Озерный фонд Европы

SDG indicator 6.6.1 «Change in the extent of water-related ecosystems over time» is designed to protect the integrity of ecological functions and preserve the biodiversity of water-related ecosystems - lakes, reservoirs, rivers, flooded wetlands, etc. The development of a national methodology for assessing the dynamics of changes in the area of water-related ecosystems is due to the need to generate data on the indicator within the framework of international approaches, to create a single database for making management decisions on the conservation of fresh water and the communities of plants, animals and microorganisms interacting with it.According to the concept of the international methodology, taking into account national characteristics, a project of national methodology was developed to calculate the proxy indicator 6.6.1.1 «Dynamics of changes in the area of surface water bodies», which is formed by comparing data on the area of lakes, reservoirs and rivers of the base period with the subsequent target five-year period. Based on the comparison of the base and target periods, the percentage change in spatial extent is calculated. To calculate the percentage change in spatial extent, data on the areas of lakes, reservoirs and rivers included in the Register of Surface Water Bodies of the Republic of Belarus, geoportal data on land types, certification results, cartographic calculations based on GIS, VIS and UAV own aerial photography are used. The calculation of changes in the water surface area for lakes, reservoirs, rivers was carried out. It is shown that in the study period (2018-2022), compared with the previous one (2008-2012), the water surface area of lakes (total number 2041), reservoirs (total number 84), rivers (total number 19) decreased respectively by 7,83 km2 or 0,63 %, 2,03 km2 or 0,31 %, 2,15 km2 or 0,27 %. Data, in accordance with the developed methodology, will be generated for a five-year period starting from 2023.

The lakes of Jianghan Plain, as an important component of the water bodies in the middle and lower reaches of the Yangtze River plain, have made significant contributions to maintaining the ecological health and promoting the sustainable development of the Jianghan Plain. However, there is a relatively limited understanding regarding the trends of lake area change for different types of lakes and their dominant factors over the past three decades in the Jianghan Plain. Based on the Google Earth Engine (GEE) platform, combined with the water body index method, the changes in area of three different types of lakes (area > 1 km2) in the Jianghan Lake Group from 1990 to 2020 were extracted and analyzed. Additionally, the Partial least squares structural equation model (PLS-SEM) was utilized to analyze the driving factors affecting the changes in water body area of these lakes. The results show that from 1990 to 2020, the area of the lakes of the wet season and level season exhibited a decreasing trend, decreasing by 893.1 km2 and 77.9 km2, respectively. However, the area of dry season lakes increased by 59.27 km2. The areas of all three types of lakes reached their minimum values in 2006. According to the PLS-SEM results, the continuous changes in the lakes’ area are mainly controlled by environmental factors overall. Furthermore, human factors mainly influence the mutation of the lakes’ area. This study achieved precise extraction of water body areas and accurate analysis of driving factors, providing a basis for a comprehensive understanding of the dynamic changes in the lakes of Jianghan Plain, which is beneficial for the rational utilization and protection of water resources.

The study of Lake Hamrin is of enormous importance in understanding changes in water levels and their impact on the local environment. It helps to clarify the role of climatic and human factors in the decline of its water resources from 2013 to 2023 in order to ensure sustainable solutions. Satellite imagery from Landsat 8 is used, and processing is performed using ArcGIS Pro and ENVI. The normalized vegetation index (NDVI) is applied to show the changes in the vegetation cover and reveal the amount of change in the surface water area of the lake. It is noted that the water area changes in one year as it increases in the rainy season and decreases in the summer, and it also varies from one year to another with the difference in the amount of rainfall. In March 2019, the largest area of the water surface of Hemrin Lake appeared, as the water surface area of the lake reached 326.87190 km² due to the increase in rainfall this year. It is noted that the lake's water area decreased during the rainy season due to the lack of rain, as the lake's water area decreased to 121.52340 km² in January 2021, while the smallest area of Hemrin Lake's water surface is reached in October 2021, as the lake's water surface area reached 28.79910 km², and the surface water area became 59.23530 km² in February 2022 due to the lack of rain during this year. In 2023, a relative increase in the lake's water area began to appear due to the return of seasonal rains this year, reaching 80.88570 km² in April, which proves to us that the change in rainfall in the region is one of the main reasons for the change in the area and water level in Hemrin Lake. The values of the earth's surface temperature are also revealed, and these values varied in the study area according to the different elements of the earth's surface and differed from one season to another and from one year to another. It is natural for the surface temperature of Hemrin Lake to rise in the summer and decrease in the winter. As the smallest area of the lake's water surface is 28.79910 km², the value of the lake's water surface temperature reached 24.9°C in October 2021. It is assumed that the area and water level of the lake decrease with the rise in temperature, but in August 2023, when the lake's water surface temperature is 31.9742°C, the lake's water surface area reached 69.75360 km². The main reasons for the decline in the water levels of Hemrin Lake are the lack of rainfall in recent years due to climate change and the lack of water releases from Lake Darbandikhan due to the lack of water coming to it from the Iranian side, as well as the significant decline in the waters of the Alwend River due to its interruption by the Iranian side due to the construction of dams and the change in its course and the cessation of its flow in the summer.

ABSTRACTLakes and ponds are important ecosystem components in arctic lowlands, and they are prone to rapid changes in surface area by thermokarst expansion and by sudden lake drainage. The 30 m resolution Landsat record (1984–2018) was used to derive a record of changes in the area of lakes and ponds in the five National Parks of northern Alaska. Surface-water area declined significantly in portions of the study area with ice-rich permafros t and water bodies of thermokarst origin. These declines were associated with rapid lake drainage events resulting from the thermoerosion of outlets. Thermoerosion was probably favored by the record warm mean annual temperatures in the study area, combined with precipitation that fluctuated near long-term normals. The rate of lake loss by rapid drainage was greatest in 2005–2007 and 2018. In landscapes with permafrost of lower ice content and water bodies in depressions of non-thermokarst origin, surface-water area generally fluctuated in response to year-to-year changes in precipitation, without a long-term trend, and lake drainage events were rare. Loss of surface water in ice-rich lowlands is likely to continue as the climate warms, with associated impacts on aquatic wildlife.

Lake level, area, and volume changes can describe the fluctuation of water bodies. In this study, ICESat/Hydroweb and Landsat data recorded with irregularly time intervals from 1975 to 2015 were used to examine changes in lake level and area which were combined to indirectly estimate water volume variations of Lake Hulun. The time series of lake level, area, and volume variations of Lake Hulun exhibited a fluctuating trend from 1975 to 1984 and the mean value were about 542.57m, 2065.76km2, and -0.045km3, respectively and an increasing trends showed during 1984-2000. Lake Hulun revealed the fastest decrease in lake level (-0.42m/a), volume (-0.83km3/a) from 2000 to 2009, and the fastest shrinking in surface area (-33.88km2/a) during 2000-2012. There was a seasonal variation of water level and lake volume variations during 2009-2012 and the mean value were 539.98m and -5.72km3, respectively. From 2012 to 2015, a faster increasing trends were shown in water level, area, and volume variations with a rate of 0.73m/a (the amount of change was 2.92m), 81.95km3/a (the amount of change was 327.8km2), and 0.42km3/a (the amount of change was1.67km3), respectively. The lake level and area showed strong correlations for Lake Hulun (R 2=0.93). The water volume changes were in very good agreement for lake level changes (R 2>0.99) and surface area variations (R 2=0.92). Combining with lake level and area changes, the sum of lake volume variation of Lake Hulun was obtained and it showed a positive water budgets of 0.24km3 during past 40years. River and groundwater discharge, the pan evaporation, the net pan evaporation, and the water diversion project were reasons for the lake level, area, and volume variations in Lake Hulun. This study demonstrates that remote sensing data can be used as a source of information for monitoring comprehensively the fluctuation of large water bodies.

The dynamics of surface water play a crucial role in the hydrological cycle and are sensitive to climate change and anthropogenic activities, especially for the agricultural zone. As one of the most populous areas in China’s river basins, the surface water in the Huai River Basin has significant impacts on agricultural plants, ecological balance, and socioeconomic development. However, it is unclear how water areas responded to climate change and anthropogenic water exploitation in the past decades. To understand the changes in water surface areas in the Huai River Basin, this study used the available 16,760 scenes Landsat TM, ETM+, and OLI images in this region from 1989 to 2017 and processed the data on the Google Earth Engine (GEE) platform. The vegetation index and water index were used to quantify the spatiotemporal variability of the surface water area changes over the years. The major results include: (1) The maximum area, the average area, and the seasonal variation of surface water in the Huai River Basin showed a downward trend in the past 29 years, and the year-long surface water areas showed a slight upward trend; (2) the surface water area was positively correlated with precipitation (p < 0.05), but was negatively correlated with the temperature and evapotranspiration; (3) the changes of the total area of water bodies were mainly determined by the 216 larger water bodies (>10 km2). Understanding the variations in water body areas and the controlling factors could support the designation and implementation of sustainable water management practices in agricultural, industrial, and domestic usages.

In this study, a framework to monitor the volumetric fluctuation of the inland water body by the combination of a bathymetry map, an optical satellite imagery & multiple satellite altimetry measurements is presented. In spite of the recent studies in monitoring water level changes in lakes using satellite altimetry & optical satellite imagery, it’s still evident that these methods are limited to the water level, surface area and volume changes. However, to effectively study the lakes, it’s important to quantify the total lake volume. This hasn’t been possible as the existing satellite methods cannot estimate the bathymetry depth. The methodology was developed over Lake Victoria during 1993–2016. The results indicate that the water level, area, and volume of Lake Victoria decreased over the past 23 years. The water level shows a slight decrease (−0.005 m/year) of a total of −0.115 m from 1993 to 2016. The changes in water level translates to a reduction in lake area (−100 km2) and volume (−5 km3). Despite the inconsistent changes in area and volume, significant reduction occurred between 1998 and 2006 where (3484 km2) and (122.87 km3) reduction in area and volumes respectively were observed.

Spatiotemporal changes in the surface area of inland water bodies have important implications in regional water resources, flood control, and drought hazard prediction. Although inland water bodies have been investigated intensively, few studies have looked at the effect of human activities and climate variability on surface area of inland waters at a larger scale over time and space. In this study, we used MODIS (MOD13Q1) images to determine water surface area extent at 250 m spatial resolution. We then applied this algorithm with MOD13Q1 images taken at 16-day intervals from 2000 to 2018 to a large river basin in China’s northeast high latitude region with dense stream network and abundant wetlands to investigate spatiotemporal distribution and dynamics of inland water bodies. The study identified 209 ponds, lakes, and reservoirs with an average total surface area of 2080 km2 in the past 19 years. The total water surface area fluctuated largely from 942 km2 to 5169 km2, corresponding to rainfall intensity and flood. We found that the total water surface area in this high latitude river basin showed an increasing trend during the study period, while the annual precipitation amount in the river basin also had an increasing trend concurrently. Precipitation and irrigation significantly contributed to the monthly change of water surface area, which reached the highest during June and August. The increase of water surface area was significant in the lower basin floodplain region, where agricultural irrigation using groundwater for rice production has progressed. Four nationally important wetland preserves (Zhalong, Xianghai, Momoge, and Chagan Lake) in the river basin made up nearly 50% of the basin’s total water surface area, of which Zhalong, Xianghai, and Momoge are designated by The Ramsar Convention as wetland sites of international importance. Seasonally, these water bodies reached their maximal surface area in August, when both the monsoon weather and agricultural discharge prevailed. This study demonstrates that water surface area in a high latitude river basin is affected by both human activities and climate variation, implying that high latitude regions will likely experience more changes in surface water distribution as global climate change continues and agriculture becomes intensified.

Errors in the estimates of changes in the area of individual water bodies and their groups have been studied in the central part of Yamal Peninsula based on Landsat satellite imagery. For the first time, the overlap zone of two satellite images along satellite trajectory has been proposed for the choice of the most efficient algorithm for identifying water surfaces and estimating the errors in water body area measurements. Empirical equations have been derived to describe the relationship between the error in estimates of changes in the areas of individual water bodies and their groups and the areas themselves. The seasonal and many-year variations in water body areas have been identified in the zone of intense construction of facilities at the Bovanenkovskoe oil-gas condensate field.

The use of Remote Sensing data in monitoring water bodies and reservoirs is a new and advanced approach that helps to study large areas covered by water in a short time and at a reasonable amount compared to traditional methods. Accordingly, the Remote Sensing technique integrated with Geographic Information Systems (GIS) has been used in several studies to monitor changes in water surface body area locally and regionally. In this study, several studies in Iraq were reviewed, using Remote Sensing techniques with the help of GIS to detect changes in the areas covered by the water bodies using satellite images captured in different periods. Natural and artificial lakes represent a large proportion of the water bodies in Iraq, including eight (8) lakes distributed in different regions of Iraq. An evaluation of the hydrological system of the studied water bodies showed that the changes in the area and size of natural and artificial lakes are affected by political, economic, and climatic conditions as the areas increase and decrease over years.

The change of inland lake area has a great impact on human life, ecological environment, and animal and plant habitats. But, the systematic and quantitative research on the long time change of water area and its human activity and climate change causes is very difficult. Here, taking the largest and youngest natural inland freshwater lake Hongjian Nur (HJN) in China as the study object, the long-term changes of water surface area of natural lakes and artificial reservoirs were retrieved. The impact of climate change on the change of HJN lake area is analyzed from the point of view of overall meteorological water shortage. The results showed that the natural lake area continued to decrease for 19 years from 1996 to 2015. On the contrary, the area of the reservoirs increased continuously. The determination coefficient of reservoir area and natural lake area is 0.357, which indicates that the impoundment of reservoirs has a significant impact on the decrease area of natural lake. The drought period for 13 years before 2011 also has a direct impact on the water area reduction of the lake. The future 1-2 years drought period or the transition from drought to humidity will threaten the maintenance of the existing water volume. Historical impact of multisource precipitation changes on the HJN water area proved the important influence of precipitation on the change of lake area. It is concluded that human activities and climate change will be the key constraints for maintaining ecological balance in lakes in the desert.

Qingtu Lake is located between Tengger Desert and Badain Jilin Desert, Gansu Province, Northwest China. It is the terminal lake of Shiyang River. In recent years, Qingtu lake has maintained a certain area of water surface and vegetation by artificial water conveyance. It is of great significance in preventing the convergence of the two deserts and restraining the trend of ecological deterioration of Shiyang River Basin. The relationship between the water surface area and the ecological water conveyance have not been thoroughly investigated. This study analyzed the spatial and temporal distribution of water surface area of Qingtu Lake and surrounding reeds by interpreting remote sensing data; the change of water surface area under the influence of meteorological factors and water conveyance by linear regression; the water conveyance to maintain current water surface area by water balance method, as well as the reasonable ecological water delivery in high flow year, normal flow year and low flow year by the means of analyzing the upstream inflow and water consumption in Minqin Basin. The results showed that there is a significant correlation between the water surface area of Qingtu Lake, evaporation and ecological water conveyance, and the minimum and maximum water surface areas generally appear before and after water delivery, indicating that the ecological water delivery and evaporation are the two main factors affecting the water surface area change of Qingtu Lake. The result calculated by linear regression indicated that the ecological water delivery volume to maintain current water surface area of Qingtu Lake is 3.146 × 107 m3/yr, while the value was 3.136 × 107 m3/yr calculated by water balance method. These two results are similar and can be verified with each other. Reasonable ecological water conveyance of Qingtu Lake in high flow year, normal flow year and low flow years were 4 × 107 m3/yr, 3.2 × 107 m3/yr and 2.3 × 107 m3/yr, respectively.

The surface area changes of 151 natural lakes over 37 months in the Yellow River Basin, based on remote sensing data and 21 meteorological indicators, employing spatial distribution feature analysis, principal component analysis (PCA), correlation analysis, and multiple regression analysis, identify key meteorological factors influencing these variations and their interrelationships. During the study period, lake area averages were from 0.009 km2 to 506.497 km2, with standard deviations ranging from 0.003 km2 to 184.372 km2. The coefficient of variation spans from 3.043 to 217.436, indicating considerable variability in lake area stability. Six primary meteorological factors were determined to have a significant impact on lake surface area fluctuations: 24 h precipitation, maximum daily precipitation, hours of sunshine, maximum wind speed, minimum relative humidity, and lakes in the source region of the Yellow River generally showed a significant positive correlation. For maximum wind speed (m/s), 28 lakes showed significant correlations, with five positive and twenty-three negative correlations, correlation coefficients ranging from −0.34 to −0.63, average −0.47, indicating an overall negative correlation between lake surface area and maximum wind speed. For maximum daily precipitation (mm), 36 lakes had 21 showing a positive correlation, indicating a positive correlation between lake surface area and daily precipitation in larger lakes. Furthermore, of the 117 lakes with sufficient data to model, the predictive capabilities of various models for lake surface area changes showcased distinct advantages, with the random forest model outperforming others in a dataset of 65 lakes, Ridge regression is best for 28 lakes, Lasso regression performs best for 20 lakes, Linear model is only best for 4 cases. The random forest model provides the best fit due to its ability to handle a large number of feature variables and consider their interactions, thereby offering the best fitting effect. These insights are crucial for understanding the influence of meteorological factors on lake surface area changes within the Yellow River Basin and are instrumental in developing predictive models based on meteorological data.

Distinctive water bodies surrounding lakes: An effective indicator for drought monitoring and assessment

The methods and results of a comparative analysis of the effects of climate changes on the dynamics of the areas of thermokarst lakes over the past 36 years in the Arctic regions on the Yamal, Gydan, and Taimyr peninsulas are considered, the areas of which are 114, 175 and 426 thousand km2, respectively. All three regions are located within the permafrost zone of the Siberian Arctic. Using images of the Landsat 4, 5, 7, and 8 satellites, time series of data on average values of lake areas for the indicated regions were obtained on the basis on averaging areas of lakes over 23 test (key) areas. The total area of the test sites is about 800 km2. Using the ERA5 reanalysis system, time series of data on the mean annual air temperature in these territories have been generated, which show a rise of the temperature over the studied period 1985–2021. A comparison of trends in changes in regional mean areas of lakes and mean annual air temperature shows that with approximately the same rate of the temperature rise on these peninsulas, different trends in the dynamics of the lake areas are observed, which are manifested, on the one hand, in a noticeable reduction in the areas of lakes in the territories of Yamal and Gydan and, on the other hand, in their growth in Taimyr. Air temperature averaged over the period 1985–2021 and coefficients of the linear trend of changes in the lake areas in each of the above regions were compared. The results show that on the territories of Yamal and Gydan, where the lake areas decrease, the mean air temperature for the same period is equal to –8.1±0.9 and –8.9±0.9 °С, respectively. On the Taimyr territory, where the lake areas increase, the mean air temperature is significantly lower: –12.8±0.94 °С. Thus, this makes possible to make a conclusion that these considered regions differ significantly from each other by values of mean air temperature, and respectively, they are characterized by different trends in changes in areas of the thermokarst lakes.