Hydrodynamic and biochemical impacts on the development of hypoxia in the Louisiana–Texas shelf – Part 2: statistical modeling and hypoxia prediction

Abstract

Abstract. This study presents a novel ensemble regression model for forecasts of the hypoxic area (HA) in the Louisiana–Texas (LaTex) shelf. The ensemble model combines a zero-inflated Poisson generalized linear model (GLM) and a quasi-Poisson generalized additive model (GAM) and considers predictors with hydrodynamic and biochemical features. Both models were trained and calibrated using the daily hindcast (2007–2020) by a three-dimensional coupled hydrodynamic–biogeochemical model embedded in the Regional Ocean Modeling System (ROMS). Compared to the ROMS hindcasts, the ensemble model yields a low root-mean-square error (RMSE) (3256 km2), a high R2 (0.7721), and low mean absolute percentage biases for overall (29 %) and peak HA prediction (25 %). When compared to the shelf-wide cruise observations from 2012 to 2020, our ensemble model provides a more accurate summer HA forecast than any existing forecast models with a high R2 (0.9200); a low RMSE (2005 km2); a low scatter index (15 %); and low mean absolute percentage biases for overall (18 %), fair-weather summer (15 %), and windy-summer (18 %) predictions. To test its robustness, the model is further applied to a global forecast model and produces HA prediction from 2012–2020 with the adjusted predictors from the HYbrid Coordinate Ocean Model (HYCOM). In addition, model sensitivity tests suggest an aggressive riverine nutrient reduction strategy (92 %) is needed to achieve the HA reduction goal of 5000 km2.

The second-generation real-time ecological environment prediction system for the Guangdong–Hong Kong–Marco Greater Bay Area: Model setup, validation, improvements, and online visualization

“…The prediction tool that we have constructed is based on realistic representations of ocean circulation coupled with biogeochemical and ecological models, which can forecast short-term (days to weeks) to seasonal (months) time intervals. In addition to this type of model, statistics based artificial intelligence models have emerged and have been applied to coastal ocean biogeochemistry studies in recent years (Chen et al, 2019;Li X et al, 2020;Ou et al, 2022;Yu et al, 2022). Compared with the coupled hydrodynamicbiogeochemistry model, artificial intelligent models are more efficient and require less computational resources.…”

Using dissolved oxygen variance to investigate the influence of nonextreme wind events on hypoxia in Mobile Bay, a shallow stratified estuary

“…Statistical analysis typically identifies potential driving variables by estimating the correlation between bottom DO concentration, nutrient loading, stratification/mixing metrics, and wind (Hagy et al, 2004;Kemp et al, 2009;Bianchi et al, 2010;Scully, 2010a;Feng et al, 2012). The stratification and mixing metrics that are usually linked to hypoxia are water column stability and vertical mixing as quantified by buoyancy frequency (Bianchi et al, 2010;Coogan et al, 2021), Richardson number (Park et al, 2007), potential energy anomaly (Ou et al, 2022) and vertical exchange time (Du et al, 2018b). However, the statistical approach tends not to separate the relative contributions of advection and vertical diffusion to DO dynamics during different wind conditions.…”