Barnali Dixon

Coastal, low‐lying Onsite Sewage Treatment and Disposal Systems (OSTDS) are increasingly vulnerable to sea‐level rise (SLR) and groundwater inundation. This study developed three spatially explicit GIS‐based multi‐criteria evaluation frameworks: Bathtub‐Current, Bathtub‐Future under 2 ft (0.6 m) of SLR and MODFLOW‐Future under 2 ft (0.6 m) of SLR, which assessed OSTDS vulnerability for current and future SLR and how alternative representations of SLR‐modeled groundwater response affect vulnerability classification and prioritization by integrating SLR responses, depth to water table, saltwater intrusion, recurrent flooding, storm surge and OSTDS density using Delphi elicitation, pairwise comparison weighting and Weighted Linear Combination. At 2 ft of SLR, the bathtub approach reproduced 86% of the MODFLOW inundation extent. The three vulnerability surfaces showed strong agreement, with Pearson correlations of 0.966–0.994 and RMSE values of 0.036–0.077, although individual OSTDS classes differed. At least one framework classified 1961 OSTDS as highly vulnerable, while all three frameworks classified 577 OSTDS as highly vulnerable, among the 56,080 OSTDS evaluated. Results show hydrostatic modeling is an acceptable screening tool for regional vulnerability assessments; however, it is not interchangeable with groundwater modeling or field verification. The framework provides a screening tool with a tiered approach to prioritize monitoring and resource allocation for at‐risk OSTDS.

Hydrodynamic models are essential for understanding flood processes, but their computational demands make them unsuitable for updating flood risk in dynamic storm conditions. This study presents an alternative approach that combines crowdsourced information with a Bayesian Neural Network (BNN) to generate full-field, near–real-time flood risk maps with modest computational requirements. This framework, applied to Pinellas County, Florida, and tested across two storms, leverages community-sourced information to update model predictions. Another key contribution is the use of full predictive distributions rather than single deterministic estimates. While crowdsourced information provided voluntarily can validate strong model performance where observations are available, such information may be inherently incomplete and spatially biased. The predictive distributions reveal a broad spectrum of flooding outcomes and highlight areas where averaged estimates, although operationally useful for decision-making, may mask high-flood-risk scenarios. This range of uncertainty provides transparent guidance for targeted verification thereby mitigating the inherent bias of voluntary reporting.

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Gender is one aspect of faculty diversity that benefits recruitment and retention of faculty and supports the engagement and success of students (Drury et al. 2011; Fox Tree and Vaid 2021). Diverse...