Natural and socioeconomic variables have an impact on ecosystem services (ESs). The ESs trade-offs/synergies are informed by the reality that the same inputs have varying impacts on different ESs. Changing scales and time can alter dominant drivers and biophysical linkages of ESs, affecting their relationships. Although it is often assumed that ES relationships vary across scales, quantitatively testing this assumption with multiple ES is rare. Therefore, this study evaluated the five key ESs in the Pearl River Delta (PRD) from 1990 to 2015. We also employed a statistical approach to investigate the temporal variations, scale dependency, and spatial heterogeneity of ES trade-offs and synergies. The results demonstrated that: (1) The PRD’s synergetic interaction among ESs has been steadily improving over time; (2) The interaction between ESs dramatically altered as the research scale increased; (3) We discovered that the linkages among the soil conservation (SC), carbon sequestration (CS), water yield (WY), and habitat quality (HQ) were primarily synergistic. ESs of SC, CS, WY, and HQ were found to have negative correlations with grain production. This study will strengthen the understanding of the temporal changes and spatial scales of ESs relationships for decision-makers, which is beneficial to ecosystem management.

Earthquake-induced landslides are one of the most dangerous secondary disasters in mountainous areas throughout the world. The nowcasting of coseismic landslides is crucial for planning land management, development, and urbanization in mountainous areas. Taking Wenchuan County in Western Sichuan Plateau (WPS) as the study area, a landslide inventory was built using historical records. Herein, eight causative factors were selected for a library of factors, and then a landslide susceptibility assessment (LSA) was performed based on the machine learning techniques of Random Forest (RF) and Artificial Neural Network (ANN) models, respectively. The prediction abilities of the above two LSM models were assessed using the area under curve (AUC) value of the receiver operating characteristics (ROC) curve, precision, recall ratio, accuracy, and specificity. The performances of both machine learning techniques were found to be excellent, but RF outperformed in accuracy. There were still some differences between the models’ performances shown by the results: RF (AUC = 0.966) outperformed ANN (AUC = 0.914). The RF model demonstrated a higher degree of correlation between the areas classified as very low and high susceptibility in comparison to the ANN model. The results provided a theoretical framework upon which machine learning applications could be applied (e.g., RF and ANN), a reliable and low-cost tool to assess landslide susceptibility. This comparative study will provide a useful description of earthquake-induced landslides in the study area, which can be used to anticipate the features of landslides in the future, and have played a very important role in proper anthropogenic activities, resource management, and infrastructural development of the mountainous areas.

In recent years, large-scale afforestation in the Yellow River Basin (YRB) has attracted widespread attention due to its significant impact on surface water, playing a crucial role in the ecological sustainability and high-quality development of the basin. In this study, we used a combination of Theil–Sen and Mann–Kendall trend analysis to detect the spatiotemporal dynamic changes of NDVI, surface water storage (SWS), and its components in the YRB from 2001 to 2020, and explored the time lag and instantaneous effects between them using methods such as cross-correlation. The results show that from 2001 to 2020, NDVI and SWS in the YRB increased at rates of 0.41%/year and 1.95 mm/year, respectively, with fluctuations. Spatially, NDVI exhibited a significant upward trend in most areas of the YRB, while regions with significant increases in SWS, canopy surface water (CSW), snow water equivalent (SWE), and soil moisture (SM) were primarily located in the upper reaches. There was a time lag effect of about 2 months between NDVI and SWS in the YRB, and the time lags between SWE, SM, and NDVI were 5 months and 2 months, respectively. Except for CSW, the lag between NDVI and SWE was longer than that between NDVI and SWS or SM across all land cover types. Regarding the instantaneous effect, we found that the effect of vegetation on SWS in the upstream area is mainly the water storage function. In some areas of the middle and lower reaches, vegetation intensifies the consumption of SWS. Our study provides valuable insights into the response mechanism between vegetation restoration and SWS changes, facilitating better coordination between water resource management and ecological conservation in the YRB, thereby achieving sustainable regional economic and ecological development.

The Yellow River Basin (YRB) is an important grain production base, and exploring the spatiotemporal heterogeneity and driving factors of soil erosion in the YRB is of great significance to the ecological environment and sustainable agricultural development. In this study, we employed the Revised Universal Soil Loss Equation (RUSLE) in conjunction with Transport-Limited Sediment Delivery (TLSD) to explore a modified RUSLE-TLSD for use assessing net water erosion. This modification was performed using sediment data, and the explanatory power of driving factors was assessed utilizing an optimal parameters-based geographical detector (OPGD). The results demonstrated that the modified RUSLE-TLSD can accurately simulate the spatiotemporal distribution of net water erosion (NSE = 0.5766; R² = 0.6708). From 2000 to 2020, the net water erosion modulus in the YRB ranged between 1.62 and 5.33 t/(ha·a). Specifically, the net water erosion modulus decreased in the YRB and the middle reaches of the YRB (MYRB), but it increased in the upper reaches of the YRB (UYRB). The erosion occurred mainly in the Loess Plateau region, while the deposition occurred mainly in the Hetao Plain and Guanzhong Plain. The Normalized Difference Vegetation Index (NDVI) and slope emerged as significant driving factors, and their interaction explained 31.36% of YRB net water erosion. In addition, the redistribution of precipitation by vegetation and the slope weakened the impact of precipitation on the spatial pattern of net water erosion. This study provides a reference, offering insights to aid in the development of soil erosion control strategies within the YRB.