Testing a sequential stochastic simulation method based on regression kriging in a catchment area in Southern Hungary

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Published: Sep 16, 2015
DOI: https://doi.org/10.4154/GC.2015.21
Keywords:
geostatistics, regression kriging, sequential stochastic simulation, spatial uncertainty, soil organic matter

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Gábor Szatmári
Department of Physical Geography and Geoinformatics, University of Szeged
Károly Barta
Department of Physical Geography and Geoinformatics, University of Szeged
Andrea Farsang
Department of Physical Geography and Geoinformatics, University of Szeged
László Pásztor
Department of Environmental Informatics, Institute for Soil Sciences and Agricultural Chemistry, Hungarian Academy of Sciences

Abstract

Modelling spatial variability and uncertainty is a highly challenging subject in soil- and geosciences. Regression kriging (RK) has several advantages; nevertheless it is not able to model the spatial uncertainty of the target variable. The main aim of this study is to present and test a sequential stochastic simulation approach based on regression kriging (SSSRK), which can be used to generate alternative and equally probable realizations in order to model and assess the spatial variability and uncertainty of the target variable, meanwhile the advantages of the RK technique are retained. The SSSRK method was tested in a catchment area, in Southern Hungary for the modelling of spatial variability and uncertainty of soil organic matter (SOM) content. In the first step, the auxiliary information was derived according to the soil forming factors; then the RK system was built up, which provides the base of SSSRK. 100 realizations were generated, which reproduced the model statistics and honored the input dataset. These realizations provide 100 simulated values for each grid node, which number is appropriate to calculate the cumulative distributions. Using these distributions the following maps were derived: map of expected values, the corresponding 95% confidence interval’s width, furthermore the probability of the event of {SOM < 1.5%}, since this threshold value is highly informative in soil protection and management planning. The resulted maps showed that, SSSRK is a valuable technique to model and assess the spatial variability and uncertainty of the target variable. Furthermore, the comparison of RK and SSSRK showed that, the SSSRK’s E-type estimation and the RK estimation gave almost the same results due to the fairly high R2 value of the regression model (R2=0.809), which decreased the smoothing effect.

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Article Details

Issue
Vol. 68 No. 3 (2015)
Section
Original Scientific Papers

Authors have copyright and publishing rights on all published manuscripts.

References

DEUTSCH, C.V & JOURNEL, A.G. (1998): GSLIB: Geostatistical Software Library and User’s Guide. – Oxford University Press, New York, 369 p.

DÖVÉNYI, Z. (ed.) (2010): Magyarország kistájainak katasztere [The cadastre of the Hungarian small regions – in Hungarian]. – MTA FKI, Budapest, 876 p.

GEIGER, J. (2006): Szekvenciális gaussi szimuláció az övzátonytestek kisléptékű heterogenitásának modellezésében [Sequential Gaussian simulation in the modelling of small-scale heterogeneity of point-bars – in Hungarian, with an English Abstract]. – Földtani közlöny, 136/4, 527–546.

GEIGER, J. (2007): Geomatematika [Geomathematics – in Hungarian]. – JATEPress, Szeged, 117 p.

GEIGER, J. (2012): Some thoughts on the pre- and post-processing in sequential Gaussian simulation and their effects on reservoir characterization. – In: GEIGER, J., PÁL-MOLNÁR E. & MALVIĆ, T. (eds.): New horizons in central european geomathematics, geostatistics and geoinformatics. – GeoLitera, Szeged, 17–34.

GEIGER, J. & MUCSI, L. (2005): A szekvenciális sztochasztikus szimuláció előnyei a talajvízszint kisléptékű heterogenitásának térképezésében [The advantages of stochastic simulation in mapping the small-scale heterogeneity of water tables – in Hungarian, with an English Abstract]. – Hidrológiai közlöny, 85/2, 37–47.

GOOVAERTS, P. (1997): Geostatistics for Natural Resources Evaluation. – Oxford University Press, New York, 512 p.

GOOVAERTS, P. (2000): Estimation or simulation of soil properties? An optimization problem with conflicting criteria. – Geoderma, 97, 165–186.

GOOVAERTS, P. (2001): Geostatistical modelling of uncertainty in soil science. – Geoderma, 103, 3–26.

GOOVAERTS, P. (2010): Combining Areal and Point Data in Geostatistical Interpolation: Applications to Soil Science and Medical Geography. – Math. Geosci., 42, 535–554.

GOOVAERTS, P. (2011): A coherent geostatistical approach for combining choropleth map and field data in the spatial interpolation of soil properties. – Eur. J. Soil Sci., 62, 371–380.

HENGL, T., HEUVELINK, G.B.M. & STEIN, A. (2004): A generic framework for spatial prediction of soil variables based on regression-kriging. – Geoderma, 120, 75–93.

JENNY, H. (1941): Factors of Soil Formation: A System of Quantitative Pedology. – McGraw-Hill, New York, 281 p.

KERRY, R., GOOVAERTS, P., RAWLINS, B.G. & MARCHANT, B.P. (2012): Disaggregation of legacy soil data using area to point kriging for mapping soil organic carbon at the regional scale. – Geoderma, 170, 347–358.

MALVIĆ, T. (2008): Kriging, cokriging or stochastical simulations, and the choice between deterministic or sequential approaches. – Geol. Croat., 61/1, 37-47.

MALVIĆ, T., NOVAK ZELENIKA, K. & CVETKOVIĆ, M. (2012): Indicator vs. gaussian geostatistical methods in sandstone reservoirs – case study from the Sava Depression, Croatia. – In: GEIGER, J., PÁL-MOLNÁR E. & MALVIĆ, T. (eds.): New horizons in central european geomathematics, geostatistics and geoinformatics. – GeoLitera, Szeged, 37–45.

MUCSI, L., GEIGER, J. & MALVIĆ, T. (2013): The advantages of using sequential stochastic simulations when mapping small-scale heterogeneities of the groundwater level. – J. En. Geo., 6/3-4, 39–47. DOI: 10.2478/jengeo-2013-0005

MSZ 21470–52:1983 (1983): Környezetvédelmi talajvizsgálatok: Talajok szervesanyag-tartalmának meghatározása [Environmental protection. Testing of soils. Determination of organic matter – in Hungarian]. – Hungarian Standard Association, Budapest, 4 p.

NOVAK ZELENIKA, K. & MALVIĆ, T. (2011): Stochastic simulations of dependent geological variables in sandstone reservoirs of Neogene age: A case study of the Kloštar Field, Sava Depression. – Geol. Croat., 64/2, 173–183.

NOVAK ZELENIKA, K., VELIĆ, J. & MALVIĆ, T. (2012): Application of geostatistics in the description of turbiditic depositional environments, a case study of the Kloštar Field, Sava Depression, Croatia. – In: GEIGER, J., PÁL-MOLNÁR E. & MALVIĆ, T. (eds.): New horizons in central european geomathematics, geostatistics and geoinformatics. – GeoLitera, Szeged, 61–71.

PÁSZTOR, L., KÖRÖSPARTI, J., BOZÁN, CS., LABORCZI, A. & TAKÁCS, K. (2014a): Spatial risk assessment of hydrological extremities: Inland excess water hazard, Szabolcs-Szatmár-Bereg County, Hungary. – Journal of Maps, DOI: 10.1080/17445647.2014.954647

PÁSZTOR, L., SZABÓ, J., BAKACSI, ZS., LABORCZI, A., DOBOS, E., ILLÉS, G. & SZATMÁRI, G. (2014b): Elaboration of novel, countrywide maps for the satisfaction of recent demands on spatial, soil related information in Hungary. – In: ARROUAYS, D., MCKENZIE, N., HEMPEL, J., RICHER DE FORGES, A. & MCBRATNEY, A.B. (eds.): Global Soil Map: Basis of the global spatial soil information system. – Taylor & Francis, London, 207–212.

SIMBAHAN, G.C., DOBERMANN, A., GOOVAERTS, P., PING, J. & HADDIX, M.L. (2006): Fine-resolution mapping of soil organic carbon based on multivariate secondary data. – Geoderma, 132, 471–489.

SZATMÁRI, G. & BARTA, K. (2013): Csernozjom talajok szervesanyag-tartalmának digitalis térképezése erózióval veszélyeztetett mezőföldi területen [Digital mapping of the organic matter content of chernozem soils on an area endangered by erosion in the Mezőföld region – in Hungarian, with an English Abstract]. – Agrokem. Talajtan, 62/1, 47-60.

SZATMÁRI, G., LABORCZI, A., ILLÉS G. & PÁSZTOR, L. (2013): A talajok szervesanyag-készletének nagyléptékű térképezése regresszió krigeléssel Zala megye példáján [Large-scale mapping of soil organic matter content by regression kriging in Zala County – in Hungarian, with English Abstract]. – Agrokem. Talajtan, 62/2, 219-234.

WEBSTER, R. & OLIVER, M.A. (2007): Geostatistics for Environmental Scientists (2nd Ed.). – Wiley, Chichester, 330 p.

WISCHMEIER, W.H. & SMITH, D.D. (1978): Predicting rainfall erosion losses: A guide to conservation planning. – U.S. Government Printing Office, Washington D.C., 58 p.