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The soil organic carbon (SOC) pool in farmland is changing rapidly due to human activities, thereby greatly affecting the regional and global environment, as well as influencing soil fertility and crop yields. The present study investigated the effects of underground coal mining on the regional SOC pool in farmland in the Jiuli Mining Area of Xuzhou City in China as a typical coal mining region based on field sampling, chemical analysis, model construction, and spatial analysis using the software of ArcGIS. The results showed that in the mining subsidence area, spatial variations in the SOC content and soil bulk density were mainly caused by structural factors (mining subsidence, subsidence waterlogging, and other structural factors due to coal mining) at a regional scale. SOC storage in farmland soil decreased sharply in non-waterlogged subsidence farmland and seasonally waterlogged subsidence farmland in the areas with mining, whereas the SOC storage increased in waterlogged wetland after coal mining. The SOC was reduced by 102,882 tonnes (32.81%) compared with the original SOC stock as a consequence of coal mining, and thus the effect of underground coal mining on the regional SOC pool in farmland was characterized as a carbon loss process. Land-use changes, soil degradation and erosion contributed almost equally to the carbon loss process in the study area. The results of this study may facilitate evaluations of low-carbon land reclamation and ecological compensation in mining areas.

Many researches have been conducted to understand the scale-location specific variations of soil properties in one-dimensional soil samples. However, these results did not enable us to properly understand the 2D (two-dimensional) distributions of soil characteristics, especially in the coal mining areas. Therefore, the objective of this study is to reveal the scale-location dependent variation of two-dimensional patterns for soil properties and to develop a soil organic matter (SOM) prediction based on those scale-location effects in the coal mining area. To this end, SOM, porosity, and silt were measured in the top (0-20 cm), middle (20-40 cm), and bottom (40-60 cm) layers of soil in Changhe watershed, China. The scale-specific spatial patterns of soil properties were extracted using two-dimensional empirical mode decomposition (2DEMD), and SOM was predicted using stepwise multiple linear regression (SMLR) and 2DEMD GS. It was found that the spatial distributions of SOM residue (>24 km) at middle layer and porosity residue at top and bottom layers could reveal the differentiation between coal mining and non-coal mining areas. The scale-effects on the relationships between SOM and its covariates was stronger than that of land uses, and the relationships of SOM with the covariates in different land use types were distinctive at top layer, and were similar at bottom layer. 2DEMD GS performed better than SMLR on SOM prediction for its involvement of scale and location effects. Therefore, the relationship between SOM and influencing factors is a function of scale, location, and land uses activities, and the scale-and location-dependent method should be considered for SOM prediction. K E Y W O R D S bidimensional intrinsic mode function (BIMF), coal mining area, land use type, soil organic matter (SOM), two-dimensional empirical mode decomposition (2DEMD) 1 | INTRODUCTION Soil is one of the most valuable nonrenewable resources (Lal, 2015) and serves as an nexus for the atmosphere, hydrosphere, biosphere, and lithosphere (Alcañiz, Outeiro, Francos, & Úbeda, 2018). Previous studies indicated that minesoils undergo rapid changes in chemical, physical, and biological properties as a result of mining activities and pedogenic weathering processes (Shrestha & Lal, 2007; Skousen, 1995), and their spatial variation differed greatly compared with those in the non

Water-induced erosion of iron tailings is a serious problem affecting ecological restoration, but, little is known about how the occurrence of erosion on tailings slopes and types of reclaimed substrates that are beneficial to reducing slope erosion. This study measured the slope erosion characteristics of six reclaimed substrates including loose tailings (Lt), crusty tailings (ct), tailings incorporating mushroom residues (tM), tailings incorporating soil (tS), tailings incorporating soil and mushroom residues (TSM) and soil (S) in experimental soil flumes under three simulated intermittent rainfall events, with intensity of 60, 90 and 120 mm h −1 for the first, second and third event, respectively. Significant differences (p < 0.05) were found in erosion characteristics among the six reclaimed substrates. TM had the lowest sediment yield but the highest runoff volume without obvious rills. LT, CT and TS had the highest sediment yield rates and severe slope erosion morphology. With the increased number of rainfall events, the runoff rates of the six substrates all increased, but only the sediment yield rates of LT, CT and TS increased, the sediment yield rates of other substrates increased first and then decreased. therefore, adding agricultural organic wastes such as mushroom residues to tailings and reducing soil addition may be an effective way to reduce erosion and promote ecological restoration in soilless tailings areas. Mining is essential for maintaining society's current life style and economic development. However, mining also impacts the environment 1-3. One of the most significant environmental impacts from mining is soil erosion, which has received increasing attention 3-8. Waste dumps generated from mining usually consist of unconsolidated waste rock, parent material, tailings or their mixtures 9,10. The waste dumps are piled on the land surface in the form of overburden dump. They usually become the material source for debris flow, river sediment, and hyper-concentrated sediment flow of artificially accelerated erosion 11,12 as well as the source of wind erosion 13,14. The erosion forms and characteristics of mining waste dumps are different from those occuring naturally 5. Accumulated overburden dump material is prone to gravity erosion 12 , causing abrupt increases or decreases in sediment yield 15. Erosion in coal waste slag has shown extreme sediment fluctuation at the beginning of runoff, accompanied by local debris flow 16. Martín Duque et al. 1 observed that tailing deposits underwent severe erosion after long-term abandonment, and as a result, caused a mosaic-like erosion and sedimentary landforms with gully formations upon and piping within deposits. Therefore, at a larger scale, it is important to reconstruct stable post-mining landforms and control erosion, which have been claimed in mine reclamation regulations in different countries 17,18. Most of the traditional landform reconstruction is in the form of terraces and contoured banks 3. Recent studies indicated that geomorphic-based mi...

1) Background: Coal mining operations caused severe land subsidence and altered the distributions of soil nutrients that influenced by multiple environmental factors at different scales. However, the prediction performances for soil nutrients based on their scale-specific relationships with influencing factors remains undefined in the coal mining area. The objective of this study was to establish prediction models of soil nutrients based on their scale-specific relationships with influencing factors in a coal mining area. (2) Methods: Soil samples were collected based on a 1 × 1 km regular grid, and contents of soil organic matter, soil available nitrogen, soil available phosphorus, and soil available potassium were measured. The scale components of soil nutrients and the influencing factors collected from remote sensing and topographic factors were decomposed by two-dimensional empirical mode decomposition (2D-EMD), and the predictions for soil nutrients were established using the methods of multiple linear stepwise regression or partial least squares regression based on original samples (MLSR Ori or PLSR Ori ), partial least squares regression based on bi-dimensional intrinsic mode function (PLSR BIMF ), and the combined method of 2D-EMD, PLSR, and MLSR (2D-EMD PM ).(3) Results: The correlation types and correlation coefficients between soil nutrients and influencing factors were scale-dependent. The variances of soil nutrients at smaller scale were stochastic and non-significantly correlated with influencing factors, while their variances at the larger scales were stable. The prediction performances in the coal mining area were better than those in the non-coal mining area, and 2D-EMD PM had the most stable performance. (4) Conclusions: The scale-dependent predictions can be used for soil nutrients in the coal mining areas.