DEPLETION OF HIGH PLAINS AQUIFER IN  NEW MEXICO (1995 -2005).

BACKGROUND: An aquifer is an underground layer from which groundwater can be usefully extracted using a well. Usually the underground layer is of water-bearing permeable unconsolidated materials or permeable rock. In many countries aquifers are the main source of irrigation. The High Plains Aquifer in U.S.A has an area of 174,000 sq miles in parts of eight states namely Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas and Wyoming. Fig 1 shows the location of the High Plains Aquifer and also the location of major cities.

locationmap.jpg

Fig 1: The location of High Plains Aquifer and its boundaries along with the major cities and counties (USGS).

Mean annual temperature: 43˚F in the North to 63˚F in the South (U.S.G.S).

Mean annual precipitation: Ranges from 12 inches in the West to 33 inches in the East (U.S.G.S).

OBJECTIVE: Main objective of the project is to determine the decrease of the water table using various tools of Arc Map and Arc Catalog.

DATA DETAILS: The data is obtained from USGS website (http://water.usgs.gov/) and the datum for the data is North American datum of 1983 (NAD 1983). The well depth below the land surface in feet was missing for some wells and these missing well depths are taken as -999 feet.  Fig 2 gives a view of New Mexico counties and well locations.

METHODS: The methods that can be used to create virtual surface using interpolation are Inverse Distance Method (IDW), Spline Method, Natural Neighbor Method and Kriging.

Inverse Distance Method: IDW literally depends on the concept of spatial autocorrelation. “It assumes that the nearer a sample point is to the cell whose value is to be estimated, the more closely the cell’s value will resemble the sample point’s value” ESRI.

Spline Method:  “Spline virtually guarantees you a smooth-looking surface. Imagine stretching a rubber sheet so that it passes through all of your sample points” ESRI.

Natural Neighbor Method: “Natural Neighbor calculates the value of an estimated location as a weighted average of the values of the natural neighbors” ESRI. This interpolation method can efficiently handle large numbers of input points.

Kriging: “Kriging is one of the most complex and powerful interpolators. It applies sophisticated statistical methods that consider the unique characteristics of your dataset. In order to use Kriging interpolation properly, you should have a solid understanding of geostatistical concepts and methods” ESRI.

Difference between interpolation methods: An IDW surface never exceeds the highest or lowest values in the sample point set unlike a Spline surface where the highest and the lowest values can exceed the values in the data set. But in Spline the surface must be such that it passes through all the points in the data set (ESRI). Whereas in Kriging the surface can exceed the highest and lowest values of the data set but they may not pass through any data points. The difference between these methods can be observed in the Fig 3.

I choose Kriging method. The reason I choose kriging method is, both IDW and Spline method are directly dependent on the surrounding data, measurements or some mathematical equations, whereas kriging has an advantage that it not only gives you the predicted surface but it also gives the certainty or accuracy of the prediction (ESRI).

 

countie.jpg

Fig 2: New Mexico counties and well locations of High Plains Aquifer.

http://training.esri.com/Courses/LearnSA/M4/InterpRules3.gif

Fig 2: The ranges of cell values in the resulting surfaces will differ according to the method used. With courtesy of ESRI.

PROCEDURE: Initially the data is in text format, it is coped into excel sheet and the unnecessary data for this analysis are removed. The important fields of the data is formatted to 6 decimal points and then changed to dBase (IV) form which is compatible in Arc GIS.

Once the data is converted into dBase (IV) form it is imported into Arc Map and the XY data (latitude – longitude) is displayed.  After displacing the XY data, virtual surface for the given well depths using interpolation is created. The methods that can be used to create virtual surface using interpolation are Inverse Distance Method (IDW), Spline Method, Natural Neighbor Method and Kriging. I choose Kriging method to create virtual surface.

Kriging generates an estimated surface from a scattered set of points with z-values; my z-values here in this analysis are well depths. Virtual surfaces for years from 1995 to 2005 are created. Thus by using Minus (3D Analyst) tool the difference between two virtual surfaces is obtained, which will help us to analyze the patterns or trends of the depletion of the water table if any. A 3D analyst tool from Arc Catalog is used to analyze the virtual surfaces. Minus (3D Analyst) is a subtraction tool, which “subtracts the value of the second input raster from the value of the first input raster on a cell-by-cell basis within the Analysis window” ESRI. Using 3D Analyst Minus tool the difference between the virtual surfaces of each year with respect to 1995 is obtained.

RESULTS: By using the Minus 3D analyst tool the difference in the virtual surfaces of different years is obtained. Fig 4 and fig 5 shows the virtual surfaces 1995 and 2005 respectively that create using Kriging.

Fig 4: The virtual surface created for the year 1995.

With the result obtained from the analysis and by observation it is clear that the depletion of water table is not following a trend or pattern from 1995 to 2005. Fig 6 shows the depletion of water table from 1995 to 2005. So the factors responsible for depletion of water table in High Plains Aquifer must be various and many. Hence depletion of water table with years is compared to increase in population. Results obtained are convincing. Fig 7 shows cities with high population in the year 2000. By observation we can see that highly populated cities are the areas where more number of wells is found as well as more depletion in the water table. The rest of the figures and procedure followed are in appendix.

 

Fig 5: Virtual surface created for the year 2005.

CONCLUSION: The maximum depletion of the water table since 1995 to 2005 is 983 feet with an average depletion of 460 feet. As anticipated the water level fell mostly in and around the densely populated areas.

Fig 6: Depletion of water table from 1995 – 2005.

Fig 7: Population density map for the year 2000 (Gurie 2007).

FUTURE WORKS: Future works in this area should focus on the Characteristics of bed rocks surrounding the wells and to see if their influence the depletion of water table is considerable. Also the influence of a depleted aquifer (when it is completely dried) on the surrounding or other aquifers, whether they deplete or project.

More tools like hydrology tool set in arc catalogue can be used to get indepth analysis .(taking porosity, permeability and transmissibility into account.)

 

 REFERENCE:

esri.com/virtual training courses.

McGuire, V.L., 2007, “Water-Level Changes in the High Plains Aquifer, Predevelopment to 2005 and 2003 to 2005”. U.S. Geological Survey Scientific Investigations Report 2006-5324

USGS website.

 

 

 

 

    Fig A.1: Virtual surface created for the year 1996

Fig A.2: Virtual surface created for the year 1997.

Fig A.3: Virtual surface created for the year 1998.

Fig A.4: Virtual surface created for the year 1999.

Fig A.5: Virtual surface created for the year 2000.

Fig A.6: Virtual surface created for the year 2001.

Fig A.7: Virtual surface created for the year 2002.

Fig A.8: Virtual surface created for the year 2003.

Fig A.9: Virtual surface created for the year 2004.

Fig A.9: Depletion in the water table between the years 1995 and 1996.

Fig A.10: Depletion in the water table between the years 1995 and 1997.

Fig A.11: Depletion in the water table between the years 1995 and 1998.

Fig A.12: Depletion in the water table between the years 1995 and 1999.

Fig A.13: Depletion in the water table between the years 1995 and 2000.

Fig A.14: Depletion in the water table between the years 1995 and 2001.

Fig A.15: Depletion in the water table between the years 1995 and 2002.

Fig A.16: Depletion in the water table between the years 1995 and 2003.

Fig A.17: Depletion in the water table between the years 1995 and 2004.

SAMPLE dBASE file.

state_cd

cnty_cd

site_cd

otid

site_name

lat_dd_NAD83

long_dd_NAD83

land_alt_NGVD29

well_depth_ft

35

25

USGS 323405103044501

 

20S.39E.19.122122

32.56817

-103.08

3546.3

-999

35

25

USGS 323735103075001

 

19S.38E.34.22122

32.6265

-103.131

3595.4

120

35

25

USGS 324455103283501

 

18S.35E.17.41111

32.74873

-103.477

3941

190

35

25

USGS 324615103083001

 

18S.38E.03.31333

32.77094

-103.142

3656

100

35

25

USGS 324715103113001

 

17S.38E.31.311111

32.78761

-103.192

3689.5

110

35

25

USGS 324745103055501

 

17S.38E.36.212122

32.79594

-103.099

3657

130

35

25

USGS 324745103082001

 

17S.38E.34.113143

32.79594

-103.144

3664.2

126

35

25

USGS 324755103145501

 

17S.37E.34.11111

32.79873

-103.249

3730.4

100

35

25

USGS 324820103204501

 

17S.36E.27.13134

32.80568

-103.346

3844

100

35

25

USGS 324850103060901

 

17S.38E.24.314444

32.814

-103.103

3657

175

35

25

USGS 324946103082801

 

17S.38E.15.313111

32.82955

-103.142

3687.1

110

35

25

USGS 325113103125001

 

17S.37E.12.11321

32.85373

-103.214

3747.8

140

35

25

USGS 325115103101501

 

17S.38E.08.21113

32.85428

-103.171

3714.8

145

35

25

USGS 325115103141501

 

17S.37E.10.211111

32.85428

-103.238

3762.1

130

35

25

USGS 325132103112501

 

17S.38E.07.111311

32.85547

-103.196

3728.1

125

35

25

USGS 325151103054201

 

17S.38E.01.23214

32.86427

-103.095

3722

-999

35

25

USGS 325250103082501

 

16S.38E.34.13131

32.88067

-103.141

3707.4

130

35

25

USGS 325307103110001

 

16S.38E.30.21111

32.88539

-103.184

3747.1

118

35

25

USGS 325350103123501

 

16S.37E.25.111113

32.89734

-103.21

3767

-999

35

25

USGS 325415103081501

 

16S.38E.27.111111

32.90428

-103.138

3714.1

130

35

25

USGS 325435103035001

 

16S.39E.29.23332

32.90983

-103.064

3678.7

172

35

25

USGS 325436103191001

 

16S.36E.23.241324

32.91012

-103.32

3860

95

35

25

USGS 325448103035001

 

16S.39E.17.34422

32.91344

-103.064

3679.5

-999

35

25

USGS 325455103250501

 

16S.35E.24.111241

32.9154

-103.419

3966.4

100

35

25

USGS 325545103132001

 

16S.37E.14.211114

32.92928

-103.223

3786.9

130

35

25

USGS 325545103243001

 

16S.35E.13.112312

32.92929

-103.409

3970

82

35

25

USGS 325619103323101

 

16S.34E.10.21431

32.93873

-103.542

4099.7

-999

35

25

USGS 325622103191501

 

16S.36E.11.241131

32.93956

-103.321

3886

100

35

25

USGS 325628103275601

 

16S.35E.09.11120

32.94123

-103.466

4024.6

154

35

25

USGS 325645103171501

 

16S.37E.07.114224

32.94595

-103.288

3859

125

35

25

USGS 325655103081501

 

16S.38E.03.33333

32.94872

-103.138

3727.1

140

35

25

USGS 325658103200001

 

16S.37E.11.11111

32.94956

-103.334

3794.6

118

35

25

USGS 325730103213901

 

16S.36E.04.32232 LOVINGT

32.95806

-103.361

3922

212

35

25

USGS 325750103123001

 

16S.37E.02.211141

32.964

-103.209

3801

120

35

25

USGS 325750103124001

 

16S.37E.01.311123

32.964

-103.212

3789.7

105

35

25

USGS 325813103123601

 

15S.37E.33.311341

32.97039

-103.21

3798.9

127

35

25

USGS 325815103041001

 

15S.38E.35.13133

32.97094

-103.07

3714.6

140

35

25

USGS 325826103142601

 

15S.37E.31.132221

32.97401

-103.241

3828.3

107

35

25

USGS 325920103185501

 

15S.36E.28.131331

32.98901

-103.316

3905

138

35

25

USGS 325920103203001

 

15S.36E.30.411114

32.98901

-103.342

3924

120

35

25

USGS 325926103113401

 

15S.37E.27.111342

32.99067

-103.193

3793.3

126

35

25

USGS 325926103134101

 

15S.37E.29.111313

32.99067

-103.229

3828.6

120

35

25

USGS 325945103195001

 

15S.36E.29.112121

32.99595

-103.331

3916

105

35

25

USGS 330045103103001

 

15S.37E.23.112130

33.01261

-103.175

3791.4

122

35

25

USGS 330138103051001

 

15S.38E.10.321134

33.02733

-103.087

3734.5

155

35

25

USGS 330255103205501

 

15S.36E.01.311113

33.04873

-103.349

3879.8

120

35

25

USGS 330315103192001

 

15S.36E.05.211120

33.05428

-103.323

3939.7

128

35

25

USGS 330330103144501

 

14S.37E.31.33131

33.05845

-103.246

3873.5

130

35

25

USGS 330349103165301

 

14S.36E.35.111112

33.06373

-103.282

3910.1

136