Examining the relationship between coal mining subsidence and crop failure in plains with a high underground water table

Xiao, Wu; Zheng, Wenxiu; Zhao, Yuemin; Chen, Jiale; Hu, Zhenqi

doi:10.1007/s11368-021-02991-2

Cited by 24 publications

(10 citation statements)

References 50 publications

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“…Mining subsidence in high groundwater table coal mines has an ecological impact, not only on soil moisture, but also on vegetation growth for agricultural production. The decay trend of coal mining disturbance accompanied by distance has been found in other studies, showing an excess of severe to minor effects on AGB biomass of corn (Ren et al, 2020;Xiao et al, 2019).…”

Section: Accuracy Validation Of Soil Moisture Estimationsupporting

confidence: 74%

Identifying coal mining subsidence impacts by soil moisture based on optical trapezoid model in Google Earth Engine

Chen

Xiao

et al. 2023

Land Degrad Dev

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Many coal mining areas overlapped with agricultural land in the world. However, surface subsidence and waterlogging brought on by coal mining inexorably harm the agricultural land. Soil moisture can reflect the variation of mining impact significantly according to the geographic features of the high underground water table, which shows significant spatial gradient variation within the subsidence basin. Previous studies have used site survey and unmanned aerial vehicle (UAV), which are not applicable to large‐scale assessments of the ecological impacts and defining the scale of impacts. Here, we used the Google Earth Engine (GEE) platform and an optical trapezoid model (OPTRAM) to estimate the soil moisture distribution around 50 mining subsidence waterbody samples in eastern China. Based on the spatial variation in soil moisture, the impact boundary of coal mining was determined using buffer analysis and trend fitting. The results showed that: (1) The OPTRAM can well reflect the soil moisture distribution which shows a good correlation between estimated and measured values, with R = 0.83 and RMSE = 0.054. (2) The spatial trend fitting of soil moisture is suitable for evaluating mining impact, and the impact distances are in the range of 96–762 m, 80% of which are located at 128–436 m. We also find that 28% of subsidence waterbody buffer basins were heavily influenced by human activities, and the coal mining impact boundaries cannot be effectively identified. The study provides a new idea to quantify the ecological and agricultural impact boundary of coal mining, which could be applied to other similar regions across the globe.

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Section: Accuracy Validation Of Soil Moisture Estimationsupporting

confidence: 74%

Identifying coal mining subsidence impacts by soil moisture based on optical trapezoid model in Google Earth Engine

Chen

Xiao

et al. 2023

Land Degrad Dev

View full text Add to dashboard Cite

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“…The quantity and quality of soil TN directly affect soil characteristics and can reflect the level of soil fertility [23,25,26]. Therefore, an in-depth understanding of the three-dimensional spatial variation characteristics of soil total nitrogen will help to improve the sustainable development of mining land and provide valuable guidance and a basis for land consolidation and precision agriculture in mining areas [27][28][29]. Soil is a continuous three-dimensional entity with spatial variation in both horizontal and vertical directions.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Spatial Distribution and Influential Factors of Soil Total Nitrogen in a Coal Mining Subsidence Area

et al. 2022

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Soil nitrogen is very important for crop growth and development. However, the factors affecting the three-dimensional spatial distribution of soil total nitrogen (TN), particularly in coal mining subsidence areas, are unclear. In this study, Markov geostatistics was used to analyse the three-dimensional spatial distribution characteristics and influential factors of TN by examining 180 soil samples from the Zhaogu mine in China. The results showed that the TN content was significantly different at different soil depths (0–20, 20–40, 40–60 cm) and decreased with increasing soil depth. The variation coefficient of the TN content decreased gradually from top to bottom, ranging from 18.18 to 25.62%. In addition, the TN content was greatly affected by mining subsidence, rainfall, irrigation, fertilization and management mode. The factors that influenced the TN content also varied across different slope positions. The TN content of the upslope was the highest, and the TN content of the middle slope was the lowest. These results can provide research ideas and technical countermeasures for ecological environment improvement and sustainable land development in coal mining subsidence areas.

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“…However, underground mining can cause damage to the mining area environment, including the destruction of buildings and structures [6], reduction of agricultural output [7], soil contamination [8], surface ponding and pollution of surface waters [9,10], vegetation damage [11,12], and alteration of landscapes [13]. In coal-grain overlapping areas, mining subsidence destroys cultivated soil's physical and chemical environments, ultimately reducing the grain yield [7,[14][15][16][17]. In addition, mining subsidence-induced cropland ponding has flooded significant tracts of farmland, leading to irreversible damage [18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Calculation and Optimization Method for the Theoretical Reclamation Timing of Cropland

Yin,

Guo,

et al. 2024

Land

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In mining areas with high groundwater tables, mining subsidence can lead to the inundation of cropland by water, causing damage to cropland and posing a threat to national food security. The implementation of concurrent mining and reclamation techniques can effectively enhance the reclamation rate of cropland. This technique requires engineers to initiate reclamation measures before cropland waterlogging occurs. Therefore, when mining a panel underground, an accurate calculation of the time when cropland becomes waterlogged, known as the theoretical reclamation timing, is crucial. To address this issue, this study proposes a computational method for the theoretical reclamation timing of cropland under the conditions of single-panel mining based on intelligent optimization algorithms. In addition, this paper also proposes an optimization method for the theoretical reclamation timing of cropland within a district based on an intelligent optimization algorithm. Utilizing this method makes optimizing the layout of multiple panels possible, thereby delaying the theoretical reclamation timing for cropland within a district. This approach aims to shorten the duration of reclamation projects and minimize their interference with agricultural activities. Through experimental validation, this paper demonstrates the reliability of these two methods. This study is beneficial for the rational planning of reclamation projects.

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Examining the relationship between coal mining subsidence and crop failure in plains with a high underground water table

Cited by 24 publications

References 50 publications

Identifying coal mining subsidence impacts by soil moisture based on optical trapezoid model in Google Earth Engine

Identifying coal mining subsidence impacts by soil moisture based on optical trapezoid model in Google Earth Engine

Three-Dimensional Spatial Distribution and Influential Factors of Soil Total Nitrogen in a Coal Mining Subsidence Area

A Calculation and Optimization Method for the Theoretical Reclamation Timing of Cropland

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