climate change in the headwaters of the medina river...
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CLIMATE CHANGE IN THE
HEADWATERS OF THE
MEDINA RIVER
WATERSHED BAEN 673 Final Report
JASON MURRAY
Texas A&M University
Formatting:
Journal of Hydrometeorology
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Abstract
Global climate change is a growing concern for water resources. Many water resources
are strained more and more every year due to constant increases in population. Lake Medina in
south central Texas and to the west of San Antonio is one example as it has been nearly
completely drained for municipal water supply. In this study the headwaters of the Medina River
are modeled using the Soil and Water Assessment Tool to examine the effect that climate change
will have on the river basin and ultimately to attempt to predict the future of Lake Medina. It was
shown that a climate change scenario of a 5°C temperature increase and 20% rainfall reduction
in spring and fall, a 10% reduction in summer, and a 5% reduction in fall would result in a 53%
reduction of streamflow from the observed streamflow between 1983 and 2014. Additionally a
non-climate change scenario was ran in that the climate change scenario was a 39% reduction of
streamflow.
1. Introduction
Global climate change is expected to cause alterations to the hydrology of many different
regions on different levels. There are many climate scenarios that are primarily based upon the
future greenhouse gas emissions. Some common changes are shifts in precipitation patterns,
increased average global temperatures, changes in runoff timing and magnitude, and changes to
streamflow volume (Meehl, 2007; USGCRP, 2009; Moss et al., 2010). This creates uncertainty
for water resources in regions that will be receiving decreased amounts of precipitation and even
more so if that region is also prone to drought.
Two commonly used climate scenarios are the B1 and A2 scenarios. The B1 scenario
uses ongoing globalization and predicts a future world that doesn’t have as much difference
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between regions while the A2 scenario accounts for regional differences in socioeconomic
factors, environmental development, and other differences (Zhang et al., 2007). One common
major predicted change is the increase of average surface air temperatures (Figure 1) (USGCRP,
2009). Another major commonly predicted change is a shift in precipitation patterns where some
areas are expected to receive less precipitation while other areas are expected to receive more
precipitation (Figure 2) (USGCRP, 2009).
The river basin examined in this study is the Medina River, HUC 12100302, located in
south central Texas and has a total drainage area of 474 mi2. The study area is that of the
headwaters of the Medina River upstream of Lake Medina (Figure 3) that represents a 328 mi2
drainage area. The Medina River flows from the northwest of San Antonio, TX down to the west
of San Antonio then curves to the south of San Antonio where it empties into the San Antonio
River. The river is used as a water source for the San Antonio Water System (SAWS) that is
contracted for 19,974 acre-feet per year from Lake Medina (SAWS, 2012). As a result of the
current ongoing drought the water level of the reservoir has been near empty at about 3-4%
(Figure 4) because there is not adequate streamflow to fill the reservoir as well as providing the
water supply for San Antonio.
As a result of the reservoir remaining near empty for so long the tourism of the area has
decreased and is near non-existent. This has raised questions about whether the local tourism-
driven economy surrounding Lake Medina would be able to return following the drought, but
this question cannot be adequately addressed while uncertainties surrounding climate change and
how it would affect the river basin. In addition to future climate being an issue, the San Antonio
region had a population of 2,460,599 people and is projected to grow to about 4.3 million by
2060 (Vaughan et al., 2012). This creates a greater need of understanding what the future of the
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Medina River may be so policy makers and water resources specialists have the greatest ability
to enact plans that will make the best use of the resources they have.
Using climate change scenario projections have been successfully implemented into the
Soil and Water Assessment Tool (SWAT) for the purpose of analyzing changes to streamflow
(Zhang et al., 2007; Lu et al., 2010). Zhang et al. (2007) found that within the Luohe River Basin
the analyzed climate change scenarios would not cause a dramatic change in streamflow but did
find that there would be some variability in streamflow of ±20% for most months. Lu et al.
(2010) successfully modeled the upper Mississippi River basin using SWAT and found that
monthly changes to temperature and precipitation were important, but temperature changes
dominated model outputs for future predictions. Therefore, for this study the SWAT model is
implemented in an attempt to quantify possible effects of increasing temperatures and decreased
rainfall due to climate change on the streamflow of the Medina River’s headwaters as a result of
climate change.
II. Methods
a. SWAT model
To model the Medina River headwaters this study utilized the SWAT version 2012, a
watershed-scale hydrological model, which subdivides a watershed into smaller subbasins that
represent the contributing area of a segment of stream and divides those subbasins even
hydrologic response units (HRUs) that represent a combination of land use, soil, and slope
(Arnold et al. 2012). The SWAT model delineates a watershed based upon a source digital
elevation model (DEM) raster and also uses the DEM for calculating a stream network using
flow accumulation. To compare the generated flowlines to the actual flow lines shapefiles of
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flow lines and stream gage locations were downloaded from the USGS National Hydrography
Dataset (NHD).
b. SWAT setup and input data
The DEM raster grid was obtained from the National Elevation Dataset with a 30m
resolution. Two DEM rasters were combined using ArcMap 10.1 and the mosaic Raster tool. To
increase the speed of delineation an arbitrarily drawn mask was applied around the desired study
area. The land use data was obtained from the National Land Cover Database 2001 and soils data
was loaded from the SWAT provided ArcSWAT US STATSGO database.
The model delineated an area of the headwaters that was 328 mi2 and the outlet position
was manually chosen to be aligned with United States Geological Survey (USGS) stream gauge
08180500 Medina River at Bandera, TX. Initially the stream network was calculated with a flow
accumulation of 1000, 1500, and 2000 hectares. This flow accumulation proved to be too
minimal as the resulting stream network was overly complex and not representative of the actual
stream network and all attempts at calibration were met with failure. Following this, the stream
network was calculated with a flow accumulation area of 5000 hectares which gave more
favorable results as shown in Figure 5.
To define the HRUs the land use, soil class, and slope class percentages were all set to
10%. Slope was defined with four classes first being 0-8, second being 16-24, and fourth being
24-9999. The results of the HRU definitions are shown in Table 1 for land use, soils is reported
in Table 2, and slope classes are reported in Table 3.
Precipitation data was obtained from the National Climatic Data Center for station
GHCND:USC00415742 - Medina 1 NE, TX US. There were several periods where the station
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had no data, so the data was filled in with -99.0 for these days. The built-in weather generator
WGEN_US_COOP_1980_2010 was used to generate all other weather data.
c. SWAT default, calibration, and validation runs
The default SWAT run was ran from 1988 to 1993 and the first 3 years were discarded
due to aquifer filling. The streamflow results of the initial SWAT run were compared to the
observed streamflow from January 1992 to December 1993 and the using Equation 1 we find the
Nash-Sutcliffe Coefficient of Suitability (R2) was -0.397 (Figure 6). An R2 of 0.5 or greater is
considered to be a suitable simulated result. Therefore, calibration was necessary to improve the
model results.
To calibrate the model the following input files were edited: groundwater (.gw), HRU
(.hru), management (.mgt), routing (.rte), and soils (.sol). The parameters that were edited in
these input files for all subbasins and their values are reported in Table 4. After calibration the
model was ran and the resulting R2 value was 0.671 (Figure 7) for validation the model was ran
from January 1, 1994 to December 31, 1999 and the resulting R2 was 0.613 (Figure 8).
d. Climate change simulation setup
To run the SWAT model for predicting future streamflow conditions of the Medina River
the decrease in precipitation was interpolated from Figure 2. This entailed a decrease of rainfall
in winter (December, January, February) of 20%, decrease in spring (March, April, May) of
20%, decrease in summer (June, July, August) of 10%, and decrease in fall (September, October,
November) of 5%. Furthermore, a temperature increase of 5°C (Mellilo et al., 2014) was
applied. These values were implemented through the subbasins (.sub) input file’s Weather
Adjustments options. In addition to running the climate change scenario, a SWAT run was
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conducted with no changes to the parameters to simulate as if there is no climate change
occurring for the sake of comparison. The SWAT weather generator was used to generate future
weather. The simulation was ran from 2010-2050, with the first 4 years being discarded to
prevent any interference from the aquifer filling.
III. Results and Discussion
The results of the SWAT climate change run showed a decrease in average streamflow
from the measured values of 53% and a decrease of 39% from the simulated streamflow without
climate change (Figure 9). The average streamflow of the measured data was 145.4 cfs and the
climate change scenario had an average streamflow of 68.4 cfs. This shows a drastic drop in
streamflow from the past. However, it is also apparent that the output yearly average streamflow
hydrograph does not show the same high peak years that is shown in the measured past data.
This is possibly due to the SWAT weather generator not predicting the wet years that were
observed in the past. But, it could also be that those years were anomalous of a particularly wet
period in the stream basin’s history. The hydrograph for measure data before 1983 at USGS
stream gauge 08179000 (Figure 10) which is located at Pipe Creek, about 2 miles downstream of
the gage used for this study shows less high peaks and shows a higher base flow for the 1970s
and to the end of its recordings in 1982. But before the 1970s it shows long periods of low flow
and few high flow years, but none that compare to the magnitude of high flow years shown
between 1983 and 2003. The values for this time period appear to be closer resembled by the
non-drought simulated stream flows which leads one to think that the three years of very high
stream flows in 1987, 1992, and 2002 were anomalous years.
Within the future simulated stream flows it is not unreasonable to believe that Lake
Medina could begin to be filled once again due to the historical data having similar average
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annual flows. However, with San Antonio’s projected population growth it is also plausible that
the reservoir would see more times of emptying due to the need for municipal water supply. This
would only become more realized if periods of drought are more frequent with climate change.
IV. Conclusion
Global climate change and population growth combine to create uncertainties with
respect to the planning and management of water resources. The Medina River in south central
Texas is one such location with these growing uncertainties. Lake Medina, located on the
Medina River highlights the river basin’s uncertainty as it has been near empty from 2011 to the
present because of an ongoing drought and the water supply needs of San Antonio.
Temperatures in this location are expected to rise while rainfall is expected to decrease.
In this study the SWAT model was used to simulate a future climate change scenario to observe
the effects on the headwaters of the river, upstream of Lake Medina. The simulation results
showed a large decrease of 53% in streamflow from the previous two decades which means a
great decrease in the amount of inflow to Lake Medina. Through observing historical streamflow
data and the future simulated streamflow it can be interpreted that the period of high flow years
from 1983 to 2003 were an abnormally wet period for the watershed. Additionally, the climate
change scenario streamflow was 39% less than the simulation without climate change factors.
Simulating reservoir operations would have been a worthwhile endeavor for this study,
however, the complexity and time constraints related to setting up and running reservoir
operations in SWAT created too many obstacles that could be overcome in a timely fashion. For
adequate simulation of the reservoir operations it would be required to have minimum outflow
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data as well as future water needs projections from the reservoir. Furthermore, not having data
on the past operations of the reservoir made calibration too difficult for this study.
While the climate change scenario for this study was one of the more severe climate
change scenarios, it is still worth noting that the non-climate change scenario showed periods of
low flow that could be considered drought conditions when compared to the current drought. As
the population of San Antonio continues to grow the water resource is expected to continue to be
stressed and the recreational use of Lake Medina may sporadically return, but may also not last
long enough for the full recreational economy to return. Further studies with more detailed
climate projection data is recommended to create projections of future conditions with greater
confidence.
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References
Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J., Srinivasan, R., ...
& Jha, M. K. (2012). SWAT: Model use, calibration, and validation. Transactions of the
ASABE, 55(4), 1491-1508.
Lu, E., Takle, E. S., & Manoj, J. (2010). The relationships between climatic and hydrological
changes in the Upper Mississippi River Basin: A SWAT and multi-GCM study. Journal of
Hydrometeorology, 11(2), 437-451.
Meehl, G. A. (2007). Global Climate Projections in Climate Change 2007: The physical Science
Basis: contributions of Working Group I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change.
Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., Van Vuuren, D. P.,
... & Wilbanks, T. J. (2010). The next generation of scenarios for climate change research and
assessment. Nature, 463(7282), 747-756.
Melillo, J. M., Richmond, T. C., & Yohe, G. W. (2014). Climate change impacts in the United
States: the third national climate assessment. US Global change research program, 841.
SAWS (2012). 2012 Water Management Plan. San Antonio Water System. San Antonio, TX,
USA.
USGCRP (2009). Global Climate Change Impacts in the United States. Thomas R. Karl, Jerry
M. Melillo, and Thomas C. Peterson (eds.). United States Global Change Research Program.
Cambridge University Press, New York, NY, USA.
Vaughan, E. G., Crutcher, J. M., Labatt III, T. W., McMahan, L. H., Bradford, B. R., & Cluck,
M. (2012). Water for Texas 2012 State Water Plan Texas Water Development Board.
Zhang, X., Srinivasan, R., & Hao, E. (2007). Predicting hydrologic response to climate change in
the Luohe River basin using the SWAT model. Transactions of the ASABE, 50(3), 901-910.
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Figures & Tables.
Figure 1. Projected change in average surface air temperature (°F) for the end of the century
(2071-2099) under climate scenarios B1 and A2 which are lower emissions and higher emissions
respectively. (Figure source: NOAA NCDC / CICS-NC; Obtained from: Mellilo et al., 2014)
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Figure 2. Predicted changes in seasonal precipitation under climate scenario A2 which entails
higher emissions. Northern areas in blue are expected to receive increased rainfall while southern
areas in brown are expected to receive reduced rainfall. Areas overlain with diagonal lines
indicate high confidence in predictions. (Figure source: NOAA NCDC / CICS-NC; Obtained
from Melillo et al., 2014)
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Figure 3. Map of the Medina River with study area highlighted in red.
Figure 4. Lake Medina reservoir storage over time at USGS gauge 08179500. (Figure source:
TWDB - Water Data for Texas)
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Table 1. Land Use of the study area
LANDUSE Percent of
Watershed
Area (mi2)
Water 0.18 % 0.57666
Residential-Low Density 1.99 % 6.517147
Residential-Medium Density 0.13 % 0.414221
Residential-High Density 0.02 % 0.078635
Industrial 0.01 % 0.027689
Southwestern US (arid) Range <0.01 % 0.007384
Forest-Deciduous 5.79 % 18.98216
Forest-Evergreen 44.48 % 145.9087
Forest-Mixed 0.01 % 0.049101
Range-Brush 37.33 % 122.431
Range-Grasses 9.62 % 31.56569
Hay 0.26 % 0.839517
Agricultural - Row Crops 0.15 % 0.482519
Wetlands-Forested 0.04 % 0.129583
Table 2. Soils of the study area
SOIL Percent of
Watershed
Area (mi2)
TX022 6.8 % 22.29556
TX155 72.6 % 238.1389
TX190 11.95 % 39.20256
TX527 8.65 % 28.37301
Table 3. Slope classes of the study area
SLOPE Percent of
Watershed
Area (mi2)
0-8 36.22 % 118.7939
8-16 22.88 % 75.05517
16-24 14.33 % 46.99227
24-9999 26.58 % 87.16873
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Figure 6. Results of the initial SWAT run without calibration. R2 = -0.397
Table 4. Input files edited for calibration and the variables edited
File / Variable Default
Value
Calibrated
Value
Groundwater (.gw)
GWQMN 1000 5000
GW_REVAP 0.02 0.2
Management (.mgt)
FRSE TX155 CN2 77 64
RNGB TX155 CN2 80 60
RNGE TX155 CN2 84 64
Routing (.rte)
CH_K2 0 25
Soils (.sol)
AWC - Layer 1 0.12 0.15
AWC - Layer 2 0.08 0.15
AWC - Layer 3 0.01 0.15
HRU (.hru)
OV_N 0.1-0.15 2
0
200
400
600
800
1000
1200
1400
1600
1800
Stre
amfl
ow
(cf
s)Default SWAT Run
Observed Simulated
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Figure 7. SWAT Calibration results. R2=0.671
Figure 8. SWAT model validation. R2=0.613
R2 = 1 − ∑ (𝑄𝑜
𝑖 − 𝑄𝑠𝑖 )
2𝑖𝑖=1
∑ (𝑄𝑜𝑖 −𝑄𝑜
)2𝑖
𝑖=1
Equation 1. Nash-Sutcliffe Coefficient of Suitability. Where Q0 is the observed streamflow, Qs is
the simulated streamflow, and 𝑄𝑜 is the average observed streamflow.
0
200
400
600
800
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1200
Stre
amfl
ow
(cf
s)SWAT Calibration
Observed Simulation
0
200
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1200
1400
Stre
amfl
ow
(cf
s)
Validation
Observed Simulated
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Figure 9. Average yearly streamflow of Medina River. Past measured and future simulated
climate scenarios. Average measured streamflow is 145.4 cfs, average climate change simulated
streamflow is 68.4 cfs, and average simulated without climate change streamflow is 112.7 cfs.
Figure 10. Historical yearly average streamflows for the Medina River at Pipe Creek. USGS stream gage
08179000. Average streamflow 148.1 cfs.
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Stre
amfl
ow
(cf
s)
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Medina River Streamflow with Climate Scenarios
Measured Climate Change No Climate Change
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amfl
ow
(cf
s)
Historical Medina River Annual Average Streamflow
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