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Ecological restoration impact on total terrestrial water storage

Nature Sustainability volume 4, pages 56–62 (2021)Cite this article

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Abstract

Large-scale ecological restoration (ER) has been successful in curbing land degradation and improving ecosystem services. Previous studies have shown that ER changes individual water flux or storage, but its net impact on total water resources remains unknown. Here we quantify ER impact on total terrestrial water storage (TWS) in the Mu Us Sandyland of northern China, a hotspot of ER practices. By integrating multiple satellite observations and government reports, we construct a TWS record that covers both the pre-ER (1982–1998) and the post-ER (2003–2016) periods. We observe a significant TWS depletion (P < 0.0001) after ER, a substantial deviation from the pre-ER condition. This contrasts with a TWS increase simulated by an ecosystem model that excludes human interventions, indicating that ER is the primary cause for the observed water depletion. We estimate that ER has consumed TWS at an average rate of 16.6 ± 5.0 mm yr−1 in the analysed domain, equivalent to a volume of 21 km3 freshwater loss during the post-ER period. This study provides a framework that directly informs the water cost of ER. Our findings show that ER can exert excessive pressure on regional water resources. Sustainable ER strategies require optimizing ecosystem water consumption to balance land restoration and water resource conservation.

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Fig. 1: Conceptual diagram of the potential impact of ER on total TWS.
Fig. 2: Vegetation restoration in our study region.
Fig. 3: TWS trends for the post-ER and pre-ER periods.
Fig. 4: Separation of ER impact.
Fig. 5: Schematic of future TWS trends under different ER strategies in the analysed domain.

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Data availability

GRACE TWS are available from NASA JPL (https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/). NDVI from GIMMS-3g are available at https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1/. NDVI from MODIS are available at https://lpdaac.usgs.gov/products/mod13c2v006/. Land cover change data are available at https://www.esa-landcover-cci.org. Station precipitation data are from the National Meteorological Administration of China (https://data.cam.cn). Gridded precipitation data are from the Climate Research Unit (https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.03/) and the Tropical Rainfall Measuring Mission (https://pmm.nasa.gov/data-access/downloads/trmm). Runoff data is provided by C. Shi from the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, China. Yellow River Resource Bulletins are available at http://www.yrcc.gov.cn/other/hhgb/. Results from the dynamic vegetation model are available from the corresponding author upon request.

Code availability

Computer code used in this study is available from the corresponding author upon reasonable request.

References

  1. Bryan, B. A. et al. China’s response to a national land-system sustainability emergency. Nature 559, 193–204 (2018).

    Article  CAS  Google Scholar 

  2. Ouyang, Z. et al. Improvements in ecosystem services from investments in natural capital. Science 352, 1455–1459 (2016).

    Article  CAS  Google Scholar 

  3. Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).

    Article  Google Scholar 

  4. Tong, X. et al. Increased vegetation growth and carbon stock in China karst via ecological engineering. Nat. Sustain. 1, 44–50 (2018).

    Article  Google Scholar 

  5. Lu, F. et al. Effects of national ecological restoration projects on carbon sequestration in China from 2001 to 2010. Proc. Natl Acad. Sci. USA 115, 4039 (2018).

    Article  CAS  Google Scholar 

  6. Feng, X. et al. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat. Clim. Change 6, 1019 (2016).

    Article  Google Scholar 

  7. Jia, X., Shao, M. A., Zhu, Y. & Luo, Y. Soil moisture decline due to afforestation across the Loess Plateau, China. J. Hydrol. 546, 113–122 (2017).

    Article  Google Scholar 

  8. Chen, Y. et al. Balancing green and grain trade. Nat. Geosci. 8, 739–741 (2015).

    Article  CAS  Google Scholar 

  9. Tong, X. et al. Forest management in southern China generates short term extensive carbon sequestration. Nat. Commun. 11, 129 (2020).

    Article  CAS  Google Scholar 

  10. Jackson, R. B. et al. Trading water for carbon with biological carbon sequestration. Science 310, 1944–1947 (2005).

    Article  CAS  Google Scholar 

  11. Li, Y. et al. Divergent hydrological response to large-scale afforestation and vegetation greening in China. Sci. Adv. 4, eaar4182 (2018).

    Article  Google Scholar 

  12. Green, J. K. et al. Regionally strong feedbacks between the atmosphere and terrestrial biosphere. Nat. Geosci. 10, 410–414 (2017).

    Article  CAS  Google Scholar 

  13. Branch, O. & Wulfmeyer, V. Deliberate enhancement of rainfall using desert plantations. Proc. Natl Acad. Sci. USA 116, 18841–18847 (2019).

    Article  CAS  Google Scholar 

  14. Ellison, D. et al. Trees, forests and water: cool insights for a hot world. Glob. Environ. Change 43, 51–61 (2017).

    Article  Google Scholar 

  15. McDonnell, J. J. et al. Water sustainability and watershed storage. Nat. Sustain. 1, 378–379 (2018).

    Article  Google Scholar 

  16. Rodell, M. et al. Emerging trends in global freshwater availability. Nature 557, 651–659 (2018).

    Article  CAS  Google Scholar 

  17. Mirzabaev, A. et al. in IPCC Special Report on Climate Change and Land (eds Akhtar-Schuster, M., Driouech, F. & Sankaran, M.) Ch. 3 (IPCC, Cambridge Univ. Press, 2019).

  18. Rodell, M., Velicogna, I. & Famiglietti, J. S. Satellite-based estimates of groundwater depletion in India. Nature 460, 999–1002 (2009).

    Article  CAS  Google Scholar 

  19. Scanlon, B. R. et al. Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data. Proc. Natl Acad. Sci. USA 115, E1080 (2018).

    Article  CAS  Google Scholar 

  20. Tapley, B. D., Bettadpur, S., Ries, J. C., Thompson, P. F. & Watkins, M. M. GRACE Measurements of Mass Variability in the Earth System. Science 305, 503–505 (2004).

    Article  CAS  Google Scholar 

  21. Tapley, B. D. et al. Contributions of GRACE to understanding climate change. Nat. Clim. Change 9, 358–369 (2019).

    Article  Google Scholar 

  22. Tian, H. et al. Response of vegetation activity dynamic to climatic change and ecological restoration programs in Inner Mongolia from 2000 to 2012. Ecol. Eng. 82, 276–289 (2015).

    Article  Google Scholar 

  23. Zhang, Z. & Huisingh, D. Combating desertification in China: monitoring, control, management and revegetation. J. Clean. Prod. 182, 765–775 (2018).

    Article  Google Scholar 

  24. Huang, Y., Wang, N.-a, He, T., Chen, H. & Zhao, L. Historical desertification of the Mu Us Desert, Northern China: A multidisciplinary study. Geomorphology 110, 108–117 (2009).

    Article  Google Scholar 

  25. Xu, D. Y., Kang, X. W., Zhuang, D. F. & Pan, J. J. Multi-scale quantitative assessment of the relative roles of climate change and human activities in desertification–a case study of the Ordos Plateau, China. J. Arid Environ. 74, 498–507 (2010).

    Article  Google Scholar 

  26. Yan, F., Wu, B. & Wang, Y. Estimating spatiotemporal patterns of aboveground biomass using Landsat TM and MODIS images in the Mu Us Sandy Land, China. Agric. For. Meteorol. 200, 119–128 (2015).

    Article  Google Scholar 

  27. Li, S. et al. Vegetation changes in recent large-scale ecological restoration projects and subsequent impact on water resources in China’s Loess Plateau. Sci. Total Environ. 569–570, 1032–1039 (2016).

    Article  CAS  Google Scholar 

  28. Xu, Z. et al. Recent greening (1981–2013) in the Mu Us dune field, north-central China, and its potential causes. Land Degrad. Dev. 29, 1509–1520 (2018).

    Article  Google Scholar 

  29. Poulter, B. et al. Plant functional type classification for earth system models: results from the European Space Agency’s Land Cover Climate Change Initiative. Geosci. Model Dev. 8, 2315–2328 (2015).

    Article  Google Scholar 

  30. Xu, Z., Mason, J. A. & Lu, H. Vegetated dune morphodynamics during recent stabilization of the Mu Us dune field, north-central China. Geomorphology 228, 486–503 (2015).

    Article  Google Scholar 

  31. Review of the Kubuqi Ecological Restoration Project: A Desert Green Economy Pilot Initiative (United Nations Environment Programme, 2015).

  32. Cheng, D.-h et al. Estimation of groundwater evaportranspiration using diurnal water table fluctuations in the Mu Us Desert, northern China. J. Hydrol. 490, 106–113 (2013).

    Article  Google Scholar 

  33. Yu, X., Huang, Y., Li, E., Li, X. & Guo, W. Effects of rainfall and vegetation to soil water input and output processes in the Mu Us Sandy Land, northwest China. CATENA 161, 96–103 (2018).

    Article  Google Scholar 

  34. Li, Q. et al. Feasibility of the combination of CO2 Geological storage and saline water development in sedimentary basins of China. Energy Proc. 37, 4511–4517 (2013).

    Article  CAS  Google Scholar 

  35. Xie, X., Xu, C., Wen, Y. & Li, W. Monitoring groundwater storage changes in the Loess Plateau using GRACE satellite gravity data, hydrological models and coal mining data. Remote Sens. 10, 605 (2018).

    Article  Google Scholar 

  36. Griffin-Nolan, R. J. et al. Legacy effects of a regional drought on aboveground net primary production in six central US grasslands. Plant Ecol. 219, 505–515 (2018).

    Article  Google Scholar 

  37. Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).

    Article  CAS  Google Scholar 

  38. Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).

    Article  CAS  Google Scholar 

  39. Cho, S., Ser-Oddamba, B., Batkhuu, N.-O. & Seok Kim, H. Comparison of water use efficiency and biomass production in 10-year-old Populus sibirica and Ulmus pumila plantations in Lun soum, Mongolia. For. Sci. Technol. 15, 147–158 (2019).

    Google Scholar 

  40. Swenson, S. C. & Lawrence, D. M. A GRACE-based assessment of interannual groundwater dynamics in the Community Land Model. Water Resour. Res. 51, 8817–8833 (2015).

    Article  Google Scholar 

  41. Guo, J., Huang, G., Wang, X., Li, Y. & Lin, Q. Investigating future precipitation changes over China through a high-resolution regional climate model ensemble. Earth’s Future 5, 285–303 (2017).

    Article  Google Scholar 

  42. Gong, T., Lei, H., Yang, D., Jiao, Y. & Yang, H. Monitoring the variations of evapotranspiration due to land use/cover change in a semiarid shrubland. Hydrol. Earth Syst. Sci. 21, 863–877 (2017).

    Article  Google Scholar 

  43. Feng, W. et al. Evaluation of groundwater depletion in North China using the Gravity Recovery and Climate Experiment (GRACE) data and ground-based measurements. Water Resour. Res. 49, 2110–2118 (2013).

    Article  Google Scholar 

  44. Famiglietti, J. S. Satellites measure recent rates of groundwater depletion in California’s Central Valley. Geophys. Res. Lett. 38, L03403 (2011).

    Article  Google Scholar 

  45. Wang, J. et al. Recent global decline in endorheic basin water storages. Nat. Geosci. 11, 926–932 (2018).

    Article  CAS  Google Scholar 

  46. Chen, X. et al. Detecting significant decreasing trends of land surface soil moisture in eastern China during the past three decades (1979–2010). J. Geophys. Res. Atmos. 121, 5177–5192 (2016).

    Article  Google Scholar 

  47. Peng, D. & Zhou, T. Why was the arid and semiarid northwest China getting wetter in the recent decades? J. Geophys. Res. Atmos. 122, 9060–9075 (2017).

    Article  Google Scholar 

  48. Grassi, G. et al. The key role of forests in meeting climate targets requires science for credible mitigation. Nat. Clim. Change 7, 220–226 (2017).

    Article  Google Scholar 

  49. Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645 (2017).

    Article  CAS  Google Scholar 

  50. Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).

    Article  CAS  Google Scholar 

  51. Watkins, M. M., Wiese, D. N., Yuan, D.-N., Boening, C. & Landerer, F. W. Improved methods for observing Earth’s time variable mass distribution with GRACE using spherical cap mascons. J. Geophys. Res. Solid Earth 120, 2648–2671 (2015).

    Article  Google Scholar 

  52. Wiese, D. N., Landerer, F. W. & Watkins, M. M. Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution. Water Resour. Res. 52, 7490–7502 (2016).

    Article  Google Scholar 

  53. Myneni, R. B., Hall, F. G., Sellers, P. J. & Marshak, A. L. The interpretation of spectral vegetation indexes. IEEE Trans. Geosci. Remote Sens. 33, 481–486 (1995).

    Article  Google Scholar 

  54. Glenn, E. P., Huete, A. R., Nagler, P. L., Hirschboeck, K. K. & Brown, P. Integrating remote sensing and ground methods to estimate evapotranspiration. Crit. Rev. Plant Sci. 26, 139–168 (2007).

    Article  Google Scholar 

  55. Pinzon, J. & Tucker, C. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014).

    Article  Google Scholar 

  56. Fan, X. & Liu, Y. Multisensor normalized difference vegetation index intercalibration: A comprehensive overview of the causes of and solutions for multisensor differences. IEEE Geosci. Remote Sens. Mag. 6, 23–45 (2018).

    Article  Google Scholar 

  57. Huffman, G. J. et al. The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeorol. 8, 38–55 (2007).

    Article  Google Scholar 

  58. Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).

    Article  Google Scholar 

  59. Zhou, Y., Shi, C., Du, J. & Fan, X. Characteristics and causes of changes in annual runoff of the Wuding River in 1956–2009. Environ. Earth Sci. 69, 225–234 (2013).

    Article  Google Scholar 

  60. Rodell, M. Basin scale estimates of evapotranspiration using GRACE and other observations. Geophys. Res. Lett. 31, L20504 (2004).

    Article  Google Scholar 

  61. Gerten, D., Schaphoff, S., Haberlandt, U., Lucht, W. & Sitch, S. Terrestrial vegetation and water balance—hydrological evaluation of a dynamic global vegetation model. J. Hydrol. 286, 249–270 (2004).

    Article  CAS  Google Scholar 

  62. Smith, B. et al. Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences 11, 2027–2054 (2014).

    Article  Google Scholar 

  63. Haxeltine, A. & Prentice, I. C. BIOME3: An equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types. Glob. Biogeochem. Cycles 10, 693–709 (1996).

    Article  CAS  Google Scholar 

  64. Prestele, R. et al. Current challenges of implementing anthropogenic land-use and land-cover change in models contributing to climate change assessments. Earth Syst. Dyn. 8, 369–386 (2017).

    Article  Google Scholar 

  65. Piao, S. et al. Lower land-use emissions responsible for increased net land carbon sink during the slow warming period. Nat. Geosci. 11, 739–743 (2018).

    Article  CAS  Google Scholar 

  66. Tian, H. et al. The Global N2O Model Intercomparison Project. Bull. Am. Meteorol. Soc. 99, 1231–1251 (2018).

    Article  Google Scholar 

  67. Etheridge, D. M. et al. Natural and anthropogenic changes in atmospheric CO2 over the last 1000 years from air in Antarctic ice and firn. J. Geophys. Res. Atmos. 101, 4115–4128 (1996).

    Article  CAS  Google Scholar 

  68. Keeling, C. D., Whorf, T. P., Wahlen, M. & van der Plichtt, J. Interannual extremes in the rate of rise of atmospheric carbon dioxide since 1980. Nature 375, 666–670 (1995).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank C. Shi from the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, China for providing Wuding river runoff data. We thank Z. He from the Department of Earth System Science, University of California Irvine, for designing Fig. 5. We thank D. N. Wiese at NASA JPL for helpful discussion about potential signal leakage in JPL mascon solution. We thank Z. Wu from the Department of Physical Geography and Ecosystem Science, Lund University and W. Chao from the Sierra Nevada Research Institute, University of California, Merced for helpful discussion about LPJ-GUESS modelling. We acknowledge funding from the NASA Earth and Space Science Fellowship programme and the terrestrial hydrology programme.

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Authors and Affiliations

  1. Department of Earth System Science, University of California, Irvine, CA, USA

    Meng Zhao, Geruo A & Isabella Velicogna

  2. Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA

    Jien Zhang

  3. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    Isabella Velicogna

  4. School of Ecology and Environment, Inner Mongolia University, Hohhot, Inner Mongolia, China

    Cunzhu Liang & Zhiyong Li

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Contributions

J.Z. initiated the research. M.Z. and G.A. designed the study with I.V. M.Z. conducted the analyses with G.A., J.Z. and I.V. M.Z. and G.A. wrote the manuscript. M.Z., G.A., J.Z. and I.V. edited the manuscript. J.Z., C.L. and Z.L. prepared weather station data.

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Correspondence to Meng Zhao.

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Extended data

Extended Data Fig. 1 GRACE TWS, satellite and gauge precipitation time series.

a, GRACE TWS time series and domain-averaged annual total precipitation from the Climate Research Unit (CRU) and the Tropical Rainfall Measuring Mission (TRMM). b, Gridded precipitation data (TRMM and CRU) with precipitation time series averaged at six weather stations (Fig. 2b in the main text). The correlation coefficients of station vs. CRU and station vs. TRMM are 0.8 and 0.9, respectively.

Extended Data Fig. 2 ET estimates from 1982–2016.

Error bars represent 1-σ errors.

Extended Data Fig. 3 ET-NDVI relationship.

Scatterplot of interannual MODIS NDVI values and GRACE water budget-based ET estimates during 2003–2016. The dashed line represents the best fit regression line.

Extended Data Fig. 4 JPL Mascon solution.

a, The location of JPL mascons (that is #822 and #823) that include Mu Us Sandyland. The land-cover legend is the same as in Fig. 2b in the main text. b, and c, are GRACE TWS time series for the two mascons, respectively. The shaded blue area represents 1σ error of TWS.

Extended Data Fig. 5 Potential signal leakage from the groundwater depletion in North China Plain (NCP) to our study domain.

a, NCP groundwater depletion rate derived from previous research. b, Mascon representation of (a). The two black boxes in (b) represent JPL mascon #822 and #823.

Extended Data Fig. 6 Runoff analysis.

a, Drainage basins in our study region. The green basin is the Ordos endorheic basin. The red basin is the Wuding River drainage basin. Baijiachuan is a hydrological gauge station that measures the total runoff out of the Wuding River basin. The background raster represents the long-term mean annual total precipitation from TRMM during 1998–2018. b, Time series of estimated runoff for our entire study region.

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Zhao, M., A, G., Zhang, J. et al. Ecological restoration impact on total terrestrial water storage. Nat Sustain 4, 56–62 (2021). https://doi.org/10.1038/s41893-020-00600-7

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