Fusion-Based Hypoxia Estimates: Combining Geostatistical and Mechanistic Models of Dissolved Oxygen Variability

Abstract

The need to characterize and track coastal hypoxia has led to the development of geostatistical models based on in situ observations of dissolved oxygen (DO) and mechanistic models based on a representation of biophysical processes. To integrate the benefits of these two distinct modeling approaches, we develop a space−time geostatistical framework for synthesizing DO observations with hydrodynamic−biogeochemical model simulations and meteorological time series (as covariates). This fusion-based approach is used to estimate hypoxia in the northern Gulf of Mexico across summers from 1985 to 2017. Deterministic trends with dynamic covariates explain over 35% of the variability in DO. Moreover, cross-validation results indicate that 58% of DO variability is explained when combining these trends with spatiotemporal interpolation, which is substantially better than mechanistic or conventional geostatistical hypoxia modeling alone. The fusion-based approach also reduces hypoxic area uncertainties by 11% on average and up to 40% in months with sparse sampling. Moreover, our new estimates of mean summer hypoxic area changed by >10% in a majority of years, relative to previous geostatistical estimates. These fusion-based estimates can be a valuable resource when assessing the influence of hypoxia on the coastal ecosystem.

Advancing estuarine ecological forecasts: seasonal hypoxia in Chesapeake Bay

“…While these HV disruptions are often relatively short-lived, they increase variability at monthly scales and may lead to substantial overprediction on short time scales (Testa et al 2017a). Similar disruptions of seasonal hypoxia occur in other systems (Turner et al 2012;Bocaniov and Scavia 2016), leading to either incorporate weather-related drivers or to shift to hypoxia metrics that better integrate conditions throughout the year (Bever et al 2013(Bever et al , 2018Feng et al 2012;Obenour et al 2015;Matli et al 2018Matli et al , 2020.…”

“…Matli et al. ( 2020 ) combined these two approaches by using output from a three‐dimensional ecological model as covariates in their space–time geostatistical analysis for the Gulf of Mexico, reducing prediction uncertainty by 11–40% compared to using measurement alone. As these modeling and geostatistical approaches improve, together with the ever‐increasing availability of high‐frequency sensors and remote sensing products, the ability to expand beyond the limitations of traditional monitoring will allow for more integrative and accurate ecosystem metrics used in forecast and scenario development.…”

“…Geostatistical techniques are also being used to augment discrete monitoring data and generate enhanced estimates of algal blooms and hypoxia dynamics integrated over space and time with quantified uncertainty (Murphy et al 2011;Obenour et al 2013;Zhou et al 2013Zhou et al , 2014Matli et al 2018;Fang et al 2019). Matli et al (2020) combined these two approaches by using output from a three-dimensional ecological model as covariates in their space-time geostatistical analysis for the Gulf of Mexico, reducing prediction uncertainty by 11-40% compared to using measurement alone.…”

“…Geostatistical techniques are also being used to augment discrete monitoring data and generate enhanced estimates of algal blooms and hypoxia dynamics integrated over space and time with quantified uncertainty (Murphy et al 2011, Zhou et al 2013, 2014, Matli et al 2018, Fang et al 2019. Matli et al (2020) combined these two approaches by using output from a three-dimensional ecological model as covariates in their space-time geostatistical analysis for the Gulf of Mexico, reducing prediction uncertainty by 11-40% compared to using measurement alone. As these modeling and geostatistical approaches improve, together with the ever-increasing availability of high-frequency sensors and remote sensing products, the ability to expand beyond the limitations of traditional monitoring will allow for more integrative and accurate ecosystem metrics used in forecast and scenario development.…”

Trends and drivers of hypoxic thickness and volume in the Northern Gulf of Mexico: 1985-2018

“…Fennel et al, (2016) developed a hydrodynamic-biogeochemical model capable of predicting hypoxic volume across the summer, but results have only been systematically compared to midsummer estimates (Obenour et al, 2013). Thus, there remains the need for data-driven estimates of hypoxic volume across the summer season, as intra-seasonal variability is important for assessing fisheries and ecosystem health (Scavia et al, 2019; Matli et al, 2020).…”