Mapping global lake dynamics reveals the emerging roles of small lakes

Restore polder and aquaculture enclosure to the lake: Balancing environmental protection and economic growth for sustainable development

The restoration of lakes and their buffer zones is crucial for understanding the intricate interplay between human activities and natural ecosystems resulting from the implementation of environmental policies. In this study, we investigated the ecological restoration of shallow lakes and buffer zones in the Yangtze-Huaihe River Basin, specifically focusing on the removal of polder and aquaculture enclosure areas within the lakes. By examining data from eight shallow lakes and their corresponding buffer zones, encompassing lake morphology, water quality parameters, and land use/land cover (LULC) data spanning from 2008 to 2022, which shed light on the complex relationships involved. During the process of restoring polder and aquaculture enclosure areas, we observed a general decrease in the extent of polders and aquaculture enclosures within the lakes. Notably, the removal of aquaculture enclosures had a more pronounced effect (reduction rate of 83.37 %) compared to the withdrawal of polders (reduction rate of 48.76 %). Linear regression analysis revealed a significant decrease in the concentrations of seven water quality parameters, including COD, CODMn, TN, TP, NH3-N, Chl-a, and F, while pH and DO factors exhibit a distinct increasing trend. The results of redundancy analysis and Pearson correlation analysis demonstrated significant correlations between the area of polders and aquaculture enclosures and the changes in lake water quality. Encouragingly, the withdrawal of polders and the removal of aquaculture enclosures had a positive impact on the lake water quality improvement. In contrast, the LULC in the buffer zones of the lakes experienced a gradual decline owing to land degradation, resulting in a reduction in ecosystem service value (ESV). These results offer valuable support for policymakers in their endeavors to restore lake water quality, mitigate the degradation of buffer zones land, and promote the sustainable development of land and water resources.

Thermal structure regulates the dynamics of carbon dioxide flux in alpine saline lake on the Qinghai-Tibet Plateau, China

Thermal stratification and mixing play important roles in the physicochemical composition of lakes and affect the geochemical cycle. However, the regulation of lake carbon exchange at the water-air interface by seasonal thermal structures remains unclear, especially for alpine saline lake on the Qinghai-Tibet Plateau (QTP). Based on continuous field sampling, carbon dioxide flux (FCO2) at the water-air interface in Qinghai Lake during the ice-free period was quantitatively analyzed by thin boundary layer model, as well as the driving factors of the change in FCO2 at the water-air interface. The findings revealed that the FCO2 was -22.16 ± 11.73 mmol m-2d-1 during the stratification period, and - 45.32 ± 29.67 mmol m-2d-1 during the mixing period. We found that thermal stratification limits the matter-energy exchange between the upper and bottom water columns, and carbonate precipitation results in a higher FCO2 than during mixing stage. However, the mixing process reduces the limiting effect of thermal stratification. During the carbonate process, water with higher salinity and pH at the bottom of the water column enters the upper part of the water column, reducing the partial pressure of carbon dioxide (pCO2) in the water column and causing the absorption of CO2 by the lake. Thermal stratification affects the vertical material-energy exchange and atmospheric CO2 uptake of lake. The present study further explains the possible underlying regulation of CO2 uptake in saline lake on the QTP involving the varied thermal structure.

Distribution characteristics and microbial synergistic degradation potential of polyethylene and polypropylene in freshwater estuarine sediments

The microbial degradation of polyethylene (PE) and polypropylene (PP) resins in rivers and lakes has emerged as a crucial issue in the management of microplastics. This study revealed that as the flow rate decreased longitudinally, ammonia nitrogen (NH4+-N), heavy fraction of organic carbon (HFOC), and small-size microplastics (< 1 mm) gradually accumulated in the deep and downstream estuarine sediments. Based on their surface morphology and carbonyl index, these sediments were identified as the potential hot zone for PE/PP degradation. Within the identified hot zone, concentrations of PE/PP-degrading genes, enzymes, and bacteria were significantly elevated compared to other zones, exhibiting strong intercorrelations. Analysis of niche differences revealed that the accumulation of NH4+-N and HFOC in the hot zone facilitated the synergistic coexistence of key bacteria responsible for PE/PP degradation within biofilms. The findings of this study offer a novel insight and comprehensive understanding of the distribution characteristics and synergistic degradation potential of PE/PP in natural freshwater environments.

Centennial loss of lake wetlands in the Yangtze Plain, China: Impacts of land use changes accompanied by hydrological connectivity loss

Humans have played a fundamental role in altering lake wetland ecosystems, necessitating the use of diverse data types to accurately quantify long-term changes, identify potential drivers, and establish a baseline status. We complied high-resolution historical topographic maps and Landsat imagery to assess the dynamics of the lake wetlands in the Yangtze Plain over the past century, with special attention to land use and hydrological connectivity changes. Results showed an overall loss of 45.6 % (∼11,859.5 km2) of the lake wetlands over the past century. The number of lakes larger than 10 km2 decreased from 149 to 100 due to lake dispersion, vanishing, and shrinkage. The extent of lake wetland loss was 3.8 times larger during the 1930s-1970s than that in the 1970s-1990s. Thereafter, the lake wetland area remained relatively stable, and a net increase was observed during the 2010s-2020s in the Yangtze Plain. The significant loss of lake wetland was predominately driven by agricultural activities and urban land expansion, accounting for 81.1 % and 4.9 % of the total losses, respectively. In addition, the changes in longitudinal and lateral hydrological connectivity further exacerbated the lake wetland changes across the Yangtze Plain through isolation between lakes and the Yangtze River and within the lakes. A total of 130 lakes have been isolated from the Yangtze River due to the construction of sluices and dykes throughout the Yangtze Plain, resulting in the decrease in the proportion of floodplain marsh from 28.3 % in the 1930s to 8.0 % in the 2020s. Furthermore, over 260 sub-lakes larger than 1 km2 (with a total area of 1276.4 km2) are experiencing a loss of connectivity with their parent lakes currently. This study could provide an improved historical baseline of lake wetland changes to guide the conservation planning to wetland protection and prioritization area in the Yangtze Plain.

Multivariate extremes in lakes

Extreme within-lake conditions have the potential to exert detrimental effects on lakes. Here we use satellite observations to investigate how the occurrence of multiple types of extremes, notably algal blooms, lake heatwaves, and low lake levels, have varied in 2724 lakes since the 1980s. Our study, which focuses on bloom-affected lakes, suggests that 75% of studied lakes have experienced a concurrent increase in at least two of the extremes considered (27% defined as having a notable increase), with 25% experiencing an increase in frequency of all three extremes (5% had a notable increase). The greatest increases in the frequency of these extremes were found in regions that have experienced increases in agricultural fertilizer use, lake warming, and a decline in water availability. As extremes in lakes become more common, understanding their impacts must be a primary focus of future studies and they must be carefully considered in future risk assessments.

Re-estimating China's lake CO2 flux considering spatiotemporal variability

The spatiotemporal variability of lake partial carbon dioxide pressure (pCO2) introduces uncertainty into CO2 flux estimates at the lake water-air interface. Knowing the variation pattern of pCO2 is important for obtaining accurate global estimation. Here we examine seasonal and trophic variations in lake pCO2 based on 13 field campaigns conducted in Chinese lakes from 2017 to 2021. We found significant seasonal fluctuations in pCO2, with decreasing values as trophic states intensify within the same region. Saline lakes exhibit lower pCO2 levels than freshwater lakes. These pCO2 dynamics result in variable areal CO2 emissions, with lakes exhibiting different trophic states (oligotrophication > mesotrophication > eutrophication) and saline lakes differing from freshwater lakes (-23.1 ± 17.4 vs. 19.3 ± 18.3 mmol m-2 d-1). These spatiotemporal pCO2 variations complicate total CO2 emission estimations. Using area proportions of lakes with varying trophic states and salinity in China, we estimate China's lake CO2 flux at 8.07 Tg C yr-1. In future studies, the importance of accounting for lake salinity, seasonal dynamics, and trophic states must be noticed to enhance the accuracy of large-scale carbon emission estimates from lake ecosystems in the context of climate change.

Analysis of taiga and tundra lake browning trends from 2002 to 2021 using MODIS data

Lakes in taiga and tundra regions may be silently undergoing changes due to global warming. One of those changes is browning in lake color. The browning interacts with the carbon cycle, ecosystem dynamics, and water quality in freshwater systems. However, spatiotemporal variabilities of browning in these regions have not been well documented. Using MODIS remote sensing reflectance at near ultraviolet wavelengths from 2002 to 2021 on the Google Earth Engine platform, we quantified long-term browning trends across 7616 lakes (larger than 10 km2) in taiga and tundra biomes. These lakes showed an overall decreased trend in browning (Theil-Sen Slope = 0.00015), with ∼36% of these lakes showing browning trends, and ∼1% of these lakes showing statistically significant (p-value <0.05) browning trends. The browning trends more likely occurred in small lakes in high latitude, low ground ice content regions, where air temperature increased and precipitation decreased. While temperature is projected to increase in response to climate change, our results provide one means to understand how biogeochemical cycles and ecological dynamics respond to climate change.

Global annual wetland dataset at 30 m with a fine classification system from 2000 to 2022

Wetlands play a key role in maintaining ecological balance and climate regulation. However, due to the complex and variable spectral characteristics of wetlands, there are no publicly available global 30-meter time-series wetland dynamic datasets at present. In this study, we present novel global 30 m annual wetland maps (GWL_FCS30D) using time-series Landsat imagery on the Google Earth Engine platform, covering the period of 2000-2022 and containing eight wetland subcategories. Specifically, we make full use of our prior globally distributed wetland training sample pool, and adopt the local adaptive classification and spatiotemporal consistency checking algorithm to generate annual wetland maps. The GWL_FCS30D maps were found to achieve an overall accuracy and Kappa coefficient of 86.95 ± 0.44% and 0.822, respectively, in 2020, and show great temporal variability in the United States and the European Union. We expect the dataset would provide vital support for wetland ecosystems protection and sustainable development.

Dynamics of algal blooms in typical low-latitude plateau lakes: Spatiotemporal patterns and driving factors

The alpine lakes distributed on the plateau are crucial for the hydrological, and biogeochemical cycle, and also serve as a guarantee for regional economic development and human survival. However, under the influence of human interference and climate fluctuations, lakes are facing problems of eutrophication and subsequent algal blooms (ABs) with acceleration, and the development and driving factors of this phenomenon need to be considered as a whole. In this study, ten lakes located on the Yunnan-Guizhou Plateau were selected as the study area to analyze the spatiotemporal distribution of ABs and possible controlling forces. The FAI (Floating Algae Index) derived from multiple MODIS products and water quality data under high-frequency monitoring were selected as the data sources for characterizing ABs. Three nutrient parameters and five meteorological variables were used to explore the driving factors affecting ABs. Various methods of trend detection and correlation analysis have been applied. The main results are as follows: (1) Dianchi Lake (in lake area) and Xingyun Lake (in area proportion) are the two lakes with the most serious ABs in the historical period; (2) ABs are mainly distributed on the shoreline and northern edge of lakes, and tend to stay away from the lake center during high-temperature periods of the day; (3) Six lakes show a decreasing trend in ABs, especially after 2018, while other lakes (including Fuxian, Chenghai, Yangzong, and Erhai) are increasing, not only in peak value but also in duration; (4) Lakes with severe ABs are all P-restricted lakes, the minimum temperature is the most sensitive meteorological factor, while the impact of precipitation against ABs has a time lag; (5) Establishing a warning system of temperature and nutrient concentration is critical in ABs adaptive strategy. This study is expected to provide scientific references for regional water management and the restoration of the eutrophic aquatic ecosystem.

An optical mechanism-based deep learning approach for deriving water trophic state of China's lakes from Landsat images

Widespread eutrophication has been considered as the most serious environment problems in the world. Given the critical roles of lakes in human society and serious negative effects of water eutrophication on lake ecosystems, it is thus fundamentally important to monitor and assess water trophic status of lakes. However, a reliable model for accurately estimating the trophic state index (TSI) of lakes across a large-scale region is still lacking due to their high complexity. Here, we proposed an optical mechanism-based deep learning approach to remotely estimate TSI of lakes based on Landsat images. The approach consists of two steps: (1) determining the optical indicators of TSI and modeling the relationship between them, and (2) developing an approach for remotely deriving the determined optical indicator from Landsat images. With a large number of in situ datasets measured from lakes (2804 samples from 88 lakes) across China with various optical properties, we trained and validated three machine learning methods including deep neural network (DNN), k-nearest neighbors (KNN) and random forest (RF) to model TSI with the optical indicators and TSI and derive the determined optical indicator from Landsat images. The results showed that (1) the total absorption coefficients of optically active constituents at 440 nm (at-w(440)) performs best in characterizing TSI, and (2) DNN outperforms other models in the inversion of both TSI and at-w(440). Overall, our proposed optical mechanism-based deep learning approach demonstrated a robust and satisfactory performance in assessing TSI using Landsat images (root mean squared error (RMSE) = 5.95, mean absolute error (MAE) = 4.81). This highlights its merit as a nationally-adopted method in lake water TSI estimation, enabling the convenience of the acquisition of water eutrophic information in large scale, thereby assisting us in managing lake ecology. Therefore, we assessed water TSI of 961 lakes (>10 km2) across China using the proposed approach. The resulting at-w(440) and TSI ranged from 0.01 m-1 to 31.42 m-1 and from 6 to 96, respectively. Of all these studied lakes, 96 lakes (11.40 %) were oligotrophic, 338 lakes were mesotrophic (40.14 %), 360 lakes were eutrophic (42.76 %), and 48 were hypertrophic (5.70 %) in 2020.

Ecological evolution of algae in connected reservoirs under the influence of water transfer: Algal density, community structure, and assembly processes

Reservoir connectivity provides a solution for regional water shortages. Understanding the water quality of reservoirs and the response of algal communities to water transfer could provide the basis for a long-term evolutionary model of reservoirs. In this study, a water-algal community model was established to study the effects of water transfer on water quality and algal communities in reservoirs. The results showed that water transfer significantly decreased total nitrogen and nitrate concentrations. However, the water transfer resulted in an increase in the CODMn concentration and conductivity in the receiving reservoir. Additionally, the algal density and chlorophyll-a (chl-a) concentration showed an increase with water transfer. Bacillariophyta, Cyanophyta, and Chlorophyta were the dominant algal phyllum in all three reservoirs. Water transfer induced the evolution of the algal community by driving changes in the chemical parameters of the receiving reservoir and led to more complex relationships within the algal community. The effects of stochastic processes on algal communities were also enhanced in the receiving reservoirs. These results provide specific information for water quality safety management and eutrophication prevention in connected reservoirs.

Identification of Playa Lakes and tracking their evolution pathways using geochemical models in the Great Indian Thar desert

Playa Lakes of the great Indian Thar desert are unique and intricate systems with pronounced scientific and ecological significance. In this study, the combined use of geospatial and field data assisted in depicting and understanding the changes within these natural systems. The purpose of this study is to provide a comprehensive dataset of Playa Lakes, which can help with an overall understanding of playas from a geochemical, ecological, and economic perspective. The 1163 surface depressions were accounted for as possible playas considering the threshold area of >5 km2. A total of 42 representative surface water samples were collected from the selected playas (Sambhar, Didwana, Pachpadra, and Pokhran) and hydrochemical analysis was carried out to identify the geochemical evolution of these playas. The major water types were Na-Cl and Na-Cl-SO4 type. Hardie and Eugster's model was used to explain the possible sequence of evolutionary pathways and brine shifts in the selected playas. Simulated evaporation modeling suggests precipitation of major evaporites (chlorides, carbonates, and sulphates) during progressive evaporation. Surface sediment analysis (X-ray diffraction) confirmed the presence of calcite, halite, and dolomite minerals in the playa sediments. The greatest economic value of these playas may be mineral production and providing food and habitat for migratory birds, though conservation and policy intervention is required for their sustainable utilization.

Fine-scale monitoring of lake ice phenology by synthesizing remote sensed and climatologic features based on high-resolution satellite constellation and modeling

Lake ice, as a crucial component of the cryosphere, serves as a sensitive indicator of climate change. Fine-scale monitoring of spatiotemporal patterns in lake ice phenology holds significant importance in scientific research and environmental management. However, the rapid and dynamic nature of the freeze-thaw process of lake ice poses challenges to existing methods, resulting in their limited application in small lakes. In this study, we propose a novel approach of investigating ice phenology of lakes in various sizes. We conducted a case study in Hoh Xil, known for its vulnerability to climate change and a wide distribution of small lakes, to analyze the ice phenology of 372 lakes (>1 km2) during 2017-2021. Firstly, ensemble machine-learning model was developed for lake ice identification from Landsat-8/9 and Sentinel-2 A/B imagery. The accuracy evaluation reveals the overall good performance for ice extraction results based on Landsat-8/9 (97.03 %) and Sentinel-2 A/B (96.89 %). Next, the XGBoost models were employed to reconstruct ice coverages on unobserved dates for the freezeup and breakup periods, respectively. Totally, 744 XGBoost models were constructed for the study lakes, and the majority of them perform well. Based on the reconstructed daily ice coverage, phenology parameters could be extracted for examining the spatiotemporal characteristics of ice cover and possible relationships with lake sizes and terrains. From early-October to early-November, the Hoh Xil lakes freeze from the northwest to the southeast, while the breakup period starts in late-March and lasts until late-June. Moreover, the results indicate relatively small variability in freezeup-end dates among lakes, but significant differences in breakup dates, showing a greater sensitivity to temperature variations. Furthermore, ice phenology in small lakes exhibit stronger consistency with subtle climatic fluctuations. The results highlight the significant role of ice phenology in small lakes, as they dominate the overall tendency of ice phenology in Hoh Xil.

Dynamic monitoring and analysis of chlorophyll-a concentrations in global lakes using Sentinel-2 images in Google Earth Engine

Remote estimation of Chlorophyll-a (Chl-a) has long been used to investigate the responses of aquatic ecosystems to global climate change. High-spatiotemporal-resolution Sentinel-2 satellite images make it possible to routinely monitor and trace the spatial distributions of lake Chl-a if reliable retrieval algorithms are available. In this study, Sentinel-2 images and in-situ measured data were used to develop a Chl-a retrieval algorithm based on 13 optical water types (OWTs) with a satisfying performance (R2 = 0.74, RMSE = 0.42 mg/m3, MAE = 0.33 mg/m3, and MAPE = 55.56 %). After removing the disturbance of algal blooms and other factors, the distribution of Chl-a in 3067 of the largest global lakes (≥50 km2) was mapped using the Google Earth Engine (GEE). From 2019 to 2021, the average Chl-a concentration was 16.95 ± 5.95 mg/m3 for the largest global lakes. During the COVID-19 pandemic, global lake-averaged Chl-a concentration reached its lowest value in 2020. From the perspective of spatial distribution, lakes with low Chl-a concentrations were mainly distributed in high-latitude, high-elevation, or economically underdeveloped areas. Among all the potential influencing factors, lake surface temperature had the largest contribution to Chl-a and showed a positive correlation with Chl-a in approximately 92.39 % of the lakes. Conversely, factors such as precipitation and tree cover area around the lake were negatively correlated with Chl-a concentration in nearly 61.44 % of the lakes.

Is ebullition or diffusion more important as methane emission pathway in a shallow subsaline lake?

Methane (CH4) emissions via ebullition contribute significantly to greenhouse gas emissions from freshwater bodies. According to the literature, the ebullition pathway may even be the most important pathway in some cases, particularly in shallow lakes. Ebullition rates are not often estimated because of the high uncertainty associated with episodic releases, leading to difficulties in their determination. This study provides an estimate of such emissions in a large, shallow, subsaline lake in eastern Austria, Lake Neusiedl, and compares them to the diffusion pathway. Ebullition gas sampling was conducted every 5-10 days over a period of 107 days from late March to mid-July 2021, using ebullition traps placed in three distinct locations: Reed belt, Channel and Open water/Lake. The aim was to study the temporal and spatial heterogeneity of ebullition and its contribution to total emissions. At the same time, several water quality and other environmental parameters were measured and then tested against the CH4 ebullition rates to explore them as potential drivers for this pathway. The carbon isotope fractionation factor (αC) of the measured CH4 ebullition gas, ranging from 1.03 to 1.06, indicates a dominance of the acetoclastic methanogenesis in the sediments of Lake Neusiedl, regardless of the location. The Reed belt location showed the highest mean CH4 ebullition rate (17 ± 28 mg CH4 m-2 d-1), which is >340-fold higher than the mean of the other two locations, and demonstrated also a strong temperature dependency. In all locations at Lake Neusiedl, the median CH4 fluxes via diffusion are significantly higher than via ebullition. Our analyses do not confirm the dominance of the ebullition pathway in any of the studied locations. Whereas at the Reed belt, ebullition accounts for 48 % of the CH4 emissions, in the other two locations, is responsible only for about 1 %.

Increased nitrous oxide emissions from global lakes and reservoirs since the pre-industrial era

Lentic systems (lakes and reservoirs) are emission hotpots of nitrous oxide (N2O), a potent greenhouse gas; however, this has not been well quantified yet. Here we examine how multiple environmental forcings have affected N2O emissions from global lentic systems since the pre-industrial period. Our results show that global lentic systems emitted 64.6 ± 12.1 Gg N2O-N yr-1 in the 2010s, increased by 126% since the 1850s. The significance of small lentic systems on mitigating N2O emissions is highlighted due to their substantial emission rates and response to terrestrial environmental changes. Incorporated with riverine emissions, this study indicates that N2O emissions from global inland waters in the 2010s was 319.6 ± 58.2 Gg N yr-1. This suggests a global emission factor of 0.051% for inland water N2O emissions relative to agricultural nitrogen applications and provides the country-level emission factors (ranging from 0 to 0.341%) for improving the methodology for national greenhouse gas emission inventories.

Metabolism and carbonate buffering drive seasonal dynamics of CO2 emissions from two German reservoirs

Biological metabolism drives much of the variation in CO2 in terrestrial ecosystems but does not explain CO2 oversaturation and emission in net autotrophic lakes and reservoirs. The unexplained CO2 could be attributed to the equilibria between CO2 and the carbonate buffering system, which is seldom integrated into CO2 budgets, let alone its interplay with metabolism on CO2 emissions. Here, we perform a process-based mass balance modeling analysis based on an 8-year dataset from two adjacent reservoirs with similar catchment sizes but contrasting trophic states and alkalinity. We find that in addition to the well-acknowledged driver of net metabolic CO2 production, carbonate buffering also determines the total amount and seasonal dynamics of CO2 emissions from the reservoirs. Carbonate buffering can contribute up to nearly 50% of whole-reservoir CO2 emissions, by converting the ionic forms of carbonate to CO2. This results in similar seasonal CO2 emissions from reservoirs with differing trophic state, even in low alkalinity system. We therefore suggest that catchment alkalinity, instead of trophic state, may be more relevant in predicting CO2 emissions from reservoirs. Our model approach highlights the important role of carbonate buffering and metabolism that generate and remove CO2 throughout the reservoirs on a seasonal scale. The inclusion of carbonate buffering could diminish a major uncertainty in the estimation of reservoir CO2 emissions and increase the robustness of aquatic CO2 emission estimates.

An intra-annual 30-m dataset of small lakes of the Qilian Mountains for the period 1987-2020

Small lakes (areas between 0.01 km² and 1 km²) on the Qinghai-Tibet Plateau (QTP) are prone to fluctuations in number and area, with serious implications for the surface water storage and water and carbon cycles of this fragile environment. However, there are no detailed long-term datasets of the small lakes of the QTP. Therefore, the intra-annual changes of small lakes in the Qilian Mountains region (QMR) in the northeastern part of the QTP were investigated. The small lake water bodies (SLWB) in the QMR were extracted by improving existing commonly used waterbody extraction algorithms. Using the Google Earth Engine platform and 13,297 Landsat TM/ETM + /OLI images, the SLWB of the QMR were extracted from 1987 to 2020 applying the improved algorithm, cross-validation and manual corrections. The reliability, uncertainty and limitations of the improved algorithm were discussed. An intra-annual small lake dataset for QMR (QMR-SLD) from 1987 to 2020 was released, containing eight attributes: code, perimeter (km), area (km²), latitude and longitude, elevation (m), area error, relative error (%), and subregion.