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[Display omitted] • NDVI shows an overall increasing trend from 2000 to 2020. • The Hexi region shows a warming and wetting trend. • Extreme precipitation is more important for NDVI. • The lag effect of NDVI on climate extremes varies by indicators and spaces. Climate extremes significantly affects vegetation dynamics, and understanding the relationship between dryland vegetation and climate extremes is crucial for regional ecological security and disaster warning. According to meteorological and satellite remote sensing data, the paper analyses the dynamics of normalized difference vegetation index (NDVI) and climate extremes in the Hexi region from 2000 to 2020 using Theil-Sen Median, Mann-Kendall and linear regression methods; the relationship between climate extremes and vegetation dynamics is revealed using Pearson correlation and boosted regression trees (BRT) models. On this basis, the time-lag effect of NDVI in response to climate extremes was further investigated. The results show that the NDVI in the Hexi region increased from 2000 to 2020, with significant greening (67.72 %) and a small amount of degradation concentrated in the desert in the northwest and the oasis in the southeast. The correlation between climate extremes indicators and NDVI shows a large spatial heterogeneity, with extreme precipitation showing a high importance for all regions except oasis. NDVI has no lag or 1 month lag for extreme temperature, no lag for max 1-day precipitation amount (Rx1day) for areas other than mountainous areas, and a 3-month lag for max 5-day precipitation amount (Rx5day) for extensive areas. The results of this study could serve as a reference for the study of the relationship between vegetation dynamics and climate extremes in arid and semi-arid regions, and provide a theoretical basis for the establishment of scientific and reasonable strategies for regional vegetation restoration and conservation. [ABSTRACT FROM AUTHOR]

• Drivers of vegetation greening were examined at the inter-annual and spatial scales. • Human activities were the dominant driver of variation in vegetation during 2001–2020. • Forests following row-belt distribution with low coverage is more suitable for application in drylands. Vegetation growth of the Chinese Loess Plateau (CLP) has improved since the launch of the Grain to Green Programme. This study examined the mechanism of spatial differentiation of the Normalized Difference Vegetation Index (NDVI) and the relative contributions of climate variations (CV) and human activities (HA) to variations in inter-annual NDVI in the CLP for 2001–2020. The response of NDVI to afforestation was examined and the trend of projected future NDVI was identified using the Moderate Resolution Imaging Spectroradiometer and other correlative data. The results showed that: (1) precipitation, sunshine duration, relative humidity, solar radiations, and vegetation type were the dominant factors affecting the spatial distribution of NDVI. All interactive explanatory powers of two factors exceeded those of any individual factor, and the interactions between natural and anthropogenic factors had significant effects on NDVI. (2) Variation in NDVI in 88.77% of the total area was influenced by the combined effect of HA and CV, and HA was the dominant factor with relative a contribution of 68.44%. (3) There was an increasing (2001–2005) followed by a decreasing (2006–2019) pattern of annual area of afforestation in each province. The seven provinces showed extremely significant correlations (p < 0.01) between the cumulative area of afforestation and NDVI. (4) The trend in the change in NDVI exhibited the unsustainability of development of the CLP, with 63.20% of regions at risk of vegetation degradation. Finally, the present study suggests that forests in arid and semi-arid areas following a row-belt distribution with low coverage composed of narrow forest belts (covering an area of 15–25%) and wide natural vegetation restoration (covering an area of 75–85%) may be more appropriate for restoration of the regional ecology. [ABSTRACT FROM AUTHOR]

• Precipitation drove f PV variation, temperature dominated f NPV variation. • The time-lag of temperature and precipitation effect on f NPV were 1.28 ± 1.2 and 1.19 ± 1.24 months. • Accumulated precipitation had positive effect on f PV and negative effect on f NPV. • Accumulated temperature and accumulated precipitation have opposite effects on f PV , f NPV. Efficient prediction of the response of vegetation to future climate change requires an in depth understanding of the effect of climate change on the land cover by photosynthetic (f PV) and non-photosynthetic vegetation (f NPV) and its driving mechanism. Here, we aimed to estimate f PV and f NPV , and analyze the impacts of climate factors on their seasonal variability in the arid desert of northwestern China. First, optimal vegetation indices (VIs) were selected to estimate f PV and f NPV based on measured spectral reflectance, and the accuracy of estimated f PV and f NPV using Sentinel-2 images was evaluated with data from an unmanned aerial vehicle (UAV). Then, partial correlation, multi-correlation and time‐lag effect analyses were used to obtain the correlation coefficients and lag times between f PV , f NPV and climate change. The results showed that Normalized Difference Vegetation Index (NDVI) and Dead Fuel Index (DFI) were the optimal VIs to estimate f PV and f NPV in the desert, with the accuracy of 67 and 52 % for f PV and f NPV , respectively. The average f PV and f NPV were 8.15 and 9.26 % from March to November, their seasonal variability was consistent with plant phenology, and there was a decrease in f PV and f NPV from east to west with a decrease in precipitation. The variability in f PV was driven by precipitation while that of f NPV was dominated by temperature. The time-lag of temperature and precipitation effect on f PV was 1.28 ± 1.28 and 1.61 ± 1.16 months, respectively, and on f NPV was 1.28 ± 1.2 and 1.19 ± 1.24 months, respectively. The time-lag of temperature-precipitation interaction on f PV and f NPV were 1.4 ± 1.22 and 1.2 ± 1.28 months, respectively. Accumulated precipitation had a positive effect on f PV and a negative effect on f NPV , while the impact of accumulated temperature was the opposite. We conclude that there were time-lag and cumulative effects of climate change on f PV and f NPV variability in desert areas. This study helps us better understand the climate-vegetation interactions and provides a scientific basis for desert vegetation management under climate change. [ABSTRACT FROM AUTHOR]

• Holocene vegetation and climate were reconstructed in the Mu Us Sandy Land. • Steppe dominated vegetation during a climate optimum at 8.3–2.6 ka. • Ice cover and solar radiation drove Holocene millennial-scale climate change. Knowledge of changes in vegetation and climate during the Holocene is important for improved assessments of future climate change and for mitigating climate warming. Owing to the relative difficulty in obtaining high-resolution sedimentary records and identifying pollen, few paleovegetation reconstructions are available from the Mu Us Sandy Land, China. This study analyzes a naturally-exposed fluvio-lacustrine section in the hinterland of the Mu Us Sandy Land. Based on a chronological framework constructed by AMS 14C, a pollen-based random forest model was used to quantitatively reconstruct changes in Holocene climate and normalized difference vegetation index (NDVI) changes. The relationships between NDVI, pollen and precipitation, as well as pollen assemblage characteristics were combined to reconstruct the evolution of the Holocene vegetation in the Mu Us Sandy Land. Our results show that the climate was cold and humid, and the dominant vegetation type was Ephedra shrub-encroached steppe between 11.2 and 8.3 ka BP; the climate reached an optimum, and vegetation evolved into Artemisia – Taraxacum -type– Thalictrum steppe from 8.3 to 2.6 ka BP; and finally, the climate gradually deteriorated and vegetation became an Artemisia –Chenopodiaceae steppe after 2.6 ka BP. In comparisons with other climate records, we found that the Mu Us Sandy Land climate optimum occurred synchronously at 8.3–2.6 ka BP, and the pattern of climate change is closely consistent with the East Asian summer monsoon. We suggest that millennial-scale Holocene climatic change in this region was mainly driven by the influences of high-latitude ice volume and summer solar radiation on the East Asian summer monsoon. [ABSTRACT FROM AUTHOR]

[Display omitted] • Runoff changes in the Yarlung Zangbo River Basin were attributed based on the Budyko framework. • 78.81% of runoff changes is attributed to climate change and 21.19% to human activities. • Increasing precipitation and accelerating snowmelt may not offset water dissipation induced by rising temperature. • Combined effects of multiple factors on runoff were explored. • Precipitation is the best predictor of runoff variations in the region. The runoff in Tibetan Plateau (TP) is sensitive to environmental changes, and it is thus challenging to assess the variations and influencing factors of runoff in the context of global warming and increased human activities. We addressed this challenge by using the most comprehensive dataset, Budyko hypothesis, and wavelet theory to attribute runoff variations in the Yarlung Zangbo River Basin (YZRB), and its interaction with regional environmental variables (e.g., precipitation PCP, relative humidity RH, average temperature TMPave, maximum temperature TMPmax, minimum temperature TMPmin, wind speed WS, snow depth SD, potential evapotranspiration ET0, and normalized difference vegetation index NDVI). The results indicated that, 1) the warming of the YZRB was 0.47, 0.36, and 0.65 °C/decade in TMPave, TMPmax, and TMPmin, two to three times the global mean rate, and the increasing trend of precipitation was nonsignificant; 2) ET0 increased significantly by 2.25 mm/decade, and SD decreased rapidly at a rate of 2.54 mm/decade. 3) The streamflow (lower Nuxia) showed a decreasing trend from 1960 to 2015, primarily caused by climate change, which accounted for 78.81%. Human activities contributed 21.19%; 4) Seasonal streamflow increased in Spring and Winter and declined in Summer and Autumn. 5) PCP was the single variable mostly associated with runoff. When PCP-SD and PCP-RH-ET0 were combined, they demonstrated the most robust relationships with runoff, respectively; 6) A modest increase in PCP and accelerating snowmelt may not offset water dissipation due to severe warming. Further research is necessary to improve understanding of the driving mechanisms behind seasonal and inter-annual variability in the runoff and to provide strategies for mitigation and adaptation to environmental change. [ABSTRACT FROM AUTHOR]

• Land use and land cover classes homogenize within-class differences in vegetation density, type and other characteristics that influence surface erosion. • When these classes are used to estimate current and projected future RUSLE C-factor for native vegetation, this introduces significant error. • Remote sensing methods can be used to assess intra-class variation in C-factor, improving the soil loss estimate. • A strong, predictive relationship exists between remotely-sensed C-factor values and climate. • Current and projected changes in climate can be used to directly estimate C-factor, improving the estimate of current and future soil loss rates. This new method provides a low-cost, efficient and reliable estimate of future C. Understanding how reservoir sedimentation rates may evolve due to climate change is essential for projecting future changes in reservoir water storage capacity. The Revised Universal Soil Loss Equation (RUSLE) is commonly used to assess regional soil loss rates because of its suitability for working with the coarse temporal and spatial scale of climate model outputs. Application of the RUSLE for projecting future erosion rates is constrained by the relatively limited number of classes used in projected changes of native vegetation: this typically reduces a wide range of variation in RUSLE cover (C) factors to a single value per class, and these classes may be insensitive to changes in species composition and canopy density with time and space. This paper develops a low-cost, efficient, and objective approach to projecting future C values directly based on widely available Landsat data, downscaled climate model data, and a limited number of terrain variables. Observed C is estimated from Landsat-derived Normalized Difference Vegetation Index (NDVI) and Modified Soil-Adjusted Vegetation Index (MSAVI) values using standard methods. A linear relationship is established between the observed C values and average annual antecedent temperature, average annual antecedent precipitation, latitude, and percent sand (adjusted R2 = 0.75604, 0.7543, and RMSE = 0.09448, 0.07043, respectively). A proof of principal study demonstrates that, unlike when land cover classes are used to estimates C, a RUSLE model driven by the regression-based C provides a correct order-of-magnitude estimate of observed long-term erosion and sediment yield rates (e.g., an estimate of 2.9–3.2 t ha−1 y-1 is obtained where observed rates range from 1.2 to 3.9 t ha−1 y-1 for the same time period, and up to 8 t ha−1 y-1 historically, while standard formulations of RUSLE yield 16.1 and 0.3 t ha−1 y-1). Using climate model data to estimate both precipitation intensity (R factor) and C in the RUSLE model results in basin wide projected end 21st century soil erosion rates for relative concentration pathway 8.5 of 5.5–7.5 t ha−1 y-1, consistent with expectations for accelerated soil loss in the study area as climate change reduces soil moisture while increasing precipitation intensity. The same model driven by land cover class estimates of C produces soil loss rates an order of magnitude smaller (0.3–0.4 t ha−1 y-1), consistent with a much more heavily vegetated landscape than could be supported under the increasingly arid projected climate. [ABSTRACT FROM AUTHOR]