Test of statistical means for the extrapolation of soil depth point information using overlays of spatial environmental data and bootstrapping techniques |
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| Authors: | Helen E Dahlke Thorsten Behrens Jan Seibert Lotta Andersson |
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| Institution: | 1. Biological and Environmental Engineering, Cornell University, 165 Riley‐Robb Hall, Ithaca, New York, 14853, USA;2. Physical Geography, Institute of Geography, University of Tuebingen, Ruemelinstrasse 19‐23, 72070 Tübingen, Germany;3. Department of Geography, University of Zurich, CH‐8057 Zurich, Switzerland;4. Department of Physical Geography and Quaternary Geology, Stockholm University, SE‐106 91, Stockholm, Sweden;5. Swedish Meteorological and Hydrological Institute, Department of Research and Development, SE‐601 76 Norrk?ping, Sweden |
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| Abstract: | Hydrological modelling depends highly on the accuracy and uncertainty of model input parameters such as soil properties. Since most of these data are field surveyed, geostatistical techniques such as kriging, classification and regression trees or more sophisticated soil‐landscape models need to be applied to interpolate point information to the area. Most of the existing interpolation techniques require a random or regular distribution of points within the study area but are not adequate to satisfactorily interpolate soil catena or transect data. The soil landscape model presented in this study is predicting soil information from transect or catena point data using a statistical mean (arithmetic, geometric and harmonic mean) to calculate the soil information based on class means of merged spatial explanatory variables. A data set of 226 soil depth measurements covering a range of 0–6·5 m was used to test the model. The point data were sampled along four transects in the Stubbetorp catchment, SE‐Sweden. We overlaid a geomorphology map (8 classes) with digital elevation model‐derived topographic index maps (2–9 classes) to estimate the range of error the model produces with changing sample size and input maps. The accuracy of the soil depth predictions was estimated with the root mean square error (RMSE) based on a testing and training data set. RMSE ranged generally between 0·73 and 0·83 m ± 0·013 m depending on the amount of classes the merged layers had, but were smallest for a map combination with a low number of classes predicted with the harmonic mean (RMSE = 0·46 m). The results show that the prediction accuracy of this method depends on the number of point values in the sample, the value range of the measured attribute and the initial correlations between point values and explanatory variables, but suggests that the model approach is in general scale invariant. Copyright © 2009 John Wiley & Sons, Ltd. |
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| Keywords: | soil‐landscape modelling hydrological modelling soil depth bootstrapping soil attributes soil attribute prediction statistical mean root mean square error |
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