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Rainfall-Runoff Event Detection and Identification (RREDI) toolkit

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Created: Nov 14, 2022 at 3:44 p.m.
Last updated: Feb 01, 2024 at 8:47 p.m.
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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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# Rainfall-Runoff Event Detection and Identification (RREDI) alogrithm - ReadMe.txt
# Canham, H., Lane, B. (2022). Rainfall-Runoff Event Detection and Identification (RREDI) algorithm, HydroShare,

# Utah Water Research Laboratory
# Utah State University

# November 2022
# Updated January 2024 - Additional operational details and streamline of input file names. Include file.

# Contact
	Haley Canham -
	Belize Lane -
	RREDI is still in development and not in final release.

# Contents
	ReadMe.txt -> read me file for resource
	RREDI -> folder containing all algorithm code -> python script containing diurnal cycling filter function to be called by
	func-flow-master -> folder containing adapted FFC code (Patterson et al., 2019) for rainfall-runoff event identification. -> python script containing required functions for rainfall-runoff event attribution. -> python script for rainfall-runoff event attribution -> python script for flagging of rainfall-runoff event issues (NAN events, duplicate events, no end events) -> python script to get and format USGS flow data -> python script to check aligned period of record and formatting for FFC input files -> python script to generate storms from instantaneous precipitation data
	ExampleFiles -> folder containing all example files
	FlowData_I.csv -> formatted 15-min flow data file from
	FlowData_D.csv -> formatted daily flow data file from
	Precip.csv -> formatted instantaneous precipitation data file (no script provided) 
	Storms.csv -> output file from with attributes for all storms from
		Magnitude folder -> contains .csv files for incremental cumulative magnitude for each storm from - NOTE: not inlcuded due to file size
		Intensity folder ->  contains .csv files for incremental 15-min intensity for each storm from - NOTE: not included due to file size
	P_Input -> formatted preciptation (storm) input file for event detection and identification from
	Q_D_Input -> formatted daily flow input file for event detection and identification from
	Q_I_Input -> formatted instantaneous (15-min) input file for event detection and identification from
	FPEvents.csv -> daily event identification (FFC) output file from (FFC)
	Event_Attirbutes_Output.csv -> event attribution output file from 
	FinalEvents.csv -> list of identified and attributed events that meet RREDI requirements output file fro
# Order of Operations
	1. -> retrieve and format 15-min and daily flow data for USGS gage of interest for specified POR
	2. Get and format precipitation data to instantaneous datetime (retreival and formatting script not inlcuded due to variety of data sources)
	3. -> create storms out of instant precip data based on specified storm time gap
	4. -> run to ensure that preciptation and flow pors meet the requirements for FFC (aligned, >1wy)
	5. (within FFC) -> run to identify rainfall-runoff events using daily data
	6. -> run to attribute rainfall-runoff events using 15-min flow data
	7. -> run to filter out mis-attributed events inlcuding situations with NAN events, duplicate events, and no end events

# Applying RREDI
	RREDI is developed to automatie the identification and attributions of rainfall-runoff events using feature detection and signal processing of timeseries.  
	RREDI performs well in un-altered watersheds with distrinct rainfall-runoff events and has been applied in watersheds of varying size across the western US. 
	RREDI is not applicabale for seperating rainfall-runoff events from diurnal cycling (from melt cycles or irrigation) or other human caused hydrograph influence (ie below dams).
	RREDI should be applied for time periods greater than 1 wy otherwise error will be returned in the FFC. To decrease error, recommend running the prior to subsequent scripts.
	RREDI should be applied for flow values >1. If values are less than 1, peaks will not be detected. Can alter flow units or magnify flow if all/majority of flow values are <1. Example: convert cms to cfs.

# Tips and Tricks
	Ensure exact file formatting otherwise code will not run.
	File paths will need to be specified and changed for each sub step along the way. - Update coming in the future.

# Future plans
	Main wrapper script - so that everything can be run together and not individually. 
	File path unification for simplicity and ease of transferability between users.


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

Canham, H., B. Lane (2024). Rainfall-Runoff Event Detection and Identification (RREDI) toolkit, HydroShare,

This resource is shared under the Creative Commons Attribution CC BY.


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