S1: Complexity, Uncertainty and Sensitivity Issues in Integrated Environmental Models

Objective

Scope

• Workshop 2 "Sensitivity and Uncertainty Assessment of Integrated Environmental Models"
  This workshop offers an opportunity for: 1) increasing awareness of the significance of various sensitivity and uncertainty analysis techniques in the development and application of integrated environmental models; and 2) discussing and critically evaluating the contribution of these techniques to improved modeling of environmental systems. Objectives include communicating state-of-the-art information on sensitivity and uncertainty methodologies, and identifying research directions and potential collaborations for improving these methods in the context of integrated environmental modeling.
• Workshop 8 "TIAS Workshop on Complexity and Uncertainty and a New Role for Models"
  If there is one characteristic of today's environmental problems it is complexity. We are now faced with the challenge of solving problems where time delays, feedback loops, non-linearity, and system interconnectedness make prediction particularly difficult. When dealing with these types of problems, computer models play a central role. They constitute general and flexible tools that can help dealing with complex and uncertain phenomena. In this workshop we investigate what complexity and uncertainty mean for models and the way we approach modeling. We aim to revise the assumptions upon which models are being built and used, claiming that the modeling activity needs to shift its goals of predictions to a new role of communication, exploration and understanding. Particularly our focus is on models and methods that allow a pluralistic representation of reality, embracing different types of uncertainties and ambiguities, and that advance the modeling activity by combining with other methods of knowledge integration.
• Workshop 10 "Uncertainty Management, Spatial and Temporal Reasoning, and Validation of Intelligent Environmental Decision Support Systems"
  The workshop will create a discussion platform for Artificial Intelligence (AI) and Environmental researchers involved in the development of applications in the Intelligent Environmental Decision Support Systems (IEDSS) area. Nowadays, AI techniques such as rule-based reasoning, fuzzy models, case-based reasoning, qualitative reasoning, artificial neural networks, genetic algorithms and programming, model-based reasoning, Bayesian networks, multiagent systems, etc., enable the construction of reliable and real applications. IEDSS are present in the environmental management process at different levels such as hazard identification, risk assessment, risk evaluation and intervention decision-making. Workshop participants may come from all environmental science and AI fields. Although there are numerous topics that could be discussed, this workshop will focus on the following challenging and cutting edge issues within the interdisciplinary field of Artificial Intelligence and Environmental Sciences.
• Workshop 15 "ISEM Workshop on Dealing with Uncertainty and Sensitivity Issues in Process-Based Models of Carbon and Nitrogen Cycles in Northern Forest Ecosystems"
  Several process-based models on the carbon and nitrogen cycles have been developed for forest ecosystems. These models have made significant contributions to better understand the processes that govern both cycles. Thus, there is increasing pressure from policy makers to use process-based models to predict the effects of global change, as it is believed that this model type can best deal with the mathematical representation of the dynamics of complex processes in forest ecosystems. However, the modelling efforts have focused on model development. Less attention has been given to uncertainty and sensitivity issues. It is important to address both issues to determine the extent to which the natural variability in forest ecosytems affects the precision of the predictions and the degree to which predictions are affected by the amplitude of change in initial conditions.

Schedule

11:15. Sensitivity Analysis of Historical Aerial Photography: A Case Study of Barrier Island Marshes. Joanne Halls

11:30. Rank-Equivalence method for Sensitivity Analysis of an Integrated Model of a River Catchment. Jakin Ravalico, Graeme Dandy, Holger Maier

11:45. Using Optimization for Environmental Simulation Model Calibration Uncertainty Analysis. Bryan Tolson, Christine Shoemaker

12:00. Avoiding the finite difference sensitivity analysis deathtrap by using the complex-step derivative approximation technique. Dirk J.W. De Pauw, Peter A. Vanrolleghem

12:15. Practical Sensitivity and Uncertainty Analysis Techniques Applied to Agricultural System Models. James C. Ascough II

14:15. Using the Monte Carlo method to quantify uncertainty in predictions of a soil carbon cycle model in balsam fir (Abies balsamea (L.) Mill.) and black spruce (Picea mariana (Mill.) B.S.P.) forest ecosystems in the boreal forest. Guy R. Larocque, David Parè, Robert Boutin, Valèrie Lacerte

14:30. Quantifying uncertainties associated with estimates of greenhouse gas emissions and removals from Canada's managed forests. Thomas White, Werner Kurz, Graham Stinson

14:45. Impacts of Forest Disturbances on the Carbon Cycle in the Laurentian Plains and Hills of the United States. Jinxun Liu, Shuguang Liu, Linda S. Heath, Thomas R. Loveland, Larry L. Tieszen

15:00. Improving the Use of Data for Quantifying Uncertainty in Parameters and Predictions of Forest Dynamic Models by Bayesian Approach. Alexander Komarov, Pavel Grabarnik, Alexey Mikhaylov, Oleg Chertov

15:45. Uncertainty analysis of WETLAND-DNDC: Errors and critical parameters for predicting carbon dynamics in wetland ecosystems. Harbin Li, Changsheng Li, Carl Trettin

16:00. Application of an Artificial Neural Network and Stochastic Conditional Simulation at the Schuyler Falls Landfill, NY. Lance Besaw

16:15. A Retrospective Analysis of Model Uncertainty for Forecasting Hydrologic Change. Darius Semmens, William Kepner, David Goodrich, Mariano Hernandez

16:30. Parameter, data input and structural uncertainty propagating through coupled models in a river water quality modelling system. Karl-Erich Lindenschmidt

16:45. Forecasting and managing sustainable systems: A framework based on optimal control and options theory. Urmila Diwekar, Yogendra Shastri