A Catastrophe Model to Assess Soil Restoration Under Ecological Restoration in the Red Soil Hilly Region of China

Our understanding of how varying clipping intensities affect the efficiency of phytoextraction remains limited, particularly with co‐cropping systems. This study focuses on assessing the effects of different clipping intensities on phytoextraction efficiency within co‐cropping systems dominated by the key species Dicranopteris pedata and Pinus massoniana in rare‐earth mines of southern China. Our results confirmed the following. Firstly, the compensation index, dry aboveground biomass, and rare‐earth elements (REE) accumulation of clipped D. pedata per unit area were highest under 100% clipping (p < 0.05). Meanwhile, the bioabsorption coefficient, bioconcentration factor, and translocation factor of P. massoniana were less than 1. Secondly, the soil catalase, sucrase, and α‐diversity indices of bacteria and fungi separately indicated no significant variation between the clipping treatments and control. The REE content in tree rings increased, and the total REE contents in thicker tree rings were higher than those in the thinner tree rings of P. massoniana from 1997 to 2016. Additionally, the soil erosion intensity diminished significantly in the growth area of P. massoniana from 1995 to 2019. Thirdly, both D. pedata and P. massoniana, when subjected to 100% clipping, could effectively remove excess REEs from the 0–20 cm soil layer within 18.57–61.19 years. In contrast, P. massoniana alone achieved a removal rate of excess REEs in the 20–60 cm layer, within 923.16–1899.62 years. Thus, D. pedata with 100% clipping can phytoextract the maximum amount of REEs. It is noted that P. massoniana cannot be used for REEs phytoextraction or phytostabilization. Co‐cropping with 100% clipping would have a less negative impact on growth and REEs extraction, and the co‐cropping system can phytoextract excess REEs from the surface soil within decades. However, it is not efficient for the phytoextraction of excess REEs from deep soil layers.

Section: The Soil Erosion Intensity Decreased Significantly In Zhuotianmentioning

confidence: 99%

Rare‐earth element phytoextraction efficiency in southern Chinese mines under different clipping intensities using a co‐cropping system (Dicranopteris pedata and Pinus massoniana)

Chen,

Feng,

Lin

et al. 2024

Land Degrad Dev

Self Cite

“…Because of severe soil erosion and economic development in the past, native zonal vegetation no longer exists and has been degraded into sparse forests, subtropical thickets, or forestless land. The poorly growing Masson’s pine ( Pinus massoniana ) is the dominant species in the arboreal forest (Chen et al, 2013, 2017).…”

Section: Case Studymentioning

confidence: 99%

Spatial optimization of watershed best management practice scenarios based on boundary-adaptive configuration units

Qin

Progress in Physical Geography: Earth and Environment

2020

Spatial optimization of watershed best management practice (BMP) scenarios based on watershed modeling is an effective decision support tool for watershed management. During such optimization, existing types of BMP configuration units for configuring BMPs (or BMP configuration units, e.g. subbasins, hydrologic response units, farms) remain fixed boundaries once they have been created through spatial discretization prior to BMP scenario optimization. This sort of “boundary-fixed” method does not allow for adjustments to the spatial characteristics of BMP configuration units. Hence, it runs the risk of missing superior BMP scenarios that could have been obtained by adjusting unit boundaries and may produce less effective spatial optimization. In this article, we propose a new approach to the spatial optimization of BMP scenarios based on boundary-adaptive configuration units. The proposed optimization approach adopts slope positions (basic landform units along hillslopes inherently related to physical hillslope processes) as BMP configuration units and dynamically adjusts their boundaries by using quantitative information about their spatial gradation (i.e. fuzzy slope positions) during the optimization. A case study conducted in the Youwuzhen watershed in Fujian, China, showed that the proposed optimization approach can significantly enlarge the search space and obtain optimal BMP scenarios with better cost-effectiveness and higher optimization efficiency than with boundary-fixed units. The proposed optimization approach provides a new alternative framework for spatial optimization of BMP scenarios, in which other watershed models, intelligent optimization algorithms, and BMP configuration units available for boundary adjustment can be applied to BMP scenario optimization in a boundary-adaptive manner. This study also exemplifies the potential for transforming qualitative, vague, and empirical geographical knowledge about slope position units related to physical hillslope processes and BMPs into quantitative, explicit, and automated geospatial algorithms for effectively resolving environmental management problems in a more geographically meaningful way.

“…As shown in Table 1, four BMPs that had been widely implemented in Changting County for ecological restoration and soil and water conservation were selected in this study, i.e., Closing measures (CM), Arbor-bush-herb mixed plantation (ABHMP), Low-quality forest improvement (LQFI), and Orchard improvement (OI) [2,40].…”

Section: Bmp Knowledge Base and Bmp Configuration Strategiesmentioning

confidence: 99%

Effects of Different Spatial Configuration Units for the Spatial Optimization of Watershed Best Management Practice Scenarios

Qin

et al. 2019

Water

Different spatial configurations (or scenarios) of multiple best management practices (BMPs) at the watershed scale may have significantly different environmental effectiveness, economic efficiency, and practicality for integrated watershed management. Several types of spatial configuration units, which have resulted from the spatial discretization of a watershed at different levels and used to allocate BMPs spatially to form an individual BMP scenario, have been proposed for BMP scenarios optimization, such as the hydrologic response unit (HRU) etc. However, a comparison among the main types of spatial configuration units for BMP scenarios optimization based on the same one watershed model for an area is still lacking. This paper investigated and compared the effects of four main types of spatial configuration units for BMP scenarios optimization, i.e., HRUs, spatially explicit HRUs, hydrologically connected fields, and slope position units (i.e., landform positions at hillslope scale). The BMP scenarios optimization was conducted based on a fully distributed watershed modeling framework named the Spatially Explicit Integrated Modeling System (SEIMS) and an intelligent optimization algorithm (i.e., NSGA-II, short for Non-dominated Sorting Genetic Algorithm II). Different kinds of expert knowledge were considered during the BMP scenarios optimization, including without any knowledge used, using knowledge on suitable landuse types/slope positions of individual BMPs, knowledge of upstream-downstream relationships, and knowledge on the spatial relationships between BMPs and spatial positions along the hillslope. The results showed that the more expert knowledge considered, the better the comprehensive cost-effectiveness and practicality of the optimized BMP scenarios, and the better the optimizing efficiency. Thus, the spatial configuration units that support the representation of expert knowledge on the spatial relationships between BMPs and spatial positions (i.e., hydrologically connected fields and slope position units) are considered to be the most effective spatial configuration units for BMP scenarios optimization, especially when slope position units are adopted together with knowledge on the spatial relationships between BMPs and slope positions along a hillslope.