Occurrence, removal, emission and environment risk of 32 antibiotics and metabolites in wastewater treatment plants in Wuhu, China.

Wastewater treatment plants (WWTPs) are considered important sources of antibiotics and metabolites in aquatic environments and pose a serious threat to the safety of aquatic organisms. In this study, we investigated the seasonal occurrence, removal, emission, and environmental risk assessment (ERA) of 32 antibiotics and metabolites at four WWTPs located in Wuhu, China. The main findings of this study are as follows: Ofloxacin concentrations dominated all WWTPs, and large quantities of sulfachinoxalin were only detected in WWTP 2 treating mixed sewage. The average apparent removal of individual parent antibiotics or metabolites ranged from -94.7 to 100 %. There was a noticeable seasonal emission pattern (independent t-test, t = 9.89, p < 0.001), with lower emissions observed during summer. WWTPs discharged 85.2 ± 43.8 g of antibiotics and metabolites each day. Approximately 87 % of emissions were discharged into the mainstream of the Yangtze River, while the remainder were discharged into its tributary, the Zhanghe River. The total emissions of 21 parent antibiotics were approximately 18 % of the prescription data, indicating that a considerable and alarming amount of prototype drugs entered the receiving water body. Based on the risk quotient (RQ) of the ERA, the Zhanghe River has a moderate risk of ofloxacin (RQ = 0.111-0.583), a low or insignificant risk of sulfamethoxazole (RQ = 0.003-0.048), and an insignificant risk of other antibiotics or metabolites. However, the risk of antibiotics or metabolites in the mainstream of Yangtze River is insignificant. This study could help understand the seasonal emission patterns of antibiotics and metabolites, as well as more antibiotics sensitive of environmental risks in tributary than that in mainstream.

Global warming is increasing the discrepancy between green (actual) and thermal (potential) seasons of temperate trees.

Over the past decades, global warming has led to a lengthening of the time window during which temperatures remain favorable for carbon assimilation and tree growth, resulting in a lengthening of the green season. The extent to which forest green seasons have tracked the lengthening of this favorable period under climate warming, however, has not been quantified to date. Here, we used remote sensing data and long-term ground observations of leaf-out and coloration for six dominant species of European trees at 1773 sites, for a total of 6060 species-site combinations, during 1980-2016 and found that actual green season extensions (GS: 3.1 ± 0.1 day decade-1 ) lag four times behind extensions of the potential thermal season (TS: 12.6 ± 0.1 day decade-1 ). Similar but less pronounced differences were obtained using satellite-derived vegetation phenology observations, that is, a lengthening of 4.4 ± 0.13 and 7.5 ± 0.13 day decade-1 for GS and TS, respectively. This difference was mainly driven by the larger advance in the onset of the thermal season compared to the actual advance of leaf-out dates (spring mismatch: 7.2 ± 0.1 day decade-1 ), but to a less extent caused by a phenological mismatch between GS and TS in autumn (2.4 ± 0.1 day decade-1 ). Our results showed that forest trees do not linearly track the new thermal window extension, indicating more complex interactions between winter and spring temperatures and photoperiod and a justification of demonstrating that using more sophisticated models that include the influence of chilling and photoperiod is needed to accurately predict spring phenological changes under warmer climate. They urge caution if such mechanisms are omitted to predict, for example, how vegetative health and growth, species distribution and crop yields will change in the future.

Drought propagation under global warming: Characteristics, approaches, processes, and controlling factors.

Drought is a costly natural hazard with far-reaching impacts on agriculture, ecosystem, water supply, and socio-economy. While propagating through the water cycle, drought evolves into different types and affects the natural system and human society. Despite much progress made in recent decades, a synthesis of the characteristics, approaches, processes, and controlling factors of drought propagation is still lacking. We bridge this gap by reviewing the recent progress of drought propagation and discussing challenges and future directions. We first introduce drought propagation characteristics (e.g., response time scale, lag time), followed by different approaches, including statistical analysis and hydrological modeling. The recent progress in the propagation from meteorological drought to different types of drought (agricultural drought, hydrological drought, and ecological drought) is then synthesized, including the basic process, commonly used indicators, data sources, and main findings of drought propagation characteristics. Different controlling factors of drought propagations, including climate (e.g., aridity, seasonality, and anomalies of meteorological variables), catchment properties (e.g., slope, elevation, land cover, aquifer, baseflow), and human activities (e.g., reservoir operation and water diversion, irrigation, and groundwater abstraction), are then summarized. Challenges in drought propagation include the discrepancy in drought indicators (and approaches) and difficulty in characterizing the full propagation process and isolating influencing factors. Future analysis of drought propagation should shift from single indicators to multiple indicators, from individual drivers to combined drivers, from uni-directional analysis to feedbacks, from hazards to impacts, and from stationary to nonstationary assumptions. This review is expected to be useful for drought prediction and management across different regions under global warming.

Systematic assessment of the development and recovery characteristics of hydrological drought in a semi-arid area.

Studies have documented the significant effect of various factors on hydrological drought events. However, few studies have quantified drought's development and recovery process under environmental changes. This study focused on identifying hydrological drought's development and recovery characteristics and their potential causes in a typical semi-arid area. The Standardized Streamflow Index (SSI) was used as a metric for hydrological droughts, while the run theory was applied to identify the development and recovery processes of droughts. Changes in observed (human disturbed scenario) and simulated (natural scenario) droughts by employing the SWAT (Soil and Water Assessment Tool) model were also investigated from 1970 to 2016. The "simulated-observed" approach was used to assess the impacts of human regulations on hydrological drought development and recovery characteristics. Results showed that hydrological droughts occurred mainly during 1980-1990 and 2000-2016. In the natural condition, drought duration and intensity were higher, while lower severity in the drought recovery stage than development stage was observed. The drainage characteristics of the basin played the most critical role in the development and recovery characteristics of drought, which were also influenced by climatic conditions. Human activities had exacerbated recent natural hydrological drought. When considering the contribution of human activities, the reservoir operation was the dominant anthropic factor affected the development and recovery process of drought in the study area. Under the effects of reservoir regulation, long-duration hydrological droughts became rare. Moreover, the recovery ability of drought had been weakened. The effects of the reservoir were progressively crucial gradually. Although the water got from the river by the reservoir had been reduced, the negative impact on aggravating drought remains stronger than the reservoir was initially constructed. The results of our study will help improve the optimal management of reservoirs in semi-arid areas and enhance drought early warning and forecasting system.

Multi-scale assessment of water security under climate change in North China in the past two decades.

Climate change is projected to affect the hydrological cycles in China, while the effects are expected to vary spatiotemporally. Understanding the variations in water security conditions and their sensitivity to climatic variables is crucial for assessing regional ecosystem responses to climate change. In the present study, we estimated the water yield capacity, an important indicator of water security in North China (NC), at a spatial resolution of 1 km during the last two decades based on the Budyko framework and quantified the sensitivity of water yield change to climate change among different vegetation types. The results showed that the performances of the Budyko framework were reliable both at the pixel scale and across large watersheds. The annual water yield in North China was estimated to be 7.61 ± 2.67 ∗ 1010 m3/yr, with an average mean water yield (MWY) of 49.51 ± 17.49 mm/yr. The spatial pattern of mean water yield change (MWYC) exhibited high heterogeneity; 46% of the study region was dominated by an increasing trend, while 9.84% was statistically significant (P < 0.05). Compared with temperature, the water yield capacity was more sensitive to precipitation variation. A consistent trend of variation was found in cropland between water yield and precipitation, while negative sensitivity coefficients were found in natural vegetation types. The variation in sensitivity coefficients (Swyp) in natural vegetation showed that in regions with a decrease in precipitation, the variation in water yield capacity also decreased, while in regions with an increase in precipitation from 0 to 8 mm/yr, the water yield capacity first decreased and then increased with precipitation. Our findings suggest that grass and shrubs would be more beneficial to regional water security in North China's revegetation, while afforestation would provide protection for the regional environment from extreme rainfall events.

A follow-up study shows that recovered patients with re-positive PCR test in Wuhan may not be infectious.

BACKGROUND: Previous studies showed that recovered coronavirus disease 2019 (COVID-19) patients can have a subsequent positive polymerase chain reaction (PCR) test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) after they are discharged from the hospital. Understanding the epidemiological characteristics of recovered COVID-19 patients who have a re-positive test is vital for preventing a second wave of COVID-19. METHODS: This retrospective study analyzed the epidemiological and clinical features of 20,280 COVID-19 patients from multiple centers in Wuhan who had a positive PCR test between December 31, 2019, and August 4, 2020. The RT-PCR test results for 4079 individuals who had close contact with the re-positive cases were also obtained. RESULTS: In total, 2466 (12.16%) of the 20,280 patients had a re-positive SARS-CoV-2 PCR test after they were discharged from the hospital, and 4079 individuals had close contact with members of this patient group. All of these 4079 individuals had a negative SARS-CoV-2 PCR test. CONCLUSIONS: This retrospective study in Wuhan analyzed the basic characteristics of recovered COVID-19 patients with re-positive PCR test and found that these cases may not be infectious.

Influences of Shifted Vegetation Phenology on Runoff Across a Hydroclimatic Gradient.

Climate warming has changed vegetation phenology, and the phenology-associated impacts on terrestrial water fluxes remain largely unquantified. The impacts are linked to plant adjustments and responses to climate change and can be different in different hydroclimatic regions. Based on remote sensing data and observed river runoff of hydrological station from six river basins across a hydroclimatic gradient from northeast to southwest in China, the relative contributions of the vegetation (including spring and autumn phenology, growing season length (GSL), and gross primary productivity) and climatic factors affecting the river runoffs over 1982-2015 were investigated by applying gray relational analysis (GRA). We found that the average GSLs in humid regions (190-241 days) were longer than that in semi-humid regions (186-192 days), and the average GSLs were consistently extended by 4.8-13.9 days in 1982-2015 period in six river basins. The extensions were mainly linked to the delayed autumn phenology in the humid regions and to advanced spring phenology in the semi-humid regions. Across all river basins, the GRA results showed that precipitation (r = 0.74) and soil moisture (r = 0.73) determine the river runoffs, and the vegetation factors (VFs) especially the vegetation phenology also affected the river runoffs (spring phenology: r = 0.66; GSL: r = 0.61; autumn phenology: r = 0.59), even larger than the contribution from temperature (r = 0.57), but its relative importance is climatic region-dependent. Interestingly, the spring phenology is the main VF in the humid region for runoffs reduction, while both spring and autumn growth phenology are the main VFs in the semi-humid region, because large autumn phenology delay and less water supply capacity in spring amplify the effect of advanced spring phenology. This article reveals diverse linkages between climatic and VFs, and runoff in different hydroclimatic regions, and provides insights that vegetation phenology influences the ecohydrology process largely depending on the local hydroclimatic conditions, which improve our understanding of terrestrial hydrological responses to climate change.

Climate warming increases spring phenological differences among temperate trees.

Climate warming has substantially advanced spring leaf flushing, but winter chilling and photoperiod co-determine the leaf flushing process in ways that vary among species. As a result, the interspecific differences in spring phenology (IDSP) are expected to change with climate warming, which may, in turn, induce negative or positive ecological consequences. However, the temporal change of IDSP at large spatiotemporal scales remains unclear. In this study, we analyzed long-term in-situ observations (1951-2016) of six, coexisting temperate tree species from 305 sites across Central Europe and found that phenological ranking did not change when comparing the rapidly warming period 1984-2016 to the marginally warming period 1951-1983. However, the advance of leaf flushing was significantly larger in early-flushing species EFS (6.7 ± 0.3 days) than in late-flushing species LFS (5.9 ± 0.2 days) between the two periods, indicating extended IDSP. This IDSP extension could not be explained by differences in temperature sensitivity between EFS and LFS; however, climatic warming-induced heat accumulation effects on leaf flushing, which were linked to a greater heat requirement and higher photoperiod sensitivity in LFS, drove the shifts in IDSP. Continued climate warming is expected to further extend IDSP across temperate trees, with associated implications for ecosystem function.

Considering atmospheric N2O dynamic in SWAT model avoids the overestimation of N2O emissions in river networks.

Modeling studies have focused on N2O emissions in temperate rivers under static atmospheric N2O (N2Oairc), with cold temperate river networks under dynamic N2Oairc receiving less attention. To address this knowledge and methodological gap, the dissolved N2O concentration (N2Odisc) and N2Oairc algorithms were integrated with an air-water gas exchange model (FN2O) into the SWAT (Soil and Water Assessment Tool). This new model (SWAT-FN2O) allows users to simulate daily riverine N2O emissions under dynamic atmospheric N2O. The spatiotemporal fluctuations in the riverine N2O emissions was simulated and its response to the static and dynamic atmospheric N2O were analyzed in a middle-high latitude agricultural watershed in northeastern China. The results show that the SWAT-FN2O model is a useful method for capturing the hotspots in riverine N2O emissions. The model showed strong riverine N2O absorption and weak N2O emissions from September to February, which acted as a sink for atmospheric N2O in this cold temperate area. High N2O emissions occurred from April to July, which accounted for 83.34% of the yearly emissions. Spatial analysis indicated that the main stream and its tributary could contribute 302.3-1043.7 and 41.5-163.4 µg N2O/(m2·d) to the total riverine N2O emissions (15.02 t/a), respectively. The riverine N2O emissions rates in the subbasins dominated by forests and paddy fields were lower than those in the subbasins dominated by arable and residential land. Riverine N2O emissions can be overestimated under the static atmospheric N2O rather than under the increasing atmospheric N2O. This overestimation has increased from 1.52% to 23.97% from 1990 to 2016 under the static atmospheric N2O. The results of this study are valuable for water quality and future climate change assessments that aim to protect aquatic and atmospheric environments.

Monitoring Consumption of Common Illicit Drugs in Kuala Lumpur, Malaysia, by Wastewater-Cased Epidemiology.

Southeast Asian countries including Malaysia play a major role in global drug trade and abuse. Use of amphetamine-type stimulants has increased in the past decade in Malaysia. This study aimed to apply wastewater-based epidemiology for the first time in Kuala Lumpur, Malaysia, to estimate the consumption of common illicit drugs in urban population. Influent wastewater samples were collected from two wastewater treatment plants in Kuala Lumpur in the summer of 2017. Concentrations of twenty-four drug biomarkers were analyzed for estimating drug consumption. Fourteen drug residues were detected with concentrations of up to 1640 ng/L. Among the monitored illicit drugs, 3,4-methylenedioxy-methamphetamine (MDMA) or ecstasy had the highest estimated per capita consumptions. Consumption and dose of amphetamine-type stimulants (methamphetamine and MDMA) were both an order of magnitude higher than those of opioids (heroin and codeine, methadone and tramadol). Amphetamine-type stimulants were the most prevalent drugs, replacing opioids in the drug market. The prevalence trend measured by wastewater-based epidemiology data reflected the shift to amphetamine-type stimulants as reported by the Association of Southeast Asian Nations Narcotics Cooperation Center. Most of the undetected drug residues were new psychoactive substances (NPSs), suggesting a low prevalence of NPSs in the drug market.

Heavy metal accumulation, geochemical fractions, and loadings in two agricultural watersheds with distinct climate conditions.

The main aim of this study was to explore the effects of climate conditions on the transport and transformation of heavy metals. Sedimentary geochemical analysis and watershed modeling were used to investigate the distinctions between heavy metal pollution under different climate conditions. The results showed that the average concentrations of Cu, Cd, and Pb in sediments of the subtropical watershed (36.64, 0.60, and 133.69 mg/kg, respectively) were higher than those of the temperate watershed (26.58, 0.19, and 23.17 mg/kg, respectively) because of surface runoff-induced heavy metal loadings under higher precipitation. Also, the labile fractions, which mainly originated from anthropogenic sources, showed higher percentages in the subtropical watershed (67.84-91.33%), thereby indicating that the transport of heavy metals was promoted by surface runoff. Moreover, higher percentages of acid-soluble fractions of Cu and Pb (23.55-33.60%) in the subtropical watershed suggested that higher temperatures accelerated the transformation of heavy metal fractions, thus contributing to the transportation of heavy metals. Overall, climate conditions were the dominant factors for the differences between the subtropical and temperate watersheds. The results of this study suggest that the effects of climate conditions on the transport, enrichment, and bioavailability of heavy metals are of great significance. Such effects should therefore be the focus of future studies.

Monitoring consumption of methadone and heroin in major Chinese cities by wastewater-based epidemiology.

BACKGROUND: Methadone maintenance treatment (MMT) services have been used in China for treatment of heroin dependence. But no study has been conducted to assess the appropriateness of MMT distribution and the potential abuse of methadone in China. This study aims to do that through a nationwide estimation of methadone consumption in China via wastewater-based epidemiology and subsequently compare it with MMT data and level of heroin abuse. METHODS: Wastewater samples were collected from 53 wastewater treatment plants in 27 major cities that cover all geographic regions of China. Methadone and pure heroin consumptions were estimated based on influent concentrations of methadone, 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP), morphine and codeine. RESULTS: Drug residues were detected in most samples. The ratio of EDDP/methadone was around 2 in influents and methadone and EDDP loads were strongly correlated, indicating that they originated from human consumption. Both influent methadone and EDDP loads in Southwest and Northwest China were significantly higher than those in other regions. The highest estimated consumptions of methadone and heroin in China were 22.0 ±â€¯2.1 mg/1000 in./d and 263.9 ±â€¯115.9 mg/1000 in./d, respectively. There was a significant positive correlation between methadone and heroin consumptions. CONCLUSIONS: Consumption of methadone in China was primarily from MMT services. The use of methadone and heroin displayed a clear geographical pattern: it is higher in the western inland regions than in the eastern regions. This study has shown that the distribution of MMT services is reflective of the level of heroin abuse in different regions of China.

Probabilistic evaluation of the impact of compound dry-hot events on global maize yields.

Weather and climate extremes, such as droughts and hot extremes, may result in marked damages to crop yields and threaten regional and global food security. Understanding the relationship between climate extremes and crop yields is of critical importance for food security under a changing climate. The objective of this study is to investigate the probabilistic variability of maize yields with respect to compound dry-hot events, which has been shown to be more stressful to crops compared with individual dry or hot events. A multivariate model is first constructed to model the joint behavior of the dry condition, hot condition, and crop yields. The response of crop yields under different dry, hot, and compound dry-hot conditions at national and global scales is then investigated based on the conditional distribution. For the major maize producing countries (top 5), the probability of maize yield reduction could increase by from 0.07 to 0.31 (from 0.04 to 0.31) when the individual extreme drought (extreme hot) conditions changed to compound dry-hot conditions. The probabilistic evaluation of compound dry-hot events' impacts on maize yields is expected to provide useful insights for the mitigation of compound events and their impacts under a changing climate.

Shortened temperature-relevant period of spring leaf-out in temperate-zone trees.

Temperature during a particular period prior to spring leaf-out, the temperature-relevant period (TRP), is a strong determinant of the leaf-out date in temperate-zone trees. Climatic warming has substantially advanced leaf-out dates in temperate biomes worldwide, but its effect on the beginning and length of the TRP has not yet been explored, despite its direct relevance for phenology modeling. Using 1,551 species-site combinations of long-term (1951-2016) in situ observations on six tree species (namely, Aesculus hippocastanum, Alnus glutinosa, Betula pendula, Fagus sylvatica, Fraxinus excelsior, and Quercus robur) in central Europe, we found that the advancing leaf-out was accompanied by a shortening of the TRP. On average across all species and sites, the length of the TRP significantly decreased by 23% (p < .05), from 60 ± 4 days during 1951-1965 to 47 ± 4 days during 2002-2016. Importantly, the average start date of the TRP did not vary significantly over the study period (March 2-5, DOY = 61-64), which could be explained by sufficient chilling over the study period in the regions considered. The advanced leaf-out date with unchanged beginning of the TRP can be explained by the faster accumulation of the required heat due to climatic warming, which overcompensated for the retarding effect of shortening daylength on bud development. This study shows that climate warming has not yet affected the mean TRP starting date in the study region, implying that phenology modules in global land surface models might be reliable assuming a fixed TRP starting date at least for the temperate central Europe. Field warming experiments do, however, remain necessary to test to what extent the length of TRP will continue to shorten and whether the starting date will remain stable under future climate conditions.

Exploring the application of artificial intelligence technology for identification of water pollution characteristics and tracing the source of water quality pollutants.

Point sources are important routes through which pollutants enter rivers. It is important to identify the characteristics of and trace the origins of water pollutants. In this study, an artificial intelligence system called the integrated long short-term memory network (LSTM), using cross-correlation and association rules (Apriori), was used to identify the characteristics of water pollutants and trace industrial point sources of pollutants. Water quality monitoring data from Shandong Province, China, were used to verify the applicability of the artificial intelligence system using a cross-correlation method to develop a water quality cross-correlation map. The map was used to identify highly correlated pollutants affecting water quality, then the association rules (Apriori) were used to track the pollutants to industries common in the study area. The highly correlated water pollutants and relevant industries were used as inputs for the LSTM to determine how well the LSTM traced sources of water pollutants. The results showed that (1) changes in water quality were affected in different ways by different industries and different distributions and production cycles of the pollutant point sources; (2) water quality correlation maps can be used to identify regular and abnormal fluctuations in point source pollutant emissions by identifying changes in water quality characteristics and frequent itemsets in water quality indices can be used to trace the industries that most strongly affect water quality; and (3) the LSTM accurately traced point sources of future changes in water quality. In conclusion, the artificial intelligence scheme described here can be applied to aquatic systems.

Assessment of lake eutrophication using a novel multidimensional similarity cloud model.

Lake eutrophication is characterized by a variety of indicators, including nitrogen and phosphorus concentrations, chemical oxygen demand, chlorophyll levels, and water transparency. In this study, a multidimensional similarity cloud model (MSCM) is combined with a random weighting method to reduce the impacts of random errors in eutrophication monitoring data and the fuzziness of lake eutrophication definitions on the consistency and reliability of lake eutrophication evaluations. Measured samples are assigned to lake eutrophication levels based on the cosine of the angle between the cloud digital characteristics vectors of each sample and those of each eutrophication grade. To field test this method, the eutrophication level of Nansi Lake in Shandong Province was evaluated based on monitoring data collected in 2009-2016. Results demonstrate that, in 2009 and in 2011-2015, the upper lake of Nansi Lake exhibited moderate eutrophication while the lower lake exhibited mild eutrophication. In 2010, 2016, elevated concentrations of total nitrogen and total phosphorus led to an increase in the eutrophication level of the lower lake, matching that of the upper lake. Based on the results of these field tests, we conclude that the MSCM presented in this study provides a more flexible and effective method for evaluating lake eutrophication data than the existing multidimensional normal cloud model.

Nutrient availability alters the correlation between spring leaf-out and autumn leaf senescence dates.

Leaf senescence (LS) affects tree fitness, species distribution and ecosystem structure and functioning. The drivers of LS and the processes underlying it have been studied, but the studies have mainly focused on environmental cues and have mainly been based on statistical analyses using in situ data sets. Experimental investigation and field verification of the processes and drivers are thus urgently needed. We conducted a nutrient-addition experiment after a spring-warming experiment in which an ~40-day range of leaf-out (LO) dates was induced in horse chestnut (Aesculus hippocastanum) and beech (Fagus sylvatica) saplings. We found that both increased nutrient supply and advanced LO date significantly affected the timing of LS, but their effects were opposite, as the former delayed and the latter advanced the senescence. The effects of nutrient supply and LO interacted species specifically. In chestnut, the delay of senescence caused by fertilization increased with the delay of LO and was thus stronger for individuals that flushed late in the spring. On the contrary, in beech the delay of senescence caused by fertilization decreased with the delay of LO and was insignificant for individuals with the latest LO. The experimental findings for beech were confirmed with mature trees at a regional scale. The interactive effect between nutrients and LO on senescence may be associated with variable sensitivity to photoperiod, growth sink limitation and/or direct effect of foliar nutrition on the timing of senescence. Our novel results show that the interactive effects of LO and nutrient supply on the timing of LS should be further addressed experimentally in forthcoming studies. It would also be interesting to consider our results in the further development of phenological models used in assessing the effects of climatic change. The differences found in the present study between horse chestnut and beech suggest that the results found for one species cannot necessarily be generalized to other species, so studies with different temperate tree species are called for.

Daylength helps temperate deciduous trees to leaf-out at the optimal time.

Global warming has led to substantially earlier spring leaf-out in temperate-zone deciduous trees. The interactive effects of temperature and daylength underlying this warming response remain unclear. However, they need to be accurately represented by earth system models to improve projections of the carbon and energy balances of temperate forests and the associated feedbacks to the Earth's climate system. We studied the control of leaf-out by daylength and temperature using data from six tree species across 2,377 European phenological network (www.pep725.eu), each with at least 30 years of observations. We found that, in addition to and independent of the known effect of chilling, daylength correlates negatively with the heat requirement for leaf-out in all studied species. In warm springs when leaf-out is early, days are short and the heat requirement is higher than in an average spring, which mitigates the warming-induced advancement of leaf-out and protects the tree against precocious leaf-out and the associated risks of late frosts. In contrast, longer-than-average daylength (in cold springs when leaf-out is late) reduces the heat requirement for leaf-out, ensuring that trees do not leaf-out too late and miss out on large amounts of solar energy. These results provide the first large-scale empirical evidence of a widespread daylength effect on the temperature sensitivity of leaf-out phenology in temperate deciduous trees.

Variations of compound precipitation and temperature extremes in China during 1961-2014.

Concurrent precipitation and temperature extremes usually have significant impacts on the society, economy and ecosystem. Changes in precipitation or temperature extremes in China have been extensively studied; however, less attention has been paid to their concurrence (or compound extremes) to date. This study explores the historical variations of compound extremes including dry/warm, dry/cold, wet/warm, and wet/cold combinations based on monthly precipitation and temperature observations during summer and winter from 1961 to 2014 over China. Results show that there is a significant increase in the frequency of compound dry/warm and wet/warm extremes while a decrease in compound dry/cold and wet/cold extremes for the period 1988-2014 relative to 1961-1987. In addition, statistically significant increase in the spatial extent exists in compound dry/warm and wet/warm extremes, while the areas affected by the compound dry/cold and wet/cold extremes present a decrease across China, which is shown to be partly related to the large-scale circulation patterns. The results of this study could improve our understanding of changes of compound precipitation and temperature extremes from a multivariate perspective.

Short photoperiod reduces the temperature sensitivity of leaf-out in saplings of Fagus sylvatica but not in horse chestnut.

Leaf phenology is one of the most reliable bioindicators of ongoing global warming in temperate and boreal zones because it is highly sensitive to temperature variation. A large number of studies have reported advanced spring leaf-out due to global warming, yet the temperature sensitivity of leaf-out has significantly decreased in temperate deciduous tree species over the past three decades. One of the possible mechanisms is that photoperiod is limiting further advance to protect the leaves against potential damaging frosts. However, the "photoperiod limitation" hypothesis remains poorly investigated and experimentally tested. Here, we conducted a photoperiod- and temperature-manipulation experiment in climate chambers on two common deciduous species in Europe: Fagus sylvatica (European beech, a typically late flushing species) and Aesculus hippocastanum (horse chestnut, a typically early flushing species). In agreement with previous studies, we found that the warming significantly advanced the leaf-out dates by 4.3 and 3.7 days/°C for beech and horse chestnut saplings, respectively. However, shorter photoperiod significantly reduced the temperature sensitivity of beech only (3.0 days/°C) by substantially increasing the heat requirement to avoid leafing-out too early. Interestingly, the photoperiod limitation only occurs below a certain daylength (photoperiod threshold) when the warming increased above 4°C for beech trees. In contrast, for chestnut, no photoperiod threshold was found even when the ambient air temperature was warmed by 5°C. Given the species-specific photoperiod effect on leaf phenology, the sequence of the leaf-out timing among forest tree species may change under future climate warming conditions. Nonphotoperiodic species may benefit from warmer springs by starting the growing season earlier than photoperiodic sensitive species, modifying forest ecosystem structure and functions, but this photoperiod limitation needs to be further investigated experimentally in numerous species.