Skip to main content
SpringerLink
Log in

Assessing spatial uncertainty in mapping soil erodibility factor using geostatistical stochastic simulation

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

Soil erosion is one of most widespread process of degradation. The erodibility of a soil is a measure of its susceptibility to erosion and depends on many soil properties. Soil erodibility factor varies greatly over space and is commonly estimated using the revised universal soil loss equation. Neglecting information about estimation uncertainty may lead to improper decision-making. One geostatistical approach to spatial analysis is sequential Gaussian simulation, which draws alternative, equally probable, joint realizations of a regionalised variable. Differences between the realizations provide a measure of spatial uncertainty and allow us to carry out an error analysis. The objective of this paper was to assess the model output error of soil erodibility resulting from the uncertainties in the input attributes (texture and organic matter). The study area covers about 30 km2 (Calabria, southern Italy). Topsoil samples were collected at 175 locations within the study area in 2006 and the main chemical and physical soil properties were determined. As soil textural size fractions are compositional data, the additive-logratio (alr) transformation was used to remove the non-negativity and constant-sum constraints on compositional variables. A Monte Carlo analysis was performed, which consisted of drawing a large number (500) of identically distributed input attributes from the multivariable joint probability distribution function. We incorporated spatial cross-correlation information through joint sequential Gaussian simulation, because model inputs were spatially correlated. The erodibility model was then estimated for each set of the 500 joint realisations of the input variables and the ensemble of the model outputs was used to infer the erodibility probability distribution function. This approach has also allowed for delineating the areas characterised by greater uncertainty and then to suggest efficient supplementary sampling strategies for further improving the precision of K value predictions.

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

Access this article

Log in via an institution

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Agassi M (1996) Soil erosion, conservation, and rehabilitation. Marcel Dekker Inc., New York

    Google Scholar 

  • Aitchison J (1982) The statistical analysis of compositional data (with discussion). J R Stat Soc B 44:139–177

    Google Scholar 

  • Aitchison J (1984) The statistical analysis of geochemical compositions. Math Geol 16:531–564

    Article  Google Scholar 

  • Aitchison J (1986) The statistical analysis of compositional data. Chapman and Hall, Ltd, London

    Book  Google Scholar 

  • Aitchison J (1994) Principles of compositional data analysis. In: Anderson TW, Olkin I, Fang K (eds) Multivariate analysis and its applications. Institute of Mathematical Statistics, Hayward, pp 73–81

  • ARSSA (2003) I suoli della Calabria. Carta dei suoli in scala 1:250000 della Regione Calabria, Monografia divulgativa, ARSSA-Agenzia Regionale per lo Sviluppo e per i Servizi in Agricoltura, Servizio Agropedologia, Rubbettino, Soveria Mannelli (CZ), Italy, pp 387

  • Bleines C, Deraisme J, Geoffrey F, Jeannée N, Perseval S, Rambert F, Renard D, Torres O, Touffait Y (2008) Isatis Software Manual, 10th edn. Géovariances & Ecole des Mines de Paris, Avon

    Google Scholar 

  • Burrough PA (2001) GIS and geostatistics: essential partners for spatial analysis. Environ Ecol Stat 8:361–377

    Article  Google Scholar 

  • Calcaterra D, Parise M (2010) Weathering in the crystalline rocks of Calabria, Italy, and relationships to landslides. In: Calcaterra D, Parise M (eds) Weathering as predisposing factor to slope movements. Geological Society of London, Engineering Geology Series, Special Publication 2

  • Castrignanò A, Buttafuoco G (2004) Geostatistical stochastic simulation of soil water content in a forested area of South Italy. Biosyst Eng 87:257–266

    Article  Google Scholar 

  • Castrignanò A, Buttafuoco G, Canu A, Zucca C, Madrau S (2008) Modelling spatial uncertainty of soil erodibility factor using joint stochastic simulation. Land Degrad Dev 19:198–213

    Article  Google Scholar 

  • Chilès JP, Delfiner P (1999) Geostatistics: modelling spatial uncertainty. Wiley, New York

    Book  Google Scholar 

  • Conforti M (2009) Studio geomorfopedologico dei processi erosivi nel bacino del T. Turbolo (Calabria settentrionale) con il contributo della spettrometria della riflettenza. PhD Thesis, University of Calabria, Italy, pp 310

  • Davis JC (1986) Statistics and data analysis in Geology, 2nd edn. Wiley, New York, pp 107–148

    Google Scholar 

  • Deutsch CV, Journel AG (1998) GSLIB: Geostatistical Software Library and User’s Guide. Oxford University Press, New York

    Google Scholar 

  • Faulkner H, Spivey D, Alexander R (2000) The role of some site geochemical processes in the development and stabilisation of three badland sites in Almeria, Southern Spain. Geomorphology 35:87–99

    Article  Google Scholar 

  • Geovariances (2010) ISATIS Software (Version 10.05) Avon, France. http://www.geovariances.fr

  • Gomez-Hernandez JJ, Journel AG (1992) Joint sequential simulation of multigaussian fields. In: Soares A (ed) Geostatistics Troia 1992, vol I. Kluwer Academic Publishers, Dordrecht, pp 85–94

  • Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York

    Google Scholar 

  • Goovaerts P (1994) Study of spatial relationships between two sets of variables using multivariate geostatistics. Geoderma 62:93–107

    Article  Google Scholar 

  • Heuvelink GBM (1998) Error propagation in environmental modelling with GIS. Taylor and Francis, London

    Google Scholar 

  • Heuvelink GBM, Pebesma EJ (1999) Spatial aggregation and soil process modelling. Geoderma 89:47–65

    Article  Google Scholar 

  • Hudson N (1995) Soil conservation. Iowa State University Press, Ames

    Google Scholar 

  • Isaaks EH, Srivastava RM (1989) Applied geostatistics. Oxford University Press, New York

    Google Scholar 

  • Jansen MJW (1998) Prediction error through modelling concepts and uncertainty from basic data. Nutr Cycl Agroecosyst 50:247–253

    Article  Google Scholar 

  • Journel AG (1983) Non-parametric estimation of spatial distributions. Math Geol 15:445–468

    Article  Google Scholar 

  • Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic Press, New York

    Google Scholar 

  • Journel AG, Alabert F (1989) Non-Gaussian data expansion in the earth sciences. Terra Nova 1:123–134

    Article  Google Scholar 

  • Köppen W (1936) Das geographische System der Klimate. In: Köppen W, Geiger R (eds) Handbuch der Klimatologie. Band 5, Teil C. Gebrüder Bornträger, Berlin, pp 1–46

    Google Scholar 

  • Lanzafame G, Zuffa G (1976) Geologia e petrografia del foglio Bisignano (Bacino del Crati, Calabria). Geol Rom 15:223–270

    Google Scholar 

  • Lark RM, Bishop TFA (2007) Cokriging particle size fractions of the soil. Eur J Soil Sci 58:763–774

    Article  Google Scholar 

  • Lewis PAW, Orav EJ (1989) Simulation methodology for statisticians, operations analysts, and engineers, vol. 1. Wadsworth Publ. Co, Belmont

    Google Scholar 

  • McBratney AB, De Gruijter JJ, Brus DJ (1992) Spacial prediction and mapping of continuous soil classes. Geoderma 54:39–64

    Article  Google Scholar 

  • Morgan RPC (2005) Soil erosion and conservation, 3rd edn. Wiley, New York

    Google Scholar 

  • Pagliai M (ed) (1997) Metodi di Analisi Fisica del Suolo, Ministero per le Politiche Agricole e Forestali. Franco Angeli, Milan

    Google Scholar 

  • Parysow P, Wang GX, Gertner G, Anderson AB (2003) Spatial uncertainty analysis for mapping soil erodibility based on joint sequential simulation. Catena 53:65–78

    Article  Google Scholar 

  • Pawlowsky-Glahn V, Burger H (1992) Spatial structure analysis of regionalised compositions. Math Geol 24:675–691

    Article  Google Scholar 

  • Pawlowsky-Glahn V, Egozcue JJ (2006) Compositional data and their analysis: an introduction. In: Buccianti A, Mateu-Figueras G, Pawlowsky-Glahn V (eds) Compositional data analysis in the geosciences: from theory to practice, Special Publications 264. Geological Society, London, pp 1–10

    Google Scholar 

  • Pulice I, Scarciglia F, Leonardi L, Robustelli G, Conforti M, Cuscino M, Lupiano V, Critelli S (2009) Studio multidisciplinare di forme e processi denudazionali nell’area di Vrica (Calabria orientale). Bollettino della Società Geografica Italiana, vol 87, no I–II, pp 403–417

  • Renard KG, Foster GR, Weesies GA, Mccool DK, Yoder DC (1997) Predicting soil erosion by water: a guide to conservation planning with the revised soil loss equation (RUSLE). U.S. Dept. of Agriculture. Agric. Handbook No. 703, p 404

  • Shirazi MA, Boersma L, Hart JW (1988) A unifying quantitative analysis of soil texture: improvement of precision and extension of scale. Soil Sci Soc Am J 52:181–190

    Article  Google Scholar 

  • Singh MJ, Khera KL (2009) Nomographic estimation and evaluation of soil erodibility under simulated and natural rainfall conditions. Land Degrad Dev 20:471–480

    Google Scholar 

  • Soil Survey Staff (2010) Keys to soil taxonomy, 11th edn. USDA-Natural Resources Conservation Service, Washington, DC, p 338

    Google Scholar 

  • Terranova O, Antronico L, Coscarelli R, Iaquinta P (2009) Soil erosion risk scenarios in the Mediterranean environment using RUSLE and GIS: an application model for Calabria (southern Italy). Geomorphology 112:228–245

    Article  Google Scholar 

  • Terranova O, Catalano E, Langellotti M, Sorriso-Valvo M (1989) Individuazione dei parametri caratterizzanti i fenomeni idraulici ed erosivi di un piccolo bacino (T. Turbolo-Calabria). In: Proceedings of the 1st Workshop on Informatica e Scienze della Terra, October 1989, Sarnano (MC), Italy. Gruppo Informatica Applicata alle Scienze della Terra del CNR, De Frede, Napoli, Italy, pp 32/1–32/22

  • Tolosana Delgado R (2005) Geostatistics for constrained variables: positive data, compositions and probabilities. Applications to environmental hazard monitoring. PhD Thesis, Universitat de Girona

  • Tolosana-Delgado R, Otero N, Pawlowsky-Glahn V (2005) Some basic concepts of compositional geometry. Math Geol 37:673–680

    Article  Google Scholar 

  • Torri D, Poesen J, Borselli L (1997) Predictability and uncertainty of the soil erodibility factor using a global dataset. Catena 31:1–22

    Article  Google Scholar 

  • Torri D, Poesen J, Borselli L (2002) Corrigendum to ‘Predictability and uncertainty of the soil erodibility factor using a global dataset’ [Catena 31 (1997) 1–22] and to ‘‘Erratum to Predictability and uncertainty of the soil erodibility factor using a global dataset’’ [Catena 32 (1998) 307–308]. Catena 46:309–310

    Article  Google Scholar 

  • Violante P (ed) (2000) Metodi di Analisi Chimica del Suolo Ministero per le Politiche Agricole e Forestali. Franco Angeli, Milan

    Google Scholar 

  • Wackernagel H (2003) Multivariate geostatistics: an introduction with applications. Springer, Berlin

    Google Scholar 

  • Wang G, Gertner GZ, Liu X, Anderson AB (2001) Uncertainty assessment of soil erodibility factor for revised universal soil loss equation. Catena 46:1–14

    Article  Google Scholar 

  • Webster R, Oliver MA (1990) Statistical methods in soil and land resource survey. Oxford University Press, Oxford, pp 291–298

    Google Scholar 

  • Webster R, Oliver MA (2007) Geostatistics for environmental scientists, 2nd edn. Wiley, Chichester

    Book  Google Scholar 

  • Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses. A guide to conservation planning. U.S. Department of Agriculture, Agriculture Handbook No 537, p 58

Download references

Acknowledgments

The authors thank Dr Raimon Tolosana-Delgado and an anonymous reviewer for their critical comments and suggestions, which greatly improved the quality of our manuscript.

Author information

Authors and Affiliations

  1. CNR, Istituto per i Sistemi Agricoli e Forestali del Mediterraneo (ISAFOM), 87036, Rende (CS), Italy

    G. Buttafuoco

  2. Dipartimento di Scienze della Terra, Università della Calabria, Via P. Bucci-Cubo 15B, 87036, Arcavacata di Rende (CS), Italy

    M. Conforti, G. Robustelli & F. Scarciglia

  3. Dipartimento di Scienze Ambientali (DiSAm), Università degli studi di Napoli “Parthenope”, Centro Direzionale di Napoli, Isola C4, 80143, Napoli, Italy

    P. P. C. Aucelli

Authors
  1. G. Buttafuoco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Conforti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. P. C. Aucelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. Robustelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. F. Scarciglia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G. Buttafuoco.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Buttafuoco, G., Conforti, M., Aucelli, P.P.C. et al. Assessing spatial uncertainty in mapping soil erodibility factor using geostatistical stochastic simulation. Environ Earth Sci 66, 1111–1125 (2012). https://doi.org/10.1007/s12665-011-1317-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12665-011-1317-0

Keywords

Search

Navigation