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In ice-covered polar lakes, a narrow ice-free moat opens up in spring or early summer, and then persists at the edge of the lake until complete ice loss or refreezing. In this study, we analyzed the horizontal gradients in Ward Hunt Lake, located in the Canadian High Arctic, and addressed the hypothesis that the transition from its nearshore open-water moat to offshore ice-covered waters is marked by discontinuous shifts in limnological properties. Consistent with this hypothesis, we observed an abrupt increase in below-ice concentrations of chlorophyll a beyond the ice margin, along with a sharp decrease in temperature and light availability and pronounced changes in benthic algal pigments and fatty acids. There were higher concentrations of rotifers and lower concentrations of viruses at the ice-free sampling sites, and contrasts in zooplankton fatty acid profiles that implied a greater importance of benthic phototrophs in their inshore diet. The observed patterns underscore the structuring role of ice cover in polar lakes. These ecosystems do not conform to the traditional definitions of littoral versus pelagic zones but instead may have distinct moat, ice-margin, and ice-covered zones. This zonation is likely to weaken with ongoing climate change.

High-resolution lake ice/water observations retrieved from satellite imagery through efficient, automated methods can provide critical information to lake ice forecasting systems. Synthetic aperture radar (SAR) data is well-suited to this purpose due to its high spatial resolution (approximately 50 m). With recent increases in the volume of SAR data available, the development of automated retrieval methods for these data is a priority for operational centres. However, automated retrieval of ice/water data from SAR imagery is difficult, due to ambiguity in ice and open water signatures, both in terms of image tone and in terms of parameterized texture features extracted from these images. Convolutional neural networks (CNNs) can learn features from imagery in an automated manner, and have been found effective in previous studies on sea ice concentration estimation from SAR. In this study the use of CNNs to retrieve ice/water observations from dual-polarized SAR imagery of two of the Laurentian Great Lakes, Lake Erie and Lake Ontario, is investigated. For data assimilation, it is crucial that the retrieved observations are of high quality. To this end, quality control measures based on the uncertainty of the CNN output to eliminate incorrect retrievals are discussed and demonstrated. The quality control measures are found to be effective in both dual-polarized and single-polarized retrievals. The ability of the CNN to downscale the coarse resolution training labels is demonstrated qualitatively.

Further to systematic experiments on the flexural strength of laboratory-grown, fresh water ice loaded cyclically, this paper describes results from new experiments of the same kind on lake ice harvested in Svalbard. The experiments were conducted at −12 °C, 0.1 Hz frequency and outer-fiber stress in the range from ~ 0.1 to ~ 0.7 MPa. The results suggest that the flexural strength increases linearly with stress amplitude, similar to the behavior of laboratory-grown ice.

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.

Snow and ice were monitored by thermistor-string-based Snow and Ice Mass Balance Array (SIMBA) in Lake Orajärvi in northern Finland. An existing automatic SIMBA-algorithm was further developed to derive air/snow, snow/ice and ice/water interfaces based on the SIMBA environment temperature (ET) profiles. The identified interfaces agreed with in situ observations made in 2011/12 winter season. The method was capable to identify upward-moving snow/ice interface that was also visible from SIMBA heating temperature (HT) profiles, which responds to differences in the thermal diffusivities of air, snow, ice and water. The SIMBA data obtained in winters 2017/18 and 2018/19 were used to investigate snow and ice mass balance. An upward-moving snow/ice interface was detected as a result of meteoric ice (snow ice and superimposed ice) formation. Snow contributed to granular lake ice formation up to 40–55% of the total ice thickness on the seasonal mean. Heavy snowfalls and low air temperature in early winter are favourable for granular ice formation. The seasonal mean snow depth on nearby land was 2.7–2.9 times of that on the lake. The estimation of freeboard from snow and ice mass-balance measurement is sensitive to the snow density. Accurate ice freeboard calculation is still a challenge.

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.

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

Antarctic lakes with perennial ice covers provide the opportunity to investigate in-lake processes without direct atmospheric interaction, and to study their ice-cover sensitivity to climate conditions. In this study, a numerical model – driven by radiative, atmospheric and turbulent heat fluxes from the water body beneath the ice cover – was implemented to investigate the impact of climate change on the ice covers from two Antarctic lakes: west lobe of Lake Bonney (WLB) and Crooked Lake. Model results agreed well with measured ice thicknesses of both lakes (WLB – RMSE= 0.11 m over 16 years of data; Crooked Lake – RMSE= 0.07 m over 1 year of data), and had acceptable results with measured ablation data at WLB (RMSE= 0.28 m over 6 years). The differences between measured and modeled ablation occurred because the model does not consider interannual variability of the ice optical properties and seasonal changes of the lake's thermal structure. Results indicate that projected summer air temperatures will increase the ice-cover annual melting in WLB by 2050, but that the ice cover will remain perennial through the end of this century. Contrarily, at Crooked Lake the ice cover becomes ephemeral most likely due to the increase in air temperatures.

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.

Climate change is expected to decrease ice coverage and thickness globally while increasing the variability of ice coverage and thickness on midlatitude lakes. Ice thickness affects physical, biological, and chemical processes as well as safety conditions for scientists and the general public. Measurements of ice thickness that are both temporally frequent and spatially extensive remain a technical challenge. Here new observational methods using repurposed soil moisture sensors that facilitate high spatial–temporal sampling of ice thickness are field tested on Lake Mendota in Wisconsin during the winter 2015/16 season. Spatial variability in ice thickness was high, with differences of 10 cm of ice column thickness over 1.05 km of horizontal distance. When observational data were compared with manual measurements and model output from both the Freshwater Lake (FLake) model and General Lake Model (GLM), ice thickness from sensors matches manual measurements, whereas GLM and FLake results showed a thinner and thicker ice layer, respectively. The FLake-modeled ice column temperature effectively remained at 0°C, not matching observations. We also show that daily ice dynamics follows the expected linear function of ice thickness growth/melt, improving confidence in sensor accuracy under field conditions. We have demonstrated a new method that allows low-cost and high-frequency measurements of ice thickness, which will be needed both to advance winter limnology and to improve on-ice safety.

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.

New systematic experiments reveal that the flexural strength of saline S2 columnar-grained ice loaded normal to the columns can be increased upon cyclic loading by about a factor of 1.5. The experiments were conducted using reversed cyclic loading over ranges of frequencies from 0.1 to 0.6 Hz and at a temperature of −10 ∘C on saline ice of two salinities: 3.0 ± 0.9 and 5.9 ± 0.6 ‰. Acoustic emission hit rate during cycling increases with an increase in stress amplitude of cycling. Flexural strength of saline ice of 3.0 ± 0.9 ‰ salinity appears to increase linearly with increasing stress amplitude, similar to the behavior of laboratory-grown freshwater ice (Murdza et al., 2020b) and to the behavior of lake ice (Murdza et al., 2021). The flexural strength of saline ice of 5.9 ± 0.6 ‰ depends on the vertical location of the sample within the thickness of an ice puck; i.e., the strength of the upper layers, which have a lower brine content, was found to be as high as 3 times that of lower layers. The fatigue life of saline ice is erratic. Cyclic strengthening is attributed to the development of an internal back stress that opposes the applied stress and possibly originates from dislocation pileups.

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.

© 2020 Copernicus GmbH. All rights reserved. Lake ice is a strong climate indicator and has been recognised as part of the Essential Climate Variables (ECV) by the Global Climate Observing System (GCOS). The dynamics of freezing and thawing, and possible shifts of freezing patterns over time, can help in understanding the local and global climate systems. One way to acquire the spatiooral information about lake ice formation, independent of clouds, is to analyse webcam images. This paper intends to move towards a universal model for monitoring lake ice with freely available webcam data. We demonstrate good performance, including the ability to generalise across different winters and lakes, with a state-of-the-art Convolutional Neural Network (CNN) model for semantic image segmentation, Deeplab v3+. Moreover, we design a variant of that model, termed Deep-U-Lab, which predicts sharper, more correct segmentation boundaries. We have tested the model's ability to generalise with data from multiple camera views and two different winters. On average, it achieves Intersection-over-Union (IoU) values of ≈71% across different cameras and ≈69% across different winters, greatly outperforming prior work. Going even further, we show that the model even achieves 60% IoU on arbitrary images scraped from photo-sharing websites. As part of the work, we introduce a new benchmark dataset of webcam images, Photi-LakeIce, from multiple cameras and two different winters, along with pixel-wise ground truth annotations., ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 5 (2), ISSN:2194-9042, ISSN:2194-9050

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).

The changing characteristics of lake ice phenology over the Tibetan Plateau (TP) are investigated using historical satellite retrieved datasets during 2002–15 in this study. The results indicate that the freezing process mainly starts in December, and the ice melting process generally occurs in April for most lakes. However, the changes in lake ice phenology have varied depending on the location in recent years, with delayed break-up dates and prolonged ice durations in the southern TP, but no consistent changes have occurred in the lakes in the northern TP. Further analysis presents a close connection between the variation in the lake ice break-up date/ice duration over the southern TP and the winter North Atlantic Oscillation (NAO). The positive NAO generally excites an anomalous wave activity that propagates southward from the North Atlantic to North Africa and, in turn, strengthens the African–Asian jet stream at its entrance. Because of the blocking effect of the TP, the enhanced westerly jet can be divided into two branches and the south branch flow can deepen the India–Myanmar trough, which further strengthens the anomalous cyclonic circulation and water vapor transport. Therefore, the increased water vapor transport from the northern Indian Ocean to the southern region of the TP can increase the snowfall over this region. The increased snow cover over the lake acts as an insulating layer and lowers the lake surface temperature in the following spring by means of snow–ice feedback activity, resulting in a delayed ice break-up date and the increased ice duration of the lakes over the southern TP in recent years.

In this study, we assess the effect of the lake size on the accuracy of a threshold-based classification of ground-fast and floating lake ice from Sentinel-1 Synthetic Aperture Radar (SAR) imagery. For that purpose, two new methods (flood-fill and watershed method) are introduced and the results between the three classification approaches are compared regarding different lake size classes for a study area covering most of the Yamal Peninsula in Western Siberia. The focus is on April, the stage of maximum lake ice thickness, for the years 2016 and 2017. The results indicate that the largest lakes are likely most prone to errors by the threshold classification. The newly introduced methods seem to improve classification results. The results also show differences in fractions of ground-fast lake ice between 2016 and 2017, which might reflect differences in temperatures between the winters with severe impact on wildlife and freshwater fish resources in the region. Patterns of low backscatter responsible for the classification errors in the centre of the lakes were investigated and compared to the optical Sentinel-2 imagery of late-winter. Strong similarities between some patterns in the optical and SAR data were identified. They might be zones of thin ice, but further research is required for clarification of this phenomenon and its causes.

This article is an attempt to analyse and compare several selected parameters regarding ice phenomena using the correlation analysis of two lakes, which are benchmark lakes located in the central part of the Kashubian Lakeland. These lakes are: Raduńskie Górne, a larger one (387.2 hm2) and Ostrzyckie, a smaller one covering an area of 308.0 hm2. The analysis covered measurement sequences for the period 1971–2010. The material for analysis regarding data on ice phenomena on Lake Raduńskie Górne was obtained from the University of Gdańsk Limnological Station in Borucino while the data for Lake Ostrzyckie was obtained from the Institute of Meteorology and Water Management (IMGW). Relations between the following parameters of the ice regime were analysed: duration of the ice phenomena, duration of the ice cover, average and maximum thickness of the ice cover. The analysis and data comparisons have revealed that there are strong and very strong relations between the analysed parameters, which made it possible to develop proper statistical models. Hence, should there be a lack of data on the ice-cover pattern for one of these lakes it is possible to recreate them using the elaborated empirical models and data for the other.

Monitoring and analyzing the (decreasing) trends in lake freezing provides important information for climate research. Multi-temporal satellite images are a natural data source to survey ice on lakes. In this paper, we describe a method for lake ice monitoring, which uses low spatial resolution (250 m–1000 m) satellite images to determine whether a lake is frozen or not. We report results on four selected lakes in Switzerland: Sihl, Sils, Silvaplana and St. Moritz. These lakes have different properties regarding area, altitude, surrounding topography and freezing frequency, describing cases of medium to high difficulty. Digitized Open Street Map (OSM) lake outlines are back-projected on to the image space after generalization. As a pre-processing step, the absolute geolocation error of the lake outlines is corrected by matching the projected outlines to the images. We define the lake ice detection as a two-class (frozen, non-frozen) semantic segmentation problem. Several spectral channels of the multi-spectral satellite data are used, both reflective and emissive (thermal). Only the cloud-free (clean) pixels which lie completely inside the lake are analyzed. The most useful channels to solve the problem are selected with xgboost and visual analysis of histograms of reference data, while the classification is done with non-linear support vector machine (SVM). We show experimentally that this straight-forward approach works well with both MODIS and VIIRS satellite imagery. Moreover, we show that the algorithm produces consistent results when tested on data from multiple winters., International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2, ISSN:1682-1750, ISSN:2194-9034, ISSN:1682-1777

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.

A dataset of annual freshwater ice phenology was compiled for the largest river (Danube) and the largest lake (Lake Balaton) in eastern–central Europe, extending regular river and lake ice monitoring data through the use of historical observations and documentary records dating back to AD 1774 and AD 1885, respectively. What becomes clear is that the dates of the first appearance of ice and freeze-up have shifted, arriving 12–30 and 4–13 days later, respectively, per 100 years. Break-up and ice-off have shifted to earlier dates by 7–13 and 9–27 days/100 years, except on Lake Balaton, where the date of break-up has not changed significantly. The datasets represent a resource for (paleo)climatological research thanks to the strong, physically determined link between water and air temperature and the occurrence of freshwater ice phenomena. The derived centennial records of freshwater cryophenology for the Danube and Balaton are readily available for detailed analysis of the temporal trends, large-scale spatial comparison, or other climatological purposes. The derived dataset is publicly available via PANGAEA at https://doi.org/10.1594/PANGAEA.881056.

Freshwater lakes are an important source of atmospheric methane ([Formula: see text]); however, uncertainties associated with quantifying fluxes limit the accuracy of climate warming projections. Among emission pathways, ebullition (bubbling) is the principal and most challenging to account for given its spatial and temporal patchiness. When lakes freeze, many methane-rich bubbles escaping from lake-bottom sediments are temporarily trapped by downward-growing lake ice. Because bubble position is then seasonally fixed, we postulate that it should be possible to locate bubbles using a geophysical approach sensitive to perturbations in the ice-water interface and ice sheet structure generated by bubbles. We use ground-penetrating radar (GPR) to noninvasively quantify the amount of ebullition gas present in lake ice. To do this, an appropriate petrophysical transformation is required that relates radar wave velocity and volumetric gas content. We use laboratory experiments to show that electromagnetic models and volumetric mixing formulas were good representations of the gas volume-permittivity relationship. We found a standard deviation in dielectric permittivity between the models of 0.03, 0.03, and 0.02 for 20%, 50%, and 70% gas content, respectively. Second, by combining two GPR geometries (common and multioffset), we were able to locate bubbles and estimate gas volume with low uncertainty, with [Formula: see text] being the lowest uncertainty found and [Formula: see text] the largest. Finally, we found that GPR reflection patterns were associated with different previously identified ice-bubble classes. These geophysical results coupled with ancillary field measurements and ice-growth models also suggest how GPR can contribute to estimates of seasonal and annual ebullition fluxes over large spatiotemporal scales within and among lakes, thereby helping to reduce uncertainties in upscaled estimates of ecosystem methane emissions.

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.

Satellite observations have revealed decreases in the duration of the seasonal snow and ice coverage of Great Slave Lake (GSL) and Great Bear Lake (GBL), large freshwater lakes in Northern Canada. However, limited information is available about ice thickness changes. Here, we present and validate a method to retrieve lake ice thickness using the CryoSat-2 (CS2) radar altimeter. These are the first satellite altimeter retrievals of lake ice thickness. Under optimal conditions, the CS2 signal is scattered from both the snow–ice and the ice–water interfaces, with returns from each interface being of sufficient power to be identified in the radar waveform. The distance between the scattering horizons is used to determine the ice thickness, similar to traditional ground penetrating radar measurements. The seasonal evolution of ice thickness of GBL and GSL is compared with in situ measurements, modeled ice thicknesses, and previous studies. The impact of ice and snow properties on signal penetration and the thickness retrieval are examined with synthetic aperture radar imagery. The CS2 ice thickness retrievals are able to observe the seasonal thickening of the lake ice and closely match the in situ measurements over both lakes ( $R > 0.65$ , $\text {RMSE} m). Thickness retrievals of thin ice are limited by a minimum waveform peak separation of 2 range bins, approximately 0.26 m in ice. Although not designed for lake ice studies, CS2 and future SAR satellite altimeter missions offer new possibilities to monitor the ice and water levels of climatically sensitive and influential lakes.

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...

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.

Synthetic Aperture Radar (SAR) remote sensing has become a valuable tool for sea ice monitoring. A recently proposed SAR configuration for Earth observation called compact polarimetric (CP) SAR could be a good compromised choice between conventional (single or dual) and fully polarimetric SAR for operational sea ice observation. Given its enhanced radar target information compared to conventional SAR systems over wider swath coverage compared to fully polarimetric SAR systems, CP SAR systems could play important role in the new generation of Earth observation systems. In this study, fully polarimetric SAR images were collected over the Resolute Bay area during the fall of 2017. Acquired images are used for the simulation of CP SAR images and the derivation of a set of 23 CP SAR parameters from each image. The derived CP parameters were analysed in relation to the ice thickness and salinity of lake ice and fast sea ice. Results are compared against backscattering and decomposition parameters derived from the fully polarimetric SAR imagery.

Algorithms for the generation of a bedfast/floating lake ice product from Sentinel-1A/B synthetic aperture radar (SAR) data were implemented, cross-compared, and validated for various permafrost regions (Alaska, Canada and Russia). The algorithms consisted of: 1) thresholding; 2) Iteration Region Growing with Semantics (IRGS); and 3) K-means. The thresholding algorithm (92.4%) was found to perform slightly better on average than the IRGS algorithm (90.1%), and to outperform K-means (85.3%). The thresholding algorithm was therefore selected for implementation of a processing chain to generate a novel bedfast/floating lake ice product. Using a time series of Sentinel-1 SAR data, the new map product shows the day of year (DOY) when the ice becomes bedfast or remains afloat for individual lake sections.

Winter is an important season for many limnological processes, which can range from biogeochemical transformations to ecological interactions. Interest in the structure and function of lake ecosyst ...

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.

Seepage of geological methane through sediments of Arctic lakes might contribute conceivably to the atmospheric methane budget. However, the abundance and precise locations of such seeps are poorly quantified. For Lake Neyto, one of the largest lakes on the Yamal Peninsula in Northwestern Siberia, temporally expanding regions of anomalously low backscatter in C-band SAR imagery acquired in late winter and spring have been suggested to be related to seepage of methane from hydrocarbon reservoirs. However, this hypothesis has not been verified using in-situ observations so far. Similar anomalies have also been identified for other lakes on Yamal, but it is still uncertain whether or how many of them are related to methane seepage. This study aimed to document similar lake ice backscatter anomalies on a regional scale over four study regions (the Yamal Peninsula and Tazovskiy Peninsulas, the Lena Delta in Russia, the National Petroleum Reserve Alaska) during different years using a time series based approach on Google Earth Engine (GEE) that quantifies changes of σ0 from the Sentinel-1 C-band SAR sensor over time. An algorithm for assessing the coverage that takes the number of acquisitions and maximum time between acquisitions into account is presented, and differences between the main operating modes of Sentinel-1 are evaluated. Results show that better coverage can be achieved in extra wide swath (EW) mode, but interferometric wide swath (IW) mode data could be useful for smaller study areas and to substantiate EW results. A classification of anomalies on Lake Neyto from EW Δσ0 images derived from GEE showed good agreement with the classification presented in a previous study. Automatic threshold-based per-lake counting of years where anomalies occurred was tested, but a number of issues related to this approach were identified. For example, effects of late grounding of the ice and anomalies potentially related to methane emissions could not be separated efficiently. Visualizations of Δσ0 images likely reflect the temporal expansions of anomalies and are expected to be particularly useful for identifying target areas for future field-based research. Characteristic anomalies that clearly resemble the ones observed for Lake Neyto could be identified solely visually in the Yamal and Tazovskiy study regions. All data and algorithms produced in the framework of this study are openly provided to the scientific community for future studies and might potentially aid our understanding of geological lake seepage upon the progression of related field-based studies and corresponding evaluations of formation hypotheses.

Lake ice models are a vital tool for studying the response of ice-covered lakes to changing climates throughout the world. 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 ice composition between northern and temperate region lake 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 High Arctic lake 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 good ice-on timing, but an under-representation of ice thickness, and earlier complete ice-off timing (~3 to 5 weeks). Field measurements were used to adjust the albedo values used in CLIMo, which resulted in improvements to both simulated ice thickness (~3 cm MAE compared to manual measurements), and ice-off timing, within 0 to 7 days (2 days MAE) of observations. These findings suggest regionally specific measurements of albedo can improve the accuracy of lake ice simulations, which further our knowledge of the response of temperate and High Arctic lake ice regimes to climate conditions.