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Numerical modeling efforts in support of restoration and protection activities in coastal Louisiana have traditionally been conducted externally to any stakeholder engagement processes. This separation has resulted in planning-and project-level models built solely on technical observation and analysis of natural processes. Despite its scientific rigor, this process often fails to account for the knowledge, values, and experiences of local stakeholders that often contextualizes a modeled system. To bridge this gap, a team of natural and social scientists worked directly with local residents and resource users to develop a participatory modeling approach to collect and utilize local knowledge about the Breton Sound Estuary in southeast Louisiana, USA. Knowledge capture was facilitated through application of a local knowledge mapping methodology designed to catalog local understanding of current and historical conditions within the estuary and identify desired ecological and hydrologic end states. The results of the mapping endeavor informed modeling activities designed to assess the applicability of the identified restoration solutions. This effort was aimed at increasing stakeholder buy-in surrounding the utility of numerical models for planning and designing coastal protection and restoration projects and included an ancillary outcome aimed at elevating stakeholder empowerment regarding the design of nature-based restoration solutions and modeling scenarios. This intersection of traditional science and modeling activities with the collection and analysis of traditional ecological knowledge proved useful in elevating the confidence that community members had in modeled restoration outcomes.

[1] A variable residence time (VART) model is developed for longitudinal dispersion and transport of solutes in natural streams. The VART model is based on a ''double-layer'' conceptual model of transient storage in streams. The upper layer is an advectiondominated transient storage zone which includes instream and shallow hyporheic storage. The lower layer is an effective diffusion-dominated storage zone that is deeper in the streambed and farther beneath the banks. The VART model is characterized by the following features. First, a varying residence time is used to simulate the transient storage process. Second, no user-specified residence time distribution (RTD) functions are required. Third, there are only four parameters to estimate. Fourth, the VART model is also able to accommodate stream water/solute gains or losses in the reach caused by groundwater exchanges. Fifth, the simple VART model is able to produce various compound solute concentration breakthrough curves (BTCs) commonly observed in streams. The early portion of BTCs follows exponential RTDs. The late portion of BTCs can essentially be any distribution. Sixth, performance of the VART model in reproducing various RTDs is comparable to other widely used 1-D solute transport models that commonly utilize five or more fitting parameters and are limited to the simulation of user-specified RTDs or underlying RTDs within them. Applications in hydrologically and geomorphically varied streams have indicated that the VART model is a powerful and flexible numerical tool for simulating solute transport in natural streams.

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