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Get Free AccessFlash floods are a common, but poorly understood feature of arid environments. Much of the uncertainty associated with flash flooding events is associated with a lack of accurate environmental data. In addition to limiting the understanding of hydrological processes, this situation handicaps human use and development in such regions, necessitating the use of modelling approaches for environmental prediction. Here, a hydrological model driven mainly by information on land cover distribution (derived by satellite remote sensing) and soil properties (derived from field measurement) was used to predict sites at risk from large peak flows associated with flash flooding in a wadi located in the Eastern Desert of Egypt. The land cover map was derived from a maximum likelihood classification of a Landsat TM image and had an estimated accuracy of 89.5%. The soils of the classes depicted in this map differed markedly in terms of texture and permeability, with the field based estimates of infiltration capacity ranging from 0.07 cm h−1 for desert pavement through 14.01 cm h−1 for unconsolidated wadi bed deposits. Using the derived information within the hydrological modelling system, the discharge from the wadi and its sub-basins was predicted for an assumed severe storm scenario. The outputs of the model indicated two locations within the wadi where a very large peak discharge (>115 m3 s−1) could be expected. These sites corresponded to those that suffered flood damage in a recent storm event. The results indicate the potential to drive an integrated hydrological model from limited data to derive important and useful hydrological information in a region where data are scarce.
Giles Foody, Eman Ghoneim, Nigel W. Arnell (2004). Predicting locations sensitive to flash flooding in an arid environment. Journal of Hydrology, 292(1-4), pp. 48-58, DOI: 10.1016/j.jhydrol.2003.12.045.
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Type
Article
Year
2004
Authors
3
Datasets
0
Total Files
0
Language
English
Journal
Journal of Hydrology
DOI
10.1016/j.jhydrol.2003.12.045
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