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The considerable impact of oxygen deficient waters on marine resources in St Helena Bay has generated interest in exploring the vulnerability of South Africa's largest and most productive bay to further deoxygenation in response to climate change. Seasonal, sub-seasonal and spatial fluctuations in bottom dissolved oxygen (DO) are examined in St Helena Bay to facilitate better interpretation of historical data. DO measurements in relation to physical, chemical and biological variables were made between November 2013 and November 2014. Alongshore bay characteristics were assessed through comparison of variables along the 50 m depth contour. A mean coeffcient of variation of 0.35 provided a measure of the relative variability of near-bottom DO concentrations along this contour. Across-shelf transects captured the seasonal development of hypoxia in relation to the distribution of phytoplankton biomass. DO was lowest in autumn in the south of the bay prior to winter ventilation of the bottom waters. Exceptional dinoflagellate blooms forming extensive subsurface thin layers preceded the autumn DO minima. The development of hypoxia at inner and central stations prior to expansion beyond the boundaries of the bay provided evidence of local drawdown. Coincident with the seasonal decline of DO within the bay was an increase in macronutrient concentrations which tended to mirror DO concentrations. Indication of denitrification in the suboxic waters in the south of the bay was provided through evidence of a nitrate deficit in autumn supported by elevated nitrite concentrations. Superimposed on the seasonal decline of DO concentrations in the bottom waters were sub-seasonal events of hypoxia and anoxia linked to episodic deposition of organic matter as indicated by increases in bottom Chl a concentrations. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) vegetation plays an important role in the local ecosystem, which responds significantly to climate change and can affect local and large-scale weather and climate anomalies. However, little attention has been paid to its year-to-year variation. In this paper, using two NDVI datasets (GIMMS and MODIS) originated from satellite remote sensing, the variability characteristics of NDVI over the TP on the interannual time scale and associated local climatic factors were investigated. The results show that two primary patterns of NDVI governed TP during the main growing season (June–September, JJAS) for the period 1982–2020. The first one is a uniform pattern, with a consistent spatial variation over the entire TP, and the second is a dipole pattern, with an out-of-phase spatial variation of NDVI between the northern and southern TP. Interannual variations of the different climatic factors regulate the NDVI variability over the different regions of the TP. The interannual variability of the uniform NDVI pattern is mainly affected by the two local climatic factors, the preceding May–August precipitation and simultaneous JJAS sunshine duration. Specifically, NDVIs over the southern and eastern TP have a more significant response to the preceding precipitation and simultaneous sunshine duration, respectively. The variability of the dipole NDVI pattern is primarily modulated by the preceding May–August precipitation and simultaneous surface air temperature, ground surface temperature, and sunshine duration. However, NDVIs over the northern and southern TP have different degrees of response to the four climatic factors, with the most significant response being to preceding precipitation. The combined effect of these factors contributes to the formation of the interannual variability in the uniform and dipole patterns. This paper may shed light on deeply understanding the reasons for the inconsistency in variations of vegetation over the different regions of the TP under climate change. In addition to the effect of local climatic factors that this study focuses on, the influence of external climatic factors on the variability of the TP NDVI deserves further research in the future. [ABSTRACT FROM AUTHOR]

Fast and accurate fire severity mapping can provide an essential resource for fire management and studying fire-related ecological and climate change. Currently, mainstream fire severity mapping approaches are based only on pixel-wise spectral features. However, the landscape pattern of fire severity originates from variations in spatial dependence, which should be described by spatial features combined with spectral features. In this paper, we propose a morphological attribute profiles-based spectral–spatial approach, named Burn Attribute Profiles (BAP), to improve fire severity classification and mapping accuracy. Specifically, the BAP method uses principal component transformation and attributes with automatically determined thresholds to extract spatial features, which are integrated with spectral features to form spectral–spatial features for fire severity. We systematically tested and compared the BAP-based spectral–spatial features and spectral index features in the extremely randomized trees machine learning framework. Sentinel-2 imagery was used for seven fires in the Mediterranean region, while Landsat-8 imagery was used for another seven fires in the northwestern continental United States region. The results showed that, except for 2 fires (overall accuracy (OA) for EMSR213_P: 59.6%, EL: 59.5%), BAP performed well for the other 12 fires (OA for the 2 fires: 60–70%, 6 fires: 70–80%, 4 fires: >80%). Furthermore, compared with the spectral indices-based method, the BAP method showed OA improvement in all 14 fires (OA improvement in Mediterranean: 0.2–14.3%, US: 4.7–12.9%). Recall and Precision were also improved for each fire severity level in most fire events. Moreover, the BAP method improved the "salt-and-pepper" phenomenon in the homogeneous area, where the results are visually closest to the reference data. The above results suggest that the spectral–spatial method based on morphological attribute profiles can map fire severity more accurately. [ABSTRACT FROM AUTHOR]

Previous studies have explored the long time series and large-scale cultivated land nutrient sensitivity and its spatial differentiation characteristics in arid zones from human activities in the context of climate change. This study is based on 10-year interval data on soil nutrient content of cultivated land in the oasis in Xinjiang, China, cultivated land use intensity (LUI) and climate data sets. Using sensitivity and GIS analysis methods, this paper studies soil nutrient sensitivities and their spatial distribution patterns in the context of LUI and climate change. The results showed significant response differences and spatial heterogeneity regarding the sensitivity of soil nutrient systems to LUI and climate change. Among them, soil nutrients were the most sensitive to temperature changes, followed by LUI, while precipitation was the weakest. Soil nutrient sensitivity showed a decreasing spatial distribution pattern from the northeast to the southwest. The soil nutrient system had a strong adaptability to LUI and climate change. However, there were differences in different sensitivity states. These results provide scientific guidance for the spatial selection and implementation of soil fertility enhancement and land remediation projects in similar arid areas. [ABSTRACT FROM AUTHOR]

The Yalong River Basin is an important hydropower energy base in our country. In the context of global warming, it is of great significance to study the characteristics of precipitation changes in the Yalong River Basin and predict future trends in precipitation. In this paper, SDSM is used to downscale the GCM global climate model data to simulate the future precipitation changes in Yalong River Basin. Through trend analysis, M-K trend test and mutation test, Morlet wavelet analysis and other methods, the temporal and spatial trends and characteristics of precipitation in the Yalong River Basin in the historical period and the future three concentration path scenarios are analyzed. The results show that the annual precipitation in the Yalong River Basin in each period shows an increasing trend, the increase range is RCP8.5 scenario>RCP4.5 scenario>historical period>RCP2.6 scenario. Its spatial characteristics are expressed as the increase in upstream precipitation higher than downstream. There are certain differences in the seasonal changes in the amount of water falling in different periods. Except for the RCP2.6 scenario, there is a sudden change in precipitation increase in each period of the basin. The sudden change in the upstream of the historical period is 1986, and there is no significant change in the downstream. In the future, the abrupt years are mostly concentrated on the mid-range period (2041-2070).Regarding cyclical changes, the historical period of the basin and the cyclical changes under the RCP2.6 scenario are more significant, and there are multiple cycles at the same time: 1-4a short cycle and 4-8 a medium cycle. In the RCP4.5 and RCP8.5 scenarios, the frequency of periodic occurrence is higher, and both are short periods of 1-4 a, indicating that in the future, these two scenarios will show the characteristics of a long cycle duration and short frequency cycle changes.In general, the results simulated by the SDSM statistical downscaling model can well reflect the trend of precipitation changes in the Yalong River Basin. The statistical analysis method is used to conduct a specific research on the characteristics of future precipitation changes, which can be used to rationally plan the water in the Yalong River Basin. This paper provides a scientific basis for resource utilization, optimized reservoir dispatch and power generation dispatch. [ABSTRACT FROM AUTHOR]

Damage from climate change is widespread throughout the world. This change has brought about calamities, the most prevalent of which is the emergence of numerous droughts which are increasingly threatening human lives. In this paper, we studied the spatial and temporal variations of drought under the effect of climate change in the Huaibei Plain, which is a very important agricultural zone in China. Drought has attracted increasing attention in research due to its heavy impact on agriculture, the environment, livelihood, and food security. The SPEI (Standardized Precipitation Evapotranspiration Index) has been used in this study to express and identify drought events in the Huaibei Plain due to climate change. A general circulation model (GCM), HadGEM2-AO, which was the most appropriate for the study area's precipitation simulation, and three Representative Concentration Pathways (RCP), RCP 2.6, RCP 4.5, and RCP 8.5, were used to analyze and compare the drought effect for the baseline (1985–2017) and the future climate scenarios (2025–2090). At 3 and 6 months, the SPEI successfully detects agricultural drought in temporal and spatial variation. However, according to the analysis, more severe agricultural drought events are foreseen in the future than in the baseline because of climate change. SPEI performed better than SPI in detecting drought in the baseline and simulated data due to increased evapotranspiration. Between the SPEI-3 and SPEI-6, the Pearson coefficient correlation reveals a positive association. The Mann-Kendall test was used to cover the two studied periods in order to establish the drought trend. Both decreasing and increasing trends, in different timescales, were detected by Sen's Slope in the baseline and future periods with all RCPs. [ABSTRACT FROM AUTHOR]

Nitrous oxide (N2O) emissions from agricultural land are variable at the landscape scale due to variability in land use, management, soil type, and topography. A field experiment was carried out in a typical mixed farming landscape in Denmark, to investigate the main drivers of variations in N2O emissions, measured using static chambers. Measurements were done over a period of 20 months, and sampling was intensified during two weeks in spring 2009 when chambers were installed at ten locations or fields to cover different crops and topography and slurry was applied to three of the fields. N2O emissions during the spring 2009 period were relatively low, with maximum values be low 20 ng N m-2 s-1. This applied to all land use types including winter grain crops, grassland, meadow, and wetland. Slurry application to wheat fields resulted in short-lived two-fold increases in emissions. The moderate N2O fluxes and their moderate response to slurry application were attributed to dry soil moisture conditions due to the absence of rain during the four previous weeks. Measured cumulated annual emissions from two arable fields that were both fertilized with mineral fertilizer and manure were large (17 kg N2O-N ha-1 yr-1 and 5.5 kg N2O-N ha-1 yr-1, respectively) during the previous year when soil water conditions were favourable for N2O production during the first month following fertilizer application, confirming the importance of the climatic regime on N2O fluxes. [ABSTRACT FROM AUTHOR]

Globally, tropical forest soils represent the second largest source of N2O and NO. However, there is still considerable uncertainty on the spatial variability and soil properties controlling N trace gas emission. To investigate how soil properties affect N2O and NO emission, we carried out an incubation experiment with soils from 31 locations in the Nyungwe tropical mountain forest in southwestern Rwanda. All soils were incubated at three different moisture levels (50, 70 and 90% water filled pore space (WFPS)) at 17 °C. Nitrous oxide emission varied between 4.5 and 400 µg N m-2 h-1, while NO emission varied from 6.6 to 265 µg N m-2 h-1. Mean N2O emission at different moisture levels was 46.5±11.1 (50% WFPS), 71.7±11.5 (70% WFPS) and 98.8±16.4 90% WFPS) µg N m-2 h-1, while mean NO emission was 69.3±9.3 (50% WFPS), 47.1±5.8 (70% WFPS) and 36.1±4.2 (90% WFPS) µg N m-2 h-1. The latter suggests that climate (i.e. dry vs. wet season) controls N2O and NO emissions. Positive correlations with soil carbon and nitrogen indicate a biological control over N2O and NO production. But interestingly N2O and NO emissions also showed a negative correlation (only N2O) with soil pH and a positive correlation with free iron. The latter suggest that chemo-denitrification might, at least for N2O, be an important production pathway. In conclusion improved understanding and process based modeling of N trace gas emission from tropical forests will not only benefit from better spatial explicit trace gas emission and basic soil property monitoring, but also by differentiating between biological and chemical pathways for N trace gas formation. [ABSTRACT FROM AUTHOR]

Natural hazards are dynamic and unpredictable events that are a continuous threat to global socio-economic development. Humans' reactions to these catastrophes are influenced by their proximity to the hazards and their ability to anticipate, resist, cope with, and recover from their consequences. Due to climatic changes, the risk of multiple natural hazards is expected to increase in several regions of Pakistan. There is a pressing need to understand the spatial discrepancies of natural hazards due to climate change and identifying the regions that require special measures to increase resilience, achieve adaptation, and sustainable development goals. This paper synthesizes the related literature to understand spatial variations of natural hazards due to climate changes across Pakistan. The Emergency Events Database (EM-DAT), National Aeronautics and Space Administration Global Landslide Catalog (NASA-GLC), National Disaster Management Authority (NDMA), and Pakistan Meteorological Department (PMD) are utilized to analyze spatial discrepancies and vulnerabilities to natural hazards. This study unveils that Pakistan's current risk analysis and management strategies seem to be obsolete compared to global trends. Because of spatial variations of hazards, most research work on hazard risk assessments and risk management focuses on a single hazard, neglecting the co-occurrence impact of different natural hazards. Very limited studies are included in comprehensive multi-hazard risk strategies. Therefore, in Pakistan, risk management would require integrated multi-hazard risk assessment approaches to detect, analyze, measure, and evaluate various natural hazards, their effects, and interconnections. Moreover, the Pakistan governmental institutes dealing with natural hazards should focus on pre-disaster mitigation and resilience techniques instead of investing only in post-disaster relief activities. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the temporary variation of the area-orientated annual maximum daily rainfall (AMDR) with respect to the three spatial properties: location, size and shape of the region-of-interest (ROI) in Great Britain and Australia using two century-long datasets. The Maximum Likelihood and Bayesian Markov-Chain-Monte-Carlo methods are employed to quantify the time-varying frequency of AMDR, where a large proportion of the ROIs shows a non-decreasing level of most frequent AMDR. While the most frequent AMDR values generally decrease with larger-sized ROIs, their temporal variation that can be attributed to the climate change impact does not show the same dependency on the size. Climate change impact on ROI-orientated extreme rainfall is seen higher for rounded shapes although the ROI shape is not as significant as the other two spatial properties. Comparison of the AMDR at different return levels shows an underestimation by conventionally used stationary models in regions where a nonstationary (i.e., time-varying) model is preferred. The findings suggest an overhaul of the current storm design procedure in view of the impact of not only climate change but also spatial variation in natural processes. [ABSTRACT FROM AUTHOR]

In recent years, the frequent occurrence of extreme climate disasters has seriously affected agriculture in Northeast China. Based on precipitation data derived from 83 geographical stations in the study area for 60 years (1960–2019), we chose the reliable statistical methods of the Mann–Kendall test, Sen's slope, and the Standardized Precipitation Index, and regarded drought and flood as a whole in this paper, to identify the temporal and spatial variation characteristics of precipitation and extreme climate events in Northeast China. The results show that an increasing trend of disasters was detected at the 95% confidence level (Z = 2.3). According to the timescale analysis, abrupt climate changes started in 2006. Temporal and spatial distribution of extreme climate disasters, mainly drought and flood disasters, showed a significant upward trend from 2006 to 2019. According to the spatial analysis, the precipitation in Northeast China decreased from south to north and fluctuated less from east to west. Moreover, stations with extreme climate trends (trend of climatic anomaly with confidence level > 90%) followed the same spatial pattern as those with a high frequency of extreme climate disasters (more than 17.87 times/decade). The severity and frequency of extreme climate have increasingly threatened Northeast China in the past decade. In particular, the Northeast Plain experienced the most severe and extreme climate events that seriously threatened the study area in 2007, 2009, 2010, and 2019. Our results highlight the urgent need for the development of monitoring and early warning of droughts and flood disasters to reduce economic losses. [ABSTRACT FROM AUTHOR]

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Land surface albedo directly determines the distribution of radiant energy between the surface and the atmosphere, and it is a key parameter affecting the energy balance on the land surface. However, the spatiotemporal dynamics of land surface albedo and associated influencing factors in the Qilian Mountains (QM) have been rarely reported. By using the long-time series data products of MODIS shortwave albedo, normalized difference vegetation index (NDVI), and snow cover with a spatial resolution of 0.05° from 2001 to 2020, this paper analyzes the temporal and spatial variations of land surface albedo in the QM over the past 20 years and its influencing factors. The analysis results show that the multi-year average surface albedo in the QM has obvious differences in spatial distribution: it increases with the altitude, and it is high in the west (at the west of 98° E) and low in the east. Meanwhile, the surface albedo has different distribution characteristics in different seasons: the spatial distribution of surface albedo is similar in spring and autumn; the areas with a high surface albedo in summer are significantly fewer than those in other seasons. Besides, in the past 20 years, the annual average surface albedo has shown a weak growth trend in the QM, with a change rate of 5 × 10−3/10a, and the minimum and maximum values were reached in 2001 and 2019, respectively. In addition, the annual variation of the surface albedo in the QM showed a "U" shape, with the largest variation in January and the smallest variation in August. The annual variation of surface albedo is significantly positively correlated with snow cover (r = 0.96) and significantly negatively correlated with NDVI (r = −0.91). Moreover, the interannual variation of the surface albedo in the QM is closely related to land surface cover and is greatly affected by snow cover. Spatially, the annual variation of surface albedo in most areas of the QM is dominated by the change of snow cover, and the increase of surface albedo in the middle area is consistent with the increase of snow cover, while the decrease of albedo in the edge area is related to the improvement of vegetation cover. The results of this study provide a scientific basis for studying the climate and environmental changes caused by changes in the surface of the QM and making ecological environment restoration strategies. [ABSTRACT FROM AUTHOR]

This paper presents the spatial variation of area‐orientated annual maximum daily rainfall (AMDR), represented by well‐fitted generalized extreme value (GEV) distributions, over the last century in Great Britain (GB) and Australia (AU) with respect to three spatial properties: geographic locations, sizes, and shapes of the region‐of‐interest (ROI). The results show that the spatial variation of GEV location‐scale parameters is dominated by geographic locations and area sizes. In GB, there is an eastward‐decreasing banded pattern compared with a concentrically increasing pattern from the middle to coasts in AU. The parameters tend to decrease with increased area sizes in both studied regions. Although the impact of the ROI shapes is insignificant, the round‐shaped regions usually have higher‐valued parameters than the elongated ones. These findings provide a new perspective to understand the heterogeneity of extreme rainfall distribution over space driven by the complex interactions between climate, geographical features, and the practical sampling approaches. Key Points: Rainfall series are extracted and processed from more than 11,000 regions of interest over the last 100 years in Great Britain and Australia.Spatial distribution of extreme rainfall modeled qualitatively and quantitatively shows impact from location, size, and shape of regions.Methods and findings provide new perspectives for understanding the heterogeneous nature of climate variability and climatic change impact. [ABSTRACT FROM AUTHOR]

The aim of this paper is to understand the historical and future climate change situation using 15 extreme precipitation indices in the Pare watershed of Arunachal Pradesh, India. Historical period (1981–2019) and future period (2021–2050) precipitation data are used to compute extreme precipitation indices in RClimDex software. The Pare watershed was divided into 13 subwatersheds; however, the results of the study showed no significant spatial variation. This study found that majority of the precipitation extreme indices are showing decreasing trends during the historical period and most of them are statistically insignificant at 95% confidence level. Only three indices such as SDII, CWD and MRI are found significant at 0.05 level in the Pare watershed. Though not significant, the annual precipitation amount in the Pare watershed was found decreasing at the rate of 3.3 mm per year during the study period. The trend analysis over the whole watershed indicated significant decreasing trends for CWD and MRI while indicating significant increasing trend for SDII. The representative concentration pathway (RCP) 4.5 and 8.5 projected the extreme precipitation indices in a very similar way. The results of the trend analysis under RCP 8.5 showed significant decreasing trend only at SW10 for the index-moderate rainfall index (MRI). Various cases of RX1DAY and RX5DAY not falling during the months of monsoon were observed in both the historical and future periods. The percentage departures of the monsoon from its annual total had increased in RCP 4.5 and RCP 8.5 scenarios as compared to the historical periods. The results of this climatic investigation suggest that the precipitation regime in the study area had been accompanied and also expected by overall reduction in precipitation amount, milder rainfall events, reduction in monsoon (June–September) rainfall and drier climatic conditions. With the prevalent historical scenario and future projected scenarios of the extreme precipitation indices, the water resource potential in the study area is expected to be greatly reduced, for which the authors seek the attention of various stakeholders in water and allied sectors to come together and discuss on the construction of water conservation structures so that agricultural activities can be expanded and remain sustainable. [ABSTRACT FROM AUTHOR]

The accuracy of hydrological assessments in mountain regions is often hindered by the low density of gauges coupled with complex spatial variations in climate. Increasingly, spatial datasets (i.e. satellite and other products) and new computational tools are merged with ground observations to address this problem. This paper presents a comparison of approaches of different complexities to spatially interpolate monthly precipitation and daily temperature time series in the upper Aconcagua catchment in central Chile. A generalised linear mixed model (GLMM) whose parameters are estimated through approximate Bayesian inference is compared with simpler alternatives: inverse distance weighting (IDW), lapse rates (LRs), and two methods that analyse the residuals between observations and WorldClim (WC) data or Climate Hazards Group Infrared Precipitation with Station data (CHIRPS). The assessment is based on a leave-one-out cross validation (LOOCV), with the root-mean-squared error (RMSE) being the primary performance criterion for both climate variables, while the probability of detection (POD) and false-alarm ratio (FAR) are also used for precipitation. Results show that for spatial interpolation of temperature and precipitation, the approaches based on the WorldClim or CHIRPS residuals may be recommended as being more accurate, easy to apply and relatively robust to tested reductions in the number of estimation gauges. The GLMM has comparable performance when all gauges were included and is better for estimating occurrence of precipitation but is more sensitive to the reduction in the number of gauges used for estimation, which is a constraint in sparsely monitored catchments. [ABSTRACT FROM AUTHOR]

The achievement of dual carbon goals varies significantly across Chinese provinces due to the differences in resource endowments and socioeconomic development levels. These variations impose distinct pressures for transitioning along different socioeconomic development paths. Exploration of orderly carbon reduction paths for each province is, thus, crucial, but current predictions have not adequately integrated provincial development plans and carbon emission convergence trends. This study combines the Intergovernmental Panel on Climate Change (IPCC's) shared socioeconomic pathways (SSPs) with a dual-layer target-setting approach based on trends in carbon emissions and each province's five-year plans. The scalable stochastic impacts by regression on population, affluence, and technology (STIRPAT) model and ridge regression method were employed to identify the factors influencing carbon emissions. Carbon peak values and timing were then predicted for provinces under different scenarios. The results indicate that total carbon emissions are primarily influenced by provincial economic level and population, with spatial and temporal variations in the driving factors. Carbon emissions are ranked from low to high in the following order: SSP1, SSP2, and SSP5. Provinces were categorized into four tiers based on their peak times: early, on-time, delayed, and significantly delayed peaking. Under SSP1, 24 provinces would achieve carbon peaking as scheduled. Under SSP2, only 17 provinces would achieve carbon peaking on time. Among the first-tier provinces, Beijing and Shanghai would achieve peak carbon five to eight years ahead of schedule; the second-tier provinces of Henan, Fujian, Guangdong, and Inner Mongolia would achieve peak carbon by 2030; the third tier would achieve carbon peak before 2035; and the fourth tier would gradually reach peak carbon before 2050. Policy implications for differentiated carbon peak paths are proposed for different regions based on these findings. [ABSTRACT FROM AUTHOR]

Aim: Variation in spatial predictions of species' ranges made by various models has been recognized as a significant source of uncertainty for modelling species distributions. Consensus approaches that combine the results of multiple models have been employed to reduce the uncertainty introduced by different algorithms. We evaluate how estimates of habitat suitability, projected using species distribution models (SDMs), varied among different consensus methods relative to the variation introduced by different global climate models (GCMs) and representative concentration pathways (RCPs) used for projection. Location: California Floristic Province (California, US portion). Methods: We modelled the current and future potential distributions of 82 terrestrial plant species, developing model predictions under different combinations of GCMs, RCPs, time periods, dispersal assumptions and SDM consensus methods commonly used to combine different species distribution modelling algorithms. We assessed how each of these factors contributed to the variability in future predictions of species habitat suitability change and aggregate measures of proportional change in species richness. We also related variability in species‐level habitat change to species' attributes. Results: Assuming full dispersal capacity, the variability between habitat predictions made by different consensus methods was higher than the variability introduced by different RCPs and GCMs. The relationships between species' attributes and variability in future habitat predictions depended on the source of uncertainty and dispersal assumptions. However, species with small ranges or low prevalence tended to be associated with high variability in range change forecasts. Main Conclusions: Our results support exploring multiple consensus approaches when considering changes in habitat suitability outside of species' current distributions, especially when projecting species with low prevalence and small range sizes, as these species tend to be of the greatest conservation concern yet produce highly variable model outputs. Differences in vulnerability between diverging greenhouse gas concentration scenarios are most readily observed for end‐of‐century time periods and within species' currently occupied habitats (no dispersal). [ABSTRACT FROM AUTHOR]

The seasonal dynamics and spatial variability of macroinvertebrate assemblages in Alpine streams are becoming better understood. However, the implications of this knowledge for indices used in monitoring programs have yet to be fully considered. Establishing the ecological status of such streams using macroinvertebrates may then be difficult if what is expected to be found, and where, varies across small distances between streams at a given altitude, or between years in response to different climatic characteristics. In this paper, we evaluated the degree of spatial variability in macroinvertebrate abundance and diversity for 14 tributaries at similar altitude that feed a 6-km reach of an Alpine stream, and how this spatial variability differs within and between years. Environmental variables and macroinvertebrates were sampled on 10 dates for assessment. The data showed that differences in environmental characteristics over relatively short distances lead to between tributary differences in the "windows of opportunity" and hence when a tributary should be sampled for monitoring purposes. Superimposed on this spatial variability was inter-annual variability linked to climate differences that shifted this "window of opportunity" in time, and thus altered when monitoring was optimal for any one tributary in any one year. If biological indices are not rendered sensitivity to these variations, the results obtained may reflect more natural variability than possible human impacts. [ABSTRACT FROM AUTHOR]

Ecotones are considered as unique environments. The concepts of edge effect and ecotone species (flora and fauna) are widely used. Considering the fact that the majority of the species found in ecotones are usually at their physiologically determined limits of distribution, how they react to global climate changes becomes crucial. Ecotones are reputed to be more biologically diverse than areas close to them, and therefore possesses a high conservation value, yet little is known on how soil properties vary across ecotones. In this paper, we firstly highlighted the roles ecotones play in assessing the effect on global climate change, the mediatory role they play in the movement of material (water and nutrients) into and out of the region. Secondly, we reviewed studies on how soil properties change across ecotones and it is worthwhile to note that soil properties tend to differ across various ecotones (e.g. increasing pH and decreasing P & N across forest-glade ecotones, decreasing pH across ancient-recent forest ecotones) in a manner that defines the character of the ecotones existing. [ABSTRACT FROM AUTHOR]

Traditionally, the daily average air temperature at a weather station is computed by taking the mean of two values, the maximum temperature (Tmax) and the minimum temperature (Tmin), over a 24-h period. These values form the basis for numerous studies of long-term climatologies (e.g., 30-yr normals) and recent temperature trends and changes. However, many first-order weather stations-such as those at airports-also record hourly temperature data. Using an average of the 24 hourly temperature readings to compute daily average temperature has been shown to provide a more precise and representative estimate of a given day's temperature. This study assesses the spatial variability of the differences in these two methods of daily temperature averaging [i.e., (Tmax 1 Tmin)/2; average of 24 hourly temperature values] for 215 first-order weather stations across the conterminous United States (CONUS) over the 30-yr period 1981-2010. A statistically significant difference is shown between the two methods, as well as consistent overestimation of temperature by the traditional method [(Tmax 1 Tmin)/2], particularly in southern and coastal portions of the CONUS. The explanation for the long-term difference between the two methods is the underlying assumption for the twice-daily method that the diurnal curve of temperature is symmetrical. Moreover, this paper demonstrates a spatially coherent pattern in the difference compared to the most recent part of the temperature record (2001-15). The spatial and temporal differences shown have implications for assessments of the physical factors influencing the diurnal temperature curve, as well as the exact magnitude of contemporary climate change. [ABSTRACT FROM AUTHOR]

Due to global warming and increased human activity, the wild population of Thuja koraiensis Nakai (T. koraiensis) has dropped, placing it in danger. An understanding of the response of T. koraiensis to climate change and the determination of priority conservation areas are tremendously critical for proper conservation. Using sixty-nine T. koraiensis distribution points and seven environmental factors, the Maxent model was used to predict potentially suitable areas and spatial variation patterns of T. koraiensis and the Marxan conservation planning model was used to evaluate conservation gap areas. Research shows that the dominant environmental factors affecting the distribution of potentially suitable areas for T. koraiensis included elevation, precipitation of the driest month, isothermality and precipitation of the wettest quarter. Under the current climatic conditions, highly suitable areas for T. koraiensis are mainly distributed in the Changbai Mountains within Samjiyon County and Baishan City, the Hamgyong Mountains within the western part of Hamgyong-Bukto Province, and the T'aeback-Sanmaek Mountains within Gangwon-do, Kumgangsan Special Administrative Region and Kangwon-do. Under future climate conditions, suitable areas for T. koraiensis show a decreasing trend, and the suitable area will be reduced to higher elevations, and the Hamgyong Mountains may become a refuge. Based on GAP analysis, 69.69% of the priority conservation areas of T. koraiensis are located outside of the nature reserve, and these conservation gap areas are primarily in the southern part of the Changbai Mountains and Kangwon-do. [ABSTRACT FROM AUTHOR]

Assessing Sustainable Development Goal (SDG) indicator 15.3.1, which refers to the proportion of degraded land to total land area, and analysing its status and drivers is essential for the development of policies to promote the early achievement of SDG target 15.3 of Land Degradation Neutrality (LDN). In this study, Northeast China was selected as the study area, and the progress of indicator 15.3.1 was assessed based on the perspective of Net Primary Productivity (NPP) calculated by the CASA model. WorldPop population spatial distribution data were used as a proxy for human activities, combined with climate data to calculate the effects of changes in temperature, precipitation and population spatial distribution on vegetation NPP based on the partial correlation coefficient method and the Geodetector method. The results showed that 92.81% of the areas that passed the test of significance showed an increasing trend in vegetation NPP from 2000 to 2020. The vegetation NPP was affected by a combination of temperature, precipitation and population. The effects of temperature and precipitation on spatial differences in NPP for various vegetation types were significantly greater than those of population, but in high-density population zones, the effects of population on spatial differences in NPP were generally greater than those of temperature and precipitation. Precipitation was the main driver for spatial variation in NPP in deciduous broad-leaved forests, cultivated vegetation and thickets, while temperature was the main driver for spatial variation in NPP in evergreen coniferous forests. Generally, the warming and wetting trend in Northeast China contributed to the accumulation of NPP in cultivated vegetation, thickets, steppes and grasslands. The sensitivity of NPP to temperature and precipitation in deciduous broad-leaved and deciduous coniferous forests varied according to geographical location. [ABSTRACT FROM AUTHOR]

Soil pH is crucial for the quality of the farmland and crop growth. The objective of this study is to analyze the spatial and temporal variations of farmland soil pH in Xinjiang (XJ), and to provide a scientific basis for soil improvement and agricultural production optimization. Based on soil pH data from XJ farmland in two periods, 2008~2010 and 2019~2021, geostatistical methods and kriging spatial interpolation techniques were employed to analyze the spatiotemporal changes in soil pH and to investigate the relationship between relevant influencing factors and pH over more than a decade. The results indicate that the spatiotemporal distribution of soil pH in XJ farmlands is uneven. Between 2019 and 2021, the average soil pH was 8.11, generally being on the higher side, with a coefficient of variation (CV) of 3.85%, indicating minimal spatial variability. In the farmland soil of Southern Xinjiang (S-XJ), the average pH value was 8.17, significantly higher than that of Northern Xinjiang (N-XJ), which was 8.10, demonstrating a spatial pattern of "higher in the south, lower in the north". Over the past decade, soil pH in XJ has significantly increased from 8.11 to 8.13, with a 0.05 unit increase in the north and a 0.01 unit increase in the south (p < 0.05). Regionally, Altay saw the largest increase of 0.22 units, while Bortala Mongol Autonomous Prefecture (Bortala) experienced the most significant decrease, dropping by 0.59 units. Furthermore, this study found that factors such as topography, nutrients, and irrigation methods all have certain influences on the spatial distribution of soil pH in XJ farmland, while variations in climate factors and fertilization levels may affect its long-term temporal changes. These research findings will provide new insights for adjusting and updating agricultural management measures related to soil pH regulation in XJ. [ABSTRACT FROM AUTHOR]

The agro-pastoral transitional zone in northern China is an important agricultural and pastoral production base. This study focuses on the eastern section of the agro-pastoral transitional zone in northern China. Combined with spatio-temporal analysis, stepwise regression, and gray relation analysis, we analyzed the average annual growth rate, spatio-temporal changes, and influencing factors of agricultural and pastoral production in the study area during 2000–2020. The results show that, in the past 20 years, the agricultural and pastoral production of the 50 municipal districts and counties has significantly changed, among which the agricultural production of 38 municipal districts and counties has shown an extremely significant increase. Generally, the growth rate of agricultural production is higher than that of pastoral production. Agricultural and pastoral production in the study area is influenced by socio-economic and land use/cover factors, with an average correlation degree of 0.79 and 0.88, respectively. Climate change affects agricultural production in agricultural counties, with a correlation degree of 0.85. The results of this research provide valuable insights into understanding the long-term temporal and spatial changes in agricultural and pastoral production and help to develop sound agriculture and pastoral management practices in the eastern section of the agro-pastoral transitional zone in Northern China. [ABSTRACT FROM AUTHOR]

The vertical transition zone of mountain vegetation is characterized by high species diversity, and the width of the transition zone may serve as an indirect indicator of climate change. However, research into the differential characteristics of vegetation response to climate changes at the boundary of vertical transition zones has been limited. This study employs MODIS and climate data spanning 2001 to 2018 to investigate spatiotemporal trends in precipitation (PRE), temperature (TMP), radiation (RAD), and Normalized Difference Vegetation Index (NDVI) across nine montane deciduous broad-leaved forests in the eastern monsoon region of China. It explores the time-lag and -accumulation effects of climatic variables on NDVI, quantifying their relative contributions to both its short-term and interannual variations. Results show that, notably, with the Qinling-Daba Mountains as a demarcation, northern regions exhibit significant increases in RAD (0.874–2.047 W m−2/a), whereas southern regions demonstrate notable rises in TMP (0.59–0.73 °C/10a). Areas of lower annual PRE correspond to the most rapid increases in annual average NDVI (5.045 × 10−3/a). NDVI's lag time and cumulative duration responses to TMP are the shortest (0 and 2~4 periods), while its correlation with RAD is the strongest (0.815–0.975), generally decreasing from higher to lower latitudes. TMP significantly affects NDVI variations, impacting both short-term and interannual trends, with PRE driving short-term fluctuations and RAD dictating long-term shifts. This research provides critical data and a theoretical framework that enhances our understanding of how regional vegetation's vertical zonation responds to climate change, thereby making a substantial contribution to the study of mountain vegetation's diverse adaptability to climatic variations. [ABSTRACT FROM AUTHOR]

Dispersal—the movement of an individual from the site of birth to a different site for reproduction—is an ecological and evolutionary driver of species ranges that shapes patterns of colonization, connectivity, gene flow, and adaptation. In plants, the traits that influence dispersal often vary within and among species, are heritable, and evolve in response to the fitness consequences of moving through heterogeneous landscapes. Spatial and temporal variation in the quality and quantity of habitat are important sources of selection on dispersal strategies across species ranges. While recent reviews have evaluated the interactions between spatial variation in habitat and dispersal dynamics, the extent to which geographic variation in temporal variability can also shape range‐wide patterns in dispersal traits has not been synthesized. In this paper, we summarize key predictions from metapopulation models that evaluate how dispersal evolves in response to spatial and temporal habitat variability. Next, we compile empirical data that quantify temporal variability in plant demography and patterns of dispersal trait variation across species ranges to evaluate the hypothesis that higher temporal variability favors increased dispersal at plant range limits. We found some suggestive evidence supporting this hypothesis while more generally identifying a major gap in empirical work evaluating plant metapopulation dynamics across species ranges and geographic variation in dispersal traits. To address this gap, we propose several future research directions that would advance our understanding of the interplay between spatiotemporal variability and dispersal trait variation in shaping the dynamics of current and future species ranges. [ABSTRACT FROM AUTHOR]

As a region susceptible to the impacts of climate change, evaluating the temporal and spatial variations in ecological environment quality (EEQ) and potential influencing factors is crucial for ensuring the ecological security of the Tibetan Plateau. This study utilized the Google Earth Engine (GEE) platform to construct a Remote Sensing-based Ecological Index (RSEI) and examined the temporal and spatial dynamics of the Tibetan Plateau's EEQ from 2000 to 2022. The findings revealed that the RSEI of the Tibetan Plateau predominantly exhibited a slight degradation trend from 2000 to 2022, with a multi-year average of 0.404. Utilizing SHAP (Shapley Additive Explanation) to interpret XGBoost (eXtreme Gradient Boosting), the study identified that natural factors as the primary influencers on the RSEI of the Tibetan Plateau, with temperature, soil moisture, and precipitation variables exhibiting higher SHAP values, indicating their substantial contributions. The interaction between temperature and precipitation showed a positive effect on RSEI, with the SHAP interaction value increasing with rising precipitation. The methodology and results of this study could provide insights for a comprehensive understanding and monitoring of the dynamic evolution of EEQ on the Tibetan Plateau amidst the context of climate change. [ABSTRACT FROM AUTHOR]

The variation of polar sea ice is an indicator of polar environmental change, which plays an important role in the study of regional and global climate change. In this paper, the latest sea ice data sets from the European Space Agency Climate Change Initiative were firstly combined to comprehensively analyze the spatial and temporal variation of Arctic sea ice concentration (SIC) and sea ice thickness (SIT) from 2002 to 2017. The results show that during this period, the SIC of the Kara Sea and the Barents Sea decreases the most significantly, reaching −1.11%·year−1. The Arctic annual average sea ice extent (SIE) and sea ice area (SIA) exhibit noticeable decreasing trends, reaching −0.0592 × 106 km·year−1 and −0.0628 × 106 km·year−1, respectively. Both of Arctic SIE and SIA vary seasonally, and they reach the minimum values of 4.97 × 106 km2 and 3.96 × 106 km2 in September and the maximum values of 14.58 × 106 km2 and 13.35 × 106 km2 in March, respectively. Moreover, the Arctic SIE and SIA anomaly series from 2002 to 2017 were analyzed based on the linear regression method. It is found the most noticeable decreasing trends of the Arctic SIE and SIA are from July to October. The maximum variation of the SIE is −1.06 × 106 km2(10a)−1, and the maximum variation of the SIA is −1.09 × 106 km2(10a)−1. The Arctic SIE and SIA present the smallest decreasing trends from April to May, which are −0.19 × 106 km2(10a)−1 and −0.2 × 106 km2(10a)−1, respectively. The Arctic SIT also displays a pronounced decreasing trend with −0.015 m·year−1 in autumn and −0.018 m·year−1 in winter. The annual average SIT reaches a minimum of 1.28 m in 2013 and a maximum of 1.66 m in 2005. Particularly, the SIT of the Hudson Bay decreases the most significantly, reaching −0.051 m·year−1. Finally, the Arctic average SIT was tested by the Mann‐Kendall method. It is found that the SIT has a sudden change in 2007, from a slight increase in 2003–2007 to a significant decrease in 2007–2017. Overall, the decreasing trend of the Arctic sea ice (both SIC and SIT) is significant in the past decade. These findings can contribute to future research on polar regions and global climate change. Key Points: We analyzed the whole and regional variation of Arctic sea ice by combining use of the latest sea ice concentration and sea ice thickness data sets from European Space AgencyThe sea ice concentration in the Barents Sea and the Kara Sea and the sea ice thickness in the Hudson Bay decreased the most obviouslyThe sea ice thickness has a sudden change in 2007 based on Mann‐Kendall mutation test [ABSTRACT FROM AUTHOR]

The dominant paradigm for assessing ecological responses to climate change assumes that future states of individuals and populations can be predicted by current, species-wide performance variation across spatial climatic gradients. However, if the fates of ecological systems are better predicted by past responses to in situ climatic variation through time, this current analytical paradigm may be severely misleading. Empirically testing whether spatial or temporal climate responses better predict how species respond to climate change has been elusive, largely due to restrictive data requirements. Here, we leverage a newly collected network of ponderosa pine tree-ring time series to test whether statistically inferred responses to spatial versus temporal climatic variation better predict how trees have responded to recent climate change. When compared to observed tree growth responses to climate change since 1980, predictions derived from spatial climatic variation were wrong in both magnitude and direction. This was not the case for predictions derived from climatic variation through time, which were able to replicate observed responses well. Future climate scenarios through the end of the 21st century exacerbated these disparities. These results suggest that the currently dominant paradigm of forecasting the ecological impacts of climate change based on spatial climatic variation may be severely misleading over decadal to centennial timescales. [ABSTRACT FROM AUTHOR]

Since the beginning of the new century, there has been a notable enhancement in China's ecological environment quality (EEQ), a development occurring in tandem with climate change and the extensive ecological restoration projects (ERPs) undertaken in the country. However, comprehensive insights into the spatial and temporal characteristics of China's EEQ, and its responses to both climate change and human activities over the past two decades, have remained largely elusive. In this study, we harnessed a combination of multi-source remote-sensing data and reanalysis data. We employed Theil–Sen median trend analysis, multivariate regression residual analysis, and the Hurst index to examine the impacts and changing patterns of climatic factors and human activities on China's EEQ during the past two decades. Furthermore, we endeavored to forecast the future trajectory of EEQ. Our findings underscore a significant improvement in EEQ across most regions of China between 2002 and 2019, with the most pronounced enhancements observed in the Loess Plateau, Northeast China, and South China. This transformation can be attributed to the combined influence of climatic factors and human activities, which jointly accounted for alterations in EEQ across 78.25% of China's geographical expanse. Human activities (HA) contributed 3.93% to these changes, while climatic factors (CC) contributed 17.79%. Additionally, our projections indicate that EEQ is poised to continue improving in 56.70% of China's territory in the foreseeable future. However, the Loess Plateau, Tarim Basin, and Inner Mongolia Plateau are anticipated to experience a declining trend. Consequently, within the context of global climate change, the judicious management of human activities emerges as a critical imperative for maintaining EEQ in China. This study, bridging existing gaps in the literature, furnishes a scientific foundation for comprehending the evolving dynamics of EEQ in China and informs the optimization of management policies in this domain. [ABSTRACT FROM AUTHOR]

As the main expenditure item in water balance, evapotranspiration has an important impact on the surface ecosystem. Assessing the impact of changes in meteorological elements on evapotranspiration is essential to identify the spatiotemporal heterogeneity of hydrographic responses to climate changes. Based on the actual evapotranspiration (ETa) product (GPR-ET) generated by Gaussian process regression (GPR), as well as temperature and precipitation datasets, our study employed various statistical analysis methods, including geographic detector, the center of gravity migration model, spatial variation coefficients, and partial differential models, to investigate the spatiotemporal variation in ETa in China from 2000 to 2018. The analysis covered future trends in ETa changes and the contribution of meteorological factors. Our results showed that the ETa in northwest China had stronger spatial heterogeneity and the mean value was generally lower than that in the southeast. But the center of gravity of ETa was shifting towards the northwest. In most areas, the future trend was expected to be inconsistent with the current stage. ETa in the regions of north and west was mainly driven by precipitation, while its increase in southeast China was largely attributed to temperature. In addition to spatial variations, the joint enhancement effect of temperature and precipitation on ETa exists. According to the contribution analysis, precipitation contributed more to the change in ETa than temperature. These findings have enhanced our comprehension of the contribution of climate variability to ETa changes, providing scientific proof for the optimization apportion of future water resources. [ABSTRACT FROM AUTHOR]

This research analyzes extreme precipitation events in the Huaihe River Basin in China, a densely populated region with a history of human settlements and agricultural activities. This study aims to explore the impact of extreme precipitation index changes and provide decision-making suggestions for flood early warning and agricultural development in the Huaihe River Basin. The study utilizes the NEX-GDDP-CMIP6 climate model dataset and daily value dataset (V3.0) from China's national surface weather stations to investigate temporal and spatial changes in the extreme precipitation indices from 1960 to 2014 and future projections. At the same time, this study adopts the RclimDex model, Taylor diagram, and Sen+Mann–Kendall trend analysis research methods to analyze the data. The results reveal a slight increase in extreme precipitation indices from the northwest to southeast within the basin, except for the CDD, which shows a decreasing trend. Regarding the spatial variation, the future increase in extreme precipitation in the Huaihe River Basin shows a spatial variation characteristic that decreases from the northwest to southeast. These findings suggest that extreme precipitation events are intensifying in the region. Understanding these trends and their implications is vital for adaptation strategy planning and mitigating the risks associated with extreme precipitation events in the Huaihe River Basin. [ABSTRACT FROM AUTHOR]

Effective Strength Index (ESI) is the relative strength of NAOand SO. ESI tendency is the algebraic difference between April-ESI and January-ESI and it represents the simultaneous evolution of NAO and SO from winter to spring. During positive (negative) phase of ESI tendency, NAO restores positive (negative) phase and SO restores negative (positive) phase before the beginning of summer season. Thus during contrasting phases (positive and negative) of ESI tendency, the evolution of NAO and SOis out of phase. In this paper we have studied the spatial and temporal variability of winter-time temperature field over Europe, Arabian Sea and Bay of Bengal during contrasting phases of ESI tendency. The study reveals that during positive (negative) ESI tendency, smaller (larger) region of Europe is showing significant winter-time cooling (warming) at surface. The relationship between winter-time surface temperature over above regions and Indian summer monsoon rainfall (ISMR) also shows spatial and temporal variability. The probable explanation for this change in the relationship is discussed in the paper. Two sets of temperature parameters for two different phases of ESI tendency are found out. Multiple regression equations are developed for the prediction of ISMR in each phase of ESI tendency. The performance of these equations is also discussed in this paper. [ABSTRACT FROM AUTHOR]

The problem of fit is about the interplay between the human and ecosystem dimensions in social-ecological systems that are not just linked but truly integrated. This interplay takes place across temporal and spatial scales and institutional and organizational levels in systems that are increasingly being interpreted as complex adaptive systems. In 1997, we were invited to produce one of three background papers related to a, at that time, new initiative called Institutional Dimensions of Global Environmental Change (IDEG), a research activity of the International Human Dimensions Program of Global Environmental Change (IHDP). The paper, which exists as a discussion paper of the IHDP, has generated considerable interest. Here we publish the original paper 10 years later with an extended introduction and with reflections on some of the issues raised in the original paper concerning problems of fit. [ABSTRACT FROM AUTHOR]

• Based on the principles of SPEI, we develop a GDAI. • The relationship between GDAI and AI is closer. • The correlation between GDAI and SPEI is also better. Climate change has increased the risk of drought in both arid and non-arid regions of the globe. Therefore, in order to monitor and evaluate global drought and aridity, based on the principle of standardized precipitation evapotranspiration index (SPEI), this paper selected precipitation and potential evapotranspiration data and used a frequency calculation method to develop a multi-scale global drought aridity index (GDAI) that concurrently considers spatial and temporal variation characteristics: the spatiotemporal standardized precipitation evapotranspiration index. The index was compared with the aridity index (AI) and SPEI. The results showed that the correlation coefficients between GDAI and AI were greater than 0.8 in most areas, and the relationship was closer and could reveal the global aridity and wetness zoning. In addition, the correlation between GDAI and SPEI was also better, which could capture the temporal change characteristics of drought. Moreover, this study also compared the grades of GDAI and SPEI with the actual drought grades in Yunnan from September 2009 to August 2010, and found that GDAI was better than SPEI, and GDAI was the most accurately evaluated at the 3 month time scale, with an accuracy rate of 86.11%. Therefore, this index is more helpful for the assessment and monitoring of global drought and aridity. [ABSTRACT FROM AUTHOR]

Net primary productivity (NPP) can indirectly reflect vegetation's capacity for CO 2 fixation, but its spatiotemporal dynamics are subject to alterations to some extent due to the influences of climate change and human activities. In this study, NPP is used as an indicator to investigate vegetarian carbon ability changes in the vital ecosystems of the Yangtze River Basin (YRB) in China. We also explored the NPP responses to climate change and human activities. We conducted a comprehensive analysis of the temporal dynamics and spatial variations in NPP within the YRB ecosystems from 2003 to 2020. Furthermore, we employed residual analysis to quantitatively assess the contributions of climate factors and human activities to NPP changes. The research findings are as follows: (1) Over the 18-year period, the average NPP within the basin amounted to 543.95 gC/m 2 , displaying a noticeable fluctuating upward trend with a growth rate of approximately 3.1 gC/m 2 ; (2) The areas exhibiting an increasing trend in NPP account for 82.55% of the total study area. Regions with relatively high stability in the basin covered 62.36% of the total area, while areas with low stability accounted for 2.22%, mainly situated in the Hengduan Mountains of the western Sichuan Plateau; (3) NPP improvement was jointly driven by human activities and climate change, with human activities contributing more significantly to NPP growth. Specifically, the contributions were 65.39% in total, with human activities contributing 59.28% and climate change contributing 40.01%. This study provides an objective assessment of the contributions of human activities and climate change to vegetation productivity, offering crucial insights for future ecosystem development and environmental planning. [ABSTRACT FROM AUTHOR]

Compound heat–precipitation events exert significant impacts on severe weather occurrences. Intense vertical air movement, driving vigorous convection, primarily contributes to the formation of extreme precipitation. Nevertheless, such compound events' temporal and spatial variation patterns at convection-permitting resolutions remain inadequately explored. This study assesses the performance of the Convection-Permitting Model (CPM) against a model of convection parameterization while investigating the spatial dynamics of compound heat–precipitation events in Guangdong, China. Our findings indicate that the CPM exhibits heightened reliability and precision in simulating temperature and precipitation patterns, especially in extreme precipitation simulation, which would be highly underestimated without a convection-permitting process. Projections from the CPM reveal that, across historical and future periods, the occurrence frequency and fraction of T-P events (instances of extreme heat followed by extreme precipitation) surpass those of P-T events (occurrences of extreme precipitation followed by extreme heat). For T-P events, the CPM exhibits better capability in capturing high-frequency occurrence areas, whereas the results of the relatively low-resolution model show less distinct spatial variations. Both types of events exhibit noticeable upward trends yearly within each period. By the close of this century, the provincial average frequency of P-T events is anticipated to decrease from 20.32 times to 14.55 times. In contrast, the frequency of T-P events is projected to increase from 87.7 times to 101.38 times. These projected changes underscore the shifting dynamics of compound heat–precipitation events in the study region. [ABSTRACT FROM AUTHOR]

The frequency of precipitation extremes is set to change in response to a warming climate. Thereby, the change in extreme precipitation event occurrence is influenced by both a shift in the mean and a change in variability. How large the individual contributions are from either of them (mean or variability) to the change in precipitation extremes is largely unknown. This is, however, relevant for a better understanding of how and why climate extremes change. For this study, two sets of forcing experiments from the regional CRCM5 initial-condition large ensemble are used: a set of 50 members with historical and RCP8.5 forcing and a 35-member (700-year) ensemble of pre-industrial natural forcing. The concept of the probability risk ratio is used to partition the change in extreme-event occurrence into contributions from a change in mean climate or a change in variability. The results show that the contributions from a change in variability are in parts equally important to changes in the mean and can even exceed them. The level of contributions shows high spatial variation, which underlines the importance of regional processes for changes in extremes. While over Scandinavia or central Europe the mean influences the increase in extremes more, the increase is driven by changes in variability over France, the Iberian Peninsula, and the Mediterranean. For annual extremes, the differences between the ratios of contribution of mean and variability are smaller, while on seasonal scales the difference in contributions becomes larger. In winter (DJF) the mean contributes more to an increase in extreme events, while in summer (JJA) the change in variability drives the change in extremes. The level of temporal aggregation (3, 24, 72 h) has only a small influence on annual and winterly extremes, while in summer the contribution from variability can increase with longer durations. The level of extremeness for the event definition generally increases the role of variability. These results highlight the need for a better understanding of changes in climate variability to better understand the mechanisms behind changes in climate extremes. [ABSTRACT FROM AUTHOR]

The spatio-temporal variation of precipitation under global warming had been a research hotspot. Snowfall is an important part of precipitation, and its variabilities and trends in different regions have received great attention. In this paper, the Haihe River Basin is used as a case, and we employ the K-means clustering method to divide the basin into four sub-regions. The double temperature threshold method in the form of the exponential equation is used in this study to identify precipitation phase states, based on daily temperature, snowfall, and precipitation data from 43 meteorological stations in and around the Haihe River Basin from 1960 to 1979. Then, daily snowfall data from 1960 to 2016 are established, and the spatial and temporal variation of snowfall in the Haihe River Basin are analyzed according to the snowfall levels as determined by the national meteorological department. The results evalueted in four different zones show that (1) the snowfall at each meteorological station can be effectively estimated at an annual scale through the exponential equation, for which the correlation coefficient of each division is above 0.95, and the relative error is within 5%. (2) Except for the average snowfall and light snowfall, the snowfall and snowfall days of moderate snow, heavy snow, and snowstorm in each division are in the order of Zones III > IV > I > II. (3) The snowfall and the number of snowfall days at different levels both show a decreasing trend, except for the increasing trend of snowfall in Zone I. (4) The interannual variation trend in the snowfall at the different levels are not obvious, except for Zone III, which shows a significant decreasing trend. [ABSTRACT FROM AUTHOR]

In the karst areas, the epikarst system is a very sensitive environment, due to its position at the interface between soil and vadose zone. The epikarst is a weathered zone that develops as a result of both abiotic and biotic processes. In this paper we present the result of the complex investigations of epikarst zone which overlap dolines within two typical karst areas (Gömör-Torna and Tapolca) from Hungary, based on multi-criteria analysis techniques (microbiological activity assessed as biomass amount and CO2 production, seasonality of air and soil microclimate, slope orientation and exposition), in order to reveal control factors of karst processes, the territori-al and local distinctions of karst dissolution that occur in the epikarst zone. The data were compared, taking into account the human activities' impact on both sampled study areas. After four years of monitoring, the results show that there are significant season-al and diurnal variations of physical, chemical and biotic parameters of soil that cover and affect the epikarst zone. Spatial variations of these parameters were recorded as well. [ABSTRACT FROM AUTHOR]

Motivation: Climate change plays an important role in the generation and maintenance of biodiversity by driving processes such as diversification and range shifts. As a result, biodiversity patterns are often found to carry the imprints of palaeoclimatic changes. However, we still know little about the spatial and temporal variation in climate over the scale of millennia affecting eco‐evolutionary dynamics, mostly because of the scarcity of user‐friendly and freely available spatio‐temporal palaeoclimate series at such temporal scales. Here, we address this gap by presenting PALEO‐PGEM‐Series, a global spatio‐temporal dataset of the last 5 Myr, with 1 kyr resolution, spatially downscaled from emulations performed with the intermediate‐complexity atmosphere–ocean general circulation model PALEO‐PGEM. PALEO‐PGEM‐Series holds the potential to advance our understanding of the mechanisms behind the strong relationship between biodiversity and climate, a pressing need given projected biodiversity responses to anthropogenic climatic change. Main Types of Variables Contained: Spatio‐temporal series of monthly temperature and precipitation and 17 derived bioclimatic variables over the Pliocene–Pleistocene, along with standard error estimates from multiple runs of the emulator. Spatial Location and Grain: Global landmasses, at 1° × 1°. Time Period and Grain: Last 5 Myr at 1000 year resolution. Major Taxa and Level of Measurement: Not applicable. Software Format: Tab‐delimited text files and accompanying R code to derive bioclimatic variables. [ABSTRACT FROM AUTHOR]

Understanding the impact of drought on fire dynamics is crucial for assessing the potential effects of climate change on wildfire activity in China. In this study, we present a series of multiple linear regression (MLR) models linking burned area (BA) during mainland China's fire season from 2001 to 2019, across seven regions, to concurrent drought, antecedent drought, and time trend. We estimated burned area using Collection 6 Moderate Resolution Imaging Spectradiometer (MODIS) and drought indicators using either the Standardized Precipitation Evapotranspiration Index (SPEI) or the self-calibrated Palmer Drought Severity Index (sc-PDSI). Our findings indicate that the wildfire season displays a spatial variation pattern that increases with latitude, with the Northeast China (NEC), North China (NC), and Central China (CC) regions identified as the primary areas of wildfire occurrence. Concurrent and antecedent drought conditions were found to have varying effects across regions, with concurrent drought as the dominant predictor for NEC and Southeast China (SEC) regions and antecedent drought as the key predictor for most regions. We also found that the Northwest China (NWC) and CC regions exhibit a gradual decrease in burned area over time, while the NEC region showed a slight increase. Our multiple linear regression models exhibited a notable level of predictive power, as evidenced by the average correlation coefficient of 0.63 between the leave-one-out cross-validation predictions and observed values. In particular, the NEC, NWC, and CC regions demonstrated strong correlations of 0.88, 0.80, and 0.76, respectively. This indicates the potential of our models to contribute to the prediction of future wildfire occurrences and the development of effective wildfire management and prevention strategies. Nevertheless, the intricate relationship among fire, climate change, human activities, and vegetation distribution may limit the generalizability of these findings to other conditions. Consequently, future research should consider a broad range of factors to develop more comprehensive models. [ABSTRACT FROM AUTHOR]

We explore past trends and future projections of mean sea level (MSL) at the Finnish coast, in the northeastern Baltic Sea, during the period 1901–2100. We decompose the relative MSL change into three components: regional sea level rise (SLR), postglacial land uplift, and the effect of changes in wind climate. Past trends of regional SLR can be calculated after subtracting the other two components from the MSL trends observed by tide gauges, as the land uplift rates obtained from the semi-empirical model NKG2016LU are independent of tide gauge observations. According to the results, local absolute SLR trends are close to global mean rates. To construct future projections, we combine an ensemble of global SLR projections in a probabilistic framework. In addition, we use climate model results to estimate future changes in wind climate and their effect on MSL in the semi-enclosed Baltic Sea. This yields probability distributions of MSL change for three scenarios representing different future emission pathways. Spatial variations in the MSL projections result primarily from different local land uplift rates: under the medium-emission scenario RCP4.5/SSP2-4.5, for example, the projected MSL change (5 % to 95 % range) over the 21st century varies from -28 (-54 to 24) cm in the Bothnian Bay to 31 (5 to 83) cm in the eastern Gulf of Finland. [ABSTRACT FROM AUTHOR]

The climate of High Mountain Asia (HMA) has changed in recent decades. While the temperature is consistently increasing at a higher rate than the global warming rate, precipitation changes are inconsistent, with substantial temporal and spatial variation. Climate warming will have enormous consequences for hydroclimatic extremes. For the higher altitudes of the HMA, which are a significant source of water for the large rivers in Asia, often trends are calculated using a limited number of in situ observations mainly observed in valleys. This study explores the changes in mean, extreme, and compound-extreme climate variables and their seasonality along the full altitudinal range in HMA using daily ERA5 reanalysis data (1979–2018). Our results show that winter warming and summer wetting dominate the interior part of HMA. The results indicate a coherent significant increasing trend in the occurrence of heatwaves across all regions in HMA. The number of days with heavy precipitation shows more significant trends in southern and eastern basins than in other areas of HMA. The dry period occurrence shows a distinct demarcation between lower- and higher-altitude regions and is increasing for most basins. Although precipitation and temperature show variable tendencies, their compound occurrence is coherent in the monsoon-dominated basins. These changes in indicators of climatic extremes may imply substantial increases in the future occurrence of hazards such as floods, landslides, and droughts, which in turn impact economic production and infrastructure. [ABSTRACT FROM AUTHOR]

Soil moisture is closely related to the hydrosphere, atmosphere, and biosphere, which makes it one of the most significant climate variables. Using data from the National Environmental Forecasting Center (NCEP), this paper analyzes the temporal and spatial characteristics of soil moisture at a depth of 0–10 cm in China for the period of 1948 to 2014. In addition, the soil moisture's possible interaction with air temperature is explored. Mainly using statistical analysis, the results showed that annual soil moisture decreased significantly (p < 0.01) in most areas. The tendency of decreasing soil moisture was relatively higher in spring and autumn than that in summer and winter. As to the national annual average soil moisture, there was a sudden change in the 1970s. The soil moisture had a relatively high value with a larger deviation before the abrupt change, but after that, the soil moisture was at a relatively low level with a smaller deviation. It was also found that the soil moisture at 0–10 cm showed a negative correlation with the 2-m air temperature above ground in the northern part of China, where the speed of the temperature rise was higher. The results are expected to help improve the understanding of the link between regional soil moisture variation and climate change. [ABSTRACT FROM AUTHOR]

One nation with the highest risk of climate catastrophes is Pakistan. Pakistan's geographical nature makes it susceptible to natural hazards. Pakistan is facing regional differences in terms of climate change. The frequency and intensity of natural hazards due to climate change vary from place to place. There is an urgent need to recognize the spatial variations in natural hazards inside the country. To address such problems, it might be useful to map out the areas that need resources to increase resilience and accomplish adaptability. Therefore, the main goal of this research was to create a district-level map that illustrates the multi-hazard zones of various regions in Pakistan. In order to comprehend the geographical differences in climate change and natural hazards across Pakistan, this study examines the relevant literature and data currently available regarding the occurrence of natural hazards in the past. Firstly, a district-level comprehensive database of Pakistan's five natural hazards (floods, droughts, earthquakes, heatwaves, and landslides) was created. Through consultation with specialists in related areas, hazard and weighting factors for a specific hazard were specified based on the structured district-level historical disaster database of Pakistan. After that, individual and multi-hazard ratings were computed for each district. Then, using estimated multi-hazard scores, the districts of Pakistan were classified into four zones. Finally, a map of Pakistan's multi-hazard zones was created per district. The study results are essential and significant for policymakers to consider when making decisions on disaster management techniques, that is, when organizing disaster preparedness, mitigation, and prevention plans. [ABSTRACT FROM AUTHOR]

Aim: Soil organic carbon (SOC) stabilization has become an important topic in recent years in the context of global climate change. Microbial residues represent a significant component of stabilized SOC pools. However, spatial variations in the contributions of bacterial and fungal residues to SOC and their determinants at a continental scale remain poorly understood. We aimed to evaluate the spatial variations and controls of the contributions of microbial residues to SOC in forest topsoil. Location: North–south transect in eastern China. Time period: 2014. Major taxa studied: Forest ecosystems. Methods: A total of 195 surface (0–10 cm) soils were sampled from 28 forest sites across tropical and boreal forests in eastern China from July to August to assess how biotic and abiotic factors govern the geographic patterns of the contributions of soil microbial residues (indicated by amino sugars) to SOC. Results: Fungal residues (30.0%) had a greater average contribution to SOC than bacterial residues (15.5%). The contributions of bacterial (CBR) and total microbial residues (CMR) to SOC showed negative latitudinal patterns and were positively correlated with mean annual temperature (MAT). In contrast, the contribution of fungal residues to SOC (CFR) showed no clear geographic or climatic patterns. On average, the CBR (9.7%), CFR (21.1%) and CMR (30.8%) were lower in boreal forests than in other biome forests. SOC concentration negatively mediated CBR, CFR and CMR. The piecewise structural equation model results showed that MAT was the primary driver of the geographic pattern of CBR, whereas SOC and soil carbon : nitrogen ratio were more directly associated with the CFR. Additionally, plant factors and microbial properties (i.e., microbial biomass and composition) played relatively little roles in regulating CBR, CFR and CMR. Main conclusions These findings advance the current knowledge of the different geographic patterns of CBR and CFR regulated by different potential mechanisms in forest ecosystems. This highlights that the dynamics of microbial residues could potentially have unexpected consequences for topsoil SOC stocks under climate change. [ABSTRACT FROM AUTHOR]