Comprehensively revealing the intensity of drought propagation from meteorological to hydrological drought is crucial for effective drought monitoring and management. However, existing assessments often fail to integrate multiple drought characteristics, resulting in incomplete evaluations. To address this limitation, this study introduced the drought comprehensive propagation intensity (DCPI) index, a systematic tool that quantifies propagation intensity and incorporates five drought characteristic indicators (drought frequency, total duration, maximum duration, coverage, and degree) to assess the comprehensive drought intensity in the upper Shiyang River Basin, China from 1961 to 2023. The results indicated that pre-1980s drought propagation was relatively weak (DCPI<0.964), reflecting stable hydrological homeostasis. After the 1980s, the intensity significantly increased, peaking at 5.530 (rather strong drought) in the 2000s due to human-induced alterations in surface runoff and ecological changes. Spatially, the western tributaries (e.g., the Xida River Watershed) presented stronger hydrological drought intensity, whereas the eastern tributaries (e.g., the Huangyang, Gulang, and Dajing river watersheds) presented higher meteorological drought intensity. The DCPI values decreased from west to east, with near peer-to-peer propagation observed in the Dongda, Huangyang, and Jinta river watersheds, suggesting minimal human interference. A nonlinear relationship between meteorological and hydrological droughts was identified, with severe drought frequency and duration emerging as critical drivers of propagation intensity. Notably, trends of meteorological humidification coexist with hydrological aridification, highlighting systemic challenges for water resource management. The DCPI framework enhances the understanding of drought mechanisms by enabling a structured evaluation of drought impacts, which is essential for developing effective water resource strategies and ecological restoration efforts in arid areas. This study underscores the importance of integrating multi-dimensional drought characteristics to improve prediction accuracy and inform policy decisions.

Climate change and human activities are primary drivers of runoff variations, significantly impacting the hydrological balance of river basins. In recent decades, the Yellow River Basin, China has experienced a marked decline in runoff, posing challenges to the sustainable development of regional water resources and ecosystem stability. To enhance the understanding of runoff dynamics in the basin, we selected the Dahei River Basin, a representative tributary in the upper reaches of the Yellow River Basin as the study area. A comprehensive analysis of runoff trends and contributing factors was conducted using the data on hydrology, meteorology, and water resource development and utilization. Abrupt change years of runoff series in the Dahei River Basin was identified by the Mann-Kendall and Pettitt tests: 1999 at Dianshang, Qixiaying, and Meidai hydrological stations and 1995 at Sanliang hydrological station. Through hydrological simulations based on the Variable Infiltration Capacity (VIC) model, we quantified the factors driving runoff evolution in the Dahei River Basin, with climate change contributing 9.92%-22.91% and human activities contributing 77.09%-90.08%. The Budyko hypothesis method provided similar results, with climate change contributing 13.06%-20.89% and human activities contributing 79.11%-86.94%. Both methods indicated that human activities, particularly water consumption, were dominant factors in the runoff variations of the Dahei River Basin. The integration of hydrological modeling with attribution analysis offers valuable insights into runoff evolution, facilitating adaptive strategies to mitigate water scarcity in arid and semi-arid areas.

Vegetation change is the most intuitive and sensitive bioindicator reflecting seasonal and interannual variations in the external environment, and it can directly reflect the rapid response of terrestrial ecosystems to climate change. Using remote sensing and meteorological data, this study revealed the spatiotemporal characteristics of leaf area index (LAI) in the north of China during 1982-2022, clarified the response of LAI change to different meteorological factors, quantified the impacts of climate change and human activities on LAI change, and predicted the future trends in LAI change. From 1982 to 2022, the vegetation in the north of China generally showed a greening trend with a change rate of 0.0071 m²/(m²•a). Temperature was strongly positively correlated with LAI and was the main climate factor driving LAI change. Residual analysis revealed that vegetation improvement occurred in across 74.53% of the study area, and vegetation improvement in about 96.83% of the improved zone was attributed to a combination of climate change and human activities. The regions where anthropogenic contribution exceeded 60.00% covered 36.83% of human-affected areas, while the regions where climatic contribution exceeded 60.00% covered 19.77% of climate-affected areas, demonstrating that human activities influenced the intensity of LAI change more deeply despite the broad spatial impact of climate change. Human activities such as afforestation and the Three-North Protective Forest Program played the dominant role in vegetation greening compared to climate change. Hurst index analysis indicated that 80.30% of vegetation in the north of China is expected to experience a non-sustained improvement in the future. These findings will provide a scientific basis for optimizing the protection strategies of the national ecological barrier areas and evaluating the effectiveness of major ecological projects.

Fires are one of the most destructive natural disasters and have serious long-term effects on the environment, economy, and human health. In Inner Mongolia Autonomous Region, China, frequent fire disturbance occurs due to the intensification of climate change and human activities. It is crucial to understand the fire regime and estimate the probability of regional fire occurrence and reducing fire losses. However, most studies have primarily focused on the dynamic changes, probability of occurrence, and driving mechanisms of wildfires in the grassland and forest land ecosystems in Inner Mongolia, while insufficient research has been conducted on the spatiotemporal variations in active fires and their impact on the wildfire risk in forest land and grassland. Therefore, in this study, we analyzed the active fire regime based on Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomalies and burned area products from 2000 to 2022. Combined with climate, topographic, landscape, anthropogenic, and vegetation datasets, logistic regression (LR), support vector machine (SVM), random forest (RF), and convolutional neural network (CNN) models were chosen to estimate the probability of active fire occurrence at the seasonal timescale. The results revealed that: (1) a total of 100,343 active fires occurred in Inner Mongolia and the burned area reached 6.59×10⁴ km². The number of ignition point exhibited a significant increasing trend, while the burned area exhibited a nonsignificant decreasing trend; (2) four active fire belts were detected, namely, the Hetao-Tumochuan Plain fire belt, Xiliao River Plain fire belt, Songnen Plain fire belt, and Hailar River Eroded Plain fire belt. The centroid of the active fires has shifted 456.4 km toward the southwest; (3) RF model achieved the highest accuracy in estimating the probability of active fire occurrence, followed by CNN, and LR and SVM models had lower accuracies; and (4) the distribution of the high and extremely high fire risk areas largely aligned with the four fire belts. The probability of active fire occurrence was the highest in spring, followed by that in autumn, and it gradually decreased in summer and winter. Our results revealed active fires migrated to the southwest and ignition sources increased, despite reduction of the burned area was not significant. The RF model outperformed the other models in predicting the probability of active fire occurrence. These findings contribute to future fire prevention and prediction in Inner Mongolia.

In addition to sequestering carbon in soil, biochars can also play a role in changing the potassium equilibration and dynamics of the soil. Nowadays, acidification of biochar is commonly used to improve its properties, which can impact the potassium content in the soil. Simultaneous application of acidified biochar and sodium bentonite can complicate this effect. In the present study, the effects of adding two types of biochars prepared from municipal waste and used coffee grounds and their acidified types, along with sodium bentonite at three levels (0.00%, 1.00%, and 2.00%), on soil physical-chemical properties (pH, salinity, cation exchange capacity, concentration of soluble cations and their ratio, and sodium adsorption ratio) and the release of potassium from a calcareous soil were investigated. The results showed that the addition of coffee ground biochar increased the concentration of soluble potassium and decreased the ratio of calcium to potassium, while the acidified coffee ground biochar decreased the amount of soluble potassium and increased the ratio of calcium to potassium. Alkaline and acidified municipal waste biochars had no effect on soluble potassium and soluble cations ratio. Application of bentonite increased the amount of soluble calcium and sodium and the ratio of calcium to potassium. Addition of bentonite also increased the amount of exchangeable potassium and exchangeable sodium percentage, but use of different biochars reduced negative effect of bentonite. Use of bentonite also caused an increase in the exchangeable potassium and a decrease in the non-exchangeable potassium contents. Alkaline and acidified coffee ground biochars increased the amount of exchangeable, non-exchangeable, and total potassium, but this effect was greater by alkaline biochar. Application of municipal waste biochar did not affect the amount of exchangeable potassium but increased the amount of non-exchangeable and total potassium, with no significant difference observed between alkaline and acidified biochars. Potassium saturation percentage was not affected by bentonite, but coffee ground biochar increased its amount and municipal waste biochar had no effect on it. Acidified and alkaline coffee ground biochars were able to release more potassium from the soil (475 and 71 mg/kg, respectively), while alkaline municipal waste biochar did not affect it and acidified municipal waste biochar reduced it by 113 mg/kg. In general, it can be concluded that alkaline biochars in calcareous soils can improve potassium fertility by reduction of the ratio of calcium to potassium and increasing its various forms, while acidified biochars and bentonite may aggravate potassium deficiency in these soils. Considering the lack of significant change in the pH of calcareous soils with the use of different biochars, it is suggested to use alkaline biochars, which can improve the potassium status of the soil while reducing the costs associated with biochar modification.

Understanding how dominant plants respond to nitrogen (N) addition is critical for accurately predicting the potential effects of N deposition on ecosystem structure and functionality. Biomass partitioning serves as a valuable indicator for assessing plant responses to environmental changes. However, considerable uncertainty remains regarding how biomass partitioning shifts with increasing N inputs in sandy ecosystems. To address this gap, we conducted a greenhouse N fertilization experiment in April 2024, using seeds from 20 dominant plant species in the Horqin Sandy Land of China representing 5 life forms: annual grasses, annual forbs, perennial grasses, perennial forbs, and shrubs. Six levels of N addition (0.0, 3.5, 7.0, 14.0, 21.0, and 49.0 g N/(m²•a), referred to as N0, N1, N2, N3, N4, and N5, respectively) were applied to investigate the effects of N inputs on biomass partitioning. Results showed that for all 20 dominant plant species, the root biomass:shoot biomass (R:S) consistently declined across all N addition treatments (P<0.050). Concurrently, N addition led to a 23.60% reduction in root biomass fraction, coupled with a 12.38% increase in shoot biomass fraction (P<0.050). Allometric partitioning analysis further indicated that N addition had no significant effect on the slopes of the allometric relationships (leaf biomass versus root biomass, stem biomass versus root biomass, and shoot biomass versus root biomass). This suggests that plants can adjust resource investment—such as allocating more resources to shoots—to optimize growth under favorable conditions without disrupting functional trade-offs between organs. Among different life forms, annual grasses, perennial grasses, and annual forbs exhibited increased allocation to aboveground biomass, enhancing productivity and potentially altering community composition and competitive hierarchies. In contrast, perennial forbs and shrubs maintained stable biomass partitioning across all N addition levels, reflecting conservative resource allocation strategies that support long-term ecosystem resilience in nutrient-poor environments. Taken together, these findings deepen our understanding of how nutrient enrichment influences biomass allocation and ecosystem dynamics across different plant life forms, offering practical implications for the management and restoration of degraded sandy ecosystems.

Soil moisture is a key parameter in the exchange of energy and water between the land surface and the atmosphere. This parameter plays an important role in the dynamics of permafrost on the Qinghai-Xizang Plateau, China, as well as in the related ecological and hydrological processes. However, the region's complex terrain and extreme climatic conditions result in low-accuracy soil moisture estimations using traditional remote sensing techniques. Thus, this study considered parameters of the backscatter coefficient of Sentinel-1A ground range detected (GRD) data, the polarization decomposition parameters of Sentinel-1A single-look complex (SLC) data, the normalized difference vegetation index (NDVI) based on Sentinel-2B data, and the topographic factors based on digital elevation model (DEM) data. By combining these parameters with a machine learning model, we established a feature selection rule. A cumulative importance threshold was derived for feature variables, and those variables that failed to meet the threshold were eliminated based on variations in the coefficient of determination (R²) and the unbiased root mean square error (ubRMSE). The eight most influential variables were selected and combined with the CatBoost model for soil moisture inversion, and the SHapley Additive exPlanations (SHAP) method was used to analyze the importance of these variables. The results demonstrated that the optimized model significantly improved the accuracy of soil moisture inversion. Compared to the unfiltered model, the optimal feature combination led to a 0.09 increase in R² and a 0.7% reduction in ubRMSE. Ultimately, the optimized model achieved a R² of 0.87 and an ubRMSE of 5.6%. Analysis revealed that soil particle size had significant impact on soil water retention capacity. The impact of vegetation on the estimated soil moisture on the Qinghai-Xizang Plateau was considerable, demonstrating a significant positive correlation. Moreover, the microtopographical features of hummocks interfered with soil moisture estimation, indicating that such terrain effects warrant increased attention in future studies within the permafrost regions. The developed method not only enhances the accuracy of soil moisture retrieval in the complex terrain of the Qinghai-Xizang Plateau, but also exhibits high computational efficiency (with a relative time reduction of 18.5%), striking an excellent balance between accuracy and efficiency. This approach provides a robust framework for efficient soil moisture monitoring in remote areas with limited ground data, offering critical insights for ecological conservation, water resource management, and climate change adaptation on the Qinghai-Xizang Plateau.

Texture analysis methods offer substantial advantages and potential in examining macro-topographic features of dunes. Despite these advantages, comprehensive approaches that integrate digital elevation model (DEM) with quantitative texture features have not been fully developed. This study introduced an automatic classification framework for dunes that combines texture and topographic features and validated it through a typical coastal aeolian landform, namely, dunes in the Namib Desert. A three-stage approach was outlined: (1) segmentation of dune units was conducted using digital terrain analysis; (2) six texture features (angular second moment, contrast, correlation, variance, entropy, and inverse difference moment) were extracted from the gray-level co-occurrence matrix (GLCM) and subsequently quantified; and (3) texture-topographic indices were integrated into the random forest (RF) model for classification. The results show that the RF model fused with texture features can accurately identify dune morphological characteristics; through accuracy evaluation and remote sensing image verification, the overall accuracy reaches 78.0% (kappa coefficient=0.72), outperforming traditional spectral-based methods. In addition, spatial analysis reveals that coastal dunes exhibit complex texture patterns, with texture homogeneity being closely linked to dune-type transitions. Specifically, homogeneous textures correspond to simple and stable forms such as barchans, while heterogeneous textures are associated with complex or composite dunes. The complexity, periodicity, and directionality of texture features are highly consistent with the spatial distribution of dunes. Validation using high-resolution remote sensing imagery (Sentinel-2) further confirms that the method effectively clusters similar dunes and distinguishes different dune types. Additionally, the dune classification results have a good correspondence with changes in near-surface wind regimes. Overall, the findings suggest that texture features derived from DEM can accurately capture the dynamic characteristics of dune morphology, offering a novel approach for automatic dune classification. Compared with traditional methods, the developed approach facilitates large-scale and high-precision dune mapping while reducing the workload of manual interpretation, thus advancing research on aeolian geomorphology.