Python Essentials for Water Resources Engineers
Before Getting Started:
Many of the notebooks below have been developed using Google Colab, an incredible platform that has streamlined my coding process and enhanced the efficiency of my projects. For those who are curious about Google Colab and eager to explore its capabilities, there's a valuable resource that can serve as a great starting point. I highly recommend watching these videos:
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ChatGPT and Coding
ChatGPT can be incredibly useful for learning coding, especially for beginners and those looking to deepen their understanding of programming concepts. Here's how ChatGPT can aid in programming and learning coding through an example that demonstrates how you can ask ChatGPT to visualize a hydrograph for you:
Meteostat opens the door to a diverse array of data sourced from various reputable public interfaces, including national weather services like NOAA (National Oceanic and Atmospheric Administration) and DWD (Germany's national meteorological service). Through practical examples and step-by-step guidance, this guide aims to equip you with the knowledge to access to a wealth of open weather and climate data directly through Pandas.
This tutorial serves as a gateway for enthusiasts and professionals to explore streamflow data using Python and the Hydrofunctions library. A step-by-step journey unfolds, showcasing how to query, analyze, and visualize water data from the USGS's National Water Information System. With an emphasis on simplicity and practical application, learners are equipped with the skills to transform raw data into insightful hydrologic analyses.
In this tutorial, we'll use the py3dep library in a Google Colab environment to fetch high-resolution digital elevation model (DEM) data and export it as a GeoTIFF file. We'll also visualize the selected area on an interactive map.
In this tutorial, we'll explore land cover and surface roughness analysis using pygeohydro, a powerful Python package for accessing hydrological spatial data. We'll learn how to download land cover data, visualize it alongside surface roughness information, and export our findings as GeoTIFF files for further analysis.
In this tutorial, we will explore how to create dynamic visualizations depicting the distribution of dams in the contiguous USA over time using Python. Through step-by-step instructions, we will cover data processing, spatial analysis, and visualization techniques, culminating in the creation of a GIF animation that illustrates the evolving landscape of dam construction and distribution.
AI Fills the Gaps: Transforming Hydrological Data Analysis (Coming Soon!)
This video explores cutting-edge AI techniques that are transforming the field, from predictive modeling to anomaly detection. Learn how these advancements provide more accurate forecasts, improve decision-making, and offer solutions to some of the most pressing environmental challenges today.
AI for Advanced Land Cover Classification from Satellite Data (Coming Soon!)
This tutorial will guide you through the entire process, from obtaining satellite imagery to using AI algorithms for accurate land cover classification. Tailored for beginners and intermediate learners, this tutorial includes step-by-step instructions, practical examples, and tips to enhance your understanding of geographical data analysis.