Abstract
Growing urbanisation and imperviousness have augmented magnitudes of peak flows, resulting in flooding especially during extreme events. Flood forecast of extreme events can rely on real‐time ensemble flood forecasting systems. Such systems often use predictions from physical models and precipitation ensembles to predict downstream urban flood hydrographs. However, these methods are seldom used in small catchments, where flood predictions may assist emergency management. We explore the relative utility of two models, the Sacramento Model (SAC‐SMA) and an adaptive neuro‐fuzzy inference system (ANFIS) for ensemble flood prediction for nine small urban catchments located near New York City. The models were used to reforecast streamflow for Hurricane Irene (160 mm) and a 35 mm storm across lead times from 3 to 24 hr. Differences in performance between models were small for short (3 hr) lead times, and were similar for the 35 mm storm. Reforecasts of hurricane Irene at 24‐hr lead times show strong performance for SAC‐SMA, but a decline in performance for ANFIS. Model performance did not vary systematically with either catchment size or imperviousness. Our results suggest that model selection is especially important when reforecasting large rain events with longer lead times in small urban catchments.
Highlights
As a consequence of rapid urbanization and increased surface imperviousness, many urban watersheds worldwide are threatened by greater frequency and depth of flooding (Du et al, 2012; Liu et al, 2005; Nirupama and Simonovic, 2007; Qaiser et al, 2012; Suriya and Mudgal, 2012; Wang and Yang, 2013; Zope et al, 2015)
We focus primarily on these two storm events based on an extensive survey of all storms between 2004 and 2011 contained within the Global Ensemble Forecast System Reforecast (GEFS/R), one of the most reliable sources for ensemble precipitation data for reforecasting extreme events
Relative Bias (RelBIAS) values, SAC-SMA calibration datasets, and calibration hydrographs are presented for individual watersheds in Supplementary Material/Appendix (Table A1, Table A2, and Figure A3)
Summary
As a consequence of rapid urbanization and increased surface imperviousness, many urban watersheds worldwide are threatened by greater frequency and depth of flooding (Du et al, 2012; Liu et al, 2005; Nirupama and Simonovic, 2007; Qaiser et al, 2012; Suriya and Mudgal, 2012; Wang and Yang, 2013; Zope et al, 2015). Urban floods endanger human lives, damage property, and initiate a cascade of environmental and health impacts (Jha et al, 2012; World Bank, 2013) To mitigate this damage, emergency management authorities may rely on real-time flood forecast systems to provide sufficient lead time for evacuation and asset protection in urban watersheds during extreme rainfall events. The rainfall forecast ensembles are used to generate a series of future flood hydrographs, called spaghetti hydrographs (Emerton et al, 2016) This procedure is performed on a real-time basis using a flood forecast model that is continuously calibrated up to the current time from historical weather and streamflow observations. A spaghetti hydrograph can be used to inform emergency managers about possible future flooding scenarios and can guide strategies for evacuation and rescue
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