Down2Earth CUWALID Project

Project Overview

The EU-funded Down2Earth project has created CUWALID, a modelling system to support a range of decision makers to make informed decisions on evaluating and managing climate in the Horn of Africa drylands (HAD) where seasonal climatic hazards have a major impact on people's lives especially in rural communities.

My Role in the Project

• Contributed to the development of a PyPi package for water hazard forecasting using large datasets.
• Wrote Python modules for data processing and handling.
• Planned and executed the code structure for package development.
• Developed forecasting algorithms for predicting water hazards.
• Informed on key decisions during the development.
• Ensured the package was efficient, scalable, and user-friendly.
• Managed the project's documentation for easy usability

I collaborated with researchers to refine the application’s performance, optimized data pipelines, fixed bugs, and efficiently structured code.

Technologies Used

• Python (PyPi Package Development)
• Data Processing with Pandas and NumPy
• Geospatial Data Libraries (e.g., GeoPandas)
• Sphinx (Python documentation generator)

Challenges and Solutions

One of the primary challenges I faced was handling testing for the packages across all operating systems, for example I fixed a bug relating to compiled Fortran not correctly working on Windows systems.

To overcome this, I built testing functions so I could easily check all systems were correctly functioning after each time I updated the code. This testing was also crucial for intergrating different parts of the model together efficiently.

Impact and Future Goals

The Down2Earth CUWALID project is being piloted around the HAD region, providing information to help build early warning systems for climate events like flooding. Moving forward, the project plans to integrate more functions into the model to accuratly provide information, along with streamlining data presentation.

My contributions to this project have given me valuable experience in data science, collaboration, structuring projects and critical decision making.

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