scholarly journals Seasonality modulates wind-driven mixing pathways in a large lake

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Bieito Fernández Castro ◽  
Damien Bouffard ◽  
Cary Troy ◽  
Hugo N. Ulloa ◽  
Sebastiano Piccolroaz ◽  
...  
Keyword(s):  
Climate Change ◽  
Turbulent Mixing ◽  
Summer Season ◽  
Large Lake ◽  
Lake Geneva ◽  
Large Lakes ◽  
Environmental Forcing ◽  
Vertical Transfer ◽  
Stratified Lakes ◽  
Observational Record

AbstractTurbulent mixing controls the vertical transfer of heat, gases and nutrients in stratified water bodies, shaping their response to environmental forcing. Nevertheless, due to technical limitations, the redistribution of wind-derived energy fuelling turbulence within stratified lakes has only been mapped over short (sub-annual) timescales. Here we present a year-round observational record of energy fluxes in the large Lake Geneva. Contrary to the standing view, we show that the benthic layers are the main locus for turbulent mixing only during winter. Instead, most turbulent mixing occurs in the water-column interior during the stratified summer season, when the co-occurrence of thermal stability and lighter winds weakens near-sediment currents. Since stratified conditions are becoming more prevalent –possibly reducing turbulent fluxes in deep benthic environments–, these results contribute to the ongoing efforts to anticipate the effects of climate change on freshwater quality and ecosystem services in large lakes.

scholarly journals Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

2020 ◽  
Vol 13 (1) ◽  
pp. 10
Author(s):  
Andrea Sulova ◽  
Jamal Jokar Arsanjani
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Random Forest ◽  
Google Earth ◽  
Summer Season ◽  
Driving Factors ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Training Dataset ◽  
Google Earth Engine

Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.

scholarly journals A Bayesian Climate Change Detection and Attribution Assessment

2005 ◽  
Vol 18 (13) ◽  
pp. 2429-2440 ◽  
Author(s):  
Terry C. K. Lee ◽  
Francis W. Zwiers ◽  
Gabriele C. Hegerl ◽  
Xuebin Zhang ◽  
Min Tsao
Keyword(s):  
Climate Change ◽  
Strong Evidence ◽  
Climate Model ◽  
Consistency Test ◽  
Change Question ◽  
Detection And Attribution ◽  
Complex Process ◽  
Detection Approach ◽  
Limited Length ◽  
Observational Record

Abstract A Bayesian analysis of the evidence for human-induced climate change in global surface temperature observations is described. The analysis uses the standard optimal detection approach and explicitly incorporates prior knowledge about uncertainty and the influence of humans on the climate. This knowledge is expressed through prior distributions that are noncommittal on the climate change question. Evidence for detection and attribution is assessed probabilistically using clearly defined criteria. Detection requires that there is high likelihood that a given climate-model-simulated response to historical changes in greenhouse gas concentration and sulphate aerosol loading has been identified in observations. Attribution entails a more complex process that involves both the elimination of other plausible explanations of change and an assessment of the likelihood that the climate-model-simulated response to historical forcing changes is correct. The Bayesian formalism used in this study deals with this latter aspect of attribution in a more satisfactory way than the standard attribution consistency test. Very strong evidence is found to support the detection of an anthropogenic influence on the climate of the twentieth century. However, the evidence from the Bayesian attribution assessment is not as strong, possibly due to the limited length of the available observational record or sources of external forcing on the climate system that have not been accounted for in this study. It is estimated that strong evidence from a Bayesian attribution assessment using a relatively stringent attribution criterion may be available by 2020.

scholarly journals Assessing the Effectiveness of Supplemental Irrigation to Improve Soil Moisture in an Arid Ecosystem with an Emphasis on Climate Change: A Case Study from the State of Kuwait

2020 ◽  
Vol 12 (21) ◽  
pp. 9104
Author(s):  
Ahmed Alqallaf ◽  
Bader Al-Anzi ◽  
Meshal Alabdullah
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Hydrological Cycle ◽  
Winter Season ◽  
Climate Change Impacts ◽  
Summer Season ◽  
Arid Ecosystems ◽  
Arid Ecosystem ◽  
Supplemental Irrigation ◽  
The State Of Kuwait

Arid ecosystems are extremely vulnerable to climate change, which is considered one of the serious global environmental issues that can cause critical challenges to the hydrological cycle in arid ecosystems. This work focused on assessing the effectiveness of supplemental irrigation to improve the actual soil moisture content in arid ecosystems and considering climate change impacts on soil moisture. The study was conducted at two fenced protected sites in Kuwait. The first site is naturally covered with Rhanterietum epapposum, whereas the other study site is a supplemented irrigated site, containing several revegetated native plants. The results showed that supplemental irrigation highly improved soil moisture (∆SM) during the winter season by >50%. However, during the summer season, the rainfed and irrigated site showed low ∆SM due to the high temperature and high evapotranspiration (ET) rates. We also found that ∆SM would negatively get impacted by climate change. The climate change projection results showed that temperature would increase by 12%–23%, ET would increase by 17%–19%, and precipitation would decrease by 31%–46% by 2100. Such climate change impacts may also shift the current ecosystem from an arid to a hyper-arid ecosystem. Therefore, we concluded that irrigation is a practical option to support the ∆SM during the low-temperature months only (spring and winter) since the results did not show any progress during the summer season. It is also essential to consider the possibility of future shifting in ecosystems and plant communities in restoration and revegetation planning.

scholarly journals Climate change negatively impacts dominant microbes in the sediments of a High Arctic lake

2019 ◽  
Author(s):  
Graham A. Colby ◽  
Matti O. Ruuskanen ◽  
Kyra A. St. Pierre ◽  
Vincent L. St. Louis ◽  
Alexandre J. Poulain ◽  
...  
Keyword(s):  
Climate Change ◽  
Community Structure ◽  
Environmental Changes ◽  
High Arctic ◽  
The Arctic ◽  
Large Lake ◽  
Spatial Gradients ◽  
Functional Potential ◽  
High Runoff ◽  
Sediment Microbes

AbstractTemperatures in the Arctic are expected to increase dramatically over the next century, yet little is known about how microbial communities and their underlying metabolic processes will be affected by these environmental changes in freshwater sedimentary systems. To address this knowledge gap, we analyzed sediments from Lake Hazen, NU Canada. Here, we exploit the spatial heterogeneity created by varying runoff regimes across the watershed of this uniquely large lake at these latitudes to test how a transition from low to high runoff, used as one proxy for climate change, affects the community structure and functional potential of dominant microbes. Based on metagenomic analyses of lake sediments along these spatial gradients, we show that increasing runoff leads to a decrease in taxonomic and functional diversity of sediment microbes. Our findings are likely to apply to other, smaller, glacierized watersheds typical of polar or high latitude / high altitudes ecosystems; we can predict that such changes will have far reaching consequences on these ecosystems by affecting nutrient biogeochemical cycling, the direction and magnitude of which are yet to be determined.

scholarly journals Movement Ecology of Potamodromous Top Predator in a Large Lake: Synchrony and Coexistence of Distinct Migratory Patterns 

2020 ◽  
Author(s):  
Silviya V. Ivanova ◽  
Timothy B Johnson ◽  
Aaron T Fisk
Keyword(s):  
Native Species ◽  
Life Histories ◽  
Community Dynamics ◽  
Lake Trout ◽  
Lake Ontario ◽  
Predator Population ◽  
Large Lake ◽  
Top Predator ◽  
Large Lakes ◽  
Migratory Patterns

Abstract Migrations are a key component of the life-histories of many highly mobile animals. The study of potamodromous migrations occurring within large lakes have lagged and are poorly understood for most species. This is an issue for restoration efforts and adaptive management, as understanding the movement of species, and underlying patterns and mechanisms are essential for identifying key habitat and quantifying the species role in the ecosystem. Using acoustic telemetry, this study quantified the spatio-temporal movements and migratory patterns of lake trout (Salvelinus namaycush), an iteroparous, potamodromous predator in Lake Ontario, the 13th largest lake by volume in the world that is highly managed and supports a diverse fish community of native and non-native species. Over 2.5 years (December 2016 to April 2019), the movements of 41 lake trout were quantified across a large array of 196 acoustic receivers in Lake Ontario. Individual analysis revealed annual convergence in the fall at a location other than the spawning grounds, followed by synchronized migrations to spawning sites. Consistent with divergent migrations, out-migration was asynchronous, stretching over a longer period of time than pre-spawning movements and across multiple routes. At least two groups of individuals with distinct migratory behaviors, i.e. contingents, were identified in the population. These results illustrate the presence of contingents and provide key information on migratory patterns, convergence points and routes in a potamodromous top predator population in a large lake. Thus, we provide evidence that contingents with different behavior used different habitats across seasons. As such, this study informs management on the potential success and implications of employing different rehabilitation strategies, such as diversifying a species’ population through selective strain stocking in large deep lakes to aid reestablishment across habitats. This knowledge would improve modelling of community dynamics, understanding of nutrient cycling, and overall ecosystem function of large lakes.

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