Intra-annual Variability Research Articles

Intra-annual compositions and diversity variations of waterbird communities in China

Waterbirds are often used as indicators of wetland biodiversity and ecosystem health due to their sensitivity to environmental changes. However, most studies have focused on long-term variations of waterbird communities, while the intra-annual changes have rarely been investigated. Here, we analyzed the seasonal dynamics of compositions and the intra-annual variations of species richness and abundance of waterbird communities among the nine major river basins of China, and focused on the effects of different residential types and taxonomic groups on the diversity variations. In most of the nine basins, the waterbird communities in spring and autumn showed high similarity, while those in summer and winter were quite different. Such a seasonal cycle of species composition was caused by the seasonal migration of waterbirds. The season with higher species richness shifted from summer to winter as the latitude of the basin decreased within China, but almost all basins had the lowest community abundance in summer. This was because the variation of richness was directly influenced by the variation in species composition, but the variation of abundance was more influenced by the population size of different waterbird assemblages. Our results emphasized the importance of seasonal dynamics of waterbird communities to the understanding of their migration patterns, and highlighted the impact of different waterbird assemblages on intra-annual community variations. Additionally, we also noted the potential effects of population size and behaviors at different parts of the waterbird life circle on the observations of them. The information gathered in this study will provide guidance for the conservation of waterbird species and their habitats.

Intra-annual variations in isotopic composition of atmospheric water vapour in a semi-arid monsoon region: observations and inferences

The isotopic composition of atmospheric water vapour dynamically changes due to the complex interplay between the moisture-contributing sources (precipitation, evaporating soil moisture, transpiration, evaporating water bodies, etc.), operating ecohydrological processes, and the climatic conditions. In the present study, the isotopic composition of different sources of water was sampled; air moisture (n = 47) twice a month for the period December, 2021 to January, 2023; transpiration samples (n = 76); evaporation and rain samples (n = 12 each). The samples were analyzed using the mass spectrometry to investigate the dynamic nature of the isotopic composition of the atmospheric vapour and the factors influencing it. The research findings showed that the isotopic composition of atmospheric moisture rapidly depleted after the onset of the monsoon, indicating the replacement of pre-monsoon local vapour by monsoon moisture of depleted isotopic composition. The process of evaporation from the soil surface during the post-monsoon period was found to progress parallel to other soil hydrological processes, such as capillary action and diffusion-driven downward flux of enriched water molecules. This resulted in the broadening of an isotopically enriched layer of soil moisture in the vadose zone with time, and accordingly, there was transpiration of this enriched soil moisture from the deeper layers of soils with time by plants/trees. Evaporation of soil water and the transpiration of plants and trees of enriched water vapour gradually altered the isotopic composition of the atmospheric water vapour composition from the depleted isotopic composition of the monsoon period to an enriched isotopic composition. The transpiration of enriched water vapour from deep-rooted trees indicated more prolonged arid climate conditions. The findings of the study are useful in understanding the regional hydrological and hydroclimatic processes which can be implemented in agro-climatic studies and water resource management.

Intra-annual and regional variability of the oleic acid and linoleic acid content of pumpkin seed oil over a 10-year period

Pumpkin seed oil, extracted from roasted and hulled pumpkin seeds, is prized for its distinct nutty flavor and deep amber color. Its significance in gastronomy lies in its rich flavor profile and nutritional benefits, which enhance dishes with both taste and health-promoting properties. Despite a notable increase in production, particularly in Croatia and other regions, there remains a gap in research regarding how factors such as production year and geographic origin influence its quality and health-related compounds. In this study, we analyzed the fatty acid composition of 928 samples of pumpkin seed oil from Croatia, Slovenia, Austria, and Hungary, produced between 2014 and 2023, using GC-FID, with a focus on linoleic and oleic acids. Our findings revealed significant year-to-year variability, particularly evident in 2019 samples, which exhibited higher oleic acid and lower linoleic acid content, resulting in the highest oleic acid to linoleic acid ratio. Additionally, cold-pressed oils showed a slightly higher ratio, and a significant regional influence was observed, with Austrian oils having a lower ratio compared to those from Croatia and Slovenia. This observed variation may be attributed to changing climatic conditions or shifts in producer practices, though further research is needed to validate these hypotheses.

Intra-annual variability of urban effects on streamwater quality: An examination of watersheds in the Atlanta, Georgia USA region

There has been considerable research on urban impacts on streamwater quality, but studies have mostly ignored intra-annual variations in water-quality variables and thus seasonally-varying impacts. Therefore, this study uses daily water-quality data over eight years for 38 streams in the Atlanta, Georgia USA region and watershed-level, urbanization variables to assess month-specific impacts of urbanization on water quality. The water-quality variables were mean specific conductance, mean temperature, mean dissolved oxygen, median pH, and median turbidity. The watershed-level variables were population density, housing density, proportion of watershed that is high-intensity developed land, imperviousness, proportion of watershed that is urban land use, an urban-intensity index, and change in developed land. Both differences in water quality between urban, suburban, and exurban streams and correlations between water quality and the watershed-level variables were examined. During all months, urban streams had significantly higher specific conductance than suburban and exurban streams, and the urban intensity of a watershed was significantly positively correlated with specific conductance. Differences and correlations were intensified during winter and spring. For temperature and dissolved oxygen, only during non-winter months did urban streams have significantly higher temperatures and significantly lower dissolved oxygen as well as significant positive correlations between urban intensity and temperature and significant negative correlations between urban intensity and dissolved oxygen. Urban streams had the highest pH during all months, but none of the differences were significant. Significant correlations between pH and urban intensity were restricted generally to winter and spring months. Turbidity was highest for exurban watersheds and thus was not significantly positively correlated with urban intensity. Seasonal and stream-group differences in the weathering of concrete, use of road salts, solar heating of streams, urban surface temperatures, baseflow, and land-disturbance activities explain the water-quality findings. Most of the findings should be applicable to urban areas in humid subtropical climates within the Piedmont physiographic region.

Non-negligible clear-sky biases of satellite thermal infrared observations for analyzing surface urban heat island intensity: A case study in China

Surface urban heat island (SUHI) intensity generally determined by satellite-derived clear-sky land surface temperature (LST) has ignored the impacts of cloud coverage and cannot reflect the real SUHI intensity. Only a few studies focus on the effects of this issue based on short-time LST datasets, which could contain non-negligible uncertainties to summarize reliable rules. To investigate the influence, the SUHI intensity (SUHII) clear-sky bias (CSB), which is defined as the SUHII difference between clear-sky and all-weather conditions, was investigated in 35 cities in China, based on clear-sky and all-weather LST datasets from 2003 to 2022. Results show that the two SUHIIs show similar spatial distribution patterns, with stronger SUHIs in southern China at daytime and weaker at nighttime. However, a non-negligible difference can be found between these two SUHIIs, with a SUHII CSB range of −1.43 to 2.27 °C at daytime and − 2.17 to 0.91 °C at nighttime. In terms of intra-annual variation, SUHII CSBs in similar climate regions exhibit similar patterns but different ranges due to their different natural properties. Generally, intra-annual variations of SUHII CSB can be divided into three groups, i.e., “Table Mountain”, single peak, and single valley, varying across climate regions and years. The main reason for SUHII CSB was analyzed, i.e., spatial gaps of the data directly caused the SUHII CSB, and the thermal properties and meteorological conditions of the missing pixels affect the magnitude of the SUHII CSB. Taking the urban system as an example, this study has provided evidence of the non-negligible SUHII clear-sky bias to emphasize the importance of using all-weather LST for relevant studies.

Contrasting nature of aerosols over South Asian cities and its surrounding environment

Cross-country assessment of aerosol loading was made over several South Asian megacities using multiple high-resolution remote-sensing database to assess how aerosols vary within the city and its suburbs. Parameters sensitive to aerosol optical and microphysical properties were processed over city-core and its surrounding, separated by a buffer. Cities across the Indo-Gangetic Plain (IGP; AOD:0.52–0.72) along with Mumbai (0.47) and Bangalore (0.46) denote comparatively high aerosol loading against non-IGP cities. City-core specific AOD was invariably high compared to surrounding, however with varying gradient having robust geographical signature. Exceptions to this general trend were in Kathmandu (ΔAOD: 0.07) and Dhaka (ΔAOD: 0.01) while strong positive AOD gradient was noted in Bangalore (+0.11), Colombo (+0.08) and in Mumbai (+0.07). While all mainland cities exhibited robust intraannual variability, distinction between city-core and its surrounding AOD exhibited varying seasonality. City-specific geometric coefficient of variation indicated insignificant association with mean AOD as opposed to European and American cities. Both pixel-based and city-specific analysis revealed a strong increasing trend in AOD with highest magnitude in Varanasi and Bangalore. Aerosol sub-types based on aerosols’ sensitivity to UV-absorption and particle size denotes higher relative abundance of carbonaceous smoke aerosols within city-core, without having significant distinction for mineral dusts and urban aerosols.

Simulation based Ecological Risk Assessment of the Black Sea Ecosystem

The article presents results of the conducted simulation research in ecological risk for three shelf areas of the Black Sea exposed to different combinations of the external stressors. Simulated risk calculations were performed for the aggregated phytoplankton taking into account the intra-annual variations in biomass values obtained from the observation data. The results revealed significant dependence of the ecological risk on the intra-annual state of phytoplankton, and also made it possible to calculate dependence of the acceptable probability of impacts on the ecological risk. The latter identified areas of probable errors of the second kind, which is of practical importance for ensuring ecological safety in the shelf resources development.

Interannual variability and seasonality of litterfall in three temperate and boreal forest ecosystems of eastern Canada: A synthesis of long-term monitoring

Litterfall is a major pathway for transferring aboveground biomass to the forest floor and thus plays an important role in building forest soil carbon stocks. However, inter- and intra-annual variability of litterfall remains poorly documented, especially in North American temperate and boreal forests, due to the lack of recent long-term studies at high sampling frequencies. This potentially creates uncertainties in estimates of forest carbon budget models. The objectives of the present study were to 1) quantify the mean annual flux, interannual variability, and seasonality of litterfall in three sites (dominated respectively by sugar maple (Acer saccharum Marsh.), balsam fir (Abies balsamea (L.) Mill. 1768), and black spruce (Picea mariana (Mill.) B.S.P.)) in eastern Canada over a period of 22–32 years, 2) relate the litterfall amounts and temporal variations to the changes in the size of major organic matter pools in these ecosystems, and 3) compare our litterfall estimates with reference values used in national greenhouse gas inventories. Litterfall production decreased from the sugar maple to the balsam fir and black spruce sites, preponderantly due to species composition. Litterfall evolution was related to the aboveground biomass of live trees in both conifer sites; in contrast, in the broadleaf site, changes in forest composition and structure were apparently the main drivers. The litterfall seasonality varied between broadleaf and conifer sites and could be explained by a sigmoidal model. Substantial departures from the seasonality for some given years were likely due to important climatic anomalies. Forest floor biomass remained stable over time at all three sites despite the increase in litterfall at the balsam fir and sugar maple sites and rapid forest floor turnover at the latter site. Our analyses of litterfall suggest that reference values from the literature used for national greenhouse gas inventories underestimate annual litterfall and forest floor carbon stocks for temperate and boreal forests.

Using citizen science data for predicting the timing of ecological phenomena across regions.

The scarcity of long-term observational data has limited the use of statistical or machine-learning techniques for predicting intraannual ecological variation. However, time-stamped citizen-science observation records, supported by media data such as photographs, are increasingly available. In the present article, we present a novel framework based on the concept of relative phenological niche, using machine-learning algorithms to model observation records as a temporal sample of environmental conditions in which the represented ecological phenomenon occurs. Our approach accurately predicts the temporal dynamics of ecological events across large geographical scales and is robust to temporal bias in recording effort. These results highlight the vast potential of citizen-science observation data to predict ecological phenomena across space, including in near real time. The framework is also easily applicable for ecologists and practitioners already using machine-learning and statistics-based predictive approaches.

Hydrologic variability governs GHG emissions in rice-based cropping systems of Eastern India

Reducing methane (CH4) emissions is increasingly recognized as an urgent greenhouse gas mitigation priority for avoiding ecosystem ‘tipping points’ that will accelerate global warming. Agricultural systems, namely ruminant livestock and rice cultivation are dominant sources of CH4 emissions. Efforts to reduce methane from rice typically focus on water management strategies that implicitly assume that irrigated rice systems are consistently flooded and that farmers exert a high level of control over the field water balance. In India most rice is cultivated during the monsoon season and hydrologic variability is common, particularly in the Eastern Gangetic Plains (EGP) where high but variable rainfall, shallow groundwater, and subtle differences in topography interact to create complex mosaics of field water conditions. Here, we characterize the hydrologic variability of monsoon season rice fields (n = 207) in the Indian EGP (‘Eastern India’) across two contrasting climate years (2021, 2022) and use the Denitrification Decomposition (DNDC) model to estimate GHG emissions for the observed hydrologic conditions. Five distinct clusters of field hydrology patterns were evident in each year, but cluster characteristics were not stable across years. In 2021, average GHG emissions (8.14 mt CO2-eq ha−1) were twice as high as in 2022 (3.81 mt CO2-eq ha−1). Importantly, intra-annual variability between fields was also high, underlining the need to characterize representative emission distributions across the landscape and across seasons to appropriately target GHG mitigation strategies and generate accurate baseline values. Simulation results were also analyzed to identify main drivers of emissions, with readily identified factors such as flooding period and hydrologic interactions with crop residues and nitrogen management practices emerging as important. These insights provide a foundation for understanding landscape variability in GHG emissions from rice in Eastern India and suggest priorities for mitigation that honor the hydrologic complexity of the region.

Assessment of Changes in Hydrometeorological Indicators and Intra-Annual River Runoff in the Ile River Basin

Water management strategies in the Ile River basin (Republic of Kazakhstan) have traditionally relied on historical data without fully considering the potential impacts of climate change. This gap can lead to underestimating the changes in temperature, precipitation patterns, and runoff, hindering effective water resource management. This study aimed to analyze how a changing climate is affecting the Ile River basin’s water regime. Specifically, it investigated trends in temperature, precipitation, and runoff within the basin, emphasizing the importance of incorporating these intra-annual variations when planning water management strategies and hydraulic structures. A detailed analysis of the long-term data was conducted, focusing on changes in meteorological indicators. This included average air temperatures and annual precipitation for elevations above and below 1500 m during cold and warm periods. The analysis aimed to identify and quantify trends of increase or decrease. Meteorological stations were strategically chosen to represent both arid and humid areas within the basin, accounting for the region’s significant altitude variations. The investigation revealed several key findings. Rising average annual air temperatures are leading to a larger area experiencing snowmelt and a longer warm period within the runoff formation zone. This directly impacts the water balance of the basin. Additionally, an increase in total annual precipitation, particularly during the cold season within the runoff formation zone, suggests a potential for future water resource growth, assuming that these trends persist. This study highlights the importance of considering intra-annual variations in water regimes when developing water management strategies. The observed changes in temperature and precipitation patterns within the Ile River basin necessitate adjustments to existing plans to ensure sustainable water resource management in a changing climate.

The Influence of Wildfire Climate on Wildfire Incidence: The Case of Portugal

Although the influence of climate on the fire regime is unanimously recognized, most publications and studies on this influence are on a global scale. Therefore, this study aims to demonstrate the role of climate in wildfire incidence at the country and regional scale using multivariate statistical analysis and machine learning methods (clustering and classification algorithms). Mainland Portugal was chosen as a case study due to its climate and because it is the European region most affected by wildfires. The results demonstrate the climate signature in the spatial and temporal distribution of the wildfire incidence. The conclusions of the study include (i) the existence of two pyro-regions, with different types of climate (Csb and Csa) composed of NUTS II regions: the northern region composed of the Norte and Centro regions and the southern region composed of Alentejo and Algarve; (ii) the intra-annual variability in the wildfire incidence, characterized by two peaks, one in the spring and the other in the summer, are a consequence of the country’s type of climate; and (iii) how the annual cycle of wildfire incidence varies over the years depends on the weather conditions throughout each year. These results are of fundamental importance for wildfire managers, especially in the context of climate change.

A Radar-Based 10-Year Climatology of Convective Snow Events in Central Pennsylvania

Abstract Convective snow (CS) presents a significant hazard to motorists and is one of the leading causes of weather-related fatalities on Pennsylvania roadways. Thus, understanding environmental factors promoting CS formation and organization is critical for providing relevant and accurate information to those impacted. Prior research has been limited, mainly focusing on frontal CS bands often called “snow squalls”; thus, these studies do not account for the diversity of CS organizational modes that is frequently observed, highlighting a need for a robust climatology of broader CS events. To identify such events, a novel, radar-based CS detection algorithm was developed and applied to WSR-88D radar data from 10 cold seasons in central Pennsylvania, during which 159 cases were identified. Distinct convective organization modes were identified: linear (frontal) snow squalls, single cells, multicells, and streamer bands. Each algorithm-flagged radar scan containing CS was manually classified as one of these modes. Interestingly, the most-studied frontal mode only occurred <5% of the time, whereas multicellular modes dominated CS occurrence. Using the times associated with each CS mode, synoptic and local environmental information from model analyses was investigated. Key characteristics of CS environments compared to null cases include a 500-hPa trough in the vicinity, lower-tropospheric conditional instability, and sufficient moisture. Environments favorable for the different CS modes featured statistically significant differences in the 500-hPa trough axis position, surface-based CAPE, and the unstable layer depth, among others. These results provide insights into forecasting CS mode, explicitly presented in a forecasting decision tree. Significance Statement Convective snow events such as snow squalls are a leading cause of weather-related deaths on Pennsylvania roads. Research into these events is limited, thus negatively impacting forecast skill. To better understand convective snow event frequency of occurrence, inter- and intra-annual variability, and their supporting environments, we performed a 10-yr radar-based climatology of these events. We report the results of this climatology and on the statistically significant differences in their supporting environments. The latter are used to propose a forecasting framework for convective snow, which may improve the predictability of convective snow in an operational setting.

Intra-annual vegetation changes and spatial variation in China over the past two decades based on remote sensing and time-series clustering.

Vegetation is an important link between land, atmosphere, and water, making its changes of great significance. However, existing research has predominantly focused on long-term vegetation changes, neglecting the intra-annual variations of vegetation. Hence, this study is based on the Enhanced Vegetation Index (EVI) data from 2000 to 2022, with a time step of 16days, to analyze the intra-annual patterns of vegetation changes in China. The average intra-annual EVI values for each municipal-level administrative region were calculated, and the time-series k-means clustering algorithm was employed to divide these regions, exploring the spatial variations in China's intra-annual vegetation changes. Finally, the ridge regression and random forest methods were utilized to assess the drivers of intra-annual vegetation changes. The results showed that: (1) China's vegetation status exhibits a notable intra-annual variation pattern of "high in summer and low in winter," and the changes are more pronounced in the northern regions than in the southern regions; (2) the intra-annual vegetation changes exhibit remarkable regional disparities, and China can be optimally clustered into four distinct clusters, which align well with China's temperature and precipitation zones; and (3) the intra-annual vegetation changes demonstrate significant correlations with meteorological factors such as dew point temperature, precipitation, maximum temperature, and sea-level pressure. In conclusion, our study reveals the characteristics, spatial patterns and driving forces of intra-annual vegetation changes in China, which contribute to explaining ecosystem response mechanisms, providing valuable insights for ecological research and the formulation of ecological conservation and management strategies.

Dust Events over the Urmia Lake Basin, NW Iran, in 2009–2022 and Their Potential Sources

Nowadays, dried lake beds constitute the largest source of saline dust storms, with serious environmental and health issues in the surrounding areas. In this study, we examined the spatial–temporal distribution of monthly and annual dust events of varying intensity (dust in suspension, blowing dust, dust storms) in the vicinity of the desiccated Urmia Lake in northwestern (NW) Iran, based on horizontal visibility data during 2009–2022. Dust in suspension, blowing dust and dust storm events exhibited different monthly patterns, with higher frequencies between March and October, especially in the southern and eastern parts of the Urmia Basin. Furthermore, the intra-annual variations in aerosol optical depth at 500 nm (AOD550) and Ångström exponent at 412/470 nm (AE) were investigated using Terra/Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) data over the Urmia Lake Basin (36–39°N, 44–47°E). Monthly distributions of potential coarse aerosol (AE < 1) sources affecting the lower troposphere over the Urmia Basin were reconstructed, synergizing Terra/Aqua MODIS AOD550 for AE < 1 values and HYSPLIT_4 backward trajectories. The reconstructed monthly patterns of the potential sources were compared with the monthly spatial distribution of Terra MODIS AOD550 in the Middle East and Central Asia (20–70°E, 20–50°N). The results showed that deserts in the Middle East and the Aral–Caspian arid region (ACAR) mostly contribute to dust aerosol load over the Urmia Lake region, exhibiting higher frequency in spring and early summer. Local dust sources from dried lake beds further contribute to the dust AOD, especially in the western part of the Urmia Basin during March and April. The modeling (DREAM8-NMME-MACC) results revealed high concentrations of near-surface dust concentrations, which may have health effects on the local population, while distant sources from the Middle East are the main controlling factors to aerosol loading over the Urmia Basin.

Using Sentinel-2 Imagery to Measure Spatiotemporal Changes and Recovery across Three Adjacent Grasslands with Different Fire Histories

As a result of the advocacy of Indigenous communities and increasing evidence of the ecological importance of fire, California has invested in the restoration of intentional burning (the practice of deliberately lighting low-severity fires) in an effort to reduce the occurrence and severity of wildfires. Recognizing the growing need to monitor the impacts of these smaller, low-severity fires, we leveraged Sentinel-2 imagery to reveal important inter- and intra-annual variation in grasslands before and after fires. Specifically, we explored three methodological approaches: (1) the complete time series of the normalized burn ratio (NBR), (2) annual summary metrics (mean, fifth percentile, and amplitude of NBR), and (3) maps depicting spatial patterns in these annual NBR metrics before and after fire. We also used a classification of pre-fire vegetation to stratify these analyses by three dominant vegetation cover types (grasses, shrubs, and trees). We applied these methods to a unique study area in which three adjacent grasslands had diverging fire histories and showed how grassland recovery from a low-severity intentional burn and a high-severity wildfire differed both from each other and from a reference site with no recent fire. On the low-severity intentional burn site, our results showed that the annual NBR metrics recovered to pre-fire values within one year, and that regular intentional burning on the site was promoting greater annual growth of both grass and shrub species, even in the third growing season following a burn. In the case of the high-severity wildfire, our metrics indicated that this grassland had not returned to its pre-fire phenological signals in at least three years after the fire, indicating that it may be undergoing a longer recovery or an ecological shift. These proposed methods address a growing need to study the effects of small, intentional burns in low-biomass ecosystems such as grasslands, which are an essential part of mitigating wildfires.

Night lights observations significantly improve the explainability of intra-annual vegetation growth globally

Understanding the underlying mechanism of vegetation growth is of great significance to improve our knowledge of how vegetation growth responds to its surrounding environment, thereby benefiting the prediction of future vegetation growth and guiding environmental management. However, human impacts on vegetation growth, especially its intra-annual variability, still represent a knowledge gap. Night Lights (NL) have been demonstrated as an effective indicator to characterize human activities, but little is known about the potential improvement of intra-annual vegetation growth using seasonal NL observations. To address this gap, we investigated and quantified the explainability improvement of intra-annual vegetation growth by establishing a multiple linear regression model for vegetation growth (indicated by Normalized Difference Vegetation Index, NDVI) with human factor (indicated by NL observations here) and three climatic factors, i.e., temperature, water availability, and solar radiation using the Principal Components Regression (PCR) method. Results indicate that NL observations significantly improve our understanding of intra-annual vegetation growth globally. Model explainability, i.e., adjusted R2 metric of the PCR model, was comparatively improved by 54 % on average with a median value of 11 % when taking NL observations into consideration. Such improvement occurred in 82 % of the whole investigation pixels. We found that the improvement of model explanatory power was significant in regions where both NL and NDVI trends were large, except for the case where both of their trends were negative. At the country-level, the improvement of model explanatory power increases as GDP decreases, illustrating a greater improvement in a lower middle-income country than that in a high-income country. Our findings emphasize the importance of considering human activities (indicated by NL here) in vegetation growth, offering novel insights into the explanation of intra-annual vegetation growth.

Three years of CO2, CH4 and N2O fluxes from different sheepfolds in a semiarid steppe region, China

To better assess greenhouse gas (GHG) emissions from livestock folds in semi-arid steppe zones and reduce uncertainties in regional and national GHG emission inventories, we measured the fluxes of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) from sheepfolds under contrasting management regimes (i.e., summer sheepfolds under continuous and rotational grazing strategies and the winter sheepfold) for 3 consecutive years. Our results showed that these GHG fluxes had high intra-annual and interannual variations, emphasizing the importance of multi-year measurement for achieving temporally representative annual budgets. Sheep presence and temperature appeared to be the key factors driving CH4, CO2 and N2O fluxes from sheepfolds, e.g., higher GHG emissions usually occurred in seasons with sheep presence. However, the sheepfold type exerted a distinct influence on the temperature sensitivity of GHG fluxes, i.e., the Q10 values for GHG fluxes were generally higher in summer sheepfolds than in winter sheepfold. The annual CH4, CO2 and N2O emissions for the 3 sheepfolds were estimated to be 1.5–16.5 kg C ha−1 yr−1 (or 1.9–2.6 g C yr−1sheep−1), 8.6–16.0 t C ha−1 yr−1 (or 5.1–6.6 kg C yr−1sheep−1) and 28.3–41.9 kg N ha−1 yr−1 (or 19.0–26.8 g N yr−1sheep−1), respectively. Averaging across the 3 years, the annual net GHG emissions (CH4 + CO2 + N2O) for all sheepfolds ranged from 47 to 71 t CO2-eq ha−1 yr−1 (or 27–36 kg CO2-eq yr−1 sheep−1), of which CO2 and N2O emissions contributed the most; moreover, the annual net GHG emissions had no significant differences between sheepfold types or grazing strategies. Given that local steppe soils have a lower magnitude of soil respiration (CO2) and N2O emissions and are also net sink for atmospheric CH4, the sheepfold sites in this region are undoubtedly one of the significant hotspots for GHG emissions and could be key areas to focus mitigation action.

Soil Hydrothermal Dynamics in the Hengduan Mountains of Southeast Tibet and Associated Influencing Factors

Soil water and soil temperature are important ecological factors and driving forces for ecosystem restoration and sustainable development, possessing great significance for climate modeling and prediction. The Hengduan Mountains in southeastern Tibet, China, are located in a climate-change-sensitive area, and the study of soil hydrothermal dynamics in this area is of great significance for local and global climatic change and water resource utilization. This study, based on the soil hydrothermal and meteorological data of the Hengduan Mountain area in Southeast Tibet, analyzes the dynamic change patterns of soil hydrothermal and meteorological factors and explores their influencing relationships. It was found that the dynamic change in soil water content affected by precipitation was “bimodal” type. Among the meteorological factors, soil water content has the strongest correlation with relative humidity. The intra-annual variation curve of soil temperature is similar to that of the atmospheric temperature, showing a “unimodal” type, and has the highest correlation with atmospheric temperature. Specifically, it takes 70 mm and 170 mm of precipitation to change the soil water content and soil temperature at the 150 cm depth. For every 20 °C change in atmospheric temperature, soil temperature above 150 cm changes by an average of 7.2 °C.

Investigation of biological-rhythm patterns: physiological and biochemical effects in herbaceous plants exposed to low-level chronic radiation – part 1: nonirradiated seeds

Hypothesis Because reactive oxygen species are involved in the regulation of biological rhythms, we hypothesized that intra-annual variability of seed progeny quality at low doses of ionizing radiation (LDIRs) would differ from that of background plants. Materials and methods We conducted 12 consecutive experiments using the roll culture method by germinating seeds (monthly for 3 weeks) of six herbaceous plant species (Bromus inermis, Geum aleppicum, Plantago major, Rumex confertus, Silene latifolia, and Taraxacum officinale) growing under conditions of chronic radiation in the East Ural Radioactive Trace (EURT). We assessed physiological (seed viability and abnormality frequency) and biochemical (low-molecular-weight antioxidants, LMWAs) parameters of seedlings. Results Total absorbed dose rates of maternal plants (TADRplants) and seed embryos (TADRseeds) in the EURT exceeded background levels by 1–3 and 1–2 orders of magnitude, respectively. Nonlinear dependencies on TADR were mainly characteristic of physiological and biochemical parameters. For most populations of the studied species (B. inermis, G. aleppicum, R. confertus, and S. latifolia), seedling survival and root length decreased in the autumn–winter period, while the frequency of abnormal seedlings increased. The content of LMWAs could be ranked as R. confertus > B. inermis > G. aleppicum > S. latifolia, in good agreement with the presence of anthocyanin pigmentation in the plants. The lowest synthesis of antioxidants in seedlings was observed in winter. A high LMWA content promoted growth and reduced the frequency of abnormal seedlings. Conclusions These results underscore a multistage nature of the impact of LDIRs on intra-annual biological rhythms in plants. High heterogeneity in reference group ‘wild grasses’ and diversity of their radiobiological effects should help to develop methods of radiation protection for natural ecosystems and facilitate approaches used by the International Commission on Radiological Protection. Highlights Absorbed dose rates for six plant species in the East Ural Radioactive Trace (EURT) area range from 0.11 to 73.89 µGy h−s (plants) and 0.11 to 6.88 µGy h−s (seed embryos). Intra-annual rhythms of physiological and biochemical parameters in the EURT zone differ from those in background seedlings. Plants in the EURT area exhibit a wide range of trait variability, asynchrony of the manifestation of the effects, nonlinear dose–response relations, and hormesis. A high content of low-molecular-weight antioxidants (LMWAs) is associated with low frequency of developmental abnormalities and high viability of seed progeny.