Changes In Temperature Patterns Research Articles

Spatial heterogeneity in climate change effects across Brazilian biomes

We present a methodology designed to study the spatial heterogeneity of climate change. Our approach involves decomposing the observed changes in temperature patterns into multiple trend, cycle, and seasonal components within a spatio-temporal model. We apply this method to test the hypothesis of a global long-term temperature trend against multiple trends in distinct biomes. Applying this methodology, we delve into the examination of heterogeneity of climate change in Brazil—a country characterized by a spectrum of climate zones. The findings challenge the notion of a global trend, revealing the presence of distinct trends in warming effects, and more accelerated trends for the Amazon and Cerrado biomes, indicating a composition between global warming and deforestation in determining changes in permanent temperature patterns.

The use of time-dynamic patterns of temperature and flexible generalized models to clarify the relations between temperature and semen quality

BackgroundSperm quality has decreased over the last decades worldwide. It is affected, among others, by season and heat. This study aimed to address the association between ambient temperature and sperm quality by assessing its shape using flexible multivariate models and identifying distinct time-dynamic patterns of temperature change based on unsupervised analysis. Material and methodsA retrospective population-based study has been conducted, including all samples of males attending the Fertility and In-Vitro-Fertilization unit at a single medical center during 2016–2022. Flexible generalized models were fitted to characterize the relations between sperm quality and temperature while accounting for patients characteristics, and to identify temperature levels that correspond with the optimal sperm quality. This information was then used to estimate adjusted slope coefficients at specified time-windows. ResultsIn total, 4555 sperm samples were provided by 3229 individuals. Sperm concentration, motility and progressive motility were higher by 8 %, 11 % and 16 %, respectively, during the spring versus the fall season. Furthermore, their quality during early spermatogenesis improved with temperature, until a certain optimum around 23 °C–24 °C. Increasing temperature at later developmental stages was associated with lower sperm concentration and higher motility. Sperm concentration and motility were highest following a period of moderate gradual warming. Motility was higher and sperm concentration was lower, following a period with heatwaves or summer. ConclusionsThis study assessed temperature role in sperm production quality by considering both average and time-dynamic temperatures. It identified several temperature change patterns over time and stratified the analysis by them. The differences in the relations across stages of spermatogenesis were addressed. Several mechanisms may explain the associations found, including heat-induced apoptosis of the sperm cells, and destruction of sperm cells DNA integrity by over-production of reactive oxygen species. The gradual global warming necessitates exploration of individual response to outdoor temperature in relations to genetic predisposition, lifestyle, and other health characteristics.

Longitudinal changes in the volume of residual lung lobes after lobectomy for lung cancer: a retrospective cohort study.

It is unclear how the residual lobe volume changes over time after lobectomy. This study aims to clarify the temporal patterns of volume changes in each remaining lung lobe post-lobectomy. A retrospective review was conducted on patients who underwent lobectomy for lung cancer at Yueyang Central Hospital from January to December 2021. Lung CT images were reconstructed in three dimensions to calculate the volumes of each lung lobe preoperatively and at 1, 6, and 12months postoperatively. A total of 182 patients were included. Postoperatively, the median total lung volume change rates relative to preoperative values were -20.1%, -9.3%, and -5.9% at 1, 6, and 12months, respectively. Except for the right middle lobe in patients who underwent right upper lobectomy, the volumes of individual lung lobes exceeded preoperative values. The volume growth of the lung on the side of the resection was significantly more than that of the lung on the opposite side. For left lobectomy patients, the right lower lobe's volume change rate exceeded that of the right upper and middle lobes. Among right lobectomy patients, the left lower lobe and the relatively inferior lobe of right lung had higher volume change rates than the superior one. Right middle lobe change rate was more in patients with right lower lobectomy than right upper lobectomy. Six months postoperatively, FEV1% and right middle lobectomy were positively correlated with the overall volume change rate. One year postoperatively, only age was negatively correlated with the overall volume change rate. 75 patients had pulmonary function tests. Postoperative FEV1 change linearly correlated with 1-year lung volume change rate, but not with theoretical total lung volume change rate or segmental method calculated FEV1 change. Time-dependent compensatory volume changes occur in remaining lung lobe post-lobectomy, with stronger compensation observed in the relatively inferior lobe compared to the superior one(s). Preoperative lung function and age may affect compensation level.

Monitoring Long-Term Land Cover Change in Central Yakutia Using Sparse Time Series Landsat Data

Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s.

Subtropical mangroves poleward shift to the Yangtze Estuary under different carbon emission scenarios

Mangroves are crucial plant communities in the coastal intertidal zones, providing various ecological services and supporting biodiversity. To identify the primary factors affecting the poleward shift of subtropical mangroves and the northernmost extent of subtropical mangroves under various carbon emission scenarios, the MaxEnt model was utilized with presence data of subtropical mangrove and environmental data under different carbon emission scenarios. Kandelia obovata, Avicennia marina, and Aegiceras corniculatum in China were included for modeling, utilizing 22 bioclimate variables to predict mangrove distributions, and kernel density analysis was further employed to identify conservation and afforestation hotspots for mangroves. The results indicated that the number of days below 10 °C per year is the major climatic factor shaping the distribution of subtropical native mangroves, while precipitation exerts a relatively lower contribution. Under the low and medium emissions scenarios (SSP126 and SSP245), the northern boundary of the distribution of K. obovata remains stable, while A. marina and A. corniculatum are projected to shift northward to Fuzhou, Fujian Province (26.1°N) from 24.9°N by 2100. Under higher emissions scenarios (SSP370 and SSP585), the suitable habitat of K. obovata is expected to shift north to the mouth of the Yangtze River (31.9°N) from 26.8°N, and A. marina and A. corniculatum are projected to move northward to the coastal area of Ningde, Fujian Province (26.8°N) by 2100. In conclusion, with the increase in carbon emissions, there is a significant northward migration observed in subtropical native mangroves (p < 0.01), and suitable areas for restoration in China have been identified, with the northernmost region located in Zhejiang Province. The spatial and temporal patterns of range change predicted in this study provide valuable information for conservation and afforestation strategies for these ecologically important species.

Changes in the Water Area of an Inland River Terminal Lake (Taitma Lake) Driven by Climate Change and Human Activities, 2017–2022

Constructed from a dataset capturing the seasonal and annual water body distribution of the lower Qarqan River in the Taitma Lake area from 2017 to 2022, and combined with the meteorological and hydraulic engineering data, the spatial and temporal change patterns of the Taitma Lake watershed area were determined. Analyses were conducted using Planetscope (PS) satellite images and a deep learning model. The results revealed the following: ① Deep learning-based water body extraction provides significantly greater accuracy than the conventional water body index approach. With an impressive accuracy of up to 96.0%, UPerNet was found to provide the most effective extraction results among the three convolutional neural networks (U-Net, DeeplabV3+, and UPerNet) used for semantic segmentation; ② Between 2017 and 2022, Taitma Lake’s water area experienced a rapid decrease, with the distribution of water predominantly shifting towards the east–west direction more than the north–south. The shifts between 2017 and 2020 and between 2020 and 2022 were clearly discernible, with the latter stage (2020–2022) being more significant than the former (2017–2020); ③ According to observations, Taitma Lake’s changing water area has been primarily influenced by human activity over the last six years. Based on the research findings of this paper, it was observed that this study provides a valuable scientific basis for water resource allocation aiming to balance the development of water resources in the middle and upper reaches of the Tarim and Qarqan Rivers, as well as for the ecological protection of the downstream Taitma Lake.

Temporal pattern in foraging behaviour of Vanellus malabaricus in relation to different seasons and habitats

This study monitored the temporal pattern of the diurnal foraging behaviour of yellow-wattled lapwing Vanellus malabaricus Boddaert, 1783 (Charadriiformes: Charadriidae) in different seasons (breeding and non-breeding) and habitats (uncultivated, cultivated, ponds and rivers). The focal observation method was employed to investigate the foraging behaviours of yellow-wattled lapwings. Foraging techniques showed non-significant (p = 0.85) variation in different habitat types; however, walk-halt pecking (WHP) was observed to be the most commonly utilized foraging technique across all habitats. Furthermore, foraging success varied significantly in seasons (p &lt;0.05) as well as in habitat types (p &lt;0.05). Due to the availability of ample food items, foraging success reached its peak in uncultivated habitats. We documented considerably high feeding rates in the uncultivated habitat in the breeding season due to the high prey abundance. Additionally, seasons and habitat both greatly affected the rate of walking. Uncultivated habitat represented the highest value of % successful attempts while the lowest value of % successful attempts was recorded in river habitat. Moreover, the % successful attempts were significant (p &lt;0.05) in the breading season as compared to the non-breeding season. Finally, we found substantial (p &lt;0.05) changes in temporal pattern with regard to daily foraging behaviour. The behaviours appear to be bimodal, with morning and evening types for both seasons and habitat types.

Diagnosis of Forme Fruste Keratoconus Using Corvis ST Sequences with Digital Image Correlation and Machine Learning.

This study aimed to employ the incremental digital image correlation (DIC) method to obtain displacement and strain field data of the cornea from Corvis ST (CVS) sequences and access the performance of embedding these biomechanical data with machine learning models to distinguish forme fruste keratoconus (FFKC) from normal corneas. 100 subjects were categorized into normal (N = 50) and FFKC (N = 50) groups. Image sequences depicting the horizontal cross-section of the human cornea under air puff were captured using the Corvis ST tonometer. The high-speed evolution of full-field corneal displacement, strain, velocity, and strain rate was reconstructed utilizing the incremental DIC approach. Maximum (max-) and average (ave-) values of full-field displacement V, shear strain γxy, velocity VR, and shear strain rate γxyR were determined over time, generating eight evolution curves denoting max-V, max-γxy, max-VR, max-γxyR, ave-V, ave-γxy, ave-VR, and ave-γxyR, respectively. These evolution data were inputted into two machine learning (ML) models, specifically Naïve Bayes (NB) and Random Forest (RF) models, which were subsequently employed to construct a voting classifier. The performance of the models in diagnosing FFKC from normal corneas was compared to existing CVS parameters. The Normal group and the FFKC group each included 50 eyes. The FFKC group did not differ from healthy controls for age (p = 0.26) and gender (p = 0.36) at baseline, but they had significantly lower bIOP (p < 0.001) and thinner central cornea thickness (CCT) (p < 0.001). The results demonstrated that the proposed voting ensemble model yielded the highest performance with an AUC of 1.00, followed by the RF model with an AUC of 0.99. Radius and A2 Time emerged as the best-performing CVS parameters with AUC values of 0.948 and 0.938, respectively. Nonetheless, no existing Corvis ST parameters outperformed the ML models. A progressive enhancement in performance of the ML models was observed with incremental time points during the corneal deformation. This study represents the first instance where displacement and strain data following incremental DIC analysis of Corvis ST images were integrated with machine learning models to effectively differentiate FFKC corneas from normal ones, achieving superior accuracy compared to existing CVS parameters. Considering biomechanical responses of the inner cornea and their temporal pattern changes may significantly improve the early detection of keratoconus.

Incorporating physical constraints in a deep learning framework for short-term daily prediction of sea ice concentration

Grasping the rapidly changing sea ice patterns has become the key to Arctic navigation. Starting from meteorology and oceanography, this study combines the empirical orthogonal function (EOF) analysis with convolutional long short-term memory (ConvLSTM) model to realize short-term sea ice prediction. To further improve the effectiveness and predictability of the ConvLSTM model, we adopt the EOF analysis to incorporate the spatial and temporal patterns of sea ice changes into the ConvLSTM model in three ways, namely, model input factors, model internal network structure, and model loss function. Through the mean absolute error (MAE), anomaly correlation coefficient (ACC), and anomaly metrics, we find that embedding the EOF physical constraints into the model's internal structure fully employs the spatial pattern and time series information, affording an optimal prediction effect. Additionally, the impact of the weight proportion between the physical constraint and the base loss and the effect of combining the three ways are discussed. The experimental results reveal that tuning the weight proportion in the loss function can improve the model's training effect. However, multiple combinations of the same physical law make it difficult to introduce new effective information.

Temporal tracking of cysteine 34 oxidation of plasma albumin as a biomarker of muscle damage following a bout of eccentric exercise

Abstract Purpose Exercise-induced muscle damage (EIMD) results in the generation of reactive oxygen species (ROS), but little is known about the temporal profile of change in ROS post-EIMD and how ROS levels relate to the onset of and recovery from EIMD. Our primary aim was to examine the effect of EIMD on the pattern of change in the blood level of thiol-oxidised albumin, a marker of oxidative stress. Methods Seven male participants were subjected on separate days to eccentric muscle contraction to cause EIMD or a no-exercise condition. After each session, the participants collected daily dried blood spots to measure thiol-oxidised albumin and returned to the laboratory every 2 days for the assessment of indirect markers of EIMD, namely maximal voluntary contraction (MVC), delayed onset muscle soreness (DOMS), creatine kinase (CK), and myoglobin. Results Eccentric exercise resulted in a significant decrease in MVC and increase in DOMS, CK, myoglobin, and thiol-oxidised albumin with the latter reaching above baseline level within 24–48 h post-exercise. All the markers of EIMD returned to baseline level within 6 days post-exercise, but not the level of thiol-oxidised albumin which remained elevated for 10 days after exercise. There was a moderate correlation between changes in thiol-oxidised albumin and DOMS, but no significant relationship between any other markers of muscle damage. Conclusion The levels of thiol-oxidised albumin increase in response to EIMD and remain elevated for several days post-exercise. The temporal pattern of change in the level of thiol-oxidised albumin suggests that this may be a useful biomarker of muscle repair post-EIMD.

Spatiotemporal Land Use/Land Cover Changes and Impact on Urban Thermal Environments: Analyzing Cool Island Intensity Variations

This study pioneers the comprehensive evaluation of the spatiotemporal evolution of land use/land cover (LULC) in Hangzhou city, introducing the novel water body shape index (WBSI) to analyze its seasonal impacts on the urban thermal environment and urban cool island (UCI) effects, uncovering distinct patterns of thermal regulation. It particularly investigates how distance gradients and the water body shape index (WBSI) influence land surface temperature (LST) in the urban core. The region’s climate, featuring hot summers and cold winters, highlights significant seasonal LST variations. Addressing a gap in existing UCI research, the analysis extends beyond the typical large-scale planning focus to include small-scale, high-resolution aspects. Employing remote sensing and geographic information system (GIS) analysis techniques, this study analyzes the seasonal dynamics in Hangzhou’s central urban area. High-resolution LST data, obtained through single-channel inversion and resolution enhancement algorithms, are crucial to this analysis. This study employs the maximum likelihood classification method to analyze land use and land cover changes from 1990 to 2020. This analysis reveals potential drivers of urban thermal environment changes, such as the expansion of residential and commercial areas and the reduction in green spaces. Different regions in LST data are delineated to assess the cool island effect, and the complexity of water body boundaries is quantified using the water body shape index. Spatial and temporal patterns of LST changes are investigated using multivariate regression and time-series analysis models. We identified significant changes in LULC over the past 30 years in Hangzhou, closely correlating with a continuous rise in LST. This observation underscores a clear finding: the strategic importance of blue–green infrastructure in mitigating urban heat, a novel insight that extends the current understanding of urban thermal dynamics. A clear and novel finding of this study is that the intensity of the cool island effect from large water bodies not only diminishes with distance but is intricately influenced by the complexity of their shapes, as quantified by the WBSI, whereas the complexity of their boundaries enhances this effect. Additionally, the regulatory role of the cool island effect is observed to vary seasonally, being most pronounced in summer and less so in autumn and winter, thereby demonstrating a positive impact. In conclusion, our findings innovatively highlight how the specific shapes of water bodies, quantified through the water body shape index (WBSI), emerge as critical, yet previously underappreciated, drivers in modulating the urban thermal environment. This underscores a new avenue for urban planning, advocating for the strategic design of water bodies within urban landscapes. It also finds that spatial factors and seasonal variations significantly affect the intensity of the cool island effect. These findings offer valuable evidence for urban planning and climate change adaptation, emphasizing balancing natural elements with the built environment in urban design.

A Spatiotemporal Assessment of the Precipitation Variability and Pattern and an Evaluation of the Predictive Reliability of Global Climate Models over Bihar

Climate change is significantly altering precipitation patterns, leading to spatiotemporal changes throughout the world. In particular, the increased frequency and intensity of extreme weather events, leading to heavy rainfall, floods, and droughts, have been a cause of concern. A comprehensive understanding of these changes in precipitation patterns on a regional scale is essential to enhance resilience against the adverse effects of climate change. The present study, focused on the state of Bihar in India, uses a long-term (1901–2020) gridded precipitation dataset to analyze the effect of climate change. Change point detection tests divide the time series into two epochs: 1901–1960 and 1961–2020, with 1960 as the change point year. Modified Mann–Kendall (MMK) and Sen’s slope estimator tests are used to identify trends in seasonal and annual time scales, while Centroidal Day (CD) analysis is performed to determine changes in temporal patterns of rainfall. The results show significant variability in seasonal rainfall, with the nature of pre-monsoon and post-monsoon observed to have flipped in second epoch. The daily rainfall intensity during the monsoon season has increased considerably, particularly in north Bihar, while the extreme rainfall has increased by 60.6 mm/day in the second epoch. The surface runoff increased by approximately 13.43% from 2001 to 2020. Further, 13 Global Climate Models (GCMs) evaluate future scenarios based on Shared Socioeconomic Pathways (SSP) 370 and SSP585. The suitability analysis of these GCMs, based on probability density function (PDF), monthly mean absolute error (MAE), root mean square error (RMSE) and percentage bias (P-Bias), suggests that EC-Earth3-Veg-LR, MIROC6, and MPI-ESM1-2-LR are the three best GCMs representative of rainfall in Bihar. A Bayesian model-averaged (BMA) multi-model ensemble reflects the variability expected in the future with the least uncertainty. The present study’s findings clarify the current state of variability, patterns and trends in precipitation, while suggesting the most appropriate GCMs for better decision-making and preparedness.

Patterns of light pollution on sea turtle nesting beaches in the Egyptian Red Sea

We examined the probability of past sea turtle nesting as a function of light intensity and patterns of temporal changes of light along nesting beaches in the Egyptian Red Sea. Beaches had a lower probability of past sea turtle nesting as light intensity increased. Light has been significantly increasing on mainland nesting beaches between 1992 and 2021 except for temporary declines. Island beaches historically had lower light pollution, but there was a sudden increase of light starting in 2014 that continued through 2021, except for the precipitous decline in 2020 during the Covid 19 pandemic. Light pollution on past nesting beaches has now approached levels that may be too polluted and discourage nesting. The impacts of the increased light pollution on nesting density and hatchling survival of hawksbill, Eretmochelys imbricata, and green turtles, Chelonia mydas, is likely negative.

TEMPORAL PATTERN OF AEDES SOLLICITANS AND AEDES TAENIORHYNCHUS IN AN INTERTIDAL WETLAND SYSTEM, NORTHEASTERN FLORIDA: A LITERATURE REVIEW

Temporal and spatial patterns of mosquito species distributions are influenced by weather conditions and climate, changes in water quality, and many other factors. Aedes taeniorhynchus and Aedes sollicitans are common salt marsh mosquitoes that cause many nuisance problems for human populations. St. Johns County (north-eastern Florida) has, over the past decades, been one of the fastest growing counties in the U.S.A. This is related to changes in land use and land cover, and local climate variations. It has been recently noticed that two species, Ae. taeniorhynchus and Ae. sollicitans, switch their temporal pattern in St. Johns County. To explore factors influencing potential changes between the temporal distribution of the two species of salt marsh mosquitoes, a literature search was conducted. Based on the literature, there are many factors influencing the potential species switch or temporal patterns of the two species. These include meteorological and tidal patterns as well as land use and land cover changes. The results of the literature analysis indicate that the switch and change in temporal pattern between the two species needs to be further explored, especially with respect to temperature, tidal flooding, water quality (especially salinity), sea level rise and land use/cover.

Analysis of Land Cover Changes to Increase Land Surface Temperature in Surabaya using Landsat Satellite

Surabaya has experienced very significant development in the last few decades. Changes in land use will cause the Urban Heat Island phenomenon. This study aims to determine how far the impact of land cover changes on the increase in surface temperature in the Surabaya. The use of Landsat satellite imagery is considered very effective in describing land cover and surface temperature because it has good spatial resolution and long data availability. During 1991 – 2020 there was a significant decrease in the amount of vegetation by 24.3%, decrease in the number of water bodies by 4.9%, and increase in the number of buildings by 29.2%. The average increase in Land Surface Temperatures was 1.40°C between decades 2 and 1, and an increase of 2.19°C between decades 3 and 2. The development of Surabaya began in the city center and then developed mainly in the west and east. The urban development model is consistent with the pattern of land surface temperature changes. Each type of land cover has special characteristics on the value of NDVI, NDBI, and surface temperature. Changes in cover from water bodies to buildings have the highest contribution to increasing the and surface temperature. There was a significant increase in hotspots in decade 3 in Surabaya which indicated an increasingly severe UHI phenomenon.

Time series retrieval of Multi-wavelength Aerosol optical depth by adapting Transformer (TMAT) using Himawari-8 AHI data

Accurate aerosol optical depth (AOD) with high temporal resolution is required for dynamic monitoring changes of aerosol properties. The Himawari-8 Advanced Himawari imager (AHI) data with 10-min temporal resolution has been widely used for AOD retrieval but usually involves radiative transfer models and assumptions on surface reflection and aerosol properties that are hard to satisfy. This paper introduces a data-driven algorithm without any priori assumptions on surface or aerosol properties called Time series retrieval of Multi-wavelength AOD by adapting Transformer (TMAT). TMAT directly retrieves AOD at eight-wavelengths every 10-min throughout the day, using AHI top of atmosphere (TOA) reflectances in 6 wavebands and ancillary data. TMAT explores diurnal temporal change patterns of AOD and surface reflection using time series data. TMAT was trained and evaluated using samples derived by matching a year (2019) of AHI full-disk data and AERONET/SONET observations with 12,768 diurnal time series, each of which includes up to 81 10-min 6-band AHI TOA observations and 81 (time) × 8 (wavelength) AOD values per day. There are up to 273,413 (<12,768 × 81) TOA observations in 6 wavebands and up to 1,420,716 (<12,768 × 81 × 8) AOD values in all the time series samples, with filled values (−9999) to represent no data due to cloud contamination and incomplete wavelength configurations of AOD stations. The masking mechanism in Transformer is leveraged to exclude the contribution of the AHI filled values in time series AOD retrieval and exclude the contribution of AOD filled values in training. TMAT was trained with a physical constraint that the retrieved AOD decreases toward longer wavelengths. TMAT AOD was evaluated by separating training and validation samples using site-specific leave-one-station-out validation. TMAT AOD shows extremely high accuracy at all wavelengths, with RMSE = 0.101, R = 0.949 and 81.69% of retrievals within ±0.05+15%∗AODsites for the AOD at 550 nm. We also validated the accuracy of the NASA multiangle implementation of atmospheric correction (MAIAC) AHI AOD over these sites with RMSE = 0.186, R = 0.832 and 52.10% of retrievals within ±0.05+15%∗AODsites. The Ångström exponent (AE) derived from the multi-wavelength TMAT AOD also shows good agreement with AERONET/SONET AE, with a correlation coefficient of 0.620 and an RMSE of 0.291. The TMAT effectiveness to capture diurnal temporal change patterns of surface reflection and AOD was evidenced by the significant reduction of AOD accuracy when using a non-time series deep learning method. The effectiveness of multi-wavelength joint retrieval was evidenced by the significant reduction of AE accuracy when using single wavelength retrieval models. Evidence shows the training and evaluation split methods have a large impact on evaluation accuracy and this study further used ground network independent training and evaluation split to confirm the leave-one-station-out validation accuracy. Despite this, future study will focus on more spatially independent validation and more comprehensive comparison between the MAIAC and TMAT results.

Spatial–temporal patterns of ecological changes in the Dongting Lake region and their responses to climate factors and human activities

ABSTRACT The Dongting Lake region is an important ecological and environmental protection zone in the Yangtze River Economic Belt of China. Long-term monitoring and evaluation of its ecological environment is of great significance. In this study, the spatio-temporal pattern of ecological condition change and its response to climate factors and human activities in the Dongting Lake region was quantitatively analyzed using RSEI (Remote Sensing Ecological Index) model. The primary conclusions are as follows: (1) The temporal pattern of ecological changes has a small fluctuation period (1990–2007) and a continuous improvement period (2007–2020) with an ecological improvement rate of 1.17% per year. (2) The spatial pattern of ecological changes is directly tied to the land use types in this area. 17.36% of the regions with an excellent ecological level are primarily woodland, whereas 4.45% of the areas with a low ecological level are mostly urban. (3) The deterioration in ecological conditions in urban areas is primarily attributable to human activities, whereas the decline in ecological conditions in surrounding mountainous regions and central agricultural regions is primarily attributable to climate factors. Our research provides valuable support for ecological environment monitoring and management strategy formulation in the Dongting Lake region.

Satellite-Derived Indicators of Drought Severity and Water Storage in Estuarine Reservoirs: A Case Study of Qingcaosha Reservoir, China

Estuarine reservoirs are critical for freshwater supply and security, especially for regions facing water scarcity challenges due to climate change and population growth. Conventional methods for assessing drought severity or monitoring reservoir water level and storage are often limited by data availability, accessibility and quality. We present an approach for monitoring estuarine reservoir water levels, storage and extreme drought via satellite remote sensing and waterline detection. Based on the CoastSat algorithm, Landsat-8 and Sentinel-2 images from 2013 to 2022 were adopted to extract the waterline of Qingcaosha Reservoir, the largest estuarine reservoir in the world and a key source of freshwater for Shanghai, China. This study confirmed the accuracy of the satellite-extracted results through two main methods: (1) calculating the angle of the central shoal slope in the reservoir using the extracted waterline data and measured water levels and (2) inverting the time series of water levels for comparison with measured data. The correlation coefficient of the estimated water level reached ~0.86, and the root mean square error (RMSE) of the estimated shoal slope was ~0.2°, indicating that the approach had high accuracy and reliability. We analyzed the temporal and spatial patterns of waterline changes and identified two dates (21 February 2014 and 15 October 2022) when the reservoir reached the lowest water levels, coinciding with periods of severe saltwater intrusions in the estuary. The extreme drought occurrences in the Qingcaosha Reservoir were firstly documented through the utilization of remote sensing data. The results also indicate a strong resilience of the Qingcaosha Reservoir and demonstrate that the feasibility and utility of using satellite remote sensing and waterline detection for estuarine reservoir storage can provide timely and accurate information for water resource assessment, management and planning.

Cultural differences in the ICE-Group experience: Diaries of French and British antarctic winteroverers

Cultural differences may influence the psychosocial adaptation to prolonged isolation and confinement in extreme environments. Recent results showed that the temporal pattern of mood changes may also be affected by cultural differences. The aim of the study was to gain deeper insight into the structure and temporal patterns of group-related themes mentioned in the diaries of Antarctic winteroverers in two different stations with different national and organizational backgrounds. The diaries of the corresponding 8-8 French and British crew members of the Concordia and Halley VI research stations were subjected to content analysis. Method. A total of 18324 sentences were coded. In the first phase of the analysis, we identified 5 major thematic groups (Self focus, Group focus, Antarctica focus, Outworld focus, and Castoff sentences). One-third of these sentences were Group Focus, which were classified into 4 code groups, depending on whether the sentences referred to Experienced or Observed Comfort or Discomfort in the ingroup, respectively. These 4 code groups were subject to further analyses to test whether each code cluster showed a significant difference between British and French winteroverers during the 4 quarters of the mission. The Q3 phenomenon was also investigated. Results. Throughout all four quarters, the British crewmembers used significantly more Experienced Comfort sentences, while the French expressed significantly more Observed Discomfort sentences. Regarding the temporal patterns, the amount of Experienced Comfort sentences was significantly lower in the French diaries compared to the British diaries in the third quarter of the mission. Conclusions. Content analysis detected the impact of cultural differences on the composition and temporal pattern of social experiences of winterovering crews. It thus can diagnose and predict the psychodynamics of groups in isolated, confined, and extreme (ICE) environments, such as polar stations or long-duration missions in space.

Spatial patterns of Holocene temperature changes over mid-latitude Eurasia

The Holocene temperature conundrum, the discrepancy between proxy-based Holocene global cooling and simulated global annual warming trends, remains controversial. Meanwhile, reconstructions and simulations show inconsistent spatial patterns of terrestrial temperature changes. Here we report Holocene alkenone records to address spatial patterns over mid-latitude Eurasia. In contrast with long-term cooling trends in warm season temperatures in northeastern China, records from southwestern Siberia are characterized by colder conditions before ~6,000 years ago, thus long-term warming trends. Together with existing records from surrounding regions, we infer that colder airmass might have prevailed in the interior of mid-latitude Eurasian continent during the early to mid-Holocene, perhaps associated with atmospheric response to remnant ice sheets. Our results challenge the proposed seasonality bias in proxies and modeled spatial patterns in study region, highlighting that spatial patterns of Holocene temperature changes should be re-considered in record integrations and model simulations, with important implications for terrestrial hydroclimate changes.