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Moving Hydrologic Prediction Forward — A software integration meeting at the Alabama Water Institute

· 10 min read
Martyn Clark
Professor of Hydrology at University of Calgary
James Halgren
Assistant Director of Science
Matthew Denno
Senior Engineering Applications Developer at RTI International
Arpita Patel
Assistant Director of DevOps and IT
Josh Cunningham
Software Engineer
Quinn Lee
Programmer Analyst
Sam Lamont
Environmental Applications Developer at RTI International
Darri Eythorsson
Postdoctoral Researcher at University of Calgary
Cyril Thebault
Postdoctoral Associate at University of Calgary
Group photo from the software integration meeting at the Alabama Water Institute

Last week, at the invitation and expert coordination of James Halgren, teams from RTI International (Sam Lamont and Matt Denno) and the University of Calgary (Darri Eythorsson, Cyril Thebault, and Martyn Clark) met at AWI for an intensive working session focused on weaving recent CIROH research into AWI’s fork of the NOAA Office of Water Prediction (OWP) Next Generation Water Resources Modeling Framework (nicknamed “NextGen”). James took the lead in developing the agenda, lining up the right scientific and technical expertise and ensuring that the week targeted the most critical software integration challenges. Throughout the visit, the RTI and UCalgary teams collaborated closely with AWI software engineers Quinn Lee, Josh Cunningham, and James himself. The days were filled with whiteboards, deep technical conversations, and strategic planning around the future of NextGen water prediction. This recap captures the key themes and the momentum that carried through the week.

Getting started

The U Calgary team arrived quite late Sunday night. The plane landed around 9:30 p.m., but with no dinner on board, the drive from Birmingham to Tuscaloosa became an unexpectedly comprehensive tour of places that were neither open nor in possession of a single vegetable. The group checked into the Indigo just before midnight, somewhat hungry but otherwise intact.

After Team Calgary’s epic late-night journey, we kicked off early on Monday morning with a whiteboard session to map out the “assets” we rely on for hydrologic modeling—datasets, models, tools—everything from the CAMELS dataset to TOPMODEL to NOAH-OWP-Modular, plus the assortment of geospatial processing and calibration methods that we all depend on. The aim was to start corralling this sprawling collection of community resources into something a bit more coherent, mostly because no single group has the expertise (or the workforce) needed to tackle all the challenges of building state-of-the-art prediction systems. The conversation naturally focused on software integration—the whole point of the trip—as we tried to figure out how to get these pieces to play nicely with each other.

A high point of the morning was meeting with our operational colleagues at the NOAA Office of Water Prediction—Dr. Fred Ogden, Dr. Fernando Salas, and Dr. Jason Regina. They shared updates on OWP’s vision for future versions of the National Water Model and outlined plans for defining model configurations and calibratng them across the national domain.

Refueled by a terrific lunch at the Curry Kitchen, we reconvened to focus on two key integration efforts: introducing the SYMFLUENCE integration architecture for large-domain modeling and enabling SUMMA’s process-based simulations to run seamlessly within the NextGen workflow.

Darri Eythorsson walked the group through SYMFLUENCE, an integration architecture designed to make hydrologic modeling workflows more structured and efficient, describing how SYMFLUENCE supports everything from defining spatial domains and preparing data to model setup, calibration, optimization, and evaluation. He emphasized that SYMFLUENCE can also drive simulations from NextGen, giving NextGen access to capabilities such as large-sample calibration and geospatial preprocessing. Darri noted that the system runs on laptops, HPC clusters, and cloud-compute platforms, making it adaptable to modeling at different scales.

We closed out Monday with a deep dive into bringing SUMMA into NOAA/OWP’s NextGen framework. The meeting was scheduled late in the day so that Ashley van Beusekom, one of SUMMA’s primary developers, could join from her home in Okinawa, Japan. A couple of years ago, Ashley added BMI support to SUMMA, making it possible to run the model within the NextGen architecture. Ahead of the meeting, she and Darri Eythorsson worked together to set up SUMMA as a spatially distributed model in NextGen, and they successfully reproduced the Provo and Oyster River test cases to verify that everything was running as intended. Quinn and Josh from AWI were excited by the results and immediately started weaving SUMMA capabilities into the broader NextGen framework.

The U Calgary crew wrapped up the day on the Indigo Hotel rooftop, sharing giant pretzels, vegetables, and the warm Alabama night air. They toasted the momentum of the visit and the possibilities ahead. As the night settled in, Team RTI’s Matt Denno and Sam Lamont arrived mid-evening Monday, ready to jump into the work the next morning.

Technical deep dives

The RTI and U Calgary teams met up for breakfast on Tuesday morning at Heritage House where they exchanged high-fives and consumed the caffeine and carbohydrates needed to tackle another ambitious day of software integration.

Once everyone settled in at AWI, the group dug into the two main challenges of the week: integrating models into the NextGen framework (the U Calgary effort) and evaluating those simulations with shared protocols (the RTI focus). The conversation broadened as Arpita Patel (UA), Jordan Laser (Lynker), Zach Wills (Lynker), Nels Frazier (Lynker), Giovanni Romero (Aquaveo), Jonathan Frame (UA), Trupesh Patel (UA), Harsha Vemula (UA) and Manjila Singh (UA) joined to discuss their work on making NextGen accessible and useful to the wider community. Together, the group reviewed progress on SYMFLUENCE, the NextGen/SUMMA integration, and the TEEHR data model. As always, James Halgren kept everything running seamlessly, turning lunch into a productive working session so the team could update the University of Alabama’s computer science leads on recent advances and ongoing challenges.

Before long, the team divided into smaller groups so they could move quickly on the most pressing software-integration tasks on the schedule. Throughout the day, the big question hovered: How do we create a seamless pipeline from model execution data ingest→ evaluation → model improvement?

On the RTI side, much of the effort focused on preparing outputs for seamless use within the TEEHR Evaluation System. This involved translating the output from NextGen simulations into the TEEHR data schema and determining how best to extract the metadata needed to track which model configuration produced each forecast. The team also worked on defining crosswalks to link simulation points to USGS gage IDs, ensuring that model results could be cleanly tied back to the appropriate metadata and hydrofabric, and they began documenting key operational details such as run intervals and latency characteristics of near-real-time runs. By the time the dust settled, the RTI and AWI teams had stitched the pieces together and emerged with the gratifying sense that they had made good initial progress on the problem they came to solve.

On the U Calgary side, the team split off into two groups. In one corner Darri Eythorsson and Cyril Thebault met with Josh Cunningham to make progress on integrating SUMMA in to NextGen. There were lots of issues to consider, including appropriately linking with different software libraries and ensuring that SUMMA could run effectively in the containerized NextGen instantiation “NextGen In a Box” (NGIAB). The software integration progress was rapid, thanks in large part to the Herculean efforts of Ashley van Beusekom prior to the visit.

In another corner, Martyn Clark sat down with James Halgren and Quinn Lee to put together a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis for the AWI-led efforts to transition CIROH research into NOAA/OWP operations. The timing mattered: James was preparing to drive to Utah the following week for a faculty position at Brigham Young University, and leadership of his group was in the process of transitioning to Quinn Lee and Josh Cunningham. The SWOT analysis brought a sense of optimism and excitement as the team saw the promise in recent hydrologic prediction advances and brainstormed several clear steps to make the research-to-operations path smoother and more effective.

Later in the day, Arpita Patel and Matt Denno met with NOAA/OWP Chief Scientist Fred Ogden to walk through the latest NextGen Framework design diagram and discuss how their development of NextGen In A Box (NGIAB) aligns with NOAA/OWP’s component-based architecture. Their conversation highlighted that NGIAB already follows many of these principles, including modular components, stand-alone tools, and fully BMI-compliant model execution. They also identified clear opportunities to strengthen the alignment, particularly around refining the runtime environment, enhancing component decoupling, and mapping NGIAB’s architecture onto OWP’s emerging design. The meeting set the stage for ongoing coordination.

Dinner at the River Café was a highlight of the day. With a table on the terrace and the Black Warrior River drifting by below, the team enjoyed an evening of easy conversation and good-natured reflection on the progress over the past two days.

A springboard for the future

Wednesday saw the RTI and U Calgary teams peel off in different directions. RTI paired with AWI software engineers to advance the TEEHR model evaluation manager, tightening the evaluation protocols, metadata requirements, and workflow linkages needed for basin-scale and national-scale verification. U Calgary spent the day with CIROH leadership —Steve Burian, Sagy Cohen, and Erin White—reviewing their major research advances and the steps required to improve the research-to-operations pipeline. A clear highlight of the day came when SUMMA was successfully integrated into NextGen: Martyn Clark marked the moment with a celebratory dance—hands clasped, fingers interlocked, and his upper body swaying with unmistakable satisfaction.

That evening, everyone gathered at Steve Burian’s home for a farewell celebration in honor of James Halgren. Steve and his wife Cindy laid out an extraordinary taco spread, enough to nourish the lively crowd that had gathered to see James off in style. James brought with him a tall stack of mounted photographs he had taken around the U.S., and he spent the night offering warm goodbyes and writing personal messages on the back of each one. It was a lovely gesture, and a reminder of how much his enthusiasm and kindness had meant to the community.

The joint visit wrapped up on Thursday morning, when the full team gathered to chart a path forward. Together they mapped out the key components of the NextGen Research Data Stream (NRDS—affectionately pronounced “nerds”) and clarified how NRDS fits within the broader NOAA/OWP NextGen framework, including the orchestration manager, calibration manager, and evaluation manager that had occupied so much attention throughout the week. The discussion concluded with a clear set of immediate priorities, identifying the specific steps needed over the next six months to realize near-term successes in the transition of research to operations.

CIROH Cyberinfrastructure: Accelerating Hydrologic Research

This collaborative work exemplifies how CIROH's cyberinfrastructure investments are accelerating the pace of hydrologic research. The platforms developed by CIROH's DevOps team - the Research to Operations Hydrologic Community (R2OHC) platform and tools developed by CIROH's Science team, Lynker team, RTI team and Aquaveo team —including NextGen In A Box (NGIAB), NRDS, and containerized modeling environments—provide researchers with immediate access to computational resources and standardized workflows. By eliminating the traditional barriers of infrastructure setup and configuration, these platforms allow research teams like University of Calgary, RTI International, Lynker, Aquaveo and AWI to focus their energy on scientific innovation rather than technical implementation. This meeting at AWI represents the kind of rapid, collaborative progress that becomes possible when robust cyberinfrastructure removes technical friction from the research process.

Building Bridges: CIROH–Penn State Collaboration Formalizes Differentiable Modeling for NRDS

· 6 min read
Leo Lonzarich
Graduate Researcher
Quinn Lee
Programmer Analyst
Josh Cunningham
Software Engineer
Arpita Patel
Assistant Director of DevOps and IT
James Halgren
Assistant Director of Science

Almost from the start, 2025 has been a banner year in hydrologic modeling, with advancements in capabilities on both sides of the aisle of CIROH's research-to-operations (R2O) pipeline.

  • From the research skunkworks, Penn State's MHPI group, led by Dr. Chaopeng Shen introduced a new generation of distributed, differentiable hydrologic models spearheaded by δHBV 2.0. Capable of high-resolution, continental-scale streamflow forecasting across the CONUS Hydrofabric, δHBV 2.0 fuses process-based modeling and machine learning to enable efficient parameter calibration and interpretable predictions at scale -- with demonstrated viability as a National Water Model 3.0 successor.

  • Meanwhile, from the operations core, CIROH's Science and Cyberinfrastructure team at the Alabama Water institute (AWI), headed by Arpita Patel and James Halgren, debuted an operational pipeline composed chiefly of flagships NextGen In a Box (NGIAB) and the Next Generation Research Datastream (NRDS), forming an open-source software stack powering an accessible operational implementation of NOAA's Next Generation National Water Model. NRDS, in particular, executes a regularly scheduled set of continental-scale, NextGen-based hydrologic forecasts on CIROH's cloud-based cyberinfrastructure, and is designed to be a canvas for showcasing community modeling advances as potential R2O opportunities for NextGen.

As destined in CIROH, these two efforts converged in early October during a week-long collaboration hosted by AWI, bringing Penn State researcher Leo Lonzarich south to Tuscaloosa, Alabama, to join in facilitating the formal introduction of differentiable models like δHBV 2.0 into the NRDS.

Keyboard Accomplishments 👾

During the visit, the CIROH Science and Cyberinfrastructure team worked with Leo to finalize the formal integration of the δHBV 2.0 model into the NextGen ecosystem. Between extensive code reviews, model refactoring, and validation runs, we verified δHBV 2.0's operations-ready performance through its Basic Model Interface (BMI) within the NextGen runtime, ensuring full compliance with BMI standards and alignment with previously published benchmarks. We enhanced the model to support both batch and sequential timestep simulations — the latter mirroring the runtime behavior of NextGen — while maintaining high computational efficiency and reproducibility across environments.

Once δHBV 2.0 was verified and tuned, we expanded the scope to broader ecosystem integration. The model was finalized on GitHub with modular configuration capabilities, daily and hourly (model coming soon) simulation modes, and confirmed compatibility with the T-route routing component. Significant performance optimizations were implemented to improve scalability across large basin networks. We added δHBV 2.0 support to the NGIAB distribution, enabling cloud-based state loading from AWS S3 (PR coming soon), and extended its reach into the NGIAB Data Preprocess to allow on-demand generation of forcings, static catchment attributes, and model realizations for NextGen. Finally, the static catchment attributes were incorporated into the CIROH Community Hydrofabric, ensuring the model is represented and interoperable within the hydrologic framework used across the consortium.

The beauty of these efforts lies in the fact that δHBV 2.0 is built upon a model-agnostic, differentiable modeling framework — δMG. Therefore, the formalization accomplished during this visit scales to all new research products and advancements made through the framework, streamlining future NGIAB and NRDS integrations and reducing development overhead across projects.

Beyond the Keyboard

Out of the office, this week also offered opportunities for cross-team engagement and a deeper look into CIROH's computational backbone. In one such case, Leo and the team toured the University of Alabama's data center, meeting with Josh Lotfi and other members of the Office of Information Technology (OIT) who help manage CIROH's on-premises high-performance computing resources. Naturally, this included 1-on-1 time with the flashing lights and silicon of Wukong (which supports a bulk of Penn State's R&D) and Pantarhei HPCs. Across the board, these interactions gave valuable insight into work going on behind the scenes at AWI and about how CIROH's cyberinfrastructure supports researchers and partners across academia, government, and industry.

It is easy to see these collaborations as purely technical exchanges. However, the deep friendships that were built during this visit are at least as important, they inevitably inspire open flows of ideas, mentorship, and future synergies in service of CIROH's mission and the broader community.

Looking Forward

With δHBV 2.0's integration near completion, differentiable models will soon be running as part of CIROH's NRDS nightly forecasts — accessible through the NRDS Visualizer where other researchers and community members will be able to explore, assess, and iterate on simulations from this latest crop of hydrologic models in real time.

We anticipate continuing this AWI-Penn State collaboration, with future projects e.g., being additions to NGIAB and NRDS including more differentiable models, a formal hourly δHBV 2.0 model, and differentiable routing. Some of this work may also arrive in a DevCon to showcase the efforts of both of groups.

Overall, this visit exemplifies how CIROH's approach to developing a community of practice catalyzes scientific advancements, fosters lasting inter-institutional relationships, and makes normally lab-bound research products salient to the broader R2O community. In these collaborations, CIROH's core mission, strengthening the link between scientific discovery and operational forecasting, remains clear and in view.

Many thanks are owed to Arpita, James, and the whole of CIROH's Science and Cyberinfrastructure team -- git merge on research products has never been easier.

Resources for the Curious

Publication

  1. Song, Y., Bindas, T., Shen, C., Ji, H., Knoben, W. J. M., Lonzarich, L., et al. (2025). High-resolution national-scale water modeling is enhanced by multiscale differentiable physics-informed machine learning. Water Resources Research, 61, e2024WR038928. https://doi.org/10.1029/2024WR038928

Evaluating NextGen’s Performance in the MARFC Region with NGIAB

· 7 min read
Hudson Finley Davis
Hydrologist, NOAA Office of Water Prediction
Seann Reed
Hydrologist, NOAA Office of Water Prediction
Josh Cunningham
Software Engineer
Arpita Patel
DevOps Manager and Enterprise Architect
James Halgren
Assistant Director of Science
Sifan A. Koriche
Research [Hydrologic] Scientist
Trupesh Patel
Research Software Engineer

The National Weather Service's Middle Atlantic River Forecast Center (MARFC) sees large variations in the performance of the National Water Model 3.0. Through its support for regionalized parameters and models, NOAA-OWP’s Next Generation Water Resources Modeling Framework (NextGen framework) offers a potential solution to address these inconsistencies. As such, this study took advantage of NextGen in a Box (NGIAB) to evaluate the NextGen framework’s performance in the MARFC region.

This study evaluated three operational hydrologic modeling frameworks targetted at the National Water Model (NWM): the Community Hydrologic Prediction System (CHPS), the NextGen framework, and version 3.0 of the National Water Model itself.

  • CHPS is the current operational framework used by NOAA's River Forecast Centers. It incorporates the SNOW-17 model for snowmelt and the Sacramento Soil Moisture Accounting (SAC-SMA) model for runoff generation.
  • For the early phases of this study, the NextGen framework was used with the default model configuration provided by the NGIAB ecosystem, which combines the Noah-OWP-Modular land surface model and the Conceptual Functional Equivalent (CFE) rainfall runoff model [2].
    • After initial runs with the baseline configuration, Noah-OWP-Modular was replaced with SNOW-17 output and simplified Potential Evapotranspiration (PET) values from the MARFC database.
    • The models were calibrated using two objective functions: Kling-Gupta Efficiency (KGE) [6][7] and Nash-Sutcliffe Efficiency (NSE) [4][5].
  • The National Water Model 3.0 uses the Noah-MP land surface model coupled with the Weather Research and Forecasting Hydrologic model (WRF-Hydro) [2][3] to simulate hydrological processes across CONUS.

The case studies focused on the Westfield and Elkland basins in North-Central Pennsylvania. These basins provide good locations for comparison due to the presence of USGS stream gages and their "flashy" behavior, characterized by rapid and unpredictable rises and falls in streamflow. Additionally, both Westfield and Elkland were sites of catastrophic flooding during Tropical Storm Debby in 2024, which allowed for the models to be evaluated on a recent extreme flood event. Results from Westfield, PA are shown in Figure 1.

A bar graph titled 'Westfield, PA Nash-Sutcliffe Efficiency values'. SAC-SMA displayed the best performance, closely followed by SAC-SMA Uncalibrated and NGen Calibrated (NSE OFunc). NGen Calibrated (KGE OFunc) fell slightly further behind, while NGEN Uncalibrated was by far the lowest.

Figure 1) Nash-Sutcliffe Efficiency (NSE) Metric for simulations from 2007 to 2020.

Cross-Institutional Collaboration Enhances Hydrologic Modeling in the Logan River Watershed

· 3 min read
Bhavya Duvuri
Machine Learning Researcher
Ayman Nassar
Postdoctoral Researcher
James Halgren
Assistant Director of Science
Arpita Patel
DevOps Manager and Enterprise Architect
Josh Cunningham
Software Engineer
David Tarboton
Professor at Utah Water Research Laboratory
A before-and-after comparison of the corrected catchment for the Logan River. Text: 'Correcting hydrofabric by removing catchments that do not drain into gauge'
Figure 1. A corrected reach arising from the UA-USU collaboration.

Recent collaboration between researchers in the Cooperative Institute for Research to Operations in Hydrology (CIROH) from University of Alabama (UA) and Utah State University (USU) highlighted the value of cross-institutional partnerships in improving community hydrologic modeling. Focused on the Logan River watershed, this joint effort demonstrated how sharing tools, knowledge, and infrastructure can accelerate both model development and scientific discovery.

Through this engagement, USU researchers gained deeper understanding of the NextGen framework and T-Route modeling library, empowering them to improve physical process representations for the Logan River watershed for heightened simulation fidelity. The collaboration also provided valuable exposure to the developmental side of complex modeling tools, offering insights into framework design, automation workflows, and best practices for model setup and calibration. Both teams benefited from exposure to alternative research tools and methods, which helped enhance and refine the community development pipeline.

NGIAB Reaches 10,000 Docker Pulls: NextGen In A Box Makes Water Modeling More Accessible

· 4 min read
Arpita Patel
DevOps Manager and Enterprise Architect

NGIAB Banner

We're thrilled to announce that NextGen In A Box (NGIAB) has surpassed 10,000 Docker pulls — a significant milestone reflecting the growing adoption of water modeling tools that are accessible to all. This achievement creates opportunities for researchers, practitioners, and students worldwide to leverage advanced water prediction frameworks without infrastructure barriers, accelerating global water science innovation.

Update, 8/29: NGIAB Journal Paper now available in Environmental Modelling and Software
→ Read the full paper

From Research Innovation to Community Tool

When we first containerized the NextGen Water Resources Modeling Framework into NGIAB, our goal was simple yet ambitious: remove the technical barriers that prevented many researchers from accessing NOAA's next-generation water modeling capabilities.

Today, with over 10,000 downloads, it's clear the community was ready for this transformation.

The University of Alabama recently highlighted NGIAB's impact in their news feature, "UA Software Makes Water Modeling More Accessible", recognizing how this tool is changing the landscape of hydrologic research and education. As the article notes, NGIAB turns what was once a complex, infrastructure-heavy process into something that researchers can run on their laptops in minutes.