Katie Cerretani
Intro to GIS
Final Paper
April 17, 2009
The goal of my analysis was to characterize agricultural water use in Massachusetts, and then
determine whether or not the areas with most intense water use overlaps with areas experiencing
stress on their water resources. The central questions are:
Where is agricultural water use concentrated in Massachusetts?
Is there any overlap with areas classified as having high water stress?
…areas with predicted high population growth/land use change?
A secondary question relates to hypothesized changes in irrigation in the future, as climate
change scenarios play out. Climate change is expected to bring warmer average temperature and
longer growing seasons with little change in total precipitation. This is predicted to cause more
frequent short-term droughts; additionally, precipitation is predicted to become more variable
(1). Thus, irrigation is expected to become increasingly important for Massachusetts farmers as
relying on rainfall alone will become riskier. The expected increase in irrigation may come into
conflict with other uses, including residential, and in-stream. The question became:
Are there agricultural areas that might significantly increase their water use that overlap
with areas experiencing water stress?
Data:
Data Layer (Year)
Water Management
Act (WMA)
Registrations and
Permits (2009)
Description
Includes all current permits and registrations held
by agricultural facilities as of February 2009, with
the following information:
 Industry (CRAN = cranberry; AGRI = all
other agriculture)
 Permit/registration number
 Type (permit or registration)
 Facility name, address, town, and zip code
 Permit/registration start and end dates
 Allowed water withdrawals
 Actual water withdrawals (1997 – 2007)
 Water source type – surface or ground (one
farm may have several surface or ground
water sources)
Source
Massachusetts
Department of
Environmental
Protection
Stressed River
Basins (2001)
1:25,000
In 2001, the Massachusetts Water Resources
Commission published a report in which it
identified a preliminary list of stressed river basins
in the state. The authors compared low flow data
for 72 stream gages in Massachusetts, classifying
those rivers and streams with the lowest flows per
Massachusetts
Department of
Conservation and
Recreation; based
on
*An update is
expected by the end
of calendar year
2009
Town and County
Boundaries
Zip Codes (2006)
National Land
Cover Database
(NCLD)
square mile of drainage area as having hydrologic
stress. These classifications cannot be generalized
outside of Massachusetts due to the use of relative
low flow rather than absolute numbers.
Gages had from 25 to over 50 years of data that
was compiled to calculate median values. Not all
gages had sufficient data (namely those on the
Cape and the islands) so were not included in the
analysis.
MassGIS
Contains rasters (30 m x 30 m) that are coded for
different types of land cover, including cropland
and pastureland.
ESRI
http://www.mrlc.
gov/multizone_do
wnload.php?zone
=13
Steps in the analysis
Describe Agricultural Water Use in Massachusetts
My primary data source for agricultural water use comes from the MA DEP’s WMA permit
database. The permit data needed to be prepared for the analysis, which included deleting any
permits that had already expired, making sure there were no duplicates, calculating the annual
permitted allowances, and calculating the annual reported withdrawals.
In order to visualize the spatial distribution of agricultural water use, I geocoded the WMA
permits, using the ESRI zip code polygon layer as the reference data. After loading the zip code
layer, I transformed the projection to match the coordinate system of the rest of my layers (NAD
1983 Massachusetts State Plane). I next selected by attribute, where [state=MA] to clip the layer
to Massachusetts, and exported this as the “ma_zip_poly” layer to create my address locator.
The initial result of the geocode was 78% match with 60% as the minimum match score and
10% as the minimum candidate score. I was able to rematch the remainder by locating the
facility using google maps and the town name and matching to the nearest zip code. To assist in
this process I loaded US Census Tiger roads as reference. The result of the geocode was a data
layer that contains agricultural WMA permits points at the resolution of zip code.
For my poster, I chose to display the permits as graduated symbols to convey the message that,
not only are agricultural WMA permits concentrated in the southeast, the larger permit amounts
are also concentrated in this area.
I next summarized the permit points by zip code to aggregate permitted and actual withdrawal
amounts, and then joined the summary table to the “ma_zip_poly” layer to see spatial
distribution of withdrawal allowances and amounts:
Millions of Gallons per Year
0
1 - 50
51 - 500
501 - 1,500
1,501 - 3,000
3,001 - 6,000
6,001 - 15,319
Since this map conveys a message similar to the map showing permits as graduated symbols, I
chose to leave it off of my poster.
Join Permit and Stressed Basins Data
In order to look at where agricultural water use overlaps with stress on water resources, I
performed a spatial join of the permit points to stressed water basins.
This join provides me with a summary of permit characteristics by basin. To identify the regions
where water stress overlaps with agricultural water use (in the form of WMA permits), I selected
by attribute where a basin is classified as having high or medium stress and contains at least 1
permit: "Count_" > 0 AND ( "FINAL_STRE" = 'HIGH' OR "FINAL_STRE" = 'MEDIUM' ).
The resulting map is displayed in the poster.
Describe Agricultural Land Use in Stressed Basins
In order assess whether or not there are areas with high potential to increase irrigation, I first
wanted to find out how much agricultural land was in each river basin. To summarize cropland
per basin, I used the spatial analyst – reclassify tool to reclassify NCLD data such that rasters
classified as cropland were coded as 1 and all other rasters as 0:
The same process was repeated for pastureland. Next, I used zonal statistics to aggregate
cropland and pastureland rasters to the stressed basins polygons:
Once these statistics were joined with the stressed basins layer, I used the field calculator to
calculate the acres of cropland and pastureland in each basin. This was done by multiplying the
count of cropland (or pastureland) rasters by the area of the rasters
(25.6429918*25.6429918meters) and then the square meter to acre conversion factor (.0002471):
I also calculated the amount of acres in each basin, then used the new fields to calculate percent
of cropland and percent of pastureland in each basin. I added the cropland and pastureland fields
together to get total agricultural land.
With this information, I used selection tools to identify basins with a relatively high percentage
of agricultural land but no WMA permits. Though this data is very general, the assumption is
that areas with a lot of agricultural land may be likely to increase their irrigation as average
temperatures increase. To identify these basins, I selected from the joined stressed and permit
points layer basins which had no permits, were classified as having high or medium stress, and at
least 10% of the land cover as agricultural land. For the agricultural land cover threshold, I used
the unique value closest to, but greater than, 10%.
The resulting map was included in my poster.
Limitations
The primary limitation in my analysis was the lack of data on irrigation water withdrawals in
Massachusetts. Some very general data is available at the county level, but this is not very useful
in Massachusetts as stress on water resources may vary within counties. The WMA permit
information serves as a good estimate of withdrawals by the larger irrigators, but misses smaller
farms that may also be irrigating.
As a way to roughly approximate areas that may contain small irrigating farms (or the potential
for new irrigators), I used national land cover data to calculate the amount of agricultural land in
each basin. Without more specific information (crop type for instance) it is very difficult to say
anything about whether or not the agricultural land is more or less likely to be irrigated in the
future.
Another data limitation was the quality of the address field in the WMA permit database. To get
around this, I matched the permits to zip codes. While this was sufficient for my analysis, with
more time I would have liked to revise addresses by doing a simple internet search for the farm
names and towns. While this may not have yielded 100%, it would have been preferable to
display multiple permits within zip codes or towns.
Conclusions
This project was a good preliminary look at the spatial distribution of agricultural water use in
the state; however data and time limitations prevented a more accurate analysis. Additionally,
the stressed basins layer is quite dated. A new report is expected by the end of 2009, and will
likely have more detailed information on the type of stress experienced in each river basin. It
would be preferable to use the most recent information for any future analysis.
I would also modify the analysis to spend time getting more detailed data on farm type by zip
code or town. One possible source for this information is the U.S. Census of Agriculture, which
offers zip code tabulations. Data available online only includes numbers of farms by zip code,
but the National Agricultural Statistics Service might have information on acres of different
types of cropland at that level. With even more time, a student could also do a survey of farms in
the state to collect the information.
1. Frumhoff, Peter C, et al. Confronting Climate Change in the U.S. Northeast: Science,
Impacts, and Solutions. s.l. : Union of Concerned Scientists, 2007. A report of the Northeast
Climate Impacts Assessment.