Feedback between structured vegetation and soil water in a changing climate: A simulation study

  • Heike Lischke
  • Bärbel Zierl
Part of the Advances in Global Change Research book series (AGLO, volume 10)


Structure and composition of vegetation influence the local water budget by transpiration and interception. On the other hand soil water content crucially affects plant physiological processes such as nutrient transport or photosynthesis. These processes in turn partly determine biomass production, plant growth, survival and competition and, thus, vegetation structure. This vegetation-hydrology feedback has the potential to influence the impacts of a changing climate on vegetation and hydrology.

However, only few simulation studies include this feedback explicitly. In these studies vegetation models are linked to hydrological or climate models. On the local scale, the used vegetation models are complex and slow. On larger scales, the vegetation and its dynamics are usually modelled in a simplified manner. Particularly vegetation structure is either ignored completely, e.g. by using the leaf area index (LAI) averaged over large areas as state variable, or reduced to few canopy layers and plant functional types.

To examine the effect of vegetation structure and the effect of the vegetationhydrology feedback on drought and vegetation under a changing climate, we coupled the dynamic forest model DisCForM with the soil water budget model WAWAHAMO. DisCForM is a highly efficient model, designed for regional scale applications over decades and centuries. It simulates horizontal, vertical, and species structure in a forest stand.

Simulations of forest development and drought stress occurrence were performed with the coupled and decoupled models at several typical sites in Switzerland. The results indicate that vegetation plays an important stabilizing role regarding the impact of a climatic shift on hydrology. By adaptation, i.e. the lowering of LAI and the reduction of cover grade by changed mortality and growth and the exchange of tree species, the vegetation itself is able to counterbalance the influence of climate change on drought. This stabilizing effect is particularly pronounced at the currently dry forest sites. At wet forest sites, the positive effect of a warmer climate on biomass production appears to overwhelm the negative effect of the strengthening of drought.

To study the effect of structure, simulations with reduced horizontal and vertical structure and a reduced species set were compared to those with full structure. Reducing structure had minor effects on drought and moderate effects on vegetation in the long-term average. Under medium dry conditions, however, reducing all three kinds of structure simultaneously resulted in lower drought and LAI values. Furthermore, deviations in single years were considerable.

We concluded that neglecting structure in vegetation-hydrology-models can lead to wrong drought values for single years and to a systematic underestimation of drought in the long-term average under dry conditions. In contrast, neglecting the feedback can lead to an overestimation of drought.


Soil Water Drought Stress Drought Index Couple Simulation Vegetation Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Band, L. E., Patterson, P., Nemani, R. and Running, S. W., 1993. Forest ecosystem processes at the watershed scale: incorporating hillslope hydrology. Agricultural and Forest Meteorology, 63: 63–126.CrossRefGoogle Scholar
  2. Beniston, M. A., Ohmura, A., Rotach, M., Tschuk, P., Wild, M. and Marinucci, R. M., 1995. Simulation of Climate Trends over the Alpine Region: Development of a Physically-Base Modeling System for Application to Regional Studies of Current and Future Climate.. 198 pp., Department of Geography, ETH Zürich, Zürich.Google Scholar
  3. Betts, R. A., Cox, P. M., Lee, S. E. and Woodward, F. I., 1997. Contrasting physiological and structural vegetation feedbacks in climate change simulations. Nature (London), 387: 797–799.CrossRefGoogle Scholar
  4. Bolker, B. M., Pacala, S. W. and Levin, S. A., 1997. Moment methods for stochastic processes in continuous space and time. In: U. Dieckmann and J. Metz (Eds.), Low Dimensional Dynamics of Spatial Ecological Systems, Laxenburg.Google Scholar
  5. Brang, P., 1998. Sanasilva-Bericht 1997. Gesundheit und Gefährdung des Schweizer Waldeseine Zwischenbilanz nach 15 Jahren Waldschadenforschung, Berichte der Eidg. Forschungsanstalt für Wald, Schnee und Landschaft, Vol. 345, pp. 102. Eidg. Forschungsanstalt für Wald, Schnee und Landschaft, Birmensdorf.Google Scholar
  6. Bugmann, H., 1994. On the ecology of mountainous forests in a changing climate: A simulation study. Diss. ETH, No. 10638, Environmental Sciences, Swiss Federal Institute of Technology Zurich, Zurich.Google Scholar
  7. Bugmann, H., 1996. A simplified forest model to study species composition along climate gradients. Ecology, 77 (7): 2055–2074.CrossRefGoogle Scholar
  8. Bugmann, H. and Cramer, W., 1997. Improving the behaviour of forest gap models along drought gradients. No. 24, PIK — Potsdam Institute for Climate Impact Research, Potsdam. Bundesamt für Raumplanung, 1980. Bodeneignungskarte der Schweiz. Grundlagen für die Raumplanung., Bundesamt für Raumplanung, Bern,Google Scholar
  9. Churkina, G., Running, S. W., Schloss, A. L., Bondeau, A., Cramer, W., Colinet, G., Collatz, J., Dedieu, G. W. E., Esser, G., Field, C., Francois, L., Friend, A., Haxeltine, A., Heimann, M., HOffstadt, J., Kaduk, J., Kergoat, L., Kicklighter, D. W., Knorr, W., Kohlmaier, G., Lurin, B., Maisongrande, P., Martin, P., McKEown, R., Meeson, B., Moore III, B., Nemani, R., Nemry, B., Olson, R., Otto, R., Parton, W., Plöchl, M., Prince, S., Randerson, J., Rasool, L, Rizzo, B., Ruimy, A., Running, S., Sahagian, D., Saugier, B., Schloss, A. L., Scurlock, J., Steffen, W., Warnant, P. and Wittenberg, U., 1999. Comparing global models of terrestrial net primary productivity (NPP): the importance of water availability. Global Change Biology, 5 (Suppl. 1): 46–55.CrossRefGoogle Scholar
  10. Ciencala, E., Running, S. W., Lindroth, A., Grelle, A. and Ryan, M. G., 1998. Analysis of carbon and water fluxes from the NOPEX boreal forest: comparison of measurements with FOREST-BGC simulations. Journal of Hydrology, 212–213 : 62–78.CrossRefGoogle Scholar
  11. Clark, J. S., 1993. Paleoecological perspectives on modeling broad-scale responses to global change. In: P. M. Kareiva, J. G. Kingsolverand R. B. Huey (Eds.), Biotic interactions and global change, pp. 315–332. Sinauer Associates, Sunderland, Massachusetts.Google Scholar
  12. Deutschman, D. H., Levin, S. A. and Pacala, S. W., 1997. Scaling from trees to forest landscapes: The role of fine-scale heterogeneity in light. Ecological Monographs: submitted.Google Scholar
  13. Dobbertin, M., 1996. Relationship between basal area increment, tree crown defoliation, and tree and site variables, IUFRO Conference on Effects of environmental factors on tree and stand growth, pp. 33–44. Technische Universität Dresden, Berggiesshübel near Dresden.Google Scholar
  14. EAFV, 1988. Schweizerisches Landesforstinventar. Ergebnisse der Erstaufnahme 1982–1986, Berichte Eidgenössische. Forschungsanstalt für Wald, Schnee und Landschaft, Vol. 305, pp. 375. Eidgenössische Anstalt für das forstliche Versuchswesen in Zusammenarbeit mit dem Bundesamt für Forstwesen und Landschaftsschutz, Birmensdorf.Google Scholar
  15. Foley, J. A., Levis, S., Costa, M. H., Cramer, W. and Pollard, D., 2000. Incorporating dynamic vegetation cover within global climate models. Ecological Applications, 10 (6): 1620–1632.CrossRefGoogle Scholar
  16. Foley, J. A., Levis, S. and Prentice, C, 1998. Coupling dynamic models of climate and vegetation. Global change biology, 4 (5): 561–579.CrossRefGoogle Scholar
  17. Foley, J. A., Prentice, C, Ramankutty, N., Levis, S., Pollard, D., Sitch, S. and Haxeltine, A., 1996. An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Global Biogeochemical Cycles, 10 : 603–628.CrossRefGoogle Scholar
  18. Friend, A. D., Stevens, A. K., Knox, R. G. and Cannell, M. G. R., 1997. A process-based, terrestrial bioshpere model of ecosystem dynamics (Hybrid v3.0). Ecological Modelling, 96 : 249–287.CrossRefGoogle Scholar
  19. Gurtz, J., Baltensweiler, A., Lang, H., Menzel, L. and Schulla, J., 1997. Auswirkungen von klimatischen Variationen auf den Wasserhaushalt und Abfluss im Flussgebiet des Rheins. Projektschlussbericht im Rahmen des nationalen Forschungsprogramms (NFP31): Klimaänderungen und Naturkatastrophen, vdf, Hochschul-Verlag an der ETH, Zürich.Google Scholar
  20. Haeupler, H., 1995. Diversität. In: W. Kuttler and K. Steinecke (Eds.), Handbuch zur Ökologie, pp. 99–104. Analytica, Berlin.Google Scholar
  21. Heck, P., Lüthi, D. and Schär, C, 1999. The influence of vegetation on the summertime evolution of European soil moisture. Physics and Chemistry of the Earth. Part B Hydrology, Oceans and Athmosphere, 24 (6): 609–614.Google Scholar
  22. Hieke, K., 1989. Praktische Dendrologie. VEB Deutscher Landwirtschaftsverlag, Berlin.Google Scholar
  23. Hurtt, G. C, Moorcroft, P. R., Pacala, S.W. and Levin, S. A., 1998. Terrestrial models and global change: challenges for the future. Global Change Biology, 4 : 581–590.CrossRefGoogle Scholar
  24. Huston, M. A., 1991. Use of individual-based forest succession models to link physiological whole-tree models to landscape-scale ecosystem models. Tree Physiology, 9 : 293–306.CrossRefGoogle Scholar
  25. Hutjes, R., Kabat, P., Running, S., Shuttleworth, W., Field, C, Bass, B., Dias, M., Avissar, R., Becker, A., Claussen, M., Dolman, A., Feddes, R., Fosberg, M., Fukushima, Y., Gash, J., Guenni, L., Hoff, H., Jarvis, P., Kayane, L, Krenke, A., Liu, C, Meybeck, M., Nobre, C., Oyebande, L., Pitman, A., Pielke, R., Raupach, M., Saugier, B., Schulze, E., Sellers, P., Tenhunen, J., Valentini, R., Victoria, R. and Vorosmarty, C, 1998.Biospheric aspects of the hydrological cycle — Preface.Journal of Hydrology, 213 (1–4): 1–21.CrossRefGoogle Scholar
  26. Kergoat, L., 1998. A model for hydrological equilibrium of leaf area index on a global scale. Journal of hydrology, 212–213 : 268–286.CrossRefGoogle Scholar
  27. Korol, R. L., Running, S. W. and Milner, K. S., 1995. Incorporating intertree competition into an ecosystem model. Can. J. For. Res., 25 (3): 413–424.CrossRefGoogle Scholar
  28. Kräuchi, N., 1994. Modelling forest succession as influenced by a changing environment. Mitteilungen der Schweizerischen Anstalt fur das forstliche Versuchswesen, 69 : 125–271.Google Scholar
  29. Kräuchi, N. and Kienast, F., 1993. Modelling subalpine forest dynamics as influenced by a changing environment. Water, Air, & Soil Pollution, 68 : 185–197.CrossRefGoogle Scholar
  30. Kremer, R. G., Hunt, E. R., Running, S. W. and Coughlan, J. C, 1996. Simulating vegetational and hydrologic responses to natural climatic variation and GCM-predicted climate change in a semi-arid ecosystem in Washington, USA. Journal of Aride Environments, 33 (1): 23–38.CrossRefGoogle Scholar
  31. Kucharik, C. J., Foley, J. A., Delire, C., Fisher, V. A., Coe, M. T., Lenters, J. D., Young, M. C, Ramankutty, N., Norman, J. M. and Gower, S., 2000. Testing the performance of a Dynamic Global Ecosystem Model: Water balance, carbon balance, and vegetation structure. Global Biogeochemical Cycles, 14 (3): 795–825.CrossRefGoogle Scholar
  32. Levis, S., Foley, J. A. and Pollard, D., 2000. Large-scale vegetation feedbacks on a doubled C02 climate. Journal of Climate, 13 (7): 1313–1325.CrossRefGoogle Scholar
  33. Lexer, M., 1995. Anwendung eines “big-leaf-Modelies zur Simulation des Bodenwasserhaushaltes in Fichtenbeständen (Picea abies(L.) Karst.). Applying a ” bigleaf’-model to simulate seasonal sooil water dynamics in Norway spruce stands. Centralblatt für das gesamte Forstwesen, 112 (4): 209–225.Google Scholar
  34. Lexer, M. J., Honninger, K., Scheifinger, H., Matulla, C, Groll, N. and Kromp-Kolb, H., 2000. The sensitivity of central European mountain forests to scenarios of climatic change: Methodological frame for a large-scale risk assessment. Silva Fennica, 34 (2): 113–129.Google Scholar
  35. Lischke, H., 2001. New developments in forest modeling: Convergence between applied and theoretical approaches. Natural Resource Modeling, 14 (1): in press.Google Scholar
  36. Lischke, H., Löffler, T. J. and Fischlin, A., 1997a. Calculating temperature dependence over long time periods: A comparison and study of methods. Agric. For. Meteorol., 86 : 169–181.CrossRefGoogle Scholar
  37. Lischke, H., Löffler, T. J. and Fischlin, A., 1997b. Calculating temperature dependence over long time periods: Derivation of methods. Ecol. Model., 98 (2–3): 105–122.CrossRefGoogle Scholar
  38. Lischke, H., Löffler, T. J. and Fischlin, A., 1998. Aggregation of individual trees and patches in forest succession models — Capturing variability with height structured random dispersions. Theoretical Population Biology, 54 : 213–226.CrossRefGoogle Scholar
  39. Löffler, T. J. and Lischke, H., 2001. Incorporation and Influence of Variability in an Aggregated Forest Model. Natural Resource Modeling, 14 (1): in press.Google Scholar
  40. Lüdecke, M. K. B., Badeck, F.-W., Otto, R. D., Hager, C, Dönges, S., Kindermann, J., Würth, G., Lang, T., Jäkel, U., Klaudius, A., Ramge, P., Habermehl, S. and Kohlmaier, G. H., 1994. The Frankfurt Biosphere Model: a global process-oriented model of seasonal and long-term C02 exchage between terrestrial ecosystems and the atmosphere. I. Model description and illustrative results for cold deciduous and boreal forests. Clim. Res., 4 :143–166.CrossRefGoogle Scholar
  41. Marinucci, R. M, Giorgi, F., Beniston, M., Wild, M., Tschuck, P. and Bernasconi, A., 1995. High resolution simulations of January and July climate over the western Alpine region with a nested regional modeling system. Theoretical and Applied Climatology, 51 : 119–138.CrossRefGoogle Scholar
  42. McMurtrie, R. E. and Landsberg, J. J., 1992. Using a simulation model to evaluate the effects of water and nutrients on the growth and carbon partitioning of Pinus radiata. Forest Ecology and Management, 52 : 243–260.CrossRefGoogle Scholar
  43. Neilson, R. P. and Drapek, R. J., 1998. Potentially complex biosphere responses to transient global warming. Global change biology, 4 (5): 505–521.CrossRefGoogle Scholar
  44. Neilson, R. P. and Marks, D., 1994. A global perspective of regional vegetation and hydrologic sensitivities from climatic change. Journal of Vegetation Science, 5 : 715–730.CrossRefGoogle Scholar
  45. Pacala, S. W. and Deutschman, D. H., 1995. Details that matter: the spatial distribution of individual trees maintains forest ecosystem function. OIKOS, 74 : 357–365.CrossRefGoogle Scholar
  46. Pielke, R. A., Avissar, R., Raupach, M., Dolman, A. J., Zeng, X. B. and Denning, A. S., 1998. Interactions between the atmosphere and terrestrial ecosystems: influence on weather and climate. Global change biology, 4 (5): 461–475.CrossRefGoogle Scholar
  47. Potter, C. S., Randerson, J. T., Field, C. B., Matson, P. A., Vitousek, P. M., Mooney, H. A. and Klooster, S. A., 1993. Terrestrial ecosystem production: A process model based on global satellite and surface data. Global Biogeochem. Cycles, 7 (4): 811–841.CrossRefGoogle Scholar
  48. Prentice, I. C. C, W. Harrison, S.P. Leemans, R. Monserud, R.A. and Solomon, A. M., 1992. A global biome model based on plant physiology and dominance, soil properties and climate. J. Biogeogr., 19: 117–134.CrossRefGoogle Scholar
  49. Running, S. W., Gower, ST., 1991. FOREST-BGC, A general model of forest ecosystem processes for regional applications. II. Dynamic carbon allocation and nitrogen budgets. Tree Physiol., 9 : 147–160.CrossRefGoogle Scholar
  50. Running, S. W., 1994. Testing FOREST-BGC ecosystem process simulations across a climatic gradient in Oregon. Ecological Applications, 4 (238–247).CrossRefGoogle Scholar
  51. Running, S. W. and Coughlan, J. C, 1988. A general model of forest ecosystem processes for regional applications. I. Hydrologic balance, canopy gas exchange and primary production processes. Ecol. Model., 42 : 125–154.CrossRefGoogle Scholar
  52. Shannon, C. E. and Weaver, W., 1976. Mathematische Grundlagen der Informationstheorie. R. Oldenbourg-Verlag, München, Wien.Google Scholar
  53. Tiktak, A. and Bouten, W., 1992. Modelling soil water dynamics in a forested ecosystem. Ill: Model description and evaluation of discretization. Hydrological Processes, 6 : 455–465.CrossRefGoogle Scholar
  54. Tiktak, A. and VanGrinsven, H. J. M., 1995. Review of sixteen forest-soil-atmosphere models. Ecological Modelling, 83 : 35–53.CrossRefGoogle Scholar
  55. Waldispühl, P., Swiss Institute of forest, snow and landscape research, soil ecology section, 1998.,, Birmensdorf, Switzerland.Google Scholar
  56. Wilson, K. B., Carlson, T. N. and Bunce, J. A., 1999. Feedback significantly influences the simulated effect of C02 on seasonal evapotranspiration from two agricultural species. Global Change Biology, 5 (8): 903–917.CrossRefGoogle Scholar
  57. Woodward, F. I., Smith, T. M. and Emanuel, W. R., 1995. A global land primary productivity and phytogeography model. Global Biogeochemical Cycles, 9 (4): 471–490.CrossRefGoogle Scholar
  58. Zierl, B., 2000. WAWAHAMO — a hydrological model to simulate drought in forested ecosystems. PhD thesis no. 1320, Geography, University of Fribourg, Switzerland. Swiss Federal Research Institute WSL, Birmensdorf — Switzerland.Google Scholar
  59. Zierl, B., 2001. A water balance model to simulate drought in forested ecosystems and its application to the entire forested area of Switzerland. Journal of Hydrology, 242 (1–2):115–136.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2002

Authors and Affiliations

  • Heike Lischke
    • 1
  • Bärbel Zierl
    • 1
  1. 1.Swiss Federal Research Institute for Forest, Snow and Landscape (WSL)BirmensdorfSwitzerland

Personalised recommendations