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.
Urbanisation is an important driver of changes in streamflow. These changes are not uniform across catchments due to the diverse nature of water sources, storage, and pathways in urban river systems. While land cover data are typically used in urban hydrology analyses, other characteristics of urban systems (such as water management practices) are poorly quantified which means that urbanisation impacts on streamflow are often difficult to detect and quantify. Here, we assess urban impacts on streamflow dynamics for 711 catchments across England and Wales. We use the CAMELS-GB dataset, which is a large-sample hydrology dataset containing hydro-meteorological timeseries and catchment attributes characterising climate, geology, water management practices and land cover. We quantify urban impacts on a wide range of streamflow dynamics (flow magnitudes, variability, frequency, and duration) using random forest models. We demonstrate that wastewater discharges from sewage treatment plants and urban land cover dominate urban hydrology signals across England and Wales. Wastewater discharges increase low flows and reduce flashiness in urban catchments. In contrast, urban land cover increases flashiness and frequency of medium and high flow events. We highlight the need to move beyond land cover metrics and include other features of urban river systems in hydrological analyses to quantify current and future drivers of urban streamflow.
more » « less- Award ID(s):
- 2124923
- PAR ID:
- 10523029
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 19
- Issue:
- 8
- ISSN:
- 1748-9326
- Format(s):
- Medium: X Size: Article No. 084016
- Size(s):
- Article No. 084016
- Sponsoring Org:
- National Science Foundation
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