Tidal response to sea‐level rise (SLR) varies in different coastal systems. To provide a generic pattern of tidal response to SLR, a systematic investigation was conducted using numerical techniques applied to idealized and realistic estuaries, with model results cross‐checked by analytical solutions. Our results reveal that the response of tidal range to SLR is nonlinear, spatially heterogeneous, and highly affected by the length and bathymetry of an estuary and weakly affected by the estuary convergence with an exception of strong convergence. Contrary to the common assumption that SLR leads to a weakened bottom friction, resulting in increased tidal amplitude, we demonstrate that tidal range is likely to decrease in short estuaries and in estuaries with a narrow channel and large low‐lying shallow areas.

Hurricane Harvey, one of the worst hurricanes that hit the United States in recent history, poured record-breaking rainfall across the Houston metropolitan area. Based on a comprehensive set of data from various sources, we examined the dramatic responses in hydrodynamic and sedimentary processes of Galveston Bay to this extreme event. Using a freshwater fraction method that circumvents the uncertainties in surface runoff and groundwater discharge, the freshwater load into the bay during Harvey and the following month was estimated to be 11.1 × 109 m3, about 3 times the bay volume, which had completely refreshed the entire bay. Harvey also delivered 9.86 × 107 metric tons of sediment into the bay, equivalent to 18 years of average annual sediment load. At a site inside the San Jacinto Estuary, acute bed erosion of 48 cm followed by deposition of 22 cm of new sediment was observed from the sediment cores. Slow salinity recovery (~2 month) and a thick flood deposit (~10.5 cm average over the entire bay) had likely impacted the ecosystem in the bay and the adjacent inner shelf. Estuaries with similar bathymetric and geometric characteristics, i.e., shallow bathymetry with narrow outlets, are expected to experience similar dramatic estuarine responses while extreme precipitation events are expected to occur more frequently under the warming climate.

With a warming climate and a more humid atmosphere, extreme precipitation events are projected to occur more frequently in future. Understanding how coastal systems respond to and recover from such acute events is of fundamental significance for environmental assessment and management. A hydrodynamic model was used to examine the estuarine responses in Galveston Bay to Hurricane Harvey, an extreme precipitation event with a return period of larger than 1000 years. The enormous freshwater input during Harvey caused long-lasting elevated water level, extraordinarily strong along-channel velocity, sharp decreases in salinity, and huge river plumes, all of which were well reproduced by the model. The salinity recovery time (TR) was estimated as a measure of the system resiliency to stormwater input. Over the entire bay, the TR had a mean of two months, but with great variability ranging from less than 10 days near the bay entrance to over three months in the inner part of Trinity Bay and the middle of East Bay. The spatially varying TR was explained by different contributions of exchange flow and tidal pumping to salt flux. At the bay entrance, tidal pumping facilitated by the shelf current was the dominant mechanism for salt influx, while exchange flow and tidal pumping had a comparable contribution to salt influx to Trinity Bay. The spatial pattern of the TR appears consistent with the changes in the phytoplankton community in the bay. A series of numerical experiments with different amounts of stormwater reveals a non-linear relationship between the bay-wide mean TR and the amount of stormwater, with the rate of increase in TR decreasing when stormwater input increases. The present approach using a hydrodynamic model will be able to provide a quick assessment of the environmental pressure from extreme events.

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.