Objective. Introduction and Background. Emily Finkelman

Transcription

1 2011 County Bell Falls Total Well Data Points Recent Well Data Points Recent Well Data Within Trinity Formation Texas Groundwater Future Investigation of the data our government uses to make laws and policy decisions affecting future availability of what is rapidly most important resource, water. Finkelman, Emily C GIS Final Project 12/1/2011

2 Objective There is a policy in Texas which requires each Groundwater Management Area (GMA) to state future goals for drawdown in the aquifers that underlie the area. The latest published Desired Future Conditions (DFC) for GMA 8 gives desired conditions for the Trinity aquifer in drawdown by the layer of the aquifer in each county. Given that the aquifer is laterally continuous underneath multiple counties the desired drawdown in one county and thereby the projected pumping rates over the next 50 years will affect the aquifer in neighboring counties. This method of deciding desired future conditions seems to be based solely on population growth rather than geologic data. For instance neighboring Bell and Falls county have projected drawdowns in aquifer layers that differ by nearly 200 feet. I would like to investigate the geologic and hydrologic data to figure out the feasibility of these DFCs. Introduction and Background In 1995 the Texas congress passed a law requiring Groundwater Management Areas to be created In order to provide for the conservation, preservation, protection, recharging, and prevention of waste of the groundwater, and of groundwater reservoirs or their subdivisions, and to control subsidence caused by withdrawal of water from those groundwater reservoirs. Since then 16 Groundwater Management Areas (GMAs) have been created to pass laws and make policy for the protection of water resources in their jurisdiction. (Figure 1) Part of the requirements of these policy making bodies is to set a goal for the maximum effects that the areas projected pumping will have on the underlying groundwater reservoirs over the next 50 years. This goal or set of goals is called the Desired Future Conditions (DFC) of that GMA. To simplify the decision process of the DFC boundaries for the GMAs were created roughly along aquifer lines so as to minimize the number reservoirs to be analyzed for each GMA. (Figure 2) Figure 1 Page 1

3 Figure 2 Decision making processes to implement the creation of these DFCs, rather than a standardized method, are entirely unique to the GMA in question, which causes inconsistencies for the goals across the state. Each GMA takes into account a Groundwater Availability model run with projected future conditions as well as other factors such as projected population growth, mining, and drought conditions when making decisions for Desired Future Conditions. With this information at hand, some GMAs decide their DFCs with one number for expected drawdown in the whole of the aquifer underlying the region, and others, such as GMA 8, decide their DFCs by aquifer layer within each county. My area of interest is GMA 8, whose method provides an overwhelming amount of data that is difficult to understand or quantify. The DFCs for each county are given in four projected drawdown numbers which are based on the layer of the Trinity aquifer. To further the confusion with the DFC report, desired future drawdowns in these layers varied drastically across neighboring county boundaries. This observation incited further analysis in hopes of finding lithologic or hydrologic reasoning to explain these inconsistencies. Page 2

4 Figure 3 Figure 4 I began to research this by going to the Texas Water Development Board site get a better idea of the area and the aquifers. I first obtained the DFCs for GMA 8 and looked up the method in which they delineated between the aquifer layers. A schematic table showing the lithologic layers (Figure 5) has the Layers of the aquifer outlined in red. The DFCs for the aquifer are given for four units in the trinity formation: Glenrose, Pauluxy, Hensell and Hosston. After doing this I then went back to the map and found two neighboring counties that had drastically different drawdowns for the Trinity aquifer layers. An example of this is Bell County and Falls county (Figures 3 and 4) with up to 199 feet of difference in drawdown for a layer across county lines. I believed that this may have been due to a similarly drastic difference in lithology but I then found a cross section across these counties in the GAM report evidencing the contrary. (Figure 6) After exhausting the superficial answers provided by TWDB without a satisfactory explanation, I decided to use Arc GIS to process and analyze the raw data and maps provided by TWDB. I did this in efforts to draw my own conclusions for the realistic viability of these DFC predictions and to understand the effects that they have on the Groundwater Policy in our state that is built around them. Page 3

5 Figure 5 Figure 6 Page 4

6 Data Collection To carry out this investigation I relied mainly on the data made available by the Texas Water Development Board. For background information I read the reports for the Desired Future Conditions and the Groundwater Availability Model (GAM) runs that the DFCs are based on. For geographic representations of boundaries and aquifers I used Shapefiles from the TWDB GIS data page. For the county boundaries I used the county map of Texas found in the Labs folder from our class. TWDB TWDB GIS o Shapefile data used: Major Aquifers of Texas Groundwater Management Areas GMA o o Texas Water Code (Acts of 1995, 74th Leg., ch. 933, 2, eff. Sept. 1, 1995) GMA 8 o Home o DFCs o GAM Report used by GMA 8 for DFCs Well Data o eports/gwdatabaserpt.asp Lab Folders Lab 1 o o Texas Data Counties of Texas Shapefile Page 5

7 Data Preprocessing Downloading files from TWDB I first downloaded all of the shapefiles from the GIS data page; these were all in NAD These maps required little preprocessing for my first map showing the GMA and the aquifers except for defining their projections. After this I downloaded the well text files from the TWDB website for Well Data. The Data was organized by county as seen in Figure 8. The data available on this site was very helpful in that the raw data as well as reports were available so when I didn t understand something I went back to the Reports or the Dictionaries for those reports. Figure 7 To analyze the data for these two counties I needed the well data for both of these counties. I used the well data and water level tables for my analysis. When opened these text files open in a new tab which cannot be easily imported into excel, see Figure 9. Page 6

8 Figure 8 Because of this difficulty I decided to copy the text file into notepad and then I was able to import those files from notepad to excel using the Import data from text button under the Data tab. I then needed to specify that my data was a comma delimited file when I imported it. This created an excel workbook that I could then work with in Arc Map, once exported as a text file. Once I had done this for all four files, Well data and Water level tables for both Falls and Bell County I looked at what would be necessary for my analysis. For starters I wanted to look at the aquifer layers that each well was drilled to so I would need the aquifer_code information from the well data file. I also wanted to make a surface defining the top of the water table for which I would need the water levels of the wells which was in the water level file but that file lacked a spatial reference. The well data table had the locations of the wells in decimal latitude and longitude so I knew I could use that spatial reference if I could somehow match the records. Fortunately all of the data for the wells is organized by well number so I could use the well_id field to join the tables. After finding the important data fields I then cleaned up the data tables by taking out some of extra fields and saved the new excel files as text files and added the new text files to my arc map and joined the well data and water level tables by the well_id field(figures 10 and 11) Page 7

9 Figure 9 Figure 10 After joining these tables I exported them from Arc Map as datasets so I could use them as layers rather than tables. At this point I finished my data preprocessing, with the exception of going back and fixing re-preprocessing mistakes I didn t realize I had made, or at times, realizing were even possible. ArcGIS Processing and Methodology of Analysis I first wanted to take a look at GMA 8 in better detail so I took the Texas county boundaries shapefile and overlaid that with the GMA and Aquifer shapefiles. I then made the GMA and Major Aquifer layers 40% transparent by right clicking on the layer > properties> display. This allowed me to see the Page 8

10 relationships between the aquifers and the GMAs and the counties that were involved in each. After creating this Map I zoomed into GMA 8 by selecting only GMA 8 and creating a layer from the selection. With this layer I could then clip the counties and the aquifers to this area so I had a map showing only the area I was interested in. I did this by going to the Toolbox> Analysis Tools> Extract>Clip with the Input Features as the aquifer and county layers and the Clip Features layer as my newly created GMA 8 layer. I then symbolized and put the finishing touches on the map as seen below. (Figure 12) Page 9

11 Figure 11 I then zoomed in further on this map to my area of specific interest, being the neighboring Bell and Falls counties. In this view I showed the Trinity aquifer coverage of the two counties for reference when the wells were added. I did this by following the same steps as outlined above for clipping to the two counties I was interested in rather than GMA 8. (Figure 13) Page 10

12 Figure 12 With these maps providing geographic reference for the area I was interested in I was then ready to add the wells to my map to begin my analysis. After exporting the text files as datasets from Arc Map I then added them back into the map as X_Y data onto my county shapefile which resulted in a map with the wells that were supposed to be in my counties a world away from their proper home. (Figure 14) Page 11

13 Figure 13 I realized that I must have a disconnect between the projections in my data. I then had to go back and define the projections of my data correctly. My county shape file was in which was in the Texas Albers Projection and the Well files were NAD 1983 Data. Texas Albers is in meters while NAD 1983 was in Decimal degrees so they did not project well when accidentally difined wrongly. Figure 14 I found that my well data was mis-defined as Texas Albers (Figure 15) data so I corrected that in the Layer> Properties>Source. With this correct I then wanted to standardize all the layers in my map so I then went to the Toolbox>Data Management Tools>Projections and Transformations>Project to change the projections of my layers. I changed all of the layers to Texas State Plane NAD 1983 Feet. (Figure 16) Page 12

14 Figure 15 I then added the wells as X_Y data with the settings below in Figure 17 and they showed up at home in my map! (Figure 18) Figure 16 Page 13

15 Figure 17 Now that I had the wells correctly in the map I wanted to start using the data that I had collected but with the combined well data and water level tables from TWDB there were far too many vaguely named fields to do anything useful with my attribute tables without doing some cleanup work. To do this I had to go back to TWDB to find a Rosetta stone to translate these column titles. databaserpt.asp Page 14

16 Figure 18 After translating the column titles from the Dictionary (Figure 19) I found the data that I would need and turned off the fields I didn t need in Arc Map. (Fields I didn t need highlighted in blue) (Figure20) Figure 19 From this well data dictionary I found that the water level in the well was given in feet below land surface depth_from and the datum of the land surface was given by elev_lsd. Water level in the well was the information that I needed but since the level is given from the land surface where the well was drilled I would have to take into account the variation in the land surface that the water level was measured from. To do this I added a field in my cleaned up attribute table. (Figure 21) Page 15

17 Figure 20 I then used the field calculator (Figure 20) in my new field wtr_lvl_ms for the corrected water level in relation to mean sea level rather than in relation to the land surface. (Figure 22) Figure 21 Page 16

18 Figure 22 After doing this calculation I realized that many of the wells had water level data for multiple years going back to the 1930 s. While this could have been useful for analyzing the water levels over time I was only interested in in recent water levels. I chose to further cleanup my data set by finding the water level data for the trinity aquifer over the last 20 years. I did this by selecting by an attribute with an SQL Query for the Year field>= I then took this data from each county and created a new layer file from the selection. This gave me all of the recent well data in the area. (Figure 24) Figure 23 Page 17

19 I then wanted to see which of these recent well data points were for wells that were screened within the Trinity Aquifer. Because the codes for the aquifers are for general combinations of formation members I chose all of the wells with codes that had one or more aquifer members listed. (see Figure 25) I followed the same steps of creating a layer from the selected attributes for each county as before and then had a map of all of the wells in the Trinity Aquifer formation that had water level data from the last 20 years. (Figure 26) Figure 24 Figure 25 The number of data points shown in Figure 26 was shocking to me. I had a hard time understanding how Policy Makers could make decisions on such a limited data set. I wanted to then show how much this Page 18

20 changed by making a map that symbolized the progression of the data available and how quickly it dwindled with each parameter I set. Shown below in Figure 27 is a map that shows the large amount of well data available for these two counties in pink dots, of these wells those with recent data from the last 20 years are symbolized by light blue dots and of those recent wells those that are pumping from the Trinity aquifer are shown in small dark blue spots. Table seen immediately below provides well counts as a companion to Figure 27. County Bell Falls All Well Data Points Recent Well Data Points Recent Well Data Within Trinity Aquifer Figure 26 Page 19

21 After looking at this data I decided that I wanted to see the distribution of wells that were in the different layers of the Trinity aquifer. I did this because the DFCs for GMA 8 delineate that there would be a certain draw down in each of the aquifer layers being the Paluxy, Glenrose, Hensell and Hosston formations. What I found was that the wells were not delineated in such a way but were organized by the formations that intersected each well. Some of these delineations were very specific as would be assumed in the DFCs but others were as general as Trinity member. This was a further disappointment in the investigation, I had hoped that the wells would at least show data for the layers specified in the DFC but to the contrary the Paluxy layer which was supposed to have a projected drawdown of 134 and 279 in Bell and Falls counties respectively had no mention what so ever in the data available. Furthermore the only recent well data available for the Trinity aquifer in Falls county was data for wells in the Hosston formation. I decided to show this data by aquifer code in a map shown below, Figure 28. Figure 27 Page 20

22 This graphical representation of the data along with the others then begged the question: If this is the only recent data that we have then how can GMA 8 have such specific projected drawdown numbers for their Desired Future Conditions? Are these decisions truly based on scientific observation or something else? I decided to give the GMA 8 the benefit of the doubt and try one more time to justify the differences in the DFCs between Bell and Falls counties. I then took the recent trinity well data that I had made in the creation of the previous maps and then used that data to create a surface for the water table in these two counties to evaluate for a difference in water level data that I may have over looked before. I used this new data to create a surface defining the water table from well levels from mean sea level. I used the exact method of the spline tool by Arc Toolbox>3-D analyst> Interpolation>Spline for each of the counties well data separately. I input the wells as point features as shown below and used my calculated wtr_lvl_ms as the Z value Field (Figure 29) Figure 28 The Surface that I created did not come out very well; with no continuity between the counties and the surfaces created fell outside the county lines. (Figure 30) I decided that I needed to figure out a way to get the surface to be as continuous as possible to I needed to combine the tables. After looking around for a while I learned that the only way to do this easily was to append the files. Page 21

23 Figure 29 When I attempted to append the files through Toolboxes>Data Management>Append I found that I could not do so because the arrangement of the attribute fields did not match up correctly in the attribute tables. Since you cannot choose to omit or add a field in the append process and you cannot easily reorganize the tables within Arc Map I decided that the fastest way to solve my problem was to export my edited well data files and re-arrange them manually in Excel. After Manually joining the tables I could then import them back into Arc Map as before and export them as a Shapefile that could then be added as X_Y Data in the Texas Stateplane Feet Projection of NAD I then used the same method of splining that I had before with the exception that I used a Mask to keep my interpolated surface within the county lines. I did this by going to: Arc Toolbox>3-D analyst> Interpolation>Spline> Environments>Raster Analysis> Mask, I then chose my counties shapefile to mask the spline to so it would create a cohesive surface within my area of interest. (see Figure 31) Page 22

24 Figure 30 I put the finishing touches on this map (contours, symbology, classification of labels and symbology) and created a graphic to demonstrate the relatively continuous level surface of the water level in the trinity aquifer from the well data provided by the Texas Water Development Board. This map reinforces the argument against the validity for the DFCs of GMA 8 by failing to provide a reason for such a discrepancy in projected drawdown in the Trinity aquifer over the next 50 years. (Figure 32) Page 23

25 Conclusions and Future Work In conclusion I found very little evidence to support the very specific drawdown DFCs for GMA 8. I used Bell and Falls counties as my study area for their proximity to each other, available lithologic information and highly varied DFCs across the county boarders. I studied the GAMs that these decisions were based upon and found that while the GAMs and DFCs were in agreement with each other there was little explanation for the inconsistencies of drawdown between the counties. I demonstrated the available data that was given in multiple ways to investigate the true amount of data that was available to base the DFCs upon through graphically showing recent well data compared to the total data available, the layers that the recent wells pumped from and water table level in the trinity aquifer over the two counties in question. After analyzing this data I have found that the GAMs were not very representative of scientific observation or data. Instead, I believe that these decisions were made largely based upon projected population growth rather than lithologic or hydrologic data. While this method does take into account projected pumping, the highly fragmented decision making system done by county does not Page 24

26 allow for the reality that aquifers are laterally continuous regardless of county boarders. The DFCs for GMA 8 have provided a dangerously superficial answer to a very important question for our future; will there be enough water for our children? By ignoring the lateral continuity of aquifers throughout the area the policy makers are turning a blind eye to the emergent detrimental effects that choices made in one county about DFCs will most certainly affect neighboring counties in one of the most densely populated regions in Texas. I would like to continue this investigation by delving deeper into the GAM runs and analyzing the raw data created by the GAM runs, rather than just looking at the GAM reports. Along with this I would like to get in contact with geologist that provided this information and the policy makers that decided upon this method of DFC creation. Page 25