Mean Minimum Temperature Research Articles

Spatiotemporal Evolution Analysis of Surface Deformation on the Beihei Highway Based on Multi-Source Remote Sensing Data

Under the interference of climate warming and human engineering activities, the degradation of permafrost causes the frequent occurrence of geological disasters such as uneven foundation settlement and landslides, which brings great challenges to the construction and operational safety of road projects. In this paper, the spatial and temporal evolution of surface deformations along the Beihei Highway was investigated by combining the SBAS-InSAR technique and the surface frost number model after considering the vegetation factor with multi-source remote sensing observation data. After comprehensively considering factors such as climate change, permafrost degradation, anthropogenic disturbance, and vegetation disturbance, the surface uneven settlement and landslide processes were analyzed in conjunction with site surveys and ground data. The results show that the average deformation rate is approximately −16 mm/a over the 22 km section of the study area. The rate of surface deformation on the pavement is related to topography, and the rate of surface subsidence on the pavement is more pronounced in areas with high topographic relief and a sunny aspect. Permafrost along the roads in the study area showed an insignificant degradation trend, and at landslides with large surface deformation, permafrost showed a significant degradation trend. Meteorological monitoring data indicate that the annual minimum mean temperature in the study area is increasing rapidly at a rate of 1.266 °C/10a during the last 40 years. The occurrence of landslides is associated with precipitation and freeze–thaw cycles. There are interactions between permafrost degradation, landslides, and vegetation degradation, and permafrost and vegetation are important influences on uneven surface settlement. Focusing on the spatial and temporal evolution process of surface deformation in the permafrost zone can help to deeply understand the mechanism of climate change impact on road hazards in the permafrost zone.

Geographical variation and genetic diversity of Parashorea chinensis germplasm resources.

Parashorea chinensis is a rare monodominant species in southwest China known for its production of high-quality timber, is facing decline due to its narrow distribution, human interference and habitat destruction. However, there are no reports on genetic diversity and geographical variation of phenotypic traits of P. chinensis. In this study, phenotypic characters and genetic diversity of 15 germplasms resources from five provenances in southwest China were investigated, and their relationships with geographical and environmental factors was discussed. Our results revealed a rich phenotypic diversity among the germplasms, with variation coefficients ranging from 3.63% to 45.49%. Among the studied germplasms, NP03 from Napo and ML02 from Mengla region exhibited superior phenotypic traits. Notably, NP03 also demonstrated the highest genetic diversity. Genetic differentiation analyses including genetic differentiation coefficient (0.6264) and gene flow (0.3736) illustrated that genetic variation was most prevalent among populations. Furthermore, redundancy analysis showed that temperature related factors (maximum air temperature, annual mean temperature and minimum air temperature) significantly affected phenotypic variation. Similarly, altitude, longitude, latitude, annual mean precipitation and the minimum air temperature significantly impacted the level of genetic diversity. The molecular variation of the natural population of P. chinensis followed a certain geographical pattern. Our finding indicated abundant phenotypic variation among P. chinensis germplasms. However, populations exhibited low levels of genetic diversity alongside high genetic differentiation, potentially contributing to the species' rarity. Based on our results, NP03 and ML02 germplasm could be used as the parents for breeding superior germplasm of P. chinensis. Overall, this study provides valuable insights into germplasm diversity and conservation, genetic improvement, and utilization of P. chinensis.

Alien aquatic plants in Poland: Temporal and spatial distribution patterns and the effects of climate change

Distribution of alien aquatic plants in European freshwater ecosystems is still not fully recognized. Little is also known about the impact of climatic factors on the expansion of particular neophyte species in freshwater ecosystems. In this article, we present the first comprehensive assessment of the occurrence of non-native aquatic vascular plants in Poland (CE Europe), with a particular focus on: (1) inventory of the species, (2) temporal and spatial changes in their distribution, and (3) the climate impact on species occurrence and diversity. For this purpose, we collected both published and unpublished data on alien macrophytes occurrence in Poland covering past 70 years. In order to investigate the mechanism of alien aquatic plant expansion, climate data from 24 meteorological stations were analyzed. Redundancy analysis (RDA) was used to analyze the relationship between species composition and microclimate variables. In addition, Generalized Additive Model (GAM) was used to examine the response curves of neophyte species to temperature parameters.In total, we found records of 15 neophytes of alien aquatic plant taxa in Poland, including 14 species and collective taxon of Nymphaea cultivars. The most frequent was Elodea canadensis (over 25,000 records). Excluding this species, we counted more than 300 records of the occurrence of alien aquatic plants, of which three (E. nuttallii, Azolla filiculoides and Lemna turionifera) were the most abundant. Ten species occurred only in isolated sites. In addition, analyses showed a substantial increase in the number of the aquatic neophyte sites in the last 20 years that could be linked with the raised average monthly and yearly air temperatures in Poland.Our results showed a strong relationship between raised average temperatures in Poland and the spread of some species, e.g. Azolla filiculoides, while other species such as Elodea nuttallii and Lemna turionifera expanded irrespectively of this factor. The occurrence of some species (e.g. Vallisneria spiralis, Hygrophila polysperma) was exclusively associated to high thermally altered or thermally contaminated waters, and thus was limited to only a small part of the country. Our results confirm the major role of elevated temperatures (mean annual temperatures and minimum monthly temperatures) and thermally polluted freshwater ecosystems in the distribution of alien aquatic plants.

Dendroclimatological study of ancient trees integrating non-destructive techniques.

Based on the need to protect previous ancient trees and the development of dendroclimatology, the use of non-destructive technologies in tree-ring research has gained increasing attention. This study focuses on the ancient Pinus tabulaeformis in Yu Xiang Forest Farm in Henan Province. Firstly, samples were collected using the traditional Increment borers and the Resistograph, a non-destructive method. Subsequently, the peak-valley analysis was used to filter the data obtained by the Resistograph to extract the tree ring width sequence, and the data's accuracy was verified by correlation analysis with tree ring width sequence by the Increment borers. Then, the optimal filtering method and an appropriate comprehensive threshold were determined, and tree ring width and density sequences were successfully extracted. Following that, the growth trend and residual resistance in the measurement process were corrected using linear fitting and Ensemble Empirical Mode Decomposition (EEMD) technology, thereby establishing the tree-ring width and density index series, which were further validated through correlation analysis and t-tests. Finally, analysis of the correlation with climatic factors, identified the main limiting factors for tree growth, and the accuracy of the tree-ring information extracted by the Resistograph was further verified. The results showed that spite of certain differences between the tree-ring width indices extracted by the Resistograph and the Increment borer, they were generally reliable. The radial growth of the ancient P.tabulaeformis in Yu Xiang Forest Farm is primarily influenced by temperature, with the maximum density of the tree rings responding more significantly to the mean maximum temperature, while the minimum density of the tree rings responded more significantly to the mean minimum temperature. These results not only provide a scientific and accurate age for the protection of ancient trees and verify the reliability of the data obtained by the Resistograph, but also facilitate the use of non-destructive technology for in-depth study of ancient trees, therefore enhancing our understanding of how climate change affects tree growth and provide valuable insights for the future protection and management of these ancient trees.

A century of medical records reveal earlier onset of the malaria season in Haut-Katanga induced by climate change

BackgroundDespite worldwide efforts to eradicate malaria over the past century, the disease remains a significant challenge in the Democratic Republic of the Congo (DRC) today. Climate change is even anticipated...

Enhancing Coffee Agroforestry Systems Suitability Using Geospatial Analysis and Sentinel Satellite Data in Gedeo Zone, Ethiopia.

The Gedeo zone agroforestry systems are the main source of Ethiopia's coffee beans. However, land-use and suitability analyses are not well documented due to complex topography, heterogeneous agroforestry, and lack of information. This research aimed to map the coffee coverage and identify land suitability for coffee plantations using remote sensing, Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP) in the Gedeo zone, Southern Ethiopia. Remote sensing classifiers often confuse agroforestry and plantations like coffee cover with forest cover because of their similar spectral signatures. Mapping shaded coffee in Gedeo agroforestry using optical or multispectral remote sensing is challenging. To address this, the study identified and mapped coffee coverage from Sentinel-1 data with a decibel (dB) value matched to actual coffee coverage. The actual field data were overlaid on Sentinel-1, which was used to extract the raster value. Pre-processing, classification, standardization, and reclassification of thematic layers were performed to find potential areas for coffee plantation. Hierarchy levels of the main criteria were formed based on climatological, edaphological, physiographic, and socioeconomic factors. These criteria were divided into 14 sub-criteria, reclassified based on their impact on coffee growing, with their relative weights derived using AHP. From the total study area of 1356.2 km2, the mapped coffee coverage is 583 km2. The outcome of the final computed factor weight indicated that average annual temperature and mean annual rainfall are the primary factors, followed by annual mean maximum temperature, elevation, annual mean minimum temperature, soil pH, Land Use/Land Cover (LULC), soil texture, Cation Exchange Capacity (CEC), slope, Soil Organic Matter (SOM), aspect, distance to roads, and distance to water, respectively. The identified coffee plantation potential land suitability reveals unsuitable (413 km2), sub-suitable (596.1 km2), and suitable (347.1 km2) areas. This study provides comprehensive spatial details for Ethiopian cultivators, government officials, and agricultural extension specialists to select optimal coffee farming locations, enhancing food security and economic prosperity.

Warming events and their causes at the Bering Sea section B in summer of 1999–2019

Based on hydrological data for the Bering Sea from 1999 to 2019 during the Chinese National Arctic Research Expeditions (CHINAREs), along with oceanographic and meteorological data from the National Centers for Environmental Information (NCEI) and the European Centre for Medium-Range Weather Forecasts (ECMWF), we investigated trends in summer at the Bering Sea section B over the past 20 years and their underlying causes. The results revealed a pronounced rise in temperature at the Bering Sea section B in 2003, 2014, 2016, 2018, and 2019, suggesting a shift towards a new phase of warmth starting in 2014. In warm years (2003, 2014, 2016, 2018, and 2019), the mean maximum temperature in the upper layer in the basin north of 57°N and on the continental shelf were roughly 2.7 °C, 2.6 °C higher than that in cold years (1999, 2008, 2010, and 2012). Additionally, the mean minimum temperature of the cold water in the middle layer in the basin was roughly 0.7 °C higher in the warm years compared to the cold years. This difference between the warm and cold years was evident in the heat content: the average heat content in the upper to middle layers in the basin north of 57°N and on the continental shelf were about 1.2 and 0.9 GJ/m2 higher, respectively, in the warm years than the cold years. However, there was a modest warming trend in the basin south of 57°N, and the temperature in the mid-layer on the continental shelf showed year-to-year stability, in contrast to the significant warming observed elsewhere. Further study suggested that temperature fluctuation in the basin was closely tied to seasonal 2-m air temperature. In addition, the southern basin, divided at 57°N, experienced larger positive or negative anomalous wind stress curl compared to the northern basin. This discrepancy resulted in differing causes of temperature variation between the two regions. Temperature change in the upper layer on the continental shelf was attributed to an increase in cumulative net heat flux resulting from reduced sea ice, while the temperature in the middle layer on the continental shelf was limited by the enhanced density gradient between the upper and middle layer caused by melt ice, preventing significant warming. Overall, the thermal state for the Bering Sea section B was influenced by a combination of factors, including net heat flux, sea ice, and atmospheric conditions, contributing to the year-to-year variation characterized by alternating cold and warm regimes.

The effects of ambient temperature on non-accidental mortality in the elderly hypertensive subjects, a cohort-based study

BackgroundThe association between ambient temperature and mortality has yielded inconclusive results with previous studies relying on in-patient data to assess the health effects of temperature. Therefore, we aimed to estimate the effect of ambient temperature on non-accidental mortality among elderly hypertensive patients through a prospective cohort study conducted in northeastern China.MethodsA total of 9634 elderly hypertensive patients from the Kailuan research who participated in the baseline survey and follow-up from January 1, 2006 to December 31, 2017, were included in the study. We employed a Poisson generalized linear regression model to estimate the effects of monthly ambient temperature and temperature variations on non-accidental mortality.ResultsAfter adjusting for meteorological parameters, the monthly mean temperature (RR = 0.989, 95% CI: 0.984–0.993, p < 0.001), minimum temperature (RR = 0.987, 95% CI: 0.983–0.992, p < 0.001) and maximum temperature (RR = 0.989, 95% CI: 0.985–0.994, p < 0.001) exhibited a negative association with an increased risk of non-accidental mortality. The presence of higher monthly temperature variation was significantly associated with an elevated risk of mortality (RR = 1.097, 95% CI:1.051–1.146, p < 0.001). Further stratified analysis revealed that these associations were more pronounced during colder months as well as among male and older individuals.ConclusionsDecreased temperature and greater variations in ambient temperature were observed to be linked with non-accidental mortality among elderly hypertensive patients, particularly notable within aging populations and males. These understanding regarding the effects of ambient temperature on mortality holds clinical significance for appropriate treatment strategies targeting these individuals while also serving as an indicator for heightened risk of death.

Long-term meteorological characteristics and extreme climate indices over Tirupati: a rapidly developing tropical city

Tirupati’s climate has undergone significant changes in both temperature and precipitation patterns. While there has been a consistent increase in rainfall during the southwest monsoon, there is a concerning long-term trend of a decrease in total annual precipitation over the last 30 years. The city has experienced a rise in wet days during both the southwest and northeast monsoons, yet a recent decrease over the past three decades. Heavy precipitation events, particularly during the southwest monsoon, have shown a positive trend, whereas there have been no significant changes in heavy rainfall days during the northeast monsoon. Temperature trends reveal that there has been a warming scenario, with a significant positive trend in annual maximum temperatures and a consistent annual rise in mean minimum temperatures. A substantial decrease in cold and very cold days, especially during the last 30 years, suggests a broader warming trend impacting seasonal temperature variations in Tirupati. These findings highlight the complex interplay of monsoons, temperature variations, and changing precipitation patterns in Tirupati's climate over the years.

Impact of Greenhouse Size on the Growth and Yield of Warm Season Vegetables During the Summer Ladakh, India

No guidelines exist on the size of naturally ventilated passive solar greenhouses in mountain regions, especially above 10,000 feet above mean sea level (AMSL). Two different-sized greenhouses in Ladakh were studied, and it was figured out that a large greenhouse (60 feet in length, 24 feet in width, 9 feet 6 inches in height) was better than a small greenhouse (32 feet in length, 18 feet width, 9 feet 6 inches height) for growing cucumber, capsicum, and brinjal in summer. The mean maximum and minimum temperature of the large greenhouse was 3.6±4.1°C and 1.3±1.6°C warmer in comparison to the small greenhouse. Cucumber was harvested seven days earlier, while capsicum and brinjal were harvested 17 days earlier in the large greenhouse. The marketable fruit numbers in cucumber, capsicum, and brinjal were 50.5 %, 46.9 %, and 67.7 % higher in the large greenhouse. The average marketable fruit yield of cucumber, capsicum and brinjal was 112 %, 55 % and 71.4 % higher in the large greenhouse than in the small greenhouse. Hence, large greenhouses are suggested for high-altitude Ladakh regions for growing warm-season crops in summer.

Interactive and lag effects of environmental factors on the density of schistosome-transmitting Oncomelania hupensis: A twelve-year monthly repeated survey.

Schistosomiasis is a significant public health threat, and Oncomelania hupensis is the only intermediate host for schistosoma japonicum. We conducted 12-year monthly repeated surveys to explore the interactive and lag effects of environmental factors on snail density and to monitor their long-term and seasonal trends in a bottomland around the Dongting Lake region in China. Relevant environmental data were obtained from multiple sources. A Bayesian kernel machine regression model and a Bayesian temporal model combined with a distributed lag model were constructed to analyze interactive and lag effects of environmental factors on snail density. The results indicated the average annual snail density in the study site exhibited an increasing and then decreasing trend, peaking in 2013. Snail densities were the highest in October and the lowest in January in a year. Normalized Difference Vegetation Index (NDVI) and water level were the most effective predictors of snail density, with potential interactions among temperature, precipitation, and NDVI. The mean minimum temperature in January, water level, precipitation and NDVI were positively correlated with snail density at lags ranging from 1 to 4 months. These findings could serve as references for relevant authorities to monitor the changing trend of snail density and implement control measures, thereby reducing the occurrence of schistosomiasis.

Mapping Ixodes pacificus and Borrelia burgdorferi Habitat Suitability Under Current and Mid-Century Climate in the Pacific Northwest (BC and WA).

Introduction: Lyme disease is the most common vector-borne disease in the United States and Canada. The primary vector for the causative agent of Lyme disease, Borrelia burgdorferi, in the Pacific Northwest is the western blacklegged tick, Ixodes pacificus. Materials and Methods: Using active tick surveillance data from British Columbia, Canada, and Washington State, USA, habitat suitability models using MaxEnt (maximum entropy) were developed for I. pacificus to predict its current and mid-century geographic distributions. Passive surveillance data both from BC and WA were also visualized. Results: According to the constructed models, the number of frost-free days during the winter is the most relevant predictor of its habitat suitability, followed by summer climate moisture, ecoregion, and mean minimum fall temperature. The ensemble geographic distribution map predicts that the coastal regions and inland valleys of British Columbia and the Puget Lowlands of Washington State provide the most suitable habitats for I. pacificus. The density map of ticks submitted from passive surveillance data was overlaid on the current distribution map and demonstrates the correlation between numbers of submissions and habitat suitability. Mid-century projections, based on current climate change predictions, indicate a range expansion, especially of low and moderate suitability, from current distribution. Regarding Lyme disease risk, I. pacificus identified from both active and passive surveillance and tested positive for B. burgdorferi were found to be in areas of moderate to very high suitability for I. pacificus. Conclusion: According to developed models, the total suitable habitat area for I. pacificus will expand in the interior regions of British Columbia and Washington State. However, the risk remains small given relatively low infection rates among I. pacificus. Further studies are required to better understand how this might change in the future.

Impacts of future climate change on rice yield based on crop model simulation—A meta-analysis

Rice is one of the world's major food crops. Changes in major climatic factors such as temperature, rainfall, solar radiation and carbon dioxide (CO2) concentration have an important impact on rice growth and yield. However, many of the current studies that predict the impact of future climate change on rice yield are affected by uncertainties such as climate models, climate scenarios, model parameters and structure, and showing great differences. This study was based on the assessment results of the impact of climate change on rice in the future of 111 published literature, and comprehensively analyzed the impact and uncertainty of climate change on rice yield. This study utilized local polynomial (Loess) regression analysis to investigate the impact of changes in mean temperature, minimum temperature, maximum temperature, solar radiation, and precipitation on relative rice yield variations within a complete dataset. A linear mixed-effects model was used to quantitatively analyze the relationships between the restricted datasets. The qualitative analysis based on the entire dataset revealed that rice yields decreased with increasing average temperature. The precipitation changed between 0 and 25 %, it was conducive to the stable production of rice, and when the precipitation changed >25 %, it would cause rice yield reduction. The change of solar radiation was less than −1.15 %, the rice yield increases with the increase of solar radiation, and when the change of solar radiation exceeds −1.15 %, the rice yield decreases. Elevated CO2 concentrations and management practices could mitigate the negative effects of climate change. The results of a quantitative analysis utilizing the mixed effects model revealed that average temperature, precipitation, CO2 concentration, and adaptation methods all had a substantial impact on rice production, and elevated CO2 concentrations and management practices could exert positive influences on rice production. For every 1 °C and 1 % increase in average temperature and precipitation, rice yield decreased by 3.85 % and 0.56 %, respectively. For every 100 ppm increase in CO2 concentration, rice yield increased by 7.1 %. The variation of rice yield under different climate models, study sites and climate scenarios had significant variability. Elevated CO2 concentrations and management practices could compensate for the negative effects of climate change, benefiting rice production. This study comprehensively collected and analyzed a wide range of literature and research, which provides an in-depth understanding of the impacts of climate change on rice production and informs future research and policy development.

Spatiotemporal interpretable mapping framework for soil heavy metals

Mapping the spatiotemporal distribution of soil heavy metals is a prerequisite for soil pollution prevention and control. However, current mapping methods make it difficult to balance robustness and interpretability. In this study, we proposed a spatiotemporal interpretable mapping framework for soil heavy metals and conducted a case study of soil cadmium (Cd) in cropland of the Poyang Lake region in China. The results showed that the average soil Cd concentration increased significantly (p < 0.01) from 0.13 mg/kg in 1983 to 0.19 mg/kg in 2010. Soil Cd in 1983 was mainly controlled by human activities and topography, whereas it was jointly driven by human activities, topography, and climate in 2010. In addition, the framework revealed the spatial distribution of soil Cd and their uncertainties at a 12.5-m resolution in 1983 (R2 = 0.94) and 2010 (R2 = 0.97). The top three covariates in 1983 were nighttime-light, PM10, and land surface temperature (day), whereas those in 2010 were mean annual minimum temperature, gross domestic product, and industrial enterprises, with Shapley additive explanation (SHAP) values of 0.44, 0.42, and 0.27, and 0.31, 0.11, and 0.09 mg/kg, respectively. By integrating machine learning, variable and model selection, and the SHAP module, the proposed framework achieves a balance between the robustness and interpretability in mapping the spatiotemporal distribution of soil Cd, going beyond the limitations of the classical statistical “smoothing effect” and the artificial intelligence “black box.” Our study emphasizes the importance of considering time, space, models, variables, and mechanisms in digital soil mapping, which will enable more accurate and scientific predictions of soil contaminants across time and space.

Simulating Climatic Patterns and Their Impacts on the Food Security Stability System in Jammu, Kashmir and Adjoining Regions, India

This study investigated the historical climate data and future projections under the SSP5-8.5 scenario for Jammu, Kashmir (J&amp;K), and its adjoining regions in India. Agriculture is a critical economic pillar of this region, making it highly vulnerable to climate change. This study focused on temperature and precipitation trends. Statistical analysis and modeling methods, including cloud computing, were employed to predict changes and assess their impact on agricultural productivity and water resources. The results indicated that by 2100, the mean maximum and minimum temperatures are projected to increase by approximately 2.90 °C and 2.86 °C, respectively. Precipitation variability is expected to rise, with a mean increase of 2.64 × 10−6 mm per day. These changes have significant consequences for crop yield, water stress, and ecosystem dynamics. An analysis of Gross Primary Productivity (GPP) as a proxy for agricultural productivity using linear regression revealed a concerning trend. Although the total GPP of the study area remained stable over time, it declined by −570 g yr−1 in 2010, coinciding with a 1 °C temperature rise. Projections based on the expected 3 °C temperature increase by 2100 suggest a total GPP loss of −2500 g yr−1. These findings highlight the urgent need for proactive adaptation measures, including sustainable agricultural practices, improved water management, and enhanced socioeconomic infrastructure, to mitigate the impact of climate change and ensure long-term resilience and food security in the region.

PREDICTIVE MODEL TO MINIMIZE THE EFFECT OF EXTREME TEMPERATURE IN SKARDU AND ASTORE, GILGIT BALTISTAN

Climate is a fundamental factor of the natural environment that has a role in both natural and human existence. Temperature is an important climatic element that influences snow melting, evaporation, and frost directly. Current study has used Mean Monthly Minimum Temperature (MMMT) of Skardu from 1972 to 2021 and of Astore from 1993 to 2021 based on the availability of data. In this work; we have used SARIMA (Seasonal Auto Regressive Integrated Moving Average Model) to forecast mean monthly minimum temperature. Skardu data is stationary at level form, which suggests SARMA model for Skardu station and Astore data is stationary at first difference so SARIMA time series is appropriate for mean monthly minimum temperature of Astore. Using Box Jenkins’s approach it is found that the most appropriate model for Skardu is SARMA(1,0)(1,0)12 and Astore is SARIMA (0,1,1)(4,1,4)12 respectively. These models have been utilized to forecast MMMT from 2022 to 2036. Yearly mean minimum temperature forecasts show that the mean minimum temperature at Skardu and Astore stations is slightly decreasing. The yearly mean minimum temperature at Skardu station is 4.0 °C in 2022 and will decrease to 2.3 °C till 2036, while at Astore station it is 4.0 °C in 2022 and will fall to 2.6 °C in 2036. Our results will be useful for decision makers and insurance companies for better future planning to minimize the effect of lowest temperature.

Analysis of Long-Term Vegetation Trends and Their Climatic Driving Factors in Equatorial Africa

Understanding vegetation seasonality and its driving mechanisms improves decision-making in the management of ecological systems in a warming global climate. Using multiple statistical methods (i.e., trend analysis, abrupt changes, and partial correlation analysis), this study analyzed the spatiotemporal variations in the Normalized Difference Vegetation Index (NDVI) in the Equatorial Africa (EQA) region and their responses to climate factors from 1982 to 2021. The NDVI values declined at a rate of 0.00023 year−1, while the precipitation (P) and mean temperature (TMEAN) values increased at rates of 0.22 mm year−1 and 0.22 °C year−1, respectively. The mean minimum temperature (TMIN) had a higher rate of 0.2 °C year−1 than the mean maximum temperature (TMAX) at 0.02 °C year−1. An abrupt change analysis showed that the TMAX, P, and NDVI breakpoints occurred in 2000, 2002, and 2009, respectively; TMEAN and TMIN breakpoints occurred in 2001. The NDVI trends declined in forest and cropland areas but increased in shrubland and grassland areas. The summer NDVI trends declined for all vegetation types and were reversed in the winter season. The NDVI positively correlated with the P (r = 0.50) and TMEAN (r = 0.60). All seasonal analyses varied across four seasons. A temporal analysis was conducted using partial correlation analysis (PCR), and the results revealed that TMIN had a greater impact on the NDVI (PCR = −0.45), followed by the TMAX (PCR = 0.31) and then the P (PCR = −0.19). The annual trend showed that areas with significant greening were consistent with stronger wetter and weaker warming trends. Both precipitation and temperature showed a positive relationship with vegetation in semi-arid and arid regions but a negative relationship with humid regions. Our findings improve our insight into scientific knowledge on ecological conservation.

Anomalous Warm Temperatures Recorded using Tree Rings in the Headwater of the Jinsha River during the Little Ice Age

As a feature of global warming, climate change has been a severe issue in the 21st century. A more comprehensive reconstruction is necessary in the climate assessment process, considering the heterogeneity of climate change scenarios across various meteorological elements and seasons. To better comprehend the change in minimum temperature in winter in the Jinsha River Basin (China), we built a standard tree-ring chronology from Picea likiangensis var. balfouri and reconstructed the regional mean minimum temperature of the winter half-years from 1606 to 2016. This reconstruction provides a comprehensive overview of the changes in winter temperature over multiple centuries. During the last 411 years, the regional climate has undergone seven warm periods and six cold periods. The reconstructed temperature sensitively captures the climate warming that emerged at the end of the 20th century. Surprisingly, during 1650–1750, the lowest winter temperature within the research area was about 0.44 °C higher than that in the 20th century, which differs significantly from the concept of the “cooler” Little Ice Age during this period. This result is validated by the temperature results reconstructed from other tree-ring data from nearby areas, confirming the credibility of the reconstruction. The Ensemble Empirical Mode Decomposition method (EEMD) was adopted to decompose the reconstructed sequence into oscillations of different frequency domains. The decomposition results indicate that the temperature variations in this region exhibit significant periodic changes with quasi-3a, quasi-7a, 15.5-16.8a, 29.4-32.9a, and quasi-82a cycles. Factors like El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and solar activity, along with Atlantic Multidecadal Oscillation (AMO), may be important driving forces. To reconstruct this climate, this study integrates the results of three machine learning algorithms and traditional linear regression methods. This novel reconstruction method can provide valuable insights for related research endeavors. Furthermore, other global climate change scenarios can be explored through additional proxy reconstructions.

Broadscale spatial synchrony in a West Nile virus mosquito vector across multiple timescales

Insects often exhibit irruptive population dynamics determined by environmental conditions. We examine if populations of the Culex tarsalis mosquito, a West Nile virus (WNV) vector, fluctuate synchronously over broad spatial extents and multiple timescales and whether climate drives synchrony in Cx. tarsalis, especially at annual timescales, due to the synchronous influence of temperature, precipitation, and/or humidity. We leveraged mosquito collections across 9 National Ecological Observatory Network (NEON) sites distributed in the interior West and Great Plains region USA over a 45-month period, and associated gridMET climate data. We utilized wavelet phasor mean fields and wavelet linear models to quantify spatial synchrony for mosquitoes and climate and to calculate the importance of climate in explaining Cx. tarsalis synchrony. We also tested whether the strength of spatial synchrony may vary directionally across years. We found significant annual synchrony in Cx. tarsalis, and short-term synchrony during a single period in 2018. Mean minimum temperature was a significant predictor of annual Cx. tarsalis spatial synchrony, and we found a marginally significant decrease in annual Cx. tarsalis synchrony. Significant Cx. tarsalis synchrony during 2018 coincided with an anomalous increase in precipitation. This work provides a valuable step toward understanding broadscale synchrony in a WNV vector.

Linking weather and health outcomes: Examining the potential influences of weather factors and particulate matter pollution on adverse pregnancy outcomes in the Kavre district, Nepal

IntroductionAdverse pregnancy outcomes (APOs) include stillbirth, preterm birth, and low birthweight (LBW). Studies exploring the impact of weather factors and air pollution on APOs are scarce in Nepal. We examined the impacts of prenatal exposure to temperature, precipitation, and air pollution (PM2.5) on APOs among women living in Kavre, Nepal. MethodsWe conducted a hospital and rural health centers-based historical cohort study that included health facility birth records (n = 1716) from the Nepali fiscal year 2017/18 through 2019/20. We linked health records to temperature, precipitation, and PM2.5 data for Kavre for the six months preceding each birth. A random intercept model was used to analyze birthweight, while a composite APO variable, was analyzed using multivariable logistic regression in relation to environmental exposures. ResultsThe proportion of LBW (<2500 gm), preterm birth (babies born alive before 37 weeks of gestation), and stillbirth was 13%, 4.3%, and 1.5%, respectively, in this study. Overall, around 16% of the study participants had one or more APOs. Total precipitation (β: 0.17, 95% CI 0.01 to 0.33, p = 0.03) had a positive effect on birthweight in the wetter season. Negative effects for mean maximum (β: 33.37, 95% CI -56.68 to −10.06, p = 0.005), mean (β: 32.35, 95% CI -54.44 to −10.27, p = 0.004), and mean minimum temperature (β: 29.28, 95% CI -49.58 to −8.98, p = 0.005) on birthweight was also observed in the wetter season. ConclusionA positive effect of temperature (mean maximum, mean, and mean minimum) and total precipitation on birthweight was found in the wetter season. This study emphasizes the need for future research using larger cohorts to elucidate these complex relationships in Nepal.