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

This resource, RREDI toolkit V2, updates and supersedes:
Canham, H., Lane, B. (2022). Rainfall-Runoff Event Detection and Identification (RREDI) toolkit, HydroShare, http://www.hydroshare.org/resource/797fe26dfefb4d658b8f8bc898b320de

Paired Paper:
Canham, H. A., Lane, B., Phillips, C. B., and Murphy, B. P. (2025). Leveraging a time-series event separation method to disentangle time-varying hydrologic controls on streamflow – application to wildfire-affected catchments, Hydrol. Earth Syst. Sci., 29, 27–43, https://doi.org/10.5194/hess-29-27-2025.

ABSTRACT:

This Grizzly Creek Repository includes precipitation data, discharge data, survey data, and the scripts used to analyze it.

ABSTRACT:

This resource has been updated and superseded by:
Canham, H., Lane, B. (2025). Rainfall-Runoff Event Detection and Identification (RREDI) toolkit V2, HydroShare, http://www.hydroshare.org/resource/c0b8d3aee3434df88adc1f495b3121f6

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

Paired Paper:
Canham, H. A., Lane, B., Phillips, C. B., and Murphy, B. P. (2025). Leveraging a time-series event separation method to disentangle time-varying hydrologic controls on streamflow – application to wildfire-affected catchments, Hydrol. Earth Syst. Sci., 29, 27–43, https://doi.org/10.5194/hess-29-27-2025.

ABSTRACT:

This resource includes a python Jupyter notebook to evaluate 2021 drought conditions in the Blacksmith Fork, UT using data retrieved from the USGS using the python dataretrieval library. The analysis contained within the Jupyter notebook is reproducible. This is created to fulfill Hydroinformatics Assignment 8 requirements.