Annals of Forest Science

, 74:71 | Cite as

Fuzzy modelling and mapping soil moisture for observed periods and climate scenarios. An alternative for dynamic modelling at the national and regional scale?

Original Article
Part of the following topical collections:
  1. ICP Forests

Abstract

Key message

Average soil moisture was spatially modelled for observed periods and climate scenarios using a fuzzy logic approach. Accordingly, a significant decline of soil moisture until 2070 in Germany and the Kellerwald National Park could be evidenced for soils influenced by ground water and by stagnant water and at sites on steep slopes and on southerly slopes.

Context

Soil moisture is an essential environmental factor affecting the condition of forests throughout time with high spatial variance. To adapt forests to climate change, assessments of ecological integrity and services in forest management and nature conservation need spatio-temporal estimations of current and future soil moisture. Dynamic modelling of soil moisture even with rather simple models needs numerous data which are often not available for areas of large spatial extent.

Aims

Therefore, the objectives of this investigation were to (1) spatio-temporally estimate ecological soil moisture with available data covering the whole territory of Germany, (2) to specify these estimates for the regional scale, (3) to statistically analyse temporal trends of modelled soil moisture for the time period 1961–2070 and (4) to map soil moisture changes (drying-out) at both national and regional levels.

Methods

A fuzzy rule-based model was developed allowing the combination of a pedological and an ecological soil moisture classification. The fuzzy modelling approach was applied for mapping average soil moisture at two spatial scales.

Results

Soil moisture was modelled and mapped on a scale of 1:500,000 across Germany and regionally specified on a scale of 1:25,000 for the Kellerwald National Park for the time intervals 1961–1990, 1991–2010, 2011–2040 and 2041–2070. The model validation gave a root mean squared error (RMSE) of 0.86 and a coefficient of determination (pseudo R2) of 0.21. Average soil moisture was expected to decline significantly until 2070 concerning soils influenced by ground water and by stagnant water and at sites on steep slopes (> 25%) and on southerly slopes (120–240°).

Conclusion

The model allows mapping of mean soil moisture at the national and regional scale as shown by the example of Germany and the Kellerwald National Park across observed periods and climate scenarios. It should be combined with available ecological data on forest ecosystem types (Jenssen et al. 2013; Schröder et al. 2015) and tested at the European scale.

Keywords

Soil moisture Climate change Fuzzy logic GIS mapping 

Supplementary material

13595_2017_667_MOESM1_ESM.doc (809 kb)
ESM 1(DOC 809 kb)

References

  1. Ad-Hoc-AG Boden (2006a) Verknüpfungsregel 3.30 - Korrektur des Klimabereichs im Rahmen der Methode zur Ermittlung des ackerbaulichen Ertragspotentials. www.bgr.bund.de/DE/Themen/Boden/Netzwerke/Adhocag/Downloads/Ergaenzungsregel_3_30.pdf. Accessed 21 April 2017
  2. Ad-Hoc-AG Boden (2006b) Verknüpfungsregel 4.14 - Ermittlung der bodenkundlichen Feuchtestufe im Rahmen der Methode zur Ermittlung des ackerbaulichen Ertragspotentials www.bgr.bund.de/DE/Themen/Boden/Netzwerke/Adhocag/Downloads/Ergaenzungsregel_4_14.pdf. Accessed 21 April 2017
  3. AG Boden (1982) Bodenkundliche Kartieranleitung. 3. Aufl., HannoverGoogle Scholar
  4. AG Boden (2005) Bodenkundliche Kartieranleitung. 5. verbesserte und erw. Aufl., E. Schweizerbart, StuttgartGoogle Scholar
  5. Archaux F, Wolters V (2006) Impact of summer drought on forest biodiversity: what do we know? Ann For Sci 63:645–652CrossRefGoogle Scholar
  6. Bardossy A, Duckstein L (1995) Fuzzy rule-based modeling with applications to geophysikal, biological and engineering systems. CRC, Boca RatonGoogle Scholar
  7. Beierle C, Kern-Isberner G (2006) Methoden wissensbasierter Systeme. Grundlagen, Algorithmen, Anwendungen. 3. erw. Aufl. Viehweg-Verlag, WiesbadenGoogle Scholar
  8. Benzler JH, Eckelmann W, Oelkers KH (1987) Ein Rahmenschema zur Kennzeichnung der bodenkundlichen Feuchtesituation. Mitteilungen der Deutschen Bodenkundlichen Gesellschaft 53:95–101Google Scholar
  9. BGR (2007) Nutzungsdifferenzierte Bodenübersichtskarte der Bundesrepuplik Deutschland 1:1.000.000 (BÜK 1000). Bundesamt für Geowissenschaften und Rohstoffe (BGR), Hannover. [Dataset] https://download.bgr.de/bgr/Boden/BUEK1000N/Gesamt/pGDB/buek1000n_v231.zip. Accessed 13 October 2017
  10. Borgelt C, Timm H, Kruse R (2003) (Hg): Einführung in die Künstliche Intelligenz. In: Görz G, Rollinger CR, Schneeberger J (eds) Unsicheres und vages Wissen. Oldenbourg Verlag, MünchenGoogle Scholar
  11. Bréda N, Huc R, Granier A, Dreyer E (2006) Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences. Ann For Sci 63:625–644CrossRefGoogle Scholar
  12. Burkhard B, Maes J (eds) (2017) Mapping ecosystem services. Pensoft Publishers, Sofia, p 376Google Scholar
  13. Calder IR (2007) Forests and water-ensuring forest benefits outweigh water costs. Forest Ecol Manag 251:110–120CrossRefGoogle Scholar
  14. Christensen JH, Hewitson B, Busuioc A, Chen A, Gao X, Held I, Jones R, Kolli RK, Kwon W-T, Laprise R, Rueda VM, Mearns L, Menéndez CG, Räisänen J, Rinke A, Sarr A, Whetton P (2007) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Regional climate projections. Cambridge University Press, Cambridge, pp 847–940Google Scholar
  15. Ciais P, Reichstein M, Viovy N, Granier A, Ogee J, Allard V, Aubinet M, Buchmann N, Bernhofer C, Carrara A, Chevallier F, De Noblet N, Friend AD, Friedlingstein P, Grunwald T, Heinesch B, Keronen P, Knohl A, Krinner G, Loustau D, Manca G, Matteucci GMF, Ourcival JM, Papale D, Pilegaard K, Rambal S, Seufert G, Soussana JF, Sanz MJ, Schulze ED, Vesala T, Valentini R (2005) Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Science 437:529–533Google Scholar
  16. Cullum C, Rogers KH, Brierley G, Witkowski ETF (2016) Ecological classification and mapping for landscape management and science: foundations for the description of patterns and processes. Prog Phys Geogr 40:38–65CrossRefGoogle Scholar
  17. Dahmen FW, Dahmen G, Heiss W (1976) Neue Wege der graphischen und kartographischen Veranschaulichung von Vielfaktorenkomplexen. Decheniana 129:145–178Google Scholar
  18. De Cáceres M, Martínez-Vilalta J, Coll L, Llorens P, Casals P, Poyatos R, Pausas JG, Brotons L (2015) Coupling a water balance model with forest inventory data to predict drought stress: the role of forest structural changes vs. climate changes. Agric Forest Meteorol 213:77–90CrossRefGoogle Scholar
  19. Desprez-Loustau M, Marcais B, Nageleisen L, Piou D, Vannini A (2006) Interactive effects of drought and pathogens in forest trees. Ann For Sci 63:597–612CrossRefGoogle Scholar
  20. Dury M, Hambuckers A, Warnant P, Henrot A, Favre E, Ouberdous M, François L (2011) Responses of European forest ecosystems to 21st century climate: assessing changes in interannual variability and fire intensity. iForest 4:82–99CrossRefGoogle Scholar
  21. Einecke M (2005) Entwicklung bodenhydraulischer Pedotransferfunktionen für kohlehaltige Kippböden der Niederlausitzer Bergbaufolgelandschaft. Dissertation, Brandenburg University of Technology CottbusGoogle Scholar
  22. Ellenberg H, Weber HC, Düll R, Wirth V, Werner W, Paulissen D (1991) Zeigerwerte von Pflanzen in Mitteleuropa, 3rd edn. Scripta Geobotanica 18, Verlag Erich Goltze KG, GöttingenGoogle Scholar
  23. Forest Europe (2011) State of Europe’s forests 2011. Status and trends in sustainable forest management in Europe. Jointly prepared by Forest Europe Liaison Unit Oslo, the United Nations Economic Commission for Europe (UNECE) and the Food and Agricultural Organization of the United Nations (FAO). http://www.foresteurope.org/documentos/State_of_Europes_Forests_2011_Report_Revised_November_2011.pdf. Accessed 13 October 2017
  24. Gessler A, Keitel C, Nahm M, Rennenberg H (2004) Water shortage affects the water and nitrogen balance in Central European beech forests. Plant Biol 6:289–298CrossRefPubMedGoogle Scholar
  25. Green TR, Taniguchi M, Kooi H, Gurdak JJ, Allen DM, Hiscock KM, Treidel H, Aureli A (2011) Beneath the surface of global change: impacts of climate change on groundwater. J Hydrol 405:532–560CrossRefGoogle Scholar
  26. Hasenauer S, Komma J, Parajka J, Wagner W, Blöschl G (2009) Bodenfeuchtedaten aus Fernerkundung für hydrologische Anwendungen. Österreichische Wasser- und Abfallwirtschaft 61:117–123CrossRefGoogle Scholar
  27. Hirschi M, Mueller B, Dorigo W, Seneviratne SI (2014) Using remotely sensed soil moisture for land-atmosphere coupling diagnostics: the role of surface vs. root-zone soil moisture variability. Remote Sens Environ 154:246–252CrossRefGoogle Scholar
  28. Hofmann G (2002) Entwicklung der Waldvegetation des nordostdeutschen Tieflandes unter den Bedingungen steigender Stickstoffeinträge in Verbindung mit Niederschlagsarmut. In: Anders S (ed) Ökologie und Vegetation der Wälder Nordostdeutschlands. Verlag Kessel, Remagen-Oberwinter, pp 24–41Google Scholar
  29. IMSE-CNM (2012) Xfuzzy3 – Fuzzy logic design tools. Instituto de Microelectrónica de Sevilla - Centro National de Microelectrónica. http://www2.imse-cnm.csic.es/Xfuzzy/Xfuzzy_3.3/Xfuzzy3.3_en.pdf (02.09.2015)
  30. IMSE-CNM (2012) Xfuzzy3 – Fuzzy logic design tools. Instituto de Microelectrónica de Sevilla – Centro National de Microelectrónica. http://www2.imse-cnm.csic.es/Xfuzzy/Xfuzzy_3.3/Xfuzzy3.3_en.pdf. Accessed 02 September 2015
  31. Jasper K, Calanca P, Fuhrer J (2006) Changes in summertime soil water patterns in complex terrain due to climatic change. J Hydrol 327(3–4):550–563CrossRefGoogle Scholar
  32. Jenssen M (2009) Climate-adaptive forests—ecological basics of a forest ataptation strategy. Forst und Holz 64:14–17Google Scholar
  33. Jenssen M (2010) Modellierung und Kartierung räumlich differenzierter Wirkungen von Stickstoffeinträgen in Ökosysteme im Rahmen der UNECE-Luftreinhaltekonvention. Teilbericht IIII: Modellierung der Wirkung der Stickstoff-Deposition auf die biologische Vielfalt der Pflanzengesellschaften von Wäldern der gemäßigten Breiten, UBA-Texte 09/2010. Dessau. https://www.umweltbundesamt.de/publikationen/modellierung-kartierungraeumlich-differenzierter-0. Accessed 13 October 2017
  34. Jenssen M, Hofmann G, Nickel S, Pesch R, Riediger J, Schröder W (2013) Bewertungskonzept für die Gefährdung der Ökosystemintegrität durch die Wirkungen des Klimawandels in Kombination mit Stoffeinträgen unter Beachtung von Ökosystemfunktionen und -dienstleistungen. UBA-Texte 87/2013. Dessau. https://www.umweltbundesamt.de/publikationen/bewertungskonzept-fuer-die-gefaehrdung-der. Accessed 13 October 2017
  35. Kempeneers P, Sedano F, Seebach L, Strobl P, San-Miguel-Ayanz J (2011) Data fusion of different spatial resolution remote sensing images applied to forest type mapping. IEEE Trans Geosci Remote Sens 49:4977–4986CrossRefGoogle Scholar
  36. Kendall MG (1975) Rank correlation methods, 4th edn. Charles Griffin, LondonGoogle Scholar
  37. Kiendl H (1997) Fuzzy control methodenorientiert. Oldenbourg Verlag, MünchenGoogle Scholar
  38. Kruse R, Gebhardt T, Klawonn F (1993) Fuzzy-system, 2. Aufl edn. Teubner-Verlag, StuttgartGoogle Scholar
  39. Lindner M, Maroschek M, Netherer S, Kremer A, Barbati A, Garcia-Gonzalo J, Seidl R, Delzon S, Corona P, Kolström M, Lexer MJ, Marchetti M (2010) Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. For Ecol Manag 259:698–709CrossRefGoogle Scholar
  40. Lippe WM (2006) Soft-Computing mit Neuronalen Netzen, Fuzzy-Logic und Evolutionären Algorithmen. Springer-Verlag, Berlin / HeidelbergGoogle Scholar
  41. Maes J, Teller A, Erhard M, Liquete C, Braat L, Berry P, Egoh B, Puydarrieux P, Fiorina C, Santos F, Paracchini ML, Keune H, Wittmer H, Hauck J, Fiala I, Verburg PH, Condé S, Schägner JP, San Miguel J, Estreguil C, Ostermann O, Barredo JI, Pereira HM, Stott A, Laporte V, Meiner A, Olah B, Royo Gelabert E, Spyropoulou R, Petersen JE, Maguire C, Zal N, Achilleos E, Rubin A, Ledoux L, Brown C, Raes C, Jacobs S, Vandewalle M, Connor D, Bidoglio G (2013) Mapping and assessment of ecosystems and their services. An analytical framework for ecosystem assessments under Action 5 of the EU Biodiversity Strategy to 2020. Discussion paper—final, April 2013. Publications office of the European Union, LuxembourgGoogle Scholar
  42. Maes J, Liquete C, Teller A, Erhard M, Paracchini ML, Barredo JI, Grizzetti B, Cardoso A, Somma F, Petersen J-E, Meiner A, Royo Gelabert E, Zal N, Kristensen P, Bastrup-Birk A, Biala K, Piroddi C, Egoh B, Degeorges P, Fiorina C, Santos-Martín F, Naruševicius V, Verboven J, Pereira HM, Bengtsson J, Gocheva K, Marta-Pedroso C, Snäll T, Estreguil C, San-Miguel-Ayanz J, Pérez-Soba M, Grêt-Regamey A, Lillebø AI, Malak DA, Condé S, Moen J, Czúcz B, Drakou EG, Zulian G, Lavalle C (2016) An indicator framework for assessing ecosystem services in support of the EU Biodiversity Strategy to 2020. Ecosyst Serv 17:14–23Google Scholar
  43. Mann HB (1945) Non-parametric tests against trend. Econometrica 13:163–171CrossRefGoogle Scholar
  44. Marks R (1979) Ökologische Landschaftsanalyse und Landschaftsbewertung als Aufgaben der Angewandten Physischen Geographie. Materialien zur Raumordnung XXI, BochumGoogle Scholar
  45. Menziani M, Rivasi MR, Pugnaghi S, Santangelo R, Vincenzi S (1996) Soil volumetric water content measurements using TDR technique. Ann Geophys 39:91–96Google Scholar
  46. Mosbrugger V, Brasseur G, Schaller M, Stribrny B (Hrsg.) (2012) Klimawandel und Biodiversität: Folgen für Deutschland. Wissenschaftliche Buchgesellschaft, Darmstadt, 420 SGoogle Scholar
  47. Müller U, Waldeck A (2011) Auswertungsmethoden im Bodenschutz - Dokumentation zur Methodenbank des Niedersächsischen Bodeninformationssystems (NIBIS), GeoBerichte 19. LBEG, Hannover. http://www.lbeg.niedersachsen.de/download/61889/GeoBerichte_19.pdf. Accessed 21 April 2017
  48. Nickel S, Schröder W, Jenssen M (2015) Veränderungen deutscher Wälder durch Klimawandel und Stickstoffdeposition. Schweiz Z Forstwes 166:325–334CrossRefGoogle Scholar
  49. Orlowsky B, Gerstengarbe FW, Werner PC (2008) A resampling scheme for regional climate simulations and its performance compared to a dynamical RCM. Theor Appl Climatol 92:209–223CrossRefGoogle Scholar
  50. Peters R, Schwärzel K, Feger KH (2011) Fuzzy-Inference-Systeme zur Regionalisierung des Standortwasserhaushaltes von Wäldern. Waldökol, Landschaftsforsch Natursch 12:111–117Google Scholar
  51. Peyke G, Wolf W (1999) Genauere Aussagen in der Geographie durch Betrachtung der Unschärfe - Plädoyer für eine vermehrte Anwendung der Fuzzy-Theorie. In: Geographisches Institut der Humboldt-Universität zu Berlin; Schultz HD (ed): Quodlibet Geographicum - Einblicke in unsere Arbeit, self-published, Berlin, pp 159–179Google Scholar
  52. Pilaš I, Feger KH, Vilhar U, Wahren A (2011) Forest management and the water cycle—an ecosystem-based approach. In: Bredemeier M, Cohen S, Godbold DL, Lode E, Pichler V, Schleppi P (eds) Multidimensionality of scales and approaches for forest–water interactions. Ecol Stud 212, Springer, Heildelberg, pp 351–380Google Scholar
  53. Posch M, Reinds GJ (2009) A very simple dynamic soil acidification model for scenario analyses and target load calculations. Environ Model Softw 24:329–340CrossRefGoogle Scholar
  54. Rennenberg H, Loreto F, Polle A, Brilli F, Fares S, Beniwal RS, Gessler A (2006) Physiological responses of forest trees to heat and drought. Plant Biol 8:556–571CrossRefPubMedGoogle Scholar
  55. Rouault G, Candau JN, Lieutier F, Nageleisen LM, Martin JC, Warze’e N (2006) Effects of drought and heat on forest insect populations in relation to the 2003 drought in Western Europe. Ann For Sci 63:613–624CrossRefGoogle Scholar
  56. Saint-André L, Sainte-Marie J, Leguedois S, Ferry B, Lafolie F, Marsden C, Van Der Heijden G, Dufrene E, Bontemps J-D, Legout A (2014) Les avancées de la recherche dans le domaine de la modélisation des interactions sol-arbre. Revue Forestière Française 66:479–490 (Advances in modeling interactions between soils and trees. Revue Forestière Française, Spec Iss:83-93)Google Scholar
  57. Scheinost A (1995) Pedotransferfunktionen zum Wasser- und Stoffhaushalt einer Bodenlandschaft. Dissertation, Technical University of MunichGoogle Scholar
  58. Schröder W, Nickel S, Jenssen M, Riediger J (2015) Methodology to assess and map the potential development of forest ecosystems exposed to climate change and atmospheric nitrogen deposition: a pilot study in Germany. Sci Total Environ 521–522:108–122CrossRefPubMedGoogle Scholar
  59. Schröder W, Nickel S, Jenssen M, Hofmann G, Schlutow, A, Nagel H-D, Burkhard B, Dworczyk C, Elsasser P, Lorenz M, Meyerhoff J, Weller P, Altenbrunn K (2017) Anwendung des Bewertungskonzepts für die Ökosystemintegrität unter Berücksichtigung des Klimawandels in Kombination mit Stoffeinträgen. Final Report 10/2017. Dessau, Vechta.Google Scholar
  60. Schwärzel K, Peters R, Petzold R, Häntzschel J, Menzer A, Clausnitzer F, Spank U, Köstner B, Bernhofer C, Feger KH (2011) Räumlich-differenzierte Berechnung und Bewertung des Standortswasserhaushaltes von Wäldern des Mittelgebirges. Waldökol, Landschaftsforsch Natursch 12:119–126Google Scholar
  61. Scibek J, Allen DM (2006) Modeled impacts of predicted climate change on recharge and groundwater levels. Water Resour Res 42:1–18CrossRefGoogle Scholar
  62. Stocker TF, Qin D, Plattner GK, Alexander LV, Allen SK, Bindoff NL, Bréon FM, Church UCJA, Emori S, Forster P, Friedlingstein P, Gillett N, Gregory JM, Hartmann DL, Jansen E, Kirtman B, Knutti R, Krishna Kumar K, Lemke P, Marotzke J, Masson-Delmotte V, Meehl GA, Mokhov II, Piao S, Ramaswamy V, Randall D, Rhein M, Rojas M, Sabine C, Shindell D, Talley LD, Vaughan DG, Xie SP (2013) Technical summary. In: climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 1–115Google Scholar
  63. Strietzel R (1996) Fuzzy-Regelung. Oldenbourg-Verlag, MünchenGoogle Scholar
  64. Topp GC, Davis JL, Annan AP (1980) Electromagnetic determination of soil water con-tent: measurements in coaxial transmission lines. Water Resour Res 16:574–582CrossRefGoogle Scholar
  65. Wahren A, Feger KH (2011) Model-based assessment of forest land management on water dynamics at various hydrological scales. In: Bredemeier M, Cohen S, Godbold DL, Lode E, Pichler V, Schleppi P (eds) Forest management and the water cycle. An ecosystem-based approach. Springer Science+Business Media B.V., Dordrecht, pp 453–469. https://doi.org/10.1007/978-90-481-9834-4_1
  66. Western AW, Grayson RB, Blöschl G (2002) Scaling of soil moisture: a hydrologic perspective. Annu Rev Earth Planet Sci 30:149–180CrossRefGoogle Scholar
  67. Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1:80–83CrossRefGoogle Scholar
  68. Williams AP, Allen CD, Millar CI, Swetnam TW, Michaelsen J, Still CJ, Leavitt SW (2010) Forest responses to increasing aridity and warmth in the southwestern United States. Proc Natl Acad Sci U S A 107:21289–21294CrossRefPubMedPubMedCentralGoogle Scholar
  69. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353CrossRefGoogle Scholar
  70. Zadeh LA (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst 1:3–28CrossRefGoogle Scholar
  71. Zadeh LA (2008) Is there a need for fuzzy logic? Inf Sci 178:2751–2779CrossRefGoogle Scholar
  72. Zeng JY, Chen KS, Bi HY, Chen Q (2016) A Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product over United States and Europe Using Ground-Based Measurements. IEEE Transactions on Geoscience and Remote Sensing 54:4929–4940. https://doi.org/10.1109/TGRS.2016.2553085
  73. Zepp H (1995) Klassifikation und Regionalisierung von Bodenfeuchteregime-Typen. Relief, Boden, Paläoklima, Bd. 9. Gebrüder Borntraeger, BerlinGoogle Scholar
  74. Zepp H, Müller MJ (1999) Landschaftsökologische Erfassungsstandards. Ein Methodenbuch. Forschungen zur deutschen Landeskunde Bd. 244. Deutsche Akademie für Landeskunde, FlensburgGoogle Scholar

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Authors and Affiliations

  1. 1.Chair of Landscape EcologyUniversity of VechtaVechtaGermany

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