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Python-Based HEC-RAS Controller for Coupling National Water Model with HEC-RAS for River Ice-Informed Streamflow Modeling
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| Created: | Nov 12, 2024 at 7:35 p.m. | |
| Last updated: | Nov 13, 2024 at 3:01 p.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Discoverable (Accessible via direct link sharing) |
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Abstract
Accurate streamflow modeling under ice-affected conditions is critical for reliable flood prediction. This resource integrates satellite-derived ice data and meteorological inputs with HEC-RAS and the National Water Model (NWM) to capture seasonal streamflow variations. By automating the HEC-RAS setup in Python, the framework updates boundary conditions and composite channel roughness based on river ice climatology, improving the model’s adaptability to dynamic conditions.
The resource performs batch runs of the unsteady 2D HEC-RAS model, pausing monthly to restart with a warm-start file. It applies monthly updates to boundary conditions via the Qmult parameter in the unsteady-flow file and adjusts Manning’s roughness values for each calibration region within the geometry files.
Subject Keywords
Coverage
Spatial
Content
README.md
Python-Based HEC-RAS Controller for Coupling National Water Model with HEC-RAS for River Ice-Informed Streamflow Modeling
This repository aims to enhance streamflow accuracy during ice presence. It was created to address limitations in HEC-RAS model batch runs and other necessary interactions with the model.
Background
Accurate streamflow modeling for ice-affected conditions is essential for reliable flood prediction. This framework integrates satellite-derived ice data and meteorological inputs with HEC-RAS and NWM to capture seasonal changes in streamflow. By automating the HEC-RAS model setup in Python, the framework updates boundary conditions and channel roughness based on ice climatology, enhancing model adaptability for dynamic conditions.
What This Repository Contains
In this repository, you will find the controller demo code and example input data for updating the HEC-RAS model. The example data includes Mannings_n.csv, which contains Manning's n values for each month and calibration region, and Qmult_Calibrated.csv, where boundary condition multiplier factors are defined. Column names in Qmult_Calibrated.csv refer to the specific boundary condition names. Important Note: Please locate your HEC-RAS model in the "model" file.
Dependencies
- numpy
- pandas
- win32com.client
- calendar
- datetime
- h5py
Contributing
We welcome contributions to this repository to further enhance flood prediction accuracy and explore new techniques for addressing ice-induced flood challenges. If you are interested in contributing, please feel free to open a pull request.
Contact
For any questions or inquiries, please contact Berina Mina Kilicarslan (bkilicar@stevens.edu).
We hope this repository will contribute to improved streamflow predictions in the presence of ice and support disaster resilience efforts. Thank you for your interest and support!
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| National Aeronautics and Space Administration | NASA Science Mission Directorate Earth Science Division Applied Sciences Program | 80NSSC22K0924 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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