James River Watershed Model Development of Hydrodynamic Model

Transcription

1 James River Watershed Model Development of Hydrodynamic Model Modeling Report February 2016 Prepared for: Virginia Department of Environmental Quality 629 East Main Street Richmond, VA Prepared by: Virginia Institute of Marine Science College of William & Mary Jian Shen, Rico Wang, Mac Sisson P.O. Box 1346 Gloucester Point, VA

2 2.1 Introduction The James River is a western tributary of the Chesapeake Bay (Figure 2-1-1). The downstream portion of the James River is distinguished by a meandering main channel. An abrupt bend of the river occurs at Newport News Point (NNP), approximately 10.5 km from its mouth, where the orientation of the river changes from northeast-southwest in the lower river to southeast-northwest in the upper river. Hampton Flats is the shoal flanking the northern side of the deep channel in the lower James River. Water depth over the Hampton Flats is less than 5 m. The tidal range of the James is about 0.8 m near the mouth. The tidal range decreases to 0.6 m towards the upstream to approximately 90 km from the mouth and increases again due to the reflection of the tidal wave and change of convergence of the channel width. Two sub-estuaries, the Chickahominy and Elizabeth Rivers, are connected to the James mainstem in the mesohaline and hyperhaline regions, respectively. In the upper James River, algal blooms are strongly influenced by hydrodynamic fields because of the limited mobility of phytoplankton. Bukaveckas et al. (2011) found that the location of the chlorophyll-a maximum in the tidal freshwater region of the James River is determined in part by natural geomorphic features of the channel. The transition from a riverine-type (narrow, deep) cross-sectional morphology to a broad channel with shallow lateral areas provides favorable light conditions and results in increased phytoplankton production and abundance. The increase of cross-sectional area also likely reduces local water velocities, allowing more efficient phytoplankton utilization of nutrients from the catchment and local sources. It is well-known that the lateral expansions of the channel width can slow down water transport and cause the formation of recirculation, resulting in an increase of residence time. To simulate these features, an accurate three-dimensional (3D) hydrodynamic model with sufficient spatial resolution is necessary. The lower James River exhibits the strongest and most persistent classical estuarine circulation patterns (Pritchard, 1956) among the major tributary estuaries of the Chesapeake Bay (Kuo and Neilson, 1987). The strong circulation patterns transport a large amount of the cleaner lower Bay water into the tidal James River and enhance flushing capabilities. This results in the least occurrence of hypoxic conditions among the Chesapeake Bay s major tributary estuaries. In addition to this strong circulation in the vertical plane, there is an extensive topographic eddy controlling lateral circulation in the lower James River and Hampton Roads (Shen and Kuo, 1999). The water exchange between the James and Elizabeth Rivers is another unique feature of the James River. The development of the harmful algal bloom in the James is due to the transport of phytoplankton, initiated in the Lafayette River, one of the tributaries of the Elizabeth River (Morse et al., 2011). Considering the interaction of tidal, wind, and baroclinic forcings (result of the density difference between fresh and salt waters), water movement in the tidal James River is truly multidimensional and quite complicated. The circulation features span time scales of hours to months. A hydrodynamic model must successfully reproduce these circulation features to properly supply the transport framework for a water quality model. To support modeling efforts to address the establishment of phytoplankton criteria, we developed a new hydrodynamic model. Although several models have been applied to the James River in the past (Shen and Kuo, 1999; Shen and Lin, 2006), the early model resolution is not high enough to accurately simulate geometry effects in both the upstream and down- stream portions of the river, while the model used for sea level rise study (Rice et al., 2012) has a very high resolution, but it is computationally too expensive for long-term water quality simulations as a very small timestep is required. Besides, the model calibration does not consider local and lateral flows. The purpose of the development of this new James River model is to provide an accurate long-term transport field for the eutrophication model for the James, while the computation is sufficiently efficient to conduct long-term water quality simulations. 2

3 A linked watershed, hydrodynamic, eutrophication, and bottom sediment modeling approach is applied to the James River for assisting in the establishment of algal water quality standards for the James. The daily flows from upstream and lateral watersheds are discharged to the hydrodynamic model domain. The hydrodynamic model, driven by tide at the mouth and flow and wind forcing at the surface, simulates surface elevation, current, salinity, temperature, and turbulent mixing. The dynamic fields are used to drive the eutrophication model. The eutrophication model receiving nutrient loads from watersheds, simulates the nutrient cycle, algal, and dissolved oxygen dynamics in the estuary. A bottom sediment model is coupled to the eutrophication model in the water column and simulates nutrient mineralization processes and resultant nutrient fluxes and sediment oxygen dement. A diagram of the modeling system is shown in Figure Figure 2-1-1: Location of James River and model grid. 3

4 Figure 2-1-2: A schematic diagram of the modeling system for the James River. 2.2 Hydrodynamic Model Description and Model Configuration Hydrodynamic Model Description The Environmental Fluid Dynamics Code (EFDC) is applied to the James River. The EFDC model is an EPA-approved modeling tool ( It is a generalpurpose modeling package for simulating 1D, 2D, or 3D flow, transport, and bio-geochemical processes in surface water systems, including rivers, lakes, estuaries, reservoirs, wetlands, and coastal regions. EFDC was originally developed by Hamrick (1992; 1997) at the Virginia Institute of Marine Science for estuarine and coastal applications and is a public domain software. The EFDC model uses curvilinear, orthogonal horizontal coordinates and sigma vertical coordinates to represent the physical characteristics of a water body. The vertical mixing is parameterized using the Mellor and Yamada (1982) level 2.5 turbulence closure scheme as modified by Galperin et al. (1988). A high-order transport with anti-numerical diffusion scheme is implemented in the model that provides accurate transport for salinity and pollutants. In addition to the simulation of hydrodynamics, salinity, and temperature, EFDC is capable of simulating cohesive and non-cohesive sediment transport, near-field and far-field discharge dilution from multiple sources, eutrophication processes, the transport and fate of toxic contaminants in the water, and sediment phases (Shen et al., 2012), and the transport and fate of various life stages of finfish and shellfish. The model has been applied successfully to study James River transport processes (Shen and Lin, 2006), impact of dynamics on HAB in the lower James (Morse et al., 2011), and Chesapeake Bay (Hong and Shen, 2013), and widely throughout the country for regulatory studies (including criteria development in Florida and California, TMDL development, contaminated sediment modeling, and mixing zone studies) Model configuration The model grid and computational domain of this study is presented in Fig The model grid cells were designed to follow the main channel of the James River. High resolution was placed in the main stem of the river to obtain the best representation of the topography in this area. There are a total of 3,066 grid cells in the horizontal and eight layers in the vertical. The bottom bathymetry is interpolated using NOAA coastal relief model with 90-m resolution ( For the Elizabeth River, data measured during a survey conducted by the Army Corps were used. For small creeks, NOAA charts were used to obtain depths in shallow areas. The water quality model simulation periods are from and from The first period was used by the Bay Program to develop TMDLs for the James River. The second period was selected because new data are available, including continuous monitoring data and data flow data collected by DEQ. To allow the model spin-up, the model started from 1990 and 2006, respectively, a full year in advance of each simulation period. Therefore, the impact of the initial conditions can be efficiently removed. Daily river discharges from upstream and lateral watersheds are obtained from the James watershed model developed by TetraTech, Inc. (this report), which includes inflows from 87 subwatersheds. The three main upstream freshwater discharges are from Richmond, the Appomattox River, 4

5 and the Chickahominy River, respectively. Wind forcing data were obtained from the Norfolk and Richmond International Airports, which are located near the Sewells Point station at the mouth and near the fall line of the James, respectively. The open boundary conditions from 1990 to 2000 (which included hourly time-varying water level, temperature, and salinity profiles) were obtained from the 3D model of the Chesapeake Bay Program (Cerco and Noel, 2004). Because the Chesapeake Bay model does not simulate the period from , the tide boundary condition for this period used Sewells Point data with corrections of phase and amplitude. The mean differences of phase and amplitude were estimated based on Bay Program model outputs and measured tidal elevations at Sewells Point. The hourly salinity boundary conditions used are outputs from the large domain Chesapeake Bay model for the period (Du and Shen, 2014; Hong and Shen, 2013). The temperature boundary condition used hourly measurements at Sewells Point at the surface and monthly temperature data measured at CBP Station CB8.1 from Differences of surface and bottom temperatures at Station CB8.1 were interpolated in time and applied to the hourly surface temperature data to obtain an estimation of bottom temperature. Because the temperature is highly determined by the air-sea exchange, and the open boundary has less impact on temperature inside the James, this approach yields good model-data agreement. Hourly wind forcing, surface pressure, humidity, and solar radiation obtained from the hourly meteorological data from Norfolk and Richmond Airports were used for temperature simulations Model Calibration Surface elevation The mean calibration of tidal range along the James River was conducted first. The purpose of the mean calibration is to ensure that the tidal wave propagation inside James is correctly simulated. The locations of NOAA tidal table stations are shown in Fig The mean tidal ranges at multiple stations obtained from the NOAA tidal table were used for the model calibration. The EFDC model uses the logarithmic velocity profile to compute the bottom shear stress based on the velocity at the bottom layer. The bottom roughness was adjusted so that the tidal range along the James main stem matches the tidal range at 12 stations obtained from the NOAA tidal table. Without information of measured bottom roughness in the James, a constant roughness height throughout the entire James River is assumed and adjusted accordingly. The roughness height of cm is used for the model, which yields the best fit. A comparison of modeled and observed tidal range is shown in Figure The model skill, which is defined as SS = 1 (A!"#$% A!"# )! / ( A!"# A!"# )! was used to evaluate the model performance. Performance levels are categorized by the SS value as: >0.65 excellent; very good; good; <0.2 poor (Ralston et al., 2010; Wilmott, 1981). SS is 0.84 in the James, indicating a good prediction skill. Because the tidal ranges published in the tidal table were based on the observation data collected many years age, some changes can be expected due to changes of geometry. We use these ranges to calibrate the characteristics of tidal wave propagation rather than to match each station. It can be seen that the characteristics of the tidal wave propagation along the James are simulated correctly. A nodal point is located nearly 90 km from the mouth. Tidal range decreases initially, but then increases. The highest tidal range is located near the fall line, where a tidal wave reflection occurs. 5

6 Figure 2-3-1: Location map for NOAA tidal stations published in the NOAA tidal table. Figure 2-3-2: Comparison of modeled mean tidal range against NOAA tidal table predictions. 6

7 Figure 2-3-3(a): Map of observation stations for surface elevation and current calibration. 7

8 Figure 2-3-3(b): Map of observation stations for salinity and temperature calibration. There is only one permanent tidal gage, which is located at Sewells Point. In recent years, the Virginia Estuarine and Coastal Observing System (VECOS) ( has maintained several shallow water monitoring stations in the James, which measure total water depth. We have selected the period for tidal calibration as we have hourly data records at 5 stations. The locations of the stations are shown in Figure 2-3-3a. The model predictions of surface elevation at several stations are compared with tidal elevation measurement at monitoring stations. Besides Sewells Point, only the total water depth was measured. Therefore we removed the long-term mean sea-level and compared the tidal variation. Use of spatial varying bottom roughness can improve surface elevation. By adjusting the roughness, the surface elevation can be improved over a certain period, but it could worsen during other periods as the surface elevation (especially peaks) is also affected by the open boundary condition, freshwater discharge, and wind forcing. The approach is to optimize the overall predictive skill. A constant roughness was used in the model. The performance is summarized using a Taylor diagram (Figure 2-3-4). Three axes represent correlation coefficients, the centered root-mean-square difference, and standard deviation. The data were normalized by the standard deviation at Station 6 (JMS073.37). The results show that a high correlation with a low root-mean-square error is achieved. The root-mean-square error is less than 0.5 of one standard deviation (~0.28 m). Examples of time series comparing of modeled and observed elevation are shown in Figure and for Days It can be seen the modeled tidal elevations agree with observations. As 8

9 there is no perfect boundary condition for the model and we used water depth data for model calibration, some deviations can be expected. A scatterplot comparison is shown in Fig Model predictive skill values, SS, range from , indicating a good predictive skill. Figure 2-3-4: Taylor diagrams representing model-data comparisons at 6 tidal stations in the James River. Three axes represent correlation coefficients (blue lines), the centered root-meansquare difference (green lines), and normalized standard deviation (black lines) (Station location: 1= Sewells Point, 2= JMS043.78, 3=JMS018.23, 4= JMS002.55, 5= CHK015.12, 6= JMS073.37). 9

10 Figure 2-3-5: Comparison of modeled and observed tidal elevation at 4 selected stations. 10

11 Figure 2-3-6: Comparison of modeled and observed tidal elevation at 4 selected stations. 11

12 Figure 2-3-7: Comparison of modeled and observed tidal elevation at 4 selected stations. 12

13 2.3.2 Current NOAA has surface current measure in the lower James River. We download the current data at three stations shown in Figure 2-3-3(a). Model comparisons at the dominant current direction for four years from were conducted and results are shown in Appendix II-A. The scatter plots at these three stations at each year are shown in Figure It can be seen the current vary each year. In general, the SS ranges from 0.17 to 0.77, indicating a good predictive skill

14

15 Figure 2-3-8: Comparison of modeled and observed current. 15

16 2.3.2 Salinity The long-term transport processes are driven by sub-tidal circulation. Correct calibration of salinity is important for accurate simulation of sub-tidal circulation. The calibration of salinity is focused on stratification and salinity intrusion. We used monthly data to conduct the salinity calibration. The salinity calibration was conducted from and the model validation was conducted from Unlike the model calibration of surface elevation, there are no model parameters to calibrate for salinity for a 3D model. The discrepancy of salinity between modeled and observed is mainly caused by flow, boundary condition(s), wind, and bathymetry. Both the transport scheme and turbulent scheme used in the model play important roles. EFDC uses a second-order transport scheme with anti-numerical diffusion; it can simulate salinity well in general. Initial model calibration results show that the salinity intrusion is not enough and the salinity near the Stations RET5.2 and TF5.6 is under-predicted. By examining the bathymetric data, it appears that there is narrow channel located in mesohaline region, which is not well represented by the model grid depth. As the NOAA data was compiled based on early measurements and the channel is not well represented. We revised the model grid by increasing the deep between Chickahominy River and downstream mesohaline. With the change of the depth, the model results are improved. The summary of model prediction skill is shown in Figure as a Taylor diagram, in which salinity is compared at seven monitoring stations along the James River (locations shown in Figure 2-3-3(b)). It can be seen that the model has a high correlation with a low root-mean-square error at each station. Selected time series comparisons for the period at Stations LE5.4 and LE5.1 are shown in Figures and , respectively. Comparisons of modeled and observed salinities for the model simulation period from at Stations LE5.4 and LE5.1 are shown in Figures to , respectively. The station TF5.6 located the James River fresh water dominated area is well predicted by the hydrodynamic model. The time series of the station TF5.6 is shown Figures All the calibration results are presented in Appendix II-A. In general, the model can simulate salinity well in the James. 16

17 ( ) ( ) Figure 2-3-9: Taylor diagrams representing model-data comparisons at 7 monitoring stations in the James River. Three axes represent correlation coefficients (blue lines), the centered rootmean-square difference (green lines), and normalized standard deviation (black lines)(station Location: 1=LE5.5, 2=LE5.6, 3=LE5.4, 4=LE5.3, 5=LE5.2, 6=LE5.1, 7=RET5.2). 17

18 Figure : Comparison of modeled and observed salinity (surface, middle, and bottom layers) from and at Station LE

19 Figure : Comparison of modeled and observed salinity (surface, middle, and bottom layers) from and at Station LE

20 Figure : Comparison of modeled and observed salinity (surface, middle, and bottom layers) from and at Station LE

21 Figure : Comparison of modeled and observed salinity (surface, middle, and bottom layers) from at Station LE

22 Figure : Comparison of modeled and observed salinity (surface, middle, and bottom layers) from at Station LE5.1. The model stratification is examined for each station. We use salinity difference between bottom and surface to quantify the magnitude of the stratification. Examples of model results at Stations LE5.4, LE5.3, and LE5.1 located in lower James from are shown in Figs to Because of the influence of forcing and the boundary condition, some mismatch can be expected. Overall, the results are satisfactory. 22

23 20 15 L E5. 4 O M ΔS /15/07 02/15/07 03/15/07 04/15/07 05/15/07 06/15/07 07/15/07 08/15/07 09/15/07 10/15/07 11/15/07 12/15/07 Date L E5. 4 O M ΔS /15/08 02/15/08 03/15/08 04/15/08 05/15/08 06/15/08 07/15/08 08/15/08 09/15/08 10/15/08 11/15/08 12/15/08 Date L E5. 4 O M ΔS /15/09 02/15/09 03/15/09 04/15/09 05/15/09 06/15/09 07/15/09 08/15/09 09/15/09 10/15/09 11/15/09 12/15/09 Date L E5. 4 O M ΔS /15/10 02/15/10 03/15/10 04/15/10 05/15/10 06/15/10 07/15/10 08/15/10 09/15/10 10/15/10 11/15/10 12/15/10 Date L E5. 4 O M ΔS /15/11 02/15/11 03/15/11 04/15/11 05/15/11 06/15/11 07/15/11 08/15/11 09/15/11 10/15/11 11/15/11 12/15/11 Date Figure : Example of comparison of modeled and observed stratification of salinity at Station LE5.4 from (O is observation and M is modeled). 23

24 20 15 L E5. 3 O M ΔS /15/07 02/15/07 03/15/07 04/15/07 05/15/07 06/15/07 07/15/07 08/15/07 09/15/07 10/15/07 11/15/07 12/15/07 Date L E5. 3 O M ΔS /15/08 02/15/08 03/15/08 04/15/08 05/15/08 06/15/08 07/15/08 08/15/08 09/15/08 10/15/08 11/15/08 12/15/08 Date L E5. 3 O M ΔS /15/09 02/15/09 03/15/09 04/15/09 05/15/09 06/15/09 07/15/09 08/15/09 09/15/09 10/15/09 11/15/09 12/15/09 Date L E5. 3 O M ΔS /15/10 02/15/10 03/15/10 04/15/10 05/15/10 06/15/10 07/15/10 08/15/10 09/15/10 10/15/10 11/15/10 12/15/10 Date L E5. 3 O M ΔS /15/11 02/15/11 03/15/11 04/15/11 05/15/11 06/15/11 07/15/11 08/15/11 09/15/11 10/15/11 11/15/11 12/15/11 Date Figure : Example of comparison of modeled and observed stratification of salinity at Station LE5.3 from

25 20 15 L E5. 1 O M ΔS /15/07 02/15/07 03/15/07 04/15/07 05/15/07 06/15/07 07/15/07 08/15/07 09/15/07 10/15/07 11/15/07 12/15/07 Date L E5. 1 O M ΔS /15/08 02/15/08 03/15/08 04/15/08 05/15/08 06/15/08 07/15/08 08/15/08 09/15/08 10/15/08 11/15/08 12/15/08 Date L E5. 1 O M ΔS /15/09 02/15/09 03/15/09 04/15/09 05/15/09 06/15/09 07/15/09 08/15/09 09/15/09 10/15/09 11/15/09 12/15/09 Date L E5. 1 O M ΔS /15/10 02/15/10 03/15/10 04/15/10 05/15/10 06/15/10 07/15/10 08/15/10 09/15/10 10/15/10 11/15/10 12/15/10 Date L E5. 1 O M ΔS /15/11 02/15/11 03/15/11 04/15/11 05/15/11 06/15/11 07/15/11 08/15/11 09/15/11 10/15/11 11/15/11 12/15/11 Date Figure : Example of comparison of modeled and observed stratification of salinity at Station LE5.1 from Temperature Temperature is a key parameter for eutrophication model as all the kinetic parameters depend on temperature. A summary of model prediction skill for temperature simulation is shown in Figure

26 as a Taylor diagram, in which temperature is compared at ten monitoring stations along the James River (Figure 2-3-3). It can be seen that the model results include a high correlation at each station with a low root-mean-square error. There is no difference for all stations statistically for model calibration and validation. Examples of time series comparisons of temperature at surface, middle, and bottom layers at stations upstream (TF5.5), middle (RET5.2), and downstream (LE5.4) are shown in Figs to for the period It can be seen that temperatures are simulated well. The period was used for temperature verification and the results are shown in Figs to The model results are satisfactory. Figure : Taylor diagrams representing model-data comparisons at 7 monitoring stations in the James River. Three axes represent correlation coefficients (blue lines), the centered rootmean-square difference (green lines), and normalized standard deviation (black lines)(station Locations: 1=LE5.5, 2=LE5.6, 3=LE5.4, 4=LE5.3, 5=LE5.2, 6=LE5.1, 7=RET5.2, 8=TF5.6, 9=TF5.5, 10=TF4.5). 26

27 Figure : Comparison of modeled and observed temperature (surface, middle, and bottom layers) from ( ) at Station TF

28 Figure : Comparison of modeled and observed temperature (surface, middle, and bottom layers) from ( ) at Station RET

29 Figure : Comparison of modeled and observed temperature (surface, middle, and bottom layers) from at Station LE

30 Figure : Comparison of modeled and observed temperature (surface, middle, and bottom layers) from at Station TF

31 Figure : Comparison of modeled and observed temperature (surface, middle, and bottom layers) from at Station RET

32 Figure : Comparison of modeled and observed temperature (surface, middle, and bottom layers) from at Station LE Sensitivity Tests A series of model sensitivity tests were conducted for roughness height, boundary condition, and wind forcing Bottom roughness The bottom roughness is the only parameter to calibrate for the surface elevation. We use one uniform roughness height along the entire James. As there is insufficient information to specify roughness locally, the use of a global roughness specification was made. Changes of tidal range with respect to different values of roughness are shown in Fig It can be seen that the model is very sensitive to the selection of roughness height. It appears that the model needs to use a relatively low roughness height. 32

33 Figure 2-4-1: Sensitivity tests for different bottom roughness heights Freshwater Estuarine stratification is a competition between barotrophic and baroclinic forcings. The large buoyant forcing is from freshwater discharge, which flows out of the estuary on top of the salty, dense water. It can be expected that a change of freshwater discharge can cause a change of salinity. In the current model configuration, we used watershed model output to drive the model. The model predictive skill is very high, but some discrepancy can be expected, as shown in Fig A sensitivity run was conducted to replace three freshwater discharge input records from locations upstream of Richmond, Appomattox River, and Chickahominy River with USGS flows. These three stations account for the majority of the flow. An example of flow difference at Richmond between watershed model results and USGS data is shown in Fig

34 Figure 2-4-2: Comparisons of discharge at the fall line between USGS observations and watershed model simulation results. Figure shows the comparison of salinity difference when using watershed runoff and USGS flow. It can be seen that the model is very sensitive to the flow. The salinity can differ by 2-4 psu, which is about one standard deviation, which is on the same order as the root-mean-square error of model calibration (less than one standard deviation). Therefore, some discrepancy during salinity calibration can be expected. Figure 2-4-3: Sensitivity tests for flow at Station RET5.2 and LE5.4 (blue lines show results of model simulation using watershed flow and green line show results of simulation using USGS flow at three upstream stations). 34

35 It is interesting to know if the change of salinity, which is within the accepted error due to flow or other forcings, will affect the long-term transport or export of nutrients, as the retention of nutrients and eutrophication are highly determined by the residence time (Boynton et al., 1995). The transport property of a substance can be quantified by the transport timescales such as residence time and water age (Deleersnijder et al., 2001; Shen and Lin, 2006). The age of water is defined as the time elapsed since it leaves the headwaters. The age at each location indicates the time required for the water or conservative substance to travel from the headwaters to a specified location (Shen and Lin, 2006). The age and the residence time are often sufficient to characterize the motions of a conservative substance. We computed the freshwater age along the James River using different flows and compared the results in Fig The method for computing age can be found in Deleersnijder et al. (2001) and Shen and Lin (2006). It can be seen that the transport process is very sensitive to the flow condition. The difference can be about 5 days for a given period for this example or at a particular day. The change of flow can affect short-term transport processes. But mean water age does not change much as shown in Fig As the USGS flow increases slightly, the transport speeds slightly. The results indicate that it will not affect the long-term transport of nutrients in the estuary. Figure 2-4-4: Example of water age at 4/16/2008 (left panel shows results using watershed flow and right panel shows results using USGS flow). 35

36 Figure 2-4-5: Mean water age and difference along the James River for the period of Wind Wind is a very important forcing to change estuarine circulation. Downstream wind can enhance the estuarine circulation, while upstream wind can increase mixing in estuary. Wind plays an important role in the modulation of hypoxia in the Chesapeake Bay (Scully, 2010; Shen et al., 2013). A sensitivity test to determine the influence of wind on salinity was conducted. It is unknown if wind will have a large impact on salinity for this relatively small estuary. We reduced wind forcing by 10% and ran the model from and compared the salinity at Stations LE5.4 and RET5.2, which represents the range of salinity intrusion in estuary. The sensitivity results are shown in Fig It can be seen that a 10% reduction of wind can change salinity up to 2 psu at Station LE5.4, but causes less than a 0.5-psu change at Station RET5.2. Because the period of wind forcing fluctuation is 3-5 days in the Chesapeake Bay, the short-term change of wind on long-term transport may not be important in this narrow estuary. We compared computed water age. Examples of time series of age at Stations LE5.3 and RET5.2 are shown in Fig It can be seen that there is no difference in water age, which suggests that a 10% error in wind forcing will not affect the long-term transport of nutrients. 36

37 Figure 2-4-6: Comparison of differences of salinity with wind forcing reductions by 10% at Stations LE5.4 and RET5.2, respectively, from Figure 2-4-7: Comparison of differences of water age (days) with wind forcing reductions by 10% at Stations LE5.3 and TF5.5 respectively, from

38 2.4.4 Open Boundary We used the output of salinity from the large model as the open boundary condition. The influence of the open boundary condition of salinity on the model was evaluated by running the model with a reduction of 5% of the salinity at the open boundary. Comparisons of model results at Stations LE5.4 and RET5.2 are shown in Fig The salinity decreases about 1.5 psu at Station LE5.4, but only decreases up to 1 psu at Station RET5.2. It can be seen that the salinity simulation is sensitive to the open boundary specification. Based on the sensitivity runs, the calibration results are satisfactory, although there are errors in the salinity open boundary condition. Figure 2-4-8: Comparison of differences of salinity with open boundary salinity reductions by 5% at Stations LE5.4 and RET5.2, respectively, from

39 2.5 Summary A three-dimensional hydrodynamic model has been developed for the James River. The Environmental Fluid Dynamics Computer Code (EFDC) is used for developing the James River hydrodynamics model. The daily watershed model outputs from upstream and laterally adjacent watershed were used for the flows discharging to the model domain. The model was forced by hourly wind and metrological data, and hourly tidal elevation and salinity and temperature at the mouth. The model was calibrated for surface elevation in from and current from , and for salinity and temperature from , and it is validated from for salinity and temperature. The model validation results indicate that the model is robust, it adequately simulates the dynamics and temperature, and it is suitable for the water quality model development for the James River. A series of model sensitivity simulations were conducted with respect to bottom roughness height, freshwater discharge, wind, and boundary forcings. The model is sensitive to bottom roughness and freshwater discharge and boundary condition of salinity. Change of freshwater discharge can affect the salinity downstream. However, driven by watershed model discharge, it will not affect the long-term transport property for the estuary based on the comparison of the transport timescale of water age. References Boynton, W. R., J. H. Garber, R. Summers, and W. M. Kemp Inputs, transformations, and transport of nitrogen and phosphorus in Chesapeake Bay and selected tributaries. Estuaries 18, Bukaveckas, P. A. and L. E. Barry Factors determining the location of the chlorophyll maximum and the fate of algal production within the tidal freshwater James River. Estuaries and Coasts, 34: Cerco, C. and M. Noel The 2002 Chesapeake Bay eutrophicationmodel. US Army Engineer Research and Development Center, Vicksburg, MS. (EPA 903-R ). Deleersnijder, E., J. M. Campin, and E. J. M. Delhez The concept of age in marine modeling, I. Theory and preliminary model results. Journal of Marine Systems 28, Du, J. and J. Shen Decoupling the influence of biological and physical processes on the dissolved oxygen in the Chesapeake Bay, J. Geophys. Res. Oceans, 119, doi: / 2014JC Galperin, B., L. H. Kantha, S. Hassid, and A. Rosati A quasi-equilibrium turbulent energy model for geophysical flows. J. Atmos. Sci., 45, Hamrick, J. M A Three-Dimensional Environmental Fluid Dynamics Computer Code: Theoretical and Computational Aspects. Special Report in Applied Marine Science and Ocean Engineering. No Virginia Institute of Marine Science, College of William and Mary, Gloucester Point, Virginia. 39

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