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.