Sheila M. Saia
I work alongside a team of scientists in Tetra Tech's Center for Ecological Analytics and Modeling (CEAM). My current research uses data analytics (i.e., data wrangling, data modeling and analyzing, and data visualizing) to predict and minimize human impacts on our climate and water resources at regional and national scales. I’m passionate about open science and interested in research questions exploring the relationships between humans, climate, and water resources.
Session
Addressing complex water quantity and water quality challenges in agricultural landscapes across the United States requires creative conservation approaches. To rise to this challenge, the Ecosystem Services Market Consortium (ESMC) recognizes and rewards agricultural producers who implement sustainable water management practices on their fields. However, it is difficult to quantity the impact of spatially-explicit water management practices for thousands of individual agricultural fields across the United States. This is where Python comes in. This presentation will provide an overview of the collaborative effort to develop a Python-based tool capable of quantifying the impacts of water quantity and water quality management practices on agricultural fields. Specifically, we will discuss ESMC’s needs, the general structure and function of the tool, and how we leveraged open-source Python libraries, publicly available geospatial datasets, and scientifically-based hydrological approaches to meet project goals. We will end with lessons learned and tips for PyLadies looking to venture into Python tool development for scientific applications.