Abstract
The complexity of agricultural droughts requires a consistent, reliable, and systematic method for monitoring and reporting. Amongst the various indices used to monitor this phenomenon, the soil moisture anomaly has been proven to be a more reliable predictor. However, the datasets required for computing this index are often large and computationally demanding. To address this challenge, we have developed SMODEX, a Python package that enables scalable, fast, and open-source standard-compliant computation and visualization of soil moisture anomalies.
SMODEX simplifies the computation and visualization of time-series for soil moisture and soil moisture anomalies from high-dimensional climate datasets. It allows for quick and easy parallelization of the computation on a daily, weekly, and monthly timescale. Additionally, SMODEX implements a straightforward workflow for automating the use of FAIR (Findable, Accessible, Interoperable, and Reusable) principles in producing and sharing outputs by leveraging the open source STAC API. The package is extendible and provides information on how to contribute to the project, test suites, test coverage, and a use case for the South Tyrol region, all provided in the package repository. In the future, additional agricultural drought indices and indicators would be included to serve to even larger community of researchers, policy makers, and individual users.