Probabilistic Conditioning and Recalibration of an Event-Based Flood Forecasting Model Using Real-Time Streamflow Observations
Publication: Journal of Hydrologic Engineering
Volume 28, Issue 4
Abstract
Flood warnings provide information about the timing and magnitude of impending floods, which can help mitigate the adverse impacts of flooding. Flood forecasts are highly influenced by uncertainty associated with rainfall forecasts as well as initial catchment wetness. Event-based models are simple and parsimonious and are widely favored by practitioners for flood estimation. However, these models require loss parameters to be manually specified for each simulated event, and this represents an additional source of uncertainty that needs to be considered along with errors in observations and rainfall forecasts. Little attention has been given to the coupling of updating techniques with event-based models to reduce the uncertainty associated with catchment wetness. To this end, we devised a sequential recalibration scheme to characterize uncertainty in ensemble forecasts derived using an event-based flood model. This scheme uses information on both observation and model errors to filter and update catchment loss estimates to improve the accuracy of the forecasts. Analysis of flood forecasts for 22 events showed that although initially, there was low skill in forecasts derived solely from external estimates of catchment wetness, the reliability and accuracy of the forecasts improved rapidly once the flood event commenced and the flood model was coupled with an updating scheme. Compared with forecasts made without any updating scheme, the conditioning and recalibration steps progressively improved the accuracy of the forecasts as measured by Nash-Sutcliffe efficiency from to 0.88, bias was reduced by 78%, and root-mean square error reduced by 67%. The use of such schemes thus reinforces the advantages of using parsimonious models that have long been favored by practitioners for design and other purposes.
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Data Availability Statement
Some data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Some data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
The authors appreciatively acknowledge the financial support provided by the Australian Research Training Program (RTP) to fund the first author’s Ph.D. scholarship. Also, the authors acknowledge the availability of rainfall and streamflow data provided by the Australian Bureau of Meteorology. The daily and pluviograph rainfall data may be freely downloaded from http://www.bom.gov.au/climate/data/, and the streamflow data from http://www.bom.gov.au/waterdata/. Forecast data are freely available to registered users by contacting [email protected].
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Journal of Hydrologic Engineering
Volume 28 • Issue 4 • April 2023
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© 2023 American Society of Civil Engineers.
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Received: Jan 11, 2022
Accepted: Aug 30, 2022
Published online: Jan 19, 2023
Published in print: Apr 1, 2023
Discussion open until: Jun 19, 2023
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