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• The aim of the findings is to scrutinize the association between rice production, technical progress and climatic factors from 1985 to 2020 by applying the autoregressive distributed lag (ARDL) approach of cointegration. • The study finds that pesticide use and fertilizer used has no impact on the rice production in South Korea. • The expected elasticity of the short-run illustrates that area under rice, and mean temperature during the rice growth has significant positive effect on the rice production. • Climatic factors such as rainfall, wind speed and humidity in the rice growing period is a challenge for rice production in South Korea. Rice is a staple crop that is highly concerned with climate change and technical progress. Therefore, an autoregressive distributed lag approach (ARDL) was applied to measure the association between rice production, technical progress, and climate factors. The current study uses time series data from South Korea, covering a time frame from 1985 to 2020. The bounded F-test provides evidence of existing long-run relations between rice yield, technical factors, and climatic factors. The study finds that in technical inputs only areas under rice have a significant positive impact on rice production. Similarly, in climatic factors, an average temperature has a positive impact on rice production while rainfall, wind speed and humidity during growth have a significant declining impact in long run. The estimated coefficient of the input used in the short run illustrates that area under rice shows inclining flexibility towards rice production whereas in climatic factors, meant temperature shows an inclining impact on rice production while rainfall, wind speed and humidity show a significant declining flexibility towards rice production. The overall diagnostic tests used in the study are satisfactory. The study concludes that government needs to allot arable land to deserving farmers to avoid future food security threats in South Korea. The government and non-government organizations need to come forward and support the farmer awareness program about fertilizer and pesticide use through extension workers and other media sources. [ABSTRACT FROM AUTHOR]

This study examines the effects of environmental conditions on venom production in four prevalent scorpion species in Morocco: Androctonus amoreuxi , Androctonus mauretanicus , Buthus mardochei , and Scorpio maurus. The research focuses on how climatic factors such as temperature, humidity, and diet—specifically mealworm consumption—affect physical parameters including size and weight, and ultimately venom yield. We conducted a comprehensive investigation involving the capture, acclimatization, and systematic venom extraction from 480 scorpions. Our findings indicate considerable variations in venom volumes, with Scorpio maurus showing a reduced yield compared to Androctonus amoreuxi. Key determinants of venom production were identified as humidity and dietary intake, pointing to significant implications for interspecific diversity and acclimatization processes. These insights have crucial implications for medical and pharmaceutical applications, emphasizing the need for species-specific husbandry protocols to optimize venom extraction for therapeutic use. [Display omitted] • Significant size differences among species, especially between Androctonus mauritanicus and Buthus mardochei/Scorpio maurus. • Significant weight differences across all groups, with the most substantial difference between Androctonus mauritanicus and Buthus mardochei. • Significant differences in venom extraction between Scorpio maurus and other groups, no differences among the others. • First two principal dimensions' capture 40% of data variance, primarily driven by group, weight, size, and humidity. • Humidity and nutrition are the most influential variables for predicting venom extraction. [ABSTRACT FROM AUTHOR]

[Display omitted] • First analysis of the correlation between climatic factors and nucleoside content. • ResNet model successfully traced the origin of Boletus bainiugan. • PLSR model smoothly predicts three nucleoside compounds. Boletus bainiugan has high nutritional and economic values. As one of the potential medicinal active ingredients, nucleosides have important research significance. Porcini mushrooms fraud is frequently detected on the market, including substitute inferior into superior and lack of geographical origin's certification. This behavior results in economic loss and health damage to consumers. Fourier transform near-infrared (FT-NIR) spectroscopy is a fast, efficient and reliable analytical tool. In the present study, the effect of source environment (climatic factors) on nucleoside content is analyzed for the first time. Then, the FT-NIR spectroscopy to study the origin traceability and content prediction of Boletus bainiugan are utilized. The results indicate that the nucleoside content is associated with precipitation and temperature. The combination of synchronous two-dimensional correlation spectroscopy (2DCOS) with residual neural networks (ResNet) model obtains the precise identification of the origin of Boletus bainiugan , with an accuracy of 100%. In the prediction models of content for uridine, guanosine, and adenosine, the optimal coefficient of determination of predictive set (R2 P) is 0.901, and the optimum residual prediction deviation (RPD) is 3.178. FT-NIR spectroscopy has proven to be an environmentally friendly and non-destructive analytical tool for accurate origin traceability and content prediction. [ABSTRACT FROM AUTHOR]

Yield is a complex trait reflecting the interaction between genotype, environment and farm management. The challenge of adapting spring crops to climate change involves unravelling the contribution of climatic factors that impact yield performance according to phenological stages. The aim of the present study was to identify the main Environmental Covariates (EC) – climatic variables calculated over phenological stages – driving spring barley yield levels. Five contrasting European agro-climatic (AC) regions were defined as follows: United Kingdom and Ireland (UK-IE), Denmark and Sweden (DK-SE), France (FR), Northeast Germany, Czech Republic and Poland (N.E. DE-CZ-PL) and South Germany and Austria (S. DE-AT). Yield data from 270 two-row spring barley accessions/varieties, grown in 125 environments between 2015 and 2021, were collected from a multi-environment trials network. Using the phenology-calibrated CERES-Barley model (DSSAT), 91 ECs were calculated in each environment based on collected weather data and simulated phenological stages. Partial Least Squares (PLS) regression analyses were carried out to sort out the main ECs impacting yield performance in each of the five AC regions. Results showed that elevated temperatures and solar radiation were the main yield-drivers in all AC regions. Associations between water availability and yield were detected in most AC regions. The strongest contrasts were observed for the critical phenological stages during pre-anthesis, which govern grain number per unit area. Cool temperatures (days with minimum temperature <0°C or <7°C and average temperature <15°C) during emergence and tillering, and solar radiation intensity between emergence and grain filling, were the most yield contributing ECs. This study showed the importance of considering climate during early stages to predict yields. The identification of major yields EC drivers suggests the need to adjust agricultural practices in spring barley production across Europe for climate adaptation. This study unraveled the complexity of yield ecophysiology affecting spring barley in Europe. In order to improve the adaptation of spring barley to climate change, the perspective is to examine the role of ECs on genotype x environment interactions for yield and develop stable cultivars that outperform existing germplasm. [ABSTRACT FROM AUTHOR]

Global climate change has profound impacts on agricultural production, and it includes increasing temperature, global dimming, altered precipitation patterns, and elevated CO 2 concentration. However, the comprehensive assessment of the impact of different individual climatic factors and their interactions on crop production is relatively limited. Here we assessed the impacts of climate change and different climatic factors on winter wheat yields, interannual yield variability, and resource use efficiency in China from 1980 to 2020, with four wheat crop models (DSSAT-CERES-Wheat, DSSAT-Nwheat, WheatGrow, and APSIM-Wheat). The results showed that climate change was estimated to decrease wheat yields and increase interannual yield variability in the main winter wheat production region of China, especially in the Middle-lower Reaches of the Yangzi River Subregion, where yield reduction and the coefficient of variation increase could be 4.3 % and 30.2 %, respectively. Without considering the CO 2 effect, the primary reason for yield decrease and interannual yield variability increase was the interactions of temperature and solar radiation across the main winter wheat production region of China, and wheat yields were estimated to decrease by 9.2 % in the Southwest Subregion while the interannual yield variability increased by 49.5 % in the Middle-lower Reaches of the Yangzi River Subregion. The elevated CO 2 concentration was mostly beneficial, manifested as increasing the yield and decreasing interannual yield variability, but it could not fully offset negative impacts of climate change. Moreover, radiation use efficiency increased while heat use efficiency and precipitation use efficiency decreased during the study period. It is imperative to consider the diverse climatic factors and their respective regional impacts when adapting to climate change in China. [Display omitted] • Yield variability increased by 30 % in the Middle-lower Reaches of Yangzi River region. • Yield changes were mainly due to temperature and radiation interactions. • Elevated CO 2 failed to fully offset negative impacts of climate change. • Radiation use efficiency of wheat increased in the past decades. • Heat and precipitation use efficiency of wheat decreased in the past decades. [ABSTRACT FROM AUTHOR]

Over the past few decades, grassland productivity in mountainous regions has been increasingly affected by livestock grazing and land use/cover changes. Human appropriation of net primary production (HANPP) is a valuable metric for assessing human impacts on grasslands. To date, knowledge of the patterns and drivers of HANPP in montane grasslands remains limited. In this study, we developed a 1 km × 1 km resolution HANPP dataset for the grasslands of the Qilian Mountains from 2000 to 2020 using two process-based models. We aimed to examine the spatial distribution characteristics and temporal trends of HANPP and explore the effects of climatic conditions, rural populations, cultivated areas, and livestock numbers on the interannual variability of HANPP. The results showed that HANPP decreased significantly with increasing elevation due to the corresponding decrease in population density. During the study period, HANPP showed a widespread decline, which was significant in 15.4% of the grassland area, mainly in alpine meadows. Climatic factors and human activities explained 60.8% and 39.2% of the temporal variability in HANPP, respectively. In particular, precipitation was negatively correlated with HANPP, whereas air temperature and number of livestock were positively correlated with HANPP based on correlation analysis. By providing a refined spatial assessment of human impacts on montane grasslands, this study suggests that ecological restoration projects, especially the Grain to Green and Grazing Withdrawal Programs, can reduce human pressures on grassland productivity by controlling population density and grazing intensity. Furthermore, the adoption of enclosed paddocks and supplemental feeding may mitigate montane grassland degradation near villages. This study elucidates the primary factors influencing the spatiotemporal patterns of HANPP in an area with typical montane grasslands, thereby supporting research-based decision making. [Display omitted] • A refined spatial assessment of human impacts on montane grasslands was provided. • HANPP was positively correlated with population density. • HANPP decreased at a rate of −2.1 g C m−2 yr−1. • Human activities accounted for 39.2% of the HANPP changes. • Ecological restoration measures were effective in reducing human activity intensity. [ABSTRACT FROM AUTHOR]

Predicting electricity demand at city level is vital for stakeholders. There are various data-driven methods in electricity consumption prediction, but their applicability to forecast monthly electricity demand has yet to be systematically explored. This study examines several widely used data-driven methods, namely multiple linear regression (MLR), machine learning (ML) method including support vector machine (SVM) and random forest (RF), and deep learning (DL) method including long short-term memory network (LSTM) and LSTM-gated recurrent unit (GRU), for long-term energy prediction using climatic and historical electricity datasets in Singapore (2005–2019) and Hong Kong (1975–2019). In Singapore, ML outperforms other methods in terms of statistical criteria and time series plots, and SVM provides the best accuracy with mean absolute percentile error (MAPE) of 2.55 %, while MLR exhibits the worst accuracy with MAPE of 3.1 %. In Hong Kong, DL surpasses ML in terms of statistical criteria, time series stability, and generalization ability. MLR achieves the best overall prediction accuracy with R 2 up to 0.95 but shows poor ability for predicting peak and low electricity consumption, while LSTM exhibits the smallest bias in these months. RF shows strong overfitting issues in both cities. Overall, SVM and LSTM are recommended for small and large datasets, respectively. • Regression, machine and deep learning methods were used to predict electricity consumption. • Temperature is the most explanatory climatic factor in Singapore, while it is cooling degree days in Hong Kong. • Machine learning performs best on small datasets, and support vector machine is the most suitable model. • Deep learning shows overall superiority over machine learning on larger datasets, and long-short term memory is recommended. [ABSTRACT FROM AUTHOR]

Vegetation plays a crucial role in terrestrial ecosystems, and there has been a substantial shift in global vegetation cover in recent decades. China is recognized for its substantial impact on global vegetation cover changes, which are influenced by both climate change and human activities. Therefore, this research aims to assess the respective influences of climate modification and human activities on vegetation variations in China. First, the changes in vegetation cover are explored between 1982 and 2020 using satellite-image derived vegetation index, known as the Normalized Difference Vegetation Index (NDVI). Second, a multiple regression model based on time-lag analysis is used to simulate NDVI. In addition to common climatic factors such as temperature, precipitation, solar radiation intensity and relative humidity, the atmospheric CO 2 concentration to directly reflect climate change is considered in this model. Finally, the relative influence of climate variation and human activities on the alteration of vegetation cover is determined based on reconstructed NDVI. Results: (1) Precipitation and solar radiation are the most important influences, while carbon dioxide concentration and relative humidity have the least influence. (2) The simulation error before 2000 was 0.875%, which was considerably lower than the error after 2000. (3) After 2000, human activities favorably affected vegetation recovery in most of the study area, with an average degree of influence of >30%. • Explored the change in vegetation cover in China from 1982 to 2020 using NDVI; • A multiple regression model based on time-lag analysis was used to simulate NDVI; • Tried to quantify the human activities effects on the change of vegetation in China. [ABSTRACT FROM AUTHOR]

Long chain n- alkanes of leaf waxes are widely used in paleoclimatic reconstructions. The application of these proxies depends on profound understandings gained from studies of the relationship between climatic factors and long chain n- alkanes in living plants. However, a lot of uncertainties still exist in the relationship due to the interactions among climatic factors, geographical and topographical factors, and phylogeny. In addition, unrealistic or inaccurate climatic data also introduce errors. To evaluate the effect of climate with minimized interfering factors, we conducted long-term measurements of leaf wax n- alkanes of the plants growing on Dongling Mountain, north China from 2004 to 2019, where a meterological observatory is located. On the whole, total n- alkane concentration (Σalk) was positively correlated mean annual precipitation (MAP); average chain length (ACL) was negatively related to MAP; carbon preference index (CPI) had poor correlations with all climate factors. Compared with Σalk, ACL is a better indicator that reflects annual precipitation. The coefficient of ACL vs. MAP is 0.0015/mm. However, MAP accounted for only 40% of the variability in the year-averaged ACL of all plants together due to the significant interspecific differences in the response of leaf wax n- alkane distribution to climatic parameters; thus, ACL should be employed in parallel with other proxies to quantitatively reconstruct paleoprecipitation. We conducted a sample reconstruction of paleoprecipitation using ACL records preserved in the annually laminated sediments of Maar Lake Twintaung, Myanmar. The results of reconstruction using ACL are completely consistent with the results of reconstruction using carbon isotope ratios of n- alkanes, and are highly consistent with historical documents. [ABSTRACT FROM AUTHOR]

• Explored permafrost temperature time–frequency traits in four alpine grasslands. • Alpine meadows were more climate-sensitive than the other three alpine grasslands. • Precipitation and wind speed had significant effects on permafrost. • The varying impacts of vegetation and a sand layer on permafrost were discussed. Permafrost temperature is a vital indicator of climate and permafrost changes, benefiting ecosystem development and informing local climate strategies. Alpine grasslands impact moisture and heat exchange between the surface and atmosphere, thereby affecting the thermal state of underlying permafrost. This study analyzed permafrost temperatures (2004–2019) from various alpine grasslands (including alpine meadow, alpine steppe, alpine desert grassland, and barren land) in the Beiluhe region of the Tibetan Plateau and revealed their connections to climate change and controlling factors, using time–frequency analysis. The findings revealed that in the time–frequency domain, permafrost temperatures exhibited multiple time scales characteristics, driven by climate fluctuations. Changes in the active layer closely followed monthly climate variations, while permafrost dynamics responded to annual climate changes. Significant oscillations with periods of 10–11, 8–9, and 14 years were observed in the surface, permafrost table, and deep permafrost layers, respectively. Among the different types of alpine grasslands, alpine meadows proved to be the most sensitive to climate change, with the intensity of periodic fluctuations initially decreasing and then increasing with depth in alpine meadows, while it consistently decreased with depth in the other three alpine grasslands. The impact of air temperature, precipitation, and wind speed on permafrost dynamics exhibited depth-dependent variations in the time–frequency domain, contrasting with the time domain where permafrost temperature changes were predominantly associated with air temperature across all depths. [ABSTRACT FROM AUTHOR]

• Significant variation was found in the seasonal and annual litterfall biomass of the three types of forest stands. • The seasonal dynamics of litterfall biomass of twigs, leaf and fruits exhibited a 'unimodal pattern' with a peak in October. • Temperature played a key role in the climatic factors affecting forest litterfall processes. Litterfall is a link in the energy flow and material cycling of ecosystems, which maintain the primary productivity of forests. However, there is no consensus regarding the factors driving for the litterfall biomass dynamics because of the high spatial–temporal heterogeneity. Herein, we investigated and compared the stand litter biomass, litterfall biomass, including its components in the pure Robinia pseudoacacia forest (RL), pure Platycladus orientalis forest (PL), and mixed forest of Robinia pseudoacacia and Platycladus orientalis (ML) from 2020 to 2022 in the gully erosion area of the Loess Plateau. Correlation and multiple regression analyses were used to study the relationships among the amount of stand litter, litterfall biomass, and seasonal temperature and precipitation. Significant variation was found in the seasonal and annual litterfall biomass of the three types of forest stands. Annual stand litter biomass was in the order PL > RL > ML, whereas the annual litterfall biomass was in the order RL > ML > PL. The seasonal dynamics of litterfall biomass of twigs, leaf and fruits exhibited a 'unimodal pattern' with a peak in October. Annual leaf litterfall biomass formed, the main body of litter in all the components, accounting for 57.14–67.05 % of the total litterfall biomass. The correlation and regression analysis results showed that the stand litter biomass was primarily affected by the average temperature. Total litterfall biomass and twigs, leaf, and fruits were significantly affected by the precipitation and the maximum temperature, however, flower litterfall biomass was mainly affected by the average temperature. Overall, our results indicated that temperature plays a key role in the climatic factors affecting forest litterfall processes, while different climatic conditions and biological characteristics probably caused the differences in litterfall biomass. In the long term, temperature will potentially play a leading role in altering carbon storage and nutrient cycling as the climate warms. [ABSTRACT FROM AUTHOR]

Understanding the intricate relationship between climate variables and the Normalized Difference Vegetation Index (NDVI) is essential for effective ecosystem management. This study focuses on the spatiotemporal dynamics of NDVI and its interaction with climate variables in the ecologically diverse Khyber Pakhtunkhwa (KPK) Province, Pakistan, from 2000 to 2022. The research methodology involves analyzing satellite images and meteorological datasets to examine NDVI and surface latent heat flux (SHF), total precipitation (TPP), temperature (T), soil temperature (ST), and total pressure (TP). KPK Province's ecological significance and complex climate-vegetation interactions drive the selection of this study area. The study uses multiple linear regression analysis to investigate how T, TPP, SHF, and TP influence NDVI. The Mann-Kendall test detects trends, with Sen's slope estimator quantifying trend magnitudes. Additionally, correlation coefficients provide insights into long-term changes and association strengths. The findings highlight a consistent upward trend in mean NDVI over the 23 years, revealing an overall increase in NDVI, particularly in vegetation-dense areas where it rose from 0.27 to 0.32. The research showed an annual growth rate of 0.84% in the entire area, with specific vegetated zones exhibiting a slightly lower rate of 0.80%. However, the average yearly increase in NDVI is higher in vegetation-specific zones (0.00237) compared to the whole area (0.00151). This increase in NDVI occurs alongside a statistically significant decrease in SHF and PPT, suggesting a complex adaptation of vegetation to changing climate conditions in the KPK Province. In contrast, SHF exhibits a statistically significant negative slope of −5.952e-06 (p < 0.05), indicating a pronounced downward trend. Similarly, Sen's slope estimate for precipitation demonstrates a significant negative trend of −0.0001 (p < 0.05), showing diminishing precipitation. The study uncovers intricate linkages between climate variables and vegetation dynamics within KPK Province. These insights have far-reaching implications, guiding decision-making in land management, conservation efforts, and global climate resilience strategies. Ultimately, the research underscores the critical role of data-driven approaches in shaping a greener and more sustainable future. • Upward NDVI trend shows dense area growth over 23 years. • Zones show 0.80% annual growth, revealing nuanced vegetation dynamics in Province. • Clear SHF decrease & diminishing precipitation show complex vegetation adaptation. • Complex climate-NDVI dynamics revealed, highlighting changing conditions' impact. • Data insights guide global land, conservation, and climate strategies. [ABSTRACT FROM AUTHOR]

The carbon and oxygen isotopic composition of land snail shells (δ13C shell and δ18O shell) has been used to reconstruct paleoenvironmental changes worldwide. Adding to the limited calibration information available for the monsoon marginal regions, we present δ13C and δ18O results for modern snails, plants and soils, collected from the Helan Mountains, in North China, spanning an altitudinal gradient of 1733–2851 m a.s.l. Linear regression analysis show that δ13C shell is positively correlated with altitude on both slopes, with similar rates of altitudinal variation; in contrast, δ18O shell is negatively correlated with altitude on the western slopes, but this effect is absent on the eastern slopes. The variations in the integrated δ13C signals of living vegetation and the flux of absorbed atmospheric CO 2 attributed to changes in metabolic rates are likely responsible for the altitudinal effect on δ13C shell above 2000 m a.s.l., while changes in vegetation type and/or carbonate ingestion by snails may be important in inducing the positive shift in δ13C shell below 2000 m a.s.l. δ18O shell is determined by the rainwater δ18O and by the fractionation processes between shell carbonate and snail body water, and both are controlled by the temperature of the alpine environment. In addition, evaporative enrichment of the body fluid or of the environmental water prior to ingestion by snails exerts an important influence on δ18O shell. Notably, both δ13C shell and δ18O shell in the study region respond more sensitively to climate than in the monsoon core area. The rate of change of δ13C shell in response to temperature and precipitation above the altitude of 2000 m a.s.l. are −0.87‰/°C and +2.76‰/100 mm, while the corresponding rates for δ18O shell on the western slopes are +0.41‰/°C and −1.29‰/100 mm. Our results expand the range of application of δ13C shell and δ18O shell as climatic indicators, and emphasize the differences in the climatic sensitivity of these proxies between the monsoon margin and core areas. In addition, the differences in shell isotope composition in response to environment changes between adjacent locations (i.e. on different slopes) in mountainous terrain need to be considered in future studies. [ABSTRACT FROM AUTHOR]

Analyzing the influencing factors of fine particulate matter and ozone formation and identifying the coupling relationship between the two are the basis for implementing the synergistic pollutants control. However, the current research on the synergistic relationship between the two still needs to be further explored. Using the Geodetector model, we analyzed the effects of meteorology and emissions on fine particulate matter and ozone concentrations over the "2 + 26" cities at multiple timescales, and also explored the coupling relationship between the two pollutants. Fine particulate matter concentrations showed overall decreasing trends on inter-season and inter-annual scale from 2015 to 2021, whereas ozone concentrations showed overall increasing trends. While ozone concentrations displayed an inverted U-shaped distribution from month to month, fine particulate matter concentrations displayed a U-shaped fluctuation. On inter-annual scale, climatic factors, with planet boundary layer height as the main determinant, have higher effects for both pollutants than human precursors. In summer and autumn, sunshine duration had the most influence on fine particulate matter, while planet boundary layer height was the greatest factor in winter. Fine particulate matter is the leading impacting factor on ozone concentrations in summer, and there were positive associations between them on both annual and seasonal scale. The impact of nitrogen oxides and volatile organic compounds for both pollutants concentrations varied significantly between seasons. The two pollutants concentration were enhanced by the interactions between the various components. On inter-annual scale, interactions between the planet boundary layer height and other factors dominated the concentrations of the two pollutants, whereas in summer, interactions between fine particulate matter and other factors dominated the concentrations of ozone. The study has implications for the treatment of atmospheric pollution in China and other nations and can serve as an important reference for the creation of integrated atmospheric pollution regulation policies over the "2 + 26" cities. [Display omitted] • Meteorology have stronger effects than emissions on inter-annual scale. • PM 2.5 plays the dominant role on O 3 formation in summer. • There is positive correlations between PM 2.5 and O 3 on inter-annual and inter-season scale. • Interactions between the factors exerted enhanced effects on the PM 2.5 and O 3 concentrations. [ABSTRACT FROM AUTHOR]

Paddy fields constitute a substantial anthropogenic reservoir of methane, with their inundation management fostering an ideal habitat for anaerobic methane oxidation. Within this context, a novel clade of anaerobic methanotrophic (ANME) archaea, known as ANME-2d, has been identified as capable of catalyzing anaerobic methane oxidation in conjunction with nitrate and metal oxide reduction processes within paddy fields. Nevertheless, our comprehension of the mechanisms governing anaerobic methane oxidation and its pivotal role in regulating methane emissions within rice paddies remains limited. This study quantified the rates of nitrate- and iron (III)-driven anaerobic methane oxidation through 13C-labeled stable isotope tracing experiments in Chinese paddy fields spanning diverse climate zones. Additionally, it investigated the ANME-2d archaeal community using quantitative polymerase chain reaction and high-throughput sequencing techniques. The nitrate-driven anaerobic methane oxidation contributed 10.9% to methane emission reduction. This contribution is equal to the previously identified contribution of nitrite-driven anaerobic methane oxidation (11.2%) mediated via NC10 bacteria, but played more important roles than iron-driven one (4.1%). The rates of nitrate- and nitrite-driven anaerobic methane oxidation differed significantly among climate zones and showed positive correlation with the mean annual temperature. Furthermore, their rates were more sensitive to temperature increases at higher and lower latitudes, respectively, under both representative concentration pathways 2.6 and 8.5. The rate of anaerobic methane oxidation driven by nitrate exhibited a positive correlation with nitrogen fertilization rate but displayed a negative correlation with phosphorus fertilization rate. Conversely, the rate of anaerobic methane oxidation driven by iron showed no significant correlation with either nitrogen or phosphorus fertilization rates. This study underscores the great potential of anaerobic methane oxidation in mitigating global warming, particularly under the conditions of future climate change and elevated nitrogen loading. These findings underline the necessity of incorporating anaerobic methane oxidation as a crucial parameter in methane emission prediction models. [Display omitted] • Nitrate-driven anaerobic methane oxidation contributes 10.9% to methane mitigation. • Nitrate-driven anaerobic methane oxidation outperforms iron-driven one (4.1%). • Nitrate-driven anaerobic methane oxidation rates increase with air temperature. • Nitrate-driven anaerobic methane oxidation rates increase with nitrogen fertilizer. [ABSTRACT FROM AUTHOR]

The impact of climate change on the crop water footprint (WF) is highly uncertain, hindering effective agricultural water use in response to climate change. This study proposes a factor sensitivity analysis method, which can screen out statistically significant climatic factors and interactions on crop WF under various uncertainties and determine water resource management measures to mitigate drought for different crops. A special case study was conducted in Heilongjiang Province, China. The results showed that (1) the annual change in crop WF showed a downward trend from 1988 to 2018. The crop WF was dominated by green water footprint (WF green), and the average occupancy rate of WF green in crops was 58.7%–74.1 %; the spatial distribution of WF has latitude zonality. (2) Drought has different effects on WF of different crops, and WF of soybean is susceptible to drought. Wind speed, sunshine hours, and humidity have a greater impact on crop WF in most growth stages. (3) The effect of climatic factors on crop WF varies in different months. The rice WF is mainly affected by the climate in May, and there is an interaction between May humidity and May rain. The WF of maize and soybeans are affected primarily by the climate in July, especially sunshine hours. The proposed approach attempts to analyze that crop WF is affected by not only an individual climatic factor but also their interactions. Crop water management practices should be adjusted based on the results to mitigate the adverse impact of climatic conditions on crop WF during different growing months. [ABSTRACT FROM AUTHOR]

[Display omitted] • There was a negative correlation between Chl a and TP. • The interannual ETP variation was dominated by TP. • The seasonal ETP characteristics were primarily related to drivers other than TP. • Extremely high and low ETP determined the tightness of nutrition thresholds. In recent decades, climate change has significantly affected water quality and algal blooms in lakes worldwide. Studying the temporal dynamics patterns of algal production efficiency and its drivers in individual lakes is useful for analyzing the evolutionary trajectory of Chl a -TP changes, and identifying the main controlling factors of eutrophication to guide lake management. In this study, we used a dataset for the Lake Gehu (1986–2020) to analyze the drivers of the ETP variability on interannual and seasonal time scales and to quantify the relative impacts and contributions of nutrient and climatic factors on the ETP. The results showed a negative correlation of the Chl a -TP relationship appeared in years with very high ETP. The effects of nutritional and climatic factors on the ETP at different time scales were quantified using a generalized additive model. The results showed that the total phosphorus (TP) concentration had a significant effect on the interannual ETP variation, explaining up to 28.9 %. At the intra-annual scale, climatic variables had a much greater effect on the ETP than the nutrient concentration, and the explanatory effect was ranked as temperature > wind speed > rainfall > total nitrogen (TN). Based on the characteristics of seasonal ETP variations, the TPa of Lake Gehu were 50 μg/L, 33 μg/L, 20 μg/L with ETP levels of 0.2, 0.3, 0.5, respectively. This research highlights the significance of nutrient and climatic factors in interannual and seasonal scale, respectively. In addition, ETP influenced by the coupling of nutrient and climate provides a criterion for establishing the seasonal nutrient threshold of lakes. [ABSTRACT FROM AUTHOR]

• Evaluated the best alternative dataset for precipitation in global land. • Identified the spatiotemporal variation of the extreme precipitation in global land. • Analyzed the change of duration and intensity proportion in total precipitation. • Quantified and explored the influence of major climate factors on extreme precipitation and its proportion. Changes in extreme precipitation (EP) have a significant impact on the ecology and sustainable development of most regions, especially when considered against a backdrop of global warming. Using trend analysis and other methods, we analyze the changes in nine extreme precipitation indices (EPIs), looking at the proportion of EP in total precipitation from the perspectives of intensity, frequency, and duration. We also utilize a geo-detector method to investigate the impacts of 15 climate factors on EPIs. Our results indicate that although EP and their proportions show an overall increasing trend on a global scale, they had undergone a process of initial decrease followed by increase over the past approximately 70 years. And this overall global increases are charged by the significant increases happened in Northern Hemisphere and western of South America, especially EP intensity and frequency indices, and their proportion. Whereas CDD shows an opposite trend in spatial distribution, the CWD is decreasing globally. The EPIs and their proportions are all highest in the low elevation belt (≤2000 m), followed by the high elevation belt (≥4000 m), and lowest in the mid-elevation belts (2000-4000 m). Most of them have returned to or exceeded the levels of the mid-20th century in all three elevation belts. Only CWD shows a decreasing trend, which indicates that more EP amounts are occurring with less precipitation duration. In some arid areas, this makes it possible that a short period of precipitation may complete the total amount of precipitation for that year. Global temperature is proven to be the most important factor influencing the EPI globally, and followed by the temperature of each region. And the regions of high latitudes of the Northern Hemisphere and Antarctica which are greatly influenced by global temperature demonstrate the Polar amplification effect. The East Atlantic-West Russia Pattern, Atlantic Multi-Decadal Oscillation, and El Nino-Southern Oscillation indices are the secondly important factors leading to changes in the proportion of global EP. At the same time, we also find that the influence of non-monsoon factors on EP is gradually strengthening. This study clarifies changes in EP globally and in various regions, which will help provide a scientific reference for preventing disasters caused by extreme climate change. [ABSTRACT FROM AUTHOR]

Highlights • Tree height and stem diameter are positively correlated to tree water use. • Environmental factors are secondary to morphological traits as determinants of whole tree water use. • Thermodynamic methods are most commonly used to quantify tree water use. Abstract Although tree transpiration (T) studies across multiple spatial scales have been conducted, the synthesis of the driving factors of tree water use, especially for a variety of species under different climatic conditions has not yet been made. This paper analyses T data from 94 published studies conducted in various sites between 1970 and 2016, representing 196 data points to seek relations between morphological traits; tree height (H), diameter at breast height (DBH) and environmental factors; mean annual precipitation (MAP), mean annual temperature (MAT) and altitude (Z) on whole tree water use for 130 species of trees. Techniques used in the studies for T measurement were also analysed. Log transformed T (ln T) varied between 0 and 7.1 L day−1. Univariate correlation and regression analysis revealed that ln T was positively and significantly correlated with H (Spearman correlation coefficient (r s) = 0.55) and DBH (r s = 0.62) at P < 0.1. A weak positive correlation was found between ln T and MAP (r s = 0.16) at P < 0.1. The results further showed that during the study period (1970–2016), 82% of the studies used thermodynamic methods to measure T, in particular thermal heat dissipation probes were used by 60% of the studies, while 21% reported use of heat pulse velocity. The results contribute to a better understanding of T in forest ecosystems, and the factors of control to inform global scale modelling and ecosystem management. Thermodynamic methods, especially thermal heat dissipation probes and heat pulse velocity are the most prevalent techniques used for whole tree T measurement. [ABSTRACT FROM AUTHOR]

Highlights • Sustainable land management for land degradation neutrality is a global challenge. • Calvillo-Cañones region is of national significance for the production of guava. • The plantations in this region occupy 91.3 km2 and it represents 3% of the total area. • The productive potential is primarily related to the SLM and biophysical conditions of the land. • The variables associated with the edaphic component are key in the SLM assessing. Abstract The need to promote a sustainable land management (SLM) approach for land degradation neutrality (LDN) is a global challenge, and we must rely on local assessments of how rural participants are conducting their work. Therefore, the objectives of the present research are as follows: 1) evaluate the fruit culture of the guava producers (Psidium guajava, L.) in Calvillo, Aguascalientes, Mexico; and 2) to show whether it is possible to detect key variables in SLM by using an artificial neural network to improve productivity. The study unit (SU) was located in the community of San Tadeo (21.917 °N and 102.701 °W) in the municipality of Calvillo, Aguascalientes. We considered a total of 430 ha established with this fruit and nine orchards that consisted of 9.05 ha, which is equivalent to 2.09% of the total area. Each hectare was randomly sampled, with a significance of 95% (α ≥ 0.05). The information was collected by using empirical field techniques and a questionnaire sent to the producers. The results revealed that the productive success of the SU is based on the knowledge and ability of the fruit farmers to take advantage of bioclimatic and biophysical conditions for guava cultivation. It was demonstrated in approximately 90% of the analyzed cases that the producer's SLM was between regular and excellent. This result was confirmed by the index of edaphic adequacy (Iea), which was created by nine of thirteen variables measured in the field, that indicated in 47% of the orchards (OR), the edaphic condition is optimum (Iea = 0) or close to optimum (-0.2 ≥ Iea ≤ 0.2). In addition, by using a Multilayer Percetron (MLP), it was possible to detect two key aspects in the producers' work: cava design (DC) and soil management (SM). [ABSTRACT FROM AUTHOR]

Abstract This paper describes the hydraulic performance of a start-up, pilot-scale, horizontal sub-surface flow constructed wetland (CW), located outdoors at the Helmholtz UFZ, Leipzig. This paper aims to investigate the impact of the method of hydraulic calculation in a pilot-scale system. Impulse-response tracer tests were conducted at multiple depths and locations throughout the system and the uranine concentration was measured using a fluorometer. In addition, the volumetric flow rate was closely monitored and climatic data was gathered to support the hydraulic results. Werner and Kadlec's modified residence time distribution (RTD) theory (originally developed for systems with large flow rate and volume fluctuations) was applied and the results compared to those obtained using classic RTD theory. Progressive uranine dispersion, broadening of the RTD base, a change in peak shape and extended tailing were observed with increasing distance. All of these factors indicated deviation from plug flow and mixing effects with low-to-moderate dead volume. As this was a non-steady flow system, application of modified RTD theory ensured that the first moments of the normalized breakthrough curves and RTD functions were always unity. The Student's t -test (95% confidence) showed that the outlet RTDs calculated assuming steady-flow were significantly different, but those determined using the modified theory were closely comparable. In general, a decrease in flow rate from inlet to outlet was observed and fluctuations in the outflow were linked to climatic conditions. August was characterized by the highest temperatures, high global radiation and high rates of evapotranspiration. Low or no outflow was recorded in conjunction with high evapotranspiration. The lowest temperatures, low global radiation, low evapotranspiration and high humidity were recorded in October, as well as the second highest rainfall (82 mm) after June (115 mm). Surges in outflow were observed with rain events. Graphical abstract Unlabelled Image Highlights • Impulse-response tracer tests in a start-up, non-steady flow, pilot-scale HSSF CW • Fluctuations in outflow linked to vegetation and climatic conditions. • Comparison of results of classic RTD theory with Werner & Kadlec's modified method • Significant difference between outlet RTDs calculated using standard RTD theory. • Variable flow method should be used preferentially as outlet RTDs are comparable. [ABSTRACT FROM AUTHOR]

Climate change is producing an increase on extreme weather events around the world such as flooding, drought and extreme ambient temperatures impacting animal production and animal welfare. At present, there is a lack of studies addressing the effects of climatic conditions associated with energy intake in finishing cattle in South American feed yards. Therefore, two experiments were conducted to assess the effects of environmental variables and level of metabolizable energy intake above maintenance requirements (MEI) on performance and carcass quality of steers. In each experiment (winter and summer), steers were fed with 1.85 or 2.72 times of their requirements of metabolizable energy of maintenance. A total of 24 crossbred steers per experiment were used and located in four pens (26.25 m2/head) equipped with a Calan Broadbent Feeding System. Animals were fed with the same diet within each season, varying the amount offered to adjust the MEI treatments. Mud depth, mud scores, tympanic temperature (TT), environmental variables, average daily gain, respiration rates and carcass characteristics plus three thermal comfort indices were collected. Data analysis considered a factorial arrangement (Season and MEI). In addition, a repeated measures analysis was performed for TT and respiration rate. Mean values of ambient temperature, solar radiation and comfort thermal indices were greater in the summer experiment as expected (P <0.005). The mean values of TT were higher in steers fed with higher MEI and also in the summer season. The average daily gain was greater during summer v. winter (1.10±0.11 v. 0.36±0.06) kg/day, also when steers were fed 2.72 v. 1.85 MEI level (0.89±0.12 v. 0.57±0.10) kg/day. In summer, respiration rate increased in 41.2% in the afternoon. In winter, muddy conditions increased with time of feeding, whereas wind speed and rainfall had significant effects on TT and average daily gain. We conclude that MEI and environmental variables have direct effects on the physiology and performance of steers, including TT and average daily gain, particularly during the winter. In addition, carcass characteristics were affected by season but not by the level of MEI. Finally, due to the high variability of data as well as the small number of animals assessed in these experiments, more studies on carcass characteristics under similar conditions are required. [ABSTRACT FROM AUTHOR]

• Soil C:N ratio ranged from 9.68 to 18.61 in woody plants communities on the QTP. • Soil C:N ratio varied significantly among lifeforms and community types. • Factors influencing soil C:N ratio: soil > topography > biology > climate > lifeform. • EcM species dominance had a significant positive correlation with soil C:N ratio. • DON was an important attribute influencing soil C:N ratio. Soil carbon: nitrogen ratio (Soil C:N ratio) is a crucial and stable indicator of soil organic matter quality, but little is known about how it is driven in woody plant communities on the northeastern Qinghai-Tibetan Plateau (QTP). For addressing the gap, we sampled 92 tree field plots and 200 shrub field plots during the plants' growing seasons based on Vegetation Map of the People's Republic of China (1:1,000,000) to investigate the vegetations and soils. Our results showed that soil C:N ratios in tree communities were higher than those in shrub communities, with values of 14.58 ± 1.85 and 13.16 ± 1.66, respectively. Random forest (RF) algorithm and multiple comparison analysis further suggested that soil C:N ratios varied significantly among lifeforms and community types. Interestingly, our findings challenged the long-held view that climate had the most important impact on soil C:N ratios at the regional scale, as the hierarchy of importance for integrated factors was found to be soil > topography > biology > climate > lifeform for all woody plants. Pearson correlation analysis and ordinary least squares (OLS) regression analysis revealed that dissolved organic nitrogen (DON), ectomycorrhizal fungi (EcM) species proportion and downward shortwave radiation (Shwara) had significant positive correlations with soil C:N ratios in both lifeforms. The variation partitioning analyses (VPA) results showed that edaphic properties solely explained most of the variance. Multiple linear regression (MLR) models exhibited that dissolved total nitrogen (DTN) and DON were the main attributes influencing soil C:N ratios in tree communities while DON had the largest positive effect in shrub communities. Overall, these findings provide new insights into the regulatory mechanisms of C and N cycling in the woody plant communities of the northeastern QTP and shed light on the controlling factors of soil C:N ratios in this region. [ABSTRACT FROM AUTHOR]

• ADG improves greenhouse efficiency. • Reduces energy costs. • Microclimate control. • TRNSYS. The growing significance of covered crops in recent decades has prompted the establishment of objectives for greenhouse design and management, aiming to strike a balance between productivity and environmental sustainability. Specifically, ongoing efforts are focused on optimizing greenhouse design to enhance solar radiation absorption and maintain a stable temperature environment for crops throughout their growth period. This paper presents a study that utilizes TRNSYS 18 simulation and experimental validation techniques to investigate the impact of Argon Double Glazing (ADG) on the distribution and uniformity of climatic factors within the greenhouse. Additionally, an energy analysis was conducted, revealing that ADG has a positive influence on reducing heating and cooling requirements compared to other cover materials. The results demonstrate the high efficiency of ADG as a construction material for greenhouses, leading to a reduction in greenhouse production costs. [ABSTRACT FROM AUTHOR]

• Long-term natural environment experiments of composite ice were performed. • The cumulative effect of composite ice sublimation rate was explored. • A dynamic model for composite ice sublimation prediction was proposed. • The changes in the mechanical properties of composite ice were tested. The climatic adaptability of ice-shell architecture under long-term natural influence remains underexplored. This study investigates the weatherability of paper fiber reinforced ice (PFRI) as envelopes, including the changes in the sublimation mechanism and mechanical properties changes. The PFRI specimens were layered poured and exposed to nature. The sublimation rates, climatic parameters, and stress–strain data were recorded. The results suggest that PFRI sublimation is a dynamic process with cumulative effects. A calculation model is proposed to predict sublimation in engineering. Considering fibre escape, PFRI with 2% or 3% fibre content has better weatherability, with a 30% decrease in elastic modulus on the sunny side and a 15% decrease on the shaded side. [ABSTRACT FROM AUTHOR]

Highlights • NDVI change in the farming-pastoral ecotone of northern China (FPEN) was analyzed. • Human activities dominated trends of NDVI in FPEN after 2000. • Human activities were responsible for increased NDVI in southwestern FPEN. Abstract Separating anthropogenic contributions from observed vegetation change is helpful for improving our understanding of the effects of human activities on regional ecosystems. In this study, using 1982–2015 GIMMS3g normal difference vegetation index (observed NDVI), monthly climatic variables and land use data, we investigated anthropogenic contributions on vegetation change in the farming-pastoral ecotone of northern China (FPEN). Specifically, given that large-area ecological engineering was practiced since 1999 and large-area land use changes were recorded after 2000, we assumed that human activities only had little impact before 1999. Based on this assumption, we developed a climate-based NDVI model (also termed as TPR-based NDVI model) by using 1982–1999 observed NDVI and assembled monthly precipitation, temperature and solar radiation. Subsequently, the TPR-based NDVI model as well as the residual analysis method were used to separate anthropogenic contributions from observed NDVI in the period of 2000–2015. Results showed that most FPEN performed a greening trend for the period of 1982–2015. Yet a browning trend was also found in central and northern FPEN. The browning trend was largely related to changes of observed NDVI after 2000. Spatial statistics for the best related climatic variable with observed NDVI displayed that temperature, precipitation and solar radiation separately accounted for 42.45%, 31.05% and 26.50% of FPEN, implying their similar importance for vegetation growth in space. Importantly, this study found that anthropogenic contributions dominated trends of observed NDVI over FPEN. Human activities significantly increased NDVI in western and southeastern FPEN (p < 0.05), but small decreased NDVI was also observed in central and northeastern FPEN. The findings of this study suggest that applications of anthropogenic ecological engineering and associated conservation measures should be suitable for features of eco-climatic zones. [ABSTRACT FROM AUTHOR]

Abstract Plant herbivore interactions can be influenced by abiotic factors such as climate or resource availability. Nevertheless, the influence of climatic variation on the temporal dynamics of plant-herbivore networks has been scarcely studied. In this study we evaluated the influence of temperature and precipitation on the structure and selectiveness of plant-herbivore networks associated to a seasonal tropical ecosystem in the Gulf of Mexico. Although a significant turnover was observed in plant and herbivore species across seasons, high modularity and selectivity of the networks remained relatively constant despite the temporal variation in climatic variables. However, precipitation and temperature was negatively associated with niche overlap for herbivores and positively related to evenness of network interactions. In other words, less stressful conditions are likely to promote the diversification in the use of resources by herbivores, and increase evenness of interactions in the network. An increase in niche overlap and a decrease in the evenness of interactions during the driest and coldest months could be promoted by the presence of less specialized herbivores when availability and quality of host resources is lower. We suggest that the constancy in network selectiveness and modularity facilitates the coexistence of species through the fine distribution of niches and the equitable distribution of food resources in periods of greater precipitation and temperature, when the availability of host plants is greater. Overall, we show for the first time how abiotic factors can influence the emergent structural properties of an antagonistic tropical plant-herbivore network. [ABSTRACT FROM AUTHOR]

Seed traits related to recruitment can directly affect plant fitness and persistence. Phenotypic variability in seed traits among populations could increase species resilience and reduce the risk of extinction under climate change. However, seed trait variations along geographic gradients of relict mountain tree species remain poorly explored despite their vulnerability to environmental changes. Here, we collected seeds of Euptelea pleiospermum from 18 populations across its natural distribution in China, measured seed morphology and seed nutrients, and performed a germination test. We investigated geographic patterns of seed traits and analyzed the relationships between environmental factors and seed traits. We also analyzed the relationships between seed intrinsic attributes and seed germination percentage. In addition, we explored the direct and indirect effects of climatic and edaphic variables on seed germination percentage. We found substantial variation in seed traits of this species among populations. Seed mass decreased from low to high latitude, and seed size (length and width) decreased with longitude from west to east. Seed germination timing increased from low to high altitude. Temperature and soil phosphorus determined the geographic variation of seed traits. Seed mass and seed nitrogen had positive effects on seed germination percentage. Seed intrinsic attributes, rather than maternal environmental factors, were the dominant drivers of the variability in seed germination percentage. However, maternal environmental variables could indirectly affect the seed germination percentage through their effects on seed morphology and nutrients. These results demonstrate that among-population seed trait variations are mainly driven by climatic variables and soil nutrients, and indicate that climate warming is likely to alter seed germination patterns by shifting seed intrinsic attributes. Our study provides insight into how mountain tree species regulate seed traits and germination time to adapt to heterogeneous environments and improves our power to predict how relict plants may respond to climate change. [ABSTRACT FROM AUTHOR]

Carbon isotope compositions of land snail shells (δ 13 C shell ) are shown to be indicative of local climate conditions. However, it is largely unknown how the responses of δ 13 C shell to climatic factors changes amongst different species. In this study, we collected 3 species of land snail shells across the East Asian monsoon region of China to explore the overall relationship between δ 13 C shell as well as the response of individual species to the regional climate. Results show that, whereas all species collectively can provide a consensus relation between δ 13 C shell and local climatic factors such as temperature and precipitation; the response of individual species to the fluctuations of these factors is not uniform. Specifically, while the southerly species Bradybaena similaris exhibits robust δ 13 C shell – mean precipitation correlation in both linearity and sensitivity, a common northerly species, Cathaica fasciola , only finds limited utility as a climate indicator, particularly for precipitation. Meanwhile, the south-central species Acusta ravida appears to be able to faithfully record past climate conditions despite showing a wider distribution and a broader habitat. Such species-dependent nature in the relations between δ 13 C shell and local climatic factors can be attributed to the effect of ingested carbonate and variations in eco-physiological factors of different species, and is expected to be widespread, suggesting the need to be taken into consideration for future studies. [ABSTRACT FROM AUTHOR]

Latitudinal patterns of leaf stoichiometry and nutrient resorption were not consistent among published studies, likely due to confounding effects from taxonomy (e.g., plant distribution and community composition), and environment, which is also influenced by altitude and longitude. Thus, the latitudinal patterns and environmental mechanism could be best revealed by testing a given species along a latitude gradient with similar altitude and longitude. We determined nitrogen (N) and phosphorus (P) concentrations of green (leaf) and senesced leaves (litter) from eight Metasequoia glyptostroboides forests along the eastern coastline of China, with similar altitude and longitude. Leaf N, P concentrations increased along latitude, mainly driven by mean annual temperature (MAT), mean annual precipitation (MAP), annual evaporation (AE), aridity index (AI), and annual total solar radiation (ATSR); While leaf N:P ratio was stable with no latitudinal pattern. Nitrogen resorption efficiency (NRE) increased along latitude, and was also mainly influenced by MAT, MAP, AE, and AI. Phosphorus resorption efficiency (PRE) first increased and then decreased with latitude, which was impacted by soil available P. These results indicated that only climate (such as heat, water, and light) controlled the shift in leaf stoichiometry and NRE, while soil nutrient was likely responsible for the shift in PRE along eastern China. Our findings also suggested that leaf N, P stoichiometry and NRE displayed similar latitudinal patterns at regional scale when studied for a given species (this study) or multi-species (previous studies). [ABSTRACT FROM AUTHOR]

Background Dengue is one of the most important and widespread viral infection comprises 4 related serotypes (DEN-1, 2, 3, and 4). Infection with one serotype does not protect against the others, and sequential infections put people at greater risk for dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). This study determines the epidemiology of prevailing viral infections with dengue and molecular characterization of serotype-specific DENV circulation in Odisha of eastern India. Methods During the year 2013, 1980 blood samples with suspected dengue cases were obtained between days 1–10 of illness and analyzed by NS1 Ag-RDT, NS1 Ag-ELISA, and RT-PCR. The differential detection of dengue infections and DENV serotyping were carried out by IgM/IgG Ab-ELISA and RT-PCR, respectively. Results Of the 1980 samples, 733 (37.0%) were positive for dengue RNA by RT-PCR. The confirmed cases of dengue were more in males (73.6%) in comparing to females (26.4%). The age group of 15–44 years (527 cases, 71.9%) were more susceptible to dengue infections. 656 (89.5%) cases had infected with monotypic infection by different DENV serotype and 77 (10.5%) cases had multitypic infections by multiple serotypes of DENV. Of the total multitypic infections, there were 74 (10.1%) cases had infected with DENV-2 and DENV-3 serotypes at a time; and only 3 (0.4%) cases had the concurrent infections of all three serotypes that were, DENV-1, DENV-2, and DENV-3. Of the 28 DHF cases, there were 17 (2.3%) cases had infected with multitypic infections and 11 (1.5%) cases had infected with monotypic infection. Conclusion Dengue infections have prevailed from the month of July and grasped it's the peak in September. Rain, temperature and relative humidity have favored the dengue infections. Young adults and males are more susceptible to dengue infections. Serotypes DEN-2 followed by DEN-3 was dominant among the confirmed dengue cases. Co-circulation of multitypic infections with multiple DENV serotypes and the emergence of DHF cases suggested that eastern Indian state Odisha was becoming a hyper-endemic province for dengue; therefore, continuous surveillance is suggested for understanding the epidemiology of the diseases and monitoring the changes in the characteristics of circulating DENV strains. [ABSTRACT FROM AUTHOR]

This paper investigates the use of solar energy in urban areas as exemplified by Famagusta in Cyprus. All the climatic and geographic factors were analyzed to compute the solar energy potential for Famagusta City. Next, the solar energy utilization potential in the Social Housing Complex (SHC) district of Famagusta was investigated in detail. The effective parameters of solar energy, including climatic factors, radiation types, geographic parameters, orientation techniques, height to width ratio (H/W) and landscape analysis, were evaluated based on Famagusta using DuffieBeckman and Stephenson’s cousin methods. The rate of irradiation solar energy was analyzed for horizontal, vertical and tilted surfaces of blocks and routes in the SHC in Famagusta City using Ladybug for Rhino and MS Excel software programs. The results of this study showed that the district landscape is very poor from a solar energy point of view. While there is a great source of solar energy producing extreme heat, the solar energy is not utilized properly, as the district is not walkable, especially in the afternoons. It is recommended that pedestrians be helped by adding hardwood trees, by using solar panels to generate renewable power for lightning, and by using permeable materials for pavements. [ABSTRACT FROM AUTHOR]

Information regarding the relationship between the incidence of foodborne diseases (FBD) and climatic factors is useful in designing preventive strategies for FBD based on anticipated future climate change. To better predict the effect of climate change on foodborne pathogens, the present study investigated the combined influence of multiple climatic factors on bacterial FBD incidence in South Korea. During 2011–2015, the relationships between 8 climatic factors and the incidences of 13 bacterial FBD, were determined based on inpatient stays, on a monthly basis using the Pearson correlation analyses, multicollinearity tests, principal component analysis (PCA), and the seasonal autoregressive integrated moving average (SARIMA) modeling. Of the 8 climatic variables, the combination of temperature, relative humidity, precipitation, insolation, and cloudiness was significantly associated with salmonellosis ( P < 0.01), vibriosis ( P < 0.05), and enterohemorrhagic Escherichia coli O157:H7 infection ( P < 0.01). The combined effects of snowfall, wind speed, duration of sunshine, and cloudiness were not significant for these 3 FBD. Other FBD, including campylobacteriosis, were not significantly associated with any combination of climatic factors. These findings indicate that the relationships between multiple climatic factors and bacterial FBD incidence can be valuable for the development of prediction models for future patterns of diseases in response to changes in climate. [ABSTRACT FROM AUTHOR]

The linkage between root zone soil moisture and groundwater is either neglected or simplified in most land surface models. The fully-coupled subsurface-land surface model TerrSysMP including variably saturated groundwater dynamics is used in this work. We test and compare five data assimilation methodologies for assimilating groundwater level data via the ensemble Kalman filter (EnKF) to improve root zone soil moisture estimation with TerrSysMP. Groundwater level data are assimilated in the form of pressure head or soil moisture (set equal to porosity in the saturated zone) to update state vectors. In the five assimilation methodologies, the state vector contains either (i) pressure head, or (ii) log-transformed pressure head, or (iii) soil moisture, or (iv) pressure head for the saturated zone only, or (v) a combination of pressure head and soil moisture, pressure head for the saturated zone and soil moisture for the unsaturated zone. These methodologies are evaluated in synthetic experiments which are performed for different climate conditions, soil types and plant functional types to simulate various root zone soil moisture distributions and groundwater levels. The results demonstrate that EnKF cannot properly handle strongly skewed pressure distributions which are caused by extreme negative pressure heads in the unsaturated zone during dry periods. This problem can only be alleviated by methodology (iii), (iv) and (v). The last approach gives the best results and avoids unphysical updates related to strongly skewed pressure heads in the unsaturated zone. If groundwater level data are assimilated by methodology (iii), EnKF fails to update the state vector containing the soil moisture values if for (almost) all the realizations the observation does not bring significant new information. Synthetic experiments for the joint assimilation of groundwater levels and surface soil moisture support methodology (v) and show great potential for improving the representation of root zone soil moisture. [ABSTRACT FROM AUTHOR]

[Display omitted] This study demonstrated the use of multiple techniques for big data processing and subsequent exploratory analyses, and investigated the relationships between climatic factors and a typical asphalt pavement distress - roughness, through three types of analyses - correlation, regression and causality. Analyses were conducted separately for four different climate regions using the data of 1823 pavement sections collected from Long-term Pavement Performance (LTPP) InfoPave™. The analyses revealed that key climatic factors affecting asphalt pavement roughness differ in different climate regions. The climatic factors reflecting the regional climate characteristics were captured by Kendall's correlation coefficient and grey relational analysis. Regional climate characteristics were further distinguished by the relative contribution from beta coefficients and average accuracy change of ridge regression models. Relative to the averaged climatic factors, the cumulative climatic factors showed higher levels of correlation with International Roughness Index (IRI) but lower transfer entropy to IRI. [ABSTRACT FROM AUTHOR]

The study found that seasonal variation of δ13C was mainly affected by climatic factors like average relative humidity. The values of δ13C had various responses to the available soil water under the influence of topography, and soil water content of different layers was the key factor for the spatial variation of WUE of individuals in communities. The research indicated that mature plantations had adopted more stable water use pattern than that of the youngers in alpine desert in Qinghai-Tibet Plateau. Fig. 1. Pearson correlation coefficients between δ13C of different kinds of sand-fixing species and climatic factors in the QLED. [Display omitted] • Water use efficiency of plantations was lower as restoration year increasing in alpine semiarid desert. • The seasonal variation of water use efficiency for sand-fixing plantations was mainly affected by monthly relative humid. • The spatial dynamics of water use efficiency for plantations was primarily depended on soil water content at site scale. Afforestation is an effective measure for ecological restoration in the desert ecosystem. The long-term water use efficiency (WUE) of leaves is an important indicator for evaluating the water adaptation strategy of sand-fixing species. However, the WUE of typical sand-fixing plants in the alpine desert and its responses to local climatic, micro-geomorphology, environmental conditions and nutrient limitations are still unclear. In this study, sand-fixing vegetation community along a revegetation chronosequence (Hippophae rhammoides planted on dunes in 1987, 2008, and 2015, respectively. H. rhammoides with the longest recovery period were defined as mature plantations, and the shortest recovery period were defined as juveniles.) at the alpine desert at the eastern shore of the Qinghai Lake were used as the research plots. Stable carbon isotope was used to evaluate species WUE. The results showed that: (1) the δ13C values of H. rhammoides showed a decreasing trend as plants grew. The seasonal δ13C variation was mainly affected by average relative humidity (MRH). (2) The δ13C in the dunes revegetated in 2008 and 2015 were significantly higher than that in 1987 at the windward slope (P < 0.05). The values of δ13C had various responses to the available soil water under the influence of topography, and soil water content was the key factor for the WUE of individuals in communities. There was a significant negative correlation between the ratio of carbon and nitrogen content in H. rhammoides leaves and WUE. The research indicated that mature plantations had adopted a more stable water use pattern than the juveniles, which provide an effective insight for ecological restoration in fragile ecological regions. [ABSTRACT FROM AUTHOR]

• The variations in TWS, SWS, and GWS in northwestern China show different trends. • Precipitation was the main factor for the increase in surface water storage. • The increasing population and economic led to decreases in GWS and TWS in the northwestern China. The northwestern China (NWC) generally suffers from severe water shortages. To improve the management and utilization of the limited water storage in NWC, it is crucial to explore the distribution and variation of water storage. Here, we obtained the variations of terrestrial water storage (TWS), surface water area (SWA), and groundwater storage (GWS) in NWC (which refers to Xinjiang Uyghur Autonomous Region in the study) from 2002 to 2018 based on multi-source satellite data, and analyzed the factors influencing water storage changes. The results indicated that (1) SWA exhibited an increasing trend with 40.90 km2/year from 2002 to 2018 based on main lakes/reservoirs and rivers water area in NWC, due to the increase of precipitation and glacier melting. TWS and GWS showed a decreasing trend with a mean rate of −1.25 mm/year and −1.08 mm/year from 2002 to 2018 in NWC, respectively, due to the increase of arable land area and water consumption. (2) SWA in northern and southern NWC showed a decrease (-0.72 km2/year) and an increase (51.14 km2/year) from 2002 to 2018, respectively. The different rates are due to regional precipitation and temperature variations. TWS in northern and southern NWC showed a declining of −0.14 mm/year and −1.94 mm/year, respectively. GWS in northern and southern NWC showed a declining of −0.55 mm/year and −1.72 mm/year, respectively. The differences are related to the greater evapotranspiration and irrigation water consumption in southern than northern NWC. This study enriches the information on water storage in NWC and provides useful guidance for local water resource management and protection. [ABSTRACT FROM AUTHOR]

Climatic variables constitute important extrinsic determinants of transmission and seasonality of influenza. Yet quantitative evidence of independent associations of viral transmissibility with climatic factors has thus far been scarce and little is known about the potential effects of interactions between climatic factors on transmission. This study aimed to examine the associations of key climatic factors with risk of influenza transmission in subtropical Guangzhou. Influenza epidemics were identified over a 17-year period using the moving epidemic method (MEM) from a dataset of N = 295,981 clinically- and laboratory-confirmed cases of influenza in Guangzhou. Data on eight key climatic variables were collected from China Meteorological Data Service Centre. Generalized additive model combined with the distributed lag non-linear model (DLNM) were developed to estimate the exposure-lag-response curve showing the trajectory of instantaneous reproduction number (R t) across the distribution of each climatic variable after adjusting for depletion of susceptible, inter-epidemic effect and school holidays. The potential interaction effects of temperature, humidity and rainfall on influenza transmission were also examined. Over the study period (2005–21), 21 distinct influenza epidemics with varying peak timings and durations were identified. Increasing air temperature, sunshine, absolute and relative humidity were significantly associated with lower R t , while the associations were opposite in the case of ambient pressure, wind speed and rainfall. Rainfall, relative humidity, and ambient temperature were the top three climatic contributors to variance in transmissibility. Interaction models found that the detrimental association between high relative humidity and transmissibility was more pronounced at high temperature and rainfall. Our findings are likely to help understand the complex role of climatic factors in influenza transmission, guiding informed climate-related mitigation and adaptation policies to reduce transmission in high density subtropical cities. [ABSTRACT FROM AUTHOR]

The optical losses associated with the attenuation of the reflected direct irradiance by the heliostats along the optical path to the receiver may be significant in large solar tower plants. This phenomenon, known as atmospheric attenuation loss, may have a stronger impact at those tower plants where high aerosol loads are expected. Performance models like the System Advisor Model (SAM) and the ray-tracing models (DELSOL, MIRVAL) usually estimate the atmospheric attenuation loss by a polynomial expression which is function of the slant range (the optical path between the heliostat and the receiver). Most of the polynomial models proposed to determine this optical loss use two established extreme attenuating conditions corresponding to a clear or hazy atmosphere. This paper presents a sensitivity study of the impact of time-dependent variability of the atmospheric attenuation in the yield performance of two reference large solar tower plants (one similar to Ivanpah 1 and the other one to Crescent Dunes as examples of direct steam and molten salt tower plants, respectively). Five sites have been selected from the AERONET ground station network to obtain the aerosol loading at different time-scales: annual, monthly and daily. Multiple SAM runs have been performed to simulate the annual yield of each plant and site creating different inputs to the code corresponding to each time-scale condition. The results show a significant impact of the time-scale for modeling the atmospheric attenuation on the annual yield and daily energy output of the plant. Although the annual and monthly means produce some compensation of the impact, the differences in several particular days can be significant. Up to 20% difference in the daily energy output is found when the extinction is modeled as a steady-state polynomial representing the annual mean compared to the case of daily time-dependent variability of the attenuation. The sensitivity results presented here show that for more realistic yield performance calculations in solar tower plants, particularly at desert and arid climates, the modeling of the atmospheric attenuation should be performed in a time-dependent way according to the climatological variability conditions characteristic of the site. [ABSTRACT FROM AUTHOR]

Soil microorganisms play a key role in terrestrial ecological processes, especially in soil organic matter (SOM) formation and nutrient recycling. To perform these essential functions, microbes require mineral nutrients and labile C from the soil solution in amounts that roughly follow the Redfield ratio. However, it remains unclear how soil microorganisms maintain their C:N stoichiometry in grassland ecosystems. Here, we explored the patterns of microbial biomass C (MBC) and N (MBN), along with their controlling factors, using published data on alpine and temperate grasslands across northwestern China (from Inner Mongolia to Tibet). We tested the hypothesis that soil microbial biomass is more sensitive to climate factors than to edaphic factors. The results showed that MBC (670.0 mg kg −1 ), MBN (78.5 mg kg −1 ), and the ratio of MBC to MBN (10.9:1) in alpine grasslands were greater than in temperate grasslands (MBC = 317.4 mg kg −1 , MBN = 59.9 mg kg −1 , MBC:MBN = 6.2:1). Soil MBC in temperate grasslands had a stronger relationship with MBN ( y = 0.83 x - 0.33, r 2 = 0.53, p < 0.01) than in alpine grasslands ( y = 0.62 x + 0.09, r 2 = 0.29, p < 0.01). Climate factors were more sensitive than edaphic factors for MBC and MBN in both grasslands, but climate factors explained only 26% of the variation. These findings demonstrate that climate generally affects microbial C:N stoichiometry more strongly than do edaphic factors in alpine and temperate grasslands. However, some biotic factors could be more influential than climatic factors, necessitating further investigation. [ABSTRACT FROM AUTHOR]

The glacial aquifer system in the United States is a major source of water supply but previous work on historical groundwater trends across the system is lacking. Trends in annual minimum, mean, and maximum groundwater levels for 205 monitoring wells were analyzed across three regions of the system (East, Central, West Central) for four time periods: 1964–2013, 1974–2013, 1984–2013, and 1994–2013. Trends were computed separately for wells in the glacial aquifer system with low potential for human influence on groundwater levels and ones with high potential influence from activities such as groundwater pumping. Generally there were more wells with significantly increasing groundwater levels (levels closer to ground surface) than wells with significantly decreasing levels. The highest numbers of significant increases for all four time periods were with annual minimum and/or mean levels. There were many more wells with significant increases from 1964 to 2013 than from more recent periods, consistent with low precipitation in the 1960s. Overall there were low numbers of wells with significantly decreasing trends regardless of time period considered; the highest number of these were generally for annual minimum groundwater levels at wells with likely human influence. There were substantial differences in the number of wells with significant groundwater-level trends over time, depending on whether the historical time series are assumed to be independent, have short-term persistence, or have long-term persistence. Mean annual groundwater levels have significant lag-one-year autocorrelation at 26.0% of wells in the East region, 65.4% of wells in the Central region, and 100% of wells in the West Central region. Annual precipitation across the glacial aquifer system, on the other hand, has significant autocorrelation at only 5.5% of stations, about the percentage expected due to chance. [ABSTRACT FROM AUTHOR]

In this paper, a new data fusion-based methodology is presented for spatio-temporal (S-T) redesigning of Groundwater Level Monitoring Networks (GLMNs). The kriged maps of three different criteria (i.e. marginal entropy of water table levels, estimation error variances of mean values of water table levels, and estimation values of long-term changes in water level) are combined for determining monitoring sub-areas of high and low priorities in order to consider different spatial patterns for each sub-area. The best spatial sampling scheme is selected by applying a new method, in which a regular hexagonal gridding pattern and the Thiessen polygon approach are respectively utilized in sub-areas of high and low monitoring priorities. An Artificial Neural Network (ANN) and a S-T kriging models are used to simulate water level fluctuations. To improve the accuracy of the predictions, results of the ANN and S-T kriging models are combined using a data fusion technique. The concept of Value of Information (VOI) is utilized to determine two stations with maximum information values in both sub-areas with high and low monitoring priorities. The observed groundwater level data of these two stations are considered for the power of trend detection, estimating periodic fluctuations and mean values of the stationary components, which are used for determining non-uniform sampling frequencies for sub-areas. The proposed methodology is applied to the Dehgolan plain in northwestern Iran. The results show that a new sampling configuration with 35 and 7 monitoring stations and sampling intervals of 20 and 32 days, respectively in sub-areas with high and low monitoring priorities, leads to a more efficient monitoring network than the existing one containing 52 monitoring stations and monthly temporal sampling. [ABSTRACT FROM AUTHOR]

Agriculture is very sensitive to climate change, and correct forecasting of climate change is a great help to accurate allocation of irrigation water. The use of irrigation water is influenced by crop water demand and precipitation. Potential evapotranspiration (ET 0 ) is a measure of the ability of the atmosphere to remove water from the surface through the processes of evaporation and transpiration, assuming no control on water supply. It plays an important role in assessing crop water requirements, regional dry–wet conditions, and other factors of water resource management. This study analyzed the spatial and temporal evolution processes and characteristics of major meteorological parameters at 10 stations in the Loess Plateau of northern Shaanxi (LPNS). By using the Mann–Kendall trend test with trend–free pre–whitening and the ArcGIS platform, the potential evapotranspiration of each station was quantified by using the Penman–Monteith equation, and the effects of climatic factors on potential evapotranspiration were assessed by analyzing the contribution rate and sensitivity of the climatic factors. The results showed that the climate in LPNS has become warmer and drier. In terms of the sensitivity of ET 0 to the variation of each climatic factor in LPNS, relative humidity (0.65) had the highest sensitivity, followed by daily maximum temperature, wind speed, sunshine hours, and daily minimum temperature (− 0.05). In terms of the contribution rate of each factor to ET 0 , daily maximum temperature (5.16%) had the highest value, followed by daily minimum temperature, sunshine hours, relative humidity, and wind speed (1.14%). This study provides a reference for the management of agricultural water resources and for countermeasures to climate change. According to the climate change and the characteristics of the study area, farmers in the region should increase irrigation to guarantee crop water demand. [ABSTRACT FROM AUTHOR]

A Late Cretaceous to Early Oligocene episode of paleoweathering and supergene activity has been identified within the Jurassic-age (170 Ma) Humboldt mafic complex (HMC) and associated Boyer Ranch Formation of west-central Nevada, USA. This episode is responsible for significant subaerial weathering, erosion, oxidation and most importantly local supergene metal enrichment which could potentially have a major economic impact upon iron oxide copper-gold (IOCG)-style mineralization within the complex and in the Boyer Ranch Formation. Paleoweathering profiles that developed on the exposed Jurassic surface during this time now mark an angular unconformity (J-T unconformity) with a stratigraphically overlying, Tertiary (Oligocene) rhyolitic volcanic-volcaniclastic sequence. Differential uplift of the Jurassic surface is a direct result of the Middle and/or Late Jurassic Luning-Fencemaker compressional overthrust faulting event during which the entire complex was tectonically transported to its present location. The uplifted Jurassic paleosurface experienced one or more cycles of weathering, erosion, depression of the water table and uninterrupted supergene activity beginning at the end of the Cretaceous and possibly accelerating during the Paleocene-Eocene Thermal Maximum Event (∼56 Ma). Geochemically mature supergene Cu-oxide profiles developed over exhumed mineralized sections in terrains with favorable fault block movements. These paloweathering systems evolved from moderately acidic pH to near-neutral or slightly alkaline pH environments over time. High protolith reactivity and low hypogene pyrite concentrations modified acidity of supergene fluids thus limiting Cu mobility. Profiles that survived erosion were later buried and preserved by the Oligocene-age rhyolitic volcanic-volcaniclastic sequence before the onset of mid-Tertiary Basin and Range normal faulting. Subsequent partial unroofing of overlying Tertiary volcanic cover resulted in further erosion of some profiles. Erosional remnants mapped in the field include all or some components of the original supergene Cu-oxide profiles. Drilling results suggest profiles that remain buried are generally intact. Although this field-oriented study is considered preliminary, it concludes that known Jurassic-age IOCG-style mineral occurrences in the central HMC have been subjected to prolonged subaerial paleoweathering and supergene activity that has not been previously recognized. Where complete or nearly complete profiles are preserved, efficient cumulative Cu-enrichment to potentially ore grades has been documented in well defined oxide zones. These findings can be extrapolated to the entire J-T unconformity along which concealed and enriched deposits may exist. As such, the unconformity is considered a prime exploration target that is highly prospective for new discoveries of economically viable, supergene-enriched IOCG resources. [ABSTRACT FROM AUTHOR]

Global warming and the associated risks for natural and human systems have been major global concerns. The International Panel on Climate Change (IPCC) has projected average global surface temperature to increase by between 0.3 °C and 4.8 °C by the end of this century, depending on the greenhouse gas emission scenarios assessed. In the tropical and sub-tropical regions increases in temperature will lead to greater use of electricity for space cooling, a development that is undesirable from energy and sustainability viewpoints. We investigate how temperature increases affect electricity consumption in Singapore and Hong Kong. This is done by consuming sector, i.e. residential, commercial and industrial. Singapore and Hong Kong are respectively two tropical and subtropical cities with comparable physical, population and economy sizes. Two different approaches are used to relate their sectoral electricity consumption to temperature using regression analysis. It is estimated that total annual electricity consumption would increase by between 3% and 4% in Singapore in 2015 if there were to be a 1 °C rise in temperature. The corresponding estimates for Hong Kong are between 4% and 5%. In both cities, increases would be the greatest in the residential sector, followed by the commercial sector and the industrial sector. [ABSTRACT FROM AUTHOR]