hu Y F, Wang J B, Kuang W H. 2011. Preliminary study on research progress and methods of land use change Impact on global climate (in Chinese). Earth Sci Prog, 26: 1015–1022. Google Scholar

[21] Luo L H, Zhang Y N, Zhou J, Pan X D, Sun W J. 2013. Research on land surface process simulation of Qinghai-Tibet Plateau based on CLM model driven by WRF (in Chinese). J Glaciol Geocryol, 35: 553–564. Google Scholar

[22] 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

[23] 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

[24] 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

[25] Piao S L, Zhang X P, Chen A P, Liu Q, Lian X, Wang X H, Peng S S, Wu X C. The impacts of climate extremes on the terrestrial carbon cycle: A review. Sci China Earth Sci, 2019, 62: 1551-1563 CrossRef ADS Google Scholar

[26] Ren H C, Shi X L, Zhang Z Q. 2014. Analysis on the Characteristics of the Change of Leaf Area Index in China from 2003 to 2009 (in Chinese). Meteorol Sci, 34: 171–178. Google Scholar

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

[28] 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

[29] 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 Interact, 2011, 15: 1-38 CrossRef ADS Google Scholar

[30] 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

[31] Tang Q H. 2020. Global change hydrology: Terrestrial water cycle and global change (in Chinese). Sci Sin Terr, 50: 436–438. Google Scholar

[32] Wang C H, Sun C. 2013. The establishment and preliminary test of a regional climate model (WRFC) based on WRF+CLM (in Chinese). J Plateau Meteorol, 32: 1626–1637. Google Scholar

[33] Wang F, Wang Z M, Yang H B, Zhao Y. Study of the temporal and spatial patterns of drought in the Yellow River basin based on SPEI. Sci China Earth Sci, 2018, 61: 1098-1111 CrossRef ADS Google Scholar

[34] Wang G Q, Zhang C C, Liu J H, Wei J H, Xue H, Li T J. 2006. Vegetation cover change and benefit analysis of water and sediment reduction in the sediment-rich and coarse sand regions of the Yellow River basin (in Chinese). Sed Res, 2: 10–16. Google Scholar

[35] 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

[36] 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

[37] 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

[38] Wang Y Y, Xie Z H, Jia B H, Yu Y. 2015. Simulation and evaluation of China’s regional vegetation total primary productivity based on the land surface process model CLM4 (in Chinese). J Clim Environ Res, 20: 97–110. Google Scholar

[39] 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

[40] 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

[41] 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

[42] Xiao Z Q, Wang J D, Wang S. 2008. MODIS LAI products in China and their improvements (in Chinese). J Remote Sens, 12: 993–1000. Google Scholar

[43] Xiong J S, Zhang Y, Wang S Y, Shang L Y, Chen Y G, Shen X Y. 2014. Effect of CLM4.0 soil moisture transmission scheme improvement in simulation of land surface process in Qinghai-Tibet Plateau (in Chinese). J Plateau Meteorol, 33: 323–336. Google Scholar

[44] Yang D W, Zhang S L, Xu X Y. 2015. Analysis of the attribution of runoff changes in the Yellow River basin based on the water and heat coupled equilibrium equation (in Chinese). Sci Sin Tech, 45: 1024–1034. Google Scholar

[45] Yang L, Zhang H D, Chen L D. Identification on threshold and efficiency of rainfall replenishment to soil water in semi-arid loess hilly areas. Sci China Earth Sci, 2018, 61: 292-301 CrossRef ADS Google Scholar

[46] 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

[47] 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

[48] Yang Y, Zuo H C, Yang Q D, Du B, Wang X X, Wang M X, Wu J J. 2015. Numerical simulation research on the rapidly changing land surface process of the Desert-Steppe transition zone in arid regions by CLM4.0 model (in Chinese). J Plateau Meteorol, 34: 923–934. Google Scholar

[49] 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

[50] 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

[51] Zhang B Q, Wu P T, Zhao X N. 2011. Monitoring and analysis of the spatiotemporal evolution of vegetation cover in the Loess Plateau in the past 30 years (in Chinese). J Agricul Eng, 27: 287–293. Google Scholar

  • Figure 1

    Spatiotemporal change of LAI ((a) and (d)), vegetation fraction ((b) and (e)), and albedo ((c) and (f)) in the Loess Plateau for the period 2000 to 2015. The dots in the top plots denote the statistical significance of the linear trend at the 95% confidence level. The dashed lines in the bottom plots denote linear trends in the time series. The rate of change (%) for each variable is also shown in the bottom plots, and the asterisk denotes the statistical significance of the linear trend at the 95% confidence level.

  • Figure 2

    Comparison of sensitivity tests for different categories of the parameterization schemes in the WRF model. (a) Microphysics schemes; (b) planetary boundary layer schemes; (c) cumulus schemes. The pentagram in (c) denotes the precipitation simulated by WRF with dynamic vegetation, while the purple dot denotes the precipitation simulated by WRF without dynamic vegetation.

  • Figure 3

    Comparison between WRF precipitation simulations (DYN and DEF) and CMFD observation (OBS). (a) Inter-annual variations; (b) intra-annual variations. The red line and bar represent WRF simulated precipitation with the dynamic vegetation scenario (DYN), the blue line and bar represent that with the default vegetation scenario (DEF). Meanwhile, the black line and bar represent the CMFD observations.

  • Figure 4

    The climatology (2000–2015) of annual precipitation (mm yr−1) in the two WRF precipitation simulations. (a) DYN and (b) CTL. The difference (DYN minus CTL) of annual precipitation climatology between DYN and CTL is shown in (c), and the dots in plot (c) denote statistical significance at the 95% confidence level.

  • Figure 5

    Spatial patterns of the linear trend (mm yr−2) in WRF simulated precipitation over the Loess Plateau during 2000–2015. WRF simulation under the dynamic vegetation scenario (a), and under the static vegetation scenario (b). (c) The difference in trends between the dynamic and static vegetation scenarios.

  • Figure 6

    Schematic diagram of the mechanism responsible for the positive effect of GfGP on precipitation. The plus sign, “+”, denotes a positive contribution (green line), and the minus sign, “−”, denotes a negative contribution (red line). All the values in the diagram represent differences between the climatological values of the default scenario and dynamic scenario. Percentages are differences relative to the default scenario, also shown as the corresponding absolute values (in brackets).

  • Table 1   Summary of various datasets used in this study

    Dataset

    Variable

    Time period

    Temporal resolution

    Spatial resolution

    ERA-Interim Reanalysis Dataset

    Wind, specific humidity, temperature, etc.,at different pressure levels

    1999–2015

    6 h

    0.5°

    ESA CCI Land Cover Dataset

    Land use/land cover

    1999–2015

    Yearly

    0.3 km

    GLASS

    LAI, vegetation fraction, and surface albedo

    2000–2015

    Daily

    1 km

    CMFD

    Precipitation, and air temperature

    2000–2015

    3 h

    0.1°

qqqq

Contact and support