The mechanisms by which the soil physical structure, nutrient conditions, understory vegetation diversity and forest meteorological factors influence fine root (<2 mm diameter) characteristics mediated by soil moisture content (SMC) and soil heat flux (SHF) remain uncertain under climate change. Therefore, in this research, continuous observations were made of the fine root growth, death and turnover of cypress plantations, as well as the SMC and SHF under the management of four thinning intensities in hilly areas in central Sichuan from 2021 to 2023. The fine root data were obtained using the microroot canals (minirhizotron) in the study, and the soil hydrothermal data were obtained using the ECH2O soil parameter sensor and the PC-2R SHF data logger. In the time series, the fine root growth, death and turnover of the cypress plantations with different thinning intensities first increased and then decreased throughout the year; the vertical center of the gravity of the fine roots of cypress was concentrated in the 30–50 cm range. This research also revealed that the variability in the SMC decreased with increasing soil depth. Additionally, the SHF was transmitted from greater soil depths to the surface in unthinned cypress plantation at a rate of 0.036 per year, which decreased the heat in the fine root region. However, SHF was transmitted from the soil surface to greater depths at rates of 0.012 per year, 0.08 per year and 0.002 per year, which increased the heat in the fine root area. The redundancy analysis (RDA) and structural equation model (SEM) results indicated that the SMC and soil heat energy distribution pattern obviously affected fine root growth, death and turnover in the cypress plantation. However, the climate conditions in the forest, the characteristics of vegetation in the understory and the physical and chemical characteristics of the soil directly or indirectly affect the characteristics of the fine roots of cypress plantations with changes in thinning intensity. This research provides a basis for understanding ecosystem structure, nutrient cycling and carbon balance and may guide artificial plantation development and management.

Southwestern China has a large area of artificial forests and has experienced massive environmental and social losses due to forest fires. Evaluating the flammability of fuels from dominated forests in this region can help assess the fire risk and predict potential fire behaviors in these forests, thus guiding forest fire management. However, such studies have been scarcely reported in this region. In this study, the flammability of litter from nine forest types, which are common in southwestern China, was evaluated by measuring organic matter content, ignition point, and calorific value. All these flammability characteristics of fuels varied significantly across forest types. By using principal component analysis and K-means clustering, litters were classified into three groups: highly susceptible to ignition with low fire intensity (Pinus densata, Pinus densata-Populus simonii, Pinus yunnanensis, Larix gmelini, Pinus armandii), less susceptible to ignition with high fire intensity (Abies fabri-Populus simonii), and median ignitibility and fire intensity (Abies fabri, Abies fabri-Picea asperata, Platycladus orientalis). Our study can help predict the risk and intensity of fires in the studied forests and serve as a source of information for fire management in southwestern China.

Topography is an important factor that impacts the hydrological processes on sloping farmlands. Yet, few studies have reported the combined influences of slope gradient and slope position on infiltration characteristics and hydrodynamic parameters on sloping croplands in purple soil regions, an important area for agricultural productivity in Southwest China. Here, laboratory-simulated rainfall experiments were conducted in a steel trough (5 m long, 2 m wide, and 0.45 m deep), and rainfall lasted for 1 h at a rate of 90 mm h⁻¹ to examine the variations in the infiltration rates and hydrodynamic parameters under varying slope gradients (i.e., 3°, 6°, 10°, 15°, 21°, and 27°) and slope positions (i.e., upper, middle, and lower), and explore the relationships between the infiltration rate and the soil detachment rate. The results showed that the infiltration rate decreased gradually with duration rainfall and ultimately approached a steady state in the six slope treatments. Cumulative infiltration ranged from 15.54 to 39.32 mm during rainfall, and gradually reduced with the increase of slope gradient. The Horton’s model outperforms other models for predicting the infiltration rate with an R² value of 0.86. Factors such as Darcy–Weisbach friction, flow shear force, Manning friction coefficient, unit energy, and runoff depth varied in the following order: upper slope > middle slope > lower slope, whilst the Reynolds number and Froude number gradually increased along the slope transect from the upper to lower slope positions. A significant linear function was fitted between the soil detachment rate and the infiltration rate at the gentle slopes (3°, 6°, 10°), whereas an exponential relationship was observed at the steep slopes (15°, 21°, and 27°). Observation also suggested that 15° was the critical slope gradient of sediment detachment, infiltration characteristics, and hydrodynamic parameters. Our results provide theoretical insight for developing models that predict the impacts of topographic factors on hydrological characteristic and soil erosion in hilly agricultural landscapes of purple soil fields.

Landslides caused by earthquakes bring about dramatic changes in soil erodibility. In order to understand the change characteristics of soil erodibility during a vegetation restoration period after the 5.12 Wenchuan earthquake, a non-landslide area, landslide area, and transition area in Leigu Town, Beichuan County were selected as research areas. Field soil sampling, geostatistics, and spatial interpolation were used to explore the spatiotemporal changes in soil physicochemical properties and soil erodibility during a natural restoration in 2013 (5 years after the earthquake) and in 2022 (14 years after the earthquake). The results showed that the comprehensive soil erodibility index (CSEI) was mainly composed of five soil factors, which were soil pH, soil total nitrogen (TN), mean weight diameter of soil aggregates (MWD), fractal dimension of soil water stable aggregates (D), and soil erodibility (K_epic). The CSEI of the landslide area was slightly lower than that of the non-landslide area. The CSEI was gradually increasing during the process of natural restoration after earthquake. From 2013 to 2022, the increase rates of the CSEI were 6.9%, 10.0%, and 41.5% for the landslide area, non-landslide area, and transition area, respectively. Along attitude segments, the spatial distribution of soil erodibility in 2022 is more uniform than that in 2013. The higher value of CSEI was located in the upper part of research areas. The spatial distribution of the CSEI in 2013 and 2022 appeared as a moderate autocorrelation. The variable ranges of CSEI in 2013 and 2022 were about 20 m. In the early stage of vegetation restoration, soil and water conservation engineering was recommended in the landslide area.

Soil erosion in the purple soil region presents severe challenges with complex driving mechanisms. At the same time, evaluation and prediction of runoff and sediment dynamics are lacking for natural vegetation restoration in bare areas. The Mann–Kendall and Pettitt tests were employed to identify abrupt shift points in runoff and sediment dynamics, utilizing monitoring data from the Suining Soil and Water Conservation Experimental Station over the period from 1984 to 2018. Therefore, the research periods were divided into a baseline period (1984–1992) and an evaluation period (1993–2018). Subsequently, encompassing rainfall, runoff, sediment, topography, soil properties, and vegetation parameters, a Water Erosion Prediction Project (WEPP) model was established to quantify the reduction benefits of runoff and sediment during the period of forest restoration. We found that the calibrated WEPP model demonstrated satisfactory performance based on Nash–Sutcliffe efficiency coefficients (NSE > 0.5) and determination coefficients (R² > 0.5) for runoff and sediment simulations. The WEPP model and double-mass curve analysis method revealed that forest restoration reduced runoff and sediment by more than 80%. It is recommended to implement artificial vegetation restoration before reaching the threshold for natural vegetation restoration to achieve soil and water conservation goals.