Yu Xin

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Yu
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Xin
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  • Article
    Massive pollutants released to Galveston Bay during Hurricane Harvey: Understanding their retention and pathway using Lagrangian numerical simulations
    (Elsevier, 2019-11-21) Du, Jiabi ; Park, Kyeong ; Yu, Xin ; Zhang, Yinglong J. ; Ye, Fei
    Increasing frequency of extreme precipitation events under the future warming climate makes the storm-related pollutant release more and more threatening to coastal ecosystems. Hurricane Harvey, a 1000-year extreme precipitation event, caused massive pollutant release from the Houston metropolitan area to the adjacent Galveston Bay. 0.57 × 106 tons of raw sewage and 22,000 barrels of oil, refined fuels and chemicals were reportly released during Harvey, which would likely deteriorate the water quality and damage the coastal ecosystem. Using a Lagrangian particle-tracking method coupled with a validated 3D hydrodynamic model, we examined the retention, pathway, and fate of the released pollutants. A new timescale, local exposure time (LET), is introduced to quantitatively evaluate the spatially varying susceptibility inside the bay and over the shelf, with a larger LET indicating the region is more susceptible to the released pollutants. We found LET inside the bay is at least one order of magnitude larger for post-storm release than storm release due to a quick recovery in the system's flushing. More than 90% of pollutants released during the storm exited the bay within two days, while those released after the storm could stay inside the bay for up to three months. This implies that post-storm release is potentially more damaging to water quality and ecosystem health. Our results suggest that not only the amount of total pollutant load but also the release timing should be considered when assessing a storm's environmental and ecological influence, because there could be large amounts of pollutants steadily and slowly discharged after storm through groundwater, sewage systems, and reservoirs.
  • Article
    A machine-learning-based model for water quality in coastal waters, taking dissolved oxygen and hypoxia in Chesapeake Bay as an example
    (American Geophysical Union, 2020-08-25) Yu, Xin ; Shen, Jian ; Du, Jiabi
    Hypoxia is a big concern in coastal waters as it affects ecosystem health, fishery yield, and marine water resources. Accurately modeling coastal hypoxia is still very challenging even with the most advanced numerical models. A data‐driven model for coastal water quality is proposed in this study and is applied to predict the temporal‐spatial variations of dissolved oxygen (DO) and hypoxic condition in Chesapeake Bay, the largest estuary in the United States with mean summer hypoxic zone extending about 150 km along its main axis. The proposed model has three major components including empirical orthogonal functions analysis, automatic selection of forcing transformation, and neural network training. It first uses empirical orthogonal functions to extract the principal components, then applies neural network to train models for the temporal variations of principal components, and finally reconstructs the three‐dimensional temporal‐spatial variations of the DO. Using the first 75% of the 32‐year (1985–2016) data set for training, the model shows good performance for the testing period (the remaining 25% data set). Selection of forcings for the first mode points to the dominant role of streamflow in controlling interannual variability of bay‐wide DO condition. Different from previous empirical models, the approach is able to simulate three‐dimensional variations of water quality variables and it does not use in situ measured water quality variables but only external forcings as model inputs. Even though the approach is used for the hypoxia problem in Chesapeake Bay, the methodology is readily applicable to other coastal systems that are systematically monitored.