Topographic and Edaphic Influences on the Spatiotemporal Soil Water Content Patterns in Underground Mining Regions

Abstract

Understanding the dynamics of soil water content (SWC) is essential for effective land management, particularly in regions affected by underground mining. This study investigates the spatial and temporal patterns of SWC and its interaction with topographic and edaphic factors in coal mining and non-coal mining areas of the Chenghe watershed, located in the southeast of the Chinese Loess Plateau, which is divided by a river. Our findings revealed that the capacity to retain moisture in the top layer of coal mining areas is significantly higher (25.21%) compared to non-coal mining areas, although deeper layers exhibit lower SWC, indicating altered moisture dynamics due to underground mining disturbances. Coal mining areas show greater spatial and temporal variability in SWC, suggesting increased sensitivity to moisture fluctuations, which complicates water management practices. Additionally, underground mining activities introduce more intense effects on the relationship between SWC and topographic factors (i.e., GCVR across soil profile of 0–60 cm; slope at depth of 50 cm) or edaphic factors (i.e., soil organic matter and available potassium at depth of 30 cm; pH at depth of 50 cm) compared to non-coal mining areas. This variability is evident in the temporal shifts from positive to negative correlations, particularly in coal mining areas, reflecting modifications in both soil physical and chemical properties resulting from mining activities. In contrast, non-coal mining areas maintain a more stable moisture regime, likely due to preserved natural soil structures and processes. These contrasting findings emphasize the necessity for tailored management strategies in coal mining regions to address the unique challenges posed by altered soil characteristics and water dynamics.

1. Introduction

Soil water content (SWC) refers to the amount of water present in soil, typically expressed as a percentage of the soil's total weight or volume. SWC plays a critical role in maintaining ecosystem health, supporting agricultural productivity, and regulating hydrological processes [1,2], and it is also closely linked to sustainable development goals [3]. Understanding the factors that influence SWC is essential, especially in regions impacted by land-use changes, such as coal mining. Mining activities significantly alter the physical, chemical, and biological properties of soils, leading to changes in water retention and nutrient availability [4].

In coal mining areas, the extensive disturbance on the soil layer may result in increased variability in SWC, as well as alterations in soil structure and composition [5]. The disturbance in coal mining areas can involve direct soil removal, such as surface cuts or excavation, as well as the creation of underground mining shafts, both of which disrupt the natural soil structure and can alter broader groundwater circulation patterns. The effects of these changes may not be uniform across different soil depths, which can complicate our understanding of moisture dynamics in such environments. In contrast, non-coal mining areas often maintain more stable soil characteristics, providing a valuable reference for assessing the impacts of mining.

Previous studies have demonstrated that topographic factors, such as altitude and slope, significantly influence SWC [6,7,8]. Meanwhile, rainfall significantly influences the spatiotemporal variation in SWC by affecting soil moisture dynamics, infiltration rates, and water retention properties. Increased rainfall can lead to higher soil moisture levels, but the impact varies based on soil properties and topography. For instance, areas with high permeability may experience rapid drainage, while finer-textured soils retain moisture longer. Research has shown that rainfall patterns affect SWC distribution differently across landscapes, influencing agricultural productivity and ecosystem health [9,10]. However, the interactions between these factors and soil properties in coal mining regions remain poorly understood. The relationship between soil chemical properties—such as pH, organic matter, and nutrient content—and SWC is particularly important, as these properties can dictate soil’s capacity to retain moisture [11].

This study aims to compare SWC and its relationships with environmental factors in coal mining and non-coal mining areas. By examining both topographic and edaphic factors, we intend to elucidate the impact of mining activities on SWC dynamics and to pinpoint key differences between these environments. To ensure a valid comparison, we selected study sites based on criteria, focusing on areas with similar topographic factors, soil types, and climatic conditions. The coal mining areas were chosen for their active mining operations, while non-coal mining areas were selected from regions with comparable environmental factors but no mining influence. Specifically, we hypothesize that (1) SWC and edaphic indicators will undergo substantial changes as a result of coal mining activities; (2) the relationships between SWC and environmental factors will be significantly altered by mining activities; and (3) rainfall will differentially influence these relationships in coal mining versus non-coal mining areas. This study's findings will contribute to a comprehensive understanding of soil–water interactions in disturbed landscapes, providing crucial insights for developing land management strategies that address the adverse effects of mining and promote sustainable soil and water resource management in affected ecosystems.

2. Materials and Methods

2.1. Study Area

The study area is the Chenghe watershed, located in southeastern Shanxi Province, China (Figure 1a). The watershed is located between latitudes 35°30′14″ N and 35°38′04″ N and longitudes 112°27′39″ E and 112°46′13″ E, covering an area of approximately 113 km², with elevations ranging from 730 to 1185 m (Figure 1b). The region is bordered by mountains to the east and west, with a landscape that consists of 60.8% mountainous areas, 30.9% hills, and 8.3% plains. Elevations range from 730 m to 1185 m. The climate is a semi-humid continental monsoon, with an average annual temperature of 11 °C, annual rainfall of 533.3 mm, and 188 frost-free days.

The Chang River flows through the area from northeast to southwest, dividing the study region into eastern and western sections. Coal mines are primarily concentrated in the western part, which has been heavily impacted by mining activities. Much of the land has been damaged, though some areas have been reclaimed.

The land use of the study area, including the damaged region, is illustrated in Figure 1c. The dominant land use is dryland, covering 52.8% of the area. Irrigated lands (4.6%) are primarily located along both sides of the Chang River, while forested areas (7.9%) are mainly distributed at higher elevations along the eastern and western borders. The geological formations in this region are composed of 10 types. One example is the Lishi Formation (Figure 1d), which is characterized by loess deposit in the middle and upper sections, along with grayish-yellow to brownish-yellow sub-sand and sub-clay layers. These are interbedded with ancient soil layers that range from brownish red to light red. The Taiyuan Formation is defined by cyclical layers consisting of shale interbedded with sandstone, coal, and limestone, with lake deposits (iron–aluminum ore) found at the base. Additionally, the Mid-Subformation of the Majiagou Formation is another dominant geological type, represented by the 5th and 6th sections of the Majiaogou Formation. The dominant soil type is calcaric cambisols, and the major crops grown in both dryland and irrigated areas include maize, potatoes, and wheat.

2.2. Coal Mining Regions and the Damaged Areas

There are 12 coal mining enterprises belonging to different owners, as shown in Figure 1a. Mining activities, based on longwall mining (Figure 1c), were carried out within the boundaries of each respective region. As a result, different levels of damage were caused, as illustrated in Figure 2b. The damaged areas were mostly concentrated on the left side of the Chang River, with the distribution of coal seams shown in Figure 2d.

2.3. Data Acquisition

A total of 106 sampling points were systematically established using a 1 × 1 km regular grid (Figure 1b), and soil samples were collected from 15 July to 31 July 2015. At each point, three soil samples were taken within a 5 m radius using a soil auger and then combined to form a composite sample from the topsoil layer (0–20 cm). The samples were air-dried, gently crushed, and sieved through a 2 mm mesh for analysis of edaphic chemical and physical indicators. Soil organic matter (SOM) was measured using the dichromate oxidation method, while soil available nitrogen (SAN) was determined by isotope analysis. Soil available phosphorus (SAP) was extracted using alkaline sodium bicarbonate in a 20:1 ratio, and soil available potassium (SAK) was extracted using the ammonium acetate extraction method and analyzed with a flame photometer.

Sand, silt, and clay content were determined by the pipette method in the lab of agricultural resources and environment. Undisturbed soil samples were collected using a metallic core cylinder with a volume of 100 cm³ (5 cm in height and 5 cm in diameter) from the 0–20 cm depth. Soil bulk density (SBD) was measured by the oven-dry method, and soil porosity (SP) was calculated as follows:

$$\mathrm{SP} \left(\%\right)=\left(1-{\displaystyle \frac{{\rho }_b}{{\rho }_d}}\right)\times 100$$

(1)

where ${\rho }_b$ is the SBD and ${\rho }_d$ is particle density of 2.65 g/cm³.

The gravimetric SWC was determined by weighing samples before and after oven-drying, expressed as a percentage of SWC in dry soil weight. Volumetric SWC at depths of 10, 30, and 50 cm was obtained from a data center (https://data.tpdc.ac.cn/home, (accessed on 5 January 2025)) and validated using the method of Universal Triangle [12]. The digital elevation model (DEM) data with 30 m resolution was used to extract elevation information. Slope and aspect were calculated, and the cosine of the azimuth angle of aspect was used to determine the aspect factor. Additionally, secondary topographic factors related to hydrological processes, such as convergence index (CGI), general curvature (GCVR), topographic wetness index (TWI), and valley depth (VAD), were calculated. The main null hypothesis for these factors is that topographic features have no significant effect on the spatial distribution of SWC or hydrological behavior. By analyzing these topographic factors, we aim to investigate how variations in terrain morphology may contribute to the differences in SWC in the coal and non-coal mining areas, thus addressing the research questions regarding the influence of topography on SWC dynamics.

2.4. Data Analysis

The traditional statistics for SWC in both coal mining and non-coal mining areas were calculated based on the spatial distribution and temporal trend. Additionally, the statistics for topographic factors and edaphic indicators, which relate to soil properties, were calculated separately for coal mining and non-coal mining areas. The correlation coefficients between SWC and edaphic or topographic factors were also determined for both areas. Furthermore, the effects of rainfall on the relationships between SWC and environmental factors were assessed, with the distinctive results between coal mining and non-coal mining areas highlighted. The parameter of percent area of significant coherence (PASC) was used to evaluate the strength of these correlations. Lastly, the common factors showing significant differences between the two areas across different soil layers were identified and the relationships between the correlation coefficients for the two areas were calculated.

3. Results

3.1. SWC and Environmental Factors in Coal and Non-Coal Mining Areas

As shown in

Table 1. Descriptive statistics of SWC (m³ m⁻³) under different depths in coal mining and non-coal mining areas.

Areas	Spatial Variables	Temporal Variables	10 cm	30 cm	50 cm
Coal mining area	Mean	Mean	25.21	22.78	22.69
		SDt	3.50	3.06	3.18
		CVt	13.89	13.43	14.01
	SDs	Mean	5.72	9.25	9.45
		SDt	2.73	6.45	8.37
		CVt	47.82	69.76	88.58
	CVs	Mean	23.31	39.88	41.19
		SDt	11.71	26.29	32.45
		CVt	50.23	65.92	78.79
Non-coal mining area	Mean	Mean	25.09	22.96	22.60
		SDt	3.47	3.39	2.97
		CVt	13.82	14.75	13.16
	SDs	Meant	4.32	7.86	8.30
		SDt	2.60	6.53	7.31
		CVt	60.13	83.07	88.03
	CVs	Mean	17.51	33.90	35.98
		SDt	10.62	26.87	27.92
		CVt	60.64	79.27	77.60

The spatial variables of Mean, SD_s, and CV_s refer to the mean, standard deviation, and coefficient of variation in the spatial SWC; the temporal variables of Mean, SD_t, and CV_t refer to the mean, standard deviation, and coefficient of variation in the temporal SWC.

, the mean SWC was higher in the top layer of the coal mining area (25.21%) compared to the non-coal mining area, and it also exhibited greater temporal variation. In contrast, the SWC in the second layer was lower in the coal mining area than in the non-coal mining area, with a temporal variation of 13.43%. However, the temporal variation at the deeper layer was more pronounced in the coal mining area. Overall, both the top and deep layers in the coal mining area showed greater temporal variations in SWC compared to the non-coal mining area.

The spatial variations in SWC in the coal mining area were 23.31%, 39.88%, and 41.19% at depths of 10 cm, 30 cm, and 50 cm, respectively, showing a gradual increase from the top to the deeper layers. Similarly, in the non-coal mining area, the spatial variations were 17.51%, 33.90%, and 35.98% for the same depths, also indicating an increasing trend with depth. Additionally, at each corresponding layer, the spatial variation in SWC was greater in the coal mining area compared to the non-coal mining area.

The spatiotemporal variations in SWC in the coal mining area were 50.23%, 65.92%, and 78.79% for the three layers, respectively, while, in the non-coal mining area, they were 60.64%, 79.27%, and 77.60%. The variations in the coal mining area were lower than those in the non-coal mining area at the 10 cm and 30 cm depths. This indicated that the temporal variation in SWC was greater in the non-coal mining area compared to the coal mining area at these layers. However, at the 50 cm depth, the spatiotemporal variations were similar in both the coal mining and non-coal mining areas.

Both areas have comparable slope values, with a mean slope of 0.17 in the coal mining area and 0.16 in the non-coal mining area, indicating that the terrain in both areas is generally flat to gently sloping (

Table 2. Descriptive statistics of edaphic and topographic properties in coal mining and non-coal mining areas and p-value from ANOVA analysis for assessing the statistical differences between the indicators of coal and non-coal mining areas.

Areas	Environmental Factors		Min.	Mean	Max.	SD	CV	p
Coal mining area	Topographic indicators	Altitude (m)	730.00	849.02	1035.00	68.98	8.13	0.43
		Aspect (-)	−1.00	−0.11	1.00	0.70	−614.21	0.14
		Slope (°)	0.00	0.17	0.45	0.12	71.78	0.73
		TWI (-)	3.97	7.68	20.83	4.05	52.70	0.45
		CGI (-)	−29.11	−1.03	13.74	8.38	−810.66	0.21
		GCVR (-)	−0.04	0.00	0.04	0.01	4671.26	0.66
		VAD (-)	5.62	63.19	121.62	31.12	49.25	0.41
	Soil physical indicators	Sand (-)	0.09	0.23	0.64	0.09	40.43	0.03 *
		Silt (-)	0.22	0.48	0.66	0.10	21.55	0.17
		Clay (-)	0.14	0.29	0.48	0.08	27.02	0.69
		SBD (g/cm3)	1.03	1.24	1.53	0.11	8.82	0.05 *
		SP (%)	42.08	53.38	60.98	4.11	7.70	0.05 *
	Soil chemical indicators	pH (-)	6.49	8.02	8.45	0.30	3.73	0.20
		SOM (g/kg)	1.10	17.82	36.42	7.95	44.64	0.42
		SAN (mg/kg)	7.42	32.21	57.48	12.01	37.29	0.03 *
		SAP (mg/kg)	0.95	8.15	23.18	4.29	52.61	0.16
		SAK (mg/kg)	60.30	151.94	311.55	40.87	26.90	0.06
Non-coal mining area	Topographic indicators	Altitude (m)	743.00	858.64	977.00	53.21	6.20	—
		Aspect (-)	−1.00	0.09	1.00	0.72	800.65	—
		Slope (°)	0.00	0.16	0.53	0.12	77.65	—
		TWI (-)	4.73	7.15	15.10	2.77	38.79	—
		CGI (-)	−22.91	1.03	28.23	8.40	816.98	—
		GCVR (-)	−0.02	0.00	0.02	0.01	−1202.27	—
		VAD (-)	0.00	44.20	98.16	25.35	57.35	—
	Soil physical indicators	Sand (-)	0.09	0.19	0.67	0.10	52.84	—
		Silt (-)	0.18	0.53	0.70	0.11	20.02	—
		Clay (-)	0.13	0.28	0.56	0.08	29.57	—
		SBD (g/cm3)	0.99	1.18	1.36	0.09	7.30	—
		SP (%)	48.56	55.46	62.62	3.25	5.86	—
	Soil chemical indicators	pH (-)	7.03	7.94	8.51	0.27	3.43	—
		SOM (g/kg)	4.72	18.99	40.17	6.38	33.62	—
		SAN (mg/kg)	5.56	39.05	66.75	12.30	31.51	—
		SAP (mg/kg)	1.52	9.71	32.30	7.05	72.62	—
		SAK (mg/kg)	70.35	166.86	244.55	36.85	22.08	—

* represents significant difference between coal mining and non-coal mining regions at significant level of p < 0.05.

). The topographic wetness index (TWI) is also quite similar between the two regions, with mean values of 7.97 in coal mining and 7.15 in non-coal mining areas, suggesting that the potential for soil moisture accumulation is nearly the same. Additionally, the standard deviations for slope and altitude are fairly close, demonstrating that the variability in these topographic features is consistent across both areas. These shared characteristics indicate that, despite differences in land use, both coal mining and non-coal mining areas maintain similar foundational topographic structures and soil properties, with the exception of a few indicators (i.e., SBD).

Soil chemical properties between coal mining and non-coal mining areas show notable differences. In the coal mining area, soil pH is slightly higher (mean = 8.02) compared to the non-coal mining area (mean = 7.94), indicating slightly more alkaline conditions. SOM content is marginally lower in coal mining areas (mean = 17.82) compared to non-coal areas (mean = 18.99). The coal mining area also has a lower mean concentration of soil available nutrients, with SAN averaging at 32.21 mg/kg, SAP at 8.15 mg/kg, and SAK at 151.94 mg/kg, suggesting lower nutrient enrichment, possibly due to mining activities or runoff. In contrast, the non-coal mining area has significantly lower soil nutrients, which may indicate differences in mineral composition or soil fertility. These variations in soil chemical properties suggest that coal mining activities influence soil nutrient availability and composition, creating distinct chemical environments compared to non-coal mining regions.

Soil physical properties between coal mining and non-coal mining areas differ in several aspects. In the coal mining areas, the sand content is marginally higher (mean = 0.23) than in non-coal mining areas (mean = 0.19), indicating a looser soil texture in coal regions. Conversely, there is a slightly lower silt content (mean = 0.48) compared to non-coal mining areas (mean = 0.53), which results in a better soil structure in non-coal mining areas. The clay content is almost similar in both areas, but the SBD is slightly greater in coal mining areas (mean = 1.24) compared to non-coal mining areas (mean = 1.18), suggesting more compacted soils in coal mining regions. Moreover, SP is higher in non-coal mining areas (mean = 55.46) compared to coal mining areas (mean = 53.38), indicating better aeration and water infiltration in non-coal mining soils. These differences suggest that coal mining activities lead to more compacted soils with coarser textures, while non-coal mining areas maintain looser and more porous soil structures.

3.2. Relationships Between SWC and Environmental Factors in Coal and Non-Coal Mining Areas

The descriptive statistics in

Table 3. Descriptive statistics of correlation coefficients between SWC of topsoil and topographic or edaphic factors.

Areas	Environmental Factors		Min.	Mean	Max.	SD	Mean (-)	Mean (+)
Coal mining area (n = 59)	Topographic indicators	Altitude	−0.60 **	0.30 *	0.82 **	0.33	−0.20	0.45 **
		Aspect	−0.20	0.04	0.23	0.08	−0.06	0.08
		Slope	−0.33 **	0.16	0.50 **	0.19	−0.11	0.25 *
		TWI	−0.29	−0.05	0.22	0.11	−0.11	0.08
		CGI	−0.32 *	−0.03	0.30 *	0.10	−0.08	0.09
		GCVR	−0.33 *	−0.03	0.33 *	0.12	−0.10	0.12
		VAD	−0.56 **	−0.12	0.28 *	0.15	−0.18	0.08
	Soil physical indicators	Sand	−0.22	0.04	0.32 *	0.10	−0.06	0.10
		Silt	−0.32 *	−0.02	0.41 **	0.15	−0.13	0.12
		Clay	−0.44 **	−0.02	0.28 *	0.15	−0.14	0.12
		SBD	−0.16	0.11	0.37 **	0.11	−0.06	0.15
		SP	−0.37 **	−0.11	0.16	0.11	−0.15	0.06
	Soil chemical indicators	pH	−0.30 *	0.00	0.40 **	0.16	−0.13	0.13
		SOM	−0.53 **	0.04	0.60 **	0.29	−0.24	0.27 *
		SAN	−0.52 **	0.01	0.54 **	0.24	−0.20	0.20
		SAP	−0.42 **	−0.05	0.43 **	0.22	−0.20	0.20
		SAK	−0.37 **	−0.14	0.38 **	0.13	−0.18	0.13
Non-coal mining area (n = 47)	Topographic indicators	Altitude	−0.52 **	0.19	0.77 **	0.26	−0.16	0.30 *
		Aspect	−0.19	−0.01	0.20	0.08	−0.07	0.07
		Slope	−0.28 *	0.12	0.35 **	0.13	−0.08	0.17
		TWI	−0.25	−0.05	0.28 *	0.09	−0.09	0.06
		CGI	−0.23	−0.01	0.24	0.10	−0.08	0.08
		GCVR	−0.24	0.02	0.29 *	0.09	−0.05	0.08
		VAD	−0.54 **	−0.08	0.30 *	0.17	−0.17	0.10
	Soil physical indicators	Sand	−0.27	0.03	0.25	0.12	−0.09	0.11
		Silt	−0.29 *	0.02	0.40 *	0.15	−0.11	0.14
		Clay	−0.28 *	−0.06	0.21	0.10	−0.11	0.07
		SBD	−0.32 *	0.09	0.36 *	0.15	−0.11	0.16
		SP	−0.36 *	−0.09	0.32 *	0.15	−0.16	0.11
	Soil chemical indicators	pH	−0.27	−0.06	0.25	0.12	−0.13	0.09
		SOM	−0.45 **	−0.03	0.45 **	0.22	−0.19	0.20
		SAN	−0.42 **	−0.02	0.43 **	0.19	−0.18	0.14
		SAP	−0.30 *	−0.09	0.22	0.12	−0.15	0.08
		SAK	−0.33 *	−0.14	0.34 *	0.14	−0.19	0.10

Mean (-) represents the mean of negative correlation coefficients, and Mean (+) represents the mean of positive correlation coefficients. * indicates a signficant correlation at the p < 0.05 level, and ** indicates a significant correlation at the p < 0.01 level.

present the correlation coefficients between top-layer SWC and various topographic or edaphic factors in both coal mining and non-coal mining areas. In both coal mining and non-coal mining areas, altitude showed a notable positive mean correlation with SWC, but the strength of this relationship was stronger in the coal mining area (mean = 0.30) compared to the non-coal mining area (mean = 0.19). The maximum correlation value for altitude was also higher in coal mining areas (0.82 vs. 0.77), suggesting that altitude might have a more significant influence on SWC in coal mining areas. Regarding slope, the mean correlation was slightly higher in coal mining areas (0.16) than in non-coal mining areas (0.12), indicating that slope is more influential in coal mining regions. The TWI and VAD generally showed negative correlations with SWC in both areas, but the values were slightly more positive in the non-coal mining area, suggesting these factors might be less favorable for SWC retention in coal mining zones.

For soil texture components such as sand, silt, and clay, both areas showed generally weak correlations with SWC, although there were significant relations at some specific times. The coal mining area exhibited slightly higher mean positive correlations for sand (mean = 0.04) and clay (mean = −0.02) compared to non-coal mining areas (sand mean = 0.03; clay mean = −0.06), indicating a more pronounced but still modest influence in coal mining environments. SBD or SP had a higher positive correlation with SWC in the non-coal mining area (mean (+) = 0.16 or 0.11) compared to the coal mining area (mean (+) = 0.15 or 0.06), suggesting that they might play a more critical role in controlling SWC in the former. Overall, SBD and SP seem to have a slightly stronger impact on SWC in non-coal mining areas.

Soil chemical properties showed more variation between the two mining areas. For instance, soil pH had a similar mean negative correlation with SWC in coal mining areas and non-coal mining area (mean = −0.13) but a greater positive mean in coal mining areas (0.13 vs. 0.09), indicating that SWC in coal mining areas might be more influenced by pH variations. SOM and N also displayed more substantial positive correlations in coal mining areas compared to non-coal mining areas, suggesting that nutrient availability may be more closely linked to SWC in coal mining regions. For instance, SOM exhibited a mean negative correlation of −0.24 in coal mining areas and −0.19 in non-coal mining areas, a mean positive correlation of 0.27 in coal mining areas and 0.20 in non-coal mining areas, highlighting that SOM could be more strongly associated with SWC in coal mining regions.

Overall, the coal mining area showed generally stronger correlations between topographic and soil properties with SWC compared to the non-coal mining area, indicating that mining activities might have altered the soil and landscape characteristics in ways that intensify these relationships. In particular, altitude, slope, and soil chemical properties like SOM demonstrated more substantial influences on SWC in coal mining areas, while non-coal mining areas exhibited weaker but still present relationships. SBD demonstrated more substantial influences on SWC in non-coal mining areas. The differences in correlation values could reflect how mining activities impact soil structure, nutrient availability, and water retention properties in these two distinct environments.

3.3. Differences Between Coal and Non-Coal Mining Areas

A distinct band of strong correlation around the 1-year scale is observed for almost all indicators in both coal and non-coal mining regions at a depth of 10 cm (Figure 3). Specifically, for altitude, a strong and significant correlation is present at approximately the 1-year scale between 2005 and 2010 in the coal mining area (Figure 3a), while the non-coal mining area exhibits a similar but less pronounced pattern (Figure 3b). Regarding GCVR, both regions display significant correlations around the 1-year scale; however, in the coal mining area, these correlations are more widespread, longer-lasting, and extend significantly beyond the 4-year scale (Figure 3c,d). Additionally, both coal and non-coal mining areas demonstrate strong and significant correlations for SOM (Figure 3i,j), indicating that SOM plays a critical role in influencing the rainfall–SWC relationship over multi-year scales.

The PASC between SWC and altitude shows a positive correlation across all temporal scales in the coal mining area, with the highest coherence at the sub-1-year scale (20.52%) and a notable decrease at scales greater than 4 years (0.69%) (

Table 4. Percent area of significant coherence (PASC) between rainfall and coefficients between SWC and topographic or edaphic indicators at different temporal scales (< 1 year, 1–4 years, and > 4 years).

Depths	Areas	Factors	Scales				Type of Correlation
			<1 Year	1–4 Year	>4 Year	All
10 cm	Coal mining area	Altitude	20.52	14.84	0.69	16.39	Positive
		GCVR	17.17	16.95	55.74	25.45	1 year negative and >4 year positive
		VAD	13.93	12.67	38.75	19.05	1 year positive and >4 year negative
		Sand	15.47	4.10	0.00	9.48	Positive
		SOM	19.59	15.36	9.09	17.63	Around 1 year negative
		SAN	16.64	12.18	7.57	14.62	Negative
		SAP	15.41	10.69	9.09	13.68	Positive
		SAK	15.00	8.02	0.00	10.78	Negative
	Non-coal mining area	Altitude	20.85	15.31	0.00	16.62	Positive
		GCVR	16.95	12.80	0.00	13.65	Negative
		VAD	14.29	14.79	30.24	18.54	1 year positive and >4 year negative
		Sand	13.40	8.80	0.00	10.27	Positive
		SOM	23.20	16.32	0.00	18.21	Negative
		SAN	17.71	12.18	6.59	14.98	Negative
		SAP	14.18	4.61	10.47	10.91	Unstable
		SAK	18.21	11.04	12.64	15.88	Negative
30 cm	Coal mining area	Altitude	30.02	11.64	0.00	19.84	Positive
		Aspect	4.54	5.30	62.80	15.71	Negative
		CGI	21.78	11.89	4.67	16.59	Negative
		GCVR	26.19	18.04	2.03	20.78	Negative at around 1 year
		Silt	21.78	7.79	0.00	14.13	Mainly negative
		SOM	30.33	8.28	0.00	18.67	Mainly negative
		SAN	22.55	6.56	0.00	14.04	Mainly negative
		SAK	27.61	16.20	7.71	21.79	Mainly negative
	Non-coal mining area	Altitude	28.78	3.15	18.44	19.19	Positive
		Aspect	11.03	15.55	0.00	11.72	Positive
		CGI	21.91	5.11	26.84	17.99	<1 year negative and >4 year positive
		GCVR	8.85	1.54	55.31	15.08	Positive
		Silt	6.72	10.09	59.36	18.09	Positive
		SOM	13.17	15.97	0.00	12.98	Negative
		SAN	9.95	0.61	27.16	10.20	Negative
		SAK	6.08	10.13	22.46	11.12	Mainly positive
50 cm	Coal mining area	Altitude	20.54	18.33	9.05	19.28	1 year positive and >4 year negative
		Slope	10.56	20.37	76.60	27.20	Negative
		CGI	12.84	13.50	69.29	24.33	1 year negative and >4 year positive
		GCVR	13.94	16.60	0.00	13.62	Negative
		Clay	10.76	3.09	0.00	6.68	Mainly positive
		SBD	12.49	12.83	5.76	12.44	Mainly positive
		SOM	18.92	10.19	1.99	13.98	Negative
		pH	20.99	7.15	64.18	25.05	1 year positive and >4 year negative
	Non-coal mining area	Altitude	10.90	13.16	73.45	23.96	Negative
		Slope	8.43	5.58	0.00	6.48	Mainly positive
		CGI	9.14	14.76	71.13	23.28	Mainly positive
		GCVR	5.80	6.51	29.77	10.87	Mainly positive
		Clay	15.54	12.22	75.26	26.28	Negative
		SBD	6.00	9.15	52.92	16.19	Positive
		SOM	13.38	11.49	37.52	18.09	Mainly negative
		pH	7.08	9.83	0.00	7.47	Negative

). In contrast, the non-coal mining area also exhibits a positive correlation but with slightly higher overall PASC values (20.85% at sub-1-year scales and 0% at scales greater than 4 years). For GCVR, a key difference emerges: in the coal mining area, the correlation shifts from negative at the 1-year scale to positive at scales greater than 4 years, whereas the non-coal mining area maintains a consistently negative correlation across all scales. The coherence percentage for SOM also reveals contrasting trends between the two regions: in the coal mining area, there is a negative correlation around the 1-year scale, while the overall negative trend is stronger in the non-coal mining area. The factors that significantly impact SWC also differ between the regions. In the coal mining area, the most influential factors are GCVR, VAD, and SOM, likely due to the disturbances caused by mining activities that affect soil properties. In contrast, the non-coal mining area is more strongly influenced by altitude, VAD, and SOM, reflecting the natural variability of these factors in undisturbed environments. These distinctions suggest that the influence of edaphic factors on SWC is more complex and variable in coal mining areas due to the direct and indirect effects of mining activities, compared to the more stable conditions in non-coal mining areas.

There are more pronounced differences between coal mining and non-coal mining areas at a depth of 30 cm (Figure 4). Across nearly all environmental factors, coal mining areas exhibit more extensive and intense red regions, indicating stronger correlations between rainfall and SWC at various time scales. This is particularly evident in the subplots for CGI (e, f), GCVR (g, h), and SOM (k, l), where coal mining areas show larger regions of significant correlation. In contrast, non-coal mining areas generally display weaker correlations, with fewer or smaller red patches. For instance, the plots for aspect (c, d) and SAN (m, n) show less intense correlations in non-coal mining areas, suggesting that these factors have a less pronounced effect on SWC in non-mining regions.

At a depth of 30 cm, the coal mining area demonstrates significant coherence between SWC and altitude, with a strong positive correlation at time scales of less than 1 year (30.02%), which diminishes over time. Conversely, in the non-coal mining area, there is a positive correlation at less than 1 year (28.78%) but a reversal to a negative correlation at time scales greater than 4 years (18.44%). For GCVR, coal mining areas exhibit a predominantly negative trend around the 1-year scale, while the non-coal mining area shows a predominantly positive trend, especially at time scales greater than 4 years (55.31%). A notable difference is also observed in the aspect factor, which shows a negative correlation in coal mining areas and a positive correlation in non-coal mining areas, highlighting the contrasting influence of topographic orientation on SWC in these environments. These differences suggest that topographic features, such as aspect, may play a more consistent role in influencing SWC in non-coal mining areas.

At a depth of 50 cm, more pronounced differences between coal mining and non-coal mining areas are observed, particularly with respect to altitude (a, b) and slope (c, d) (Figure 5). The PASC between SWC and altitude at this depth reveals opposing trends: in coal mining areas, there is a “1-year positive and >4-year negative” trend, while, in non-coal mining areas, a strong negative correlation dominates at time scales greater than 4 years (73.45%). This contrast suggests that mining activities may be altering the long-term influence of altitude on soil moisture retention. The correlation with slope also differs notably between the regions. In coal mining areas, it is predominantly negative, especially at time scales greater than 4 years (76.60%), whereas, in non-coal mining areas, the correlation remains mostly positive. Other factors, such as CGI and pH, display similarly contrasting patterns, with coal mining areas showing more pronounced shifts between positive and negative correlations across different temporal scales. This suggests a greater variability in the response to environmental factors due to mining disturbances.

Overall, coal mining activities introduce more variability and often more intense effects on the relationship between SWC and topographic or edaphic factors compared to non-coal mining areas. This variability is evident in the temporal shifts from positive to negative correlations, particularly in coal mining areas, which may result from mining-induced changes in soil structure, composition, and water retention properties. In contrast, non-coal mining areas show more stable and consistent correlations, indicating less disturbance and a more predictable relationship between SWC and influencing factors

4. Discussion

4.1. SWC and Environmental Factors

The higher mean SWC observed in the top layer of the coal mining area (25.21%) suggests enhanced moisture retention, likely due to the disturbance of soil layers that can trap water [13]. Firstly, mining activities often cause significant disturbances to soil structure, leading to soil compaction, which reduces pore space and impairs the soil's ability to drain water. Specifically, the compacted layers can form barriers that slow down water percolation, allowing water to accumulate in the upper layers. Secondly, mining can modify the natural soil horizons by mixing them or creating artificial layers. For instance, deeper horizons might be brought to the surface or foreign soil may fill the cracks created by mining. These alterations could create a more favorable environment for water retention, especially in the top layers where the interaction between water and soil is most direct. However, the lower SWC at the second layer in coal mining areas, alongside greater temporal variation, indicates a complex interaction between soil structure and moisture dynamics [14,15]. The pronounced spatial variations in SWC, particularly in coal mining areas, highlight the impact of mining on soil heterogeneity, which may affect ecosystem functions and agricultural practices in the region [16,17].

Interestingly, despite similar slope values and TWI between the two areas, the distinct chemical compositions suggest that mining alters nutrient dynamics. The increased nutrient levels (SAN and SAP) in coal mining areas could be attributed to runoff from disturbed landscapes, which may enhance fertility but also pose risks of nutrient leaching into surrounding ecosystems [18]. Specifically, mining activities often result in significant disruption to the landscape, including the removal of vegetation, soil disturbance, and alteration in natural drainage patterns. These disturbances increase surface runoff, as soil's natural ability to absorb and retain water is reduced. Runoff can carry nutrients, such as nitrogen and phosphorus, from disturbed soils into the surrounding environment, leading to higher concentrations of SAN and SAP in topsoil. In contrast, the higher potassium levels in non-coal mining areas reflect a more balanced nutrient profile, potentially supporting sustainable soil health [19,20].

4.2. Relationships Between SWC and Environmental Factors

The correlation analysis underscores the stronger relationships between altitude, soil chemical properties, and SWC in coal mining areas. The positive correlation between altitude and SWC, particularly in coal mining regions, suggests that altitude may influence moisture retention differently due to the modified soil structure and chemistry resulting from mining [21]. Specifically, geological processes, such as the formation of mountainous terrain or hills, often result in different hydrological characteristics at higher altitudes. Mining in these areas could change the natural water flow patterns, influencing how water is retained in the soil. In higher-altitude coal mining regions, water may accumulate at the surface due to altered drainage patterns, creating pockets of water retention in disturbed soil layers. These changes in hydrology, combined with altered soil properties, contribute to the observed correlation between altitude and SWC. Conversely, the weaker correlations in non-coal mining areas imply a more stable environmental condition, where traditional relationships between topographic features and moisture content remain intact [22].

The more substantial correlations between SWC and soil chemical properties in coal mining areas—especially for nitrogen—highlight how mining can enhance certain nutrient interactions with water retention [23]. Mining disturbs the natural soil horizons, exposing previously deep soil layers and minerals that were not directly in contact with the surface. This disturbance can alter the chemical composition of soil, especially increasing the availability of certain nutrients like nitrogen, which are often trapped in deeper layers. The disruption of these layers allows for better nutrient mobilization and increased interactions between water and soil minerals. This dynamic poses both opportunities and challenges; while it may improve short-term fertility, the long-term implications for soil health and sustainability warrant attention [24]. Several potential solutions could be beneficial. Firstly, to mitigate the long-term negative effects of mining on soil health and sustainability, a geological approach could involve soil restoration and stabilization techniques, such as the use of organic amendments (e.g., compost or biochar) to restore SOM. This would not only help in improving nitrogen retention but also stabilize the soil structure to reduce nutrient leaching. Secondly, revegetation with native or adaptive plant species could help restore soil balance, as plant roots can act as conduits for nitrogen and other nutrients, reducing leaching and promoting nutrient cycling.

4.3. Differences Between Coal and Non-Coal Mining Areas

The differences in temporal coherence between SWC and altitude at various depths further illustrate the complex influence of mining activities on soil hydrology. In the coal mining area, the shift from negative to positive correlations indicates a dynamic response of SWC to environmental changes, likely driven by mining-induced disturbances [25]. Conversely, in non-coal mining areas, the more stable correlations reflect a less disturbed environment, where topographic and edaphic factors exert a consistent influence on SWC [26,27]. The negative trends observed in the correlations between SWC and aspects or slopes in the coal mining area suggest disruptions in natural drainage patterns and moisture distribution, indicating that mining activities might alter topographical control on water movement [13]. These shifts emphasize the need for adaptive management strategies that account for the variability introduced by mining [28].

Wavelet coherency reveals significant differences in the relationship between rainfall and SWC at depths of 30 and 50 cm in both coal mining and non-coal mining areas. This provides critical insights into how mining affects soil properties and hydrological processes. In coal mining areas, environmental factors such as altitude, slope, GCVR, CGI, clay, SBD, SOM, and pH exhibit stronger and more persistent correlations with SWC over longer periods and broader temporal scales compared to non-coal mining areas. This suggests that mining activities could cause substantial alterations to soil, possibly due to changes in soil structure, compaction, and vegetation loss, which amplify the influence of these environmental factors on SWC [29]. The increased and sustained correlations, particularly for factors like SOM and clay, underline their critical role in maintaining water retention in disturbed soils. As mining activities disrupt soil properties, SOM and soil texture become pivotal in controlling moisture dynamics. Furthermore, phase relationships in the coal mining area demonstrate more variability, indicating disrupted hydrological cycles where rainfall and SWC are often out of phase or exhibit lagged responses. This is in contrast to non-coal mining areas, where the correlations between SWC and environmental factors like altitude and slope are weaker and more sporadic, reflecting a more natural and stable hydrological interaction [21]. The comparative analysis highlights the substantial impact of coal mining on the soil's water retention capacity and its responsiveness to environmental factors. The long-term consequences of these alterations may include reduced ecosystem services and diminished hydrological functionality in coal mining regions. In non-coal mining areas, where correlations are more stable and predictable, environmental factors maintain a more natural relationship with SWC, suggesting less disturbance to soil and water processes.

Overall, coal mining activities significantly alter the relationships between SWC and key environmental factors, leading to more variable and intense interactions than those observed in non-coal mining areas. This variability underscores the importance of developing tailored land management practices to mitigate the adverse effects of mining while promoting the sustainability of soil and water resources. Further research is essential to understand the long-term consequences of these alterations on soil health and ecosystem functionality, particularly in mining-disturbed landscapes [30].

5. Conclusions

This study provides crucial insights into the spatial and temporal dynamics of soil water content (SWC) in coal mining and non-coal mining areas, revealing the significant effects of mining activities on soil health and moisture retention. Our findings demonstrate distinct differences in SWC levels and their relationships with environmental factors, underscoring the complexity of mining-induced disturbances.

In coal mining areas, SWC showed higher mean values in topsoil but exhibited greater temporal variability because mining activities often cause significant distances to soil structures and can modify the natural soil horizons by mixing them or creating artificial layers, compared to non-coal mining regions. At deeper layers, however, SWC was lower in coal mining areas, indicating that mining activities have altered moisture dynamics throughout the soil profile. The increased spatial and spatiotemporal variability in these regions suggests heightened sensitivity to moisture fluctuations, posing challenges for effective water management.

The relationships between SWC and environmental factors, such as topography and soil chemistry, were generally stronger in coal mining areas. Notably, altitude and nutrient availability (e.g., nitrogen and phosphorus) showed significant correlations with SWC, suggesting that mining has not only affected the physical structure of the soil but also its chemical properties, impacting its capacity to retain moisture.

In contrast, non-coal mining areas exhibited more stable SWC dynamics, likely due to the preservation of natural soil structures and processes. This stability highlights the importance of conserving non-mined landscapes, which serve as vital benchmarks for understanding and managing disturbed environments.