Rainfall-Runoff Event Detection and Identification (RREDI) toolkit
| Authors: | |
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| Owners: | Haley Canham |
| Type: | Resource |
| Storage: | The size of this resource is 131.1 MB |
| Created: | Nov 14, 2022 at 3:44 p.m. |
| Last updated: | Feb 01, 2024 at 8:47 p.m. |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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| Views: | 1039 |
| Downloads: | 151 |
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Abstract
To automate the analysis of post-wildfire rainfall-runoff events across numerous storms and watersheds, the hydrologic time-series analysis Rainfall-Runoff Event Detection and Information (RREDI) algorithm was developed. The RREDI algorithm first uses feature detection and signal processing of storm precipitation and flow data to identify rainfall-runoff events. Then each rainfall-runoff event is extracted using 15-minute flow and instantaneous precipitation data and the timing and magnitude of the start, peak, and end of event is extracted. These identifiers are then used to calculate a set of event attributes including time to peak, response time, duration, volume, and percent rise. These attributes from the identified rainfall-runoff events can then analyzed to answer research questions regarding variability in rainfall-runoff patterns within and between watersheds. This algorithm utilizes the open-source Python.
Utah Water Research Laboratory, Utah State University
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Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| National Science Foundation | Monitoring and modeling watershed-scale post-wildfire streamflow response through space and time | 2051762 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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