matBRAT - Beaver Restoration Assessment Tool (Matlab based - version 1 to 2.03)
Participants will learn about the beaver dam capacity model in BRAT and how to modify the model and run it for their own study areas.
Most of the day will be devoted to nuts and bolts of the GIS processing of BRAT. We’ll need and want to move reasonably quick through the different steps and I want to make sure we don’t digress into a basic ArcGIS training session (see here if you need a refresher). To facilitate this I am going to ask that all participants for the rest of the day will need to:
Choose a tractably sized watershed or study area for workshop (e.g. Logan River Watershed as opposed to the Mississippi).
The data requirements for the beaver dam building capacity model we run are spelt out here.
Come prepared with raw inputs for one study area you want to run the capacity model for having:
If possible, please also come with at least a report from the USGS National Streamflow Statistics for the State(s) your watershed is located in. We will use regional curve regression equations from these reports to estimate baseflow and Q2 streampower.
You may also find it helpful to bring some context GIS layers, such as:
I will have backup datasets to use for each exercise, should someone’s ‘own’ data fall short.
If you are joining us remotely for the Background & Theory Portion (9:00 - 10:30), please use the GoTo Meeting Links or numbers below. If you wish to run through the hands-on session, you will need to be in Logan.
- Please join my meeting.
https://global.gotomeeting.com/meeting/join/299690645
- Use your microphone and speakers (VoIP) - a headset is recommended. Or, call in using your telephone.
United States: +1 (773) 897-3015
Access Code: 299-690-645
Audio PIN: Shown after joining the meeting
Meeting ID: 299-690-645
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This workshop was made possible by Wilburforce and the efforts of Mary O’Brien at the Grand Canyon Trust. Thanks to Enid Kelly and Wes James for assisting with set up.
Subpages (3): 1. Getting NHD Data 2. Getting DEM Data 3. Getting Vegetation Data