Adam Weinberg
12/13/2012
UEP 232
Final Paper For GIS 232
I.
Introduction
The Town of Sharon obtains it’s water supply from a network of six groundwater pumping wells. There
has been considerable effort over the years to improve conservation practices. However, the town
population has been growing at a slow but consistent pace, and it is anticipated that demand for water will
exceed the capacity of the existing wells before 2030.
In response to this anticipated demand shortfall, the Sharon Water Department commissioned a study to
identify potential locations for new water supply wells. Although Sharon is not a large town, drilling test
wells at random would be an expensive and inefficient way to explore potential well sites. It is therefore
critical to employ some type of screening tool to narrow the options down to the sites with the most
probable likelihood of success. This requires evaluating a defined spatial extent based on a number of
traits that can serve as indicators that a given location may or may not be worth pursuing further as a
potential future well location.
The method for this study utilized GIS to evaluate the suitability of each 35x35 meter quadrant of the
town for siting a new well based on 7 categories: aquifer yield, water quality, permit restrictions,
proximity to water mains, land cost, watershed recharge, and proximity to roads. Each of these categories
is an important consideration for determining the affordability of well construction, and also predicting
the ability of the well to provide plentiful and safe resources for town residents.
A map was made to evaluate the favorability of every quadrant for each category. Point values were
assigned to each quadrant to reflect the favorability of the location for each category based on a set of
criteria. Finally, all the maps were overlain, and the points for each quadrant of each map were added
together. The quadrants with the lowest point values on the final map were considered ideal potential
locations for a new water supply well.
My project attempted to replicate this study. Almost all of the data for this study is available from the
MassGIS database, unless otherwise noted. A more detailed discussion of the data sources, methods, and
difficulties are detailed below.
II.
Categories, data sources, and data layers
The majority of data was obtained from MassGIS. These layers are provided with thorough metadata
including, in most cases, detailed explanations of the methods and primary source material used to create
the layers.
The evaluation category Aquifer Yield describes whether a given quadrant is located over a sand and
gravel aquifer that can be easily exploited for extracting groundwater, as well as the anticipated pumping
rate from that aquifer. The datalayer providing this information is the polygon vector “Aquifers_Poly”.
The layer was obtained from MassGIS and derived from United States Geological Survey (USGS) Water
Resources Division (WRD) quadrangle maps, researched and published from the 1960s to the present. It
is unknown what year the quadrangle that includes Sharon, MA was created. A quandrant located on a
high yield aquifer (>300 gallons per minute (gpm)) is a highly favorable location compared to a medium
yield or non-aquifer location.
The evaluation category Water Quality describes whether a given quadrant is located on or near a known
pollutant source. Agricultural and pastural lands are considered a contaminant risk, likely due to the
possible presence of pesticides, fertilizers, and animal waste. The location of these properties was
identified by the polygon vector layer “Landuse_poly” obtained from MassGIS. Serious contaminant
risks are posed by any location within 400 feet of sources with registered Groundwater Discharge Permits
(multipoint vector “GWP_PT”), Solid Waste Facilities (polygon vector “SW_Poly”), and Oil and
Hazardous Release Sites (multipoint vector “C21E_PT”). Each of these datalayers were obtained from
MassGIS and originally compiled by Massachusetts Department of Environmental Protection (MassDEP)
staff from 1989 to the present.
The evaluation category Permit Restrictions describes whether a given quadrant is located in an
environmentally protected area. Well construction in these locations is difficult and expensive due to an
extensive permitting process, and potential restrictions to construction and access. There were eight data
sources utilized for this category, all obtained from MassGIS:
National Heritage and Endangered Species Protection (NHESP) Priority Habitats of Rare Species
(polygon vector “Prihab_Poly”)
NHESP Estimated Habitats of Rare Wildlife (polygon vector “Esthab_Poly”)
NHESP Certified and estimated vernal pools (multipoint vector “CVP_PT”)
NHESP Potential Vernal Pools (multipoint vector “PVP_PT”)
Areas of Critical Environmental Concern (polygon vector “ACECS_Poly”)
100’ wetland protection buffer (polygon vector “Wetlandsdep_poly”)
150’ riverfront protection area (arc vector “hydro25k_arc”)
200’ riverfront protection area (arc vector “hydro25k_arc”)
100 year flood zone (polygon vector “Q3flood_poly”)
Quadrants that are not located in any protected areas are considered most favorable. Those located within
the 100’ wetlands buffer, ACEC, 100-year flood zone, or 200’ of river or stream is considered less
favorable, and locations within NHESP site or 150’ of river or stream are least favorable. Again, this is
based on the anticipated cost and restrictions of obtaining permits in these areas.
The evaluation category Proximity To Roads describes whether a given quadrant is located within 400
feet of a road. It is prohibited by public health codes to install a well within 100 feet of a wellhead, and an
added buffer is desirable to ensure the safety of the water supply. The arc vector “Eotroads_arc” was
obtained from MassGIS.
The evaluation category Watershed Recharge describes whether a given quadrant is located in a
watershed which is already stressed by groundwater extraction, versus a watershed which has a
groundwater surplus due to factors such as septic system return flows. A watershed experiencing a
significant groundwater recharge surplus is considered highly favorable, whereas a watershed
experiencing a significant recharge deficit is much less favorable. The category was created by using the
polygon vector “subbasins_poly” obtained from MassGIS, and adding a field that includes basin water
balance data extracted from the Town of Sharon Water Department Master Plan report (WDMP). The
WDMP was downloaded from the Town of Sharon Water Department website at:
http://www.townofsharon.net/Public_Documents/SharonMA_DPW/water
The evaluation category Proximity to Existing Water Main describes the relative cost of obtaining
right- of-way and installing the piping necessary to connect a well at a given location into the existing
water mains. Therefore, quadrants closer to the existings mains (<500 feet) are more favorable than
locations far away from existing mains (>1,000 feet). The data for this category was obtained from the
Town of Sharon Department of Public Works: Engineering/GIS Division. The datalayer showing water
main locations is an arc vector file “pipes_with_info_WSE”. No metadata is available for this datalayer,
which was obtained by requesting the data from the GIS Coordinator. The contact info is given at the
following website: http://www.townofsharon.net/Public_Documents/SharonMA_DPW/engineering_gis
The evaluation category Land Cost Criteria describes the anticipated cost and difficulty of obtaining the
land for a given quadrant. It is assumed that unprotected land owned by the Town of Sharon is the most
affordable, that privately owned land would be more expensive, and property owned by the state, county,
federal government, or conservation land protected by a land trust or non-profit would be prohibitive. The
datalayer utilized for this category was a parcels map (polygon vector “parcels_2012”) containing
ownership information. This datalayer was downloaded from the Town of Sharon GIS website:
http://www.mainstreetmaps.com/MA/Sharon/
III.
Data preparation and analysis
All operations used in data processing for this project utilized the same Geoprocessing Environment
settings, a cell size of 35 square meters, and a mask using the town borders (polygon vector “towns_poly”
from MassGIS) to set the spatial extent of the analysis. The goal of the following steps was to create a
raster layer for each category, with a point value assigned to each quadrant of each category raster.
Aquifer Yield involved converting the layer to a raster, and reclassifying the layer in a new raster using
an assigned criteria point system. The point system for all categories was that the most favorable criteria
were assigned a value of 1 (in this case, high yield), the less favorable criteria were assigned a value of 2
(medium yield), and the least favorable criteria were assigned a value of 3 (non-aquifer zones). This was
accomplished using the Reclassify Tool in the Reclass Toolbox.
Two of the data categories – Proximity To Water Mains and Proximity To Roads – involved using the
Euclidean Distance tool in the Spatial Analyst Toolbox. The classifications were set for the ranges (ie.
<500 ft, 500 to 1,000 ft, etc) specified in Section II above. The data layers were then reclassified in a new
raster.
The Water Quality category utilized the Euclidean Distance tool for data sources requiring 400 foot
buffer zones, resulting in new raster data layers. The fourth data layer – Land Use 2005 –required
selecting the crop and pasture lands and creating a new data layer with properties for only those land uses.
The new Crop and Pasture Lands polygon layer was then converted to a raster. All four new rasters were
then merged using the Mosaic to New Raster Tool. Finally, this raster was reclassified in a new raster
using the assigned criteria point system.
The Permit Restrictions category involved first converting each of the NHESP areas, the ACECS layer,
and the flood zone layer to rasters. Next, the Euclidean Distance Tool was used to create raster layers for
the wetland buffer and riverfront protection areas. The NHESP rasters were then merged together with the
150’ riverfront protection raster, and the 200’ riverfront protection raster were merged with the 100’
wetland buffer, the ACECS raster, and the Flood Zone raster. The two new rasters were then reclassified
using the assigned criteria point system, and combined using the Cell Statistics Tool in the Spatial
Analyst-Local Toolbox. This function looked at each overlapping quadrant of the new raster layers, and
assigned the highest value attached to that quadrant in each component raster to that quadrant in the new
raster.
The category Land Cost required adding a new field to the “Parcels_2012” polygon layer, and coding
each property according to the criteria point system. The majority of property ownership is private
residential, so the field calculator was used to initially code every property a value of “2” (private
property), and this value was then updated as necessary. The Parcels polygon layer was then converted to
a raster.
The category Watershed Recharge required adding a new field to the attribute table of the Subbasins
polygon for the basin water balance. The basin water balance was found in the Town of Sharon Water
Department Master Plan report. The Subbasins polygon was then converted to a raster, and reclassified as
a new raster using the assigned criteria point system.
After the above steps, there will be a single raster for each category. The value for each 35 square meter
quadrant was then calculated using the Cell Statistics Tool in the Spatial Analyst- Local Toolbox to find
potential options for future groundwater supply wells. Each category was weighted equally with the
exception of the roads criteria. The study which this project was based on weighted the location of roads
much more heavily, so that quadrants located within 400 feet of a road would be prohibited from being
options for well construction.
IV.
Difficulties encountered
The single greatest difficulty that I encountered in conducting my analysis was figuring out how to
combine several different datalayers with different file formats. In the case of the Permit Restrictions
category, I had multipoint, polygon, and raster datalayers that I needed to combine into a single layer. The
key was deciding to convert the vector layers (multipoint and polygon) into their own rasters. At that
point, I was able to merge these layers as a mosaic raster, and reclassify the mosaic raster as a new raster
using the criteria point system. It was very difficult for me to figure out how to make this process work
and I received a great deal of assistance. However, after figuring out this sequence, the rest of the project
analysis proceeded rapidly.
The next difficult consideration was the best way to represent the results. There were ultimately about 70
different values which lends itself towards a gradational map. However, it was a little difficult to identify
the ideal locations. A closer look at the data also indicated that the data is somewhat “clumped” into
groups, as shown on the following figure:
The reason for the “clumping” of data in the classification window is that the Proximity To Roads
category was weighted far more heavily than all other categories. This means that any value over 53 (as
shown on the above figure) could effectively be in a single category considered prohibited for well
construction. After playing around with visualization I finally settled on a three category classification
(most favorable, less favorable, prohibited). In addition to being more easily understood from a visual
stand point, the three categories also were consistent with the theme of the criteria point assignment
system.
V.
Concluding Thoughts
The results of my project were consistent with the results of the study which my project attempted to
replicate. This should not be surprising, given that the data sources and analysis approaches were the
same. Nonetheless, it indicates that the specific steps that I used to conduct my analysis were consistent
with that used by GIS professionals, and that the results of such a study are replicable, which lends some
confidence to the steps described earlier in this paper. This is not to say that the analytical approach isn’t
flawed. Changing the weights of the different categories does yield different results, so depending on your
priorities (ie. Environmental protection vs. cost) the “most favorable” locations will be different. It would
have been very interesting to see a variety of maps showing various iterations of prioritizing different
categories. Unfortunately, there is only so much room on a poster. Overall, I feel that the process of using
map overlays and raster calculations to create a screening tool is a credible way to identify potential areas
for well exploration. Obviously, it is completely necessary to conduct ground-truthing and much more
detailed research into any property identified as “most favorable” by this project prior to authorizing the
purchase of land for construction a new well. Nonetheless, this tool has value, which is very exciting.
VI.
Background Literature
A. Noorollahi, Younes and Ryuichi Itoi, Hikari Fujii, Toshisaki Tanak. GIS model for geothermal
resource exploration in Akita and Iwate prefectures, northern Japan. Computers and Geosciences,
33 (2007), pp. 1008-1021.
This paper demonstrates how GIS can be utilized for resource and energy exploration. Layers were
created for geological, geochemical, and thermal data sets. The proximity from productive geothermal
wells to various features were then calculated to characterize areas for potential exploration. These
potential exploration areas were then prioritized by assigning weights to each overlying data layer. The
intersect tool was used to identify intersecting areas of interest in the various data layers. Those areas of
interest intersected in some layers, but not all, and the ranking of areas was accomplished based on the
number of intersecting layers. The union method was used to create new layers from the combining of
two intersecting areas of interest within a category of data; ie. areas with volcanic rocks, craters, and
active faults. The weighted overlay raster then creates a new data layer by adding together the values
assigned to each component data layer. Weights can be added to the component data layers to increase or
decrease the influence of that data on determining the suitability of each map region for future geothermal
exploration. The final product is a decision support tool to aid the decision making process of where to
drill new test wells. This sounds like exactly the kind of approach that I could use for my project. The
goals are also similar: siting wells based on categories of data. I will likely be using much of these
methodologies.
B. El-Naqa, Ali and Nezar Hammouri, Khalil Ibrahim, and Masdouq El-Taj. Integrated Approach
for Groundwater Exploration in Wadi Araba Using Remote Sensing and GIS. Jordan Journal of
Civil Engineering, Vol. 3, No. 3 (2009), pp. 209-243.
GIS and remote sensing data is utilized for water source exploration due to the exorbitant cost of
gathering data in a deep aquifer by other means in Jordan. The research utilized satellite images,
topographic, geological, and structural data. Maps were made for each data source. Weights were applied
to the categories within each layer. A groundwater potential (GP) equation was formulated such that the
GP of a given location was equal to the sum of values assigned to annual rainfall, lithology, lineament
(fault) density, elevation, slope steepness, and drainage density. A new layer was created by dividing
these GP values into three categories: low, medium, and high potential, and displaying the results on a
new map. The study that my project is based on also uses a three category approach to divide up areas
into zones of high probability versus low and infeasible locations using weighted categorical data.
C. Kamaraju, M.V.V. and A. Bhattacharya, G. Sreenivasa Reddy, G. Chanrasekhar Rao, G,S,
Murthy, and T. Ch. Malleswara Rao. Ground-Water Potential Evaluation of West Godavari
District, Andhra Pradesh State, India – A GIS Approach. Ground Water Vol. 34, No. 2 (1996),
pp. 318-325.
In this case, similar methodologies utilizing GIS were used for evaluating groundwater resources. The
data sources include lithological, landform, structural, and recharge conditions. The inputs were converted
into a ground-water favorability index (GWFI) ranging from 0 (non-favorable) to 10 (favorable). This
index was applied to each data layer. Weighting was utilized to account for the fact that lithology has a
profound impact on groundwater availability and water quality. Weighted values are assigned to each of
the four components rated by the GWFI. Overlay analysis combines the four layers to establish a groundwater potentiality index (GWPI) that ranges from 2.35 to 10. I am seeing a pattern where categorical
datalayers are overlain and weighted to create a range of favorability, in this case, a variable index.
D. Evans, Barry M. and Wayne L. Myers. A GIS-based approach to evaluating regional groundwater
pollution potential with DRASTIC. Journal of Soil and Water Conservation (March-April 1990),
pp. 242-245.
This paper uses similar methodologies, utilizing an additive overlay process. The study defines two
separate components. The first component involves creating raster layers from mappable hydrogeologic
units with categories such as topography, elevation, aquifer transmissivity, etc. The second component
superimposes these layers with weights, using a relative rating system. There are considerable similarities
between the methodologies of the above papers. While I don’t yet know the exact steps that I will use in
my study, the approach of overlaying layers of categorical data and assigning weights based on some
logic seems very sound.