Lake Ice Thickness Research Articles

Improving the Estimation of Lake Ice Thickness with High-Resolution Radar Altimetry Data

Lake ice thickness (LIT) is a sensitive indicator of climate change, identified as a thematic variable of Lakes as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). Here, we present a novel and efficient analytically based retracking approach for estimating LIT from high-resolution Ku-band (13.6 GHz) synthetic-aperture radar (SAR) altimetry data. The retracker method is based on the analytical modeling of the SAR radar echoes over ice-covered lakes that show a characteristic double-peak feature attributed to the reflection of the Ku-band radar waves at the snow–ice and ice–water interfaces. The method is applied to Sentinel-6 Unfocused SAR (UFSAR) and Fully Focused SAR (FFSAR) data, with their corresponding tailored waveform model, referred to as the SAR_LIT and FFSAR_LIT retracker, respectively. We found that LIT retrievals from Sentinel-6 high-resolution SAR data at different posting rates are fully consistent with the LIT estimations obtained from thermodynamic lake ice model simulations and from low-resolution mode (LRM) Sentinel-6 and Jason-3 data over two ice seasons during the tandem phase of the two satellites, demonstrating the continuity between LRM and SAR LIT retrievals. By comparing the Sentinel-6 SAR LIT estimates to optical/radar images, we found that the Sentinel-6 LIT measurements are fully consistent with the evolution of the lake surface conditions, accurately capturing the seasonal transitions of ice formation and melt. The uncertainty in the LIT estimates obtained with Sentinel-6 UFSAR data at 20 Hz is in the order of 5 cm, meeting the GCOS requirements for LIT measurements. This uncertainty is significantly smaller, by a factor of 2 to 3 times, than the uncertainty obtained with LRM data. The FFSAR processing at 140 Hz provides even better LIT estimates, with 20% smaller uncertainties. The LIT retracker analysis performed on data at the higher posting rate (140 Hz) shows increased performance in comparison to the 20 Hz data, especially during the melt transition period, due to the increased statistics. The LIT analysis has been performed over two representative lakes, Great Slave Lake and Baker Lake (Canada), demonstrating that the results are robust and hold for lake targets that differ in terms of size, bathymetry, snow/ice properties, and seasonal evolution of LIT. The SAR LIT retrackers presented are promising tools for monitoring the inter-annual variability and trends in LIT from current and future SAR altimetry missions.

Analyzing the Vertical Recharge Mechanism of Groundwater Using Ion Characteristics and Water Quality Indexes in Lake Hulun

The water level of Lake Hulun has changed dramatically in recent years. The interannual interaction between groundwater and lake water is an important factor affecting Lake Hulun’s water level. Vertical recharge between groundwater and the lake is particularly important. Based on an analysis of differences between the hydrogeochemical and water quality characteristics of the spring water, the lake water, and the surrounding groundwater, the source and recharge mechanism of the spring water in the vertical recharge lake are determined. The results show that spring water is exposed at the bottom of Lake Hulun, and there are obvious differences between spring water and lake water in lake ice thickness, ion characteristics, and water quality characteristics. For example, the ice thickness at the spring site is only 6.8% of the average ice thickness of the lake, and there is a triangular area directly above the spring water area that is not covered by ice; the ion contents of the spring water at the lake bottom were less than 50% of those in the lake water; and the NH4+-N content of the spring water at the lake bottom was only 3.0% of the mean content of the lake water. In addition, the total nitrogen (TN), dissolved oxygen (DO), and NH4+-N contents of the spring water at the lake bottom all fall outside the range of contents of the surrounding groundwater. In general, the source of the spring water at the lake bottom is not recharged by the infiltration recharge of the phreatic aquifer but by the vertical recharge of the confined aquifer. Additionally, the Lake Hulun basin may be supplied with confined water through basalt channels while it is frozen. The vertical groundwater recharge mechanism may be that spring water at the lake bottom is first supplied by the deep, confined aquifer flowing through the fault zone to the loose-sediment phreatic aquifer under the lake, and finally interacts with the lake water through the phreatic aquifer.

Remote Sensing and Modeling of the Cryosphere in High Mountain Asia: A Multidisciplinary Review

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate–cryosphere–hydrology–hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze–thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole.

Modeling Climate Characteristics of Qinghai Lake Ice in 1979–2017 by a Quasi-Steady Model

Lakes on the Qinghai Tibet Plateau (QTP) are widely distributed spatially, and they are mostly seasonally frozen. Due to global warming, the thickness and phenology of the lake ice has been changing, which profoundly affects the regional climate evolution. There are a few studies about lake ice in alpine regions, but the understanding of climatological characteristics of lake ice on the QTP is still limited. Based on a field experiment in the winter of 2022, the thermal conductivity of Qinghai Lake ice was determined as 1.64 W·m−1·°C−1. Airborne radar ice thickness data, meteorological observations, and remote sensing images were used to evaluate a quasi-steady ice model (Leppäranta model) performance of the lake. This is an analytic model of lake ice thickness and phenology. The long-term (1979–2017) ice history of the lake was simulated. The results showed that the modeled mean ice thickness was 0.35 m with a trend of −0.002 m·a−1, and the average freeze-up start (FUS) and break-up end (BUE) were 30 December and 5 April, respectively, which are close to the field and satellite observations. The simulated trend of the maximum ice thickness from 1979 to 2017 (0.004 m·a−1) was slightly higher than the observed result (0.003 m·a−1). The simulated trend was 0.20 d·a−1 for the FUS, −0.34 d·a−1 for the BUE, and −0.54 d·a−1 for the ice duration (ID). Correlation and detrending analysis were adopted for the contribution of meteorological factors. In the winters of 1979–2017, downward longwave radiation and air temperature were the two main factors that had the best correlation with lake ice thickness. In a detrending analysis, air temperature, downward longwave radiation, and solar radiation contributed the most to the average thickness variability, with contributions of 42%, 49%, and −48%, respectively, and to the maximum thickness variability, with contributions of 41%, 45%, and −48%, respectively. If the six meteorological factors (air temperature, downward longwave radiation, solar radiation, wind speed, pressure, and specific humidity) are detrending, ice thickness variability will increase 83% on average and 87% at maximum. Specific humidity, wind, and air pressure had a poor correlation with ice thickness. The findings in this study give insights into the long-term evolutionary trajectory of Qinghai Lake ice cover and serve as a point of reference for investigating other lakes in the QTP during cold seasons.

Lake Ice Thickness Retrieval Method with ICESat-2-Assisted CyroSat-2 Echo Peak Selection

Lake ice thickness (LIT) is one of the key climate variables in the lake ice domain, but there are currently large uncertainties in the retrieval of LIT. We present and validate a new LIT retrieval method that utilizes ICESat-2 data to assist CryoSat-2 echo peak selection, aiming to improve the accuracy of LIT retrieval and enable data acquisition without on-site measurements. The method involves screening out similar ICESat-2 and CryoSat-2 tracks based on time and space constraints. It also involves dynamically adjusting the range constraint window of CryoSat-2 waveforms based on the high-precision lake ice surface ellipsoid height obtained from ICESat-2/ATL06 data. Within this range constraint window, the peak selection strategy is used to determine the scattering interfaces between snow-ice and ice-water. By utilizing the distance between the scattering horizons, the thickness of the lake ice can be determined. We performed the ice thickness retrieval experiment for Baker Lake in winter and verified it against the on-site measurement data. The results showed that the accuracy was about 0.143 m. At the same time, we performed the ice thickness retrieval experiment for Great Bear Lake (GBL), which does not have on-site measurement data, and compared it with the climate change trend of GBL. The results showed that the retrieval results were consistent with the climate change trend of GBL, confirming the validity of the proposed method.

Climatic sensitivity of seasonal ice-cover, water temperature and biogeochemical cycling in Lake 239 of the Experimental Lakes Area (ELA), Ontario, Canada

Regional climate plays an important role on in-lake physical and biogeochemical processes and determines the state of water quality and ecology of the lakes. This paper presents the result of a one-dimensional numerical modelling of lake ice cover, water temperature, as well as nutrient and phytoplankton dynamics in Lake 239 (Rawson Lake) located in the Experimental Lakes Area (ELA), Ontario, Canada. Using observed meteorological and inflow/outflow data, the Multi-year Lake simulation model (MyLake) was setup to replicate measured lake water temperature, ice-cover, dissolved organic carbon, particulate and dissolved phosphorus, and chlorophyll-a concentrations over the entire depth of the lake. The sensitivity of the lake to changes in climatic drivers, specifically to changes in surface air temperature and wind speed, was examined by incrementally changing the model input forcing for temperature and wind speed and mapping the resulting changes in the simulated outputs. The results show that lake processes are generally as sensitive to changes in wind speed as they are to changes in air temperature. Simulated changes in lake variables corresponding to projected changes in Global Climate Models (GCMs) under RCP4.5 and RCP8.5 emission scenarios reveal a general increase in lake water temperature and the corresponding decrease in lake ice thickness and ice cover duration under the changing climate. While lake water temperature increases at all depths, the shallow epilimnion layer is more sensitive to the changing climate than the deeper hypolimnion layer, reaching mean annual change value of +3.7 °C for RCP 8.5 and the 2090s as compared to the 1990s baseline period. While the simulated change in lake ice thickness reaches −20 cm by 2090s for the RCP 8.5 scenario, the corresponding change in ice-cover duration reaches -47 days. These changes are shown to result in seasonal changes in the biogeochemical cycling over the depth of the lake with projected increases in chlorophyll-a concentration during ice covered season of December to May reaching up to +0.15 mg/m3 for RCP 8.5 and the 2090s followed by a decrease during open water season of June to November reaching −0.15 mg/m3 compared to the 1990s baseline period.

Water temperature, mixing, and ice phenology in the arctic–alpine Lake Darfáljávri (Lake Tarfala), northern Sweden

ABSTRACT In the rapidly warming circumpolar Arctic, recent research of lakes has focused on their climatology and ecology but is challenged by sparsity of wintertime data. At the c. 48-m-deep and c. 0.5-km2 large proglacial Darfáljávri (Lake Tarfala), located in an arctic–alpine environment in the Scandinavian Mountains, year-round water temperatures were previously reported for 2016 to 2019. Here, this record is continued for 2019–2020 and 2021–2022, complemented by time-lapse imagery records of the state of the lake surface, as well as degree-day modeling of ice phenology (timing of ice-on and ice-off). Darfáljávri is cryostratified during winter, with interannual variations in the thermocline’s thickness and temperature range. The ice season lasts from October to July. Modeled ice-on dates match observed ones reasonably well; however, observed ice-off dates occur much later than modeled ones, likely because of cold impact from Darfáljávri’s glacial environment as inferred from a comparison with a close tundra lake. Though new insights into the complex lake mixing and ice phenology are provided, it remains to attribute the characteristics of Darfáljávri’s winter stratification to additional potential drivers, such as lake ice thickness, atmospheric heat fluxes, and the water balance of the lake.

Climate change reductions in lake ice cover duration and thickness help regulate the carbon sink potential of plateau type lakes

Climate change reductions in lake ice cover duration and thickness help regulate the carbon sink potential of plateau type lakes

Lake Ice Simulation and Evaluation for a Typical Lake on the Tibetan Plateau

This study aims to simulate the lake ice conditions in the Nam Co lake using a lake ice model, which is a one-dimensional physics-based model that utilizes enthalpy as the predictor variable. We modified the air density schemes within the model to improve the accuracy of the lake ice simulation. Additionally, the process of lake ice sublimation was included, and the effect of lake water salinity on the freezing point was considered. Using the improved lake ice model, we simulated lake surface water temperature, lake ice thickness, and interannual variations in lake ice phenology, and we compared these results with observations at Nam Co. The results demonstrate that the improved model better reproduces the lake surface water temperature, lake ice thickness, and lake ice phenology at Nam Co. Additionally, the thin air density affects lake processes by weakening sensible heat and latent heat, which ultimately leads to a delayed ice-on date and a slightly earlier ice-free date in Nam Co. This study contributes to an enhanced understanding of the freeze–thaw processes in Nam Co and reduces the biases in lake ice simulation on the Tibetan Plateau through the lake model improvement.

Ice Concentration Scaling Laws for Freshwater Lakes in Numerical Weather and Climate Prediction

AbstractIf lake ice is assumed to deform and fail as a linear viscoelastic material under the action of wind stress, then a simple ice concentration scaling law can be constructed suitable for one‐dimensional lake models embedded within environmental prediction systems. Most 1‐D lake models assume no ice mechanics at all, while others adapt the viscous‐plastic rheology common in ice‐ocean models for the purpose of estimating ice fraction. Elastic buckling is generally disregarded as a significant failure mechanism in ice under low stress conditions at geophysical scales. However, by adding viscosity to the constitutive equation, the conditions for viscoelastic buckling seem quite plausible over a wide range of lake size and ice thickness. An ice concentration scaling law based on this process is evaluated here in multiannual simulations over North America and found to produce superior ice phenology statistics compared with simulations based on plastic failure or no ice mechanics.

Mapping snow depth on Canadian sub-arctic lakes using ground-penetrating radar

Abstract. Ice thickness across lake ice is mainly influenced by the presence of snow and its distribution, which affects the rate of lake ice growth. The distribution of snow depth over lake ice varies due to wind redistribution and snowpack metamorphism, affecting the variability of lake ice thickness. Accurate and consistent snow depth data on lake ice are sparse and challenging to obtain. However, high spatial resolution lake snow depth observations are necessary for the next generation of thermodynamic lake ice models to improve the understanding of how the varying distribution of snow depth influences lake ice formation and growth. This study was conducted using ground-penetrating radar (GPR) acquisitions with ∼9 cm sampling resolution along transects totalling ∼44 km to map snow depth over four Canadian sub-arctic freshwater lakes. The lake snow depth derived from GPR two-way travel time (TWT) resulted in an average relative error of under 10 % when compared to 2430 in situ snow depth observations for the early and late winter season. The snow depth derived from GPR TWTs for the early winter season was estimated with a root mean square error (RMSE) of 1.6 cm and a mean bias error of 0.01 cm, while the accuracy for the late winter season on a deeper snowpack was estimated with a RMSE of 2.9 cm and a mean bias error of 0.4 cm. The GPR-derived snow depths were interpolated to create 1 m spatial resolution snow depth maps. The findings showed improved lake snow depth retrieval accuracy and introduced a fast and efficient method to obtain high spatial resolution snow depth information. The results suggest that GPR acquisitions can be used to derive lake snow depth, providing a viable alternative to manual snow depth monitoring methods. The findings can lead to an improved understanding of snow and lake ice interactions, which is essential for northern communities' safety and wellbeing and the scientific modelling community.

Microphone recording of flexural waves for estimation of lake ice thickness

In this study we took an intentionally low-tech approach, aiming to estimate key physical parameters of lake ice using a single, inexpensive microphone. We consider this approach highly relevant to the issue of transport safety and the relatively high number of accidents involving breakthrough failure of thin ice underlines the importance of this topic. We conducted a range of experiments on three frozen lakes in the Tromsø region of Northern Norway and found that the monochromatic air-coupled flexural wave was a robust feature of impulsively excited frozen lakes. Ice thickness was estimated via a closed form solution that only depends on the measured monochromatic frequency of the air-coupled flexural wave and a set of assumed physical parameters for the ice, air and water. We discuss the impact of uncertainty in the assumed parameters on estimated ice thickness and bearing capacity, finding the uncertainty to be quite small, particularly when physical observation of ice type or drilled thickness are available to constrain the assumed Young's modulus of the ice. Ice thicknesses estimated from air-coupled flexural waves were typically within 5–10% of ice thickness measured in holes drilled in the vicinity of the microphone for both artificial sources including hammer strikes, jumping and tapping with ice skates and natural ice quakes. The thickness estimates were also similarly accurate whether the microphone was resting on the ice, placed on land along the shoreline or handheld. We also showed that it is possible to record the dispersive ice flexural wave using a microphone, particularly when it was resting on the ice surface. Since thickness was constrained by the air-coupled flexural wave, we were able to estimate the propagation distance, corresponding to the horizontal offset between source and microphone, by inversion of the time dispersed arrival of the chirp signal corresponding to the ice flexural wave. We also demonstrated that a microphone can record inharmonic monochromatic overtones of the air-coupled flexural wave, that were linked to the geometry and boundary conditions of the frozen lakes using finite element modal analysis. This study leads us to conclude that a simple microphone can be a powerful tool giving a surprising amount of information on the lake-ice system. Using a microphone to record air-coupled flexural waves appears to be a promising additional tool for evaluation of ice conditions, convenient enough that it could substantially increase the availability of timely and accurate information on ice thickness and thereby contribute to safer travel on floating ice.

Prediction of subglacial lake melt source regions from site characteristics

AbstractSubglacial melt has important implications for ice-sheet dynamics. Locating and identifying subglacial lakes are expensive and time-consuming, requiring radar surveys or satellite methods. We explore three methods to identify source regions for lakes using seven continent-wide environmental characteristics that are sensitive to or influenced by ice-sheet temperature. A simple comparison of environmental properties at lake locations with their continent-wide distributions suggests a statistical relationship (high Kolmogorov-Smirnov statistic) between stable lake locations and ice thickness and surface temperatures, indicating melting under passive conditions. Active lakes, in contrast, show little correlation with direct thermally influenced parameters, instead exhibiting large statistical differences with horizontal velocity and bedrock elevation. More sophisticated techniques, including principal component analysis (PCA) and machine learning (ML) classification, provide better spatial identification of lake types. Positive PCA scores derived from the environmental characteristics correlate with stable lakes, whereas negative values correspond to active lakes. ML methods can also identify regions where subglacial lake melt sources are probable. While ML provides the most accurate classification maps, the combination of approaches adds deeper knowledge of the primary controls on lake formation and the environmental settings in which they are likely to be found.

Estimation of Lake Ice Thickness with Satellite Radar Altimeter Waveforms

Background and Relevance: The phenology of seasonally ice covered lakes has been altered with anthropogenic climate change; recent studies point out an overall reduction in the number of days in which the lake is covered with ice [1]. Lake ice is essential for many northern communities in fishing and travel. Reductions in the thickness in ice is a major safety risk, especially when those who travel on ice have historically been able to trust it. Remote sensing efforts have majorly focused on sea ice and glacial changes, and little is known about changes in lake ice thickness besides the seasonal length of ice coverage. Previous researchers have determined that the Ku-band (13 to 17 GHz) is capable of penetrating through freshwater ice and is scattered from both the snow/ice and the ice/water interfaces [1]. A radar altimeter operating at the Ku-band holds the potential to measure the height of the sensor above the snow/ice interface and above the ice/water interface. The difference between these two measurements yields the ice thickness. This method has been confirmed with data from the Cryosat-2 satellite [1], but only tested with arctic lakes. There is a gap in knowledge of how midlatitude lakes have responded to changing climates in terms of their seasonal ice thickness. Objectives and Methods: Ku-band radar altimeter measurements from the JASON series of satellites can be used to determine lake ice thickness by calculating the distance between dualpeak waveforms, representative of snow/ice and ice/water interfaces. A study area will be chosen after a review of the available data coverage and an overall analysis of scattering. Snowpack, bubbles, and wetness of the ice adds noise to the waveform and complicates the ice thickness retrieval. A lake that minimizes these factors will be selected for the study area. Data will be extracted from an orbit passing over the lake for each month of a year and in intervals of 5 years. The JASON series has the following advantages to the Cryosat-2 satellite: Improved temporal resolution – CryoSat-2 covers polar regions, while the JASON Series covers the area between N: 66.15° N and S: -66.15°; and greater coverage – Jason-1/2/3 have the shortest revisit cycle (~10 days) among all existing satellite altimeters and have been operating since 1992 [2]. The radar altimeter measurements will be displayed as the travel distance as a function of mean normalized power. The difference between peaks on the waveform will be calculated and logged as the ice thickness during that time. The results will be validated by comparing the estimated lake ice thickness values with ice fishing logs from similar dates. There is potential for the ice thickness data to be interpolated across the entirety of the lake using ArcGIS Pro, but this is contingence on the specifics of the study area. Anticipated Contributions: This study will: (1) estimate ice thickness in areas where in situ drillhole measurements cannot be taken, (2) introduce another factor in climate change discussions and models, (3) further our ability to monitor biophysical factors with remote sensing at global scales. [1] J. F. Beckers, J. Alec Casey and C. Haas, "Retrievals of Lake Ice Thickness From Great Slave Lake and Great Bear Lake Using CryoSat-2," in IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 7, pp. 3708-3720, July 2017, doi: 10.1109/TGRS.2017.2677583. [2] C. J. Donlon et al., “The Copernicus Sentinel-6 mission: Enhanced continuity of satellite sea level measurements from space,” Remote sensing of environment, vol. 258, p. 112395–, 2021, doi: 10.1016/j.rse.2021.112395.

Ice thickness and water level estimation for ice-covered lakes with satellite altimetry waveforms and backscattering coefficients

Abstract. Lake ice, serving as a sensitive indicator of climate change, is an important regulator of regional hydroclimate and lake ecosystems. For ice-covered lakes, traditional satellite altimetry-based water level estimation is often subject to winter anomalies that are closely related to the thickening of lake ice. Despite recent efforts made to exploit altimetry data to resolve the two interrelated variables, i.e., lake ice thickness (LIT) and the water level of ice-covered lakes, several important issues remain unsolved, including the inability to estimate LIT with altimetric backscattering coefficients in ungauged lakes due to the dependence on in situ LIT data. It is still unclear what role lake surface snow plays in the retrieval of LIT and water levels in ice-covered lakes with altimetry data. Here we developed a novel method to estimate lake ice thickness by combining altimetric waveforms and backscattering coefficients without using in situ LIT data. To overcome complicated initial LIT conditions and better represent thick ice conditions, a logarithmic regression model was developed to transform backscattering coefficients into LIT. We investigated differential impact of lake surface snow on estimating water levels for ice-covered lakes when different threshold retracking methods are used. The developed LIT estimation method, validated against in situ data and cross-validated against modeled LIT, shows an accuracy of ∼ 0.2 m and is effective at detecting thin ice that cannot be retrieved by altimetric waveforms. We also improved the estimation of water levels for ice-covered lakes with a strategy of merging lake water levels derived from different threshold methods. This study facilitates a better interpretation of satellite altimetry signals from ice-covered lakes and provides opportunities for a wider application of altimetry data to the cryosphere.

Thermal modeling of three lakes within the continuous permafrost zone in Alaska using the LAKE 2.0 model

Abstract. Lakes in the Arctic are important reservoirs of heat with much lower albedo in summer and greater absorption of solar radiation than surrounding tundra vegetation. In the winter, lakes that do not freeze to their bed have a mean annual bed temperature >0 ∘C in an otherwise frozen landscape. Under climate warming scenarios, we expect Arctic lakes to accelerate thawing of underlying permafrost due to warming water temperatures in the summer and winter. Previous studies of Arctic lakes have focused on ice cover and thickness, the ice decay process, catchment hydrology, lake water balance, and eddy covariance measurements, but little work has been done in the Arctic to model lake heat balance. We applied the LAKE 2.0 model to simulate water temperatures in three Arctic lakes in northern Alaska over several years and tested the sensitivity of the model to several perturbations of input meteorological variables (precipitation, shortwave radiation, and air temperature) and several model parameters (water vertical resolution, sediment vertical resolution, depth of soil column, and temporal resolution). The LAKE 2.0 model is a one-dimensional model that explicitly solves vertical profiles of water state variables on a grid. We used a combination of meteorological data from local and remote weather stations, as well as data derived from remote sensing, to drive the model. We validated modeled water temperatures with data of observed lake water temperatures at several depths over several years for each lake. Our validation of the LAKE 2.0 model is a necessary step toward modeling changes in Arctic lake ice regimes, lake heat balance, and thermal interactions with permafrost. The sensitivity analysis shows us that lake water temperature is not highly sensitive to small changes in air temperature or precipitation, while changes in shortwave radiation and large changes in precipitation produced larger effects. Snow depth and lake ice strongly affect water temperatures during the frozen season, which dominates the annual thermal regime of Arctic lakes. These findings suggest that reductions in lake ice thickness and duration could lead to more heat storage by lakes and enhanced permafrost degradation.

Determinants of Spatial Variability of Ice Thickness in Lakes in High Mountains of the Temperate Zone—The Case of the Tatra Mountains

Vertical and horizontal variation in the ice cover of mountain lakes in the temperate climate zone has not been thoroughly studied. The study concerned ice phenomena in four lakes located in the Tatra National Park in the Tatra Mountains (the Czarny Staw Gąsienicowy, Czarny Staw pod Rysami, Morskie Oko, and Smreczyński Staw). The research, which was conducted in the 2018/2019 winter season, included an analysis of variability in atmospheric conditions, an analysis of presence of ice phenomena on satellite images, field work (measurements of ice layer and of snow and slush layer thickness were conducted at a total of 151 sites), and statistical analyses. It was determined that negative air temperature was just one factor among those that determined the maximum thickness of the ice forming on lakes in high mountains. It was found that in addition to ambient thermal conditions, a major factor affecting the magnitude of variation in lake ice thickness was the thickness of the snow overlying the ice and its spatial variability. Thicker ice cover tended to form in areas where a thick layer of snow was deposited. The decisive factor that contributed to a significant variation in ice thickness between lakes was the uneven accretion of snow ice from above. The maximum ice thickness values modeled using Stefan’s formula were significantly underestimated (accounting for 38–61% of the ice thickness measured) relative to the highest ice thickness values found empirically at the end of the winter season. Study results fill a gap in our knowledge and methodology related to vertical and horizontal variation in the ice cover of mountain lakes; they also have significant applications, indicating the risk of winter use of water bodies with different ice cover structure, thickness, and extent.

Analysis of the Variability and Influencing Factors of Ice Thickness during the Ablation Period in Qinghai Lake Using the GPR Ice Monitoring System

As a reliable indicator of regional climate change, the growth and decline of lake ice thickness affect the regional intra–annual heat and energy balance. In this study, a ground-penetrating radar (GPR) ice monitoring system, located approximately 1.7 km west of Bird Island in Qinghai Lake, in the territory of Qinghai Province and located in northwest China, was designed to carry out continuous fixed–point observations of local ice thickness and meteorological elements from 7 to 24 March 2021. The characteristics of continuous daily changes in ice thickness during the ablation period of Qinghai Lake and their relationship with meteorological elements were analyzed. The results showed that the average daily ice thickness of Qinghai Lake increased and then decreased during the observation period, with an average ice thickness of 42.83 cm, an average daily ice thickness range of 39.35~46.15 cm, and a growth rate of 0.54 cm/day during 8–13 March 2021, with an ice melting rate of −0.61 cm/day during 14–24 March 2021. The daily ice thickness variations were divided into two phases, which were relatively stable before dawn and followed a decreasing, increasing, and then decreasing trend during 8–13 March 2021 and a decreasing, increasing (for several hours), and then decreasing trend during 14–24 March 2021. There was a significant positive correlation (R = 0.745, p < 0.01) between near-surface air temperature and ice surface temperature during the observation period, but a significant negative correlation (R = −0.93, p < 0.05) between the average daily ice thickness and cumulative temperature of the ice surface. Temperature was the dominant factor affecting lake ice thickness, as compared to near-surface air humidity, wind speed, and illuminance. However, a sudden increase in wind speed have also played an important role at certain periods. A large number of cracks appeared on the ice surface on 26 March 2021, which, combined with the forces of wind speed, wind direction, and temperature, contributed to the rapid melt of the lake ice. This study filled the gap in situ measurement data on the continuous ice thickness variability during the ablation period in Qinghai Lake. It provided scientific support for the further study of lake ice on the Qinghai–Tibet Plateau (QTP).

Thermal Responses of the Largest Freshwater Lake in the Tibetan Plateau and Its Nearby Saline Lake to Climate Change

There are thousands of lakes in the Tibetan Plateau (TP), and most are saline. However, little is known about the responses of TP lakes to climate change, especially saline ones. We investigated the thermal responses of the largest freshwater lake (Ngoring Lake) in the TP and its nearby small saline lake (Hajiang Salt Pond) to climate change using the improved lake scheme in the Community Land model (CLM4-LISSS), in which we primarily developed the salinity parameterizations previously evaluated in the Great Salt Lake in USA and further considered the effect of salinity on the temperature of the maximum density of saline water in the present study. The improved lake model with salinity parameterizations was first applied to a saline lake in the TP, where saline lakes make up the majority of water bodies. The CLM4-LISSS model could effectively simulate lake surface water temperature (LSWT), lake water temperature (LT) and ice thickness in Ngoring Lake. Additionally, the model including our salinity parameterizations significantly improved simulations of LSWT and LT in Hajiang Salt Pond, especially in winter. The LSWT of the two completely opposite lakes were warming in the simulations at a rate above 0.6 °C/decade. Meteorological forces were the main driving factor, with increasing downward longwave radiation, air temperature and air humidity, as well as weakening winds contributing to LSWT increase. Compared to a hypothetical shallow freshwater lake, the greater depth of Ngoring Lake made its surface warm faster, and salinity slightly accelerated the warming of Hajiang Salt Pond. Monthly mean LSWT differences between the two lakes were induced by salinity effects in cold periods and lake depth in the unfrozen period. In response to a warming climate, the LSWT in the ice-free Hajiang Salt Pond rapidly increased from January to April due to the warming climate, whereas the LSWT of Ngoring Lake increased faster in the first and last month of the ice-cover period due to later ice-on and earlier ice-off. This study will provide a useful tool for saline lakes in the TP and help deepen our knowledge about the responses of TP lakes, especially the saline lakes, to climate change, as well as response differences between freshwater and saline lakes and the reasons for these differences.

Retrieval of Lake Ice Characteristics from SAR Imagery

Boreal lakes ecosystems can remain partially or completely covered by ice and snow during an important portion of the year. Alterations of lake and river ice properties can deteriorate the conditions of local ice roads, negatively influencing Nordic communities and economical activities. Monitoring of lake ice characteristics and dynamics is therefore crucial. In this study, Sentinel-1 Synthetic Aperture Radar (SAR) satellite imagery is exploited to evaluate changes in the ice regime over shallow and deep high-latitude lakes during the winters of 2018 and 2019. The methodology proposed, based on the combined analysis of SAR intensity and interferometric coherence maps, enables the extraction of important characteristics of ice dynamics. Overall, the lake ice thickness change derived from Differential Interferometric SAR (D-InSAR) increases with the lake depth. The D-InSAR-derived mean rate of growth, in general, agrees with the one estimated from records of in-situ ice thickness measurements. The methodology presented herein could be temporally extended to support the understanding of historical and current climate conditions. This could be done by using archived and newly available imagery to improve lake ice models.