Skip to main content

Advertisement

Log in

The total suitability of water yield and carbon sequestration under multi-scenario simulations in the Weihe watershed, China

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

Global climate change and national policies play an important role in regional ecosystem services, both of which should be fully considered when exploring their effective use and management. Bayesian belief network (BBN) is often used in complicated system modelling. Using a BBN to construct a network framework of ecosystem services under climate and policy scenarios for exploring the total suitability distribution of ecosystem services is of great significance. In this study, we develop BBN for the total suitability of water yield and carbon sequestration based on hydro-biogeochemical process. And then we predict the probabilities of the total suitability in 2050 through the BBN under multi-scenario simulations accounting for climate change, birth control and carbon tax policies. Finally, total suitability priority regions are mapped, which are synergy development, water yield suitability, carbon sequestration suitability and non-suitability, respectively. Our results indicate forest, cropland, urban area, and grassland have the largest areas under the representative concentration pathway (RCP) 2.6, RCP 4.5, RCP 6.0 and RCP 8.5, respectively. The abolition of the one-child policy has led to a significant expansion of urban areas, and the implementation of the carbon tax policy has greatly increased forest areas. Additionally, temperature, Normalized Vegetation Index (NDVI), precipitation and land use are the key driving factors that influence suitability. The suitable priority regions of different alternatives help policy makers consider ecological protection priorities while addressing management options.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Arnold JG, Allen PM, Bernhardt G (1993) A comprehensive surface-groundwater flow model. J Hydrol 142(1–4):47–69

    Article  Google Scholar 

  • Bryan BA, Nolan M, Harwood TD, Connor JD, Navarro-Garcia J, King D et al (2014) Supply of carbon sequestration and biodiversity services from Australia's agricultural land under global change. Global Environ Chang 28(1):166–181

  • Chen SH, Pollino CA (2012) Good practice in Bayesian network modelling. Environ Model Softw 37(17):134–145

    Article  Google Scholar 

  • Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1):35–46. https://doi.org/10.1016/0034-4257(91)90048-B

    Article  Google Scholar 

  • Dai EF, Huang Y, Wu Z, Zhao DS (2016) Analysis of spatio-temporal features of a carbon source/sink and its relationship to climatic factors in the Inner Mongolia grassland ecosystem. J Geogr Sci 26(3):297–312. https://doi.org/10.1007/s11442-016-1269-0

    Article  Google Scholar 

  • Daily G (1997) Nature's services. Societal dependence on natural ecosystems. Island Press

  • Dal Ferro N, Quinn C, Morari F (2018) A Bayesian belief network framework to predict SOC dynamics of alternative management scenarios. Soil Tillage Res 179:114–124

    Article  Google Scholar 

  • Dang KB, Windhorst W, Burkhard B, Müller F (2019) A Bayesian belief network – based approach to link ecosystem functions with rice provisioning ecosystem services. Ecol Indic 100:30–44. https://doi.org/10.1016/j.ecolind.2018.04.055

    Article  Google Scholar 

  • Engel BA, Srinivasan R, Arnold J, Rewerts C, Brown SJ (1993) Nonpoint source (NPS) pollution modeling using models integrated with geographic information systems (GIS). Water Sci Technol 28(3–5):685–690. https://doi.org/10.2166/wst.1993.0474

    Article  CAS  Google Scholar 

  • Field CB, Randerson JT, Malmstroem CM (1995) Global net primary production. Combining ecology and remote sensing. Remote Sens Environ 51(1):74–88

  • Fu BJ, Zhang LW (2014) Land-use change and ecosystem services. Concepts, methods and progress. Prog Geogr 33(4):441–446

  • Gonzalez-Redin J, Luque S, Poggio L, Smith R, Gimona A (2016) Spatial Bayesian belief networks as a planning decision tool for mapping ecosystem services trade-offs on forested landscapes. Environ Res 144:15–26

    Article  CAS  Google Scholar 

  • Hao L, Jiang CY, Sun X, He HG (2013) Impact factors and policy implications of carbon emissions from energy consumption in Shaanxi province. Res Soil Water Conserv 20(6):326–332

  • Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25(15):1965–1978. https://doi.org/10.1002/joc.1276

    Article  Google Scholar 

  • Jackson RB, Jobbágy EG, Avissar R, Roy SB, Barrett DJ, Cook CW et al (2005) Trading water for carbon with biological carbon sequestration. Science 310(5756):1944–1947

    Article  CAS  Google Scholar 

  • Jenks, GF (1967) The Data Model Concept in Statistical Mapping. International Yearbook of Cartography. 7:186–190

  • Landuyt D, Broekx S, D'hondt R, Engelen G, Aertsens J, Goethals PLM (2013) A review of Bayesian belief networks in ecosystem service modelling. Environ Model Softw 46:1–11

    Article  Google Scholar 

  • Landuyt D, Broekx S, Goethals PLM (2016) Bayesian belief networks to analyse trade-offs among ecosystem services at the regional scale. Ecol Indic 71:327–335

    Article  Google Scholar 

  • Lautenbach S, Jungandreas A, Blanke J, Lehsten V, Mühlner S, Kühn I, Volk M (2017) Trade-offs between plant species richness and carbon storage in the context of afforestation – examples from afforestation scenarios in the Mulde Basin, Germany. Ecol Indic 73:139–155. https://doi.org/10.1016/j.ecolind.2016.09.035

    Article  CAS  Google Scholar 

  • Li X, Yang QS, Liu XP (2007) Genetic algorithms for determining the parameters of cellular automata in urban simulation. Sci China Ser D Earth Sci 50(12):1857–1866

    Article  Google Scholar 

  • Li T, Li J, Wang YZ (2019) Carbon sequestration service flow in the Guanzhong-Tianshui economic region of China. How it flows, what drives it, and where could be optimized? Ecol Indic 96:548–558. https://doi.org/10.1016/j.ecolind.2018.09.040

  • Liu JY, Li J, Qin KY, Zhou ZX, Yang XN, Li T (2017) Changes in land-uses and ecosystem services under multi-scenarios simulation. Sci Total Environ 586:522–526. https://doi.org/10.1016/j.scitotenv.2017.02.005

  • Loheide SP, Gorelick SM (2007) Riparian hydroecology: a coupled model of the observed interactions between groundwater flow and meadow vegetation patterning. Water Resour Res 43(7):30. https://doi.org/10.1029/2006WR005233

    Article  Google Scholar 

  • McMaster R (1997) In memoriam. George F. Jenks (1916-1996). Cartogr Geogr Inf Sci 24(1):56–59. https://doi.org/10.1559/152304097782438764

  • Millennium Ecosystem Assessment (2005) Ecosystems and human well-being: a framework for assessment. Island Press, Washington

    Google Scholar 

  • Pearl J (1988) Morgan Kaufmann series in representation and reasoning. In: Probabilistic reasoning in intelligent systems. Networks of plausible inference. Morgan Kaufmann, San Mateo

    Google Scholar 

  • Potter CS, Randerson JT, Field CB, Matson PA, Vitousek PM, Mooney HA, Klooster SA (1993) Terrestrial ecosystem production. A process model based on global satellite and surface data. Glob Biogeochem Cycle 7(4):811–841

  • Saunders MJ, Jones MB, Kansiime F (2007) Carbon and water cycles in tropical papyrus wetlands. Wetl Ecol Manag 15(6):489–498

    Article  CAS  Google Scholar 

  • Soil Survey Staff (1999) Soil taxonomy: A basic system of soil classification for making and interpreting soil surveys. 2nd edition: Natural Resources Conservation Service. U.S. Department of Agriculture Handbook, 436

  • Su CH, Fu BJ (2013) Evolution of ecosystem services in the Chinese Loess Plateau under climatic and land use changes. Glob Planet Change 101:119–128

  • Thorne CR, Lawson EC, Ozawa C, Hamlin SL, Smith LA (2018) Overcoming uncertainty and barriers to adoption of blue-green infrastructure for urban flood risk management. J Flood Risk Manag 11(3):S960–S972. https://doi.org/10.1111/jfr3.12218

    Article  Google Scholar 

  • Tian HQ, Chen GS, Liu ML, Zhang C, Sun G, Lu CQ et al (2010) Model estimates of net primary productivity, evapotranspiration, and water use efficiency in the terrestrial ecosystems of the southern United States during 1895–2007. For Ecol Manag 259(7):1311–1327 https://doi.org/10.1016/j.foreco.2009.10.009

  • Voinov A, Fitz C, Boumans R, Costanza R (2004) Modular ecosystem modeling. Environ Model Softw 19(3):285–304. https://doi.org/10.1016/S1364-8152(03)00154-3

    Article  Google Scholar 

  • Wang ZM, Guo ZX, Song KS, Liu DW, Zhang B, Zhang SQ et al (2009) Effects of land use/cover change on net primary productivity of Sanjiang Plain, during 2000–2005. J Nat Resour 24(1):136–146

  • Wang X, Jiang YL, Jia BR, Wang FY, Zhou GS (2010) Comparison of soil respiration among three temperate forests in Changbai Mountains, China. Can J For Res 40(4):788–795

  • Wang W, Liao YC, Wen XX, Guo Q (2013) Dynamics of CO2 fluxes and environmental responses in the rain-fed winter wheat ecosystem of the Loess Plateau, China. Sci Total Environ 461–462:10–18. https://doi.org/10.1016/j.scitotenv.2013.04.068

  • Williams JR, Nicks AD, Arnold JG (1985) Simulator for water resources in rural basins. J Hydraul Eng 111(6):970–986

    Article  Google Scholar 

  • Wu D, Shao QQ, Liu JY, Cao W (2016) Spatiotemporal dynamics of water regulation service of grassland ecosystem in China. Res Soil Water Conserv 23(05):256–260

  • Xu HL, Zhang H (2009) Perspectives on soil respiration of Alpine meadow in China. Prataculture & Animal Husbandry 2:1–5

  • Xue J, Gui DW, Lei JQ, Sun HW, Zeng FJ, Feng XL (2017) A hybrid Bayesian network approach for trade-offs between environmental flows and agricultural water using dynamic discretization. Adv Water Resour 110:445–458. https://doi.org/10.1016/j.advwatres.2016.10.022

  • Zeng L, Li J (2019) A Bayesian belief network approach for mapping water conservation ecosystem service optimization region. J Geogr Sci 29(6):1021–1038. https://doi.org/10.1007/s11442-019-1642-x

    Article  Google Scholar 

  • Zhang L, Mao JF, Shi XY, Ricciuto D, He HL, Thornton P et al (2016) Evaluation of the Community Land Model simulated carbon and water fluxes against observations over ChinaFLUX sites. Agric For Meteorol 226–227:174–185. https://doi.org/10.1016/j.agrformet.2016.05.018

  • Zhao FB, Wu YP, Qiu LJ, Sivakumar B, Zhang F, Sun YZ et al (2018) Spatiotemporal features of the hydro-biogeochemical cycles in a typical loess gully watershed. Ecol Indic 91:542–554. https://doi.org/10.1016/j.ecolind.2018.04.027

  • Zhou T, Shao ZY, Luo JY, et al.(2008) Estimation of soil organic carbon based on remotesensing and process model. Front For China 3(2):139–147.

Download references

Funding

This study is supported by National Natural Science Foundation of China, No.41771198, No.41771576; The NSFC-NRF Scientific Cooperation Program (Grant no. 41811540400). The Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2018JM4010). The Fundamental Research Funds for the Central Universities, Shaanxi Normal University (GK201901009).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jing Li.

Additional information

Responsible editor: Marcus Schulz

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zeng, L., Li, J., Qin, K. et al. The total suitability of water yield and carbon sequestration under multi-scenario simulations in the Weihe watershed, China. Environ Sci Pollut Res 27, 22461–22475 (2020). https://doi.org/10.1007/s11356-020-08205-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-020-08205-5

Keywords

Navigation