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Aim Poleward range shifts of species are among the most obvious effects of climate change on biodiversity. As a consequence of these range shifts, species communities are predicted to become increasingly composed of warm‐dwelling species, but this has only been studied for a limited number of taxa, mainly birds, butterflies and plants. As species groups may vary considerably in their adaptation to climate change, it is desirable to expand these studies to other groups, from different ecosystems. Freshwater macroinvertebrates, such as dragonflies (Odonata), have been ranked among the species groups with highest priority. In this paper, we investigate how the occurrence of dragonflies in Europe has changed in recent decades, and if these changes are in parallel with climate change. Location Europe. Methods We use data from 10 European geographical regions to calculate occupancy indices and trends for 99 (69%) of the European species. Next, we combine these regional indices to calculate European indices. To determine if changes in regional dragonfly communities in Europe reflect climatic warming, we calculate Species Temperature Indices (STI), Multi‐species Indicators (MSI) and Community Temperature Indices (CTI). Results 55 of 99 considered species increased in occupancy at European level, 32 species remained stable, and none declined. Trends for 12 species are uncertain. MSI of cold‐dwelling and warm‐dwelling species differ in some of the regions, but increased at a similar rate at European level. CTI increased in all regions, except Cyprus. The European CTI increased slightly. Main conclusions European dragonflies, in general, have expanded their distribution in response to climate change, even though their CTI lags behind the increase in temperature. Furthermore, dragonflies proved to be a suitable species group for monitoring changes in communities, both at regional and continental level.

Droughts and heat waves are currently recognized as two of the most serious threats associated with climate changes. Drought is characterized by prolonged dry periods, low precipitation, and high temperature, while heat wave refers to an extended period of exceptionally high temperature, surpassing the region’s average for that time of year. There is a close relationship between droughts and heat waves, as both are often caused by similar weather patterns and can exacerbate each other’s impacts. Therefore, it is crucial to monitor and quantify both droughts and heat waves jointly at a regional level in order to develop sustainable policies and effectively manage water resources. This article develops a new index, the standardized composite index for climate extremes (SCICE), for joint monitoring and probabilistic quantification of extreme climate events at regional level. The procedure of SCICE is mainly based on the joint standardization of standardized precipitation index (SPI) and standardized temperature index (STI). In the application of SCICE, results reveal that the long-term probabilities of the joint occurrence of dry and hot events are significantly greater than those of wet and cold events. Furthermore, the outcomes of the comparative assessment support the validity of using SCICE as a compact statistical approach in regional drought analysis. In summation, the study demonstrates the capability of SCICE to effectively characterize and assess the joint monitoring of drought and heat waves at a regional level, providing a comprehensive approach to understanding the joint impact of climate extremes.

Young-of-the-year fish communities are widely used as bioindicators of various environmental disturbances. This study was conducted from 1997 to 2015 and aims to develop fish trait–based indices of changes in the temperature regime and eutrophication of water bodies in the Dnipro River basin. We identified fish traits that significantly correlate with both temperature and chlorophyll-a concentration optimum: reproduction habitat, oxygen tolerance, and toxicity tolerance. Compared to other ecological groups, lithophilic species exhibited the lowest degree of thermal and eutrophication optimum, indicating this species’ greater vulnerability to environmental alteration. Fish species that are intolerant to water quality and low oxygen concentration were the most sensitive to changes in temperature regime and eutrophication level. Salinity preferences and water quality tolerance emerged as reliable predictors of temperature optimum. Freshwater fish had an average temperature optimum that was 4.5% higher than that of freshwater-brackish and freshwater-brackish-marine fish. Species tolerance to the temperature factors and nutrient loads correlated only with rheophily, with rheophilic species having an average 13.8% higher temperature tolerance than other fish species and a 10.4% higher chlorophyll-a concentration tolerance. The fish temperature index increased over time during the study period in all the studied water bodies, consistent with ongoing warming affecting all sites. In contrast, the Fish Eutrophication Index showed greater temporal heterogeneity in studied water bodies, indicating various adaptative potentials of fish communities to eutrophication. These indices can be relevant for assessing disturbed situations caused by changes in climatic and anthropogenic impacts on water bodies.

The current study on spatiotemporal variability of temperature presents a holistic approach for quantifying the joint space–time variability of extreme temperature indices over the physio-climatically heterogeneous Tapi River basin (TRB) using two unsupervised machine learning algorithms, i.e., principal component analysis (PCA) and cluster analysis. The long-term variability in extreme temperature indices, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), was evaluated for 1951–2016. The magnitude and statistical significance of the temporal trend in extreme temperature indices were estimated using non-parametric Sen’s slope estimator and modified Mann Kendall (MMK) tests, respectively. The multivariate assessment of temporal trends using PCA resulted in four principal components (PCs) encapsulating more than 90% variability. The cluster analysis of corresponding PCs resulted in two spatial clusters exhibiting homogeneous spatiotemporal variability. Cluster 1 is characterized by significantly increasing hottest, very hot, and extremely hot days with rising average maximum temperature and intraday temperature variability. On the other hand, cluster 2 showed significantly rising coldest nights, mean minimum, mean temperature, and Tx37 with significantly decreasing intraday and interannual temperature variability, very cold, and extremely cold nights with reducing cold spell durations. The summertime heat stress computation revealed that the Purna sub-catchment of the Tapi basin is more vulnerable to various health issues and decreased work performance (> 10%) for more than 45 days per year. The current study dealing with the associated effects of rising temperature variability on crop yield, human health, and work performance would help policymakers formulate better planning and management strategies to safeguard society and the environment.

Against the backdrop of intensified global climate change, the frequency and intensity of extreme weather events in mainland China continue to rise due to its unique topography and complex climate types. In-depth research on the trends and impacts of climate extremes can help develop effective adaptation and mitigation strategies to protect the environment and enhance social resilience. In this research, temperature data from 2029 meteorological stations for the period 1961–2021 were used to study 15 extreme temperature indices and 3 extreme composite temperature indices. Linear propensity estimation and the Mann–Kendall test were applied to analyze the spatial and temporal variations in extreme temperatures in China, and Pearson’s correlation analysis was used to reveal the relationship between these indices and atmospheric circulation. The results show that in the past 60 years, the extreme temperature index in China has shown a trend of decreasing low-temperature events and increasing high-temperature events; in particular, the increase in warm nights is significantly higher than that of warm days. In terms of spatial distribution, daily maximum temperature less than the 10th percentile (TX10P) and daily minimum temperature greater than the 90th percentile (TN90P) increased significantly in the warm temperate sub-humid (WTSH) region, north subtropical humid (NSH) region, and marginal tropical humid (MTH) region, whereas frost days (FD0) and diurnal temperature range (DTR) decreased significantly. In the extreme composite temperature index, extreme temperature range (ETR) showed a downward trend, while compound heatwave (CHW) and compound heatwave and relative humidity (CHW-RH20) increased, with the latter mainly concentrated in the WTSH and NSH regions. Correlation analysis with climate oscillation shows that Arctic Oscillation (AO), Atlantic Multiannual Oscillation (AMO), and El Niño–Southern Oscillation (ENSO) are positively correlated with extremely high temperatures, whereas North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO) are negatively correlated.

To explore the spatiotemporal evolution of extreme temperature events in Guangxi (1940–2023), reveal regional response mechanisms, and assess future trends of persistence under climate warming, a multi-scale analysis was conducted using ERA5 reanalysis data. Methodologies included RH tests for homogeneity correction, collaborative kriging for data optimisation, Mann–Kendall tests for trend and abrupt change detection, Morlet wavelet analysis for cyclic pattern identification, Exploratory Spatio-Temporal Data Analysis (ESTDA) for spatial heterogeneity quantification, and Rescaled Range (R/S) analysis to calculate Hurst indices for future persistence assessment. Results showed the following: (1) The ERA5 dataset exhibited high applicability in Guangxi (R = 0.9989, RMSE = 1.9492 °C), supporting robust evidence of continuous warming—warm indices (e.g., SU25, TX90p) increased significantly (SU25 at 0.2044 d/10a), while cold indices (e.g., TN10p, FD0) declined (TN10p at −0.0519 d/10a); abrupt changes of cold indices were concentrated in 1942–1950, with warm indices accelerating post-2000 and TXx exhibited the highest warming rate (0.23 °C/decade). (2) Extreme temperature indices displayed a primary 19–21-year oscillation cycle (dominant in warm indices) and a secondary 13-year cycle (prominent in cold indices). (3) Spatial heterogeneity featured northwest–southeast cold–heat inversion, coastal–inland intensity gradients, and latitudinal zonation of extreme indices; ESTDA revealed intensified polarisation, with warm indices clustering in low-latitude regions (e.g., Baise) and cold indices declining homogeneously in mountainous areas (e.g., Guilin), indicating an irreversible transition to a warming steady state. (4) R/S analysis indicated all indices had Hurst indices of 0.65–0.92, reflecting persistent future trends consistent with historical evolution, with warm indices (e.g., TNn, SU25) showing stronger persistence (H > 0.85). This work clarifies the spatial polarisation mechanism and future persistence of extreme temperature dynamics in Guangxi, providing a multi-scale scientific basis for disaster early warning and adaptation planning in climate-sensitive karst-monsoon regions.

The change in the spatial distribution of agroclimatic conditions based on the sum of active temperatures (SAT), growing degree days (GDD), and latitude–temperature index (LTI) is discussed in this article. Data from 20 meteorological stations of IMGW-PIB (Institute of Meteorology and Water Management—National Research Institute) in Poland from the years 1966–2020 were used. The temporal and spatial diversity of mean air temperature and the chosen indices were analyzed for the period from April to October. Designating areas of diverse thermal conditions with respect to plant comfort on the basis of agroclimatic indices was attempted, together with mean air temperature and its temporal changes. The clustering, using the Ward’s method, yielded four regions with different thermal resources in Poland. The study period showed an increase in the values of all agroclimatic indices and air temperature during the growing season, suggesting an increase in the thermal resources in the territory of Poland.

The extreme temperature indices (ETI) are an essential indicator of climate change. The detection of their changes over the next years can play an essential role in the climate action plan (CAP). In this study, four temperature indices (mean of daily minimum temperature (TN), mean of daily maximum temperature (TX), cold-spell duration index (CSDI), and warm-spell duration index (WSDI)) were defined by ETCCDI and two new indices,the maximum number of consecutive frost days (CFD) and the maximum number of consecutive summer days (CSU), were used to examine ETIs in Iran under climate change conditions. We used minimum and maximum daily temperatures of five general circulation models (GCMs), including HadGEM2-ES, IPSL-CM5A-LR, GFDL-ESM2M, MIROC-ESM-CHEM, and NorESM1-M, from the set of CMIP5 bias-correction models. We investigated two representative concentration pathway (RCP) scenarios of RCP4.5 and RCP8.5 during the historical (1965–2005) and future (2021–2060 and 2061–2100) periods. The performance of each model evaluated using the Taylor diagram on a seasonal scale. Among models, GFDL-ESM2M and HadGEM2-ES showed the highest, and NorESM1-M and IPSL-CM5A-LR showed the lowest performance in Iran. Then, an ensemble model was generated using independence weighted mean (IWM) method. The results of multi-model ensembles (MME) showed a higher performance compared to individual CMIP5 models in all seasons. Also, the uncertainty value significantly reduced, and the correlation value of the MME model reached 0.95 in all seasons. Additionally, it is found that WSDI and CSU indices showed positive anomalies in future periods, and CSDI and CFD showed negative anomalies throughout Iran. Also, at the end of the twenty-first century, no cold spells are projected in almost every part of Iran. The CSU index showed that summer days are increasing sharply; according to the results of the RCP8.5 scenario in spring (MAM) and autumn (SON), the CSU will increase by 18.79 and 20.51 days, respectively, at the end of the twenty-first century. It projected that in the future, the spring and autumn seasons will be shorter and summers will be much longer than before.

Modelling streamflow in snow-covered mountainous regions with complex hydrology and topography poses a significant challenge, particularly given the pronounced influence of temperature lapse rate (TLAPS) and precipitation lapse rate (PLAPS). The Present study area covers 54,990 km2 in the western Himalayas, including the Tibetan Plateau and the Indian portion of the USRB up to Bhakra Dam in Himachal Pradesh. In order to estimate the snowmelt and rainfall runoff contributions to the catchment, an integrated Soil and Water Assessment Tool (SWAT) model incorporates a Temperature Index with an Elevation Band approach. The uncertainty analysis of the SWAT model has been conducted using the Sequential Uncertainty Fitting algorithm (SUFI-2). Furthermore, machine-learning models such as Long Short-Term Memory (LSTM) neural networks and Random Forest (RF) are integrated with the SWAT model to enhance the accuracy of streamflow predictions resulting from snowmelt. The performance indices of a model for the monthly calibration period are R2 = 0.83, NSE = 0.82, P-BIAS = 2.3, P-factor = 0.82, and R-factor = 0.81. The corresponding values for the validation period are R^2 = 0.78, NSE = 0.77, P-BIAS = 5.7, P-factor = 0.72 and R-factor = 0.66. The results show that 63.08% of the Bhakra gauging station’s annual streamflow has attributed to snow and glacier melt. The highest snow and glacier melt occur from May to August, while the minimum is observed from November to February. Regarding snowmelt forecasting, the LSTM model outperforms the RF model with an R 2 value of 0.86 and 0.85 during training and testing, respectively. Additionally, sensitivity analysis highlights that soil and groundwater flow parameters, specifically SOL_K, SOL_AWC, and GWQMN, are the most sensitive parameters for streamflow modelling. The study confirms the effectiveness of SWAT for water resource planning and management in the mountainous USRB.

Many species have suffered large population declines due to the anthropogenic influence on ecosystems. Understanding historical population trends is essential for informing best efforts to preserve species. We propose a new method to reconstruct the past structure of a regional species pool, based on historical naturalist literature. Qualitative information collected from annotated checklists and reports can be relevant to identify major long-term community changes. We reviewed ornithological literature on the Camargue, the largest wetland in France. We reconstructed the entire breeding bird community from 1830 to 2009 and translated historical data into semi-quantitative data. This data permitted a calculation of a Community Commonness Index to measure the average level of abundance of species in a community. The Community Specialization and Community Temperature Indices were used to evaluate the potential long-term impact of land-use and climate changes on the composition of the regional bird species pool. We found a decrease in average abundance and specialization between 1950 and 1989, suggesting that changes in land-use negatively impacted the structure and composition of the local bird community by reducing species abundance and removing habitat-specialists (e.g. Southern Grey Shrike, Greater Short-toed Lark). These results are likely to be linked with a major loss of natural habitats in the Camargue between 1942 and 1984 when natural areas and traditional farmland were converted into intensive cultivated lands. We also found fluctuations among species with high versus low temperature preference. However, long-term effects of climate change on the bird community might be blurred by the impact of land-use changes. Overall, our results contrast with those obtained from well-monitored colonial waterbirds showing long-term increases. Our results plead for a more regular use of historical naturalist data when examining long-term changes in species communities as they allow the establishment of an older temporal point of reference and consideration of species not covered by traditional monitoring schemes.