
Lars Tierolf developed advanced coastal and flood risk modeling capabilities for the GEB-model/GEB repository, focusing on scalable, region-aware simulations and robust data workflows. He engineered integrations for geospatial datasets and coastal hazard modules using Python, Pandas, and GeoPandas, enabling unified coastal–riverine analysis and streamlined data ingestion. His work included refactoring legacy modules, implementing data adapters, and enhancing model fidelity through improved configuration management and code quality practices. By introducing ASC/TIFF GIS support, YAML-based configuration, and rigorous testing, Lars ensured maintainable, production-ready pipelines that support reliable risk assessments and faster deployment cycles for complex environmental modeling scenarios.

February 2026: Delivered foundational repo setup, data adapter integration aligned with the data catalog, and key feature enhancements across the GEB project, with a strong emphasis on data governance, code quality, and expanded regional data capabilities. Improvements in stability, maintainability, and documentation enable faster, reliable future iterations and stronger business value.
February 2026: Delivered foundational repo setup, data adapter integration aligned with the data catalog, and key feature enhancements across the GEB project, with a strong emphasis on data governance, code quality, and expanded regional data capabilities. Improvements in stability, maintainability, and documentation enable faster, reliable future iterations and stronger business value.
January 2026 (2026-01) performance summary for GEB-model/GEB: Delivered core data-processing and GIS capability enhancements, expanded format support, and robustness improvements. The work focused on delivering tangible business value through reliable data workflows, region-aware analytics, coastline-aware data management, and maintainable code. Key outcomes include restored GIS data format support (ASC/TIFF) with basins data, region-based initialization with region geometry filtering and unary union handling, improved tile handling and configuration reliability, and coastline/data catalog integration with Delta-TM considerations. Public-facing documentation and changelog updates accompanied the delivery, reflecting a verifiable baseline for future coastal modeling runs.
January 2026 (2026-01) performance summary for GEB-model/GEB: Delivered core data-processing and GIS capability enhancements, expanded format support, and robustness improvements. The work focused on delivering tangible business value through reliable data workflows, region-aware analytics, coastline-aware data management, and maintainable code. Key outcomes include restored GIS data format support (ASC/TIFF) with basins data, region-based initialization with region geometry filtering and unary union handling, improved tile handling and configuration reliability, and coastline/data catalog integration with Delta-TM considerations. Public-facing documentation and changelog updates accompanied the delivery, reflecting a verifiable baseline for future coastal modeling runs.
December 2025 performance: Delivered core coastal risk modeling enhancements and data processing improvements that drive reliable, production-ready simulations and faster iteration cycles. Key outcomes include enabling coastal flood simulations with proper model-loading order, improved robustness across model variants, a cleaned and filtered data pipeline, and strengthened developer tooling and code quality.
December 2025 performance: Delivered core coastal risk modeling enhancements and data processing improvements that drive reliable, production-ready simulations and faster iteration cycles. Key outcomes include enabling coastal flood simulations with proper model-loading order, improved robustness across model variants, a cleaned and filtered data pipeline, and strengthened developer tooling and code quality.
November 2025 achieved foundational architectural improvements and reliability fixes for GEB, enabling scalable coastal and inland modeling, improved data handling, and faster, more reliable simulations. Key work includes refactoring the coastal model into the MultiSfincs framework, initiating a unified coastal–riverine SFINCS architecture, hardening boundary parsing and masks for GEBCO, gating coastal initialization by domain presence, and extending data workflows with Open Building Map integration and typing/cleanup for maintainability. These changes improve domain fidelity, reduce unnecessary computation, and lay groundwork for business-ready risk analyses and deployment readiness.
November 2025 achieved foundational architectural improvements and reliability fixes for GEB, enabling scalable coastal and inland modeling, improved data handling, and faster, more reliable simulations. Key work includes refactoring the coastal model into the MultiSfincs framework, initiating a unified coastal–riverine SFINCS architecture, hardening boundary parsing and masks for GEBCO, gating coastal initialization by domain presence, and extending data workflows with Open Building Map integration and typing/cleanup for maintainability. These changes improve domain fidelity, reduce unnecessary computation, and lay groundwork for business-ready risk analyses and deployment readiness.
October 2025 monthly summary for GEB-model/GEB focusing on delivering coastal modeling capabilities, refining region handling, and tightening data integration to enable scalable coastal analysis and faster deployment.
October 2025 monthly summary for GEB-model/GEB focusing on delivering coastal modeling capabilities, refining region handling, and tightening data integration to enable scalable coastal analysis and faster deployment.
September 2025: Delivered a major refactor and feature enablement for flood decision logic and coastal hazard modelling in GEB-model/GEB, with a focus on maintainability, correctness, and readiness for coastal risk assessments. The work includes removal of the legacy DecisionModule, alignment of interfaces with updated typing and tests, coastal region-aware modelling enhancements, and fixes to data-path references.
September 2025: Delivered a major refactor and feature enablement for flood decision logic and coastal hazard modelling in GEB-model/GEB, with a focus on maintainability, correctness, and readiness for coastal risk assessments. The work includes removal of the legacy DecisionModule, alignment of interfaces with updated typing and tests, coastal region-aware modelling enhancements, and fixes to data-path references.
August 2025 monthly summary for GEB-model/GEB focusing on coastal flood risk modeling enhancements and data robustness. Delivered integrated surge hydrographs using the dullaarts hgrapher method and produced storm surge hydrographs for stations within model bounds. Built the coastal sfincs app and generated all return-period coastal flood maps, then merged these with riverine flood maps to enable unified flood risk visualization. Underwent a codebase refactor for renaming and restructuring to improve maintainability and reduce complexity. Implemented robustness/data handling improvements across the pipeline, including floodmap CRS updates, handling GDL regions with no buildings, bounds buffers for GT historical stations, and filtering inland-related data. Refactored the station data pipeline to remove for loops and leverage model files in hydrographs, and began exporting GTSM data to a table for performance considerations. Initiated OECD income distribution data integration and vector scanner alignment; expanded code quality, typing, and documentation efforts; disabled long-running progress bars to improve UX. Added tests for household decisions and decision horizon to strengthen reliability.
August 2025 monthly summary for GEB-model/GEB focusing on coastal flood risk modeling enhancements and data robustness. Delivered integrated surge hydrographs using the dullaarts hgrapher method and produced storm surge hydrographs for stations within model bounds. Built the coastal sfincs app and generated all return-period coastal flood maps, then merged these with riverine flood maps to enable unified flood risk visualization. Underwent a codebase refactor for renaming and restructuring to improve maintainability and reduce complexity. Implemented robustness/data handling improvements across the pipeline, including floodmap CRS updates, handling GDL regions with no buildings, bounds buffers for GT historical stations, and filtering inland-related data. Refactored the station data pipeline to remove for loops and leverage model files in hydrographs, and began exporting GTSM data to a table for performance considerations. Initiated OECD income distribution data integration and vector scanner alignment; expanded code quality, typing, and documentation efforts; disabled long-running progress bars to improve UX. Added tests for household decisions and decision horizon to strengthen reliability.
July 2025 monthly summary for GEB-model/GEB. Focused on delivering feature updates, reliability improvements, and performance optimizations that increase model fidelity and operational stability. Key work included updating GLOPOP grids, integrating OSM building data, expanding the damage scanner, and broad code quality improvements, while fixing docker-related build issues and restoring SFINCS path export.
July 2025 monthly summary for GEB-model/GEB. Focused on delivering feature updates, reliability improvements, and performance optimizations that increase model fidelity and operational stability. Key work included updating GLOPOP grids, integrating OSM building data, expanding the damage scanner, and broad code quality improvements, while fixing docker-related build issues and restoring SFINCS path export.
June 2025 monthly summary for GEB-model/GEB focused on stabilizing the spin-up/adaptation cycle, expanding data integration, and improving model fidelity. Delivered key data integration, targeted bug fixes, and parameter tuning that reduce risk and improve reliability for flood risk assessment and agent deployment.
June 2025 monthly summary for GEB-model/GEB focused on stabilizing the spin-up/adaptation cycle, expanding data integration, and improving model fidelity. Delivered key data integration, targeted bug fixes, and parameter tuning that reduce risk and improve reliability for flood risk assessment and agent deployment.
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