@article{katin_del giudice_obenour_2022, title={Temporally resolved coastal hypoxia forecasting and uncertainty assessment via Bayesian mechanistic modeling}, volume={26}, ISSN={["1607-7938"]}, url={https://doi.org/10.5194/hess-26-1131-2022}, DOI={10.5194/hess-26-1131-2022}, abstractNote={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.
                    }, number={4}, journal={HYDROLOGY AND EARTH SYSTEM SCIENCES}, publisher={Copernicus GmbH}, author={Katin, Alexey and Del Giudice, Dario and Obenour, Daniel R.}, year={2022}, month={Feb}, pages={1131–1143} }
 @article{katin_del giudice_hall_paerl_obenour_2021, title={Simulating algal dynamics within a Bayesian framework to evaluate controls on estuary productivity}, volume={447}, ISSN={["1872-7026"]}, DOI={10.1016/j.ecolmodel.2021.109497}, abstractNote={The Neuse River Estuary (North Carolina, USA) is a valuable ecosystem that has been affected by the expansion of agricultural and urban watershed activities over the last several decades. Eutrophication, as a consequence of enhanced anthropogenic nutrient loadings, has promoted high phytoplankton biomass, hypoxia, and fish kills. This study compares and contrasts three models to better understand how nutrient loading and other environmental factors control phytoplankton biomass, as chl-a, over time. The first model is purely statistical, while the second model mechanistically simulates both chl-a and nitrogen dynamics, and the third additionally simulates phosphorus. The models are calibrated to a multi-decadal dataset (1997–2018) within a Bayesian framework, which systematically incorporates prior information and accounts for uncertainties. All three models explain over one third of log-transformed chl-a variability, with the mechanistic models additionally explaining the majority of the variability in bioavailable nutrients (R2 > 0.5). By disentangling the influences of riverine nutrient concentrations, flows, and loadings on estuary productivity we find that concentration reductions, rather than total loading reductions, are the key to controlling estuary chl-a levels. The third model indicates that the estuary, even in its upstream portion, is rarely phosphorus limited, and will continue to be mostly nitrogen limited even under a 30% phosphorus reduction scenario. This model also predicts that a 10% change in nitrogen loading (flow held constant) will produce an approximate 4.3% change in estuary chl-a concentration, while the statistical model suggests a larger (10%) effect. Overall, by including a more detailed representation of environmental factors controlling algal growth, the mechanistic models generate chl-a forecasts with less uncertainty across a range of nutrient loading scenarios. Methodologically, this study advances the use of Bayesian methods for modeling the eutrophication dynamics of an estuarine system over a multi-decadal period.}, journal={ECOLOGICAL MODELLING}, author={Katin, Alexey and Del Giudice, Dario and Hall, Nathan S. and Paerl, Hans W. and Obenour, Daniel R.}, year={2021}, month={May} }
 @article{scavia_bertani_obenour_turner_forrest_katin_2017, title={Ensemble modeling informs hypoxia management in the northern Gulf of Mexico}, volume={114}, ISSN={["0027-8424"]}, DOI={10.1073/pnas.1705293114}, abstractNote={Significance}, number={33}, journal={PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA}, author={Scavia, Donald and Bertani, Isabella and Obenour, Daniel R. and Turner, R. Eugene and Forrest, David R. and Katin, Alexey}, year={2017}, month={Aug}, pages={8823–8828} }