Monitoring of Caspian Sea level change affected by atmospheric parameters using remote sensing data

Monitoring of Caspian Sea level change affected by atmospheric parameters using remote sensing data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Monitoring of Caspian Sea level change affected by atmospheric parameters using remote sensing data morteza sharif, Ata Abdollahi, Maedeh Sadat Hosseini ‎ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1666521/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Caspian Sea level (CSL) fluctuations are driven by reciprocal hydro-meteorological processes that extend not only over the entire catchment area but also far beyond. According to the research literature, CSL has dropped approximately 3 m in the short-term (1929–1977). In this study, change in precipitation, runoff, and evaporation in the catchment area, river discharge, sea surface temperature (SST), and also CSL change were investigated over 40-year decades. Study results reveal two major change in the increasing trend of SST anomalies. With an increasing trend of 0.03 at the CS level, the results indicate an increase in temperature of more than 1.5°C during the study period, which can be a response to the increase in air and land surface temperature on a regional and global scale. The decline in river discharge levels has also been significant in recent decades. Reconstruction of long-term change in CSL with daily water level data between 1981–2020 through fluxes also showed that the average sea level rise was about 20 cm/yr in the 1981–1995 period and a decrease of 6 cm/yr during the 1996–2020 period was well visible in the results of the water balance equation. Moreover, according to the observed peak values of the four parameters precipitation, evaporation, runoff, and SST, the trend of increasing sea surface temperature and decreasing precipitation (with a slope of -1.8 mm/40 year) is more consistent with CSL change. Caspian Sea level hydrological Volga climate change Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction Many researchers believe that change in the water level of landlocked seas and inland lakes can disrupt the interactions of various ecosystems associated with them. This sea level change can be due to the water balance of inland seas (or lakes) or other reasons such as rising SST (Cook et al., 2014 ; Wang et al., 2018 ), land temperature change, and global climate change. One of these landlocked seas is the Caspian Sea (CS) (Naderi Beni et al., 2013 ; Nandini Weiss et al., 2020 ). Change in the water level of enclosed seas can pose a serious threat to plant ecosystems, wetlands that depend on the water flowing from these lakes, and consequently a series of other hazards that threaten wildlife and aquatic life in these wetlands. Each of these threats will trigger a chain of other challenges. Many countries are looking for consistent and accurate information that would enable them to reduce these threats. The impact of these change on the surface of the enclosed seas will have far-reaching implications for coasts and coastal ports that could indirectly affect the livelihoods and economies of millions of people (Prange et al., 2020 ; Nandini Weiss et al., 2020 ). This is a very serious warning signal forecasting a catastrophic decline in the CSL, as one of the most important enclosed lakes in the world. This decline has serious implications for all the countries around it. Previous studies have shown that rising surface temperatures during the 21st century will cause water to evaporate on landlocked seas and lakes (Cook et al., 2014 ; Dai et al., 2018 ; Wang et al., 2018 ). This can cause a drop in the water level of enclosed seas such as CS (K Arpe et al., 2011 ), which is intensified by decreasing precipitation ( Dai et al., 2018 ). Natural lakes that do not have an outflow stream are more sensitive to climate change (Prange et al., 2020 ) because their water level is determined by the balance of precipitation and runoff discharge into the lake with the evaporation of the lake water. The drying up of the internal continental spaces due to climate is recognized as an important problem in terms of freshwater scarcity (Naderi Beni et al., 2013 ). Meanwhile, sea surface temperature is one of the most important parameters for understanding oceanographic processes and climate (López García, 2020 ). Hence, it is defined by the Global Climate Observing System (GCOS) as one of the basic Essential Climate Variables (ECV) that is required for the systematic observation of climate change on Earth ( GCOS, 2011 ; Bojinski et al., 2014 ; Brewin et al., 2018 ). In the current context of global warming, the availability of accurate sea surface temperature (SST) time series is invaluable for monitoring and predicting ocean conditions. These images can provide a comprehensive view of the changing trend of water temperature in enclosed seas. Based on previous studies and the observed declining trend of CS water level as one of the most important enclosed seas, it is predicted that between 1993 and 2020, by the end of the 21st century, its water level will decrease by 9–18 m in moderate to high conditions (Prange et al., 2020 ). This change result from a significant increase in evaporation (Chen et al., 2017 ; A. Shirvani et al., 2020 ) overexploitation, and a reduction in the amount of water entering the CS due to damming projects in some upstream countries, such as Russia, which is not compensated by increasing water flow from rivers or precipitation (K. Arpe et al., 2012 ). According to new forecasts, the decline in CSL in the 21st century will be almost twice the predictions of previous climate models (Prange et al., 2020 ). A decrease of 9–18 m means that a large part of CS in the north, areas located in the Turkmen Sahara area in the southeast, and most of the coastal areas in the central and southern parts of CS will come above the sea level (Nandini Weiss et al., 2020 ; Prange et al., 2020 ). In general, a 9 m decrease in CSL means a 23% drop, while an 18 m decrease is equal to a 34% drop (Prange et al., 2020 ). These change can have serious consequences that threaten the environment. Despite the disconcerting conditions of CSL change as well as the tensions created on its surrounding ecosystems (e.g. wetlands) following the declining water levels, there is still no comprehensive plan to reduce future crises among the countries bordering the CS. As this risk continues, a chain of serious risks, including the disruption of plant ecosystems threatens the related water-dependent wetland ecosystems (such as the internationally renowned Anzali Wetland, Miankaleh Wetland, and Amir - kalaye wetland in Iran and wetlands in the northern part of the Volga Delta in Russia), the wildlife around CS-dependent wetlands, the coastline, coastal ports, and the life of millions of people indirectly dependent on the CS (Prange et al., 2020 ). On the other hand, the decline of CSL will have geopolitical consequences and will also affect the economy of the whole region. Likewise, transportation in and out of the CS, which connects to the global ocean via the Volga-Baltic waterway and the Volga-Don canal, will be severely affected by the declining water level of the CS (Prange et al., 2020 ). This is important for maritime trade and maritime access. On the other hand, declining sea levels will seriously damage coastal infrastructure, including ports. Maritime jurisdiction and exclusive fishing rights will change. These conditions may also lead to growing international political tensions over the reallocation of fishing territories or national water harvesting and desalination programs envisioned to help meet the growing demand for the agricultural, industrial and household sectors in areas under water stress. Therefore, given the Sustainable Development Goals of the United Nations or Intergovernmental Science, the policy platform on biodiversity and ecosystem services, there is a dire need for a global committee that can formulate and coordinate mitigation strategies. This is because the environmental, economic and political effects of declining CS levels will be precarious. Due to the sensitive conditions of the enclosed seas, any imbalance between the water inflow (precipitation and runoff) and outflow (evaporation) will lead to serious challenges and change in the water level of these seas or inland lakes. All three mentioned inflow and outflow fluxes are the most important control parameters of sea level, which is determined using the following equation: dV/dt = R-A (E-P) (Crétaux & Birkett, 2006 ; Chen, Pekker et al., 2017 ). In this equation, R represents runoff, A is the area of the lake, and E and P are evaporation and precipitation, respectively. However, some studies have referred to the snow water equivalent (SWE) and soil moisture (SM) parameters for hydrological estimates (Saxe et al., 2021 ). This equation can estimate the share of the annual, monthly, or daily balance of a catchment. Recognizing these precise models is essential for future sea-level change as a scientific basis for adjustment, adaptation, and sustainability prediction strategies for different ecosystems. The predicted effects of declining CS water levels are likely to lead to a major reorganization of the surrounding ecosystems. This decline threatens its unique environment that has evolved over millions of years. Therefore, in this study, first, the trend of changes in the CSL is investigated through the water balance average (dV/dt = R-A (E-P)). The obtained results are then compared with anomali altimetry data. Next, the effect of six important parameters on changes in water level decrease, such as changes in sea surface temperature (SST), changes in temperature of the catchment, changes in inflows of streams that flow into the CS, changes in precipitation, runoff and Evaporation of the entire CS catchment is assessed on changes in the CSL. The results of this study provide answers to two important questions: First, how much the parameters of precipitation, runoff, evaporation, catchment surface temperature and SST in the long run affect the changes in the CSL. Second, in the short ten-year periods, which parameters are more in line with changes in the CSL?. Also, the general results of the research will provide a comprehensive view of the changes in the studied parameters during the period 1981 to 2020 in the whole watershed and their relationship with CSL. 2. Study Area CS is the largest enclosed lake in the world, with an approximate area of 422000 km 2 recorded in 1929, which has dropped to 355000 km 2 in 1977 and in 371000 km 2 2009 (Akbari et al., 2020 ). It was divided between Iran and Russia until 1999 (before the collapse of the Soviet Union). However, its political division changed after the collapse of the Soviet Union, which resulted in the creation of new borders with five countries (Iran, Turkmenistan, Azerbaijan, Russia, and Kazakhstan) and a rise in its commercial, military, and economic importance, especially in the field of oil and gas. It stretches 1204 km from north to south, with an average width of 200 to 400 km and a shoreline of about 6500 km (Lebedev & Kostianoy, 2005 ). The total area of the CS catchment is approximately 3.7×10 6 km 2 (Nandini Weiss et al., 2020 ), which is 10 times the area of this sea and about 10% of the enclosed basins worldwide (Chen et al., 2017 ). About 130 rivers flow into the CS (Lebedev, 2012b ; Nematollahi et al., 2020 ), including Babol River, Gorgan River, Haraz, Talar River, Safid River, Tajan, Astara Çay, Shahr Chay, Zarjub, and several other seasonal rivers in Iran, Araz and Kura rivers in Azerbaijan, and the Volga and Ural rivers in Russia (Fig. 1 ). According to previous studies, the highest amount of water flowing into this sea is through the Volga River, estimated at more than 80% (K. Arpe et al., 2000 ; Lebedev, 2012a ; Amin. Shirvani et al., 2020 ). The CS is also one of the most important reserves of gas resources, which seriously endangers its aquatic ecosystem considering the increasing investment by large oil companies in the region and the world. This may have also contributed to the increase in water surface temperature and, consequently, the evaporation of CS water. 3. Materials And Methods In this study, various remote sensing (RS) data were used to examine CSL change (Table 1). The measured data of the altimetry change and the results of previous studies have been used to examine the CS water level in different periods (K Arpe et al., 2011 ; Chen, Pekker, et al., 2017 ; Chen, Wilson, et al., 2017 ; Dolukhanov et al., 2010 ; Kakroodi et al., 2012 , 2014 ; Kazancı et al., 2004 ; Lebedev & Kostianoy, 2005 ; Medvedev et al., 2020 ). Moreover, change were analyzed using the surface runoff data in the CS catchment basin. Topographic data of the CS catchment basin and the drainage data of the rivers flowing into the CS, SST and precipitation change (Muñoz Sabater, 2019 ), the images of the European Reanalysis (ERA5-Land) climate parameters including LST, runoff, and precipitation and evaporation in the sea surface and the entire catchment basin for estimating the CS water balance were obtained from the Google Earth Engine (GEE) platform. ERA5-Land is the latest reanalysis product by the European Centre for Medium-Range Weather Forecasting (ECMWF) (Cao et al., 2020 ). These data provide users with a total of 50 parameters to describe the climate cycle and energy fluxes on land and ocean environments globally and with daily cycles at different spatial resolutions whose latest products are 0.1°× 0.1° (Muñoz-Sabater et al., 2021 ). SST images were only selected for the nighttime to prevent the effects of intense daily heating on the results, especially in spring and summer and in the region near the Mediterranean (Minnett et al., 2019 ). Table 1 shows the data used in this study. Table 1 Study data. Name Product Data Time Resolution Reference precipitation ERA5-Land 1981–2020 HOUR, 0.1 arc degrees https://cds.climate.copernicus.eu Runoff ERA5-Land 1981–2020 HOUR, 0.1 arc degrees https://cds.climate.copernicus.eu Evaporation ERA5-Land 1981–2020 HOUR, 0.1 arc degrees https://cds.climate.copernicus.eu Skin temperature ERA5-Land 1981–2020 HOUR, 0.1 arc degrees https://cds.climate.copernicus.eu Sea Surface Temperature AVHRR 1981–2020 Day, 4 km https://www.ncei.noaa.gov Bathymetry 2019 500 × 500 m www.gebco.net Altimetry TOPEX/Poseidon, Jason-1, 2, & 3 1992–2020 https://ipad.fas.usda.gov River runoff http://daac.ornl.gov/RIVDIS/rivdis.shtm 3.1. Altimetry Data Satellite altimeter observation of change in the Sea Surface Height (SSH) and inland lakes since September 1992, with the launch of the TOPEX/Poseidon altimeter mission (Chen et al., 2017 ), has made it possible to investigate the effects of climate change on the water of inland seas and lakes in different parts of the world. The data has been available online ( http://hydroweb.theia-land.fr ) for the world's largest lakes since 2003 through an automated algorithm. In this study, altimeter measurements of TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3 satellites have been used (Fig. 2 ). The SSH measurement accuracy for these sensors is about ~ 4 cm, which provides sufficient accuracy for research (Lebedev, 2012a ). They also have an orbital repetition period (~ 10 days) that makes it possible to analyze sea-level change (Ji et al., 2016 ). On the other hand, they are the longest series of satellite altimeters (from September 1992 to December 2020) with the possibility of expanding the data in the coming years. Therefore, data related to sensor images that show change with better consistency were received and analyzed. Moreover, considering the historical background of CSL change from the analysis of previous studies by sedimentological measurements during the period 1980–1991 (excluding seasonal and short-term change) (Kakroodi et al., 2012 , 2014 ), a series of satellite altimeter data from CSL change were interconnected in this study to extend the CSL series between 1992 and December 2020. This volume of data provides a more comprehensive view of the trend of CSL change in the long term, including the period from 1980 to December 2020. 3.2. Sea Surface Temperature Data Advanced Very High Resolution Radiometer (AVHRR) was the first infrared sensor in the Tiros-NOAA series of satellites to provide information on global terrestrial and sea surface temperature (López García, 2020 ). The first satellite of the series (Tiros N) was launched in 1978, followed by NOAA-6 in 1980, and finally NOAA-19 in 2009. AVHRR offers images in five spectral bands: Visible range (0.58 0.68µm), near-infrared (0.78–1.10µm), mid-infrared (3.55–3.93µm), and two thermal infrared bands (10.3–11.3 and 11.5–12.5µm) that allow accurate SST recovery. Therefore, considering the long-term trend that these images provide to the public, the trend of change in CS surface temperature were received daily using the AVHRR-v02 product time series between 1981–2020, and then the monthly and annual mean values were calculated. For accurate observation, the CS SST trend was investigated in two ways. First, the Eastern Caspian Basins, the Middle Caspian Basins, and the Western Caspian Basins between 45°83' to 37°26' N were examined separately through regions of interest (ROIs). Then, CS was divided into three parts: Northern Caspian, Middle Caspian, and Southern Caspian, whose SST change were examined using ROIs. The distribution of these samples is shown in Fig. 5 . This was done to more accurately observe the trend of SST change and compare the most common anomalies of the CS water surface. 3.3. Data analysis To calculate the change in the main factors controlling CSL (including evaporation, precipitation, runoff, and SST change), it is necessary to calculate the correlation between the largest variations of each parameter with CSL change. Hence, after obtaining the change of these parameters through software (OriginLab Corp., Northhampton, MA, USA), the investigation of the trend of change, correlation, graph plotting, and trend analysis was undertaken. 4. Results The most important parameters affecting CS change due to variations in the CS surface evaporation include evaporation in the whole catchment, change in rainfall in the whole catchment, runoff change in important rivers such as Volga, Ural, Araz, and Kura as four important rivers in the CS catchment, SST change, catchment LST change, and sea flow change. Groundwater flows, although small, are very important in controlling water level changes. In this study, the results of the most important parameters controlling CSL change were reviewed in the following sections. 4.1. Precipitation pattern in the CS catchment Demonstrating the trend of precipitation change is a key part of investigations of climate change and weather conditions (Randles et al., 2017 ), because it links aspects of the water and energy cycle. However, there might be differences between satellite measurements and field surveys. meanwhile, due to the complex climatic conditions prevailing in CS, the seasonal rainfall cycle varies greatly in different regions. The highest precipitation of the northern part (Volga basin) occurs in summer, the western part (Kura /Turk /Araz region) in spring, and the southern part in autumn and winter (Molavi Arabshahi et al., 2016 ; Nandini Weiss et al., 2020 ). In addition, the eastern shores of the CS have an almost desert climate (Nandini Weiss et al., 2020 ). Therefore, it is necessary to fully study the precipitation catchment in order to observe the precipitation change affecting CS. The northern and eastern coastal areas of the CS are mostly arid with an annual rainfall of 100–300 mm, while the western coasts are semi-arid and the southern and southwestern coasts are wet with an annual rainfall of 400–1200 mm (Stolberg et al., 2006 ). In general, the amount of precipitation decreases from west to east. However, the northern part of the Volga basin, which is the main source of the water balance of CS, has a wet climate, which is mostly in the form of snowfall. The results of precipitation in the whole catchment area showed that 1996, 2005, 2010, 2014, and 2018 had the lowest rainfall, while 1983, 1990, 1993, 1997, 2004 had the highest rainfall in the study period (Fig. 3 a). The precipitation between latitude 37° to 60° N in the CS catchment area was studied and evaluated separately to examine the trend of change. The average trend of precipitation change in 1981–2020 (-1.8 mm/40 year) showed a declining rate in the whole catchment (Fig. 3 a). This decreasing trend was 0.3 mm/15 year in the 1981–1995 period, 6.2 mm/7 year in the 1996–2002 period, -6.6 mm/3 year in the 2003–2005 period, and − 2.5 mm/15 year from 2010 to December 2020 (Fig. 3 a). This variation was observed in the vlga catchment with a decrease − 2.4 mm/40 year, which is more than the entire CS catchment (Fig. 3 b). The highest decreasing trend was seen in the latitudes 40° N (-8.87 mm/40 year) and 37° N (-3.1 mm/40 year). Latitudes 55 to 60° N had the highest rainfall in the CS catchment (Fig. 3 c). These results are consistent with the climatic conditions and topography of the CS catchment. 4.2. LST Variation LST, or land surface temperature derived from satellite data, has become an essential tool for tracking climate change in recent decades. This atmospheric parameter is recognized as one of the essential climate variables (ECVs) by the World Meteorological Organization, which can be used directly to extract global warming trends and anomalies since it is a key parameter in the exchange of energy at the ground level (Reiners et al., 2021 ). Therefore, ERA5-Land Skin Temperature product was used to understand how the surface temperature change in the Caspian catchment. The course of surface temperature change throughout the CS catchment and the northern part of the Volga basin is shown in Fig. 4 . The trend of temperature change in the whole catchment was about 1.4°C (Fig. 4 a). The northern part of the Volga catchment trend of temperature change was 1.2°C (Fig. 4 b). Due to the climatic conditions and geographical location of this part of the CS catchment, the difference in observed surface temperature change is reasonable. Maximum surface temperature in the northern part of the Volga basin was 18.7°C in the 1981–1995 period, 19°C in the 1996–2002 period, 19.4°C in the 2003–2005 period, and 19.8°C in the 2006–2020 period, which indicates an increase of 1.1°C. These change in surface temperature in the second half of the year show a greater difference between 2003–2020 than in 1981–2002, reaching the highest difference (0.5-1°C) between October and the end of December (Fig. 4 c). Although the first half of the year was colder than other periods during the 2003–2005 period, the temperature increased from July to the end of December. In addition, change in the catchment surface temperature in the four periods with change in the CSL indicate a greater temperature slope from 1996 to 2002 and from 2003 to 2005 (Fig. 4 a). This discrepancy may be due to more data in each time period. 4.3. Caspian Sea surface temperature In this study, the trend of SST was investigated using ROIs from the east, west, and central coasts of the CS, as well as three parts of the northern, central, and southern Caspian. This was done to observe the rate of increase in SST. The measurement results in both methods show the average annual SST has an increasing trend (0.03°C/yr) between 1981 and December 2020 (Fig. 6 a). In 1986, 1992, 1996, 2003, 2009, and 2014, SST was at its lowest, whereas in 1981, 1991, 2000, and 2007 it was at its highest. The results showed that SST conditions are affected by latitude that create a higher temperature in the southern Caspian than in the northern Caspian. Moreover, the eastern coast shows a higher average temperature and a higher upward trend in the study period than in the western coast and the middle strip (Fig. 6 b, c). These temperature change are more common on the southeastern shores of the CS, which could be due to the weather conditions in Turkmenistan. The SST data indicates a further increase in SST in the central Caspian with a slope of 0.04°C/yr (Fig. 6 c). This is consistent with Abdolazim and Gholamreza ( 2017 ). The average temperature observed in the CSL decrease and increase periods and the mean temperature during the entire research period (1981 − 1920) are shown in Fig. 5 e. The mean temperature was about 14.8°C in the 1981–1995 period (positive water level period) (sea Fig. 5 a), 15.7°C in the 1996–2002 period (sea Fig. 5 b), 15.8°C in the 2003–2005 period (sea Fig. 5 c), and 15.9°C in the 2006–2020 period (negative water level period) (sea Fig. 5 d), which indicates an increase of about 1.1°C. Moreover, this shows the expansion of temperature increase to the Middle Caspian Basin, which indicates a significant change in the temperature regime of this sea (Fig. 5 ). The average temperature of about 20°C/yr during the 2006–2020 period and its stabilization in the Middle Caspian during the past 15 years can be one of the main reasons for the increase in evaporation and the declining water level (Fig. 5 d). This increase in temperature can be affected by several important factors. However, some studies argue that these change correlate to the phases of the South Oscillation (El Nino) and the North Atlas (Nav) fluctuations (Ginzburg et al., 2008 ). 4.4. River discharge CS water balance is very sensitive to climatic diversity in the surrounding areas due to its confinement on land. Climate change will cause change in rainfall as well as surface runoff, so its decreasing and increasing trends will have a great impact on CSL. There are three sections with a runoff of < 70 mm/month on the southern shores of the CS, the northern sections of the Volga River basin, and the eastern part of the catchment that forms the Ural River. Azerbaijan, with a runoff rate of 10–30 mm/month, directing the existing runoff to CS through the Araz and Kura rivers, is also considered an important part of the sea catchment. The minimum runoff rate is between 5–20 mm/month on the eastern coast of CS (Fig. 9 , 2 ). Therefore, runoff change and the impact of climate change in the northern part of the CS catchment are more important than in other parts. These geographical conditions have caused the hydrological balance of CS to be affected by the confluence of the Volga River with runoff flowing over 8118 m³/s/yr. However, rivers such as the Kura, Araz, Ural, Sefidrud, Gorgan River, and other rivers that flow into the CS are also important. The trend of change in the water flow of these rivers was prepared according to the available information (Table 1), as presented in Fig. 7 . Reducing the discharge of the Volga River will have a direct effect on CSL reduction. The discharge rate of this river from the beginning of 1897 to the end of 2010 is shown in Fig. 6 c. The discharge of the Volga River decreased significantly from 1925 to 1980, which is directly related to the CS water level. One of the sharpest declines in CSL between 1950 and 1980, reaching about 3 m, is shown in Fig. 8 a. Rainfall data were not available during these years to identify the main reason for this decline in the Volga River discharge. The average discharge of the Volga River in 1982 was 7103 m³/s/yr. According to meteorological data, it was 8664 m³/s/Decade for the 1920s and 7501 m³/s/Decade for the 1950s, during which the Volga River discharge decreased by about 19%. This drop can be due to the construction of many dams, especially in the Volga basin with a capacity of about 223 km³ (Akbari et al., 2020 ). Some studies Georgievsky & Shiklomanov ( 1994 ) have also shown a significant relationship between the decrease in CSL and the increase in the volume of dam reservoirs before the 1980s in the Caspian catchment. It was also observed in 1980 to 1989 (8163 m³/s/Decade), 1990 to 1999 (8454 m³/s/Decade), and 2000 to 2010 (7924 m³/s/Decade 7924), indicating a decrease of 7% compared to the 90s and 80s. The greatest decrease in the Volga River runoff (with a decrease of about 30%) was observed in the 1880s (9309 m 3 /s/Decade) compared to the 1970s (7219 m 3 /s/Decade 7219). However, due to the year-to-year fluctuations of weather conditions in different river basins, it is expected that the true percentage of the Volga share will fluctuate differently. In general, from 1980 to 2010, the flow of the Volga River has experienced a decreasing trend of -151.7 m 3 /s/yr (Fig. 7 c). Although there was not much information about other rivers evaluated in this study, all available data showed a decreasing trend in the discharge of these rivers, namely Gorgan river with − 2.3 m³/s/yr, SefidRud with − 0.9 m³/s/yr, Kura with − 33.5 m³/s/yr, with only Araz River having an increasing trend of 0.02 m³/s/yr (Fig. 7 a, b). These results may indicate one of the main factors in CSL change. Therefore, the river discharge, especially in the Volga River, directly controls the hydrological change in large areas of the CS. These results are also consistent with other research works (K. Arpe et al., 2000 , 2012 ; Ozyavas et al., 2010 ; Roshan et al., 2012 ). 4.5. Change in the Water regime Annual change in rainfall, evaporation, and runoff rates are the main features of the CS hydrological regime. As a result of this hydrological regime, change in CSL lead to change in the sea level and volume (Medvedev et al., 2020 ). However, depending on the topographic characteristics and geographical conditions of the CSL change, the northern parts and the eastern, western, and southern coasts will experience different stress levels. Considering the hydrodynamics in the northern section, the sea retreat will follow a wider range due to the shallow shores. This tension will be significant on the coasts of Kazakhstan and Turkmenistan, especially on the Gulf of Karabakh. In the southern strip of the CS, although the retreat of the sea is less than in other coasts, this will create serious challenges for natural ecosystems such as international wetlands and northern ports of Iran. The amount of CS water balance was examined using P, E, and R fluxes (in cm/Day) obtained through daily images and the trend of parameters affecting CSL in a period of 40 years (1981 − 1920). Figure 8 shows the trend of three parameters (P, E, R). A normal increasing trend of precipitation and runoff was observed from 1981 to July 1995. This slope of change was lower in the period from August 1995 to December 2002 compared to 1981–1995. The highest change trend was observed in the whole CS catchment from 2006 to December 2020 for all three parameters. Change in precipitation, runoff, and evaporation in the whole catchment show that the greatest change took place in the eastern and northern parts of the CS catchment. These change, which have a significant effect on water balance as well as evaporation, can be seen in Fig. 9 . The CS catchment balance rate in the southern part compared to the eastern coasts and the northern parts of the catchment during the 2010–2020 period shows positive change compared to the 1980s. As shown in Fig. 9 , in the periods 1981–1995, 1996–2002, 2003–2005, and 2006–2020, the mean rainfall was 552.8, 523, 528.9, and 503 mm (Fig. 9 , 1 ), mean runoff was 136, 92.7, 95, 82.5 mm, (Fig. 9 , 2 ), and mean evaporation was 466.6, 465, 480, 476 mm (Fig. 9 , 3 ). These change show that precipitation decreased by 3.4% in the first negative balance period (1996–2002), 4.4% in the second negative balance period (2003–2005), and 9.1% in the third negative balance period (2006–2020) compared to the positive period (1981–1995); while runoff decreased by about 32%, 30%, and 40% in these periods. Meanwhile, evaporation has increased from 0.4–2.8%- 2.1% in the 2003–2020 period. The trend of precipitation change in the catchment area shows that in the coastal strip of CS, the distribution of rainfall in Gilan has become more concentrated in recent years. Whereas in the 1980s, the average monthly rainfall was more than 60 mm in the southern strip of the sea compared to the western and eastern part of CS. These change are consistent with the droughts of the last decade in Iran. The change in the total runoff of the CS catchment can also be seen in Azerbaijan and the southern coastal strip. In the 1981–2020 period, the average runoff changed from 3.8 to 3.2 cm per year, evaporation from − 3.9 to -3.3, and precipitation from 14.2 to 16.8 mm per month. These results indicate a decrease (-0.6 mm) in the runoff, an increase (2.6 mm) in precipitation, and a decrease (-15 mm) in evaporation. High change in CS evaporation can also be a reaction to rising air temperature and surface temperature. These change at different time periods are shown in Fig. 9 ,c. Nandini Weiss et al. ( 2020 ) predicted that CS evaporation would increase by 15–20% by the end of the 21st century. This increase in evaporation for the world's largest lakes is estimated at 16% (Wang et al., 2018 ). 4.6. Sea level change The range of change in CS water level according to sedimentological results was − 17.4 m in 915 (Brückner, 1890 ; Naderi Beni et al., 2013 ), but in -27.78 m on 2020/12/30 obtained through altimetry data. The CSL change between 1980 and 1995 (18 years) there is a suitable condition with an increase of 3.30 m. However, a declining trend of more than 2 m can be observed from July 1995 to December 2020 (25 years) (Fig. 10 ). This sharp decline is more disconcerting than ever, given the phenomenon of climate change as well as the increase in human activities. However, between 2003 and December 2005, there is a short-term upward trend. The balance of the Volga River decreased in the 1950s compared to the 1930s (-15.1%), which shows that it is directly related to the decrease of CS water level from − 26.6 to -28 (about − 2.6) meters. It also experienced an increase (13%) in the 1980s, which could be one of the main reasons for the increase of about 3 m in CSL in the 1980s (Fig. 10 ). Results of CSL change show that major challenges in the region's ecosystem include the increase in CS water consumption by its five neighboring countries, the policy of building dams on rivers flowing into CS (such as the Volga, Ural, Kura, Aras, Gorgan, Babol, and Sefidrud rivers), and global warming ( increased evapotranspiration). Although previous studies have shown many change in the sea level, recent increases in exploitation and human activities can impose more serious risks on the aquatic ecosystem and the environment in a shorter time than before. The average rate of change in CSL (2002–2005) is estimated to be about 6.6 cm per year (Chen et al., 2017 ), which has generally decreased by about 1.5 m (Fig. 10 a). Due to the very dry conditions of the areas around this sea, except for the northern catchment of the Volga and the Urals, it seems that climate change will accelerate the process of change in this sea. 5. Discussion The three principal parameters of precipitation, runoff, and evaporation are the main controlling factors of change in enclosed seas. However, other parameters such as atmospheric currents, the direction of marine winds and cyclones, and the sensible and latent heat fluxes also have a significant effect on change in the water level of enclosed seas. One of the reasons for the CSL change seen so far is climate change affecting its catchment area ( K. Arpe et al., 2011 ; Ibrayev et al., 2010 ; Naderi Beni et al., 2013 ). Furthermore, human factors such as the construction of a dam on the Volga River after the 1940s Ibrayev et al. ( 2010 ) and other rivers that affect the CS water balance are other important factors contributing to these change. 5.1. Caspian Sea catchment balance According to the available data concerning the estimation of the water balance of the CS catchment, the CSL change were compared with the results from the 1981–2020 period. The trend of change in the CS catchment water balance during the 1981–2020 period was about − 1.2 cm/Day, which is very close to the RS data − 1.1 cm/Day (Fig. 11 a). these change show that there is a positive correlation between the three parameters of precipitation, evaporation and runoff in some periods according to different CSL peaks. Temporal change based on the results show four different period, including two short trends between 1995 and 2002, where the level was at the height of -25.85 and − 26.74, respectively, (an 89 cm decrease); a short increasing period between 2003 and July 2005 (from − 26.74 to -26.1) (a 64 cm increase) (Fig. 3 ); followed by the fourth period, where from August 2005 to December 2020 (from − 26.12 to -27.84), the water level of the CS decreased by 1.72 m. Only, Between March 1981 and July 1995, the CSL was rising at a rate of + 5.4 cm/day (Fig. 11 b). The period from 2003 to the end of 2005 showed an increasing trend in the CS level (5.6 cm/Day). However, from 2006 to 2020, the sea level change was much steeper than the water balance change obtained in this study (Fig. 11 b). These results show that the water balance between 1981 and 1995 and 2003 to July 2005 led to a positive CS water balance. Similarly, the water balance was positive from 1996 to 2002 (1.3 cm/Day) and from 2006 to December 2020 (5.8 cm/Day), while the sea level (-2.4 cm/Day) was decreasing (Fig. 11 b). These results indicate that there are factors other than precipitation, evaporation, and runoff influencing the severity of the water-level decline in the 2006–2020 period. The average annual water balance in the 1981–1995 period was 20 cm/yr, which shows a 6 cm decrease compared to the 1996–2020 period (Fig. 12 b). This is consistent with the results in another study Chen et al. ( 2017 ) calculated through the Volga River runoff from 1979 to 2015. The peak increase in CSL change shown by the Gaussian filter in Fig. 12 a also indicates a direct correlation between the three major fluxes of precipitation, runoff, and evaporation with long-term change in CS water levels. The highest CSL change occurred in 1994–1996, followed by a sharp downward trend in 2006, which could be affected by the drought periods between 2000–2009 as one of the most important drought periods in the Caspian Basin. The average SST has increased more than other years during this period (Fig. 6 a). The impact rates of all three parameters of precipitation, evaporation and runoff were calculated separately and in different time periods according to CSL peaks (Table 2). In all four periods, simultaneously with changes in sea level, the highest impact with 49.7, 50.4, 49.6 and 49%, respectively, were related to rainfall and the lowest with rates (12.8, 103, 10.9 and 10%) was related to runoff (Table 2). These results show that as the amount of evaporation increases, the amount of runoff in the catchment area decreases too. During the 2006–2020 period, the highest rate of evaporation and the lowest rate of precipitation and runoff compared to the other three time periods can be among the factors affecting the decreasing water balance. The evaporation change over the entire basin in all four water level periods can be seen in Fig. 9 , 3 . Increasing change in evaporation in the whole basin and the CS surface shows a direct relationship with the increasing temperature of the catchment surface, especially in the northern parts of the Volga basin. Tabel 2 Influence rate of atmospheric parameters of precipitation, evaporation and runoff on the water level of the CS in different periods. Period Precipitation Evaporation Runoff 1981/1/1-1995/7/30 49.7% 37.5% 12.8% 1995/8/1-2002/12//30 50.4% 39.3% 10.3% 2003/1/1-2005/12/30 49.6% 39.5% 10.9% 2006/1/1-2020/12/30 49% 41% 10% 1981/1/1-2020/12/30 49.7% 39.3% 11% 5.2. Atmospheric parametrs on the Caspian Sea The negative effects on CSL change in recent studies indicate an increase in SST and sea evaporation (A. Shirvani et al., 2020 ). According to the results obtained in this study, the rising of the Caspian SST began observed about a decade earlier than change in evaporation in the entire basin (Fig. 13 ). This change can be a response to an increase in ground surface temperature (by about 1.4 ° C/yr) and an increase in air temperature. The most significant CSL change in relation to SST occurred in 1995 (during the 1981–2020 period). Between 1982 and 2020, the average increase in SST was more than 1.5°C. This is consistent with Amin Shirvani et al. ( 2020 ), which showed a rise of about 1.2°C up to 2016. Moreover, the observed SST peak obtained through the Gaussian relation shows that there is a significant correlation between the temperature increase limit in 1995 and the decreasing trend of CSL. This increase in CS temperature eventually led to an increase in evaporation, which has been well documented since 1998 (Fig. 13 ). The rainfall change in the whole CS catchment show a significant relationship with CSL, with a period of positive balance (1981–1995) and a period of negative balance (1996–1996) in the long run (Fig. 13 ). The long-term average of CS catchment rainfall (with a slope of -1.8 mm/yr between 1981 and 2020) was decreasing. Still, it is not possible to provide an accurate estimate of the river flow rates because precipitation and the resulting runoff in different areas such as the river catchment of Volga, Ural, Aras, Kura, Sefidrud, Atrak, Gorgan, and other seasonal rivers face important obstacles such as dams. This may be the reason for severe runoff decline in the catchment area in different periods and especially from 2002 to the end of 2020 (Fig. 9 , 2 ). However, previous studies A. Shirvani ( 2017 ) and A. Shirvani et al. ( 2020 ) between the years 1951 to 2016 also show the lowest annual rainfall change in the southern part of CS, i.e. the coastal stations of Bandar Anzali and Babolsar. The results obtained from the parameters of surface temperature, evaporation in the catchment, and SST also show that evaporation and temperature increase had a greater effect in the CS catchment than precipitation. Considering the evaporation at sea level and the whole catchment is a reaction to global and regional temperature changes in this catchment, SST changes were analyzed in more detail. The results showed two major changes in CS surface temperature in the eastern coasts and the middle strip with an increasing trend of 0.033°C/yr and also in the middle Caspian section compared to the northern and southern areas with an increasing trend of 0.04°C/ yr. These temperature changes are very complex and driven by the atmospheric characteristics affecting CS, which are affected by four different climatic conditions, namely (1) Arctic cold air, (2) temperate humid air masses of the Atlantic Ocean, (3) tropical and subtropical air masses of the Black Sea, and (4) continental dry air masses from the east (Molavi Arabshahi et al., 2016 ; Nandini Weiss et al., 2020 ). The analysis of the increase in SST observed in the east and middle coasts, which was obtained between latitudes 45° 83' N to 37° 26' N (Fig. 6 b), and in the southern and middle Caspian (Fig. 6 -c) show that the SST increase in the eastern coast is one of the most influential drivers of CSL changes. This increase in temperature in the north of the CS catchment could lead to an increase in snowmelt. The trend of increasing SST has also led to an increase in evaporation in the northern and eastern parts of the CS, which had a wider range during the 2010–2020 decade with an evaporation of more than 30 mm/month from 1981 to 1989. Also, the increase in SST between 1996 and the end of 2002 is most in line with the declining CS water level in this period. Whereas, the water balance of the whole catchment is positive (Fig. 11 ). However, these two factors are self-affecting and a response to rising temperatures and surface temperatures. Excessive evaporation, which occurs with increasing SST and consequent increase in LST, has an immediate effect on CSL, but the decrease in water inflow of the Volga and Ural catchment seem to be consistent with the abnormal drop in CSL. These changes are exacerbated by the sharp decline and even in some months of the year the drying up of some seasonal rivers that flow into the CS from Iran and Azerbaijan and the increase in global temperatures. On the other hand, increasing the exploitation of oil and gas fields in different parts of CS ‌ can be another factor contributing to increasing SST. As previous studies K. Arpe et al. ( 2012 )d Arpe & Leroy ( 2007 ) have shown, the decrease in rainfall on the Volga River and its catchment area and the decrease in its inflow into CS are directly related to the decrease in CSL. The drop in rainfall over the volga river basin does not lead to an immediate CSL drop, as the Caspian water balance is primarily regulated by snowmelt north of the catchment and takes several months to reach the CS (K. Arpe et al., 2012 ). This delay is 1–3 months for heavy rainfall in summer, but higher for the slow winter rainfall. The process of direct evaporation from the CS surface (excluding evaporation throughout the catchment) has been cited as a factor in the simultaneous decrease in CSL from July to September. Because the effect of reduced Volga River discharge on the CSL occurs in the warm months of the year due to the rainfall deficit. Due to the existence of several dams that affect water flow, this drop in rainfall has also undergone various changes in recent years. Changes in the CS hydrological balance are decreasing in all rivers shown in Fig. 7 . In general, there is strong evidence that the sharp decline in sea level coincide with the inflow of rivers flowing into the CS. These decreasing changes can be seen from 1980 to 2020 for the Volga River, which supplies more than 80% of the CS water (Fig. 7 c). Meanwhile, the decreasing river discharge trend for the Kura River was − 33.5 m³/s/yr from 1980 to 2017. Gorgan River has an annual discharge of 200 to 300 m³/s/yr, but this amount has been decreasing since 1980, and dropped to less than − 50 m³/s/yr in 2017 (Fig. 7 a). The trend of precipitation changes from 1981 to 2020 shows that the CS level has experienced a total drop of -1.8 mm/40 year (Fig. 3 a). But this slope is different in different latitudes. 6. Conclusions In recent decades, advances in technology, from satellite altimeters to satellite imagery capable of recording changes in LST, SST, and water area, have greatly aided climate change studies, especially hydrological modeling. Therefore, meteorological, hydrological, and CSL change data that affect CS aquatic ecosystem changes were collected from different sources and evaluated in this study. The main purpose of this study was to investigate the relationship of CSL changes with important parameters such as river discharge, trend of SST changes, precipitation, evaporation, runoff, and LST changes of the CS catchment during the last four decades. The final results affecting the reduction of water level indicate several important factors, including the following. The main controlling factors of changes in the CSL during the study period are changes in SST, precipitation, and river discharge. Peaks of evaporation changes in the CS catchment, a temperature increase of 1.4°C in the CS catchment and 1.2°C in the northern parts of the Volga catchment were determined as the main controller of CS water balance. Monitoring of environmental factors, namely precipitation, evaporation, and runoff, was used as a method to calculate the water balance. The findings show that precipitation (by 49.7%), evaporation (by 39.3%), and runoff (by 11%) were effective in changes in CS water level in the long run. But this rate varied in different periods of CSL fluctuations. Precipitation changes in the first period of negative balance (1992–1996) compared to the positive balance (1981–1995) decreased by 3.4%; runoff decreased by 32%; and evaporation increased by 0.4%. These changes in the balance period 2003–2005 were 4.4% for precipitation, -30% for runoff, and 2.8% for evaporation; and in the balance period 2006–2020 they were − 9.1% for precipitation, -40% for runoff, and 2.1% for evaporation. The results of CS water balance in the whole catchment area (1981–2020) show a decrease of about − 1.2 cm per day, which is consistent with a drop of -1.1 in CSL. These results indicate a direct relationship between climatic parameters of precipitation, evaporation, and runoff in the long run. In the short run, precipitation and evaporation parameters seem to play a more important role. The ERA5-Land data provided a good estimate of water balance changes in the CS catchment, which can be used in future studies to monitor changes in climate parameters and their impact on the CS catchment. Considering the CS water depth, with a sharp decrease in CSL, the ecosystems of the eastern coasts, including Turkmenistan, the northeastern parts of Iran, and Kazakhstan, will be most vulnerable. However, most of the water retreat will be in the northern parts of the CS. Therefore, it is important to have an inclusive governance approach engages all countries around CS as key players in the sustainable management of water resources in CS as a single ecosystem. Declarations “The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.” The authors declare that this manuscript is original, has not been published before and is not ‎ currently being considered for publication elsewhere. We confirm that the manuscript has been ‎ read and approved by all named authors and that there are no other persons who satisfied the ‎ criteria for authorship but are not listed. We further confirm that the order of authors listed in the ‎ manuscript has been approved by all of us. We understand that the Corresponding Author is the ‎ sole contact for the Editorial process. He/She is responsible for communicating with the other ‎ authors about progress, submissions of revisions and final approval of proofs. ‎ References Abdolazim, G., & Gholamreza, R. (2017). Identify different patterns of sea surface temperature using a cluster analysis. 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Part I: System Description and Data Assimilation Evaluation. Journal of Climate , 30 (17), 6823–6850. https://doi.org/10.1175/JCLI-D-16-0609.1 Reiners, P., Asam, S., Frey, C., Holzwarth, S., Bachmann, M., Sobrino, J., Göttsche, F.-M., Bendix, J., & Kuenzer, C. (2021). Validation of AVHRR Land Surface Temperature with MODIS and In Situ LST—A TIMELINE Thematic Processor. Remote Sensing , 13 (17), 3473. https://doi.org/10.3390/rs13173473 Roshan, G., Moghbel, M., & Grab, S. (2012). Modeling Caspian Sea water level oscillations under different scenarios of increasing atmospheric carbon dioxide concentrations. Iranian Journal of Environmental Health Science & Engineering , 9 (1), 24. https://doi.org/10.1186/1735-2746-9-24 Saxe, S., Farmer, W., Driscoll, J., & Hogue, T. S. (2021). Implications of model selection: a comparison of publicly available, conterminous US-extent hydrologic component estimates. Hydrol. Earth Syst. Sci. , 25 (3), 1529–1568. https://doi.org/10.5194/hess-25-1529-2021 Shirvani, A. (2017). Change point detection of the Persian Gulf sea surface temperature. Theoretical and Applied Climatology , 127 (1–2), 123–127. https://doi.org/10.1007/s00704-015-1625-5 Shirvani, Amin. (2017). Change in annual precipitation in the northwest of Iran. Meteorological Applications , 24 (2), 211–218. https://doi.org/10.1002/met.1619 Shirvani, Amin., Arpe, K., & Jahandideh, M. (2020). Analysis of trends and change points in meteorological variables over the south of the Caspian Sea. Theoretical and Applied Climatology , 141 , 959–966. https://doi.org/org/10.1007/s00704-020-03233-0 Stolberg, F., Borysova, O., Mitrofanov, I., Barannik, V., & Eghtesadi, P. (2006). Caspian Sea, GIWA Regional assessment 23. University of Kalmar, Kalmar, Sweden. , 148 , 9–71. Wang, W., Lee, X., Xiao, W., Liu, S., Schultz, N., Wang, Y., Zhang, M., & Zhao, L. (2018). Global lake evaporation accelerated by changes in surface energy allocation in a warmer climate. Nature Geoscience , 11 (6), 410–414. https://doi.org/10.1038/s41561-018-0114-8 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1666521","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":114391714,"identity":"734b35c4-9dca-4e22-bdaa-43d5ca06888d","order_by":0,"name":"morteza sharif","email":"","orcid":"","institution":"Faculty of Geography","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"morteza","middleName":"","lastName":"sharif","suffix":""},{"id":114391715,"identity":"40d0d816-aa74-4a71-b4d6-29c6463befa0","order_by":1,"name":"Ata Abdollahi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYJACxgaGBBBtcOADVOQA0VoOzmAwIFELMw9UC17A38D8gHFGTVo+P3vzxsO2bX8Y+NsPMB6uwKNF4gCbAeOGYzmWM3uOFRzObTNgkDiTwHDwDD5rDjAYMD5gqzAwuJFjANbCcIOB4WADHh3yB9g/MD74B9ViCdQiT0iLwQEeA8aNbTkQLYxALQaEtBge5ik4OLMvzUAS6JeDPeeMeQzPJDbg1SJ3vH3jw55vyQbAENv84UeZnJzc8cOHP+LTwsCMFnE84HgaBaNgFIyCUUAZAAD8/0/9mHe7YwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Tehran","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ata","middleName":"","lastName":"Abdollahi","suffix":""},{"id":114391716,"identity":"9885c2fc-8fda-4489-96fa-a76bc23bab3f","order_by":2,"name":"Maedeh Sadat Hosseini ‎","email":"","orcid":"","institution":"Natural Resources and Environment","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Maedeh","middleName":"Sadat Hosseini","lastName":"‎","suffix":""}],"badges":[],"createdAt":"2022-05-17 17:15:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1666521/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1666521/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":23234763,"identity":"1d2021da-ca79-43d2-a587-6a64288affa9","added_by":"auto","created_at":"2022-06-29 15:17:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2362992,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical location and borders of the CS region.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1666521/v1/7a337cb6600a0d1fd8c3e9c3.jpg"},{"id":23235155,"identity":"1e970d7a-88bb-47f5-8920-16685f0d70e9","added_by":"auto","created_at":"2022-06-29 15:22:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1148460,"visible":true,"origin":"","legend":"\u003cp\u003eThe anomalies CSL from 1992 to December 2020 based on altimetry data.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1666521/v1/05ac6f4819aeeba7ddd2298f.jpg"},{"id":23234760,"identity":"b91a9778-b42c-4bb8-9943-728e0a6e57a3","added_by":"auto","created_at":"2022-06-29 15:17:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":100261,"visible":true,"origin":"","legend":"\u003cp\u003e\t(a) Mean Precipitation in the whole CS catchment, (b) Volga catchment, and mean precipitation in latitude 60° to 37° N (c) of the CS catchment between 1981-2020.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1666521/v1/a219576ea66d054d4ca12a3d.jpg"},{"id":23234761,"identity":"c718969e-f590-4fd1-b585-dea724699c26","added_by":"auto","created_at":"2022-06-29 15:17:28","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96929,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Time series of LST change in the entire CS catchment. (b) Volga basin, as well as the average monthly temperature in the four periods 1981-1995, 1996-2002, 2003-2005, and 2006-2020 in the Volga basin (c).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1666521/v1/69b936d4090f80e5bf84effd.jpg"},{"id":23234772,"identity":"d69bc304-649e-4ede-9c35-9e7ed5cf4fb8","added_by":"auto","created_at":"2022-06-29 15:17:29","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":55468,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of Mean SST for the periods 1981-1995 (a), 1996-2002 (b), 2003-2005 (c), 2006-2020 (d), and 1981-2020 (e).\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1666521/v1/9f2a6f2ba1cff01b865fd91d.jpg"},{"id":23234769,"identity":"6eff60b8-fa88-4427-90f4-4f6951d6ba7b","added_by":"auto","created_at":"2022-06-29 15:17:28","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":81446,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of\u0026nbsp;mean SST in the whole CS (1981-2020) (a), and based on ROIs selected from east coast or E, west coast or W, and middle strip or M (b), SST change in north, middle, and south CS (c).\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1666521/v1/c287b0bda766c854add6ce30.jpg"},{"id":23234770,"identity":"0248394f-69c2-4997-8a9b-360cf05259fe","added_by":"auto","created_at":"2022-06-29 15:17:29","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":82543,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of the discharge of rivers flowing into CS, (a) Sefidrud, Gorgan, Araz, (b) Kura, (c) Volga river.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1666521/v1/30d6017d42c7bff627e59d65.jpg"},{"id":23234771,"identity":"ede46cdd-064e-47d3-9c6d-d87fc2402b89","added_by":"auto","created_at":"2022-06-29 15:17:29","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":73560,"visible":true,"origin":"","legend":"\u003cp\u003eDaily change in precipitation, evaporation, and runoff in the CS catchment (1981-2020). (In this study, the amount of evaporation, as an outflowing flux, was negative. The more the positive values, the lower the rate of evaporation at the catchment).\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1666521/v1/12992c324bfc02225317d598.jpg"},{"id":23234764,"identity":"07b8a93c-74d6-47f7-b158-f0a014d05233","added_by":"auto","created_at":"2022-06-29 15:17:28","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":267261,"visible":true,"origin":"","legend":"\u003cp\u003e(1) Spatial distribution of Mean precipitation. (2) runoff and\u0026nbsp;(3) evaporation, in the periods 1995-1981 (a), 2002-1996 (b), 2005-2003 (c), 2006-2020 (d), and 2081-1920 (e).\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1666521/v1/7f24b5a648e5b8dc4898c5ec.jpg"},{"id":23235324,"identity":"9a119c0e-0ff7-4d83-ba08-0cb107469686","added_by":"auto","created_at":"2022-06-29 15:27:28","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1234811,"visible":true,"origin":"","legend":"\u003cp\u003e(a) The anomalies CSL curve for period 1980-2020.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1666521/v1/176e9ef01e9ab3d6206b8657.jpg"},{"id":23235323,"identity":"3f7d6d43-151e-4219-90bb-60a69534b7d0","added_by":"auto","created_at":"2022-06-29 15:27:28","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":2975071,"visible":true,"origin":"","legend":"\u003cp\u003e(a) The CSL change and water balance change smoothed by Savitzky-Golay filter in the whole surface of CS catchment. (b) The anomalies CSL and daily (P-E+R) budget CS catchment in period 1981-2020.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1666521/v1/9d3191730108e19b22d26942.jpg"},{"id":23234765,"identity":"662d01c8-ad72-47d8-8501-c0662065d95a","added_by":"auto","created_at":"2022-06-29 15:17:28","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":1768131,"visible":true,"origin":"","legend":"\u003cp\u003e(a) The annual CSL and daily (P-E+R) budget CS catchment smoothed by Gaussian filter. (b) Yearly (P-E+R) budget for CS catchment from 1981 to 2020.\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1666521/v1/7cc7f56ae2687d49e539e12a.jpg"},{"id":23235156,"identity":"fe67f7a2-6e6a-42f9-8ec3-96c13d724528","added_by":"auto","created_at":"2022-06-29 15:22:28","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":1274801,"visible":true,"origin":"","legend":"\u003cp\u003eThe annual in runoff, precipitation, evaporation, and SST parameters smoothed by Gaussian filter.\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1666521/v1/dcd2d5f85710e02b60de8943.jpg"},{"id":23363571,"identity":"a7fd4ea4-824c-4f8a-895d-d0aaeb862699","added_by":"auto","created_at":"2022-07-02 15:49:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1528195,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1666521/v1/60d62d55-c869-4342-8dfb-fe57cf904ff7.pdf"}],"financialInterests":"","formattedTitle":"Monitoring of Caspian Sea level change affected by atmospheric parameters using remote sensing data","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMany researchers believe that change in the water level of landlocked seas and inland lakes can disrupt the interactions of various ecosystems associated with them. This sea level change can be due to the water balance of inland seas (or lakes) or other reasons such as rising SST (Cook et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), land temperature change, and global climate change. One of these landlocked seas is the Caspian Sea (CS) (Naderi Beni et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Nandini Weiss et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Change in the water level of enclosed seas can pose a serious threat to plant ecosystems, wetlands that depend on the water flowing from these lakes, and consequently a series of other hazards that threaten wildlife and aquatic life in these wetlands. Each of these threats will trigger a chain of other challenges. Many countries are looking for consistent and accurate information that would enable them to reduce these threats. The impact of these change on the surface of the enclosed seas will have far-reaching implications for coasts and coastal ports that could indirectly affect the livelihoods and economies of millions of people (Prange et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nandini Weiss et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This is a very serious warning signal forecasting a catastrophic decline in the CSL, as one of the most important enclosed lakes in the world. This decline has serious implications for all the countries around it.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that rising surface temperatures during the 21st century will cause water to evaporate on landlocked seas and lakes (Cook et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dai et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This can cause a drop in the water level of enclosed seas such as CS (K Arpe et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which is intensified by decreasing precipitation ( Dai et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Natural lakes that do not have an outflow stream are more sensitive to climate change (Prange et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) because their water level is determined by the balance of precipitation and runoff discharge into the lake with the evaporation of the lake water. The drying up of the internal continental spaces due to climate is recognized as an important problem in terms of freshwater scarcity (Naderi Beni et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Meanwhile, sea surface temperature is one of the most important parameters for understanding oceanographic processes and climate (L\u0026oacute;pez Garc\u0026iacute;a, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Hence, it is defined by the Global Climate Observing System (GCOS) as one of the basic Essential Climate Variables (ECV) that is required for the systematic observation of climate change on Earth ( GCOS, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bojinski et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Brewin et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the current context of global warming, the availability of accurate sea surface temperature (SST) time series is invaluable for monitoring and predicting ocean conditions. These images can provide a comprehensive view of the changing trend of water temperature in enclosed seas.\u003c/p\u003e \u003cp\u003eBased on previous studies and the observed declining trend of CS water level as one of the most important enclosed seas, it is predicted that between 1993 and 2020, by the end of the 21st century, its water level will decrease by 9\u0026ndash;18 m in moderate to high conditions (Prange et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This change result from a significant increase in evaporation (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; A. Shirvani et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) overexploitation, and a reduction in the amount of water entering the CS due to damming projects in some upstream countries, such as Russia, which is not compensated by increasing water flow from rivers or precipitation (K. Arpe et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e ). According to new forecasts, the decline in CSL in the 21st century will be almost twice the predictions of previous climate models (Prange et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A decrease of 9\u0026ndash;18 m means that a large part of CS in the north, areas located in the Turkmen Sahara area in the southeast, and most of the coastal areas in the central and southern parts of CS will come above the sea level (Nandini Weiss et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Prange et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In general, a 9 m decrease in CSL means a 23% drop, while an 18 m decrease is equal to a 34% drop (Prange et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These change can have serious consequences that threaten the environment.\u003c/p\u003e \u003cp\u003eDespite the disconcerting conditions of CSL change as well as the tensions created on its surrounding ecosystems (e.g. wetlands) following the declining water levels, there is still no comprehensive plan to reduce future crises among the countries bordering the CS. As this risk continues, a chain of serious risks, including the disruption of plant ecosystems threatens the related water-dependent wetland ecosystems (such as the internationally renowned Anzali Wetland, Miankaleh Wetland, and Amir\u003cem\u003e-\u003c/em\u003ekalaye wetland in Iran and wetlands in the northern part of the Volga Delta in Russia), the wildlife around CS-dependent wetlands, the coastline, coastal ports, and the life of millions of people indirectly dependent on the CS (Prange et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). On the other hand, the decline of CSL will have geopolitical consequences and will also affect the economy of the whole region. Likewise, transportation in and out of the CS, which connects to the global ocean via the Volga-Baltic waterway and the Volga-Don canal, will be severely affected by the declining water level of the CS (Prange et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This is important for maritime trade and maritime access. On the other hand, declining sea levels will seriously damage coastal infrastructure, including ports. Maritime jurisdiction and exclusive fishing rights will change. These conditions may also lead to growing international political tensions over the reallocation of fishing territories or national water harvesting and desalination programs envisioned to help meet the growing demand for the agricultural, industrial and household sectors in areas under water stress. Therefore, given the Sustainable Development Goals of the United Nations or Intergovernmental Science, the policy platform on biodiversity and ecosystem services, there is a dire need for a global committee that can formulate and coordinate mitigation strategies. This is because the environmental, economic and political effects of declining CS levels will be precarious.\u003c/p\u003e \u003cp\u003eDue to the sensitive conditions of the enclosed seas, any imbalance between the water inflow (precipitation and runoff) and outflow (evaporation) will lead to serious challenges and change in the water level of these seas or inland lakes. All three mentioned inflow and outflow fluxes are the most important control parameters of sea level, which is determined using the following equation: dV/dt\u0026thinsp;=\u0026thinsp;R-A (E-P) (Cr\u0026eacute;taux \u0026amp; Birkett, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Chen, Pekker et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this equation, R represents runoff, A is the area of the lake, and E and P are evaporation and precipitation, respectively. However, some studies have referred to the snow water equivalent (SWE) and soil moisture (SM) parameters for hydrological estimates (Saxe et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This equation can estimate the share of the annual, monthly, or daily balance of a catchment. Recognizing these precise models is essential for future sea-level change as a scientific basis for adjustment, adaptation, and sustainability prediction strategies for different ecosystems. The predicted effects of declining CS water levels are likely to lead to a major reorganization of the surrounding ecosystems. This decline threatens its unique environment that has evolved over millions of years.\u003c/p\u003e \u003cp\u003eTherefore, in this study, first, the trend of changes in the CSL is investigated through the water balance average (dV/dt\u0026thinsp;=\u0026thinsp;R-A (E-P)). The obtained results are then compared with anomali altimetry data. Next, the effect of six important parameters on changes in water level decrease, such as changes in sea surface temperature (SST), changes in temperature of the catchment, changes in inflows of streams that flow into the CS, changes in precipitation, runoff and Evaporation of the entire CS catchment is assessed on changes in the CSL. The results of this study provide answers to two important questions: First, how much the parameters of precipitation, runoff, evaporation, catchment surface temperature and SST in the long run affect the changes in the CSL. Second, in the short ten-year periods, which parameters are more in line with changes in the CSL?. Also, the general results of the research will provide a comprehensive view of the changes in the studied parameters during the period 1981 to 2020 in the whole watershed and their relationship with CSL.\u003c/p\u003e"},{"header":"2. Study Area","content":"\u003cp\u003eCS is the largest enclosed lake in the world, with an approximate area of 422000 km\u003csup\u003e2\u003c/sup\u003e recorded in 1929, which has dropped to 355000 km\u003csup\u003e2\u003c/sup\u003e in 1977 and in 371000 km\u003csup\u003e2\u003c/sup\u003e 2009 (Akbari et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It was divided between Iran and Russia until 1999 (before the collapse of the Soviet Union). However, its political division changed after the collapse of the Soviet Union, which resulted in the creation of new borders with five countries (Iran, Turkmenistan, Azerbaijan, Russia, and Kazakhstan) and a rise in its commercial, military, and economic importance, especially in the field of oil and gas. It stretches 1204 km from north to south, with an average width of 200 to 400 km and a shoreline of about 6500 km (Lebedev \u0026amp; Kostianoy, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The total area of the CS catchment is approximately 3.7\u0026times;10\u003csup\u003e6\u003c/sup\u003e km\u003csup\u003e2\u003c/sup\u003e (Nandini Weiss et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which is 10 times the area of this sea and about 10% of the enclosed basins worldwide (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). About 130 rivers flow into the CS (Lebedev, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e; Nematollahi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), including Babol River, Gorgan River, Haraz, Talar River, Safid River, Tajan, Astara \u0026Ccedil;ay, Shahr Chay, Zarjub, and several other seasonal rivers in Iran, Araz and Kura rivers in Azerbaijan, and the Volga and Ural rivers in Russia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). According to previous studies, the highest amount of water flowing into this sea is through the Volga River, estimated at more than 80% (K. Arpe et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Lebedev, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e ; Amin. Shirvani et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The CS is also one of the most important reserves of gas resources, which seriously endangers its aquatic ecosystem considering the increasing investment by large oil companies in the region and the world. This may have also contributed to the increase in water surface temperature and, consequently, the evaporation of CS water.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. Materials And Methods","content":"\u003cp\u003eIn this study, various remote sensing (RS) data were used to examine CSL change (Table\u0026nbsp;1). The measured data of the altimetry change and the results of previous studies have been used to examine the CS water level in different periods (K Arpe et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chen, Pekker, et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Chen, Wilson, et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dolukhanov et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kakroodi et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kazancı et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e; Lebedev \u0026amp; Kostianoy, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Medvedev et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, change were analyzed using the surface runoff data in the CS catchment basin. Topographic data of the CS catchment basin and the drainage data of the rivers flowing into the CS, SST and precipitation change (Mu\u0026ntilde;oz Sabater, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), the images of the European Reanalysis (ERA5-Land) climate parameters including LST, runoff, and precipitation and evaporation in the sea surface and the entire catchment basin for estimating the CS water balance were obtained from the Google Earth Engine (GEE) platform. ERA5-Land is the latest reanalysis product by the European Centre for Medium-Range Weather Forecasting (ECMWF) (Cao et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). These data provide users with a total of 50 parameters to describe the climate cycle and energy fluxes on land and ocean environments globally and with daily cycles at different spatial resolutions whose latest products are 0.1\u0026deg;\u0026times; 0.1\u0026deg; (Mu\u0026ntilde;oz-Sabater et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSST images were only selected for the nighttime to prevent the effects of intense daily heating on the results, especially in spring and summer and in the region near the Mediterranean (Minnett et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Table\u0026nbsp;1 shows the data used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy data.\u003c/p\u003e\n\n\u003cdiv class=\"gridtable\"\u003e\n \u003c/div\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Taba\"\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eName Product\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eData\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResolution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eprecipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eERA5-Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1981\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOUR, 0.1 arc degrees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRunoff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eERA5-Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1981\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOUR, 0.1 arc degrees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEvaporation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eERA5-Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1981\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOUR, 0.1 arc degrees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkin temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eERA5-Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1981\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOUR, 0.1 arc degrees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSea Surface Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAVHRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1981\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDay, 4 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncei.noaa.gov\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBathymetry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500 \u0026times; 500 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.gebco.net\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltimetry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTOPEX/Poseidon, Jason-1, 2, \u0026amp; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1992\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ipad.fas.usda.gov\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRiver runoff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://daac.ornl.gov/RIVDIS/rivdis.shtm\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003e3.1. Altimetry Data\u003c/h2\u003e\n \u003cp\u003eSatellite altimeter observation of change in the Sea Surface Height (SSH) and inland lakes since September 1992, with the launch of the TOPEX/Poseidon altimeter mission (Chen et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), has made it possible to investigate the effects of climate change on the water of inland seas and lakes in different parts of the world. The data has been available online (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hydroweb.theia-land.fr\u003c/span\u003e\u003c/span\u003e) for the world\u0026apos;s largest lakes since 2003 through an automated algorithm. In this study, altimeter measurements of TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3 satellites have been used (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The SSH measurement accuracy for these sensors is about\u0026thinsp;~\u0026thinsp;4 cm, which provides sufficient accuracy for research (Lebedev, \u003cspan class=\"CitationRef\"\u003e2012a\u003c/span\u003e). They also have an orbital repetition period (~\u0026thinsp;10 days) that makes it possible to analyze sea-level change (Ji et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). On the other hand, they are the longest series of satellite altimeters (from September 1992 to December 2020) with the possibility of expanding the data in the coming years. Therefore, data related to sensor images that show change with better consistency were received and analyzed. Moreover, considering the historical background of CSL change from the analysis of previous studies by sedimentological measurements during the period 1980\u0026ndash;1991 (excluding seasonal and short-term change) (Kakroodi et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), a series of satellite altimeter data from CSL change were interconnected in this study to extend the CSL series between 1992 and December 2020. This volume of data provides a more comprehensive view of the trend of CSL change in the long term, including the period from 1980 to December 2020.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003e3.2. Sea Surface Temperature Data\u003c/h2\u003e\n \u003cp\u003eAdvanced Very High Resolution Radiometer (AVHRR) was the first infrared sensor in the Tiros-NOAA series of satellites to provide information on global terrestrial and sea surface temperature (L\u0026oacute;pez Garc\u0026iacute;a, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The first satellite of the series (Tiros N) was launched in 1978, followed by NOAA-6 in 1980, and finally NOAA-19 in 2009. AVHRR offers images in five spectral bands: Visible range (0.58 0.68\u0026micro;m), near-infrared (0.78\u0026ndash;1.10\u0026micro;m), mid-infrared (3.55\u0026ndash;3.93\u0026micro;m), and two thermal infrared bands (10.3\u0026ndash;11.3 and 11.5\u0026ndash;12.5\u0026micro;m) that allow accurate SST recovery. Therefore, considering the long-term trend that these images provide to the public, the trend of change in CS surface temperature were received daily using the AVHRR-v02 product time series between 1981\u0026ndash;2020, and then the monthly and annual mean values were calculated. For accurate observation, the CS SST trend was investigated in two ways. First, the Eastern Caspian Basins, the Middle Caspian Basins, and the Western Caspian Basins between 45\u0026deg;83\u0026apos; to 37\u0026deg;26\u0026apos; N were examined separately through regions of interest (ROIs). Then, CS was divided into three parts: Northern Caspian, Middle Caspian, and Southern Caspian, whose SST change were examined using ROIs. The distribution of these samples is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. This was done to more accurately observe the trend of SST change and compare the most common anomalies of the CS water surface.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003e3.3. Data analysis\u003c/h2\u003e\n \u003cp\u003eTo calculate the change in the main factors controlling CSL (including evaporation, precipitation, runoff, and SST change), it is necessary to calculate the correlation between the largest variations of each parameter with CSL change. Hence, after obtaining the change of these parameters through software (OriginLab Corp., Northhampton, MA, USA), the investigation of the trend of change, correlation, graph plotting, and trend analysis was undertaken.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe most important parameters affecting CS change due to variations in the CS surface evaporation include evaporation in the whole catchment, change in rainfall in the whole catchment, runoff change in important rivers such as Volga, Ural, Araz, and Kura as four important rivers in the CS catchment, SST change, catchment LST change, and sea flow change. Groundwater flows, although small, are very important in controlling water level changes. In this study, the results of the most important parameters controlling CSL change were reviewed in the following sections.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Precipitation pattern in the CS catchment\u003c/h2\u003e \u003cp\u003eDemonstrating the trend of precipitation change is a key part of investigations of climate change and weather conditions (Randles et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), because it links aspects of the water and energy cycle. However, there might be differences between satellite measurements and field surveys. meanwhile, due to the complex climatic conditions prevailing in CS, the seasonal rainfall cycle varies greatly in different regions. The highest precipitation of the northern part (Volga basin) occurs in summer, the western part (Kura /Turk /Araz region) in spring, and the southern part in autumn and winter (Molavi Arabshahi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nandini Weiss et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, the eastern shores of the CS have an almost desert climate (Nandini Weiss et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, it is necessary to fully study the precipitation catchment in order to observe the precipitation change affecting CS. The northern and eastern coastal areas of the CS are mostly arid with an annual rainfall of 100\u0026ndash;300 mm, while the western coasts are semi-arid and the southern and southwestern coasts are wet with an annual rainfall of 400\u0026ndash;1200 mm (Stolberg et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In general, the amount of precipitation decreases from west to east. However, the northern part of the Volga basin, which is the main source of the water balance of CS, has a wet climate, which is mostly in the form of snowfall.\u003c/p\u003e \u003cp\u003eThe results of precipitation in the whole catchment area showed that 1996, 2005, 2010, 2014, and 2018 had the lowest rainfall, while 1983, 1990, 1993, 1997, 2004 had the highest rainfall in the study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The precipitation between latitude 37\u0026deg; to 60\u0026deg; N in the CS catchment area was studied and evaluated separately to examine the trend of change. The average trend of precipitation change in 1981\u0026ndash;2020 (-1.8 mm/40\u0026nbsp;year) showed a declining rate in the whole catchment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). This decreasing trend was 0.3 mm/15\u0026nbsp;year in the 1981\u0026ndash;1995 period, 6.2 mm/7\u0026nbsp;year in the 1996\u0026ndash;2002 period, -6.6 mm/3\u0026nbsp;year in the 2003\u0026ndash;2005 period, and \u0026minus;\u0026thinsp;2.5 mm/15\u0026nbsp;year from 2010 to December 2020 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). This variation was observed in the vlga catchment with a decrease \u0026minus;\u0026thinsp;2.4 mm/40\u0026nbsp;year, which is more than the entire CS catchment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The highest decreasing trend was seen in the latitudes 40\u0026deg; N (-8.87 mm/40\u0026nbsp;year) and 37\u0026deg; N (-3.1 mm/40\u0026nbsp;year). Latitudes 55 to 60\u0026deg; N had the highest rainfall in the CS catchment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). These results are consistent with the climatic conditions and topography of the CS catchment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2. LST Variation\u003c/h2\u003e \u003cp\u003eLST, or land surface temperature derived from satellite data, has become an essential tool for tracking climate change in recent decades. This atmospheric parameter is recognized as one of the essential climate variables (ECVs) by the World Meteorological Organization, which can be used directly to extract global warming trends and anomalies since it is a key parameter in the exchange of energy at the ground level (Reiners et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, ERA5-Land Skin Temperature product was used to understand how the surface temperature change in the Caspian catchment. The course of surface temperature change throughout the CS catchment and the northern part of the Volga basin is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The trend of temperature change in the whole catchment was about 1.4\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eThe northern part of the Volga catchment trend of temperature change was 1.2\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Due to the climatic conditions and geographical location of this part of the CS catchment, the difference in observed surface temperature change is reasonable. Maximum surface temperature in the northern part of the Volga basin was 18.7\u0026deg;C in the 1981\u0026ndash;1995 period, 19\u0026deg;C in the 1996\u0026ndash;2002 period, 19.4\u0026deg;C in the 2003\u0026ndash;2005 period, and 19.8\u0026deg;C in the 2006\u0026ndash;2020 period, which indicates an increase of 1.1\u0026deg;C. These change in surface temperature in the second half of the year show a greater difference between 2003\u0026ndash;2020 than in 1981\u0026ndash;2002, reaching the highest difference (0.5-1\u0026deg;C) between October and the end of December (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Although the first half of the year was colder than other periods during the 2003\u0026ndash;2005 period, the temperature increased from July to the end of December. In addition, change in the catchment surface temperature in the four periods with change in the CSL indicate a greater temperature slope from 1996 to 2002 and from 2003 to 2005 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). This discrepancy may be due to more data in each time period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Caspian Sea surface temperature\u003c/h2\u003e \u003cp\u003eIn this study, the trend of SST was investigated using ROIs from the east, west, and central coasts of the CS, as well as three parts of the northern, central, and southern Caspian. This was done to observe the rate of increase in SST. The measurement results in both methods show the average annual SST has an increasing trend (0.03\u0026deg;C/yr) between 1981 and December 2020 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). In 1986, 1992, 1996, 2003, 2009, and 2014, SST was at its lowest, whereas in 1981, 1991, 2000, and 2007 it was at its highest. The results showed that SST conditions are affected by latitude that create a higher temperature in the southern Caspian than in the northern Caspian. Moreover, the eastern coast shows a higher average temperature and a higher upward trend in the study period than in the western coast and the middle strip (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, c). These temperature change are more common on the southeastern shores of the CS, which could be due to the weather conditions in Turkmenistan.\u003c/p\u003e \u003cp\u003eThe SST data indicates a further increase in SST in the central Caspian with a slope of 0.04\u0026deg;C/yr (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). This is consistent with Abdolazim and Gholamreza (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The average temperature observed in the CSL decrease and increase periods and the mean temperature during the entire research period (1981\u0026thinsp;\u0026minus;\u0026thinsp;1920) are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee. The mean temperature was about 14.8\u0026deg;C in the 1981\u0026ndash;1995 period (positive water level period) (sea Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), 15.7\u0026deg;C in the 1996\u0026ndash;2002 period (sea Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), 15.8\u0026deg;C in the 2003\u0026ndash;2005 period (sea Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec), and 15.9\u0026deg;C in the 2006\u0026ndash;2020 period (negative water level period) (sea Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed), which indicates an increase of about 1.1\u0026deg;C. Moreover, this shows the expansion of temperature increase to the Middle Caspian Basin, which indicates a significant change in the temperature regime of this sea (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The average temperature of about 20\u0026deg;C/yr during the 2006\u0026ndash;2020 period and its stabilization in the Middle Caspian during the past 15 years can be one of the main reasons for the increase in evaporation and the declining water level (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). This increase in temperature can be affected by several important factors. However, some studies argue that these change correlate to the phases of the South Oscillation (El Nino) and the North Atlas (Nav) fluctuations (Ginzburg et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.4. River discharge\u003c/h2\u003e \u003cp\u003eCS water balance is very sensitive to climatic diversity in the surrounding areas due to its confinement on land. Climate change will cause change in rainfall as well as surface runoff, so its decreasing and increasing trends will have a great impact on CSL. There are three sections with a runoff of \u0026lt;\u0026thinsp;70 mm/month on the southern shores of the CS, the northern sections of the Volga River basin, and the eastern part of the catchment that forms the Ural River. Azerbaijan, with a runoff rate of 10\u0026ndash;30 mm/month, directing the existing runoff to CS through the Araz and Kura rivers, is also considered an important part of the sea catchment. The minimum runoff rate is between 5\u0026ndash;20 mm/month on the eastern coast of CS (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e,\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Therefore, runoff change and the impact of climate change in the northern part of the CS catchment are more important than in other parts. These geographical conditions have caused the hydrological balance of CS to be affected by the confluence of the Volga River with runoff flowing over 8118 m\u0026sup3;/s/yr. However, rivers such as the Kura, Araz, Ural, Sefidrud, Gorgan River, and other rivers that flow into the CS are also important. The trend of change in the water flow of these rivers was prepared according to the available information (Table\u0026nbsp;1), as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eReducing the discharge of the Volga River will have a direct effect on CSL reduction. The discharge rate of this river from the beginning of 1897 to the end of 2010 is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec. The discharge of the Volga River decreased significantly from 1925 to 1980, which is directly related to the CS water level. One of the sharpest declines in CSL between 1950 and 1980, reaching about 3 m, is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea. Rainfall data were not available during these years to identify the main reason for this decline in the Volga River discharge. The average discharge of the Volga River in 1982 was 7103 m\u0026sup3;/s/yr. According to meteorological data, it was 8664 m\u0026sup3;/s/Decade for the 1920s and 7501 m\u0026sup3;/s/Decade for the 1950s, during which the Volga River discharge decreased by about 19%. This drop can be due to the construction of many dams, especially in the Volga basin with a capacity of about 223 km\u0026sup3; (Akbari et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Some studies Georgievsky \u0026amp; Shiklomanov (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) have also shown a significant relationship between the decrease in CSL and the increase in the volume of dam reservoirs before the 1980s in the Caspian catchment. It was also observed in 1980 to 1989 (8163 m\u0026sup3;/s/Decade), 1990 to 1999 (8454 m\u0026sup3;/s/Decade), and 2000 to 2010 (7924 m\u0026sup3;/s/Decade 7924), indicating a decrease of 7% compared to the 90s and 80s. The greatest decrease in the Volga River runoff (with a decrease of about 30%) was observed in the 1880s (9309 m\u003csup\u003e3\u003c/sup\u003e/s/Decade) compared to the 1970s (7219 m\u003csup\u003e3\u003c/sup\u003e/s/Decade 7219). However, due to the year-to-year fluctuations of weather conditions in different river basins, it is expected that the true percentage of the Volga share will fluctuate differently. In general, from 1980 to 2010, the flow of the Volga River has experienced a decreasing trend of -151.7 m\u003csup\u003e3\u003c/sup\u003e/s/yr (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). Although there was not much information about other rivers evaluated in this study, all available data showed a decreasing trend in the discharge of these rivers, namely Gorgan river with \u0026minus;\u0026thinsp;2.3 m\u0026sup3;/s/yr, SefidRud with \u0026minus;\u0026thinsp;0.9 m\u0026sup3;/s/yr, Kura with \u0026minus;\u0026thinsp;33.5 m\u0026sup3;/s/yr, with only Araz River having an increasing trend of 0.02 m\u0026sup3;/s/yr (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, b). These results may indicate one of the main factors in CSL change. Therefore, the river discharge, especially in the Volga River, directly controls the hydrological change in large areas of the CS. These results are also consistent with other research works (K. Arpe et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ozyavas et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Roshan et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Change in the Water regime\u003c/h2\u003e \u003cp\u003eAnnual change in rainfall, evaporation, and runoff rates are the main features of the CS hydrological regime. As a result of this hydrological regime, change in CSL lead to change in the sea level and volume (Medvedev et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, depending on the topographic characteristics and geographical conditions of the CSL change, the northern parts and the eastern, western, and southern coasts will experience different stress levels. Considering the hydrodynamics in the northern section, the sea retreat will follow a wider range due to the shallow shores. This tension will be significant on the coasts of Kazakhstan and Turkmenistan, especially on the Gulf of Karabakh. In the southern strip of the CS, although the retreat of the sea is less than in other coasts, this will create serious challenges for natural ecosystems such as international wetlands and northern ports of Iran.\u003c/p\u003e \u003cp\u003eThe amount of CS water balance was examined using P, E, and R fluxes (in cm/Day) obtained through daily images and the trend of parameters affecting CSL in a period of 40 years (1981\u0026thinsp;\u0026minus;\u0026thinsp;1920). Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the trend of three parameters (P, E, R). A normal increasing trend of precipitation and runoff was observed from 1981 to July 1995. This slope of change was lower in the period from August 1995 to December 2002 compared to 1981\u0026ndash;1995. The highest change trend was observed in the whole CS catchment from 2006 to December 2020 for all three parameters. Change in precipitation, runoff, and evaporation in the whole catchment show that the greatest change took place in the eastern and northern parts of the CS catchment. These change, which have a significant effect on water balance as well as evaporation, can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The CS catchment balance rate in the southern part compared to the eastern coasts and the northern parts of the catchment during the 2010\u0026ndash;2020 period shows positive change compared to the 1980s.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, in the periods 1981\u0026ndash;1995, 1996\u0026ndash;2002, 2003\u0026ndash;2005, and 2006\u0026ndash;2020, the mean rainfall was 552.8, 523, 528.9, and 503 mm (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e,\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), mean runoff was 136, 92.7, 95, 82.5 mm, (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e,\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and mean evaporation was 466.6, 465, 480, 476 mm (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e,\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These change show that precipitation decreased by 3.4% in the first negative balance period (1996\u0026ndash;2002), 4.4% in the second negative balance period (2003\u0026ndash;2005), and 9.1% in the third negative balance period (2006\u0026ndash;2020) compared to the positive period (1981\u0026ndash;1995); while runoff decreased by about 32%, 30%, and 40% in these periods. Meanwhile, evaporation has increased from 0.4\u0026ndash;2.8%- 2.1% in the 2003\u0026ndash;2020 period. The trend of precipitation change in the catchment area shows that in the coastal strip of CS, the distribution of rainfall in Gilan has become more concentrated in recent years. Whereas in the 1980s, the average monthly rainfall was more than 60 mm in the southern strip of the sea compared to the western and eastern part of CS. These change are consistent with the droughts of the last decade in Iran. The change in the total runoff of the CS catchment can also be seen in Azerbaijan and the southern coastal strip. In the 1981\u0026ndash;2020 period, the average runoff changed from 3.8 to 3.2 cm per year, evaporation from \u0026minus;\u0026thinsp;3.9 to -3.3, and precipitation from 14.2 to 16.8 mm per month. These results indicate a decrease (-0.6 mm) in the runoff, an increase (2.6 mm) in precipitation, and a decrease (-15 mm) in evaporation. High change in CS evaporation can also be a reaction to rising air temperature and surface temperature. These change at different time periods are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e,c. Nandini Weiss et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) predicted that CS evaporation would increase by 15\u0026ndash;20% by the end of the 21st century. This increase in evaporation for the world's largest lakes is estimated at 16% (Wang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Sea level change\u003c/h2\u003e \u003cp\u003eThe range of change in CS water level according to sedimentological results was \u0026minus;\u0026thinsp;17.4 m in 915 (Br\u0026uuml;ckner, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1890\u003c/span\u003e; Naderi Beni et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), but in -27.78 m on 2020/12/30 obtained through altimetry data. The CSL change between 1980 and 1995 (18 years) there is a suitable condition with an increase of 3.30 m. However, a declining trend of more than 2 m can be observed from July 1995 to December 2020 (25 years) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). This sharp decline is more disconcerting than ever, given the phenomenon of climate change as well as the increase in human activities. However, between 2003 and December 2005, there is a short-term upward trend. The balance of the Volga River decreased in the 1950s compared to the 1930s (-15.1%), which shows that it is directly related to the decrease of CS water level from \u0026minus;\u0026thinsp;26.6 to -28 (about \u0026minus;\u0026thinsp;2.6) meters. It also experienced an increase (13%) in the 1980s, which could be one of the main reasons for the increase of about 3 m in CSL in the 1980s (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResults of CSL change show that major challenges in the region's ecosystem include the increase in CS water consumption by its five neighboring countries, the policy of building dams on rivers flowing into CS (such as the Volga, Ural, Kura, Aras, Gorgan, Babol, and Sefidrud rivers), and global warming ( increased evapotranspiration). Although previous studies have shown many change in the sea level, recent increases in exploitation and human activities can impose more serious risks on the aquatic ecosystem and the environment in a shorter time than before. The average rate of change in CSL (2002\u0026ndash;2005) is estimated to be about 6.6 cm per year (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which has generally decreased by about 1.5 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea). Due to the very dry conditions of the areas around this sea, except for the northern catchment of the Volga and the Urals, it seems that climate change will accelerate the process of change in this sea.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe three principal parameters of precipitation, runoff, and evaporation are the main controlling factors of change in enclosed seas. However, other parameters such as atmospheric currents, the direction of marine winds and cyclones, and the sensible and latent heat fluxes also have a significant effect on change in the water level of enclosed seas. One of the reasons for the CSL change seen so far is climate change affecting its catchment area ( K. Arpe et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ibrayev et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Naderi Beni et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Furthermore, human factors such as the construction of a dam on the Volga River after the 1940s Ibrayev et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and other rivers that affect the CS water balance are other important factors contributing to these change.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Caspian Sea catchment balance\u003c/h2\u003e \u003cp\u003eAccording to the available data concerning the estimation of the water balance of the CS catchment, the CSL change were compared with the results from the 1981\u0026ndash;2020 period. The trend of change in the CS catchment water balance during the 1981\u0026ndash;2020 period was about \u0026minus;\u0026thinsp;1.2 cm/Day, which is very close to the RS data \u0026minus;\u0026thinsp;1.1 cm/Day (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ea). these change show that there is a positive correlation between the three parameters of precipitation, evaporation and runoff in some periods according to different CSL peaks.\u003c/p\u003e \u003cp\u003eTemporal change based on the results show four different period, including two short trends between 1995 and 2002, where the level was at the height of -25.85 and \u0026minus;\u0026thinsp;26.74, respectively, (an 89 cm decrease); a short increasing period between 2003 and July 2005 (from \u0026minus;\u0026thinsp;26.74 to -26.1) (a 64 cm increase) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e); followed by the fourth period, where from August 2005 to December 2020 (from \u0026minus;\u0026thinsp;26.12 to -27.84), the water level of the CS decreased by 1.72 m. Only, Between March 1981 and July 1995, the CSL was rising at a rate of +\u0026thinsp;5.4 cm/day (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eThe period from 2003 to the end of 2005 showed an increasing trend in the CS level (5.6 cm/Day). However, from 2006 to 2020, the sea level change was much steeper than the water balance change obtained in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eb). These results show that the water balance between 1981 and 1995 and 2003 to July 2005 led to a positive CS water balance. Similarly, the water balance was positive from 1996 to 2002 (1.3 cm/Day) and from 2006 to December 2020 (5.8 cm/Day), while the sea level (-2.4 cm/Day) was decreasing (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eb). These results indicate that there are factors other than precipitation, evaporation, and runoff influencing the severity of the water-level decline in the 2006\u0026ndash;2020 period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe average annual water balance in the 1981\u0026ndash;1995 period was 20 cm/yr, which shows a 6 cm decrease compared to the 1996\u0026ndash;2020 period (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eb). This is consistent with the results in another study Chen et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) calculated through the Volga River runoff from 1979 to 2015. The peak increase in CSL change shown by the Gaussian filter in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ea also indicates a direct correlation between the three major fluxes of precipitation, runoff, and evaporation with long-term change in CS water levels. The highest CSL change occurred in 1994\u0026ndash;1996, followed by a sharp downward trend in 2006, which could be affected by the drought periods between 2000\u0026ndash;2009 as one of the most important drought periods in the Caspian Basin. The average SST has increased more than other years during this period (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eThe impact rates of all three parameters of precipitation, evaporation and runoff were calculated separately and in different time periods according to CSL peaks (Table\u0026nbsp;2). In all four periods, simultaneously with changes in sea level, the highest impact with 49.7, 50.4, 49.6 and 49%, respectively, were related to rainfall and the lowest with rates (12.8, 103, 10.9 and 10%) was related to runoff (Table\u0026nbsp;2). These results show that as the amount of evaporation increases, the amount of runoff in the catchment area decreases too. During the 2006\u0026ndash;2020 period, the highest rate of evaporation and the lowest rate of precipitation and runoff compared to the other three time periods can be among the factors affecting the decreasing water balance. The evaporation change over the entire basin in all four water level periods can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e,\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Increasing change in evaporation in the whole basin and the CS surface shows a direct relationship with the increasing temperature of the catchment surface, especially in the northern parts of the Volga basin.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTabel 2\u003c/b\u003e \u003c/p\u003e \u003cp\u003eInfluence rate of atmospheric parameters of precipitation, evaporation and runoff on the water level of the CS in different periods.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEvaporation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRunoff\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1981/1/1-1995/7/30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1995/8/1-2002/12//30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2003/1/1-2005/12/30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2006/1/1-2020/12/30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1981/1/1-2020/12/30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Atmospheric parametrs on the Caspian Sea\u003c/h2\u003e \u003cp\u003eThe negative effects on CSL change in recent studies indicate an increase in SST and sea evaporation (A. Shirvani et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to the results obtained in this study, the rising of the Caspian SST began observed about a decade earlier than change in evaporation in the entire basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). This change can be a response to an increase in ground surface temperature (by about 1.4 \u0026deg; C/yr) and an increase in air temperature. The most significant CSL change in relation to SST occurred in 1995 (during the 1981\u0026ndash;2020 period). Between 1982 and 2020, the average increase in SST was more than 1.5\u0026deg;C. This is consistent with Amin Shirvani et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which showed a rise of about 1.2\u0026deg;C up to 2016. Moreover, the observed SST peak obtained through the Gaussian relation shows that there is a significant correlation between the temperature increase limit in 1995 and the decreasing trend of CSL. This increase in CS temperature eventually led to an increase in evaporation, which has been well documented since 1998 (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). The rainfall change in the whole CS catchment show a significant relationship with CSL, with a period of positive balance (1981\u0026ndash;1995) and a period of negative balance (1996\u0026ndash;1996) in the long run (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). The long-term average of CS catchment rainfall (with a slope of -1.8 mm/yr between 1981 and 2020) was decreasing. Still, it is not possible to provide an accurate estimate of the river flow rates because precipitation and the resulting runoff in different areas such as the river catchment of Volga, Ural, Aras, Kura, Sefidrud, Atrak, Gorgan, and other seasonal rivers face important obstacles such as dams. This may be the reason for severe runoff decline in the catchment area in different periods and especially from 2002 to the end of 2020 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e,\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, previous studies A. Shirvani (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and A. Shirvani et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) between the years 1951 to 2016 also show the lowest annual rainfall change in the southern part of CS, i.e. the coastal stations of Bandar Anzali and Babolsar. The results obtained from the parameters of surface temperature, evaporation in the catchment, and SST also show that evaporation and temperature increase had a greater effect in the CS catchment than precipitation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConsidering the evaporation at sea level and the whole catchment is a reaction to global and regional temperature changes in this catchment, SST changes were analyzed in more detail. The results showed two major changes in CS surface temperature in the eastern coasts and the middle strip with an increasing trend of 0.033\u0026deg;C/yr and also in the middle Caspian section compared to the northern and southern areas with an increasing trend of 0.04\u0026deg;C/ yr. These temperature changes are very complex and driven by the atmospheric characteristics affecting CS, which are affected by four different climatic conditions, namely (1) Arctic cold air, (2) temperate humid air masses of the Atlantic Ocean, (3) tropical and subtropical air masses of the Black Sea, and (4) continental dry air masses from the east (Molavi Arabshahi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nandini Weiss et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The analysis of the increase in SST observed in the east and middle coasts, which was obtained between latitudes 45\u0026deg; 83' N to 37\u0026deg; 26' N (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), and in the southern and middle Caspian (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e-c) show that the SST increase in the eastern coast is one of the most influential drivers of CSL changes. This increase in temperature in the north of the CS catchment could lead to an increase in snowmelt. The trend of increasing SST has also led to an increase in evaporation in the northern and eastern parts of the CS, which had a wider range during the 2010\u0026ndash;2020 decade with an evaporation of more than 30 mm/month from 1981 to 1989. Also, the increase in SST between 1996 and the end of 2002 is most in line with the declining CS water level in this period. Whereas, the water balance of the whole catchment is positive (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). However, these two factors are self-affecting and a response to rising temperatures and surface temperatures. Excessive evaporation, which occurs with increasing SST and consequent increase in LST, has an immediate effect on CSL, but the decrease in water inflow of the Volga and Ural catchment seem to be consistent with the abnormal drop in CSL. These changes are exacerbated by the sharp decline and even in some months of the year the drying up of some seasonal rivers that flow into the CS from Iran and Azerbaijan and the increase in global temperatures. On the other hand, increasing the exploitation of oil and gas fields in different parts of CS \u0026zwnj; can be another factor contributing to increasing SST.\u003c/p\u003e \u003cp\u003eAs previous studies K. Arpe et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)d Arpe \u0026amp; Leroy (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) have shown, the decrease in rainfall on the Volga River and its catchment area and the decrease in its inflow into CS are directly related to the decrease in CSL. The drop in rainfall over the volga river basin does not lead to an immediate CSL drop, as the Caspian water balance is primarily regulated by snowmelt north of the catchment and takes several months to reach the CS (K. Arpe et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This delay is 1\u0026ndash;3 months for heavy rainfall in summer, but higher for the slow winter rainfall. The process of direct evaporation from the CS surface (excluding evaporation throughout the catchment) has been cited as a factor in the simultaneous decrease in CSL from July to September. Because the effect of reduced Volga River discharge on the CSL occurs in the warm months of the year due to the rainfall deficit. Due to the existence of several dams that affect water flow, this drop in rainfall has also undergone various changes in recent years. Changes in the CS hydrological balance are decreasing in all rivers shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. In general, there is strong evidence that the sharp decline in sea level coincide with the inflow of rivers flowing into the CS. These decreasing changes can be seen from 1980 to 2020 for the Volga River, which supplies more than 80% of the CS water (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). Meanwhile, the decreasing river discharge trend for the Kura River was \u0026minus;\u0026thinsp;33.5 m\u0026sup3;/s/yr from 1980 to 2017. Gorgan River has an annual discharge of 200 to 300 m\u0026sup3;/s/yr, but this amount has been decreasing since 1980, and dropped to less than \u0026minus;\u0026thinsp;50 m\u0026sup3;/s/yr in 2017 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). The trend of precipitation changes from 1981 to 2020 shows that the CS level has experienced a total drop of -1.8 mm/40\u0026nbsp;year (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). But this slope is different in different latitudes.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eIn recent decades, advances in technology, from satellite altimeters to satellite imagery capable of recording changes in LST, SST, and water area, have greatly aided climate change studies, especially hydrological modeling. Therefore, meteorological, hydrological, and CSL change data that affect CS aquatic ecosystem changes were collected from different sources and evaluated in this study. The main purpose of this study was to investigate the relationship of CSL changes with important parameters such as river discharge, trend of SST changes, precipitation, evaporation, runoff, and LST changes of the CS catchment during the last four decades. The final results affecting the reduction of water level indicate several important factors, including the following.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe main controlling factors of changes in the CSL during the study period are changes in SST, precipitation, and river discharge. Peaks of evaporation changes in the CS catchment, a temperature increase of 1.4\u0026deg;C in the CS catchment and 1.2\u0026deg;C in the northern parts of the Volga catchment were determined as the main controller of CS water balance.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMonitoring of environmental factors, namely precipitation, evaporation, and runoff, was used as a method to calculate the water balance. The findings show that precipitation (by 49.7%), evaporation (by 39.3%), and runoff (by 11%) were effective in changes in CS water level in the long run. But this rate varied in different periods of CSL fluctuations. Precipitation changes in the first period of negative balance (1992\u0026ndash;1996) compared to the positive balance (1981\u0026ndash;1995) decreased by 3.4%; runoff decreased by 32%; and evaporation increased by 0.4%. These changes in the balance period 2003\u0026ndash;2005 were 4.4% for precipitation, -30% for runoff, and 2.8% for evaporation; and in the balance period 2006\u0026ndash;2020 they were \u0026minus;\u0026thinsp;9.1% for precipitation, -40% for runoff, and 2.1% for evaporation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe results of CS water balance in the whole catchment area (1981\u0026ndash;2020) show a decrease of about \u0026minus;\u0026thinsp;1.2 cm per day, which is consistent with a drop of -1.1 in CSL. These results indicate a direct relationship between climatic parameters of precipitation, evaporation, and runoff in the long run. In the short run, precipitation and evaporation parameters seem to play a more important role. The ERA5-Land data provided a good estimate of water balance changes in the CS catchment, which can be used in future studies to monitor changes in climate parameters and their impact on the CS catchment.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eConsidering the CS water depth, with a sharp decrease in CSL, the ecosystems of the eastern coasts, including Turkmenistan, the northeastern parts of Iran, and Kazakhstan, will be most vulnerable. However, most of the water retreat will be in the northern parts of the CS. Therefore, it is important to have an inclusive governance approach engages all countries around CS as key players in the sustainable management of water resources in CS as a single ecosystem.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026ldquo;The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors declare that this manuscript is original, has not been published before and is not\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026lrm;\u003c/em\u003e\u003cem\u003ecurrently being considered for publication elsewhere. We confirm that the manuscript has been\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026lrm;\u003c/em\u003e\u003cem\u003eread and approved by all named authors and that there are no other persons who satisfied the\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026lrm;\u003c/em\u003e\u003cem\u003ecriteria for authorship but are not listed. We further confirm that the order of authors listed in the\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026lrm;\u003c/em\u003e\u003cem\u003emanuscript has been approved by all of us. We understand that the Corresponding Author is the\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026lrm;\u003c/em\u003e\u003cem\u003esole contact for the Editorial process. He/She is responsible for communicating with the other\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026lrm;\u003c/em\u003e\u003cem\u003eauthors about progress, submissions of revisions and final approval of proofs.\u003c/em\u003e\u003cem\u003e\u0026lrm;\u003c/em\u003e\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdolazim, G., \u0026amp; Gholamreza, R. (2017). Identify different patterns of sea surface temperature using a cluster analysis. \u003cem\u003eJournal of Wetlnd Ecobiology\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2), 53\u0026ndash;70. https://doi.org/http://jweb.iauahvaz.ac.ir/article-1-567-en.html\u003c/li\u003e\n \u003cli\u003eAkbari, M., Baubekova, A., Roozbahani, A., Gafurov, A., Shiklomanov, A., Rasouli, K., Ivkina, N., Kl\u0026oslash;ve, B., \u0026amp; Haghighi, A. T. (2020). 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Analysis of trends and change points in meteorological variables over the south of the Caspian Sea. \u003cem\u003eTheoretical and Applied Climatology\u003c/em\u003e, \u003cem\u003e141\u003c/em\u003e, 959\u0026ndash;966. https://doi.org/org/10.1007/s00704-020-03233-0\u003c/li\u003e\n \u003cli\u003eStolberg, F., Borysova, O., Mitrofanov, I., Barannik, V., \u0026amp; Eghtesadi, P. (2006). Caspian Sea, GIWA Regional assessment 23. \u003cem\u003eUniversity of Kalmar, Kalmar, Sweden.\u003c/em\u003e, \u003cem\u003e148\u003c/em\u003e, 9\u0026ndash;71.\u003c/li\u003e\n \u003cli\u003eWang, W., Lee, X., Xiao, W., Liu, S., Schultz, N., Wang, Y., Zhang, M., \u0026amp; Zhao, L. (2018). Global lake evaporation accelerated by changes in surface energy allocation in a warmer climate. \u003cem\u003eNature Geoscience\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(6), 410\u0026ndash;414. https://doi.org/10.1038/s41561-018-0114-8\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Caspian Sea level, hydrological, Volga, climate change","lastPublishedDoi":"10.21203/rs.3.rs-1666521/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1666521/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCaspian Sea level (CSL) fluctuations are driven by reciprocal hydro-meteorological processes that extend not only over the entire catchment area but also far beyond. According to the research literature, CSL has dropped approximately 3 m in the short-term (1929\u0026ndash;1977). In this study, change in precipitation, runoff, and evaporation in the catchment area, river discharge, sea surface temperature (SST), and also CSL change were investigated over 40-year decades. Study results reveal two major change in the increasing trend of SST anomalies. With an increasing trend of 0.03 at the CS level, the results indicate an increase in temperature of more than 1.5\u0026deg;C during the study period, which can be a response to the increase in air and land surface temperature on a regional and global scale. The decline in river discharge levels has also been significant in recent decades. Reconstruction of long-term change in CSL with daily water level data between 1981\u0026ndash;2020 through fluxes also showed that the average sea level rise was about 20 cm/yr in the 1981\u0026ndash;1995 period and a decrease of 6 cm/yr during the 1996\u0026ndash;2020 period was well visible in the results of the water balance equation. Moreover, according to the observed peak values of the four parameters precipitation, evaporation, runoff, and SST, the trend of increasing sea surface temperature and decreasing precipitation (with a slope of -1.8 mm/40\u0026nbsp;year) is more consistent with CSL change.\u003c/p\u003e","manuscriptTitle":"Monitoring of Caspian Sea level change affected by atmospheric parameters using remote sensing data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-29 15:17:26","doi":"10.21203/rs.3.rs-1666521/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c2fa35f-10f3-40e5-bfa3-05bdafc73d92","owner":[],"postedDate":"June 29th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-07-02T15:49:01+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-29 15:17:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1666521","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1666521","identity":"rs-1666521","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}