4 posts tagged with "Community Spotlight"

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:

• AORC_LL_PointRetrieval.ipynb: For retrieving and aggregating data from the latitude-longitude gridded dataset for a specific point using geographic coordinates.
• AORC_LL_ZoneRetrieval.ipynb: For retrieving and aggregating data from the latitude-longitude gridded dataset for an area defined by a polygon shapefile.
• AORC_NWMProj_PointRetrieval.ipynb: For retrieving and aggregating data from the NWM projected dataset for a specific point using geographic coordinates.
• AORC_NWMProj_ZoneRetrieval.ipynb: For retrieving and aggregating data from the NWM projected dataset for an area defined by a polygon shapefile.

These Jupyter Notebooks, containing instructions and Python code to access the data, enable researchers to retrieve AORC data from AWS. From there, the notebooks offer options to subset and aggregate the data over user-defined time intervals (beyond the original hourly resolution) and spatial area. These serve as examples for how you could write or modify code to access AORC data in your work. The notebooks are publicly available on HydroShare and are compatible with JupyterHub computing platforms such as CIROH 2i2c JupyterHub linked to HydroShare.

To use these notebooks, go to the HydroShare resource, select "Open With" at the top right, and choose "CIROH 2i2c JupyterHub". This will copy the resource contents (notebooks and data) into the CIROH JupyterHub environment, where you can open and work through them to access the data. Note that you will need a CUAHSI HydroShare account to access "Open With" in HydroShare, and you will also need to request CIROH-2i2c JupyterHub access using a GitHub account.

Our work also includes a comparative analysis of the two AORC datasets with a summary of findings. While we mostly observed small differences, mainly due to projections, users should be aware of potential discrepancies between the datasets.

By providing these user-friendly tools and highlighting the characteristics of both AORC datasets, our work aims to support and facilitate more efficient hydrological and climate-related research within the CUAHSI and CIROH community.

References:​

• Salehabadi, H., D. Tarboton, A. Nassar, A. M. Castronova, P. Dash (2025). Jupyter Notebooks for the Retrieval of AORC Data for Hydrologic Analysis, HydroShare, http://www.hydroshare.org/resource/72ea9726187e43d7b50a624f2acf591f
• The development versions of these notebooks are available on GitHub: https://github.com/CUAHSI/notebooks in the Data Access Examples / AORC - Retrieval of AORC Data for Hydrologic Analysis folder.
• Patel, A., A. Castronova (2025). CIROH 2i2c JupyterHub, HydroShare, http://www.hydroshare.org/resource/2dd1ac86e8854d4fb9fe5fbafaec2b98

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

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

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

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

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

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

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

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.

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. 🚀