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IMPACT OF TWO LAND-COVER DATA SETS ON STREAM FLOW AND TOTAL
NITROGEN SIMULATIONS USING A SPATIALLY DISTRIBUTED
HYDROLOGIC MODEL
Pei-yu Chen, Assistant Research Scientist Mauro Di Luzio,
Assistant Research Scientist
Texas Agricultural Experiment Station 720 East Blackland
Road
Temple, TX 76502, U.S.A
[email protected]@brc.tamus.edu
Jeff G. Arnold, Research Leader
USDA-ARS, Grassland, Soil and Water Research Lab 808 East
Blackland Road
Temple, TX 76502, U.S.A. [email protected]
ABSTRACT The availability of satellite-based land-cover data
becomes critical for environmental studies. A spatially distributed
hydrologic model, Soil and Water Assessment Tool (SWAT), requires
the inputs of land-cover data for delineating hydrologic response
units (HRUs) and assessing water, sediment and agricultural
chemical yields over watersheds. The National Land Cover Dataset
(NLCD), based on 1992 LANDSAT-Thematic Mapper (TM) data at 30-m
resolution for the United States, has been used to support
land-cover information for the SWAT modeling to date. Another
land-cover dataset, the Global Land Cover Characteristics (GLCC),
was produced for the entire globe at 1-km nominal spatial
resolution based on the Advanced Very High Resolution Radiometer
(AVHRR) data from April 1992 through March 1993. Both LANDSAT-TM
and AVHRR data have different spatial, spectral and temporal
resolutions, but the two land-cover data sets were expected to
contribute similar land-cover information. This study investigated
the effect of the land-cover data on stream flow and total nitrogen
simulations using the SWAT model. Our analyses showed that the
source of land-cover information did not affect the SWAT simulation
of stream flows. However, the impact of land-cover information was
significant on the total nitrogen simulations. The new NLCD using
2000 LANDSAT data is still under processing by several federal
agencies; meanwhile, several global land-cover maps have been
produced over the last few years for research purposes.
Conditionally, the recently developed global land-cover datasets
could serve as an input for the SWAT modeling to assist current
hydrological studies.
INTRODUCTION
Land-cover information has become essential and critical for
environmental studies and land-use planning. The appropriate use of
this information is usually a consequence of the derived spatial
scale. In the United States, an intermediate-scale data set, the
National Land-Cover Dataset (NLCD) (Vogelmann et al., 2001) is
often used for regional as well as national scale investigations.
The seamless NLCD, based on 1992 satellite data at 30-m resolution,
was consistently classified for the entire country. It provides a
suitable land-cover dataset to support national environmental
assessments using basin-scale water quality models such as Soil and
Water Assessment Tool (SWAT) (Arnold et al., 1998). A general
concern with NLCD is the update of the information content that
takes extensive time and tremendous processing effort. In fact, the
new version of the NLCD, Multi-Resolution Land Characteristics
Consortium (MRLC) based on the 2000 vintage Landsat data, is still
under completion by several federal agencies (Homer et al.,
2002).
The Global Land-Cover Characteristics (GLCC) was developed using
satellite data collected across 1992 through 1993, like the NLCD.
The GLCC was produced at 1-km nominal spatial resolution for the
entire globe (Brown et al., 1993; Yang et al., 2001). Numerous
global environmental and climatic studies have relied on the
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mailto:[email protected]:[email protected]:[email protected]
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GLCC to provide land-cover information. Over the last few years,
international research institutes and government organizations have
developed several global land-cover maps. For example, the Joint
Research Center (JRC) of the European Commission (EC) implemented
the Satellite Pour I’Observation de la Terre (SPOT) VEGETATION
satellite data to produce a global land-cover for the year 2000
(Giri et al., 2005). Boston University generated a global
land-cover data using the Moderate Resolution Imaging Spectrometer
(MODIS) satellite data (Friedl et al., 2002). All of these newly
produced land-cover database were generated at the 1-km
resolution.
The land-cover data is one of the essential inputs for the SWAT
model. It is important that land-cover data be based on the most
current data available, since the land-cover changes over time. The
SWAT model has consistently relied on the 1992 NLCD in 30-meter
resolution to provide land-cover information to delineate
hydrological response units (HRUs) and further simulate stream
flows and water quality. The unreleased MRLC 2000 is strongly
anticipated due to current environmental assessments requiring more
recent land-cover information.
Although the GLCC and NLCD were developed based on different
satellites with distinctive spectral and spatial resolutions, the
two data sets are expected to contribute similar land-cover
information over the conterminous United States. The objective of
this study was to investigate the impact of land-cover data from
different sources at different spatial resolutions on the stream
flow and total nitrogen simulations based on the SWAT model using
the NLCD and GLCC, respectively. Three watersheds in Iowa and
Missouri were selected for this study, and the land-cover
information between the two land-cover data sets varied for the
three watersheds (Figure 1). The results of this study will
contribute useful hydrologic information regarding the possibility
and condition of coarse-resolution land-cover data for large-scale
assessments. Several updated land-cover maps based on
coarse-resolution satellite images have been produced over the last
few years. It would be a great advantage for current environmental
studies if the recently developed global data could provide
suitable land-cover information for national environmental
assessment.
Figure 1. The distributions of three watersheds in the states of
Iowa and Missouri.
MATERIALS AND METHODS
The NLCD is a 21-class land-cover classification scheme
resembling the well-established Anderson land-use/land-cover
classification system (Anderson et al., 1976) (Table 1). The GLCC
datasets in 24 classes based on the U.S. Geological Survey (USGS)
land-use and land-cover classification scheme was adopted for this
study due to
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Table 1. The classification schemes of GLCC and NLCD,
respectively based on the modified land-cover classes of
Anderson Level I. The numbers in parentheses represent
land-cover codes.
Modified land-cover classes of Anderson Level I
GLCC classification scheme NLCD classification scheme
Low Intensity Residential (21) High Intensity Residential
(22)
Urban or Built-up Land (1)
Urban and Built-up Land (100)
Commercial/Industrial/Transport-ation (23)
Dryland Cropland and Pasture (211)
Orchards/Vineyard (61)
Irrigated Cropland and Pasture (212)
Pasture/Hay (81)
Mixed Dryland/Irrigated Cropland and Pasture (213)
Row crops (82)
Cropland/Grassland Mosaic (280)
Small Grains (83)
Cropland/Woodland Mosaic (290)
Fallow (84)
Agricultural Land (2)
Urban/Recreational Grasses (85) Grassland (311)
Grasslands/Herbaceous (71) Shrubland (321) Shrubland (51) Mixed
Shrubland/Grassland (330)
Rangeland (3)
Savanna (332)
Deciduous Broadleaf Forest (411) Deciduous Needleleaf Forest
(412)
Deciduous Forest (41)
Evergreen Broadleaf Forest (421) Evergreen Needleleaf Forest
(422)
Evergreen Forest (42)
Forest Land (4)
Mixed Forest (430) Mixed Forest (43) Water (5) Water Bodies
(500) Open Water (11)
Wooded Wetland (610) Woody Wetlands (91) Wetland (6) Herbaceous
Wetland (620) Emergent/Herbaceous Wetlands (92)
Bare Rock (31) Quarries/Mines (32)
Barren Land (7) Barren or Sparsely vegetated (770)
Transitional (33) Wooded Tundra (810) Herbaceous Tundra (820)
Bare Ground Tundra (830)
Tundra (8)
Mixed Tundra (850)
Perennial Snow or Ice (9) Snow or Ice (900) Perennial Ice/Snow
(12) similarity to the Anderson classification scheme (Table 1).
The positional accuracy of each pixel is important for land-cover
related studies. Each Landsat Thematic Mapper (TM) image used to
create the NLCD was terrain-corrected using digital terrain
elevation data and geo-registered using ground control points,
resulting in a root mean square registration error of less than one
pixel (30-m) (Vogelmann et al., 2001). The registration accuracy of
GLCC data has not been officially published yet. The USGS web
documentation (US Geological Survey, 2005) stated that
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the goal of positional accuracy is 1-km or less for the Advanced
Very High Resolution Radiometer (AVHRR) images used to create the
GLCC. Both land-cover data sets are available at the USGS web
site.
Accuracy assessment of land-cover maps derived from satellite
data has been an important concern of the remote sensing community
(Latifovic and Olthof, 2004). The 1992 NLCD has accuracy ranging
from 37% (central U.S.) to 69% (western coast)
(http://edcwww.usgs.gov/programs/lccp/accuracy). Much of the
classification error occurred among the NLCD classes that aggregate
into a single Anderson level I class. For example, pasture/hay was
often confused with row crops, and mixed forest with deciduous
forest and evergreen forest. For the GLCC datasets, the averaged
classification accuracy was 59.4% (Scepan, 1999). The highest
individual class accuracies occurred in the classes of evergreen
broadleaf forests (78%) and barren (95%). The wetlands had the
lowest accuracy about 33%. Both classes of deciduous broadleaf
forests and savannas had relatively poor accuracies around 40%.
Most errors occurred when shrublands were identified as wetlands,
and croplands as deciduous forest.
The subsets of GLCC and NLCD were prepared for each of three
watersheds in the states of Iowa and Missouri. The NLCD were
re-sampled from 30-m to 25-m resolution. A total of 1,600 (40 x 40)
small pixels in 25-m resolution constitute one large pixel in 1-km
resolution. The land-cover distribution of NLCD within the major
GLCC class at 1-km unit in the correspondent location was
calculated in percentage for each watershed. Only the large pixels
completely covered by 1,600 small pixels were used for this study.
Most edge pixels along each watershed boundary were discarded due
to incomplete information. A database was established to store the
land-cover data of GLCC and NLCD for the major GLCC class. Each
record of the database represented one 1-km x 1-km pixel, and
consisted of the location of the pixel, the land-cover type of GLCC
and the geo-correspondent land-cover composition of NLCD in
percentage. The average and standard deviation for each
geo-correspondent NLCD class were calculated for the major GLCC
class for each watershed (Chen et al., 2005).
Geographic information system (GIS) data for topography, soils
and land-cover were used in the AVSWAT, an ArcView-GIS interface
for the SWAT model (Table 2) (Di Luzio et al., 2004). Observed
daily rainfall and temperature data were needed for modeling. The
topography of watershed was defined by a Digital Elevation Model
(DEM). The DEM was used to calculate sub-basin parameters such as
slope and to define the stream network. The soil data is required
by the SWAT to define soil characteristics and attributes. The
land-cover data provides vegetation information on ground and their
ecological processes in lands and soils. Two simulations were made
for each watershed, one with the NLCD and the other with the GLCC.
Each watershed was divided into several sub-basins based on the
DEM, stream network and outlets, and each sub-basin was split into
several HRUs based on the land-cover and soil data (Table 3).
Monthly stream flow and annual total nitrogen at the outlet of each
watershed were simulated from 1987 to 1998 using the SWAT
model.
Table 2. Essential input data for the SWAT model
Data Type Data Source Type Topography USGS 30 meter
resolution
Soil NRCS-STATSGO 1:250,000 scale Land-cover LANDSAT-TM or AVHRR
30 meter or 1,000 meter
Climate National Weather Station 6-12 gages
Table 3. Basic information for each watershed
Area (km2) Sub-basin Hydrologic Response Units Watershed 1 4529
51 346 Watershed 2 2540 51 200 Watershed 3 2025 44 121
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More than 7,000 USGS gauges were distributed around the U.S. to
collect daily stream flow data (http://waterdata.usgs.gov/nwis).
One set of averaged monthly measurements between 1987 and 1998 for
each watershed was collected for this study. The monthly
measurements of total nitrogen were not available for the study
outlets. Hence, historical annual total nitrogen data for
watersheds 2 and 3 were used as ancillary information in this study
(Arnold et al., 2001). The performance of the model was evaluated
using statistical approaches to assess the quality and reliability
of the prediction when compared to measured values. The predicted
and measured values of stream flow were compared using the Nash and
Sutcliffe (1970) equation:
⎟⎟⎟⎟
⎠
⎞
⎜⎜⎜⎜
⎝
⎛
−
−−=
∑
∑
=
=n
immi
n
icimi
QQ
QQE
1
2
1
2
)(
)(1 (1)
where E is the coefficient of efficiency; n is the number of
data samples; Qmi is the measured value; Qci is the estimated
value; Qm is the mean measured value. The value of E could range
from negative infinity to 1.0, where E = 1.0 indicates a perfect
model. The E is similar to a correlation coefficient obtained from
linear regression; however, the E compares the measured values to
the 1:1 line of measured equals predicted (perfect fit) rather than
to the best-fit regression line (Saleh et al., 2000). This
statistics has been widely used for evaluating the performance of
hydrologic simulation models (Legates and McCabe, 1999).
RESULTS AND DISCUSSION
The row crops and pasture/hay were treated as two different
classes according to the NLCD classification scheme, but
categorized as one class in the GLCC since they were indivisible in
1-km unit. Both NLCD and GLCC data showed the three watersheds were
mainly occupied by croplands and pasture/hay. Other land-cover
types such as grassland and deciduous forest were scattered between
the croplands and pasture. Watershed 1 had the highest land-cover
diversity, and watershed 3 had nearly homogeneous land-cover
distributions. The land-cover variety for watershed 2 was in
between (Table 4). The NLCD row crops dominated central and
northern Iowa, where watersheds 2 and 3 were located, respectively.
The percentage of row crops gradually decreased toward the south,
where watershed 1 was located. Southern Iowa and northern Missouri
had more pasture/hay than row crops, and the deciduous forest was
another major land-cover in the area. The GLCC distribution was not
as complicated as the NLCD for the three watersheds. All three
watersheds were dominated by the GLCC dryland cropland and pasture
(Table 4). The GLCC irrigated cropland and pasture as well as mixed
dryland/irrigated cropland and pasture were omitted from this study
due to barely existing in the study sites. (Table 4). Land-cover
Relationship Assessment
The analysis of spatial relationship of land-cover distributions
between the NLCD and GLCC is a required procedure to evaluate the
influence of land-cover data sets on the SWAT outputs. The
relationship assessment presented the spatial relationship of
land-cover information between the two data sets by investigating
NLCD distribution within each major GLCC pixel in 1-km unit. The
percentages of the GLCC cropland/pasture were 91.39% for watershed
1, 99.77% for watershed 2 and 97.05% for watershed 3 (Table 4).
According to the NLCD results, watershed 1 was dominated by the
pasture/hay (41.36%), row crops (31.08%) and deciduous forest
(12.24%), watershed 2 by the row crops (69.43%) and pasture/hay
(18.21%), and watershed 3 solely by the row crops (87.29%) (Table
4).
The percentage of NLCD row crops within each GLCC
cropland/pasture was clearly related to the locations of watersheds
(Figure 2). Watershed 3 in the northern Iowa with nearly
homogeneous land-cover distributions had the strongest relationship
that each 1-km x 1-km pixel of cropland/pasture of GLCC was
corresponded to more than 88% of row crops of NLCD. The
relationship for watershed 2 in the central Iowa was around 70% due
to less homogeneous land-cover. Watershed 1 across Iowa and
Missouri had relationship as low as 32% because of diverse
land-cover types in each 1-km unit.
The relationship between the NLCD pasture/hay and GLCC
cropland/pasture were related to the locations of watersheds as
well (Figure 2). The watershed 1 had a higher relationship than the
watersheds 2 and 3. Each GLCC pixel of cropland/pasture at 1-km
unit corresponded to 41% of the NLCD pasture/hay for the watershed
1, while
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18% for the watershed 2 and 3% for the watershed 3. The
watershed 1 had higher proportion of pasture/hay than row crops of
NLCD, which is opposite to the other two watersheds (Table 4).
Moreover, each GLCC pixel of cropland/pasture was related to NLCD
deciduous forest for the watershed 1, since the deciduous forest
was mixed with pasture/hay in the area.
Table 4. The percentage proportions (%) of land-cover
distributions of NLCD and GLCC for the studied watersheds.
Watershed NLCD classes
1 (%)
2 (%)
3 (%)
Watershed GLCC classes
1 (%)
2 (%)
3 (%)
Water 0.63 0.40 0.40 Urban/Built-up land 0.12 0.23 0.59
Perennial Ice/Snow 0 0 0 Dryland Cropland and Pasture
91.39 99.77 97.05
Low Intensity Residential
0.38 0.53 0.45 Irrigated Cropland and Pasture
0 0 0
High Intensity Residential
0.03 0.08 0.20 Mixed Dryland/Irrigated Cropland and Pasture
0 0 0
Commerical/Industrial/ Transpotation
1.11 0.98 1.93 Cropland/Grassland Mosaic
6.81 0 0
Bare Rock 0.01 0.01 0 Cropland/Woodland Mosaic
1.14 0 0
Quarries/Mines 0.04 0.05 0.02 Grassland 0.05 0 2.26
Transitional
-
020406080
100
WaterLow Intensity Residential
Highintensity Residential
Commerical/Industrial
Bare Rock
Quarries/Mines
Deciduous Forest
Evergreen Forest
Mixed Forest
Grassland
Pasture/Hay
Row Crops
Small Grains
Urban/Recreational Grasses
Woody Wetlands
Herbaceous Wetlands
Classes of NLCD
Occ
urra
nce
(%) Watershed 1
Watershed 2Watershed 3
Figure 2. The spatial information of NLCD distributions within
each studied GLCC pixel of cropland/pasture at 1-
km unit for three watersheds in Iowa and Missouri. The marker in
each bar represented the stand deviations for each NLCD class for
each watershed.
SWAT Output Analyses
The correspondence degree between the NLCD and GLCC did not have
a strong influence on the SWAT outputs of water quantity but water
quality (Table 5). The differences between two simulated annual
stream flows ranged between 7% (watershed 3) and 22% (watershed 1),
which was consistent with the similarity degree of two land-cover
data in the three watersheds. Our results showed that SWAT using
NLCD produced higher stream flow predictions than using the GLCC.
The analyses exhibited that the simulated errors of annual stream
flows were higher when using the GLCC for SWAT model than using the
NLCD. The outputs of total nitrogen showed that SWAT/NLCD generated
lower predictions than SWAT/GLCC (Table 5). The differences between
two simulated annual total nitrogen were over 50% for watershed 1
and around 10% for watershed 3, which was significantly related to
the correspondence of two land-cover data for each watershed. The
simulated errors of annual total nitrogen for watershed 2 and 3
were close to or greater than 100%. Normally, model calibration is
needed to compensate for overestimation. Since this study was
focused on the impact of different land-cover on the water quantity
and quality, no calibration work was applied to this study.
Overall, the SWAT outputs showed that both NLCD and GLCC produced
similar simulations when the study site has homogeneous land-cover
distributions, which allowed the NLCD and GLCC provided nearly
identical land-cover information. The NLCD produced annual
simulations closer to the annual measurements if the study area has
a high diversity of land-covers.
Table 5. The measurements and SWAT outputs of averaged annual
stream and total nitrogen from 1987 to 1998 for
the three watersheds based on different types of land-cover
data.
SWAT outputs
Stream Flow (mm)
Total Nitrogen (kg/ha)
Measurement Simulation Measurement Simulation Watershed NLCD
GLCC NLCD GLCC
1 271.61 304.41 238.16 - 12.153 24.78 2 235.97 205.77 183.31
6.747 14.697 22.663 3 187.36 172.80 160.45 3.606 7.114 7.832
Another analysis was performed for the measured against
simulated monthly stream flows. The results showed
that the coefficients of efficiency (E) were similar for each
watershed (Table 6), which explained that land-cover did not have
significant impacts on monthly stream flow data. The watershed 3
had R-square values similar to E values due to the measurements and
simulations scatter around the 1:1 line. The R-square values were
greater than the E values for the other two watersheds, because the
simulated data were greater than the measurements. The
regression
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line was skew to the axis of simulation. The relatively low E
values for the watershed 2 indicated that calibration is needed for
further studies.
Table 6. The coefficient of efficiency (E) and R-square (R2) of
measured against simulated monthly stream flows from 1987 to 1998
for three studied watersheds based on different land-cover data
using the SWAT model.
Land-cover
NLCD GLCC
Water shed E R2 E R2
1 0.82 0.93 0.88 0.92 2 0.63 0.81 0.68 0.80 3 0.80 0.83 0.78
0.83
CONCLUSIONS
This study assessed the impact of different land-cover data sets
(NLCD and GLCC) on water quantity and quality simulations using the
basin-scale SWAT model. Three watersheds in Iowa and Missouri
selected for this study had various land-cover distributions.
According to the NLCD results, the watershed 1 had the highest
land-cover diversity including pasture/hay, row crops and deciduous
forest. A near homogeneous land-cover type of row crops dominated
the entire watershed 3. The watershed 2 was in the between occupied
by the row crops and pasture/hay. The GLCC data showed the
cropland/pasture was the major land-cover type for the three
watersheds. Our investigation showed that both NLCD and GLCC data
provided very similar land-cover information for the watershed 3.
Both land-cover data sets had the lowest spatial correspondence for
the watershed 1. The analyses exhibited that correspondence between
the NLCD and GLCC did not have a strong influence on the SWAT
outputs of annual stream flows but annual total nitrogen.
Furthermore, the various sources of land-cover data did not have
significant impacts on monthly stream flows for each of the three
watersheds according to the coefficient of efficiency (E). The SWAT
using GLCC produced similar nitrogen simulations as using NLCD for
watershed 3, which revealed that the GLCC is conditionally suitable
for the SWAT modeling if the study area has homogeneous land-cover
distributions. This study did not involve sensitivity analyses and
calibrations. Advanced investigations including multiple
simulations for water quality and model calibration are necessary
to explicitly determine the conditions of using coarse resolution
land-cover data for the SWAT model.
ACKNOWLEDGEMENTS
The authors would like to thank the USDA-Agricultural Research
Service (ARS) in Temple, TX for supporting this research through
the Specific Cooperate Agreement No. 58-6206-1-005.
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http://eosims.cr.usgs.gov:5725/CAMPAIGN_DOCS/avhrr_gc_proj_camp.html
IMPACT OF TWO LAND-COVER DATA SETS ON STREAM FLOW AND TOTAL
NITROGEN SIMULATIONS USING A SPATIALLY DISTRIBUTED HYDROLOGIC MODEL
Pei-yu Chen, Assistant Research Scientist Mauro Di Luzio, Assistant
Research Scientist Jeff G. Arnold, Research Leader INTRODUCTION
MATERIALS AND METHODS GLCC classification schemeNLCD classification
scheme
RESULTS AND DISCUSSION Land-cover Relationship Assessment
Watershed NLCD classes Watershed GLCC classes
SWAT Output Analyses WatershedGLCCWater shed
E1CONCLUSIONS
ACKNOWLEDGEMENTS REFERENCES
IMPACT OF TWO LAND-COVER DATA SETS ON STREAM FLOW AND TOTAL
NITROGEN SIMULATIONS USING A SPATIALLY DISTRIBUTED HYDROLOGIC
MODEL
Pei-yu Chen, Assistant Research Scientist
Mauro Di Luzio, Assistant Research Scientist
Texas Agricultural Experiment Station
720 East Blackland Road
Temple, TX 76502, U.S.A
[email protected]
[email protected]
Jeff G. Arnold, Research Leader
USDA-ARS, Grassland, Soil and Water Research Lab
808 East Blackland Road
Temple, TX 76502, U.S.A.
[email protected]
ABSTRACT
The availability of satellite-based land-cover data becomes
critical for environmental studies. A spatially distributed
hydrologic model, Soil and Water Assessment Tool (SWAT), requires
the inputs of land-cover data for delineating hydrologic response
units (HRUs) and assessing water, sediment and agricultural
chemical yields over watersheds. The National Land Cover Dataset
(NLCD), based on 1992 LANDSAT-Thematic Mapper (TM) data at 30-m
resolution for the United States, has been used to support
land-cover information for the SWAT modeling to date. Another
land-cover dataset, the Global Land Cover Characteristics (GLCC),
was produced for the entire globe at 1-km nominal spatial
resolution based on the Advanced Very High Resolution Radiometer
(AVHRR) data from April 1992 through March 1993. Both LANDSAT-TM
and AVHRR data have different spatial, spectral and temporal
resolutions, but the two land-cover data sets were expected to
contribute similar land-cover information. This study investigated
the effect of the land-cover data on stream flow and total nitrogen
simulations using the SWAT model. Our analyses showed that the
source of land-cover information did not affect the SWAT simulation
of stream flows. However, the impact of land-cover information was
significant on the total nitrogen simulations. The new NLCD using
2000 LANDSAT data is still under processing by several federal
agencies; meanwhile, several global land-cover maps have been
produced over the last few years for research purposes.
Conditionally, the recently developed global land-cover datasets
could serve as an input for the SWAT modeling to assist current
hydrological studies.
INTRODUCTION
Land-cover information has become essential and critical for
environmental studies and land-use planning. The appropriate use of
this information is usually a consequence of the derived spatial
scale. In the United States, an intermediate-scale data set, the
National Land-Cover Dataset (NLCD) (Vogelmann et al., 2001) is
often used for regional as well as national scale investigations.
The seamless NLCD, based on 1992 satellite data at 30-m resolution,
was consistently classified for the entire country. It provides a
suitable land-cover dataset to support national environmental
assessments using basin-scale water quality models such as Soil and
Water Assessment Tool (SWAT) (Arnold et al., 1998). A general
concern with NLCD is the update of the information content that
takes extensive time and tremendous processing effort. In fact, the
new version of the NLCD, Multi-Resolution Land Characteristics
Consortium (MRLC) based on the 2000 vintage Landsat data, is still
under completion by several federal agencies (Homer et al.,
2002).
The Global Land-Cover Characteristics (GLCC) was developed using
satellite data collected across 1992 through 1993, like the NLCD.
The GLCC was produced at 1-km nominal spatial resolution for the
entire globe (Brown et al., 1993; Yang et al., 2001). Numerous
global environmental and climatic studies have relied on the GLCC
to provide land-cover information. Over the last few years,
international research institutes and government organizations have
developed several global land-cover maps. For example, the Joint
Research Center (JRC) of the European Commission (EC) implemented
the Satellite Pour I’Observation de la Terre (SPOT) VEGETATION
satellite data to produce a global land-cover for the year 2000
(Giri et al., 2005). Boston University generated a global
land-cover data using the Moderate Resolution Imaging Spectrometer
(MODIS) satellite data (Friedl et al., 2002). All of these newly
produced land-cover database were generated at the 1-km
resolution.
The land-cover data is one of the essential inputs for the SWAT
model. It is important that land-cover data be based on the most
current data available, since the land-cover changes over time. The
SWAT model has consistently relied on the 1992 NLCD in 30-meter
resolution to provide land-cover information to delineate
hydrological response units (HRUs) and further simulate stream
flows and water quality. The unreleased MRLC 2000 is strongly
anticipated due to current environmental assessments requiring more
recent land-cover information.
Although the GLCC and NLCD were developed based on different
satellites with distinctive spectral and spatial resolutions, the
two data sets are expected to contribute similar land-cover
information over the conterminous United States. The objective of
this study was to investigate the impact of land-cover data from
different sources at different spatial resolutions on the stream
flow and total nitrogen simulations based on the SWAT model using
the NLCD and GLCC, respectively. Three watersheds in Iowa and
Missouri were selected for this study, and the land-cover
information between the two land-cover data sets varied for the
three watersheds (Figure 1). The results of this study will
contribute useful hydrologic information regarding the possibility
and condition of coarse-resolution land-cover data for large-scale
assessments. Several updated land-cover maps based on
coarse-resolution satellite images have been produced over the last
few years. It would be a great advantage for current environmental
studies if the recently developed global data could provide
suitable land-cover information for national environmental
assessment.
Figure 1. The distributions of three watersheds in the states of
Iowa and Missouri.
MATERIALS AND METHODS
The NLCD is a 21-class land-cover classification scheme
resembling the well-established Anderson land-use/land-cover
classification system (Anderson et al., 1976) (Table 1). The GLCC
datasets in 24 classes based on the U.S. Geological Survey (USGS)
land-use and land-cover classification scheme was adopted for this
study due to
Table 1. The classification schemes of GLCC and NLCD,
respectively based on the modified land-cover classes of Anderson
Level I. The numbers in parentheses represent land-cover codes.
Modified land-cover classes of Anderson Level I
GLCC classification scheme
NLCD classification scheme
Urban or Built-up Land (1)
Urban and Built-up Land (100)
Low Intensity Residential (21)
High Intensity Residential (22)
Commercial/Industrial/Transport-ation (23)
Agricultural Land (2)
Dryland Cropland and Pasture (211)
Orchards/Vineyard (61)
Irrigated Cropland and Pasture (212)
Pasture/Hay (81)
Mixed Dryland/Irrigated Cropland and Pasture (213)
Row crops (82)
Cropland/Grassland Mosaic (280)
Small Grains (83)
Cropland/Woodland Mosaic (290)
Fallow (84)
Urban/Recreational Grasses (85)
Rangeland (3)
Grassland (311)
Grasslands/Herbaceous (71)
Shrubland (321)
Shrubland (51)
Mixed Shrubland/Grassland (330)
Savanna (332)
Forest Land (4)
Deciduous Broadleaf Forest (411)
Deciduous Forest (41)
Deciduous Needleleaf Forest (412)
Evergreen Broadleaf Forest (421)
Evergreen Forest (42)
Evergreen Needleleaf Forest (422)
Mixed Forest (430)
Mixed Forest (43)
Water (5)
Water Bodies (500)
Open Water (11)
Wetland (6)
Wooded Wetland (610)
Woody Wetlands (91)
Herbaceous Wetland (620)
Emergent/Herbaceous Wetlands (92)
Barren Land (7)
Barren or Sparsely vegetated (770)
Bare Rock (31)
Quarries/Mines (32)
Transitional (33)
Tundra (8)
Wooded Tundra (810)
Herbaceous Tundra (820)
Bare Ground Tundra (830)
Mixed Tundra (850)
Perennial Snow or Ice (9)
Snow or Ice (900)
Perennial Ice/Snow (12)
similarity to the Anderson classification scheme (Table 1). The
positional accuracy of each pixel is important for land-cover
related studies. Each Landsat Thematic Mapper (TM) image used to
create the NLCD was terrain-corrected using digital terrain
elevation data and geo-registered using ground control points,
resulting in a root mean square registration error of less than one
pixel (30-m) (Vogelmann et al., 2001). The registration accuracy of
GLCC data has not been officially published yet. The USGS web
documentation (US Geological Survey, 2005) stated that the goal of
positional accuracy is 1-km or less for the Advanced Very High
Resolution Radiometer (AVHRR) images used to create the GLCC. Both
land-cover data sets are available at the USGS web site.
Accuracy assessment of land-cover maps derived from satellite
data has been an important concern of the remote sensing community
(Latifovic and Olthof, 2004). The 1992 NLCD has accuracy ranging
from 37% (central U.S.) to 69% (western coast)
(http://edcwww.usgs.gov/programs/lccp/accuracy). Much of the
classification error occurred among the NLCD classes that aggregate
into a single Anderson level I class. For example, pasture/hay was
often confused with row crops, and mixed forest with deciduous
forest and evergreen forest. For the GLCC datasets, the averaged
classification accuracy was 59.4% (Scepan, 1999). The highest
individual class accuracies occurred in the classes of evergreen
broadleaf forests (78%) and barren (95%). The wetlands had the
lowest accuracy about 33%. Both classes of deciduous broadleaf
forests and savannas had relatively poor accuracies around 40%.
Most errors occurred when shrublands were identified as wetlands,
and croplands as deciduous forest.
The subsets of GLCC and NLCD were prepared for each of three
watersheds in the states of Iowa and Missouri. The NLCD were
re-sampled from 30-m to 25-m resolution. A total of 1,600 (40 x 40)
small pixels in 25-m resolution constitute one large pixel in 1-km
resolution. The land-cover distribution of NLCD within the major
GLCC class at 1-km unit in the correspondent location was
calculated in percentage for each watershed. Only the large pixels
completely covered by 1,600 small pixels were used for this study.
Most edge pixels along each watershed boundary were discarded due
to incomplete information. A database was established to store the
land-cover data of GLCC and NLCD for the major GLCC class. Each
record of the database represented one 1-km x 1-km pixel, and
consisted of the location of the pixel, the land-cover type of GLCC
and the geo-correspondent land-cover composition of NLCD in
percentage. The average and standard deviation for each
geo-correspondent NLCD class were calculated for the major GLCC
class for each watershed (Chen et al., 2005).
Geographic information system (GIS) data for topography, soils
and land-cover were used in the AVSWAT, an ArcView-GIS interface
for the SWAT model (Table 2) (Di Luzio et al., 2004). Observed
daily rainfall and temperature data were needed for modeling. The
topography of watershed was defined by a Digital Elevation Model
(DEM). The DEM was used to calculate sub-basin parameters such as
slope and to define the stream network. The soil data is required
by the SWAT to define soil characteristics and attributes. The
land-cover data provides vegetation information on ground and their
ecological processes in lands and soils. Two simulations were made
for each watershed, one with the NLCD and the other with the GLCC.
Each watershed was divided into several sub-basins based on the
DEM, stream network and outlets, and each sub-basin was split into
several HRUs based on the land-cover and soil data (Table 3).
Monthly stream flow and annual total nitrogen at the outlet of each
watershed were simulated from 1987 to 1998 using the SWAT
model.
Table 2. Essential input data for the SWAT model
Data Type
Data Source
Type
Topography
USGS
30 meter resolution
Soil
NRCS-STATSGO
1:250,000 scale
Land-cover
LANDSAT-TM or AVHRR
30 meter or 1,000 meter
Climate
National Weather Station
6-12 gages
Table 3. Basic information for each watershed
Area (km2)
Sub-basin
Hydrologic Response Units
Watershed 1
4529
51
346
Watershed 2
2540
51
200
Watershed 3
2025
44
121
More than 7,000 USGS gauges were distributed around the U.S. to
collect daily stream flow data (http://waterdata.usgs.gov/nwis).
One set of averaged monthly measurements between 1987 and 1998 for
each watershed was collected for this study. The monthly
measurements of total nitrogen were not available for the study
outlets. Hence, historical annual total nitrogen data for
watersheds 2 and 3 were used as ancillary information in this study
(Arnold et al., 2001). The performance of the model was evaluated
using statistical approaches to assess the quality and reliability
of the prediction when compared to measured values. The predicted
and measured values of stream flow were compared using the Nash and
Sutcliffe (1970) equation:
÷
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ö
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ç
ç
ç
è
æ
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-
-
=
å
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=
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ci
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E
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where E is the coefficient of efficiency; n is the number of
data samples; Qmi is the measured value; Qci is the estimated
value; Qm is the mean measured value. The value of E could range
from negative infinity to 1.0, where E = 1.0 indicates a perfect
model. The E is similar to a correlation coefficient obtained from
linear regression; however, the E compares the measured values to
the 1:1 line of measured equals predicted (perfect fit) rather than
to the best-fit regression line (Saleh et al., 2000). This
statistics has been widely used for evaluating the performance of
hydrologic simulation models (Legates and McCabe, 1999).
RESULTS AND DISCUSSION
The row crops and pasture/hay were treated as two different
classes according to the NLCD classification scheme, but
categorized as one class in the GLCC since they were indivisible in
1-km unit. Both NLCD and GLCC data showed the three watersheds were
mainly occupied by croplands and pasture/hay. Other land-cover
types such as grassland and deciduous forest were scattered between
the croplands and pasture. Watershed 1 had the highest land-cover
diversity, and watershed 3 had nearly homogeneous land-cover
distributions. The land-cover variety for watershed 2 was in
between (Table 4). The NLCD row crops dominated central and
northern Iowa, where watersheds 2 and 3 were located, respectively.
The percentage of row crops gradually decreased toward the south,
where watershed 1 was located. Southern Iowa and northern Missouri
had more pasture/hay than row crops, and the deciduous forest was
another major land-cover in the area. The GLCC distribution was not
as complicated as the NLCD for the three watersheds. All three
watersheds were dominated by the GLCC dryland cropland and pasture
(Table 4). The GLCC irrigated cropland and pasture as well as mixed
dryland/irrigated cropland and pasture were omitted from this study
due to barely existing in the study sites. (Table 4).
Land-cover Relationship Assessment
The analysis of spatial relationship of land-cover distributions
between the NLCD and GLCC is a required procedure to evaluate the
influence of land-cover data sets on the SWAT outputs. The
relationship assessment presented the spatial relationship of
land-cover information between the two data sets by investigating
NLCD distribution within each major GLCC pixel in 1-km unit. The
percentages of the GLCC cropland/pasture were 91.39% for watershed
1, 99.77% for watershed 2 and 97.05% for watershed 3 (Table 4).
According to the NLCD results, watershed 1 was dominated by the
pasture/hay (41.36%), row crops (31.08%) and deciduous forest
(12.24%), watershed 2 by the row crops (69.43%) and pasture/hay
(18.21%), and watershed 3 solely by the row crops (87.29%) (Table
4).
The percentage of NLCD row crops within each GLCC
cropland/pasture was clearly related to the locations of watersheds
(Figure 2). Watershed 3 in the northern Iowa with nearly
homogeneous land-cover distributions had the strongest relationship
that each 1-km x 1-km pixel of cropland/pasture of GLCC was
corresponded to more than 88% of row crops of NLCD. The
relationship for watershed 2 in the central Iowa was around 70% due
to less homogeneous land-cover. Watershed 1 across Iowa and
Missouri had relationship as low as 32% because of diverse
land-cover types in each 1-km unit.
The relationship between the NLCD pasture/hay and GLCC
cropland/pasture were related to the locations of watersheds as
well (Figure 2). The watershed 1 had a higher relationship than the
watersheds 2 and 3. Each GLCC pixel of cropland/pasture at 1-km
unit corresponded to 41% of the NLCD pasture/hay for the watershed
1, while 18% for the watershed 2 and 3% for the watershed 3. The
watershed 1 had higher proportion of pasture/hay than row crops of
NLCD, which is opposite to the other two watersheds (Table 4).
Moreover, each GLCC pixel of cropland/pasture was related to NLCD
deciduous forest for the watershed 1, since the deciduous forest
was mixed with pasture/hay in the area.
Table 4. The percentage proportions (%) of land-cover
distributions of NLCD and GLCC for the studied watersheds.
Watershed
NLCD classes
1
(%)
2
(%)
3
(%)
Watershed
GLCC classes
1
(%)
2
(%)
3
(%)
Water
0.63
0.40
0.40
Urban/Built-up land
0.12
0.23
0.59
Perennial Ice/Snow
0
0
0
Dryland Cropland and Pasture
91.39
99.77
97.05
Low Intensity Residential
0.38
0.53
0.45
Irrigated Cropland and Pasture
0
0
0
High Intensity Residential
0.03
0.08
0.20
Mixed Dryland/Irrigated Cropland and Pasture
0
0
0
Commerical/Industrial/
Transpotation
1.11
0.98
1.93
Cropland/Grassland Mosaic
6.81
0
0
Bare Rock
0.01
0.01
0
Cropland/Woodland Mosaic
1.14
0
0
Quarries/Mines
0.04
0.05
0.02
Grassland
0.05
0
2.26
Transitional
