Zhengyang Li

Small watersheds are fundamental units for natural processes and social management in Southwestern China. Accurately assessing carbon sinks in small watersheds is crucial for formulating carbon sink management policies. However, there has been a lack of assessment of the dynamics of carbon fluxes in the major ecosystems of small watersheds. Here, we selected the Reshuihe River watershed, which is a typical small watershed in Southwestern China, to measure carbon fluxes using eddy covariance systems for two years (October 2021 to September 2023) from three major ecosystems, namely forest, cropland, and non-timber forest. We compared variations and controlling factors of net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (Re) among different ecosystems, and estimated annual watershed carbon flux based on the land cover areas of the three ecosystems. This study found that three ecosystems were net annual carbon sinks during the study period. Forest was the strongest (−592.8 and −488.1 gC m⁻² a⁻¹), followed by non-timber forest (−371.0 gC m⁻² a⁻¹), and cropland was the smallest (−92.5 and −71.6 gC m⁻² a⁻¹), after taking fallow period into account. Weeds were a significant source of carbon flux in non-timber forest ecosystems. It was also found that variations in daily NEE were controlled by photosynthetically active radiation and soil volumetric water content, with weak effects related to temperature also being observed. However, when the temperature exceeded 21 °C, GPP and Re were significantly reduced in cropland. Finally, it was discovered that the total carbon sink of the three ecosystems in the watershed for one year was −52.15 Gg C. Overall, we found that small watersheds dominated by forest ecosystems in Southwestern China have a strong carbon sink capacity.

The development of the tertiary industry is of great significance for promoting industrial structure, optimizing and upgrading it, and achieving regional energy conservation and emission reduction goals. This study adopts a quantitative method to analyze the spatio-temporal pattern of carbon emissions from China’s tertiary industry from 2004 to 2019. In order to analyze emissions from aspects such as energy structure, energy intensity, energy carrying capacity, industrial structure, level of industrial development, income level, consumption capacity, energy consumption intensity, and population size, this study establishes a hybrid factor decomposition model called the “energy-industry-consumption” research framework. The study shows that carbon emissions from China’s tertiary industry have been increasing year by year from 2004 to 2019, with a growth rate of 353.10%. Transportation is the largest contributor to the increase in carbon emissions from China’s tertiary industry. The carbon emissions from the tertiary industry in each province show four types: high-speed growth, low-speed growth, fluctuating growth, and stable growth. During the study period, carbon emissions produce a spatial heterogeneity with the highest emissions in the south and lowest in the northwestern part of China. The spatial pattern of per capita carbon emissions is not significant. Guangdong has the highest carbon emissions, and Shanghai and Beijing have higher per capita carbon emissions. Industrial factors and consumption factors have a positive effect on carbon emissions in China’s tertiary industry, while energy factors have a negative effect. The leading factor of carbon emissions in China’s tertiary industry has gradually shifted from energy to industry.

Background The Panxi region in China is among the areas that are most severely impacted by forest fires. Despite this, there is currently a lack of comprehensive and systematic research on the spatial and temporal distribution patterns, as well as the drivers, of forest fires in the region. To reveal bio-geo-climatic and anthropogenic influences, this study investigated the temporal and spatial characteristics of forest fires and migration patterns of the center of gravity of forest fires in Panxi. A parametric optimal geographical detection model was utilized to quantify the influence of various individual factors and their combinations on the spatial patterns of forest fire occurrence in the whole Panxi region and sub-region, by analyzing the forest fire data-set from 2004-2020. Results From 2004 to 2020, the Panxi region experienced an upward trend in the number of forest fires and the area burned. However, the trends were not consistent over the entire period. Between 2004 and 2014, both the number of fires and the area burned showed fluctuations and an overall increase. In contrast, between 2015 and 2020, there was a significant decrease in the number of fires, while the area burned showed a continued upward trend. The study identified abrupt changes in the frequency of forest fires and burned areas, primarily in 2007 and 2016. Spatially, forest fires in Panxi exhibited a positive correlation and local clustering. The river valley basin and hilly regions displayed a higher incidence of forest fires, which were concentrated mainly along the hill edges. In the whole area of Panxi, climatic factors have a predominant influence on forest fire occurrences. Specifically, evaporation, maximum temperature, average temperature, number of days without rain, and minimum temperature demonstrated the strongest explanatory power. Furthermore, this relationship was found to be reinforced when combined with topographical, human activities, and vegetation factors. The spatial variation of drought within each sub-district has a stronger explanatory power for the distribution characteristics of forest fires in the region than at the Panxi-wide scale. The factor with the maximum interaction in most regions was the dual factor of rainfall and drought. Conclusion The study’s findings validate the applicability of geographic probes for identifying the drivers of fire occurrence and enhance our understanding of the drivers and their combined effects on the spatial context of the fire-incident study area.

Maintaining and improving the soil conservation function of an ecosystem is of positive significance to the sustainable and stable development of that ecosystem. We used the RUSLE model to evaluate the soil conservation function of the Qinling-Daba Mountains from 1982, 1995, 2005, and 2015 in order to analyze the spatio-temporal evolution characteristics of soil conservation. Our conclusions are as follows: (1) During the study period, the amount of average actual soil erosion in the Qinling-Daba Mountains was 955.39 × 10⁸ t, the amount of actual soil erosion fluctuated greatly from year after year, there were obvious spatial aggregation and temporal and spatial transfer phenomena, and there was serious soil nutrient loss in the east. (2) From 1982 to 2015, soil conservation in the Qinling-Daba Mountains increased by 27.75 × 10⁸ t during fluctuations. The soil conservation was negatively correlated with elevation and slope, and was positively correlated with vegetation coverage. (3) The average soil conservation of forest ecosystems and farmland ecosystems accounts for 78.11% of the total soil conservation, but there are differences in the ways in which to achieve soil conservation function. The order for soil conservation function of different vegetation types is crops > shrub > broad-leaved forest > coniferous forest > grass > meadow > grassland > coniferous and broad-leaved mixed forest > alpine plant > swamp. (4) The average retention of N, P and K elements in soil were 75.57 × 10⁴ t, 25.35 × 10⁴ t and 737.28 × 10⁴ t, respectively. The soil elements had the consistency of spatial difference in spatial distribution and were time scaled. The soil nutrient loss in the eastern region is serious. Shrubs, broadleaf forests and crops have the greatest effect on soil nutrient retention. Alpine plants retain the greatest amount of soil nutrients per unit area. Therefore, the establishment of reasonable soil conservation strategies and scientific vegetation interplanting measures will help to enhance the soil conservation function of the Qinling-Daba Mountains ecosystem and improve the ecosystem production capacity.