The Kubuqi Desert represents a key ecologically fragile region in northern China, primarily functioning as a windbreak and sand-fixation barrier while also contributing to regional ecological balance. However, the area’s ecological vulnerability is pronounced, and investigating the spatiotemporal dynamics of vegetation carbon storage and associated driving mechanisms is essential for the scientific formulation of ecological restoration strategies. This research incorporates multi-source remote-sensing datasets (including Landsat 8 OLI/TIRS Level 2, Sentinel-1 Synthetic Aperture Radar (SAR), ERA5 daily meteorological data, GEDI Level 4B, SRTM GL1 v003, and ESA WorldCover v100) based on the Google Earth Engine (GEE) platform, and employs multiple machine-learning algorithms (validation metrics of the machine learning model: $R^2$ = 0.917, RMSE = 0.251) to develop a dynamic monitoring model of vegetation carbon storage in the Kubuqi Desert during the period 2019–2023. The analysis systematically evaluates the influence of climatic variables and anthropogenic activities on the spatiotemporal differentiation of carbon storage. The results indicate a slight upward trend in overall carbon storage across the study area (average annual increase of 0.4%), with high values predominantly concentrated in vegetated regions (up to 5.22 Mg/Ha) and low values distributed in bare lands and desert zones (0.5–0.7 Mg/Ha). Altitude, temperature, and slope serve as the primary driving factors governing carbon-storage variability. The findings suggest that scientifically guided vegetation restoration and optimized water-resource management can enhance the carbon-sink capacity of the Kubuqi Desert, offering a robust scientific basis for ecological governance and carbon budget assessment in arid and semi-arid desert ecosystems.

Standing biomass stocks represent a balance between a number of processes that lead to biomass accumulation or to biomass loss. The average time of biomass residence (ATr) of an ecosystem is the average period of time that carbon is locked up in the living biomass before it is transferred to the litter pool and is an important variable influencing the process leading to biomass loss. Variation in terrestrial ATr with climate is thought to originate from a direct influence of temperature and precipitation on plant mortality. However, variation in ATr may also result from an indirect influence of climate by means of plant age and growing season length. To identify the relative importance of direct and indirect climate effects, we analyzed published data of ecosystem woody biomass and productivity from forest plots across climate gradients, using three approaches: bayesian linear regression, multiple regression, and structural equation modeling. The three approaches provided special insights and they converged in supporting climate as an indirect driver of ATr across climate gradients. Notably, age and growing season length explained most of the variation in ATr, whereas mean annual temperature and precipitation explained almost none, suggesting that climate indirectly influenced ATr. Our analyses provide novel evidence not only supporting that the old-age forests could be a carbon sink with a longer time of biomass residence at a large scale, but also modifying the key drivers of ecosystem processes for vegetation dynamic models.

Arid regions are ecologically fragile and occupy a substantial portion of the global terrestrial surface. In these regions, ecosystem services (ESs) are strongly constrained by water availability and, more importantly, by the balance between water supply and demand. However, the nonlinear responses and threshold mechanisms linking water supply–demand balance to ES dynamics remain unclear. Taking the Kubuqi Desert in the “Great Bend” of the Yellow River as the study area, this study quantified the Comprehensive Ecosystem Service Index (CESI) and the Water Supply–Demand Ratio (WSDR) by integrating the InVEST model, RWEQ model, the RUSLE model, Water Balance Method, and so on. The dual-constraint line method and elasticity coefficient approach were integrated to identify the constraint effects and critical thresholds of WSDR on CESI. Ecological management zones were further delineated by integrating the inflection-point intervals of the dual-constraint lines with the threshold intervals identified by elasticity coefficients. The results showed that CESI remained relatively low, with a maximum value of approximately 0.5, suggesting that the overall ES level was still limited, but exhibited a continuous increasing trend. The regional water supply–demand pattern gradually shifted from deficit toward relative balance, although agricultural water use still accounted for about three-quarters of total consumption. CESI showed a nonlinear threshold response to WSDR: mild water deficit suppressed CESI growth, whereas moderate water surplus promoted CESI recovery by alleviating water constraints and improving ecosystem functioning. Thresholds identified by elasticity coefficients mainly occurred near critical transitions between water deficit and surplus. Based on ES supply and threshold sensitivity, nine ecological management zones were identified, with priority enhancement areas accounting for approximately 75%. These findings provide a threshold-based basis for ecological zoning and differentiated restoration in arid regions.

Permafrost wetlands are important components of the global carbon cycle, functioning as carbon stores while also releasing methane (CH₄). Although biochar amendment has been proposed as a strategy to mitigate greenhouse gas (GHG) emissions, its effectiveness in permafrost wetland soils under contrasting moisture regimes remains uncertain. We conducted a 100-day incubation experiment to examine the effects of pine-sawdust biochar produced at 300 or 800 °C and applied at five rates (0%, 1%, 3%, 5%, and 8%, w/w) on soil properties, enzyme activities, and CO₂ and CH₄ emissions under non-flooded and waterlogged conditions. Biochar addition altered soil pH, carbon and nitrogen pools, and enzyme activities, with responses varying between moisture regimes. Under non-flooded conditions, 300 °C biochar reduced soil pH and increased nitrate nitrogen (NO₃⁻-N), whereas under waterlogged conditions, biochar treatments increased soil pH and were associated with higher ammonium nitrogen (NH₄⁺-N) and lower NO₃⁻-N concentrations. Biochar generally reduced CO₂ emissions under non-flooded conditions and CH₄ emissions under waterlogged conditions. The 8% amendment of 800 °C biochar under waterlogged conditions yielded the lowest reported global warming potential (GWP; 5.49 ± 0.15 g·kg⁻¹). Enzyme responses also depended on moisture regime: dehydrogenase and urease activities generally declined under waterlogged conditions, whereas 300 °C biochar increased catalase activity. Pearson correlation and SHapley Additive exPlanations (SHAP) analyses identified NH₄⁺-N (29.3%) and catalase activity (17.4%) as the two leading predictors of GWP in the fitted model. These findings suggest that biochar amendment can reduce GHG emissions during laboratory incubation, although the magnitude and direction of the response depend on pyrolysis temperature, application rate, and moisture regime. Among the treatments tested, the 8% amendment of 800 °C biochar under waterlogged conditions produced the lowest GWP.