NOAA-OWP/inundation-mapping
Jan 2025 – Feb 2026
7 Months active
Languages Used
PythonShellMarkdownSQLBash
Technical Skills
Data ProcessingGeospatial AnalysisHydrologyMachine Learning Data IntegrationPythonShell Scripting
HeidiSafa-NOAA
PROFILE
Heidisafa-noaa
Safa Hamideh contributed to the NOAA-OWP/inundation-mapping repository by engineering features that improved flood inundation mapping accuracy and data quality. Over seven months, Safa integrated AI-based bathymetry data, optimized Manning’s roughness coefficients, and developed calibration workflows to align model predictions with observed flood extents. Using Python, SQL, and shell scripting, Safa implemented longitudinal adjustments to rating curves, enhanced data pipelines for robustness, and introduced data quality control scripts for ripple terrain metrics. The work addressed challenges in hydraulic modeling and geospatial analysis, resulting in reproducible, scalable workflows and more reliable flood risk assessments across diverse hydrologic environments.
Overall Statistics
Feature vs Bugs
89%Features
Repository Contributions
12Total
Bugs
1
Commits
12
Features
8
Lines of code
6,372
Activity Months7
Your Network
2 peopleShared Repositories
2
Work History
February 2026
2 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for NOAA-OWP/inundation-mapping focused on delivering a feature to improve discharge estimation accuracy through surface-area adjustments and SRC calibration, along with alignment of model parameters nationwide. Implemented longitudinal adjustments to surface-area calculations in the rating curve to improve discharge value accuracy and updated optimized roughness values across the USA to align with the new SRC calibration framework. No major bugs fixed this month; the work prioritized calibration framework updates and reproducible model fidelity, with clear versioned changes.
2 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for NOAA-OWP/inundation-mapping focused on delivering a feature to improve discharge estimation accuracy through surface-area adjustments and SRC calibration, along with alignment of model parameters nationwide. Implemented longitudinal adjustments to surface-area calculations in the rating curve to improve discharge value accuracy and updated optimized roughness values across the USA to align with the new SRC calibration framework. No major bugs fixed this month; the work prioritized calibration framework updates and reproducible model fidelity, with clear versioned changes.
February 2026
October 2025
2 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for NOAA-OWP/inundation-mapping focused on feature delivery and data quality tooling. Delivered two key system enhancements and introduced a new data quality script, driving downstream reliability and QC capability for Hydrovis data products. Delivered features: - Longitudinal adjustment enhancements for rating curves: added new parameters and scripts; separated thalweg notch adjustment into its own script; reordered post-processing steps; replaced the minimum filter with the 10-percentile of discharge. Commit: 819d4665d090ef08430decf2554035541c23f961 (v4.8.12.0). - Ripple terrain agreement metrics data quality script: new Python script to analyze ripple terrain agreement metrics across diverse data sources, flags low-quality ripple models, and generates blacklist files for outlier streams and reaches to support Hydrovis data quality control. Commit: 5d3ef07f16f9d2eef351db0ea539fb0cf074055c (v4.8.14.1). Impact and accomplishments: - Increased accuracy and robustness of rating-curve adjustments through modularized scripts and updated post-processing sequence, improving model fidelity in production workloads. - Enhanced data quality governance with a dedicated ripple terrain metrics script, enabling proactive QC and streamlined Validation for Hydrovis datasets. Technologies/skills demonstrated: - Python scripting and data processing, cross-source data integration, modular code design, and version-controlled delivery with clear commit messages.
October 2025
2 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for NOAA-OWP/inundation-mapping focused on feature delivery and data quality tooling. Delivered two key system enhancements and introduced a new data quality script, driving downstream reliability and QC capability for Hydrovis data products. Delivered features: - Longitudinal adjustment enhancements for rating curves: added new parameters and scripts; separated thalweg notch adjustment into its own script; reordered post-processing steps; replaced the minimum filter with the 10-percentile of discharge. Commit: 819d4665d090ef08430decf2554035541c23f961 (v4.8.12.0). - Ripple terrain agreement metrics data quality script: new Python script to analyze ripple terrain agreement metrics across diverse data sources, flags low-quality ripple models, and generates blacklist files for outlier streams and reaches to support Hydrovis data quality control. Commit: 5d3ef07f16f9d2eef351db0ea539fb0cf074055c (v4.8.14.1). Impact and accomplishments: - Increased accuracy and robustness of rating-curve adjustments through modularized scripts and updated post-processing sequence, improving model fidelity in production workloads. - Enhanced data quality governance with a dedicated ripple terrain metrics script, enabling proactive QC and streamlined Validation for Hydrovis datasets. Technologies/skills demonstrated: - Python scripting and data processing, cross-source data integration, modular code design, and version-controlled delivery with clear commit messages.
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for NOAA-OWP/inundation-mapping. Focused on enhancing inundation mapping accuracy through Manning's roughness coefficient optimization. Implemented an optimization workflow to calibrate Manning's n for channel and overbank areas against benchmark inundation data, enabling iterative coefficient adjustment to minimize differences between predicted and observed flood extents. This work increases the reliability of mapping for flood risk assessment and planning.
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for NOAA-OWP/inundation-mapping. Focused on enhancing inundation mapping accuracy through Manning's roughness coefficient optimization. Implemented an optimization workflow to calibrate Manning's n for channel and overbank areas against benchmark inundation data, enabling iterative coefficient adjustment to minimize differences between predicted and observed flood extents. This work increases the reliability of mapping for flood risk assessment and planning.
July 2025
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for NOAA-OWP/inundation-mapping: Delivered a Global Manning's N roughness data input update to enable global optimized Manning's N values. Changes include updating the runtime data path via bash_variables.env to point to the new CSV for variable Manning's roughness, and documenting the change in the release notes. Release tagged as v4.8.1.0 dev-optz-mannings (#1552).
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for NOAA-OWP/inundation-mapping: Delivered a Global Manning's N roughness data input update to enable global optimized Manning's N values. Changes include updating the runtime data path via bash_variables.env to point to the new CSV for variable Manning's roughness, and documenting the change in the release notes. Release tagged as v4.8.1.0 dev-optz-mannings (#1552).
May 2025
4 Commits • 2 Features
May 1, 2025May 2025 monthly summary for NOAA-OWP/inundation-mapping. Focused on delivering data-pipeline enhancements to improve inundation mapping accuracy and reliability, while hardening the pipeline against numerical issues and streamlining deployment.
4 Commits • 2 Features
May 1, 2025May 2025 monthly summary for NOAA-OWP/inundation-mapping. Focused on delivering data-pipeline enhancements to improve inundation mapping accuracy and reliability, while hardening the pipeline against numerical issues and streamlining deployment.
May 2025
February 2025
1 Commits
Feb 1, 2025February 2025 monthly summary for NOAA-OWP/inundation-mapping focusing on data quality improvements to Hydrotables and inundation mapping accuracy.
February 2025
1 Commits
Feb 1, 2025February 2025 monthly summary for NOAA-OWP/inundation-mapping focusing on data quality improvements to Hydrotables and inundation mapping accuracy.
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025: Delivered a major enhancement to inundation mapping in NOAA-OWP/inundation-mapping by integrating AI-based bathymetry data and optimizing Manning's N to improve mapping accuracy. Introduced bathymetry processing functions and updated configuration files, culminating in v4.5.14.0. This work is anchored by commit deb8b2ea95915193849bac1e782fed2294a04522 ("v4.5.14.0 Rating curves adjustments with ml-bathymetry data and optimized mannN (#1340)"). Business value includes more accurate flood extent delineation, improved risk assessment, and better decision support for coastal planning. Technologies demonstrated include ML-based data integration, hydrodynamic parameter tuning, bathymetry processing, and configuration management. No major bugs fixed this period.
1 Commits • 1 Features
Jan 1, 2025January 2025: Delivered a major enhancement to inundation mapping in NOAA-OWP/inundation-mapping by integrating AI-based bathymetry data and optimizing Manning's N to improve mapping accuracy. Introduced bathymetry processing functions and updated configuration files, culminating in v4.5.14.0. This work is anchored by commit deb8b2ea95915193849bac1e782fed2294a04522 ("v4.5.14.0 Rating curves adjustments with ml-bathymetry data and optimized mannN (#1340)"). Business value includes more accurate flood extent delineation, improved risk assessment, and better decision support for coastal planning. Technologies demonstrated include ML-based data integration, hydrodynamic parameter tuning, bathymetry processing, and configuration management. No major bugs fixed this period.
January 2025
Activity
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Quality Metrics
Correctness87.6%
Maintainability83.4%
Architecture85.8%
Performance75.8%
AI Usage30.0%
Skills & Technologies
Programming Languages
BashMarkdownPythonSQLShell
Technical Skills
Configuration ManagementData AnalysisData CleaningData ProcessingData Quality ControlDevOpsDocumentationGeospatial AnalysisHydraulic EngineeringHydraulic ModelingHydrologyMachine Learning Data IntegrationOptimization AlgorithmsPythonPython Scripting
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