Results
Hydrologic Model
SWAT calculates hydrological processes based on HRUs in each sub-basin
in the basin. HRUs were produced by the unique combination of soil, land
use, and slope in each sub-basin. After the physical and chemical
parameters of the soil, land use characteristics, pollution sources and
management processes of the basin were defined in the model, together
with the climate data, the model was simulated between 1991-2007, after
3-years warming period, using the monthly time step in the model.
Asymmetric distribution was used for precipitation distribution, and SCS
(Soil Conservation Service) method was used for surface runoff (USDA
1986). Bagnold method (Bagnold 1966) was applied for the sediment
transport. The results of the simulated flow rate were compared to the
values of the stream gauge station on the Namazgah river results of the
model. Model accuracy was determined based on Nash-Sutcliffe efficiency
(NSE), and coefficient determination (R2). According
to the comparison between simulated and observed discharge values on the
stream gauge station on the Namazgah river, NSE, and R2 are 0.48 and
0.58. Although the accuracy values of the model indicate that the model
did not reflect the real world enough, the graphical comparison of the
flow rate between 1991 and 2007 shows that features of the simulated
flow rate are similar to observed flow rates.
While the 1991-2002 period was used for the calibration of the model,
the 2003-2007 period was used for the validation. The initial model run
had r2=0.58, NS=0.48 improved to
r2=0.63, NS=0.56, after the calibration by using the
FACT with 200 simulation numbers. The model run had
r2=0.80, NS = 0.68 after the validation (Fig. 4). The
accuracy of the model results is good based on Moriasi et al. (2007)
model performance assessment. Unfortunately, there is no monitoring
station for water quality assessment on the Namazgah river. So, the
model accuracy assessment in terms of water quality measurements
couldn’t be made. However, SWAT is the most popular model since ungauged
watersheds can be modelled accurately by SWAT (Gassman et al. 2014).
Moreover, SWAT simulates long-term impacts of land use, land management
practices and buildup of pollutants with a continuous time model
(Neitsch et al. 2005).
Climate Change Impacts
The General Directorate of Meteorology has developed climate projections
for the 2016-2099 period using HadGEM2-ES, MPI-ESM-MR, GFDL-ESM2M global
model data sets in order to reveal how climate change will affect Turkey
in the future. In this study, global model data sets RegCM4.3.4 regional
model and dynamic scale-down method, according to the RCP 4.5 and RCP
8.5 scenarios, the results of the projection of 20 km resolution with
1971-2000 reference period 2016-2040, 2041-2070, 2071-2099 future
periods were obtained. Climate change data obtained from the study of
the General Directorate of Meteorology was used to show the effects of
climate change on the flow rates and NPS pollution loads in the Namazgah
dam basin.
According to the Fifth Assessment Report of the Intergovernmental Panel
on Climate Change (IPCC 2014), Representative Concentration Pathways are
new scenarios considering global greenhouse gas and aerosol
concentrations and alternative future scenarios (SRES) as a prelude.
There are four RCPs defined as 2.5, 4.5, 6.0 and 8.5 depending on the
total radiative forcing path and level until 2100. In this study, RCP
4.5 (540 ppm CO2) and RCP 8.5 (940 ppm CO2) scenarios were selected to
reveal the effects of future climate projections on the Namazgah dam
basin. While RCP 4.5 assumes a long-term level of medium greenhouse gas
concentrations with broadly pre-defined strain stabilization
constraints, RCP 8.5 acknowledges that greenhouse gas emissions will
increase over time in the 21st century and approach very high levels by
2100 (IPCC 2014). According to the RCP 4.5 and RCP 8.5 scenarios, the
estimated monthly mean total precipitation and changes in average
temperature for the years 2021-2090 were compared with the measurements
of the meteorological station in the basin between 1979-2014. A decrease
in the monthly average total amount of precipitation is estimated
between 2021-2090. Although more rainfall was observed in autumn and
spring between 1979 and 2014, the highest rainfall is expected in the
summer period in 2022-2090 (Fig. 5a). An increase of approximately 0.49oC is expected in average monthly temperatures
2021-2090 compared to 1979-2014 concerning the RCP 8.5 scenario, while a
decrease of 0.35 oC is expected concerning the RCP 4.5
scenario (Fig. 5b).
After the hydrological model was run for 2021-2099 according to the RCP
4.5 and RCP 8.5 scenarios, the effect of climate change on flow rate and
NPS pollution loads was shown. Mean flow rate values according to RCP
8.5 and RCP 4.5 scenarios, respectively; It is estimated at 0.67 and
0.68 m3/sec. When predicted flow rate values based on
RCP 4.5 and RCP 8.5 were examined for 2022-2047, 2048-2072, and
2073-2099 periods, 0.64, 0.70; 0.71, 0.68, and 0.80, 0.64,
m3/sec, respectively were predicted. TN values based
on RCP 4.5 and RCP 8.5 for 2022-2047, 2048-2072, and 2073-2099 periods
changes 43947.02, 48987.89; 50653.91, 46405.73, and 54913.97, 46246.08,
respectively. TP values based on RCP 4.5 and RCP 8.5 for 2022-2047,
2048-2072, and 2073-2099 periods varies 11474.19, 13808.91; 13943.31,
11959.92, and 14174.19, 12035.26, respectively (Fig. 6, Table 2).
Scenario Analysis
Land-use scenarios were developed and explored to understand the
sensitivity of model outputs to understand the impact of land-use/cover
changes on the flow rates and NPS pollution loads of the Namazgah river.
The land-use scenarios were chosen according to the Regulation on
Protection of Drinking and Utility Water Basins (PDUWB 2017). Forest
areas are protected and enhanced, the current agricultural areas are
protected and it isn’t permitted to increase their areas. Thus, based on
the regulation, two different scenarios were chosen to observe land use
changes impacts on water quantity and quality, which are; conversion of
shrubland to the forest and, conversion of agricultural areas to the
forest
conversion of shrubland to forest
Examining the impacts of the conversion of shrubland to forest areas
shows that mean flow rate values according to RCP 4.5 and RCP 8.5
scenarios were estimated as 0.8 and 0.67 m3/sec,
respectively. Predicted flow rate values based on RCP 4.5 and RCP 8.5
for 2022-2047, 2048-2072, and 2073-2099 periods were 0.72, 0.79; 0.79,
0.77; and 0.87, 0.73 m3/sec, respectively. TN values
based on RCP 4.5 and RCP 8.5 for 2022-2047, 2048-2072, and 2073-2099
periods changes 75200.2, 83661.618; 106754, 112049.514, and 125218,
104668.712, respectively. TP values based on RCP 4.5 and RCP 8.5 for
2022-2047, 2048-2072, and 2073-2099 periods varies 13562.3, 16497.508;
15897.7, 14410.515; 15750.6, 14627.425, respectively (Fig. 7, table 2).
conversion of agricultural areas to forest
According to the RCP 4.5 and RCP 8.5 scenarios, the average flow rates
were estimated as 0.51 and 0.77 m3/sec, respectively,
based on this scenario. Estimated flow rate values based on RCP 4.5 and
RCP 8.5 were examined for 2022-2047, 2048-2072, and 2073-2099 periods,
0.727, 0.5298; 0.798, 0.504, and 0.876, 0.48 m3/sec were predicted,
respectively. TN values based on RCP 4.5 and RCP 8.5 for 2022-2047,
2048-2072, and 2073-2099 periods changes 75200.2, 36282.471; 106754,
32743.9884 and 125218, 32592.5749, respectively. TP values based on RCP
4.5 and RCP 8.5 for 2022-2047, 2048-2072, and 2073-2099 periods varies
13562.3, 12034.256; 15897.7, 10499.158, and 15750.6, 10543.612,
respectively (Fig. 8, table 2).
Statistical Assessment of Model Results and Scenario
Analyses
Descriptive statistics of the results obtained from the modeling studies
are presented in Table 3. Considering two climate scenarios and two land
use scenarios, TN load, TP load and Q Tukey HSD were compared using
multiple benchmarks. Statistical significance level of two and higher
interaction terms was determined using full factorial design analysis of
variance (ANOVA). LSM values (which can also be defined as adjusted
means) are values predicted by the model for certain level combinations
of categorical variables when all other model factors are set to neutral
values. This approach was chosen due to unequal sample sizes (Table 4).
According to the Tukey HSD multiple comparison test, it was concluded
that there would be no significant change in the TP load in the future
depending on the climate and land use scenarios. On the other hand, it
is estimated that there may be significant changes in TN load and Q
between some scenarios. There is no significant difference between S1,
S4 and S5 scenarios in terms of TN load. Again, it is estimated that
there will be no difference between S2, S3 and S6 scenarios in terms of
TN load. On the other hand, it is expected that there will be a
significant difference between scenarios S1, S4 and S5 and scenarios S2,
S3 and S6. In terms of flow rate (Q), a significant difference is
expected between the S5 scenario and other scenarios. On the other hand,
it is predicted that there will be no significant difference between
scenarios S1, S2, S3, S4 and S6.
The changes that may occur in TN, TP and Q depending on the climate and
land use scenarios were also compared with the data of the previous
period (1991-2007) by using Dunnet’s test (Table 5). Depending on the
climate and land use scenarios, when the 2022-2099 period is compared
with the 1991-2007 period, it is estimated that there will be an
increase in phosphorus loads and a decrease in stream flow rate. On the
other hand, it is expected that the TN load will decrease only in the S5
scenario and increase in all other scenarios. When evaluated together
with the data presented in Table 5, the changes that may occur in the TP
load in all scenarios will not be significant when compared to S0. On
the other hand, the change that may occur when compared with the flow
rate of all scenarios will be statistically significant. In terms of TN
load, the difference between S0 and S1, S4, S5 scenarios is
statistically insignificant, whereas the difference between S0 and S2,
S3 and S6 scenarios will be significant.