The Loess Plateau region of China suffers from severe soil erosion, and the selection of effective slope-protection vegetation is essential to prevent soil and water loss. This study focused on individual plants of common species in the Loess Plateau, such as Caragana korshinski Kom., Hippophae rhamnoides Linn., Pinus tabuliformis Carr., Robinia Pseudoacacia Linn., Populus tomentosa Carr., Prunus armeniaca Lam. The root spatial distribution, geometric morphology, and fractal characteristics of these plants were measured using the whole-root-excavation method, and the vertical pull-out force of their root systems was quantified using the in situ whole-plant root-pulling method. The results showed that H. rhamnoides dominates in the vertical spatial distribution of its root system through a larger number of inclined roots. C. korshinskii, P. tomentosa, R. pseudoacacia, and P. armeniaca dominate in the horizontal spatial distribution of their root systems through a greater number of horizontal roots. P. tabuliformis, on the other hand, achieves a relatively balanced distribution in both horizontal and vertical spaces through its well-developed taproot and numerous lateral roots. In terms of the geometric morphology and fractal characteristics of their root systems, H. rhamnoides and C. korshinskii exhibit a larger number of fine roots and complex branching, resulting in a higher total-root length, total-root surface area, and root fractal dimension. The soil-stabilizing ability of H. rhamnoides, C. korshinskii, and R. pseudoacacia was stronger, mainly influenced by their total-root length, total-root surface area, and inclined root quantity, and these species can be prioritized as typical vegetation for soil and water conservation in the construction of Loess Plateau vegetation. From the perspective of slope stabilization and soil conservation alone, we strongly recommend planting shrub vegetation in the Chinese Loess Plateau.

The root systems of vegetation significantly contribute to enhancing slope stability. The shear strength of soil–root systems is a crucial parameter for assessing slope stability. This study focuses on six types of vegetation in the Yellow River Basin of China (woodland: Populus przewalskii and Broussonetia papyrifera; shrubland: Periploca sepium and Ziziphus jujuba; grassland: Artemisia hedinii and Setaria viridis), employing in situ shear tests and the Wu–Waldron model (Wu model) to investigate the shear strength of soil–root systems. The results show that the shear stress–displacement curves for P. przewalskii, B. papyrifera, and Z. jujuba are higher and steeper, with clear inflection points. The tensile strength of the roots from the six vegetation types decreases as the root diameter increases. According to the Wu model, the additional root cohesion is ranked as follows: A. hedinii > B. papyrifera > P. przewalskii > Z. jujuba > P. sepium > S. viridis. Based on the in situ shear tests, the shear strength increments are ranked as follows: Z. jujuba > B. papyrifera > P. przewalskii > A. hedinii > P. sepium > S. viridis. Overall, the additional root cohesion obtained by the Wu model in each soil layer is greater than the shear strength increment measured from the in situ shear tests. In the 0–30 cm soil layers, the soil–root systems of Z. jujuba, B. papyrifera, P. przewalskii, and A. hedinii exhibit a better shear strength, whereas P. sepium and S. viridis perform poorly. A principal component analysis reveals that the shear strength of the soil–root systems of different vegetation types is primarily influenced by the soil moisture content and root mass density. Z. jujuba, B. papyrifera, P. przewalskii, and A. hedinii are recommended for ecological restoration projects in the Yellow River Basin of China.

Quantitatively estimating the grazing intensity (GI) effects on vegetation in semiarid hilly grassland of the Loess Plateau can help to develop safe utilization levels for natural grasslands, which is a necessity of maintaining livestock production and sustainable development of grasslands. Normalized difference vegetation index (NDVI), field vegetation data, and 181 days (one goat per day) of GPS tracking were combined to quantify the spatial pattern of GI, and its effects on the vegetation community structure. The spatial distribution of GI was uneven, with a mean value of 0.50 goats/ha, and 95% of the study area had less than 1.30 goats/ha. The areas with utilization rates of rangeland (July) lower than 45% and 20% made up about 95% and 60% of the study area, respectively. Grazing significantly reduced monthly aboveground biomass, but the grazing effects on plant growth rate were complex across the different plant growth stages. Grazing impaired plant growth in general, but the intermediate GI appeared to facilitate plant growth rate at the end of the growing seasons. Grazing had minimal relationship with vegetation community structure characteristics, though Importance Value of forbs increased with increasing GI. Flexibility in the number of goats and conservatively defining utilization rate, according to the inter-annual variation of utilization biomass, would be beneficial to achieve ecologically healthy and economically sustainable GI.

Heterobasidion, a significant forest pathogen affecting coniferous forests in the Northern Hemisphere, can infect 27 species of coniferous trees, leading to widespread forest mortality. It has already caused considerable damage to both natural and plantation forests in Europe. As essential components of the global ecosystem, forests are increasingly affected by ongoing environmental changes. The ability to accurately predict and effectively respond to pathogen outbreaks across different habitats is becoming increasingly critical. This study employs an optimized MaxEnt model in conjunction with six Global Climate Models (GCMs) to simulate and predict the potentially suitable distributions and changes of three Heterobasidion species in Europe (Heterobasidion abietinum, Heterobasidion annosum sensu stricto, Heterobasidion parviporum) under current conditions and four future climate scenarios (SSP126, SSP245, SSP370, and SSP585) for the period 2081–2100. The objective of this analysis is to assess the potential effects of climate change on the distribution of Heterobasidion species. The results indicate that the distributions of the three Heterobasidion species are influenced by factors such as temperature and precipitation. By 2081–2100, under most climate scenarios, except for the SSP585 scenario, the distribution areas of the three Heterobasidion species show an expansion trend. Notably, Heterobasidion abietinum showed the most significant expansion of its suitable habitat, while the expansion of low-suitability areas for Heterobasidion parviporum and Heterobasidion annosum sensu stricto was more pronounced. Heterobasidion abietinum is projected to shift southward due to factors such as precipitation, while Heterobasidion parviporum and Heterobasidion annosum sensu stricto are expected to migrate northward, influenced by factors such as temperature and host tree species.

Against the background of global warming and frequent extreme precipitation events, the changes in drought characteristics in the middle reaches of the Yellow River Basin (YRMB) have been particularly pronounced in recent years. Under the new situation, it is necessary to further our understanding of drought risk in the basin and its response mechanisms to climate change. In this study, YRMB was utilized as the research area. Based on the precipitation (P) and potential evapotranspiration (PET) data under four emission scenarios (historical and future), five timescales (SPEI-1, SPEI-3, SPEI-6, SPEI-9, and SPEI-12) of SPEI drought indices were estimated in this study. Drought events were identified using the run-length theory, and the spatial distribution values of drought frequency, duration, intensity, and severity were also examined. Based on the preferred copula function, the two-dimensional joint distribution of drought variables was established, and the two-dimensional return period of drought indices in the historical (1901–2014) and future (2022–2100 under SSP245 and SSP585 emission scenarios) periods were calculated. The results show that the SPEI index in the middle reaches of the YRMB is increasing in the future period and the basin tends to be more humid. Under the SSP245 and SSP585 scenarios, the frequency of long drought durations, high drought intensities, or severe drought events in the basin may be higher. The univariate return period is always higher than the joint return period, but lower than the co-occurrence return period, and both types of return periods can be used to assess range estimations in the future. The research results can provide support for understanding the spatiotemporal distribution characteristics of drought in the YRMB and improving the accuracy of drought decision making.

The relationship between climate change and extreme precipitation is extremely complex. From a probabilistic perspective, a proper understanding of the response of extreme precipitation to climate change is of significant importance. This study was based on daily precipitation provided by CMIP6 climate models and employed copula functions to construct joint distributions of precipitation amount and precipitation intensity indices at different quantile levels. A spatial–temporal assessment of the susceptibility areas for extreme precipitation in the Yellow River Basin was conducted while considering bivariate return periods and design values. The results indicate that there were significant spatial differences in the bivariate return periods. Taking the R90P-SDII (90) index for a 20a return period as an example, the difference between the maximum and minimum joint return periods within the Yellow River Basin was 1.4 times, while the co-occurring return period was 7.0 times, and the Kendall return period was 4 times. Moreover, this difference increased with the increase in the return period. The magnitude order of the four return periods is as follows: T_And > T_Kendall > T_{Single-variable} > T_Or. Joint return periods (Or) and co-occurring return periods (And) could be considered as the extreme cases under single-variable return periods, serving as an estimation interval for actual return periods. Under the influence of climate change, the bivariate design values for future periods exhibited a variability increase of 6.76–28.8% compared to historical periods, and this increase grew with higher radiative forcing scenarios, ranking as SSP126 < SSP245 < SSP585. The bivariate design values showed a noticeable difference in variability compared to the single-variable design values, ranging from −0.79% to 18.67%. This difference increased with higher quantile values, with R95P-SDII (95) > R90P-SDII (90) > PRCPTOT-SDII.