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.

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This resource has been updated and superseded by: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 Grizzly Creek Repository includes precipitation data, discharge data, survey data, and the scripts used to analyze it.

ABSTRACT:

To automate the analysis of the influence of wildfire on runoff events across numerous storms and watersheds the Paired Storms Framework was developed. The Framework applies the concepts of the established paired watersheds approach but exchanges time for space by identifying and comparing post-fire flood-producing storm characteristics to those of similar (i.e., paired) unburned storms in the same watershed. The Paired Storms Framework first retrieves and processes hourly 1 km2 gridded precipitation data from the NOAA Analysis of Record for Calibration (AORC) data product. Then storms are created using the RREDI Toolkit (Canham & Lane, 2024) and storm temporal, spatial, and interannual and seasonal context are calculated. Post-fire floods of interest are selected and for each post-fire flood, undisturbed paired storms are identified from the storm record as those with similar parameterized characteristics. Finally, the influence of the wildfire on the post-fire flood is calculated as a multiplier of how many times greater the post-fire runoff peak magnitude is than that of the paired storms. This Framework utilizes the open-source Python.

Utah Water Research Laboratory, Utah State University
Associated text: Canham, H., B. Lane (in review). Paired storms approach reveals post-fire flood characteristics and drivers. In review at Water Resources Research.

Associated code repository: Canham, H., Lane, B. (2024). Rainfall-Runoff Event Detection and Identification (RREDI) toolkit, HydroShare, http://www.hydroshare.org/resource/797fe26dfefb4d658b8f8bc898b320de

ABSTRACT:

To visualize post-fire flood-producing storms and quantify change in post-fire runoff using dataset developed by the Paired Storms Framework (Canham & Lane, 2025).

Utah Water Research Laboratory, Utah State University

Associated text: Canham, H., B. Lane (in review). Paired storms approach reveals post-fire flood characteristics and drivers. In review at Water Resources Research.

Associated code repository: Canham, H., Lane, B. (2025). Paired Storms Framework for Post-Fire Flood Analysis, HydroShare, http://www.hydroshare.org/resource/e232f1ee789a4d03aa276008da2b7afb

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.