Search

We are far from understanding the spatial patterns of dryland soil carbon and nitrogen stocks and how they vary among different land cover types. We used data from 12,000 sites from 129 countries in global drylands to estimate soil organic carbon (SOC) and total nitrogen (STN) stocks in different land cover types, explore the factors driving their spatial distribution, and predict the trends under different climate scenarios in global drylands. SOC and STN stocks in the upper 100 cm reached 419.5 and 38.2 Pg, respectively, with the upper 0–30 cm accounting for half of them. The largest SOC stocks were found in forests, shrublands and grasslands, while STN stocks peaked in forests, bare areas and croplands. The factors driving the spatial patterns of SOC and STN varied among soil depths, with mean annual temperature, pH and aridity being the main factors driving the spatial patterns in SOC and STN density for 0–30 cm, and soil texture the strongest factor for 60–100 cm. Under the Representative Concentration Pathways (RCP) 4.5 scenario, SOC and STN stocks were predicted to decrease by 3.6 % and 4.0 %, respectively, from 2020 to 2100, whereas under the RCP 8.5 scenario, the projected decreases were 5.9 % and 6.4 % respectively. Our results indicate that if we want to accurately predict C and N accumulation, and design effective mitigation measures in terrestrial ecosystems under future climatic scenarios, we need to better explore the drivers that operate at the deeper soil depths, which also accumulate a significant amount of SOC and STN.

The accuracy of data-driven landslide susceptibility prediction depends heavily on the quality of non-landslide samples and the selection of machine-learning algorithms. Current methods rely on artificial prior knowledge to obtain negative samples from landslide-free regions or outside the landslide buffer zones randomly and quickly but often ignore the reliability of non-landslide samples, which will pose a serious risk of including potential landslides and lead to erroneous outcomes in training data. Furthermore, diverse machine-learning models exhibit distinct classification capabilities, and applying a single model can readily result in over-fitting of the dataset and introduce potential uncertainties in predictions. To address these problems, taking Chenxi County, a hilly and mountainous area in southern China, as an example, this research proposes a strategy-coupling optimised sampling with heterogeneous ensemble machine learning to enhance the accuracy of landslide susceptibility prediction. Initially, 21 landslide impact factors were derived from five aspects: geology, hydrology, topography, meteorology, human activities, and geographical environment. Then, these factors were screened through a correlation analysis and collinearity diagnosis. Afterwards, an optimised sampling (OS) method was utilised to select negative samples by fusing the reliability of non-landslide samples and certainty factor values on the basis of the environmental similarity and statistical model. Subsequently, the adopted non-landslide samples and historical landslides were combined to create machine-learning datasets. Finally, baseline models (support vector machine, random forest, and back propagation neural network) and the stacking ensemble model were employed to predict susceptibility. The findings indicated that the OS method, considering the reliability of non-landslide samples, achieved higher-quality negative samples than currently widely used sampling methods. The stacking ensemble machine-learning model outperformed those three baseline models. Notably, the accuracy of the hybrid OS–Stacking model is most promising, up to 97.1%. The integrated strategy significantly improves the prediction of landslide susceptibility and makes it reliable and effective for assessing regional geohazard risk.

Rainfall infiltration and artificial excavation are important factors inducing the loess landslides. To study the deformation process of loess landslides in the Guanzhong area and its influence on stability under the two inducements, the Laomiao landslide in Yangchang Village, Changwu County, Shaanxi Province is taken as the research object.Through field investigation, geological mapping and borehole exploration, the deformation characteristics of the landslide are determined, and the deformation evolution process of the landslide is qualitatively analyzed. Based on the measured daily rainfall within 15 days before the landslide deformation, the process of landslide formation under continuous rainfall after excavation actions at the slope foot was simulated by using finite element software. Based on the strength reduction method, the stability variation law of the landslide is studied. The results show that: ① the special formation structure in the Guanzhong area is the internal cause of landslide deformation, and rainfall is the most important inducing factor; ②the deformation evolution process of the landslide: at first the slope was in a state of creep. After the excavation actions, the anterior edge of the slope became unstable, and then the trailing edge of the slope was drawn downward, resulting in tension cracks. Under the influence of rainfall, rainfall infiltrated along the fissures of the slope, while the shear strength of the rock and soil mass in the central part was reduced, resulting in the sliding interface between the soil layer and red clay layer. Ultimately a deep landslide was induced. ③ After the landslide excavation, the stability coefficient decreased by 0.102, as compared with the initial state. Then, influenced by continuous rainfall, the stability coefficient decreased slowly at an average rate of 0.010/d in the first 10 d, and rapidly decreased to the lowest rate of 0.034/d in 10-13 d, and began to rise after 13 d. The research results are expected to provide effective basis for the prevention and control of such landslides.

Different land utilization types have unignorable impacts on adjacent aquifers, so studying the effects of varying land utilization types on groundwater balance and groundwater table in arid and semi-arid areas is crucial to facilitate the rational development of territorial space and groundwater resource management. This study investigated the relationship between land utilization type variations and groundwater dynamics from spatial and temporal scales in Huocheng plain, Xinjiang, China, via taking advantage of different land utilization maps provided by remote sensing techniques in 1990, 2000, and 2015. Based on the analysis of results, the findings can be summarized as follows. First, the cultivated and construction land has expanded significantly in the past 25 years. The migration of the regional center of cultivated land was insignificant, as it is still located almost in the center of the entire study area. However, the transfer span of the location center of construction land was more prominent, and its moving direction was mainly from south to northeast, directly affecting the groundwater balance state and groundwater table. Second, the total amount of groundwater storage exhibited a remarkable reduction, from a recharge in 1990 to a continuous discharge in 2000–2015, during which the total recharge and discharge had both continuously declined. Meanwhile, recharging and discharging elements were also changed profoundly as less groundwater recharges from ambient aqueous environments and more groundwater extraction. Thus, human activities were the fundamental driving terms causing the changes in groundwater sources. Third, the groundwater table gradually and continuously dropped from south to northeast in the study area over time. The drawdown of spatially averaged groundwater tables was positively correlated with construction and cultivated area extension. In addition, compared to the former literature, this study offers an alternative approach for groundwater protection and optimization of territorial space development and utilization in arid and semi-arid areas.

Drylands are highly vulnerable to climate change and human activities. The drylands of China account for approximately 10.8% of global drylands, and China is the country most severely affected by aridity in Asia. Therefore, studying the spatial variation characteristics in soil multifunctionality (SMF) and investigating the driving factors are critical for elucidating and managing the functions of dryland ecosystems in China. Based on the environmental factors (mean annual precipitation (MAP), mean annual temperature (MAT), solar radiation (Srad), soil acidity (pH), enhanced vegetation index (EVI), and cation exchange capacity (CEC)) and aridity from the Dataset of soil properties for land surface modeling over China, we used non-linear regression, ordinary least square (OLS) regression, structural equation model (SEM), and other analytical methods to investigate the relationships of SMF with environmental factors across different aridity levels in China. SMF in different dryland regions varied significantly and showed a patchy distribution, with SMF index values ranging from −1.21 to 2.42. Regions with SMF index values from −0.20 to 0.51 accounting for 63.0% of dryland area in China. OLS regression results revealed that environmental factors like MAP, MAT, Srad, pH, EVI, and CEC were significantly related to SMF (P ​