Climate Change Impact on Inflow and Nutrient Loads to a Warm Monomictic Lake
1
Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran
2
Graduate Faculty of Environment, University of Tehran, Tehran 1417853111, Iran
3
Faculty of Governance, University of Tehran, Tehran 1439814151, Iran
4
Faculty of Civil, Water and Environmental Engineering, Technical and Engineering College, Shahid Beheshti University, Tehran 1983969411, Iran
*
Author to whom correspondence should be addressed.
Water 2023, 15(17), 3162; https://doi.org/10.3390/w15173162
Submission received: 7 August 2023
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Revised: 29 August 2023
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Accepted: 30 August 2023
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Published: 4 September 2023
Abstract
:This study analyses the impact of climate change on the inflows, sediment loads, and nutrient inputs to the Sabalan dam reservoir, a warm monomictic lake located northwest of Iran. For this purpose, the Soil and Water Assessment Tool (SWAT) was calibrated (2005–2018) and validated (2001–2004). Future climate-based data under the AR5 emission scenarios were obtained from the HadGEM2–ES general circulation model and then downscaled using the LARSWG 6.0. The tuned SWAT model was used to investigate the climate change impact on the hydrological processes and pollution loads to the Sabalan dam reservoir. Our findings based on the Nash–Sutcliffe efficiency coefficient and the coefficient of determination indicated an acceptable performance of the SWAT model in the simulation of inflows, sediment loads, and nutrient inputs to the reservoir. Inflow and sediment load to the reservoir will increase during the period of 2030–2070 compared to the base period (1998–2018). The annual total nitrogen (phosphorus) load to the reservoir will increase by 8.5% (9.4%), 7.3% (8.2%), and 5% (3.4%) under the emission scenarios of RCP2.6, RCP4.5, and RCP8.5, respectively. An increase in sediment loads and nutrient inputs to the Sabalan dam reservoir will significantly exacerbate the reservoir eutrophic condition, leading to water quality deterioration with acute consequences for the positive functions of the dam.
1. Introduction
Water resources, especially in (semi)arid areas, e.g., Iran, play a key role in socioeconomic development and environmental sustainability [1]. In many parts of Iran, water resources have been impacted by climate change along with population growth and imbalanced development [2]. Climate change has reduced the country’s long-term precipitation from ~250 to 225 mm/yr and raised the annual average air temperature to ~one degree of Celsius during the past half-century, leading to a significant decline in the annual renewable water nationwide from ~130 to ~95 km3 [3]. These changes happened during a period in which the country’s population grew rapidly; consequently, the demand for water increased in the agricultural, industrial, and sanitary sectors. The major national plan for providing the water needed under the mentioned conditions has been to build dams and store water for different purposes. Although dams have some benefits, they can bring about negative changes in aquatic ecosystems and prevent their sustainable use [4]. These changes mainly include the undesirable alteration of hydrodynamic conditions in rivers, the accumulation of contaminants under the approximately steady-state conditions of the reservoirs, increase in the evaporation rate, and decrease in river flow due to river regulation/fragmentation [5,6,7]. These factors are threatening Iran’s dams, as less than ~50% of the total capacity of the country’s reservoirs has remained unfilled with water during the past two decades, and also there is no dam reservoir which is safe from eutrophication [8,9]. Hence, it is essential to understand the changes in inflows and nutrient loads to reservoirs to plan sustainable practices for water use in the future [10].
Ardabil province, located in northwest Iran, is characterized by the highest rate of agricultural development in recent decades. The authorities of Ardabil Regional Water Company planned the construction of five large dams, including the Sabalan dam on the Qarasu river, to supply the water needed for a growing population and, specifically, for the agriculture sector. Although the Sabalan dam reservoir impoundment started in 2006, it suffers from reduced inflow impacted by climate change and a wide range of water quality crises, especially nutrient inputs to the reservoir. The discharge rate of the river entering the dam reservoir has sharply declined during the past few years compared with the average long-term discharge rate. As a consequence, the authorities have had to further store water in the reservoir to answer the needs of downstream water. This operation strategy led to an increase in the water residence time in the reservoir with acute consequences for water quality and eutrophication. Noori et al. [7] stated that the concentration of nitrogen and phosphors compounds in this reservoir was much higher than those in many other artificial lakes in the world. In addition to nutrients, high concentrations of heavy metals have also been detected in the water column and the bed sediment samples taken from this reservoir. Noori et al. [11] observed that water quality in the Sabalan dam reservoir was polluted by nutrients even during the cold months of the year when the reservoir was homo-thermal.
Given the deteriorated water quality and reduced inflows to the Sabalan dam reservoir that supports water needs for its surrounding residential areas, it is essential to analyze the potential impact of climate change on inflows, sediment loads, and nutrient inputs to the reservoir. This requires comprehensive modeling of the reservoir watershed using (semi)distributed hydrological–water quality models such as the Soil and Water Assessment Tool (SWAT) [12] integrated with the downscaled climatic data from General Circulation Models (GCMs) [13]. The SWAT model, which can properly estimate the watershed-based hydrological–water quality processes under the impact of climate change, has been introduced as a useful tool for making decisions on projects and prioritizing them to improve the water quality of river–reservoir systems.
2. Materials and Methods
2.1. Study Area and Data
With an altitude of ~1000 m, the Sabalan dam is located in Ardabil province in northwestern Iran within a distance of 55 and 40 km from Ardabil and Meshgin Shahr cities, respectively (Figure 1a). This dam was constructed on Qarasu river in 2006 with a water storage capacity of ~105 × 106 m3 and an annual regulation capacity of ~115 × 106 m3 from the Qarasu river’s flow. The Sabalan dam was built to irrigate the farmlands in Meshgin Shahr plain and provide ~107 m3 of drinking water needed by this city.
Atmospheric precipitation, water transferred via the network of Lai chai, Nirchai, Ghorichai, Balukhlochai, Khalifa Lu, Namin Chai, Qarasu, and Saqqezchi tributaries, and the outflows from the upstream dams feed the Sabalan dam reservoir (Figure 1b). Phosphate and nitrate fertilizers are usually used in farmlands in the watershed more than what is required by plants to grow. The excessive fertilizers are washed away from the farmlands and transferred to the river network and finally reach out to the dam reservoir.
Here, we planned a fifteen-month sampling campaign to measure the data required for water quality modeling at a monitoring station located at the entrance of the Sabalan dam reservoir, i.e., W1, as shown in Figure 1. The data sampled mainly included total nitrogen (TN) and total phosphorus (TP). We used the land use/cover map created in 2016 by the Ardabil Regional Water Company (Figure 1a). The watershed of the Sabalan dam reservoir, which is located in mountainous areas with elevation ranging from 1059 to 4796 m, is mainly covered by grasslands (48%), farmlands (28%), forests (10%), agricultural lands–shrublands (8%), and rural areas and brownfields (4%). The reservoir watershed has a cold climate with a relative humidity of 54%. The average annual evaporation and precipitation in the study region are 1683 mm and 312 mm, respectively, which show a very high evaporation rate compared with precipitation. The mean daily temperature and daily absolute minimum and maximum temperatures recorded in the region are 10.9, −28.6, and 42 °C, respectively.
2.2. Hydrological Processes and Water Quality Modeling
The hydrological SWAT model was employed to simulate inflows, sediment loads, and nutrient inputs to the Sabalan dam reservoir affected by climate change. The SWAT model, which is a hydrological model based on integrated watershed processes, predicts the long-term impact of water resources management on inflows and pollution loads in small- to large-scale watersheds [12]. For this purpose, a digital elevation model (DEM) with a 30 m spatial resolution was utilized to divide the watershed into 121 subbasins. The SWAT model simulated hydrological processes and pollution loads in hydrological response units (HRUs), for which the maps of land use/cover were available. Soil types and slope classes (here, 0–10, 15–33, 33–60, and >60%) were introduced into the model, leading to the creation of 532 HRUs in the reservoir watershed. According to Figure 1a, agricultural activities are dominant in the watershed of the reservoir. Therefore, the required agricultural information, such as the planting and harvesting dates of crops, irrigation scheduling, water demand for irrigated crops, tillage, and application rates of fertilizers to farmlands, sediments, and nutrient loads were introduced to the SWAT model. Further information for the SWAT model inputs in the Sabalan dam watershed is explained in Table 1.
We used the SWATCUP with SUF12 algorithms for calibration and validation of the SWAT model [14]. The data from hydrometric and synoptic stations for the entire simulation period (1998–2018) and the 15-month measured data for total nitrogen and total phosphorus at station W1 (May 2017–July 2018) were entered into the model for calibration and validation processes. The 1998–2000, 2001–2004, and 2005–2018 periods were considered as the warm-up, validation, and calibration periods, respectively. These periods were selected because the water quality data (i.e., total nitrogen and total phosphorus) were measured close to the end of the simulation period. To follow the calibration process suggested by Arnold et al. [15], the same periods were used for streamflow and sediment load, total phosphorus, and total nitrogen calibration. Moreover, a validation period was not considered for the nutrient loads because no data were available for total nitrogen and total phosphorus from 2001 to 2004. However, both validation and calibration periods included wet and dry periods so that the model could experience different hydro climatological conditions in the reservoir watershed [16].
Abbaspour et al. [17] recommended that parameters such as precipitation lapse rate (PLAPS), temperature lapse rate (TLAPS), and the parameters related to snow, which are classified as forcing factors, be separated from the other factors in the calibration process. Accordingly, the parameters related to hydrology, nutrient loads, and sediments were recognized as sensitive through the Global Sensitivity Analysis based on a higher t-stat and lower p-value. The initial ranges of the parameters were limited in the model through regional studies to guide the calibration process manually [17]. The Nash–Sutcliffe efficiency (NSE) [18] was used as the objective function to evaluate the model performance in the calibration and validation steps. The coefficient of determination (R2) [19,20,21] was also used to further analyze the model’s performance. Table 2 lists the acceptable ranges of NSE and R2 values for various hydrological and water quality variables [15,22].
2.3. Downscaling and Projection of Climatic Data
Climate change has serious effects on hydrological processes and water quality by increasing temperature and changing the form, amount, and pattern of precipitation [13,23]. Accordingly, analysis of the impact of climate change on hydrology and water quality in the future is necessary for the suitable management of water resources. Atmosphere–Ocean General Circulation Models are a common tool for the projection of weather data under different climate scenarios [24,25]. The Intergovernmental Panel on Climate Change (IPCC) introduced different scenarios, called the Representative Concentration Pathways (RCPs), in which different conditions of economic growth, technology advancement, environmental protection, population growth, deforestation, and types of energy used in transportation were considered. The outputs of the HadGEM2-ES general circulation model (GCM) in the IPCC Fifth Assessment Report (AR5) [26] were used in this study to project the air temperature and precipitation in the Sabalan dam watershed. For this purpose, the output of the HadGEM2-ES model, which has shown desirable results in Iran [11], was utilized under the lowest (RCP2.6), average (RCP4.5), and highest (RCP8.5) radiative forcing (IPCC, 2014). Since the HadGEM2-ES model generates precipitation and air temperature data on large spatial scales, these data must be downscaled through suitable tools/models for use in smaller spatial scales (e.g., Sabalan dam watershed) [27]. We used LARSWG 6.0, a tool for generating random weather data, to downscale the projected results of the HadGEM2-ES model. LARSWG 6.0, introduced by Semenov [28], performed desirably in the studies conducted in northern and northeastern Iran, which also included our study region [26,29].
However, the data on daily precipitation and minimum and maximum temperatures were obtained at two weather stations from Ardabil Regional Water Company, for the 1998–2018 period. Then, the LARSWG 6.0 model was used to downscale the climatic data projected by the HadGEM2–ES model for the period of 2030–2070.
3. Results and Discussion
3.1. Projected Precipitation and Air Temperature in the Study Region
The R2 values for the performance of the LARSWG 6.0 model in the estimation of monthly mean precipitation, minimum temperature, and maximum temperature in synoptic stations varied from 0.94 to 0.96, 0.96 to 0.98, and 0.96 to 0.98, respectively. These results indicate the good performance of the LARSWG 6.0 model in the downscaling process of both precipitation and air temperature, which could provide accurate information for the hydrological–water quality simulation of the SWAT model in the Sabalan dam watershed [30].
Changes in weather components, including air temperature and form, amount, and pattern of precipitation caused by climate change, can substantially affect the water and nutrient cycles in the watershed of the Sabalan dam reservoir, thereby affecting the positive functions of the reservoir. Figure 2 and Figure 3 show both annual and seasonal changes in minimum and maximum air temperature and precipitation, respectively, during 2030–2070 compared with those of the base period at stations C1 and C2 under the RCP2.6, RCP4.5, and RCP8.5 scenarios. Under the RCP2.6 scenario, the maximum (minimum) annual temperature will increase by 1.6 °C (1.25 °C) and 1.8 °C (1.43 °C) at synoptic stations C1 and C2, respectively, from 2030 to 2070. The foregoing increase in temperature will be larger under the RCP4.5 and even RCP8.5 scenarios. During the 2030–2070 period, the maximum (minimum) temperature will increase by 1.89 °C (1.52 °C) and 2.15 °C (1.62 °C) under the RCP4.5 scenario at stations C1 and C2, respectively. Concerning RCP8.5 (2030–2070), the maximum (minimum) temperature will increase by 2.55 °C (2.26 °C) and 3.01 °C (2.33 °C) at stations C1 and C2, respectively, compared with the base period. The foregoing increase in air temperature in the watershed of the Sabalan dam reservoir, especially when it continues during winter, will change the precipitation form from snow to rain, leading to a decline in the main sources of streamflow in warm seasons [31].
According to Figure 3, it is clear that the mean precipitation at the synoptic stations will change under the impacts of climate change compared with the base period. During the projection period (i.e., 2030–2070), the mean annual precipitation at the synoptic station C1 (C2) will increase by 16.5% (20.2%), 13.4% (18.4%), and 17.8% (18%) under the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. Compared with the base period, the seasonal precipitation will increase in the watershed of the Sabalan dam reservoir under the RCP2.6, RCP4.5, and RCP8.5 scenarios (except for summer). Changes in the precipitation pattern in the reservoir watershed can greatly affect the water budget, sediments, and nutrient loads to the dam reservoir. In sum, the predicted increase in precipitation and air temperature caused by the impact of climate change is in line with previous studies conducted in northwestern Iran [32].
3.2. Sensitivity Analysis of SWAT Model
The PLAPS, TLAPS, SFTMP (snowfall temperature), and SMTMP (snowmelt base temperature) parameters, known as the factors affecting the hydrology of the watershed, were adjusted to 10–22 mm, 2.8–5.1 °C, 2 °C, and 1 °C, respectively. The SUF12 algorithm in SWAT–CUP was then employed to determine the best parameters in order to simulate runoff, sediments, and nutrient loads for the watershed of the Sabalan dam reservoir as well as for its subbasins. Table 3 presents the parameters sensitive to discharge, sediments, total nitrogen, and total phosphorus, which were extracted by running the model 500 times. These parameters were selected based on higher t-stat and lower p-values compared to the other parameters involved in the sensitivity analysis process [17]. Based on the suggestion of Arnold et al. [15], the parameters sensitive to the hydrology of the watershed were calibrated first in the sensitivity analysis process and then the parameters sensitive to sediments and total phosphorus were adjusted, followed by the parameters sensitive to total nitrogen. Our selected sensitive parameters in the watershed of the Sabalan dam reservoir have also been used in other watersheds worldwide [33].
3.3. Calibration and Validation Results of SWAT
Table 4 reports the results of the calibration and validation of the SWAT model in SWAT–CUP through the SUFI2 algorithm for the streamflow and sediment load in selected hydrometric stations and water quality monitoring station W1. The NSE and R2 values resulting from this simulation indicate a good match between the simulated and observed values for the streamflow and sediment load in the calibration and validation period (NSE > 0.5, R2 > 0.5) [27,34]. However, Figure 4 reveals that the model underestimated discharge in certain stations (S5, S1, C1, C2, and S4 stations) in a few months, mainly due to the lack of precipitation, snowmelt, or air temperature data in those stations. On the contrary, the model overestimates the base flow in some stations (C2, S3, S2, and C1 stations), likely due lack of water extraction data from the river for agricultural purposes by local farmers. With respect to sediment (Figure 4) and nutrient loads (Figure 5), the simulated results also match the observed ones well. In this regard, the NSE, R2, R-factor, and P-factor demonstrate the good performance of the SWAT model. Similar to streamflow and sediment simulation, the SWAT model underestimates the nutrient loads (TN and TP) in high values. Arnold et al. suggested that sediment load and nutrient load simulation performance depends on how streamflow is calibrated. Then, it can be concluded that the poor performance of the SWAT model in the simulation of peak nutrient loads is a result of its poor performance in the simulation of high extreme flows [15]. It should be noted that nitrogen and total phosphorus data were not measured in station C1 during the verification period; as a result, verification was not conducted. In sum, given the good performance of the SWAT model for the simulation of streamflow, sediments, and nutrient loads, the model can be used to analyze the response of the studied watershed to the changing climate in the future.
3.4. Climate Change Impact on Streamflow, Sediment, and Nutrient
Compared to the base period, the seasonal and annual changes in inflows, sediment loads, total phosphorus, and total nitrogen to the Sabalan dam reservoir under different RCPs (2030–2070) are shown in Figure 6. The inflow to the dam reservoir will increase by 10.3%, 8.3%, and 6.5% under the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively, likely due to the projected increase in precipitation. The higher temperature in the watershed of the Sabalan dam reservoir compared with the base period, especially under the RCP8.5 scenario, will increase evapotranspiration in the watershed. Hence, the streamflow entering the dam will be higher under the RCP4.5 scenario, whereas the increase in annual precipitation under the RCP8.5 and RCP4.5 scenarios will approximately be in the same range. The increase in the produced runoff in the watershed of the Sabalan dam reservoir through soil erosion, which has steep slopes, will enhance the generation of sediment yield [35]. These sediments that are carried by the runoff are more sensitive to climate change than to changes in discharge. This will increase the annual amount of sediments in the dam. Other studies have also pointed out the increased impact of climate change on sediment generation compared to the water cycle [14,36]. In addition, nutrients such as total nitrogen and total phosphorus, which are moved in watersheds by runoff and sediments, will finally reach the rivers. Accordingly, since runoff and generated sediments will increase in the watershed of the Sabalan dam reservoir under the impact of climate change, it is predicted that higher total nitrogen and total phosphorus loads will enter the Sabalan reservoir under the different emission scenarios. It is predicted that the annual increase in mean total nitrogen and total phosphorus loads to the Sabalan dam reservoir under the RCP2.6 scenario will be larger compared to the RCP4.5 and RCP8.5 scenarios due to the increase in the factors that affect nutrient movement (i.e., sediments and runoff) (Figure 5). The watershed of the Sabalan dam reservoir is located in a cold and mountainous region, and the increase in air temperature, especially under the RCP8.5 scenario, will prolong the growing season of the plants [13]. Therefore, they can absorb the available mineral nitrogen and phosphorus, limiting the increase in nitrogen and phosphorus loads to the reservoir. This is another factor that can prevent excessive input of total nitrogen and total phosphorus to the Sabalan dam reservoir compared to sediment load and streamflow. It also shows the importance of natural vegetation such as forests in controlling the total nitrogen and total phosphorus produced in the reservoir watershed. The results of this research indicate that the impact of climate change will increase the total nitrogen and total phosphorus loads to the Sabalan dam compared to the base period. This will further worsen the quality of the Sabalan dam with respect to eutrophication as the dam is already exposed to nutrient enrichment in the base period [7].
The increase in winter air temperature in the watershed of the Sabalan dam reservoir (Figure 2) will accelerate winter snowmelt—a phenomenon that often occurs in spring during the base period. This increases the winter runoff, leading to more sediment yield through soil erosion in the watershed of the reservoir compared with the base period. Consequently, an increase in runoff and sediment yield washed out more nitrogen and phosphorus compounds and exposed the reservoir to higher nutrient loads. In winter, the increase in precipitation and air temperature will be larger under the RCP4.5 and RCP8.5 scenarios than the RCP2.6, leading to more nutrient inputs to the Sabalan dam in the former scenarios than the latter. These results highlight the significant impact of changes in the form, amount, and timing of precipitation, and also increased temperature on water cycle and nutrient loads in mountainous watersheds such as the Sabalan dam watershed. In addition, vegetation cover in the dam watershed is much less in winter than in other seasons. Hence, smaller amounts of nutrients are used by plants, and the mineral nutrients (mainly total phosphorus) are easily washed away from farmlands and discharged into the Sabalan dam reservoir. Due to winter snowmelt caused by climate change, the inflow into the reservoir will decrease in spring under the RCP2.6, RCP4.5, and RCP8.5 scenarios, although the average amount of rainfall in spring will increase compared to the base period. The reduction in runoff and the sediment yield in the watershed in spring will also reduce the nutrient loads to the dam reservoir. The decrease in inflow discharge is not limited to spring. In summer, the inflow to the Sabalan dam will also decrease due to the growing temperature and the declining average precipitation under the impacts of climate change. Such a decline in inflow to the dam in these warm and growing seasons can collapse the role of the Sabalan dam in supplying the demands of agriculture and drinking water.
In autumn, the sediment loads to the Sabalan dam will increase by about 50% under the RCP2.6 scenario. In addition, an increase in autumn air temperature could extend the growing season, which can be translated into more fertilizer use by farmers [37]. The more fertilizer which is used, the more nutrient inputs there will be to the environment during autumn [37]. Our projected results also reveal an increase in total nitrogen and total phosphorus loads to the Sabalan dam reservoir by about 2% and 4.3%, respectively.
4. Conclusions
In this study, we investigated the impact of climate change on the inflow and pollution loads to the Sabalan dam reservoir in Iran. Our findings showed an increase in inflow, sediment, and nutrient loads to the Sabalan dam reservoir under the RCP2.6, RCP4.5, and RCP8.5 scenarios by 2070. More specifically, we understood that increasing air temperature in cold months would further change the precipitation form from snow to rain, which could reduce the main source of water for streamflow in warm months when the water needed is the maximum amount to support agricultural activities in the watershed. The results also suggested that the vegetation in the watershed of the Sabalan dam can cope with the increase in sediment and nutrient loads to the Sabalan dam reservoir. Considering the hypereutrophic condition in the Sabalan dam reservoir, the projected increase in sediment and nutrient loads will likely create intensive blooms of algae and cyanobacteria, with severe consequences for the quality of the reservoir’s water. In addition, internal pollution loads in the reservoir (e.g., bed sediment and nutrient re-suspension) would further deteriorate the reservoir’s water quality. Therefore, the authorities must take action to preserve the reservoir’s water quality by implementing the suggested relevant catchment practices (e.g., increasing vegetation cover in the dam watershed and decreasing fertilizer-use in farmlands) and in-lake management strategies (e.g., improve dissolved oxygen conditions and dredging the reservoir).
It should be noted that we did not consider the impact of land use/cover changes on our investigations. Given that changes in land use/cover can affect the hydrological processes and water quality in the watershed, further works are required to better paint a picture of the future of inflows and nutrient and sediment loads into the Sabalan dam reservoir under the combined impacts of climate and land use/cover changes.
Author Contributions
Conceptualization, R.N. and S.A.H.; methodology, B.P. and R.N.; software, B.P.; validation, R.N. and S.A.H.; formal analysis, B.P.; investigation, R.N. and B.P.; resources, R.N.; data curation, B.P. and R.N.; writing—original draft preparation, B.P.; writing—review and editing, R.N., S.A.H. and M.S.; visualization, B.P. and M.S.; supervision, R.N., S.A.H. and M.S.; project administration, R.N. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Data Availability Statement
Data are available upon reasonable request from the corresponding author.
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1.
(a) The watershed of the Sabalan dam reservoir together with the locations of the hydrometric, synoptic, and water quality stations, the Sabalan dam, and the land use/cover map. (b) The main tributaries in the Sabalan dam watershed together with the main cities and dams. C1 and C2: Arbab Kandi and Barough synoptic stations, respectively; S1 to S5: Kouzeterapi, Polealmas, Samiyan, Shamsabad, and Gilandeh hydrometric stations; W1: Arbab Kandi water quality station.
Figure 1.
(a) The watershed of the Sabalan dam reservoir together with the locations of the hydrometric, synoptic, and water quality stations, the Sabalan dam, and the land use/cover map. (b) The main tributaries in the Sabalan dam watershed together with the main cities and dams. C1 and C2: Arbab Kandi and Barough synoptic stations, respectively; S1 to S5: Kouzeterapi, Polealmas, Samiyan, Shamsabad, and Gilandeh hydrometric stations; W1: Arbab Kandi water quality station.
Figure 2.
Annual and seasonal changes in maximum and minimum air temperature at two synoptic stations under the RCP2.6, RCP4.5, and RCP8.5 scenarios in the watershed of Sabalan dam reservoir during the period of 2030–2070 compared with the base period (1998–2018).
Figure 2.
Annual and seasonal changes in maximum and minimum air temperature at two synoptic stations under the RCP2.6, RCP4.5, and RCP8.5 scenarios in the watershed of Sabalan dam reservoir during the period of 2030–2070 compared with the base period (1998–2018).
Figure 3.
Annual and seasonal changes in precipitation at two synoptic stations under the RCP2.6, RCP4.5, and RCP8.5 scenarios in the watershed of Sabalan dam reservoir during the period of 2030–2070 compared with the base period (1998–2018).
Figure 3.
Annual and seasonal changes in precipitation at two synoptic stations under the RCP2.6, RCP4.5, and RCP8.5 scenarios in the watershed of Sabalan dam reservoir during the period of 2030–2070 compared with the base period (1998–2018).
Figure 4.
Time series of observed and simulated monthly streamflow and sediment load in both calibration and validation of SWAT model during the study base period.
Figure 4.
Time series of observed and simulated monthly streamflow and sediment load in both calibration and validation of SWAT model during the study base period.
Figure 5.
Time series of observed and simulated monthly total phosphor and total nitrogen in both calibration and validation of SWAT model during the study base period.
Figure 5.
Time series of observed and simulated monthly total phosphor and total nitrogen in both calibration and validation of SWAT model during the study base period.
Figure 6.
Seasonal and annual changes in inflows, sediment loads, total nitrogen, and total phosphorus inputs to the Sabalan dam reservoir under the RCP2.6, RCP4.5, and RCP8.5 scenarios during 2030–2070 compared to the base period.
Figure 6.
Seasonal and annual changes in inflows, sediment loads, total nitrogen, and total phosphorus inputs to the Sabalan dam reservoir under the RCP2.6, RCP4.5, and RCP8.5 scenarios during 2030–2070 compared to the base period.
Table 1.
Information is required for the Soil and Water Assessment Tool (SWAT) model in the watershed of the Sabalan dam reservoir. 1 DEM: digital elevation model; 2 SRTM: the Shuttle Radar Topography Mission; 3 USGS: the United States Geological Survey; 4 FAO: the Food and Agriculture Organization.
Table 1.
Information is required for the Soil and Water Assessment Tool (SWAT) model in the watershed of the Sabalan dam reservoir. 1 DEM: digital elevation model; 2 SRTM: the Shuttle Radar Topography Mission; 3 USGS: the United States Geological Survey; 4 FAO: the Food and Agriculture Organization.
| Classification | Data | Resolution/Scale | Sources |
|---|---|---|---|
| Hydrology | DEM 1 | 30 m | USGS 2/SRTM 3 |
| River network | 1:200,000 | Ardabil Regional Water Company | |
| Land use/cover (in 2016) | 30 m | Ardabil Regional Water Company | |
| Soil type and properties | 10,000 m | FAO 4 digital soil map | |
| Precipitation and temperature | Daily | Ardabil Regional Water Company | |
| River flow and sediment concentration | Monthly | Ardabil Regional Water Company | |
| Nutrients | Urea fertilizer | 300 kg/ha | Environmental Studies and Ardabil Agriculture Jihad Management |
| Phosphate fertilizer | 50 kg/ha | Agricultural Jihad Organization of Ardabil Province | |
| Agricultural information | The watershed of Sabalan dam reservoir | Agricultural Jihad Organization of Ardabil Province | |
| Total nitrogen and total phosphorus concentration | Monthly measurements at the entrance of the dam, i.e., station W1 (May 2017–July 2018) | Filed studies |
Table 2.
Acceptable ranges of the Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2) for the simulation of hydrological and water quality parameters using SWAT model [17].
Table 2.
Acceptable ranges of the Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2) for the simulation of hydrological and water quality parameters using SWAT model [17].
| Variable | NSE | R2 |
|---|---|---|
| Stream flow | >0.5 | >0.6 |
| Sediment load | >0.5 | >0.6 |
| Nitrogen load | >0.35 | >0.3 |
| Phosphorus load | >0.4 | >0.4 |
Table 3.
The list of the parameters sensitive to discharge, sediments, total nitrate, and total phosphorus loads in the watershed of the Sabalan dam reservoir.
Table 3.
The list of the parameters sensitive to discharge, sediments, total nitrate, and total phosphorus loads in the watershed of the Sabalan dam reservoir.
| Sensitive for: | Parameter | Name | t-Test | p-Value |
|---|---|---|---|---|
| Hydrology | R__CN2.mgt | Initial SCS runoff curve number for moisture condition II | −23.99 | <0.05 |
| Hydrology | R__SOL_BD (…).sol | Moist bulk density | 3.92 | <0.05 |
| Hydrology | V__ALPHA_BF.gw | Baseflow alpha factor | −10.35 | <0.05 |
| Hydrology | V__LAT_TTIME.hru | Lateral flow travel time | −1.88 | <0.05 |
| Hydrology | V__RCHRG_DP.gw | Deep aquifer percolation fraction | 1.57 | <0.05 |
| Hydrology | V__GW_DELAY.gw | Groundwater delay time | −1.48 | <0.05 |
| Hydrology | V__CH_K2.rte | Effective hydraulic conductivity in main channel alluvium | 4.15 | <0.05 |
| Hydrology | v_ESCO.bsn | Soil evaporation compensation factor. | 4.3 | <0.05 |
| Hydrology | v_EPCO.bsn | Plant uptake compensation factor | 4.5 | < 0.05 |
| Hydrology | r_SOL_AWC.sol | Available water capacity of the soil layer | 4.9 | <0.05 |
| Hydrology | v_OV_N.hru | Manning’s “n” value for overland flow | 5.3 | <0.05 |
| Sediment and total phosphorus | v_PRF.bsn | Peak rate adjustment factor for sediment routing in the main channel | −14.05 | <0.05 |
| Sediment and total phosphorus | V_BC4.bsn | Rate constant for mineralization of organic P to dissolved P in the reach at 20 °C | −9.01 | <0.05 |
| Sediment and total phosphorus | r_USLE_K.sol | USLE equation soil erodibility (K) factor | −8.09 | <0.05 |
| Sediment and total phosphorus | R_ USLE_P.mgt | USLE equation support practice factor | 6.08 | <0.05 |
| Sediment and total phosphorus | V_PHOSKD.bsn | Phosphorus soil partitioning coefficient | 5.76 | <0.05 |
| Sediment and total phosphorus | v_CH_COV1 | The channel erodibility factor | 5.08 | <0.05 |
| Sediment and total phosphorus | V_SOL_ORGP.sol | Initial organic P concentration in soil layer | 3.54 | <0.05 |
| Sediment and total phosphorus | V_ERORGP.HRU | Phosphorus enrichment ratio for loading with sediment | −3.12 | <0.05 |
| Total nitrogen | V_CDN.bsn | Denitrification exponential rate coefficient | −25.01 | <0.05 |
| Total nitrogen | R_SOL_CBN.sol | Organic carbon content | −22.98 | <0.05 |
| Total nitrogen | R_ANION_EXCL.sol | Fraction of porosity (void space) from which anions are excluded | −14.54 | <0.05 |
| Total nitrogen | V_SDNCO.bsn | Denitrification threshold water content | 8.76 | <0.05 |
| Total nitrogen | V_ERORGN.HRU | Organic N enrichment ratio for loading with sediment | 5.43 | <0.05 |
| Total nitrogen | V_HLIFE_NGW.gw | Half-life of nitrate in the shallow aquifer | −3.24 | <0.05 |
Table 4.
Statistical performance of the SWAT model in simulating discharge, sediments, total nitrogen, and total phosphorus at the measured stations in both calibration and validation phases during the study’s base period.
Table 4.
Statistical performance of the SWAT model in simulating discharge, sediments, total nitrogen, and total phosphorus at the measured stations in both calibration and validation phases during the study’s base period.
| Validation (2001–2004) | Calibration (2005–2018) | Station | |||||||
|---|---|---|---|---|---|---|---|---|---|
| P-factor | R-factor | R2 | NSE | P-factor | R-factor | R2 | NSE | ||
| Flow | |||||||||
| 1.08 | 0.66 | 0.88 | 0.56 | 1.19 | 0.71 | 0.69 | 0.65 | S4 | Shamsabad |
| 1.32 | 0.71 | 0.91 | 0.58 | 1.42 | 0.76 | 0.71 | 0.7 | S2 | Polealmas |
| 1.65 | 0.68 | 0.92 | 0.52 | 1.32 | 0.7 | 0.77 | 0.48 | S5 | Gilandeh |
| 1.32 | 0.75 | 0.82 | 0.63 | 1.16 | 0.78 | 0.86 | 0.73 | C1,W1 | Arbab Kandi |
| 1.39 | 0.72 | 0.98 | 0.56 | 1.52 | 0.77 | 0.82 | 0.61 | S3 | Samiyan |
| 1.27 | 0.69 | 0.76 | 0.63 | 1.43 | 0.71 | 0.76 | 0.53 | S1 | Kouzeterapi |
| 1.09 | 0.76 | 0.63 | 0.53 | 1.09 | 0.78 | 0.87 | 0.62 | C2 | Barough |
| Sediment | |||||||||
| 1.38 | 0.66 | 0.87 | 0.52 | 1.45 | 0.65 | 0.88 | 0.56 | S4 | Shamsabad |
| 1.28 | 0.69 | 0.85 | 0.48 | 1.32 | 0.71 | 0.8 | 0.59 | S2 | Polealmas |
| 1.77 | 0.6 | 0.78 | 0.58 | 1.87 | 0.56 | 0.78 | 0.46 | S5 | Gilandeh |
| 1.67 | 0.71 | 0.85 | 0.6 | 1.53 | 0.66 | 0.7 | 0.67 | C1,W1 | Arbab Kandi |
| 1.21 | 0.69 | 0.78 | 0.52 | 1.09 | 0.73 | 0.81 | 0.58 | S3 | Samiyan |
| 1.87 | 0.75 | 0.89 | 0.54 | 1.75 | 0.71 | 0,76 | 0.56 | S1 | Kouzeterapi |
| 1.26 | 0.59 | 0.71 | 0.56 | 1.31 | 0.65 | 0.71 | 0.5 | C2 | Barough |
| Total phosphorus | |||||||||
| 2.13 | 0.63 | 0.75 | 0.41 | C1,W1 | Arbab Kandi | ||||
| Total nitrogen | |||||||||
| 1.94 | 0.73 | 0.82 | 0.65 | C1,W1 | Arbab Kandi | ||||
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Parmas, B.; Noori, R.; Hosseini, S.A.; Shourian, M. Climate Change Impact on Inflow and Nutrient Loads to a Warm Monomictic Lake. Water 2023, 15, 3162. https://doi.org/10.3390/w15173162
AMA Style
Parmas B, Noori R, Hosseini SA, Shourian M. Climate Change Impact on Inflow and Nutrient Loads to a Warm Monomictic Lake. Water. 2023; 15(17):3162. https://doi.org/10.3390/w15173162
Chicago/Turabian StyleParmas, Behnam, Roohollah Noori, Seyed Abbas Hosseini, and Mojtaba Shourian. 2023. "Climate Change Impact on Inflow and Nutrient Loads to a Warm Monomictic Lake" Water 15, no. 17: 3162. https://doi.org/10.3390/w15173162
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