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Brazos River and Surfside, TX Case Study

Abstract

Since the Brazos River dilutes nearby water at its mouth and changes the salinity, it must also alter other aspects of the ocean as well. Water samples were collected on the beachside and bayside of Surfside, Texas and tested for salinity, pH, nitrate and ammonia concentration. These variables determine the fish species and population that can live in these waters. The results for all variables were highly varied on the bayside, while minimal changes were observed on the beachside. Nitrate and ammonia concentrations were found to have a positive correlation with the salinity on the bayside of the island. The Brazos River dilutes the nearby water and changes the salinity and could affect the nitrate and ammonia concentrations near its mouth.

Introduction

The Brazos River is the longest river flowing through Texas, starting from its watershed in New Mexico and emptying into the Gulf of Mexico. The water carries water runoff from nearby cities and towns from North Texas to the southeast coast near Surfside, Texas. The Brazos is known to change the salinity of the water near its mouth, a variable that determines species and population density of fish (Texas Water Development Board, 2012). For example, speckled trout prefer waters with 18-25 ppt salinity for their mating grounds while adults prefer higher salinities (Tompkins, 2011).  

 

Since the Brazos River changes the salinity of the water surrounding its mouth, what are other variables that determine fish species and population that the river alters? In addition to the salinity, the ammonia concentration, nitrate concentration, and pH of the water on the beachside and bayside of Surfside, Texas was collected. If the pH, ammonia and nitrate concentrations are determined to correlate with changes in salinity, then the Brazos River could also be changing these variables.

 

These variables should be monitored, because changes in their values can have serious impacts on sealife. The EPA, Environmental Protection Agency has identified ammonia to be toxic to fish at high concentrations, because it inhibits organisms’ ability to release ammonia into the water (2013). In comparison, high nitrate concentrations can cause algae blooms to quickly grow and deplete oxygen supplies (Kim et al., 2011). Both ammonia and nitrate concentration increases are associated with fertilizers in agricultural water runoff and pollution from waste treatment plants. Water having a lower, acidic pH causes a decrease in carbonate ions that are used by oysters, corals, and calms to build their shells (National Oceanic and Atmospheric Administration, 2013). Ocean acidification is caused by the ocean absorbing an increasing amount of carbon dioxide released into the atmosphere by burning fossil fuels.

Data Collection

The 16 samples were collected at depths between 6 and 12 inches of water on the beach and bayside on February 2, 2020. The sample point locations were determined by what beach entrances had bayside access across from them and the amount of sample points taken gave full coverage of the island. Google Maps was used to document the exact coordinates of each sample point to ensure that each one is represented accurately on maps and graphs. There were more beachside sample points taken closer to the mouth, because there were more opportunities to take samples on that side. Having sample points that are close together will show any gradual changes in these variables if any. In contrast, having sample points that are farther apart will show abrupt changes.

 

Using primary data provides the most up to date and recent data available. The most recent salinity dataset is made available by the NOAA and was updated February 27, 2019. Although this data would suffice, all of the data should be collected at similar times to strengthen any arguments that the Brazos River has any influence over the variables bayside or beachside of the island.

 

A Total Dissolved Salts (TDS) and Conductivity meter was used to test the salinity of each sample three times and then averaged to ensure that the reading was accurate. In contrast, the Saltwater Master Test Kit was used to test the pH, ammonia and nitrate concentrations only once due to only having a limited amount of testing supplies. Both of these tools are made to measure their respective variables in saltwater aquariums, making them suitable for use in this project.

 

In contrast, Njuguna et al. collected over 50 water samples during both wet season, May, and dry season, August, to gain an understanding of water pollution in the Tana River (2020). This approach would give a better idea of the changes in these variables, since flooding upstream increases the rate that the river’s water empties into the Gulf and would emphasize water qualities changed by the Brazos. Samples during dry season would provide a baseline to compare the wet season results to, because the Brazos would not have a strong flow.

BAYSUDE VS BEACHSIDE.png

The bayside dataset contains the sample points in pink and sample point 16 in green. Meanwhile, the beachside dataset contains the sample points in blue and the sample point 16 in green. The distance was measured from each sample point to point 16 along the coastline in ArcGIS.

Bayside Sample Point Data
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Beachside Sample Point Data
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Methodologies & Analysis

Once the data was collected, it was divided into bayside and beachside datasets since they are separate bodies of water. Since sample point 16 is the furthest into the mouth of the Brazos River, it is included in both datasets and used as the origin when measuring the distance between the mouth and each sample point.

 

Once the sample points were divided, descriptive statistics were calculated to understand characteristics of the data. The high standard deviation, long range, and high variance values found in Bayside Description Statistics Table show that the values of the salinity changed significantly between sample points. In comparison, the salinity values collected on the beachside have a smaller range, standard deviation, and variance value and no significant changes in salinity value was observed. There was more variation and change in the salinity on the bayside than on the beachside.

 

Just like the salinity, more variation in pH, ammonia, and nitrate values were observed on the bayside than on the beachside of the island. The standard deviations, ranges, and variances are consistently higher for all variables for the bayside dataset. This suggests that the Brazos has less influence on the Gulf of Mexico than on the waterway to Christmas Bay.

Bayside Descriptive Statistics
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Beachside Descriptive Statistics
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Methodologies & Analysis

The variables found in Bayside & Beachside Sample Point Data Tables were then graphed against the distance between each sample point and point 16 to identify any relationship between them and the distance from the mouth of the Brazos. Bayside Graph Distance v Salinity graph shows an overall increase in salinity approaching the mouth, but Beachside Distance v Salinity graph shows a decrease in the salinity approaching the mouth. The ammonia concentration displayed in the Bayside Distance v Ammonia graph sharply increases as the sample points are closer to the mouth and declines at point 16. Beachside Distance v Ammonia graph shows that the ammonia spikes randomly regardless of the distance from the mouth. Beachside Distance v Nitrate shows no change in the ammonia concentration, but Bayside Distance v Nitrate shows a sharp increase at the two points closest to the mouth of the Brazos River. Both Beachside and Bayside Distance v pH show that the pH randomly spikes and declines with no relationship with the distance to point 16.

Bayside Graphs
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distance_ammonia.PNG
distance_pH.PNG
distance_salinity.PNG
distance_nitrate.PNG
Beachside Graphs
distance_salintiy_pH_ammonia.PNG
distance_ammonia.PNG
distance_salinity.PNG
distance_pH.PNG
distance_nitrate.PNG

Methodologies & Analysis

The Explanatory Regression tool in ArcGIS was then used to identify relationships between the measured variables and the salinity data. The results showed that there was a strong correlation between the salinity and the ammonia and nitrate variables on the bayside. Ammonia was significant at the 0.01 level and contributed 87% to the variation of the salinity. Nitrate was significant at the 0.05 level and contributed 58% to the variation of the salinity. Ammonia has a stronger correlation with the salinity than the nitrate concentration does. These results mean that increases in salinity are seen with increases in ammonia and nitrate concentrations.

 

The distance was also significant at the 0.05 level, but only contributed 38% to the variation of the salinity. There is a correlation between the salinity and the distance from the mouth, but it is not as strong as the relationship between salinity and the nitrate and ammonia concentration. The pH does not have any correlation with the salinity since it was not found to be statistically significant.

 

The statistically significant variables were then graphed against the distance from the mouth to visually show the relationships identified by the Explanatory Regression tool. Multivariate analysis conducted by Honglei et al was also conducted by graphing variables predicted to have positive or negative relationship (2018). Simple graphs are powerful visuals that are easy to understand.

 

Bayside Distance v Salinity v Nitrate v Ammonia graph shows that there is a positive correlation between salinity, nitrate and ammonia concentration. This graph also supports a weak negative correlation between the variables and the distance from the mouth of the river. While using graphs to visually see relationships can be helpful, these graphs provide an incomplete understanding of any correlation between variables. The salinity appears to increase approaching the mouth of the river, but the Explanatory Regression tool determined that correlation between these variables is weak and unimportant. Using a combination of graphs and the Explanatory Regression tool strengthens multivariate relationships that are identified using these tools.

 

The Explanatory Regression results for the beachside data did not identify any variables that relate to changes in salinity. The data collected on the beachside showed that the variables did not significantly change, making it difficult to identify relationships. The spread of the beachside data is significantly smaller than the spread of the bayside data when comparing the standard deviation, variance, and range for all variables. Beachside Distance v Salinity v pH v Ammonia graph shows no correlation between variables by comparing the variables with the distance.

 

Since it is known that water from the Brazos River dilutes the salinity of the water nearby the mouth, identifying relationships between the salinity and other variables describes other aspects of the Gulf of Mexico that the Brazos changes. While Explanatory Regression was used in this project to identify statistically significant variables, Njuguna et al used Principal Component Analysis (PCA) to identify correlated water quality variables (2020). Explanatory Regression was chosen for this project, because does not assume any preconceived relationships like PCA does.

 

Njuguna et al. also classified their data based on the concentration of each variable and its threat to human health (2020). Although this technique works well with a large amount of highly varied data, it is unnecessary for this project. There were only 16 locations where data was collected and only four variables were measured in this study. Having a smaller sample size and number of variables makes changes in the data apparent and simplifies analyzing the data.

Conclusions & Recommendations

All of the variables that were measured at the sample points have been graphed in relation to the points’ distance from the mouth of the Brazos River. Distance v Salinity v Nitrate v Ammonia graph for the bayside data points clearly shows that as the salinity increases, both the ammonia and nitrate concentration increases as well. This graph supports the Explanatory Regression results that suggested that the change in salinity could be explained by the increase in ammonia and nitrate concentrations. Since the Brazos River changes the salinity of the Gulf of Mexico, its water could also increase the ammonia and nitrate concentration.  

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Distance v Salinity v pH v Ammonia graph displays the variables measured on the beachside of the island in relation to the points’ distance from the mouth of the Brazos River. The variables do not relate to each other in any way and support the results of the Explanatory Regression results. Since none of the variables have any correlation with each other, the Brazos River does not impact the beachside as strongly as the bayside.

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Sample Point Data at Surfside, Texas map shows the values of salinity, pH, ammonia and nitrate concentrations measured at each sample point. The bar graphs representing the variables highlights significant jumps in value compared to their neighboring points. Having all of the information represented in one map gives a quick overview of the data. The map shows that the variables measured on the bayside highly vary and change, but the variables on the beachside are similar. Since the bayside has more varied data, the Brazos River changes the variables in this water the most and does not change the beachside water. 

 

The Bayside Data-Surfside map, Texas map displays the salinity, ammonia and nitrate concentration measured at each sample point on the bayside of the island. The changes and fluctuations in the variables are easily seen since the bars are three-dimensional and larger without the beachside data. Only the variables that have been determined to have a relationship, the salinity, ammonia and nitrate concentrations, were included to highlight changes.

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The results of the Explanatory Regression and the graphs show that salinity has a positive correlation with the ammonia and nitrate concentration on the bayside. Since the Brazos River is responsible for changes in salinity, it could also be responsible for the changes in ammonia and nitrate concentration.

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The Explanatory Regression results and the graphs show that there is no relationship between the measured variables collected on the beachside of the island. There is no significant change in any of the variables as shown in Distance v Salinity v pH v Ammonia graph and it can be concluded that the Brazos River does not change the variables on the beachside. 

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The amount of samples taken and the geographic area that was covered was limited by time and testing supplies. Further studies with unlimited resources would have samples taken along the Brazos River upstream of the mouth to provide more information about the attributes of the river. Further sampling could also be used to pinpoint causes of changes in salinity, pH, ammonia and nitrate concentrations along the river. These sources could then be studied and brought to the attention of officials to implement laws that will reduce human impact on the variables.

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This project would have also benefited from sampling a larger geographic area along the coast would provide more information of how the Brazos River flows interacts with the Gulf of Mexico. Taking samples on Surfside and Matagorda Bay would give insight on both sides of the mouth of the Brazos River. Having more sample points would also increase the area coverage of the project.

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Additional analysis should also be conducted to identify what environmental factors cause the Brazos River to have a larger impact on the bayside than the beachside. Data would need to be collected over the course of several days after significant rainfall upstream from the Brazos River to increase flow into the Gulf of Mexico. Flooding in areas upstream from the mouth of the Brazos are known to lower salinity near the mouth than usual. 

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Current flow mapping would be a crucial element to further investigate the aspects of the Gulf of Mexico that the Brazos River changes. Did the current prevent the Brazos from interacting with the water on the beachside? If it was known that the current was heading south at the time that the data was collected, then it could be said that the Brazos River’s water did not mix with the beachside water. The current map would also inform how the Brazos flows through the bayside and interacts with other bodies of water. 

Sample Point at Surfside, TX.png
Bayside map.png

Bibliography

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Honglei, Liu, Qiang, Wu, Wang, Mingjun, & Zhange, Meng. (2018). Multivariate Analysis of Water Quality of the Chenqi Basin, Inner Mongolia, China. Mine Water & the Environment, 37(2), 249-262. Doi: 10.1007/s10230-018-0533-1

 

Kim, Tae-Wook, Lee, Kitack, Najjar, Raymond G., Jeong, Hee-Dong, & Jeong Hae Jin. (2011). Increasing N Abundance in the Northwestern Pacific Ocean Due to Atmospheric Nitrogen Deposition. Science, 334(6055), 505-509. DOI:  10.1126/science.1206583

 

National Oceanic and Atmospheric Administration. (2013). Oceanic acidification. Retrieved from https://www.noaa.gov/education/resource-collections/ocean-coasts-education-resources/ocean-acidification

 

National Oceanic and Atmospheric Administration. (2020). NGOFS-Galveston Bay Currents Nowcast. Retrieved from https://www.tidesandcurrents.noaa.gov/ofs/ofs_animation.shtml?ofsregion=ng&subdomain=gb&model_type=currents_nowcast

 

Njuguna, Samwel Maina, Onyango, Janet Atieno, Githaiga, Kelvin Babu, Gituru, Robert Wahiti, & Yan, Xue. (2020). Application of multivariate statistical analysis and water quality index in health risk assessment by domestic use of river water. Case study of Tana River in Kenya. Process Safety & Environmental Protection: Transactions of the Institution of Chemical Engineers Part B, 133, 149-158. Doi: 10.1016/j.psep.2019.11.006

 

Proper salinity the key to locating speckled trout right now along Louisiana coast. (2014). Energy Monitor Worldwide. Retrieved from https://link-gale-com.ezproxy.snhu.edu/apps/doc/A377019433/ITOF?u=nhc_main&sid=ITOF&xid=7fce7c1e

 

Suhr, Diana D. (n.d.) Principal Component Analysis vs. Exploratory Factor Analysis [PDF File]. Retrieved from https://support.sas.com/resources/papers/proceedings/proceedings/sugi30/203-30.pdf

 

Texas Water Development Board (2012). Brazos River Basin and Bay Expert Science Team Environmental Flow Regime Recommendations Report [PDF File]. Retrieved from http://www.twdb.texas.gov/publications/reports/contracted_reports/doc/1248311377_Brazos.pdf

 

Tompkins, Shannon. (2011). Speckled trout spawn chances for anglers. Retrieved from chron.com/sports/article/Tompkins-Speckled-trout-spawn-chances-for-anglers-1689940.php

 

United States Environmental Protection Agency. (2013). Aquatic Life Ambient Water Quality Criteria for Ammonia-Freshwater [PDF File]. Retrieved from https://www.epa.gov/sites/production/files/2015-08/documents/fact_sheet_aquatic-life-ambient-water-quality-criteria-for-ammonia-freshwater-2013.pdf

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