Temporally resolved coastal hypoxia forecasting and uncertainty assessment via Bayesian mechanistic modeling

Alexey Katin,Dario Del Giudice,Daniel R Obenour

doi:10.5194/hess-26-1131-2022

Alexey Katin, Dario Del Giudice + Show 1 more

Open Access

https://doi.org/10.5194/hess-26-1131-2022

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Abstract

Abstract. Low bottom water dissolved oxygen conditions (hypoxia) occur almost every summer in the northern Gulf of Mexico due to a combination of nutrient loadings and water column stratification. Several statistical and mechanistic models have been used to forecast the midsummer hypoxic area, based on spring nitrogen loading from major rivers. However, sub-seasonal forecasts are needed to fully characterize the dynamics of hypoxia over the summer season, which is important for informing fisheries and ecosystem management. Here, we present an approach to forecasting hypoxic conditions at a daily resolution through Bayesian mechanistic modeling that allows for rigorous uncertainty quantification. Within this framework, we develop and test different representations and projections of hydrometeorological model inputs. We find that May precipitation over the Mississippi River basin is a key predictor of summer discharge and loading that substantially improves forecast performance. Accounting for spring wind conditions also improves forecast performance, though to a lesser extent. The proposed approach generates forecasts for two different sections of the Louisiana–Texas shelf (east and west), and it explains about 50 % of the variability in the total hypoxic area when tested against historical observations (1985–2016). Results also show how forecast uncertainties build over the summer season, with longer lead times from the nominal forecast release date of 1 June, due to increasing stochasticity in riverine and meteorological inputs. Consequently, the portion of overall forecast variance associated with uncertainties in data inputs increases from 26 % to 41 % from June–July to August–September, respectively. Overall, the study demonstrates a unique approach to assessing and reducing uncertainties in temporally resolved hypoxia forecasting.

Highlights

The northern Gulf of Mexico has one of the largest hypoxic zones in the world, forming virtually every summer over the last 3 decades (Rabalais and Turner, 2019)
We describe the approach to evaluating the bottom water dissolved oxygen (BWDO) and hypoxic area (HA) forecast performance and analyze how the forecast varies in relation to alternative combinations of data inputs
We demonstrate a novel approach for forecasting intraseasonal variability in BWDO and HA in the northern Gulf of Mexico by leveraging a Bayesian mechanistic model

Summary

Introduction

The northern Gulf of Mexico has one of the largest hypoxic zones in the world, forming virtually every summer over the last 3 decades (Rabalais and Turner, 2019). The approaches developed to predict hypoxia in the Gulf of Mexico included statistical regressions (Forrest et al, 2011; Greene et al, 2009; Turner et al, 2012) and both parsimonious (Obenour et al, 2015; Scavia et al, 2013) and complex (Justicand Wang, 2014; Yu et al, 2015) mechanistic models. Among these alternatives, parsimonious process-based models attempt to balance biophysical detail with computational ef-

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