Open-pit coal mining poses a severe threat to regional ecological security. Rapid and accurate monitoring of ecological quality changes is crucial for regional ecological restoration. In this study, taking the Wujiata open-pit coal mine as an example, the Red-Edge Normalized Difference Vegetation Index (RENDVI), Salinity Index (SI-T), WETness index (WET), Normalized Differential Built Soil Index (NDBSI), Land Surface Temperature (LST), and Desertification Index (DI) were used to construct the Open-pit Mine Remote Sensing Ecological Index (OM-RSEI) through Principal Component Analysis (PCA). The ecological quality and restoration conditions of typical mining areas in arid and semi-arid regions were monitored and evaluated. The results shown that: (1) The contribution rates and eigenvalues of OM-RSEI were higher than those of conventional RSEI, OM-RSEI was more applicable in open-pit mining areas. (2) From 2018 to 2023, the OM-RSEI of the Wujiata open-pit coal mine showed a 'V' shaped fluctuation that was damaged and then gradually recovered. (3) The degraded area of Wujiata open-pit coal mine and its 5 km buffer zone accounted for 78.02%, and the improved area accounted for 19.16%. (4) The average Moran's I index of OM-RSEI in the study area was 0.8189, and the high-high clustering corresponded to the 'good' and 'excellent' distributions, while the low-low clustering corresponded to the 'poor' and 'less-poor' distributions. The OM-RSEI provided a new indicator for monitoring and evaluation of ecological restoration in open-pit coal mines, which can provide theoretical support for ecological restoration in open-pit coal mining areas.

Natural farming, introduced by Masanobu Fukuoka, provides a sustainable alternative to traditional farming by tackling issues like soil damage, chemical use, and environmental harm. This study looks at global research on natural farming from 2018 to 2024, focusing on research trends, themes, and factors influencing its adoption. A review of 210 publications was conducted using Scopus, following PRISMA guidelines, with analysis done using R Studio and VOS viewer. India is the leader in natural farming research, with 135 publications, driven by government policies that support sustainability. Other key contributors include China, Italy, and the United States. The analysis identified three main themes: environmental sustainability, economic benefits, and adoption strategies. The citation analysis highlighted important researchers like Bharucha and Venkateshmurthy. The main reasons farmers adopt natural farming include better soil health, less chemical use, lower costs, and greater climate resilience. Techniques like ZeroBudget Natural Farming (ZBNF), which includes practices like Jeevamritha and organic mulching, are vital for improving soil fertility and cutting down on input costs. This study shows that natural farming can help balance the environment, improve farmer incomes, and encourage more sustainable practices. Future research should explore the role of digital technologies and cooperation between countries to expand natural farming worldwide.

This study analyzes the forest flammability hazard in the south of Tyumen Oblast (Western Siberia, Russia) and identifies variation patterns in fire areas depending on weather and climate characteristics in 2008-2023. Using correlation analysis, we proved that the area of forest fires is primarily affected by maximum temperature, relative air humidity, and the amount of precipitation, as well as by global climate change associated with an increase in carbon dioxide in the atmosphere and the maximum height of snow cover. As a rule, a year before the period of severe forest fires in the south of Tyumen Oblast, the height of snow cover is insignificant, which leads to insufficient soil moisture in the following spring, less or no time for the vegetation to enter the vegetative phase, and the forest leaf floor remaining dry and easily flammable, which contributes to an increase in the fire area. According to the estimates of the CMIP6 project climate models under the SSP2-4.5 scenario, by the end of the 21st century, a gradual increase in the number of summer temperatures above 35 degrees C is expected, whereas the extreme SSP5-8.5 scenario forecasts the tripling in the number of such hot days. The forecast shows an increase of fire hazardous conditions in the south of Tyumen Oblast by the late 21st century, which should be taken into account in the territory's economic development.

Black carbon (BC) is one of the major aerosol components with relatively high implications on climatic patterns through its radiative forcing (RF). South Asia has recently experienced an increased concentration of pollution; however, relatively fewer studies have been carried out on long-term assessment of BC and its implications. The present study analyzed the long-term concentration of BC in selected urban locations over South Asia using the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). The study employed statistical analysis, including linear regression techniques, to assess the long-term concentration of BC. The results show that a rapid increase of BC is observed over most urban locations of South Asia with the predominance in winter and hence requires strict regional control measures to reduce the excess concentration of BC in the atmosphere. High concentration of BC in winter is attributed to anthropogenic activities and changes in meteorological conditions that enhance the accumulation of pollutants in the atmosphere. The relationship of BC with cloud top temperature and cloud effective radius demonstrates the direct and indirect effect of BC on cloud properties in this region. The RF results reveal that aerosol optical depth has positive aerosol RF in the atmosphere and negative RF at the top of the atmosphere (TOA) as well as at the bottom of the atmosphere (BOA). Negative RF at the TOA indicates less forcing efficiency due to fewer BC aerosols. On the other hand, averaging aerosol RF within the atmosphere reveals positive forcing, which suggests the efficiency force exerted by BC aerosols after absorbing solar radiation.

Water temperature extremes can pose serious threats to the aquatic ecosystems of mountain rivers. These rivers are influenced by snow and glaciermelt, which change with climate. As a result, the frequency and severity of water temperature extremes may change. While previous studies have documented changes in non-extreme water temperature, it is yet unclear how extreme water temperatures change in a warming climate and how their hydro-meteorological drivers differ from those of non-extremes. This study aims to assess temporal changes and spatial variability in water temperature extremes and enhance our understanding of the driving processes across European mountain rivers in the current climate, at both a regional and continental scale. First, we describe the characteristics of extreme events and explore their relationships with catchment characteristics. Second, we assess trends in water temperature extremes and compare them with trends in mean water temperature. Third, we use random forest models to identify the main driving processes of water temperature extremes. Last, we conduct a co-occurrence analysis to examine the relationship between water temperature extremes and hydro-climatic extremes. Our results show that mean water temperature has increased by +0.38 +/- 0.14 ${+}0.38\pm 0.14$degrees C per decade, leading to more extreme events at high elevations in spring and summer. While non-extreme water temperatures are mainly driven by air temperature, water temperature extremes are also importantly influenced by soil moisture, baseflow, and meltwater. Our study highlights the complexity of water temperature dynamics in mountain rivers at the regional and continental scale, especially during water temperature extremes.

The seasonal mountain snowpack of the Western US (WUS) is a key water resource to millions of people and an important component of the regional climate system. Impurities at the snow surface can affect snowmelt timing and rate through snow radiative forcing (RF), resulting in earlier streamflow, snow disappearance, and less water availability in dry months. Predicting the locations, timing, and intensity of impurities is challenging, and little is known concerning whether snow RF has changed over recent decades. Here we analyzed the relative magnitude and spatio-temporal variability of snow RF across the WUS at three spatial scales (pixel, watershed, regional) using remotely sensed RF from spatially and temporally complete (STC) MODIS data sets (STC-MODIS Snow Covered Area and Grain Size/MODIS Dust Radiative Forcing on Snow) from 2001 to 2022. To quantify snow RF impacts, we calculated a pixel-integrated metric over each snowmelt season (1st March-30th June) in all 22 years. We tested for long-term trend significance with the Mann-Kendall test and trend magnitude with Theil-Sen's slope. Mean snow RF was highest in the Upper Colorado region, but notable in less-studied regions, including the Great Basin and Pacific Northwest. Watersheds with high snow RF also tended to have high spatial and temporal variability in RF, and these tended to be near arid regions. Snow RF trends were largely absent; only a small percent of mountain ecoregions (0.03%-8%) had significant trends, and these were typically decreasing trends. All mountain ecoregions exhibited a net decline in snow RF. While the spatial extent of significant RF trends was minimal, we found declining trends most frequently in the Sierra Nevada, North Cascades, and Canadian Rockies, and increasing trends in the Idaho Batholith. This study establishes a two-decade chronology of snow impurities in the WUS, helping inform where and when RF impacts on snowmelt may need to be considered in hydrologic models and regional hydroclimate studies.

Coal mining in arid western regions is damaging the fragile ecology, causing problems such as surface damage, vegetation destruction, and soil erosion. These issues are obstacles to the development of green coal, as mining activities can disrupt the distribution of surface vegetation, leading to its spread outside the mining area and affecting surrounding areas. Based on Landsat data, the binary pixel model was used to calculate the vegetation coverage (FVC) in mining area from 2005 to 2021. Through vegetation coverage classification and regression trend analysis, the temporal and spatial changes and evolution trends of vegetation disturbance caused by coal mining and climate were analyzed. Correlation analysis revealed the range of ecological disturbance caused by coal mining at the coal mine scale and mining area scale. The results show that the vegetation coverage of the mining area showed a decreasing trend from 2005 to 2021. Winter and spring precipitation was the primary factor affecting vegetation growth in the area, while coal mining had indirect and secondary effects on vegetation. Human activities played a significant role in improving vegetation, and between 2015 and 2018, the area of vegetation improvement increased by 133.41% compared to that of 2009-2014. Compared to the reference area, the impact range of vegetation disturbance in the mining area is 2.5-5 km, while the impact range of vegetation disturbance in the coal mine is less than 500 m. Therefore, this study provides a theoretical basis for studying the impact of mining activities on vegetation and boundary identification.

Ground temperature's sensitivity to climate change has garnered attention. This study aimed to monitor and analyze temporal trends and estimate Active Layer Thickness from a monitoring point at Fildes Peninsula, King George Island, in Antarctica. Quality control and consistency analysis were performed on the data. Methods such as serial autocorrelation, Mann-Kendall, Sen-Slope, Pettitt, and regression analysis tests were applied. Spearman's correlation examined the relationship between air temperature and ground depths. The active layer thickness was estimated using the maximum monthly temperature, and the permafrost lower limit used the minimum monthly temperature. Significant summer seasonal trends were observed with Mann- Kendall tau, positive Sen-Slope, and Pettitt slope at depths of 67.5 and 83.5 cm. The regression analysis was significant and positive for all ground depths and in different seasons. The highest correlation (r=0.82) between air temperature and surface ground depth was found. Freezing prevailed at all depths during 2008-2018. The average Active Layer Thickness (ALT) was 92.61 cm. Temperature is difficult to monitor, and its estimation is still complex. However, it stands out as a fundamental element for studies that refer to the impacts of climate change

The extreme floods of recent years underline the urgency of studying long-term changes of floods and their driving processes. This paper reports results on this issue obtained within the framework of subproject 6 of the DFG research group SPATE (Space-Time Dynamics of Extreme Floods). The analyses use an extensive dataset of flood observations at rivers and complementary information to determine and explain significant changes in flood probabilities. The data show that the flood-rich periods of the last 500 years in Europe have been significantly colder than usual. Over the last 60 years, the number of flood-rich periods in north-western Europe has increased. This increase is due to more intense precipitation. In medium-sized and large catchments of southern and eastern Europe, on the other hand, lower soil moisture and less snow cover have led to decreasing flood probabilities. These results are intended as a basis for more reliable design flood estimates in a changing world.

Tree-ring width chronologies are a critically important material to reconstruct past precipitation variability on the northeastern Tibetan Plateau (NTP). However, temperature signals are often encoded in these chronologies, which complicate the precipitation reconstructions and should be carefully assessed. Here, a dataset of 487 Qilian juniper (Juniperus przewalskii Kom.) tree-ring width series from 16 sites on the NTP were collected to investigate the influence of different temperature signals on the precipitation reconstructions. Correlation analysis showed that all tree-ring series recorded similar precipitation information, but had positive (p 0.05, Group1), weak (p 0.05, Group2), and negative (p < 0.05, Group3) correlations with temperature, respectively. In view of this, all tree-ring series were divided into three groups to develop chronologies to reconstruct local precipitation. During the calibration period of 1957?2011 CE, the Group1 reconstruction had the fastest uptrend, which almost overlapped the observed precipitation; the Group2 reconstruction showed a slower uptrend, whereas the Group3 reconstruction lacked an uptrend. As a result, we get different results when the reconstructions were used to assess the current precipitation status over the past millennium. The Group1 (Group2) reconstructions showed that the recent 20 (10) years were the highest precipitation period over the past millennium, whereas the Group3 reconstruction did not capture this phenomenon. Therefore, we caution that the temperature effects should be evaluated carefully before tree-ring width chronologies being employed to study past precipitation variability.