Rapidly predicting and revealing the spatiotemporal characteristics and driving factors of land-use changes in carbon storage within megacities under different scenarios is crucial to achieving sustainable development. In this study, Jinan City (JNC) is taken as the study area, and the Markov-FLUS-InVEST model is utilized to predict and analyze the spatiotemporal variation in carbon storage in 2030 under three scenarios, namely, the natural development scenario (S1), the ecological conservation scenario (S2), and the economic development scenario (S3). The drivers of carbon storage changes were identified using an optimal parameter-based geographic detection (OPGD) model. The findings indicate that (1) land use from 2010 to 2018 shows a trend of continuous expansion of construction land and reduction in arable land. (2) The main types of carbon pools were cropland, forest, and grassland, accounting for more than 96% of the total amount. Carbon storage showed a decreasing trend from 2010 to 2018, and the main type of carbon pool that decreased was cropland. The center of gravity of carbon storage increases and decreases was located in the southern Lixia District, and the center of gravity of increase and decrease moved to the southwest by 3057.48 m and 1478.57 m, respectively. (3) From 2018 to 2030, the reductions in carbon stocks were 3.20 × 10⁶ t (S1), 2.60 × 10⁶ t (S2), and 4.26 × 10⁶ t (S3), and the carbon release was about 9 times (S1), 4 times (S2), and 10 times (S3) that of the carbon sink. (4) The contribution of slope (A2) ∩ nighttime light index (B6) and elevation (A1) ∩ nighttime light index (B6) to the regional heterogeneity of carbon stocks was the largest among the interaction drivers. To sum up, this study deepens the simulation of spatial and temporal dynamics of carbon storage under land-use changes in megacities and the related driving mechanism, which can provide the basis for scientific decision-making for cities to conduct territorial spatial planning and ecological protection and restoration.

Assessing the spatiotemporal evolution characteristics of habitat quality, human footprint, and coupling coordination between two systems in continuous cycles and on national scales is of great significance to maintaining biodiversity and sustainable development. This study took China as an example, based on land-use data from 2000 to 2020, using the Integrated Valuation of Ecosystem Services and Tradeoffs—Habitat Quality (InVEST-HQ) model and the human footprint framework, coupling trend analysis methods such as Theil–Sen Median Analysis, Mann–Kendall Test, and Grid Transition Matrix (GTM) Method and combining the four-quadrant model and the coupling coordination degree model (CCDM) to reveal the spatiotemporal evolution characteristics of habitat quality, human footprint, and CCDM in China for 21 consecutive years and the response relationship between the two systems of habitat quality and human footprint. The results show that the land cover change area from 2000 to 2020 accounted for 4.2% of the total area. Both habitat quality and human footprints exhibit apparent spatial heterogeneity along the “Hu Line” and generally fall into two evolutionary stages: “degradation–improvement”. The proportions of degradation and improvement were 14.37% and 8.36%, respectively, and the mutation point was in the year 2013; the average human footprint increased by 16.75%, and the increased and decreased area proportions were 63.40% and 21.53%, respectively. The mutation occurred in 2014. The right side of the “Hu Line” primarily hosts areas with high values of the coordinated coupling index of human footprints and habitat quality systems. The four quadrants generally have the following characteristics: “quadrant IV on the right is dominant, quadrants II and III on the left are dominant, and quadrant I is located in the transition zone of the ‘Hu Line’”. The coupling coordination degree (CCD) and human footprints have a weak, nonlinear “inverted U-shaped” relationship. This study provides compelling evidence for the spatiotemporal evolution and coupling relationship between habitat quality and human footprint in China, provides scientific decision-making support for biodiversity protection and sustainable economic development, and maintains the bottom line of ecological security for a beautiful China.

The Southwest Alpine Canyon Area of China was one of the regions in the world that suffered from severe soil erosion. The focus of this research was to understand the response of land use change on soil loss by water erosion in alpine canyon on Southeast Tibet, we analyzed the characteristics of temporal and spatial dynamics of land use in the Niyang River Basin from 1990 to 2020 using GIS and statistical methods, assessed the spatial and temporal change characteristics of soil erosion in the Niyang River Basin using RUSLE model. For this purpose, the meteorological, soil, topographic, remote sensing data were collected. The results showed the area of cropland and construction land in the Niyang River Basin increased to 2.4 and 6.1 times the original from 1990 to 2020, respectively, the area of forest and water continued to decrease. Grassland, unused land and forest have always been the dominant land types in the Niyang River Basin. Tolerable soil erosion was distributed in the flat areas of the valley terraces in the main stream and its tributaries, slight soil erosion was mainly distributed in the flat areas at high altitudes, and moderate, severe, very severe and destructive soil erosion was sporadically distributed in the Niyang River Basin. The areas of tolerable and slight soil erosion exceeded 82% of the total area, The area of severe, very severe and destructive soil erosion was less than 6%, but the total soil loss amount of severe, very severe and destructive erosion was more than 27%. From 1990 to 2020, the soil erosion area decreased from 9219.34 km2 to 8,090.84 km2 in Niyang River Basin, more than 85% of the soil erosion class has not changed, and the change of total soil loss amount also shows a significant reduction trend as a whole. 99% of the total soil loss amount in the Niyang River basin is generated by grassland, forest and unused land. The conversion of various land use types to cropland will cause the enhancement in soil erosion grade. We recommend that the focus of soil erosion control in the Niyang River Basin is to protect grassland, forest and carry out scientific farming.

In the Sanjiang Plain, agricultural drainage ditches effectively alleviate agricultural non-point source nitrogen pollution. However, limited information is known about the characteristics of bidirectional trans-ports of nitrogen between sediment and overlying and pore water in different patterns of ditches undergoing seasonal freezing-thawing cycles. It is vital to better understand nitrogen interception and purification by ecological ditches. In order to clarify the interception of ecological ditches on internal and external nitrogen, overlying water and sediment samples of two typical agricultural ditches were collected and analyzed in Sanjiang Plain during the growing seasons of 2015–2017. The results indicated that the N-$\mathrm{N}{\mathrm{O}}_3^{-}$ in overlying water, which was higher than N-$\mathrm{N}{\mathrm{H}}_4^{+}$, was the dominant inorganic nitrogen, whilst, in the sediment, N-$\mathrm{N}{\mathrm{H}}_4^{+}$ was much higher than N-$\mathrm{N}{\mathrm{O}}_3^{-}$, which should be attributed to the soil’s adsorption of N-$\mathrm{N}{\mathrm{H}}_4^{+}$. In contrast to the dryland ditch, the paddy ditch had a more significant amount of inorganic nitrogen both in overlying water and sediment, which means that the non-point source nitrogen pollution caused by paddy fields was more severe than that of drylands. Compared with dryland ditches, N-$\mathrm{N}{\mathrm{H}}_4^{+}$ in the sediment of pad ditches seemed to be much easier to migrate to a deeper layer, which may cause a greater risk of nitrogen pollution to groundwater. Both in the overlying water and the sediment of ditches, nitrogen content fluctuated during different periods, and inter-annual variation was noticeable, which results means that estimation or prediction of the non-point source pollution output needs to extend the monitoring period and increase sampling frequency to reduce the great uncertainty. The findings may provide a foundation for forecasting agricultural nitrogen pollution and guide best management practices (BMPs) of non-point source nitrogen pollution control in seasonally frozen areas.