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Volume 5, Issue 10 (1-2015)
Volume 5, Issue 10 (1-2015)
Abstract
Statistical downscaling methods are widely used for prediction of climatic variables e.g. temperature because of importance of these factors in environmental planning and management. In this study, the performance of Statistical Downscaling Model (SDSM) was investigated to predict temperature. The input data of the study include minimum, maximum and mean temperature of Kerman and Bam Synoptic stations, NCEP (National Centers for Environmental Prediction) data and the A2 and B2 emission scenarios HadCM3 for the reference period, 1971-2001. The first 15 years data (1971-1985) was applied for the calibration and the second 15 years data (1986-2001) for model validation. Temperature for three periods including 2010-2039, 2040-2069 and 2070-2099 was predicted and then compared with the temperature data of reference period i.e.1971-2001 using HadCM3A2, B2 data. Statistical measures of model performance such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Nash-Sutcliffe efficiency (NS) and the analysis of output results from SDSM model shown that this model is able to predict temperature indexes more accurately in arid climate than in hyper-arid climate. The results indicate temperatures rising in all months for both stations.
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Volume 5, Issue 10 (1-2015)
Volume 5, Issue 10 (1-2015)
Abstract
Due to spatial correlations between climatic variables and natural features like altitude, the gradients finding is the first option in zoning of them. Various gradients in large area and lack of knowledge of them is resulted in applying of geostatistical and deterministic interpolation techniques by researchers. Geographically Weighted Regression (GWR) is a method which can identify local regressions as well and in spite of gradient increases interpolation accuracy. Accordingly, this research was conducted in order to evaluation of GWR’s accuracy than another interpolation methods based on 30 years average of air temperature and relative humidity data of 240 synoptic and climatologically station in Iran. The results of this research on air temperature which was a function of altitude showed that GWR method had significant difference than other methods, while there was no significant difference on relative humidity and These results may be due to no specific correlation between altitude and relative humidity.
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Volume 6, Issue 11 (7-2015)
Volume 6, Issue 11 (7-2015)
Abstract
The lack of enough meteorological stations at high elevations in mountainous regions causes a limitation for using regression relations to interpolate air temperatures at different time scales. Therefore, in this study, geostatistical methods include Kriging, Co-Kriging and Weighting moving average has been compared with elevation-temperature relation using a data series of 25 years (1975-2000). Evaluating the selected interpolation methods has been done based on Root Mean Square Error, Mean Absolute Error and Mean Biass Error. Also the physical conception of estimated values has been analyzed based on extracted maps. Results showed that using elevation as a covariate in Cokriging method caused a 28% increase in effective range. Although Kriging method overestimate the monthly and yearly air temperature, no significant difference was observed in evaluating indices between Kriging and Cokriging methods. Results also demonstrated that both Kriging and Cokriging method could be used instead of linear regression to interpolate air temperature in the study area. Analyzing prepared maps showed a decreasing pattern in air temperature from east to west parts.
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Volume 7, Issue 14 (2-2017)
Volume 7, Issue 14 (2-2017)
Abstract
Fluctuations in the meteorological variables such as: temperature, precipitation, is one of the atmospheric circulation characteristics. Meanwhile, the sharp decrease of precipitation and dry periods caused by that has many negative effects on water resources. Climatic fluctuating in a region has severe effect on soil and water resources. Nowadays, generally it has been accepted that any change in climate system is important in the water and soil resources management. Among the climate elements, precipitation has the most fluctuation; this matter especially in Iran that the average rainfall is 250 mm is more importance. Drought and wet periods, especially drought period has been affected the Kaju watershed for a long time; and because of its long durability has been exposed to a lot of damages and injuries. The purpose of this article is Investigation of temperature, precipitation and discharge change trend the basin watershed a period of 20 years. Mann-Kndall parametric test was used to determine trend parametric. The results show precipitation decrease trend, temperature is ascendant trend and discharge River region is decrease trend.
Dr. Ebrahim Omidvar, Eng. Maryam Rezaei, Dr. Abdollah Pirnia,
Volume 9, Issue 18 (1-2019)
Volume 9, Issue 18 (1-2019)
Abstract
Atmosphere–ocean coupled global climate models (GCMs) are the main source to simulate the climate of the earth climate. The computational grid of the GCMs is coarse and so, they are unable to provide reliable information for hydrological modelling. To eliminate such limitations, the downscaling methods are used. The present study is focused on simulating the impact of climate change on the behavior of precipitation and temperature of Sirjan synoptic station in Kerman Province. At first, the capability of artificial neural network to downscaling of climate variables that predicted by CanESM2 is tested. Then, using the most appropriate models, the mean monthly temperature and precipitation amounts forecast for future periods under RCP 4.5 scenario. Results of this study for monthly temperature downscaling indicated that the artificial neural network with 2 hidden layer, 8 neurons, with Tangent and Log sigmoid activation function was the best model, so that RMSE, NS and R2 were 0.387 , 0.973 and 0.917 respectively. Also, for precipitation variable, the structure with 2 hidden layer feed forward perceptron, 8 neurons, Tangent and Log sigmoid activation function and Levenberg-Marquardt algorithm had better performance, so that RMSE, NS and R2 were 2.867, 0.849 and 0.924, respectively. Results indicate that until 2099, amount of monthly mean temperature under RCP 4.5 emission scenario will be increased by 3 (˙C) and the highest increase is predicted for August by 3.9 (˙C) and a lower increase in April by 1.8 (˙C). The results also showed considerable increase of precipitation for June to November and noticeable decrease for March and May months. However, no change occure in annaul scale (inter-annual).
Hossein Salmani, Vahed Berdi Sheikh, Abdolrassoul Salman Mahiny, Majid Ownegh, Abolhasan Fathabadi,
Volume 10, Issue 19 (5-2019)
Volume 10, Issue 19 (5-2019)
Abstract
Given the importance of climate change issue, consideration of the climate and hydrological data trend has become very important. This study aimed to evaluate the effectiveness of different methods to remove the effect of autocorrelation in assessing the trend of parameters such as temperature, precipitation and discharge of the Eastern Gorganrood Basin, Golestan Province. For this purpose, in addition to conventional Mann-Kendall test, nonparametric tests like MK-PW, MK-TFPW, MK-VCA, MK-SA and MK-HSA were used for trend analysis. As well as determining the slope of the trend line and identifying the jump points, San slope estimator and Mann-Kendall sequence (SQMK) were used. The results showed that the values of temperature and precipitation at all stations uptrend and this was significant for temperature in Lazoreh stations and Ramian. Also, discharge flow in Arazkuseh station had significant descending trend. Using nonparametric tests decreased Z statistic. In most stations, MK-PW method had the minimum significance and MK-TFPW and MK-VCA methods showed better performance and reduced the risk of Type I error. In the station where there was no significance in the first and higher order autocorrelation, MK-VCA and MK-PW methods provided similar results. MK-TFPW showed a different behavior due to calculating the autocorrelation after the removal process. Considering of turning points in Mann-Kendall sequence test indicated that at Arazkuseh, Ramiyan, Nodeh, Tamar and Ghazaghli stations, the begining point of trend was related to the years 2000, 1987, 1988, 2001 and 2001 and only in 2 stations including Tamar and Ghazaghli, it was significant in the years 2006 and 2005 (at 5%). In all of the stations, temperature had an abrupt change point of different trend and the time of increasing temperature was equal to the point jump of discharge decreasing trend in 1993 in Arazkuseh station.
Dr Zahra Shirmohammadi-Aliakbarkhani, Dr Seyed Farhad Saberali, Dr Mansoureh Kouhi,
Volume 11, Issue 22 (10-2020)
Volume 11, Issue 22 (10-2020)
Abstract
One of the most fundamental processes, which influence climate and weather, both global and local scales that a fact which gives it the status of agriculture, is Evapotranspiration (ET). In irrigation and water resources management, ecosystem modelers, environmental assessment and solar energy system, accurate assessment of evapotranspiration is essential. Potential evapotranspiration (ET) has commonly applied to calculate the actual evapotranspiration, which was difficult to estimate by lysimeter measurement and water balance approach under field conditions. Until now, many methods have reported to estimating ET, however, due to the availability of the observed data, it is difficult to choose the optimal method. In this study, to determination of the best potential evapotranspiration method for the Khorasan Razavi Province, three temperature-based methods, Hargreaves–Samani (HS), Hamon (HAM) and Linacre (LIN) and five radiation-based methods, Jensen-Haise (JH), Makkink (MAK), McGuinness and Bordne (MB), Abtew (ABT), and Priestley–Taylor (PT), were compared with PM at yearly scale, using long-term (11-67 years) data from 13 meteorology stations. Indicators, viz. The correlation coefficient (CC), Relative bias (BIAS), normalized root mean squared error (NRMSE) and Relative error(Re) were used to evaluate the performance of ET estimations by the above-mentioned eight methods. The results showed that the performance of the methods in ET estimation varied among regions; ETLIN overestimated ET, while others underestimated. In Dargaz and Golmakan, ETHS yielded similar estimations to that of ETPM, while, in Torbate-jam and Khaf, ETLIN showed better performances. Also in Mashhad and Gonabad, ETHAM, in Neyshabour, ETJH and in Torbat-e Heydarieh, ETABT showed better performances. But in Fariman, Kashmar, Sabzevar, Sarakhs and Quchan, all methods showed poor performance. It indicated that ETPM is acceptable for ET simulation for the yearly timescale in these areas.
Elham Mahmudzadeh, Sedigheh Anvari,
Volume 12, Issue 24 (9-2021)
Volume 12, Issue 24 (9-2021)
Abstract
Extended Abstract
Introduction and Objective: The accurate estimation of actual evapotranspiration (ETA), i.e. crop water requirements, is an important issue for irrigation water allocation at fields and improving water efficiency. In the present study, ETA for Sorghum crop was estimated using surface energy balance algorithm for land (SEBAL) and by employing Landsat8 satellite images; meteorological data and DEM map and then compared with the measured values of lysimeter.
Material and Methods: For carrying out the sensitivity analysis, the key parameters of SEBAL algorithm was altered at the range of ±10 , ±20 , ±30 , ±40 , ±50 and resultantly the variation of ETA corresponding to decrease and increase of these parameters was investigated. The changes of key parameters on the days of 206, 238 and 254, based on Julian days calendar, were evaluated at eight points with different vegetation densities. Albedo parameters have moderate to high sensitivity, so that the albedo parameter has medium and high sensitivity in the areas with ET> 10 and ET <10, respectively.
Results: The results of SEBAL showed the value of accuracy indices were acceptable at a significant level of 95%, while compared with those of lysimeter measurements. The results of the sensitivity analysis also showed the surface temperature and input shortwave radiation are more sensitive especially at the areas having low ETA values. The wind speed and
Conclusion: In general, in addition to high economic efficiency compared to other conventional methods, the SEBAL algorithm has a good efficiency in estimating ET. Also, performing sensitivity analysis and determining the key input parameters, while increasing the accuracy of measuring those parameters, can greatly improve the modeling results.
Introduction and Objective: The accurate estimation of actual evapotranspiration (ETA), i.e. crop water requirements, is an important issue for irrigation water allocation at fields and improving water efficiency. In the present study, ETA for Sorghum crop was estimated using surface energy balance algorithm for land (SEBAL) and by employing Landsat8 satellite images; meteorological data and DEM map and then compared with the measured values of lysimeter.
Material and Methods: For carrying out the sensitivity analysis, the key parameters of SEBAL algorithm was altered at the range of ±10 , ±20 , ±30 , ±40 , ±50 and resultantly the variation of ETA corresponding to decrease and increase of these parameters was investigated. The changes of key parameters on the days of 206, 238 and 254, based on Julian days calendar, were evaluated at eight points with different vegetation densities. Albedo parameters have moderate to high sensitivity, so that the albedo parameter has medium and high sensitivity in the areas with ET> 10 and ET <10, respectively.
Results: The results of SEBAL showed the value of accuracy indices were acceptable at a significant level of 95%, while compared with those of lysimeter measurements. The results of the sensitivity analysis also showed the surface temperature and input shortwave radiation are more sensitive especially at the areas having low ETA values. The wind speed and
Conclusion: In general, in addition to high economic efficiency compared to other conventional methods, the SEBAL algorithm has a good efficiency in estimating ET. Also, performing sensitivity analysis and determining the key input parameters, while increasing the accuracy of measuring those parameters, can greatly improve the modeling results.
Kameleh Aghajanloo, Hossein Fathi Almalou,
Volume 15, Issue 2 (10-2024)
Volume 15, Issue 2 (10-2024)
Abstract
Extended Abstract
Background: Climate changes can significantly affect socioeconomic activities and quality of life, especially in countries that are currently facing water tensions. Climate models play a key role in assessing the impact of climate change and developing adaptation and resilience strategies. Considering the importance of food security and then water security in resilience against climate change, as well as the significant contribution of Mazandaran province in the production of agricultural products and food supply of the country, it is very important to examine the drought situation of this province and its climate change process. In this study, therefore, the wet and dry durations in the 20-year base period of Mazandaran province were evaluated using the standardized standard precipitation index. Then, projections of temperature and precipitation at the local scale were made in future periods using the five global circulation models (GCM) available in phase 6 of the climate output project (CMIP6) under three scenarios SSP2.6, SSP4.5, and SSP8.5.
Methods: In this research, six meteorological stations, viz. Ramsar, Noshahr, Siyabisheh, Babolsar, and Qarakhil, were selected due to the coverage of the most statistical years and suitable spatial distribution in the region. Time series of precipitation and daily maximum/minimum temperatures were collected for six selected stations in the region with a base statistical period of 20 years (January 1999 to December 2018). After ensuring the quality of the data, the trend of their changes was analyzed using Mann-Kendall and age slope tests. Standard precipitation index values were calculated and evaluated in different intervals. Finally, large-scale data from five general circulation models (ACCESS-CM2, CanESM2, CNRM-CM-6-1, MRI-ESM2-0, and NESM3) were downscaled by the LARS-WG6 climate generator. Thus, predictions of seasonal and annual changes for Tmax, Tmin, and precipitation in two future periods (2040-2060 and 2080-2100) were made using the average of selected GCMs.
Results: As a result of the statistical analysis of the above data, minimum and maximum temperatures increased and precipitation decreased during the standard period, but no significant trend was found at the 0.05 level. The analysis also shows that the state's worst droughts occurred in 2007, 2009, late 2011, early 2012, and 2018, with SPI values below-1.0 at stations. The wettest years in the region are 2004-2006 and 2017. The frequency of wet periods is higher than dry periods for all seasons in the region. In the microscale aspect, the results confirmed the ability of the LARS-WG6 model to simulate temperature more accurately than local precipitation, with more precipitation errors in wet seasons. Among these results, the lowest value of the correlation coefficient (0.941) was obtained for maximum and minimum monthly temperatures, which means that the squared error value is between 1.05 and 3.82°C. The largest differences between modeled precipitation and observations occurred during the rainy season when GCMs underestimated precipitation. The analysis of future climate changes revealed that all five GCMs indicated a continued increase in temperature in the study area. However, differences in the magnitude of signal changes were observed in different GCMs and SSPs. These predicted temperature changes are significant and reliable because all models agree on the direction of temperature change across the province. Overall, the increase in average Tmax and Tmin is significant in SSP8.5 compared to SSP4.5 because of no reduction in greenhouse gas emissions. Thus, the largest mean changes in the SSP8.5 scenario for 2050 and 2090 at the provincial level for maximum temperature are increases of 2.64 and 4.72 °C during spring, and the largest mean changes in minimum temperature were calculated to rise to 2.97 and 4.83 °C during autumn. Future changes in precipitation proved to be more complex and unpredictable than temperature. The largest incremental changes in local precipitation in 2090 under the SSP4.5 (40.5%) and SSP8.5 (51.9%)scenarios were shown by the Ramsar station. In the study area, it is projected 38.86% and 43.95% on average in the feature period (2040-2060) and 45.11% and 65.94% in the feature period (2080-2100) under SSP4.5 and SSP8.5. Thus, the results of this study show that the shift toward wetter seasons at the provincial level in the future will cause more precipitation in the western part of the province.
Conclusion: Examining the drought situation in the base period shows the occurrence of periods with near-normal rainfall in longer intervals, and Sari and Qarakhail stations report more drought than stations in the west of the province. All GCM forecasting models presented the same results in significant warming trends in this province. For precipitation, however, it is suggested to investigate other models in this regard due to the sensitivity of the issue and the uncertainties involved in the issue. Considering the effect of changes in temperature parameters and precipitation on water resources and floods in the region, it is necessary to adopt suitable management strategies for the future to be resilient against climate change.
Background: Climate changes can significantly affect socioeconomic activities and quality of life, especially in countries that are currently facing water tensions. Climate models play a key role in assessing the impact of climate change and developing adaptation and resilience strategies. Considering the importance of food security and then water security in resilience against climate change, as well as the significant contribution of Mazandaran province in the production of agricultural products and food supply of the country, it is very important to examine the drought situation of this province and its climate change process. In this study, therefore, the wet and dry durations in the 20-year base period of Mazandaran province were evaluated using the standardized standard precipitation index. Then, projections of temperature and precipitation at the local scale were made in future periods using the five global circulation models (GCM) available in phase 6 of the climate output project (CMIP6) under three scenarios SSP2.6, SSP4.5, and SSP8.5.
Methods: In this research, six meteorological stations, viz. Ramsar, Noshahr, Siyabisheh, Babolsar, and Qarakhil, were selected due to the coverage of the most statistical years and suitable spatial distribution in the region. Time series of precipitation and daily maximum/minimum temperatures were collected for six selected stations in the region with a base statistical period of 20 years (January 1999 to December 2018). After ensuring the quality of the data, the trend of their changes was analyzed using Mann-Kendall and age slope tests. Standard precipitation index values were calculated and evaluated in different intervals. Finally, large-scale data from five general circulation models (ACCESS-CM2, CanESM2, CNRM-CM-6-1, MRI-ESM2-0, and NESM3) were downscaled by the LARS-WG6 climate generator. Thus, predictions of seasonal and annual changes for Tmax, Tmin, and precipitation in two future periods (2040-2060 and 2080-2100) were made using the average of selected GCMs.
Conclusion: Examining the drought situation in the base period shows the occurrence of periods with near-normal rainfall in longer intervals, and Sari and Qarakhail stations report more drought than stations in the west of the province. All GCM forecasting models presented the same results in significant warming trends in this province. For precipitation, however, it is suggested to investigate other models in this regard due to the sensitivity of the issue and the uncertainties involved in the issue. Considering the effect of changes in temperature parameters and precipitation on water resources and floods in the region, it is necessary to adopt suitable management strategies for the future to be resilient against climate change.
Mis Ameneh Yahyavi Dizaj, Dr. Tayebeh Akbari Azirani, Dr. Khadijeh Javan,
Volume 16, Issue 1 (2-2025)
Volume 16, Issue 1 (2-2025)
Abstract
Introduction and Objective: Climate change as an effective phenomenon on temperature variables, precipitation and reference evapotranspiration; In this century, it has had significant negative effects in different regions. Since the reference evapotranspiration represents the climatic conditions of different regions, therefore, knowing its changes for water resources management and agricultural planning, especially in Urmia synoptic station, which is dependent on agriculture, is very important. Therefore, in order to know the average changes of minimum and maximum temperature and reference evapotranspiration in the study area, the effect of climate change on reference evapotranspiration at Urmia station was evaluated using CMIP6 models.
Material and Methods: In order to predict the mentioned variables, observational data of Urmia synoptic station and CESM2 and IPSL-CM6A-LR models of the sixth report were used. Microscaling with the LS method during the base period (1975-2014) and two future periods (2020-2059 and 2060-2099); It was done under optimistic and pessimistic scenarios. R2 and MAE measures were used to validate climate models. Finally, the Thornthwaite method was used to calculate reference evapotranspiration.
Results: The results indicate that the average minimum temperature changes in Urmia synoptic station in the near and far future will be between 0.03 to 3.69 and 0.55 to 5.59 ℃ and the maximum temperature will be between 0.25 to 3.84 and 0.55 to 5.01 ℃ compared to the base period and incremental observations.
Conclusion: The values of the average changes in reference evapotranspiration will increase between 0.01 to 21.08 and 0.08 to 29.86 mm/month in the near future and in the far future, which is a serious threat to the studied area.
Material and Methods: In order to predict the mentioned variables, observational data of Urmia synoptic station and CESM2 and IPSL-CM6A-LR models of the sixth report were used. Microscaling with the LS method during the base period (1975-2014) and two future periods (2020-2059 and 2060-2099); It was done under optimistic and pessimistic scenarios. R2 and MAE measures were used to validate climate models. Finally, the Thornthwaite method was used to calculate reference evapotranspiration.
Results: The results indicate that the average minimum temperature changes in Urmia synoptic station in the near and far future will be between 0.03 to 3.69 and 0.55 to 5.59 ℃ and the maximum temperature will be between 0.25 to 3.84 and 0.55 to 5.01 ℃ compared to the base period and incremental observations.
Conclusion: The values of the average changes in reference evapotranspiration will increase between 0.01 to 21.08 and 0.08 to 29.86 mm/month in the near future and in the far future, which is a serious threat to the studied area.
Mr Aboozar Sadeghi, Dr Bromand Salahi, Mrs Roghaye Azari Sanjebad,
Volume 16, Issue 1 (2-2025)
Volume 16, Issue 1 (2-2025)
Abstract
Extended Abstract
Introduction and Objectives: The phenomenon of climate change and its effects and consequences has become a challenging issue for managers and planners, especially for water resources. Currently, climate change has attracted the attention of scientists due to its effects on human societies. Snow plays an important role in the protection of biodiversity and the changes in the amount of snow cover affect animal and plant life as well as the structure of ecosystems. Snow cover is very important in mountainous areas. Since snow is considered solid water, it is an important source for providing drinking water. Because snow cover contains a lot of air, it is a weak conductor of heat, so the snow cover can protect agricultural products and trees from extreme cold. The current research aims to investigate the changes in snow cover concerning the components of land surface temperature, evapotranspiration, and vegetation cover in the Aras Basin using MODIS sensor data products in annual, seasonal, and monthly periods.
Materials and Methods: The studied area is the Aras Basin, which is considered a part of the sub-basin of the western Caspian Lake and forms the political border between the countries of Azerbaijan, Iran, Turkey, and Armenia. In this research, Terra satellite images were used to calculate snow cover, land surface temperature, vegetation cover, and evapotranspiration. In this way, for each of the mentioned variables, the annual average, monthly average, and seasonal average were calculated based on solar date. Daily products of snow, vegetation, surface temperature, and 8-day evapotranspiration product of Terra satellite were used. Finally, the images were transferred to the ArcMap 10.8 environment for calculation. To calculate the averages of the studied variables in the period of 2011-2018, using coding in Google Earth Engine, 8644, 8642, 8325, and 1058 images were processed for snow cover, land surface temperature, vegetation cover, and evapotranspiration, respectively.
Results: The results showed that in 2000-1401, the hottest and highest temperatures were in 2000, 2001, and 2014, respectively, with the average maximum temperature of 42, 40, and 40 degrees Celsius. 2017 was the coldest year of the studied statistical period with an average maximum temperature of 35 degrees Celsius and minimum temperature of 1 degree Celsius. The highest average amount of greenness was related to the years 2019 and 2021 with a value of 0.44 and the lowest average amount of greenness was related to the years 2007 and 2003 with a value of 0.34. In the studied years, 2019 had the lowest annual average of evapotranspiration, and 2022 had the highest annual average of evapotranspiration. The transpiration evaporation in 2018 was at the highest level, 20.96, and at the lowest level, 3.57 kg/m3. In 2022, evapotranspiration was 37.42 in the highest state and 2.60 kg/m3 in the lowest state. In all the studied years, the southeastern and northern parts of the studied basin have the highest average evapotranspiration. In 2018, the maximum average land surface temperature was equal to 37.12 and the minimum was equal to 0.14 degrees Celsius. In 2022, the maximum temperature of the land surface temperature was 39.80 and its minimum was 5.66 degrees Celsius. As can be seen, there has been a direct relationship between temperature and evapotranspiration in these years. During the years 2000-2022, the average NDSI in 2000 had the lowest value (17.31) and in 2017 the highest value (26.23). In all the studied years, the most snow-bearing areas were the high-altitude areas located in the southern, southeastern, and southwestern parts of the Aras Basin.
Conclusion: The results of the survey of the surface temperature maps showed that the years 2000 and 2001 started with an average maximum temperature of 42.37 and 40.20 degrees Celsius and continued with a decrease in the average maximum temperature. In 2020 and 2021, the maximum temperature reached 39 degrees Celsius, after which evapotranspiration also changed according to the land surface temperature. The trend of changes in the vegetation cover of Aras Basin generally shows an increase in vegetation cover during the 22 years, but the trend of changes in the snow cover has been a slight decrease. Higher temperatures are seen in the low and flat parts of the northeast and northwest of the Aras Basin, and lower temperatures are seen in the high areas of the southeast and west of the basin. Evapotranspiration is often observed in the northern and southeastern parts of the Aras Basin, which has more snow cover. Although these parts have higher altitudes and lower temperatures than other regions, they have more evapotranspiration. Winter and autumn are the snowy seasons of the study area. The highest amount of snow cover was in February with an area of 37234.32 square kilometers and the lowest was in August with 4.71 square meters. The high areas with snow cover (southeast, west, and north of the basin) had snow cover even in the years when the surface average snow cover was at its lowest
Introduction and Objectives: The phenomenon of climate change and its effects and consequences has become a challenging issue for managers and planners, especially for water resources. Currently, climate change has attracted the attention of scientists due to its effects on human societies. Snow plays an important role in the protection of biodiversity and the changes in the amount of snow cover affect animal and plant life as well as the structure of ecosystems. Snow cover is very important in mountainous areas. Since snow is considered solid water, it is an important source for providing drinking water. Because snow cover contains a lot of air, it is a weak conductor of heat, so the snow cover can protect agricultural products and trees from extreme cold. The current research aims to investigate the changes in snow cover concerning the components of land surface temperature, evapotranspiration, and vegetation cover in the Aras Basin using MODIS sensor data products in annual, seasonal, and monthly periods.
Materials and Methods: The studied area is the Aras Basin, which is considered a part of the sub-basin of the western Caspian Lake and forms the political border between the countries of Azerbaijan, Iran, Turkey, and Armenia. In this research, Terra satellite images were used to calculate snow cover, land surface temperature, vegetation cover, and evapotranspiration. In this way, for each of the mentioned variables, the annual average, monthly average, and seasonal average were calculated based on solar date. Daily products of snow, vegetation, surface temperature, and 8-day evapotranspiration product of Terra satellite were used. Finally, the images were transferred to the ArcMap 10.8 environment for calculation. To calculate the averages of the studied variables in the period of 2011-2018, using coding in Google Earth Engine, 8644, 8642, 8325, and 1058 images were processed for snow cover, land surface temperature, vegetation cover, and evapotranspiration, respectively.
Results: The results showed that in 2000-1401, the hottest and highest temperatures were in 2000, 2001, and 2014, respectively, with the average maximum temperature of 42, 40, and 40 degrees Celsius. 2017 was the coldest year of the studied statistical period with an average maximum temperature of 35 degrees Celsius and minimum temperature of 1 degree Celsius. The highest average amount of greenness was related to the years 2019 and 2021 with a value of 0.44 and the lowest average amount of greenness was related to the years 2007 and 2003 with a value of 0.34. In the studied years, 2019 had the lowest annual average of evapotranspiration, and 2022 had the highest annual average of evapotranspiration. The transpiration evaporation in 2018 was at the highest level, 20.96, and at the lowest level, 3.57 kg/m3. In 2022, evapotranspiration was 37.42 in the highest state and 2.60 kg/m3 in the lowest state. In all the studied years, the southeastern and northern parts of the studied basin have the highest average evapotranspiration. In 2018, the maximum average land surface temperature was equal to 37.12 and the minimum was equal to 0.14 degrees Celsius. In 2022, the maximum temperature of the land surface temperature was 39.80 and its minimum was 5.66 degrees Celsius. As can be seen, there has been a direct relationship between temperature and evapotranspiration in these years. During the years 2000-2022, the average NDSI in 2000 had the lowest value (17.31) and in 2017 the highest value (26.23). In all the studied years, the most snow-bearing areas were the high-altitude areas located in the southern, southeastern, and southwestern parts of the Aras Basin.
Conclusion: The results of the survey of the surface temperature maps showed that the years 2000 and 2001 started with an average maximum temperature of 42.37 and 40.20 degrees Celsius and continued with a decrease in the average maximum temperature. In 2020 and 2021, the maximum temperature reached 39 degrees Celsius, after which evapotranspiration also changed according to the land surface temperature. The trend of changes in the vegetation cover of Aras Basin generally shows an increase in vegetation cover during the 22 years, but the trend of changes in the snow cover has been a slight decrease. Higher temperatures are seen in the low and flat parts of the northeast and northwest of the Aras Basin, and lower temperatures are seen in the high areas of the southeast and west of the basin. Evapotranspiration is often observed in the northern and southeastern parts of the Aras Basin, which has more snow cover. Although these parts have higher altitudes and lower temperatures than other regions, they have more evapotranspiration. Winter and autumn are the snowy seasons of the study area. The highest amount of snow cover was in February with an area of 37234.32 square kilometers and the lowest was in August with 4.71 square meters. The high areas with snow cover (southeast, west, and north of the basin) had snow cover even in the years when the surface average snow cover was at its lowest
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