Integrating parametric uncertainty and modeling results into an advisory system for watershed management

摘要

This paper describes an approach to integrate complex modeling experience into a decision support framework for non-point source pollution modeling of a watershed. The approach employs probabilistic reasoning techniques and derives probability distributions from previous model simulations. Thus, the sensitivity of a given model to its inputs is captured in such a way that the system can be used to find solutions to management problems through the application of probabilistic inference. A graphical probability model is a visual formalism encoding random variables and relationships between random variables as nodes and directed links in a graph. In our model, the nodes represent individual parameters for each of the fields in a watershed. Directed links connect correlated nodes, such as nodes representing the management practice in a field to nodes representing soil loss rates. The directed links between the nodes in the probability model follow the drainage network of the watershed. The relationships (or links) between the nodes are quantified via two methods. The first method integrates data (cases) derived from Monte Carlo simulation of a non-point source (NPS) pollution model. In the second method, deterministic functions defined in the NPS model are used to specify the relationships. The Monte Carlo simulations are performed to include the influence of parametric uncertainty on model results. The network for an entire watershed is complex with a large number of nodes, therefore, a spatial analysis/visualization tool was developed for interacting with the large probability model.