iEMSs 2022 Conference - Brussels, Belgium

iEMSs 2022 - Session D.2

Complexity, Sensitivity, and Uncertainty Issues in Environmental Models

Stream    : D - System Identification and Uncertainty in Environmental Computing

Session Leader: Giorgio Mannina, Francesca Pianosi, Timothy Green, Thorsten Wagener and Olaf David

Session Organizers: Giorgio Mannina1, Francesca Pianosi2, Timothy Green3, Thorsten Wagener4 and Olaf David5

1 Engineering Department, Palermo University, Viale delle Scienze ed 8, 90128, Palermo Italy
2 Department of Civil Engineering, Queen's School of Engineering, University of Bristol 1.51 Queen's Building, University Walk, Bristol BS8 1TR, UK
3 USDA-ARS, Center for Agricultural Resources Research, Water Management & Systems Research Unit Fort Collins, CO 80526
4 Institute of Environmental Science and Geography University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam-Golm, Germany
5 Department of Civil and Environmental Engineering, Colorado State University, 1372 Campus Delivery, Fort Collins, CO 80523-1372

The purpose of the following session is to provide a forum for a group of presentations focusing on complexity, sensitivity, and uncertainty issues in environmental models. The session offers an opportunity for the creation of a discussion platform for researchers involved in the development and application of modelling for the environmental models. More specifically, the session would present the last trends in system-wide modelling (mechanistic, data-driven, etc) and the techniques used to calibrate and validate these models (Bayesian, multiobjective optimization, etc) including uncertainty analysis. Further related topics regard: scale effects in uncertainty analysis (UA) of environmental models; uncertainty propagation in complex, environmental models with large parameter sets or high computational requirements. development and evaluation of UA methods that appropriately consider subjective and qualitative factors; evaluation of uncertainty in model outputs with respect to decision making or risk management objectives; assessing and quantifying information requirements (e.g., theories, data, models) to reduce predictive uncertainty in environmental models; methods for identifying and managing structural uncertainty and bias in environmental models.