Spatiotemporal impacts of climate change and human activities on water resources and ecological sensitivity in the Mekong subregion in Cambodia

An investigation into the zoning of ecosystem sensitivity control areas in Mentougou District (Beijing, China)

To achieve a more precise delineation of ecosystem sensitivity control areas, this study examines the Mentougou District in Beijing and introduces the Ecosystem Sensitivity Control Area Classification Method (ESCACM). This novel approach combines single-factor sensitivity analysis, multi-factor comprehensive sensitivity assessments, and strategies for zoning based on various ecological scenarios. The study categorizes the region into three specific zones: first-level management and control areas, second-level management and control areas, and non-management and control areas. The key findings are: (1) By systematically categorizing relevant factors, the method creates independent, non-overlapping groups, effectively reducing dimensionality. (2) After conducting a comprehensive sensitivity assessment using multiple factors, scientifically quantified values are assigned to each zone. These values are processed through mathematical overlay algorithms, which generate composite results with different sensitivity levels. This method places a strong emphasis on quantitative analysis, thereby enhancing the objectivity and precision of the results. (3) The use of multi-scenario ecosystem sensitivity zoning strategies enhances the adaptability and flexibility of the zoning process. This method significantly improves the accuracy and scientific credibility of ecosystem sensitivity zoning, providing a versatile approach to meet the varied zoning needs of different regions. This model serves as a valuable framework for promoting ecological protection efforts in support of sustainable urban development objectives.

Tracing sediment sources in a plain river network area by using optimized experimental design and reflectance spectroscopy

Soil erosion in a plain river network area with dense rivers, fertile land, and agricultural development is easily causes river siltation, agricultural non-point source pollution, and water eutrophication. Therefore, the negative impact of the sediment on the environment cannot be underestimated. Most traditional sediment fingerprint tracing studies have focused on mountain basins and lack a scheme suitable for plain river network sediment tracing. Here, a typical plain river network in the Taihu Basin was selected as the study area. The flow structure and characteristics were analysed, and a sampling scheme for the stream segment and a two-step model of sediment tracing in a plain river network were proposed to quantitatively distinguish the types of sediment sources. The results indicated that the traditional discriminant function analysis adequately distinguishes the contribution rate of basin soil and has a good validation accuracy (R2 = 0.96, root mean square error of calibration = 5.91 %), whereas Random Forest obtains better discrimination results by mining non-linear information in the soil spectra of different land types, with R2 values of 0.89, 0.83, and 0.80 for farmland, forest, and grassland, respectively. The average proportion of soil in the sediment in the watershed was 23 %, and the proportion of soil in the watershed increased from upstream to downstream. The sediment sources of the Caoqiao, Yincun, and Shaoxiang Rivers mainly came from grassland (44 %), forest (39 %), and farmland (42 %), respectively. Land-use distribution, water conservation facilities, and soil particle size were the main factors affecting these sources. Each river adopts measures to remove the corresponding pollutants, optimise water and soil conservation measures for riverbank green belts and forest, and regularly clean up silt in water conservancy ditches and rivers, which can reduce the pollution impact caused by sediment.