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Jian
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Jian
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ArticleChallenges associated with modeling low-oxygen waters in Chesapeake Bay : a multiple model comparison(Copernicus Publications on behalf of the European Geosciences Union, 2016-04-06) Irby, Isaac D. ; Friedrichs, Marjorie A. M. ; Friedrichs, Carl T. ; Bever, Aaron J. ; Hood, Raleigh R. ; Lanerolle, Lyon W. J. ; Li, Ming ; Linker, Lewis ; Scully, Malcolm E. ; Sellner, Kevin G. ; Shen, Jian ; Testa, Jeremy M. ; Wang, Hao ; Wang, Ping ; Xia, MengAs three-dimensional (3-D) aquatic ecosystem models are used more frequently for operational water quality forecasts and ecological management decisions, it is important to understand the relative strengths and limitations of existing 3-D models of varying spatial resolution and biogeochemical complexity. To this end, 2-year simulations of the Chesapeake Bay from eight hydrodynamic-oxygen models have been statistically compared to each other and to historical monitoring data. Results show that although models have difficulty resolving the variables typically thought to be the main drivers of dissolved oxygen variability (stratification, nutrients, and chlorophyll), all eight models have significant skill in reproducing the mean and seasonal variability of dissolved oxygen. In addition, models with constant net respiration rates independent of nutrient supply and temperature reproduced observed dissolved oxygen concentrations about as well as much more complex, nutrient-dependent biogeochemical models. This finding has significant ramifications for short-term hypoxia forecasts in the Chesapeake Bay, which may be possible with very simple oxygen parameterizations, in contrast to the more complex full biogeochemical models required for scenario-based forecasting. However, models have difficulty simulating correct density and oxygen mixed layer depths, which are important ecologically in terms of habitat compression. Observations indicate a much stronger correlation between the depths of the top of the pycnocline and oxycline than between their maximum vertical gradients, highlighting the importance of the mixing depth in defining the region of aerobic habitat in the Chesapeake Bay when low-oxygen bottom waters are present. Improvement in hypoxia simulations will thus depend more on the ability of models to reproduce the correct mean and variability of the depth of the physically driven surface mixed layer than the precise magnitude of the vertical density gradient.
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ArticleSimple relationships between residence time and annual nutrient retention, export, and loading for estuaries(Association for the Sciences of Limnology and Oceanography, 2022-02-27) Shen, Jian ; Du, Jiabi ; Lucas, Lisa V.Simple mathematical models are derived from mass balances for water and transported substance to provide insight into the relationships between import, export, transport, and internal removal for nonconservative substances in an estuary. Extending previous work, our models explicitly include water and substance inputs from the ocean and are expressed in terms of timescales (i.e., mean residence time and the timescale for net removal). Steady-state, timescale-based expressions for ratios of export to import, retention to import, and net export to loading, as well as for loading and annually averaged concentration, are provided. The net export:loading model explains the underlying mechanisms for a well-known empirical relationship between fractional net export and residence time derived by other authors. Although our simplified models are first-order approximations, the relative importance of physical and biochemical processes influencing export or retention of a substance can be assessed using mean residence time and the timescale for net removal. Assumptions employed in deriving the simplified models (e.g., well-mixed, dynamic steady state) may not be met for real estuaries. However, model application to Chesapeake Bay for 1985–2012 demonstrates that interannual variations in total nitrogen (TN) net export:loading can be evaluated, and annual nutrient loadings can be well estimated using numerically modeled time-varying mean residence time, observation-based mean concentration, freshwater inflow, and an appropriately estimated removal timescale. Our model shows that net fractional export of TN loading ranges from 0.3 to 0.5 over the 28-yr period. The models can be employed for other substances and water bodies if the underlying assumptions are applicable.
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ArticleA 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, JiabiHypoxia 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.