Fusion-Based Hypoxia Estimates: Combining Geostatistical and Mechanistic Models of Dissolved Oxygen Variability
Venkata Rohith Reddy Matli, Arnaud Laurent, Katja Fennel, Kevin J. Craig, Jacob R. Krause, Daniel R. Obenour
Abstract
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.