Meeting Highlights: Transforming Research into Real-World Impact
Over four days, our community showed how scientific innovation translates into tools and systems that support communities, enhance resilience, and improve decision-making across the nation. From AI advances in water prediction to flood inundation mapping, the presentations and discussions demonstrated the collaborative, open-science approach that defines CIROH.
We are excited to share a comprehensive collection of assets from the 2025 CIROH Science Meeting, including session recordings, presentation slides, and group photos! Whether you attended in person, joined virtually, or could not participate, these materials are available to you.
Please note that materials are being uploaded continuously as we receive images and additional files from presenters and participants. We encourage you to check back periodically for updates. If you are a presenter and prefer not to have your slides or poster included in the shared drive, please contact Charity McCalpin (cnmccalpin@ua.edu) for removal.
If you attended the meeting, either in person or virtually, we kindly ask that you complete our post-event survey. Your feedback is invaluable in helping us improve future meetings and ensure they continue to serve our community's needs effectively.
Looking Forward: Building on Our Collaborative Foundation
The 2025 Science Meeting reinforced CIROH's commitment to building a collaborative, open-science community that transforms research into real-world impact. The connections made and innovations shared during these four days will continue to drive our mission forward throughout the year.
Thank you for your ongoing support and participation in making the 2025 CIROH Science Meeting a tremendous success. Your commitment to advancing water science and translating research into operational tools that benefit communities nationwide is what makes CIROH's mission possible.
Together, we continue building a future where scientific innovation directly serves society's water-related challenges and opportunities.
The CIROH Science Meeting 2025 was held September 15-18 in Tuscaloosa, Alabama. For more information about future meetings and CIROH initiatives, visit our website (https://ciroh.ua.edu/) and stay connected with our community.
Reliable and high-resolution streamflow data are essential for hydrologic research, flood forecasting, and water resource management. Streamflow gages provide necessary measurements but can be difficult and expensive to build and operate. Camera-based monitoring offers a promising, non-contact alternative to or augmentation of traditional streamflow gages. However, broad use of camera-based streamflow monitoring has been limited by operational challenges including how to collect, store, manage, and process the large volume of image and video data produced by monitoring cameras.
With help from Arpita Patel and the CIROH Cyberinfrastructure and DevOps Team, who assisted our team with access to Amazon Web Services and the Google Cloud Platform, we developed and tested new cyberinfrastructure that advances camera-based hydrologic monitoring.
Traditional dataloggers used in hydrologic monitoring focus on interfacing with conventional sensors (e.g., pressure transducers, float gages, etc.) and lack some capabilities required for camera-based monitoring. Low-cost field computers like the Raspberry Pi provide a capable alternative, but lack out-of-the-box software required to support high-resolution image and video capture, management of the large volume of data that accumulates, data processing, and cloud uploading processes. Because of this, we had to build the functionality required to combine low-cost field computers with cloud computing services to produce an operational, real-time, cloud-integrated, camera-based streamflow monitoring system.
Figure 1. Segmented images showing pixels identified as water by the HydrocamCompute software. Quantifying water pixels within the rectangular areas of interest provide an estimate of stream stage and related discharge.
We developed two new Python-based software programs compatible with inexpensive field computers such as Raspberry Pi: one that captures and processes the data from cameras, and one that automatically extracts hydrologically relevant data from image data in the cloud.
Hydrocam Collect: automatically captures images and videos at user-specified intervals, manages local storage on field computers, and uploads captured image and video data to the cloud. Real-time data monitoring and alerts minimize potential for data loss.
Hydrocam Compute: This serverless cloud computing workflow extracts relevant hydrologic data from camera images and videos, such as depth/stage and velocity. Automated and scalable, the workflow can handle a large number of independent monitoring sites, each executing their own serverless functions using isolated cloud resources.
In an operational context, automation is key to a scalable, robust system capable of integrating camera-based monitoring with larger hydrologic monitoring networks such as those managed by the United States Geological Survey (USGS). We tested the automation of the system we developed using commercial cloud services via Amazon Web Services and Google Cloud Platform with access provided through CIROH. By leveraging CIROH-provided cloud services, our team was able to prototype, test, and scale the software to bridge the gap between proof-of-concept research and practical field use.
This linked software system we developed responds to data events (i.e., capture and upload of an image or video to the cloud) instantly and minimizes human intervention and error correction. The serverless and event driven design of Hydrocam Compute reduces infrastructure management overhead and cost because cloud resources are only used during execution of data processing tasks with no need to manage or maintain physical compute infrastructure. The models used for processing images and video can be swapped out to meet the needs of particular monitoring locations.
The Hydrocam workflows enhance the feasibility, reliability, and efficiency of camera-based streamflow monitoring. As hydrological conditions become more variable, access to high-resolution, real-time data becomes increasingly important for flood forecasting, water resource management, and long-term climate adaptation strategies. This work supports these broader goals in making camera-based monitoring more practical and scalable.
Access the pre-prints of papers related to the Hydrocam software and camera-based monitoring:
Neupane, S., Horsburgh, J. S., Bin Issa, R., Young, S. (2025). HydrocamCollect: A robust data acquisition and cloud data transfer workflow for camera-based hydrological monitoring. Environmental Modelling & Software (submitted). Available at SSRN: https://ssrn.com/abstract=5451102 or https://doi.org/10.2139/ssrn.5451102
Neupane, S., Horsburgh, J. S., Bin Issa, R., Young, S. (2025). HydrocamCompute: Serverless cloud computing workflow for camera-based hydrological monitoring. Environmental Modelling & Software (submitted). Available at SSRN: https://ssrn.com/abstract=5451100 or https://doi.org/10.2139/ssrn.5451100
Bin Issa, R., Neupane, S., Khan, S., Horsburgh, J. S., Young, S. (2025). Towards Real-Time Water Level and Discharge Measurements using Imagery, Machine Learning, and Edge Computing. Journal of Hydrology (submitted). Available at SSRN: https://ssrn.com/abstract=5531964 or https://doi.org/10.2139/ssrn.5531964
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.
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
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.
CIROH team at NHWC 2025 in Tucson, Arizona, standing by the event’s official banner.
CIROH had a strong showing at the 15th Biennial National Hydrologic Warning Conference (NHWC 2025), with our researchers presenting innovative solutions and engaging with the broader hydrologic warning community. The conference brought together field personnel, innovators, engineers, hydrologists, forecasters, water resource managers, and emergency management officials from across the country to advance flood warning systems and address emerging challenges in evolving climate and drought management.
The Analysis of Record for Calibration (AORC) dataset is recognized as a high-value resource for the CUAHSI and CIROH community.
This dataset is hosted by NOAA via Amazon Web Services (AWS) and is available in two primary formats:
a latitude-longitude gridded dataset
and the National Water Model (NWM) projected dataset, part of the NWM Retrospective archive.
To enhance accessibility and illustrate analysis capabilities, we developed four user-friendly Jupyter Notebooks that enable data retrieval for both specific points of interest and spatial domains defined by shapefiles:
The recent DevCon 2025 event showcased not just cutting-edge development practices, but also demonstrated how modern DevOps principles and cloud infrastructure can seamlessly support large-scale technical workshops. Our team had the privilege of providing IT infrastructure and support for over 200 attendees, creating a robust learning environment through an exemplary public-private partnership.
CIROH's Research Cyberinfrastructure and DevOps team. Left to right, top to bottom: Manjila Singh, Arpita Patel, Nia Minor, Trupesh Patel, James Halgren; Benjamin Lee.
Last week, I had the incredible opportunity to co-present a keynote at the CIROH
Developers Conference (DevCon 2025), which attracted over 200 attendees. This
presentation, which I presented alongside Dan Ames, focused on "CIROH HydroInformatics
and Research Cyberinfrastructure." It was a fantastic experience to share insights
into the powerful tools and technologies that CIROH engineers, students, researchers
have been developing to advance hydrological research and operations.
CIROH-AWI Science and Technology Team. Left to right: Sagy Cohen, Steven Burian, Manjila Singh, Saide Zand, Savalan N. Neisary, Arpita Patel, Nia Minor, Trupesh Patel, Sifan A. Koriche, Jonathan Frame, Reza S. Alipour, Hari T. Jajula, Chad Perry; Josh Cunningham.
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.
The Alabama Water Institute (AWI) at the University of Alabama (UA) recently published an article highlighting how NextGen In A Box (NGIAB) could transform hydrological modeling. This article provides great insight into NGIAB's real-world impact:
🚀30-minute setup vs days/weeks of configuration
📖 Provo River Basin Case Study demonstrating rapid deployment
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. 🚀
AGU24 brought together the world’s leading minds in Earth and space sciences. CIROH participated actively, showcasing advances in water prediction, modeling techniques and many more technologies.
The conference provided an excellent platform for CIROH researchers to present their groundbreaking work. Our team delivered impactful presentations and poster sessions highlighting CIROH’s innovative work, including advancements in water prediction systems
and community water modeling.
These sessions sparked thought-provoking discussions and fostered collaborations with other researchers. For those who missed it, posters and presentation slides are now availablehere. Feel free to explore these materials and share your thoughts. 📝
The Community NextGen framework has seen significant advancements in November 2024, with major updates across multiple components and exciting new resources for users. Let's dive into the key developments that are making hydrologic modeling more accessible and powerful than ever.
The 2024 CIROH Science Meeting was a huge success, bringing together researchers, federal partners, and consortium members both in person and virtually. We're excited to share the valuable resources from this year's meeting with the wider CIROH community.
Slides and pictures from the various sessions, keynotes, and the Federal Town Hall have all been uploaded to a shared drive for easy access. You can find links to these materials here: Access the Shared Drive with Presentation Slides
Several important historical and ongoing National Water Model (NWM) datasets are now available on Google Cloud BigQuery, which makes them queryable through SQL using Google Cloud console. Some of these data sets are also accessible through an API (e.g. using Python). These datasets and their current status are as follows:
This month, we are excited to showcase two case studies that utilized our cyberinfrastructure tools and services. These case studies demonstrate how CIROH's cyberinfrastructure is being utilized to support hydrological research and operational advancements.
CIROH's Google Cloud Account is now fully operational and managed by our team. You can find more information here.
We're in the process of migrating our 2i2c JupyterHub to CIROH's Google Cloud account.
We've successfully deployed the Google BigQuery API (developed by BYU and Google) for NWM data in our cloud. To access this API, please contact us at ciroh-it-admin@ua.edu. Please refer to NWM BigQuery API to learn more.
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.
The CIROH CyberInfrastructure team recently participated in the AWRA 2024 Spring Conference, co-hosted by the Alabama Water Institute at the University of Alabama.
Themed "Water Risk and Resilience: Research and Sustainable Solutions," the conference brought together a diverse group of water professionals to exchange knowledge and explore cutting-edge research in the field.
I recently had the incredible opportunity to attend Google Cloud Next 2024 in person for the first time, and it was truly an amazing experience. From insightful keynote presentations and workshops to vibrant booths buzzing with connections, the event was a whirlwind of innovation and inspiration.
Accelerating Innovation: CIROH's March 2024 Update
The CIROH team has been diligently accelerating research cyberinfrastructure capabilities this month. We're thrilled to share key milestones achieved in enhancing the Community NextGen project and our cloud/on-premises platforms.
Welcome to the February edition of the CIROH DocuHub blog, where we bring you the latest updates and news about the Community NextGen project and CIROH's Cloud and on-premise Infrastructure.
Our team has been hard at work enhancing CIROH's Infrastructure and Community NextGen tools. Here are some highlights from February 2024:
We successfully launched our new On-premises Infrastructure, which is now fully operational. You can find documentation for it here.
Welcome to the January edition of the CIROH DocuHub blog, where we share the latest updates and news about the Community NextGen project monthly. NextGen is a cutting-edge hydrologic modeling framework that aims to advance the science and practice of hydrology and water resources management. In this month's blog, we will highlight some of the recent achievements and developments of the Community NextGen team.
A new forcing processor tool has been made public. This tool converts any National Water Model based forcing files into ngen forcing files. This process can be an intensive operation in compute, memory, and IO, so this tool facilitates generating ngen input and ultimately makes running ngen more accessible.
