- Success Stories
- Environmental Resource Assessment & Management
Developing a Database for Ecosystem Service Models
CSS scientists have been major developers and contributors to the online U.S. Environmental Protection Agency’s EcoService Models Library (ESML) database since its inception in 2012. The ESML database contains detailed but concise descriptions of ecosystem service models to facilitate the selection of models by ecosystem scientists for a variety of management and research applications. The database contains over 290 ecosystem service models, with additional entries for specific model runs. CSS employee owners helped design the database and the framework for summarizing models. In addition, CSS has selected models from the database for generating scenarios of remediation options for specific Superfund sites to identify the ecosystem services they could provide. Models that have been applied include pollinator (e.g., bumblebee), carbon sequestration, and bird ecosystem services. The database can be accessed at www.epa.gov/eco-research/ecoservice-models-library/.
See More CSS Insights
Monitoring Algal Blooms for Harmful Toxins
CSS employee owners support NOAA’s National Centers for Coastal Ocean Science (NCCOS) Harmful Algal Blooms (HAB) Sensor Development Team in their efforts to detect and monitor these harmful algal blooms in coastal waters where they are likely to occur. HAB monitoring is critical for detecting harmful toxins produced by some algae. When present in over…
Providing Data Products for More Effective Consumption
The National Oceanic and Atmospheric Administration’s (NOAA) National Centers for Environmental Information (NCEI) is responsible for providing data, products, and services that help businesses and organizations operate more efficiently and effectively. As such, these data and products need to be available in a publicly accessible and digestible format the audience are able to understand. To…
Advancing Severe Weather Predictions with Artificial Intelligence
As artificial intelligence (AI) and machine learning (ML) technologies evolve in Earth sciences, CSS employee owners (formerly Riverside staff) are growing our expertise in this field. CSS employee owners are advancing technologies for our client, NOAA’s Center for Satellite Applications and Research (STAR). Our staff have developed a framework, exploiting modern AI/ML techniques, to rapidly…