The present investigation was carried out within the Peddavagu watershed, which is located in India. The necessary datasets, including soil, land use land cover, rainfall, and digital elevation model, were processed and analysed within a Geographic Information System framework. To evaluate soil loss within the watershed, the present investigation employed the revised universal soil loss equation (RUSLE) model. Subsequently, the sediment yield is estimated based on the sediment delivery ratio (SDR). The average annual soil loss was estimated at 17.91 tonnes/hectare/year, which is high soil erosion risk. The RUSLE model's accuracy is 82.1%. Moreover, the findings revealed that sub-watersheds (SW) 9 and SW 3 exhibited the maximum and minimum average annual soil loss. The Peddavagu watershed's SDR was 0.210. Annually, 3.76 tonnes/hectare/year of sediment were transported to the Peddavagu watershed outlet. The findings revealed that SW 9 and SW 5 exhibited the maximum and minimum average annual sediment yield. The model's performance was evaluated by comparing its predictions with gauge data for validation. The observed actual data indicated a yield of 3.66 tonnes/hectare/year, while the model predicted a yield of 3.76 tonnes/hectare/year. This resource offers significant insights for policymakers and decision-makers on sustainable watershed management techniques.

  • The revised universal soil loss equation (RUSLE) model for mean soil loss estimation was employed.

  • Average annual soil loss was estimated at 17.91 tonnes/hectare/year, indicating high erosion risk.

  • The RUSLE model achieved a very good accuracy of 82.1%.

  • The average annual sediment yield was estimated at 3.76 tonnes/hectare/year.

  • The study offers key insights for policymakers to develop sustainable watershed management techniques.

Soil erosion is the most common form of land degradation caused by a combination of natural processes and human activities (Ganasri & Ramesh 2016; Bhattacharya et al. 2020). Soil erosion is a worldwide problem that has both on-site and off-site impacts (Bogale et al. 2020). It has come to the observation of decision-makers and land managers all over the world that the fight against soil erosion and other kinds of land degradation has been a source of concern (Roder & Hill 2009). Numerous important topography factors have an impact on the mechanism of soil erosion (Ustun 2008; Mitasova et al. 2013; Saravanan et al. 2021). An important factor affecting the runoff process is aspect and slope. As the slope increases, there is a corresponding increase in runoff and a subsequent reduction in infiltration. Due to the slope's rainfall runoff's faster rate, soil erosion will occur. Erosion occurs when wind or water moves soil particles, transferring them to other areas. Erosion is initiated by a confluence of elements, including the presence of steep slopes, climatic conditions, unsuitable land utilisation, patterns of land cover, and so on (Renschler et al. 1999; Mathew & Shekar 2022; Shekar et al. 2023a).

Thlakma et al. (2018) and May & Place (2004) identify water-induced soil erosion as the most prevalent type of soil erosion when compared to other causes. Land-use land cover (LULC), soil type, surface gradient greatly, and other factors affect erosion. Erosion reduces soil nutrients and organic matter (OM), affecting soil performance. The issue of soil erosion has been recognised as a global concern due to the interconnectedness of natural resources, population dynamics, and the phenomenon of climate change (Lal 2003). The utilisation of effective modelling techniques facilitates the acquisition of valuable insights pertaining to the present state of erosion, its patterns of change over time, and the ability to conduct scenario analysis.

Soil erosion models employ mathematical formulations to establish relationships between key characteristics and land surface processes (Jetten et al. 2003). The criteria encompassed in their study consist of meteorological variables, topography characteristics, LULC, and soil properties (Merritt et al. 2003; Saravanan et al. 2010; Ochoa-Cueva et al. 2015). Soil erosion models include detachment, deposition, and transport (Nearing et al. 1994). Soil erosion models serve as pivotal planning tools due to their invaluable ability to anticipate soil loss, as highlighted in the research by Benavidez et al. (2018). The selection of an appropriate model for a specific soil erosion study hinges on a triad of critical considerations: the unique characteristics of the watershed region under examination, and the data availability or insights regarding the model's effectiveness, as elucidated by Keesstra et al. (2014).

Conventional methods for evaluating soil erosion risk are known for their high costs and time demands. A wide range of models (conceptual, physical, and empirical) has been established on a global scale to facilitate soil loss assessment (Maqsoom et al. 2020). Data inputs and complexity differ amongst these models. Soil erosion models, like USLE (Wischmeier & Smith 1978) and the revised universal soil loss equation (RUSLE) proposed by Renard et al. (1997), are used to predict the rate of gross soil loss at the plot scale. One of the models that has achieved widespread acceptance is the empirical RUSLE (Chuenchum et al. 2019; Ghosal & Bhattacharya 2020). Aspects like the availability of data or information and the intended use of the model influence the choice of models for estimating erosion, among other considerations. Moreover, the model demonstrates strong applicability in ungauged catchments. It has a moderate data requirement and, most importantly, effectively integrates with geographic information system (GIS), facilitating the upscaling of soil erosion processes. This method is utilised by scientists worldwide to study soil erosion at various spatial scales (Renard et al. 1997; Fernandez et al. 2003; Gitas et al. 2009; Yue-Qing et al. 2009; Manjulavani et al. 2016; Thlakma et al. 2018; Chuenchum et al. 2019; Fenta et al. 2020).

The evaluation of sediment discharge plays a role in the detection of diffuse source contamination as well as in the strategic development and implementation of construction endeavours. Nevertheless, the measurement of sediment production is typically not directly accessible, and instead, it is estimated through the utilisation of an sediment delivery ratio (SDR) as proposed by Verstraeten & Poesen (2002). Accurately predicting SDR holds significant importance in the management of sediments for the purpose of sustainable development of natural resources and preservation of the environment. Soil erosion represents the initial stage within the sedimentation process, encompassing the sequential occurrences of erosion, transit, and deposition of sediment. A portion of eroded soil is transported via a channel system, contributing to sediment yield (SY), whereas a component of this soil undergoes deposition within water channels. In order to accomplish the purpose of measuring sediment yields, it is possible to make use of the SDR, which provides an indication of the percentage of total soil loss that is caused by water that flows to a certain location within the channel network. This can be done by calculating the SDR at the location in question. Utilising the RUSLE model to quantify spatial gross soil loss is an efficient way to determine the amount of soil that has been lost due to erosion and the amount of sediment that has been moved within a watershed. Furthermore, the estimation of sediment delivery within watersheds can be successfully accomplished through the implementation of the SDR technique. The process of raindrops impacts detaching soil particles from the earth and results in their transportation to rivers, hence increasing the sediment load and SDR of watersheds (Beskow et al. 2009; Colman et al. 2018; Rajbanshi & Bhattacharya 2020; Ahmad et al. 2022). The use of the SDR allows for the estimation of sediment delivery in watersheds that lack sufficient data. The integration of the RUSLE and the SDR has been identified as a major technique for predicting SY and soil erosion in places with limited availability of data (Bhattacharya et al. 2020; Ahmad et al. 2022).

The objectives of this current research were (1) to estimate soil erosion using the RUSLE in a GIS environment; (2) to estimate the SDR of the Peddavagu watershed; and (3) to estimate the SY by combining RUSLE with SDR in the Peddavagu watershed.

The Indian Meteorological Department (IMD) reports that the monsoon rainfall, which occurs between the months of June and September, significantly impacts the hydrological characteristics of the watershed. This is evident from the annual average precipitation of approximately 1,150 mm. Numerous ephemeral streams and smaller rivers supply the river with its water. According to the information presented in Figure 1, the region is geographically partitioned between the states of Maharashtra and Telangana in the country of India. The region encompasses a total land area of 3,150 km2. The Peddavagu watershed has an elevation ranging from 160 to 655 m relative to the mean sea level. The study region spans from 79° 45′ 0′′ to 78° 30′ 0′′ east and from 18° 45′ 0′′ to 19° 45′ 0′′ north. The outlet locations at Bhatpalli are 79° 30′ 14′′ east and 19° 19′ 50′′ north.
Figure 1

Peddavagu watershed location map.

Figure 1

Peddavagu watershed location map.

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The watershed's boundaries were defined through the utilisation of a digital elevation model (DEM) downloaded from National Aeronautics and Space Administration (NASA) website. To create the map representation, remote sensing (RS) data from the Landsat 5 satellite were employed, and this process involved the application of supervised classification methodologies as detailed by Roy et al. (2016). The FAO (1974) launched the Digital Soil Map of the World (DSMW) soil mapping and database project. In addition, the data on rainfall that was particular to the Peddavagu watershed were extensively collected from the IMD in Pune. The major resource for this important meteorological data was the official IMD website (https://mausam.imd.gov.in/), which can be accessed here. The India-WRIS website provided suspended sediment concentration and runoff data for the Bhatpalli gauging station in the Godavari River Basin (http://www.india-wris.nrsc.gov.in/) (Das et al. 2021, 2022).

RUSLE model

The rate of soil loss in watershed regions is determined by a variety of factors, including the watershed's specific shape, the inherent properties of the soil, the prevailing climatic conditions in the area, and the LULC patterns and management strategies employed. Many models can accomplish the estimation of soil loss. The empirical models utilised in the present investigation were RUSLE (Renard et al. 1997). The reasons for this phenomenon can be attributed to the reduced data, the easy accessibility of the necessary data, and the capability to seamlessly interface with GIS (Jiang et al. 2015; Yesuph & Dagnew 2019). To estimate soil loss, variables such as LULC, rainfall, DEM, and soil were used in this study. The RUSLE model incorporates several key parameters that have been identified as significant contributors to soil loss, like the land-use management factor (C-factor), the length and steepness factor (LS factor), the agriculture practice factor (P-factor), the soil erodibility factor (K-factor: units in tonnes ha h /MJ/ha/mm), and the rainfall erosivity component (R-factor: units in MJ mm/ha/h/year). The RUSLE technique is utilised to assess the annual erosion rate within the watershed during the year 2005. Equation (1) calculates the annual rate of erosion. The model's execution led to the classification of annual soil loss. Consequently, the area of erosion loss was categorised into six. Figure 2 depicts the methodology chart of the current study.
formula
(1)
Figure 2

Flowchart for soil erosion and sediment yield mapping.

Figure 2

Flowchart for soil erosion and sediment yield mapping.

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The SE represents the mean annual soil loss (tonnes/hectare/year). The factors P, C, and LS are dimensionless.

Validation

The process of validation holds significant importance in the field of modelling, as it is essential for ensuring the scientific integrity of the models. Without undergoing validation, models lack the necessary credibility and scientific merit. The accuracy of soil erosion was verified using the receiver operating characteristic curve (ROC) and area under the curve (AUC). The present investigation employed India-WRIS data to verify the model (RUSLE)'s SY output.

Sediment delivery ratio

Due to sediment deposition in watersheds, the RUSLE model cannot quantify SY at outflows (Fernandez et al. 2003; Wu et al. 2013). The direct measurement of SY is typically unavailable, necessitating the use of an SDR for estimation purposes. The estimation of the cumulative sediment load discharged to the watershed's outfall by runoff water is achieved through the utilisation of the SDR. The SDR, which is bounded between 0 and 1, quantifies the proportion of sediment present. As stated by Fernandez et al. (2003), SY consistently exhibits a lower magnitude than the erosion rate. Ouyang & Bartholic (1997) propose that an accurate calculation of SDR is an approach that is both significant and effective for predicting the amount of sediment that would be yielded. This current study employed the SDR model to assess the SY. The calculation of the SDR entails the utilisation of Equation (2), as proposed by the United States Department of Agriculture (USDA) in 1979. This study used Equation (2), which is used in many investigations (Ouyang & Bartholic 1997; Kumar et al. 2014; Behera et al. 2020; Cheikha et al. 2021).
formula
(2)
where A is the area of each SWs (km2) and SDR is each SWs or entire watershed (no units).

SY estimation

It is the quantity of sediments that are transported or delivered to a specific location during a defined time frame. The amount of sediment carried by the flow to the outlet of the watershed is provided (Julien 2010). Both soil erosion and SY are interconnected phenomena, but they possess unique properties. Kumar et al. (2020) found that soil fragments from level fields with modest surface runoff do not reach the watershed exit. The direct measurement of SY is sometimes not possible in a watershed that lacks sufficiently collected SY data. The precise assessment of SDR correlation with regional soil loss is a significant and efficient method for forecasting sediment output (Mutua & Klik 2006). The annual SY is assessed by taking into account the annual soil loss and SDR correspondingly. The estimation of SY can be achieved by utilising Equation (3) (Fistikoglu & Harmancioglu 2002).
formula
(3)
where SE is annual soil erosion (tonnes/hectare/year) and SY is the annual sediment yield of SWs (tonnes/hectare/year).

Rainfall erosivity factor

The parameter ‘R’ denotes the capacity of precipitation and surface water flow to induce erosion. There is a positive correlation between the intensity of rainfall and the quantity and likelihood of erosion. Rainfall has a significant role as a catalyst for soil loss within the watershed. It generates kinetic energy that facilitates soil particle separation through raindrop splash erosion (Wischmeier & Smith 1978; Ganasri & Ramesh 2016; Teng et al. 2019). The existing body of literature has examined the association between rainfall features and soil detachment and has identified a robust correlation between these two factors (Ma et al. 2014). Therefore, it can be expressed as a mathematical function that is dependent on both the intensity of rainfall and the quantity. It can be determined by considering the characteristics of the falling raindrop and the intensity of the rainfall. The raindrop's kinetic energy is multiplied by the 30-min max rainfall intensity (Pandey et al. 2007). However, the availability of comprehensive meteorological data for the current investigation is limited. Different equations can be utilised to determine the R-factor. However, carefully selecting an appropriate equation is critical for accurately computing the R-factor. Renerd & Freimund (1994) and Bhattacharya et al. (2021a, 2021b) proposed the following equations to achieve this.
formula
(4)
formula
(5)
where P means annual rainfall (units in mm); R is the erosivity of annual rainfall.
This study utilised annual rainfall data from 12 in and around stations to examine rainfall distribution variance and estimate the watershed's R-factor. The R-factor map was created using 2005 IMD data. The R-factor was determined using Equations (4) and (5), utilising annual rainfall data from each site. Subsequently, the annual rainfall and R-factor values for each station were exported to a GIS environment. In ArcGIS 10.7, the inverse distance weighting (IDW) interpolation method was used to create the rainfall distribution (Figure 3) (Sathiyamurthi et al. 2023). The precipitation levels throughout the watershed exhibit a variation spanning from 1,449.1 to 1,698 mm. The R-factor is obtained through the implementation of the IDW technique, as depicted in Figure 4. The R-factor values within the watershed exhibit a range of 1,178.8–1,482.2 (Units in MJ mm/ha/h/year).
Figure 3

Rainfall map.

Figure 4

R-factor map.

Soil erodibility factor

It measures potential erosion and is impacted by the soil's underlying features. Soil types vary in water-induced erosion susceptibility. The inherent susceptibility of soil loss from rainfall and surface overflow (Wischmeier & Smith 1978; McCool et al. 1995; Thomas et al. 2017). A diverse range of chemical and physical soil factors influence the erodibility of soil. The RUSLE model takes into account various physical features, including the structure of the soil, OM content, primary particle size distribution, and permeability, which are considered to be the key elements that affect the K-factor. The quantification of soil parameters is often conducted in a laboratory setting using established conventional methodologies. Conducting a comprehensive field survey of soils in certain areas within the watershed is unfeasible. Although laboratory-based approaches continue to be widely utilised, their utilisation is accompanied by significant drawbacks. These drawbacks include high costs, time-intensive procedures, and the potential for environmental harm due to the generation of chemical residues (Nanni & Demattê 2006).

Permeability and structural code determine K-factor (Tran et al. 2011), as indicated in Table 1. The provided code incorporates various soil attributes, including its composition and permeability, which are known to impact its susceptibility to erosion. The percentages (%) such as clay, OM, sand, and silt, which are crucial variables for calculating the K-factor, are sourced from the data centre of the FAO. Two groups were recognised in the research region, namely clay and clayey loam (Figure 5). The estimation of the K-factor in this present investigation is conducted by using Equations (6) and (7), which were derived by Wischmeier & Smith (1978). Figure 6 visually displays this information.
formula
(6)
where M represents the particle size fractions in mm
formula
(7)
where ‘p’ is the code of permeability (dimensionless); ‘OM’ is the organic matter of soil; ‘s’ is the structure code (dimensionless).
Table 1

Code for permeability and structure

TextureStructure code (s)Permeability code (p)
Silty clay, clay 
Silty clay loam, sandy clay 
Sandy clay loam, clayey loam 
Loam, silty loam 
Loam sand, sandy loam 
Sand 
TextureStructure code (s)Permeability code (p)
Silty clay, clay 
Silty clay loam, sandy clay 
Sandy clay loam, clayey loam 
Loam, silty loam 
Loam sand, sandy loam 
Sand 
Figure 5

Soil classes map.

Figure 5

Soil classes map.

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Figure 6

K-factor map.

Length and steepness factor

Both slope steepness (S) and slope length (L) are important topographic parameters that have a significant effect on soil loss. The LS component in the RUSLE considers these qualities (Datta & Schack-Kirchner 2010; Prasannakumar et al. 2012). Typically, regions characterised by pronounced inclines are more prone to erosion compared to locations that are flat or have gradual slopes. Similarly, as slope length rises, soil erosion increases, and vice versa. The aforementioned analysis demonstrates the susceptibility of topographic parameters to soil erosion, underscoring the importance of obtaining precise measurements for this variable.

The LS factor map is generated by utilising significant factors including flow accumulation, the slope of the watershed, and flow direction (Figure 7). The determination of these parameters is conducted via the Shuttle Radar Topography Mission (SRTM)-DEM within the GIS environment. The LS factor analysis entails the utilisation of the following equation (Figure 8).
formula
(8)
where slope in degree and slope length are the resolution of DEM and flow accumulation.
Figure 7

Slope in degrees map.

Figure 7

Slope in degrees map.

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Figure 8

LS factor map.

Land-use management factor

It is commonly referred to as the C-factor and is of utmost importance in the evaluation of the correlation between soil erosion and LULC. Land-use change pertains to alterations in the utilisation or management of a specific land region by human beings. Land-cover change pertains to alterations in the continuous characteristics of the land, including vegetation type, soil conditions, and similar factors (Shekar & Mathew 2023; Shekar et al. 2023b, 2023c). The C-factor, as identified by McCool et al. (1995), is a significant erosion factor that can be readily worked by human activities in order to mitigate erosion. RS approaches, such as LULC categorisation, are currently preferred over conventional methods because of their cost-effectiveness, quick data analysis, and relatively high accuracy (Durigon et al. 2014). When extracting LULC classes from RS imagery, the allocated C-factor values are taken from relevant literature sources. A value of ‘1.0’ indicates a significant erosion risk, whereas ‘0’ indicates no erosion risk. As seen in Table 2, different land covers have different C-factors. A land-use map (Landsat 5) for the entire country of India was produced by Roy et al. (2016), from which the study area was cropped and utilised for soil erosion mapping (2005 year) (Figure 9). The LULC map underwent reclassification using the C-factor value to produce the C-factor mapping within a GIS environment (Figure 10).
Table 2

C-factor values

LULCC-factorReference
Deciduous broadleaf forest 0.12 Chen et al. (2019)  
Crop land 0.55 Bouguerra et al. (2017)  
Built-up land 0.07 Eniyew et al. (2021), Hurni (1985)  
Water bodies 0.0 Erdogan et al. (2007)  
Fallow land 1.0 Park et al. (2007)  
Plantations 0.13 Lahlaoi et al. (2015)  
Deciduous needleleaf forest 0.12 Chen et al. (2019)  
Wasteland 0.50 Swarnkar et al. (2018)  
Mixed forest 0.08 Ed-daoudy et al. (2023)  
Shrubland 0.014 Getachew et al. (2021), Wischmeier & Smith (1978)  
LULCC-factorReference
Deciduous broadleaf forest 0.12 Chen et al. (2019)  
Crop land 0.55 Bouguerra et al. (2017)  
Built-up land 0.07 Eniyew et al. (2021), Hurni (1985)  
Water bodies 0.0 Erdogan et al. (2007)  
Fallow land 1.0 Park et al. (2007)  
Plantations 0.13 Lahlaoi et al. (2015)  
Deciduous needleleaf forest 0.12 Chen et al. (2019)  
Wasteland 0.50 Swarnkar et al. (2018)  
Mixed forest 0.08 Ed-daoudy et al. (2023)  
Shrubland 0.014 Getachew et al. (2021), Wischmeier & Smith (1978)  
Figure 9

LULC map.

Figure 10

C-factor map.

Agriculture practice factor

The P-factor and C-factor are interconnected as they both aim to assess the efficacy of management practices in mitigating soil loss (Renard et al. 2011). Nevertheless, variable C demonstrates the favourable influence of managerial strategies on erosion, such as the existence of biomass on beneath the soil or the surface, as well as the level of surface roughness. Conversely, the positive effects of management practices on controlling runoff, particularly in terms of altering the direction and velocity of the runoff, are denoted as P (Morgan & Nearing 2011; Benavidez et al. 2018). It is critical to determine LULC characteristics and steepness within a specific catchment region. The variable P has been employed to denote the influence of agricultural practices and land management approaches on soil erosion, with the aim of mitigating the rate of soil erosion through the alteration of runoff flow direction.

The P-factor value of the study of the watershed ranges from 0.1 to 1 (Tsegaye & Bharti 2021; Saha et al. 2022). A value of 1 designates non-agricultural lands such as deciduous needleleaf forests, barren lands, deciduous broadleaf forests, water bodies, built-up, fallow land, mixed forest, wasteland, shrubland, and plantations. In the context of agricultural land, specifically crop land, distinct P-factor values are allocated based on certain slope categories. These categories include 0–5, 5–10, 10–20, 20–30, 30–50, and 50–100%, as outlined in Table 3. The values utilised in this analysis: Wischmeier & Smith (1978). GIS erosion models calculate P-factor values depending on LULC and slope (Figure 11).
Table 3

P-factor value

Type of landSlope (percentage)P-factor
Agriculture land 0–05 0.10 
05–10 0.12 
10–20 0.14 
20–30 0.19 
30–50 0.25 
50–100 0.33 
Non-agriculture land All 1.00 
Type of landSlope (percentage)P-factor
Agriculture land 0–05 0.10 
05–10 0.12 
10–20 0.14 
20–30 0.19 
30–50 0.25 
50–100 0.33 
Non-agriculture land All 1.00 
Figure 11

P-factor map.

Estimation of average annual soil erosion

GIS was employed to estimate annual soil loss using RUSLE. The creation of the RUSLE input parameters in a raster layer format was facilitated using ArcGIS 10.7. The thematic layers were subsequently subjected to analysis using the raster calculator tool in order to produce a map illustrating the annual soil loss, employing Equation (1). The average yearly soil loss using the GIS-RUSLE approach was 0–5,600 tonnes/hectare/year. The estimated average annual soil loss within the designated study region is 17.91 tonnes/hectare/year. The current study determined that the annual soil loss amounts to 5,641,113.48 tonnes/year. The watershed has six soil erosion classes (tonnes/hectare/year): low (0–5), moderate (5–10), high (10–20), very high (20–40), severe (40–80), and very severe (>80) (Dabral et al. 2008; Thapa 2020; Handique et al. 2023), as depicted in Figure 12. High soil erosion rates in the Peddavagu watershed resulted in 17.91 tonnes/hectare/year soil loss.
Figure 12

Soil erosion map.

Figure 12

Soil erosion map.

Close modal

The watershed has been partitioned into 13 distinct sub-watersheds (SW) in order to assess soil loss within each individual sub-watershed (Figures A1 and A2). Within the GIS framework, the process of extracting individual sub-watershed shapefiles from a total of 13 SW is accomplished through the utilisation of the export data function. Once all the individual SW shapefiles have been obtained, the Arc toolbox provides a tool called spatial analyst tools. Within this tool, the extraction function, namely the extract by mask feature, is utilised. The input data required for the extract by mask operation consist of a complete soil loss raster file and an individual shapefile representing the watershed. This process aims to obtain the final soil loss of sediment in each individual watershed, measured in tonnes/hectare/year. SW 3 had the lowest mean annual soil loss, 14.10 tonnes/hectare/year, while SW 9 had the highest, 51 tonnes/hectare/year (Figure A3).

Validation

Models' scientific importance is contingent upon validation reports, making them a critical stage in scientific research (Owolabi et al. 2020). The accuracy of these classifications was verified using the ROC and AUC. The ArcSDM tool in the ArcGIS software generated ROC-AUC using SE points (Prasannakumar & Vijith 2012; Osman & Das 2023; Debnath et al. 2024). This study identified erosion-vulnerable sites in the study region using Google Earth Pro. This categorisation provides a framework for understanding as well as assessing the different levels of relationship quality (Yesilnacar & Topal 2005; Andualem & Demeke 2019; Owolabi et al. 2020). The RUSLE model demonstrates a level of accuracy amounting to 82.1% (0.821), rendering it a commendable tool for soil loss prediction, as depicted in Figure 13.
Figure 13

Validation of RUSLE model.

Figure 13

Validation of RUSLE model.

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Sediment delivery ratio

Ferro & Minacapilli (1995) suggest that the SDR represents the likelihood of sediment particles eroded from a specific upland region successfully reaching the next stream channel. This parameter calculates the percentage (%) of gross soil loss that reaches the drainage network and watershed outlet, ranging from 0 to 1. The watershed physiology, texture of the eroded material sediment source, land cover, and transport system might affect SDR values (Walling 1988; Richards 1993). After computing all the factors of the RUSLE model, ArcGIS 10.7 software created thematic layers representing each specific RUSLE parameter. Subsequently, these thematic maps were multiplied together using a tool called a raster calculator. From a managerial standpoint, SY holds greater significance than potential soil loss, as it provides a quantifiable measure of the amount of dirt being transported out of a watershed. Therefore, this study has integrated an approach for estimating SY based on the SDR. The SY is the amount of erosion in a catchment area that has not yet been accumulated and is leaving the watershed within a certain timeframe. Typically, the combined effects of erosion and the magnitude of the SDR at a particular position determine the sediment output at that place. SDR denotes the percentage of total erosion that reaches the designated measurement site. This metric represents the effectiveness of sediment transport at the given location.

Equation (2) from the USDA (1979) gives the Peddavagu watershed's SDR of 0.210. According to Mohapatra (2022), there exists an inverse relationship between the area of the watershed and the soil detachment rate. Figure A4's data reveal that the sub-watershed's SDR values range from 0.266 (SW 5 Area: 367.13 km2) to 0.295 (SW 4 Area: 146.56 km2), while the overall watershed value stands at 0.210 (21.0%). These findings reveal that the channels have carried 21.0% of the soil loss. These findings suggest that approximately 21.0% of the eroded soil materials have the potential to reach the channels, while the remaining 79.0% effectively retain and deposit before entering the channel networks.

SY estimation

The SY, as determined by SDR, has a wide range across the Peddavagu watershed, spanning from 0 to 1,173.38 tonnes/hectare/year. The anticipated annual SY is calculated to be 1,184,633.81 tonnes/year. The calculated average annual SY is determined to be 3.76 tonnes/hectare/year. According to Kolli et al. (2021), SY can be divided into five categories: extremely severe (>40), severe (19–40), strong (7–19), moderate (1–7), and slight (<1), as seen in Figure 14. The average annual SY was found to be 3.76 tonnes/hectare/year in the Peddavagu watershed. The model's performance was assessed by comparing its predictions with gauge data for validation. The results indicate that the observed actual data are 3.66 tonnes/hectare/year, whereas the predicted amount is 3.76 tonnes/hectare/year.
Figure 14

Sediment yield map.

Figure 14

Sediment yield map.

Close modal

The watershed is divided into 13 SW to evaluate their sediment yields (Figure A5). Within the GIS environment, the process of extracting individual sub-watershed shapefiles from a total of 13 SW is accomplished through the utilisation of the export data function. Once all the individual SW shapefiles have been obtained, the Arc toolbox offers a tool called spatial analyst tools. Within this tool, the raster calculator is utilised as a form of map algebra. The input data for the raster calculator consist of a specific soil loss raster file for each individual sub-watershed unit, together with the corresponding SDR value for that particular SW unit. These inputs are used to calculate the final SY for each individual SW unit, expressed in tonnes/hectare/year. The study region revealed that SW 9 and SW 5 exhibited the maximum and minimum mean SY, as measured by the SDR, with values of 14.08 and 4.02 tonnes/hectare/year, respectively.

Limitation of the study

The current investigation provides information about soil erosion classification and shows high soil erosion in the Peddavagu watershed; however, it has limitations. Watershed sediment and soil erosion are major barriers to sustainable land use and stream and lake water quality (Mutua & Klik 2006). RUSLE also ignores gully erosion and only considers sheet/interill and rill erosion. Additionally, the RUSLE model does not directly represent watershed sediment output (Phinzi & Ngetar 2019).

The present investigation incorporates the analysis of rainfall, soil, LULC, and DEM data. The R, K, C, P, and LS factors are first computed within the GIS framework. Subsequently, these factors were utilised to estimate soil loss through the implementation of the RUSLE. The average annual soil loss for the watershed is 17.91 tonnes/hectare/year, indicating high erosion risk. The model's accuracy suggests a very good (82.1%) outcome for the RUSLE model results. Moreover, the study region revealed that SW 9 and SW 3 exhibited the maximum and minimum average soil loss, measuring 51 and 14.10 tonnes/hectare/year, respectively. The Peddavagu watershed's SDR was approximately 0.210. The findings also indicate that the yearly SY originating from the entirety of the watershed amounts to 3.76 tonnes/hectare/year. The study region revealed that SW 9 and SW 5 exhibited the maximum and minimum mean SY, as measured by the SDR, with values of 14.08 and 4.02 tonnes/hectare/year, respectively. The model's performance was assessed by comparing its predictions to gauge data for validation. The results indicate that the observed actual data was 3.66 tonnes/hectare/year, whereas the anticipated value was 3.76 tonnes/hectare/year. Moreover, it offers significant insights for policymakers, decision-makers, stakeholders, and international organisations collaborating to advance the implementation of sustainable watershed management techniques.

The satellite data were made accessible by the USGS, which the authors would like to appreciate for their efforts (https://earthexplorer.usgs.gov/). On their website, which can be found at https://mausam.imd.gov.in/, the Indian Meteorological Department has kindly provided data on the amount of rainfall. We are grateful that the FAO has provided us with a soil data. Also, authors would like to thank India-WRIS for providing daily data of surface runoff and suspended sediment concentration (http://www.india-wris.nrsc.gov.in/).

P.R.S. conceptualised the whole article, developed the methodology, arranged the Software, rendered support in data curation; validated the data, and wrote the original draft. A.M. supervised the article, investigated the data, visualised the process, reviewed and edited the article.

There was no funding for this project

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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