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Mapping Potential Groundwater-DependentEcosystems for
Sustainable Managementby Si Gou1, Susana Gonzales1, and Gretchen R.
Miller2
AbstractEcosystems which rely on either the surface expression
or subsurface presence of groundwater are known as groundwater-
dependent ecosystems (GDEs). A comprehensive inventory of GDE
locations at an appropriate management scale is a
necessaryfirst-step for sustainable management of supporting
aquifers; however, this information is unavailable for most areas
of concern.To address this gap, this study created a two-step
algorithm which analyzed existing geospatial and remote sensing
data to identifypotential GDEs at both state/province and
aquifer/basin scales. At the state/province scale, a geospatial
information system (GIS)database was constructed for Texas,
including climate, topography, hydrology, and ecology data. From
these data, a GDE index wascalculated, which combined vegetative
and hydrological indicators. The results indicated that central
Texas, particularly the EdwardsAquifer region, had highest
potential to host GDEs. Next, an aquifer/basin scale remote
sensing-based algorithm was created toprovide more detailed maps of
GDEs in the Edwards Aquifer region. This algorithm used Landsat
ETM+ and MODIS images to trackthe changes of NDVI for each
vegetation pixel. The NDVI dynamics were used to identify the
vegetation with high potential touse groundwatersuch plants remain
high NDVI during extended dry periods and also exhibit low seasonal
and inter-annual NDVIchanges between dry and wet seasons/years. The
results indicated that 8% of natural vegetation was very likely
using groundwater.Of the potential GDEs identified, 75% were
located on shallow soil averaging 45 cm in depth. The dominant GDE
species were liveoak, ashe juniper, and mesquite.
IntroductionMultiple ecosystems in semi-arid regions are
likely
to be stressed by the increasing pressures of climate, landuse,
and population change (Baldwin et al. 2003; Smithet al. 2003).
Groundwater-dependent ecosystems (GDEs),typically vegetative
communities that rely on the surfaceor subsurface expression of
groundwater, are especiallysensitive to these changes. Greater
understanding of GDEswill enable more informed management
strategies aschanges are observed. While previous studies have
beenable to identify and monitor individual GDEs, muchremains to be
done to document their collective spatialdistribution, influence on
the water balance, and responseto changing water availability. The
purpose of this studyis to develop a method to map GDEs using
existinggeospatial and remote sensing datasets and apply thismethod
to create state and aquifer scale maps in Texas.
1Zachry Department of Civil Engineering, Texas
A&MUniversity, College Station, TX 77843-3136.
2Corresponding author: Zachry Department of Civil Engineer-ing,
Texas A&M University, College Station, TX 77843-3136;
(979)862-2581; fax: (979) 862-1542; [email protected]
Received June 2013, accepted January 2014.2014, National
GroundWater Association.doi: 10.1111/gwat.12169
GDEs in the United States occur in a number ofpotentially
stressed ecoregions, particularly the GreatBasin in Nevada
(Naumburg et al. 2005; Steinwand et al.2006), the Edwards Plateau
in Texas (Jackson et al. 1999;McElrone et al. 2004), the Sonoran
Desert in Arizona(Scott et al. 2008), and in California, the Owens
Valley(Elmore et al. 2003; Goedhart and Pataki 2010) and
thefoothills (Miller et al. 2010) and riparian meadows of theSierra
Nevadas (Loheide et al. 2009; Loheide and Gore-lick 2005, 2007;
Lowry et al. 2011). Two distinct types ofGDEs are significant for
sustainable groundwater devel-opment (Eamus et al. 2006): (1) biota
living in and aroundsprings, groundwater-fed wetlands, and riparian
zones, allof which rely on the surface expression of
groundwater;and (2) vegetation with root access to deeper (more
than2 m) stores of water which require the subsurface pres-ence of
groundwater. A third class of GDEs, subsurfacemicrobial
communities, is also recognized. While thesepopulations and the
processes they facilitate are environ-mentally significant, they
are substantially different intheir character and thus will not be
included in this study.We considered the vegetation belongs to the
first two typesof GDEs in this study, and have referred to them as
low-land GDEs and upland GDEs, correspondingly. Eamuset al. (2006)
also suggested that the vegetation may relyon groundwater if it
meets one or more of the followingcriteria: (1) the groundwater or
the capillary fringe is
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Table 1Selected Studies of Known Phreatophytes in the
Southwestern United States
Source Location Species
McElrone et al. (2003) Texas, karst uplands Juniper (Juniperus
ashei ) and live oak (Quercus fusiformis)Wilcox et al. (2006)
Texas, riparian zones Saltcedar (Tamarix chinensis, Tamarix
ramosissima)
Texas, karst uplands Ashe juniper (Juniperus ashei Buchholz
)Texas, deep soils Mesquite (Prosopis glandulosa Torr. var.
glandulosa)
Schaeffer et al. (2000) Arizona, stream channels Willow (Salix
goodingii Ball ) and cottonwood (Populusfremontii Wats .)
Scott et al. (2008) Arizona, savannas Velvet mesquite (Prosopis
velutina Woot .)Miller et al. (2010) California, savannas Blue Oak
(Quercus douglasii )Steinwand et al. (2006) California, scrubland
Rabbitbrush (Chysothamnus nauseosus), Nevada saltbush
(Atriplex lentiformis ssp. torreyi ), and greasewood
(Sarcobatusvermiculatus)
Loheide and Gorelick(2005, 2007)
California, riparian zones Wet-meadow vegetation (sedges,
rushes, and some otherherbaceous species)
Martinet et al. (2009) New Mexico, riparian zones Cottonwood
(Populous deltoids spp. wislizeni), salt cedar(Tamarix chinesis),
Russian olive (Elaeagnus angustifolia),mesquite (Prosopis
pubescens), saltbush (Atriplex L. spp.)
within the vegetation rooting depth; (2) significant
surfaceexpressions of groundwater are present (e.g., springs),and
the vegetation associated with these expressions isdifferent from
other nearby vegetation; (3) the vegetation,or a portion of it,
remains green and physiologically activeduring extended dry
periods; (4) the vegetation showsslow seasonal changes in leaf area
index while others donot; (5) the vegetation exhibits lower water
stress than thenearby vegetation without accessing groundwater; (6)
theannual transpiration is significantly larger than the
annualrainfall and run-on rate; and (7) daily or seasonal changesin
groundwater depths are observed, not due to lateral flowor
percolation.
Table 1 shows the wide variety of known phreato-phyte species in
the southwestern United States. WithinTexas, at least six
phreatophyte species have been identi-fied; two in karst upland
areas (juniper and live oak), onein deep upland soils (mesquite),
and three lining riparianzones (willow, salt cedar, and giant reed,
Arundo donax ).
Two prior approaches have been used to predict thepresence or
absence of GDEs in a given region. The mostcommon is the creation
of an index based on key factorslinking GDEs and abiotic factors,
such as pedological,morphological, hydrological, and climate
characteristics(Bertrand et al. 2012). On the basis of these
existingdatasets, some studies created index values for
smallwatersheds; for example, studies had been conducted atthe
hydrologic unit code-12 (HUC-12, subwatershedsswith the average
area of 40 mile2) scale (Howard andMerrifield 2010) and the HUC-6
(basins with the averagearea of 10,000 mile2) scale (Brown et al.
2010). Thismethod can highlight areas with high index values
whichindicate the areas host large numbers of GDEs. The resultscan
provide useful information to incorporate GDEsinto groundwater
management at large scale. However,the previous GDE index systems
had only consideredthe factors related to the lowland GDE types
(gainingstreams, springs, riparian zones, potential
groundwater-fed
wetlands, and perennial lakes) and did not include thefactors
linked with the deep-rooted, upland phreatophytes.In addition, the
index approach considered watershedscale areas of interest (e.g.,
HUC-12) as the smallestestimation units. Thus, the detailed
information on GDEdistributions within a watershed was
unavailable.
Alternate approaches directly identified potentialGDEs based on
their own specific characteristics or behav-iors, such as
relatively slower changes in their physiolog-ical activity than
that of nearby, non-GDE plants. Variousremote sensing based
indices, such as Normalized Dif-ference Vegetation Index (NDVI),
Normalized DifferenceWater Index (NDWI), and MODIS Enhanced
VegetationIndex (EVI), have been used to detect such GDE
char-acteristics. On the basis of the changes in NDVI andNDWI
response to water-limiting conditions, Barron et al.(2012)
identified potential GDEs in Western Australiausing Landsat
imagery. Dresel et al. (2010) combinedthree remote sensing
measures, including the NDVI dataderived from Landsat images, the
changes of MODIS EVI,and the remote sensing based classification,
to identifyGDEs in Australia. GIS modeling can further
improveremote sensing based GDE identification methods. Com-bining
remote sensing derived NDVI, vegetation green-ness, and soil
moisture, and the GIS modeled groundwaterand landscape wetness
information, Munch and Conrad(2007) classified the potential GDEs
for a 2400 km2 regionin South Africa.
These remote sensing based approaches can providemore detailed
GDE distribution than the index systemapproach. However, the
previous studies did not considerthe impacts of vegetation density
and plant phenologyon NDVI dynamics. The potential biases need to
beaddressed when NDVI dynamics was used to identifyplant
groundwater use. The combination of various remotesensing measures
may ameliorate these biases. In addition,the remote sensing based
approaches usually needed highquality images combined with
additional calculations and
100 S. Gou et al. Groundwater 53, no. 1: 99110 NGWA.org
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careful user interpretation. Therefore, they were typicallyused
only for GDE identification at small scales.
Our study aims to combine the best features of bothapproaches to
address the different management needsfound at various scales and
to produce a more holisticassessment. At the large state/province
scale, a GIS-basedGDE index approach was used to generally identify
whichareas contained considerable numbers of both lowlandand upland
GDEs. The GDE index approach highlightedthe critical areas where
water and ecosystem managersshould consider GDEs in their planning.
For these criticalareas, a remote sensing-based approach was
developedto provide more thorough information about the
spatiallocation of GDEs. Thus, the objectives of this study areto:
(1) develop a GIS-based method to estimate a GDEindex value for
each subregion in Texas; (2) propose aremote sensing-based method
to delineate detailed GDEdistributions for the area highlighted in
objective (1);and (3) analyze the impacts of various factors on
GDEdistribution, including vegetation types, soil depth,
andlandforms. These mapping efforts represent a key steptowards
providing groundwater managers and modelerswith the information
they need to assess GDEs at differentscales.
MethodsWe mapped GDEs based on the criteria proposed
by Eamus et al. (2006) using a two-step approach: atthe state
scale, a GIS-based method was first used tocalculate a GDE index
for each state subdivision, thatis, groundwater management area
(GMA) or hydrologicunit code-6 (HUC-6) sized watershed in Texas. We
choseGMAs and HUCs for GDE index estimation because theseareas are
the spatial scales used for groundwater andwatershed management in
Texas (TWDB 2012). Usingpublically available data, the GIS-based
method served asa screening tool to identify critical regions with
a highpotential to host a significant number of GDEs. Next,at the
aquifer/basin scale, a remote sensing method wasapplied to a
critical region in order to identify ecosystemsthat exhibit the
physiological hallmarks of groundwaterdependence. This method
provided detailed, smaller scaleinformation on GDE
distributions.
GDE Index Method for State/Province ScalesThe criteria proposed
by Eamus et al. (2006), espe-
cially the first two criteria (see Introduction), mainly focuson
two aspectswhether the groundwater is accessibleby vegetation, and
whether the vegetations dynamicsare associated with the available
groundwater. There-fore, we created a new GDE index system, which
com-bined two categories of GDE indicatorsvegetative
andhydrological. The vegetative indicators denoted the veg-etation
with high potential to be GDEs based on ecosys-tem type, while the
hydrological indicators identified theareas where groundwater is
most likely to be accessedby ecosystems. To derive these
indicators, a GIS databasewas established with a variety of
geospatial information
on Texas topography, hydrology, and ecology, includingpreviously
generated data on springs (Brune 1975; USGS2012a), wetlands (USFWS
2012; USGS 2012b), lan-duse/landcover (USGS 2012b), vegetation
types (TPWD2012), base flow index (Wolock 2003),
gaining/losingstreams (Slade et al. 2000), HUCs (USGS 2012a),
andGMAs (TWDB 2012).
The vegetative indicators included representationof
groundwater-fed wetlands and phreatophytes, whichrepresented
dominant ecosystem types of lowland andupland GDEs. The lowland
GDEs in the riparian zonesand around the springs were excluded in
the estimationof the vegetative indicators, since our analysis
suggestedthat their areas were insignificant when compared to
thetotal area of wetlands and phreatophytes (see Results
andDiscussions). Groundwater-fed wetlands were specifiedbased on
wetland types in National Wetland Inventory ofU.S. Fish and
Wildlife Service (2012) and included allnoncoastal wetland types:
freshwater emergent wetlandand freshwater forested/shrub wetland.
In some inlandareas not covered by the National Wetland
Inventory,wetland locations were derived from the USGS NationalLand
Cover Database (2012b), and the emergent herba-ceous wetlands and
woody wetlands in these areas wereconsidered to be groundwater-fed
wetlands. To identifypotential upland GDEs, the vegetation
belonging tophreatophytic species was identified from
vegetationcover data based on the list of species known to occur
inthe southwestern United States (Table 1). The vegetativeindex was
calculated for each state subdivision (GMA orHUC-6) (Equation
1).
Vegetative Index = Phreatophyte area + Wetland areaTotal
subdivision area
(1)
A higher vegetative index value denoted that the areahad more of
the ecosystem types that are likely to usegroundwater. However, in
some areas, groundwater istoo deep to be accessed by these
ecosystems. In thatcase, even though the dominant vegetation
belongs tothe phreatophytic species, they may not be
groundwaterdependent. Therefore, a hydrological indicator was
intro-duced to show the areas where groundwater is accessibleby
ecosystems. Ideally, the hydrological indicators shouldinclude the
information on the location and depth of nearsurface water tables.
However, this information is unavail-able in many regions. Instead,
we used the USGS Base-flow Index (BFI) was used as a surrogate
hydrologicalindicator of regional groundwater-surface water
interac-tions. BFI is a measure of the contribution of baseflowto a
streams overall flow and was produced by USGSbased on their
streamgauging data (Wolock 2003; Wahland Wahl 2007). It also
included some spring flows con-tributing to the streams (Wahl and
Wahl 1995). A high BFIshows a high proportion of total flow coming
from morereliable groundwater sources, which implies a high
poten-tial that groundwater presented to land surface or watertable
rised near land surface to contribute to streamflow.
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The USGS BFI data used in this study were point-based estimates
created from and assigned to individualUSGS streamgauges, rather
than the spatially interpolatedgrids that were also available
(Wolock 2003). In somecases, a HUC-6 watershed contained multiple
USGSstreamgauges with different BFI values, while in others,a HUC-6
watershed did not have any streamgauges orhad streamgauges not
reporting BFI values. For thewatersheds with multiple USGS
streamgauges, BFI valueswere averaged. For the watersheds without
data, a BFIvalue was assigned based on the average BFI value ofthe
larger HUC-4 watershed where the HUC-6 watershedresides (e.g.,
Upper Beaver, HUC 111001, was assignedthe average BFI value for
North Canadian, HUC 1110).For each GMA, a BFI value was determined
to be thearea weighted average of corresponding HUC-6s BFIvalues
(Equation 2). The area of a specific HUC-6 withina certain GMA was
calculated in ArcGIS.
GMAj _BFI
=
ni=1
HUCi_BFI Area of HUCi within GMAj
Total Area of GMAj(2)
Finally, a GDE Index was developed to integrateboth vegetative
and hydrological indices. For each statesubdivision (GMA or HUC-6),
the vegetative index wasmultiplied by the hydrological index
(regional BFI ) tocalculate the GDE Index of each specific
area:
GDE Index = Vegetative Index Hydrological Index(3)
The two indices were combined multiplicatively,such that if one
index was not satisfied then anotherindex could not compensate for
it. Both hydrological andvegetative indices need to be above zero
in order foran area to be identified as potentially hosting a
GDE.Multiplying the two indices yielded zero if one of theindices
was not satisfied, which eliminated some directly.When both indices
were above zero, those areas withhigher multiplication results
implied a higher potential tohost GDEs. Efforts to sustainably
manage groundwaterin areas with high GDE indices should focus
attentionon these vulnerable ecosystems as potential
groundwaterconsumers.
Remote Sensing-Based Method at Aquifer ScaleFor regions
identified as highly likely to contain
GDEs, more accurate information about the spatial distri-bution
of GDE was needed to support sustainable ground-water management.
The exercise was not straightforward;many factors combined together
to impact GDE distri-bution, such as plant characteristics,
climate, soil, andgeology. Not all the plants belonging to the
phreato-phytic species depend on groundwater. For example, ifa
mesquite was on deep soil and the local precipitation
was adequate to support its water use, this mesquite mayonly
rely on water stored in deep soil from rainfall events,rather than
using groundwater. However, even in the sameclimate regime, another
mesquite may be located on shal-low soil with a lower available
water content. Undersuch conditions, a mesquite may need to access
deepergroundwater to support its water use. Even though thetwo
mesquites were the same species under the sameclimate, they may be
different in groundwater depen-dency. Additionally, different, yet
co-occurring, speciesmay have different levels of groundwater
dependence. Forexample, if a live oak is located in upland area
withshallow water table, this live oak may access ground-water
using its deep roots, while shallow-rooted grassesaround it may not
access groundwater directly. Therefore,we needed to develop a more
complex method, based onremote sensing data, to detect the
physiological signaturesof groundwater-dependent vegetation.
The criteria of Eamus et al. (2006) were also appliedto guide
the GDE identification using remote sensing.Two of these criteria
can be assessed by analyzing remotesensing data: (1) A proportion
of the vegetation that usesgroundwater remains green and
physiologically activeduring extended dry periods, and (2) the
vegetation thataccesses groundwater exhibits lower seasonal changes
inleaf area index than the other nearby vegetation does.
Inaddition, Tweed et al. (2007) highlighted a third criterionfor
GDE identification: (3) Vegetation with low inter-annual
variability of vegetation photosynthetic activity islikely to
access groundwater.
To assess these criteria remotely, the NDVI waschosen as an
indicator. NDVI is widely used to monitorvegetation cover and
biomass production. It is sensitiveto leaf area index change until
a full vegetation coverhas been reached (Carlson and Ripley 1997)
and providesuseful information about vegetation physiological
functionunder clear weather conditions (Wang et al. 2004; Tweedet
al. 2007). Two different remote sensing products fromLandsat 7
Enhanced Thematic Mapper (ETM+) andMODIS were used to relate
vegetation NDVI variability togroundwater use (Figure 1). Landsat
ETM+ has relativelyhigh spatial resolution, which helps to discern
the finescale distribution of GDEs, while MODIS has relativelyhigh
temporal resolution, enabling it to capture NDVIchanges of
vegetation within short time periods.
We selected the Edwards Aquifer region, belong-ing to GMA 10, as
case study area for the remotesensing-based methods. This area
hosts three knownphreatophytic species: live oak (Quercus
fusiformis), ashejuniper (Juniperus ashei ), and mesquite (Prosopis
glandu-losa) (McElrone et al. 2004; Wilcox et al. 2006). Numer-ous
springs appear along the Balcones fault zone. Thedata from National
Land Cover Database (USGS 2012b)showed that the dominant plant
functional types wereshrublands (48% of total natural vegetative
areas), grass-lands (22%), evergreen forests (20%), deciduous
forests(8%), and woody wetlands (1%). Data from Web SoilSurvey
(USDA 2013) showed that 57% of the EdwardsAquifer region has
shallow soils(average depth of 45 cm),
102 S. Gou et al. Groundwater 53, no. 1: 99110 NGWA.org
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Criterion 1. Vegetationremains green andactive in dry season
Criterion 2. Vegetationexhibits low seasonalchanges in LAI
Criterion 3. Vegetationhas low inter-annualvariability
Landsat ETM+ imageof mid-summer ofYear 2002 (3030 m)
MODIS NDVI imagesevery 16 days for Year2011 (250250 m)
MODIS NDVI images inJuly from Year 2002 to2011 (250250 m)
Calculate NDVI valueRemove urban areas, water, farmlands
andpasturesCluster into 5 groups using K-means fromlowest NDVI (1)
to highest NDVI (5) (3030 m)
Calculate NDVI standard deviationRemove urban areas, water,
farmlands andpasturesCluster into 5 groups using K-means fromlowest
SD (5) to highest SD (1) (250250 m)
Calculate NDVI standard deviationRemove urban areas, water,
farmlands andpasturesCluster into 5 groups using K-means fromlowest
SD (5) to highest SD (1) (250250 m)
Add the threedatasets togetherCluster the resultsinto five
GDElikelihood groupsusing K-means(3030 m)
Criteria Data Source
Resamplethe results
Data Processing Results
Figure 1. Flow chart for remote sensing method: Three criteria
were used for the remote sensing based method, which wasused to
identify GDEs at the aquifer/basin scale. For criterion 1, one
Landsat ETM+ image from mid-summer in 2002 wasprocessed using the
steps shown above; it indicated the vegetation with high NDVI
values in dry season. For criteria 2 and 3,multiple MODIS NDVI
images collected throughout 2011 and during July of 2002 to
2011were selected, in order to identifyvegetation with low seasonal
and inter-annual changes in NDVI. The results from the three
criteria were integrated togetherto classify each 30 30 m pixel
into one of five GDE likelihood groups.
and the remaining 43% has deep soils (more than 200 cm).The
region also has a subtropical to semi-arid climate.Precipitation is
highly variable in time, but is generallyhighest in May and
September. In July and August, theprecipitation is usually low,
while the potential evapo-transpiration is high. The precipitation
is out of phasewith potential evapotranspiration during this
period. Thisimplies that GDEs are most likely to rely on
groundwaterduring July and August. Therefore, satellite imagery
fromJuly was used in the analyses relating to Criteria One
andThree.
During the extended summer dry season, vegetationwith high NDVI
values was considered to be physiologi-cally active, indicating
that there was a high likelihood itwas using groundwater (Criterion
One). To verify the firstcriterion remotely, Landsat ETM+ images
(30 30 m)from July 2002 were used; these images were high
qualityand relatively cloud-free. The NDVI value of each pixelwas
calculated (Equation 4). Since we only consideredthe natural
vegetation in this study, the pixels representingurban areas,
water, farmlands, and pastures were removedfrom the NDVI results
based on land cover data fromUSGS (2012b).
NDVIj =(NIRj Rj
)(NIRj + Rj
) (4)
where j is the j th vegetation pixel, NIRj and Rj refers tothe
spectral reflectance measurements in the near-infraredand red
regions, respectively.
We chose an unsupervised classification technique,K-means, to
cluster the NDVI results into five groups.K-means is a widely used
algorithm to automaticallyclassify the data into K clusters
according to their sim-ilarity (MacQueen 1967). Unlike supervised
classification
methods, this unsupervised classification technique doesnot need
prior knowledge to define training sets. Instead,it attempts to
find the underlying cluster structure auto-matically (Canty 2007),
thus it was suitable for our regionof interest, which lacked
previous studies of GDEs. Weconducted the K-means classification in
ENVI software(version 4.8) (Canty 2007), which we used to
classifythe pixels into five groups according to the similarity
oftheir NDVI values. We then calculated the average NDVIvalue of
the pixels in each group and assigned each groupa value from 1 (the
group containing the lowest averageNDVI value) to 5 (highest NDVI
group).
Vegetation exhibiting low seasonal changes in leafarea index
over a whole year may also access groundwater(Criterion Two). For
each vegetation pixel, the standarddeviation in NDVI across a
year-long time series wascalculated by Equation 5:
SDj =1
n
nt=1
(NDVIt,j NDVImean,j
)2 (5)
where n is the number of time-series satellite images, jis the j
th vegetation pixel, NDVIt,j is the NDVI valueof the j th pixel at
time t , NDVImean is the mean NDVIvalue of the j th pixel for the n
images. A low NDVIstandard deviation implies that the vegetation
pixel hadslow changes in leaf area during the study period.
MODIS NDVI products (MOD13Q1, 250 250 m),collected every 16 d in
Year 2011, were used to analyzethe seasonal NDVI changes over the
whole year. Thedry year 2011 was chosen for the analysis because
Year2009 and Year 2010 were also dry years, minimizing thepotential
impacts of antecedent soil moisture on vegetationdynamics. As in
the previous analyses, urban areas, water,
NGWA.org S. Gou et al. Groundwater 53, no. 1: 99110 103
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farmlands, and pastures were also removed. The K-meanstechnique
was then applied to cluster the seasonal NDVIstandard deviation
into five groups. Five groups wereassigned the values from 1 (the
group with highest averageseasonal NDVI standard deviation) to 5
(lowest NDVISD). Higher group values indicated that the
vegetationlocated within a pixel had relatively higher potential to
beusing groundwater.
A similar method was used to identify the vegetationwith low
inter-annual changes in leaf area index (CriterionThree). The
MOD13Q1 data (250 250 m), from imagestaken in July for each year
from 2002 to 2011, wereused to calculate the inter-annual NDVI
standard deviationvalue for each natural vegetative pixel. The
resultswere also clustered into five groups using the
K-meansalgorithm and assigned from 1 (the group with highestaverage
inter-annual NDVI standard deviation) to 5(lowest NDVI SD). Both
results from the MODIS-basedanalyses in Criteria Two and Three were
further resampledin ArcGIS to change the cell size from 250 250 m
to30 30 m resolution to correlate to the spatial resolutionof
Landsat ETM+.
Each criterion yielded a dataset containing potentiallyunique
information to identify GDEs. However, eachcriterion still had its
own disadvantages, which centeredon its biases in regard to certain
plant functional types (seeResults and Discussion). To overcome
these, we mergedall three datasets from Criteria One, Two, and
Three usingthe raster calculator in GIS. The assigned values (1
to5) were summed for each pixel and the resulting sumhad the values
ranging from 3 to 15. Using the K-meansalgorithm, these values were
further classified into fiveGDE likelihood groups of the final
resultsvery likelyto be GDEs (the group with highest average
values), likelyto be GDEs, about as likely as not to be GDEs,
unlikelyto be GDEs and very unlikely to be GDEs (the group
withlowest average values).
Results and Discussions
GDE Index at the State/Province ScaleWe estimated the GDE Index
for each GMA and
HUC-6 in Texas. Phreatophytes clustered in the middleregions of
Texas, from the High Plains through the CentralGreat Plains and
Edwards Plateau to the Southern TexasPlains. Live oak and mesquite
were the two dominantphreatophyte species (Figure 2a). Other
phreatophytes,including cottonwood, saltceder, and willow oak,
werefound in riparian areas. The woody wetlands and theemergent
herbaceous wetlands were mainly found ineastern Texas (Figure 2b).
A large number of thesewetlands were located in riparian areas,
where they maybe fed by shallow groundwater. The regions with
thehighest vegetative index were located in central Texas.BFI
values indicated that the streams in central andeastern Texas had
high baseflow ratios (Figure 2c).Correspondingly, the highest
hydrological index valueswere also found in the central Texas.
GMAs 7, 9, and 10 had the highest GDE index values,indicating
they had the highest potential to contain GDEs(Figure 2d). The
HUC-6 basins with the highest GDEindex values were the Colorado
River and the NeucesRiver (Figure 2e). The BFI values in these
regions rangedfrom 0.3 to 0.45, and they are underlain by a number
ofmajor aquifers, including the Edwards, Edwards-Trinity,Trinity,
Ogallala, Pecos Valley, and Seymour. Karstedcarbonate rocks and
other permeable formations in theseareas are known to produce
numerous springs, includingthe two largest: Comal and San Marcos.
These areaswere almost fully covered by phreatophytic plant
species,with wetlands scattered in the riparian areas and aroundthe
large springs, making upland GDEs the dominanttype. Plans for
sustainable groundwater management needto address the groundwater
use of potential GDEs andthe risks of disturbances on GDEs, such as
the landuse changes, groundwater over-extraction, and
climatechange. In addition, managers in some specific areasneed to
consider the influence of GDEs on public watersupplies, including
the potential changes to groundwaterrecharge and baseflow that may
result from their presenceor expansion (Wilcox 2002).
Remote Sensing-Based Results in the Edwards AquiferRegion
Results Using the Three Groundwater-Dependence CriteriaEach
criterion captured the groundwater use potential
of different plant functional types. Table 2 shows
thepercentages of each plant functional type, as classifiedinto
likelihood groups (with 5 being the highest). If oneplant
functional type had the largest portion in the highestlikelihood
group and the smallest portion in the lowestlikelihood group, it
was considered as the type has thehighest likelihood to use
groundwater, such as the wetlandin Criterion One.
Criterion One identified the areas with high NDVIvalues in the
dry summer (Figure 3a). The pixels in thehighest likelihood group
(group 5) had an NDVI valuegreater than 0.5, similar to the 0.35 to
0.5 range suggestedby Barron et al. (2012). Wetlands were
identified as theplant functional type most likely to use
groundwater.In contrast, a large portion of the grasslands (78%)
wasclassified into the lowest likelihood group. These grass-lands
likely depend only on soil water. There were 10%of deciduous
forests classified into the highest likelihoodgroup, while only 4%
of evergreen forests were includedinto this group. The different
results between deciduousand evergreen forests may because of
vegetation densityrather than differences in their groundwater
dependency;NDVI is tightly related to the vegetation density
(Carlsonand Ripley 1997; Purevdorj et al. 1998). Van Auken et
al.(1981) found that in central Texas, the evergreen forestshad
significantly lower density and species richnessthan the deciduous
forests. Therefore, Criterion One canexclude some non-GDEs with low
NDVI in the dryseason because they did not access groundwater,
butit may also ignore some GDEs with low NDVI due to
104 S. Gou et al. Groundwater 53, no. 1: 99110 NGWA.org
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(a)
(d) (e)
(b) (c)
Figure 2. GDE index developed by integrating the vegetative and
hydrological indicators (a, b, c) for each GMA (d) andHUC-6 (e)
watershed. Darker areas indicate a higher likelihood of supporting
significant numbers of GDEs.
Table 2Plant Functional Types Captured by Each Criterion
Percentage of Each Type (%)Criterion 1 Criterion 2 Criterion
3
Plant Functional TypesHighest
Likelihood (5)Lowest
Likelihood (1)Highest
Likelihood (5)Lowest
Likelihood (1)Highest
Likelihood (5)Lowest
Likelihood (1)
Deciduous forest 101 42 13 4 27 9Evergreen forest 4 43 23 2 36
6Shrubland 2 67 14 8 11 18Grasslands/herbaceous 2 78 9 16 10
26Wetlands 36 22 28 11 28 9Total vegetation covered areas 3 62 15 8
17 16
1Percentage was calculated as 10% = Deciduous Forest in Group
5/Total Deciduous Forest 100%. Other percentages were calculated in
the same way.
NGWA.org S. Gou et al. Groundwater 53, no. 1: 99110 105
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(a)
(b)
(c) (f)
(e)
(d)
Figure 3. Remote sensing results using three GDE criteria:The
left three figures show the individual results from thethree
criteria, shown in blue. The areas with higher criterionvalues
imply the higher probability of containing GDEs.These three results
were synthesized together to generate thefive likelihood groups in
Figure 4. The right three figuresshow the relationship between each
two criteria, shown ingreen. The areas with the highest agreement
imply the twocriteria had the same results in these areas.
their low density, such as the evergreen forests. Thesimilar
problem occurred for the shrublands due to theirlow canopy
coverage. Only 2% of total shrublands wereincluded in the highest
likelihood group.
Criterion Two indicated areas with slow seasonalchanges in NDVI
for a dry year (Figure 3b). As comparedto Criterion One, more areas
(15% of the total vegetationcovered areas) were classified into the
highest likelihoodgroup and much fewer areas (8%) were in the
lowestlikelihood group (Table 2); obvious increases occurred inthe
number of both evergreen forests and shrublands inthe highest
likelihood group. Wetlands were still the typewith the highest
potential to use groundwater, while grass-lands were still the type
with the lowest groundwater usepotential. Criterion Two focused on
the rate of change inNDVI rather than the NDVI value itself, which
eliminatedthe impacts of vegetation density on NDVI in
CriterionOne. The percentage of the deciduous forests in thehighest
likelihood group (13%) only increased slightly.Due to their growth
pattern and phenological stages, thedeciduous forests essentially
exhibited faster seasonal
NDVI changes compared to the evergreen forests. There-fore,
Criterion Two may ignore some GDEs with fasterseasonal NDVI changes
due to their essential seasonalgrowth pattern rather than their
dependence on groundwa-ter. However, Criterion Two still had its
own advantageto capture the species which may access groundwater
attimes outside of the dry season, while Criteria One andThree only
analyzed the NDVI variability in dry periodsto distinguish
vegetation with different water use patterns.
The third criterion analysis indicated areas with
lowinter-annual changes in NDVI in dry seasons for multipleyears
(Figure 3c). By using only the satellite imagescollected in July,
each plant functional type in the imageswas at a consistent
phenological stage for every year. Iteliminated the impacts of
plant growth pattern on NDVIstandard deviation in Criterion Two.
Focusing on theresponses of vegetation in various precipitation
conditionsduring the dry season, Criterion Three segregated
theeffects of annual variations in precipitation from theimpact of
vegetation growth patterns and phenologicalstages. Compared to the
results of Criterion Two, moredeciduous forests (27%) were
classified into the highestlikelihood group. Grasslands still had
the lowest potentialto use groundwater based on Criterion Three.
Evergreenforests had the highest groundwater use potential
inCriterion Three, rather than wetlands as identified inCriteria
One and Two.
We analyzed the results to determine if the threecriteria each
contained distinct information on GDEs.For one vegetation pixel, if
two criteria yielded thesame results, we considered that these
criteria had thehighest agreement. For example, if both Criteria
One andTwo classified a pixel into Group 5, they had
highestagreement in this pixel; if they classified the pixel
intoGroups 1 and 5, they had the lowest agreement. Similaritymaps
for each pair of criteria are shown in Figure 3 (partsd, e, and f).
The results indicated that Criteria One andTwo had the highest
agreement over wetlands, and thelowest agreement in shrublands.
Therefore, if the resultsof Criteria One and Two were summed, their
combinationwould strengthen the final results in wetlands and
wouldhelp ameliorate the disadvantage of Criterion One
inshrublands. Criteria One and Three also had the highestagreement
in wetlands, but the lowest agreement inevergreen forests; Criteria
Two and Three had the highestagreement in deciduous forests, but
the lowest agreementin the wetlands. In general, Criteria One and
Two hadthe most distinct results (Figure 3d): 14% of the
totalvegetation cover had the highest agreement and 9% hadthe
lowest. Criteria Two and Three had the most similarresults (Figure
3f), with 27% in this highest agreementcategory and 2% in the
lowest agreement category.
Combination of Three CriteriaAcross the Edwards Aquifer, the sum
of the three
criteria ranged from 3 to 15. The K-means algorithmwas used to
find classification thresholds for the fivegroupsvery likely to be
GDEs (values ranging from12 to 15), likely to be GDEs (10 to 11),
about as likely
106 S. Gou et al. Groundwater 53, no. 1: 99110 NGWA.org
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(d)
(a)
(c)(b)
Figure 4. GDE mapping in the Edwards Aquifer using the remote
sensing method: Three areas were highlighted to show thatGDEs were
most likely to be found around springs, along the streams, and in
the upland where groundwater is accessibleby phreatophytes. The
figure on the left corner showed the soil depth of the Edwards
Aquifer region; deep soil refers to thatwith depths over 200 cm. A
large number of pixels classified as Very Likely to Contain GDEs
(shown in dark green color)were found on shallow soils over
carbonate rocks, while the remaining were associated with deep
alluvial soils.
as not to be GDEs (8 to 9), unlikely to be GDEs (6 to 7)and very
unlikely to be GDEs (3 to 5). The five groups,from Very Likely to
Very Unlikely, were 8%, 19%,32%, 26%, and 15% of total natural
vegetation coveredareas, respectively (Figure 4). The group Very
Likely tobe GDEs was further divided into lowland and uplandGDEs.
Potential GDEs within 200 m from a stream orwithin 500 m around a
spring or a wetland were classifiedas lowland GDEs. The analysis
showed that 11% of thepotential GDEs belonged to lowland category,
with 8%occurring in riparian zones and around springs and 3%in
other groundwater-fed wetlands. The remaining 89%of the potential
GDEs were located in uplands, indicatingthat they were the dominant
GDE category in the EdwardsAquifer region.
For the areas very likely to contain GDEs, weexamined vegetation
types, the soil depth, and thelandforms to determine whether or not
the remotesensing results coincided with our understanding
ofimportant GDE characteristics. While water table depthsfor
surficial, unconfined aquifers were not available forthis area, the
soil data from Web Soil Survey (USDA
2013) indicated that more than 99% of the study areahad a water
table deeper than 2 m. Therefore, in orderfor vegetation to access
groundwater in this area, it mustpossess a deep root system;.
Previous studies have foundthat live oak, ashe juniper, and
mesquite are able todevelop such rooting patterns (Wilcox 2002;
McElroneet al. 2004).
In the Edwards Aquifer region, live oaks andashe juniper
dominated 45% of total natural vegetation,and mesquite dominated
47% (TPWD 2012). However,81% of the potential GDEs were live
oak-ashe juniperparks/woods, while only 14% were mesquite
dominatedforests and shrublands. We further determined that a
largefraction of potential GDEs (75%) were located on shallowsoils,
where live oaks and ashe junipers are chiefly found(Mesquite may be
located on both shallow and deepsoils.) Why is this significant?
Shallow soil areas hadthe average soil depth of 45 cm, which places
significantlimits on soil water storage. As a result, vegetation
mayneed to access groundwater to complement its wateruse,
especially during the dry season. However, deepsoil areas had
depths greater than 200 cm, creating with
NGWA.org S. Gou et al. Groundwater 53, no. 1: 99110 107
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large storage reservoirs which may be used to buffer theimpacts
of droughts and low rainfall periods. Therefore,plants on shallow
soils exhibited a higher potential to usegroundwater than those on
deep soils.
Landform type was also correlated with areas highlylikely to
support GDEs; 66% of potential GDEs werelocated on ridges with
shallow soils weathered fromlimestone, and 9% were located on
plains covered byshallow soils, also weathered from limestone
(Figure 4aand 4c). The remaining GDEs were found on deep
soils,mainly near streams (Figure 4b) with alluvial deposits:10% on
flood plains 12% on stream terraces and theirerosion remnants, and
3% on paleoterraces.
In the Edwards Aquifer region, the total area poten-tially
hosting GDEs was 840 km2. Assuming 90 mm/yearof groundwater is
consumed by GDEs, based on litera-ture estimates (Orellana et al.
2012), a total of 2.0 1010gallons of groundwater is used by the
potential GDEsevery year in the Edwards aquifer region. For
compari-son, this rate of water consumption is nearly 30% of
theannual net groundwater use of the City of San Antonio(TWDB
2013b), indicating the significance of GDEs togroundwater
management.
ConclusionsWe proposed a methodological framework to
identify
potential GDES and applied it to map GDEs in Texas.To address
the different management requirements atvarious scales, we
developed a two-step approach for thestate/province scale using GIS
and the aquifer/basin scaleusing remote sensing-based techniques.
We produced statescale GDE index maps for GMAs and HUC-6s in
Texasand aquifer/basin scale, 30 30 m resolution maps ofpotential
GDEs distributions in the Edwards Aquiferregion. The GDE index maps
aimed to identify criticalregions with vulnerable GDEs. These GDE
index mapsindicated that areas in central Texas, which host
streamswith high baseflow ratios, numerous springs, large areas
ofphreatophyte species, and groundwater-fed wetlands, hada high
potential to contain a significant amount of GDEs.
The remote sensing-based analysis aimed to identifyGDEs for more
specific management and study; inthis case, the Edwards Aquifer
region was used asa demonstration of the method. Three criteria
weredeveloped, and these captured the physiologic signature
ofgroundwater use associated with different plant functionaltypes.
Analysis of the criteria showed that each hadidentifiable biases
when assessing plant groundwateruse. Criterion One captured the
potential groundwateruse of wetlands, but failed to capture it in
shrublandsand evergreen forests, due to the impact of their
lowvegetation density on NDVI. This disadvantage waseliminated by
Criterion Two, but Criterion Two failed tocapture the deciduous
forests due to their relatively fastseasonal changes in their leaf
areas. These impacts weremitigated by Criterion Three, but
Criterion Three failedto capture the groundwater use potential of
wetlands.Three criteria were combined together to ameliorate
their
disadvantages and yielded a final detailed map of thelocations
of potential GDEs. The results indicated thatnot all plants
belonging to phreatophyte species or withinwetlands were
groundwater dependent. Only 9% of thetotal phreatophytes and 31% of
woody and herbaceouswetlands were classified as having the highest
potential touse groundwater. Soil depth and landforms were found
tobe the critical factors impacting vegetation groundwateruse. Of
potential GDEs, 75% were found on ridges andplains with shallow
soils. The remaining 25% of potentialGDEs were located on soils
deeper than 200 cm, and thesewere mainly associated with
streams.
The proposed methods had several limitations. Inthe GDE index
method, phreatophytes, woody wetlands,and emergent herbaceous
wetlands were considered asthe vegetative indicators . However,
this assumption ledto overestimation of the potential GDEs, as
compared tothe independent remote sensing-based results. In
someinstances, the overestimated vegetative index values mayhave
overwhelmed the effect of the hydrological indexon the overall GDE
index . In the remote sensing-basedmethod, due to the relatively
coarse spatial resolution ofthe satellite images from Landsat ETM+
and MODIS,vegetation pixels with mixed vegetation coverage
(e.g.,phreatophytes mixed with bare soil or grasses) may haveNDVI
changes that do not accurately reflect the actualvegetation water
use pattern. Also, the Criteria Twoand Three results from MODIS
data were resampled to30 30 m resolution, which produced some loss
of infor-mation. Finally, due to the lack of previous GDE studiesin
our study area, future field studies are needed to fullyverify the
results produced by the remote sensing method.
In summary, this two-step approach can provide use-ful GDE
information for decision makers. The generalunderstanding of the
occurrence of GDEs gained fromGDE index maps can help groundwater
managers screenareas and integrate the consideration of GDEs into
man-agement practices. Detailed GDE distributions obtainedfrom
remote sensing provides researchers with a guid-ing tool for the
study of GDEs, indicating priority areasfor field-based assessment
and monitoring. The resultscan also be used in numerical models
intended to sim-ulate the groundwater use of GDEs and their
potentialimpacts on water supply, including the tools developedby
the Texas Groundwater Availability Modeling pro-gram (TWDB 2013a).
In addition, the remote sensing-based method highlights the
potential to use satellites toremotely monitor GDE dynamics and
health under chang-ing hydrological and climatological
conditions.
AcknowledgmentsThe authors would like to acknowledge funding
from
the Texas Water Resources Institute in the form of a
MillsScholarship and the Schlumberger Foundation in the formof a
Faculty for the Future Fellowship, both awarded toS. Gou. We would
like to thank the Louis Stokes Alliancefor Minority Participation
at Texas A&M Universityand its Undergraduate Research Program
for support of
108 S. Gou et al. Groundwater 53, no. 1: 99110 NGWA.org
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S. Gonzalez. We would also like to acknowledge Dr.Huilin Gao and
two anonymous peer reviewers for theirhelpful feedback.
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