Quantitative assessment of the impact of land use and climate change on hydrological processes is of great importance to water resources planning and management. The main objective of this study was to quantitatively assess the response of runoff to land use and climate change in the Zhengshui River Basin of Southern China, a heavily used agricultural basin. The Soil and Water Assessment Tool (SWAT) was used to simulate the river runoff for the Zhengshui River Basin. Specifically, a soil database was constructed based on field work and laboratory experiments as input data for the SWAT model. Following SWAT calibration, simulated results were compared with observed runoff data for the period 2006 to 2013. The Nash-Sutcliffe Efficiency Coefficient (NSE) and the correlation coefficient (R²) for the comparisons were greater than 0.80, indicating close agreement. The calibrated models were applied to simulate monthly runoff in 1990 and 2010 for four scenarios with different land use and climate conditions. Climate change played a dominant role affecting runoff of this basin, with climate change decreasing simulated runoff by −100.22% in 2010 compared to that of 1990, land use change increasing runoff in this basin by 0.20% and the combination of climate change and land use change decreasing runoff by 60.8m³/s. The decrease of forestland area and the corresponding increase of developed land and cultivated land area led to the small increase in runoff associated with land use change. The influence of precipitation on runoff was greater than temperature. The soil database used to model runoff with the SWAT model for the basin was constructed using a combination of field investigation and laboratory experiments, and simulations of runoff based on that new soil database more closely matched observations of runoff than simulations based on the generic Harmonized World Soil Database (HWSD). This study may provide an important reference to guide management decisions for this and similar watersheds.

Soil cation exchange property (SCEP) is important in soil development and environmental buffering. However, the variations in SCEP and its correlation with soil-forming factors in subsurface soil are not fully understood. In this study, we quantified the surface and subsurface SCEP variations as a function of parent material, vegetation, hillslope position, and soil type. Fifty upland soil profiles from mid-subtropical China were selected. The cation exchange capacity (CEC) and effective CEC (ECEC) of subsurface soil were significantly higher in soils derived from slate, Quaternary red clay (QRC), and limestone than in soils derived from granite and sandstone. The subsurface soils derived from limestone had the highest base saturation (BS) and the sum of base cations, and the lowest aluminum (Al) saturation. The SCEP in surface soil significantly varied with vegetation and hillslope position. The surface soil CEC was the highest in mixed-forest vegetation, whereas the ECEC and exchangeable acidity (EA) were the highest in arable vegetation. Exchangeable potassium (K⁺) was lowest and the EA was highest in soil orders at the strong development phase. Exchangeable calcium (Ca²⁺), magnesium (Mg²⁺), CEC, and BS were the highest in soil orders at the intermediate development phase. The prediction accuracy of soil CEC using the random forest model was higher than that obtained using multiple stepwise regression, with the best results (R² = 0.92) obtained in the surface soil. Our study indicated that the SCEP in surface and subsurface soils was controlled by different soil-forming factors and could be effectively predicted by soil properties in subtropical China.

The accurate prediction of soil organic matter (SOM) content is essential for promoting sustainable soil management and addressing global climate change. Due to multiple factors such as topography and climate, especially in mountainous areas, SOM spatial prediction faces significant challenges. The main novelty of this study lies in proposing a geographic positional encoding mechanism that embeds geographic location information into the feature representation of a Transformer model. The encoder structure is further modified to enhance spatial awareness, resulting in the development of the Geo-Positional Transformer (GPTransformer). Furthermore, this model is integrated with a 1D-CNN to form a dual-branch neural network called the Geo-Positional Transformer-CNN (GPTransCNN). This study collected 1490 topsoil samples (0–20 cm) from cultivated land in Longshan County to develop a predictive model for mapping the spatial distribution of SOM across the entire cultivated area. Different models were comprehensively evaluated through ten-fold cross-validation, ablation experiments, and uncertainty analysis. The results show that GPTransCNN has the best performance, with an R² improvement of approximately 43% over the Transformer, 19% over the GPTransformer, and 15% over the 1D-CNN. This study demonstrates that by incorporating geographic positional information, GPTransCNN effectively combines the global modeling capabilities of the GPTransformer with the local feature extraction strengths of the 1D-CNN, which can improve the accuracy of SOM mapping in mountainous areas. This approach provides data support for sustainable soil management and decision-making in response to global climate change.