Drought, one of the most common natural disasters that have the greatest impact on human social life, has been extremely challenging to accurately assess and predict. With global warming, it has become more important to make accurate drought predictions and assessments. In this study, based on climate model data provided by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), we used the Palmer Drought Severity Index (PDSI) to analyze and project drought characteristics and their trends under two global warming scenarios—1.5 °C and 2.0 °C—in Central Asia. The results showed a marked decline in the PDSI in Central Asia under the influence of global warming, indicating that the drought situation in Central Asia would further worsen under both warming scenarios. Under the 1.5 °C warming scenario, the PDSI in Central Asia decreased first and then increased, and the change time was around 2080, while the PDSI values showed a continuous decline after 2025 in the 2.0 °C warming scenario. Under the two warming scenarios, the spatial characteristics of dry and wet areas in Central Asia are projected to change significantly in the future. In the 1.5 °C warming scenario, the frequency of drought and the proportion of arid areas in Central Asia were significantly higher than those under the 2.0 °C warming scenario. Using the Thornthwaite (TH) formula to calculate the PDSI produced an overestimation of drought, and the Penman–Monteith (PM) formula is therefore recommended to calculate the index.

Remarkable warm‒wet climate shifts and intensive cropland expansion strongly affected carbon (C) cycle and threaten agricultural sustainability in northwest China. In this study, we integrated a process-based ecosystem model and an empirical C bookkeeping model to investigate the coupled and isolated effects of arable land conversions and climate change (CLM) on regional C balance in a typical watershed of northwest China. Results revealed that the farmland area increased by 3367.31 km² during 1979–2014. The combined effects of CLM with net cropland expansion enlarged the vegetation C (VEGC) and the soil organic C (SOC) stock by 2.83 and 11.83 Tg, respectively, and were strongest in 2008–2014. The conversions between desert grassland and cropland were the major driving forces for regional C balance. Cropland expansion shared equal effects on VEGC increase with CLM, but its effect on SOC increment was 53 times larger than CLM’s. VEGC was more responsive to CLM, whereas SOC gained more benefits from land management. The C sink from reclamation suffered from high water consumption and is facing great threats due to glaciers and mountain lake shrinking and groundwater overpumping. Water-saving irrigation techniques and environmentally friendly water use strategies are essential for local agricultural sustainability.

The effect of climate change on the spatio-temporal patterns of the terrestrial carbon dynamics in Central Asia have not been adequately quantified despite its potential importance to the global carbon cycle. Therefore, the modified BioGeochemical Cycles (Biome-BGC) model was applied in this study to evaluate the impacts of climatic change on net primary productivity (NPP) and net ecosystem productivity. Four vegetation types were studied during the period 1979 to 2011: cropland, grassland, forest, and shrubland. The results indicated that: (1) The climate data showed that Central Asia experienced a rise in annual mean temperature and a decline in precipitation from 1979 to 2011; (2) the mean NPP for Central Asia in 1979–2011 was 281.79 gC m⁻² yr⁻¹, and the cropland had the highest NPP compared with the other vegetation types, with a value of 646.25 gC m⁻² yr⁻¹; (3) grassland presented as a carbon source (−0.21 gC m⁻² yr⁻¹), whereas the other three types were carbon sinks; (4) the four vegetation types showed similar responses to climate variation during the past 30 years, and grassland is the most sensitive ecosystem in Central Asia. This study explored the possible implications for climate adaptation and mitigation.

The greening of the Earth over the last decades is predominantly indicated by the enhancements of leaf area index (LAI). Quantifying the relative contribution of multiple determinants, especially changes in climate and in land management changes (LMC), remains an arduous challenge. To solve this problem, we develop a simple yet novel data‐driven method, called the Paired Land Use Experiment (PLUE), for mesoscale analysis. Using PLUE, we analyze vegetation development of the Sanjiang Plain, a transboundary plain between China and Russia, with roughly homogeneous climate but with distinct land management practices across the border‐intensified agricultural development on China side (CNSP) versus largely little‐disturbed natural vegetation on Russia side (RUSP). Both CNSP and RUSP LAI show significant trends (p < 0.05), with the annual variability reaching values of 9.8 × 10⁻³ yr⁻¹ and 11.3 × 10⁻³ yr⁻¹, respectively. However, in CNSP, the LAI increase is concentrated in the middle of the year, especially in five 8‐day periods from 26 June to 28 July. During this period, the LAI trend of CNSP is much higher than that of RUSP, at 92.7 × 10⁻³ yr⁻¹ (p < 0.01) and 43.8 × 10⁻³ yr⁻¹ (p < 0.01), respectively. Meanwhile, LAI decreased in CNSP at the begging and end of the growing season. The results show that different LMC practices lead to notably different seasonal variability in vegetation changes. The PLUE method offers a new potential tool in driver identification of vegetation greenness change based on observations. We argue for the necessity of parameterizing these different LMC in Earth system models.

Reducing the uncertainties in carbon balance assessment is essential for better pastureland management in arid areas. Climate forcing data are some of the major uncertainty sources. In this study, a modified Biome-BGC grazing model was driven by an ensemble of reanalysis data of the Climate Forecast System Reanalysis data (CFSR), the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim), and the Modern-Era Retrospective Analysis for Research and Applications (MERRA), to study the effect of climate change and grazing on the net ecosystem exchange (NEE) of the pasturelands in Central Asia. Afterwards, we evaluated the performance of corresponding climate datasets over four major pastureland types, and quantified the modeling uncertainty induced by climate forcing data. Our results suggest that (1) a significant positive trend in temperature and a negative trend in precipitation were obtained from the three climate datasets. The average precipitation is apparently higher in the CFSR and MERRA data, showing the highest temperature value among the data sets; (2) pasturelands in Central Asia released 2.10 ± 1.60 Pg C in the past 36 years. The highest values were obtained with the CFSR (−1.53 Pg C) and the lowest with the MERRA (−2.35 Pg C) data set; (3) without grazing effects, pasturelands in Central Asia assimilated 0.13 ± 0.06 Pg C from 1981–2014. Grazing activities dominated carbon release (100%), whereas climate changes dominated carbon assimilation (offset 6.22% of all the carbon release). This study offered possible implications to the policy makers and local herdsmen of sustainable management of pastureland and the adaptation of climate change in Central Asia.

Climate change significantly alters vegetation distribution patterns in arid regions, while ecological water conveyance serves as a critical intervention to modify these patterns by augmenting water availability. As a keystone species in Central Asia’s water-stressed ecosystems, Populus euphratica plays a pivotal role in maintaining arid ecosystem stability, making the investigation of its habitat suitability under combined climate change and ecological water conveyance imperative. This study selected 12 variables associated with the spatial distribution of P. euphratica, including bioclimate, groundwater resources, available water storage capacity, elevation, distance to rivers, and stocking rate. Using the maximum entropy (MaxEnt) model, we projected habitat distributions of P. euphratica across the Tarim River Basin with three scenarios: no climate change, climate change, and ecological water conveyance. The study indicated that (1) distance to rivers has the significant effect on the distribution of P. euphratica; (2) although climate change is expected to reduce the habitat suitable for P. euphratica, the implementation of ecological water conveyance is expected to lead to an expansion of its habitat; (3) the implementation of ecological water conveyance is expected to cause the habitat suitable for P. euphratica to shift toward the southeast, suggesting that this initiative has increased groundwater resources in the southeastern part of the watershed. These findings provide a scientific foundation for protecting P. euphratica and formulating effective ecological water conveyance strategies.