Enhanced precipitation variability effects on water losses and ecosystem functioning: differential response of arid and mesic regions

Abstract

Climate change will result in increased precipitation variability with more extreme events reflected in more frequent droughts as well as more frequent extremely wet conditions. The increase in precipitation variability will occur at different temporal scales from intra to inter-annual and even longer scales. At the intra-annual scale, extreme precipitation events will be interspersed with prolonged periods in between events. At the inter-annual scale, dry years or multi-year droughts will be combined with wet years or multi-year wet conditions. Consequences of this aspect of climate change for the functioning ecosystems and their ability to provide ecosystem services have been underexplored. We used a process-based ecosystem model to simulate water losses and soil-water availability at 35 grassland locations in the central US under 4 levels of precipitation variability (control, +25, +50 + 75 %) and six temporal scales ranging from intra- to multi-annual variability. We show that the scale of temporal variability had a larger effect on soil-water availability than the magnitude of variability, and that inter- and multi-annual variability had much larger effects than intra-annual variability. Further, the effect of precipitation variability was modulated by mean annual precipitation. Arid-semiarid locations receiving less than about 380 mm yr−1 mean annual precipitation showed increases in water availability as a result of enhanced precipitation variability while more mesic locations (>380 mm yr−1) showed a decrease in soil water availability. The beneficial effects of enhanced variability in arid-semiarid regions resulted from a deepening of the soil-water availability profile and a reduction in bare soil evaporation. The deepening of the soil-water availability profile resulting from increase precipitation variability may promote future shifts in species composition and dominance to deeper-rooted woody plants for ecosystems that are susceptible to state changes. The break point, which has a mean of 380-mm with a range between 440 and 350 mm, is remarkably similar to the 370-mm threshold of the inverse texture hypothesis, below which coarse-texture soils had higher productivity than fine-textured soils.

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References

  1. Anadón JD, Sala OE, Turner BL, Bennett EM (2014) The effect of woody-plant encroachment on livestock production in North and South America. Proc Natl Acad Sci U S A 111:12948–12953. doi:10.1073/pnas.1320585111

    Article  Google Scholar 

  2. Archer SR, Predick KI (2014) An ecosystem services perspective on brush management: research priorities for competing land-use objectives. J Ecol 102:1394–1407. doi:10.1111/1365-2745.12314

    Article  Google Scholar 

  3. Bailey RG (1998) Ecoregions: the ecosystem geography of the oceans and continents. Springer, New York

    Book  Google Scholar 

  4. Barger NN, Archer SR, Campbell JL, Huang Cy, Morton JA, Knapp AK (2011) Woody plant proliferation in North American drylands: a synthesis of impacts on ecosystem carbon balance Journal of Geophysical Research 116

  5. Christensen JH, Christensen OB (2003) Climate modelling: severe summertime flooding in Europe. Nature 421:805–806. doi:10.1038/421805a

    Article  Google Scholar 

  6. Coffin D, Lauenroth W (1996) Transient responses of North-American grasslands to changes in climate. Clim Chang 34:269–278. doi:10.1007/bf00224638

    Article  Google Scholar 

  7. Easterling DR, Meehl GA, Parmesan C, Changnon SA, Karl TR, Mearns LO (2000) Climate extremes: observations, modeling, and impacts. Science 289:2068–2074

    Article  Google Scholar 

  8. Field CB (2012) Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

    Book  Google Scholar 

  9. Heisler-White JL, Knapp AK, Kelly EF (2008) Increasing precipitation event size increases aboveground net primary productivity in a semi-arid grassland. Oecologia 158:129–140. doi:10.1007/s00442-008-1116-9

    Article  Google Scholar 

  10. Heisler-White JL, Blair JM, Kelly EF, Harmoney K, Knapp AK (2009) Contingent productivity responses to more extreme rainfall regimes across a grassland biome. Glob Change Biol 15:2894–2904. doi:10.1111/j.1365-2486.2009.01961.x

    Article  Google Scholar 

  11. Hennessy KJ, Gregory JM, Mitchell JFB (1997) Changes in daily precipitation under enhanced greenhouse conditions. Clim Dyn 13:667–680. doi:10.1007/s003820050189

    Article  Google Scholar 

  12. Jackson RB, Sperry JS, Dawson TE (2000) Root water uptake and transport: using phisiological processes in global predictions. Trends Plant Sci 5:482–488

    Article  Google Scholar 

  13. Janssen E, Wuebbles DJ, Kunkel KE, Olsen SC, Goodman A (2014) Observational‐and model‐based trends and projections of extreme precipitation over the contiguous United States Earth’s. Future 2:99–113

    Google Scholar 

  14. Kharin VV, Zwiers FW, Zhang XB, Hegerl GC (2007) Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations. J Clim 20:1419–1444. doi:10.1175/jcli4066.1

    Article  Google Scholar 

  15. Knapp AK et al (2008a) Consequences of more extreme precipitation regimes for terrestrial ecosystems. Bioscience 58:811–821. doi:10.1641/b580908

    Article  Google Scholar 

  16. Knapp AK et al (2008b) Shrub encroachment in North American grasslands: shifts in growth form dominance rapidly alters control of ecosystem carbon inputs. Glob Change Biol 14:615–623

    Article  Google Scholar 

  17. Lauenroth WK, Bradford JB (2009) Ecohydrology of dry regions of the United States: precipitation pulses and intraseasonal drought. Ecohydrology 2:173–181. doi:10.1002/eco.53

    Article  Google Scholar 

  18. Lauenroth WK, Bradford JB (2012) Ecohydrology of dry regions of the United States: water balance consequences of small precipitation events. Ecohydrology 5:46–53. doi:10.1002/eco.195

    Article  Google Scholar 

  19. Lauenroth WK, Urban DL, Coffin DP, Parton WJ, Shugart HH, Kirchner TB, Smith TM (1993) Modeling vegetation structure-ecosystem process interactions across sites and ecosystems. Ecol Model 67:49–80. doi:10.1016/0304-3800(93)90099-E

    Article  Google Scholar 

  20. Lauenroth WK, Sala OE, Coffin CP, Kirchner TB (1994) The importance of soil water in the recruitment of Bouteloua gracilis in the shortgrass steppe. Ecol Appl 4:741–749

    Article  Google Scholar 

  21. Lauenroth WK, Schlaepfer DR, Bradford JB (2014) Ecohydrology of dry regions: storage versus pulse soil. Water Dynam Ecosyst 17:1469–1479. doi:10.1007/s10021-014-9808-y

    Article  Google Scholar 

  22. Leiserowitz A, Maibach E, Roser-Renouf C, Hmielowski JD (2012) Extreme weather. Climate & Preparedness in the American Mind, New Haven

    Google Scholar 

  23. Lewis SL, Brando PM, Phillips OL, van der Heijden GMF, Nepstad D (2011) The 2010 amazon drought. Science 331:554–554. doi:10.1126/science.1200807

    Article  Google Scholar 

  24. Liu J, Wang B, Cane MA, Yim S-Y, Lee J-Y (2013) Divergent global precipitation changes induced by natural versus anthropogenic forcing. Nature 493:656–659

    Article  Google Scholar 

  25. Luo Y et al (2008) Modeled interactive effects of precipitation, temperature, and [CO2] on ecosystem carbon and water dynamics in different climatic zones. Glob Change Biol 14:1986–1999. doi:10.1111/j.1365-2486.2008.01629.x

    Article  Google Scholar 

  26. Malhi Y, Roberts JT, Betts RA, Killeen TJ, Li WH, Nobre CA (2008) Climate change, deforestation, and the fate of the Amazon. Science 319:169–172. doi:10.1126/science.1146961

    Article  Google Scholar 

  27. Minnick TJ, Coffin DP (1999) Geographic patterns of simulated establishment of two Bouteloua species: implications for distributions of dominants and ecotones. J Veg Sci 10:343–356

    Article  Google Scholar 

  28. Parton WJ (1978) Abiotic section of ELM. In: Innis GS (ed) Grassland simulation model, vol ecological studies, 26th edn. Springer, New York, pp 31–53

    Chapter  Google Scholar 

  29. Parton W, Schimel DS, Cole C, Ojima D (1987) Analysis of factors controlling soil organic matter levels in Great Plains grasslands. Soil Sci Soc Am J 51:1173–1179

    Article  Google Scholar 

  30. Parton WJ, Stewart JW, Cole CV (1988) Dynamics of C, N, P and S in grassland soils: a model. Biogeochemistry 5:109–131

    Article  Google Scholar 

  31. Parton W et al (2001a) Generalized model for NOx and N2O emissions from soils. J Geophys Res 106:17403–17419

    Article  Google Scholar 

  32. Parton WJ, Morgan JA, Kelly RH, Ojima DS (2001b) Modeling soil C responses to environmental change in grassland ecosystems. CRC Press, Boca Raton

    Google Scholar 

  33. Peters D (2000) Climatic variation and simulated patterns in seedling establishment of two dominant grasses at a semiarid-arid grassland ecotone. J Veg Sci 11:493–504

    Article  Google Scholar 

  34. Peters DPC, Herrick JE, Monger HC, Huang HT (2010) Soil-vegetation-climate interactions in arid landscapes: effects of the North American monsoon on grass recruitment. J Arid Environ 74:618–623. doi:10.1016/j.jaridenv.2009.09.015

    Article  Google Scholar 

  35. Poulter B et al. (2014) Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle Nature advance online publication doi:10.1038/nature13376

  36. R Development Core Team (2012) R: a language and environment for statistical computing, version: 2.14, 2nd edn. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  37. Ratajczak Z, Nippert JB, Briggs JM, Blair JM (2014) Fire dynamics distinguish grasslands, shrublands and woodlands as alternative attractors in the Central Great Plains of North America. J Ecol 102:1374–1385. doi:10.1111/1365-2745.12311

    Article  Google Scholar 

  38. Reichmann LG, Sala OE (2014) Differential sensitivities of grassland structural components to changes in precipitation mediate productivity response in a desert ecosystem. Funct Ecol 28:1292–1298. doi:10.1111/1365-2435.12265

    Article  Google Scholar 

  39. Reichmann LG, Sala OE, Peters DPC (2013) Precipitation legacies in desert-grassland primary production occur through previous-year tiller density. Ecology 94:435–443. doi:10.1890/12-1237.1

    Article  Google Scholar 

  40. Reyer CP et al (2013) A plant’s perspective of extremes: terrestrial plant responses to changing climatic variability. Glob Chang Biol 19:75–89

    Article  Google Scholar 

  41. Reynolds JF et al (2007) Global desertification: building a science for dryland development. Science 316:847–851

    Article  Google Scholar 

  42. Robinson TM, Gross KL (2010) The impact of altered precipitation variability on annual weed species. Am J Bot 97:1625–1629

    Article  Google Scholar 

  43. Sala OE, Parton WJ, Lauenroth WK, Joyce LA (1988) Primary production of the central grassland region of the United States. Ecology 69:40–45. doi:10.2307/1943158

    Article  Google Scholar 

  44. Sala OE, Lauenroth WK, Parton WJ (1992) Long term soil water dynamics in the shortgrass steppe. Ecology 73:1175–1181

    Article  Google Scholar 

  45. Schulze ED et al (1996) Rooting depth, water availability, and vegetation cover along an aridity gradient in Patagonia. Oecologia 108:503–511

    Article  Google Scholar 

  46. Seneviratne SI, Luthi D, Litschi M, Schar C (2006) Land-atmosphere coupling and climate change in Europe. Nature 443:205–209. doi:10.1038/nature05095

    Article  Google Scholar 

  47. Seneviratne SI et al. (2012) Changes in climate extremes and their impacts on the natural physical environment Managing the risks of extreme events and disasters to advance climate change adaptation:109–230

  48. Singh D, Tsiang M, Rajaratnam B, Diffenbaugh NS (2013) Precipitation extremes over the continental United States in a transient, high‐resolution, ensemble climate model experiment. J Geophys Res: Atmospher 118:7063–7086

    Google Scholar 

  49. Solomon S et al (eds) (2007) Climate change 2007: the physical science basis. Cambridge University Press, Cambridge

    Google Scholar 

  50. Tanaka JA, Brunson M, Torell LA (2011) A social and economic assessment of rangeland conservation practices. In: Briske DD (ed) Conservation benefits of rangeland practices: assessment, recommendations, and knowledge gaps. Natural Resources Conservation Service, Washington, DC, pp 371–422

    Google Scholar 

  51. Thomey ML, Collins SL, Vargas R, Johnson JE, Brown RF, Natvig DO, Friggens MT (2011) Effect of precipitation variability on net primary production and soil respiration in a Chihuahuan Desert grassland. Glob Change Biol 17:1505–1515. doi:10.1111/j.1365-2486.2010.02363.x

    Article  Google Scholar 

  52. Trenberth KE, Dai A, Rasmussen RM, Parsons DB (2003) The changing character of precipitation. Bull Am Meteorol Soc 84:1205–1218

    Article  Google Scholar 

  53. Wright SP (1992) Adjusted p-values for simultaneous inference Biometrics:1005–1013

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Acknowledgments

The authors thank Haitao Huang for model simulations, reviewers and editor for positive suggestions that significantly improved the manuscript and finally G.A. Gil and D. Correa for assistance and guidance. This research was financially supported by NSF DEB 09–17668 and DEB 12–35828.

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Correspondence to Osvaldo E. Sala.

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Sala, O.E., Gherardi, L.A. & Peters, D.P.C. Enhanced precipitation variability effects on water losses and ecosystem functioning: differential response of arid and mesic regions. Climatic Change 131, 213–227 (2015). https://doi.org/10.1007/s10584-015-1389-z

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Keywords

  • Mean Annual Precipitation
  • Precipitation Variability
  • Soil Evaporation
  • Soil Water Availability
  • Deep Percolation