NOAA-OWP/inundation-mapping
Jan 2025 – Oct 2025
6 Months active
Languages Used
PythonShellMarkdownSQL
Technical Skills
Data ProcessingGeospatial AnalysisHydrologyMachine Learning Data IntegrationPythonShell Scripting
HeidiSafa-NOAA
PROFILE
Heidisafa-noaa
Safa Hamideh developed and enhanced flood inundation mapping workflows in the NOAA-OWP/inundation-mapping repository, focusing on data-driven accuracy and reliability. Over six months, Safa integrated AI-based bathymetry, optimized Manning’s roughness coefficients, and implemented longitudinal flow consistency filters using Python and Shell scripting. Their work included building data quality control tools, refining rating curve adjustments, and automating configuration management to support robust, scalable pipelines. By addressing data cleaning, geospatial analysis, and hydraulic modeling challenges, Safa improved both the precision and maintainability of the system, enabling more accurate flood risk assessments and streamlined quality control for hydrologic data products.
Overall Statistics
Feature vs Bugs
88%Features
Repository Contributions
10Total
Bugs
1
Commits
10
Features
7
Lines of code
5,775
Activity Months6
Your Network
2 peopleShared Repositories
2
Work History
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.
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
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.
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.
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).
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
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.
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.
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.
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
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.
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.
Activity
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Quality Metrics
Correctness87.0%
Maintainability82.0%
Architecture85.0%
Performance73.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
MarkdownPythonSQLShell
Technical Skills
Configuration ManagementData AnalysisData CleaningData ProcessingData Quality ControlDevOpsDocumentationGeospatial AnalysisHydraulic EngineeringHydraulic ModelingHydrologyMachine Learning Data IntegrationOptimization AlgorithmsPythonPython Scripting
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