Week 5 Update: In-Depth Data Visualization for Wildfire and Snow Analysis | Shashwath Senthil

This week, I focused on refining my data analysis and visualization techniques to better understand the interplay between wildfire burn severity, elevation, and snow accumulation. I started by loading the snow depth data from a CSV file using pandas and created time series plots for each camera site. Each camera, identified by its unique ID, is linked to specific elevation and burn severity values. By plotting the snow depth for each site over time, I was able to observe seasonal patterns and identify any anomalies that might suggest localized effects.

Next, I divided the camera sites into two groups based on their elevation—one representing higher elevations and the other lower. This separation allowed me to compare how different elevations affect snow depth trends over the same time period. The side-by-side comparison helped to highlight the differences in weather patterns and accumulation processes that occur at varying altitudes.

I also calculated the peak snow depth for each camera and plotted these peaks against the corresponding burn severity values. This scatter plot provided a clear visual representation of the potential correlation between burn severity and the maximum amount of snow accumulated, making it easier to spot any trends or outliers that could inform further analysis. Additionally, by comparing cameras that are close in elevation, I aimed to isolate the influence of other factors, such as local environmental conditions or varying burn intensities, beyond just elevation.

Overall, these visualizations are an important step in building a comprehensive framework to analyze the complex relationship between wildfires and snowpack behavior. The insights gained from these plots will be instrumental in refining the predictive models that will eventually

 help improve water management strategies and disaster preparedness. I look forward to integrating more data and continuing to explore these relationships in the upcoming weeks.