Using fuzzy logic-based hybrid modeling to guide riparian best management practices selection in tributaries of the Minnesota River Basin

Abstract

Riparian corridors serve as buffer zones between land and water, thereby supporting essential ecosystem services. Though riparian zones occupy around 1% of land area in midwestern USA watersheds, their management has significant societal consequences on downstream ecosystem services, such as water quantity and quality. The management of riparian zones in the Minnesota River Basin (MRB) needs a comprehensive decision support framework (DSF) to guide conservationists to invest resources efficiently. This study attempts to classify riparian zones based on three classes: stream features, riparian characteristics, and riparian functions to suggest suitable Best Management Practices (BMPs). We developed a comprehensive hybrid decision support framework (DSF) by integrating fuzzy analytical hierarchy process (AHP) and fuzzy inference system (FIS) to propose riparian BMPs based on the physical characteristics of stream, riparian zone, and desired riparian functions. The DSF accommodates the uncertainty associated with the stakeholder's perceptions towards the riparian zone classes while also accounting for continuous variation in riparian dynamics. The advantage of a hybrid model is its ability to allow the stakeholders to prioritize critical criteria belonging to the classes to ensure that they have maximum impact on the riparian BMPs proposed by the model. We have demonstrated the model using three Minnesota River tributaries at the Hydrologic Unit Code (HUC)-8, HUC-10, HUC-12 scales. Results show that BMPs such as riparian revegetation, buffer strips, wetland management, the addition of woody debris, and an increase in tree cover obtained the highest scores and occupied 35%, 25%, 15%, 10%, and 10% of the observed sites, respectively.