301 / 2022-03-03 20:40:32
Coupling PLUS model and InVEST to assess the impact of coal mining and reclamation on carbon storage in high groundwater level mining areas
Carbon storage,High groundwater level mining area,Land reclamation,Mining subsidence prediction,Land use simulation
Environmental Science and Engineering > 3. Land Reclamation and Ecology Rehabilitation
Abstract Accepted
Jiazheng Han / China University Of Mining And Technology
Zhenqi Hu / China University Of Mining And Technology
Carbon sequestration in terrestrial ecosystems plays an essential role in coping with global cli-mate change and achieving regional carbon neutrality. In the mining area with high groundwater levels in eastern China, underground coal mining has severe damage to surface ecology. It is of practical significance to evaluate and predict the positive and negative effects of coal mining and land reclamation on carbon pools. This study set up three scenarios for the development of Yanzhou coalfield (YZC) in 2030, including (1) No mining activities (NMA). (2) No reclamation after mining (NRM). (3) Mining and reclamation (MR). The probability integral model is used to predict the subsidence caused by mining in YZC in 2030, and the land use and land cover (LULC) of 2010 and 2020 are interpreted by remote sensing images. Based on the classification of land damage, the LULC of different scenarios in the future is simulated by integrating various social and natural factors. Under different scenarios, the InVEST model evaluated carbon storage and its temporal and spatial distribution characteristics. The results indicated that: (1) By 2030, YZC will have 4341.13 ha land disturbed by coal mining activities; (2) NRM's total carbon storage is 36796.04 Mg C less than NMA and 17315.54 Mg C More than NRM; Especially in Nantun mine, 71.74% loss is reduced due to reclamation measures. (3) Carbon storage has a significant positive spatial correlation, and coal mining will lead to the fragmentation of carbon sink. The method of carbon storage accounting and prediction proposed in this study can provide data support for mining and reclamation planning of coal mine enterprises and carbon-neutral planning of government departments.
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