Lake Water Temperature Research Articles

Rapid changes in river plume dynamics caused by advected wind‐driven coastal upwelling as observed in Lake Geneva

AbstractBased on field investigations carried out in Lake Geneva during summer stratification in 2019, this study documents, for the first time, the rapid change of negatively buoyant river inflow dynamics caused by the passing of a coastal upwelling. Under calm conditions, the negatively buoyant Rhône River, entering at the eastern end of the lake, interacts with the lake density profile such that the river intrusion moves as an interflow in the thermocline layer straight out into the lake. A strong, large‐scale spatially homogeneous wind that lasts for several days causes a downward thermocline tilt at the western end of the lake, coastal downwelling on the northern shore, and coastal upwelling on the southern shore. This cold‐water upwelling progresses like a Kelvin wave around the lake after wind forcing ceases. When it arrives at the river inflow area, it homogenizes the lake water temperatures, and the river inflow transforms into an intrusion that spreads over the whole water column. Trapped by strong alongshore currents generated by the progressing coastal upwelling, the river plume is sharply deviated and flows along the shore in the nearshore zone, potentially bringing nutrients directly into the phototrophic near‐surface layer. Following the passage of the coastal upwelling, the river inflow transforms back into an interflow. This change in inflow dynamics, which is documented for five strong wind events between June and September by combining in situ measurements, remote sensing, and numerical simulation, can be expected to occur in other large lakes with comparable wind‐induced large‐scale thermocline displacement.

Forecasting water temperature in lakes and reservoirs using seasonal climate prediction

Seasonal climate forecasts produce probabilistic predictions of meteorological variables for subsequent months. This provides a potential resource to predict the influence of seasonal climate anomalies on surface water balance in catchments and hydro-thermodynamics in related water bodies (e.g., lakes or reservoirs). Obtaining seasonal forecasts for impact variables (e.g., discharge and water temperature) requires a link between seasonal climate forecasts and impact models simulating hydrology and lake hydrodynamics and thermal regimes. However, this link remains challenging for stakeholders and the water scientific community, mainly due to the probabilistic nature of these predictions. In this paper, we introduce a feasible, robust, and open-source workflow integrating seasonal climate forecasts with hydrologic and lake models to generate seasonal forecasts of discharge and water temperature profiles. The workflow has been designed to be applicable to any catchment and associated lake or reservoir, and is optimized in this study for four catchment-lake systems to help in their proactive management. We assessed the performance of the resulting seasonal forecasts of discharge and water temperature by comparing them with hydrologic and lake (pseudo)observations (reanalysis). Precisely, we analysed the historical performance using a data sample of past forecasts and reanalysis to obtain information about the skill (performance or quality) of the seasonal forecast system to predict particular events. We used the current seasonal climate forecast system (SEAS5) and reanalysis (ERA5) of the European Centre for Medium Range Weather Forecasts (ECMWF). We found that due to the limited predictability at seasonal time-scales over the locations of the four case studies (Europe and South of Australia), seasonal forecasts exhibited none to low performance (skill) for the atmospheric variables considered. Nevertheless, seasonal forecasts for discharge present some skill in all but one case study. Moreover, seasonal forecasts for water temperature had higher performance in natural lakes than in reservoirs, which means human water control is a relevant factor affecting predictability, and the performance increases with water depth in all four case studies. Further investigation into the skillful water temperature predictions should aim to identify the extent to which performance is a consequence of thermal inertia (i.e., lead-in conditions).

Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles

Physics-based models are often used to study engineering and environmental systems. The ability to model these systems is the key to achieving our future environmental sustainability and improving the quality of human life. This article focuses on simulating lake water temperature, which is critical for understanding the impact of changing climate on aquatic ecosystems and assisting in aquatic resource management decisions. General Lake Model (GLM) is a state-of-the-art physics-based model used for addressing such problems. However, like other physics-based models used for studying scientific and engineering systems, it has several well-known limitations due to simplified representations of the physical processes being modeled or challenges in selecting appropriate parameters. While state-of-the-art machine learning models can sometimes outperform physics-based models given ample amount of training data, they can produce results that are physically inconsistent. This article proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improves the modeling of physical processes. Specifically, we show that a PGRNN can improve prediction accuracy over that of physics-based models (by over 20% even with very little training data), while generating outputs consistent with physical laws. An important aspect of our PGRNN approach lies in its ability to incorporate the knowledge encoded in physics-based models. This allows training the PGRNN model using very few true observed data while also ensuring high prediction accuracy. Although we present and evaluate this methodology in the context of modeling the dynamics of temperature in lakes, it is applicable more widely to a range of scientific and engineering disciplines where physics-based (also known as mechanistic) models are used.

Evaluation of Global Surface Water Temperature Data Sets for Use in Passive Remote Sensing of Soil Moisture

Inland open water bodies often pose a systematic error source in the passive remote sensing retrievals of soil moisture. Water temperature is a necessary variable used to compute water emissions that is required to be subtracted from satellite observation to yield actual emissions from the land portion, which in turn generates accurate soil moisture retrievals. Therefore, overestimation of soil moisture can often be corrected using concurrent water temperature data in the overall mitigation procedure. In recent years, several data sets of lake water temperature have become available, but their specifications and accuracy have rarely been investigated in the context of passive soil moisture remote sensing on a global scale. For this reason, three lake temperature products were evaluated against in-situ measurements from 2007 to 2011. The data sets include the lake surface water temperature (LSWT) from Global Observatory of Lake Responses to Environmental Change (GloboLakes), the Copernicus Global Land Operations Cryosphere and Water (C-GLOPS), as well as the lake mix-layer temperature (LMLT) from the European Centers for Medium-Range Weather Forecast (ECMWF) ERA5 Land Reanalysis. GloboLakes, C-GLOPS, and ERA5 Land have overall comparable performance with Pearson correlations (R) of 0.87, 0.92 and 0.88 in comparison with in-situ measurements. LSWT products exhibit negative median biases of −0.27 K (GloboLakes) and −0.31 K (C-GLOPS), whereas the median bias of LMLT is 1.56 K. When mapped from their respective native resolutions to a common 9 km Equal-Area Scalable Earth (EASE) Grid 2.0 projection, similar relative performance was observed. LMLT and LSWT data are closer in performance over the 9 km grid cells that exhibit a small range of lake cover fractions (0.05–0.5). Despite comparable relative performance, ERA5 Land shows great advantages in spatial coverage and temporal resolution. In summary, an integrated evaluation on data accuracy, long-term availability, global coverage, temporal resolution, and regular forward processing with modest data latency led us to conclude that LMLT from the ERA5 Land Reanalysis product represents the most optimal path for use in the development of a long-term soil moisture product.

Chirripó hydrological research site: Advancing stable isotope hydrology in the Central American Páramo

AbstractThe Chirripó hydrological research site (CHRS) is located within the Chirripó National Park, Costa Rica (between 3100 and 3820 m asl) whereby ~100 km2 are covered by Páramo, a high‐elevation tropical grassland ecosystem. A lake district with approximately 30 lakes of glacial origin is also protected in this area. The CHRS has been monitored since April 2015 with the aim of establishing the first water isotope baseline for the Central American Páramo. At a regional scale, the water isotope ratios (δ2H and δ18O) in precipitation and surface water at CHRS are useful for describing the governing moisture transport from the Caribbean Sea and Pacific Ocean and the complex rainfall producing systems across the N–S mountain range of Central America. These data are also providing unique information about the evaporation and water balance conditions of tropical glacial lakes and the formation of orographic and convective precipitation in high‐elevation tropical ecosystems. Current data sets from CHRS include continuous lake water temperature and meteorological conditions (i.e., precipitation amount, air temperature and relative humidity), as well as water stable isotopes in precipitation, stream water, and lake water (daily to biweekly sampling frequency). Stream water is collected at several locations across the topographic gradient whereas lake water is sampled in the three main lake systems of CHRS. CHRS serves as a reference site for conducting pilot isotopic research in high‐elevation ecosystems to advance the atmospheric, hydrogeological and ecohydrological studies in these understudied biomes. All data from April 2015 to November 2020 are publicly available.

A Simple Solution for Refining Lake Water Temperature Profiles Data Arrayed from High-Frequency Monitoring Sensors

The revolutionized aquatic monitoring sensors are essential in capturing environmental patterns that traditional discrete samplings might not be able to. They allow scientists to further synthesize and better conclude processes in aquatic ecosystems. These sensors produce high-frequency data that provide information on a fine temporal scale, even near real-time. The massive quantities of the streamed data, however, create challenges for scientists to grasp the concrete information. Filtering data quality, on the other hand, is another problem scientists might have encountered as sensor accuracy and precision may drift along the line. Hence, quality assurance and quality control might be quite labouring owing to the size of datasets to handle. This paper proposed a semi-mechanistic algorithm to improved false water temperature data. Using “theoretical” thermal stratification as a reference, this algorithm fixed sensors error readings. A 5-month dataset of water temperature profiles of Lake Maninjau, West Sumatra, captured every 10 minutes from a set of sensors in thermistor chain was applied. We found that most data fit to the theoretical temperature profile, R<sup>2</sup> = 0.962, RMSE = 0.081<sup>o</sup>C. A number of errors, however, were observed in the upper layer of the lake (<20 m), the most dynamic layer in terms of its thermal variation. Sensor drifts in this active upper mixed layer can be related to the generated errors. Through this simple solution, not only improving the quality of the observed water temperature data, but was also able to identify the most probable source of errors

How does the calibration method impact the performance of the air2water model for the forecasting of lake surface water temperatures?

The air2water model is a widely used tool for the forecasting of lake water temperatures and projection of climate change. It has been applied in thousands of lakes worldwide. However, it employed the Particle Swarm Optimization (PSO) as the calibration method, which was proposed more than 20 years ago. In this study, 12 advanced optimization algorithms proposed during the recent few years were implemented to calibrate and validate the air2water model. Daily observed water temperature in temperate lakes (22 lowland lakes in Poland) were used to evaluate the model performance. Each optimizer was run 30 times with 3 different settings of the maximum number of function calls, 5000, 20,000, and 100,000. We have found that the PSO method used in the original air2water model performs relatively poor compared with most of the recent algorithms, and the majority of recently proposed algorithms reach the similar best solutions on many lakes when the highest number of function calls is allowed. However, only the HARD-DE (hierarchical archive-based mutation strategy with depth information of differential evolution) algorithm is never outperformed by any competitor, irrespective which lake and maximum number of function calls is considered. As a result, we highly recommend using HARD-DE as the calibration method for the air2water model in the future studies.

Seasonal overturn and stratification changes drive deep-water warming in one of Earth\u2019s largest lakes

Most of Earth’s fresh surface water is consolidated in just a few of its largest lakes, and because of their unique response to environmental conditions, lakes have been identified as climate change sentinels. While the response of lake surface water temperatures to climate change is well documented from satellite and summer in situ measurements, our understanding of how water temperatures in large lakes are responding at depth is limited, as few large lakes have detailed long-term subsurface observations. We present an analysis of three decades of high frequency (3-hourly and hourly) subsurface water temperature data from Lake Michigan. This unique data set reveals that deep water temperatures are rising in the winter and provides precise measurements of the timing of fall overturn, the point of minimum temperature, and the duration of the winter cooling period. Relationships from the data show a shortened winter season results in higher subsurface temperatures and earlier onset of summer stratification. Shifts in the thermal regimes of large lakes will have profound impacts on the ecosystems of the world’s surface freshwater.

Mass 47 clumped isotope signatures in modern lacustrine authigenic carbonates in Western China and other regions and implications for paleotemperature and paleoelevation reconstructions

With the development of the carbonate clumped isotope (Δ47) geothermometer, many Δ47–temperature calibrations based on biogenic and abiogenic carbonates have been generated in recent years. However, there is still not a robust empirical calibration relationship derived from lacustrine authigenic carbonates that can be used to calculate lake water temperatures from Δ47 values. In this study, we present a new calibration dataset that is comprised of measurements of the clumped isotope composition of 33 lacustrine authigenic carbonates collected from terminal lakes primarily in Western China that cover a substantial altitudinal gradient, and 5 samples from other regions. These data allow us to directly derive a temperature calibration for lacustrine authigenic carbonates. Our results show a robust correlation between modern lacustrine authigenic carbonate clumped isotope composition (Δ47carb) values and independently measured mean summer water surface temperatures (Twater) (ranging from 9.8 to 26.0°C), which confirms the utility of mass-47 clumped isotope measurements of lacustrine authigenic carbonates as a lake water temperature indicator and allows for the derivation of the following calibration equation: Δ47carb (‰) = 0.0521 ± 0.0071 × 106/Twater2 (K) + 0.0904 ± 0.0870 (R2 = 0.6224, P < 0.0001, n = 33). We observe that Δ47carb is significantly correlated to lake elevation at 35°N from 270 to 5,156 m a.s.l. The change in Δ47–derived water temperature differences through elevation gradients (i.e. lapse rate) is around 3.2°C/km. We do not observe a significant correlation between Δ47carb and carbonate oxygen isotopic composition or independently measured lake surface water oxygen isotopic composition. Additionally, in our dataset, we find that water chemistry, carbonate precipitation rate, carbonate content and mineral-specific differences do not significantly affect Δ47carb values. Hence, the mass-47 clumped isotope signal of lacustrine authigenic carbonate can be used in paleotemperature and paleoaltimetry studies if post-depositional diagenesis did not modify the isotopic composition.

Indicators of climate change impacts on the water cycle and water management

Managing water is a top social and economic responsibility and is expected to become even more critical as climate change, in addition to other human activities, alters water availability and quality. Robust indicators reflecting the effects of climate change on the US and global water cycles are needed in order to appropriately manage water resources. Here, we describe a suite of seventeen water cycle and management indicators, which are based on synthesis of available datasets. These indicators include average and heavy precipitation, standardized precipitation index, annual, 7-day low and 3-day high streamflow volume, streamflow timing, snow cover, snow water equivalent, groundwater level, lake water temperature, stream water temperature, dissolved oxygen, salinity, Palmer Drought Severity Index, water withdrawals, and water use. We also identify three indicators that could be included in the suite of water cycle and management indicators with some additional, directed work: snowfall, evapotranspiration, and soil moisture. Our conceptual framework focuses on known water cycle changes in addition to potential effects on management and addresses water quantity and quality, as well as water use and related interactions with freshwater ecosystems, societal impacts, and management. Water cycle indicators are organized into three categories: (1) hydrologic processes, (2) water quality processes, and (3) water quality and quantity impacts. Indicators described here are recommended to serve as critical references for periodic climate assessments. As such, these indicators support analyses of the effects of global change on the natural environment, agriculture, energy, and water resources, among other sectors. Additionally, we identify research gaps and needs that can be addressed to advance the development of future indicators.

Microclimates in Fumarole Ice Caves on Volcanic Edifices—Mount Rainier, Washington, USA

AbstractThe fumarole ice caves of Mount Rainier in the Cascade Volcanic Arc in Washington, USA, provide unique insight into the dynamic equilibrium between thermal flux of fumaroles on volcanic edifices and snow accumulation on summit glaciers. More than 3.5 km of surveyed cave passage nearly circumnavigate the East Crater, reaching within 19 m of the 4,392 m summit and extending to 144 m depth along the glacier‐crater boundary. The large circum‐crater passage connects entrances on the crater rim to steep transverse passages, and cave morphology is maintained by fumarole gas convection and advection. A melt‐ and condensate‐formed lake, Lake Adélie, occupies a portion of the circum‐crater passage. Hourly data were collected between August 2016 and August 2017 and included the measured temperatures at three fumaroles, the cave air temperature and pressure, the lake water temperature and depth, and the outside temperature and snow depth at Paradise Visitors Center. Time‐series analyses of these data reveal complex associations between synoptic to seasonal weather, fumarole activity, and lake level. On seasonal and longer scales, fumarole temperatures follow independent pathways connected to spatial and temporal changes in volcanic heat flux and the circulation of glacial melt. Major snowfall seals the cave entrances, increasing cave air temperature and pressure from fumarole output and causing rising lake levels from increased melt until entrances reopen. Repeating freeze‐thaw cycles observed in the cave monitoring data are a primary cause of crater mass wasting. Despite these transient variations, the scale and morphology of the caves is preserved over decadal or longer scales.

Analysis of Temporal and Spatial Variation Process of Dianchi Lake Surface Water Temperature Based on MODIS Remote Sensing Images

Lake surface water temperature (LSWT) is an important physical attribute that affects the ecological environment of lakes. It plays an important role in the ecological process of the water environment and directly affects human production and life, the ecological environment system of the water area, and the diversity of organisms. In order to adjust and improve the ecological conditions of the watershed, it is necessary to obtain a large amount of effective lake surface water temperature data, and use this as a basis to analyze the temporal and spatial change process of the LSWT. This study uses MODIS (MOD11A2) surface temperature product data to obtain the LSWT of Dianchi Lake, and then analyzes the temporal and spatial variation characteristics of the LSWT of Dianchi Lake on different time scales (year, season, month) during the day and night. The research results show that the annual, seasonal and monthly average values of the LSWT during the day and night are all increasing from 2001 to 2017. The average annual temperature rise rate of Dianchi LSWT during the day and night is 0.09°Cyr−1 and 0.05°Cyr−1, respectively. The spatial analysis results show that the LSWT increase area of Dianchi Lake is expanding.

Clumped isotope analysis of lacustrine endogenic carbonates and implications for paleo-temperature reconstruction: A case study from Dali Lake

Carbonate clumped isotope (Δ47) is a new and reliable geothermometer. Endogenic carbonates in lake sediments are good archives to reconstruct lake water temperature using clumped isotope thermometry. However, applications of carbonate clumped isotope thermometry to lacustrine carbonates are still scarce because the existed organic and/or sulfur contaminants in such samples interfere with clumped isotope analysis and cause notable temperature biases. Therefore, exploring an effective way to remove contamination is a prerequisite to widely applying carbonate clumped isotope thermometry to lake sediments. By pretreating fine-grained endogenic carbonates from Dali lake sediments with hydrogen peroxide of different concentrations for different lengths of time, we conducted a series of conditional experiments to seek the optimal pretreatment condition for clumped isotope analysis. Δ47, Δ48 offset and 49 parameter were obtained from clumped isotope measurements to assess the effect of contamination removal. Results showed that untreated samples (“0%-0”) had a very high 49 parameter. After the treatment with hydrogen peroxide, the 49 parameter was significantly reduced while the Δ48 offset evidently increased. The Δ47 , Δ48 offset and 49 parameter of samples pretreated under different conditions showed significant variability, even though the measured Δ47 temperatures changed within a relatively narrow range (i.e., from 17.0±1.3 to 22.8±1.6°C). Among various treated samples, the sample “3%-8” yielded a smaller Δ48 offset and 49 parameter, and lower Δ47 temperature of 17.0±1.3°C. Therefore, “reacting with 3% hydrogen peroxide for 8 h” was suggested to be the optimal condition for removing contaminants from lacustrine endogenic carbonates prior to clumped isotope analysis. At the time when the pretreatment condition was too intense (i.e., H2O2 concentration >3% and/or reaction time > 8 h), secondary contaminants might have been generated in the closed reaction system, whose ultimately transformed state may interfere with masses 47, 48. Using the optimal pretreatment procedure, we obtained reasonable Δ47 temperature changes during the last deglaciation and found a temperature decrease of about 6°C in Northern China during the Younger Dryas period. Our study demonstrates a great potential of applying carbonate clumped isotope thermometry to lacustrine carbonates for paleo-temperature and paleo-elevation reconstructions in the future.

Response of downstream lakes to Aru glacier collapses on the western Tibetan Plateau

Abstract. The lower parts of two glaciers in the Aru range on the western Tibetan Plateau (TP) collapsed on 17 July and 21 September 2016, respectively, causing fatal damage to local people and their livestock. The giant ice avalanches, with a total volume of 150 × 106 m3, had almost melted by September 2019 (about 30 % of the second ice avalanche remained). The impact of these extreme disasters on downstream lakes has not been investigated yet. Based on in situ observation, bathymetry survey and satellite data, we explore the impact of the ice avalanches on the two downstream lakes (i.e., Aru Co and Memar Co) in terms of lake morphology, water level and water temperature in the subsequent 4 years (2016–2019). After the first glacier collapse, the ice avalanche slid into Aru Co along with a large amount of debris, which generated great impact waves in Aru Co and significantly modified the lake's shoreline and underwater topography. An ice volume of at least 7.1 × 106 m3 was discharged into Aru Co, spread over the lake surface and considerably lowered its surface temperature by 2–4 ∘C in the first 2 weeks after the first glacier collapse. Due to the large amount of meltwater input, Memar Co exhibited more rapid expansion after the glacier collapses (2016–2019) than before (2003–2014), in particular during the warm season. The melting of ice avalanches was found to contribute to about 23 % of the increase in lake storage between 2016 and 2019. Our results indicate that the Aru glacier collapses had both short-term and long-term impacts on the downstream lakes and provide a baseline in understanding the future lake response to glacier melting on the TP under a warming climate.

Baseline Water Temperature: Estimation of the Annual Cycle of Surface Water Temperature in Lakes in North-Central Poland over the 1951–1968 Period

Water temperature is an important ecological variable that affects the functioning of lakes. Unfortunately, for many lakes there are no long-term observations enabling the assessment of changes in water temperatures. This makes it difficult to include this aspect in research into the biology, ecology and chemistry of such lakes. This paper presents a literature review related to changes of surface water temperatures in lakes and in particular describing the response of water temperatures and stratification to changing climate in Polish lakes. On this basis, a model based on the available data on water temperature in 931 Polish lakes in the years 1951–1968 was proposed, which allows to estimate the baseline water temperature on any day of the year. This model is calculated using the complementary error peak function on the 0–3 m water temperature dataset, which provides the best reduction of diurnal temperature fluctuations. It can be an alternative to the average temperature of surface waters, which are calculated on the basis of systematically collected data. Based on the average water temperature data obtained from 56 thermal profiles in 10 lakes in 2010–2019, the equation was analogically calculated. The average monthly water temperatures in June, July, August and September and the change in water temperature (0.24–0.30 °C decade−1) in the period 1951–1968/2010–2019 were estimated then. Similar regional or single lake trends have been found in studies by other authors covering a similar or longer period of time. The proposed method, which is suitable for simulating temperatures, especially in summer, enables the determination of the value of changes in surface water temperature in Polish lakes when only thermal profiles data from different dates are available, which can be especially helpful when analyzing hydrobiological results.

Warming of Near-Surface Summer Water Temperatures in Lakes of the Conterminous United States

Because warming water temperatures have widespread consequences for freshwater communities, we were interested in estimating the patterns and rates of change of near-surface summer water temperatures in United States lakes. We developed multiple regression models to relate daily surface water temperatures in lakes of the conterminous United States to 8-day average air temperatures, latitude, elevation, and sampling month and year using data from 5723 lake samples in the months of June-September during the period 1981–2018. Our model explained 79% of the variation with a root-mean-square error of 1.69 °C. We predicted monthly average near-surface water temperatures for 1033 lakes for each year from 1981 through 2018. Lakes across the conterminous United States have been warming for the period 1981–2018 at an average heating rate of 0.32 °C per decade for the summer months (June–September). The average summer warming from 1981–2018 would be the equivalent of a lake decreasing 259 m in elevation or moving 233 km south. On the basis of national air temperatures starting in 1895, it was inferred that lake water temperatures are variable from year to year and have been steadily increasing since 1964, but that maximum temperatures in the 1930s were just as warm as those in 2008–2018.

Liver Lipid Accumulation in European Bullhead (Cottus cobio) from a High-Mountain Lake: An Adaptive Strategy to Survive the Adverse Winter Season

The hypothesis that liver lipid accumulation in fish is an adaptive strategy to survive the winter in the high-altitude environment was assessed in this study. During summer 2019, specimens of Cottus gobio were sampled in 15 watercourses of Friuli Venezia-Giulia Region (Italy) to verify if hepatic steatosis is or not normally present in the species. To do this, hepatic vacuolization was assessed by histology using a semiquantitative score. Furthermore, C. gobio were also captured during the ice-free season at Dimon Lake (1872 m a.s.l.) and But Stream (520 m a.s.l.) to compare the trend in lipid accumulation: water temperature, hepatosomatic index (HSI), gonadosomatic index (GSI), Fulton’s condition factor (K), and lipid area percentage (lipid %) were measured monthly. Findings revealed that liver steatosis is rather common in C. gobio. However, the trend in lipid accumulation of this species differed between Dimon Lake and But Stream. Based on the HSI and the GSI, the reproductive cycles differed in fish from the two environments (April–May in But Stream; May–June in Dimon Lake). While K values remained unchanged in the But Stream specimens, significant changes were recorded for Dimon specimens. The increase in lipid % from July to August in the Dimon Lake specimens coincided with greater food availability. With the rapid drop in lake water temperature in October, the lipid % decreased due to slower metabolic rate and lipid utilization from liver stores. There was a slight decrease in lipid % in the But Stream specimens, indicating that lipids were not being accumulated. Introduced years ago, the Dimon Lake bullhead population has since adapted to the winter conditions at high elevation.

Study of freeze-thaw cycle and key radiation transfer parameters in a Tibetan Plateau lake using LAKE2.0 model and field observations

AbstractThe Tibetan Plateau (TP) lakes are sensitive to climate change due to its seasonal ice cover, but few studies have paid attention to the freeze-thaw process of TP lakes and its key control parameters. By combining 216 simulation experiments using the LAKE2.0 model with the observations, we evaluated the effects of ice and snow albedo, ice (Kdi) and water (Kdw) extinction coefficients on the lake ice phenology, water temperature, sensible and latent heat fluxes. The reference experiment performs well in simulating the lake temperature, with a small positive bias increasing with depth, but it underestimates the ice thickness. The increase of ice albedo, snow albedo andKdiinduce a significant decrease in water temperature. Compared with the latent heat, the sensible heat flux is more sensitive to these three parameters. The ice thickness increases almost linearly with the increase of ice albedo but decreases with the increase ofKdi. The ice thickness and frozen days vary little withKdw, but increasingKdwcan decrease the water temperature. Compared with the ice albedo, theKdiand snow albedo have a large effect on the number of frozen days. This study brings to light the necessity to improve the parameterizations of the TP lakes freeze-thaw process.

Future Projections of Water Temperature and Thermal Stratification in Connecticut Reservoirs and Possible Implications for Cyanobacteria

AbstractFuture climate warming may increase water temperature, stratification, and the occurrence of cyanobacteria blooms in drinking water reservoirs. Herein, past relationships between air temperature, water temperature, thermal stratification, and cyanobacteria prevalence are quantified in six Connecticut, U.S.A., reservoirs. Lake‐specific empirical models were developed and used to project historical (1971–2000) and future (mid‐century, 2041–2070) water temperature and thermal stratification in the study lakes. Projections are driven with downscaled air temperature projections from three general circulation models (i.e., HadGEM2‐CC365, CCSM4, and GFDL‐ESM 2M) under Representative Concentration Pathway 8.5. Results suggest that reservoirs in Connecticut are warming relatively quickly over time and that surface, average, and bottom water temperatures may continue to increase by 0.44°C, 0.30°C, and 0.16°C per decade, respectively. Future projections of thermal stratification indicate that significant increases may occur in July–September and that stratification is likely to begin 2–4 weeks earlier and last 2–4 weeks longer by mid‐century. High‐risk cyanobacteria blooms, defined as those exceeding 70,000 cells ml−1, historically occurred in three of the study reservoirs and are correlated with the occurrence of high surface water temperatures, cool bottom water temperatures, and high total Relative Thermal Resistance to Mixing (RTRM). Days per year with extreme high water temperatures and total RTRM are projected to increase significantly in the future. This shift may favor increased dominance of certain cyanobacteria species that tend to grow best in these extreme conditions, especially in reservoirs where cyanobacteria blooms are already a concern.

Toward Numerical Modeling of Interactions Between Ice-Marginal Proglacial Lakes and Glaciers

Global climate change is evidently manifest in disappearing mountain glaciers and receding and thinning ice sheet margins. Concern about contemporaneous proglacial lake development has spurred an emerging area of research seeking to quantitatively understand lake - glacier interactions. This perspectives article identifies spatio-temporal disparity between the coverage of field data, remote sensing observations and numerical modeling efforts. Throughout, an overview of the physical effects of lakes on glaciers and on ice sheet margins is provided, drawing evidence together from very recent and high-impact studies of both modern glaciology and of the Quaternary record. We identify and discuss six challenges for numerical modeling of lake - glacier interactions, namely that there are meltwater exchanges between glaciers and ice-marginal lakes, lake bathymetry and glacier bed topography are often unknown, lake - glacier interactions affect the longitudinal stress regime of glaciers, lake water temperature affects glacier melting but is very poorly constrained, the interactions persist with considerable spatio-temporal variability and with boundary migration, and data for model parameterization and validation is extremely scarce. Overall, we contend that numerical modeling is a key frontier in the cryospheric sciences to deliver process understanding of lake - glacier interactions.