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Lake ice thickness (LIT) is important for regional hydroclimate systems, lake ecosystems, and human activities on the ice, and is thought to be highly susceptible to global warming. However, the spatiotemporal variability in LIT is largely unknown due to the difficulty in deriving in situ measurements and the lack of an effective remote sensing platform. Despite intensive development and applications of lake ice models driven by general circulation model output, evaluation of the global LIT is mostly based on assumed “ideal” lakes in each grid cell of the climate forcing data. A method for calculating the actual global LIT is therefore urgently needed. Here we use satellite altimetry to retrieve ice thickness for 16 large lakes in the Northern Hemisphere (Lake Baikal, Great Slave Lake, and others) with an accuracy of ∼0.2 m for almost three decades. We then develop a 1-D lake ice model driven primarily by remotely sensed data and cross-validated with the altimetric LIT to provide a robust means of estimating LIT for lakes larger than 50 km2 across the Northern Hemisphere. Mean LIT (annual maximum ice thickness) for 1313 simulated lakes and reservoirs covering ∼840,000 km2 for 2003–2018 is 0.63 ± 0.02 m, corresponding to ∼485 Gt of water. LIT changes are projected for 2071–2099 under RCPs 2.6, 6.0, and 8.5, showing that the mean LIT could decrease by ∼0.35 m under the worst concentration pathway and the associated lower ice road availability could have a significant impact on socio-economic activities.

Lake ice phenology is regarded as a good proxy for the past and present climates. Long time series passive microwave radiometry data are used to estimate lake ice phenology variations in the Qinghai–Tibet Plateau (QTP), and a contrasting pattern of phenology change trend is found that the time series trend of lake ice freeze-up or break-up time is obviously reversed for lakes in the QTP. The reason for this contrasting trend of lake ice phenology is discussed based on factors such as salinity, water volume change, and air temperature change. Lake ice phenology data are separated based on lake salinity for the climate study: lake ice phenology of lakes with low salinity can be used as air temperature and climate change indicator, whereas lake ice phenology of lakes with high salinity and a low water volume can be used as an indicator of water volume variation under climate change. Correlation analysis of air temperature and the lake ice phenology show that air temperature is the main driving factor behind lake ice phenology variations. The lake ice phenology results suggest overall rising air temperatures during the period 1987–2017 in all regions of the QTP.

International audience; Zooplankton can spend winter actively under the ice cover of lakes. However, dietary resources under lake ice are both quantitatively and qualitatively limited, and feeding might not be energetically rewarding for most zooplankton species. Many zooplankters are expected to fast throughout the winter, exhausting their previously accumulated fat storage. We hypothesised that only a fraction of the actively overwintering zooplankton contributes to an active food web under lake ice, leading to few trophic linkages within the planktonic community.2. Zooplankton habitats and feeding were investigated under the ice of Lake Onego. Zooplankton habitats and migrations were studied by coupling zooplankton sam-pling around the clock to measurements of particle movement using an acoustic Doppler current profiler. Secondly, fatty acid-specific stable isotope compositions were used to determine whether and which zooplankton fatty acids ultimately came from the assimilation of under-ice seston.3. The algal biomass was low under ice and mostly dominated by large diatoms. Copepods dominated the zooplankton community. Species present as late cope-podite and adult instars were confined to the deeper layers, while nauplii occupied the surface layer. Diel vertical migration by Cyclops was the most tangible ob-servation of persistent feeding under the ice. Previously accumulated fat storage represented most of zooplankton fatty acids, with few, yet detectable, fatty acids recently acquired by feeding under the ice.4. Although some zooplankton taxa maintained feeding activity under the ice of Lake Onego, the food source available beneath the ice was not sufficiently rewarding to leave an isotopic imprint upon the dominant fatty acids of bulk zooplankton. The seston fatty acids that were passed on to zooplankton from feeding under ice were not provided by diatoms, although they made up most of the phytoplank-ton biovolume. Instead, the zooplankton food web was supported by mixotrophic phytoplankton (i.e. cryptophytes and chrysophytes) that represented

Spectral albedo and light transmittance through snow, ice, and water were measured in Lake Wuliangsuhai (40 degrees 36 '-41 degrees 30 ' N, 108 degrees 43 '-108 degrees 70 ' E), Inner Mongolia, China, during winter 2016. Data on the weather, structure of lake ice, and geochemistry of water were also collected during the 60-day field program. The study lake is shallow (mean depth 1.0-1.5 m) with a large wetland area. Compared with polar lakes, solar elevation is higher, snow accumulation is much lower, and the ice has more sediment. The ice was all congelation ice with a mean thickness of 36.6 cm, corresponding to a mean air temperature of -9.6 degrees C. The mean daily broadband albedo and photosynthetically active radiation (PAR) band transmittance were 0.54 and 0.08 (bare ice), 0.74 and 0.04 (new snow), and 0.30 and 0.12 (melting period), respectively. The level of light allowed photosynthesis to occur to the bottom of the lake. The ice acted as a grey filter for the sunlight with a mean attenuation coefficient of 2.1 m(-1). These results expand our knowledge of the evolution of light transfer through ice and snow cover and its role in the ecology of lakes in temperate and arid areas.

To understand the variations in surface water associated with changes in air temperature, precipitation, and permafrost in the Headwater Area of the Yellow River (HAYR), we studied the dynamics of alpine lakes larger than 0.01 km2 during 1986–2019 using Google Earth Engine (GEE) platform. The surface areas of water bodies in the HAYR were processed using mass remote sensing images consisting of Landsat TM/ETM+/OLI, Sentinel-2A, and MODIS based on automatic extraction of water indices under GEE. Besides, the lake ice phenology of the Sister Lakes (the Gyaring Lake and the Ngoring Lake) was derived by threshold segmenting of water/ice area ratio. Results demonstrate that the change of surface areas experienced four stages: decreasing during 1986–2004, increasing during 2004–2012, decreasing again during 2012–2017, and increasing again during 2017–2019. Correspondingly, the number of small lakes decreased (−26.5 per year), increased (139.5 per year), again decreased (−109.0 per year), and again increased (433.0 per year). Eight lakes larger than 1 km2 disappeared in 2004 but restored afterward. The overall trends in the area of small lakes (0.01–1 km2), large lakes (>1 km2), and all lakes during 1986–2019 were 0.4, 3.1, and 3.4 km2 per year, respectively. Although the onsets of freezing, freeze-up, breaking and the break-up of the Sister Lakes varied from year to year, there is no obvious trend regarding the lake ice phenology. Tendencies of lake variations in the HAYR are primarily related to the increased net precipitation and the declined aridity, followed by the construction of hydropower station around the outlet of the Ngoring Lake, as well as permafrost degradation.

The lake ice phenology response to climate change has been receiving growing concern in recent years. However, most studies have put emphasis on the spatial and temporal variability of lake ice phenology, and relatively few studies have been devoted to investigating the physical mechanisms of changes in lake ice phenology from the perspective of climatic dynamics. This study investigates the possible impact of the Antarctic Oscillation (AAO) on the variations in lake ice phenology over the Tibetan Plateau (TP). The results show that there is an intimate relationship between the AAO and the variations in break-up/ice duration during the period 2003–15. Further analysis indicates that the preceding boreal spring AAO-induced atmospheric circulation anomalies are favorable for generating tropical South Atlantic Ocean SST anomalies through air–sea interaction. Then the tropical SST anomalies strengthen the anomalous local-scale meridional–vertical circulation that projects into the Azores high and further induce the extratropical portion of the North Atlantic SST tripole. The anomalous warm core in the North Atlantic serves as the source of wave activity flux and stimulates a stationary wave train along the Eurasian continent to change the downstream atmospheric circulation. As a response, an abnormal cyclone and enhanced updraft are triggered over the TP, which are favorable for the formation of snowfall and then lower the surface air temperature according to the snow-albedo feedback mechanism, and thus result in the prolonged lake ice duration events. This study provides a new insight to link the AAO influence and climate over the TP and is helpful to understand the changes in lake ice phenology in response to climate change in recent years.

Lake ice phenology is a sensitive indicator reflecting global warming. In this article, the long-term changes in lake ice phenology of the second-largest lake at Tibetan Plateau (i.e., the Nam Co) in response to climate change are investigated based on mathematical modeling. The model has been testified capable of reproducing daily surface temperature of the lake in both frozen and unfrozen seasons. The lake ice phenology determined according to the simulated lake surface temperature is found consistent with that derived from satellite observations. For the Nam Co during the period 1978–2017, the freezing date has been delayed (reached up to 0.57 days/year), whereas the thawing date became earlier (−0.23 days/year). The trend of lake ice phenology is significantly correlated to the annual minimal lake surface and air temperature. Modeling experiment suggests that lake ice phenology of the Nam Co is very sensitive to a warmer climate. Under warmer future, much later freezing date and earlier thawing date are expected; hence the ice-covering duration would be significantly shortened.

More than 50% of global lakes periodically freeze, and their lake ice phenology is sensitive to climate change. However, spatially detailed quantification of the changes in lake ice at the global scale is not available. Here, we map ice cover in >33,000 lakes throughout the North Temperate Zone (23.5°–66.5°N) using 0.55 million Landsat images from 1985 to 2020. Over this period, we found a remarkable reduction in median ice cover occurrence (ICO) (61% to 43%), which was strongly related to warming terrestrial mean surface temperatures (R2 = 0.94, p < 0.05). Lakes in Europe showed the most pronounced ice loss (median ICO decreased from 50% to 24%), and extensive lake ice losses were also detected in the northern US, and central and eastern Asia. An overall increase in ice cover was identified from P2 (1999–2006) to P3 (2007–2014) due to regional decreased temperatures associated with the “global warming hiatus.” Thehigh-resolution mapping of lake ice here provides essentialbaseline information whichcan be used to elucidate ice loss-induced environmental and societal impacts. (Less)

Studies of lake ice phenology have historically relied on limited in situ data. Satellite-derived temperature data provide an opportunity to better understand the climatological factors and trends behind ice phenology. Here we developed a model that uses daytime and nighttime surface temperature observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard Aqua to approximate ice in for 13 lakes and ice out for 58 lakes in Maine from 2002–2018. Ice-in and ice-out approximations were signaled by a moving average of the data crossing the 0°C threshold. The comparison of in situ and satellite-derived ice-out and ice-in dates were both highly correlated, but the amount of bias in those estimates was influenced by the number of days in a moving average of temperature. Systematic biases in the approximation model toward predicting ice in too early and ice out too late point to the importance of in-lake dynamics in modifying the timing of stabilization or melting of ice.

Lakes sensitively respond to global and regional climate change, especially in arid areas. Using Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow products, the lake ice phenology of 23 lakes in the Xinjiang Uygur Autonomous Region of China from 2001 to 2018 was extracted based on thresholds of ice/water pixel numbers, and their change trends over 18 years were calculated. The results of MODIS-derived lake ice phenology showed consistent variations with existing ice phenology data sets derived from passive microwave data. Generally, lakes in Xinjiang begin to freeze from October to December every year, and their ice cover periods end from March to June. The average ice cover duration for the 23 lakes is 167 days, of which 16 lakes have an average shortening rate of − 1.08 days/year and seven lakes have an average extending rate of 1.18 days/year. The majority of lakes experienced later freeze-up (17 lakes) and earlier break-up (18 lakes) from 2001 to 2018. Lake ice phenology is affected by both climatic factors and lake physicochemical characteristics, in which freeze-up dates are more easily affected by lake-specific factors such as lake area (r = 0.535), while climatic factors especially water surface temperature have greater impacts on lake break-up dates (r = − 0.874). Compared to air temperature, water surface temperature changes have a more direct influence on the variations in lake ice phenology, 1° increase in water surface temperature may cause the ice cover duration to decrease by 12 days, while precipitation changes have almost no effect on the lakes in Xinjiang. In some cases, lake changes such as changes to the area and mineralization may also have dominant impacts on lake ice phenology.

Lake Ontario ice conditions are statistically linked to regional temperatures recorded in Toronto, during the most recent climate normal (1980/81–2009/10). A metric was developed to capture the net melting effect of average winter temperatures to characterize lake ice conditions, referred to as Net Melting-Degree Days (NMDD). This metric was able to account for 78% of lake ice interannual variability (R2 = 0.783, P

The article presents the ice phenology of Lake Ostrzyckie, which is a water body covering an area of 308 ha located in the central part of the Kashubian Lakeland, northern Poland. The analysis presented in the article is based on data from daily ice phenomena monitoring for the period of 1971–2010. Data including forms of lake ice, as well as the thickness of the ice cover were obtained from the Institute of Meteorology and Water. In order to present relations between the ice phenomena and air temperature the meteorological data from the Gdańsk University Limnological Station in Borucino were used. The article presents changes in the duration time of the ice seasons and changes in the ice cover duration time in relation to winter season (November–April) temperatures. The structure of the ice phenomena duration period observed on Lake Ostrzyckie consists of three different stages of ice cover formation. 94% of this time the lake is covered by permanent ice cover, the freezing period takes about 5%, and the break-up takes only 1% of the ice phenomena duration period. In general the ice phenomena in the lake can occur only in the years when the average air temperature in the winter is lower than 5.0°C, whereas the permanent ice cover is created when the average air temperature of the winter season is lower than 4.8°C. The maximum thickness of the ice cover is usually 23 cm, and the average is 14 cm.

In this study, we coupled the two-layer one-dimensional Freshwater lake (FLake) model into the Climate Forecast System (CFS) version 2 to improve simulations of the effects of the Great Lakes on winter climate forecasts in that region. We first incorporated global lake fraction and depth data into the coupled CFS-FLake through a subgridding system. An interface between the CFS and FLake was then developed to link the two models for energy and water flux exchanges. The lake scheme was triggered only if the lake fraction was more than 10% in one CFS grid cell. We conducted ensemble retrospective forecasts with CFS-FLake for the period of 1997 through 2016 with 9 monthly leads. These forecasts were assessed to obtain a better understanding of the role of the Great Lakes in the climate system for winter, when significant lake-effect precipitation often occurs. Our results indicate that forecasts of lake surface temperature (LST), lake ice coverage (LIC), and precipitation with CFS-FLake were consistently better than those with CFS. The major improvements resulted from changes in land use type for the Great Lakes, from ocean and land in CFS to lakes in CFS-FLake. With the change from ocean to lake, LST mostly decreased with increasing LIC, resulting in lower surface heat and water fluxes to the atmosphere during the winter. However, with the change from land to lake, LST increased, leading to higher heat and water fluxes. In the meantime, precipitation predicted by CFS-FLake was reduced quite significantly over the Great Lakes compared to that by CFS. This reduction was caused by suppressed rising motion due to increased stability in the lower atmosphere as a result of lowered surface heat and water fluxes. The results from this study indicate that local and mesoscale surface and atmospheric processes significantly affect regional climate forecasts, and the coupled CFS-FLake model will have a broader impact on climate and hydrology research and forecasts.

The Tibetan Plateau houses numerous lakes, the phenology and duration of lake ice in this region are sensitive to regional and global climate change, and as such are used as key indicators in climate change research, particularly in environment change comparison studies for the Earth three poles. However, due to its harsh natural environment and sparse population, there is a lack of conventional in situ measurement on lake ice phenology. The Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Snow Index (NDSI) data, which can be traced back 20 years with a 500 m spatial resolution, were used to monitor lake ice for filling the observation gaps. Daily lake ice extent and coverage under clear-sky conditions was examined by employing the conventional SNOWMAP algorithm, and those under cloud cover conditions were re-determined using the temporal and spatial continuity of lake surface conditions through a series of steps. Through time series analysis of every single lake with size greater than 3 km2 in size, 308 lakes within the Tibetan Plateau were identified as the effective records of lake ice extent and coverage to form the Daily Lake Ice Extent and Coverage dataset, including 216 lakes that can be further retrieved with four determinable lake ice parameters: Freeze-up Start (FUS), Freeze-up End (FUE), Break-up Start (BUS), and Break-up End (BUE), and 92 lakes with two parameters, FUS and BUE. Six lakes of different sizes and locations were selected for verification against the published datasets by passive microwave remote sensing. The lake ice phenology information obtained in this paper was highly consistent with that from passive microwave data at an average correlation coefficient of 0.91 and an RMSE value varying from 0.07 to 0.13. The present dataset is more effective at detecting lake ice parameters for smaller lakes than the coarse resolution passive microwave remote sensing observations. The published data are available in https://data.4tu.nl/repository/uuid:fdfd8c76-6b7c-4bbf-aec8-98ab199d9093 and http://www.sciencedb.cn/dataSet/handle/744.

Optical remote sensing images with high temporal resolution can be used to monitor lake ice phenology, a periodic freezing and thawing cycle of ice resulting from seasonal and inter-annual climate ...

Ice provides a range of ecosystem services—including fish harvest1, cultural traditions2, transportation3, recreation4 and regulation of the hydrological cycle5—to more than half of the world’s 117 million lakes. One of the earliest observed impacts of climatic warming has been the loss of freshwater ice6, with corresponding climatic and ecological consequences7. However, while trends in ice cover phenology have been widely documented2,6,8,9, a comprehensive large-scale assessment of lake ice loss is absent. Here, using observations from 513 lakes around the Northern Hemisphere, we identify lakes vulnerable to ice-free winters. Our analyses reveal the importance of air temperature, lake depth, elevation and shoreline complexity in governing ice cover. We estimate that 14,800 lakes currently experience intermittent winter ice cover, increasing to 35,300 and 230,400 at 2 and 8 °C, respectively, and impacting up to 394 and 656 million people. Our study illustrates that an extensive loss of lake ice will occur within the next generation, stressing the importance of climate mitigation strategies to preserve ecosystem structure and function, as well as local winter cultural heritage. Up to 35,000 lakes in the Northern Hemisphere may be at risk of intermittent winter ice cover at 2 °C warming, reveals an observation-based study. This would affect 394 million people reliant on lake ice for ecosystem services.

Lake ice phenology is considered a sensitive indicator of regional climate change. We utilized time series information of this kind extracted from a series of multi-source remote sensing (RS) datasets including the MOD09GQ surface reflectance product, Landsat TM/ETM+ images, and meteorological records to analyze spatiotemporal variations of ice phenology of Qinghai Lake between 2000 and 2016 applying both RS and GIS technology. We also identified the climatic factors that have influenced lake ice phenology over time and draw a number of conclusions. First, data show that freeze-up start (FUS), freeze-up end (FUE), break-up start (BUS), and break-up end (BUE) on Qinghai Lake usually occurred in mid-December, early January, mid-to-late March, and early April, respectively. The average freezing duration (FD, between FUE and BUE), complete freezing duration (CFD, between FUE and BUS), ice coverage duration (ICD, between FUS and BUE), and ablation duration (AD, between BUS and BUE) were 88 days, 77 days, 108 days and 10 days, respectively. Second, while the results of this analysis reveal considerable differences in ice phenology on Qinghai Lake between 2000 and 2016, there has been relatively little variation in FUS times. Data show that FUE dates had also tended to fluctuate over time, initially advancing and then being delayed, while the opposite was the case for BUS dates as these advanced between 2012 and 2016. Overall, there was a shortening trend of Qinghai Lake’s FD in two periods, 2000–2005 and 2010–2016, which was shorter than those seen on other lakes within the hinterland of the Tibetan Plateau. Third, Qinghai Lake can be characterized by similar spatial patterns in both freeze-up (FU) and break-up (BU) processes, as parts of the surface which freeze earlier also start to melt first, distinctly different from some other lakes on the Tibetan Plateau. A further feature of Qinghai Lake ice phenology is that FU duration (between 18 days and 31 days) is about 10 days longer than BU duration (between 7 days and 20 days). Fourth, data show that negative temperature accumulated during the winter half year (between October and the following April) also plays a dominant role in ice phenology variations of Qinghai Lake. Precipitation and wind speed both also exert direct influences on the formation and melting of lake ice cover and also cannot be neglected.

The lake ice phenology variations are vital for the land–surface–water cycle. Qinghai Lake is experiencing amplified warming under climate change. Based on the MODIS imagery, the spatio-temporal dynamics of the ice phenology of Qinghai Lake were analyzed using machine learning during the 2000/2001 to 2019/2020 ice season, and cloud gap-filling procedures were applied to reconstruct the result. The results showed that the overall accuracy of the water–ice classification by random forest and cloud gap-filling procedures was 98.36% and 92.56%, respectively. The annual spatial distribution of the freeze-up and break-up dates ranged primarily from DOY 330 to 397 and from DOY 70 to 116. Meanwhile, the decrease rates of freeze-up duration (DFU), full ice cover duration (DFI), and ice cover duration (DI) were 0.37, 0.34, and 0.13 days/yr., respectively, and the duration was shortened by 7.4, 6.8, and 2.6 days over the past 20 years. The increased rate of break-up duration (DBU) was 0.58 days/yr. and the duration was lengthened by 11.6 days. Furthermore, the increase in temperature resulted in an increase in precipitation after two years, the increase in precipitation resulted in the increase in DBU and decrease in DFU in corresponding years, and decreased DI and DFI after one year.

The prolonged ice cover inherent to alpine lakes incurs unique challenges for aquatic life, which are compounded by recent shifts in the timing and duration of ice cover. To understand the responses of alpine zooplankton, we analyzed a decade (2009–2019) of open-water samples of Daphnia pulicaria and Hesperodiaptomus shoshone for growth, reproduction and ultraviolet radiation tolerance. Due to reproductive differences between taxa, we expected clonal cladocerans to exhibit a more rapid response to ice-cover changes relative to copepods dependent on sexual reproduction. For D. pulicaria, biomass and melanization were lowest after ice clearance and increased through summer, whereas fecundity was highest shortly after ice-off. For H. shoshone, biomass and fecundity peaked later but were generally less variable through time. Among years, ice clearance date varied by 49 days; years with earlier ice-out and a longer growing season supported higher D. pulicaria biomass and clutch sizes along with greater H. shoshone fecundity. While these large-bodied, stress tolerant zooplankton taxa were relatively resilient to phenological shifts during the observation period, continued losses of ice cover may create unfavorably warm conditions and facilitate invasion by montane species, emphasizing the value of long-term data in assessing future changes to these sensitive ecosystems.

To improve the understanding of the seasonal evolution of the mass and heat budget of ice-covered lakes in the cold and arid climate zone, in-situ observations were collected during two winters (2016&ndash, 2017 and 2017&ndash, 2018) in Lake Wuliangsuhai, Inner Mongolia, China. The mean snow thickness was 5.2 and 1.6 cm in these winters, due to low winter precipitation. The mean ice thickness was 50.9 and 36.1 cm, and the ice growth rate was 3.6 and 2.1 mm day&minus, 1 at the lower boundary of ice. Analyses of mass and heat balance data from two winters revealed that the surface heat budget was governed by solar radiation and terrestrial radiation. The net heat flux loss of the ice was 9&ndash, 22 W m&minus, 2, affected by the snow and ice thickness. Compared to boreal lakes, Lake Wuliangsuhai received more solar radiation and heat flux from the water. The ice temperature had a strong diurnal variation, which was produced by the diurnal cycles of solar radiation, and air and water temperatures. These results expand our knowledge of the evolution of mass and heat balance in temperate lakes of mid-latitude arid areas.

Extending ice records and standardizing sampling protocols are among recommendations to help researchers better predict how changing ice cover will affect aquatic ecosystems.

Lake ice models can be used to study the latitudinal differences of current and projected changes in ice covered lakes under a changing climate. The Canadian Lake Ice Model (CLIMo) is a one-dimensional freshwater ice cover model that simulates Arctic and sub-Arctic lake ice cover well. Modelling ice cover in temperate regions has presented challenges due to the differences in composition between northern and temperate ice. This study presents a comparison of measured and modelled ice regimes, with a focus on refining CLIMo for temperate regions. The study sites include two temperate region lakes (MacDonald Lake and Clear Lake, Central Ontario) and two High Arctic lakes (Resolute Lake and Small Lake, Nunavut) where climate and ice cover information have been recorded over three seasons. The ice cover simulations were validated with a combination of time lapse imagery, field measurements of snow depth, snow density, ice thickness and albedo data, and historical ice records from the Canadian Ice Database (for Resolute Lake). Simulations of the High Arctic ice cover show good agreement with previous studies for ice-on and ice-off dates (MAE 6 to 8 days). Unadjusted simulations for the temperate region lakes show both an underestimation in ice thickness (~ 4 to 18 cm) and ice-off timing (~ 25 to 30 days). Field measurements were used to adjust the albedo parameterization used in CLIMo, which resulted in improvements to both simulated ice thickness, within 0.1 cm to 10 cm of manual measurements, and ice-off timing, within 1 to 7 days of observations. These findings suggest regionally specific measurements of albedo can improve the accuracy of lake ice simulations. These results further our knowledge regarding of the response of temperate and High Arctic lake ice regimes to climate conditions.

Lake ice is a dominant component of Canada’s landscape and can act as an indicator for how freshwater aquatic ecosystems are changing with warming climates. While lake ice monitoring through government networks has decreased in the last three decades, the increased availability of remote sensing images can help to provide consistent spatial and temporal coverage for areas with annual ice cover. Synthetic aperture radar (SAR) data are commonly used for lake ice monitoring, due to the acquisition of images in any condition (time of day or weather). Using Sentinel-1 A/B images, a high-density time series of SAR images was developed for Lake Hazen in Nunavut, Canada, from 2015−2018. These images were used to test two different methods of monitoring lake ice phenology: one method using the first difference between SAR images and another that applies the Otsu segmentation method. Ice phenology dates determined from the two methods were compared with visual interpretation of the Sentinel-1 images. Mean errors for the pixel comparison of the first difference method ranged 3−10 days for ice-on and ice-off, while average error values for the Otsu method ranged 2−10 days. Mean errors for comparisons of different sections of the lake ranged 0−15 days for the first difference method and 2−17 days for the Otsu method. This research demonstrates the value of temporally consistent image acquisition for improving the accuracy of lake ice monitoring.

Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming. The spatio-temporal extent of lake ice cover, along with the timings of key phenological events such as freeze-up and break-up, provide important cues about the local and global climate. We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network. In previous studies that used optical satellite imagery for lake ice monitoring, frequent cloud cover was a main limiting factor, which we overcome thanks to the ability of microwave sensors to penetrate clouds and observe the lakes regardless of the weather and illumination conditions. We cast ice detection as a two class (frozen, non-frozen) semantic segmentation problem and solve it using a state-of-the-art deep convolutional network (CNN).We report results on two winters (2016–17 and 2017–18) and three alpine lakes in Switzerland. The proposed model reaches mean Intersection-over-Union (mIoU) scores >90% on average, and >84% even for the most difficult lake. Additionally, we perform cross-validation tests and show that our algorithm generalises well across unseen lakes and winters., ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-3-2020, ISSN:2194-9042, ISSN:2194-9050

Studying of characteristics of the Lake Onega ice regime and investigating of the climate influence on the formation and destruction of the ice cover requires a continuous chronological series of data on the Lake ice coverage. Ice cover is the percentage of the ice area to the total area of the lake. In 1955–1990, calculations of the ice coverage of the Lake were based on the use of the results of airborne ice reconnaissance. On average for this period, from 5 to 15 values were annually obtained, which was not enough for a comprehensive analysis of the ice coverage variability. In this paper, for the frst time, a daily series of values of the ice coverage of the Lake Onega for the period 2000–2018 had been formed basing on the results of a combined analysis of the following satellite data sets: NSIDC, NOAA NESDIS, corrected by data of the satellite MODIS sensor. Values from November to May were grouped from the above data sets without regards for years of observations, and then the regression analysis of these values made possible to create a model (a polynomial of the 8th degree) of the chronological course of the ice coverage during the period of the ice phenomena existence on the Lake Onega. Te coefcient of determination of the model is 0.74, and the error in determining the ice coverage is 21%. Te average dates of the beginning, end, and duration of periods of the formation (from November 25th to January 19th), the destruction (from April 13th to May 17th) of the ice cover, as well as the total freeze-up time (from January 20th to April 12th) on the Lake Onega for the period 2000–2018 was determined. Te period of ice phenomena on the Lake Onega on average lasts for almost half a year (175 days), of which a signifcant part of the time is a complete freeze-up (84 days). It was found that the rate of formation of the ice cover (1.76% per day) on the Lake Onega is 1.65 times smaller than the rate of its destruction (2.90% per day), which approximately corresponds to similar results obtained for the Lake Ladoga (1.5 times).

Late-winter lake ice regimes are controlled by water depth relative to maximum ice thickness (MIT). When MIT exceeds maximum water depth, lakes freeze to the bottom with bedfast ice (BI) and when MIT is less than maximum water depth lakes have floating ice (FI). Both airborne radar and space-borne synthetic aperture radar (SAR) imagery (Ku-, X-, C-, and L-band) have been used previously to determine whether lakes have a BI or FI regime in a given year, across a number of years, or across large regions. In this study, we use a combination of ERS-1/2, RADARSAT-2, Envisat, and Sentinel-1 SAR imagery for seven lake-rich regions in Arctic Alaska to analyze lake ice regime extents and dynamics over a 25-year period (1992–2016). Our interactive threshold classification method determines a unique statistic-based intensity threshold for each SAR scene, allowing for the comparison of classification results from C-band SAR data acquired with different polarizations and incidence angles. Additionally, our novel method accommodates declining signal strength in aging extended-mission satellite SAR instruments. Comparison of SAR ice regime classifications with extensive field measurements from six years yielded a 93% accuracy. Significant declines in BI regimes were only observed in the Fish Creek area with 3% of lakes exhibiting transitional ice regimes—lakes that switch from BI to FI during this 25-year period. This analysis suggests that the potential conversion from BI to FI regimes is primarily a function of lake depth distributions in addition to regional differences in climate variability. Remote sensing of lake ice regimes with C-band SAR is a useful tool to monitor the associated thermal impacts on permafrost, since lake ice regimes can be used as a proxy for of sub-lake permafrost thaw, considered by the Global Climate Observing System as an Essential Climate Variable (ECV). Continued winter warming and variable snow conditions in the Arctic are expected and our long-term analysis provides a valuable baseline for predicting where potential future lake ice regimes shifts will be most pronounced.

Lakes play an important role in terrestrial ecosystems and have a significant impact on human production and life. Remote sensing techniques have been widely used to monitor changes in lakes. Several studies have been conducted pertaining to extraction of lake bodies using optical remote sensing images; however, owing to the influence of ice and snow in the lake and other parameters such as noise, the accuracy and stability of lake extraction still need to be improved. This study designed a new method by combing K-means clustering and the flood fill method (KCFFM) to improve the accuracy and stability of extraction results from complex backgrounds. Using Landsat 8 Operational Land Imager (OLI) data for five lakes in Central Asia, the accuracy and stability of KCFFM in summer and winter were evaluated and compared with those of five other methods. KCFFM showed the highest accuracy and stability among all the methods, especially during the lake ice period. For KCFFM, the Kappa coefficient was greater than 0.97 and 0.92 and the overall accuracy was greater than 99% and 98% in summer and winter, respectively. The area error rate of KCFFM was less than 3%, except for Lake Alakol (less than 10%). In addition, KCFFM significantly decreased the area error rate compared to other methods (1% to 15% in summer and 10% to 50% in winter). The proposed method optimized the time continuity, accuracy, and stability of lake body extraction. Thus, KCFFM can provide valuable basic data for monitoring lake water bodies.

Ice surface albedo is an important factor in various optical remote sensing technologies used to determine the distribution of snow or melt water on the ice, and to judge the formation or melting of lake ice in winter, especially in cold and arid areas. In this study, field measurements were conducted at Wuliangsuhai Lake, a typical lake in the semi-arid cold area of China, to investigate the diurnal variation of the ice surface albedo. Observations showed that the diurnal variations of the ice surface albedo exhibit bimodal characteristics with peaks occurring after sunrise and before sunset. The curve of ice surface albedo with time is affected by weather conditions. The first peak occurs later on cloudy days compared with sunny days, whereas the second peak appears earlier on cloudy days. Four probability density distribution functions-Laplace, Gauss, Gumbel, and Cauchy-were combined linearly to model the daily variation of the lake ice albedo on a sunny day. The simulations of diurnal variation in the albedo during the period from sunrise to sunset with a solar altitude angle higher than 5 degrees indicate that the Laplace combination is the optimal statistical model. The Laplace combination can not only describe the bimodal characteristic of the diurnal albedo cycle when the solar altitude angle is higher than 5 degrees, but also reflect the U-shaped distribution of the diurnal albedo as the solar altitude angle exceeds 15 degrees. The scale of the model is about half the length of the day, and the position of the two peaks is closely related to the moment of sunrise, which reflects the asymmetry of the two peaks of the ice surface albedo. This study provides a basis for the development of parameterization schemes of diurnal variation of lake ice albedo in semi-arid cold regions.

Evidence is growing that the surface water temperature increases and the duration of ice cover has decreased in many lakes worldwide during the past few decades. Here, we present changes in surface water temperature and ice-cover duration of Lake Lunz from 1921 to 2015 and evaluate how fast these changes occur over time, in particular with respect to other lakes with similar long-term data series. Since 1921, the surface water temperature of this Austrian subalpine lake has increased by 0.8 °C, with the most intense increase recorded during the spring and summer months (~1–2 °C) and less during fall (~0.3 °C). The duration of full lake ice cover has decreased significantly since 1921. During the 1921–2015 study period, Lake Lunz was ice covered for 92 of 94 winters, with ice-free winters in 2006 and 2013. The interannual decrease of the lake ice cover corresponds to 0.36 days less ice cover per year from 1921 to 2015. Freeze dates now occur 17.4 d/100 yr later, and the ice breakup date change is s...

One proposed method to detect the presence of spilled or leaked oil under the surface ice of frozen waters would rely on measuring the reverberation time of an impulsive acoustic source generated beneath that surface. Although intended for application to expansive environments such as rivers, lakes, and oceans, where deployment and subsequent recovery of equipment would make experimental testing economically difficult to perform, a low-cost, scaled experiment to study the efficacy of this method was conducted. In the design of the experiment, realistic modeling of a large underwater environment and the ability to accurately measure reverberation time within such an environment were of paramount concern. The required scaling, the dimensions of the experimental platform, and its adaptation to mimic a realistic environment are discussed. The design of an appropriate acoustic source along with practical matters such as growing the ice and injecting the oil under the ice are also discussed. Finally, experimental results are presented.

Lake ice phenology are considered as a sensitive indicator of global warming. In Tibetan Plateau (TP), differences exist in identifying lake ice phenology using remote sensing data because of the limited ground observation and difference in the sources of remote sensing and the available algorithms. In this study, four methods based on different remote sensed data (MODIS reflectance data, snow product, land surface temperature data, and microwave data) were used to identify the lake ice phenology of 18 lakes in the TP from 2002 to 2015. Lake ice duration was longer from the south to the north, and the highest consistency is occurred in monitoring the completely ice-covering duration. Temperature plays a greater role among several climatic-environmental elements. The lake ice period was shortened in the northeastern part of the TP, and extended in the middle and northern part.

The goal of this research was to evaluate the effect of Arctic climate change on the surrounding land area. In this research, we investigated a method to monitor the ice condition of shallow lakes in the Arctic region using C-band synthetic aperture radar (SAR) data and provide a strategy to evaluate the effect of climate change on the Arctic land area. A method to monitor the ice condition of these lakes was investigated using the SAR data acquired by Sentinel-1 from 2015 to 2018. We confirmed that the C-band SAR data from Sentinel-1 could monitor the ice condition of shallow lakes in northern Alaska. The backscattering threshold between floating and grounded ice was -14.8 to -16.8 dB depending on the incidence angle. We could also successfully monitor the growth of floating ice by using the interferometric SAR approach.

Many freshwater environments experience dramatic seasonal changes with some systems remaining ice-covered for most of the winter. Freshwater systems are also highly sensitive to environmental change. However, little is known about changes in microbial abundance and community composition during lake ice formation and times of persistent ice cover. The goal of this study is to characterize temporal dynamics of microbial communities during ice formation and persistent ice cover. Samples were collected in triplicate, five days per week from surface water in the Keweenaw Waterway between November and April. Environmental conditions along with microbial abundance and microbial community composition was determined. Distinct community composition was found between ice-free and ice-covered time periods with significantly different community composition between months. The microbial community underwent dramatic shifts in microbial abundance and diversity during the transitions into and out of ice cover. The richness of the microbial community increased during times of ice cover. Relatives of microbes involved in nitrogen cycling bloomed during times of ice cover as sequences related to known nitrifying taxa were significantly enriched during ice cover. These results help to elucidate how microbial abundance and diversity change over drastic seasonal transitions and how ice cover may affect microbial abundance and diversity.

The segregation of bacteria, inorganic solutes, and total organic carbon between liquid water and ice during winter ice formation on lakes can significantly influence the concentration and survival of microorganisms in icy systems and their roles in biogeochemical processes. Our study quantifies the distributions of bacteria and solutes between liquid and solid water phases during progressive freezing. We simulated lake ice formation in mesocosm experiments using water from perennially (Antarctica) and seasonally (Alaska and Montana, United States) ice-covered lakes. We then computed concentration factors and effective segregation coefficients, which are parameters describing the incorporation of bacteria and solutes into ice. Experimental results revealed that, contrary to major ions, bacteria were readily incorporated into ice and did not concentrate in the liquid phase. The organic matter incorporated into the ice was labile, amino acid-like material, differing from the humic-like compounds that remained in the liquid phase. Results from a control mesocosm experiment (dead bacterial cells) indicated that viability of bacterial cells did not influence the incorporation of free bacterial cells into ice, but did have a role in the formation and incorporation of bacterial aggregates. Together, these findings demonstrate that bacteria, unlike other solutes, were preferentially incorporated into lake ice during our freezing experiments, a process controlled mainly by the initial solute concentration of the liquid water source, regardless of cell viability.

To gain more understanding of lake ice melting process, field research was carried out in an arctic tundra lake, Kilpisjärvi (surface area 37.1 km2, maximum depth 57 m) in the melting periods of 2013 and 2014. The heat budget of the ice cover was dominated by the radiation balance; turbulent heat fluxes were large in 2013 due to warm air advection but small in 2014. Transmittance of solar radiation through ice was 0.25 in 2013 and 0.10 in 2014, snow-ice was absent in 2013 but in 2014 accounted for 50% of the ice cover. The melting rate was 4.4 cm d-1 in 2013, 1.9 cm d-1 in 2014. The portions of surface, bottom and internal melting were, respectively, 2.9, 1.0 and 0.5 cm d-1 in 2013 and 0.8, 1.0 and 0.1 cm d-1 in 2014. Internal melting was realized in increase of ice porosity. In 2013 a rapid ice breakage event completed the ice breakup in short time when ice porosity had reached 40-50%. A lake ice melting model should include the thickness and porosity of ice, with porosity connected to an ice strength criterion.

Changing Arctic climate may alter freshwater ecosystems as a result of warmer surface waters, longer open-water periods, reduced wintertime lake ice growth, and altered hydrologic connectivity. This study aims to characterize zooplankton community composition and size structure in the context of hydrologic connectivity and ice regimes in Arctic lakes. Between 2011 and 2016, we sampled the phytoplankton, zooplankton, and fish communities from a set of representative lakes on the Arctic Coastal Plain (ACP) of northern Alaska to determine potential food web responses to changing Arctic ecosystems. Multivariate analyses showed that time from ice-out had a strong influence on zooplankton community structure and that seasonal succession of zooplankton differed between lakes with varying hydrologic connectivity. Trends were observed suggesting that large-bodied zooplankton (Daphnia, calanoid copepods) may be more prevalent in poorly connected lakes with low fish diversity. Large-bodied zooplankton displayed higher biomass in lakes with high occurrences of bedfast ice, while small-bodied zooplankton (Bosmina, rotifers) displayed highest biomass in deeper lakes with low occurrences of bedfast ice. Our results contribute to limited knowledge of zooplankton in remote lakes of the ACP and suggest that the anticipated changes to aquatic ecosystems in the Arctic may include energetically less efficient plankton food webs.

The timing of lake ice breakup and freezeup are important indicators of climate change in Arctic and boreal regions because they respond rapidly and directly to variations in climate conditions. Despite its importance, lake ice phenology remains poorly documented in most lakes of Alaska. To fill this data gap, we constructed a remote sensing-derived lake ice phenology database covering all lakes in Alaska larger than 1 km2 (n = 4241) over the period 2000–2019. This dataset, which includes lake ice on/off dates and lake ice duration, was based on an automatic method using daily moderate resolution imaging spectroradiomenter (MODIS) imagery to measure lake ice fraction. This method extracts lake ice pixels from MODIS images using a dynamic threshold that was calibrated against Landsat Fmask. Different filters that account for clouds, polar night, and other sources of error were applied to increase the accuracy of lake ice phenology estimation. Trend analysis shows earlier breakup (−5.5 d decade−1) for 440 lakes and later breakup (7.5 d decade−1) for four lakes (p < 0.05). A total of 289 lakes had significant trends toward later freezeup (2.9 d decade−1) and 11 lakes towards earlier freezeup (−3.3 d decade−1). Most lakes with significant trends are north of the Brooks Range. This dataset can contribute to increased understanding of interactions between lake processes and climate change, and it supports the study of biogeochemical, limnological and ecological regimes in Alaska and pan-Arctic regions.

The primary drivers of the recent accelerated warming of the Laurentian Great Lakes from 1982 to 2012 are explored through observations, remote sensing, and regional climate model experiments. The study focuses on the abrupt warming from 1997 to 1998 as a proxy for the long-term warming trend. The lake surface warming has been heterogeneous in both space and time, ranging from moderate warming in late spring over the southern lakes and shallow areas of the northern lakes to strong warming in mid-summer over the northern, deep lake areas. The greatest lake warming between 1997 and 1998 occurs over the deepest areas of Lake Superior during mid-summer, primarily arising from enhanced heat accumulation during the mild winter of 1997/1998 and amplified by greater incoming surface solar radiation and air temperature during the spring of 1998, according to model experiments. The mild winter condition, together with the increased solar radiation and air temperature during spring, causes an earlier onset of springtime stratification, resulting in enhanced heat absorption by surface water and thereby contributing to lake surface warming during the subsequent summer in 1998 compared with 1997. In contrast, the modest peak warming over southern lakes and shallow areas of northern lakes from 1997 to 1998 is a rapid response to synchronous increases in solar radiation and air temperature during May between the 2 yr. Changes in antecedent wintertime lake ice cover are found to have played only a minor role in the accelerated warming trend of the Laurentian Great Lakes.