Huiying Liu

Against the backdrop of intensified global climate change, the frequency and intensity of extreme weather events in mainland China continue to rise due to its unique topography and complex climate types. In-depth research on the trends and impacts of climate extremes can help develop effective adaptation and mitigation strategies to protect the environment and enhance social resilience. In this research, temperature data from 2029 meteorological stations for the period 1961–2021 were used to study 15 extreme temperature indices and 3 extreme composite temperature indices. Linear propensity estimation and the Mann–Kendall test were applied to analyze the spatial and temporal variations in extreme temperatures in China, and Pearson’s correlation analysis was used to reveal the relationship between these indices and atmospheric circulation. The results show that in the past 60 years, the extreme temperature index in China has shown a trend of decreasing low-temperature events and increasing high-temperature events; in particular, the increase in warm nights is significantly higher than that of warm days. In terms of spatial distribution, daily maximum temperature less than the 10th percentile (TX10P) and daily minimum temperature greater than the 90th percentile (TN90P) increased significantly in the warm temperate sub-humid (WTSH) region, north subtropical humid (NSH) region, and marginal tropical humid (MTH) region, whereas frost days (FD0) and diurnal temperature range (DTR) decreased significantly. In the extreme composite temperature index, extreme temperature range (ETR) showed a downward trend, while compound heatwave (CHW) and compound heatwave and relative humidity (CHW-RH20) increased, with the latter mainly concentrated in the WTSH and NSH regions. Correlation analysis with climate oscillation shows that Arctic Oscillation (AO), Atlantic Multiannual Oscillation (AMO), and El Niño–Southern Oscillation (ENSO) are positively correlated with extremely high temperatures, whereas North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO) are negatively correlated.

In this paper, a coupling model of SWAT (Soil and Water Assessment Tool) and EFDC (Environmental Fluid Dynamics Code) was established, and the relationship between the pollution source and water quality response was identified. Based on the hydrodynamic water quality simulation results and the one-dimensional WEC (water environmental capacity) theoretical formula, the total nitrogen and total phosphorus WEC and the remaining WEC of the Yongzhou Section of Xiangjiang River Basin under the guaranteed rate of 90% and in 2017 were calculated, respectively. It can be seen from the results that the total nitrogen WEC of the Yongzhou Section of Xiangjiang River Basin in 2017 is 27,673.04 t, the total nitrogen WEC under the guaranteed rate of 90% is 19,497.61 t/a and the total phosphorus WEC of the Yongzhou Section of Xiangjiang River Basin in 2017 is 4,877.22 t. The total phosphorus WEC under the guaranteed rate of 90% is 2,936.64 t/a; in 2017, the remaining WECs of total nitrogen and total phosphorus in the entire basin are 14,646.69 and 3,358.67 t, respectively.

Rainfall erosivity is a key factor to predict soil erosion rate in universal soil loss equation (USLE) and revised USLE (RUSLE). Understanding rainfall erosivity characteristics, especially its spatial distribution and temporal trends, is essential for soil erosion risk assessment and soil conservation planning. In this study, the spatial-temporal variation of rainfall erosivity in the Three Gorges Reservoir Area (TGRA) of China during 1960–2010, at annual and seasonal scales, was explored based on daily rainfall data from 40 stations (26 meteorological stations and 14 hydrologic stations). The Mann–Kendall test and Co–kriging interpolation method were applied to detect the temporal trends and spatial patterns. The results showed that TGRA’s annual rainfall erosivity increased from west, south, and east to the north-central, ranging from 3647.0 to 10884.8 MJ·mm·ha⁻¹·h⁻¹ with an average value of 6108.1 MJ·mm·ha⁻¹·h⁻¹. The spatial distribution of summer and autumn rainfall erosivity was similar to the pattern of annual rainfall erosivity. Summer is the most erosive season among four seasons, accounting for 53% of the total annual rainfall erosivity, and winter is the least erosive season. July is the most erosive month with an average of 1327.3 MJ·mm·ha⁻¹·h⁻¹, and January is the least erosive month. Mean rainfall erosivity was 5969.2 MJ·mm·ha⁻¹·h⁻¹ during 1960–2010, with the lowest value of 3361.0 MJ·mm·ha⁻¹·h⁻¹ in 1966 and highest value of 8896.0 MJ·mm·ha⁻¹·h⁻¹ in 1982. Mann–Kendall test showed that the annual rainfall erosivity did not change significantly across TGRA. Seasonal rainfall erosivity showed a significant decrease in autumn and insignificant decrease in summer and winter. Monthly rainfall erosivity in TGRA showed insignificant increases from Jun to Jul and then underwent decreases from Aug to Nov. and from Dec to Feb and it rose again in Feb reaching a 0.01 level significance. The daily rainfall data of supplemental stations is very useful to interpolate rainfall erosivity map, which could help to find the credible maximum and minimum value of TGRA. In total, the findings could provide useful information both for soil erosion prediction, land management practices, and sediment control project of TGRA.