Large Lake Research Articles

Genome sequences of toxigenic cyanobacteria from a bloom in Lake Mattamuskeet, North Carolina (United States)

AbstractLake Mattamuskeet, the largest lake in North Carolina, USA, has undergone decades‐long eutrophication causing reduced water quality and promoting cyanobacterial blooms that may produce toxins. It is therefore necessary to evaluate the cyanobacterial diversity of the lake and their toxigenic potential. We present draft genomes of Microcystis, Pelatocladus, Raphidiopsis, and Umezakia strains isolated from Lake Mattamuskeet. The whole‐genome shotgun projects for Umezakia ovalisporum BLCC‐F208, Microcystis sp. BLCC‐F209, Microcystis sp. BLCC‐F210, Pelatocladus sp. BLCC‐F211, U. ovalisporum BLCC‐F215, and Raphidiopsis BLCC‐F218 have been deposited in GenBank under accession numbers JBHFLK000000000, CP169647, JBHFLL000000000, JBHFLM000000000, JBHFLN000000000, and JBHFLO000000000, respectively. Based on the genomic analysis, several biosynthetic gene clusters (BCGs) with varying degrees of similarity to known toxic and bioactive compound gene clusters were identified across the different cyanobacterial strains.

Quantifying the effects of wind wave on cyanobacterial blooms in large shallow lake from 10 years high frequency satellite observation

Quantifying the effects of wind wave on cyanobacterial blooms in large shallow lake from 10 years high frequency satellite observation

How Much Hatchery-Reared Brown Trout Move in a Large, Deep Subalpine Lake? An Acoustic Telemetry Study

Fish movement into large, deep lakes has been rarely investigated due to the complexity and extent of such ecosystems. Among the different monitoring methods available, acoustic telemetry enables the study of the spatial ecology and behavior of aquatic organisms in lentic environments. In this study, the movement of 69 hatchery-reared adult brown trout (size 43–61 cm) marked with acoustic transmitters was monitored in the large and deep subalpine Lake Lugano (Switzerland and Italy). Trout were tracked for six consecutive months by seven acoustic receivers (March–August 2022), positioned in a non-overlapping array. Trout movement was reconstructed using R packages specific for acoustic telemetry (actel and RSP), which also allowed us to translate tracking information into utilization distribution (UD) areas for each fish. The effects of different environmental variables (rainfall, water discharge of the two main tributaries of Lake Lugano, atmospheric pressure, cloud coverage, and moon phases) on trout movement were tested, but none of these variables seemed to significantly correlate with fish movement. After release, most of the tagged fish exhibited reiterative movements during the initial month, with some maintaining this behavior throughout the entire study period. This spatial behavior can be particularly evident in hatchery-reared fish due to their aggressive and bold attitude. The association of these behavioral traits, shaped by domestication, could expose hatchery-reared fish to high risks and post-release mortality in the wild. Indeed, within a few months after the release, most of the tagged fish were no longer detected by the acoustic receivers. In addition, 26% of the total tagged fish were caught by recreational or professional fishermen.

Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake

Abstract. A real-time forecast model of surface hydrodynamics in Lake Ontario (Coastlines-LO) was developed to automatically predict storm surges and surface waves. The system uses a dynamically coupled Delft3D–SWAN model with a structured grid to generate 48 h predictions for the lake that are updated every 6 h. The lake surface is forced with meteorological data from the High Resolution Deterministic Prediction System (HRDPS). The forecast model has been running since May 2021, capturing a wide variety of storm conditions. Good agreement between observations and modelled results is achieved, with root mean squared errors (RMSEs) for water levels and waves under 0.02 and 0.26 m, respectively. During storm events, the magnitude and timing of storm surges are accurately predicted at nine monitoring stations (RMSE <0.05 m), with model accuracy either improving or remaining consistent with decreasing forecast length. Forecast significant wave heights agree with observed data (1 %–12 % relative error for peak wave heights) at four wave buoys in the lake. Coastlines-LO forecasts for storm surge prediction for two consecutive storm events were compared to those from the Great Lakes Coastal Forecasting System (GLCFS) to further evaluate model performance. Both systems achieved comparable results with average RMSEs of 0.02 m. Coastlines-LO is an open-source wrapper code driven by open data and has relatively low computational requirements compared to GLCFS, making this approach suitable for forecasting marine conditions in other coastal regions.

Indications of a changing winter through the lens of lake mixing in Earth’s largest freshwater system

Abstract Global surface freshwater primarily resides in lakes, with the overwhelming majority found in Earth’s largest lakes, thus understanding potential climate change effects in these large lakes is critical. In dimictic lakes, climate change has extended the duration of summer thermal stratification and reduced the length of the ice season. These changes are relatively straightforward to evaluate in smaller, inland lakes. However in large lakes, such as the North American Great Lakes, temporally intermittent and spatially heterogeneous ice cover, and spatial thermal heterogeneity limit the utility of simple ice on-off or mixing classifications; therefore, assessing how climate change is impacting winter conditions in large lakes is challenging. Here, we use in-situ and satellite-derived surface water temperature observations from the North American Great Lakes to overcome these limitations and show that warming air temperatures are driving reductions in the number of winter days, collectively those with either ice cover or inverse thermal stratification, in favor of increases in isothermal conditions for the period 1995 to 2023. We find that on average the Great Lakes are experiencing a loss of 14 winter days per decade. Our results demonstrate how climate change has yielded disproportionate changes in the annual thermal cycle and mixing conditions of Earth’s largest freshwater system and signals the potential for fundamental ecosystem shifts due to a loss of winter.

Molecular investigation of harmful cyanobacteria reveals hidden risks and niche partitioning in Kenyan Lakes

Despite the global expansion of cyanobacterial harmful algal blooms (cHABs), research is biased to temperate systems within the global north, such as the Laurentian Great Lakes. This lack of diversity represents a significant gap in the field and jeopardizes the health of those who reside along at-risk watersheds in the global south. The African Great Lake, Lake Victoria, is understudied despite serving as the second largest lake by surface area and demonstrating year-round cHABs. Here, we address this knowledge gap by performing a molecular survey of cHAB communities in three anthropogenically and ecologically important freshwater systems of Victoria's Kenyan watershed: Winam Gulf (Lake Victoria), Lake Simbi and Lake Naivasha. We identified a bloom of non-toxic Dolichospermum and toxic Microcystis in the Winam Gulf, with data suggesting sulfur limitation shapes competition dynamics between these two bloom-formers. Though we did not detect a bloom in Naivasha, it contained the largest diversity of cHAB genera amongst the three lakes. In turn, our results indicated methane metabolism may allow non-toxic picoplankton to outcompete cHAB genera, while suggesting Synechococcus spp. serves as a methane source and sink in this system. Lake Simbi exhibited a non-toxic Limnospira bloom at the time of sampling with very low abundances of cHAB genera present. Subsequently, these results were employed to design a cHAB screening and risk assessment framework for local stakeholders. Cumulatively, this work serves to increase cHAB research efforts on the international scale while serving as an impetus for cHAB monitoring on the local scale.

Seasonal succession, host associations and biochemical roles of aquatic viruses in a eutrophic lake plagued by cyanobacterial blooms

Viruses are implicated as important biogeochemical mediators and ecological drivers in freshwater ecosystems. However, the dynamics of viruses and host associations over the seasons and blooming periods in eutrophic freshwater ecosystems remain elusive. From the water microbiomes of planktonic biomass from Lake Taihu, a large and eutrophic lake that suffered from yearly Microcystis-dominated harmful algal blooms (HABs) in China, we recovered 41,997 unique viral clusters spanning a wide taxonomic range, including 15,139 Caudovirales clusters targeting bacteria and 1,044 NCLDV clusters targeting eukaryotes. The viral community exhibited distinct seasonal succession driven by microbial communities (mainly Cyanobacteria and Planctomycetes) and environmental factors (mainly nutrients and temperature). Host prediction highlighted a more distinct viral impact on bacteria-driven nitrogen pathways than phosphate cycling through infection. HAB-induced microbial and environmental variations affected viral strategies including lifestyles, host range, and virus-encoded auxiliary metabolic genes (vAMGs) distributions. Viruses infecting Proteobacteria and Actinobacteria showed enhanced lysogenic lifestyle and condensed host ranges during HAB peak in summer, while viruses infecting Bacteroidota selected the opposite strategy. Notably, vAMGs were most abundant before HAB outbreak in spring, compensating for host bacterial metabolism including carbohydrates metabolism, photosynthesis, and phosphate regulation. The elucidated relationship between viruses, host microbes, and bloom-associated environment suggested prominent biochemical roles in the eutrophic freshwater ecosystems.

Impact of extreme rainfall and flood events on harmful cyanobacterial communities and ecological safety in the Baiyangdian Lake Basin, China

Globally, climate change has intensified extreme rainfall events, leading to substantial hydrological changes in aquatic ecosystems. These changes, in turn, have increased the frequency of harmful algal blooms, particularly those of cyanobacteria. This study examines cyanobacterial community dynamics in the Baiyangdian Lake Basin, China, after heavy rainfall and flooding events. The aim was to clarify how such extreme hydrological events affect cyanobacterial populations in floodplain ecosystems and assess related ecological risks. The results demonstrated a significant increase in cyanobacterial diversity, exemplified by an increase of the Shannon diversity index from an average of 1.7 to 2.1 (p < 0.05). Following heavy rainfall and subsequent flooding, the average relative abundance of cyanobacteria in the microbial community increased from 7.59 % to 9.62 %, along a notable rise in the abundance of harmful cyanobacteria. The community structure exhibited notable differences after flooding, showing an increase in species richness, but a decrease in community tightness and clustering, as well as a reduction in niche overlap among harmful cyanobacteria. Environmental factors such as dissolved oxygen, water temperature, and pH were identified as crucial predictors of harmful cyanobacterial community differences and abundance variations resulting from flooding. These findings provide a critical framework for predicting ecological risks associated with the expansion of bloom-forming cyanobacteria in large shallow lake basins, particularly under intensified rainfall and flooding events. This insight is essential to anticipate potential ecological disruptions in sensitive aquatic ecosystems.

Calcite precipitation: The forgotten piece of lakes' carbon cycle.

Lakes emit substantial amounts of carbon dioxide (CO2) into the atmosphere, but why they do remains debated. The long-standing vision of lakes as solely respirators of the organic matter leaking from the soils has been challenged by evidence that inorganic carbon produced by weathering of the catchment bedrock could also support lake CO2 emissions. How inorganic carbon inputs ultimately generate lake CO2 outgassing remains a blind spot. We develop and introduce a calcite module in a coupled one-dimensional physical-biogeochemical model that we use to simulate the carbon cycle of the large Lake Geneva over the past 40 years. We mechanistically demonstrate how the so-far neglected process of calcite precipitation boosts net CO2 emissions at the annual scale. Far from being anecdotal, we show that calcite precipitation could explain CO2 outgassing across various lakes globally, including some of the largest lakes in the world.

Toxigenic Cyanobacteria and Microcystins in a Large Northern Oligotrophic Lake Onego, Russia

Toxigenic cyanobacteria and microcystins in the oligotrophic pelagic zone and mesotrophic bay of Lake Onego—the second largest lake in Europe—were found for the first time. Microscopic analysis revealed that Dolichospermum lemmermannii, D. circinale and D. spiroides dominated in bloom spots in the oligotrophic zone of the lake and D. flos-aquae and Microcystis aeruginosa OKin the eutrophic bay. The abundance of cyanobacteria in bloom spots is potentially hazardous for humans and animals. PCR-analysis showed that mcyA gene involved in microcystin biosynthesis was found in cyanobacteria of the genera Dolichospermum and Microcystis. Five structural variants of intracellular microcystins were detected in a trace amount using high-performance liquid chromatography–mass-spectrometry of high resolution. The most hazardous hepatotoxin, MC-LR, was found only in the eutrophic bay. In the present study, the reasons for the low cyanotoxin content in the phytoplankton dominated by Dolichospermum are discussed. The findings of our study make a significant contribution to the accumulation of facts which state that toxigenic cyanobacterial blooms can occur in large oligotrophic lakes.

Somatic coliphages as bioindicators of contamination in Lake Guamuez, Colombia

Lake Guamuez is the second largest lake in Colombia and economically supports hundreds of families in the area. The main activities carried out in the region have focused on tourism, agriculture, livestock, and rainbow trout production; however, these activities have been associated with contamination of the lake. This research aimed to evaluate the water quality of Lake Guamuez using somatic coliphages (SCs) as bioindicators. For this purpose, periodic sampling was carried out for six months at 9 strategic points of the lake. For the detection of SCs, the method described in 9211 D of the Standard Methods for the Examination of Water and Wastewater was used. The genomic variability and presence of virulence genes in the isolated SCs were determined. Water contamination in the lake is evident, and the SCs titer is greater in areas with a high flow of anthropogenic activities. An important degree of genetic diversity and a high prevalence of virulence genes could be observed among the SCs analyzed. The results when compared with guidelines and water quality standards from various countries, showed concentrations of SCs higher than those allowed. The high prevalence of gastrointestinal diseases in the region suggests a link to water contamination.

Driving factors of phytoplankton Reynolds functional groups (RFGs) in a subtropical reservoir (São Paulo, Brazil)

Subtropical reservoirs are susceptible to the eutrophication process, largely due to inappropriate land use and occupation in the watersheds, resulting in changes in the composition of the phytoplankton community, according to the sensitivity and environmental tolerances of the different species. Application of the Reynolds functional groups (RFGs) method to the multispecies phytoplankton community is a valuable approach that can be used to answer ecological questions at different spatial and temporal scales. The Itupararanga reservoir, in the upper Sorocaba River basin (São Paulo state, Brazil), is used to supply drinking water to approximately one million people, as well as for agricultural and recreational purposes. This study examines the relationships between environmental variables and the composition of the phytoplankton community, identifying the main functional groups (RFGs) along a spatial gradient in the reservoir (from upstream to downstream), during one year. Five samplings were performed at seven points in the reservoir, in the rainy and dry seasons (from 2016 to 2017). The effects of spatial heterogeneity influenced the formation of different biomass gradients for each functional group. The most abundant RFGs were Sn (warm mixed environments), H1 (eutrophic, both stratified and shallow lakes with low nitrogen content), and Lo (deep and shallow, oligotrophic to eutrophic, medium to large lakes), which corresponded to the spatial heterogeneity of the reservoir. The variables with the greatest influence on the phytoplankton composition in the reservoir were total nitrogen, nitrite, and the N/P ratio, with phosphorus as the limiting nutrient. The presence of Cyanobacteria (codons Sn and H1) was driven by the stability of the water column, due to the long residence time of the water in the reservoir. This research demonstrated the effectiveness of using RFGs as environmental descriptors, providing an essential tool in aquatic ecology studies.

The structure of epiphytic and planktonic bacterial assemblages under two contrasting lake regimes

Abstract In contrast to the macrophyte‐dominated regime (MDR), the phytoplankton‐dominated regime (PDR) in shallow lakes is characterized by relatively high nutrient concentrations, excessive phytoplankton growth and turbid water column. Some floating‐leaved plants can grow under both regimes and aid in reducing nutrient concentrations in water bodies. Their epiphytic bacterial assemblages play an essential role in the health and adaptation of these plants; however, there is limited understanding of this plant‐bacteria interaction. Here, we used 16S rRNA gene‐based high‐throughput sequencing to compare the diversity, composition and co‐occurrence patterns of planktonic and epiphytic bacterial assemblages associated with the submerged roots and floating leaves of Trapa natans, growing under the PDR and MDR in Lake Taihu, China. Alpha diversity of PDR epiphytic bacterial assemblages was lower than in MDR. The opposite trend was observed for planktonic bacterial assemblages. We detected a strong compositional difference between epiphytic and planktonic bacterial assemblages. Bacterial assemblages found on root and leaf surfaces showed only minimal compositional overlap with those in the water column. The composition of epiphytic bacterial assemblages differed more between the two regimes than did planktonic bacterial assemblages. The PDR co‐occurrence network of bacterial assemblages was more complex than that of the MDR, with the PDR epiphytic habitats enriched more network keystone taxa. The proportion of positive edges was also greater in the PDR networks of epiphytic bacterial assemblages than in the epiphytic MDR networks, whereas it was lower in the planktonic bacterial network from the PDR than from the MDR. The comprehensive view of the diversity, composition and co‐occurrence patterns of epiphytic and planktonic bacterial assemblages of MDR and PDR in large shallow lakes provided by our results showed that the structure of epiphytic bacterial assemblages was mediated by both host and water properties, and the observed core taxa and network keystone species may play an important role in maintaining plant growth and adaptation to environmental change.

An assessment of N, P, Fe, Zn, Ni and Mo limitation on suspended nutrient diffusing substrates in nearshore areas of Lake Michigan and Lake Erie

In large lakes, metal availability sometimes limits the acquisition of nutrients (nitrogen, N and phosphorus, P) in offshore waters that are relatively isolated from tributaries and sediments. We hypothesize that metals may also be important within harmful algal blooms (HABs). HABs occur where nutrient loads are elevated, but bioassays often indicate that phytoplankton in HABs are N or P limited. Nutrient limitation may be exacerbated by corresponding limitations in several metals (i.e. nickel - Ni, molybdenum - Mo, zinc - Zn, and iron - Fe) that facilitate uptake and transformation of oxidized and organic forms of nutrients, such as urea, nitrate and organic phosphorus. The cyanotoxin microcystin has been hypothesized to have a role in metal management, so metal demand may also influence the toxicity of HABs. Here, we used nutrient diffusing substrates to measure how N, P, Ni, Mo, Zn and Fe amendments influenced the growth and toxicity of periphyton. Periphyton was grown suspended in 10 nearshore sites in Lake Michigan and Lake Erie (5 with and 5 without perennial HABs). Outside of blooms, we found no evidence for metal limitation or co-limitation. However, evidence for metal co-limitation was observed in two HABs sites (Zn in Green Bay and Zn, Mo, Ni and Fe in Sandusky Bay). N, P and Zn amendments all stimulated microcystin content in Maumee Bay. These data indicate that nutrient limitation occurs even within blooms, and the availability of metals may have an influence on growth, community composition and toxicity.

Regional distribution of anaplastic thyroid cancer in the United States

Abstract Introduction/Objective Anaplastic thyroid cancer (ATC) is an aggressive rare malignancy found in less than 2% of thyroid cancer patients. Its low occurrence rate presents challenges for collecting sufficiently large cohorts at a single institution, making statistical analysis about the disease’s spread, geographical distribution, and prevalence difficult. Nationwide data lakes such as Oracle EHR Real-World Data (ORWD, formerly Cerner Real World Data) offer a boost in cohort sizes and an opportunity to study ATC’s geographical distribution and assess regional differences. Methods/Case Report We searched ATC distribution across US regions defined by ZIP codes recorded in ORWD using multiple coding parameters, including ICD9, ICD10, and SNOMED as well as direct text searches for “anaplastic thyroid carcinoma”. We analyzed the geographical associations defined by ORWD (region) with sex, race, and age of ATC onset using the Fisher’s exact test. We combined 10 regions into 4 representative groups because of the small sample size. The groups are West Coast including the Rockies, Midwest from North Dakota to Texas, East Coast, and New England. Results (if a Case Study enter NA) Search for ATC condition codes in ORWD produced only 200 region-coded patient records across the US using multiple methods to try and identify cases. The mid-section of the US and coastal regions shows the highest numbers of ATC patients while New England and South regions show the lowest. While more women have ATC overall, the East Coast group has significantly more males (p value = 0.04). ATC is a predominantly white disease (79.8% overall) with black population increasing in East Coast group and other races (Asian/Undeclared) in West Coast (p-value &amp;lt; 0.001). There appears to be no significant association between age of ATC onset and the geographical groups. Conclusion The data shows geographic differences in distribution of ATC although this preliminary analysis neither explains the nature of these differences nor allows for a clearer view. The rarity of the disease complicates data analysis. This study was also limited by number of cases retrievable from the database itself and lack of granularity in coding available for ATC to find cases of interest, highlighting the difficulty of accurate tumor classifications in large data lakes. Further research into geographical distribution and environmental factors (radioactivity, pollution) of thyroid cancer using more advanced tools, such as GeoSpark, are planned.

Leveraging explainable machine learning for enhanced management of lake water quality

Freshwater lakes worldwide suffer from eutrophication caused by excessive nutrient loads, particularly nitrogen (N) and phosphorus (P) from wastewater and runoff, affecting aquatic life and public health. Using a large (1800 km2) subtropical lake as an example (Lake Okeechobee, Florida, USA), this study aims to (1) predict key water quality parameters using machine learning (ML) algorithms based on easily measurable variables, (2) identify spatial patterns of these parameters, and (3) determine environmental drivers influencing turbidity levels. The study employs four ML algorithms—Extreme Gradient Boosting (XGB), Light Gradient-Boosting Machine (LGBM), Support Vector Regression (SVR), and Random Forests (RFs)—to predict total phosphorus (TP), total nitrogen (TN), nitrate + nitrite (NOx-N), and turbidity, via station-specific and lake-wide modeling approaches. The station-specific models uncover spatial patterns, while the lake-wide models support operational decision-making. Results indicated that lake stage (water level), water temperature, and, most notably, turbidity were the main nutrient predictors, with XGB demonstrating superior prediction performance. Spatial analysis using K-means clustering identified three distinct lake regions based on nutrient levels and turbidity. Due to its importance, SHapley Additive exPlanations (SHAP) were employed to identify and quantify environmental factors affecting turbidity. Inflows and lake stage were found as primary drivers of turbidity near lake inlets, while wind speed and air temperature affected turbidity in the middle of the lake. This research advances the understanding of lake water quality dynamics, emphasizing the importance of frequent monitoring of turbidity and its environmental drivers for enhanced management and future mitigation efforts.

The substantial generation of photochemically produced reactive intermediates (PPRIs) in algae-type zones from one large shallow lake promoted the removal of organic pollutants

Photochemically produced reactive intermediates (PPRIs) are ubiquitously present in aquatic systems and hold significant importance in biogeochemical cycles. The photochemical reaction of dissolved organic matter (DOM), known as photosensitizers upon irradiation, is the main pathway for PPRIs generation. However, the PPRIs produced by algal-derived organic matter (ADOM) and their environmental effects remains elusive. This study confirmed that substantial PPRIs were generated by ADOM in the algal-derived areas. UV absorption spectra, fluorescence spectra and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) were then indicated a significant correlation between the molecular weight of DOM and the quantum yield of PPRIs, with lower molecular weight of DOM exhibiting a higher potential for PPRIs generation. Orthogonal partial least squares (OPLS) were used to build novel multivariate predictive models for indicating the PPRIs production in algae-type zone. Also, the higher concentrations of PPRIs could significantly removal different kinds of organic pollutants, such as bisphenol A (BPA), sulfadiazine (SDZ) and 17α-ethinylestradiol (EE2). Quenching experiments further elucidated that 3DOM⁎ was the key specie for pollutants degradation, serving as the precursor to generate a series of PPRIs. This study highlighted the importance of PPRIs generated from ADOM in the natural attenuation of pollutants and provided a new insight for understanding the self-purification in aquatic system.

Satellite-Based Detection of Algal Blooms in Large Alpine Lake Sevan: Can Satellite Data Overcome the Unavoidable Limitations in Field Observations?

Lake Sevan in Armenia is a unique, large, alpine lake given its surface, volume, and geographic location. The lake suffered from progressing eutrophication and, since 2018, massive cyanobacterial blooms repeatedly occurred. Although the lake is comparatively intensely monitored, the feasibility to reliably detect the algal bloom events appeared to be limited by the established in situ monitoring, mostly because algal bloom dynamics are far more dynamic than the realized monitoring frequency of monthly samplings. This mismatch of monitoring frequency and ecosystem dynamics is a notorious problem in lakes, where plankton dynamics often work at relatively short time scales. Satellite-based monitoring with higher overpass frequency, e.g., by Sentinel-3 OLCI with its daily overcasts, are expected to fill this gap. The goal of our study was therefore the establishment of a fast detection of algal blooms in Lake Sevan that operates at the time scale of days instead of months. We found that algal bloom detection in Lake Sevan failed, however, when it was only based on chlorophyll due to complications with optical water properties and atmospheric corrections. Instead, we obtained good results when true-color RGB images were analyzed or a specifically designed satellite-based HAB indicator was applied. These methods provide reliable and very fast bloom detection at a scale of days. At the same time, our results indicated that there are still considerable limitations for the use of remote sensing when it comes to a fully quantitative assessment of algal dynamics in Lake Sevan. The observations made so far indicate that algal blooms are a regular feature in Lake Sevan and occur almost always when water temperatures surpass approximately 20 °C. Our satellite-based method effectively allowed for bloom detection at short time scales and identified blooms over several years where classical sampling failed to do so, simply because of the unfortunate timing of sampling dates and blooming phases. The extension of classical in situ sampling by satellite-based methods is therefore a step towards a more reliable, faster, and more cost-effective detection of algal blooms in this valuable lake.

Earth observation reveals the shifting patterns of China's lake colour driven by climate change and land cover

Water colour has been recognized as one of the most important Essential Climate Variables of the lake ecosystem, as it is directly related to changes in water constituents and almost all of the lake's ecological changes could alter water colour. Given the high retrieval accuracy from existing Earth observation satellite data, water colour, in terms of Forel Ule Index (FUI), can be a realistic indicator to track the long-term changes in the lake ecosystem and further explore the lake response to environmental changes. This paper aims to comprehensively investigate the spatiotemporal variation patterns of FUI in 159 large lakes (≥25 km2) across China during 2000–2022 based on the MODIS data and detect the climatic and anthropogenic driving forces of these changes. The 23 years of MODIS records revealed an overall downward trend of lake FUI across China, indicating the lakes in China shifted to bluer colour during the past two decades. Through driving factor analyses, the complicated interplay among lake colour, lake morphology, regional climate shifts and human interference dynamics was uncovered. In the long term, it was found the pronounced change in lake colour in the western lake zones was primarily attributed to climate warming and humidification, whereas that in the eastern lake zones was mainly related to the alterations in regional land cover during the past two decades. Seasonally, lake basin's air temperature was identified as the main factor impacting the seasonal patterns of lake colour, followed by wind speed and runoff. Spatially, there was high spatial variability in lake colour across China, which was mainly associated with lake elevation and lake basin's precipitation rate, although the factors exhibited considerable divergence across different zones. Based upon the above findings, the implications for lake environment protection and management in different regions of China were further discussed.

Tracking the Filling, Outburst Flood and Resulting Subglacial Water Channel From a Large Canadian Arctic Subglacial Lake

AbstractWe use digital elevation models (DEMs) and ICESat‐2 data to study the filling and outflow from a large subglacial lake under Manson Icefield in the Canadian Arctic. When full, the lake is ∼17 × 3 km with an area of 52 km2. Early in 2021 the ice surface over the center of the lake sank by &gt;140 m implying a subglacial outburst flood of ∼4 km3. Rapid outflow occurred over ∼30 days at an average rate of ∼1,500 m3s−1 resulting in the formation of a single ∼15 km subglacial outflow path detectable from post‐outflow surface depression. The shape of the surface depression, 600–800 m wide by 2–4 m deep, reflects the shape of the subglacial channel prior to closure. Downstream ice movement appears unaffected by the outflow. After outflow ends the surface depression persisted over weeks, apparently dependent on the difference between water and overburden pressures.