Preprints
https://doi.org/10.5194/hess-2021-207
https://doi.org/10.5194/hess-2021-207

  29 Apr 2021

29 Apr 2021

Review status: this preprint is currently under review for the journal HESS.

Daily hypoxia forecasting and uncertainty assessment via Bayesian mechanistic model for the Northern Gulf of Mexico

Alexey Katin, Dario Del Giudice, and Daniel R. Obenour Alexey Katin et al.
  • Department of Civil, Construction, & Environmental Engineering, North Carolina State University, Raleigh, 27606, USA

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 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 forecast hypoxic conditions at daily resolution through Bayesian mechanistic modeling that allows for rigorous uncertainty quantification. Within this framework, we develop and test different representations and projections of hydro-meteorological 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 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 31 May, 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 dynamic hypoxia forecasting.

Alexey Katin et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'review of', Anonymous Referee #1, 25 Jun 2021
  • RC2: 'Comment on hess-2021-207', Anonymous Referee #2, 06 Aug 2021
  • RC3: 'Comment on hess-2021-207', Anonymous Referee #3, 07 Aug 2021

Alexey Katin et al.

Alexey Katin et al.

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Short summary
Low oxygen conditions (hypoxia) occur almost every summer in the Northern Gulf of Mexico. Here, we present a new approach to forecasting hypoxia from June through September, leveraging a process-based model and an advanced statistical framework. We also show how using spring hydro-meteorological information can improve forecast accuracy while reducing uncertainties. The proposed forecasting system shows potential to support management of threatened coastal ecosystems and fisheries.