This conference has been deferred to 2023 due to the ongoing impact of the COVID-19 pandemic. Please check back soon for the 2023 schedule.
The conference will serve as a venue for field scientists, data analysts, and modelers to envision how to resolve challenges in predicting catchment responses to current and future stress.
Catchment science relies on interdisciplinary approaches to understanding the physical, chemical and biological processes that interact to shape the Earth and its ecosystems. The combination of intensive field measurements with large data sets and mechanistic models has drastically improved our understanding of the hydrological and ecological functioning of catchments. However, catchments are facing increased stressors, including climate change, water abstraction, land cover change, and biodiversity loss, that extend beyond the bounds of past observations. Responses to these combined stressors may depend on processes that are poorly constrained with current models. A probing and thoughtful assessment of the uncertainties associated with the data, analyses, model assumptions, and predictions they produce can lead to productive interactions among conference attendees.
Many catchment stressors highlight the interactions between humans and catchments. This conference will explore how field-based process understanding couples with machine learning and mechanistic models to better illuminate the interactions within catchments and between humans and catchments under emergent stress. The conference will explore the limitations of both data and models, especially in a non-stationary environment, to further our understanding of hydrology, biology, ecology and geochemistry.
Conference sessions will consider how different stressors have affected catchments, how long-term records and short-term experiments have illuminated process understanding of past stress, predictions of future catchment stress responses, and the key knowledge gaps and possible routes forward within catchment science.