Presentation Title

Improving flood predictions after extreme draught using HEC HMS with Runoff Ratio Analysis.

Faculty Mentor

Helen Jung

Start Date

18-11-2017 10:00 AM

End Date

18-11-2017 11:00 AM

Location

BSC-Ursa Minor 71

Session

Poster 1

Type of Presentation

Poster

Subject Area

engineering_computer_science

Abstract

An accurate and localized flash flood predictions during an extremely dry season is crucial in semi-arid regions due to the unexpected flash flood events in the area. The flash floods in this zone carries large amounts of debris which adds to the increased runoff. The National Weather Service sends out the flood warning messages; however it is often ignored due to few reasons: inaccurate predictions of the peak flow due to accumulated debris, inaccurate predictions of the timing of the peak flow, and over-prediction of the flood area. The prediction needs to be re-calibrated to account for extremely dry seasons, and for prolonged extreme drought years. The prediction also needs to be localized to increase the accuracy.

The county of Riverside uses two methods to predict the flood flow: Rational Method and Synthetic Unit Hydrograph. Both methods are precipitation dependent. The semi-arid areas use precipitation dependent methods to model the flow due to the low-flow data. It is very hard to calibrate and draw any physical meanings from the model parameters with the low-flow data which doesn’t fluctuate much. Both methods translate the precipitation into discharge. This discharge does not include the debris flows. With prolonged drought, there is less vegetation cover and more accumulated debris which increases the debris production with small amount of rain (increase peak bulking rate per storm). In addition to existing hardship of predicting the debris flow, we are experiencing the extreme drought. Our goal is to use the runoff ratio to as a first step to select the storms that are very similar in pattern and signature. The pattern not only from the storm itself, but also the antecedent conditions such as extreme drought. With this information we can add more physical meaning to the parameter and select the sensitive range for the condition.

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Nov 18th, 10:00 AM Nov 18th, 11:00 AM

Improving flood predictions after extreme draught using HEC HMS with Runoff Ratio Analysis.

BSC-Ursa Minor 71

An accurate and localized flash flood predictions during an extremely dry season is crucial in semi-arid regions due to the unexpected flash flood events in the area. The flash floods in this zone carries large amounts of debris which adds to the increased runoff. The National Weather Service sends out the flood warning messages; however it is often ignored due to few reasons: inaccurate predictions of the peak flow due to accumulated debris, inaccurate predictions of the timing of the peak flow, and over-prediction of the flood area. The prediction needs to be re-calibrated to account for extremely dry seasons, and for prolonged extreme drought years. The prediction also needs to be localized to increase the accuracy.

The county of Riverside uses two methods to predict the flood flow: Rational Method and Synthetic Unit Hydrograph. Both methods are precipitation dependent. The semi-arid areas use precipitation dependent methods to model the flow due to the low-flow data. It is very hard to calibrate and draw any physical meanings from the model parameters with the low-flow data which doesn’t fluctuate much. Both methods translate the precipitation into discharge. This discharge does not include the debris flows. With prolonged drought, there is less vegetation cover and more accumulated debris which increases the debris production with small amount of rain (increase peak bulking rate per storm). In addition to existing hardship of predicting the debris flow, we are experiencing the extreme drought. Our goal is to use the runoff ratio to as a first step to select the storms that are very similar in pattern and signature. The pattern not only from the storm itself, but also the antecedent conditions such as extreme drought. With this information we can add more physical meaning to the parameter and select the sensitive range for the condition.