Optimal Nonpoint Source Monitoring: An Application to Redwood Creek

The research summarized in this report involves three interrelated analyses. First, we construct a theoretical nonpoint source (NPS) pollution control model and derive the optimal budget tradeoff between direct treatment of polluting sources versus data collection, which facilitates information acquisition and learning about the relative pollution loading among the sources. Second, we develop a sequential entropy filter to statistically update estimated NPS pollution loading parameters, as new data becomes available. We apply the entropy method to stream flow and ambient sediment loading data for Redwood Creek, which flows into and through Redwood National Park. Third, the theoretical and methodological results are incorporated into a sediment control model for Redwood Creek. We simulate the sediment control management program to provide policy analysis by comparing a uniform treatment policy where no data is collected against high and low-intensity data collection policies, The simulation results indicate that the high and low-intensity data collection policies can reduces uncertainty, increasing treatment effectiveness, so that sediment related damages are lower than damages under the uniform treatment policy