Assessment of spatio-temporal waterline changes of a reservoir: A case study of Ujjani wetland, Maharashtra, India

Wetland ecosystems have very high biodiversity. Changes in surface water area greatly impact the habitat of organism as well as Biodiversity in these areas. Crocodile Lake, located in the south of Cat Tien National Park, Tan Phu district, is recognized as the second Ramsar site in Vietnam and the 1499th in the world, with high value in terms of biodiversity, environment, landscape, and tourism. However, due to the significant impact of climate change, along with the influence of surrounding human activities. Crocodile Lake is facing challenges related to decreasing area, habitat loss for organisms, and declining biodiversity. Therefore, monitoring and studying the changes in surface water coverage in the Crocodile Lake area during time is extremely necessary. The main method of the research involves utilising available remote sensing data (Landsat imagery dataset), image processing and analysis, and creating image indices (NDWI - The Normalized Difference Water Index) to distinguish water coverage from other surface features in the study area from 2000 to 2023. The result of this research is creating of a map of water coverage changes in the Crocodile Lake area during this period.

Global warming, climate change, increasing population and industrialization cause spatial and temporal changes in surface water resources. Lake Burdur, located in Turkey’s Lakes Region, is a tectonic closed basin lake that has experienced significant reductions in water surface area and volume. In this study, the changes in the water surface area of Lake Burdur between 2003 and 2023 were analyzed using Google Earth Engine. Based on the obtained data the estimated water surface area values of Lake Burdur for years 2028 and 2033 were calculated. Within the scope of the study, Landsat 5 satellite images were used for the years 2003 and 2008, while Landsat 8 satellite images were utilized for the years 2013, 2018, 2023 and 2024. To calculate the water surface area values of Lake Burdur for the specified years, the Normalized Difference Water Index (NDWI) and the Automated Water Extraction Index for Shadow (AWEIsh) methods were utilized. In the study the data obtained from both methods on an annual basis were compared, and percentage difference values were calculated. Trend line functions were generated using the water surface area data obtained for the specified years to calculate future estimated values, and the accuracy of these functions was tested using the 2024 data. The findings after 2016 were also cross-verified and examined using Sentinel-2 data. The results indicate that the water surface area loss rate of Lake Burdur during the 20 year period between 2003 and 2023 was 22.82% according to the analysis conducted using NDWI, and this rate could increase to 34.56% by 2033. According to the analysis conducted using AWEIsh, the water surface area loss rate of the lake during the same period was calculated as 22.41% and it is estimated that this loss could reach 33.85% by 2033.

In this study, we conducted a comprehensive analysis of surface water area changes in 9235 small and medium-sized reservoirs across China from 1985 to 2021. Using Landsat and Sentinel-2 satellite data, our investigation delved into the spatiotemporal changes in these reservoirs and explored reservoir-based droughts. Using a robust algorithm, we examined the spatial and temporal patterns of surface water area (SWA) change on a national scale. While cumulative SWA remained stable at the national level, our analysis revealed diverse variations in individual catchments. To improve our understanding of reservoir-based hydrological drought, we introduced the Standardized Area Index (SAI). This index serves as a metric for quantifying drought severity and reveals a distinct north–south divide in China. The study shows that water-scarce northern regions experience prolonged and severe droughts, necessitating increased conservation efforts. Conversely, the water-rich southern region prioritizes increasing reservoir capacity. Our findings underscore the importance of small and medium-sized reservoirs in shaping China’s water resources landscape. Furthermore, this study provides valuable insights into the nuanced characteristics of droughts, facilitating the development of regionally tailored water management strategies.

Detection of changes in Earth surface features, for example lakes, is important for understanding the relationships between human and natural phenomena in order to manage better the increasingly scarce natural resources. This work presents a procedure of using modified normalised difference water index (MNDWI) to detect fluctuations of lake surface water area and relate it to a changing climate. The study used radiometrically and geometrically rectified Landsat images for 1986, 1995 and 2010 encompassing the Kyoga Basin lakes of Uganda, in order to investigate the changes in surface water area between the respective years. The standard precipitation index (SPI) and drought severity index (DSI) are applied to show the relationship between variability of surface water area and climate parameters. The present analysis reveals that surface water area fluctuation is linked to rainfall variability. In particular, Lake Kyoga sub-basin lakes experienced an increase in surface water area in 2010 compared to 1986. This work has important implications to water resources management for Lake Kyoga and could be vital to water resource managers across Ugandan lakes.

The Nile River morphology has changed in the last century, due to the geological, topographical and climatological conditions, as well as due to the human impacts. The main focus of the present study is to detect the surface morphological changes in the first and second Nile River reaches (south of Egypt). For this purpose, several Landsat images acquired at different dates are utilized and analysed based on Remote Sensing (RS) and GIS techniques. Different satellite-derived indices including Normalized Difference Water Index (NDWI), Water Ratio Index (WRI) and Automated Water Extraction Index (AWEI) are applied to generate the (land-water) maps from Landsat data and to create the maps of changes in order to detect the changes in the water surface areas. The results indicated high performance of NDWI in generating the (land-water) maps and creating the maps of changes in both studied reaches of Nile River. For the first reach, NDWI has the highest overall accuracy (about 99.23%) and the lowest absolute error when applied for surface change detection. For the second reach, the NDWI index gave an overall accuracy of 99.13% which indicate the effectiveness and superiority of this index in detecting the surface morphological changes. Moreover, the results for the first reach of the Nile River showed a slightly change in the water surface area during the period 1984–2011. The Nile River in the considered reach lost about 2.3% of its surface area. Meanwhile, the results for the second reach indicated an intense decreasing in the water surface area in the period 1984–2010 (about 13% of the water area in the year 1984), and the utmost of this decreasing occurred over the period from the year 2005 to the year 2010 (about 8.3%).

This short communication discusses how a specific geoarchaeological remote-sensing (RS) method, such as analyzing satellite images through NDWI (Normalized Difference Water Index), can be used to aid in searching and locating persons missing in watercourses. Thanks to its high capacity to analyze changes in the surface water area, this index can remotely detect the presence of anomalies related to disappearances in water bodies and provide valuable information that can reduce the use of human resources and help pinpoint likely areas of search. Two real-life cases of missing persons in rivers in which the NDWI index was used are presented, and the results obtained are discussed, emphasizing the importance of NDWI analysis as a complementary method to different approaches, especially non-invasive and remote-sensed ones, when positively searching for missing persons.

This paper presents a comparative analysis of surface water dynamics using Sentinel-2 satellite imagery and the Normalized Difference Water Index (NDWI) obtained through the Google Earth Engine (GEE) platform. The study focuses on assessing changes in surface water area and water ratio in the area between the winter and summer seasons of 2017 and 2023. Our results indicate a notable fluctuation in water area over the study period, with the reservoir exhibiting varying extents of surface water coverage across different seasons and years. Specifically, in the summer of 2023, the water area was measured at 14.35 km2, compared to 14.98 km2 in 2017. Conversely, during the winter months, the water area decreased to 12.54 km2 in 2023, while it was 14.68 km2 in 2017. The findings suggest a shift in surface water dynamics over time, potentially influenced by climatic and environmental factors. Furthermore, the study highlights the efficiency of utilizing GEE and remote sensing techniques for surface water mapping and monitoring. Remote sensing provides a cost-effective and reliable means of monitoring surface water resources, enabling timely assessments and informed decision-making for water resource management and conservation efforts. This research underscores the importance of leveraging remote sensing technologies for effective resource management and environmental stewardship in the face of changing climatic conditions.

{Abstract:[Arctic landscapes are in a state of transition due to changes in climate occurring during both the summer and winter seasons. Scattered observations indicate that beavers (Castor canadensis) have moved from the forest into tundra areas during the last 20 years, likely in response to broader physical and ecosystem changes occurring in Arctic and Boreal regions. The implications of beaver inhabitation in the Arctic and Boreal are unique relative to other ecosystems due to the presence of permafrost and its vulnerability associated with beaver dams and inundation. Our study specifically examines the role of beavers in controlling surface water dynamics and related thermokarst development in low Arctic tundra regions. We mapped the number of beaver dams visible in sub-meter resolution satellite images acquired between 2002 and 2019 for a 100 square kilometer study area (12 years of imagery) near Kotzebue, Alaska and a 430 square kilometer study area (3 years of imagery) encompassing the entire northern Baldwin Peninsula, Alaska. We show that during the last two decades beaver-driven ecosystem engineering is responsible for the majority of surface water area changes and inferred thermokarst development in the study area. This has implications for interpreting surface water area changes and thermokarst dynamics in other Arctic and Boreal regions that may also result from beaver dam building activities. \n This geospatial dataset provides polygon vector files representing surface water area in a 100 square kilometer study area located near Kotzebue, Alaska. Surface water area maps were created using sub-meter resolution satellite imagery for the years 2002, 2007-2014, and 2017-2019. Image selection focused on cloud-free, ice-free, and calm surface water conditions with images being acquired between late-June and mid-August in a given year. All images were resampled to a spatial resolution of 70 centimeter to match the lowest resolution image in the time series prior to analysis. Within year image dates range from 25 June to 22 August with the average date of image acquisition being 17 July (table 1). Object-based image analysis was conducted in eCognition Essentials 1.3.]}

Deltas and lagoons, which contain many flora and fauna, have rich coastal ecological and biological environments, and are wetlands of vital importance for humans. In this study, the current problems in all coastal Ramsar sites in Turkey are summarized, and changes in water surface areas are investigated using Landsat and Sentinel 1/2 satellite images on the Google Earth Engine (GEE) cloud computing platform. Landsat TM and OLI images were used in the long-term analysis, and time series were created by taking annual and July to September averages between 1985 and 2020. In the short-term analysis, monthly averages were determined using Sentinel 2 images between 2016 and 2020. Sentinel-1 Synthetic Aperture Radar (SAR) images were used in the months when optical data were not suitable for use in monthly analysis. The Normalized Difference Water Index (NDWI) was used to extract water surface areas from the optical images. Afterwards, a thresholding process was used for both optical and radar images to determine the changes. The changes were analyzed together with the meteorological data and the information obtained from the management plans and related studies in the literature. Changes in the water surface areas of all coastal Ramsar sites in Turkey were determined from 1985 to 2020 at different rates. There was a decreasing trend in the Goksu and Kızılırmak Deltas, which also have inland wetlands. The decreasing rates from 1985 to 2020 were −24.52% and −2.86%, for annual average water surfaces for the Goksu and Kızılırmak Deltas, respectively, and −21.64% and −6.34% for the dry season averages, respectively. However, Akyatan Lagoon, which also has inland wetlands, showed an increasing trend. Observing the annual average surface area from 1985 to 2020, an increase of 438 ha was seen, corresponding to 7.65%. Every year, there was an increasing trend in the Gediz Delta and Yumurtalık Lagoons, that do not have inland wetlands. The increasing rates from 1985 to 2020 were 46.01% and 17.31% for the annual average surface area, for the Gediz Delta and Yumurtalık Lagoons, respectively, and 38.34% and 21.04% for the dry season average, respectively. The obtained results reveal the importance of using remote sensing methods in formulating strategies for the sustainable management of wetlands.

For small tropical islands with limited freshwater resources, understanding how island hydrology is influenced by regional climate is important, considering projected hydroclimate and sea level changes as well as growing populations dependent on limited groundwater resources. However, the relationship between climate variability and hydrologic variability for many tropical islands remains uncertain due to local hydroclimatic data scarcity. Here, we present a case study from Kiritimati, Republic of Kiribati (2°N, 157°W), utilizing the normalized difference vegetation index to investigate variability in island surface water area, an important link between climate variability and groundwater storage. Kiritimati surface water area varies seasonally, following wet and dry seasons, and interannually, due to hydroclimate variability associated with the El Niño/Southern Oscillation. The NIÑO3.4 sea surface temperature index, satellite‐derived precipitation, precipitation minus evaporation, and local sea level all had significant positive correlations with surface water area. Lagged correlations show sea level changes and precipitation influence surface water area up to 6 months later. Differences in the timing of surface water area changes and variable climate‐surface water area correlations in island subregions indicate that surface hydrology on Kiritimati is not uniform in response to climate variations. Rather, the magnitude of the ocean–atmosphere anomalies and island–ocean connectivity determine the extent to which sea level and precipitation control surface water area. The very strong 2015–2016 El Niño event led to the largest surface water area measured in the 18‐year data set. Surface water area decreased to pre‐event values in a similarly rapid manner (<6 months) after both the very strong 2015–2016 event and the 2009–2010 moderate El Niño event. Future changes in the frequency and amplitude of interannual hydroclimate variability as well as seasonal duration will thus alter surface water coverage on Kiritimati, with implications for freshwater resources, flooding, and drought.

Long-term spatiotemporal changes of surface water and its influencing factors in the mainstream of Han River, China

The spatiotemporal changes in surface water area (SWA) in the basins of Northeast China have far-reaching impacts on their economic, agricultural, and social development and ecological sustainability. However, the long-term variation characteristics of water bodies in the Northeast basin and its main driving factors are still unclear. Based on the global surface water dataset, combined with the Meteorological and Vegetation Normalized Index (NDVI) datasets, this study used linear regression and correlation analysis to investigate the temporal and spatial variation characteristics of surface water in Northeast China and its driving factors from 1988 to 2020. The results show that (1) the seasonal surface water area (SSWA) and permanent surface water area (PSWA) in Northeast China increased at the rates of 58.408 km2/ year and 169.897 km2/ year, respectively, from 1988 to 2020. Taking 2000 as the node, PSWA and SSWA showed a trend of first decreasing and then increasing. (2) Changes in surface water types in each basin have significant space–time differences, and the transition between water bodies is dominated by the addition and reduction of seasonal water bodies. PSWA decreased significantly in western basins such as the Ulagai River Basin, the Otindag Desert, and the Liao River Basin, but increased significantly in the Songhua River Basin. (3) The driving forces of surface water change in different basins are different. Temperature and NDVI play a leading role in the change of SWA in the western arid region; permafrost degradation under the condition of air temperature rise is an indispensable factor affecting SWA change in the Argun River Basin; the eastern basin with a larger surface water area responded more strongly to changes in precipitation and evapotranspiration. Land-use conversion and water conservancy project construction were the main reasons for the increase of SWA in the Songhua River Basin under reduced precipitation. This research provides a reference for the in-depth study of the characteristics of surface water resources in Northeast China and has important practical significance for the scientific management of water resources in the basin.

Water is an essential necessity for maintaining the life cycle on Earth. These resources are continuously changing because of human activities and climate-related factors. Hence, adherence to effective water management and consistent water policy is vital for the optimal utilization of water resources. Water resource monitoring can be achieved by precisely delineating the borders of water surfaces and quantifying the variations in their areas. Since Lake Van is the largest lake in Turkey, the largest alkaline lake in the world, and the fourth largest terminal lake in the world, it is very important to determine the changes in water surface boundaries and water surface areas. In this context, the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI) and Automatic Water Extraction Index (AWEI) were calculated from Landsat-8 satellite images of 2014, 2017, 2020 and 2023 in June, July, and August using the Google Earth Engine (GEE) platform. Water pixels were separated from other details using the Canny edge detection algorithm based on the calculated indices. The Otsu thresholding method was employed to determine water surfaces, as it is the most favored technique for calculating NDWI, AWEI, and MNDWI indices from Landsat 8 images. Utilizing the Canny edge detection algorithm and Otsu threshold detection approaches yielded favorable outcomes in accurately identifying water surfaces. The AWEI demonstrated superior performance compared to the NDWI and MNDWI across all three measures. When the effectiveness of the classification techniques used to determine the water surface is analyzed, the overall accuracy, user accuracy, producer accuracy, kappa, and f score evaluation criteria obtained in 2014 using CART (Classification and Regression Tree), SVM (Support Vector Machine), and RF (Random Forest) algorithms as well as NDWI and AWEI were all 100%. In 2017, the highest producer accuracy, user accuracy, overall accuracy, kappa, and f score evaluation criteria were all 100% with the SVM algorithm and AWEI. In 2020, the SVM algorithm and NDWI produced the highest evaluation criteria values of 100% for producer accuracy, user accuracy, overall accuracy, kappa, and f score. In 2023, using the SVM and CART algorithms as well as the AWEI, the highest evaluation criteria values for producer accuracy, user accuracy, overall accuracy, kappa, and f score were 100%. This study is a case study demonstrating the successful application of machine learning with Canny edge detection and the Otsu water surfaces thresholding method.

The spatial and temporal changes in surface water area were analyzed in Dang district of south Gujarat.The time series of normalized difference water index (NDWI) with Google earth engine (GEE) open source online planetary processing platform was used.Spatial distribution precipitation, LST, NDVI and ETa were also analyzed.The surface water availability showed high positive correlation with precipitation (0.76), LST (0.93) and (0.90). ETThe surface water availability varied month wise, minimum surface water area was observed in a May while maximum in January Availability of surface water increases NDVI and ETa thus decrease LST and . .

River channel change is a dynamic process which involves erosion, accretion, lateral migration and changes in its width through the geomorphological process. Shifting of a river causes a direct impact on natural and manmade constructions which are located near the floodplain with consistent damage and devastation of the area. The objective of this study is to analyze the spatiotemporal variation along the Satluj river during the period 1990 to 2019 using Landsat TM, ETM+ and OLI satellite data. Spatiotemporal analysis of the river channel has been carried out to analyze the river channel change morphology, mid-line channel shifting and changes in surface water area of the Satluj river using twenty randomly distributed cross-sections (X1-X20). For surface water area change analysis, the satellite derived normalized difference water index (NDWI) has been used. The obtained results indicates that the river width increased by 0.033 km in overall cross-section (X1-X20) in the year 2019 as compared to the year 1990. Mid-line shifting of the river is mostly towards the left bank of the river. The maximum channel shifting of 1.364 km took place from Chak Bandala village in Jalandhar district to Madarpur village in Moga district between the years 1990 and 2019. However, the NDWI results indicate that the river surface area has been reduced by -90.33 km2 between the years 1990 and 2019. Additionally, the average shifting rate of the Satluj river during the period 1990 to 2019 is by 0.778 km.