Reproducibly Analyzing Wildfire, Drought, and Flood Risk with NASA Earthdata Cloud

This document is a proposal from 2i2c and MetaDocencia (both are fiscally sponsored projects of Code for Science & Society) to the NASA SMD call for proposals F.14 Transform to Open Science Training (NNH22ZDA001N-TOPST). This was one of 16 successfully funded proposals under the program.

In this proposed TOPS ScienceCore module, learners will learn to identify, extract, analyze, visualize, and report on data available through NASA Earthdata Cloud for three different scenarios: wildfire, drought, and flood risk. The module will build upon TOPS OpenScience 101 and reinforce principles of reproducibility and open science-based workflows. Computationally, the scenarios will estimate changes in the hydrological water mass balance for defined regions primarily using remote sensing data. We will demonstrate best practices in “data-proximate computing” by considering examples that involve computing climatologies and other statistics from long-time series using numerical methods that scale well with the data being available on the cloud. This module will leverage scientific Python libraries such as Xarray and Dask to perform the computations. The focus of this module will be on data processing and visualization and doing so in a reproducible and transparent way.