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4 posts tagged with "AI and Machine Learning"

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δHBV2.0: How NGIAB and Wukong HPC Streamlined Advanced Hydrologic Modeling

· 2 min read
Yalan Song
Research Assistant Professor
Leo Lonzarich
Graduate Researcher
Arpita Patel
DevOps Manager and Enterprise Architect
James Halgren
Assistant Director of Science

Image of graphical outputs from the δHBV2.0 model

Predicting water flow with precision across the vast U.S. landscape is a complex challenge. That's why Song et al. 2024 developed δHBV2.0, a cutting-edge hydrologic model. It’s built with high-resolution modeling of physics to deliver seamless, highly accurate streamflow simulations, even down to individual sub-basins. It's already proven to be a major improvement, performing better than older tools at about 4,000 measurement sites. We also provide a comprehensive 40-year water dataset for ~180,000 river reaches to support this.

Penn State research group pushed δHBV2.0 further, training it with even more detailed river data and integrating other trusted models, aiming to make it a key part of the NextGen national water modeling system (as a potential NWM3.0 successor). But here’s a common hurdle: making powerful scientific tools like this easy and reliable for everyone to use within a larger framework can be tough. Setup issues, runtime errors, and inconsistent results can frustrate users.

NGIAB stepped in to solve exactly this problem. Team has taken the complexity out of using the operations-ready models within NextGen by creating one unified, reliable package. Thanks to NGIAB, users don't have to worry about tricky setups or whether the model will run correctly. NGIAB ensures that our models are compatible everywhere and, most importantly, that they run exactly as designed, consistently and faithfully, every single time, no babysitting required. This means users get the full power of our advanced modeling, without the headaches.

🔗 Learn more

δHBV2.0: https://doi.org/10.5281/zenodo.14827983

Song et al. 2024: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024WR038928

Dataset: https://doi.org/10.5281/zenodo.13774373

Google Cloud Next 2025: Innovation at Scale ✨

· 4 min read
Arpita Patel
DevOps Manager and Enterprise Architect

Last week at Google Cloud Next representing our CIROH cloud-based computing efforts! With more than 30,000 participants, Google Next always amazes me! It's huge, engaging on so many levels! Engaging booths, networking opportunities, great presentations, workshops, AI coach for basketball, incredible keynote from an amazing team! Event was not just a conference, but a celebration of innovation and a glimpse into the future of cloud computing! Great to see how Gemini is transforming data manipulation in BigQuery. The ability to use natural language to query, transform, and visualize data is revolutionizing how we interact with massive datasets. Gabe Weiss's demo particularly showcased the potential for non-specialists to derive insights from complex data.

If you missed the keynote, I highly recommend watching the recording here: GCN25 Keynote Video

Pennsylvania State University Researchers Leverage CIROH Cyberinfrastructure for Advanced Hydrological Modeling

· 3 min read
Arpita Patel
DevOps Manager and Enterprise Architect
Yalan Song
Research Assistant Professor
Tadd Bindas
Graduate Researcher

Pennsylvania State University (PSU) researchers have been leveraging CIROH Cyberinfrastructure to tackle complex hydrological modeling challenges. This post highlights their innovative approach using the Wukong computing platform in conjunction with Amazon S3 bucket storage to efficiently process and analyze large-scale environmental datasets. 🚀

CIROH Developers Conference 2024

· 2 min read
Arpita Patel
DevOps Manager and Enterprise Architect

CIROH Developers Conference 2024

DevCon2024

The CIROH team recently participated in the 2nd Annual CIROH Developers Conference (DevCon24), held from May 29th to June 1st,2024. The conference brought together a diverse group of water professionals to exchange knowledge and explore cutting-edge research in the field of hydrological forecasting.