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Investigating the research trends within a scientific domain by analyzing semantic information extracted from scientific journals has been a topic of interest in the natural language processing (NLP) field. A research trend evaluation is generally based on the time evolution of the term occurrence or the term topic, but it neglects an important aspect—research publication latency. The average time lag between the research and its publication may vary from one month to more than one year, and it is a characteristic that may have significant impact when assessing research trends, mainly for rapidly evolving scientific areas. To cope with this problem, the present paper is the first work that explicitly considers research publication latency as a parameter in the trend evaluation process. Consequently, we provide a new trend detection methodology that mixes auto-ARIMA prediction with Mann–Kendall trend evaluations. The experimental results in an electronic design automation case study prove the viability of our approach. [ABSTRACT FROM AUTHOR]

The Mann-Kendall test has been proposed as a nonparametric method to evaluate trends in long-term water quality datasets with missing values, serial correlation, and non-normality. However, this test has rarely been used to evaluate long-term trends in biological data. In this study, we used the Mann-Kendall test to evaluate trends in 15 years of data on benthic macroinvertebrate communities from 17 Precambrian Shield lakes. We also used the van Belle and Hughes test of trend homogeneity to assess whether common among-lake temporal trends existed. We assumed that evidence of a common regional trend among lakes would support the hypothesis of long-term biological recovery from past acidification. We found decreasing proportions of Chironomidae and increasing proportions of Ephemeroptera, Plecoptera, and Trichoptera (EPT) in both single-lake and multi-lake trend analysis. Moreover, six of the nine lakes with significant trends in more than one benthos metric displayed a significant decrease in Chironomidae and increase in EPT concurrently, indicating a shift towards more acid-sensitive taxa. Weak trends in several of the biological metrics indicated that recovery in these lakes has been impeded. Results of this study indicate that the Mann-Kendall and van Belle and Hughes trend tests are useful statistical tools to evaluate long-term patterns in biological data. [ABSTRACT FROM AUTHOR]

Abstract: Reference evapotranspiration (ETo) is an important element of the hydrological cycle, and changes in ETo are of great significance for agricultural water use planning, irrigation system design and management. In this study, annual, seasonal and monthly trends in the Penman–Monteith ETo at 20 meteorological stations during 1966–2005 in the western half of Iran were examined using the Mann–Kendall test, the Sen''s slope estimator and the linear regression. Annual analysis of the ETo series indicated a positive trend in 70% of the stations according to the Mann–Kendall test and the Sen''s slope estimator and in 75% of the stations according to the linear regression. The magnitude of significant positive trends in annual ETo varied from (+)11.28 to (+)2.30mm/year. On the seasonal scale, stronger increasing trends were identified in ETo data in winter and summer compared with those in autumn and spring. Meanwhile, the highest numbers of stations with significant trends were found in the monthly ETo series in February, while the lowest numbers of stations with significant trends were observed in November. Analysis of the impact of climatic variables on the significant increasing trend in ETo showed that the increasing trend was mainly caused by a significant increase in air temperature during the study period. [Copyright &y& Elsevier]

Precipitation is considered to be a very important climatic element for the plain areas in Romania. Thus, variation in precipitation data ranges is more and more studied. In this paper total amounts of precipitation and the depth of the snow cover were analyzed. Annual, seasonal and monthly data were used to identify the general trends in the amount of precipitation for a period of 41 years (1965-2005) in 5 weather stations from the central part of Romanian Plain: Alexandria, Piteşti, Ro Fiorii de Vede, Turnu Măgurele and Videle. They are located in different areas: on the high plain in the North of the region, in the central area and on the Danube River banks. For snow cover only winter months series were analyzed. To detect and estimate trends in the time series, Mann-Kendall test for trend and Sen's slope estimates were used while for fluctuations in precipitations WASP cumulated curve was analyzed. Main conclusions that can be mentioned are: decreasing trends in total amounts of precipitation, especially for summer months and for annual values, but also with fluctuations that had upward trends in the first part of the analyzed period and than decreasing trends beginning with October 1981; increasing trends of the snow cover during winter months. [ABSTRACT FROM AUTHOR]

Assessing the potential impact of climate changes on a hydrologic time serie is based on identifying the trend of the annual mean discharges. Time series of annual mean liquid discharges from all the hydrometric posts situated in the upper basin of Bârlad river are studied: Băceşti and Negreşti on Bârlad (1950 -- 2007), Ţibana and Tungujei on Şacovăţ (1987 -- 2007), Frenciugi and Căzăneşti on Stavnic (1950 -- 2007). Parametric (linear, second degree polynomial and exponential equations) and non-parametric methods (Mann-Kendall test and Sen's slope) are applied in order to detect a trend in data. The parametric testing shows that the polynomial model is the most apropiate, which means that during the interannual evolution of discharges a natural fluctuation seems to be more probable that an effect of the climate warming. The results of the non-parametric tests do not show a trend in data. We can conclude that in the studied area there is no clear impact of the climate change upon the annual mean liquid discharges. [ABSTRACT FROM AUTHOR]

Climate variability and change constitute major challenges for Africa, especially West Africa (WA), where an important increase in extreme climate events has been noticed. Therefore, it appears essential to analyze characteristics and trends of some key climatological parameters. Thus, this study addressed spatiotemporal variabilities and trends in regard to temperature and precipitation extremes by using 21 models of the Coupled Model Intercomparison Project version 6 (CMIP6) and 24 extreme indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). First, the CMIP6 variables were evaluated with observations (CHIRPS, CHIRTS, and CRU) of the period 1983–2014; then, the extreme indices from 1950 to 2014 were computed. The innovative trend analysis (ITA), Sen's slope, and Mann–Kendall tests were utilized to track down trends in the computed extreme climate indices. Increasing trends were observed for the maxima of daily maximum temperature (TXX) and daily minimum temperature (TXN) as well as the maximum and minimum of the minimum temperature (TNX and TNN). This upward trend of daily maximum temperature (Tmax) and daily minimum temperature (Tmin) was enhanced with a significant increase in warm days/nights (TX90p/TN90p) and a significantly decreasing trend in cool days/nights (TX10p/TN10p). The precipitation was widely variable over WA, with more than 85% of the total annual water in the study domain collected during the monsoon period. An upward trend in consecutive dry days (CDD) and a downward trend in consecutive wet days (CWD) influenced the annual total precipitation on wet days (PRCPTOT). The results also depicted an upward trend in SDII and R30mm, which, additionally to the trends of CDD and CWD, could be responsible for localized flood-like situations along the coastal areas. The study identified the 1970s dryness as well as the slight recovery of the 1990s, which it indicated occurred in 1992 over West Africa. [ABSTRACT FROM AUTHOR]

Summary: Thirty-four Physical–chemical and chemical variables were analysed in surface water samples collected every month over a period of 24 years. They were determined from thirteen sampling stations located along the Spanish Ebro River affected by anthropogenic and seasonal influences. The trend study was performed using the Mann–Kendall Seasonal Test and the Sen′s Slope estimator. Results revealed parameter variation over time due mainly to the reduction in phosphate concentration and increasing pH levels at the Ebro Basin during the 1981–2004 period. Exploratory analysis of data was also carried out by display methods (cluster analysis), and unsupervised pattern recognition (principal component analysis) in an attempt to differentiate between sources of variation in the water quality. PCA has allowed the identification of the following factors: geologic, climatic and anthropogenic. Spatial and seasonal sources of variation were identified that affect the quality and hydrochemistry of river water. [Copyright &y& Elsevier]

• Magnitude and occurrence time in the annual maximum rainfall and the annual peak flow have shifted in Hawaiʻi. • The annual peak flow did not coincide with the annual maximum rainfall. • The occurrence time of the annual maximum rainfall and the peak flow in Hawaiʻi. Flooding is a significant threat to life and property in Hawaiʻi. As climate warming continues to alter precipitation patterns and hydrological processes in the tropics, characterizing the shifting patterns in magnitude, seasonality, and location of floods would improve our understanding of the consequences and better prepare us for future flood events. In this study, 84 rain gauges and 111 crest gauges across five major Hawaiian Islands were analyzed from 1970 to 2005. We estimated trends in the annual maximum daily rainfall (RF max) and the annual peak flow (PF max) using the Mann-Kendall test and Senʻs slope. Subsequently, we examined the association between PF max and rainfall. Then, we assessed temporal shifting by combining circular analysis with Senʻs slope. The main identified trends showed decreases in RF max and PF max (67% and 61% of all gauges, respectively). The physiography of the Hawaiian islands (i.e., windward vs. leeward) has little contribution to both trends. In addition, PF max trend cannot be fully attributed to RF max trend, and in many cases, RF max and PF max did not occur coincidently. We also found the timing of RF max and PF max occurred in early wet season during the El Niño years and shifted even earlier from 1970 to 2005, likely due to the change of El Niño. These findings have implications for assessing flood risk. Our finding will aid watershed management and flood mitigation, and can increase resilience of downstream communities and near-shore environments. [ABSTRACT FROM AUTHOR]

This research assessed the changes in spatial patterns and the seasonal trends in temperature, precipitation, and relative humidity over 36 years (1979–2014) using Climate Forecast System Reanalysis (CFSR) datasets. The evaluation of climate deviations was the prime objective of this research. The augmented Dickey–Fuller Test (ADF) was used to scrutinize whether the data was either stationary or non-stationary. The results of the ADF test showed that all the datasets were found to be stationary at lag order 3. To observe undulations in the time series data, trend analyses were done using Sen's slope (SS), Mann–Kendall (MK), and Cox and Stuart (CS) tests. For all the statistical analyses, we considered the 5% significance level (α = 0.05) and p < 0.05 to be statistically significant. We observed significant (p < 0.05) trends in spring (MAM) and autumn (SON) for minimum temperature (Tmin) in Punjab. We also noted a significant (p < 0.05) trend in precipitation during autumn (SON). Annually, all the variables showed a non-significant (p > 0.05) trend for Punjab, Pakistan, during the period 1979–2014. Climate variability, such as a decrease in precipitation, higher temperature, and relative humidity fluctuations, were the reasons for the imbalance in the sustainability of Punjab, Pakistan. [ABSTRACT FROM AUTHOR]

Rising temperature and shifting precipitation patterns due to climate change are likely to intensify droughts throughout the world. Understanding the drought characteristics of possible future scenarios under climate change requires verification of past drought events using appropriate drought indices. Consequently, this study investigates the application of two widely used drought indices, the standardized precipitation index (SPI) and standardized streamflow index (SSI), to characterize historical droughts, drought trends, and their impact on water quality and stream integrity for a selected study basin in New Jersey. Results indicated that both SPI and SSI were able to identify historical drought events, including three drought emergency periods and the most recent drought-watch periods. A significant positive meteorological drought was observed at the western side of the basin, whilst a significant positive hydrological drought was found in the eastern side. The average pollutant concentration of drought periods were lesser than non-drought periods due to reduction of different processes, such as erosion and transport of sediment and nutrients into rivers and streams, during drought periods as opposed to non-drought periods. The findings from this study will serve to bolster the ongoing efforts to formulate better drought management strategies for future climate change in the Raritan Basin. [ABSTRACT FROM AUTHOR]

The diffusion of COVID-19 or Coronavirus since last few months is the prime matter of concern for the entire world. The government of India had declared the complete lockdown from 24th March. After the second step lockdown, now third step lockdown was declared. So, India in Lockdown 3.0 situation. Although the economy of our country has severely been affected by the impact of lockdown, this situation is good for natural healing. Major metropolitan cities of India are trying to recover from various pollution. This study, therefore, attempts to analyze the trend of air pollution before and during a lockdown situation in Kolkata metropolitan and surrounding areas. To identify air pollution trends before and during the lockdown, the non-parametric Maan-Kendall test and Sen's slope estimator have been applied in this study. This research has been done based on air quality index data of the Kolkata metropolitan region's observatory stations. The time range of the data set is from mid of February to 2nd May. The study results show that air pollution has been reduced up to 80% in almost all the stations due to strict lockdown. [Display omitted] • Selected seven stations have experienced drastic decreased of air pollution. • Jadavpur area has characterized large extent of air pollution reduction i.e., about 90% of air pollution reduction. • Ballygunge area is mostly air pollution contaminated area of Kolkata due to mass traffic congestion. • Ballygunge also experienced mass scale of air pollution reduction due to shut down of traffic. [ABSTRACT FROM AUTHOR]

Mercury is a ubiquitous environmental toxicant and a cause for global concern due to its persistence and bioaccumulation in the environment. Evaluating the effectiveness of mercury emission control has become a significant issue after the entry into force of the Minamata Convention on Mercury in 2017. Atmospheric mercury concentration is an important indicator for anthropogenic emission control. Although Eulerian models are generally applied to evaluate emission reduction and policy effectiveness, the uncertainty of mercury reaction mechanisms and the insufficient grid accuracy of simulations limit the applications of this method at particular sites. In this study, we applied a statistical approach (the Generalized Additive Model, GAM) to explain the decline of atmospheric mercury concentration in Beijing, China, which followed a trend (Sen's slope) of −0.37 ng m−3 yr−1 (−8.0% yr−1). The statistical model represented 56.5% of the variance in mercury concentration and the adjusted R2 reached 0.547. Reduction of anthropogenic mercury emission, variation in meteorological condition, and change in global background level explained 51.5%, 47.1%, and 1.4% of the decrease of air mercury concentration, respectively. We validated the results using Hg emission inventories, seasonal Hg/CO value, and meteorological data. Considering the limitations of Eulerian models and the simplicity of statistical models, we suggest the application of GAM as an assessment method for long-term variation of atmospheric mercury. Image 1 • TGM concentrations in Beijing decreased at a rate of −8% yr−1 from 2015 to 2018. • GAM was developed to explain the decline of TGM concentrations in Beijing. • Reduction of anthropogenic emission explained 51.5% of the decrease of TGM. [ABSTRACT FROM AUTHOR]

The polycyclic aromatic hydrocarbon (PAH) family is of environmental concern due to its toxicity, prompting the need of monitoring their long-term trends. Three monitoring programs in Hong Kong report concentrations of ambient PAHs, namely (1) respirable suspending particle (RSP) speciation program that monitored benzo[ a ]pyrene (BaP) (1997 to March 2000), (2) total suspended particle speciation program that monitored BaP (1997–1999), and (3) toxic air pollutant monitoring program that monitors BaP and 16 other PAHs in the combined gas and particulate phases at two general urban stations once or twice a month since January 1998. In this work, we review all the available PAH measurements in Hong Kong during 1997–2016, with emphasis on the temporal trends of BaP and the other 16 PAHs. PAHs of 5–6 rings exhibit an ambiguous decline trend since 1998, with a negative Sen's slope that is statistically significant. Specifically, BaP was reduced by 78% from 1998 to 2016, with a Sen's slope of −0.013 ng m-3 year−1. Correlations of BaP with RSP major species of high source specificity and PAH diagnostic ratios are employed to explore the source origins of PAHs. Our analysis reveals that PAHs mainly come from a combination of vehicular emissions and biomass/coal combustion. The decline trend of PAHs is further found in consistence with the declined particulate matter emissions from vehicular exhaust and biomass/coal combustion. This study fills the data vacancy in the long-term trends of ambient PAHs for the Pearl River Delta region, one of the economically more advanced regions in China. Image 1 • Benzo[ a ]pyrene decreased at the rate of 0.013 ng/m3/year during 1998–2016. • Other 5–6 ring PAHs show similar declining trends. • Major sources of PAHs are biomass burning, coal combustion and vehicular exhaust. • Reductions in emission amounts of these sources have led to the declines in PAH levels. [ABSTRACT FROM AUTHOR]

Aggregated mining development has direct and indirect impacts on vegetation changes. This impact shows spatial differences due to the complex influence of multiple mines, which is a common issue in resource regions. To estimate the spatial heterogeneity of vegetation response to mining activities, we coupled vegetation changes and mining development through a geographically weighted regression (GWR) model for three cumulative periods between 1999 and 2018 in integrated resource regions of northwestern China. Vegetation changes were monitored by Sen's slope and the Mann–Kendall test according to a total of 72 Landsat images. Spatial distribution of mining development was quantified, due to four land-use maps in 2000, 2005, 2010, and 2017. The results showed that 80% of vegetation in the study area experienced different degrees of degradation, more serious in the overlapping areas of multiple mines and mining areas. The scope of influence for single mines on vegetation shrunk by about 48%, and the mean coefficients increased by 20%, closer to mining areas. The scope of influence for multiple mines on vegetation gradually expanded to 86% from the outer edge to the inner overlapping areas of mining areas, where the mean coefficients increased by 92%. The correlation between elevation and vegetation changes varied according to the average elevation of the total mining areas. Ultimately, the available ecological remediation should be systematically considered for local conditions and mining consequences. [ABSTRACT FROM AUTHOR]

Global climate change is significant, and the spatiotemporal variations of precipitation associated with it are pronounced. Based on the daily precipitation data from 10 weather stations located from southeast to northwest across China from 1961–2017, the Mann–Kendall trend test was generally applied to analyze the spatiotemporal variations of precipitation. The factors influencing the precipitation changes were investigated. The results revealed that (1) the annual, summer, and winter rainfall amount (RA) exhibited increasing rates of 16.36, 12.31, and 2.49 mm/10 year, respectively. The change rates of annual rainfall days (RD) were 2.68 day/10 year in the northwest region and −1.88 day/10 year in the southeast. The annual and seasonal daily precipitation on rainy days (RP) exhibited an increasing trend. (2) All of the RA, RD, and RP values initially increased, then decreased, and then slightly increased from Southeast to Northwest China. These results proved that the RA increased with the increase of light rain in Northwest China and heavy rain in Southeast China. In addition, changes in the monsoon have altered the rate at which RA, RD, and RP vary with distance from the sea. These findings may help to provide suggestions for the rational spatial utilization of water resources in China. [ABSTRACT FROM AUTHOR]

Many developing nations are facing severe food insecurity partly because of their dependence on rainfed agriculture. Climate variability threatens agriculture-based community livelihoods. With booming population growth, agricultural land expands, and natural resource extraction increases, leading to changes in land use and land cover characterized by persistent vegetation greening and browning. This can modify local climate variability due to changing land–atmosphere interactions. Yet, for landscapes with significant interannual variability, such as the Mount Elgon ecosystem in Kenya and Uganda, characterizing these changes is a difficult task and more robust methods have been recommended. The current study combined trend (Mann–Kendall and Sen's slope) and breakpoint (bfast) analysis methods to comprehensively examine recent vegetation greening and browning in Mount Elgon at multiple time scales. The study used both Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data and attempted to disentangle nature- versus human-driven vegetation greening and browning. Inferences from a 2019 field study were valuable in explaining some of the observed patterns. The results indicate that Mount Elgon vegetation is highly variable with both greening and browning observable at all time scales. Mann–Kendall and Sen's slope revealed major changes (including deforestation and reforestation), while bfast detected most of the subtle vegetation changes (such as vegetation degradation), especially in the savanna and grasslands in the northeastern parts of Mount Elgon. Precipitation in the area had significantly changed (increased) in the post-2000 era than before, particularly in 2006–2010, thus influencing greening and browning during this period. The greenness–precipitation relationship was weak in other periods. The integration of Mann–Kendall and bfast proved useful in comprehensively characterizing vegetation greenness. Such a comprehensive description of Mount Elgon vegetation dynamics is an important first step to instigate policy changes for simultaneously conserving the environment and improving livelihoods that are dependent on it. [ABSTRACT FROM AUTHOR]

Lake surface water temperature (LSWT) is a key parameter to help study the environmental and ecological impacts of climate change. In this work, we measured the LSWT of 1 natural and 23 artificial lakes located on the island of Sardinia in the western Mediterranean, which is a region where changes in climate are projected to have significant impacts. By integrating multi-source and multi-resolution datasets of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat and long-term in situ temperature observations, we detected, measured, and analysed the LSWT trends during the period of 2000–2018 across all the investigated lakes. Methodologically, we demonstrated that a simplified approached based on Planck's equation for Landsat thermal infrared (TIR) data could be a valid alternative to radiative transfer equation retrieval methods for the retrieval of LSWT without loss of accuracy. Moreover, we demonstrated that rescaled and independently validated MOD112A-derived LSWT showed good accuracy, efficiently filled the spatial and temporal gaps in long-term in situ LSWT, and could be used for long-term LSWT trend detection and measurement. All 24 lakes showed an annual warming trend of +0.010 °C/y, warming winter trend of +0.013 °C/y, and cooling summer trend of −0.038 °C/y during the period of 2000–2018. This study demonstrated that the measured trend rates could be explained by and were strongly correlated with the climatology of Italy for the 2000–2018 period. Finally, we demonstrated the key role and the importance of the availability of long-term in situ temperature datasets. The approach used in this study is up-scalable to other medium to low-resolution TIR sensors as well as to other long-term monitoring sites, such as LTER-Italy, LTER-Europe, or ILTER sites. Unlabelled Image • Landsat-retrieved LSWTs inverted using simplified Planck's law showed good accuracy. • Landsat and MODIS retrieved LSWT showed an accuracy better than +2.5 °C. • Landsat LSWT dataset was used to validate rescaled MODIS LSWTs. • Sardinian lakes are warming with an annual trend of +0.010 °C/yr during 2000–2018. • The LSWT trends can be explained by the Italian climatology for the same period. [ABSTRACT FROM AUTHOR]

Precipitation plays an important role in the food production of Southern Africa. Understanding the spatial and temporal variations of precipitation is helpful for improving agricultural management and flood and drought risk assessment. However, a comprehensive precipitation pattern analysis is challenging in sparsely gauged and underdeveloped regions. To solve this problem, Version 7 Tropical Rainfall Measuring Mission (TRMM) precipitation products and Google Earth Engine (GEE) were adopted in this study for the analysis of spatiotemporal patterns of precipitation in the Zambezi River Basin. The Kendall's correlation and sen's Slop reducers in GEE were used to examine precipitation trends and magnitude, respectively, at annual, seasonal and monthly scales from 1998 to 2017. The results reveal that 10% of the Zambezi River basin showed a significant decreasing trend of annual precipitation, while only 1% showed a significant increasing trend. The rainy-season precipitation appeared to have a dominant impact on the annual precipitation pattern. The rainy-season precipitation was found to have larger spatial, temporal and magnitude variation than the dry-season precipitation. In terms of monthly precipitation, June to September during the dry season were dominated by a significant decreasing trend. However, areas presenting a significant decreasing trend were rare (<12% of study area) and scattered during the rainy-season months (November to April of the subsequent year). Spatially, the highest and lowest rainfall regions were shifted by year, with extreme precipitation events (highest and lowest rainfall) occurring preferentially over the northwest side rather than the northeast area of the Zambezi River Basin. A "dry gets dryer, wet gets wetter" (DGDWGW) pattern was also observed over the study area, and a suggestion on agriculture management according to precipitation patterns is provided in this study for the region. This is the first study to use long-term remote sensing data and GEE for precipitation analysis at various temporal scales in the Zambezi River Basin. The methodology proposed in this study is helpful for the spatiotemporal analysis of precipitation in developing countries with scarce gauge stations, limited analytic skills and insufficient computation resources. The approaches of this study can also be operationally applied to the analysis of other climate variables, such as temperature and solar radiation. [ABSTRACT FROM AUTHOR]

Grassland covers 54% of the Tibetan Plateau (TP) and suffered overgrazing and degradation problems during past decades. To alleviate these problems, a series of policy measures have been implemented during recent two decades and inevitably caused changes of the grassland. To this end, this study quantitatively analyzed the grassland changes and the effects of reduced grazing intensity, and identified the hot plots of grassland degradation in the TP during 2000-2019. The grassland status was indicated by the Fractional Vegetation Cover in the green grass period (GP), i.e., FVC GP , and its changes and spatial variations were detected by analyzing the FVC GP trends and their distribution, using the Mann-Kendal, Sen's Slope, and ArcGIS buffering methods, and data of the MOD13Q1 Collection 6 products and other sources. The results showed that 62.12% of the grasslands were significantly increased in the FVC GP , and 28.34% had no apparent changes. The remaining 9.54% of the grassland significantly decreased in the FVC GP , mainly occurring in the areas nearby roads, rivers, and lakes, and distributed mostly in a point pattern. Of the total FVC GP decreased grassland area, 27.03% was clustered and identified as the hot plots of grassland degradation in six main regions. Decreased grazing intensity and increased precipitation contributed to the increase of grassland FVC in the TP, while local overgrazing could be the main cause of the FVC decrease. To strength the grassland restoration in the TP, the government supports and supervision should be enhanced to further mitigate the grassland pressure of animal grazing, particularly in the hot plot areas of degradation.

Global warming and its relevant effects have aroused increasing human concerns in recent decades. These anomalies are likely influencing vegetation dynamics and ecosystem stability. This paper aims to dissect extreme temperature variations in both space and time, and related regional responses to global warming in the Huai River Basin. Using the daily maximum and minimum air temperature at 153 stations across the Huai River Basin, China, covering the period of 1961–2014, trends and relations amongst extreme air temperature indices were analyzed, and regional responses of the Huai River Basin to global warming process were also investigated. The results indicated that: (1) widespread increasing trends can be observed in maximum and minimum air temperature, with the largest increasing magnitude of 0.4 °C per decade and 1.3 °C per decade, respectively. The minimum air temperature regimes have a larger increasing magnitude than the maximum air temperatures. (2) There is an increasing trend in the extreme maximum temperature indices, and the increasing rates of TN90p and TR20 are greater than those of the other extreme maximum temperature indices. However, the extreme minimum temperature indices, except for consecutive icy days (CID), show significant decreasing trends. The growing season length (GSL) would increase due to the joint action of (i) an increase in maximum temperature indices, and (ii) a decrease in minimum temperatures indices. Although increases in the GSL would be beneficial for increasing the growing time of crops, a decreased extreme minimum temperature and increased extreme maximum temperature may reduce the winter wheat yield. (3) Extreme low temperature indices show a larger changing magnitude than the extreme high temperature indices. Temporally, a larger changing magnitude can be identified for temperature indices during night-time than during daytime. Moreover, a larger changing magnitude and higher significance of trends of extreme temperature indices can be observed in the eastern Huai River Basin than in the western Huai River Basin. Particularly, we detect an even higher changing rate of extreme temperature indices in the southeastern Huai River Basin. These findings can be well explained by urban heat island effects resulting from urban aggregation across the Yangtze Delta. (4) The correlation between the extreme low temperature indices is slightly higher than that of the extreme high temperature indices. The changing trends of the most extreme temperature indices are closely related to latitude and altitude. Trends in the main extreme temperature indices of the Huai River Basin are essentially similar to those in other regions around the world, implying consistent spatial patterns of extreme temperature indices across the globe. [ABSTRACT FROM AUTHOR]