March 18th, 2026

Categorizing large wood deposited by snow avalanches 

Volumes of large wood deposited by snow avalanches in the channels and on the floodplains of high mountain streams can be extraordinarily large (see our previous paper). However, we know very little about the characteristics of these deposits, which makes it rather difficult to understand how they evolve through time and influence the form and the function of the streams in which they sit. Until now, that is: our new paper (first-authored by Nathaniel Cutler as his senior thesis project) creates a framework for characterizing the form of avalanche-derived wood deposits, evaluating the factors that cause them to take a characteristic form, and determining the geomorphic impact of each form. Read more about it here. 

August 26th. 2025

Enhancing streamflow forecasts in mountain watersheds

A creative and timely new paper from Mirce Morales-Velazquez and friends that demonstrates how to use machine learning and relatively low cost instrumentation to improve streamflow forecasts in mountain watersheds, where accurate forecasts are essential for timely flood warnings but difficult to do. You can read more about it here.

March 7th. 2025

Building a turbidity forecasting tool for drinking water supply 

New York City, like any community, relies on clean drinking water, but unlike many in the United States, NYC does not filter its water supply, meaning that when sediment loads are high in one particular reservoir in its supply system, it must draw from another. To better increase anticipation of these events, myself and a wonderful band of colleagues from the University of Vermont built a machine learning tool that forecasts turbidity (a surrogate for sediment content) in one of the chief supply basins in NYC's water supply system. Built entirely with open-source code and trained on publicly available data, this work creates a blueprint for anyone looking to adapt a similar approach in their watershed(s) of interest. You can read more about it here and find the code to do so yourself here. 

August 7th, 2024

Some trees are more stressed than others

A really nice new paper from our friend Richie Thaxton and a merry band of riparian ecologists and geomorphologists that finds cottonwood trees in Canyonlands National Park – where streamflow regulation is greater and things are altogether drier – have less percent healthy canopy and are shorter and more slowly growing than their compatriots upstream, where water is more readily available. Read more about it here. 

August 1st, 2024

Westward ho (again)

I'm excited to announce that I am now a postdoctoral researcher at the Utah State University. I'll be working on a variety of projects: leveraging machine learning, large-scale physical models, and big observational data sets to understand water quality drivers and forecast concentration and load; investigating the drivers of fluvial erosion in post-fire watersheds (especially regarding the connection between large wood dynamics and channel erosion "hotspots'); and continuing to expand understanding of how snow avalanches impact mountain river form and function. 

May 5th, 2024

Estimating sediment storage at large scales 

Floodplains (and other features) store sediment throughout river networks, but where, exactly? And how much? As we continue to protect and restore natural river function, understanding these questions is essential to ensure that we do so effectively and efficiently. Our new paper in River Research and Applications takes a stab at estimating sediment storage and examining its patterns across the (biased opinion here) greatest watershed there is: the Colorado River Basin. Take a look here.

December 13th, 2023

Snow avalanches as agents of wood supply in mountain watersheds

Have you ever witnessed or seen video of an avalanche plunging down a mountainside and thought to yourself "Hmm, I wonder what impact that has on the stream at the bottom of the valley?" Well, then, you're in luck: our new paper in Geophysical Research Letters does (almost) just that. Read more about here.