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13 results for “lake surface water temperature”
Globally distributed lake surface water temperatures collected in situ and by satellites; 1985-2009
Global environmental change has influenced lake surface temperatures, a key driver of ecosystem structure and function. Recent studies have suggested significant warming of water temperatures in individual lakes across many different regions around the world. However, the spatial and temporal coherence associated with the magnitude of these trends remains unclear. Thus, a global dataset of water temperature is required to understand and synthesize global, long-term trends in surface water temperatures of inland bodies of water. We assembled a database of summer lake surface temperatures for 291 lakes collected in situ and/or by satellites for the period 1985-2009. In addition, corresponding climatic drivers (air temperatures, solar radiation, and cloud cover) and geomorphometric characteristics (latitude, longitude, elevation, lake surface area, maximum depth, mean depth, and volume) that influence lake surface temperatures were compiled for each lake. This unique dataset offers an invaluable baseline perspective on global-scale lake thermal conditions as environmental change continues. This dataset accompanies a data publication in the journal Scientific Data
Historical and future Lake Surface Water Temperature for 80 major lakes in Southeast Asia [LSWT-SEA]
The present dataset is part of a study delving into the intricate relationship between lake surface temperature (LSWT) and the broader context of climate change in the ecologically diverse region of Southeast Asia (SEA). Recognizing LSWT as a highly responsive indicator of climatic shifts, the research aims to shed light on the region's vulnerability to these changes. Using a suite of predictive models (namely Multilinear Regression (MLR), Multilayer perceptron (MLP), Random Forest (RF), eXtreme Gradient Boosting (XGB), Multilayer perceptron (MLP)) the study reconstructs historical LSWT trends from 1986 to 2020 and projects future scenarios until 2100, contingent upon various Representative Concentration Pathway (RCP) trajectories. Using MODIS-derived LSWT as predicted variable. The dataset package includes the data used to carry out the research: ECMWF ERA5 and CHIRPS climatic predicting variables, MODIS-derived daytime and nighttime LSWT, historically predicted daily daytime and nighttime LSWT, future predictions of LSWT for multiple Representative Concentration Pathways (RCPs), long term historical and future trends.
Summer high frequency measurements of dissolved O2 and CO2 concentrations and water temperature at the surface of 11 northern lakes
This dataset includes high frequency paired measurements of dissolved O2 and CO2 concentrations at the surface (0.5 to 2 m depth) of 11 lakes in the Northern Hemisphere. Measurements were taken every 2 hours in summer (July and August) of various years depending on lakes (between 2011 and 2014). The dataset is used to test a conceptual framework on the controls of coupling and decoupling of these two gases. In all lakes dissolved CO2 was measured with infrared analyzer coupled with a diffusion membrane and dissolved O2 with optodes. All gas measurements are paired with water temperature provided by one of the gas probe (usually from the O2 probe).
LakeSST: Lake Skin Surface Temperatures in French inland water bodies
<p>The data set LakeSST contains skin surface temperature data for 442 French water bodies for the period 1999-2016 obtained from archives of Landsat 5 and Landsat 7 thermal infrared images. The overall accuracy of the satellite-derived temperature measurements is about 1.2 ºC, similar to other applications of satellite images to estimate freshwater surface temperatures. The spatial and temporal coverage of the data set makes it an ideal resource for studies on the temporal evolution of lake surface temperatures and for geographical studies of temperature patterns.</p>
Global LAke Surface water Temperature (GLAST): Compound thermal extremes in lakes
<p>This database contains daily maximum temperature, daily minimum temperature, and daily mean temperature for 92,245 lakes globally from 1981 to 2020. These daily time series were derived from hourly simulation data.</p> <p>The database also includes annual statistics of six types of thermal extreme events calculated from the daily lake temperature time series:</p> <ul> <li>daytime hot extreme events (hot day–mild night)</li> <li>nighttime hot extreme events (mild day–hot night)</li> <li>compound hot extreme events (hot day–hot night)</li> <li>daytime cold extreme events (cold day–mild night)</li> <li>nighttime cold extreme events (mild day–cold night)</li> <li>compound cold extreme events (cold day–cold night)</li> </ul> <p> </p> <p>The annual statistics provided for these events include metrics such as frequency, intensity, duration, and total days. Additionally, annual statistics of extreme air temperature events over the lakes are included.</p> <p>Details about the hourly-scale lake temperature simulation methodology can be found in the paper <em>"Global lakes are warming slower than surface air temperature due to accelerated evaporation"</em> (Tong et al., 2023, Nature Water). Definitions and calculation methods for thermal extreme events in lakes and atmosphere are provided in <em>"Day-night compound thermal extremes in lakes"</em> (Tong et al., 2025).</p> <p>For detailed information about the contents of each data file, please refer to the accompanying <strong>readme.docx</strong> file.</p> <p>For more datasets on global aquatic environments, please visit the official website of the Global Aqua Remote Sensing (GARS) Laboratory, led by Prof. Lian Feng: <a href="https://garslab.com/?cat=1">https://garslab.com/?cat=1</a>.</p>
An integrated dataset of daily lake surface water temperature over Tibetan Plateau
<p>A dataset for daily surface temperature of 160 lakes over Tibetan Plateau for period from 1978 to 2017. The new dataset was developed based on combination of remote sensing (MODIS) and model (slightly modified <em>air2water </em>model).</p>
The Meltwater Pulse1A Triggered an Extreme Cooling Event: Evidence From Southern China. Meltwater Pulse Cooling Event (MCE). Winter temperature data during the last deglacial of Huguangyan Maar lake, Surface water temperature and seasonal diatom assemblage data of Huguangyan and Yunlong Lake.
<p>Here we present results of The lake averaged monthly mean surface water temperature over the period from September 2013 to August 2015 from Yunlong Tianchi Lake(YL)(25°52.2′N, 99°16.8′E, altitude: 2551 m a.s.l), southwestern China. The dataset include sediment trap main diatom percentages over the period from September 2013 to August 2015 from YL. Lake water temperature profiles at different depths (1, 3, 6, 9, 11, 13, 16 m) from November 2008 to May 2009 in Huguang Maar Lake (HML)(21°9′N, 110°17′E), Southern China. AMS radiocarbon dates of plant remains and bulk sediment samples for Huguangyan Maar Lake over the last ~17 cal ka BP. The main diatom assemblage percentages (%) from 17 to 10 cal ka BP at Huguangyan Maar Lake. Diatom-based reconstruction of winter temperature (WT) from 17 to 10 cal ka BP at Huguangyan Maar Lake.</p>
Satellite-ground synchronous in-situ dataset of water optical parameters and surface temperature for typical lakes in China
<p>Remote sensing technology has the potential to significantly enhance the lakes large-scale and long-term dynamic monitoring capabilities. High-quality in-situ datasets are essential for improving the accuracy and reliability of remote sensing retrieval of water optical parameters. This dataset provides satellite-ground synchronized in-situ data on water optical parameters for typical lakes in China spanning the period between 2020 and 2023. The dataset includes quality-checked remote sensing reflectance ( ) data and water optical parameter data for chlorophyll-a (Chl-a), total suspended matter (TSM), Secchi disk depth (SDD), andwater surface temperature (WST). It encompasses 586 sampling points across 18 lakes. The dataset exhibits two significant highlights: Firstly, synchronous observations from multiple satellites are coordinated during the data collection process, effectively supporting the retrieval and validation of water remote sensing products. Secondly, it encompasses diverse data types, collecting synchronous measurements of and various water optical parameters. This dataset will be continuously updated, thereby making a substantial contribution to enhancing regional and global lake monitoring capabilities through satellite remote sensing data.</p>
Global LAke Surface water Temperature (GLAST): Global lakes are warming slower than surface air temperature due to accelerated evaporation
<p>This repository houses a dataset, known as the Global LAke Surface water Temperature (GLAST), which provides both temporal and spatial details at high resolution for 92,245 lakes worldwide during the period of 1981-2099, with 36% of them situated in Arctic regions. The dataset was established based on four decades (1982-2020) of Landsat satellite images and a physical model (FLake). For a comprehensive overview of the dataset's production methodology, please refer to the paper titled 'Global lakes are warming slower than surface air temperature due to accelerated evaporation' (Tong et al., 2023, Nature Water). Detailed information regarding each data file can be found in the 'readme.docx' file.</p>
Direct and indirect effects of anthropogenic forcing on lake surface water temperature
<p>Here are the data from this paper (Direct and indirect effects of anthropogenic forcing on lake surface water temperature)</p>
Spatial impact of urban expansion on lake surface water temperature based on the perspective of watershed scale
<p>This is the original data from the article "Spatial impact of urban expansion on lake surface water temperature based on the perspective of watershed-scale"</p>
Global lake surface water temperature layers
<p>In modeling species distributions and population dynamics, spatially-interpolated climatic data are often used as proxies for real, on-the-ground measurements. In shallow freshwater systems, this practice may be problematic as interpolations used for surface waters are generated from terrestrial sensor networks measuring air temperatures. Using these may therefore bias statistical estimates of species' environmental tolerances or population projections -- particularly among pleustonic and epilimnetic organisms. I used a global database of satellite-derived lake surface water temperatures (LSWT) to assess and correct for the statistical correspondence between air and LSWT as a function of atmospheric and topographic predictors, resulting in the creation of monthly high-resolution global maps of air-LSWT offsets, corresponding uncertainty measures, and derived LSWT-based bioclimatic layers for use by the scientific community.</p>
Global lake surface water temperature layers
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