Skip to main content

Advertisement

Log in

Estimation of spatially distributed soil information: dealing with data shortages in the Western Bug Basin, Ukraine

  • Special Issue
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

Integrated watershed models require spatially differentiated soil information. However, in many regions of the world the limited availability of soil data hinders an appropriate simulation of hydro-ecological processes. Such circumstances lead to unsupported statements, poor statistics, misrepresentations, and, ultimately, to bad resource management. The Western Bug catchment in west Ukraine is an example of such a region. In the former Soviet Union, soil classification primarily focused on soils of agricultural importance, whereas, forested, urban, industrial, and shallow soil territories were left underrepresented in the classification and soil maps. Spatially differentiated soil texture data are required to predict soil hydraulic properties using pedotransfer functions (PTFs), along with soil maps. Furthermore, the Ukrainian soil texture scheme does not match the particle size classes commonly used with PTFs. To overcome these shortcomings, a fuzzy logic methodology was applied, based on terrain and vegetation/land use analysis and soil sampling, to close the information gaps. For the application of PTFs, a procedure was tested to estimate missing values of soil texture distribution. Applied methods were evaluated using recent soil surveys, measured soil texture, and water retention properties, while having in consideration the limitations brought by scarce soil data for integrated watershed modelling purposes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Behrens T, Zhu AX, Schmidt K, Scholten T (2010) Multi-scale digital terrain analysis and feature selection in digital soil mapping. Geoderma 155(3–4):175–185

    Article  Google Scholar 

  • Blumensaat F, Wolfram M, Krebs P (2011) Sewer model development under minimum data requirements. Environ Earth Sci. doi:10.1007/s12665-011-1146-1 (this issue)

  • Boden AG (2005) Bodenkundliche Kartieranleitung. 5. Aufl. Bundesanstalt für Geowissenschaften und Rohstoffe. Hannover

  • Bouma J (1989) Using soil survey data for quantitative land evaluation. In: Stewart BA (ed) Adv. Soil Sci, vol 9. Springer Verlag, New York, pp 177–213

    Chapter  Google Scholar 

  • Breda N, Granier A, Barataud F, Moyne C (1995) Soil water dynamics in an oak stand. 1: soil-moisture, water potentials and water-uptake by roots. Plant Soil 172(1):17–27

    Article  Google Scholar 

  • Carpenter SR, Caraco NF, Correll DL, Howarth RW, Sharpley AN, Smith VH (1998) Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol Appl 8(3):559–568

    Article  Google Scholar 

  • Elsenbeer H, Coelho RM, Newton B (2002) Spatial variability of soil hydraulic conductivity along a tropical rainforest catena. Geoderma 108(1–2):79–90

    Google Scholar 

  • European Commission Environment (2011) Water scarcity and droughts in the european union. http://ec.europa.eu/environment/water/quantity/scarcity_en.htm

  • FAO Irrigation and Drainage Papers 55 (1996) Control of water pollution from agriculture. http://www.fao.org/docrep/W2598E/W2598E00.htm

  • Gessler PE, Moore ID, Mckenzie NJ, Ryan PJ (1995) Soil-landscape modelling and spatial prediction of soil attributes. Int J Geogr Inf syst 9(4):421–432

    Article  Google Scholar 

  • Guswa AJ (2010) Effect of plant uptake strategy on the water-optimal root depth. Water Ressources Research 46, W09601, 5 pp

    Google Scholar 

  • Hendrayanto, Kosugi K, Uchida T, Matsuda S, Mizuyama T (1999) Spatial variability of soil hydraulic properties in a forested hillslope. J For Res 4(2):107–114

    Article  Google Scholar 

  • Johnson N, Revenga C, Echeverria J (2001) Ecology–managing water for people and nature. Science 292(5519):1071–1072

    Article  Google Scholar 

  • Katschinski NA (1956) Die mechanische Bodenanalyse und die Klassifikation der Böden nach ihrer mechanischen Zusammensetzung. Rapports aux Sixième Congrès International de la Science du Sol, Paris, B, 321–327

  • Kalbacher T, Delfs J-O, Shao H, Wang W, Walther M, Samaniego L, Schneider C, Musolff A, Centler F, Sun F, Hildebrandt A, Liedl R, Borchardt D, Krebs P, Kolditz O (2011) The IWAS-ToolBox: Software Coupling for an Integrated Water Resources Management. Environ Earth Sci. doi:10.1007/s12665-011-1270-y (this issue)

  • Krasilnikov P, Ibáñez Marti JJ, Arnold R, Shoba S (2009) A handbook of soil terminology, correlation and classification. Earthscan, Uk and USA, pp 189–207

    Google Scholar 

  • Leidel M, Niemann S, Hagemann N (2011) Capacity Development as a key factor for Integrated Water Resources Management (IWRM) -Improving water management in the Western Bug River Basin, Ukraine. Environ Earth Sci. doi:10.1007/s12665-011-1223-5 (this issue)

  • Mallants D, Mohanty BP, Jacques D, Feyen J (1996) Spatial variability of hydraulic properties in a multi-layered soil profile. Soil Sci 161:167–181

    Article  Google Scholar 

  • McBratney AB, Mendonca Santos ML, Minasny B (2003) On digital soil mapping. Geoderma 117:3–52

    Article  Google Scholar 

  • McKeague JA, Eilers RG, Thomasson AJ, Reeve MJ, Bouma J, Grossmann RB, Favrot JC, Renger M, Strebel O (1984) Tentative assessment of soil survey approaches to the characterization and interpretation of air-water properties of soils. Geoderma 34(1):69–100

    Article  Google Scholar 

  • Milly PCD, Eagleson PS (1987) Effects of spatial variability on annual average water balance. Water Resour Res 23(11):2135–2143

    Article  Google Scholar 

  • Milne G (1935) Some suggested units of classification and mapping particularly for East African soils. Soils Research 4:3

    Google Scholar 

  • Moore ID, Gessler PE, Nielsen GA, Peterson GA (1993) Soil attribute prediction using terrain analysis. Soil Sci Soc Am J 57:443–452

    Article  Google Scholar 

  • Mueller EN, Wainwright J, Parsons AJ (2008) Spatial variability of soil and nutrient characteristics of semi-arid grasslands and shrublands, Jornada Basin, New Mexico. Ecohydrol 1:3–12

    Google Scholar 

  • METI, NASA: ASTER Global DEM – Start distribution 2009

  • Nemes A, Rawls WJ (2006) Evaluation of different representations of the particle-size distribution to predict soil water retention. Geoderma 132(1–2):47–58

    Article  Google Scholar 

  • Publishing House of Ukrainian Academy of Agrological Sciences (1998) Terminological dictionary of soilscience questions, agrochemistry and soil amelioration. Kharkiv, Ukraine, 80p

  • Pavlik D, Söhl D, Pluntke T, Mykhnovych A, Bernhofer C (2011) Dynamic downscaling of global climate projections for Eastern Europe with a horizontal resolution of 7 km, Environ Earth Sci. doi:10.1007/s12665-011-1081-1 (this issue)

  • Quinn T, Zhu AX, Burt JE (2005) Effects of detailed soil spatial information on watershed modeling across different model scales. Int J Appl Earth Obs and Geoinformation 7:324–338

    Article  Google Scholar 

  • Renger M, Strebel O (1980) Beregnungsbedarf landwirtschaftlicher Kulturen in Abhängigkeit vom Boden. Boden und Wasser 32:572–575

    Google Scholar 

  • Rousseva S (1997) Data transformations between soil texture schemes. Eur J Soil Sci 48(4):749–758

    Article  Google Scholar 

  • Schanze J, Trümper J, Burmeister C, Pavlik D, Kruglov I (2011) A methodology for dealing with regional change in integrated water resource management. Environ Earth Sci. doi:10.1007/s12665-011-1311-6 (this issue)

  • Schwärzel K, Feger KH, Häntzschel J, Menzer A, Spank U, Clausnitzer F, Köstner B, Bernhofer C (2009a) A novel approach in model-based mapping of soil water conditions at forest sites. Forest Ecol Manag 258:2163–2174

    Article  Google Scholar 

  • Schwärzel K, Menzer A, Spank U, Clausnitzer F, Häntzschel J, Grünwald T, Köstner B, Bernhofer C, Feger KH (2009b) Soil water content measurements deliver reliable estimates of water fluxes: A comparative study in a beech and a spruce stand in the Tharandt Forest (Saxony, Germany). Agric For Meteorol 149:1994–2006

    Article  Google Scholar 

  • Some’e BS, Hassanpour F, Ezani A, Miremadi SR, Tabari H (2011) Investigation of spatial variability and pattern analysis of soil properties in the northwest of Iran. Environ Earth Sci Online First, 18 März 2011. doi:10.1007/s12665-011-0993-0

  • Thompson JA, Bell JC, Butler CA (2001) Digital elevation model resolution: effects on terrain attribute calculation and quantitative soil-landscape Modeling. Geoderma 100:67–89

    Article  Google Scholar 

  • Walczak RT, Witowska-Walczak B, Sławiński C (2004) Pedotransfer studies in Poland. In: Pachepsky Ya, Rawls WJ (eds) Development of pedotransfer functions in soil hydrology. Elsevir, Boston, Heidelberg, London, pp 449–462

    Chapter  Google Scholar 

  • Walczak RT, Moreno F, Sławiński C, Fernandez E, Arrué JL (2006) Modeling of soil water retention curve using soil solid phase parameters. J Hydrol 329(3–4):527–533

    Article  Google Scholar 

  • Williams J, Ross P, Bristow K (1992) Prediction of the Campbell water retention function from texture, structure, and organic matter. In: van Genuchten MTh, Leij FJ, Lund LJ (eds) Proc. Int. Workshop on Indirect Methods for Estimating the Hydraulic Properties of Unsaturated Soils. University of California, Riverside, CA, pp 427–442

  • World Health Organization (2011) http://www.who.int/features/factfiles/water/en/

  • Wösten JHM, Lilly A, Nemes A, Le Bas C (1999) Development and use of a database of hydraulic properties of European soils. Geoderma 90(3–4):169–185

    Article  Google Scholar 

  • Wösten JHM, Pachepsky YA, Rawls WJ (2001) Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. J Hydrol 251:123–150

    Article  Google Scholar 

  • Zacharias S, Wessolek G (2007) Excluding organic matter content from pedotransfer predictors of soil water retention. Soil Sci Soc Am J 71(1):43–50

    Article  Google Scholar 

  • Zhu AX (1997) A similarity model for representing soil spatial information. Geoderma 77:217–242

    Article  Google Scholar 

  • Zhu AX (1999) A personal construct-based knowledge acquisition process for natural resource mapping. Int J Geogr Informat Sci 13:119–141

    Article  Google Scholar 

  • Zhu AX, Mackay DS (2001) Effects of spatial detail of soil information on watershed modeling. J Hydrol 248:54–77

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by funding from the Federal Ministry for Education and Research (BMBF) in the framework of the project “IWAS—International Water Research Alliance Saxony” (grant 2WM1028). This work was partly supported by FCT—Fundação para a Ciência e a Tecnologia, Portugal. We also would like to thank the co-workers of the Soil Science and Soil Geography and Applied Geography and Cartography Departments of the Lviv Ivan Franko National University for their valuable cooperative help during our field campaigns in the Ukraine.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Filipa Tavares Wahren.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tavares Wahren, F., Tarasiuk, M., Mykhnovych, A. et al. Estimation of spatially distributed soil information: dealing with data shortages in the Western Bug Basin, Ukraine. Environ Earth Sci 65, 1501–1510 (2012). https://doi.org/10.1007/s12665-011-1197-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12665-011-1197-3

Keywords

Navigation