Lake Area Changes Research Articles

Both climate and anthropogenic impacts on recent lake area change in the Erdos Plateau

It is challenging to elucidate lake changes and their causality, particularly in the regions with strong anthropogenic impacts. This study examined the changes and patterns of surface water bodies in China's Erdos Plateau from 2000 to 2022, with a particular focus on how climate and anthropogenic activities have influenced these changes. The results indicate a significant decline in the total water area of the lakes, which decreased from 192 km2 in 2000 to 157 km2 in 2011, representing a decline of 35 km2 (18.23%). The decline was driven by reduced precipitation, increased evapotranspiration (ETP), and intensified anthropogenic activities such as mining, irrigation, and grazing. However, a notable recovery was observed, with the total lake area expanding to 276 km2 by 2022, reflecting an increase of 119 km2 (75.8%), largely due to the implementation of more stringent policy regulations. A quantitative analysis of the independent contributions of influencing factors, revealing that precipitation and mining accounted for approximately 25.47% and 22.22%, respectively, of the recorded changes in lake area. Other factors, including temperature, ETP, irrigation, and grazing, also played contributing roles. This study underscores the importance of adopting comprehensive management strategies, such as improving water resource management, enforcing environmental regulations, and promoting sustainable land use practices, to ensure the long-term sustainability of lake ecosystems in the Erdos Plateau. Coordinated efforts are essential to mitigate negative impacts and ensure the conservation of these critical water bodies.

Changes monitoring in Hongjiannao Lake from 1987 to 2023 using Google Earth Engine and analysis of climatic and anthropogenic forces

The research aimed to quantify the lake area dynamics, evaluate the changes in distance and rate of lake shorelines quantitatively and spatially and investigate the key factors influencing the Hongjiannao Lake (HL) area shrinkage. The study used remote sensing (RS) data from Landsat TM/ETM+ and OLI images and Google Earth Engine (GEE), a cloud platform for obtaining the lake surface area and island information from 1987 to 2023. A modified normalized difference water index (MNDWI) was applied to water area extraction. Digital Shoreline Analysis System (DSAS) was employed to assess net shoreline movement (NSM) and depict the lake shoreline length and rate changes. Furthermore, the water level was derived by ASTER GDEM V2 using the waterline method and lake boundaries. Six climatic features (temperature, precipitation, potential and actual evaporation, aridity index, and actual water difference) were investigated to find the driving factors of lake area shrinkage by correlation and factor analysis. The results reveal that during 1987–2023, the HL area has undergone four separate phases: stable (1987–1997), shrinkage (1998–2015), growth (2016–2019), and reduction (2020–2023). The most substantial negative change (−7.45%) in the HL area was observed in 1998. NSM analysis demonstrates that the lake has experienced both expansion and shrinkage at various times and locations. According to Water Balance Method, the water volume of HL exhibited variations, ranging from −0.1895 to −0.009 km³. The average yearly change in lake volume, water level, and area displayed similar characteristics with high inconstancy. Correlation and factor analysis of lake area and climatic factors demonstrate that higher precipitation, low temperatures, less potential evaporation level, lower actual evaporation rates, and more minor differences in water levels are associated with an increase in lake area. In contrast, the opposite conditions lead to a reduction in lake size.

Spatiotemporal change of lake shrinkage and its response to the unprecedented heat waves in the middle and lower Yangtze River Basin

ABSTRACT Due to the joint influence of human activities and climate change, lakes experience significant shrinkage or expansion, posing enormous challenges for water resource management and the ecological balance of lake ecosystems. As an important region abundant in water resources in China, the balance of water resources in the middle and lower Yangtze River Basin (MLY) is crucial for region sustainable development. However, to date, few studies have quantitatively analysed the lake area during heat waves in the MLY. To fill this gap, this study selected the summer of 2022, which encountered unprecedented heat waves, to analyse the changes of lake area in the MLY. Specifically, Sentinel-1 images were used as data sources, and the Random Forest (RF) model was applied to extract the monthly area of 62 lakes (area >10 km2) in the MLY from 2019 to 2022. The results showed that: (1) Compared to the Support Vector Machine (SVM) model and the Otsu method, the RF model had the highest accuracy, with its accuracy and Kappa coefficient in the testing samples of 97.14% and 0.94, respectively. During the period from June to September in 2022, the total area of 62 lakes decreased by 3475.50 km2. (2) Compared to the same period in the summer of 2019–2021, 23 lakes (37.10%) were observed to experience shrinkage. Among them, the area of Poyang Lake and Dongting Lake decreased to the level of the previous winter dry seasons in August 2022. (3) Temperature was the most important meteorological factor that caused changes in the lake area in the MLY, compared to precipitation, evaporation, and runoff. This research can help understand the impact of climate change on lakes and provide references for decision-making in the MLY to formulate the strategies of water resource management under the changing climate.

Spatiotemporal evolution and driving mechanism of Dongting Lake based on 2005–2020 multi-source remote sensing data

As one of the largest inland lakes in China, Dongting Lake has attracted widespread attention owing to its rich natural resources, unique geographical landscape, and important ecological functions. Recently, Dongting Lake has experienced phenomena such as an early dry season and backflow during the flood season. Multi-source remote sensing data and the normalised difference water index (NDWI) threshold method were used to systematically analyse the water area of the lake from 2005 to 2020. Additionally, it employed a centre of gravity migration model and a geographic detector model to investigate the lake's evolution patterns and driving mechanisms. The research identified notable fluctuations in Dongting Lake's water area during this period, with a particularly sharp decline in 2006—from 1509.74 km2 to 815 km2, marking a decrease of 694.74 km2 and a shrinkage rate of 46.01 %. Spatial analysis indicated that the centre of gravity of these water areas changed primarily between Nandashan Town, the Dongting Lake Management Committee, Wanzihu Township, and Qingtan Township, underscoring their significant influence on lake dynamics, including runoff, surface water availability, sediment deposition, and precipitation, all of which displayed strong positive correlations (Pearson coefficients of 0.57, 0.68, and 0.63, respectively), whereas population density showed a negative correlation (Pearson coefficient of −0.56). Furthermore, the study highlighted the substantial impact of the Digital Elevation Model (DEM) and its interaction with slope and aspect on Dongting Lake's evolution, with Q values of 0.537 and 0.543, respectively, emphasising their critical roles in shaping lake area changes and providing a crucial scientific basis for enhancing the understanding and effective management of water resources in the Dongting Lake Basin through comprehensive analysis of its spatiotemporal evolution and driving mechanisms.

Tibetan lake change linked to large-scale atmospheric oscillations via hydroclimatic trajectory

Lakes are known as sentinels of climate change, but their responses may differ from one to another leading to different strategies in lake protection. It is particularly the case in the Tibetan Plateau (TP) of multiple hydrological processes. We employed the Budyko framework to study Tibetan lakes from two lake-basins of contrasting climates for the period between 1980 and 2022: Taro Co Basin (TCB) in a sub-arid climate, and Ranwu Lake Basin (RLB) in a sub-humid climate. Our results showed that total lake area, surface air temperature, evapotranspiration, and potential evapotranspiration increased in both lake-basins, while precipitation and soil moisture increased in the TCB but decreased in the RLB. In the Budyko space, two basins had contrast hydroclimatic trajectories in terms of aridity and evaporative index. The TCB shifted from wetting to drying trend, while the RLB from drying to wetting in early 2000s. Notably, lake change was generally consistent with the drying/wetting phases in the TCB, but in contrast with that in the RLB, which can be attributed to warming-induced glacier melting. Despite of significant correlation with the large-scale atmospheric oscillations, it turned to be more plausible if lake area changes were substituted with basin's hydroclimatic trajectories. Among the large-scale oscillations, El Niño-Southern Oscillation (ENSO) is the most dominant control of lake trends and their drying/wetting shifts. Our findings offer a valuable insight into lake responses to climate change in the TP and other regions.

Analysis of lake changes and their influence factors in the three river regions from 2000 to 2020 in the Sanjiangyuan Region, China

An important factor for investigating climate change in the Sanjiangyuan is the evolution of the spatio-temporal pattern of lakes in this region. The present study used the Google Earth Engine (GEE) platform to extract lakes from 2000 to 2020. The present approach created a lake distribution dataset yearly and analyzed spatial and temporal patterns over 20 years. The analysis of lakes focused on the reaction of the Sanjiangyuan Lakes area to changes in climate, glaciers, and permafrost. The findings indicated that the Sanjiangyuan region contains 143 lakes, the majority of which are predominantly small, measuring 1–10 km2. The small lakes account for 60.14 % of the total and are primarily located in the source regions of the Yangtze River and Yellow River. The findings demonstrated that the Sanjiangyuan lakes experienced a significant expansion over the past two decades, particularly from 2011 to 2020. These lakes are divided into expanded, atrophic, and stable categories. Expanded lakes showed significant inter-annual trends in expansion, while atrophic lakes showed smaller fluctuations. The area of stable lakes experienced a consistent decline after 2010, despite a consistent expansion tendency from 2001 to 2010. Moreover, the results indicated that alterations in the size of glaciers and ice reserves in the Sanjiangyuan region have had the greatest influence on the fluctuation in lake area. Among the factors that affect the climate, temperature had the most significant effect on the change in lake area, followed by precipitation.

Evaluating Lakes Boundary Change: A Case Study from Ward Number 26 of Pokhara Metropolitan City

Lakes are crucial for their multi-faceted services, including drinking water, irrigation, tourism, and more. Understanding their status, including area coverage, is important, and continuous monitoring is essential. Lakes can be monitored through various approaches, ranging from traditional ground-based surveys to advanced earth observation satellites. This study aimed to monitor several lakes in Ward Number 26 of Pokhara Metropolitan City, including Khaste, Neurini, and Gunde Lakes, using high-resolution cadastral maps, ground-based field surveys, and Sentinel-2 earth observation satellites. Additionally, focus group discussions and key informant interviews were conducted. Supervised land use and land cover classification and the Normalized Difference Water Index method were employed to determine the area of each lake in 2017 and 2021. The focus group discussions and key informant interviews investigated factors affecting lake dynamics. The study found that climate change, land use change, floods, water abstraction, and landslides are major factors contributing to changes in lake areas. The results showed that the satellite-derived lake boundaries were smaller than those indicated on the cadastral map. Ground surveys using total station equipment also revealed that Khaste Lake shrunk by 6122.73 m² (0.01 km²), Neurini Lake lost 474.95 m² (0.00005 km²), and Gunde Lake shrunk by 4226 m² (0.004 km²) due to encroachment. Although satellite images are useful for detecting large-scale changes, this study indicates that the lake boundary areas identified through satellite imagery were smaller than those delineated by cadastral maps and ground surveys. This suggests that remote sensing analysis alone is insufficient for studying small lake boundary changes and should be combined with ground-based surveys. Therefore, a combination of satellite images and ground-based surveys is recommended for accurately and comprehensively detecting changes in lake areas.

Surface water area dynamics of the major lakes of Ethiopia (1985–2023): A spatio-temporal analysis

Ethiopian lakes face multiple ecosystem threats from human and natural factors, including climate change. The long-term dynamics of water surface area in many lakes remain unknown. This study presents the first comprehensive account of major Ethiopian lakes’ long-term water surface area dynamics, providing essential information for prioritizing hydrological conservation strategies. We extracted the time-series water surface area of the lakes from Landsat images in the Google Earth Engine (GEE), using the Modified Normalized Water Index (MNDWI) and machine learning (Random Forest) methods. Random Forest (RF) outperformed MNDWI across many lakes, especially when water body spectral characteristics became complex. A comparison of the results with the Joint Research Center (JRC) Global Surface Water Mapping Layers showed a good agreement. Long-term water surface area dynamics showed both common and distinct trends. Common trends included: (a) similar recession and expansion periodicities despite lakes being in different climatic zones, (b) an increase in the inter-annual variability of water area after 2000, and (c) a common abrupt point in time (2000/2001). Distinct trends included: (i) long-term decline in five lakes (Abiyata, Langano, Shalla, Chamo, and Hayq), (ii) expansion in three lakes (Ziway, Hawassa, and Abaya), and (iii) non-homogeneous long-term trends in two lakes (Tana and Hardibo). These common and distinct trends show the interplay of climate phenomena and lake-specific factors. Climate factors govern inter-annual lake area change (decreasing/increasing) and recession and expansion cycle periods. The magnitudes of changes and recovery rates after each change cycle vary based on lake-specific factors. We recommend prioritizing conservation efforts for lakes with high water storage degradation risks, as indicated by significant water loss over the historical period and persistent long-term declining trends. This includes Abiyata, Chamo, Shalla, Langano, Hayq, and Hardibo, in order of priority.

Response of changes in lake area to drought and land use change

The lake area is a crucial parameter that characterizes the state of a lake. Under the dual pressures of climate change and human activity, the magnitude and frequency of changes in lake areas become more pronounced. This process poses a serious threat to the local ecological environment. In this study, we constructed a lake water extraction model (LakeNet) based on a fully convolutional neural network. We extracted and analyzed the spatiotemporal characteristics of the area of nine major lakes from 1987 to 2022, as well as the driving factors behind these changes. Our results indicate that: 1) LakeNet exhibits high extraction accuracy and can remove some clouds. 2) The area of the nine major lakes shows a fluctuating downward trend (−8.11km2/10a), with drought and land use changes identified as significant driving forces behind the changes in lake boundaries, drought events caused the lake area to decrease, and the expansion of cropland further reduced the lake area. 3) Due to variations in lake area, the impact of drought on the area of the nine major lakes exhibits a lag effect, smaller lakes are likely to respond more quickly to drought.

Analysis of Lake Area Changes and Driving Factors in Nam Co and Selin Co from 1976 to 2021

Analysis of Lake Area Changes and Driving Factors in Nam Co and Selin Co from 1976 to 2021

Analysis of Lake Area Dynamics and Driving Forces in the Jianghan Plain Based on GEE and SEM for the Period 1990 to 2020

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.

Catchment characteristics dominate the hydrological behavior of closed lakes across the Tibetan Plateau

Naturally closed lakes located on the Tibetan Plateau provide a more authentic depiction of climate change and have undergone significant but dissimilar changes over the past four decades. Although previous research has concentrated on closed lake changes at regional and continental scales, the dominant factors related to closed lakes at the catchment scale remain elusive. This study employed the hierarchical minimum variance clustering method, six representative topographies and land surface descriptors to classify the catchments of 322 closed lakes (>1 km2) across the Tibetan Plateau from 1981 to 2020. To better understand the factors driving the changes in catchment-scale lake areas, we assigned two distinct attribute classes to the resulting classifications: hydro-meteorological characteristics and geographical environmental aspects. Our analysis revealed that five clusters are geographically coherent and exhibit distinct hydro-meteorological characteristics. By assessing decadal-scale changes in catchment characteristics over time for lakes in five clusters with area change of greater than ± 50 %, we found that the most significant driver of catchment changes from 1981 to 2000 was a continuous decline in snowmelt, followed by a substantial increase in precipitation, temperature, and glacial meltwater from 2000 to 2020. Regarding trends and magnitude, different lake area responses may occur in areas with similar physical geography and climatology (i.e., the same cluster). During the past four decades, only 3 % of lakes (19) shrank in area and decreased in change rate. Also, intense evaporation corresponds to a period of decrease in lake area. Despite the variation in lake area change among clusters, precipitation dominates the inconsistent increase in lake area under similar physical geography and climatology, with some exceptions strongly related to groundwater and melted permafrost recharge. Our findings have laid the groundwork for understanding this region's catchment characteristics and the resulting complex hydrological behavior.

Urban lakes change extraction using time series GaoFen-1 satellite imagery

Abstract. Urban lakes serve an indispensable role in maintaining the ecological balance of cities, ensuring flood safety, and providing recreational spaces for tourism. With the development of human activities and economic, the extent of urban lakes are inevitably influenced. Currently, the ability to detect detailed temporal changes in urban lake areas using high resolution data still has limitations. This study proposed a novel method by combining time series Gaofen-1 (GF-1) remote sensing data and random forest machine learning algorithm to explore the urban lakes change Zhushan Lake located in Wuhan. The research conducted the extraction of surface water for Zhushan Lake and its surrounding pit-ponds from 2013 to 2020. And then, a quantitative analysis of the characteristics and driving factors of lake changes is conducted. We find that (1) the accuracy of surface water extraction using the random forest classification method consistently exceeded 96%. The Kappa coefficient ranges from a minimum of 0.86 to a maximum of 0.99. (2) A noticeable decline was observed in the water areas of Zhushan Lake and its surrounding pit-ponds, predominantly along the northwestern shoreline and in the eastern pond regions. This decline is primarily attributed to pressures from building construction. The methodology proposed in this study is suitable for the area management of lakes in urban areas.

Lake changes and their driving factors in circum-arctic permafrost regions from 1990 to 2022

The rapid increase in temperatures and escalating human activities have led to a reduction or even disappearance of the thickness of the permafrost layer around the Arctic. Meanwhile, the degradation of permafrost has impacted the ecosystems within the circum-Arctic permafrost regions, also triggering a series of geological environmental changes. Consequently, research in the circum-Arctic permafrost regions is crucial not only for understanding the impact of climate change on the cryosphere but also for predicting and managing future environmental changes in the area. In this study, we extracted the number of lakes and their areas within certain areas of the circum-Arctic permafrost region from 1990 to 2022, and further investigated the factors driving lake changes. Analysis revealed a stabilization of lake areas in continuous permafrost regions, expansion in discontinuous and sporadic permafrost, and reduction in isolated permafrost, indicating the significant impact of global warming on the degradation of discontinuous and sporadic permafrost, while continuous permafrost remains less affected. Lake area dynamics across various permafrost types were influenced by distinct factors: in discontinuous permafrost, lake expansions correlated positively with snowfall, rainfall, snowmelt, and evapotranspiration; in sporadic permafrost, area reductions were tied to temperature increases, despite positive correlations with snowmelt and evapotranspiration; and in isolated permafrost, lake contractions were negatively associated with air temperature. Furthermore, permafrost degradation notably influenced lake area changes, especially in sporadic and isolated permafrost, where an inverse relationship between lake area and temperature was observed.

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States.

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the "black-box" nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having "no change" (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

Impacts of Climate Change and Human Activity on Lakes around the Depression of Great Lakes in Mongolia

The western region of Mongolia is characterized by an arid climate and a fragile ecological environment. It is a sensitive zone in response to global climate change and one of the major sources of dust globally. This region is home to numerous lakes, and their dynamic changes not only reflect global climate variations but also have implications for the global ecological environment quality. In this study, Landsat images were used as the data source, and Google Earth Engine (GEE) was employed to extract lakes with an area larger than 1 km2 from 1992 to 2021. The spatiotemporal characteristics of lake water area (LWA) changes were analyzed, and a structural equation model was applied to attribute the lake changes. The results indicate an overall trend of increasing lake area followed by a decrease in the study area. Specifically, lakes in the provinces of Khovd and Gobi-Altai exhibited a decreasing trend followed by an increasing trend, while lakes in the provinces of Uvs and Zavkhan showed an increasing trend followed by a decreasing trend. Three typical types of lakes, namely, alpine lakes, throughflow lakes, and terminal lakes, all exhibited a trend of increasing area followed by a decrease. The analysis of driving forces behind lake area changes reveals that climate change and human activities primarily exert indirect influences on the lake area changes in each province. Specifically, climate change and human activities lead to changes in soil moisture, which have a significant explanatory power for lake area changes. Regarding the typical types of lakes, climate change serves as the primary driving force for alpine lakes, while human activities are the main driving forces for throughflow lakes and terminal lakes.

Beyond the ice: decoding Lake Mertzbakher’s response to global climate shifts

This study addresses the critical problem of understanding the changing dynamics of glacier meltwater in Lake Mertzbakher, a challenge heightened by ongoing global climate change. Employing the innovative method of the Google Earth Engine (GEE) platform, this research meticulously extracted surface water data at 60 time points during the years 2000, 2005, 2010, 2015, and 2021. This approach represents a significant advancement over previous methods by offering more frequent and precise data analysis. We incorporated meteorological factors such as temperature and precipitation to assess their influence on the monthly changes in the glacier lake area. Our findings indicate a pronounced outburst in July, leading to a substantial decrease in the lake’s area, which reaches its lowest in September. Through detailed partial regression analysis, we established a hierarchy of meteorological influences on the lake’s area, identifying minimum temperature (r = 0.245), mean temperature (r = −0.239), precipitation (r = 0.228), radiation (r = 0.154), and maximum temperature (r = 0.128) as key factors. Additionally, our use of a structural equation model unveiled the most impactful elements, with mean temperature (r = −3.320), minimum temperature (r = 2.870), radiation (r = 0.480), and precipitation (r = 0.470) leading the effects. These insights mark a substantial contribution to our understanding of glacier lake dynamics, offering crucial data for predicting and managing glacier lake floods. This study’s novel methodology and comprehensive analysis underscore its significance in enhancing disaster prevention and preparedness strategies amidst the challenges of global climate change.

A quantitative method to infer lake area changes based on an extensive survey of lake surface sediment grain size across the Inner Mongolia Plateau, and its application to understanding the evolution of Lake Wulanhushao in northern China since 18.59 cal. kyr BP

Grain size is commonly used as a proxy to reconstruct past lake hydrology; however, published research rarely links grain size with modern processes, and uncertainties exist for interpretation. In this paper, we decompose the grain size frequency distributions (GSFD) for 196 lake surface sediments from 61 lakes and 9 reservoirs across the Inner Mongolia Plateau and adjacent areas and divide them into five GSFD components. We statistically analyze the relationship between the GSFD components and influencing variables to determine the taphonomic dynamics and their environmental implications. Within individual lakes, we found that GSFD component 4 (74.00–209.30 μm) and GSFD component 5 (209.30–592.00 μm) dominate near the lake margin and reduce toward the lake center in proportions. By contrast, GSFD component 2 (2.75–15.56 μm) increases toward the lake center. Therefore, the proportions of GSFD component 2, 4, and 5 reflect the relative distance to the lakeshore and allow inferences about lake area changes. We apply this large modern dataset to a sediment core obtained from Lake Wulanhushao (WLHS) on the Inner Mongolia Plateau and link the lake surface sediment-based GSFD components to BEMMA end-members of grain size records to define their environmental implications over the last 18.59 cal. kyrs BP. We found that the Lake WLHS has responded to the orbital and suborbital climate changes since the last deglaciation. Specifically, maximum lake transgression prevailed during the early to middle Holocene (11.70–7.10 cal. kyr BP), medium lake transgression dominated during 17.05–15.80 cal. kyr BP, and weak transgression developed during 14.60–12.90 cal. kyr BP. Maximum lake regressions prevailed before 17.05 cal. kyr BP, during 15.80–14.60 cal. kyr BP and 12.90–11.70 cal. kyr BP. Our reconstructed early Holocene maximum lake area is consistent with regional abiotic proxy-based moisture records, but contrasts with the regional biotic proxy-based moisture records, which reconstruct increasing moisture conditions. Our new method to determine lake area changes will provide valuable data to understand the climate dynamics of northern China.

Spatiotemporal lake area changes influenced by climate change over 40 years in the Korean Peninsula

Water resources in lakes of the Korean Peninsula play a significant role in society and ecosystems in both South and North Korea. This study characterized spatiotemporal changes in the lake area during the dry season (March–May) in the Korean Peninsula over the last 40 years. The satellite images (Landsat 5–9) were used to derive annual areas of 975 lakes during the dry season from 1984 to 2023. Our analysis indicated that the MNDWI is the optimal remote sensing-based index for delineating lake areas in the Korean Peninsula, with an overall accuracy of 92.3%. Based on the selected index, the total lake areas of the dry seasons have increased from 1070.7 km2 in 1984 to 1659.3 km2 in 2023, mainly due to newly constructed dam reservoirs. While the detailed changes in lake area vary, we found divergent results based on their sizes. The large lakes (> 10 km2) showed their area increased by 0.0473 km2 (0.1%) every year and have more influences from climate change. On the contrary, the small lakes (≤ 10 km2) have area decreases by 0.0006–0.006 km2 (0.15–0.5%) every year and have less influence from climate change. This study shows that the spatiotemporal lake area changes are determined by either climate change or human activity.

Interannual variation in lake areas over 50 km² on the Tibetan Plateau from 1986 to 2020 based on remote sensing big data

ABSTRACT Lake distribution on the Tibetan Plateau (TP) is extensive, and lake area changes are key indicators of the TP's climate change response. Many multisource remote sensing big data for the TP, particularly optical images, are unusable due to cloud cover. Therefore, an improved isobath interpolation-based lake area extraction method is proposed and applied to obtain annual average lake areas (≥ 50 km²) on the TP from 1986 to 2020 using remote sensing big data. The lake area result accuracy was verified using existing lake area and level datasets, yielding correlation coefficients of ∼0.9. The change points and segmented trends of each lake's interannual area sequence were obtained. The relationships between lake area and climatic variables were investigated. The positive accumulation of the total precipitation minus total evaporation explains the overall lake area expansion trend after 1995. The exorheic lake interannual area is related to precipitation more than that of endorheic lakes, but endorheic lake area changes are stronger. The shrinking of lakes on the southern TP may not be climate-driven but probably attributed to lake bottom leakage. We explore detailed interannual variation characteristics of lake areas on the TP and provide reference data for studying lake responses to climate change.