Together with Nick Silverman and a team of researchers from University of Montana, NRCS's Sage Grouse Initiative in this paper we show that low-tech, process based restoration can make a measurable difference in productivity of mesic habitats (i.e. wet meadows).  Using three iconic restoration projects as examples:

• Gunnison, CO - Zeedyk Structures
• Bridge Creek, OR - Beaver Dam Analogues
• Maggie Creek, NV - Grazing Management & Beaver Recovery

We show not only can these cheaper and affordable restoration techniques achieve the desired results by affecting large areas, they can also be measured simply from space with satellite imagery. The calculations shown in the paper can be done by anyone, for mesic habitats in the Western US using SGI's Mapping Tools.

In this paper, Matt Nahorniak lays out how we took advantage of open-source Delft3D to automate the process of computational mesh generation from high resolution topography for reaches. We automated this for the generation of hydraulic models for over 5000 site visits in the Columbia Habitat Monitoring Program (now defunded by BPA) and automated these simulations on Amazon Web Services (AWS EC2). While we are no longer running these simulations for CHaMP, all those model runs (and the 950+ sites from which they came) are publicly available and the source code is open source. This technique is a game changer for properly leveraging high resolution topography of rivers to realize the vision laid out in Wheaton et al. (2017).

All the source code is freely available through our Riverscapes Consortium repository: 

Wally Macfarlane led this effort to get vetted the rest of the Riparian Condition Assessment Tool (RCAT - A Riverscapes Consortium Model) peer-reviewed and disseminated. We had previously (Macfarlane et al. 2016) published a key component of the model - the riparian vegetation departure. This paper shows how that line of evidence can be combined with other lines of evidence to estimate and overall riparian condition for every reach of stream in a drainage network.  In this paper, we illustrate its application by adding floodplain accessibility and land use intensity as additional lines of evidence.

Dan Hamil did his Masters research on how to make Side-Scan SONAR into useful geospatial data for mapping bed roughness.  He employed Dan Buscombe's PyHum software.  PyHum is an open-source project dedicated to provide a generic Python framework for reading and exporting data from Humminbird(R) instruments, carrying out rudimentary radiometric corrections to the data, classify bed texture, and produce some maps on aerial photos and kml files for google-earth. This paper vets some of the methods behind doing that with a case study from the Grand Canyon.