Surface Water Temperature Data Research Articles

Changes in the Water Surface Level of the Baltic Sea from Satellite Altimetry and Gravity Missions

ABSTRACT Satellite altimetry provides high-accuracy geometrical measurements of sea level changes. We analyze altimetry time series representing sea surface height anomalies over the mean sea surface provided by the TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3 satellite missions to estimate the annual rate of sea level rise. Then, we compare the results with satellite gravimetric data from GRACE and GRACE Follow-On missions and surface water temperature data, employing statistical analyses to examine the interrelationships and correlations between them. We carry out the main analyses for the period 2001–2021 with a division into 5-year periods for six different areas of the Baltic Sea. The altimetric results show that between 2001 and 2021, the water level of the Baltic Sea rose by 5.8 mm/year on average. About 72% of the changes detected by altimetry missions can be explained by satellite gravimetry from GRACE and GRACE Follow-On, which means that the mass component is responsible for most of the observed sea level change, whereas the remaining 28% can be greatly explained by thermal expansion due to the water temperature rise.

Widespread warming of Earth's estuaries

AbstractWater temperature responses to climate change may vary across Earth's estuaries. To understand how climate change influences estuarine surface water temperature, we need global, long‐term records of estuarine temperature. Here, we generated surface water temperature data over 1060 estuaries globally using Landsat 5, 7, and 8 from 1985 to 2022 and compared water warming rates with local air temperature warming rates. Forty‐seven percent of Earth's estuaries are warming, with a global average warming rate of 0.070 ± 0.004°C yr−1 (median = 0.060°C yr−1). Estuaries at higher latitudes showed rapid warming. A 1°C increase in air temperature could lead to a 0.81°C increase in estuarine surface water warming and 1.3°C increase in estuaries above 60.5°N. We inferred the potential influences over estuarine warming based on distinct global spatial patterns in water and air warming and discussed the effects of warming water temperature on estuarine metabolism and water quality.

Long-term water temperature changes in Seneca Lake and their nexus to climate change and human activities

While many freshwater lakes have witnessed a rapid increase in surface water temperatures, the trends in subsurface water temperatures are not well-understood. This study explored the long-term subsurface water temperature change and its connection to climate change and human activities in Seneca Lake. Utilizing linear regression and the Theil-Sen estimator, the study identified a significant monotonic temperature trend in the subsurface water. Principal component and contribution analyses revealed that climate changes, particularly air warming, were more critical in explaining water temperature patterns, and human activities such as land cover change could exacerbate the impact of climate change. Using remotely sensed surface water temperature data, the study found a significant positive correlation between thermal pollution and water temperatures in the northern region of the lake, and after incorporating control variables, the regression analysis suggested that the adverse effects of thermal pollution are primarily confined to the area adjacent to the power plant. This research can offer fresh insights into lake ecology improvement and management strategies.

Surface Temperature Influences the Population of Limnothrissa miodon in Lake Kariba

Global warming is a serious world problem where earth’s temperature has been reported to increase over the years; the aquatic ecosystems are also not the exceptions. But, the effects of this phenomenon on the aquatic ecosystems are not well understood. This study aims to understand the influence of surface temperature on the population density of Limnothrissa miodon in Lake Kariba. We constructed a mathematical model on the population dynamics of Limnothrissa miodon with nutrients, phytoplankton, zooplankton, and Hydrocynus vittatus. Lake surface water temperature was modelled by a cosine function, and the parameters were estimated from data fitting. Numerical simulations were used to determine the stability of the nonautonomous model. Numerical simulation results of the nonautonomous model showed a stable periodic orbit for varying initial conditions, and therefore, instability. Numerical techniques were used to investigate the influence of surface water temperature on Limnothrissa miodon. Results from the model with fitted lake surface water temperature data showed that a shift in the optimal temperature for phytoplankton growth from 25 ° C to 34 ° C, corresponding to dominance of Cyanophyceae over Chlorophyceae, resulted in a decline in the population density of Limnothrissa miodon. Numerical results showed that the population density of Limnothrissa miodon declines after an optimum temperature of 30 ° C for phytoplankton growth. Numerical simulation results suggested that warming of the lake may lead to a decline in Limnothrissa miodon population density in Lake Kariba.

LakeCoSTR: A tool to facilitate use of Landsat Collection 2 to estimate lake surface water temperatures

AbstractAlthough remote sensing of lake surface water temperature has been ongoing for decades and has led to important discoveries regarding the warming of lake surface temperature in some regions, the ability to access and use remote sensing data is still primarily limited to remote sensing experts. Effective use of remote sensing data involves technical skills in coding (often in multiple programming languages), application of appropriate atmospheric corrections, and integrating spatially heterogeneous remote sensing data with in situ data obtained at specific geographic locations. To improve access to remote sensing data and broaden the understanding of changes in lake surface water temperature over the past four decades, we created lakeCoSTR (lake Collection 2 Surface Temperature Retrieval), a user‐friendly, cloud‐based script that gives ecologists lacking specialized training in remote sensing the ability to access the Landsat Collection 2 temperature estimates for lakes with a surface area of at least 0.4 ha. Additionally, if in situ data are provided, a paired dataset can be created within the tool. To demonstrate lakeCoSTR, we retrieved surface water temperature data for a lake with a long monitoring history, Lake Sunapee, NH, USA, and compared long‐term surface temperature trends between data obtained via lakeCoSTR and in situ measurements. When compared with Landsat Collection 1 temperature estimates derived from a single‐channel algorithm, the Landsat Collection 2 data from lakeCoSTR required no calibration to in situ data. This suggests that lakeCoSTR can be used to document temporal trends in lakes and to reprocess analyses that relied on Collection 1 data. From 1983 until 2020, remotely sensed surface water temperature estimates at Lake Sunapee increased by 0.07–0.09°C year−1 during July and August, a trend that agreed with long‐term in situ record. As climate change continues to impact freshwater systems, a better understanding of long‐term temperature data will be vital. Remotely sensed data, like those acquired using lakeCoSTR, may provide a window into temperature trends for lakes.

Use of ECOSTRESS data for measurements of the surface water temperature: Significance of data filtering in accuracy assessment

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) program was launched in 2018 and provided the scientific community with extremely valuable surface temperature data distributed with a fine resolution of 70 m. This global data can be used in environmental, engineering and many other fields to measure surface temperature in hard-to-reach places or add data to discrete temperature measurements from the field. One of the aspects of data use by the broad scientific community is a clear methodology and understanding of potential error between ECOSTRESS sensor data measured by radiometer and surface temperature measured by conventional temperature sensors.This study uses three years of ECOSTRESS data (2018–2021) and surface water temperature data collected by the New York City Department of Environmental Protection on water supply reservoirs over the same time period. Data between two sources were matched spatially and temporarily. We developed a methodology of data filtering using temporal constraints and ECOSTRESS auxiliary information on clouds and signal quality indicators (flags) supplied in NASA provided documentation.The results of the study showed that the successful use of ECOSTRESS measurements requires data filtering using flags and temporal constraints. This might cause considerable reduction of original ECOSTRESS data and the number of measurements available for analysis. In our experimental settings, use of flags and temporal constraints reduced the original dataset with 442 records down to only six records; for this dataset the average temperature difference between ECOSTRESS data and measured data was found to be 2.6 °C. This number might vary according to local environmental conditions affecting surface water temperature. The proposed methodology signifies its importance in assessing the accuracy in estimating surface water temperature.

Smoothing spatio-temporal data with complex missing data patterns

We consider spatio-temporal data and functional data with spatial dependence, characterized by complicated missing data patterns. We propose a new method capable to efficiently handle these data structures, including the case where data are missing over large portions of the spatio-temporal domain. The method is based on regression with partial differential equation regularization. The proposed model can accurately deal with data scattered over domains with irregular shapes and can accurately estimate fields exhibiting complicated local features. We demonstrate the consistency and asymptotic normality of the estimators. Moreover, we illustrate the good performances of the method in simulations studies, considering different missing data scenarios, from sparse data to more challenging scenarios where the data are missing over large portions of the spatial and temporal domains and the missing data are clustered in space and/or in time. The proposed method is compared to competing techniques, considering predictive accuracy and uncertainty quantification measures. Finally, we show an application to the analysis of lake surface water temperature data, that further illustrates the ability of the method to handle data featuring complicated patterns of missingness and highlights its potentiality for environmental studies.

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.

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.

A study on the feasibility of using solar radiation energy and ocean thermal energy conversion to supply electricity for offshore oil and gas fields in the Caspian Sea

In this research, for the study of solar radiation energy and estimation of GHI in the area of radiation component, data were collected for the years during 2005–2014 from ECMWF database. To investigate the ocean thermal energy conversion, vertical changes of water temperature were studied using UNESCO data as seasonal thermocline for the Caspian Sea. Moreover, to investigate the possibility of the use of ocean thermal energy conversion (OTEC) in the offshore regions, the surface water temperature data obtained from ECMWF from 2005 to 2014 was used, the number of days that the ocean thermal energy conversion could be used in the field was calculated. The findings show that the use of the ocean thermal energy conversion is only possible in the southern basin during July–September. The fields of Sardar Jangal, Shahdeniz, Ganeshli and Azari are the only oil and gas fields that can use this energy 54, 34, 31 and 31 days a year, respectively. In the field of solar radiation, the southern basin with an annual average of 1550 kWhm2 is the most suitable basin and Sardar Jangal field with the monthly average of 131.81 kWhm2 is the most suitable field for the use of solar energy.

The Effect of the Peak Discharges of River Danube on Istanbul Strait (Bosphorus)

The Bosphorus Strait is the meeting point of Black Sea and Marmara Sea. It is under influence of many hydrological, oceanographical and meteorological variables. Clearly, it is known that River Danube discharge reaches to Bosphorus Strait. The approximate distance between outlet of River Danube and northern end of Bosphorus Strait are nearly 500km. In this study, the influence of the peak river Danube discharges (higher than 11000 m3/s) on Bosphorus Strait is investigated by using dataset of TAISEI Corporation, ISKI, Turkish State Meteorological Service and GRDC. To this end, the time series of water temperature, water salinity, and water level of the northern Bosphorus Strait is examined together with time series of River Danube discharge between 1st Oct 2004 and 31st Sept 2005. The validation is performed by checking wind speed, air pressure and precipitation data on Bosphorus Strait during that time. In addition, the power spectra of River Danube is calculated and compared with the other datasets as water level, surface layer water salinity and finally, surface water temperature data at the northern end of Bopshorus Strait.

Bayesian prediction of spatial data with non-ignorable missingness

In spatial data, especially in geostatistics data where measurements are often provided by satellite scanning, some parts of data may get missed. Due to spatial dependence in the data, these missing values probably are caused by some latent spatial random fields. In this case, ignoring missingness is not logical and may lead to invalid inferences. Thus incorporating the missingness process model into the inferences could improve the results. There are several approaches to take into account the non-ignorable missingness, one of them is the shared parameter model method. In this paper, we extend it for spatial data so that we will have a joint spatial Bayesian shared parameter model. Then the missingness process will be jointly modeled with the measurement process and one or more latent spatial random fields as shared parameters would describe their association. Bayesian inference is implemented by Integrated nested Laplace approximation. A computationally effective approach is applied via a stochastic partial differential equation for approximating latent Gaussian random field. In a simulation study, the proposed spatial joint model is compared with a model that assumes data are missing at random. Based on these two models, the lake surface water temperature data for lake Vanern in Sweden are analyzed. The results of estimation and prediction confirm the efficiency of the spatial joint model.

Monsoonal and annual energy fluxes variations in the atmospheric surface layer over a coastal region of the tropical ocean

The tropical coastal ocean is a vital jigsaw-piece to understanding exchanges of energy, moisture, and carbon between the atmosphere and the ocean biospheres. Latent and sensible heat fluxes, micro-climate variables, and surface water temperature data in the atmospheric surface layer were collected using an instrumented tower at a tropical coastal ocean (5 ∘28’6”N, 100∘12’1”E). The instrumented tower was installed on a stainless-steel platform extending a pre-existing pier. Data were collected for two years, i.e., November 2015 to October 2017, which encompassed two annual cycles of the Monsoons. The first objective of this work is to describe the Monsoonal and annual patterns of latent (LE) and sensible (H) heats while the second objective is to link the micro-climate variables to the energy fluxes on the seasonal and annual time scales. The inter-annual variation of energy input into the study is minimal (3% and 4% in the values recorded for the RG and Rn). The average albedo was 0.29 and the Bowen ratio was 0.11. Annual changes in LE and H between the two years were apparent both at 14% increased and decreased, respectively. LE is largely modulated by the Monsoons, where it is higher in the Northeast Monsoon (dry and hot) than the Southwest Monsoon (wet and cold). Conversely, H did not exhibit appreciably changes with the Monsoon and mostly followed the downward Rn trend. Variations between Monsoons are ten times larger than between years, thus highlighting its significance in controlling the regional conditions. Between the Northeast and Southwest Monsoons, the change in LE, i.e., ΔLE recorded was −30% while that of H, i.e., ΔH was ＋18%. These results have important applications in improving numerical weather models and closing the energy budget.

A methodological approach to the air-sea energy fluxes data collection and analysis at the tropical coastal ocean

The southern South China coastal oceans within the South East Asian region are much lacking in the perception of the surface energy budget and evaporation over the ocean waters in response to climatic changes. The eddy covariance method was used to measure the energy fluxes, microclimate variables, and surface water temperature from November 2015 to October 2017 at the Straits of Malacca, South China Sea; Pulau Pinang, Malaysia, situated at latitude 5°28'06?N, and longitude 100°12'01?E. This work focused on the methodological approach to the air-sea energy fluxes data collection and analysis. In this regard, the method applied for the direct measurements and analysis of energy fluxes and other meteorological parameters in the site is considered and reported.•The paper summarizes the analysis of energy fluxes, microclimate variables, and surface water temperature data in a tropical coastal ocean station using the eddy covariance method.•The methodological approach illustrates the method of analysis applied in this study which can be compared and used for similar studies in other places.•The reproducible data analysis technique matches similar comparative methods such as Matlab and Python.

Homogenised daily lake surface water temperature data generated from multiple satellite sensors: A long-term case study of a large sub-Alpine lake.

Availability of remotely sensed multi-spectral images since the 1980’s, which cover three decades of voluminous data could help researchers to study the changing dynamics of bio-physical characteristics of land and water. In this study, we introduce a new methodology to develop homogenised Lake Surface Water Temperature (LSWT) from multiple polar orbiting satellites. Precisely, we developed homogenised 1 km daily LSWT maps covering the last 30 years (1986 to 2015) combining data from 13 satellites. We used a split-window technique to derive LSWT from brightness temperatures and a modified diurnal temperature cycle model to homogenise data which were acquired between 8:00 to 17:00 UTC. Gaps in the temporal LSWT data due to the presence of clouds were filled by applying Harmonic ANalysis of Time Series (HANTS). The satellite derived LSWT maps were validated based on long-term monthly in-situ bulk temperature measurements in Lake Garda, the largest lake in Italy. We found the satellite derived homogenised LSWT being significantly correlated to in-situ data. The new LSWT time series showed a significant annual rate of increase of 0.020 °C yr−1 (*P < 0.05), and of 0.036 °C yr−1 (***P < 0.001) during summer.

Whole truths vs. half truths – And a search for clarity in long-term water temperature records

There is widespread acceptance among the scientific community that human activities are the primary cause of present day climate change. But, how a changing climate impacts ecosystems is still a source of confusion to the public. Some of this confusion is associated with a lack of clear communication among journalists and scientists, particularly when it comes to addressing variability and uncertainty in ecological datasets. Here we use long-term surface water temperature data sets from Narragansett Bay and a recent misunderstanding of long-term temperature data that occurred on the national stage as a case study. Specifically, we re-evaluate and update the record and examine the variability inherent in long-term data sets. We found that despite high year to year variations the surface waters of Narragansett Bay have increased between 1.4 °C to 1.6 °C total rise over the last fifty years. Winter warming has been especially high over this time period, increasing between 1.6 °C to 2.0 °C. Finally, we identify the need for scientists, politicians, and journalists to appropriately address data variability and we argue for increased communication among these groups.

Functional PCA for Remotely Sensed Lake Surface Water Temperature Data

Functional principal component analysis is used to investigate a high-dimensional surface water temperature data set of Lake Victoria, which has been produced in the ARC-Lake project. Two different perspectives are adopted in the analysis: modelling temperature curves (univariate functions) and temperature surfaces (bivariate functions). The latter proves to be a better approach in the sense of both dimension reduction and pattern detection. Computational details and some results from an application to Lake Victoria data are presented.

Influence of Long-Distance Climate Teleconnection on Seasonality of Water Temperature in the World's Largest Lake - Lake Baikal, Siberia

Large-scale climate change is superimposed on interacting patterns of climate variability that fluctuate on numerous temporal and spatial scales—elements of which, such as seasonal timing, may have important impacts on local and regional ecosystem forcing. Lake Baikal in Siberia is not only the world's largest and most biologically diverse lake, but it has exceptionally strong seasonal structure in ecosystem dynamics that may be dramatically affected by fluctuations in seasonal timing. We applied time-frequency analysis to a near-continuous, 58-year record of water temperature from Lake Baikal to examine how seasonality in the lake has fluctuated over the past half century and to infer underlying mechanisms. On decadal scales, the timing of seasonal onset strongly corresponds with deviation in the zonal wind intensity as described by length of day (LOD); on shorter scales, these temperature patterns shift in concert with the El Nino-Southern Oscillation (ENSO). Importantly, the connection between ENSO and Lake Baikal is gated by the cool and warm periods of the Pacific Decadal Oscillation (PDO). Large-scale climatic phenomena affecting Siberia are apparent in Lake Baikal surface water temperature data, dynamics resulting from jet stream and storm track variability in central Asia and across the Northern Hemisphere.

Paleotemperature reconstruction in tropical Africa using fossil Chironomidae (Insecta: Diptera)

Fossil assemblages of chironomid larvae (non-biting midges) preserved in lake sediments are well-established paleothermometers in north-temperate and boreal regions, but their potential for temperature reconstruction in tropical regions has never before been assessed. In this study, we surveyed sub-fossil chironomid assemblages in the surface sediments of 65 lakes and permanent pools in southwestern Uganda (including the Rwenzori Mountains) and central and southern Kenya (including Mount Kenya) to document the modern distribution of African chironomid communities along the regional temperature gradient covered by lakes situated between 489 and 4,575 m above sea level (a.s.l). We then combined these faunal data with linked Surface-Water Temperature (SWTemp: range 2.1–28.1°C) and Mean Annual Air Temperature (MATemp: range 1.1–24.9°C) data to develop inference models for quantitative paleotemperature reconstruction. Here we compare and discuss the performance of models based on different numerical techniques [weighted-averaging (WA), weighted-averaging partial-least-squares (WA-PLS) and a weighted modern analogue technique (WMAT)], and on subsets of lakes with varying gradient lengths of temperature and other environmental variables. All inference models calibrated against MATemp have a high coefficient of determination (\( r_{\text{jack}}^{2} \) = 0.81–0.97), low maximum bias (0.84–2.59°C), and low root-mean-squared error of prediction (RMSEP = 0.61–1.50°C). The statistical power of SWTemp models is generally weaker (\( r_{\text{jack}}^{2} \) = 0.77–0.95; maximum bias 1.55–3.73°C; RMSEP = 1.39–1.98°C), likely because the surface-water temperature data are spot measurements failing to catch significant daily and seasonal variation. Models based on calibration over the full temperature gradient suffer slightly from the limited number of study sites at intermediate elevation (2,000–3,000 m), and from the presence of morphologically indistinguishable but ecologically distinct taxa. Calibration confined to high-elevation sites (>3,000 m) has poorer error statistics, but is less susceptible to biogeographical and taxonomic complexities. Our results compare favourably with chironomid-based temperature inferences in temperate regions, indicating that chironomid-based temperature reconstruction in tropical Africa can be achieved.

Comparison of Temperature Data Collected from Artificial Chinook Salmon Redds and Surface Water in the Snake River

AbstractDuring three incubation periods, we collected temperature data from within artificial redds constructed in known spawning locations of Chinook salmon Oncorhynchus tshawytscha and from the surface water of the Snake River. Our objectives were to compare the data to determine (1) whether estimates of fry emergence timing differed between the two environments and (2) whether surface water data could be used to predict thermal conditions within redds. Statistical tests indicated that no differences could be detected between accumulated thermal units calculated from intraredd and surface water data (all P ≥ 0.06). We observed very little diel thermal fluctuation or daily difference within and between environments. Regression of intraredd temperature on surface water temperature was significant (r2 ≥ 0.98; all P &lt; 0.01), indicating that surface water temperature data can be used to predict intraredd temperature. We conclude that it is feasible to use surface water temperature as a surrogate for intraredd temperature in estimating Chinook salmon embryo developmental timing within the Snake River and potentially in other large rivers.