Environmental science presents statistical challenges that are particularly well suited to Bayesian methods: complex spatial and temporal dependencies, multiple sources of uncertainty, the need to combine models with observational data, and high-stakes decision-making under uncertainty. The ISBA Section on Environmental Sciences brings together researchers who develop and apply Bayesian methods to address these challenges.
Why Bayesian Methods for the Environment?
Environmental data are often sparse, noisy, and collected over irregular spatial and temporal domains. Bayesian hierarchical models provide a natural framework for combining information across different scales and data sources, propagating uncertainty through complex model chains, and incorporating prior scientific knowledge. These advantages have made Bayesian methods increasingly central to environmental statistics.
Climate science is one of the most prominent application areas for Bayesian environmental statistics. Bayesian methods are used to quantify uncertainty in climate projections, combine outputs from multiple climate models, and attribute observed changes to specific causes. The rigorous treatment of uncertainty provided by the Bayesian framework is essential for informing climate policy decisions.
Application Domains
The section's members work across a broad spectrum of environmental applications. Air and water quality assessment uses Bayesian spatial models to map pollution levels and identify sources. Ecological modeling employs Bayesian methods to estimate species distributions, population dynamics, and biodiversity patterns. Natural hazard risk analysis—including earthquakes, floods, and wildfires—benefits from Bayesian approaches that combine historical data with physical models to assess risk. Remote sensing and environmental monitoring use Bayesian methods to extract information from satellite and sensor data.
Activities and Community
The section organizes workshops, conference sessions, and webinars that showcase the latest developments in Bayesian environmental statistics. Sessions at ISBA World Meetings and related conferences feature research on topics from Bayesian emulation of computer models to spatio-temporal modeling of environmental processes. The section also fosters interdisciplinary collaboration, bringing together statisticians, climate scientists, ecologists, and environmental engineers.
"Environmental problems are fundamentally problems of uncertainty. The Bayesian framework gives us the tools to face that uncertainty honestly and make better decisions."— Noel Cressie
Methodological Contributions
Research associated with the section has advanced key areas of Bayesian methodology, including Gaussian process emulation of complex computer models, Bayesian melding of models and data, spatial and spatio-temporal modeling, and Bayesian methods for extreme value analysis. These methodological contributions have applications well beyond the environmental sciences, illustrating how domain-driven research can advance the broader field of Bayesian statistics.