Explore the world water map with Python

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Introduction

Water consumption by humans is rising to unsustainable levels, surpassing the capacity of the natural water cycle to replenish rivers and groundwater to replenish. This trend is creating a significant water gap that poses threats to humans, ecosystems, and potentially aggravates wildfires. In this blog post, you'll explore a revolutionary dataset designed to highlight these global water gaps. Additionally, we'll delve into accessing this data using Python, enabling you to create personalized charts and visualizations for your own data journalism.

Click here to access the blog's associated Jupyter Notebook so you can run your own analyses.

What is the World Water Map?

The World Water Map visualizes freshwater trends across past, present, future, offering detailed insights for water research, policy, and storytelling. It's built atop the output of a global hydrology model developed at Utrecht University led by Mark Bierkens and his team in collaboration with National Geographic and ESRI, presents the history and future forecasts of water supply and demand globally. Available through an interactive application, this dataset offers an invaluable resource for researchers, policymakers, and journalists interested in the water gap issue.

By immersing yourself in this app, you'll gain insights into where and why water gaps occur, how they may worsen, and strategies to manage them. The app provides historical and projected data from the 1980s onwards, allowing users to explore how water gaps might evolve under different climate and economic scenarios. Users can even download the data for their own analysis.

Why DIY Analysis Matters

Before we jump in, let's think about why being able to create your own analysis and visualizations matters.

1. Customization: Tailor the analysis to answer specific questions about water availability for regions you care about.

2. Transparency: Promote independent verification of water stress projections.

3. Integration: Combine this data with other datasets for a comprehensive understanding of water-related risks.

Using Python to Visualize the Water Gap

The blog's associated video guides you through accessing and analyzing the water data using Python and open source libraries, leveraging an interactive Google Colab notebook. It walks through the following steps:

1. Download Data: Start by downloading the dataset. After obtaining the zip file, uncompress it to explore the contents, focusing on rasters and vector tables.

2. Create Simple Charts: Using the Pandas library, read CSV files into a table. With Matplotlib, generate a plot to observe trends like total water demand.

3. Create Interactive Charts: For dynamic visualizations, create an interactive time series chart with the Plotly library, which allows hovering to view year-by-year values.

4. Create Interactive Maps: The Leafmap library helps you visualize historical data as heat maps, examining variations between different years. The Whitebox library further assists in showcasing demand changes over decades.

Exploring Insights: A Case Study with Mexico

An example explored in the walkthrough involves examining Mexico's water gap. Users can navigate the insights app to view data by province and download charts. In 2019, Mexico's total water gap amounted to 63 cubic kilometers—roughly equivalent to 25 million Olympic swimming pools!

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Conclusion

This blog and video outlined the resources available for understanding the global water gap through data visualization and analysis. Armed with this knowledge, users can build their narratives, contributing valuable insights into worldwide water challenges. With Python's power and open source libraries, dive into this dataset to bring more clarity to one of our most pressing global problems. I encourage you to explore the dataset and create your own meaningful visuals then share your findings.