Energy and Matter Fluxes of a Spruce Forest Ecosystem pp 355-375

Part of the Ecological Studies book series (ECOLSTUD, volume 229)

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Catchment Evapotranspiration and Runoff

  • Gunnar Lischeid
  • Sven Frei
  • Bernd Huwe
  • Christina Bogner
  • Johannes Lüers
  • Wolfgang Babel
  • Thomas Foken
Chapter

Abstract

The interplay between precipitation and evapotranspiration determines the input into the hydrological system of a catchment. Annual values of precipitation, evapotranspiration, and runoff measured at the catchment outlet for the 2002–2009 period were available. Annual precipitation clearly surmounted the sum of evapotranspiration and runoff. Part of the observed discrepancy might be due to the heterogeneity of precipitation and evapotranspiration within the catchment which has not been studied in sufficient detail. Annual evapotranspiration fluxes were remarkably constant during this period, whereas precipitation and runoff exhibited much larger interannual variability.

Short-term dynamics of soil matrix potential, groundwater head, and discharge were studied using principal component analysis. About 19 % of the spatial variance of soil matrix potential in the soil in a mature spruce forest was ascribed to the effect of spatially varying root water uptake. In addition, the analysis illustrated the effect of increasing damping and delay of the input signal with increasing depth. That of the runoff at the catchment outlet ranged between that of soil matrix potential at 40 cm depth, pointing to near-surface runoff generation. This gives clear evidence for the respective flowpath during stormflow. Thus, input signals imposed by heavy rainstorms reach the catchment outlet within a few hours. In contrast, changes of evapotranspiration become visible at the catchment outlet only with a few years’ time delay via corresponding changes of groundwater recharge that are transferred through the aquifer.

In hydrological and groundwater models, usually little attention is paid to the mutual interplay between evapotranspiration and root water uptake on the one hand and hydrological processes, especially in shallow groundwater areas. Sound assessments of effects of land use and climate change, however, need to account for that in more detail.

References

  1. Alewell C, Manderscheid B, Gerstberger P, Matzner E (2000) Effects of reduced atmospheric deposition on soil solution chemistry and elemental contents of spruce needles in NE—Bavaria, Germany. J Plant Nutr Soil Sci 163:509–516CrossRefGoogle Scholar
  2. Bayerisches Landesamt für Umwelt (2006) Deutsches Gewässerkundliches Jahrbuch. Rheingebiet, Teil II, Main. Mit einem Anhang: Bayer. Elbegebiet. 01.11.2005–31.12.2006. ISSN 2193-4851Google Scholar
  3. Beven K (2001) On fire and rain (or predicting the effects of change). Hydrol Process 15:1397–1399. doi:10.1002/hyp.458 CrossRefGoogle Scholar
  4. Bivand R, Lewin-Koh N (2014) maptools: Tools for reading and handling spatial objects. R package version 0.8-30. http://CRAN.R-project.org/package=maptools
  5. Bivand R, Rundel C (2014) rgeos: Interface to Geometry Engine – Open Source (GEOS). R package version 0.3-8. http://CRAN.R-project.org/package=rgeos
  6. Bogner C, Wolf B, Schlather M, Huwe B (2008) Analysing flow patterns from dye tracer experiments in a forest soil using extreme value statistics. Eur J Soil Sci 59:103–113. doi:10.1111/j.1365-2389.2007.00974.x CrossRefGoogle Scholar
  7. Böttcher S, Merz C, Lischeid G, Dannowski R (2014) Using Isomap to differentiate between anthropogenic and natural effects on groundwater dynamics in a complex geological setting. J Hydrol 519:1634–1641. doi:10.1016/j.jhydrol.2014.09.048 CrossRefGoogle Scholar
  8. Calder IR, Newson MD (1979) Land use and upland water resources in Britain—A strategic look. J Am Water Resour Assoc 15:1628–1639CrossRefGoogle Scholar
  9. Charuchittipan D, Babel W, Mauder M, Leps JP, Foken T (2014) Extension of the averaging time in eddy-covariance measurements and its effect on the energy balance closure. Bound-Lay Meteorol 152(3):303–327. doi:10.1007/s10546-014-9922-6 CrossRefGoogle Scholar
  10. Chen YT, Bogner C, Borken W, Stange CF, Matzner E (2011) Minor response of gross N turnover and N leaching to drying, rewetting and irrigation in the topsoil of a Norway spruce forest. Eur J Soil Sci 62:709–717. doi:10.1111/j.1365-2389.2011.01388.x CrossRefGoogle Scholar
  11. Foken T (2003) Lufthygienisch-Bioklimatische Kennzeichnung des oberen Egertales. Bayreuther Forum Ökologie. 100:69+XLVIIIGoogle Scholar
  12. Foken T (2008) The energy balance closure problem: an overview. Ecol Appl 18(6):1351–1367. http://www.jstor.org/stable/40062260Google Scholar
  13. Frei S, Lischeid G, Fleckenstein J (2010) Effects of micro-topography on surface-subsurface exchange and runoff generation in a riparian wetland. Adv Water Res 33:1388–1401. doi:10.1016/j.advwatres.2010.07.006 CrossRefGoogle Scholar
  14. Frei S, Knorr KH, Peiffer S, Fleckenstein JH (2012) Surface micro-topography causes hot spots of biogeochemical activity in wetland systems: a virtual modeling experiment. J Geophys Res 117:G00N12. doi:10.1029/2012JG002012 CrossRefGoogle Scholar
  15. Gerstberger P, Foken T, Kalbitz K (2004) The Lehstenbach and Steinkreuz chatchments in NE Bavaria, Germany. In: Matzner E (ed) Biogeochemistry of forested catchments in a changing environment, a German case study, Ecological studies, vol 172. Springer, Heidelberg, pp 15–41CrossRefGoogle Scholar
  16. Hohenbrink T, Lischeid G (2015) Does textural heterogeneity matter? Quantifying transformation of hydrological signals in soils. J Hydrol 523:725–738. doi:10.1016/j.jhydrol.2015.02.009 CrossRefGoogle Scholar
  17. Hohenbrink TL, Lischeid G, Schindler U, Hufnagel J (2016) Disentangling land management and soil heterogeneity effects on soil moisture dynamics. Vadose Zone J 15(1), DOI: 10.2136/vzj2015.07.0107
  18. Jolliffe IT (2002) Principal component analysis. Springer series in statistics. Springer, New York, 489 ppGoogle Scholar
  19. Kirchner JW, Feng X, Neal C (2000) Fractal stream chemistry and its implications for contaminant transport in catchments. Nature 43:524–527CrossRefGoogle Scholar
  20. Köstner B, Tenhunen JD, Alsheimer M, Wedler M, Scharfenberg H-J, Zimmermann R, Falge E, Joss U (2001) Controls on evapotranspiration in a spruce forest catchment of the Fichtelgebirge. In: Tenhunen JD, Lenz R, Hantschel R (eds) Ecosystem approaches to landscape management in Central Europe, Ecological studies, vol 147. Springer, Berlin, pp 379–415CrossRefGoogle Scholar
  21. Lee JA, Verleysen M (2007) Nonlinear dimensionality reduction, Information science and statistics. Springer, New YorkCrossRefGoogle Scholar
  22. Lehr C, Pöschke F, Lewandowski J, Lischeid G (2015) A novel method to evaluate the effect of a stream restoration on the spatial pattern of hydraulic connection of stream and groundwater. J Hydrol 527:394–401. doi:10.1016/j.jhydrol.2015.04.075 CrossRefGoogle Scholar
  23. Lischeid G, Bittersohl J (2008) Tracing biogeochemical processes in stream water and groundwater using nonlinear statistics. J Hydrol 357:11–28. doi:10.1016/j.jhydrol.2008.03.013 CrossRefGoogle Scholar
  24. Lischeid G, Kolb A, Alewell C (2002) Apparent translatory flow in groundwater recharge and runoff generation. J Hydrol 265:195–211CrossRefGoogle Scholar
  25. Lischeid G, Büttcher H, Hauck A (2003) Combining data-based and process-based approaches to minimize the complexity of a reactive sulfate transport model. Proceedings of the ModelCARE’2002 conference held at Prague, Czech Republic, June 2002. IAHS-Publications 277:402–408Google Scholar
  26. Lischeid G, Alewell C, Moritz K, Bittersohl J (2004) Trends in the input-output relations: the catchment budgets. In: Matzner E (ed) Biogeochemistry of forested catchments in a changing environment. A German case study, Ecological studies, vol 172. Springer, Heidelberg, pp 437–456CrossRefGoogle Scholar
  27. Lischeid G, Kolb A, Alewell C, Paul S (2007) Impact of redox and transport processes in a riparian wetland on stream water quality in the Fichtelgebirge Region, Southern Germany. Hydrol Process 21:123–132. doi:10.1002/hyp.6227 CrossRefGoogle Scholar
  28. Lischeid G, Natkhin M, Steidl J, Dietrich O, Dannowski R, Merz C (2010) Assessing coupling between lakes and layered aquifers in a complex Pleistocene landscape based on water level dynamics. Adv Water Res 33:1331–1339. doi:10.1016/j.advwatres.2010.08.002 CrossRefGoogle Scholar
  29. Matzner E, Köstner B, Lischeid G (2004) Biogeochemistry of two forested catchments in a changing environment: a synthesis. In: Matzner E (ed) Biogeochemistry of forested catchments in a changing environment, A German case study, Ecological studies, vol 172. Springer, Heidelberg, pp 475–490CrossRefGoogle Scholar
  30. Milly PCD, Betancourt J, Falkenmark M, Hirsch RM, Kundzewicz ZW, Lettenmaier DP, Stouffer RJ (2008) Stationarity is dead: whither water management? Science 319:573–574. doi:10.1126/science.1151915 CrossRefPubMedGoogle Scholar
  31. Moritz K, Bittersohl J, Müller FX, Krebs M (1994) Auswirkungen des Sauren Regens und des Waldsterbens auf das Grundwasser. Dokumentation der Methoden und Meßdaten des Entwicklungsvorhabens 1988–1992. Bayerisches Landesamt für Wasserwirtschaft, Materialien No. 40, München, 387 SGoogle Scholar
  32. Partington D, Brunner P, Frei S, Simmons CT, Werner AD, Therrien R, Maier HR, Dandy GC, Fleckenstein JH (2013) Interpreting streamflow generation mechanisms from integrated surface-subsurface flow models of a riparian wetland and catchment. Water Resour Res 49:5501–5519. doi:10.1002/wrcr.20405 CrossRefGoogle Scholar
  33. R Core Team 2014 R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/
  34. Richter D (1995) Ergebnisse methodischer Untersuchungen zur Korrektur des systematischen Meßfehlers des Hellmann-Niederschlagmessers. Berichte des Deutschen Wetterdienstes 194, Offenbach, 93 ppGoogle Scholar
  35. Roberts J (1983) Forest transpiration: a conservative hydrological process? J Hydrol 66:133–141CrossRefGoogle Scholar
  36. Schilli C, Lischeid G, Rinklebe J (2010) What processes prevail? Analyzing long-term soil-solution monitoring data using nonlinear statistics. Geoderma 158:412–420. doi:10.1016/j.geoderma.2010.06.014 CrossRefGoogle Scholar
  37. Schmitt A, Glaser B (2011) Organic matter dynamics in a temperate forest as influenced by soil frost. J Plant Nutr Soil Sci 174:754–764. doi:10.1002/jpln.201100009 CrossRefGoogle Scholar
  38. Strohmeier S, Knorr K-H, Reichert M, Frei S, Fleckenstein JH, Peiffer S, Matzner E (2013) Dynamics of dissolved organic carbon in runoff from a forested catchment: evidence from high frequency measurements. Biogeosci 10:905–916CrossRefGoogle Scholar
  39. Van Laanen HAJ, Fendeková M, Kupczyk E, Kasprzyk A, Pokojski W (2004) Flow generating processes. In: Tallaksen LM, van Lanen HAJ (eds) Hydrological drought. Processes and estimation methods for streamflow and groundwater, Developments in water science, vol 48. Elsevier, Amsterdam, pp 53–96Google Scholar
  40. Weyer C, Peiffer S, Schulze K, Borken W, Lischeid G (2014) Catchments as heterogeneous, multi-species reactors: an integral approach for identifying biogeochemical hot-spots at the catchment’s scale. J Hydrol 519:1560–1571. doi:10.1016/j.jhydrol.2014.09.005 CrossRefGoogle Scholar
  41. Zahn MT (1995) Transport von Säurebildnern im Untergrund und Bedeutung für die Grundwasserversauerung. In: Proceedings des Internationalen Symposiums zur Grundwasserversauerung durch Atmosphärische Deposition. Ursachen–Auswirkungen– Sanierungsstrategien, 26–28 Oct 1994, Bayreuth. Informationsberichte des Bayerischen Landesamtes für Wasserwirtschaft 3/95: 143–151Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gunnar Lischeid
    • 1
    • 2
  • Sven Frei
    • 3
    • 4
  • Bernd Huwe
    • 4
    • 5
  • Christina Bogner
    • 3
    • 4
  • Johannes Lüers
    • 6
  • Wolfgang Babel
    • 4
    • 7
  • Thomas Foken
    • 4
    • 8
  1. 1.Leibniz Centre for Agricultural Landscape ResearchInstitute of Landscape HydrologyMünchebergGermany
  2. 2.Institute of Earth and Environmental SciencesUniversity of PotsdamPotsdamGermany
  3. 3.University of BayreuthBayreuthGermany
  4. 4.Bayreuth Center of Ecology and Environmental ResearchUniversity of BayreuthBayreuthGermany
  5. 5.Soil Physics GroupUniversity of BayreuthBayreuthGermany
  6. 6.Bayreuth Center of Ecology and Environmental ResearchUniversity of BayreuthBayreuthGermany
  7. 7.Group of MicrometeorologyUniversity of BayreuthBayreuthGermany
  8. 8.BischbergGermany

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