The Influence of Rainfall and Catchment Critical Scales on Urban Hydrological Response Sensitivity
Elena Cristiano (TU Delft - Water Resources)
Marie Claire Ten Ten Veldhuis (TU Delft - Water Resources)
Daniel B. Wright (University of Wisconsin-Madison)
James Smith (Princeton University)
NC van de Giesen (TU Delft - Water Resources)
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Abstract
Interactions between spatial and temporal variability of rainfall and catchment characteristics strongly influence hydrological response. In urban areas, where runoff generation is fast due to high imperviousness degree, it is especially relevant to capture the high spatiotemporal rainfall variability. Significant progress has been made in the development of spatially distributed rainfall measurements and of distributed hydrological models, to represent the variability of catchment's characteristics. Interactions between rainfall and basin scales on hydrological response sensitivity, however, needs deeper investigation. A previous study investigated the hydrological response in the small urbanized catchment of Cranbrook (8 km
2
, London, UK) and proposed three dimensionless “scale factors” to identify if the available rainfall resolution is sufficient to properly predict hydrological response. We aim to verify the applicability of these scale factors to larger scales, with a distinct physiographic setting, in Little Sugar Creek (111 km
2
, Charlotte, USA), to identify the required rainfall resolution and to predict model performance. Twenty-eight events were selected from a weather radar data set from the National Weather Radar Network, with a resolution of 1 km
2
and 15 min. Rainfall data were aggregated to coarser resolutions and used as input for a distributed hydrological model. Results show that scale factors and associated thresholds are generally applicable for characterization of urban flood response to rainfall across spatiotemporal scales. Additionally, application of scale factors in observation-based analysis supports identification of event characteristics that are poorly captured and critical improvements that need to be made before the model can benefit from high-resolution rainfall.
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