36 posts tagged with "CIROH"

At the 2026 CIROH Developers Conference (University of Utah, May 27–29) participants used a variety of tools and models on cloud infrastructure they'd never had to install, configure, or even think about. To enable that seamless experience, our Research Cyberinfrastructure team provisioned cloud resources for 15 hands-on workshops across three days in close collaboration with our partners at 2i2c. Here's what we ran, and how.

What we provisioned, by the numbers​

Running an event of this scale requires more than just a single cloud context, as each workshop posed its own computing and data access requirements. CIROH Cyberinfrastructure met this challenge via a coordinated stack spanning multiple cloud providers and services. Across the 15 workshops that needed infrastructure (14 requests), the R2OHC cloud provisioned the following:

One link, no setup: the Workshop Hub​

For 12 of these workshops, their backbone was the CIROH–2i2c JupyterHub, a managed JupyterHub environment that 2i2c operates on Google Cloud. For the conference, we provisioned a dedicated Workshop Hub: an ephemeral environment that lives only for the duration of the event and then spins back down.

The work that happens before anyone logs in: custom images​

Nine of the twelve JupyterHub workshops needed a custom software image to support their unique computing needs. Sessions covering topics like the Community Asset Computation Hub (CCNH), Foundations of Machine Learning, CUAHSI Hydroinformatics, HydroServer, INFLECT-based Flood Inundation Mapping, Satellite Data Projection, Machine-Learned Model Emulators, and Water Quality Modeling from Hillslope to Watershed Scales each had distinct dependency stacks.

That's a coordination problem, and it was solved weeks before the conference. Workshop leads told us what their sessions needed, we defined those environments in our public awi-ciroh-image repository on GitHub, and 2i2c deployed them. By the time attendees arrived, the right libraries were already in place, all but eliminating the usual loop of haggling with installations and configurations that “work on my machine”. Instead, there were reproducible environments, ready and consistent on day one, demonstrating clear evidence of how tightly the CIROH and 2i2c teams work together.

CCNH: Instant Access to NextGen in JupyterHub​

Two of the twelve workshops also marked the first appearance of the CIROH Community NextGen Hub (CCNH) at a DevCon event! This JupyterHub image contains all of the dependencies required to run the NextGen Framework, bypassing the framework's notorious complexity. CCNH represents a new paradigm where the NextGen framework can be run entirely from the comfort of a web browser, further extending its accessibility and expanding the potential for research and development with the framework.

DevCon 2026 workshops using CIROH Cyberinfrastructure​

• #463 — Hands-on LSTM and Transformer for Operational Streamflow Prediction (Penn State): Brings state-of-the-art deep-learning sequence models into an operational streamflow forecasting workflow. This workshop made use of 2i2c GPU servers to enable its cutting-edge approach.
• #462 — Foundations of ML: From Architecture to Optimization (UA): Entry-level on-ramp giving hydrology researchers the core building blocks of machine learning workflow.
• #460 — Machine Learning: Satellite Data Projection and Georeferencing with Satpy (Stevens): Teaches the remote-sensing preprocessing (projection/georeferencing of polar-orbiting data) needed to feed satellite inputs into models.
• #458 — Essential Geospatial Data and Coding Skills for CIROH Researchers / CUAHSI Hydroinformatics (CUAHSI): Establishes the baseline geospatial-data and coding literacy that underpins nearly every other track.
• #455 — Physics-Based Framework to Predict Water Quality from Hillslope to Watershed Scales (U. Iowa): Applies a physics-first modeling approach to demonstrate the design decisions that go into scaling a model from local to regional contexts.
• #449 — Topographically Derived High Flow Thresholds for Flood Inundation Mapping (USU): Demonstrates an approach to improve flood-inundation mapping by deriving high-flow thresholds directly from terrain.
• #441 — Temporary API Key for CIROH NWM API: Two workshops queried large hydrologic datasets directly through the Google BigQuery API, one in Flood Inundation Mapping and one in Hydroinformatics.
• #411 — Talk to NRDS: LLM Chat for TethysDash to Explore the NextGen Research Data Stream (BYU/Aquaveo): Demonstrates a natural-language/LLM interface for querying NRDS outputs, lowering the barrier to exploring the data stream. (Notably, this workshop used 50 custom-provisioned VMs on the JetStream2 cloud to handle the heightened GPU requirements of this novel technology.)
• #410 — Machine-Learned Emulator Models for Water-Quality Uncertainty (UVM): Trains ML emulators on the output of baseline models to quantify predictive uncertainty cheaply, replacing expensive repeated runs of in-depth physical simulations.
• #408 — Integrating Models in NGIAB, NRDS (UA): The core research-to-operations session covering the model-integration lifecycle from BMI into NGIAB and onward to NRDS.
• #389 — Managing and Standards-Based Sharing of Hydrologic Observations Using HydroServer (USU): Standardizes how observational data is collected, organized, and shared via built-for-purpose software, solidifying the evaluation/ground-truth side of modeling.
• #388 — Orchestrating End-to-End Reproducible NextGen Workflows (U. Calgary): Focuses on reproducibility, chaining NextGen and adjacent steps into repeatable, shareable workflows. This session made use of both a single "Community Asset" image and a shared storage volume, with input data pre-staged in a common folder so dozens of users could work against the same datasets across both sessions.
• #383 — Uncertainty Analysis for Extreme Event Hydrometry (U. Iowa): Provides the theory and tools for quantifying uncertainty specifically in extreme-event measurement.
• #379 — Interactive CIROH Research Apps with Python-only Tethys Component Apps (Aquaveo/BYU): Lets researchers build interactive web apps for their results in pure Python, without the overhead of learning unwieldy front-end frameworks.

HydroShare integration: from a resource to a running notebook​

Many workshop materials were published as HydroShare resources and launched straight into the Workshop Hub via a single link, pulling notebooks and data into the user's environment automatically. What might otherwise have been an onerous process of managing cluttered Downloads folders and local directory structures was instead compressed into a single click.

Why the 2i2c partnership matters​

2i2c's significance in this process went far beyond just hosting the JupyterHub. 2i2c specializes in open-source cloud infrastructure for research, and that shared commitment shaped our collaboration. Our entire stack — JupyterHub, the container images, the HydroShare connector — is open source and lives in public repositories. Another institution could easily read our Dockerfiles or dependency configurations to launch similar environments.

The operational model matters too. 2i2c manages JupyterHubs for many science communities, and what they learn from one community gets shared quickly with others. Our pre-conference testing and prep was informed by dashboards 2i2c built for EarthScope's computing-intense workshops, and the temporary storage approach 2i2c and CIROH used at DevCon is now being re-used with other communities (including EarthScope).

Lessons we're carrying forward​

Pre-event automation is everything. Nine custom images don't build themselves on the morning of a workshop. Front-loading that work meant we could focus on people, not infrastructure, during the event.

Treat infrastructure as tracked engineering. Thirteen GitHub issues turned a sprawling set of requests into an auditable plan — and a head start on next year.

Reproducibility scales. Version-controlled environment images are the difference between 15 consistent workshops and 15 individual troubleshooting sessions — and they let any of these workshops be re-run on the mainline (NM2.1) 2i2c JupyterHub environment.

Open-source, public-private partnerships work. Corporate cloud providers (AWS/Google Cloud), an NSF-funded computing resource, a mission-driven infrastructure partner with 2i2c, and a university research institute (CIROH) all contributed aspects of the DevCon experience. The result was a unified experience across every workshop. That's the model we want to keep building on.

To our partners at 2i2c, and to the providers and resources that backed DevCon 2026: thank you. To our DevCon networking sponsors — Lynker, Google Cloud, Civil and Environmental Engineering at The University of Utah — thank you.

And to everyone who logged in, ran the notebooks, and pushed their work forward — that's exactly what this infrastructure is for.

Interested in CIROH's cloud resources? Learn more at hub.ciroh.org/docs/services/intro or reach the CIROH Cyberinfrastructure team at ciroh-it-support@ua.edu.

CIROH is advancing hydrologic model evaluation into a new era. Led by a core team of developers and scientists at RTI, with testing and contributions from others across the consortium, we've built TEEHR — a system purpose-built for evaluating models at scale. Combining novel approaches to data analytics with cutting-edge open data infrastructure, TEEHR enables a truly complete picture of model and forecast performance across datasets, sites, historical time periods, and forecast horizons.

Why do we need TEEHR?​

TEEHR is built on a fundamental question: “Which hydrologic model is better?”. At its simplest, this can seem trivial; the model simulation is paired with observations, a few performance metrics are calculated, and you're on your way to a common performance analysis. Things can start to get more complicated if want to go larger, dig deeper or ask more nuanced questions:

• What if we want to compare many models against each other?
• What if we want to analyze thousands of locations with 40-years of hourly timestep data at the continental scale?
• What if we want to interrogate the data with questions like:
  • “How does performance during high-flow events compare to low-flow events?”
  • “How does model performance relate to physical basin attributes?”
  • “What's the uncertainty associated with the resulting metrics?”
• What if we want to make the data easily accessible to the hydrologic community to support both historical and near-real time analyses?

These are the challenges TEEHR is designed to address.

Technological advances are evolving water prediction capabilities at a ludicrous pace. From revolutionary machine learning algorithms to dramatic advances in computational hardware, the potential for making accurate hydrologic predictions has never been higher. To meet this new potential, the hydrologic community continuously generates models and approaches based on cutting edge research that could potentially benefit operational systems. However, many of these innovations lack a path to operational deployment.

The NextGen Research Datastream (NRDS) provides a mechanism by which these ideas can be refined and make their way into operations.

Developed by Lynker and the Alabama Water Institute (a Cooperative Institute for Research to Operations in Hydrology partnership), the NRDS facilitates the actualization a research idea from the community in a scalable and deployable numerical prediction system. To evaluate each of these modeling concepts, NRDS deploys prototype models to generate a continuous “datastream”. These outputs can then be evaluated and made more accurate. This cycle of streamlined deployment and iterative design lets these prototypes mature into a product that can be picked up by an operational forecasting team.

To enable this process to be done rapidly and smoothly, the entire system is designed with reproducibility and iterative improvement as core principles. The NRDS is an automated numerical prediction system generating regular stream flow forecasts that uses the NextGen Water Resources Modeling Framework (NextGen) as the core modeling engine and NextGen In A Box (NGIAB) as the simulation environment. This system generates forecasts across the contiguous United States (CONUS) on CIROH's operational cyberinfrastructure backbone: the research-to-operations (R2O) Hybrid Cloud (R2OHC) platform, with deployment on the AWS cloud. What makes the NRDS exciting is that the entire system is open-sourced, reproducible, publicly browsable, and potentially editable by anyone in the hydrologic community.

Opening New Doors for Research with the NextGen Framework​

The NextGen framework holds great potential for hydrologic modeling, but is often inaccessible due to its strenuous setup and requirements. As such, embedding it within a cloud-based framework offers a natural solution to this problem by removing some of the administrative and technical requirements for compute resource setup and computational library configuration, thus opening the door for a wider audience to tke advantage of the strengths of the framework.

With the CIROH Community NextGen Hub (CCNH), we’ve created a cloud-based environment that addresses exactly those setup challenges, so users can focus on science instead of software.

A Preconfigured, Ready-to-Use Cloud Environment​

CCNH is a containerized, cloud-based modeling environment hosted on the CIROH-2i2c JupyterHub. It packages everything a researcher needs to run end-to-end NextGen workflows — from input preprocessing through model execution, calibration, evaluation, and output visualization — into a single, ready-to-use JupyterHub image. Built on the same containerization patterns as NGIAB, CCNH leverages a Pangeo base image and includes:

• Pre-compiled NextGen framework binaries from NGIAB based docker image
• NGIAB data preprocessing tools for automated retrieval and subsetting of hydrofabric and meteorological forcing datasets
• T-Route routing components for streamflow simulation
• SPOTPY(Statistical Parameter Optimization Tool for Python) for model calibration
• TEEHR(Tools for Exploratory Evaluation in Hydrologic Research) for performance evaluation
• PyNGIAB, a Python wrapper that lets you run NextGen simulations directly from Jupyter notebooks
• HydroShare integration tools (nbfetch, hs_files-jupyter, hsclient) for seamless data exchange to save results in HydroShare for collaboration, reproducibility and publishing
• JupyterLab with distributed computing capabilities for interactive, scalable workflows

The result: researchers can go from zero to running a calibrated NextGen simulation in a fraction of the time previously required.

In October 2025, Penn State's Multi-scale Hydrology, Processes and Intelligence group (MHPI), led by Dr. Chaopeng Shen, and the Alabama Water Institute (AWI), led by Steve Burian and Arpita Patel, achieved a milestone R2O effort: the preliminary integration of δHBV 2.0 [4] -- a daily-scale, high-resolution, distributed differentiable model -- into a NextGen ecosystem. This resulted in the first adoption of a differentiable model into NextGen In A Box (NGIAB) [2] and provided an opportunity for CIROH researchers to fine-tune the δHBV 2.0 architecture for NextGen operation.

Having proven viability for daily timescale predictions on high-resolution river networks [4], MHPI researchers recently adapted δHBV 2.0 into a multi-timescale architecture designed to parameterize HBV and simulate streamflow at hourly intervals, at scale, across the NextGen HydroFabric. This new model, δHBV 2.0 MTS (Multi-TimeScale) [5], is a fusion of a daily and hourly δHBV 2.0 model designed to efficiently handle ML training with high geospatial and temporal complexity. (See MTS Architecture for more details about this construction.)

With δHBV 2.0 MTS maintaining similar forecasting skill compared to its daily-scale counterpart [5], Penn State and AWI were once again reunited in a joint effort to embed hourly scale differentiable modeling within AWI's operational ecosystem as a demonstration of model viability and to facilitate open access to its runtime.

Differentiable Models​

δHBV 2.0 and δHBV 2.0 MTS differentiable model constructions are briefly outlined here to contextualize the development efforts. For further detail, see each model's respective citation. At their core, differentiable models embed traditional process-based equations (here, the HBV rainfall-runoff model) inside a machine learning training loop. Because these models are designed to be differentiable (e.g., in PyTorch), gradients flow end-to-end from the loss function back through the physical equations and into the neural networks that supply their parameters. This lets the model learn optimal parameterizations directly from observed data while still obeying mass-balance and storage constraints encoded in HBV -- combining interpretability and physical consistency of process-based hydrology with the flexibility of deep learning.

For the past three years, CIROH Portal and DocuHub have greatly expanded the visibility of CIROH’s research and software products, allowing hydrology professionals from across the community to explore and leverage each other’s developments to maximize their operational benefits and enhance life-saving outcomes in domains like streamflow modeling, flood inundation mapping, and public outreach. However, both sites have always experienced some overlap in their purposes, leading to occasional confusion that has hampered their efforts to make CIROH research truly accessible to the community.

To address this, we are excited to introduce CIROH Hub: a fully unified platform that brings all CIROH resources together in one place.

Visit hub.ciroh.org to explore the full breadth of CIROH’s research and resources, including dynamic collections of community datasets, applications, courses, and notebooks through HydroShare, a listing of CIROH publications through the community Zotero collection, extensive documentation on CIROH’s tools and software, guidance on accessing leading infrastructure for hydrological computing, and much more.

As we do every year, the CIROH team took on the American Geophysical Union (AGU) Annual Meeting 2025 by storm. The Amtrak shuttled us from famously frigid Tuscaloosa to balmy New Orleans, where we laissâmes les bons temps rouler a few months early. From December 15-19, our team shared presentations and posters (and beignets and Cajun food), demonstrating CIROH's commitment to advancing hydrologic science, open collaboration, and sharing technological advancements.

AI, DevOps and the Future of Cloud Infrastructure​

AWS re:Invent did not disappoint! I spent the first week of December at Amazon Web Services' flagship conference in Las Vegas. The event delivered cutting-edge technical insights, showcased the rapid evolution of cloud computing and AI, and provided countless opportunities to connect with industry leaders.

The energy across all five conference venues was more vibrant than I ever imagined it would be.

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, hydrologic scientist Sifan A. Koriche, 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.

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.

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.

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.

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

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.

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.

A screenshot of the HydroShare resource page for Jupyter Notebooks for the Retrieval of AORC Data for Hydrologic Analysis.

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.

May was a pivotal month for representing the Cooperative Institute for Research to Operations in Hydrology (CIROH) and our collective work in advancing water science. As one of CIROH's Ambassadors, I had the privilege of connecting with the broader scientific community at two key events: the Environmental and Water Resources Institute (EWRI) Congress in Anchorage, Alaska, and the 2025 CIROH Developers Conference in Burlington, Vermont.

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.

🌍 AWI News​

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

➡️ Read the full press release here!

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.

Presentations and Posters 📊​

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 available here. 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

Image Source: https://github.com/BYU-Hydroinformatics/api-nwm-gcp

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.

1. ngen-datastream and NGIAB​

We're excited to share some recent developments and updates from CIROH's Research CyberInfrastructure team:

Cloud Infrastructure​

• 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-support@ua.edu. Please refer to NWM BigQuery API to learn more.

CIROH Developers Conference 2024

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.

AWRA 2024 Spring Conference

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.

Google Cloud Next '24

Hello everyone, and thanks for stopping by!

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.

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 Hub 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:

1. 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 Hub 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.

We've released NGIAB v1.1.0! This release fixes issues:

• #21
• #67
• #44

More info: https://github.com/CIROH-UA/NGIAB-CloudInfra/releases/tag/v1.1.0

Visit NGIAB News

NextGen Framework Forcings​

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.