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Different methods for modelling the areal infiltration of a grass field under heavy precipitation
Author(s) -
Merz Bruno,
Bárdossy András,
Schiffler Gerd R.
Publication year - 2002
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.347
Subject(s) - infiltration (hvac) , surface runoff , environmental science , spatial variability , precipitation , homogeneous , soil science , mathematics , hydrology (agriculture) , meteorology , geology , statistics , geotechnical engineering , physics , ecology , combinatorics , biology
The areal infiltration behaviour of a grass field is studied using a data set of 78 sprinkler infiltration experiments. The analysis of the experimental data shows a distinct event dependency: once runoff begins, the final infiltration rate increases with increasing rainfall intensity. This behaviour is attributed to the effects of small‐scale variability. Increasing rainfall intensity increases the ponded area and therefore the portion of the plot which infiltrates at maximum rate. To describe the areal infiltration behaviour of the grass field the study uses two different model structures and investigates different approaches for consideration of subgrid variability. It is found that the effective parameter approach is not suited for this purpose. A good representation of the observed behaviour is obtained by using a distribution function approach or a parameterization approach. However, it is not clear how the parameters can be derived for these two approaches without a large measurement campaign. The data analysis and the simulations show the great importance of considering the effects of spatial variability for the infiltration process. This may be significant even at a small scale for a comparatively homogeneous area. The consideration of heterogeneity seems to be more important than the choice of the model type. Furthermore, similar results may be obtained with different modelling approaches. Even the relatively detailed data set does not seem to permit a clear model choice. In view of these results it is questionable to use very complex and detailed simulation models given the approximate nature of the problem. Although the principle processes may be well understood there is a lack of models that represent these processes and, more importantly, there is a lack of techniques to measure and parameterize them. Copyright © 2002 John Wiley & Sons, Ltd.