Wenrui Huang

Development of a Regional Neural Network for Coastal Water Level Predictions

Abstract This paper presents the development of a Regional Neural Network for Water Level (RNN_WL) predictions, with an application to the coastal inlets along the South Shore of Long Island, New York. Long-term water level data at coastal inlets are important for studying coastal hydrodynamics sediment transport. However, it is quite common that long-term water level observations may be not available, due to the high cost of field data monitoring. Fortunately, the US National Oceanographic and Atmospheric Administration (NOAA) has a national network of water level monitoring stations distributed in regional scale that has been operating for several decades. Therefore, it is valuable and cost effective for a coastal engineering study to establish the relationship between water levels at a local station and a NOAA station in the region. Due to the changes of phase and amplitude of water levels over the regional coastal line, it is often difficult to obtain good linear regression relationship between water levels from two different stations. Using neural network offers an effective approach to correlate the non-linear input and output of water levels by recognizing the historic patterns between them. In this study, the RNN_WL model was developed to enable coastal engineers to predict long-term water levels in a coastal inlet, based on the input of data in a remote NOAA station in the region. The RNN_WL model was developed using a feed-forwards, back-propagation neural network structure with an optimized training algorithm. The RNN_WL model can be trained and verified using two independent data sets of hourly water levels. The RNN_WL model was tested in an application to Long Island South Shore. Located about 60–100 km away from the inlets there are two permanent long-term water level stations, which have been operated by NOAA since the1940s. The neural network model was trained using hourly data over a one-month period and validated for another one-month period. The model was then tested over year-long periods. Results indicate that, despite significant changes in the amplitudes and phases of the water levels over the regional study area, the RNN_WL model provides very good long-term predictions of both tidal and non-tidal water levels at the regional coastal inlets. In order to examine the effects of distance on the RNN_WL model performance, the model was also tested using water levels from other remote NOAA stations located at longer distances, which range from 234 km to 591 km away from the local station at the inlets. The satisfactory results indicate that the RNN_WL model is able to supplement long-term historical water level data at the coastal inlets based on the available data at remote NOAA stations in the coastal region.

An enhanced MODIS remote sensing model for detecting rainfall effects on sediment plume in the coastal waters of Apalachicola Bay

Mapping of total suspended solids (TSS) was conducted to investigate rainstorm-induced characteristic of sediment plume in the coastal waters of Apalachicola Bay. An improved TSS quadratic polynomial regression model (Calibration: R2=0.8586, N=32; validation: RMSE of 4.76 mg/l, N=30) for MODIS remote sensing was presented in this study. TSS mapping of MODIS before and after a rainstorm event showed distinct temporal-spatial variability of TSS concentration. Driven by ocean tidal current, a storm plume of width at about 40-50 km was formed flowing towards southwest of study area. The distinct boundary separating the highly turbid (west side) and relatively clear water (east side) was found near Sikes Cut. Further, by taking the TSS mapping under the low river discharge condition due to a local rainstorm as a reference of background TSS, two thresholds of TSS (25 and 45 mg/l respectively) were used to estimate the range of rainstorm plume and the central area of sediment load from surrounding land, and the spatial extent and evolution of the sediment plume during the local rainstorm. Besides, it was found that the storm plume concentration of TSS at east side of Sites Cut was quickly diluted under 25 mg/L, forming a storm plume towards east with width at about 7-8 km. The method developed in this study may be used to support coastal storm water research and management activities.

Numerical Modeling of Hydrodynamics and Salinity Transport in Little Manatee River

Abstract This paper presents the application a 3D hydrodynamic model (EFDC) to Little Manatee River (LMR) estuary located in the southwestern of Tampa Bay. Model boundaries were specified by field observations of hourly observations for model simulations, including freshwater inputs, tides, winds, salinity and temperatures at bay boundary, and air temperatures. By comparison with observations of water levels, salinity, and temperature at several river stations, the hydrodynamic model for LMR river system has been satisfactorily calibrated and verified to support water resources and estuary ecological studies. Salinity intrusions in the river system were investigated under typical low, average, and high flow conditions.

Effects of El-Niño and La-Niña Sea Surface Temperature Anomalies on Annual Precipitations and Streamflow Discharges in Southeastern United States

ABSTRACT Clark II, C.; Nnaji, G.A., and Huang, W., 2014. Effects of El-Nino and La-Nina sea surface temperature anomalies on annual precipitations and streamflow discharges in southeastern United States. Statistical analysis shows that El Niño and La Niña are partially responsible for the amounts of annual precipitation and annual streamflow discharges in Gulf Atlantic hydrologic unit of Southeast United States. The study is based on 61-year records of precipitation and streamflow discharge stations spread across the region. The cross-correlation coefficients for both the sea surface temperature (SST) anomalies and precipitation, and SST anomalies and streamflow were calculated after the data series were prewhitened by an Autoregressive Integrated Moving Average (ARIMA) model (2,0,3). The cross-correlations between SST anomalies and both precipitations and streamflow discharges were positively significant. Using statistical relationship established relating SST anomalies with precipitation and streamflow in the area; comparison analyses of observed and filtered data series are performed at locations where there is significant influence of El Niño – Southern Oscillation (ENSO) on precipitations and streamflow discharges in the region. The degree of influence ENSO has on each hydrologic series hence can be predicted for different water sheds in the area. This can be of immense help for water management strategy and planning in the region.

Hydrodynamic Modeling of Hurricane Dennis Impact on Estuarine Salinity Variation in Apalachicola Bay

ABSTRACT Huang, W.; Hagen, S., and Bacopoulos, P., 2014. Hydrodynamic modeling of Hurricane Dennis impact on estuarine salinity variation in Apalachicola Bay. Hurricane Dennis made landfall in Florida on 10 July 2005 and caused a storm surge of 2.4 m in Apalachicola. Field observations of salinity, winds, and river inflows are available at a few stations in the bay during the hurricane event. Presented in this paper is a numerical modeling study that investigates the effects of Hurricane Dennis on estuarine mixing and transport. A previously calibrated three-dimensional (3D) estuarine hydrodynamic model was further validated by the field observations of salinity and water levels during the hurricane event. A large-scale storm-surge model was used to provide storm-surge hydrographs at the five estuarine boundaries for the 3D estuarine model. This model integration proved successful with the results indicating that the model predictions of storm-surge hydrographs and salinity match well with observations. Model predictions of spatial distributions of salinity and current fields are presented to demonstrate the fresh-saline water mixing at different times of the storm-surge event. Results indicate that the hurricane-induced storm surge caused substantial increase of salinity in the bay. Majority saline water entered the bay from the east during the storm surge event. Salinity at the oyster reef, Cat Point in the eastern bay area, is more sensitive to the increased water levels caused by storm surge than another oyster reef, Dry Bar, in the western bay area.

Sea-Level Rise Effects on Hurricane-Induced Salinity Transport in Apalachicola Bay

ABSTRACT Huang, W.; Hagen, S.C.; Bacopoulos, P., and Teng, F., 2014. Sea-level rise effects on hurricane-induced salinity transport in Apalachicola Bay. Salinity is an important indicator for estuarine ecosystem. Estuarine salinity can be affected by hurricane and sea-level rises. In this study, hydrodynamic modeling study has been conducted to investigate the effects of sea-level rise on hurricane-induced salinity in Apalachicola Bay. By using the dataset for the Hurricane Dennis occurred in July, 2005, model simulations were conducted under different sea-level rise scenarios. Results from model simulations show the effects of sea-level rise on the estuarine salinity transport during different phases of the storm surge. Generally, the increase of water level by either storm surge or sea-level rise results in the intrusion of majority saline sea water from the east to the west through East Pass. Salinity at two oyster bars responds to the storm surge and sea-level rise differently because Cat Point is located in the east and Dry Bar is in the west of the river mouth. In Cat Point, sea-level rise can cause substantial increase of salinity because it is located between the river mouth and East Pass. Salinity at the peak of the storm surge reaches 30 ppt even without sea-level rise. While salinity at the end of the storm surge reduces to about 20 ppt under no sea-level rise condition at Cat Point, it substantially increases to 30 ppt in response to a sea-level rise of 0.2 m. However, in Dry Bar, salinity is less sensitive to the sea-level rise and the storm surge. At the peak of the storm surge, salinity in Dry Bar is 30 ppt, 28 ppt, 30 ppt., under SLR 0.2 m, 0.5 m, and 1.2 m, respectively. However, near the end of the storm surge, salinity is 22 ppt, 22 ppt, and 27 ppt under 0.2 m, 0.5 m, and 1.2 m SLR conditions, respectively. This indicates that, after the storm surge, salinity in Dry Bar can recover to the normal range (below 26 ppt) if sea-level rise is less or equal to 0.5 m.

Frequency Analysis of Extreme Water Levels Affected by Sea-Level Rise in East and Southeast Coasts of China

ABSTRACT Chen, Y.; Huang, W., and Xu, S., 2014. Frequency analysis of extreme water levels in east and southeast coasts of China with analysis on effect of sea level rise. Qiantang bore as well as the storm surge are great disasters for river bank protection at the estuary of Qiantang River. Pearl River estuary is also frequently attacked by strong typhoon storm surge. The risk of damage from storm surge is expected to increase in both estuaries, exacerbated by sea level rise (SLR) and possible climate-induced increases in typhoon intensity and frequency. Adequate estimation on extreme water level will be essential to the coastal flood mitigation for both estuary areas with the effect of climate change. In this study, the popular frequency models Weibull, Lognormal, Gumbel, P-III and GEV are compared on Ganpu station located at the estuary of Qiantang River and Denglongshan station in Guangdong province, the optimal GEV model is recommended. For the risky analysis and management concern induced by the shortage of studied data, the estimated 50 year and 100 year extreme water levels respectively at Ganpu and Denglongshan stations are recommended in this study. Both studied stations located at different estuaries are all type II GEV model as the parameter of GEV distribution are higher than 0. The difference of the parameter and the reason causes the difference in the studied stations are analyzed and discussed in this paper. Furthermore, with the estimated 2.9mm/yr SLR by sea level bulletin of China, effect of SLR in frequency analysis on Denglongshan station is discussed.

Neural Network and Harmonic Analysis for Recovering Missing Extreme Water-Level Data during Hurricanes in Florida

Abstract Predictions of extreme coastal water levels are important to coastal engineering design and hazard mitigations in Florida. Annual maximum water levels are often used in frequency analysis of 1% annual chance flood in coastal flood hazard mapping. However, because of the damage to measurement instruments during hurricanes, some annual maximum water levels may be missed, which makes coastal flood hazard analysis difficult. In this study, a technique was developed to use artificial neural network and harmonic analysis for recovering extreme coastal water levels during hurricanes. The total water levels are decomposed into tidal components and storm surge. Tidal components can be derived by harmonic analysis, whereas storm surge can be predicted by neural network modeling on the basis of the observations of local wind speeds and atmospheric pressure. The neural network model uses three-layer feed-forward back-propagation structure with advanced scaled conjugate training algorithm. The method presented in this study has been successfully tested in Panama City Beach, located on the Florida coast, for Hurricane Dennis (2005), Hurricane Ivan (2004), and Hurricane Opal (1975). Model-predicted peak elevations reasonably match with observations for the three hurricane events. The decomposed storm surge hydrograph also make it possible for the analysis of potential extreme water levels if the storm surge occurs during spring high tide.

Modeling sediment concentration and transport induced by storm surge in Hengmen Eastern Access Channel

Sediment is an important factor for excavation, dredging and maintenance of Hengmen Eastern Access Channel in Pearl River Estuary. As storm surge is considered as an important role in determining sediment re-suspension and transport, as well as creating landforms in the areas of estuary and coast, along with the storm surge disaster damage in Pearl River Estuary is one of the most serious events in China, reasonable simulation of storm-induced sediment concentration, transport and channel siltation in Hengmen Eastern Access Channel is of much significance. Based on the feasibility condition of less research on numerical simulation of storm-induced sediment concentration and transport, especially channel siltation in the Pearl River Estuary, using a curvilinear grid, a nested and coupled model which combines typhoon model, hydrodynamic model (Delft3D-FLOW), wave model (Delft3D-WAVE) and sediment transport model (Delft3D-SED) was set up for the region of Pearl River Estuary. After a series of model verifications, which showed that this coupled model performed well to reflect the characteristics of the typhoon field, tidal currents, wave height, storm surge, distribution of suspended sediment in the studied region, the model was applied to study the storm-induced sediment concentration and transport in Hengmen Eastern Access Channel. Through simulation of one extra tropical storm surge process with this coupled numerical model, the storm-induced sediment concentration and transport in Hengmen Eastern Access Channel were studied, and the storm-induced erosion and deposition were further discussed. Results showed that the effect of storm surge on sediment concentration, transport and siltation was significant. Under the influence of storm surge, the velocity and bed stress around Hengmen Eastern Access Channel increased significantly, which led the re-suspension and transport of sediment, and thus, the higher sediment concentration and more channel siltation occurred. By running this coupled model, the simulated results can be employed in the optimum decision making of Hengmen Eastern Access Channel Regulation Project.

Development of a River Hydrodynamic Model for Studying Surface-Ground Water Interactions Affected by Climate Change in Heihe River, China

ABSTRACT Zhu, F.; Huang, W.; Cai, Y.; Teng, F.; Wang, B., and Zhou, Q., 2014. Development of a river hydrodynamic model for studying surface-ground water interactions affected by climate change in Heihe River, China. Over the past 50 years, glacier area in Qilian Mountain of the Heihe River basin has decreased by 29.6%, mainly resulting from the climate change. Understanding the interactions of surface and ground water interaction will be very helpful for studying climate-change impact on Hehei River basin. This paper presents the development of one-dimensional river hydrodynamic model for surface and ground water interactions (RHM-SG). Surface-water flow is described by the modified one-dimensional Saint Venant equations, which is solved by Preissmann scheme. The flow exchange between surface and ground water is described by Darcys equation. Five model validation tests include uniform flow over sloping bed, one flood process, and river flow with interactions of ground water. Model tests demonstrate that the model compares well with analytic solution, and reasonably characterize the interaction between river flow and ground water. The model has also been tested in the application to the middle reach of the Heihe River. By including surface-ground water interactions, the accuracy of model predicted flow substantially improves. The correlation coefficient increases from 0.89 to 0.98, and the root-mean-square reduces from 21.16 m3/s to 8.49 m3/s, respectively.