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

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

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

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

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

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

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