E3S Web Conf.
Volume 7, 20163rd European Conference on Flood Risk Management (FLOODrisk 2016)
|Number of page(s)||8|
|Section||Probability of floods and storms|
|Published online||20 October 2016|
Reducing uncertainty in small-catchment flood peak estimation
1 Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, UK
2 Wallingford HydroSolutions, Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, UK
3 Environment Agency, Horizon House, Deanery Road, Bristol, BS1 9AH, UK
a Corresponding author: email@example.com
Every year in the UK, many flood risk assessments are carried out on small catchments, typically draining areas of less than 25 km2. Standard hydrological practice in all UK catchments is to apply the methods presented in the Flood Estimation Handbook (FEH) and its subsequent updates. FEH methods are practical, relatively easy to apply and based on extensive statistical analyses. However, uncertainties can be large, especially in atypical catchments, and small catchments can present unique challenges in terms of heavy urbanisation and rapid flood responses. Compared to larger catchments, small catchment flood data are limited. In this study, we use a dataset of annual maxima and digital catchment descriptors at 205 small catchments to benchmark the QMED and Q100 estimation performance of current UK flood estimation methods: the FEH statistical method, ReFH2 and MacDonald and Fraser’s method, in rural and urbanised catchments separately. All methods perform similarly in rural catchments overall, although MacDonald and Fraser’s method underestimates QMED in urbanised catchments. The methods show a larger factorial standard error against this small catchment dataset than they do against typical datasets of mixed-size catchments. Further work will evaluate the performance of ReFH2 in combination with the latest FEH13 rainfall model.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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