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Taylor and Francis Group, Hydrological Sciences Journal, 2(53), p. 387-400

DOI: 10.1623/hysj.53.2.387

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Statistical analysis of the effects on overland flow of spatial variability in soil hydraulic conductivity / Analyse statistique des effets de la variabilité spatiale de la conductivité hydraulique du sol sur l'écoulement de surface

Journal article published in 2008 by Antti Taskinen, Hannu Sirviö, Michael Bruen ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

The effects of spatial variation of the saturated hydraulic conductivity (Ks) of the soil on the variation of overland flow were tested by analysing 2000 synthetic rainfall—runoff events, all generated from real, observed rainfall events but with runoff modelled by a two-dimensional distributed model using different spatially variable Ks fields in a small (12 ha) agricultural catchment. The purpose is to determine the influence of spatial variation in Ks on runoff generation. The statistical measures used to describe the variation in the generated Ks were its coefficient of variation and correlation length. Both of these had two levels of typical values obtained from field measurements in other studies. The storms were analysed at a general event level, first using simple graphical and statistical methods and then using analysis of variance (ANOVA). The observed scale of the spatial variation of Ks does cause statistically significant variation in overland flow. The graphical analysis showed that the first flow peak in a multi-event storm had the largest variation and that differences were greater in the rising part of the hydrograph than in its recession. The greatest variation in overland flow was produced by the combination of the greater coefficient of variation and the longer correlation lengths. The smallest variation in overland flow was produced by the combination of the smaller coefficient of variation and the shorter correlation lengths. ANOVA showed that the coefficient of variation and correlation length alone did not explain all the variation of the total flow. ANOVA was not very useful due to the many restrictive assumptions that were not satisfied by the nature of the data and therefore analysis methods with less restrictive assumptions need to be tested.