Uncertainty analysis of the performance of a management system for achieving phosphorus load reduction to surface waters

Abstract

The recent re-eutrophication of Lake Erie suggests an inadequate phosphorus management system that results in excessive loads to the lake. In response, governments in Canada and the U.S. have issued a new policy objective: 40% reductions in total phosphorus (TP) and dissolved reactive phosphorus (DRP) loads relative to 2008. The International Organization for Standardization (ISO) 31000 is a risk management standard. One of its analytical tools is the ISO 31010:2009 Bowtie Risk Analysis Tool, a tool that structures the cause-effect-impact pathway of risk but lacks the ability to capture the probability of reducing risk associated with different management systems. Here, we combined the Bowtie Risk Analysis Tool with a Bayesian belief network model to analyze the probability of different agricultural management systems of best management practices (BMPs) to achieve the 40% reductions in TP and DRP loads using different adoption rates. The commonly used soil conservation BMPs (e.g., reduced tillage) have a low probability of reducing TP and DRP to achieve the policy objective; while it can achieve the TP load reduction objective at increased adoptions rates >40%, it does not achieve the DRP load reduction objective, and in fact has the unintended consequence of increasing DRP loads. If decision makers continue to rely on soil conservation BMPs, the trade-offs between meeting objectives of different forms of phosphorus will require deciding whether the management priority is to achieve 40% load reduction objectives or to prevent further increases in DRP loads, the identified culprit causing the repeated algal blooms. In contrast, TP- and DRP-effective BMPS had higher probabilities of achieving the policy objective, especially at increased adoption rates >20%. The integration of Bayesian belief networks with the ISO risk management standard allows decision makers to determine the most probable outcomes of their management decisions, and to track and prepare for less probable outcomes, thereby decreasing the risk of failing to achieve policy objectives.