Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Nonlinear groundwater influence on biophysical indicators of ecosystem services


Groundwater is a fundamental control on biophysical processes underpinning essential ecosystem services (ES). However, interactions and feedbacks among groundwater, climate and multiple ES remain less well understood. We investigated groundwater effects on a portfolio of food, water and biogeochemical ES indicators in an urbanizing agricultural watershed. Our results show that food production, water quality and quantity, and flood control are most sensitive to groundwater, with the strongest responses under wet and dry climate extremes. Climate mediates groundwater effects, such that several ES have synergies during dry climate, but trade-offs (groundwater increased some ES but declined others) under wet climate. There is substantial spatial heterogeneity in groundwater effects on ES, which is driven primarily by water table depth (WTD) and is also sensitive to soil texture and land cover. Most ES indicators respond nonlinearly to WTD when groundwater is within a critical depth (approximately 2.5 m) of land surface, indicating that small WTD changes can have disproportionately large effects on ES in shallow groundwater areas. Within this critical WTD, increasingly shallow groundwater leads to nonlinear increases in surface flood risk, sediment erosion and phosphorus yield; nonlinear decreases in drainage to the deep vadose zone and thus groundwater recharge; and bidirectional responses of crop and grass production, carbon storage and nitrate leaching. Our study illustrates the complex role of groundwater in affecting multiple ES and highlights that strategically managing groundwater may enhance ES resilience to climate extremes in shallow groundwater settings.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Land use/cover, WTD and groundwater effects on ES indicators in the Yahara Watershed.
Fig. 2: Groundwater effects on ES indicators at the grid-cell level, as a function of WTD.
Fig. 3: Influence of land use/cover and soil texture on groundwater effects on ES indicators at the grid-cell level.
Fig. 4: Differences in modelled ES indicators with and without groundwater.

Data availability

The datasets generated and analysed in this study are available from the authors upon request.


  1. 1.

    Wada, Y. et al. Global depletion of groundwater resources. Geophys. Res. Lett. 37, L20402 (2010).

    Article  Google Scholar 

  2. 2.

    Famiglietti, J. S. The global groundwater crisis. Nat. Clim. Change 4, 945–948 (2014).

    Article  Google Scholar 

  3. 3.

    Dalin, C., Wada, Y., Kastner, T. & Puma, M. J. Groundwater depletion embedded in international food trade. Nature 543, 700–704 (2017).

    CAS  Article  Google Scholar 

  4. 4.

    Aeschbach-Hertig, W. & Gleeson, T. Regional strategies for the accelerating global problem of groundwater depletion. Nat. Geosci. 5, 853–861 (2012).

    CAS  Article  Google Scholar 

  5. 5.

    Gleeson, T. et al. Groundwater sustainability strategies. Nat. Geosci. 3, 378–379 (2010).

    CAS  Article  Google Scholar 

  6. 6.

    Fan, Y. Groundwater in the Earth’s critical zone: relevance to large-scale patterns and processes. Water Resour. Res. 51, 3052–3069 (2015).

    Article  Google Scholar 

  7. 7.

    Kollet, S. J. & Maxwell, R. M. Capturing the influence of groundwater dynamics on land surface processes using an integrated, distributed watershed model. Water Resour. Res. 44, W02402 (2008).

    Article  Google Scholar 

  8. 8.

    Maxwell, R. M. & Kollet, S. J. Interdependence of groundwater dynamics and land-energy feedbacks under climate change. Nat. Geosci. 1, 665–669 (2008).

    CAS  Article  Google Scholar 

  9. 9.

    Maxwell, R. M. & Condon, L. E. Connections between groundwater flow and transpiration partitioning. Science 353, 377–380 (2016).

    CAS  Article  Google Scholar 

  10. 10.

    Richardson, M. & Kumar, P. Critical zone services as environmental assessment criteria in intensively managed landscapes. Earths Future 5, 617–632 (2017).

    Article  Google Scholar 

  11. 11.

    Qiu, J. & Turner, M. G. Spatial interactions among ecosystem services in an urbanizing agricultural watershed. Proc. Natl Acad. Sci. USA 110, 12149–12154 (2013).

    CAS  Article  Google Scholar 

  12. 12.

    Qiu, J. et al. Scenarios reveal pathways to sustain future ecosystem services in an agricultural landscape. Ecol. Appl. 28, 119–134 (2018).

    Article  Google Scholar 

  13. 13.

    Raudsepp-Hearne, C., Peterson, G. D. & Bennett, E. M. Ecosystem service bundles for analyzing tradeoffs in diverse landscapes. Proc. Natl Acad. Sci. USA 107, 5242–5247 (2010).

    CAS  Article  Google Scholar 

  14. 14.

    Werling, B. P. et al. Perennial grasslands enhance biodiversity and multiple ecosystem services in bioenergy landscapes. Proc. Natl Acad. Sci. USA 111, 1652–1657 (2014).

    CAS  Article  Google Scholar 

  15. 15.

    Zipper, S. C., Qiu, J. & Kucharik, C. J. Drought effects on US maize and soybean production: spatiotemporal patterns and historical changes. Environ. Res. Lett. 11, 094021 (2016).

    Article  Google Scholar 

  16. 16.

    Qiu, J. & Turner, M. G. Importance of landscape heterogeneity in sustaining hydrologic ecosystem services in an agricultural watershed. Ecosphere 6, 1–19 (2015).

    CAS  Article  Google Scholar 

  17. 17.

    Carpenter, S. R. et al. Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol. Appl. 8, 559–568 (1998).

    Article  Google Scholar 

  18. 18.

    Keeler, B. L. et al. Linking water quality and well-being for improved assessment and valuation of ecosystem services. Proc. Natl Acad. Sci. USA 109, 18619–18624 (2012).

    CAS  Article  Google Scholar 

  19. 19.

    Lal, R. Soil carbon sequestration to mitigate climate change. Geoderma 123, 1–22 (2004).

    CAS  Article  Google Scholar 

  20. 20.

    Nedkov, S. & Burkhard, B. Flood regulating ecosystem services—mapping supply and demand, in the Etropole municipality, Bulgaria. Ecol. Indic. 21, 67–79 (2012).

    Article  Google Scholar 

  21. 21.

    Booth, E. G., Zipper, S. C., Loheide, S. P. & Kucharik, C. J. Is groundwater recharge always serving us well? Water supply provisioning, crop production, and flood attenuation in conflict in Wisconsin, USA. Ecosyst. Serv. 21, 153–165 (2016).

    Article  Google Scholar 

  22. 22.

    Hain, C. R., Crow, W. T., Anderson, M. C. & Yilmaz, M. T. Diagnosing neglected soil moisture source–sink processes via a thermal infrared–based two-source energy balance model. J. Hydrometeorol. 16, 1070–1086 (2015).

    Article  Google Scholar 

  23. 23.

    Ukkola, A. M. et al. Land surface models systematically overestimate the intensity, duration and magnitude of seasonal-scale evaporative droughts. Environ. Res. Lett. 11, 104012 (2016).

    Article  Google Scholar 

  24. 24.

    Zipper, S. C., Soylu, M. E., Booth, E. G. & Loheide, S. P. Untangling the effects of shallow groundwater and soil texture as drivers of subfield-scale yield variability. Water Resour. Res. 51, 6338–6358 (2015).

    Article  Google Scholar 

  25. 25.

    Soylu, M. E., Kucharik, C. J. & Loheide, S. P. Influence of groundwater on plant water use and productivity: development of an integrated ecosystem—variably saturated soil water flow model. Agric. For. Meteorol. 189–190, 198–210 (2014).

    Article  Google Scholar 

  26. 26.

    Dunne, T. & Black, R. D. Partial area contributions to storm runoff in a small New England watershed. Water Resour. Res. 6, 1296–1311 (1970).

    Article  Google Scholar 

  27. 27.

    Kuppel, S., Houspanossian, J., Nosetto, M. D. & Jobbágy, E. G. What does it take to flood the Pampas? Lessons from a decade of strong hydrological fluctuations. Water Resour. Res. 51, 2937–2950 (2015).

    Article  Google Scholar 

  28. 28.

    Heathwaite, A. L. & Dils, R. M. Characterising phosphorus loss in surface and subsurface hydrological pathways. Sci. Total Environ. 251, 523–538 (2000).

    Article  Google Scholar 

  29. 29.

    Helmers, M., Christianson, R., Brenneman, G., Lockett, D. & Pederson, C. Water table, drainage, and yield response to drainage water management in southeast Iowa. J. Soil Water Conserv. 67, 495–501 (2012).

    Article  Google Scholar 

  30. 30.

    Tolomio, M. & Borin, M. Water table management to save water and reduce nutrient losses from agricultural fields: 6 years of experience in North-Eastern Italy. Agric. Water Manage. 201, 1–10 (2018).

    Article  Google Scholar 

  31. 31.

    Li, Q. et al. An approach for assessing impact of land use and biophysical conditions across landscape on recharge rate and nitrogen loading of groundwater. Agric. Ecosyst. Environ. 196, 114–124 (2014).

    CAS  Article  Google Scholar 

  32. 32.

    Kang, Y., Khan, S. & Ma, X. Climate change impacts on crop yield, crop water productivity and food security – A review. Prog. Nat. Sci. 19, 1665–1674 (2009).

    Article  Google Scholar 

  33. 33.

    Qiu, J., Wardropper, C. B., Rissman, A. R. & Turner, M. G. Spatial fit between water quality policies and hydrologic ecosystem services in an urbanizing agricultural landscape. Landsc. Ecol. 32, 59–75 (2017).

    Article  Google Scholar 

  34. 34.

    Griggs, D. et al. Policy: sustainable development goals for people and planet. Nature 495, 305–307 (2013).

    CAS  Article  Google Scholar 

  35. 35.

    Kucharik, C. J. et al. Testing the performance of a dynamic global ecosystem model: water balance, carbon balance, and vegetation structure. Glob. Biogeochem. Cycles 14, 795–825 (2000).

    CAS  Article  Google Scholar 

  36. 36.

    Motew, M. et al. The influence of legacy P on lake water quality in a Midwestern agricultural watershed. Ecosystems 20, 1468–1482 (2017).

    CAS  Article  Google Scholar 

  37. 37.

    Parsen, M. J., Bradbury, K. R., Hunt, R. J. & Feinstein, D. T. The 2016 Groundwater Flow Model for Dane County, Wisconsin Bulletin 110 (Wisconsin Geological and Natural History Survey, 2016).

  38. 38.

    Qiu, J. et al. Understanding relationships among ecosystem services across spatial scales and over time. Environ. Res. Lett. 13, 054020 (2018).

    Article  Google Scholar 

  39. 39.

    Zipper, S. C., Soylu, M. E., Kucharik, C. J. & Loheide, S. P. II Quantifying indirect groundwater-mediated effects of urbanization on agroecosystem productivity using MODFLOW-AgroIBIS (MAGI), a complete critical zone model. Ecol. Model. 359, 201–219 (2017).

    Article  Google Scholar 

  40. 40.

    Wrede, S. et al. Towards more systematic perceptual model development: a case study using 3 Luxembourgish catchments. Hydrol. Process. 29, 2731–2750 (2015).

    Article  Google Scholar 

  41. 41.

    Carpenter, S. R., Booth, E. G. & Kucharik, C. J. Extreme precipitation and phosphorus loads from two agricultural watersheds. Limnol. Oceanogr. 63, 1221–1233 (2018).

    CAS  Article  Google Scholar 

  42. 42.

    Lowry, C. S. & Loheide, S. P. Groundwater-dependent vegetation: quantifying the groundwater subsidy. Water Resour. Res. 46, W06202 (2010).

    Article  Google Scholar 

  43. 43.

    Orellana, F., Verma, P., Loheide, S. P. & Daly, E. Monitoring and modeling water–vegetation interactions in groundwater‐dependent ecosystems. Rev. Geophys. 50, RG3003 (2012).

    Article  Google Scholar 

  44. 44.

    Zipper, S. C. & Loheide, S. P. II Using evapotranspiration to assess drought sensitivity on a subfield scale with HRMET, a high resolution surface energy balance model. Agric. For. Meteorol. 197, 91–102 (2014).

    Article  Google Scholar 

  45. 45.

    Green, T. R. & Anapalli, S. S. Irrigation variability and climate change affect derived distributions of simulated water recharge and nitrate leaching. Water Int. 43, 829–845 (2018).

    Article  Google Scholar 

  46. 46.

    Kucharik, C. J. & Brye, K. R. Integrated BIosphere Simulator (IBIS) yield and nitrate loss predictions for Wisconsin maize receiving varied amounts of nitrogen fertilizer. J. Environ. Qual. 32, 247–268 (2003).

    CAS  Article  Google Scholar 

  47. 47.

    Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science 339, 940–943 (2013).

    CAS  Article  Google Scholar 

  48. 48.

    Licker, R. et al. Mind the gap: how do climate and agricultural management explain the ‘yield gap’ of croplands around the world?. Glob. Ecol. Biogeogr. 19, 769–782 (2010).

    Article  Google Scholar 

  49. 49.

    Vigerstol, K. L. & Aukema, J. E. A comparison of tools for modeling freshwater ecosystem services. J. Environ. Manage. 92, 2403–2409 (2011).

    Article  Google Scholar 

  50. 50.

    Maxwell, R. M., Condon, L. E. & Kollet, S. J. A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3. Geosci. Model Dev. 8, 923 (2015).

    Article  Google Scholar 

  51. 51.

    Brooks, P. D. et al. Hydrological partitioning in the critical zone: recent advances and opportunities for developing transferable understanding of water cycle dynamics. Water Resour. Res. 51, 6973–6987 (2015).

    Article  Google Scholar 

  52. 52.

    Bennett, E. M. Research frontiers in ecosystem service science. Ecosystems 20, 31–37 (2017).

    Article  Google Scholar 

  53. 53.

    Verburg, P. H. et al. Methods and approaches to modelling the Anthropocene. Glob. Environ. Change 39, 328–340 (2016).

    Article  Google Scholar 

  54. 54.

    Bardgett, R. D., Bowman, W. D., Kaufmann, R. & Schmidt, S. K. A temporal approach to linking aboveground and belowground ecology. Trends Ecol. Evol. 20, 634–641 (2005).

    Article  Google Scholar 

  55. 55.

    Qiu, J. et al. Evidence-based causal chains for linking health, development, and conservation actions. BioScience 68, 182–193 (2018).

    Article  Google Scholar 

  56. 56.

    Mitchell, M. G. E., Bennett, E. M. & Gonzalez, A. Strong and nonlinear effects of fragmentation on ecosystem service provision at multiple scales. Environ. Res. Lett. 10, 094014 (2015).

    Article  Google Scholar 

  57. 57.

    Rieb, J. T. et al. When, where, and how nature matters for ecosystem services: challenges for the next generation of ecosystem service models. BioScience 67, 820–833 (2017).

    Article  Google Scholar 

  58. 58.

    Herron, C. & Ruark, M. The Extent of Tile Drainage in Wisconsin (Univ. Wisconsin-Extension, 2017);

  59. 59.

    Rodríguez, J. P. et al. Trade-offs across space, time, and ecosystem services. Ecol. Soc. 11, 28 (2006).

    Article  Google Scholar 

  60. 60.

    Raudsepp-Hearne, C. & Peterson, G. Scale and ecosystem services: how do observation, management, and analysis shift with scale—lessons from Québec. Ecol. Soc. 21, 16 (2016).

    Article  Google Scholar 

  61. 61.

    Groffman, P. M. et al. Ecological thresholds: the key to successful environmental management or an important concept with no practical application? Ecosystems 9, 1–13 (2006).

    Article  Google Scholar 

  62. 62.

    Foley, J. A. et al. An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Glob. Biogeochem. Cycles 10, 603–628 (1996).

    CAS  Article  Google Scholar 

  63. 63.

    Kucharik, C. J. Evaluation of a process-based agro-ecosystem model (Agro-IBIS) across the US Corn Belt: simulations of the interannual variability in maize yield. Earth Interact. 7, 14 (2003).

    Article  Google Scholar 

  64. 64.

    Šimůnek, J., van Genuchten, M. T. & Šejna, M. The HYDRUS-1D Software Package for Simulating the One-dimensional Movement of Water, Heat, and Multiple Solutes in Variably-saturated Media v.3.0 Research Report (Univ. California Riverside, 2005)..

  65. 65.

    Richards, L. A. Capillary conduction of liquids through porous mediums. Physics 1, 318–333 (1931).

    Article  Google Scholar 

  66. 66.

    Booth, E. G. et al. From qualitative to quantitative environmental scenarios: translating storylines into biophysical modeling inputs at the watershed scale. Environ. Model. Softw. 85, 80–97 (2016).

    Article  Google Scholar 

  67. 67.

    R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2016);

  68. 68.

    Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).

    Article  Google Scholar 

  69. 69.

    Bates, D. et al. lme4: Linear mixed-effects models using Eigen and S4, 2014. R Package v.1.1-7 (2015)..

  70. 70.

    Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. Package ‘lmerTest’. R Package v.2.0-29 (2015)..

Download references


Funding support is from the National Science Foundation Water Sustainability and Climate Program under grant DEB-1038759 and the North Temperate Lakes Long-Term Ecological Research (DEB-1440297). We thank P. Pinkas for computational assistance. J.Q. acknowledges the USDA National Institute of Food and Agriculture, Hatch Project (FLA-FTL-005640) and McIntire-Stennis (1014703) projects for partial financial support of this work.

Author information




J.Q. and S.C.Z. designed the research and analysed data. J.Q., S.C.Z., M.M. and E.G.B. performed the research. J.Q., S.C.Z., M.M., E.G.B., C.J.K. and S.P.L. interpreted the results. J.Q. and S.C.Z. led the writing process and all authors contributed substantially with commentary, edits and revisions.

Corresponding author

Correspondence to Jiangxiao Qiu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Notes, Supplementary Table 1, Supplementary Figs. 1–12 and Supplementary Refs. 1–75

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Qiu, J., Zipper, S.C., Motew, M. et al. Nonlinear groundwater influence on biophysical indicators of ecosystem services. Nat Sustain 2, 475–483 (2019).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing