Principles of Soil Erosion Risk Modeling

Abstract

It is anticipated that modern agriculture practice patterns will accelerate soil erosion in a negative way. Evaluating the long-term impact of various management strategies on a large farm is a gauge of the sustainable practices of soil nutrients. To find areas at risk, there are generally three different methods used: qualitative research, statistical approach, and model approach. Each of these approaches has distinctive features and applications. The use of geographic databases created using GIS technology has improved all techniques and strategies created recently. The sustainability of agricultural ecosystems worldwide is severely threatened by low or nonexistent attention given to environmental impact assessments, which also seriously threaten soil systems. Both conventional field-based methodologies and soil erosion modeling can be employed to quantify soil erosion. Agricultural automation has increased along with the accessibility of finer scale global level data, strengthening agri-environmental related modeling approaches. Due to the laborious, moment, limited flexibility, and noncomparability of field-based methods, soil erosion modeling has many advantages over these assessments. The examined models will be examined this season in the direction of wind erosion. The model is useful for forecasting and highlighting the areas most impacted by erosion while also saving time and resources.

1. Introduction

A serious issue is soil erosion, which averages 30–40 t/ha per year in South America, Africa, and Asia, and in the South Asia region is thought to be severe [1]. The agroecological efficacy in semiarid and arid regions is facing a significant impact from climate change, primarily due to an increased rate of land degradation [2, 3]. Due to the undulating to steep terrain and heavy rainfall, particularly in the first few years after establishment, soil erosion is typically higher in plantation farms. In order to maintain the productivity and fertility of the estates, appropriate soil conservation measures must be taken in order to reduce this soil erosion to a higher level. These measures included reducing soil erosion, strengthening the soil’s structure to make it more resilient to detachment and transportation and more permeable to surface water, shielding the surface from the effects of rainfall, reducing runoff, and providing secure disposal options for excess runoff. Some of the features that have been seen include drainage systems, embankments, fences, cover crops, and stone terracing [4]. Despite the fact that soil is regarded as a mass containing nutrients, topsoil with nutrients has been drained in those fields over time, owing primarily to soil erosion. As a result, reliable and timely soil erosion monitoring in agricultural and plantation regions is crucial for developing soil preservation strategies and improving agricultural practices [5]. Numerous nations in the twentieth century experienced increased land loss as a result to raise human-induced soil erosion [6]. The most fertile topsoil can be lost due to erosion, which lowers soil productivity. Investigating soil loss mechanisms and determining the risk of soil erosion are crucial for planning future management of soils, preservation, and land-use activities [7–9].

Soil erosion can be evaluated using traditional field-based techniques and soil erosion modeling [10]. Agricultural computerization has increased along with the accessibility of finer-scale global-level data, which has improved the potency of modeling techniques associated with agriculture and the environment [11]. Field-based approaches to measuring soil erosion are labor-intensive, time-consuming, limited in flexibility, and incomparable, whereas soil erosion modeling has numerous benefits over these approaches [12, 13]. Several methods for modeling soil erosion have been established in recent years with varying needs for input and complexity [5]. Applications, specifications, intended uses, and the type of data each model provides vary significantly [14]. Soil erosion modeling, which is employed in place of traditional methodologies, is the most practical and trustworthy instrument for evaluating soil erosion and enabling the appropriate selection of soil erosion management strategies [12]. One of the primary reasons for the widespread use of soil erosion modeling around the world is unquestionably its high degree of adaptability and data accessibility, as well as its sparse parameterization, broad research, and comparability of results, which allow the model to be applied to almost any situation or geographical area [13]. The Universal Soil Loss Equation (USLE) and its associated models, which are widely employed to address soil erosion, are among the most extensively used designs. These soil erosion algorithms have been applied in a variety of contexts worldwide, making them well-known [15]. In this chapter of the book, soil erosion, risk modeling, and management using the model are discussed.

2. Soil erosion

2.1 Soil erosion definition

“The dirt on top of a field is naturally eroded by forces such as wind and water. This occurrence is known as soil erosion” [16]. It leads to a constant loss of topsoil, ecological deterioration, soil collapse, etc. Soil erosion is a continuous process that can occur either slowly or quickly. Soil erosion (Figure 1) is caused by the loosening or washing away of dirt particles in ravines, seas, waterways, or distant lands [9].

Figure 1.

Soil erosion (@Frederick J. Weyerhaeuser).

2.2 Types of soil erosion

The erosive process’ rate or the agent accountable for it is used to categorize objects. Fast or gradual and natural or human-caused soil erosion can all be categorized. Water currents and windstorms are the main causes of soil erosion caused by natural processes, but human activity can also make the problem worse [17]. In Figure 2, types of soil erosion are drawn with slight modifications (by HARM VENHUIZEN Associated Press/Reporting for America).

Figure 2.

Type of soil erosion.

2.2.1 Water erosion

This sort of soil erosion, as its name implies, is brought on by water and denotes the removal of topsoil as a result of precipitation, snowmelt, floods, or improper irrigation. As a result, it can happen as a result of farming activity or extreme weather. Water is more destructive on bare land and during periods of heavy rain or melting [18].

2.2.2 Wind erosion

Another reason for erosion is dust storms, which have become more frequent in recent years, especially in arid areas. When the earth is level, acceptable, and dry, erodibility increases; nevertheless, hills reduce wind force and make it more difficult to remove rough and heavy particles [19].

2.2.3 Anthropogenic soil erosion

This typically happens as a result of anthropogenic forces, and both direct and indirect human activity can cause soil erosion. For instance, mining and quarrying have an immediate impact. The topsoil is impacted by unsustainable management in indirect ways, which aggravates agricultural and forest standing erodibility [20].

2.3 The risks of soil erosion

More than just the loss of fertile land is a consequence of soil erosion. It has resulted in increased sedimentation and pollution in streams and rivers, blocking these waterways and resulting in a fall in fish populations and other species. Degraded areas are also frequently less able to retain water, which can make floods worse [21].

2.4 Mechanisms for erosion

Detachment (from the ground), movement (by water or wind), and deposition (frequently in areas where we do not want the soil such as streams, lakes, reservoirs, or deltas) are the three processes involved in erosion [22].

2.5 Reason for soil erosion

The following factors are significant contributors to soil erosion:

2.5.1 Rainfall and flooding

Four different types of soil erosion are brought on by rainstorms of greater intensity: Sheet and rill erosion, sheet erosion, gully erosion, and splash erosion. In locations with extremely heavy and frequent rainfall, a sizable amount of soil is lost because rainfall drops scatter the soil, which is then washed away into the nearby rivers and streams. Running water during floods also obliterates a tremendous amount of soil by creating holes, rock-cut basins, etc. [16].

2.5.2 Agriculture

Soil erosion is mostly caused by agricultural practices. The ground is disturbed by agriculture. To plant new seeds, the trees are cut down, and the ground is plowed. The land is left fallow during the winter because the majority of the crops are grown in the spring. During the winter, the earth erodes the most. Tractor tires also leave grooves in the ground that operate as a natural waterway. Wind erosion removes fine soil particles [16].

2.5.3 Grazing

Grazing animals consume the grasses on the land and clear it of its flora. The dirt is disturbed by their hooves. Additionally, they remove plants from the roots. As a result, the soil becomes more permeable to erosion [23].

2.5.4 Logging and mining

The logging process necessitates the removal of multiple trees. Trees firmly hold the soil in place. The forest cover protects the soil from intense rain. The leaf litter that protects the soil from erosion is also removed during logging. Additionally, mining activities harm the ecology and increase soil erosion risk [24].

2.5.5 Construction

The soil is at risk of erosion due to the building of structures and roadways. The trees and meadows are destroyed for development purposes, exposing the soil, and making it vulnerable to erosion [25].

2.5.6 Rivers and streams

The dirt particles are carried away by the moving waters of streams and rivers, causing a V-shaped erosion action [26].

2.6 Heavy winds

Small soil particles are carried away by the wind to distant countries when the weather is dry or in semiarid regions. During dry periods or in semiarid regions, the wind carries minute soil particles to far-off nations [24].

2.7 Effects of soil erosion

Erosive processes have an effect on agricultural productivity, deteriorating rural communities’ living conditions and well-being (both for individual farmers and agricultural cooperatives). Farmlands that have been eroded over time lose their soil fertility, deteriorate, and are no longer suited for farming. In addition, erosive activities severely harm the environment, decreasing biodiversity and disrupting the balance of ecosystems. However, soil erosion is an issue for other reasons as well. Oftentimes, it goes unnoticed, causing irreversible land decay [27].

The principal consequences of soil erosion include:

2.7.1 Loss of arable land

Crop production is not supported, and agricultural output is reduced since soil erosion damages the top fertile layer of the soil, which is rich in the nutrients needed by plants and the soil [28].

2.7.2 Obstruction of waterways

In addition to fertilizers and other chemicals, agricultural soil also contains pesticides and insecticides. Thus, the waterways where the soil flows get contaminated. Flooding results from sediments building up in the water and increasing water levels [29].

2.7.3 Polluting the air

The mixing of dust particles in the atmosphere is the main source of air pollution. When inhaled, some toxic compounds, such as petroleum and insecticides, can be quite dangerous. When the winds blow, the arid and semiarid regions’ dust plumes generate extensive pollution [30].

2.7.4 Desertification

Soil erosion is a primary contributor to desertification, transforming habitable areas into deserts. Deforestation and damaging land use exacerbate the situation, resulting in biodiversity loss, soil degradation, and ecosystem changes [31].

2.7.5 Infrastructure destruction

Soil silt deposition in dams and along their banks might limit their efficiency. As a result, it has an impact on infrastructure projects such as dams, embankments, and drainage [24].

2.7.6 Losses of topsoil

The removal of topsoil by water or wind has a significant negative impact on field fertility since it is the layer of the ground that contains the most organic matter and nutrients. This is why soil erosion on agricultural land is so important. Additionally, rills or gullies make it very difficult to cultivate eroded fields [32].

2.7.7 Soil acidification

The agricultural ground may become more acidic due to a lack of organic matter, delaying crop growth, and exposing it to water and wind [33].

2.7.8 Losses in planting material

Due to agricultural losses and decreased farmer profitability, water streams and dust storms kill seedlings and remove seeds from the fields [34].

2.7.9 Water contaminant

Other implications of soil erosion include sedimentation and the deterioration of irrigation water quality due to chemical pollution of water bodies from the crops [35].

2.8 Soil erosion affects the environment

There are other problems beside the detrimental consequences on agriculture. Plants and aquatic life suffer due to soil erosion, which also results in biodiversity loss, sedimentation, and frequent flooding [36].

2.8.1 Events of regular flooding

Due to the stabilizing effects of tree roots, transforming forests into pastures or fields increases the likelihood of flooding and waterlogging since these areas lose their capacity to allow for infiltration [37].

2.8.2 Clogged waterways and polluted aquatics

In addition to causing sedimentation in areas with higher altitudes, eroded particles suffocate water pumps, dams, and grass-lined streams. Frequently, chemicals that are harmful to people, animals, and aquatic life are present in the water currents from fields [38].

2.8.3 Loss of biodiversity

Because many creatures are robbed of their native homes, eroded lands have scarce vegetation and eventually become completely naked. The loss of biodiversity causes environmental imbalance [39].

2.8.4 Reduced greenhouse gases sequestration

The loss of carbon-sequestering plants on our world might be slowed or stopped with sustainable management, lowering greenhouse gas emissions caused by deforestation. Trees and vegetation are excellent at storing carbon dioxide, but they can rarely develop on degraded terrain. Additionally, soils themselves can function as CO2 sinks. According to Aberdeen University Professor Peter Smith, the planet can store almost 5% of man-made greenhouse gases per year [40].

2.9 Solutions to soil erosion

The kind of soil, terrain, local climate, and appropriate agricultural practices such as crop rotation or tillage methods all play a role in the decision-making process for controlling soil erosion [41]. Analyzing the efficacy of implemented techniques and customizing them for different fields is crucial [42]. The key to success is early problem discovery and the selection of appropriate solutions based on the severity of the issue. In the early stages of soil erosion, for instance, replanting, growing cover crops, or mulching can be successful treatments because vegetation protects fields from harm brought on by water runoffs, rainfall, and wind. In severe cases, the effects can be lessened by terrace farming or check dams [43]. In order to stabilize the ground and slow down water streams, further soil erosion management techniques include contour cropping and perennial plantings with robust root systems [44].

2.10 Soil erosion prevention

The major environmental problem of soil erosion. It is necessary to take action to solve this issue. Some strategies for preventing soil erosion include the ones listed below:

1. To stop soil erosion, add mulch and rocks to the grass and plants below.

2. Plant trees to prevent soil erosion on bare land.

3. Mulch sheeting may be utilized to stop slopes from eroding.

4. Place a number of fiber logs to prevent any soil or water from washing away.

5. Create a wall at the bottom of the slope to aid in preventing soil erosion.

6. Proper drainage should be installed in every home so that water can be collected in the right places [45].

2.11 Key points of soil erosion

The topsoil is naturally being worn away, but human activity has sped up the process. It usually happens as a result of clearing vegetation or taking any other activity that dries out the ground. Soil erosion is a problem that can be caused by farming, grazing, mining, building, and recreational activities. One effect of soil erosion is land degradation, which has led to a rapid increase in river pollutants and sedimentation, choking up water bodies, and reducing the diversity of aquatic life. Floods occur when deteriorated areas lose their capacity to hold water. Although there are numerous obstacles to overcome when trying to stop soil erosion, there are also solutions to it. Farmers and the community that depends on agriculture for food and employment place the greatest importance on the health of the soil [46].

3. Soil erosion models

Several theoretical frameworks emphasize the significance of protracted (whether natural or geological) erosion in shaping the topography. A multitude of erosion models has been devised to gauge the potential outcomes of expedited soil erosion or soil erosion stemming from anthropogenic actions [47]. The phenomenon of tillage erosion is frequently neglected by models, and the development of soil erosion models is more commonly observed in agricultural landscapes rather than in naturally vegetated areas such as forests or rangelands. The preponderance of erosion models concentrates solely on the phenomenon of soil erosion due to water, whereas some other models center their attention exclusively on the issue of erosion at mining locations [48]. The fundamental aim of the majority of soil erosion models is to predict customary levels of soil depletion (frequently an annual mean rate) within a given area via the utilization of diverse land management practices such as a plot, a field, or a catchment/watershed [49]. Some models used to predict erosion are founded on statistical principles, whereas other models rely on mechanical or physical principles [50]. The Water Erosion Prediction Project erosion model (WEPP) and the Revised Universal Soil Loss Equation (RUSLE) represent two of the most widely utilized soil erosion models in North America [51]. The majority of mine land erosion research focuses on setting up or improving RUSLE parameters. Gully erosion is frequently excluded from soil erosion models because it is challenging to forecast these essential erosional features [52].

3.1 Principles of erosion modeling

The complex interactions among a number of elements of the Earth’s system such as precipitation, surface, subsurface, groundwater movement, vegetation growth, soil detachment, transport, and deposition, which lead to patterns of erosion and deposition in both time and space [53]. This section concentrates on the management of rainfall, runoff, vegetation cover, and soil characteristics as ingredients without going into great detail about the methodology used to calculate the values of these elements [54]. Here, an extensive mathematical representation of erosion and sediment transport processes is presented, and an association between models with various levels of complexity and experience is derived, showing the common principles [55].

3.1.1 Soil Erodibility models by type

Three types of soil erosion models can be distinguished: theoretical, physics-based, and empirical or statistical models. These models can be divided into three categories based on the physical processes they replicate, the model algorithms that represent these processes, and how much they rely on data [14]. The Universal Soil Loss Equation (USLE), along with its derivatives, the Revised Universal Soil Loss Equation (RUSLE), and the Modified Universal Soil Loss Equation (MUSLE) represent prominent instances of empirical models. They continued by saying that because they may be used in scenarios with sparse data and parameter inputs, empirical models are the most basic models. Additionally, they are particularly helpful in locating the sources of sediment and nutrient production [56].

3.1.2 Reduced erosion process models

It is possible to create equations for streamlined models of erosion processes that satisfy easy-to-compute models with freely available data, interactions between two restrictive conditions, rainfall, runoff, the local land cover’s condition, erosion, and sediment movement [57].

• Limited separation capacity.

• Sediment transfer capacity is limited.

3.1.3 Estimating erosion rates: Techniques

The classification of soil erosion assessment approaches is shown in Figure 3. Many different scientific techniques and modeling strategies have been used to build a number of soil erosion models. There exist three distinct categories of soil erosion models, namely physics-based, empirical, and conceptual models. These models are dependent on the underlying algorithms utilized, with each model type tailored to the specific nature of the algorithms employed [58, 59].

• Models that use only statistics

• A comprehensive analysis has been conducted by professionals on a compilation of data regarding erosion rates across both naturally occurring and reclaimed locations situated in close proximity to both natural and man-made ecosystems.

• Examinations of prevailing erosive or depositional structures to determine the mean erosion rates are imperative. The age of these attributes should be duly ascertained (or determined by dating deposits). The analysis of antiquated aerial photography is a common practice.

• Empirical models that are site-specific that link slope, watershed size, and rainfall

• Observations of erosion in flumes under replicated flow or rainfall situations

• Models of gully erosion with a physical basis

• Statistical or erosion mechanics-based landform and landscape scale models that are typically based on GIS are used to forecast changes in topography and erosion rates.

• Using data from watershed monitoring and sediment budget models [60].

Figure 3.

Methods for classifying soil erosion assessments.

3.1.4 The method for modeling erosion risk

An erosion risk modeling approach combined with routine field research can produce reliable decision support beneficial for the effective management of soil erosion risk [61–63]. A global initiative to predict soil loss has been the development of empirical and process-based models [14, 64–66]. The majority of the effort has gone toward thoroughly assessing the risk of soil erosion [67–69]. Habib-Ur-Rehman [70] employed a methodology centered on the process to prognosticate soil erosion on a regional scale. Numerous models of soil erosion and sediment transportation have been created on a global scale to determine the rates of sediment and nutrient movement in various land use systems. These models can be classified into three distinct groups, namely conceptual models, physical models, and empirical models, as stated by Merritt et al. [14]. The GIS-based models, namely the USLE, WEPP, AGNPS, LISEM, and EUROSEM, exhibit considerable dissimilarities in terms of their complexity, inputs and prerequisites, methodologies, visual representations, intended application domains, and output data formats [14, 71].

3.1.5 Modeling erosion using GIS

The application of GIS in erosion models enables the storing of georeferenced data, computation of input parameters for multiple scenarios, geographical analysis of modeling outputs, and effective display. GIS is utilized for the statistical analysis and modeling of erosion processes found in remote sensing data. In the early 1990s, the Geographic Resource Analysis and Support System (GRASS) provided an environment for creative work on the integration of GIS with hydrologic and erosion modeling [72, 73]. The primary use of geospatial erosion models is found in the fields of agriculture, soil conservation, minimization of silt contamination, and sustainable military management as evidenced by various sources [64, 74]. One of the initial hillslope erosion models utilized in GIS was the Universal Computing Soil Loss Equation (USLE), which was applied to analyze the impact of recent wildfires on forests and hillslopes [75]. Furthermore, topographic parameters derived from digital elevation models (DEMs) are extracted to support the analysis. Moore and Wilson come next, then Moore and Birch [73, 76], set the stage for USLEAP-Applications to Landscapes with Complex Topography and the connection between unit flow power theory and USLE. The popularity of this strategy has led to multiple USLE installations supported by GIS for challenging topographic settings [77, 78]. Among the more recent GIS applications of USLE coverage area size, large watersheds with mapped land cover from remote sensing images are just a few examples [79–82].

3.1.5.1 Making erosion estimation easy with GIS tools

The RUSLE model was originally formulated with the intent of assessing the potential for soil erosion in small and localized watersheds. Nevertheless, due to the wide distribution, rapidity, and concerns related to the quality of water, the utilization of the RUSLE framework inherently presents drawbacks in relation to expenses of implementation, adequacy of site representation, and precision of anticipated outcomes [76, 83]. The spatial distribution of soil erosion using the traditional RUSLE model is often challenging to map, thus posing a considerable difficulty [83]. The proliferation of GIS-based models at the regional level has surged significantly subsequent to the innovation of GIS technology. Various researchers have reported that the utilization of GIS technology in tandem with erosion models, such as the RUSLE, has considerably boosted the efficacy of assessing the spatial dissemination and magnitude of erosion hazards, while simultaneously reducing costs and augmenting precision. These findings have been documented in the scholarly literature [77, 84–88].

3.1.6 Soil erosion using the RUSLE model

Satellite-based remote sensing and the utilization of geographic information systems (GIS) are indispensable instruments in the evaluation of soil erosion in spatial contexts. This is due to their remarkable ability to extract, identify, and modify land features, as well as their seamless integration with the Revised Universal Soil Loss Equation (RUSLE) [83, 89–91]. The RUSLE model exhibits extensive usage and has an extensive record of validation. It is noteworthy that its limitations have been thoroughly established [92, 93]. African soil erosion rates have been predicted and assessed in a number of studies using RUSLE, with a focus on highlands and river basins [94–96]. Nevertheless, there is a dearth of research on identifying possible erosion and simulating the danger of soil erosion in built-up metropolitan settings [97–99]. In contemporary times, noteworthy progressions in urban planning and the mitigation of soil erosion have demonstrated that land managers and policymakers hold a greater degree of significance toward the spatial distribution of soil erosion risk as opposed to the factual values for soil loss [100].

3.1.6.1 Using RUSLE to estimate parameters for soil erosion risk

The preeminent and uncomplicated digital manifestation of the Universal Soil Loss Equation (USLE) was formulated with the aim of computing the yearly soil erosion per unit region predicated on erosion attributes [101, 102]. The RUSLE model is regularly employed to forecast the mean yearly soil depletions due to sheet and rill disintegration along with exhibiting the geographic arrangement of potential erosion hazard [51, 97, 102–106]. The assessment of soil erosion risk through the utilization of the RUSLE model involves the consideration of several critical factors, including the slope length and steepness factor (LS), the land cover and management component (C), the support practice factor (P), and the rainfall erosivity factor (R) [101]. In the current investigation, the peril of soil erosion was spatially allocated, and conceivable erosion was charted by using C and P factors as identifying elements (C and P = 1).

According to reference [101], the Revised Universal Soil Loss Equation (RUSLE) stipulates that:

$$\text{A}=\text{R}\ast \text{k}\ast LS\ast \text{C}\ast \text{P}$$

Figure 4 presents an exemplification of the procedure through which the model’s input parameters are procured from diverse sources that comprise rainfall data, soil attributes that include soil texture, hydraulic conductivity, and organic matter content, as well as topographical characteristics such as slope length and percentage. These attributes are acquired from elevation digital models (DEMs) and satellite imagery. The RUSLE model encompasses several inputs such as topography (LS factor), crop cover (C factor), soil erodibility (K factor), rainfall erodibility (R factor), and soil erodibility (K factor), among others [107].

Figure 4.

Flowchart for RUSLE-based estimation of soil erosion.

3.1.6.2 Techniques for evaluating RUSLE factors in a GIS environment

The application of the Revised Universal Soil Loss Equation (RUSLE) has been extensively implemented in various settings, including tropical watersheds with mountainous terrain, expansive watersheds, those dominated by agricultural practices, locales exhibiting discernible wet and dry seasons, and areas undergoing dynamic transformations in terms of land coverage patterns, agricultural farmland utilization, and developmental activities. The RUSLE model comprises three primary databases, namely the climatic and survey database, the crop database, and the soil data. The climatic and survey database contains monthly temperature and precipitation data, as well as contours essential for the computation of factors such as erosivity, slope length, and steepness (LS). On the other hand, the crop database contains crucial data required for the determination of the surface cover factor (C). Lastly, the soil data includes relevant information on soil survey and soil characterization. The RUSLE model incorporates the five variables enumerated in eq. 1 to compute the mean annual soil erosion loss [108]. Estimation of the various components of the model, which is rooted in a significant corpus of research, is a prevalent approach to employing the Revised Universal Soil Loss Equation (RUSLE). Prior scholars have employed diverse techniques to compute these variables such as utilizing meteorological data, geological and soil maps, satellite imagery obtained remotely, empirical formulas, and digital elevation models (DEM) sourced from multiple origins [109].

3.1.7 Fundamental issues with erosion modeling

The inherent complexity of landscape systems, regional variation, and a lack of data makes distributed erosion models difficult [14]. A novel investigation is imperative for soil erosion prediction due to the fact that, despite the extensive efforts devoted to soil erosion assessment at the plot or catchment level, the quantitative estimation of soil erosion, which is regionally distributed, has not been comprehensively tackled [110]. The basic difficulty with erosion risk models is validation due to a lack of data to compare model projections with actual soil loss [111]. The data sources from which empirical models were constructed limit their application to locales and ecological circumstances [14]. Smith [112] claims that empirical models are particularly useful in a number of situations since they are the only ones that can be utilized when there is little available data. They have the following restrictions, among others; They have several drawbacks, including the following: (1) they are based on statistical analysis of significant factors in the soil erosion process and produce only approximate and probable results; (2) they are not practical for event-based prediction of soil loss; (3) they estimate soil erosion on a single slope rather than within catchments; (4) they do not represent the sedimentation process; (5) they are limited to sheet and/or rill erosion; and (6) they merely take changes in soil over time into account.

Physically-based models are typically the most scientifically valid and applicable to a wide range of soils, climatic circumstances, and land use scenarios because they are based on an understanding of the physical processes that produce erosion and are flexible in both input and output [113]. Ganasri and Ramesh [81] expressed agreement on the notion that models based on physical principles necessitate a significant amount of data, much of which is not easily accessible. They further posited that this implies a typical challenge in parameterizing such models.

Conceptual models can depict the qualitative and quantitative effects of changes in land use without needing a significant amount of input data dispersed across a wide variety of locations and times [14]. Conceptual models, such as the agricultural nonpoint source (AGNPS), occupy an intermediate position between empirical and physically-based models. These models serve as a substitute for the mechanical components of the system in question [56].

3.1.8 Soil erosion: risk management

When surface vegetation is removed or physically disturbed, the soil is more susceptible to erosion. Seasonally extremely dry circumstances raise the danger of erosion, where there is less vegetation due to inadequate crop and pasture growth [34].

Important management techniques that have an impact on the risk of soil erosion:

1. How often, how intensely, and when tillage activities occur.

2. the amount and kind of surface cover [114].

The greatest risks related to feeding happen in late summer and autumn if the supply of feed and the amount of cover of each year’s crop and grass leftovers is decreasing. The majority of the erosion risk is caused by showing practices such as cultivation and burning of stubble. Grazing management is additionally a significant factor, in particular during dry years, especially if there are more than two subsequent dry seasons [115]. The safeguarding of soil from erosion has significantly increased with the implementation of more environmentally friendly land management techniques such as no-till sowing and stubble retention. In order to avoid soil disturbance and maximize residue protection on the soil surface, no-till sowing entails placing the seed in a small opening in the soil [116]. Water-repellent soils increase the danger of soil erosion by causing poor plant germination and limited development of plants. A key tool for preventing soil erosion is the spreading, digging, and spading of clay to cultivate water-repellent soil [117]. These methods of soil alteration are commonly employed in the Southern Mallee, Upper South East, and portions of the Eyre Peninsula, where there are vast expanses of naturally water-repellent soils [118]. The utilization of these methodologies engenders an escalation in the clay concentration of the topsoil, thereby fortifying and safeguarding the soil from erosion [119]. Livestock can be withdrawn from fields using confined feed before the ground covering deteriorates too far. It is a crucial method for stopping erosion during dry seasons, as well as in the late summer and early fall when vegetation is dwindling [120].

3.1.9 Risk assessment of soil erosion

Despite the potential cost of collecting field data for risk assessments at elevated levels, it remains a crucial factor in the formulation of efficacious policies and strategies concerning the conservation of soil and water resources [121]. The technological progress in geographic information systems (GIS) and satellite imagery provides practical avenues for surveying, classifying, identifying, and tracking land use and soil at diverse levels. In numerous research endeavors, models aimed at evaluating soil erosion have been established and executed through the utilization of satellite data and geographic information system (GIS). Remarkably, it has been disclosed that these models are at times more efficacious and accurate in detecting and linking the peril of soil erosion compared to field survey data. This, in turn, confers invaluable insights for resource management and soil conservation planning [122]. Overall, the assessment of soil erosion employs both qualitative and quantitative approaches. Despite the availability of measures for approximating soil erosion volumes and rates, the determination of the severity of the risk is conducted qualitatively. The methodology utilized in qualitative evaluation encompasses a broad spectrum of techniques such as picture categorization [123], index linking [124], photo analysis [125], field research statistics [126], and photo interpretation. The soil erosion risk map is produced through the utilization of the index coupling procedure, which is a qualitative evaluation technique that utilizes remote sensing images and GIS [127]. The aforementioned methodology has been demonstrated to be a plausible and economically feasible approach for gauging the likelihood of erosion [128, 129]. Several quantitative techniques, such as USLE, RUSLE, CORINE, PESERA, and WEPP, employ methodologies for constructing models [130–133]. The determination of the average annual soil loss per unit area of soil over a prolonged period can be ascertained through the utilization of the Universal Soil Loss Equation (USLE), a widely employed and uncomplicated soil erosion model [134]. Additionally, the Revised Universal Soil Loss Equation (RUSLE) has been established as a novel technique that integrates current data, which supersedes the USLE approach [92]. There exists alternative, less commonly utilized methodologies for appraising soil erosion depletion, including the collaboration of data on the environment (CORINE) model, which was derived from a USLE model for assessing the erosion vulnerabilities and attributes within the member states of the European Union (EU) [135]. The PESERA model, designed to predict long-term average erosion rates at a 1 km resolution, has been predominantly adopted by a large portion of Europe [136]. The water erosion prediction project (WEPP) computational model is an uninterrupted, simulation-based, and distributed parameter framework for soil erosion prediction that is equivalent in status to the USLE and RUSLE models [137]. However, the authentication of qualitative erosion models presents obstacles due to the requirement for extensive proof, fresh resources, and the preparation of qualified personnel [138].

3.1.10 Global soil erosion analysis studies

Water-induced soil erosion is a significant factor in the global degradation of land [139–142]. The nutritious topsoil, which contains the majority of organic matter and nutrients, is lost due to erosion [143, 144]. According to the global evaluation of land degradation (GLADA) conducted by the United Nations Environment Program (UNEP), it has been observed that there has been a degradation of 1.1 billion hectares of land globally due to soil erosion. The issue of land degradation has been most prevalent in Asia, accounting for a significant portion of the total at 48%, followed by Africa at 21%, Latin America and the Caribbean at 15%, Europe at 10%, Oceania at 8%, and North America at 5% [145]. In areas prone to drought, it was estimated that the yield loss of agricultural produce was associated with a significant decrease of 55% [146]. The regions exhibiting the greatest levels of soil erosion were observed in Africa and Asia, predominantly due to the presence of intense precipitation that is highly erosive in nature [120, 122], larger population [123], and despite the loss of natural vegetation, there has been a proportional increase in agricultural land and urban areas [124, 125]. Researchers have shown that soil erosion rates vary widely among countries, continents, and climate zones [28, 112, 118–121]. In conjunction with these alterations, a significant reduction in biodiversity and ecological services has been observed [147–149]. According to a recent scholarly investigation [113], it has been anticipated that there will be an escalation in soil erosion until the water on a global scale by 30–66% by the year 2070 due to the projected alterations in climatic conditions and land use. This phenomenon is expected to have a more pronounced impact on the global poor. This necessitates additional research to enhance our knowledge of the key factors influencing soil erosion throughout a broader variety of geographical areas [142]. This necessitates additional research to enhance our knowledge of the key variables influencing soil erosion over a larger variety of geographical regions. Additional knowledge on land cover and its management practices is of particular importance because it will help with planning, implementing, and evaluating mitigation efforts [150].

3.1.11 Limitations of the model

Recent research calls for increased field-based erosion assessment and monitoring because it is challenging to anticipate erosion potential using models [151]. In addition to being instruments for simulation and predictability, models are tools for understanding. As a result, models can be thought of as both instruments for understanding and tools for controlling variables, trends, and disparities between data that are distinct in terms of space or time [152]. There is always a potential that what models transmit is insufficient or even incorrect, and these deficiencies may not be obvious to the model user or the recipient of the model output as models are also used to communicate research and its results [153].

4. Conclusion and future directions

Soil erosion modeling is developing due to the availability of highly precise geographical and meteorological data for monitoring intra-annual fluctuations in temperature, vegetation, and management practices. Accurate soil erosion modeling is required to make informed decisions about planning, management, and policy at both small and large spatial scales. Based on a comprehensive review of the literature, it has been observed that the RUSLE model has gained widespread usage and has been proven to be an effective tool in estimating the amount of soil loss caused by erosion across various regions of the globe. The RUSLE model has proven to be a valuable tool when implemented on a local scale. However, the integration of RUSLE with GIS methodologies has greatly improved the assessment of soil erosion that is geographically distributed over large catchment areas. Based on an analysis of the existing literature, it has been determined that the fundamental components of the model can be derived from various sources of data, including digital elevation models (DEMs), meteorological data, cartographic depictions of soil, and remote sensing imagery. GIS technology facilitates the examination of a broad study region, specifically in the context of soil erosion studies, by virtue of its requisite capabilities. Nevertheless, the identification of an appropriate model that can accurately assess soil loss from all forms of erosion at a given site remains a challenging endeavor.