Land use/land cover change and its implication on soil erosion in an ecologically sensitive Himachal Himalayan watershed, Northern India

Soil erosion is a major environmental problem that affects land and w ater resources. It has many negative implications that lead to deforestation, poor agricultural practices, loss of soil fertility, and siltation that hinder socio-economic development. In view of this, the present study was conducted with the aim of estimating soil loss in relation to long-term land use/land cover change (LULC) in the Dehar watershed, Himachal Himalaya, North India. The study was carried out using Landsat and Sentinel imageries for the years 1999, 2010, and 2020. A GIS-based Revised Universal Soil Loss Equation (RUSLE) model was applied to assess the potential soil risk. The parameters used as input for computing the spatiotemporal changes of soil loss were rainfall erosivity, soil erodibility, topographic, crop management, and conservation support practice factors. The results showed a mean soil loss of 63.71, 60.99, and 66.71 t/ha/yr for the years 1999, 2010, and 2020, respectively. In the LULC class defined as Built-up Land, the mean soil loss decreased from 32.19 t/ha/yr in 1999 to 18.77 t/ha/yr in 2010, and in the year 2020 the mean soil loss slightly increased to 20.15 t/ha/yr. Moreover, the LULC class Barren Land registered a decrease in mean soil loss for the years 1999, 2010, and 2020 of 86.43, 74.60, and 73.19 t/ha/yr, respectively. Regarding the Agriculture Land class, the rate of mean soil loss slightly increased from 32.55 t/ha/yr in 1999 to 33.35 t/ha/yr in 2010, and the mean soil loss decreased to 25.43 t/ha/yr in the year 2020. Areas covered under Forest Land experienced an increase in mean soil loss from 65.30 t/ha/yr in 1999 to 65.87 in 2010 and 74.72 t/ha/yr in 2020. The study demonstrated that LULC changes apparently influenced the soil loss in the Dehar watershed. Therefore, urgent interventions are required with the involvement of scientists, policymakers, and the general public for conservation and management of soil resources.

Introduction

Land use/land cover change (LULC) change is one of the leading global environmental problems and poses an alarming threat to humankind. LULC change is a decisive factor in most of the initiatives carried out by the public, as can be evidenced by various development activities taken up in almost all parts of the globe (Steffen et al., 2001; Glaeser and Kahn, 2004; Lambin et al., 2010; Ayele et al., 2014; Haregeweyn et al., 2015). Land use change is a key factor that contributes to soil erosion and is of prime concern in environmental studies (Senanayake et al., 2020). Moreover, LULC changes are considered to affect the hydrological characteristics of a watershed and enhance land degradation problems like soil erosion and sedimentation if they are not addressed scientifically (Abdulkareem et al., 2019; Mohammadi et al., 2021). Hence, knowing LULC dynamics and their implications on soil erosion can help policymakers in decision-making processes (Waltner et al., 2020). Extensive soil erosion results in soil erosion hazards, which influence landscape processes including land productivity, hydrological activity, and eventual human wellbeing. Therefore, soil erosion assessment is important for understanding the dynamics of landscape processes.

Soil erosion plays a pivotal role in land degradation and is considered a serious environmental hazard (Eswaran et al., 2001; Panagos et al., 2017; Poesen, 2018; Steinmetz et al., 2018). In recent times, land degradation has become a major environmental concern in many regions of the world, particularly in developing countries where agriculture is a main occupation (Krishna Bahadur, 2009; Xu et al., 2013; Rawat et al., 2016; Samanta et al., 2016; Saha et al., 2018). Further, it has many negative impacts including the loss of soil fertility that hinders socio-economic development on a global scale (Kouli et al., 2009).

At a global level, nearly 85% of land degradation is mainly caused by soil erosion (Tang et al., 2015). Soil erosion has become a serious issue in almost all parts of the globe (Pimentel and Burgess, 2013; Prashanth et al., 2021, 2022). Moreover, LULC change is affecting the availability of natural resources mainly soil resources in most regions of the world (Abdulkareem et al., 2019; Mohammadi et al., 2021; Zhu et al., 2022; Moisa et al., 2023). Generally, soil erosion is a natural process but has become a significant environmental issue in the last century due to anthropogenic interventions (Alkharabsheh et al., 2013). The problem is expected to exacerbate in the 21^st century (Hurni et al., 2005). Out of the different types of soil erosion, the effects caused by water erosion are of more concern, as this disturbs soil texture, structure, and quality, as well as endangers other natural resources, such as land and water, which mankind depend upon for survival (Pimentel et al., 1993; Hrissanthou et al., 2010; Yang et al., 2013; Keno and Suryabhagavan, 2014; Srinivasan et al., 2019; Kolli et al., 2021). The immediate on-the-spot effect is a decrease in soil productivity, and ex-situ impacts include sediment deposition that triggers floods (Meshesha et al., 2012; Negash et al., 2021). Further, anthropogenic interference often intensifies the process of soil erosion on the steep slopes of highly elevated areas particularly in a mountainous ecosystem, because of various unsustainable activities such as deforestation, forest fires, intensive farming, and improper land management practices (Mandal and Sharda, 2013; Jayasekara et al., 2018).

Stocking (1993) depicted land degradation as a global environmental problem and found it to be a major environmental crisis in the developing world. Land degradation is one of the ecologically sensitive issues in India, and increasing populations, increasing urbanization, and over-exploitation of natural resources are the main drivers of land degradation. Moreover, Singh and Panda (2017) observed that out of the total geographical area of India, nearly 47.7% of the area suffers from land degradation. Further, the National Bureau of Soil Survey and Land Use Planning (NBSS and LUP, 2005) estimated that almost 146.8 Mha of land in India is degraded. Ravi et al. (2010) viewed soil erosion as one of the major processes of land degradation spreading in arid and semi-arid regions of India.

In India, soil erosion due to water is considered to be the most severe land degradation aspect that affects the topsoil and landscape of an area in general and Himalaya in particular (Mandal and Sharda, 2011; Bhattacharyya et al., 2015; Mahapatra et al., 2018). Himalaya is considered a highly land-degraded region due to various factors including soil erosion, which poses a major challenge to mountain ecosystems (Stoddart, 1969; Chalise et al., 2019). Most of the climatic regions of the world, including mountain areas such as Himalaya, are prone to soil erosion (e.g., Garcia-Ruiz et al., 2015). The main drivers of soil erosion in Himalaya are thought to be associated with both natural and anthropogenic activities (Hamilton, 1987; Jain et al., 2001; Prashanth et al., 2021, 2022). Thus, soil erosion assessment in highly ecologically sensitive Himalayan watersheds will be supportive to land degradation evaluation and to minimize the stress on sustainable agricultural practices (Singh and Singh, 2018).

Oliveira et al. (2019) observed that runoff from mountain regions toward regions of lower elevations increases the rate of erosion of an area. Further, regular land-use changes stimulate runoff and soil productivity loss, thus resulting in land degradation (Ang and Oeurng, 2018). Sharma (2008) assessed that out of the total degraded land in the Himalaya region, 79% is related to water erosion and is mostly confined to the river catchments. Significantly, lower Himalaya is more prone to soil erosion resulting in land degradation and making the land unproductive (Kaiser, 2004). About 54% of the state of Himachal Pradesh, Himachal Himalaya (North India) is prone to soil erosion, out of which, 98% is mainly because of water erosion (Kumar et al., 2014). The uneven complex dissected terrain, tectonic pressure, and diverse climatological influences supported by anthropogenic interventions have increased stress on natural resources, mainly land resources in lower Himalaya (Prashanth et al., 2021). Hence, under these circumstances, the estimation of soil loss with emphasis on land-degraded areas is quite necessary for sustainable planning and effective conservation of natural resources in Himalaya (Yadav and Sidhu, 2010).

Several models have been developed to predict soil erosion, including: empirical models such as the Universal Soil Loss Equation (USLE) developed by Wischmeier and Smith (1978); a revised model of the USLE known as the Revised Universal Soil Loss Equation (RUSLE) (Renard et al., 1997); and process-based models like the USDA-Water Erosion Prediction Project (WEPP) (Nearing et al., 1989), European Soil Erosion Model (EUROSEM) (Morgan et al., 1998), and Large Scale Catchment Model LASCAM (Viney and Sivapalan, 1999). Though many models are used for predicting soil erosion, the most widely used model for soil erosion estimation studies is the RUSLE (Shrestha, 1997; Angima et al., 2003; Prasannakumar et al., 2011, 2012; Mandal and Sharda, 2013; Wang et al., 2013; Pan and Wen, 2014; Erol et al., 2015; Uddin et al., 2016; Panditharathne et al., 2019). For assessing soil erosion, the combined application of geospatial technologies such as geographical information systems (GIS), remote sensing, and RUSLE techniques are quite economical, time-saving, and more precise than most other models (Wang et al., 2003; Prasannakumar et al., 2011; Galdino et al., 2016; Gelagay and Minale, 2016; Zerihun et al., 2018; Adongo et al., 2019; Kumar et al., 2020; Kumar and Hole, 2021; Sandeep et al., 2021). Integrating soil erosion models with GIS acts as a potential tool in land degradation assessment practices. RUSLE is one of the soil erosion models applied for calculating and predicting the spatial and temporal differences in soil erosion in a GIS environment (Islam et al., 2020). It is used to estimate long-term annual average soil loss in certain areas with specific ground slope characters (Boggs et al., 2001). It is found to be a simple and pertinent model with limited data requirements and is widely used all over the globe because of its suitability and easy computational approaches (Jha and Paudel, 2010). Further, it is mostly an applied model for hilly terrains (Ganasri and Ramesh, 2016; Dissanayake et al., 2019) to assess erosion risk with potential application capabilities (Boggs et al., 2001). The primary objectives of the present study are to: (1) evaluate the long-term LULC changes of a Himalayan watershed; (2) assess the soil erosion due to LULC changes in the watershed using remote sensing, GIS, and RUSLE techniques; and (3) understand the potential risks of soil erosion caused by changing land-use patterns. The study will help to suggest suitable measures required for the conservation, planning, and sustainable management of soil resources in the study area and watersheds of similar settings.

Materials and methods

Study area

The Dehar river is a tributary of the Beas River system. The Dehar watershed is located between the boundaries of the Kangra and Chamba districts of the state of Himachal Pradesh between latitudes 32°5′21′′ N to 32°23′47′′ N and longitudes 76°0′25′′’ E to 76°12′29′′ E. In the course of its journey, the Dehar river traverses several places, such as Trilokpur, Kotla, Kuther, Sihunta, Thulel, and Jawali, before joining the Pong Reservoir also known as Maharana Pratap Sagar (Figure 1). The Dehar watershed is covered by the Survey of India (SOI) top-sheet maps nos. 52D/3, 52D/4, and 52D/7 with 1:50000 scales. It covers an area of 450 km² and with a minimum and maximum elevation of 395 and 4080 m above mean sea level, respectively. Geographically, the area is located in Himachal Himalaya and consists of almost parallel hills disconnected by longitudinal valleys. The area experiences a varied climate, sub-tropical at lower elevations and temperate at higher altitudes. The area experiences a maximum temperature of 38°C and a minimum temperature of 0°C with an average temperature of 22°C. It has a normal annual rainfall of 2500 mm. The dominant soil varieties that cover the watershed are sandy-skeletal, coarse-loamy, fine-loamy and mesic-loamy soils (Sidhu et al., 1997). Geologically, the watershed is covered by rocks of the Lower Himalaya in the North and Siwaliks (Outer Himalaya) in the South (Srikantia and Bhargava, 1998). From north to south, it is covered by various geological formations, such as Dhauladhar Granites, Mandi Darla Volcanics, Shali Formation, Dharamshala Formation, Siwalik Group, and fluvio-glacial deposits. The Lesser Himalayan zone is represented by major rock types: Precambrian gneissic and granitic rocks (the Dhauladhar Group); slate, schist, phyllite, and limestone (Salooni Formation); older rocks include slate, quartzite, schist, basic lava flows, marl, salt, and dolomites (Sundernagar, Jutogh, and Shali Formations). The lesser Himalaya is represented by the Subathu Group which includes rocks of Cenozoic age (green shales and fossiliferous limestones), and the Siwalik Group, with sedimentary rocks consisting of shale, conglomerate, clay, and sandstone (Srikantia and Bhargava, 1998). The area is tectonically active and traversed by several northeast-southwest trending major tectonic faults/lineaments, the Chail Thrust, the MBT, the Drini Thrust, and the Jwalamukhi Thrust, which separate different geological units by bordering them (Kumar and Nanda, 1989; Srikantia and Bhargava, 1998; Kumar and Mahajan, 2001).

FIGURE 1

Figure 1. Location maps. (A) Map of India showing the location of the Himachal Pradesh state (shaded by yellow color). (B) Map of the Himachal Pradesh state showing area (shaded by yellow color) covered by the Dehar watershed. (C) Enlarged map showing the Dehar watershed.

Data sets used

Shuttle Radar Topographic Mission (SRTM) digital elevation model data of 30 m resolution was used in the extraction of basin boundaries and for determining topographic factors (LS factors) for RUSLE modeling using ArcGIS 10.8 software (Table 1). Time series data of LANDSAT-7, LANDSAT-5, and SENTINAL-2 for the years 1999, 2010, and 2020, respectively, were used for preparing LULC maps and determining crop management (C) and conservation practice (P) factors used in RUSLE modeling (Table 1). The soil maps required to determine soil classes and soil erodibility (K factor) for the study were obtained from the database of NBSS and LUP (2005). Rainfall data procured from the India Meteorological Department (IMD) of 11 nearby rain gauge stations for the duration of 1999–2020 was used in determining the rainfall erosivity (R) factor (Table 1). All factors used in RUSLE modeling for determining soil loss were derived independently.

TABLE 1

Table 1. Different data sets used in the study.

RUSLE model

To estimate soil erosion, various parameters such as precipitation, soil categories, terrain characters, and land cover changes are required (Kouli et al., 2009; Vijith et al., 2018). The annual average soil loss per unit area per year was estimated as per the following equation (Eq. 1) given by Renard et al. (1997) using the required parameters generated through DEM, rainfall data, soil types, and satellite images (Figure 2):

FIGURE 2

Figure 2. Flow chart demonstrating the methodology adopted for RUSLE model.

$$\mathrm{A}=\mathrm{R}\times \mathrm{K}\times \mathrm{LS}\times \mathrm{C}\times \mathrm{P}(1)$$

Where A denotes the average annual soil loss (t/ha/year), R represents the rainfall erosivity factor (MJ mm/ha/hr/year), K refers to the soil erodibility factor (Mg h/MJ/mm), LS indicates the topological factor expressed as slope length and steepness factor, C is crop management, and P signifies the conservation support practice factor (Figure 2). Of all these factors, LS, C, and P factors are dimensionless.

Land use/land cover dynamics

To determine the LULC changes in the study area, the satellite imageries of LANDSAT-7, LANDSAT-5, and SENTINAL-2 for the years 1999, 2010, and 2020, respectively, were used. Image enhancement techniques were adopted to categorize the LULC features. All the downloaded images were in TIFF format and therefore exported to .img format via application of a layer stack function using ERDAS Imagine 2020. These stacked images were georeferenced into the same map projection of the World Geodetic System 1984 Zone 43N (WGS 1984-43N). All images were subset (sub-mapped) with reference to the boundaries of the watershed for marking the whole study area. Red, Green, Blue (RGB) band composition was used for all the images to interpret and classify the surface features with more precision (Teng et al., 2016). False color composite (FCC) of satellite images were generated for the years 1999, 2010, and 2020 by displaying different band combinations such as band 4 (NIR), band 3 (red), and band 2 (green). Various LULC classes were identified in the study area, specifically, Built-Up, Agricultural Land, Forest, Barren, and Water Bodies. With the aim to check the level of accuracy of the generated LULC layers, nearly 200 randomly collected sample points were selected from each of the created LULC layers. These layers were again opened and overlaid in Google Earth Pro software to validate the generated LULC layers for accuracy. Further, a confusion matrix was computed with the validated data.

Rainfall erosivity factor (R factor)

R factor is the measure of the intensity of rainfall in a certain place based on the quantum of soil erosion (Koirala et al., 2019). Incessant rainfall data is required to compute the R factor, as it is valuable in knowing the influence of rainfall intensity on soil erosion triggered by water erosion (Wischmeier and Smith, 1978). From the literature, it was noticed that a multitude of equations have been developed to quantify the R factor (Kouli et al., 2009; Khosrokhani and Pradhan, 2014). Nevertheless, the formula (Eq. 2) given by Singh et al. (1981) was used for discerning the rainfall erosivity factor. In addition, the equation illustrates the relationship between the rainfall erosivity factor and the average annual precipitation, which has been found to be more reliable for soil erosion studies in India (Saha et al., 2018; Sandeep et al., 2021; Sangeetha and Ambujam, 2021). Monthly rainfall data was procured from the IMD for the 11 rain gauge stations located in and around the Dehar watershed for the duration of 1999 to 2020. Further, the mean annual rainfall data (for the duration of 1999 to 2020) of the rain gauge stations, which is essential for the computation of the R factor, was drawn from the collected rainfall data sets (Figure 3). The R factor was computed by inverse distance weighting (IDW) interpolation method and converted to a raster data set of 30 m cell grid size (Figure 9).

FIGURE 3

Figure 3. Maps of the Dehar watershed showing the average annual rainfall for the years 1999, 2010, and 2020.

FIGURE 4

Figure 4. Map showing different soil types in the Dehar watershed.

FIGURE 5

Figure 5. Digital elevation model (DEM) of the Dehar watershed.

FIGURE 6

Figure 6. Maps of the Dehar watershed showing NDVI for the years 1999, 2010, and 2020.

FIGURE 7

Figure 7. Showing LULC maps for the years 1999, 2010, and 2020 of the Dehar watershed.

FIGURE 8

Figure 8. Bar graph showing the change in different LULC classes for the years 1999, 2010, and 2020 of the Dehar watershed.

FIGURE 9

Figure 9. Maps showing R factor for the years 1999, 2010, and 2020 of the Dehar watershed.

$$\mathrm{R}=79+0.363\times \mathrm{R}{}_{\mathrm{n}}(2)$$

Where R_n denotes the average annual rainfall expressed in mm.

Soil erodibility factor (K factor)

The soil erodibility factor (K) describes the characteristic eroding capacity of soils. It measures the susceptibility of soils to detach and be transported predominantly caused by rainfall and overland flow. The K factor refers to the impact of the physical and chemical properties of soils on erodibility during precipitation events in an elevated region (Wischmeier and Smith, 1978). The K factor is affected by various soil properties that comprise soil texture, structure, organic matter, permeability, porosity, and soil profile (Prasannakumar et al., 2011). For the present study, the soil map was procured from the NBSS and LUP (2005). The three different soil classes, sandy-skeletal, coarse-loamy, and fine-loamy soils, were recognized in the watershed (Figure 4; Sidhu et al., 1997). The K-values for these three soil classes were assigned considering the K-values proposed by Jain et al. (2010) (Table 2). The soil map was initially generated in vector format to decipher the soil classes considering K-values and later converted into raster format with a 30 m × 30 m grid (Figure 10).

TABLE 2

Table 2. K-values for different soil types (after Jain et al., 2010).

FIGURE 10

Figure 10. Map showing K factor of the Dehar watershed.

Topographic factor (LS factor)

The topographic factor (LS) is generated from the two significant aspects of slope-length factor (L) and slope-gradient factor (S), and these factors are primarily created from the Digital Elevation Method (DEM). Zhang et al. (2013) determined that the LS factor is an essential component of the RUSLE model soil erosion estimation as gravitational forces play a major role in overland flow. The LS factor revealed that the landscape setting of terrain with steep slopes and heavy precipitations is more vulnerable to soil erosion. Paul et al. (2021) viewed that if the length and steepness of an area are higher, the velocity and runoff volume escalates, leading to soil erosion. The DEM is considered to be the best option for the calculation of the LS factor in a particular area (Panagos et al., 2015). The LS factor was calculated from SRTM DEM of 30 m spatial (Figure 5). The LS factor map was generated by adopting equation (Eq. 3) (Wischmeier and Smith, 1978) and using a tool developed in Arc Macro Language by Hickey (2001). The tool takes DEM in ASCII format as input to calculate the LS factor (Figure 11).

FIGURE 11

Figure 11. Map showing LS factor of the Dehar watershed.

$$\mathrm{LS}=(\mathrm{l}/72.6){}^{\mathrm{m}}(65.41\mathrm{Sin}{}^2\mathrm{\beta }+4.56\mathrm{Sin}\mathrm{\beta }+0.065)(3)$$

Where, LS represents the slope length and steepness factor, l is cumulative slope length in meters, and β is the downhill slope angle.

Crop management factor (C factor)

The crop management factor (C factor), also known as the cover management factor, shows the influence of cropping and other allied practices on soil erosion (Chalise et al., 2019; Sandeep et al., 2021). It also expresses the response of vegetative cover to water erosion. Areas with unprotected vegetative cover are commonly prone to high water erosion. On the other hand, soil erosion is low in the areas where the land is protected by vegetative cover. Nearing et al. (2004) viewed the C factor as sensitive to spatiotemporal variations, as it depends on vegetative growth and rainfall conditions. The C factor is represented as the erosion-weighted ratio of soil loss from crop/cover land to the corresponding loss from bare fallow or unprotected soil cover (Wischmeier and Smith, 1978). This factor holds a dimensionless number and the values range between 0 and 1, where 0 signifies the area has vegetative cover and is erosion-protected, whereas 1 represents the area is bare, uncovered, and prone to soil erosion. Significantly, thick vegetation protects from soil erosion by reducing runoff intensity and raindrop influence on the soil-covered ground surface (Ranzi et al., 2012). Therefore, vegetative cover is known to be a crucial factor along with the topographic factor in controlling the rate of soil loss (Zhou et al., 2008; Li et al., 2019).

In this study, an LU/LC-related method was adopted for calculating C factor values of the area. The C factor values for the respective years were generated from Normalized Differential Vegetative Index (NDVI) derived from LANDSAT-07, LANDSAT-05, and SENTINAL-2 images for the years 1999, 2010, and 2020, respectively (Figure 6). For calculating the C factor, the equation (Eq. 4) proposed by van der Knijff et al. (2000) was adopted.

$$\mathrm{C}=\exp [-\mathrm{\alpha }((\mathrm{NDVI}/(\mathrm{\beta }-\mathrm{NDVI}))](4)$$

Where α and β are unitless parameters that fix the shape of the NDVI and the C factor curve. This equation has been widely used for Indian mountainous terrains (Kumar et al., 2014).

Conservation practice factor (P factor)

The conservation practice factor reveals the positive outcome of soil and water conservation processes of soil erosion in relation to agricultural practices. Wischmeier and Smith (1978) viewed the P factor as the percentage of erosion during the period of conservation and protective conditions compared to the usual circumstances of erosion occurrence.

The P factor values range between 0 and 1, where the values falling near to 0 signify the area is under healthy soil and water conservation practices, whereas the values close to 1 represent that the area lacks appropriate conservation practices (Table 3). In this study, the P factor map was developed by adopting the existing P factor values of the related land-cover classes collected from published sources (Wischmeier and Smith, 1978; Wanielista and Yousef, 1993; Londhe et al., 2010).

TABLE 3

Table 3. P-values for different LULC classes (complied after Wischmeier and Smith, 1978; Wanielista and Yousef, 1993; Londhe et al., 2010).

Results

Land use/land cover change

It was inferred from the analysis of the LULC classes that Built-Up areas increased from 2.3 km² in the year 1999 to 3.96 km² in 2010 and 5.36 km² in 2020. Agricultural Land increased from 48.16 km² in the year 1999 to 61.09 km² in 2010 and 64.28 km² in 2020. Conversely, Barren Land decreased from 50.15 km² in the year 1999 to 34.75 km² in 2010 and 26.85 km² in 2020. Forest Land increased from 342.1 km² in 1999 to 343.2 km² in 2010 and 346.2 km² in 2020. However, in the case of Water Bodies, there was a decrease from 5.89 km² in the year 1999 to 5.6 km² in 2010, and an increase 5.91 km² in 2020 (Table 4 and Figures 7, 8). The results of the area under very low, low, moderate, high, very high, and extremely severe erosion classes during the years 1999, 2010, and 2020 with respect to different LULC classes such as Built-Up area, Agriculture Land, Forest Land, and Barren Land are presented in Table 7. The confusion matrix resulted in an overall accuracy of 86.84% for 1999, 87.43% for 2010, and 88.05% for 2020 with a Kappa coefficient value of 0.827, 0.835, and 0.842 for the years 1999, 2010, and 2020, respectively (Table 5). It was inferred from the obtained results that the image classification was performed with accuracy.

TABLE 4

Table 4. Area under different LULC classes for the years 1999, 2010, and 2020.

TABLE 5

Table 5. Confusion matrix for LULC classification for the years 1999, 2010, and 2020.

R factor

The erosivity rate is directly related to the total amount of precipitation that occurred in a particular area (Sandeep et al., 2021). The R-values of the study area varied between 713 and 985 MJ mm/ha/hr/year in 2020, 663 to 845 MJ mm/ha/hr/year in 2010, and 672 to 874 MJ mm/ha/hr/year in 1999 (Figure 9). The watershed receives high rainfall from July to September during the southwest monsoon season. The eastern part of the watershed has a high R-value in comparison to the western part (Figure 9). Thus, this indicated that high R factor values influence the rate of soil erosion in the watershed.

K factor

The prominent soil types of the area are sandy-skeletal, coarse-loamy, and fine-loamy soils. Almost 57% of the total geographical area is covered by sandy-skeletal soil followed by coarse-loamy soil (32%), then fine-loamy soil (11%) (Figure 4). The soil erodibility (K factor) values of the study area vary from 0.020 to 0.042 (Table 2). The northern part of the watershed is covered by sandy-skeletal soil signifying the highest K factor value (Figure 10), while the central and southern parts of the watershed are covered by coarse- and fine-loamy soils with lower K factor values.

LS factor

The elevation of the Dehar watershed ranges from 395 to 4080 m with a relief of 3,695 m. The LS factor was calculated using DEM and equation (Eq. 3), by considering the interface between flow accumulation and topographic features (Figure 5). The LS factor of the watershed ranged from 0 to 98 with a mean value of 5.38 (Figure 11).

C factor

The vegetative cover factor (C) was determined using the equation (Eq. 4) for the years 1999, 2010, and 2020 (Figure 12). The NDVI values for the years 1999, 2010, and 2020 ranged from−0.41 to 0.63, −0.38 to 0.67, and −0.19 to 0.55, respectively (Figure 6). The C factor values generated from the NDVI ranged from 0.03 to 1.78 for 1999, 0.01 to 1.74 for 2010, and 0.07 to 1.39 for 2020 (Figure 12). The derived C factor values revealed that higher values were found in barren land and lower values were reported in vegetative cover areas.

FIGURE 12

Figure 12. Maps showing C factor of the Dehar watershed for the years 1999, 2010, and 2020.

P factor

The P factor value of the study area varied from 0 to 1 (Figure 13 and Table 3). The lowest value (0) was assigned to a water body where conservation measures were undertaken, while a P factor value (1) was given to vegetation, barren land, built-up area, and agriculture land (less than 3% and more than 25% slope) where appropriate soil-water conservation strategies were not adopted. The different agricultural land use classes (with slopes varying from 3–8, 8–12, 12–16, 16–20, and 20–25%) were assigned P-values ranging from 0.5 to 0.9.

FIGURE 13

Figure 13. Maps showing P factor of the Dehar watershed for the years 1999, 2010, and 2020.

Effect of LULC changes on soil erosion

The different RUSLE parameters were multiplied in an ArcGIS 10.8 environment by applying a raster calculator tool to find the geoenvironmental conditions in the Dehar watershed by knowing the spatiotemporal changes and the rate of soil erosion during the years 1999, 2010, and 2020. The assessed rate of soil erosion was grouped into five classes: very low (0–1 t/ha/y), low (1–5 t/ha/y), moderate (5–10 t/ha/y), high (10–20 t/ha/y), very high (20–50 t/ha/y), and extreme (>50 t/ha/y), as shown in Table 6 (Dabral et al., 2008). The very low class (0–1 t/ha/yr) showed a higher area in percentage with respect to soil loss followed by the extremely severe class (>50 t/ha/yr) for all three years (1999, 2010, and 2020) of the LULC settings (Figure 14).

TABLE 6

Table 6. Area under different soil loss potential classes and mean soil loss for the year 1999, 2010, and 2020.

TABLE 7

Table 7. LULC class-wise area in percentage under different soil loss potential classes for the years 1999, 2010, and 2020.

FIGURE 14

Figure 14. Maps showing soil erosion trends of the Dehar watershed for the years 1999, 2010, and 2020.

In 1999, the percentages of areas that experienced very low, low, moderate, high, very high, and extremely severe erosion were 46.42, 0.45, 2.53, 5.05, 10.81, and 34.74%, respectively (Table 6). Whereas in 2010, the percentages of areas that experienced very low, low, moderate, high, very high, and extremely severe erosion were 46.52, 1.32, 3.31, 5.91, 11.69, and 31.25%, respectively. Similarly, in 2020, the areas prone to very low, low, moderate, high, very high, and extremely severe erosion were 46.1, 0.56, 2.71, 4.68, 10.03, and 35.92%, respectively. The results representing areas under very low, low, moderate, high, very high, and extremely severe erosion classes and mean soil loss within different land use classes are presented in Tables 6–8.

TABLE 8

Table 8. Mean soil loss in different LULC classes for the years 1999, 2010, and 2020.

Discussion

In the present study, the influence of LULC changes on soil loss dynamics in the Dehar watershed was estimated using the empirical model RUSLE in the GIS environment. RUSLE is widely applied in mountain terrains and very suitable for the Himalayan mountains system (Cevik and Topal, 2003; Dissanayake et al., 2019; Thapa, 2020; George et al., 2021; Kumar and Hole, 2021). It was inferred from the study that a subsequent rise in Built-Up and Agricultural Land was observed in the years 2010 and 2020 and, conversely, Barren Land decreased in the years 2010 and 2020 due to the rise in the population and expansion of habitats and agricultural fields in the lesser and Sub-Himalayan zone in recent years, leading to the conversion of Barren Land to Built-Up and Agricultural fields. The class Forest also showed a minor rise in the years 2010 and 2020 due to sustainable forest conservation and management practices (Table 4). In the Built-Up class of land use, the mean soil loss decreased from 32.19 in 1999 to 18.77 t/ha/yr in 2010, but in the year 2020 the mean soil loss slightly increased to 20.15 t/ha/yr (Table 8). Further, Barren Land registered a decrease in the mean soil loss from 86.43 t/ha/yr in the year 1999 to 74.60 t/ha/yr in 2010 and 73.19 t/ha/yr in the year 2020. Regarding the Agriculture class, the rate of mean soil loss slightly increased from 32.55 t/ha/yr in 1999 to 33.35 t/ha/yr in 2010, but then decreased to 25.43 t/ha/yr in 2020. The area covered under Forest experienced an increase in mean soil loss, from 65.30 t/ha/yr in 1999 to 65.87 in 2010 and 74.72 t/ha/yr in 2020. From the current study, it is inferred that the Barren Land and Forest classes experienced a higher rate of soil erosion in comparison to other classes because the Dehar watershed is prone to high precipitation and its presence in a high altitudinal area with hilly terrain, steep to very steep slopes, incidents of forest fires, and unsustainable forest management practices. In the Sub-Himalayas, the Barren and Forest areas experience a high rate of soil erosion due to their spread on high elevations and steep slopes (Kumar et al., 2014; Fayas et al., 2019; Uddin et al., 2019).

Rainfall erosivity (R factor) indicated a rise in the mean soil loss between the years 2010 and 2020. Further, the R factor clearly illustrated the importance of rainfall erosivity in soil loss estimation, as its values increased between the years 2010 and 2020. Interestingly, the area experienced a decrease in total mean soil loss between the years 1999 and 2010 and an increase in the total mean soil loss between the years 2010 and 2020 (Table 6).

Conclusion

This study evaluated the influence of LULC changes on soil erosion in a hilly watershed in the northern western Himalaya region in the state of Himachal Pradesh, India, for the years 1999, 2010, and 2020. The methodology adopted was to apply the RUSLE method for soil loss estimation in a GIS environment to create and compare soil loss estimation maps of 1999, 2010, and 2020. Moreover, the main aim was to identify soil loss changes that occurred due to LULC change. The RUSLE model in combination with the GIS technique was applied to estimate the rate of soil erosion in different LULC classes. The RUSLE model was successfully employed by giving due importance to different factors (R, K, LS, P, and C). The results yielded mean soil loss of 63.71, 60.99, and 66.71 t/ha/yr for the year 1999, 2010, and 2020, respectively, indicating that the LULC changes considerably influenced the rate of soil loss in the Dehar watershed. Overall, the present study clearly shows that soil erosion is a serious geoenvironmental issue and it needs the urgent attention of scientists, policymakers, and the general public for sustainable management of soil resources. Additionally, the data used in the present study was of coarser resolution and of a larger scale. There is wider scope for further studies at micro-watershed levels using high-resolution satellite imageries and more accurate rainfall data sets derived from different sources and validated with field-based models. More precise soil-loss estimations would help various stakeholders in taking suitable measures for the prevention of soil erosion.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the author, without undue reservation.

Author contributions

MP: investigation, visualization, supervision and formal analysis, writing – original draft, and reviewing and editing. AK: conceptualization, methodology, investigation, data collection and data generation, writing, and reviewing and editing. SD: conceptualization, methodology, investigation, writing, and reviewing and editing. OV, SR, and BK: writing and reviewing and editing. All authors contributed to the article and approved the submitted version.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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