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Article

Estimation of Potential Soil Erosion and Sediment Yield: A Case Study of the Transboundary Chenab River Catchment

1
Department of Irrigation and Drainage, Faculty of Agricultural Engineering and Technology, University of Agriculture, Faisalabad 38000, Pakistan
2
Department of Water Management, Delft University of Technology, 2600 GA Delft, The Netherlands
3
Department of Agricultural Engineering, Bahauddin Zakariya University, Multan 60800, Pakistan
4
Department of Agricultural Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan
5
Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23460, Pakistan
6
Department of Structures and Environmental Engineering, Faculty of Agricultural Engineering and Technology, University of Agriculture, Faisalabad 38000, Pakistan
7
Department of Land and Water Conservation Engineering, Faculty of Agricultural Engineering and Technology, PMAS Arid Agriculture University, Rawalpindi 46000, Pakistan
8
Faculty of Agriculture and Environmental Sciences, University of Rostock, 18059 Rostock, Germany
*
Authors to whom correspondence should be addressed.
Water 2021, 13(24), 3647; https://doi.org/10.3390/w13243647
Submission received: 25 October 2021 / Revised: 7 December 2021 / Accepted: 15 December 2021 / Published: 18 December 2021
(This article belongs to the Special Issue Soil Water Erosion)

Abstract

:
Near real-time estimation of soil loss from river catchments is crucial for minimizing environmental degradation of complex river basins. The Chenab river is one of the most complex river basins of the world and is facing severe soil loss due to extreme hydrometeorological conditions, unpredictable hydrologic response, and complex orography. Resultantly, huge soil erosion and sediment yield (SY) not only cause irreversible environmental degradation in the Chenab river catchment but also deteriorate the downstream water resources. In this study, potential soil erosion (PSE) is estimated from the transboundary Chenab river catchment using the Revised Universal Soil Loss Equation (RUSLE), coupled with remote sensing (RS) and geographic information system (GIS). Land Use of the European Space Agency (ESA), Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data, and world soil map of Food and Agriculture Organization (FAO)/The United Nations Educational, Scientific and Cultural Organization were incorporated into the study. The SY was estimated on monthly, quarterly, seasonal, and annual time-scales using sediment delivery ratio (SDR) estimated through the area, slope, and curve number (CN)-based approaches. The 30-year average PSE from the Chenab river catchment was estimated as 177.8, 61.5, 310.3, 39.5, 26.9, 47.1, and 99.1 tons/ha for annual, rabi, kharif, fall, winter, spring, and summer time scales, respectively. The 30-year average annual SY from the Chenab river catchment was estimated as 4.086, 6.163, and 7.502 million tons based on area, slope, and CN approaches. The time series trends analysis of SY indicated an increase of 0.0895, 0.1387, and 0.1698 million tons per year for area, slope, and CN-based approaches, respectively. It is recommended that the areas, except for slight erosion intensity, should be focused on framing strategies for control and mitigation of soil erosion in the Chenab river catchment.

1. Introduction

Soil is a precious natural resource [1], plays a key role in the functioning ecosystem [2,3], and provides valuable goods and services [4,5] essential for human security [6,7]. Soil erosion is a natural geomorphic process and environmental problem [8,9] arising from anthropogenic activities [10] agricultural intensification, deforestation, land degradation, and global climate change [11,12]. Soil erosion is also considered as one of the significant threats to the ecosystem [13,14,15], as it not only causes soil erosion from upper catchments and deposition in rivers and lakes through the geologic ages worldwide [16,17], but also carries nutrients, pesticides, chemicals, etc., and cause groundwater contamination [18,19,20]. It has been estimated that about 56% of global soil is being degraded by light to severe forms of soil erosion caused has by water [21]. Accelerated forms of soil erosion by water become a global problem [22,23], that not only cause rivers’ catchment problems [24,25] but also act as barriers to achieving the United Nations Sustainable Development Goals [26]. Therefore, estimation of soil erosion by water from river catchment is in dire need [27], so that proper soil erosion mitigation options can be focused [28].
Estimation of soil erosion (PSE and SY) from large and complex rivers’ catchments has always been a big challenge to researchers worldwide [6,29,30,31]. Initially, soil erosion research was conducted more than seven decades ago using north American datasets [32,33,34]. Several mathematical models, conceptual, empirical, process oriented, and physically based, have been applied for soil erosion modeling/ estimation at different spatiotemporal scales [35,36,37,38,39,40,41,42]. Scientists are also working on process-oriented soil erosion models such as the Water Erosion Prediction Project [43,44], European Soil Erosion Model [45], Limburg Soil Erosion Model [46], and Pan European Soil Erosion Risk Assessment [47]. The research community is also improving the empirical model known as Universal Soil Loss Equation (USLE) [48,49,50,51,52] which is not only practically sound [53,54] but can also be applied over complex and large river basins [11,55,56,57]. The USLE and RUSLE are being applied successfully for estimation of PSE and SY from rivers’ catchments throughout the world under changing spatiotemporal conditions [6,58,59,60,61,62,63]. Large-scale soil erosion modeling has been performed by using the RUSLE model in Europe [64], Canada [65], Australia [66], and China [67].
The RUSLE was developed to estimate soil erosion by water in temperate climates, and is an empirical model founded on the USLE [68]. The RUSLE model estimates the average annual rate of soil erosion from complex river basins for multiple scenarios including management practices, cropping systems, and erosion control practices [69]. The RUSLE can also be used for estimation of average annual soil erosion rate from ungauged river catchments using local hydrometeorological information and catchment characteristics [70]. The RUSLE model considers the effect of many factors such as rainfall erosivity, soil erodibility, slope length and slope steepness, cover management, and conservation practices [71]. The soil loss occurs in three steps. Soil erosion starts with the detachment of soil particles, followed by transport and sequent deposition [72]. The RUSLE model neither estimates gully/channel erosion nor discusses the sediment deposition, so there is a need to introduce SDR for estimation of sediment delivered to the outlet from the catchment [73]. The SDR is estimated by researchers based on area, slope, and CN approaches in rivers’ catchments globally [74,75,76,77,78,79]. The RUSLE model is applied along with the SDR for estimation of sediment yield from rivers’ catchments [80,81,82,83,84].
The use of RUSLE for soil erosion needs detailed spatiotemporal in situ data [85] which is not possible, as the transboundary Chenab river catchment is divided among Pakistan (402 km2), Jammu and Kashmir (20,139.7 km2), and India (7939.27 km2) [86]. This clearly reflects that 27.87%, 70.71%, and 1.42% area of the catchment lies in India, Jammu and Kashmir (Indian Control), and Pakistan, respectively. In such a complex transboundary river catchment, the global gridded and RS-based datasets can be appropriate alternatives for research purposes [87,88,89]. Recently, researchers have employed RS and GIS technologies for evaluation of PSE and SY [90]. RS and GIS technologies provide detailed information with a spatiotemporal resolution appropriate for quantifying soil erosion at a regional/local scale [91,92]. Moreover, use of RS and GIS can reasonably account for the spatiotemporal variability of parameters and catchment heterogeneity [65]. For estimation of water-based erosion, RUSLE coupled with RS and GIS is the most commonly adopted and feasible technique to quantify the magnitude and spatial distribution of soil erosion/loss from rivers’ catchments [62,93,94,95,96,97].
A 17 to 27-year study [98] of 9 sediment stations within the Chenab river catchment revealed that there are very high erosion rates. A soil erosion study using the USLE model in similar nearby area also revealed high, very high, and severe soil erosion [99]. Researchers applied the sediment yield index model to the catchment of the Marusudar tributary of the Chenab river, which revealed a high rate of soil erosion [100]. At present, no soil erosion study has been conducted on the complete transboundary Chenab river catchment using the RUSLE model. Keeping in view all the soil erosion issues of the transboundary river catchments, the present study aims to estimate the spatially distributed PSE of the transboundary Chenab river catchment using the RUSLE model integrated with RS and GIS. The spatial distribution of PSE is one of the main targets of this study, so 55 sub-basins were created from the Chenab river catchment. The study also aims to estimate SDR using area, slope, and CN-based approaches, for estimation of sediment yield from the Chenab river catchment. Both PSE and SY were estimated on annual, seasonal, quarterly, and monthly time scales from 1991 to 2020.

2. Material and Methods

2.1. Study Area

The Chenab river catchment is shown in Figure 1. The geographic extent of the Chenab river catchment lies 74°–77.85° E and 32°–34.3° N, while elevation ranges from 240 to 7085 m, and average slope of the river in the catchment is about 25 m/km. It originates in the Kangra and Kulu district of the Himachal Pradesh, India. The Bhaga and Chandra streams emerge from mega snowfields on opposing sides of the Baralcha pass and join the Tandi, Jammu and Kashmir. The climate in the catchment typically comprises two seasons in a year. The Rabi season spans from November to April, the Kharif from May to October. Another climate classification also exists consisting of four seasons, Fall (September, October, November), Winter (December, January, February), Spring (March, April, May), and Summer (June, July, August). The snow-dominant Chenab river catchment [88] receives 65% of precipitation in the monsoon (June, July, August) or pre-monsoon (March, April, May), while 26% precipitation is received in the winter season [101]. The higher altitude of the upper and middle parts of the Chenab river catchment are snow-dominant regions. The mean annual rainfall varies from 278.5 to 2214.9 mm as shown in Figure 2.

2.2. Methodology

The overall methodology of the research for estimation of PSE and SY is presented in the flowchart (Figure 3). The topographic information, soil data, land use information, and precipitation data were used to estimate all the factors to be used in the RUSLE model for estimation of PSE. The SDRs were estimated based on area, slope and CN approaches, and the SDRs were further used along with PSE to estimate the SY.

2.2.1. Datasets Used in the Study

We used the Shuttle Radar Topography Mission Digital Elevation Model (90 m) for watershed delineation, calculation of length and slope factors, and support practice factor based on slope-contour approach. The land use change detection of the river catchment is a major challenge [102] and helps to understand hydrological processes and associated systems of the river basins [103,104,105,106]. Due to lack of in situ data, the global land cover map was used at a spatial resolution of 300 m, produced by the ESA Climate Change Initiative [107]. We estimated land cover management factors using the land use map produced by ESA at a spatial resolution of 300 m. The soil types and texture are also important, along with land use, to understand the hydrological response of the river catchment [108,109]. The Digital Soil Map of the world (DSMW) was used in this study, which is produced by the FAO/ UNESCO. The erosivity factor for 30 years (1991–2020) was estimated using the CHIRPS precipitation data at a spatial resolution of 0.25°.

2.2.2. RUSLE Model for Estimation of Potential Soil Erosion

Wischmeier and Smith developed the USLE for estimation of soil erosion [68]. The new equation (RUSLE) replaces USLE’s distinctive rainfall or runoff factor as the rainfall erosivity factor [71]. We used RUSLE to estimate soil erosion at monthly, quarterly, seasonally, and yearly time scales on a surface slope based on the runoff model, soil type, farming practices, topography (slope), and supervision techniques [110]. The RUSLE is an empirical equation that estimates PSE in tons per hectare (Equation (1)).
A = R   ×   K   ×   L   ×   S   ×   C   ×   P
where A is estimated monthly soil erosion (ton ha−1 month−1), R is rainfall erosivity factor (MJ mm ha−1 h−1 month−1), K is soil erodibility factor (t MJ−1 ha−1 mm−1), L is slope length factor, S is slope steepness factor, C is cover management factor, and P is supporting practices. All these factors of RUSLE were mapped in GIS raster format at quarterly, seasonally, and annual time scales.

Rainfall Runoff Erosivity Factor (R)

Without soil surface protection, the rainfall erosivity factor (R) triggers sheet and rill erosion. Soil loss significantly depends on rainfall because it detaches soil particles from the ground surface and transports them to the river channel [111]. Heavy rainfall events having large droplets size can quickly detach soil particles, compared to droplets of smaller size. The bulk of sheet or rill erosion occurs due to the high runoff generated by a heavy rainfall storm. Rainfall has a significant effect on soil erosion due to the kinetic energy that each raindrop contains, which causes soil particles to detach from the ground surface. We used monthly CHIRPS data for 30 consecutive years (1991–2020) over the Chenab river catchment. In order to estimate the rainfall-runoff erosivity factor of the Chenab river catchment, we calculated R-factor by using Equation (2) developed by Jung et al. [112].
R = 0.0378   ×   X 1.4190
where R is rainfall runoff erosivity factor (MJ mm ha−1 h−1 month−1), and X is monthly rainfall amount (mm).

Soil Erodibility Factor (K)

The soil erodibility factor represents the soil’s vulnerability to degradation as evaluated under normal unit plot conditions. Soil erodibility is the quantity of soil loss per unit of rainfall erosive energy. We estimated the K factor by using Equation (3) developed by [113]. This depends on soil contents of organic carbon, silt, sand, and clay, obtained from FAO soil data.
K = 0.1317 · f c s a n d · f c l s i · f o r g e · f h i s a n d
f c s a n d = 0.2 + 0.3   e x p   [ 0.256   × m s   × 1 m s i l t 100 ]
f c l s i = ( m s i l t m c + m s i l t ) 0.3
f o r g e = 1 0.0256   ×   o r g C o r g C + e x p 3.72 2.95   ×   o r g C
f h i s a n d   1 0.7   × 1 m s 100 1 m s 100 + e x p 5.51 + 22.9 1 m s 100
where ms is % sand content in topsoil, msilt is % silt content in topsoil, mc is % clay content in top soil, and orgC is % organic carbon content in top soil. We estimated the K factor by Equation (3). A maximum of four soil types were identified in the soil map using the FAO soil data. The soil erodibility factor was estimated based on the sand, silt, and clay percentages.

Slope-Length and Slope-Steepness Factor (LS)

The LS factor represents the effects of topography on soil erosion by including slope-length factor (L) and slope-steepness factor (S), both of which influence overland flow velocity [114]. The Topographic slope-length (L) and slope-steepness (S) reflect a ratio of soil erosion under defined conditions compared to soil loss at a site with a “normal” slope steepness of 9% and a slope length of 22.6 m [62]. Because the LS-factor causes high runoff velocity and hence more runoff volume, the highest slope has the greatest danger of soil erosion. The equation of LS is given as;
L S   ( λ 22.13 ) m   ×   ( sin β 0.0896 ) n
β = sin θ / 0.0896 3   ×   sin θ 0.8 + 0.56
where
  • θ is Slope of watershed (degree), n is 1.3
  • λ = slope-length (m) = flow acc. × cell size
  • m = variable slope length exponent = β 1 + β

Cover Management Factor (C)

The dimensionless cover and management factor plays its role in reducing soil erosion, and depends on the land use patterns of the area [115]. The C factor is the ratio between soil loss from areas with protective cover and management to soil loss from continuously clean tilled fallow land [116]. The C factor varies from 0 to 1 depending on land use characteristics, excluding water bodies [117].

Supporting Conservation Practice Factor (P)

The P-factor is the ratio of soil loss induced by each type of conservation technique to the comparable erosion generated by uphill and downhill sloped cropping [110,118]. It modifies the volume and water discharge, hence affecting the magnitude of soil erosion. The support conservation techniques used in the catchment, such as contouring, terracing, strip cropping, etc., are referred to as the support practice factor. The P factor has a value between 0 and 1, with 0 representing very good preservation practice, and one indicating no preservation technique [45].

2.2.3. Estimation of Sediment Yield

S Y = S D R   ×   A
where SY is in tons/month, SDR (fraction), and A is PSE (tons/month).

Estimation of Sediment Delivery Ratio

The SDR is the ratio of sediments delivered at outlet to the gross erosion of the catchment upstream of the measurement location [119], and SDR represents several processes which are involved in estimation of SY [120]. Although the United States Department of Agriculture has published a handbook [121] in which the SDR is linked to drainage areas, there is no perfect process for estimating SDR. A variety of factors can influence SDR, including sediment load, texture, proximity to the mainstream, channel density, basin area, slope, length, land use, rainfall, and runoff. The Soil Conservation Service (SCS) curve is the established relationship between SDR and drainage area. For example, a watershed with a higher channel density has a higher SDR than a catchment with a lower channel density. The SDR of a watershed with steep slopes is greater than that of a watershed with flat and large valleys. The size of the area of interest should also be defined in order to predict SDR. The higher the area size, the smaller the fraction of SDR since large areas have more chance of trapping soil particles. The SDR equations have been derived by several researchers in different river basins of the world [122,123,124,125]. The researchers correlated the SDR with area [121] and developed their equation as follows:
S D R = 0.5656   ×   A 0.11
where A is the area of watershed in sq. miles (mi2).
The researchers [126] also correlated the SDR with the slope and developed the equation as follows:
S D R = 0.627   ×   S 0.403
where S is the slope of watershed in degree.
The relief-length ratio is prepared by getting the maximum and minimum value of the elevation of the catchment, then by taking the difference of maximum and minimum elevation and finally dividing it by the length of the river. SDR against CN is prepared according to the empirical equation. According to the research [74], the SDR is related to watershed area, relief-length ratio, and SCS CN, and the following equation was derived in a study conducted on 15 Texas basins:
S D R = 1.366   ×   10 11   ×   A 0.0998   ×   Z L 0.3629   ×   C N 5.444
where A is the area in km2 and ZL is the relief-length ratio in m/km.
The hydrologic soil group and ground cover are used to calculate the CN. In general, the most reliable findings are achieved when each sub-catchment is homogeneous, with as few CNs as possible. When a large number of CNs are averaged into a single sub-catchment, the results are not necessarily the same as when multiple sub-catchments are produced and combined together. A single sub-catchment, for example, will only have one peak; however, merging many sub-catchments might result in a multi-peak hydrograph.

3. Results

3.1. Factors of the RUSLE Model

The 30-year average rainfall erosivity factor maps at annual, seasonal (rabi, kharif), and quarterly time scales (fall, winter, spring, and summer) are presented in Figure 4.
The peak rainfall erosivity factor was 826.8, 277.9, 558.5, 199, 115.3, 217.7, and 426.8 MJ mm−1 ha−1 h−1 for annual, rabi, kharif, fall, winter, spring, and summer, respectively. The spatial distribution of all the maps in Figure 4 reveals that the upper parts of the catchment have lower rainfall erosivity, and the lower parts of the catchment have very high erosivity because of high rainfall over the lower part as shown in Figure 2. Higher rainfall erosivity has been observed in kharif season (May to October), and in summer season. The maps of rabi season (November to April), fall, winter and spring seasons reveal that higher erosivity has been observed in higher latitudes in the lower parts of the catchments.
The estimated soil erodibility factor values using %sand, %silt, %clay, and % OC are given in Table 1. The four soil types were found in the river catchment, and K value ranges from 0.0174 to 0.023.
The 30-year average soil erodibility, slope-length and slope-steepness, cover management, and supporting conservation practice factors map of the Chenab river catchment is presented in Figure 5. The map of K factor reveals that most of the catchment is below 0.0197, while some portions of the catchment are in areas of high soil erodibility (greater than 0.0197).
The LS factor map of the Chenab river catchment is presented in Figure 5, which reveals that most of the catchment lies under two main classes, 0 to 5 in upper and middle parts of the catchment, and 13 to 18 in lower parts of the catchment. Therefore, the effect of LS on the PSE is not higher, as a higher LS factor area is not common in the catchment. The land use map of Chenab river catchment prepared using land use data of ESA is presented in Figure 6. The upper parts of the river catchment are covered with snow, consolidated bare land, grassland, and mosaic trees and shrubs. The middle to lower parts of the catchment are covered with tree cover and mosaic cropland, while the lowest parts of the catchment near to the outlet are covered with irrigated cropland. The cover management factor values were estimated (Table 2) using the land use information of ESA.
The cover management factor map was developed based on the C value of various vegetative coverings, as illustrated in Figure 5. The snow-covered areas of the upper catchment are under the lowest C values, while the bare areas in of the upper catchment are under high C values, while most of the upper to middle parts of the catchment are under medium C value (0.0434 to 0.1232). The middle to lower parts of the catchment are under lower C values, and the extreme lower parts of the catchment are under slightly higher C values.
In this research, data from the literature [128] was obtained for estimation of conservation management factor against % slope as presented in Table 3. As in situ crop information cannot be obtained, the average of the conservation practices was used in order to estimate the P value to be used in RUSLE. The P factor map for the Chenab river catchment is presented in Figure 5. Most of the catchment is under a high value of P, while the catchment areas near to the outlet are under lower values of P.

3.2. Potential Soil Erosion

The 30-year annual average PSE from the Chenab river catchment is presented in Figure 7. The 30-year average PSE from the Chenab river catchment was estimated as 177.8, 61.5, 310.3, 39.5, 26.9, 47.1, and 99.1 tons/ha for annual, rabi, kharif, fall, winter, spring, and summer time scales, respectively. The minimum values of PSE are on the snow cover areas, and lower values are seen also on tree cover and irrigated croplands. The bare areas near the rivers and the upper latitudes of the lower parts of the catchments are under medium PSE. On an annual time-scale, there is less area of catchment which has more than 10 PSE. The kharif season shows higher PSE values compared to the rabi season, mainly due to heavy precipitation in monsoon season and high magnitude of runoff due to rainfall-runoff and snowmelt. The PSE in summer season is higher in different areas of the catchment as compared to the fall, winter, and spring seasons.
The PSE distribution for all the mentioned time-scales is grouped into six soil erosion intensity classes (slight, moderate, high, very high, severe, and very severe) as per the literature [129] as shown in Figure 8.
These six classes have value ranges from 0–5, 5–10, 10–20, 20–40, 40–80, and >80 tons/ha, respectively. On an annual time-scale, most PSE is under slight intensity, while areas are also under moderate, high, and very high, but the percentage of area in these three intensity classes is less. In the kharif season, there is more area in high and very high intensity classes as compared to the rabi season. In the summer season, there is area under very high intensity of erosion as compared to fall, winter, and spring season. It is obvious from Figure 8 that the percentage of area in 0–5 class is more than 90% for all the time periods.

3.3. Sediment Yield

The SY from the Chenab river catchment was estimated using area, slope, and CN-based approaches. The CN for the Chenab river catchment is presented in Figure 9. The CN values in the snow-covered areas and some lower parts of the Chenab river catchment are higher, the central parts of the catchment are under medium CN values, and some lower parts of the catchment are under low CN values. The sub-catchment-wise SDR based area, slope, and CN are presented in Figure 10. There is reliable agreement between the SDRs based on area and slope, while the slope-based SDR is higher than the other. The CN-based SDR was higher than the others, while the higher CN-based SDR values were observed in sub-catchment numbers 10, 11, 12, 23, and 47.
In the area-based SDR, watershed (WS)-47 exhibited the highest SDR of 0.61 having a 0.5 sq. mile area, which contributes 0.0045% of the total area of the catchment, while the WS-1 had the minimum SDR of 0.281 consisting of 565.8 sq. miles area which contributes 5.2% of the total area of the catchment/watershed. In the slope-based SDR, the WS-44 exhibited the highest SDR of 0.551 having a 270.118 sq. miles area which contributes 2.48% of the total area of the watershed, while the WS-47 had the minimum SDR of 0.08 consisting of a 0.5 sq. mile area which contributes 0.0045% of the total area of the watershed. In the CN-based SDR, the overall ratio seemed high as compared to area and slope module, in which WS-47 contributed the highest SDR of 1.53 having a 0.5 sq. mile area which contributes 0.0045% of the total area of the watershed. On the other hand, the WS-43 had the minimum SDR consisting of 265.98 sq. miles area which contributes 2.44% of the total area of the watershed.
The annual sediment yield pattern over the last 30 years is presented in Figure 11. A similar pattern among area, slope, and CN-based annual SY has been observed. The highest SY was observed in 2014, and it showed that the sediment load was highest due to intense precipitation and surface runoff. In 2014, the area, slope and C-based SY was observed as 7,886,149, 12,032,723, and 14,550,254 tons, respectively. However, in 1998, the SY had the lowest value of 1,183,469, 1,733,782, and 2,067,188 tons based on area, slope and CN, respectively. The time series trends analysis of SY indicated an increase of 0.0895, 0.1387, and 0.1698 million tons per year for area, slope, and CN-based approaches, respectively.
The SY over the last 30-year in Rabi and Kharif season is presented in Figure 12.
The overall contribution of the kharif season was higher than that of the rabi season. In 2014, the highest SY of the kharif season based on area, slope, and CN was 5,574,869, 8,405,752, and 10,124,635 tons, respectively. In 2015, the highest SY of the rabi season based on area, slope, and CN was 3,264,869, 5,015,752, and 8,104,635 tons, respectively. The SY time series trends analysis of the rabi season indicated an increase of 0.0418, 0.064, and 0.0783 million tons per year for area, slope, and CN-based approaches, respectively. The SY time series trends analysis of the kharif season indicated an increase of 0.0481, 0.0753, and 0.0922 million tons per year for area, slope, and CN-based approaches, respectively.
The SY for fall, winter, spring, and summer is presented in Figure 13.
The SY for fall period was higher in 1992, 2010, 2011, 2012, 2014, 2018, 2019, and 2020. The SY for winter period was higher in 1992, 1994, 1996, 2008–2014, 2017, 2019, and 2020. The SY for spring period was higher in 1991–1993, 1996, 2009–2012, 2014–2017. The SY for the summer season was higher in 1991–1997 and 2009–2020.
The monthly sediment yield for the last 30 years is presented in Figure 14. The highest SY values of 3,125,536 tons, 4,745,077 tons, and 5,643,741 tons for area, slope, and CN, respectively, were observed in September 2014. Similarly, the lowest values of 11,484 tons, 17,904, and 21,811 tons for area, slope, and CN, respectively, were observed in November 1998. The time series trends analysis of SY indicated an increase of 0.0006, 0.001, and 0.0012 million tons per month for area, slope, and CN-based approaches, respectively.

4. Discussion

Soil erosion is a severe issue, particularly in the Chenab river catchment, where various variables contribute to fast soil erosion and sedimentation. The rate of runoff and sedimentation is accelerated by factors such as the region’s steep slope, temperature, velocity of flowing water, and environmental conditions [130]. The impact of a raindrop on the soil surface and the cutting force of running water causes soil particles to detach. Raindrop splash, despite having a minimal effect, triggers downslope transport of eroded soil particles [131]. High intensity rainfall makes the detachment of soil particles quicker and causes the mass movement of sediments along the runoff generated. Early in the rainy season, when the rainfall is intense but the vegetation has not developed enough to protect the soil, is the most favorable time for erosion. In general, the time between plowing and crop emergence is referred to as the farmer’s interval [132]. The CHIRPS precipitation data was incorporated in this research as it has better performance over areas that usually receive high rainfall [133].
Slope also varies, decrease in slope causing velocity to decrease, and ultimately to sediment transport decreases [134], which further increases the rate of sediment deposition. The topography and high elevation are usually the main reason for high intensity precipitation [135]. The K factor of the study area varied from 0.019 to 0.023. The soil, having low moisture content and permeability, represents a low K factor value. Soil erosion is closely concerned with the state of land use and agriculture practices, and cover management, as most of the Chenab river catchment was under 0 to 5 tons ha−1. The annual average PSE rates throughout the world are estimated as 12 to 15 tons ha−1, while the river catchment areas with a PSE of lower than 3 tons ha−1 year−1 are generally below the estimated tolerable soil loss level and should be exempt from any mitigating activities.
Though different soil erosion studies have been carried out on the Chenab river catchment with various models and techniques, this is the first time that estimation of PSE and SY for the last 30 years has been performed using the RUSLE model and SDRs on annual, seasonal, quarterly and monthly time-scales The SDR is estimated using area, slope, and CN-based approaches, and slope-based estimations are being applied by researchers around the world [136,137,138,139,140,141,142,143,144]. It is also observed in the study that SDR decreased with increase in area or stream length, and this is also observed by other researchers [124,145]. Moreover, topography-based SDR (slope) is scale dependent, and the scale dependency of such SDR has also been observed by researchers [146,147,148]. The RUSLE and SDR (mainly slope-based) are being applied by the scientific community for estimation of PSE and SY [149,150,151,152,153,154,155]. The RUSLE is used at multiple spatial scales by dividing a pixel into sub-regions with similar features and linking them to a GIS data structure [110]. Such models are now commonly in use to create an environment-based information system that allows for the estimation and evaluation of various management scenarios [156]. This methodology, however, has certain limitations but provides reliable results by identifying the high PSE areas. The RUSLE model was selected and applied to nearby similar rivers’ catchments [97,135,157].
The complete lower parts of the catchments are under high rainfall erosivity, therefore soil and water conservation measures and crop management practices are needed in order to reduce the rainfall erosivity, which will ultimately reduce the overall PSE and SY. The organic content of the soil reduces soil erodibility because increase in organic matter reduces the susceptibility of soil detachment, and increases infiltration, which further reduce runoff and thus soil erosion. The organic content should be increased in high K values areas by incorporating manure. With the novelty of providing soil loss estimates at finer spatial and temporal scales, the findings of this study can be useful for assessing soil erosion in other data-scarce areas, and can be helpful to resource conservation experts for making informed decisions.

5. Conclusions

The following conclusions have been derived from a 30-year study of soil erosion from the Chenab river catchment using RUSLE and SDR approaches;
  • The analysis results depicted that the range of average annual PSE was from 0.0 to 177.8 tons/ha, while, in the Rabi and Kharif season, the range of average PSE was between 0.0 to 61.5 tons/ha and 0.0 to 310.2 tons/ha. Similarly, in Fall, Winter, Spring, and Summer timescales, the range of average PSE was from 0 to 39.5, 26.9, 47.1, and 99.1 tons/ha, respectively.
  • The time series trends analysis of SY indicated an increase of 0.0895, 0.1387, and 0.1698 million tons per year for area, slope, and CN-based approaches, respectively.
  • The annual SY estimated by area, slope and CN was highest in 2014 with 7,886,149, 12,032,723, and 14,550,254 tons, respectively. The average PSE of Kharif season was highest (70%), followed by Fall (41%), Rabi (30%), Summer (26%), Spring (22%), and Winter (11%) season.
  • The annual SY estimated by area, slope and CN was minimum in 1998 with 1,183,469, 1,733,728, and 2,067,188, respectively. The average PSE of Kharif season was highest for soil loss (71%), followed by Summer (49.5%), Rabi (29%), Spring (22%), Fall (18%), and Winter (10.5%), respectively.
  • Inter-comparison of SY estimated by SDR based on slope and CN showed the consistency pattern and thus proved the authenticity of empirical models, but the SY estimated by area-based SDR was less compared to the slope and CN-based SDR Approaches.

Author Contributions

Conceptualization, M.G.A., R.H.A. and S.A.; Methodology, M.G.A., A.N. and S.A.; Software, R.H.A. and M.M.W.; Formal Analysis, A.N., M.W. and R.A.A.; Investigation, M.M.W., R.A.A. and M.J.M.C.; Resources, M.G.A., M.J.M.C. and S.A.; Data Curation, A.N., M.W. and M.K.L.; Writing—Original Draft Preparation, M.G.A., A.N. and I.S.; Writing—Review & Editing, S.A., M.J.M.C. and I.S.; Visualization, M.G.A., I.S. and M.K.L.; Supervision, A.N. and R.H.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research has not received any external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Data belongs to the authors.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Topographic map of the Chenab River catchment representing river, 55 sub-basins and outlet.
Figure 1. Topographic map of the Chenab River catchment representing river, 55 sub-basins and outlet.
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Figure 2. The 30-year average annual rainfall of the Chenab river catchment.
Figure 2. The 30-year average annual rainfall of the Chenab river catchment.
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Figure 3. Flow chart for estimation of potential soil erosion and sediment yield.
Figure 3. Flow chart for estimation of potential soil erosion and sediment yield.
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Figure 4. 30-year average annual, seasonal, and quarterly rainfall erosivity factors for the Chenab river catchment.
Figure 4. 30-year average annual, seasonal, and quarterly rainfall erosivity factors for the Chenab river catchment.
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Figure 5. Soil erodibility, slope-length and slope-steepness, cover management, and supporting conservation practice factors in the Chenab river catchment.
Figure 5. Soil erodibility, slope-length and slope-steepness, cover management, and supporting conservation practice factors in the Chenab river catchment.
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Figure 6. Land use of the Chenab river catchment developed using European Space Agency land use.
Figure 6. Land use of the Chenab river catchment developed using European Space Agency land use.
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Figure 7. 30-year average potential soil erosion from the Chenab river catchment at different time-scales.
Figure 7. 30-year average potential soil erosion from the Chenab river catchment at different time-scales.
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Figure 8. Catchment area percentage under different soil erosion classes based on PSE (ton/ha).
Figure 8. Catchment area percentage under different soil erosion classes based on PSE (ton/ha).
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Figure 9. CN map of the Chenab river catchment.
Figure 9. CN map of the Chenab river catchment.
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Figure 10. Mean Sediment delivery ratio in each micro-catchment.
Figure 10. Mean Sediment delivery ratio in each micro-catchment.
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Figure 11. Annual sediment yield from the Chenab river catchment 1991 to 2020.
Figure 11. Annual sediment yield from the Chenab river catchment 1991 to 2020.
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Figure 12. Sediment yield from the Chenab river catchment for rabi and kharif seasons from 1991–2020.
Figure 12. Sediment yield from the Chenab river catchment for rabi and kharif seasons from 1991–2020.
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Figure 13. Sediment yield from the Chenab river catchment for fall, winter, spring, and summer seasons from 1991–2020.
Figure 13. Sediment yield from the Chenab river catchment for fall, winter, spring, and summer seasons from 1991–2020.
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Figure 14. Monthly sediment yield from the Chenab river catchment from 1991 to 2020.
Figure 14. Monthly sediment yield from the Chenab river catchment from 1991 to 2020.
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Table 1. Soil erodibility factor value calculated for soil type in the Chenab river catchment.
Table 1. Soil erodibility factor value calculated for soil type in the Chenab river catchment.
Soil Unit Symbol% Sand Topsoil% Silt Topsoil% Clay Topsoil% OC TopsoilK Factor Value
I58.916.224.90.970.0196588
Be36.437.226.41.070.0223028
Lo769.914.10.410.0174135
Jc39.639.920.60.650.0232663
Table 2. Cover management factor values of the Chenab river catchment.
Table 2. Cover management factor values of the Chenab river catchment.
Land Use and Land Cover ClassC Factor ValueSource
Snow Cover0[127]
Tree Cover Needle-leaved0.0011[127]
Mosaic tree and shrubs0.0012[127]
Tree Cover Broad-leaved0.0013[127]
Shrub land0.0219[127]
Grassland0.0434[127]
Mosaic Cropland0.1231[127]
Tree or shrub cover0.2000[127]
Sparse vegetation0.2651[127]
Cropland irrigated0.5500[127]
Cropland rainfed0.6000[127]
Consolidated bare areas0.8000[127]
Unconsolidated bare areas0.9000[127]
Table 3. Conservation management factor values for contouring, strip cropping, and terracing against slope.
Table 3. Conservation management factor values for contouring, strip cropping, and terracing against slope.
Slope (%)ContouringStrip CroppingTerracing
0.0–7.00.5500.2700.100
7.0–11.30.6000.3000.120
11.3–17.60.8000.4000.160
17.6–26.80.9000.4500.180
26.8>1.0000.5000.200
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Ali, M.G.; Ali, S.; Arshad, R.H.; Nazeer, A.; Waqas, M.M.; Waseem, M.; Aslam, R.A.; Cheema, M.J.M.; Leta, M.K.; Shauket, I. Estimation of Potential Soil Erosion and Sediment Yield: A Case Study of the Transboundary Chenab River Catchment. Water 2021, 13, 3647. https://doi.org/10.3390/w13243647

AMA Style

Ali MG, Ali S, Arshad RH, Nazeer A, Waqas MM, Waseem M, Aslam RA, Cheema MJM, Leta MK, Shauket I. Estimation of Potential Soil Erosion and Sediment Yield: A Case Study of the Transboundary Chenab River Catchment. Water. 2021; 13(24):3647. https://doi.org/10.3390/w13243647

Chicago/Turabian Style

Ali, Muhammad Gufran, Sikandar Ali, Rao Husnain Arshad, Aftab Nazeer, Muhammad Mohsin Waqas, Muhammad Waseem, Rana Ammar Aslam, Muhammad Jehanzeb Masud Cheema, Megersa Kebede Leta, and Imran Shauket. 2021. "Estimation of Potential Soil Erosion and Sediment Yield: A Case Study of the Transboundary Chenab River Catchment" Water 13, no. 24: 3647. https://doi.org/10.3390/w13243647

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