Bayesian mechanistic modeling characterizes Gulf of Mexico hypoxia: 1968–2016 and future scenarios

Giudice, Dario Del; Matli, Venkata Rohith Reddy; Obenour, Daniel R.

doi:10.1002/eap.2032

Cited by 16 publications

(37 citation statements)

References 63 publications

(335 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Predictability of different HV metrics -Hypoxic extent metrics used for forecasts, scenarios, and reporting across several systems have often been estimates of summer maximum volume or area (e.g., Liu et al 2011;Scavia et al 2003Scavia et al , 2006Scavia et al , 2016Scavia et al , 2017Testa et al 2017a;Obenour et al 2012Obenour et al , 2015Rucinski et al 2016;Bocaniov and Scavia 2016;Zhang et al 2016; but see Katin et al 2019; Accepted Article Giudice et al 2020;Ross et al 2020). However, these maxima are not necessarily representative of year-long conditions.…”

Section: Discussionmentioning

confidence: 99%

“…A rigorous and transparent characterization of forecast uncertainty enables stakeholders and policy makers to a) get a realistic picture of the current state of scientific knowledge of the process being predicted, b) quantitatively evaluate the risk associated with a range of possible future outcomes and make decisions accordingly, and c) prioritize future investments to fill knowledge gaps that are responsible for the largest sources of uncertainty (Pappenberger and Beven 2006;Dietze et al 2018). The relative magnitude of different error sources provides useful insights on where to focus future research efforts to reduce forecast error (Obenour et al 2014;Bertani et al 2016;Del Giudice et al 2020). The hierarchical approach demonstrated here provides a means to quantify multiple sources of uncertainty, including parameter uncertainty, model prediction error, and HV measurement error.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Advancing estuarine ecological forecasts: seasonal hypoxia in Chesapeake Bay

Scavia

Bertani

Testa

et al. 2021

Ecological Applications

View full text Add to dashboard Cite

Ecological forecasts are quantitative tools that can guide ecosystem management. The co-emergence of extensive environmental monitoring and quantitative frameworks allows for widespread development and continued improvement of ecological forecasting systems. We use a relatively simple estuarine hypoxia model to demonstrate advances in addressing some of the most critical challenges and opportunities of contemporary ecological forecasting, including predictive accuracy, uncertainty characterization, and management relevance. We explore the impacts of different combinations of forecast metrics, drivers, and driver time windows on predictive performance. We also incorporate multiple sets of state-variable observations from different sources and separately quantify model prediction error and measurement uncertainty through a flexible Bayesian hierarchical framework. Results illustrate the benefits of 1) adopting forecast metrics and drivers that strike an optimal balance between predictability and relevance to management, 2) incorporating multiple data sources in the calibration dataset to separate and propagate different sources of uncertainty, and 3) using the model in scenario mode to probabilistically evaluate the effects of alternative management decisions on future ecosystem state. In the Chesapeake Bay, the subject of this case study, we find that average summer or total annual hypoxia metrics are more predictable than monthly metrics and that measurement error represents an important source of uncertainty. Application of the model in scenario mode suggests that absent watershed management actions over the past decades, long-term average hypoxia would have increased by 7% compared to 1985. Conversely, the model projects that if management goals currently in place to restore the Bay are met, long-term average hypoxia would eventually decrease by 32% with respect to the mid-1980s.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Advancing estuarine ecological forecasts: seasonal hypoxia in Chesapeake Bay

Scavia

Bertani

Testa

et al. 2021

Ecological Applications

View full text Add to dashboard Cite

show abstract

“…In addition to interannual variation, the hypoxic zone within the GOM varies spatially during the summer depending on the interaction of various physical and biological factors, local bathymetry, wind forcing, hydrodynamics, solar radiation, river freshwater and nutrient inputs, phytoplankton productivity, and zooplankton grazing (Bianchi et al, 2010). The hypoxic zone typically includes a core area that is hypoxic over most summers with outer regions where DO concentrations are typically more variable in time and space (Rabalais et al, 2007;DiMarco et al, 2010). Continuous DO measurements at fixed locations often show rapid changes (on the order of ± 1-3 mg L −1 h −1 ) in bottom DO concentrations (Babin and Rabalais, 2009;Bianchi et al, 2010;Rabalais et al, 2010;Babin, 2012).…”

Section: Introductionmentioning

confidence: 99%

Effects of spatial variability on the exposure of fish to hypoxia: a modeling analysis for the Gulf of Mexico

et al. 2021

View full text Add to dashboard Cite

Abstract. The hypoxic zone in the northern Gulf of Mexico varies spatially (area, location) and temporally (onset, duration) on multiple scales. Exposure of fish to hypoxic dissolved oxygen (DO) concentrations (< 2 mg L−1) is often lethal and avoided, while exposure to 2 to 4 mg L−1 occurs readily and often causes the sublethal effects of decreased growth and fecundity for individuals of many species. We simulated the movement of individual fish within a high-resolution 3-D coupled hydrodynamic water quality model (FVCOM-WASP) configured for the northern Gulf of Mexico to examine how spatial variability in DO concentrations would affect fish exposure to hypoxic and sublethal DO concentrations. Eight static snapshots (spatial maps) of DO were selected from a 10 d FVCOM-WASP simulation that showed a range of spatial variation (degree of clumpiness) in sublethal DO for when total sublethal area was moderate (four maps) and for when total sublethal area was high (four maps). An additional case of allowing DO to vary in time (dynamic DO) was also included. All simulations were for 10 d and were performed for 2-D (bottom layer only) and 3-D (allows for vertical movement of fish) sets of maps. Fish movement was simulated every 15 min with each individual switching among three algorithms: tactical avoidance when exposure to hypoxic DO was imminent, strategic avoidance when exposure had occurred in the recent past, and default (independent of DO) when avoidance was not invoked. Cumulative exposure of individuals to hypoxia was higher under the high sublethal area snapshots compared to the moderate sublethal area snapshots but spatial variability in sublethal concentrations had little effect on hypoxia exposure. In contrast, relatively high exposures to sublethal DO concentrations occurred in all simulations. Spatial variability in sublethal DO had opposite effects on sublethal exposure between moderate and high sublethal area maps: the percentage of fish exposed to 2–3 mg L−1 decreased with increasing variability for high sublethal area but increased for moderate sublethal area. There was also a wide range of exposures among individuals within each simulation. These results suggest that averaging DO concentrations over spatial cells and time steps can result in underestimation of sublethal effects. Our methods and results can inform how movement is simulated in larger models that are critical for assessing how management actions to reduce nutrient loadings will affect fish populations.

show abstract

“…Additionally, wind controls both the structure of the river plume (Hetland, 2005) and the rates of oxygen supply to the water column (Fennel et al, 2013;Justić et al, 1996). Overall, a complex combination of biophysical factors including long-term accumulation of organic matter (Del Giudice et al, 2020;Turner et al, 2008) and short-term events like storms and droughts (Bianchi et al, 2010) control hypoxia dynamics in the NGoM.…”

Section: Introductionmentioning

confidence: 99%

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

Katin

Giudice

Obenour

2021

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Bayesian mechanistic modeling characterizes Gulf of Mexico hypoxia: 1968–2016 and future scenarios

Cited by 16 publications

References 63 publications

Advancing estuarine ecological forecasts: seasonal hypoxia in Chesapeake Bay

Advancing estuarine ecological forecasts: seasonal hypoxia in Chesapeake Bay

Effects of spatial variability on the exposure of fish to hypoxia: a modeling analysis for the Gulf of Mexico

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

Contact Info

Product

Resources

About