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Understanding the relative contributions of aboveground and belowground processes to soil accretion and carbon density may explain carbon sequestration rates in mangroves across different coastal environmental settings. We reformulated the nutrient mangrove model (NUMAN) by improving algorithms and uncertainty analysis using literature values and site-specific observations to evaluate the relative contributions of organic and inorganic sedimentation for three mangrove sites with marked soil fertility gradients reflected by nitrogen-to-phosphorus (N:P) ratios including Shark River (N:P = 28), Rookery Bay (N:P = 54–78), and Taylor Slough (N:P = 102) in south Florida. NUMAN 2.0 considers cellulose as a refractory organic-matter sub-pool and simultaneously incorporates coarse-root inputs to soil formation. The model simulation also captures root necromass accumulation. Monte Carlo (MC) simulations (N = 1000 per site) were conducted to capture uncertainty by treating five key parameters as random variables: lignin content in fine, coarse, and large roots; inorganic sediment loading; and root biomass at the surface. With robust mass balancing of organic matter, NUMAN 2.0 generates precise predictions of surface accretion and carbon density. NUMAN 2.0 simulations estimated mean (standard deviation) soil carbon sequestration rates at 130.1 (55.4) for Shark River, 72.5 (3.7) for Rookery Bay, and 130.0 (83.9) g m-2 yr-1 for Taylor Slough, compared to field values of 123.0, 86.0, and 108.8 (8.7) g m-2 yr-1 , respectively. Simulation experiments with NUMAN 2.0 suggest that belowground organic matter dominates soil formation and carbon sequestration generally in coastal environmental settings with little allochthonous input such as carbonate settings, while wood litterfall should dominate soil organic matter in top 10 cm in estuaries, and bays. 

This study describes a hybrid framework for post-hazard building performance assessments. The framework relies upon rapid imaging data collected by regional scout teams being integrated into broader data platforms that are parsed by virtual teams of hazards engineers to efficiently create robust performance assessment datasets. The study also pilots a machine-in-the-loop approach whereby deep learning and computer vision-based models are used to automatically define common building attributes, enabling hazard engineers to focus more of their efforts on precise damage quantification and other more nuanced elements of performance assessments. The framework shows promise, but to achieve optimal accuracy of the automated methods requires regional tuning.