Skip to main content

5 posts tagged with "AI and Machine Learning"

View All Tags

Assessing Streamflow Forecast Over the Hackensack River Watershed Using NGIAB

· 3 min read
Jorge Bravo
Graduate Research Assistant
Marouane Temini
Associate Professor

A poster, titled "Assessing streamflow forecast over the Hackensack River Watershed using physics- and AI-driven weather prediction models".

A poster presented by the I-SMART team at the CIROH Developers Conference, held at the University of Vermont in Burlington from May 28 to 30, 2025.

The densely populated Hackensack River watershed lies within the New York City Metropolitan Area, which spans northern New Jersey and southern New York. Accurate streamflow forecasting within this region is therefore essential to enable effective water resource management, flood prediction, and disaster preparedness.

Precipitation data is critical for effective hydrological modeling, making the identification of reliable data sources a key priority. This is why the Integrated Spatial Modeling and Remote Sensing Technologies Laboratory (I-SMART), an interdisciplinary research unit within the Davidson Laboratory at Stevens Institute of Technology in Hoboken, New Jersey, uses the latest developments in both atmospheric and hydrological modeling to address flood risks in the Hackensack Watershed with solutions that could be expanded to the entire New York City Metropolitan Area.

In the past, this work has included key early applications of the Next Generation Water Resources Modeling Framework (NextGen). Notably, the I-SMART group was among the first to force the NextGen framework with multiple atmospheric models for comparative analysis during a real-world event: the passage of Superstorm Ida over the New York metropolitan area in September 2021. The recent advent of NextGen In a Box (NGIAB) has provided an opportunity to accelerate these applications even further by taking full advantage of NGIAB's containerized, user-friendly, and easily deployed environment.

Recently, the I-SMART team has been testing various regional atmospheric models grounded in physical equations, including traditional models like WRF and next-generation atmospheric models such as MPAS. Additionally, given the increasing popularity and adoption of AI/ML-based approaches, the team has also begun exploring their potential. The goal of this work is to assess the performance of these approaches in the Hackensack Watershed, along with investigating the sensitivity of the model to various meteorological forcings, including forcings based on the National Water Model. (The initial and/or boundary conditions for all the models were determined using the Global Forecast System.)

This investigation required handling a large volume of precipitation data from various models, each with different spatial resolutions and in some cases, such as MPAS, using unstructured grids. As such, one of the key challenges was finding a hydrological modeling framework flexible enough to accommodate such diversity. This made the NextGen framework a natural choice, allowing them to integrate precipitation forcings from various sources with the appropriate pre-processing to align them with the model requirements in terms of spatial and temporal scales.

The complex implementation and execution of these models was faciliated by NextGen In A Box (NGIAB), which successfully enabled the integration of diverse precipitation sources. By simplifying local deployment and providing full control over model inputs, configurations, and runtime operations, NGIAB has given the I-SMART team the tools to conduct their groundbreaking research with even greater efficiency.

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.

Google Cloud Next 2025: Innovation at Scale ✨

· 5 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.