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.

ABSTRACT:

This resource contains the Jupyter Notebook for a surrogate model (SM) designed to improve the accuracy of conceptual flood inundation mapping (FIM) by mimicking the behavior of a high-fidelity hydrodynamic model.
The study leverages the National Oceanic and Atmospheric Administration (NOAA) Office of Water Prediction's (OWP) Height Above Nearest Drainage-Flood Inundation Mapping (HAND-FIM) method as the baseline framework, which enables large-scale flood mapping at low computational costs.
The surrogate model (SM) employs machine learning techniques, specifically a Random Forest algorithm, to replicate critical hydrodynamic characteristics derived from a 2D Hydrologic Engineering Center-River Analysis System (HEC-RAS) model, a high-fidelity representation of flood dynamics. This approach enhances the fidelity of the simpler HAND-FIM by infusing it with insights from the more detailed HEC-RAS model. The model development is applied across 14 carefully selected sub-watersheds in the Amite River Basin.

Key features of the resource include:

- Surrogate Model: Demo code built using the Random Forest algorithm to predict flood extents based on various input features.
- Input Features: These include the HAND-FIM generated for a historic flood event in August 2016 using the National Water Model (NWM) streamflow and the Office of Water Predictions (OWP) HAND-FIM Synthetic Rating Curves (SRC). Other inputs, such as the Digital Elevation Model (DEM), slope, aspect, land use/land cover (LULC) data, impervious surface data, and river network information (RiverNet).
- Target Data: High-resolution flood extent data from the HEC-RAS model (HEC-RAS-FIM), serving as the ground truth for training the surrogate model.

Contribution:
This work demonstrates how a data-driven, machine-learning approach can bridge the gap between conceptual flood models and hydrodynamic models, improving the accuracy and reliability of large-scale flood mapping while maintaining computational efficiency. The surrogate model mimics complex relationships between terrain, hydrology, and flood extents, making it a powerful tool for flood prediction in regions where detailed hydrodynamic modeling may be too costly or time-consuming.

This research was conducted as part of the NOAA National Water Center Innovators Program, Summer Institute 2023.