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Modeling mixing processes in the Columbia River estuary: a model-data comparison
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Modeling mixing processes in the Columbia River estuary" a model-data comparison Arun Chawla ~, Antdnio M. Baptista a, and Yinglong Zhang a* aCenter for Coastal and Land Margin Research, Oregon Health & Science University, 20000 NW Walker Road, Beaverton O R - 97006, USA Columbia River estuary is a macro-tidal estuary, shallow except for two narrow channels through which most of the salt transport takes place. Mixing processes in the two channels are strongly affected by Spring- Neap variations in the tidal range, river discharges (which typically exceed 10,000 m3/s during spring freshets) and coastal winds. The interactions between these forcings controls residual flows inside the estuary, which in turn impacts the extent of salt intrusion into the estuary. Using a new 3D baroclinic circulation model (ELCIRC, [12]), a year long database of numerical simulations has been developed and contrasted against data from a network of in situ instruments which form a part of CORIE. CORIE is a long term coastal-margin observatory system for the Columbia River. In this paper we (a) show that ELCIRC simulations are able to reproduce some but not all key complex interactions observed in the field, (b) study the spatial and temporal variations circulation patterns using a combination of simulations and observations, and (c) use simulations to quantify the role played by each of the forcings (tides, winds, and discharge) on the residual circulation and salinity intrusion patterns. 1. I N T R O D U C T I O N
Transport and mixing processes in an estuarine environment are a result of strong non-linear dynamic interactions between circulation processes, bathymetry, and external forcings (atmospheric, ocean, and fluvial). In many systems, the interactions between a river and the ocean is not confined to the estuary itself, and extends into the continental shelf in the form of a fresh water plume. The Columbia River, with the second largest fresh water discharge in the continental United States, forms one such system. Considerable inroads in our understanding of the Columbia River system have been made using field studies [8,7], historical records [10], and numerical studies [6]. Though these studies have provided considerable insight into the dynamics of circulation in the Columbia River estuary, they do not consider the system as a whole. For a high discharge estuarine system such as the Columbia, mixing processes in the coastal plume will affect *This research was funded by NOAA (NA17FE1486,NA17FE1026,NA87FE0405), the US Fish and Wildlife Service (133101J104), the Office of Naval Research (N00014-00-1-0301,N00014-99-1-0051), and the National Science Foundation (ACI-0121475)
1780 estuarine dynamics and vice-versa. Keeping this in mind, a 3-dimensional baroclinic circulation model [12] for the Columbia River system has been developed. A comprehensive and efficient numerical model is a valuable tool in expanding our knowledge of the system, enabling sustainable, and eco-friendly management decisions. Benchmark tests of the numerical model have been presented in a companion paper [11]. In this paper, we shall evaluate the capabilities of the model with measured field data over a year long simulation period. The aim is to explore the model's capabilities under varying time scales and external forcings. 2. C O R I E
CORIE is a coastal-margin observatory that has been designed to study oceanographic processes in the Columbia River system, and has been in place since 1996. The region of study includes the Columbia River estuary and the neighboring coastal zone, where the impact of the fresh water plume from the Columbia River can be felt. The coastal observatory consists of two parts; a modeling system to simulate the ocean - river dynamics and a network of in situ observation stations. Though the current emphasis of the CORIE system are physical processes, the design of the system does not preclude studies of chemical and/or biological processes being added to it in the future. 2.1. O b s e r v a t i o n n e t w o r k
The CORIE observation network consists of a number of stations placed at different locations inside the estuary (see Figure 1). The stations are identified by unique 5 character alpha-numeric strings (for clarity only the two CORIE stations used in this study are labeled in Figure 1). Each of these stations consist of one or more in situ instruments. Predominantly the instruments are of two t y p e s - CTD sensors that measure the salinity, temperature, and pressure (which is used to determine elevation) at the position of the instrument, and ADP sensors that determine the velocity profile over the water column. At some stations CT sensors (measuring salinity and temperature only) have been deployed at different depths to get a sense of the vertical distribution of salinity and temperature. The instruments at all the stations are deployed on a semi-permanent basis and data is retrieved in real time, via telemetry, using UHF radio links. Apart from the estuarine stations, the CORIE observation network also consists of an offshore buoy (approximately 16 miles South West of the mouth of the Columbia River estuary). Due to line of sight complications, there is no telemetric link available to the buoy. Apart from utilizing data from the CORIE stations, this study also uses external sources of data to quantify the forcings on the Columbia River system. Tidal forcings are determined from a NOAA tidal gage located inside the estuary (see Figure 1). The tides in the estuary range from less than 2 m during Neap cycles to over 3 in during the Spring cycles. River discharge data is determined from Bonneville dam at the head of the estuary. The Columbia River system is a high discharge system with the river discharge varying from less than 2000 m3/s during periods of drought to over 10000 m 3/s during / spring freshets. Ocean wind conditions are obtained from a wind gauge on an offshore NOAA buoy, located approximately 12 kin west of the estuary mouth (see Figure 1). Typically, strong southerly (from the south) winds blow during the winter months and weak northerly winds blow during the summer months.
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Figure 1. Columbia River estuary. The estuarine stations of the CORIE observation network are identified by squares. The head of the Columbia River estuary is at Bonneville dam approximately 250 km upstream of the mouth of the Columbia River estuary
2.2. Modeling system A recently developed 3D finite volume baroclinic circulation numerical model (ELC I R C ) [12] is the numerical model used in the CORIE modeling system. The model uses z-coordinates in the vertical. Since vertical mixing plays a crucial role in transport processes inside the estuary, the model provides the option of choosing from a number of sophisticated turbulence closure schemes. Due to the strong interaction between estuarine and offshore coastal processes, the numerical model is used to simulate the smaller scale estuarine as well as the larger scale coastal processes. As a result, an extensively large grid is used in these simulations (see Figure 2). Time scales of processes in the Columbia River estuary extend from days (tidal processes) to months (migration and variation of fresh water plumes). To study the long term variation of estuarine dynamics, a year long data base of numerical simulations (for the year 2002) has been constructed for the Columbia River system. The simulations are forced by discharge data from the Columbia and Willamette rivers (see Figure 1) at the river boundaries. These are the only fresh water sources considered in the model, and other sources such as the Fraser river are ignored. At the offshore boundary tides are forced using an in house developed tidal package [9]. Atmospheric boundary conditions (winds, atmospheric pressure, and heat fluxes) are obtained from simulations of the Global Forecast System (GFS) from the National Center for Environmental Prediction (NCEP). The limitation of this approach is that outputs from atmospheric models (with their inherent uncertainty) are being used to externally force the circulation processes in our simulations. On the other hand, such an approach provides invaluable information on
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Figure 2. Computational grid for numerical simulations. The grid extends upstream into the Columbia River up to the tidal head at Bonneville dam. Offshore the grid extends approximately 300 km west 1200 km south and 500 km north, to include a large part of the continental shelf region into the computational domain. The grid is highly refined inside the estuary
the spatially varying atmospheric conditions and the heat fluxes, which are not possible to obtain with direct measurements. Initial conditions for the simulations are determined from the NODC (Levitus) World Ocean Atlas 1998 provided by the NOAA-CIRES Climate Diagnostics Center [2,1].
3. M O D E L -
DATA C O M P A R I S O N S
In this study we shall use data from two CORIE stations a m 1 6 9 and r e d 2 6 to evaluate model simulations. The two stations are in the vicinity of the South Channel, which is also the main conduit for river discharge out of the system. Hence, the data at these stations is strongly influenced by river discharge. Each of these stations have CT (salinity and temperature) sensors located at three vertical positions as well as an ADP sensor in the vicinity, allowing for comparison of vertical as well as temporal variations of salinity and velocity.
3.1. Salinity comparisons Figure 3 shows the comparison between modeled and measured salinity at all the three CT instruments of r e d 2 6 for a one year period. For clarity only the daily maximum and minimum salinities have been plotted. Time series of tidal range, wind speed, and river discharge have also been plotted for comparison as they play an important role in mixing
1783 processes. Salinity data tends to have large gaps due to frequent failures caused by biofouling. Nevertheless, the long time record does provide some insight into the different processes, and the model's ability to reproduce them.
Figure 3. Model- Data comparisons of salinity at red26. The top three panels correspond to daily maximum (in red) and daily minimum (in blue) salinity at the three CT stations. Panels on the left correspond to data while the panels on the right correspond to model. The daily tidal range (from NOAA tidal gage), the daily averaged wind speed (from offshore buoy) and the daily averaged river discharge (from Bonneville dam) have also been plotted for comparison purposes
An increase in river discharge leads to a drop in minimum salinity at all the sensors in the data, indicating an efficient flushing mechanism during the ebb tides. The maximum salinity only reduces at the top sensor, indicating that a strong salt wedge still propagates past the instruments. The model also shows a drop in salinity at all the stations during high discharge periods, but that drop is much greater than in the data, indicating that the river outflow has an unduly large effect on salt transport at red26. The effect of winds can be seen from two features. One is the drop in maximum salinity in January 2002. This occurs just after a period of strong down-welling favorable winds [3], which reduce the amount of salt entering the estuary. That process can be seen in
1784 the model results as well, though the effect is more pronounced. The second feature is the overall reduction in salinity in late August 2002 (a weak wind period) when compared with January 2002 (a similar discharge but strong wind period). The same feature is not observed in the model results, probably because of the stronger influence of river discharge. The increase in salinity in the model results during the strong winds in the latter half of the year (November 2002 onwards) is puzzling. A corresponding increase in the data is not observed. The effect of tidal range on salt transport can also be seen in this comparison. In the data, during the Neap cycle of the tides we see a rise in the minimum salinity at all the sensors and at the same time a drop in the maximum salinity at the top sensor, and viceversa during the Spring cycle. This is particularly in evidence after September 2002, when all the sensors have overlapping data. The main cause for this feature is the suppression of vertical mixing during the Neap cycle, which intensifies the horizontal baroclinic density gradient near the bottom, leading to weaker flushing of salinity during tidal ebb at the lower sensors (hence an increase in minimum salinity). Weak vertical mixing also reduces the amount of salt observed at the top sensor. The N e a p - Spring cycle is also observed in the model simulations, but the rise in minimum salinity is much smaller than seen in the data, and at the same time, the fluctuations in the maximum salinity are much greater. One of the causes of model - data disparity is that the bathymetry at r e d 2 6 is deeper in the model when compared to the data. Figure 4 shows the model time series of daily maximum and minimum salinity from the deeper parts of the water column at red26. Qualitatively similar results are observed between the near bottom model results (depth = 15 m) and near bottom data results (depth = 9 m), particularly in the maximum salinity. Differences in the flushing characteristics of the estuary still exist. After September 2002, when river discharge falls below 5000 ma/s, the data shows that the minimum salinity does not fall to 0, even during the Spring tidal cycle, when vertical mixing processes are strongest and baroclinic forcing the weakest. This is true even for the sensor in the uppermost part of the water column (see Figure 3). The model simulations, on the other hand, always flush out the salt during the Spring tidal cycle, even in the cases where the flushing characteristics of the previous Neap cycle are very weak. This is due to a combination of excessive river outflow through the South Channel and overly intensified mixing during the Spring tidal cycle. Comparison of salinity at a further upstream station (am169) is given in Figure 5. The bathymetric errors between model and data at this station are much smaller at this station compared to red26. Once again, the model results show a much greater sensitivity to river discharge than the actual data. Also, model results indicate that at this station salt always gets flushed out during the ebb, while the data shows that the salt does not get flushed out in the ebb during the Neap tidal cycle. The comparisons indicate that the salt wedge does not get pushed far enough in the model results, partly because the salt is regularly flushed out during the Spring tidal cycle.
3.2. Velocity comparisons Analysis of velocity data was undertaken by first computing the principal component axes (direction of maximum and minimum variance) [4] of the velocity vectors. All corresponding analysis between model and data was carried out for the velocity component
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Figure 4. Daily maximum and minimum salinity from the model at red26. This plot is similar to Figure 3 except the salinity has been extracted from the model at different depths
along direction of maximum variance (also referred to as the along channel velocity component). The along channel velocity component accounts for over 95 % of the total variance at the instruments in the Columbia River estuary, so it is appropriate to neglect across channel velocities. Two important time scales of velocity fields are the tidal and sub-tidal (also referred to as residual) time scales. In this paper we have defined sub-tidal velocity flow fields as velocities with periods greater than 2 days, and are computed by passing the along channel velocity time series through a low-pass filter in the frequency domain. Accurate simulation of residual flow field is vital because of its significance in long term transport properties of the estuary. Figure 6 shows a comparison between model and data residual flow fields at r e d 2 6 and am169. Positive values correspond to residual flow into the estuary. At red26, residual inflow near the bottom is much stronger in the model than in the data. Part of this occurs due to significant bathymetric errors around this station. Qualitative features of the residual flow patterns are, however, represented fairly well. Just as was observed in the salinity results, enhanced vertical mixing during the Spring tidal cycle breaks up the two layer residual flow pattern. While this occurs only for discharge greater
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Figure 5. Model - Data comparisons of salinity at am169. The figure is similar to Figure 3, except that the vertical positions of the sensors are different
than 5000 m 3/s in the data, it occurs for all discharge conditions in the model At a m 1 6 9 / on the other hand, the residual inflow near the bottom is stronger in the data than in the model, while the qualitative features are once again represented fairly well. The reduction in residual transport at this station occurs due to weakened baroclinic circulation, which in turn hampers further upstream transport of the salt wedge. A comparison of residual outflow at the two stations indicates that the residual outflow in the model results are greater when the discharge falls below 5000 ma/s. A closer look at the instantaneous along-channel velocity profiles during flood and ebb tides at the two stations (Figure 7) highlights some of the differences between simulations and the data. The profile snapshots have been plotted during a Neap tidal cycle, when the edge of the salt wedge is closer to am169. Overall, the velocity profiles compare better during ebb than flood. At both the stations the data shows increased velocities near the bottom during the flood tide. These enhanced velocities near the bottom are caused by horizontal density gradients and are not seen in the data at a m 1 6 9 during the Spring cycle when the salt wedge is closer to r e d 2 6 (figure not shown). Enhanced velocities near the edge of the salt wedge have also been reported by [5] for the Fraser river. The model on the other hand does not show similar enhanced velocities during the
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Figure 6. Model - Data comparisons of residual velocities at red26 (left panel) and a m 1 6 9 (right panel) as a function of time and vertical position. Positive values correspond to flow into the estuary. Daily tidal range and river discharge have also been plotted for comparison
flood part of the tidal cycle at either station, even at r e d 2 6 where it indicates a strong two layer flow. This is probably due to a reduction in horizontal density gradients in the model simulations due to a combination of numerical diffusion and grid resolution. 4. C O N C L U S I O N S
Evaluation of a three dimensional oceanographic model (ELCIRC) has been carried out by comparing with field observations in a highly dynamic estuarine environment. The goal of this study has been to quantify how well the numerical model reproduces mixing and transport processes in this complex environment. Long term comparisons of salinity and velocity records have been carried out with two stations in the Columbia River estuary. While the comparisons at these two stations is not enough to quantify the quality of the model simulations, they still provide us with a reasonable assessment of the model capabilities in the estuary. Overall, the data shows the development of a classical stratified two layer residual flow pattern during the periods of weak tidal mixing (Neap tides) that breaks down during enhanced tidal mixing (Spring
1788 Tidal time series on 8/31/02 2 0 -2 0900
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Figure 7. Instantaneous profiles of along channel velocity for model and data at red26 and a m 1 6 9 for two tidal periods. Specific instances at which the velocity profiles have been plotted are marked on a tidal time series plot. Profiles 1-5 and 11-15 correspond to flood tides while profiles 6-10 and 16-20 correspond to ebb tides.
tides). The two layer pattern is also sensitive to discharge and wind conditions. Simulation results show similar qualitative features though there are some differences. Some of these errors arise from erroneous bathymetric data, and are currently being addressed. Model simulations show an enhanced influence of river discharge, which in turn limits the progress of the salt wedge upstream into the estuary. An important process that influences baroclinic forcing inside the estuary is the dynamics of vertical mixing. Several year long databases of simulations, with different mixing mechanisms are currently being constructed to study their influence on circulation. Large scale offshore dynamics can also influence salt transport inside the estuary. Only continental shelf and coastal processes have been accounted for in the current set of simulations that have been evaluated here. Another database of simulations that nests the results from a global circulation model for the Pacific with the model for the Columbia River system is also being constructed. The analysis presented in this paper shall be used to evaluate the several database simulations that are currently being generated. This study has highlighted both the capabilities and limitations of the numerical model
1789 in simulating transport processes in the estuary under a range of different conditions. Though there are significant challenges ahead, the ability of the ELCIRC model to simulate circulation and transport processes in an extremely dynamic environment is very encouraging. REFERENCES
1. J.I. Antonov, S. Levitus, T.P. Boyer, M.E. Conkright, T. O'Brien and C. Stephens, World Ocean Atlas 1998,Vol. 2: Temperature of the Pacific Ocean.(1998) NOAA Atlas NESDIS 28, U.S. Government Printing Office, Washington, D.C. 2. T.P. Boyer, S. Levitus, J.I. Antonov, M.E. Conkright, T. O'Brien and C. Stephens, World Ocean Atlas 1998,Vol. 5: Salinity of the Pacific Ocean.(1998) NOAA Atlas NESDIS 31, U.S. Government Printing Office, Washington, D.C. 3. A. Chawla, A.M. Baptista and M. Wilkin, Long term variability of circulation in the Columbia River estuary. (2003) in preparation. 4. W.J. Emery and R.E. Thomson, Data analysis methods in physical oceanography, Second edition. (2001). Elsevier New York. 5. W.R. Geyer, The advance of a salt wedge : Observations and dynamical model, Physical Processes in Estuaries (1988), pp 181-195, editors J. Dronkers and W. van Leussen, Springer-Verlag, New York. 6. P. Hamilton, Modeling Salinity and Circulation for the Columbia River Estuary, Progress in Oceanography (1990), 25, pp 113-156 7. D.A. Jay, and J.D. Musiak, Internal Tide Asymmetry, Mixing in Estuaries and Coastal Seas, Coastal and Estuarine Studies (1996), pp 211-249, editor C.B. Pattiaratchi, American Geophysical Union, Washington D.C. 8. D.A. Jay and J.D. Smith, Circulation, density distribution and neap-spring transitions in the Columbia River Estuary, Progress in Oceanography (1990), 25, pp 81-112 9. E. P. Myers and A. M. Baptista, Inversion for tides in the Eastern North Pacific Ocean, Advances in Water Resources (2001), 24(5), pp 505-519 10. C. Sherwood, D. Jay, R. Harvey, P. Hamilton and C. Simenstad, Historical changes in the Columbia River Estuary, Progress in Oceanography (1990),25,pp 299-3{52 11. Y.L. Zhang and A.M. Baptista, Benchmarking a new open-source 3D circulation model (ELCIRC), Int. Conf. Comp. methods in Wat. Res. (2004) 12. Y.L. Zhang and A.M. Baptista, A cross-scale model for 3D baroclinic circulation in estuary-plume-shelf systems: I. Formulation and skill assessment. Cont. Shelf Res. (2003) submitted.
Modeling mixing processes in the Columbia River estuary" a model-data comparison Arun Chawla ~, Antdnio M. Baptista a, and Yinglong Zhang a* aCenter for Coastal and Land Margin Research, Oregon Health & Science University, 20000 NW Walker Road, Beaverton O R - 97006, USA Columbia River estuary is a macro-tidal estuary, shallow except for two narrow channels through which most of the salt transport takes place. Mixing processes in the two channels are strongly affected by Spring- Neap variations in the tidal range, river discharges (which typically exceed 10,000 m3/s during spring freshets) and coastal winds. The interactions between these forcings controls residual flows inside the estuary, which in turn impacts the extent of salt intrusion into the estuary. Using a new 3D baroclinic circulation model (ELCIRC, [12]), a year long database of numerical simulations has been developed and contrasted against data from a network of in situ instruments which form a part of CORIE. CORIE is a long term coastal-margin observatory system for the Columbia River. In this paper we (a) show that ELCIRC simulations are able to reproduce some but not all key complex interactions observed in the field, (b) study the spatial and temporal variations circulation patterns using a combination of simulations and observations, and (c) use simulations to quantify the role played by each of the forcings (tides, winds, and discharge) on the residual circulation and salinity intrusion patterns. 1. I N T R O D U C T I O N
Transport and mixing processes in an estuarine environment are a result of strong non-linear dynamic interactions between circulation processes, bathymetry, and external forcings (atmospheric, ocean, and fluvial). In many systems, the interactions between a river and the ocean is not confined to the estuary itself, and extends into the continental shelf in the form of a fresh water plume. The Columbia River, with the second largest fresh water discharge in the continental United States, forms one such system. Considerable inroads in our understanding of the Columbia River system have been made using field studies [8,7], historical records [10], and numerical studies [6]. Though these studies have provided considerable insight into the dynamics of circulation in the Columbia River estuary, they do not consider the system as a whole. For a high discharge estuarine system such as the Columbia, mixing processes in the coastal plume will affect *This research was funded by NOAA (NA17FE1486,NA17FE1026,NA87FE0405), the US Fish and Wildlife Service (133101J104), the Office of Naval Research (N00014-00-1-0301,N00014-99-1-0051), and the National Science Foundation (ACI-0121475)
1780 estuarine dynamics and vice-versa. Keeping this in mind, a 3-dimensional baroclinic circulation model [12] for the Columbia River system has been developed. A comprehensive and efficient numerical model is a valuable tool in expanding our knowledge of the system, enabling sustainable, and eco-friendly management decisions. Benchmark tests of the numerical model have been presented in a companion paper [11]. In this paper, we shall evaluate the capabilities of the model with measured field data over a year long simulation period. The aim is to explore the model's capabilities under varying time scales and external forcings. 2. C O R I E
CORIE is a coastal-margin observatory that has been designed to study oceanographic processes in the Columbia River system, and has been in place since 1996. The region of study includes the Columbia River estuary and the neighboring coastal zone, where the impact of the fresh water plume from the Columbia River can be felt. The coastal observatory consists of two parts; a modeling system to simulate the ocean - river dynamics and a network of in situ observation stations. Though the current emphasis of the CORIE system are physical processes, the design of the system does not preclude studies of chemical and/or biological processes being added to it in the future. 2.1. O b s e r v a t i o n n e t w o r k
The CORIE observation network consists of a number of stations placed at different locations inside the estuary (see Figure 1). The stations are identified by unique 5 character alpha-numeric strings (for clarity only the two CORIE stations used in this study are labeled in Figure 1). Each of these stations consist of one or more in situ instruments. Predominantly the instruments are of two t y p e s - CTD sensors that measure the salinity, temperature, and pressure (which is used to determine elevation) at the position of the instrument, and ADP sensors that determine the velocity profile over the water column. At some stations CT sensors (measuring salinity and temperature only) have been deployed at different depths to get a sense of the vertical distribution of salinity and temperature. The instruments at all the stations are deployed on a semi-permanent basis and data is retrieved in real time, via telemetry, using UHF radio links. Apart from the estuarine stations, the CORIE observation network also consists of an offshore buoy (approximately 16 miles South West of the mouth of the Columbia River estuary). Due to line of sight complications, there is no telemetric link available to the buoy. Apart from utilizing data from the CORIE stations, this study also uses external sources of data to quantify the forcings on the Columbia River system. Tidal forcings are determined from a NOAA tidal gage located inside the estuary (see Figure 1). The tides in the estuary range from less than 2 m during Neap cycles to over 3 in during the Spring cycles. River discharge data is determined from Bonneville dam at the head of the estuary. The Columbia River system is a high discharge system with the river discharge varying from less than 2000 m3/s during periods of drought to over 10000 m 3/s during / spring freshets. Ocean wind conditions are obtained from a wind gauge on an offshore NOAA buoy, located approximately 12 kin west of the estuary mouth (see Figure 1). Typically, strong southerly (from the south) winds blow during the winter months and weak northerly winds blow during the summer months.
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Figure 1. Columbia River estuary. The estuarine stations of the CORIE observation network are identified by squares. The head of the Columbia River estuary is at Bonneville dam approximately 250 km upstream of the mouth of the Columbia River estuary
2.2. Modeling system A recently developed 3D finite volume baroclinic circulation numerical model (ELC I R C ) [12] is the numerical model used in the CORIE modeling system. The model uses z-coordinates in the vertical. Since vertical mixing plays a crucial role in transport processes inside the estuary, the model provides the option of choosing from a number of sophisticated turbulence closure schemes. Due to the strong interaction between estuarine and offshore coastal processes, the numerical model is used to simulate the smaller scale estuarine as well as the larger scale coastal processes. As a result, an extensively large grid is used in these simulations (see Figure 2). Time scales of processes in the Columbia River estuary extend from days (tidal processes) to months (migration and variation of fresh water plumes). To study the long term variation of estuarine dynamics, a year long data base of numerical simulations (for the year 2002) has been constructed for the Columbia River system. The simulations are forced by discharge data from the Columbia and Willamette rivers (see Figure 1) at the river boundaries. These are the only fresh water sources considered in the model, and other sources such as the Fraser river are ignored. At the offshore boundary tides are forced using an in house developed tidal package [9]. Atmospheric boundary conditions (winds, atmospheric pressure, and heat fluxes) are obtained from simulations of the Global Forecast System (GFS) from the National Center for Environmental Prediction (NCEP). The limitation of this approach is that outputs from atmospheric models (with their inherent uncertainty) are being used to externally force the circulation processes in our simulations. On the other hand, such an approach provides invaluable information on
1782
Figure 2. Computational grid for numerical simulations. The grid extends upstream into the Columbia River up to the tidal head at Bonneville dam. Offshore the grid extends approximately 300 km west 1200 km south and 500 km north, to include a large part of the continental shelf region into the computational domain. The grid is highly refined inside the estuary
the spatially varying atmospheric conditions and the heat fluxes, which are not possible to obtain with direct measurements. Initial conditions for the simulations are determined from the NODC (Levitus) World Ocean Atlas 1998 provided by the NOAA-CIRES Climate Diagnostics Center [2,1].
3. M O D E L -
DATA C O M P A R I S O N S
In this study we shall use data from two CORIE stations a m 1 6 9 and r e d 2 6 to evaluate model simulations. The two stations are in the vicinity of the South Channel, which is also the main conduit for river discharge out of the system. Hence, the data at these stations is strongly influenced by river discharge. Each of these stations have CT (salinity and temperature) sensors located at three vertical positions as well as an ADP sensor in the vicinity, allowing for comparison of vertical as well as temporal variations of salinity and velocity.
3.1. Salinity comparisons Figure 3 shows the comparison between modeled and measured salinity at all the three CT instruments of r e d 2 6 for a one year period. For clarity only the daily maximum and minimum salinities have been plotted. Time series of tidal range, wind speed, and river discharge have also been plotted for comparison as they play an important role in mixing
1783 processes. Salinity data tends to have large gaps due to frequent failures caused by biofouling. Nevertheless, the long time record does provide some insight into the different processes, and the model's ability to reproduce them.
Figure 3. Model- Data comparisons of salinity at red26. The top three panels correspond to daily maximum (in red) and daily minimum (in blue) salinity at the three CT stations. Panels on the left correspond to data while the panels on the right correspond to model. The daily tidal range (from NOAA tidal gage), the daily averaged wind speed (from offshore buoy) and the daily averaged river discharge (from Bonneville dam) have also been plotted for comparison purposes
An increase in river discharge leads to a drop in minimum salinity at all the sensors in the data, indicating an efficient flushing mechanism during the ebb tides. The maximum salinity only reduces at the top sensor, indicating that a strong salt wedge still propagates past the instruments. The model also shows a drop in salinity at all the stations during high discharge periods, but that drop is much greater than in the data, indicating that the river outflow has an unduly large effect on salt transport at red26. The effect of winds can be seen from two features. One is the drop in maximum salinity in January 2002. This occurs just after a period of strong down-welling favorable winds [3], which reduce the amount of salt entering the estuary. That process can be seen in
1784 the model results as well, though the effect is more pronounced. The second feature is the overall reduction in salinity in late August 2002 (a weak wind period) when compared with January 2002 (a similar discharge but strong wind period). The same feature is not observed in the model results, probably because of the stronger influence of river discharge. The increase in salinity in the model results during the strong winds in the latter half of the year (November 2002 onwards) is puzzling. A corresponding increase in the data is not observed. The effect of tidal range on salt transport can also be seen in this comparison. In the data, during the Neap cycle of the tides we see a rise in the minimum salinity at all the sensors and at the same time a drop in the maximum salinity at the top sensor, and viceversa during the Spring cycle. This is particularly in evidence after September 2002, when all the sensors have overlapping data. The main cause for this feature is the suppression of vertical mixing during the Neap cycle, which intensifies the horizontal baroclinic density gradient near the bottom, leading to weaker flushing of salinity during tidal ebb at the lower sensors (hence an increase in minimum salinity). Weak vertical mixing also reduces the amount of salt observed at the top sensor. The N e a p - Spring cycle is also observed in the model simulations, but the rise in minimum salinity is much smaller than seen in the data, and at the same time, the fluctuations in the maximum salinity are much greater. One of the causes of model - data disparity is that the bathymetry at r e d 2 6 is deeper in the model when compared to the data. Figure 4 shows the model time series of daily maximum and minimum salinity from the deeper parts of the water column at red26. Qualitatively similar results are observed between the near bottom model results (depth = 15 m) and near bottom data results (depth = 9 m), particularly in the maximum salinity. Differences in the flushing characteristics of the estuary still exist. After September 2002, when river discharge falls below 5000 ma/s, the data shows that the minimum salinity does not fall to 0, even during the Spring tidal cycle, when vertical mixing processes are strongest and baroclinic forcing the weakest. This is true even for the sensor in the uppermost part of the water column (see Figure 3). The model simulations, on the other hand, always flush out the salt during the Spring tidal cycle, even in the cases where the flushing characteristics of the previous Neap cycle are very weak. This is due to a combination of excessive river outflow through the South Channel and overly intensified mixing during the Spring tidal cycle. Comparison of salinity at a further upstream station (am169) is given in Figure 5. The bathymetric errors between model and data at this station are much smaller at this station compared to red26. Once again, the model results show a much greater sensitivity to river discharge than the actual data. Also, model results indicate that at this station salt always gets flushed out during the ebb, while the data shows that the salt does not get flushed out in the ebb during the Neap tidal cycle. The comparisons indicate that the salt wedge does not get pushed far enough in the model results, partly because the salt is regularly flushed out during the Spring tidal cycle.
3.2. Velocity comparisons Analysis of velocity data was undertaken by first computing the principal component axes (direction of maximum and minimum variance) [4] of the velocity vectors. All corresponding analysis between model and data was carried out for the velocity component
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Figure 4. Daily maximum and minimum salinity from the model at red26. This plot is similar to Figure 3 except the salinity has been extracted from the model at different depths
along direction of maximum variance (also referred to as the along channel velocity component). The along channel velocity component accounts for over 95 % of the total variance at the instruments in the Columbia River estuary, so it is appropriate to neglect across channel velocities. Two important time scales of velocity fields are the tidal and sub-tidal (also referred to as residual) time scales. In this paper we have defined sub-tidal velocity flow fields as velocities with periods greater than 2 days, and are computed by passing the along channel velocity time series through a low-pass filter in the frequency domain. Accurate simulation of residual flow field is vital because of its significance in long term transport properties of the estuary. Figure 6 shows a comparison between model and data residual flow fields at r e d 2 6 and am169. Positive values correspond to residual flow into the estuary. At red26, residual inflow near the bottom is much stronger in the model than in the data. Part of this occurs due to significant bathymetric errors around this station. Qualitative features of the residual flow patterns are, however, represented fairly well. Just as was observed in the salinity results, enhanced vertical mixing during the Spring tidal cycle breaks up the two layer residual flow pattern. While this occurs only for discharge greater
1786
Figure 5. Model - Data comparisons of salinity at am169. The figure is similar to Figure 3, except that the vertical positions of the sensors are different
than 5000 m 3/s in the data, it occurs for all discharge conditions in the model At a m 1 6 9 / on the other hand, the residual inflow near the bottom is stronger in the data than in the model, while the qualitative features are once again represented fairly well. The reduction in residual transport at this station occurs due to weakened baroclinic circulation, which in turn hampers further upstream transport of the salt wedge. A comparison of residual outflow at the two stations indicates that the residual outflow in the model results are greater when the discharge falls below 5000 ma/s. A closer look at the instantaneous along-channel velocity profiles during flood and ebb tides at the two stations (Figure 7) highlights some of the differences between simulations and the data. The profile snapshots have been plotted during a Neap tidal cycle, when the edge of the salt wedge is closer to am169. Overall, the velocity profiles compare better during ebb than flood. At both the stations the data shows increased velocities near the bottom during the flood tide. These enhanced velocities near the bottom are caused by horizontal density gradients and are not seen in the data at a m 1 6 9 during the Spring cycle when the salt wedge is closer to r e d 2 6 (figure not shown). Enhanced velocities near the edge of the salt wedge have also been reported by [5] for the Fraser river. The model on the other hand does not show similar enhanced velocities during the
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Figure 6. Model - Data comparisons of residual velocities at red26 (left panel) and a m 1 6 9 (right panel) as a function of time and vertical position. Positive values correspond to flow into the estuary. Daily tidal range and river discharge have also been plotted for comparison
flood part of the tidal cycle at either station, even at r e d 2 6 where it indicates a strong two layer flow. This is probably due to a reduction in horizontal density gradients in the model simulations due to a combination of numerical diffusion and grid resolution. 4. C O N C L U S I O N S
Evaluation of a three dimensional oceanographic model (ELCIRC) has been carried out by comparing with field observations in a highly dynamic estuarine environment. The goal of this study has been to quantify how well the numerical model reproduces mixing and transport processes in this complex environment. Long term comparisons of salinity and velocity records have been carried out with two stations in the Columbia River estuary. While the comparisons at these two stations is not enough to quantify the quality of the model simulations, they still provide us with a reasonable assessment of the model capabilities in the estuary. Overall, the data shows the development of a classical stratified two layer residual flow pattern during the periods of weak tidal mixing (Neap tides) that breaks down during enhanced tidal mixing (Spring
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Figure 7. Instantaneous profiles of along channel velocity for model and data at red26 and a m 1 6 9 for two tidal periods. Specific instances at which the velocity profiles have been plotted are marked on a tidal time series plot. Profiles 1-5 and 11-15 correspond to flood tides while profiles 6-10 and 16-20 correspond to ebb tides.
tides). The two layer pattern is also sensitive to discharge and wind conditions. Simulation results show similar qualitative features though there are some differences. Some of these errors arise from erroneous bathymetric data, and are currently being addressed. Model simulations show an enhanced influence of river discharge, which in turn limits the progress of the salt wedge upstream into the estuary. An important process that influences baroclinic forcing inside the estuary is the dynamics of vertical mixing. Several year long databases of simulations, with different mixing mechanisms are currently being constructed to study their influence on circulation. Large scale offshore dynamics can also influence salt transport inside the estuary. Only continental shelf and coastal processes have been accounted for in the current set of simulations that have been evaluated here. Another database of simulations that nests the results from a global circulation model for the Pacific with the model for the Columbia River system is also being constructed. The analysis presented in this paper shall be used to evaluate the several database simulations that are currently being generated. This study has highlighted both the capabilities and limitations of the numerical model
1789 in simulating transport processes in the estuary under a range of different conditions. Though there are significant challenges ahead, the ability of the ELCIRC model to simulate circulation and transport processes in an extremely dynamic environment is very encouraging. REFERENCES
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