Topographical relief is a key factor that limits population distribution and economic development in mountainous areas. The limitation is especially apparent in the mountain-plain transition zone. Taking the transition zone between the Qinling Mountains and the North China Plain (i.e. the mountainous area in western Henan Province) as an example and based on the 200-m resolution DEM data, we used the mean change-point analysis to determine the optimal statistical unit for topographical relief, and thereafter extracted the relief degree. Taking the 1:100,000 land use data, township population and county-level industrial data, population and economic spatial models were constructed, and 200-m resolution grid population and economic density maps were generated. Afterwards, statistical analysis was carried out to quantitatively reveal the impact of topographical relief on population and economy. In addition, the impacts of other topographical factors were discussed. The results showed the following. (1) The relief degree in western Henan is generally low, where 58.6% of the regional topography does not exceed half the height of a reference mountain (relative elevation ≤250 m). Spatially, the relief degree is high in the west while low in the east, and high in the middle while low in the north and south. There is a positive correlation between relief degree and elevation, and a much stronger correlation between relief degree and slope. (2) The linear fitting degree between the population and economic validation data and the corresponding simulation data are 0.943 and 0.909, respectively, indicating that the spatialized results can reflect the actual population and economic distribution. (3) The impact of topographical relief on population and economy was stronger than that of other topographical factors. The relief degree showed a good logarithmic fit relationship with population density (0.911) and economic density (0.874). Specifically, 88.65% of the population lives in areas where the topographical relief is ≤0.5 and 88.03% of the gross regional product was from areas where the relief is ≤0.3. Compared with the population distribution, the economic development showed an obvious agglomeration trend towards low relief areas.
As a major component of the north–south transition zone in China, the vegetation ecosystem of the Qinling-Daba Mountains (QBM) is highly sensitive to climate change. However, the impact of sunshine duration, specifically, on regional vegetation remains unclear. By using linear trend, correlation, and multiple regression analyses, this study systematically analyzed the spatiotemporal characteristics and trend changes of the vegetation coverage in the QBM from 2000–2020. Changes in the main climate elements in different periods and the responses to them are also discussed. Over the past 21 years, the vegetation coverage on the east and west sides of the QBM has been lower than that in the central areas. However, it is showing a continuously improving trend, especially in winters and springs. The findings indicate that change of FVC in the QBM exhibited a positive correlation with temperature, a negative correlation with sunshine hours, and both positive and negative correlation with precipitation. On an annual scale, average temperature was the main controlling climatic factor. On a seasonal scale, the area dominated by precipitation in spring was larger. In summer, the relative importance of the three was weak. In autumn and winter, sunshine duration became the main factor affecting vegetation coverage in most areas.
Soil erosion is a serious form of land degradation and poses a considerable threat to food supply, human health, and terrestrial ecosystems globally. The Qinba Mountains are an important geo-ecological transitional zone in China, and quantifying soil erosion in response to climate and land use/land cover (LULC) change can help inform plans for the area’s ecological protection. The spatiotemporal variation of soil erosion intensity in the Qinba Mountains during 2001–2020 was estimated using revised universal soil loss equation (RUSLE). Based on CMIP6 data, combined with Statistical Down Scaling Model (SDSM) and the CA-Markov model, future soil erosion intensity was predicted. The changing trend of soil erosion intensity was compared under four different shared socio-economic pathways (SSPs). The potential contributions of long-term changes in climate and LULC to soil erosion were assessed using statistical methods. The results show that future rainfall and rainfall erosivity will increase by 8%–12% and 3%–14%, respectively. Depending on the different socio-economic pathways, the soil erosion rate will increase by between 12 and 32%, with SSP2-4.5 predicted to cause greatest soil erosion. The analysis of influencing factors showed that rainfall frequency, intensity, and duration could increase the risk of soil erosion, while high temperatures could slow down the erosion rate. Barren land is the most vulnerable to erosion and should continue to be prioritized. Our spatial distribution map of soil erosion risk will help inform sustainable land practices and provide support for adaptation to future ecological environmental hazards caused by climate change.
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