20 posts tagged with "NextGen"

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.

Why the NRDS Matters​

For decades, operational hydrologic modeling has been a largely closed process. National-scale models like the National Water Model (NWM) are developed and maintained by federal agencies where HPC access is limited. While these models frequently produce insights and forecasts of excellent quality, the simulations themselves have proven difficult to reproduce and improve upon from within the hydrologic research community, hindering the knowledge flow from research to operations. By contrast, the NRDS is totally public and open. This provides both researchers and national agencies with immediate access, reducing the latency between the discovery of new and potentially valuable knowledge and its operational implementation.

Deployed on AWS cloud from the ngen-datastream repository, the NRDS regularly ingests meteorological forcing data, processes it through the NextGen framework using community-configured parameters, and produces streamflow predictions for river reaches across the country. The forcing data, model configuration, infrastructure, and simulation outputs are all publicly available through the NRDS S3 data portal and the NRDS visualizer. Researchers can browse the results, compare them against observations, and reproduce or run their own simulations with the exact tooling that supports the NRDS. This transparency and usability enables researchers to readily propose improvements to any component of the NRDS system stack.

This is a fundamentally new paradigm for how national-scale water prediction can work: an open, iterative, community-driven feedback loop where regional expertise flows directly into an operational-like system.

How the Community Can Contribute​

This open contribution model is perhaps the most exciting aspect of the NRDS. The configuration files and NextGen model formulation that drive the daily simulations are publicly hosted and directly referenced during every execution. This means that community-proposed improvements to parameterization can flow directly into the operational system just by modifying these files.

The process is intentionally straightforward. A researcher who has improved upon the parameterization of a particular VPU realization file can navigate to the ngen-datastream Issues page on GitHub, open a new issue using the dedicated "Propose NRDS/NextGen Research DataStream parameter update" template, and submit their updated configuration along with supporting evidence. The CIROH team then reviews these contributions; once validated, they are merged and deployed into the live NRDS system.

This contribution pathway was a central theme at the 2025 CIROH Developers Conference (DevCon 2025), where a dedicated workshop walked participants through the complete process of proposing a configuration change. Attendees received access to virtual machines on NSF JetStream2 with pre-installed tooling, enabling them to run local simulations, compare results, and prepare a contribution — all within a single workshop session.

The NRDS is not designed to be a product delivered by a central team. It is a platform designed to absorb and amplify the collective expertise of the hydrologic community.

Multiple Datastreams Live and Scaling​

The long-term potential of the NRDS is further amplified by its scalability in deploying additional model formulations to generate regular forecasts. The system now runs multiple concurrent datastreams, each using a different hydrologic modeling approach within the NextGen framework to generate streamflow forecasts.

Currently, the NRDS operates a CFE-NOM datastream across all CONUS and parallelized across Vector Processing Units (VPUs), which pairs the Conceptual Functional Equivalent (CFE) rainfall-runoff model with the Noah-OWP-Modular land surface model. Alongside this, an LSTM DataStream has been deployed for all CONUS, bringing cutting-edge machine learning-based streamflow prediction into an operational-like system. Both of these datastreams route the NextGen outputs using T-Route.

The most recent release, v2.2.0 (February 2026), deployed the LSTM model into the production NRDS system — a milestone that brings differentiable and machine learning-based hydrology into an automated, cloud-based production environment. This builds on the broader CIROH effort to integrate models like δHBV 2.0 from Penn State's MHPI group.

There is also a Routing-Only DataStream for VPU 03W. This datastream focuses on channel routing NWM outputs using T-Route only, a novel experiment by Quinn Lee.

The Broader Ecosystem​

The NRDS is deeply interconnected with the broader suite of CIROH tools that are making NextGen modeling accessible. NextGen In A Box (NGIAB) provides the containerized runtime environment that researchers can use locally to test configurations before proposing them to the NRDS. The DataStreamCLI (now in its own repository) automates the complete workflow from data preprocessing to NextGen execution. The ForcingProcessor (also split out) handles the conversion of gridded meteorological data into the catchment-averaged forcings that NextGen requires. Once the model outputs are generated, TEEHR (Tools for Exploratory Evaluation in Hydrologic Research) provides standardized evaluation capabilities for comparing model outputs against observations (dashboard available here).

Together, these tools form a coherent pipeline: prepare data with the ForcingProcessor and DataStreamCLI, run simulations locally with NGIAB, evaluate results with TEEHR, and — if improvements are found — contribute them back to the NRDS for operational deployment.

What's Next​

The trajectory of the NRDS points toward an increasingly capable and diverse operational system. Active development efforts include implementing the hourly, deep learning-based differentiable model δHBV 2.0 MTS, which was recently embedded into the NGIAB-NRDS ecosystem through a collaboration between Penn State and the Alabama Water Institute. This model has demonstrated continental-scale streamflow forecasting capabilities that rival the current National Water Model.

There is also ongoing work to make the NRDS outputs more accessible and useful to downstream consumers. The NRDS Visualizer now provides a web-based interface for browsing and exploring daily simulation results. The TEEHR Evaluation Dashboard now provides rapid evaluation of these simulation results, enabling researchers to gauge model performance as events are occurring and weigh the performance of each datastream (NextGen model-formulation) relative to other datastreams.

With NRDS ecosystem quickly reaching maturity, now is the time for hydrologic researchers to put their research ideas to the test and deploy them in this operational-like environment.

Get Involved​

The NextGen Research DataStream represents a new kind of infrastructure for water science: one that is as much a social and organizational innovation as it is a technical one. By making national-scale hydrologic simulations open, reproducible, and community-editable, the NRDS creates the conditions for a distributed network of research hydrologists to incrementally improve the accuracy and reliability of streamflow predictions across the country.

Whether you are a graduate student exploring NextGen for the first time, a river forecast center hydrologist with deep regional expertise, or a researcher developing novel modeling approaches, the NRDS offers a concrete pathway from your work to operational impact.

Here's how to get started:

• NRDS on the NextGen in a Box product portfolio website: ngiab.ciroh.org/#/nrds
• Explore the data: Browse daily outputs at datastream.ciroh.org or the NRDS Visualizer
• Examine the NRDS deployment status timeline to see which datastreams have been deployed, when, and at what scale: NRDS Status
• Propose an improvement: Use the NRDS Issues page to submit your idea for a new datastream or an edit to an existing deployment.
• Join the conversation: Participate in GitHub Discussions to connect with the community
• Read the documentation: Visit the CIROH Hub NRDS page for comprehensive documentation on the NRDS system and broader ecosystem.
• Try out NRDS tools: Clone the various NRDS repositories to experiment with the NRDS workflow.
  • DataStreamCLI (Figure 2): the on-server workflow tool used in NRDS simulations. This tool offers the ability to reproduce NRDS simulations, with a modular design for integrating research configurations and processing components.
  • ForcingProcessor (Figure 3): a scalable tool for processing NWM operational data files into NextGen inputs. This tool provides the processing to generate NRDS forcings.

The future of water prediction is open. Come build it with us.​

The NextGen Research DataStream is developed and maintained by CIROH at the University of Alabama, with funding under award NA22NWS4320003 from the NOAA Cooperative Institute Program. Learn more at ciroh.ua.edu.

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.

Streamlined Workflow, Clear Results​

Through guided notebooks, users can:

• Prepare and subset input data
• Run simulations
• Calibrate models
• Evaluate performance
• Explore outputs interactively

...all within a single environment designed for efficiency and discovery.

Why It Matters​

By lowering technical barriers and using a cloud-based approach, CCNH makes NextGen accessible to a broader research community. It allows scientists to spend less time on setup and more time advancing hydrologic understanding.

How to Access CCNH​

There are two primary ways to launch CCNH in the CIROH-2i2c JupyterHub environment:

Option 1: Launch Directly from CIROH-2i2c JupyterHub​

1. Go to: https://ciroh.awi.2i2c.cloud/
2. Click “Login to continue” and authenticate with your CIROH credentials. (If this is your first time, you may be prompted to authorize access through GitHub.)
3. On the Server Options page:
   • Select the desired compute profile (Small, Medium, Large, or Huge).
   • Select the CIROH Community NextGen Hub image.
4. Click Start to launch the JupyterHub.

You are now inside a CIROH 2i2c JupyterHub session running the CCNH image, which provides a fully configured NextGen modeling environment.

Option 2: Launch from a HydroShare Resource (“Open With”)​

One of the most powerful features of CIROH-2i2c JupyterHub is its integration with HydroShare. You can open a HydroShare resource directly in a CIROH-2i2c JupyterHub session running the CCNH image:

1. Navigate to a HydroShare resource. (for example: https://www.hydroshare.org/resource/27045581bdea4808a393330f2417379c/)
2. Click Open With (top-right of the resource page) and select CIROH-2i2c JupyterHub.
3. Click “Login to continue” and authenticate with your CIROH credentials. (If this is your first time, you may be prompted to authorize access through GitHub.)
4. On the Server Options page:
   • Select the desired compute profile (Small, Medium, Large, or Huge).
   • Select the CIROH Community NextGen Hub image.
5. Click Start to launch the JupyterHub.
6. (If you did this with the example resource above, click on and launch one of the Jupyter Notebooks to step through the NextGen workflows.)

References​

• Nassar, A., Tarboton, D., Baig, F., Cunningham, J., Patel, A., Halgren, J., Lee, B., Salehabadi, H., Castronova, A., and Garousi-Nejad, I. "Accelerating community research applications of the NextGen water modeling framework through CUAHSI HydroShare linked with CIROH-2i2c JupyterHub." Environmental Modelling & Software. (In-review)
• Nassar, A., Tarboton, D., Baig, F., Patel, A., Cunningham, J., and Salehabadi, H. (2026). NextGen Water Modeling Workflow for Research-Scale Applications. https://www.hydroshare.org/resource/27045581bdea4808a393330f2417379c
• I-GUIDE VCO: Enabling Community Research with the Next Generation National Water Modeling Framework
• CIROH Hub: https://hub.ciroh.org/docs/products/ngiab/distributions/nextgen-2i2c/

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.

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.

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.

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 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.

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!

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.

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.

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

We've introduced a fresh addition within DocuHub, offering the most up-to-date insights on NGIAB and NextGen monthly updates.

Visit NGIAB Release Notes Page