Hi 👋, my name is Rich.
My expertise is at intersection of hydrogeology, geospatial programming, data science/engineering, climate change, and environmental policy. I’ve built 3D computational fluid dynamics models to explain and predict patterns of groundwater flow and contaminant transport in heterogeneous, alluvial aquifer-aquitard complexes – thin layers of subsurface, freshwater-bearing sediment that support food supply and civilization, and which are in decline worldwide. I’ve developed models that predict where wells will go dry during drought and decision-support tools for natural resources management. I’ve automated California municipal water quality reports, helped build the nation’s largest publicly-accessible spatial database of water system spatial boundaries (now in use by the US EPA) and co-founded the Water Data Lab, a group within Convolve Collective. I also co-developed R for Water Resources Data Science and I occasionally write (informally) about data.
My work has appeared in Nature, the Los Angeles Times, NewScientist, and Newsweek.
Currently, I lead data and software teams in the energy sector. We build tools to help utilities adapt to climate change, assess wildfire risk, and make equitable electrification investments. I still enjoy contributing to the academic literature and to open source projects from time to time. Long ago, I taught environmental science to 5th graders and led National Geographic expeditions for high school students. I hope that one day I can return to teaching.
This website is built with blogdown and Hugo, and deployed using Netlify. My blog posts are released under a Creative Commons Attribution-ShareAlike 4.0 International License.
Thanks for being here.
Another day, another blog
Automate SMS and MMS with R, Twilio, Docker, and Github Actions
Here we’ll deploy a Docker container running R that sends an SMS (short message service, aka “text message”) via the Twilio API, and orchestrate the code to run on a timer with Github Actions.
Read moreReproducible data science with R, RStudio Server, and Docker
Although Docker has been around for years, Docker in the data science community doesn’t seem as widespread as git, which I attribute to the fact that many data scientists learn to program and work independently. However, as data scientists increasingly collaborate with others or aim to make their work more reproducible, Docker deserves an equal place alongside git in the practitioner’s toolkit.
Read moreSelected Projects
r4wrds
R for water Resources Data Science.
Read moregsp dry wells .com
Domestic well failure prediction and cost estimates in critically overdrafted basins.
Read more