As the “roof of the world”, the Tibetan Plateau (TP) is a unique geographical unit on Earth. In recent years, vegetation has gradually become a key factor reflecting the ecosystem since it is sensitive to ecological changes especially in arid and semi-arid areas. Based on the normalized difference vegetation index (NDVI) dataset of TP from 2000 to 2015, this study analyzed the characteristics of vegetation variation and the correlation between vegetation change and climatic factors at different time scales, based on a Mann–Kendall trend analyses, the Hurst exponent, and the Pettitt change-point test. The results showed that the vegetation fractional coverage (VFC) generally increased in the past 16 years, with 60.3% of the TP experiencing an increase, of which significant (p < 0.05) increases accounted for 28.79% and were mainly distributed in the north of the TP. Temperature had the largest response with the VFC on the seasonal scale. During the growing season, the correlation between precipitation and sunshine duration with VFC was high (p < 0.05). The change-points of the VFC were mainly distributed in the north of the TP during 2007–2009. Slope and elevation had an impact on the VFC; the areas with large vegetation change are mainly distributed in slopes <20° and elevation of 3000–5000 m. For elevation above 3000–4000 m, the response of the VFC to precipitation and temperature was the strongest. This study provided important information for ecological environment protection and ecosystem degradation on the Tibetan Plateau.

Seasonal freeze-thaw erosion is a form of soil erosion caused by the topographical characteristics and climatic factors of the hilly and gully loess regions. Seasonal freeze-thaw can damage the soil pores and cause its bulk density to change. The effects of thawing depth on runoff and Nitrogen and Phosphorus loss on the rainfall erosion of an artificial slope filled with loess soil were analyzed after a rainfall test that simulated the spring thaw period in China. The results showed that: (1) The maximum runoff yield was 33.35 mm at 4 cm thawing depth, and the minimum was 12.95 mm at 6 cm thawing depth. With the increase in runoff time, the slope infiltration rate had a decreasing trend. The loss rate of available and total Phosphorus increased with the increase in runoff rate. The rate of increase was fastest when the thawing depth was 4 cm. (2) The relationships between runoff rate and Nitrogen loss and Phosphorus loss rate can be explained by linear regression equations, and the loss rate increased as the runoff rate rose for all thawing depths. Within the 0–6 cm thawing depths, the loss of total phosphorus was the largest when the thawing depth was 4 cm, and the loss of available phosphorus was the smallest when the thawing depth was 6 cm. At the shallower thawing depths, the available Nitrogen loss represented a smaller proportion of the total Nitrogen loss compared to nitrate Nitrogen loss. However, there was a gradual rise in the available Nitrogen proportion in the total amount of inorganic Nitrogen as the thawing depth increased. (3) Total Phosphorus was the available Phosphorus with a quadratic function relationship with runoff energy and runoff power. Runoff energy mainly affected the total Nitrogen and available Nitrogen loss in runoff, whereas runoff power mainly affected total Nitrogen loss in runoff. The results of this paper can improve the understanding of runoff and Nitrogen and Phosphorus loss caused by runoff during freeze-thaw conditions.

Heavy metals (HMs), characterized by their non-biodegradable nature, are prone to enrichment in river sediments, thereby severely jeopardizing the equilibrium of ecosystems and human health. Given the critical importance of safeguarding valuable water resources, it is of utmost urgency to initiate research on HMs within the Yellow River Basin (YRB). This study collected river sediment samples from the Yellow River Basin and analyzed the distribution characteristics, health risks, and pollution sources of HMs utilizing the pollution index method, health risk assessment, and positive matrix factorization (PMF) model. The results demonstrate that arsenic (As), zinc (Zn), and cadmium (Cd) are the primary elements contributing to heavy metal (HM) pollution in the sediments of the YRB. The proportions of sediment samples with low HM pollution in the upstream, midstream, and downstream are 36.48%, 71.43%, and 72.73%, respectively, whereas the proportions of samples with moderate pollution are 63.16%, 28.57%, and 27.27%, respectively. The health risk assessment reveals that the non-carcinogenic risks posed by HM pollution in the sediments to adults are negligible, whereas those to children are not. Regarding carcinogenic risks, the carcinogenic risk index of As is significantly higher than that of the other HMs. The primary sources of HM pollution in the sediments are identified as traffic–industrial sources, agricultural–industrial sources, and industrial sources, with respective contribution rates of 32.47%, 44.87%, and 22.66%. As and Zn are prioritized as elements for health risk control, while agricultural–industrial sources are highlighted as the priority sources for pollution control.

In addition to greater precipitation on extreme days of precipitation, preceding and succeeding precipitation (PSP) is often an objective component of flooding in the mid–lower reaches of the Yangtze River Basin (MLRYRB). In this study, focused on the temporal distribution pattern of precipitation, the concept of an extreme precipitation event (EPE), defined as a consecutive precipitation event having at least one daily precipitation extreme, is proposed to consider PSP in an extreme event. We analyzed the spatiotemporal variation of four types of EPEs based on daily data obtained from 130 monitoring stations covering 1960–2019. Extreme precipitation increased significantly over the last 60 years (p < 0.01). The frequency and precipitation amount of single-day EPEs accounted for only 13% and 21%, respectively, while multi-day continuous EPE types that are associated with PSP accounted for 87% and 79%, respectively, confirming the connotations of EPEs. The front and late EPEs under the 100-year return level reached 250 mm and 230 mm, respectively. Furthermore, climate warming could lead to significant increases in the frequency of single-day and late EPEs, particularly in the southern region. The EPE concept may be helpful in exploring disaster-causing processes under extreme weather, and it provides a theoretical basis for deriving the precipitation hazard chain, which is more applicable to basins with long precipitation durations.

Land use and landscape patterns strongly influence water quality and are critical for ecological conservation within watersheds, as well as for effective water resource management. This study analyzed water quality data collected from 11 monitoring sites in the Minjiahe Watershed of Qinling Mountains, China. Redundancy analysis was used to analyze the impact mechanism of multi-scale landscape patterns and land use on seasonal water quality changes, while non-parametric change point analysis was used to quantify the key landscape thresholds that lead to sudden changes in water quality. The results indicated that the concentrations of ammonia nitrogen, available phosphorus, and total nitrogen in the river water were significantly higher during the rainy season than during the dry season (p < 0.05), representing increases of 18.93%, 30.33%, and 37.52%, respectively. The dissolved oxygen levels were 27.76% higher in the dry season than in the rainy season (p < 0.05). Landscape metrics at different spatial scales can strongly explain the spatiotemporal changes in water quality, with an explanatory rate exceeding 76%. The interspersion and juxtaposition index of forestland (IJI_for), largest patch index of forestland (LPI_for), landscape shape index of residential land (LSI_res), and percentage of landscape of farmland (PLAND_far) are the main factors affecting seasonal changes in water quality. Small and discontinuous grasslands did not significantly affect water quality. To effectively mitigate the impact of landscape patterns on water quality, the following thresholds are suggested as effective management targets: at the sub-watershed scale, IJI_for < 80% and LPI_for > 94%; at the 150 m buffer scale, LSI_res < 8 and PLAND_far < 8%. These determined thresholds can be used as indicators for landscape planning to reduce the risk of water quality degradation. The research results provide valuable insights for sustainable land use and multi-scale landscape planning, thereby informing strategies to enhance water quality.