Regional land use change is the main cause of the ecosystem carbon storage changes by affecting emission and sink process. However, there has been little research on the influence of land use changes for ecosystem carbon storage at both temporal and spatial scales. For this study, the Qihe catchment in the southern part of the Taihang Mountains was taken as an example; its land use change from 2005 to 2015 was analyzed, the Markov-CLUES composite model was used to predict land use patterns in 2025 under natural growth, cultivated land protection and ecological conservation scenario, and the land use data were used to evaluate ecosystem carbon storage under different scenarios for the recent 10-year interval and the future based on the carbon storage module of the InVEST model. The results show the following: (1) the ecosystem carbon storage and average carbon density of Qihe catchment were 3.16×10 7 t and 141.9 t/ha, respectively, and decreased by 0.07×10 7 t and 2.89 t/ha in the decade evaluated. (2) During 2005-2015, carbon density mainly decreased in low altitude areas. For high altitude area, regions with increased carbon density comprised a similar percentage to regions with decreased carbon density. The significant increase of the construction areas in the middle and lower reaches of Qihe and the degradation of upper reach woodland were core reasons for carbon density decrease. (3) For 2015-2025, under natural growth scenario, carbon storage and carbon density also significantly decrease, mainly due to the decrease of carbon sequestration capacity in low altitude areas; under cultivated land protection scenario, the decrease of carbon storage and carbon density will slow down, mainly due to the increase of carbon sequestration capacity in low altitude areas; under ecological conservation scenario, carbon storage and carbon density significantly increase and reach 3.19×10 7 t and 143.26 t/ha, respectively, mainly in regions above 1100 m in altitude. Ecological conservation scenario can enhance carbon sequestration capacity but cannot effectively control the reduction of cultivated land areas. Thus, land use planning of research areas should consider both ecological conservation and cultivated land protection scenarios to increase carbon sink and ensure the cultivated land quality and food safety.
To determine the dividing index between warm temperate and subtropical zones based on the spectra of altitudinal belts, this paper collected 33 spectra of altitudinal belts in the Qinling-Daba Mountains from published literatures and then analyzed the structures and the spatial patterns from south to north, from west to east and based on exposure directions. The results show that: 1) From south to north, the basal belt gradually changes from subtropical evergreen broadleaf forest to warm temperate deciduous broadleaf forest; the spectra of altitudinal belts change from complex to simple; the dominant belt changes from montane broadleaf-conifer mixed forest and evergreen-deciduous broadleaf mixed forest to deciduous broadleaf forest. 2) From west to east, the structures of the altitudinal belt spectra show complexity in the east and west but simplicity in the middle section; the upper limits of both the evergreen-deciduous broadleaf mixed forest belt and montane deciduous broadleaf forest belt present a quadratic curve distribution pattern in the longitudinal direction. However, the upper limit of the montane broadleaf-conifer mixed forest belt exhibits a nearly linear decrease in the west-east direction. 3) Both the north and south slopes in the Qinling Mountains have the similar basal belt, whereas it varies greatly between the north and south slopes in the Daba Mountains. Comparably, dominant belts are very similar in the Qinling Mountains and the north slope of the Daba Mountains, but the south slope of the Daba Mountains has its own unique dominant belt: evergreen-deciduous broadleaf mixed forest. This implies that the Daba Mountains are more appropriate than the Qinling Mountains to act as the boundary between subtropical and warm-temperate zones in central China.
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.
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