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Rich Pauloo, PhD

Hydrologist and data scientist

About me

Groundwater resources support global food supply and civilization, yet are in decline worldwide. I use my background in hydrogeology and data science to develop scalable and scientific methods to monitor, model, and manage regional-scale aquifers. I've built open source frameworks for real-time groundwater monitoring, models that predict if a well may run dry during drought or unsustainable management, and 3D physical simulations of groundwater flow and contaminant transport.

I'm passionate about data literacy and STEM education, the outdoors, and expedition behavior. Before pursuing a PhD, I taught environmental science to middle and high school students in Yosemite and the Marin Headlands for the educational nonprofit NatureBridge, and spent summers leading expeditions in the wilderness, and in Thailand for National Geographic Student Expeditions. I love the challenge of guiding tight knit groups from different cultures across difficult terrain, speaking to large crowds, communicating to diverse audiences, and improvising.

In my free time, I enjoy alpine climbing, running, surfing, tinkering on bikes, reading, playing guitar, and cooking extravagant dinners.

Interests

  • expedition behavior
  • mathematical modeling of hydrologic systems
  • real-time sensor networks
  • data science and open source software

Education

  • PhD in Hydrogeology, 2020

    UC Davis

  • BA in Integrative Biology, minor in Conflict Resolution, 2011

    UC Berkeley

Projects

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gsp dry wells .com

Domestic well failure prediction and cost estimates in critically overdrafted basins.

low cost sensor networks

Real-time sensor networks and dashboards for monitoring environmental data.

cal water quality .com

Automated water quality reports for > 3,000 California public water systems. 🏆 Winner 2019 California Water Data Challenge.

groundwater statement .org

Built a website that gathered > 1,000 signatories of leading groundwater scientists from ~100 countries in 1 month. The statement …

interpretable random forests

Cumulative variable importance.

textme

Text yourself from R when long running jobs complete.

Tulare basin TDS

Groundwater quality data visualization.

CA well report filter

Upload a shapefile of a study area to return clean OSWCR data from that area.

CA well reports

Exploratory data analysis of California's Online State Well Completion Report Database.

Fatal landslide prediction

Using random forests, boosting, LDA, and QDA with variable probability thresholds for global landslide classification.

AbstR

An adaptation of PapR to 30,000 American Geophysical Union abstracts.

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