Shorter blooms expected with longer warm periods under climate change: an example from a shallow meso-eutrophic Mediterranean lake

Satellite data from the Climate Change Initiative (CCI) lakes project were used to examine the influence of climate on chlorophyll-a (Chl-a). Nonparametric multiplicative regression and machine learning were used to explain Chl-a concentration trend and dynamics. The main parameters of importance were seasonality, interannual variation, lake level, water temperature, the North Atlantic Oscillation, and antecedent rainfall. No evidence was found for an earlier onset of the summer phytoplankton bloom related to the earlier onset of warmer temperatures. Instead, a curvilinear relationship between Chl-a and the temperature length of season above 20°C (LOS) was found with longer periods of warmer temperature leading to blooms of shorter duration. We suggest that a longer period of warmer temperatures in the summer may result in earlier uptake of nutrients or increased calcite precipitation resulting in a shortening of the duration of phytoplankton blooms. The current scenario of increasing LOS of temperature with climate change may lead to an alteration of phytoplankton phenological cycles resulting in blooms of shorter duration in lakes where nutrients become limiting. Satellite-derived information on lake temperature and Chl-a concentration proved essential in detecting trends at appropriate resolution over time.

SELF v1.0: a minimal physical model for predicting time of freeze-up in lakes

Predicting the freezing time in lakes is achieved by means of complex mechanistic models or by simplified statistical regressions considering integral quantities. Here, we propose a minimal model (SELF) built on sound physical grounds that focuses on the pre-freezing period that goes from mixed conditions (lake temperature at 4 ∘C) to the formation of ice (0 ∘C at the surface) in dimictic lakes. The model is based on the energy balance involving the two main processes governing the inverse stratification dynamics: cooling of water due to heat loss and wind-driven mixing of the surface layer. They play opposite roles in determining the time required for ice formation and contribute to the large interannual variability observed in ice phenology. More intense cooling does indeed accelerate the rate of decrease of lake surface water temperature (LSWT), while stronger wind deepens the surface layer, increasing the heat capacity and thus reducing the rate of decrease of LSWT. A statistical characterization of the process is obtained with a Monte Carlo simulation considering random sequences of the energy fluxes. The results, interpreted through an approximate analytical solution of the minimal model, elucidate the general tendency of the system, suggesting a power law dependence of the pre-freezing duration on the energy fluxes. This simple yet physically based model is characterized by a single calibration parameter, the efficiency of the wind energy transfer to the change of potential energy in the lake. Thus, SELF can be used as a prognostic tool for the phenology of lake freezing.

Evidence of Climate Change Based on Lake Surface Temperature Trends in South Central Chile

Lake temperature has proven to act as a good indicator of climate variability and change. Thus, a surface temperature analysis at different temporal scales is important, as this parameter influences the physical, chemical, and biological cycles of lakes. Here, we analyze monthly, seasonal, and annual surface temperature trends in south central Chilean lakes during the 2000–2016 period, using MODIS satellite imagery. To this end, 14 lakes with a surface area greater than 10 km2 were examined. Results show that 12 of the 14 lakes presented a statistically significant increase in surface temperature, with a rate of 0.10 °C/decade (0.01 °C/year) over the period. Furthermore, some of the lakes in the study present a significant upward trend in surface temperature, especially in spring, summer, and winter. In general, a significant increase in surface water temperature was found in lakes located at higher altitudes, such as Maule, Laja and Galletué lakes. These results contribute to the provision of useful data on Chilean lakes for managers and policymakers.

Recent warming reverses forty-year decline in Catastrophic lake drainage and hastens Gradual lake drainage across northern Alaska

Lakes represent as much as ~25% of the total land surface area in lowland permafrost regions. Though decreasing lake area has become a widespread phenomenon in permafrost regions, our ability to forecast future patterns of lake drainage spanning gradients of space and time remain limited. Here, we modeled the drivers of gradual (steady declining lake area) and catastrophic (temporally abrupt decrease in lake area) lake drainage using 45 years of Landsat observations (i.e., 1975-2019) across 32,690 lakes spanning climate and environmental gradients across northern Alaska. We mapped lake area using supervised support vector machine classifiers and object based image analyses using five-year Landsat image composites spanning ~388,968 km2. Drivers of lake drainage were determined with boosted regression tree (BRT) models, using both static (e.g., lake morphology, proximity to drainage gradient) and dynamic predictor variables (e.g., temperature, precipitation, wildfire). Over the past 45 years, gradual drainage decreased lake area between 10-16%, but rates varied over time as the 1990s recorded the highest rates of gradual lake area losses associated with warm periods. Interestingly, the number of catastrophically drained lakes progressively decreased at a rate of ~37% decade-1 from 1975-1979 (102 to 273 lakes draining year-1) to 2010-2014 (3 to 8 lakes draining year-1). However this 40 year negative trend was reversed during the most recent time-period (2015-2019), with observations of catastrophic drainage among the highest on record (i.e., 100 to 250 lakes draining year-1), the majority of which occurred in northwestern Alaska. Gradual drainage processes were driven by lake morphology, summer air and lake temperature, snow cover, active layer depth, and the thermokarst lake settlement index (R2 adj=0.42, CV=0.35, p<0.0001), whereas, catastrophic drainage was driven by the thawing season length, total precipitation, permafrost thickness, and lake temperature (R2 adj=0.75, CV=0.67, p<0.0001). Models forecast a continued decline in lake area across northern Alaska by 15 to 21% by 2050. However these estimates are conservative, as the anticipated amplitude of future climate change were well-beyond historical variability and thus insufficient to forecast abrupt “catastrophic” drainage processes. Results highlight the urgency to understand the potential ecological responses and feedbacks linked with ongoing Arctic landscape reorganization.

Subglacial discharge controls seasonal variations in the thermal structure of a glacial lake in Patagonia

Water temperature in glacial lakes affects underwater melting and calving of glaciers terminating in lakes. Despite its importance, seasonal lake temperature variations are poorly understood because taking long-term measurements near the front of calving glaciers is challenging. To investigate the thermal structure and its seasonal variations, we performed year-around temperature and current measurement at depths of 58–392 m in Lago Grey, a 410-m-deep glacial lake in Patagonia. The measurement revealed critical impacts of subglacial discharge on the lake thermal condition. Water below a depth of ~100 m showed the coldest temperature in mid-summer, under the influence of glacial discharge, whereas temperature in the upper layer followed a seasonal variation of air temperature. The boundary of the lower and upper layers was controlled by the depth of a sill which blocks outflow of dense and cold glacial meltwater. Our data implies that subglacial discharge and bathymetry dictate mass loss and the retreat of lake-terminating glaciers. The cold lakewater hinders underwater melting and facilitates formation of a floating terminus.

Predictive Model of Lake Photic Zone Temperature Across the Conterminous United States

As the average global air temperature increases, lake surface temperatures are also increasing globally. The influence of this increased temperature is known to impact lake ecosystems across local to broad scales. Warming lake temperature is linked to disruptions in trophic linkages, changes in thermal stratification, and cyanobacteria bloom dynamics. Thus, comprehending broad trends in lake temperature is important to understanding the changing ecology of lakes and the potential human health impacts of these changes. To help address this, we developed a simple yet robust random forest model of lake photic zone temperature using the 2007 and 2012 United States Environmental Protection Agency’s National Lakes Assessment data for the conterminous United States. The final model has a root mean square error of 1.48°C and an adjusted R2 of 0.88; the final model included 2,282 total samples. The sampling date, that day’s average ambient air temperature and longitude are the most important variables impacting the final model’s accuracy. The final model also included 30-days average temperature, elevation, latitude, lake area, and lake shoreline length. Given the importance of temperature to a lake ecosystem, this model can be a valuable tool for researchers and lake resource managers. Daily predicted lake photic zone temperature for all lakes in the conterminous US can now be estimated based on basic ambient temperature and location information.

SELF v1.0: A minimal physical model for predicting time of freeze-up in lakes

Predicting the freezing time in lakes is pursued by means of complex mechanistic models or by simplified statistical regressions considering integral quantities. Here, we propose a minimal model (SELF) built on sound physical grounds, which focuses on the pre-freezing period that, in dimictic lakes, goes from mixed conditions (lake temperature at 4 °C) to the formation of ice (0 °C at the surface). The model is based on the energy balance involving the two main processes governing the inverse stratification dynamics: cooling of water due to heat loss and wind-driven mixing of the surface layer. They play an opposite role in determining the time required for ice formation and contribute to the large inter-annual variability observed in ice phenology. More intense cooling, indeed, accelerates the rate of decrease of lake surface water temperature (LSWT), while stronger wind deepens the surface layer, increasing the heat capacity, and thus reduces the rate of decrease of LSWT. A statistical characterization of the process is obtained with a Monte Carlo simulation considering random sequences of the energy fluxes. The results, interpreted through an approximate analytical solution of the minimal model, elucidate the general tendency of the system, suggesting a power-law dependence of the pre-freezing duration on the energy fluxes. This simple, yet physically based model is characterized by a single calibration parameter, the efficiency of the wind energy transfer to the change of potential energy in the lake. Thus, SELF can be used as a prognostic tool for the phenology of lake freezing.

Factors of surface thermal variation in high-mountain lakes of the Pyrenees

Thermal variables are crucial drivers of biological processes in lakes and ponds. In the current context of climate change, determining which factors better constrain their variation within lake districts become of paramount importance for understanding species distribution and their conservation. In this study, we describe the regional and short-term interannual variability in surface water temperature of high mountain lakes and ponds of the Pyrenees. And, we use mixed regression models to identify key environmental factors and to infer mean and maximum summer temperature, accumulated degree-days, diel temperature ranges and three-days’ oscillation. The study is based on 59 lake-temperature series measured from 2001 to 2014. We found that altitude was the primary explicative factor for accumulated degree-days and mean and maximum temperature. In contrast, lake area showed the most relevant effect on the diel temperature range and temperature oscillations, although diel temperature range was also found to decline with altitude. Furthermore, the morphology of the catchment significantly affected accumulated degree-days and maximum and mean water temperatures. The statistical models developed here were applied to upscale spatially the current thermic conditions across the whole set of lakes and ponds of the Pyrenees.

Effects of heatwaves on lake composition derived from satellite observations

&lt;p&gt;As a consequence of the ever-increasing global temperature, not only the air, and surface, but also lakes are warming up. This is expressed by steadily increasing base temperatures, but also in increases in the frequency and intensity of lake heatwaves. Land-based organisms may adapt to a changing climate by migrating to more suitable habitats, but this is usually not an option for lake-dwellers. Because many livelihoods depend on the ecosystem services of lakes, understanding the effects of heatwaves on lake composition form &amp;#160;an important input for the assessment of climate change impacts and design of adaptation strategies.&lt;/p&gt;&lt;p&gt;Using satellite data of lake temperature and water quality observations, we here investigate the effects of heatwaves on lake composition by studying the relationship between heatwaves and water quality variables of temperature, chlorophyll-a , colored dissolved organic matter, and suspended particulate matter . The latter can be used to infer effects of heat stress on health and populations of phyto- and zooplankton communities and higher aquatic organisms. Satellite-based data sets provided by the Climate Change Initiative of the European Space Agency, &amp;#160;CCI-Lakes (https://climate.esa.int/en/projects/lakes/) are&amp;#160; used in conjunction with the 2SeaColor model to determine depth-dependent attenuation coefficients and water quality variables.These data are complemented with and compared to data from Copernicus Global Land Services (https://land.copernicus.eu/global/products/).&amp;#160;&lt;/p&gt;&lt;p&gt;The co-occurrence of heatwaves and changes in lake composition is investigated using statistical tools, and the causality is examined by comparison with biophysical models. The results from this study are discussed in light of previously published projected changes in heatwave frequency and intensity.&lt;/p&gt;

SIMO v1.0: Simplified model of the vertical temperature profile in a small warm monomictic lake

A simple 1-D energy budget model (SIMO) for the prediction of the vertical temperature profiles in small, monomictic lakes forced by a reduced number of input meteorological variables is proposed. The model estimates the net heat flux and thermal diffusion using only routinely measured hourly mean meteorological variables (namely, the air temperature, relative humidity, atmospheric pressure, wind speed, and precipitation), hourly mean ultraviolet B radiation (UVB), and climatological monthly mean cloudiness data. Except for the initial vertical temperature profile, the model does not use any lake-specific variables. The model performance was evaluated against lake temperatures measured continuously during an observational campaign in two lakes belonging to the Plitvice Lakes, Croatia (Lake 1 and Lake 12). Temperatures were measured at 15 and 16 depths ranging from 0.2 to 27 in Lake 1 (maximum depth of 37.4 m) and 0.2 to 43 m in Lake 12 (maximum depth of 46 m). A sensitivity analysis of the simulation length was performed for simulation lengths from 1 to 30 days. The model performed reasonably well and it was able to satisfactorily reproduce the vertical temperature profile at the hourly scale, the deepening of the thermocline with time, and the annual variation in the vertical temperature profile. A yearlong simulation initiated with an approximately constant vertical profile of the lake temperature (≈ 4 °C) was able to reproduce the onset of stratification and convective overturn. However, the thermocline depth was underestimated while the epilimnion temperatures were overestimated. Nevertheless, the values of the model performance measures obtained for a yearlong simulation were comparable with those reported for other more complex models. Thus, the presented model can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.