1. Nathan Howell1, Erick Butler1, Bridget Guerrero2
1School of engineering, computer science, & math, West Texas A&M University; 2Dept of agricultural sciences, West Texas A&M University
An Interactive Web Map for the Assessment of the Anthropogenic Wastewater Generation in the
Southern High Plains
Acknowledgments
This project is being funded by the USDA National
Institute of Food and Agriculture (NIFA) under award no.
2015-68007-23189.
Conclusion
Our interactive map will require continual updates and
constant refinements. There is great anticipation that
the water information collected and aggregated will
lead to a tool that can not only be used by decision
makers but also as a framework that can be applied to
a larger watershed.
References
Budreski, K. Winchell, M. Padilla, L. Bang, J.S. Brain, R.A. (2016). A probabilistic approach for estimating the spatial extent of
pesticide agricultural use sites and potential co-occurrence with listed species for use in ecological risk assessments. Integr Environ
Assess Manag 12:315-327.
Friedl, M.A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley and X. Huang. (2010). MODIS Collection 5 global land
cover: Algorithm refinements and characterization of new datasets, 2001-2012, Collection 5.1 IGBP Land Cover, Boston University,
Boston, MA, USA.
Fry et al 2011; Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., and Wickham, J. (2011). Completion of
the 2006 National Land Cover Database for the Conterminous United States. PER&S 77:858-864.
USDA National Agricultural Statistics Service Cropland Data Layer. (2009,2011). Published crop-specific data layer [Online].
Available at https://nassgeodata.gmu.edu/CropScape/ [cited 2016 June 14]. USDA-NASS, Washington, DC.
USDA National Agricultural Statistical Service. (2009,2011). Quick Stats. Published crop-specific statistics. Available at https://
www.nass.usda.gov/Quick_Stats/ [cited 2016 June 14]. USDA-NASS, Washington, DC.
Mango Map
What is Mango Map ?
An online website where individuals can publish
interactive web maps based on work completed
with a GIS-based software (e.g. ArcGIS or qGIS).
Objectives for using Mango Map
1. Inventory. Provides a web-based space to
organize all related spatial data in the watershed.
Mango Map becomes a repository for spatial
data within the watershed.
2. Communication. Becomes a method of story
telling for all items within the watershed.
Beneficial for decision makers to use as a tool for
a better understanding of water quality within the
watershed.
Abstract
The Tierra Blanca watershed consists of four counties in
the Texas Panhandle and two in Eastern New Mexico.
Situated in the Southern High Plains, the High Plains
Aquifer is the major supplier of water for the region that
includes users from the agricultural, domestic, industry,
and energy sectors. It has been estimated that 90% of
this water is for agricultural purposes and water
availability in the region is decreasing dramatically in
danger of not being able to meet the needs of its users.
As a result, water users in the region might want to
consider alternative sources of water which may
include, but are not limited to evaluating anthropogenic
wastewater generation in the region. Wastewater
generation in the watershed is primarily produced by
beef cattle feed yards. Secondary sources of generation
include two ethanol plants, a processing plant, and a
packing plant. The three largest municipalities within the
region have a population less than 20,000. With limited
resource s available, there is a need to identify
alternative water sources to satisfy the needs of the
region.
The purpose of this study is to spatially assess the quality
and quantity of anthropogenic wastewater generated
within the region. Known wastewater quality and
quantity data has been collected from the local state
environmental agency combined with other spatial
features from other agencies and potentially some field
sampling data to generate an interactive web map that
provides an overall picture of wastewater found in the
region. Understanding this data will assist in determining
possible future water uses provided appropriate
treatment measures are made.
Mango Map Data Layers
Copland Data Layer (CDL)
A rasterized data layer that provides annual crop
land cover data for each growing season in the
contiguous United States from 1997-2015. Acreage
can be approximated using pixel counting which in
many cases develops underestimates as compared
to the National Agricultural Statistical Service (NASS)
crops planted acreage. Therefore, an assessment
study was made to determine whether or not
statistical techniques would be able to improve
acreage at the state and county level.
Cropland Data Layer Assessment Techniques
1. Pixel counting
2. Bayesian probability– Computes the probability of
a crop overlapping a National Land Cover Data Set
LCD class. Bayesian probability was completed on
four crops (corn, cotton, wheat, sorghum) at the
state and county level. Consider corn as a crop—
The probability was computed for all classes and
was used as a weight to determine the number of
acres from the CDL that overlaps all NLCD classes.
Values from each class were summed and
compared to NASS data.
3. Linear regression adjustment—A linear regression
equation was made by using MODIS global land
cover data and CDL acreage to calculate new
adjusted acreage for the study crops at the
county level. The following general regression
was used—
Results from CDL Assessment
Municipal Wastewater Volume
Computation of daily wastewater volume for three
major municipalities (Canyon, Hereford, Friona) in
the watershed. Data use is as follows—
1. United States Census Bureau—American
Community Survey 5 year data sets and 2010
Decennial US Census Total Population data at the
census track level were used to determine the
populations of Canyon and Hereford. Census
block groups were used for Friona. TIGER/Line
Shapefiles assisted in properly assigning
population information to census tracts and block
groups that were included in each municipality.
2. Wastewater demand per capita—Applied TCEQ
wastewater demand and assumed an individual
produces about 100 gal/capital-day.
Future Mango Map Data Layers
1. Anthropogenic sources of wastewater—Rasterized layer
to summarize major water quality parameters for
wastewater source categories (feed yards, dairies,
industries, municipalities).
2. Water quality in aquifers—Rasterized layers have been
prepared and uploaded to the map for water
availability in Ogallala for 2000-2013 but would like to
see if Santa Rose (Dockum) water can be developed.
Also would like to include water quality for the aquifers.
3. Water value scenarios—convert modeling work in
INPLAN to spatial feature to help decision markers
complete water conservation decision.
4. Water quality in playas—Rasterized layers of water
quality in watershed playas.
Screenshot of water
availability in the
Ogallala Aquifer in
2000. Data sets are
available for
2000-2013.
The primary municipal wastewater
treatment plant process in the
watershed is an oxidation pond.
Being able to summarize all
wastewater from all sources
including these ponds will help
improve conservation, introduce
new sources of water, and reduce
reliance on groundwater in the
area. Photo taken by Erick Butler.
Screenshot of 2015 Cropland Data Layer in Tierra Blanca Watershed on
Mango Map website. Top four crops planted for subregion 1 of the
watershed.
-
100,000
200,000
300,000
400,000
500,000
600,000
Corn Cotton Sorghum Winter
Wheat
CropsPlanted(Acres)
County level Cropland Datalayer
Assessment (2009)
NASS Data
CDL Data
Bayesian Probability
MODIS Land Cover
-
100,000
200,000
300,000
400,000
500,000
600,000
Corn Cotton Sorghum Winter
Wheat
CropsPlanted(Acres)
County level Cropland Datalayer
Assessment (2011)
NASS Data
CDL Data
Bayesian Probability
MODIS Land Cover
GIS output from overlay of all CDL sorghum pixels from a growing
season on the NLCD 2006 land cover .
County level Cropland Data Layer Assessment for growing seasons
2009 and 2011. According to assessment, the success or failure of an
adjustment is contingent on the crop type and the growing season.
Screen print of Municipal Wastewater Volume for each municipal in the
watershed. Daily wastewater output for the city of Friona (2010-2014).