Intra-annual Variability Research Articles

Spatial and temporal predictions of mosquito potential breeding sites and densities: integration of satellite imagery, in-situ data, and process-based modeling

Abstract. Dengue fever, primarily transmitted worldwide by the mosquito Aedes aegypti, poses significant public health challenges in tropical and subtropical regions. While effective vector control is crucial in the absence of reliable dengue vaccines, traditional control methods face obstacles like mosquito resistance to insecticides and a very high cost. By combining geospatial data, including satellite imagery, as descriptors, and entomological surveys as target variables in a Random Forest model, we predicted the number of potential mosquito breeding sites, derived the associated environmental carrying capacity for larvae, and used the Arbocarto process-based model to predict Ae.aegypti population densities in an urban region of French Guiana, South America. Our findings highlight that remote sensing data may help predict the number of potential breeding sites over urban areas. Our simulations indicate higher mosquito densities in urban residential areas and a strong spatial and temporal heterogeneity. These densities fluctuate according to intra-annual variations in temperature and precipitation, with higher densities associated with intermediate housing. A comparison with the conventional estimation of environmental carrying capacity for larvae in the current Arbocarto procedure highlights the advantages of our approach. Our study demonstrates the utility of integrating remote sensing with predictive modeling to enhance vector surveillance and control strategies, and provides a replicable approach for monitoring a dengue vector mosquito population in dynamic urban landscapes.

How does a change in climate variability impact the Greenland ice sheet surface mass balance?

Abstract. Given the long response time of ice sheets, simulations of the Greenland ice sheet typically exceed the availability of input climate data to reliably simulate the fast processes underlying surface mass balance. Strong feedback processes are known to make the mass balance sensitive to inter- and intra-annual variability. Even simulations with climate models do not always cover the full period of interest, motivating bridging these gaps using relatively coarsely resolved climate reconstructions or temporal interpolation methods. However, both of these approaches usually only provide information about the climatological average but not variability. We investigate how this simplification impacts the surface mass balance using the BErgen Snow SImulator. The model was run for up to 500 years using the same atmospheric climatology but different synthetic variabilities. While changing inter-annual variations has an impact of less than 5 % on the surface mass balance of the Greenland ice sheet, neglecting intra-annual variability by using a daily climatology causes a 40 % change in mass balance. Decomposing the total effect into contributions from different input variables, the biggest contributor is precipitation followed by temperature. Using a daily climatology, a small amount of snowfall every day overestimates the albedo and thus surface mass balance (SMB). We propose a correction that re-captures the effect of intermittent precipitation, reducing the SMB overestimation to 15 %–25 %. We conclude that simulations of the Greenland surface mass and energy balance should be forced with a transient climate, in particular for models that are calibrated with transient data.

Understanding the effects of weather parameters on the population dynamics of an endangered geophyte supports monitoring efficiency.

Due to their complex life cycles geophytes are often neglected in conservation programs, despite they are important elements of early spring communities. Their life cycle is strongly affected by weather parameters, i.e. temperature, precipitation, and light, but the effects of these parameters are often contradictory and show high intra-annual variability even within species. Deeper knowledge about the abiotic factors affecting the population dynamics of geophytes is needed to support the designation of effective conservation plans. We aimed to explore the link between weather parameters and population dynamics of Colchicum bulbocodium, an endangered and strictly protected geophyte. We monitored three life cycle stages (flowering, growing, fruiting) of 1069 individuals in permanent plots for six consecutive years. Our results showed that life cycle of C. bulbocodium was strongly related to the actual weather parameters; the lagged effect of the previous year was weaker. Increasing temperature and lack of cold periods had negative effect on all life stages. We highlighted that population estimation based on the number of flowering individuals in a single year can underestimate population size by 40-83%. Monitoring in years following wet and cold springs and cold winters could increase the accuracy of population estimations of the flowering individuals.

Monitoring weekly δ 13С variations along the cambium-xylem continuum in the Canadian eastern boreal forest.

Intra-annual variations of carbon stable isotope ratios (δ13C) in different tree compartments could represent valuable indicators of plant carbon source-sink dynamics, at weekly time scale. Despite this significance, the absence of a methodological framework for tracking δ13C values in tree rings persists due to the complexity of tree ring development. To fulfill this knowledge gap, we developed a method to monitor weekly variability of δ13C in the cambium-xylem continuum of black spruce species [Picea mariana (Mill.) BSP.] during the growing season. We collected and isolated the weekly incremental growth of the cambial region and the developing tree ring from five mature spruce trees over three consecutive growing seasons (2019-2021) in Simoncouche and two growing seasons (2020-2021) in Bernatchez, both located in the boreal forest of Quebec, Canada. Our method allowed for the creation of intra-annual δ13C series for both the growing cambium (δ13Ccam) and developing xylem cellulose (δ13Cxc) in these two sites. Strong positive correlations were observed between δ13Ccam and δ13Cxc series in almost all study years. These findings suggest that a constant supply of fresh assimilates to the cambium-xylem continuum may be the dominant process feeding secondary growth in the two study sites. On the other hand, rates of carbon isotopic fractionation appeared to be poorly affected by climate variability, at an inter- weekly time scale. Hence, increasing δ13Ccam and δ13Cxc trends highlighted here possibly indicate shifts in carbon allocation strategies, likely fostering frost resistance and reducing water uptake in the late growth season. Additionally, these trends may be related to the black spruce trees' responses to the seasonal decrease in photosynthetically active radiation. Our findings provide new insights into the seasonal carbon dynamics and growth constraints of black spruce in boreal forest ecosystems, offering a novel methodological approach for studying carbon allocation at fine temporal scales.

Measurement report: Intra-annual variability of black carbon and brown carbon and their interrelation with meteorological conditions over Gangtok, Sikkim

Abstract. Black carbon (BC) and brown carbon (BrC) both have a versatile nature, and they have an apparent role in climate variability and changes. As anthropogenic activity is surging, BC and BrC are also reportedly increasing. So, the monitoring of BC and BrC and observations of land use land cover change (LULCC) at a regional level are necessary for the changes in various interconnected meteorological phenomena. The current study investigates BC, BrC, CO2, BC from fossil fuels (BCff), BC from biomass burning (BCbb), and LULCC and their relationship to the corresponding meteorological conditions over Gangtok in the Sikkim Himalayan region. The concentration of BC (BrC) was found to be highest during March 2022 (April 2021) at 43.5 µg m−3 (32.0 µg m−3)​​​​​​​. Surface pressure exhibits a significant positive correlation with BC, BCff, BCbb, and BrC. Higher surface pressure results in a calmer and more stable boundary layer, which effectively retains deposited contaminants. Conversely, the wind appears to facilitate the dispersion of pollutants, showing a strong negative correlation. The fact that all pollutants and precipitation have been shown to behave similarly points to moist scavenging of the pollutants. Despite the dense cloud cover, it is clear that the area is not receiving convective precipitation, implying that orographic precipitation is occurring over the region. Most of Sikkim receives convective rain from May to September, indicating that the region has significant convective activity contributed from the Bay of Bengal during the monsoon season. Furthermore, monsoon months have the lowest concentrations of BC, BCbb, BCff, and BrC, suggesting the potential of convective rain (as rainout scavenging) to remove most of the pollutants.

Inter- and Intra-Annual Variations in Oak Tree Ring δ13C Values across Different Elevations and Their Climatic Responses in Qinling Mountains

The Qinling Mountains, serving as a natural geographical and climatic boundary in China, require comprehensive climatic records to elucidate the trends in climate changes across the country. While stable isotopes in tree rings are widely employed to indicate historical environmental changes, investigations into tree ring isotopes in the Qinling Mountains, particularly within the widespread broad-leaf oaks, remain limited. In this study, we investigated both intra- and inter-annual variations in the δ13C values of tree rings and their correlations with climatic signals over the past two decades for Quercus aliena var. acuteserrata, a dominant species among oak trees on the main peak of the Qinling Mountains. Our results reveal that responses to climate differ among altitudes and individual trees, with trees at higher altitudes exhibiting higher sensitivity to extreme climate, which is low temperatures and rainfall fluctuations during the growth period in intra-annual δ13C variations. Furthermore, the positive correlations are observed between temperature during growing season and both tree growth and the inter-annual δ13C variations. However, the climate signal appears to be hampered by oak-specific factors, such as intense competition among individuals and the age of trees. Therefore, we suggest a more rigorous selection of sampling and propose further investigations into isotopic fractionation processes in oaks for future studies.

Spatiotemporal variations and driving factors of evapotranspiration in the Yunnan-Guizhou Plateau from 2003 to 2020

ABSTRACT Evapotranspiration (ET) is vital for the Earth's energy and water balance, particularly influenced by global climate change. The Yunnan-Guizhou Plateau (YGP), characterized by abundant water resources and intricate terrain, has been a subject of study. However, previous research often overlooked intra-annual climate variations in ET. This study employed high-spatiotemporal resolution ET data from 2003 to 2020 to quantitatively analyze the spatiotemporal characteristics of ET on the YGP. The annual ET showed an increasing trend of 0.18 mm/year, with monthly ET increases in January, March, November, and December, mainly influenced by vegetation transpiration, which accounts for 56% of ET. Breakpoints in ET trends and seasonal components occurred in January 2007 and June 2018. The geodetector model assessed the impact of 15 driving factors on ET, with net radiation and vegetation index playing dominant roles with q-values of 0.29 and 0.24. Factor impacts varied seasonally, with greater influence in the dry season (q-value of 0.53 for net radiation in January) and lesser in the rainy season (q-value of 0.08 in August). Pearson correlation analysis indicated that different driving factors influenced ET in different months. These findings enhance understanding of plateau ET responses to climate change mechanisms.

Analyses of sea surface chlorophyll a trends and variability from 1998 to 2020 in the German Bight (North Sea)

Abstract. Satellite remote sensing of ocean colour properties allows observation of the ocean with high temporal and spatial coverage, facilitating the better assessment of changes in marine primary production. Ocean productivity is often assessed using satellite-derived chlorophyll a concentrations, a commonly used proxy for phytoplankton concentration. We used the Copernicus GlobColour remote sensing chlorophyll a surface concentration to investigate seasonal and non-seasonal variability, temporal trends, and changes in spring bloom chlorophyll a magnitude. Complementary, we analysed the chlorophyll a relationship with sea surface temperature and mixed-layer depth in the German Bight from 1998 to 2020. Empirical orthogonal functions were employed in order to investigate dominant spatial and temporal patterns (modes) related to the main processes of chlorophyll a variability. Multi covariance analysis was used to extract the dominant structures that maximize the covariance between chlorophyll a and sea surface temperature mixed-layer depth fields. High levels of chlorophyll a were found near the coast, showing a decreasing gradient towards offshore waters. A significant chlorophyll a positive trend was observed close to the Elbe estuary and adjacent area, while 55 % of the German Bight was characterized by a significant chlorophyll a negative trend. The chlorophyll a non-seasonal variability showed that the first four modes explained around 45 %, with the first and second modes related to inter- and intra-annual variability, respectively, observed in the temporal principal components spectral analyses. Monthly chlorophyll a concentration anomalies co-varied by 45 % with sea surface temperature anomalies and 23 % with mixed-layer depth anomalies. The monthly averages of chlorophyll a anomaly fields were suitable to investigate long-term trends and variability. The rising water temperature, combined with its indirect effects on other variables, can partially explain the observed trends in chlorophyll a.

Seasonal variability in the global relevance of mountains to satisfy lowland water demand

Abstract Mountain areas play a vital role in global water resources as they often generate disproportionally high runoff and seasonally delay runoff due to storage as snow and ice. Water originating from mountains is used to satisfy human water demand further downstream in the lowlands of the corresponding river basins. Although the relevance of mountains for water supply is widely acknowledged, our current quantitative knowledge of their relevance for human water use on a global scale remains limited to decadal averages. As both water demand and mountain water supply have a strong seasonality, it is crucial to assess the global relevance of mountain areas beyond the annual time scale. To this end, we examined the share of lowland surface water abstraction (LSWA) stemming from mountain runoff in all river basins larger than 10,000 km2 globally from 1990 to 2019, focusing on the intra-annual variability. We distinguished between essential runoff contributions from low and high mountains and potential mountain runoff contributions to LSWA. Essential mountain contributions are defined as the share of water abstractions in the lowlands that can solely be satisfied by mountain runoff, whereas potential mountain contributions are the share that can originate from the mountains but does not necessarily have to. Our results confirm a strong spatial heterogeneity in the contribution of mountain runoff to LSWA. Globally, 15% of annual LSWA can solely be satisfied by mountain runoff, with monthly variations between 9 and 23%, highlighting the strong seasonality in the reliance on mountain runoff for lowland water use. The share of potential mountain contributions is much higher (51% annually). Slightly less than half of the essential mountain contributions to LSWA are sourced from high mountains. This shows the disproportional relevance of these regions, constituting only around one-third of the total mountain area. Furthermore, our results show an increasing dependence of lowlands on mountain water contributions.

Near-surface ozone concentration variability analysis in the Karadag Nature Reserve

The article presents the results of a study of a near-surface ozone concentration variability in the Crimea at the background environmental monitoring station in the Karadag Nature Reserve for 2012–2021 years with a more detailed analysis of the last six years from 2016 to 2021. A significantly high level air pollution of а near-surface ozone was revealed in the observation region, despite the absence of nearby sources of industrial emissions. The relationship of near-surface ozone concentration and meteorological parameters was investigated. Wind directions leading to increased levels of near-surface ozone pollution are established. Intra-annual variations of near-surface ozone concentration are analyzed, the factors causing the local summer minimum of surface ozone concentration in some years are established. By using the NOAA HYSPLIT model and ERA5 reanalysis, a spatial analysis of the atmospheric circulation pattern in the region was carried out. The recurrence of episodes of exceeding the ozone concentration 100 micrograms/m3 during 8 or more hours (WHO recommendation) was estimated. Possible causes of these episodes were determined. The mechanisms of long-range transport and its contribution to the near-surface ozone regime in the area of the station have been established. Annual trends of near-surface ozone concentration in the period 2012–2021 years are estimated as statistically insignificant.

Hydrokinetic energy site selection under high seasonality: The i-IHE index

A novel index is developed to select sites for hydrokinetic energy exploitation in coastal areas with high resource seasonality – the typical case being estuaries whose hydrodynamics are highly influenced by variable river discharges. The intra-annual Integrated Hydrokinetic Energy (i-IHE) index is constructed by means of a new penalty function that modifies the Integrated Hydrokinetic Energy (IHE) index. The i-IHE index thus defined is applied to a case study: the Miño Estuary (NW Iberian Peninsula), with seasonal variations of up to 1000% in the riverine discharge. The hydrodynamics are modelled by the state-of-the-art Delft3D-FLOW code using a high-resolution 2DH grid. The results confirm a strong variability in the resource, with time periods shorter than seasons. The results obtained for the penalty function throughout the Miño Estuary, with values ranging between 0.5 and 0.75, show the relevance of the i-IHE index in order to not overestimate the potential of coastal areas subject to large intra-annual variability. Compared to the conventional IHE index, the novel i-IHE index highlights smaller areas with greater energy potential. The larger exploitable energy in these areas, along with the lower costs and fewer restrictions for hydrokinetic energy operation, will be of interest to developers and stakeholders.

Inter- and intra-annual variability and climatic responses of ecosystem water use efficiency in a cool-temperate freshwater wetland

Wetland ecosystems play a pivotal role in terrestrial carbon and water cycles, thereby possessing great potential to regulate terrestrial water use efficiency (WUE), which is calculated as the ratio of gross primary productivity (GPP) to evapotranspiration (ET). However, it remains unclear the possible changes in wetland WUE under present and future climate conditions. In this research, WUE variations in a Phragmites australis-dominated freshwater wetland were determined by the eddy covariance method during 2020–2023. Further, we projected future GPP, ET, and WUE under four Representative Concentration Pathway (RCP) scenarios based on five Earth System Models. The 3-year average field observation suggested that the P. australis marsh exhibited high GPP (1149 g C m−2), but consumed large amounts of water through ET (611 mm H2O), resulting in relatively low WUE (1.89 g C mm−1 H2O). During wet years, the studied marsh consumed much more water through evaporation than through transpiration, thus exhibiting lower WUE. Contrarily, large amounts of water were utilized to maintain high primary productivity through transpiration in 2021–2022 dry year, leading to higher WUE. In future scenarios, GPP in the P. australis marsh shows consistently faster uptrends compared to ET from 2020 to 2100, consequently yielding persistent growths of WUE. Future growths of WUE indicate that P. australis marsh tends consume more water for maintaining productivity levels rather than loss via evaporation under future climate conditions, thereby intensifying the carbon–water interaction. Driven by the most rapid growth of environmental drivers, the RCP8.5 scenario shows the fastest increasing trends in GPP, ET, and WUE among four RCP scenarios, whereas the reverse ocurres under RCP2.6 scenario. Overall, our study advocates for more comprehensive research encompassing field observation and model simulation to address the current knowledge gap regarding the response of wetland WUE to contemporary and future climate change.

Effects of hydrological variability on the sustainable use of water in a regional economy. An application to Tuscany

Existing input-output (IO) models have mainly focused on water demand. Some studies have incorporated water supply (availability), but do not take into account its natural variability, an essential element when performing a water stress analysis. The present study integrates the hydrological variability of water availability into a hydroeconomic IO model, considering its exogenous effects on water supply and its exogenous effects on water demand. Two endogenous effects are considered: i) changes in blue water requirements in the agricultural industry due to variations in precipitation and evapotranspiration, and ii) changes in grey water requirements in all discharging industries due to variations in runoff and groundwater recharge. By means of a T-years hydrological series and Monte Carlo simulations, the model allows estimating T values of the Extended Water Exploitation Index (EWEI), obtaining its empirical probability distribution and confronting it with scarcity thresholds. Additionally, the model includes a methodology to incorporate intra-annual variation, obtaining the critical month EWEI and defining a more transparent and endogenous scarcity threshold. Empirically tested for the Italian region of Tuscany considering a multivariate hydrological model for the generation of a 100-year hydrological series, our results allow a more in-depth analysis of water scarcity in the region.

Assessing changes in intra- and inter-annual precipitation variability using advanced statistical inference techniques

ABSTRACT Aiming to find statistically significant changes on the usual monthly weather conditions in Alentejo, Portugal, in the past 40 years, time series of precipitation from a grid of locations in Alentejo were studied using an unconventional approach. The time series were divided into 4 decades and disposed in contingency tables with two factors, Year and Month. Then, log-linear models were fitted to those tables. The model deviances for Year and Month represent the weight of each factor in explaning precipitation varibility and Year:Month deviance is an estimate of the lack of independence between both factors, representing changes in intra-annual precipitation variability. The set of all the models deviances, per decade and location, were analysed using ANOVA techniques, first to compare the four decades, then to perform a crossed comparison between decades and between four pre-defined zones. Results indicate significant differences between the oldest and recent decades in terms of intra-annual precipitation variability, which could be interpreted as a trend towards softening the differences in precipitation between the wet months of the year. Furthermore, two homogeneous sub-regions could be defined in Alentejo, a large interior region and a smaller one close to the Atlantic Ocean.

Spatial–Temporal Variations and Driving Factors of the Albedo of the Qilian Mountains from 2001 to 2022

Surface albedo plays a pivotal role in the Earth’s energy balance and climate. This study conducted an analysis of the spatial distribution patterns and temporal evolution of albedo, normalized difference vegetation index (NDVI), normalized difference snow index snow cover (NSC), and land surface temperature (LST) within the Qilian Mountains (QLMs) from 2001 to 2022. This study evaluated the spatiotemporal correlations of albedo with NSC, NDVI, and LST at various temporal scales. Additionally, the study quantified the driving forces and relative contributions of topographic and natural factors to the albedo variation of the QLMs using geographic detectors. The findings revealed the following insights: (1) Approximately 22.8% of the QLMs exhibited significant changes in albedo. The annual average albedo and NSC exhibited a minor decline with rates of −0.00037 and −0.05083 (Sen’s slope), respectively. Conversely, LST displayed a marginal increase at a rate of 0.00564, while NDVI experienced a notable increase at a rate of 0.00178. (2) The seasonal fluctuations of NSC, LST, and vegetation collectively influenced the overall albedo changes in the Qilian Mountains. Notably, the highly similar trends and significant correlations between albedo and NSC, whether in intra-annual monthly variations, multi-year monthly anomalies, or regional multi-year mean trends, indicate that the changes in snow albedo reflected by NSC played a major role. Additionally, the area proportion and corresponding average elevation of PSI (permanent snow and ice regions) slightly increased, potentially suggesting a slow upward shift of the high mountain snowline in the QLMs. (3) NDVI, land cover type (LCT), and the Digital Elevation Model (DEM, which means elevation) played key roles in shaping the spatial pattern of albedo. Additionally, the spatial distribution of albedo was most significantly influenced by the interaction between slope and NDVI.

Improving evapotranspiration partitioning by integrating satellite vegetation parameters into a land surface model

Land evapotranspiration (ET) primarily involves vegetation transpiration, canopy interception loss, and soil evaporation. Previous studies have made significant progress in total ET estimation; however, substantial challenges remain in partitioning ET on a regional scale, largely due to the intricate water and energy balance that is disrupted by vegetation cover changes. The accuracy of land surface models in representing ET components may be constrained by their inadequate consideration of vegetation dynamics. In this study, we integrate satellite leaf area index (LAI) and fraction of vegetation coverage (FVC) into the Variable Infiltration Capacity model (VIC) to improve ET partitioning ability in the Loess Plateau of China, a region that has experienced substantial vegetation dynamics. The results showed that satellite dynamic vegetation parameters in modeling are effective in improving the estimation of ET components compared with the default/static vegetation parameters. Considering LAI dynamics in the model enhances the representation of the inter- and intra-annual variations in vegetation transpiration and canopy interception loss. Dynamic FVC reasonably allocates transpiration to soil evaporation, capturing evaporation in forest gaps effectively. This effect is particularly relevant in arid and semi-arid regions. Among the ET components, transpiration was the most sensitive to the two dynamic vegetation parameters, followed by canopy interception loss and soil evaporation. Through the VIC model with dynamic vegetation parameters, our study revealed that soil evaporation was twice that of transpiration in the Loess Plateau, which is consistent with its semi-arid region and relatively sparse vegetation coverage. Our study offers valuable insights regarding the use of vegetation coverage for partitioning ET and highlights the advantage of integrating satellite vegetation products into land surface models.

Contributions of Ionospheric Migrating Tides to Ionospheric Intra‐Annual Variations

AbstractThe Earth's ionosphere undergoes regular intra‐annual variations (IAVs) characterized by two peaks and troughs around the equinoxes and solstices. This phenomenon is crucial for analyzing the ionospheric response to geomagnetic storms. This study presents a comprehensive analysis of the IAVs contributed by diurnal and semidiurnal migrating tides (DW1 and SW2) using Global Ionospheric Maps (GIMs) data from 2017 to 2021. Through data stacking techniques, the seasonal variability and splitting phenomenon of DW1 and SW2 across different latitudes are examined. The findings indicate that the splitting of these tides can be attributed to their quasi‐periodic variations, predominantly composed of annual oscillation (AO) and semiannual oscillation (SAO). The combination of DW1, SW2, and their side‐band harmonics results in beats with annual and semiannual periodicities, enabling the restoration of the seasonal variations in DW1 and SW2. The ionospheric day‐to‐day variations were reconstructed by superimposing DW1 and SW2, and their IAVs were evaluated using the envelope method. Comparison with IAVs driven by Earth's orbital geometry reveals that tide‐driven IAVs are more significant, and both exhibit solar activity dependence. The results advance the understanding of ionospheric variability, emphasizing the critical role of tidal contributions.

Relationship of litterfall anomalies with climatic anomalies in a mangrove swamp of the Yucatan Peninsula, Mexico.

Among the set of phenological traits featuring mangrove ecosystems, litterfall production stands out with marked intra-annual and longer-term variation. Furthermore, mangrove forests resilience is one of the most important ecological attribute, reconciling the juxtaposed terrestrial and marine environment such transitional systems occupy. However, world's mangroves are nowadays facing recurrent climatic events, reflected in anomalies depicted by major drivers, including temperature and precipitation. This physical-environmental setting may either constrain or favor overall forest productivity. A combination of time series analysis (spectral density and cross-correlation techniques) and statistical model fitting (General additive model) was implemented to explore trends in total litterfall of a well-developed mangrove forest in southeastern Gulf of Mexico (Celestun Lagoon, SE Mexico) and potential association with the varying behavior of temperature (°C) and precipitation (mm month-1), highlighting their anomalies. The results are consistent with a synchronous response between litterfall production and climatic variables (mean monthly temperature and total monthly precipitation). Concurrent peak litterfall production in Celestun lagoon with high temperatures and precipitation occurred during June and October, featuring a two-month time lag for the response time. More than half of the litterfall anomalies (53.5%) could be reflecting either multiple sources of climatic anomalies (maximum, minimum, and monthly average temperature and monthly total precipitation) or single point events (cyclone landfall). This relationship dynamics showed an interannual persistence (1999-2010). The structure portrayed by the litterfall time-series was not unequivocally related to climatic anomalies. Arguably, climatic anomalies behave with different intensities and even may exhibit complex interactions among them. The study of anomalies provides a baseline for a better grasp of: i) mangrove anomalies responses and ii) their vulnerability to these extremes.

Variation in vessel traits of northern red oak (Quercus rubra L.) provenances revealed high phenotypic plasticity to prevailing environmental conditions

Key messageRed oak provenances responded with high plasticity and intra-annual variability in vessel traits to studied climatic conditions, indicating weak signals of local adaptation and providing opportunities for forest management.The climate change-induced increase in frequency and severity of extreme events has revealed a high vulnerability of various major tree species in Europe, stressing the need for selecting climate-resilient species for forest management. In this context, adaptive strategies of northern red oak (Quercus rubra L.) were examined, using wood anatomical data derived from a provenance trial. We investigated the interannual variation in vessel traits of red oak provenances planted at three sites along a precipitation gradient in Germany. We compared the climate sensitivity of German provenances with those from North America to analyze plasticity and to identify signals of local adaptation in vessel traits. The results revealed variations in vessel traits between all sites, pointing to site-specific responses to prevailing environmental conditions. Differences between provenances were prevalent for vessel size-related traits, with site-specific higher values for German provenances at wet sites. Climate signals, which varied between traits, were strongest for vessel density and the relative conductive area. Vessel traits were found to depend both on previous-year conditions as and on spring climate conditions during the onset of vessel formation. The site-specific response in extreme years deviate significantly between drought and frost events. A trade-off between resistance to extremes and vessel diameter could not be demonstrated, and provenances with larger vessel diameters showed higher frost resistance. The observed high plasticity in vessel traits and the site-specific variation to climate influences point to an adjustment in vessel formation to the prevailing environmental conditions.

Intra-annual variations and determinants of canopy layer urban heat island in China using remotely sensed air temperature and apparent temperature

Canopy layer urban heat island (CLUHI) is closely related to about 4.7 billion urban residents’ well-being. Compared with sparse observation stations, remote sensing can obtain spatially continuous temperature, which is conducive to intra-annual variation and associated factors of CLUHI over large regions. Such research is scarce, especially from apparent temperature perspective. Thus, the intra-annual spatio-temporal variations and twelve determinants of CLUHI intensities (CLUHIIs) were investigated for 917 urban region agglomerations in China’s five ecological regions, using remotely sensed near-surface air temperature, apparent temperature and other multi-source data, and several spatial analysis and mathematical statistic methods. The findings revealed that annual CLUHIIs by mean temperatures were most between 0.5 and 1.0 ℃, higher in the summer and spring, and larger in North China than South China in the summer. The annual CLUHIIs by maximum temperatures were negative in several cities, especially in rainless West China. North China generally had stronger CLUHIIs by minimum temperatures in all seasons compared to South China. The CLUHIIs by mean apparent temperatures were larger in the summer (most between 0.5 and 2.0 ℃) and varied across ecological regions. Significant positive correlations generally existed among CLUHIIs by various air temperature and land surface temperature indices (p < 0.05). Nevertheless, obvious differences existed among them. The CLUHII was significantly negatively partially correlated with total precipitation, urban–rural difference in enhanced vegetation index and albedo, positive with urban–rural difference in nighttime light intensity, population density, sulfur dioxide concentration, insignificant with total population, urban area size and landscape shape index in different degree. These findings provided valuable insights into CLUHI research.