Erosion is a worldwide threat to biodiversity conservation and agricultural yield, and it is linked to deforestation. In this study, we aim to assess soil loss in landscapes of Cachoeira River watershed, in southern Bahia, northeastern Brazil. We estimate the role of forests in diminishing soil erosion using the Revised Universal Soil Loss Equation (RUSLE). We compare real and simulated scenarios in which the forest was replaced by agricultural use, also comparing estimates of erosivity factor (R factor) derived from remote sensing and climatological station data. Real and simulated annual soil losses varied from 0 to 15.95 t/year and from 0 to 33.53 t/year along the watershed, respectively. However, only 0.04 and 1.67% of this area is highly and severely exposed to erosion, using data from climatological stations and remote sensing, respectively. Soil loss in the simulated deforested scenario was approximately two times higher than the real annual soil loss, indicating the importance of forest cover to mitigate soil erosion. Moreover, soil loss was 10.5 times greater when using precipitation data from remote sensing compared to climatological stations. Conclusively, the practice of agroforestry can be used as an alternative to avoid erosion.

  • This paper assessed soil erosion in an important river in Brazil.

  • Real and simulated scenarios of soil loss were discussed in this study.

  • Soil loss was 10.5 times greater when using precipitation data from remote sensing compared to climatological stations.

  • Soil loss is twice as high in non-forested areas as in forested areas.

Forests are crucial for safeguarding environmental sustainability and, thus, attaining key sustainable development goals. Unfortunately, the current pace at which forests are lost indicates that this process is the leading cause of biodiversity degradation and loss (Magioli & Ferraz 2021). Most importantly, such loss also leads to decay in the portfolio of services and goods provided by nature – the so-called ecosystem services – that ultimately contribute to human wellbeing. Soils are vital components of the biosphere, providing resources necessary to sustain food production, nutrient cycling, water storage, and habitats that host an amazing diversity of life on earth (Schröder et al. 2016; De Deyn & Kooistra 2021). Its presence and quality are closely linked with land use changes, with deforestation being largely responsible for soil erosion, i.e., when soil is carried away and lost. In fact, soil erosion is a global threat (Montanarella et al. 2016; Arabameri et al. 2020), particularly affecting agricultural productivity and water quality (Benavidez et al. 2018; Sidi Almouctar et al. 2021). In particular, the transport of nutrients from agricultural lands to river results in water contamination and eutrophication. Nutrients can be transported through river networks from higher to lower elevations (Sarker 2021), often influenced by external factors such as climate change and tectonics (Sarker et al. 2019). Soil type plays a crucial role in both soil water retention and nutrient transport via runoff. The interplay of soil type, slope steepness, and slope length significantly affects nutrient runoff from forested areas, influencing the watershed outlet or groundwater recharge. Indeed, water erosion is globally responsible for the loss of 75 billion metric tons of soil every year (Dabral et al. 2008), causing a loss of 8 billion dollars in the global economy due to the erosion of fertile land and a reduction in agricultural products (FAO 2019). Although soil erosion can be a natural process, human activities, including non-sustainable agricultural practices, implementation of infrastructure, and other forms of land use changes are exacerbating it. Globally, 84% of the land is degraded by soil erosion (Opeyemi et al. 2019), negatively impacting the wellbeing of more than 3.2 billion people (IPBES 2018; Borrelli et al. 2021). This major environmental and agricultural threat (Pimentel et al. 1995) mainly occurs in agricultural areas, particularly on higher slopes (Salem et al. 2014; Ibrahim et al. 2019), with the destruction of vegetation cover increasing surface runoff and decreasing infiltration (Prosser & Williams 1998).

In Brazil, a country with an economy highly dependent on agricultural production, soil erosion is a major concern. Each year, Brazil experiences an estimated loss of 800 million metric tons of soil (Merten & Minella 2013), placing the country among the global erosion ‘hotspots’ (Guerra et al. 2014). Deforestation is closely linked with runoff and erosion issues. Approximately 70% of the country's population, roughly 140 million people, according to Scarano & Ceotto (2015), reside in the 17 states that were originally part of the Atlantic Forest biome. This intense human presence has significantly diminished this once expansive biome, which was formerly the second-largest tropical forest in the Americas (Tabarelli et al. 2005), now reduced to less than 12% of its original size. While this extensive deforestation likely contributes to increased soil erosion (Zwiener et al. 2017), certain land management practices like agroforestry might alleviate some of these environmental impacts. For instance, in the southern state of Bahia, northeastern Brazil, portions of the native forests have been replaced with cacao (Theobroma cacao) agroforests. This type of land use is known for its biodiversity and climate-friendly attributes. However, the effectiveness of such practices in mitigating soil erosion remains a topic that requires further investigation. Herein we aimed to contribute to this discussion by estimating the soil loss associated with erosion in a specific municipality within agroforestry dominated landscapes in the southern state of Bahia. The study area lies within the largest forest remnants in northeastern Brazil (Joly et al. 2014), but most of the forest cover in this region comprises cacao agroforests, comprising a forest mosaic hosting most of the rich native biota (Schroth et al. 2011). This study also presents an opportunity to compare soil loss assessed using two different data sources, namely remote sensing and climatological stations. Specifically, we raised the following questions: (i) How much soil is lost when non-forested areas are compared to forested areas – comprising the above-mentioned mosaic of native stands and agroforests – and when forest areas are replaced by agricultural areas? (ii) What is the role of rain-induced soil erosion, and (iii) whether such soil loss estimates differ when using remote sensing and climatological station data for the soil erosivity (R factor)?

Study area and land use mapping

The study was carried out in Ilhéus, a municipality located in the southern part of Bahia state, northeastern Brazil (Figure 1). This region was originally dominated by a humid tropical climate (Alvares et al. 2013), with an average annual rainfall of 1,830 mm, while the annual mean relative humidity and air temperatures are 80% and 23.5 °C, respectively (Medauar et al. 2020). The Atlantic Forest biome, which is a hotspot of biodiversity and endemism globally recognized, has been significantly reduced from its original extent (SOS Mata Atlântica & INPE 2017). Most of the forest cover remaining in southern Bahia, including the Cachoeira River watershed, is a mosaic of native stands and cocoa agroforestry, mostly cultivated under a traditional mode regionally known as cabrucas (Sambuichi & Haridasan 2007).
Figure 1

Location map of 272.87 km2 Cachoeira River watershed.

Figure 1

Location map of 272.87 km2 Cachoeira River watershed.

Close modal

Within Ilhéus municipality, our study encompasses the Cachoeira River watershed, covered by different vegetation classes, including pasture, mosaic cultures, and grassland. The Cachoeira River watershed originates in the Ouricana mountain range in the city of Itororó and meets the Salgado River before passing along the coast of the municipality of Ilhéus (Torres et al. 2001). It is limited to the north by basins of the Rios de Contas and Almada, to the south by basins of the Pardo and Una Rivers, to the west by the Rio Pardo basin, and to the east by the Atlantic Ocean (Trindade et al. 2010).

Modeling

Land use maps from Mapbiomas (https://mapbiomas.org) were used for automatic classifications to identify the spatial distribution of the following classes: forests (including native stands and cacao agroforestry systems), pastures, forestry, open areas, urban centers, exposed soil, and water bodies (e.g., rivers, ponds). Using the QGIS tools we converted vector images to shapefile layers with a 30 m resolution. Two scenarios were considered: real and simulated. In the real scenario, the actual land use land cover (LULC) was used, whereas in the simulated scenario, the forest class was replaced by the agricultural use.

RUSLE model description

The Revised Universal Soil Loss Equation (RUSLE) is one of the most popular and widely used models to estimate the soil erosion for agricultural watersheds (Udayakumara et al. 2010). This empirical equation considers layers of digital terrain, rainfall erosivity, and interpolated soil erodibility, and the topography for modeling soil erosion (Barbosa et al. 2015). In this study, we used the RUSLE model due to its minimal statistics and computation needs (Prasannakumar et al. 2012), its flexibility in modeling soil erosion, and its facility to integrate with GIS for spatial analysis (Wischmeier & Smith 1965; Bonilla et al. 2010; Prasannakumar et al. 2012). According to Renard et al. (1997), the RUSLE model can be expressed by multiplying its different factors, as shown in Equation (1):
formula
(1)
where R represents the erosivity factor (R factor) and is expressed in MJ mm ha−1 hr−1 year−1. K is the erodibility factor expressed in t.hr.(MJ.mm)−1. LS is unitless and represents the topographical factor. C is the cover management factor, and P is the support practice factor. The RUSLE was implemented according to the methodological flowchart shown in Figure 2.
Figure 2

Methodological flowchart of the RUSLE model.

Figure 2

Methodological flowchart of the RUSLE model.

Close modal

Rainfall erosivity (R factor)

The R factor is the power of rain to cause soil erosion by water (Cardoso et al. 2020). It is the mathematical expression of erosive power used to determine the average impact of rainfall and runoff on soil erosion in a specific locality and period (Farhan et al. 2013). We calculated R factor using two sources of data: (1) Using Google Earth Engine to generate the mean precipitation values in our study area between 1992 and 2021 and (2) actual precipitation data provided directly from climatological stations (Table 1). Notably, the Google Earth Engine (GEE)-based R factor was derived using the Kriging interpolation method, whereas we used the Inverse Distance Weighted interpolation for the R factor based on climatological station data.

Table 1

Coordinates of climatological stations used in this study

Climatological stationCodeLatitudeLongitudePeriod
Salvador (Ondina) 83229 −13.00583333 −38.50583333 1992–2021 
Vitoria da Conquista 83344 −14.88638888 −40.80138888 1992–2021 
Salinas 83441 −16.154862 −42.284921 1992–2021 
Vitoria 83648 −20.31583333 −40.31694443 1992–2021 
Guaratinga 83446 −16.58081 −39.783182 1992–2021 
Caravelas 83498 −17.73944444 −39.25861111 1992–2021 
Itamarandiba 83488 −17.85972222 −42.85277777 1992–2021 
Climatological stationCodeLatitudeLongitudePeriod
Salvador (Ondina) 83229 −13.00583333 −38.50583333 1992–2021 
Vitoria da Conquista 83344 −14.88638888 −40.80138888 1992–2021 
Salinas 83441 −16.154862 −42.284921 1992–2021 
Vitoria 83648 −20.31583333 −40.31694443 1992–2021 
Guaratinga 83446 −16.58081 −39.783182 1992–2021 
Caravelas 83498 −17.73944444 −39.25861111 1992–2021 
Itamarandiba 83488 −17.85972222 −42.85277777 1992–2021 

Based on a previous study carried out by Bertoni & Lombardi Neto (1999), the values of the R factor of both precipitation data sources were calculated using Equations (2) & (3).
formula
(2)
where R is the erosivity factor.
formula
(3)

EI is the monthly mean erosion index (MJ mm ha−1 h year);

r is the average monthly precipitation (mm); P is the average accumulated annual precipitation (mm). Based on previous studies, the calculation of the R factor in this study relied on daily to annual data for extrapolation in space and time (Meusburger et al. 2012; Diodato et al. 2017).

Soil erodibility (K factor)

The K factor determines the natural susceptibility to erosion, which is particularly influenced by soil's properties, such as texture, permeability, shear strength, organic matter, and chemical composition (Efthimiou 2020). The K factor is determined by two key factors: the first is the soil's infiltration capacity to withstand detachment and transport by rainfall, and the second is the runoff process (Wischmeier & Mannering 1969). Obtaining K factor values is challenging due to the need for long-term field experiments under natural precipitation conditions (Schick et al. 2014). For this reason, we utilized a soil map provided by Mapbiomas to identify soil types prior to gathering K factor values from the scientific literature. As these values particularly depend on soil classification (Podhrázská et al. 2015) and pedology maps, the K value for each soil type was inserted into the attribute table in the soil map layer, thus converting this vector layer into a raster type. The higher the coefficient of soil erodibility, the higher the soil loss.

Topographical factor (LS)

Slope length (L) and slope steepness (S) estimate the erosion effect of slope length and slope steepness, respectively (Sidi Almouctar et al. 2021). Specifically, slope length is the distance between the point of origin of surface runoff and the point where the slope decreases enough for deposition to begin (Mustefa et al. 2020), while steepness is the gradient factor (Ajibade et al. 2020). Thus, LS is the combination of the L factor and S factor used to characterize the topographical factor, which has a significant influence on soil erosion. Thus, the LS factor represents the influence of topography on soil erosion (Belasri & Lakhouili 2016; Ajibade et al. 2020), facilitating the assessment of total sediment production in a specific area. The greater the slope, the greater the runoff (Kadam et al. 2018). Using QGIS, the LS factor was calculated through the digital elevation model (DEM), particularly Copernicus Global Digital Surface Model (DSM) of a 30-m spatial resolution, utilizing the Equation (4) developed by Desmet & Govers (1996):
formula
(4)
is the length of slope for cell (i,j), is the flow accumulation area for each cell with coordinates (i,j), D is the cell grid resolution of value 30 m, m is the length exponent of the RUSLE factor, Xij is the flow direction angle (sin i,j + cos i,j). The same software can be used to calculate the Xi,j, which indicates the flow direction with reference to the north. The exponent ‘m’ can be calculated using the equation developed by Foster et al. (1977).
formula
(5)
β represents the quotient between in-furrow and inter-furrow erosion and is calculated employing the expression developed by Mccool et al. (1987).
formula
(6)
where θ is the slope in degrees.
The S factor is calculated from the expression developed by Mccool et al. (1987).
formula
(7)

Vegetation cover, the management factor (C) and erosion control practice factor (P)

Vegetation cover and the management factor (C) are dimensionless indexes that depend on the extent to which soils are covered by vegetation, as well as the land topography and previous models of land use (Renard et al. 1997). Similarly, the P factor is a dimensionless index that accounts for control practices that reduce the erosion potential of runoff (Renard et al. 1997). It expresses the ratio between expected soil erosion for a given soil conservation practice for up and down tillage (Wischmeier & Smith 1978). Herein the C and P factors were lumped together (as CP), with values ranging from 0 to 1, where 0 corresponds to soil with a high vegetation cover and 1 means the soil is entirely exposed to erosion (Ajibade et al. 2020). The LULC map for 2021 was used to extract information on vegetation class, which was then searched for in the scientific literature to find the values of CP.

R factor based on remote sensing and climatological station data

The R factor varied from 55,541.06 to 67,874.84 MJ mm ha−1hr−1 year−1 when using remote sensing as the source for precipitation data, whereas such variation using data from climatological stations varied from 5,807.53 to 6,096.82 MJ mm ha−1hr−1 year−1 (Figures 3(a) & (b)). Studies have shown that the R factor is one of the most important factors impacting the average soil erosion assessments (Dabral et al. 2008; Ganasri & Ramesh 2016), greatly influencing soil concentration loss (Wischmeier & Smith 1978). Low rainfall regimes can lead to an average small amount of soil loss. Clearly, the R factor values obtained in our study varied according to the source of precipitation data applied, as shown in Figure 3. The R factor appears to be a significant parameter in assessing soil loss with the RUSLE model. This result is supported by a previous study pointing out that the R factor is the most important component in RUSLE and is mainly responsible for soil erosion in an area (Sidi Almouctar et al. 2021). One of the reasons that explain the difference between these two values of the R factor in this study is because the precipitation data coming from remote sensing are coarser than those coming from climatological stations.
Figure 3

(a) R factor map based on precipitation data from remote sensing and (b) R factor map based on climatological stations.

Figure 3

(a) R factor map based on precipitation data from remote sensing and (b) R factor map based on climatological stations.

Close modal

The results displayed in Figure 3 indicate that the values of the R factor are influenced by the sources of precipitation. Similarly, findings from a previous study confirmed that soil erosion is driven by the R factor, particularly during the rainy season (Yao et al. 2016). Notably, this study corroborates a previous finding that the R factor greatly influences the soil erosion potential in a GEE-based study (Sud et al. 2024).

Soil erodibility (K factor)

The K factor values for the Cachoeira River watershed vary from 0 to 0.03, representing thiormorphic gleyssol and chromic luvisol, respectively (Figure 4 and Table 2). These values are near 0, indicating that the soils in these regions demonstrate resilience to erosion, as noted by Ajibade et al. (2020). Considering these K factor values, and despite the high precipitation levels, the study area exhibits a low susceptibility to erosion.
Table 2

The values of K factors of the Cachoeira River watershed

Soil typesK factorReferences
Chromic Luvisols 0.024 Embrapaa 
Thiomorphic Gleysoil 0.004 Embrapa 
Humiluvic Spodosol 0.03 Embrapa 
Dystrophic Yellow Latosol 0.017 Embrapa 
Dystrophic Red Latosol 0.023 Pereira & Cabral (2021)  
Soil typesK factorReferences
Chromic Luvisols 0.024 Embrapaa 
Thiomorphic Gleysoil 0.004 Embrapa 
Humiluvic Spodosol 0.03 Embrapa 
Dystrophic Yellow Latosol 0.017 Embrapa 
Dystrophic Red Latosol 0.023 Pereira & Cabral (2021)  

Note: Embrapa is part of the national agricultural research system in Brazil.

Figure 4

K factor map of the Cachoeira River watershed.

Figure 4

K factor map of the Cachoeira River watershed.

Close modal

Table 2 describes the values of the K factor along with different soil types in the study areas. The values of the K factor varied from 0.004 to 0.03, which explained that the degrees of limitation of K factor varied from null to moderate (Giboshi 1999).

Topographic factor (LS)

The LS factor shows how slope length and steepness influence the process of soil erosion (Kadam et al. 2018). In this study, the LS factor varied from 0.03 to 12.53. It is noted that higher LS values led to higher soil erosion (Fayas et al. 2019).

CP factor

In the Cachoeira River watershed, CP values varied from 0.00004 (low) to 0.2 (high) and from 0.0001 (low) to 0.2 (high) in the real and simulated scenarios, respectively (Figures 5(a) & (b)).
Figure 5

(a) Real CP factor map and (b) simulated CP factor map of the Cachoeira River watershed.

Figure 5

(a) Real CP factor map and (b) simulated CP factor map of the Cachoeira River watershed.

Close modal

CP values are low as they are close to zero. This means that the soil is covered with vegetation, which is important for reducing the erosion risk. This result is supported by previous studies that have highlighted how agroforestry improves physical, biological, and chemical properties (Fahad et al. 2022; François et al. 2023). The real and simulated CP factors had different effects on soil loss (Figures 5 (a) & (b)). Therefore, the utilization of cacao agroforestry systems can be a promising alternative for soil erosion reduction and soil conservation in the study areas. It is noted that the LULC has a low contribution to soil erosion in the study area and, therefore, does not represent a potential risk for erosion.

Annual soil loss

The case of the soil in the Cachoeira River watershed using data from remote sensing

The real and simulated annual soil erosion varied from 0 to 1,865.22 t/ha/year and from 0 to 3,909.00 t/ha/year, respectively, along the Cachoeira River watershed when using precipitation data from remote sensing (Figure 6(a) & (b)). In other words, soil erosion in the real scenario in the Cachoeira River watershed ranged from 0 to 167.87 t/year, with a mean of 0.95 t/year and a standard deviation of 5.76 t/year, whereas in the simulated scenario, soil erosion varied from 0 to 351.81 t/year, with a mean of 16.22 t/year and a standard deviation of 30.60 t/year. Such results show the importance of forests for environmental planning and soil conservation. Our results corroborate a previous study confirming that erosion occurred where the soil had a lack of vegetation protective cover (Pimentel & Kounang 1998). In other words, natural forest, or agroforestry systems, reduces soil loss caused by erosion (Lense et al. 2022). Accordingly, vegetation cover removal led to a very high risk of erosion (Asadi et al. 2017).
Figure 6

(a) Real soil loss map and (b) simulated soil loss based on precipitation data from remote sensing.

Figure 6

(a) Real soil loss map and (b) simulated soil loss based on precipitation data from remote sensing.

Close modal

The highest value in t/ha/year of the simulated scenario of soil loss was 2.1 times greater than that of the actual soil loss scenario in the Cachoeira River watershed. This suggests that forests/agroforests can significantly contribute to soil protection against erosion. The results of this study show the sensitivity of CP values used in soil loss assessment, in which the soil erosion rate is categorized as very high. Comparably, the soil loss values of this study are greater than those of a study in Nigeria, where soil erosion ranged from 0 to > 756.60 t/ha/year (Opeyemi et al. 2019) and in the extreme south of Bahia, Teixeira de Freitas, with Argisol, which reported a total soil loss of 686.85 Mg ha−1 (Guimaraes et al. 2017). Another study conducted in the municipality of Teixeira de Freitas, southern Bahia, indicated that the area covered by eucalyptus had significantly less soil erosion compared to the uncovered soil area (Ferreira et al. 2019). As expected, simulated CP factors led to higher soil erosion than the real scenario. In this study, the topography seems not to have a great influence on soil erosion. Kadam et al. (2018) highlighted that the higher the slope area, the more intense the runoff and flow produced.

The case of the soil in the Cachoeira River watershed using data from climatological stations

When using precipitation data from climatological stations, the actual soil loss assessment varied from 0 to 177.22 t/ha/year, with a mean of 1.01 t/ha/year and a standard deviation of 6.07 t/ha/year. In contrast, the soil loss of the simulated scenario ranged from 0 to 372.52 t/ha/year, with a mean of 17.37 t/ha/year and a standard deviation of 32.69 t/ha/year (Figure 7(a) & (b)). The results of this study corroborate a previous study in Brazil, which indicated that the expansion of agriculture in forest areas was the major cause of soil erosion in savannah and rainforest biomes (Merten & Minella 2013). As expected, it was found that the soil loss was 2.10 times greater in the simulated scenario compared to the actual soil loss scenario when using climatological station data for R factor calculation utilized in the RUSLE model.
Figure 7

(a) Real soil loss map and (b) simulated soil loss based on precipitation data from climatological stations.

Figure 7

(a) Real soil loss map and (b) simulated soil loss based on precipitation data from climatological stations.

Close modal

Based on the findings of Figures 6(a) & 7(a), soil erosion is influenced by the precipitation source used for R factor calculation. Notably, higher precipitation values resulted in greater soil erosion, as evidenced by the fact that precipitation data from remote sensing sources led to greater soil erosion compared to data from climatological stations. Importantly, it was found that the mean soil erosion calculated using data from remote sensing sources was 10.5 times greater than that assessed using climatological precipitation data. The findings of this study corroborate the results of another study, indicating that higher R values lead to higher soil erosion (Fayas et al. 2019). However, at this stage, we cannot assert that one precipitation source is better than the other. This result only indicates that the soil loss estimation is a nominal value that can depend on the source of precipitation data used for the R factor calculation.

Severity of soil loss in the Cachoeira River watershed when using data from remote sensing and climatological stations

In terms of classes of soil loss severity, only the real scenarios were considered in this study. It was discovered that the erosion risk ranged from none or slight to very high in the Cachoeira River watershed when precipitation data from remote sensing sources were used. Soil erosion is classified as very low, low, moderate, high, very high, and severe (Table 3), according to the classification used by İrvem et al. (2007). Most of the areas were classified as having no or slight soil erosion. Notably, more than 85% of this river watershed had low soil erosion. Only 1.67% of the Cachoeira River watershed experienced severe erosion (Table 3). Similarly, 96.06 and 0.04% of this river watershed experienced very low and very high soil erosion, respectively (Table 3).

Table 3

Severity range and severity class of soil loss in the Cachoeira River watershed when using remote sensing and climatological station data

Severity rangeSeverity classCachoeira River watershed/R factor data from remote sensing
Cachoeira River watershed/R factor data from climatological stations
Area (ha)Area (%)Area (ha)Area (%)
≤5 Very low 21,807.09 88.67 23,671.35 96.06 
5–12 Low 1,385.28 5.63 342.36 1.39 
12–50 Moderate 462.87 1.88 531.54 2.16 
50–100 High 246.87 1.00 85.59 0.35 
100–200 Very high 305.64 1.24 10.35 0.04 
>200 Severe 412.56 1.67 — — 
Severity rangeSeverity classCachoeira River watershed/R factor data from remote sensing
Cachoeira River watershed/R factor data from climatological stations
Area (ha)Area (%)Area (ha)Area (%)
≤5 Very low 21,807.09 88.67 23,671.35 96.06 
5–12 Low 1,385.28 5.63 342.36 1.39 
12–50 Moderate 462.87 1.88 531.54 2.16 
50–100 High 246.87 1.00 85.59 0.35 
100–200 Very high 305.64 1.24 10.35 0.04 
>200 Severe 412.56 1.67 — — 

The areas of high, very high, and severe vulnerability along the Cachoeira River watershed were 246.87, 305.64, and 412.56 ha, respectively (Table 3). These vulnerabilities are likely associated with agricultural practices. Based on the results displayed in Table 3, it is noted that the precipitation source used for the R factor in the RUSLE model affects the severity class of soil erosion.

Soil erosion assessment is crucial for environmental management and ecosystem services. Various factors, including the LS factor and CP factor, can be sources of uncertainty in soil erosion rate estimation. A previous study indicated that errors and uncertainties in the DEM influence soil erosion rate estimation (Abd Aziz et al. 2012). The LS factor influences soil loss over space and time (Wang et al. 2002) due to overlaying in a geospatial environment (Luvai et al. 2022). Similarly, Anjitha Krishna et al. (2019) underlined that the major uncertainty in soil loss assessment is derived from the LS factor. These challenges can be overcome by analyzing the global sensitivity of RUSLE and using different equations for LS factor calculation to be used in the RUSLE model. Additionally, DEM from different sources, including the Advanced Spaceborne Thermal Emission and Reflection Radiometer, with multiple resolution grids, can be used to partially clarify these issues. A previous study outlined that different DEM sources and resampled grid sizes led to different soil loss estimations (Pandey et al. 2021). Moreover, the input model and method used for soil loss estimation can both be sources of uncertainty (Schürz et al. 2020). Furthermore, another limitation of this study is that K and CP values were collected from scientific literature. These factors may influence the soil erosion rate, as it is better to collect data from the study field. Importantly, the utilization of RUSLE model may overestimate soil loss and has some limitations as it does not predict sediment yield or other elements, including gullies (Croke & Nethery 2006). Further research is needed to collect data from the study field for soil erosion assessment while performing global sensitivity and uncertainty analyses of the RUSLE model.

Anthropogenic activities (e.g., deforestation) are among the drivers causing climate change, erosion, and contamination of natural resources (e.g., water and air quality). The world today is about 0.85 °C higher compared to the average temperatures from the period 1950–1980 (Sarker 2022). In terms of environmental studies, this study shows that forests can not only be crucial for reducing soil erosion but also for mitigating climate change through carbon sequestration. In other words, the findings of this study demonstrate that forest restoration, afforestation, or tree plantations can serve as techniques for soil conservation and reclaiming damaged lands. Additionally, forests are fundamental for providing a range of services, including preserving biodiversity, wildlife habitat, and freshwater supply (Jackson et al. 2008). Indeed, this study demonstrates that anticipatory planning of reforestation is a plausible alternative that can greatly contribute to environmental protection and sustainable soil management.

Managing soil is crucial for soil conservation, environmental sustainability, sustainable development, and human wellbeing. This study was carried out in the Cachoeira River watershed in the Atlantic Forest in Bahia State, northeastern Brazil, to assess soil erosion using the RUSLE model. Soil erosion ranged from 0 to 167.87 t/year in the real scenario in the Cachoeira River watershed, whereas it ranged from 0 to 351.81 t/year in the simulated scenario when using remote sensing data. Conversely, when using data from climatological stations, soil losses ranged from 0 to 15.95 t/year in the real scenario and from 0 to 33.53 t/year in the simulated scenario. The novelty of this study is that soil erosion in simulated scenario was two times higher than in the real scenario. Similarly, soil erosion calculated using precipitation data from remote sensing for R factor calculation was 10.5 times higher than the soil loss assessed when climatological stations were used for the calculation of the R factor. Consequently, the sources of precipitation data used for R factor calculation are one of the parameters that can significantly influence soil erosion assessment. These findings highlight the importance of forests for soil conservation and the choice of precipitation source used for R factor calculation. In conclusion, the findings of this study may assist decision-makers in adopting a more effective approach to conservation and ecosystem services in the Cachoeira River watershed.

This study was financed in part by the Brazilian National Council for Scientific and Technological Development (CNPq Grant 142018/2020-1).

M.F. contributed to conceptualization, methodology, design, material preparation, data curation, writing-original draft, writing-review and editing, validation. E.M.-N. contributed to conceptualization, methodology, supervision, writing-review and editing, validation. D.F. contributed to conceptualization, supervision, writing-review and editing, validation. M.C.G.P., U.C.d.O., and H.P.d.S. contributed to writing-review and editing, validation. R.N.d.V. contributed to data curation, writing-review and editing, validation.

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

The authors declare there is no conflict.

Abd Aziz
S.
,
Steward
B. L.
,
Kaleita
A.
&
& Karkee
M.
2012
Assessing the effects of DEM uncertainty on erosion rate estimation in an agricultural field
.
American Society of Agricultural and Biological Engineers
55
,
785
798
.
https://doi.org/10.13031/2013.41514
.
Ajibade
F. O.
,
Nwogwu
N. A.
,
Adelodun
B.
,
Abdulkadir
T. S.
,
Ajibade
T. F.
,
Lasisi
K. H.
,
Fadugba
O. G.
,
Owolabi
T. A.
&
Olajire
O. O.
2020
Application of RUSLE integrated with GIS and remote sensing techniques to assess soil erosion in Anambra State, South-Eastern Nigeria
.
Journal of Water and Climate Change
11
,
407
422
.
https://doi.org/10.2166/wcc.2020.222
.
Alvares
C. A.
,
Stape
J. L.
&
Sentelhas
P. C.
2013
Köppen's climate classification map for Brazil
.
Meteorologische Zeitschrift
22
(
6
),
711
728
.
Anjitha Krishna
P. R.
,
Lalitha
R.
,
Shanmugasundaram
K.
&
& Nagarajan
M.
2019
Assessment of topographical factor (LS-Factor) estimation procedures in a gently sloping terrain
.
Journal of the Indian Society and Remote Sensing
47
,
1031
1039
.
https://doi.org/10.1007/s12524-019-00953-3
.
Arabameri
A.
,
Chen
W.
,
Loche
M.
,
Zhao
X.
,
Li
Y.
,
Lombardo
L.
,
Cerda
A.
,
Pradhan
B.
&
Bui
D. T.
2020
Comparison of machine learning models for gully erosion susceptibility mapping
.
Geoscience Frontiers
11
,
1609
1620
.
https://doi.org/10.1016/j.gsf.2019.11.009
.
Asadi
H.
,
Honarmand
M.
,
Vazifedoust
M.
&
Mousavi
A.
2017
Assessment of changes in soil erosion risk using RUSLE in Navrood Watershed, Iran
.
Journal of Agricultural Science and Technology
19
,
231
244
.
Barbosa
A. F.
,
De Oliveira
E. F.
,
Mioto
C. L.
&
Filho
A. C. P.
2015
Aplicação da equação universal de perda do solo (USLE) em softwares livres e gratuitos
.
Anuário do Instituto de Geociências
38
(
1
),
170
179
.
Bertoni
J.
&
Lombardi Neto
F.
1999
Conservacao do solo
, 2. edn.
Editora Icone
,
Sao Paulo
, pp.
352
.
Bonilla
C. A.
,
Reyes
J. L.
&
Magri
A.
2010
Water erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in a GIS framework, central Chile
.
Chilean Journal of Agricultural Research
70
(
1
),
159
169
.
Borrelli
P.
,
Alewell
C.
,
Alvarez
P.
,
Anache
J. A. A.
,
Baartman
J.
,
Ballabio
C.
,
Bezak
N.
,
Biddoccu
M.
,
Cerdà
A.
,
Chalise
D.
,
Chen
S.
,
Chen
W.
,
De Girolamo
A. M.
,
Gessesse
G. D.
,
Deumlich
D.
,
Diodato
N.
,
Efthimiou
N.
,
Erpul
G.
,
Fiener
P.
,
Freppaz
M.
,
Gentile
F.
,
Gericke
A.
,
Haregeweyn
N.
,
Hu
B.
,
Jeanneau
A.
,
Kaffas
K.
,
Kiani-Harchegani
M.
,
Villuendas
I. L.
,
Li
C.
,
Lombardo
L.
,
López-Vicente
M.
,
Lucas-Borja
M. E.
,
Märker
M.
,
Matthews
F.
,
Miao
C.
,
Mikoš
M.
,
Modugno
S.
,
Möller
M.
,
Naipal
V.
,
Nearing
M.
,
Owusu
S.
,
Panday
D.
,
Patault
E.
,
Patriche
C. V.
,
Poggio
L.
,
Portes
R.
,
Quijano
L.
,
Rahdari
M. R.
,
Renima
M.
,
Ricci
G. F.
,
Rodrigo-Comino
J.
,
Saia
S.
,
Samani
A. N.
,
Schillaci
C.
,
Syrris
V.
,
Kim
H. S.
,
Spinola
D. N.
,
Oliveira
P. T.
,
Teng
H.
,
Thapa
R.
,
Vantas
K.
,
Vieira
D.
,
Yang
J. E.
,
Yin
S.
,
Zema
D. A.
,
Zhao
G.
&
Panagos
P.
2021
Soil erosion modelling: A global review and statistical analysis
.
Science of the Total Environment
780
,
146494
.
https://doi.org/10.1016/j.scitotenv.2021.146494
.
Cardoso
D. P.
,
Silva
E. M.
,
Avanzi
J. C.
,
Muniz
J. A.
,
Ferreira
D. F.
,
Silva
M. L. N.
,
Acuña-Guzman
S. F.
&
Curi
N.
2020
Rainfallerosivityfactor: An R package for rainfall erosivity (R-factor) determination
.
Catena
189
,
104509
.
https://doi.org/10.1016/j.catena.2020.104509
.
Croke
J.
&
Nethery
M.
2006
Modelling runoff and soil erosion in logged forests: Scope and application of some existing models
.
Catena
67
,
35
49
.
https://doi.org/10.1016/j.catena.2006.01.006
.
Dabral
P. P.
,
Baithuri
N.
&
Pandey
A.
2008
Soil erosion assessment in a hilly catchment of north eastern India using USLE, GIS and remote sensing
.
Water Resources Management
22
(
12
),
1783
1798
.
De Deyn
G. B.
&
Kooistra
L.
2021
The role of soils in habitat creation, maintenance and restoration
.
Philosophical Transactions of the Royal Society B: Biological Sciences
376
,
20200170
.
https://doi.org/10.1098/rstb.2020.0170
.
Desmet
P. J. J.
&
Govers
G. A.
1996
GIS-procedure for automatically calculating the USLE LS factor on topographically complex landscape units
.
Journal of Soil and Water Conservation
51
(
5
),
427
433
.
Diodato
N.
,
Borrelli
P.
,
Fiener
P.
,
Bellocchi
G.
&
Romano
N.
2017
Discovering historical rainfall erosivity with a parsimonious approach: A case study in Western Germany
.
Journal of Hydrology
544
,
1
9
.
https://doi.org/10.1016/j.jhydrol.2016.11.023
.
Efthimiou
N.
2020
The new assessment of soil erodibility in Greece
.
Soil and Tillage Research
204
,
104720
.
https://doi.org/10.1016/j.still.2020.104720
.
Fahad
S.
,
Chavan
S. B.
,
Chichaghare
A. R.
,
Uthappa
A. R.
,
Kumar
M.
,
Kakade
V.
,
Pradhan
A.
,
Jinger
D.
,
Rawale
G.
,
Yadav
D. K.
,
Kumar
V.
,
Farooq
T. H.
,
Ali
B.
,
Sawant
A. V.
,
Saud
S.
,
Chen
S.
&
Poczai
P.
2022
Agroforestry systems for soil health improvement and maintenance
.
Sustainability
14
,
14877
.
https://doi.org/10.3390/su142214877
.
FAO
2019
The State of Food and Agriculture 2019. Moving Forward on Food Loss and Waste Reduction
.
FAO, Rome. Licence: CC BY-NC-SA 3.0 IGO
.
Fayas
C. M.
,
Abeysingha
N. S.
,
Nirmanee
K. G. S.
,
Samaratunga
D.
&
Mallawatantri
A.
2019
Soil loss estimation using rusle model to prioritize erosion control in KELANI river basin in Sri Lanka
.
International Soil and Water Conservation Research
7
,
130
137
.
https://doi.org/10.1016/j.iswcr.2019.01.003
.
Ferreira
A. B. R.
,
Pereira
G.
,
Fonseca
B. M.
,
Costa
J. C.
&
Cardozo
F. D. S.
2019
Estimativa de perda de solo no oeste da Bahia (Brasil) a partir da alteração do uso e cobertura da terra
.
Floresta e Ambiente
20
,
560
573
.
https://doi.org/10.14393/RCG207245866
.
Foster
G. R.
,
Meyer
L. D.
&
Onstad
C. A.
1977
An erosion equation derived from basic erosion principles
.
Transactions of the ASAE
20
,
0678
0682
.
François
M.
,
Pontes
M. C. G.
,
Lima Da Silva
A.
&
Mariano-Neto
E.
2023
Impacts of cacao agroforestry systems on climate change, soil conservation, and water resources: A review
.
Water Policy
25
,
564
581
.
https://doi.org/10.2166/wp.2023.164
.
Ganasri
B. P.
&
Ramesh
H.
2016
Assessment of soil erosion by RUSLE model using remote sensing and GIS – A case study of Nethravathi Basin
.
Geoscience Frontiers
7
,
953
961
.
https://doi.org/10.1016/j.gsf.2015.10.007
.
Giboshi
M. L.
1999
Desenvolvimento de um sistema especialista para determinar a capacidade de uso da terra
.
MSc Dissertation.
Faculdade de Engenharia Agrícola, Universidade de Campinas (Campinas – SP – Brazil)
, p.
77
.
Guerra
A. J. T.
,
Fullen
M. A.
,
Jorge
M. C. O.
&
Alexandre
S. T.
2014
Erosão e Conservação de Solos no Brasil
.
Anuário do Instituto de Geociências
37
(
1
),
81
91
.
https://doi.org/10.11137/2014_1_81_91
.
Guimaraes
D. V.
,
Silva
M. L. N.
,
Bispo
D. F. A.
,
Martins
S. G.
,
Melo Neto
J. D. O.
,
Martins
R. P.
&
Curi
N.
2017
Water erosion associated with rainfall patterns in the extreme South of Bahia in eucalyptus post-planting
.
Semina: Ciências Agrárias
38
,
2463
.
https://doi.org/10.5433/1679-0359.2017v38n4Supl1p2463
.
Ibrahim
G. R. F.
,
Rasul
A.
,
Ali Hamid
A.
,
Ali
Z. F.
&
Dewana
A. A.
2019
Suitable site selection for rainwater harvesting and storage case study using Dohuk Governorate
.
Water
11
,
864
.
https://doi.org/10.3390/w11040864
.
IPBES
2018
The IPBES Assessment Report on Land Degradation and Restoration, Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services ed
. p.
744
.
İrvem
A.
,
Topaloğlu
F.
&
Uygur
V.
2007
Estimating spatial distribution of soil loss over Seyhan River Basin in Turkey
.
Journal of Hydrology
336
,
30
37
.
https://doi.org/10.1016/j.jhydrol.2006.12.009
.
Jackson
R. B.
,
Randerson
J. T.
,
Canadell
J. G.
,
Anderson
R. G.
,
Avissar
R.
,
Baldocchi
D. D.
,
Bonan
G. B.
,
Caldeira
K.
,
Diffenbaugh
N. S.
,
Field
C. B.
,
Hungate
B. A.
,
Jobbágy
E. G.
,
Kueppers
L. M.
,
Nosetto
M. D.
&
Pataki
D. E.
2008
Protecting climate with forests
.
Environmental Research Letters
3
,
044006
.
https://doi.org/10.1088/1748-9326/3/4/044006
.
Joly
C. A.
,
Metzger
J. P.
&
Tabarelli
M.
2014
Experiences from the Brazilian Atlantic Forest: Ecological findings and conservation initiatives
.
New Phytologist
204
,
459
473
.
https://doi.org/10.1111/nph.12989
.
Kadam
A. K.
,
Umrikar
B. N.
&
Sankhua
R. N.
2018
Assessment of soil loss using Revised Universal Soil Loss Equation (RUSLE): A remote sensing and GIS approach
.
Remote Sensing of Land
2
(
1
),
65
75
.
https://doi.org/10.21523/gcj1.18020105
.
Lense
G.
,
Servidoni
L.
,
Parreiras
T.
,
Santana
D.
,
Bolleli
T.
,
Ernesto
J.
,
Spalevic
V.
&
Mincato
R.
2022
Modeling of soil loss by water erosion in the Tietê river hydrographic basin, São Paulo, Brazil
.
Semina Ciencias Agrarias
43
,
1403
1422
.
https://doi.org/10.5433/1679-0359.2022v43n4p1403
.
Luvai
A.
,
Obiero
J.
&
Omuto
C.
2022
Soil Loss Assessment Using the Revised Universal Soil Loss Equation (RUSLE) model
.
Applied and Environmental Soil Science
2022
,
1
14
.
https://doi.org/10.1155/2022/2122554
.
Magioli
M.
&
Ferraz
K. M. P. M. D.
2021
Deforestation leads to prey shrinkage for an apex predator in a biodiversity hotspot
.
Mammal Research
66
,
245
255
.
https://doi.org/10.1007/s13364-021-00556-9
.
Mccool
D. K.
,
Brown
L. C.
&
Foster
G. R.
1987
Revised slope steepness factor for the universal soil loss equation
.
Transactions of the ASAE
30
(
5
),
1387
1396
.
Medauar
C. C.
,
Almeida Menezes
A.
,
Ramos
A.
,
Monteiro Galvão
Í
&
De Assis Silva
S.
2020
Climatic characterization and evaluation of the need for supplementary irrigation for cacao in southern Bahia, Brazil
.
Agronomia Colombiana
38
,
272
279
.
https://doi.org/10.15446/agron.colomb.v38n2.84700
.
Merten
G. H.
&
Minella
J. P. G.
2013
The expansion of Brazilian agriculture: Soil erosion scenarios
.
International Soil and Water Conservation Research
1
,
37
48
.
https://doi.org/10.1016/S2095-6339(15)30029-0
.
Meusburger
K.
,
Steel
A.
,
Panagos
P.
,
Montanarella
L.
&
Alewell
C.
2012
Spatial and temporal variability of rainfall erosivity factor for Switzerland
.
Hydrology and Earth System Sciences
16
,
167
177
.
https://doi.org/10.5194/hessd-8-8291-2011
.
Montanarella
L.
,
Pennock
D. J.
,
McKenzie
N.
,
Badraoui
M.
,
Chude
V.
,
Baptista
I.
,
Mamo
T.
,
Yemefack
M.
,
Singh Aulakh
M.
,
Yagi
K.
,
Young Hong
S.
,
Vijarnsorn
P.
,
Zhang
G.-L.
,
Arrouays
D.
,
Black
H.
,
Krasilnikov
P.
,
Sobocká
J.
,
Alegre
J.
,
Henriquez
C. R.
,
De Lourdes Mendonça-Santos
M.
,
Taboada
M.
,
Espinosa-Victoria
D.
,
AlShankiti
A.
,
AlaviPanah
S. K.
,
Elsheikh
E. A. E. M.
,
Hempel
J.
,
Camps Arbestain
M.
,
Nachtergaele
F.
&
Vargas
R.
2016
World's soils are under threat
.
Soil
2
,
79
82
.
https://doi.org/10.5194/soil-2-79-2016
.
Mustefa
M.
,
Fufa
F.
&
Takala
W.
2020
GIS estimation of annual average soil loss rate from Hangar River watershed using RUSLE
.
Journal of Water and Climate Change
11
,
529
539
.
https://doi.org/10.2166/wcc.2019.181
.
Opeyemi
O. A.
,
Abidemi
F. H.
&
Victor
O. K.
2019
Assessing the impact of soil erosion on residential areas of Efon-Alaaye Ekiti, Ekiti-State, Nigeria
.
International Journal of Environmental Planning and Management
5
,
23
31
.
Pandey
A.
,
Gautam
A. K.
,
Chowdary
V. M.
,
Jha
C. S.
&
Cerdà
A.
2021
Uncertainty assessment in soil erosion modelling using RUSLE, multisource and multiresolution DEMs
.
Journal of the Indian Society of Remote Sensing
49
,
1689
1707
.
https://doi.org/10.1007/s12524-021-01351-4
.
Pereira
M. D. R.
&
Cabral
J. B. P.
2021
Perda de Solo no Alto Curso das Bacias Hidrográficas dos Ribeirões Taquaruçu Grande e Taquaruçuzinho, Palmas (TO)
.
Revista Brasileira de Geografia Física
14
,
332
339
.
https://doi.org/10.26848/rbgf.v14.1.p332-339
.
Pimentel
D.
&
Kounang
N.
1998
Ecology of soil erosion in ecosystems
.
Ecosystems
1
,
416
426
.
https://doi.org/10.1007/s100219900035
.
Pimentel
D.
,
Harvey
C.
,
Resosudarmo
P.
,
Sinclair
K.
,
Kurz
D.
,
McNair
M.
,
Crist
S.
,
Shpritz
L.
,
Fitton
L.
,
Saffouri
R.
&
Blair
R.
1995
Environmental and economic costs of soil erosion and conservation benefits
.
Science
267
,
1117
1123
.
https://doi.org/10.1126/science.267.5201.1117
.
Podhrázská
J.
,
Kučera
J.
,
Karásek
P.
&
Konečná
J.
2015
Land degradation by erosion and its economic consequences for the region of South Moravia (Czech Republic)
.
Soil and Water Research
10
(
2
),
105
113
.
https://doi.org/10.17221/143/2014-SWR
.
Prasannakumar
V.
,
Vijith
H.
,
Abinod
S.
&
Geetha
N.
2012
Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using Revised Universal Soil Loss Equation (RUSLE) and geo-information technology
.
Geoscience Frontiers
3
,
209
215
.
https://doi.org/10.1016/j.gsf.2011.11.003
.
Prosser
I. P.
&
Williams
L.
1998
The effect of wildfire on runoff and erosion in native eucalyptus forest
.
Hydrology Process
12
,
251
265
.
Renard
K.
,
Foster
G. R.
,
Weesies
G. A.
,
Mccool
D.
&
Yoder
D.
1997
Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Soil Loss Equation (RUSLE)
.
Agriculture Handbook, 703
,
U.S. Department of Agriculture
,
Washington DC
, p.
404
.
Salem
H. M.
,
Valero
C.
,
Muñoz
M. Á.
,
Gil-Rodríguez
M.
&
Barreiro
P.
2014
Effect of reservoir tillage on rainwater harvesting and soil erosion control under a developed rainfall simulator
.
Catena
113
,
353
362
.
https://doi.org/10.1016/j.catena.2013.08.018
.
Sambuichi
R. H. R.
&
Haridasan
M.
2007
Recovery of species richness and conservation of native Atlantic forest trees in the cacao plantations of southern Bahia in Brazil
.
Biodiversity and Conservation
16
,
3681
3701
.
https://doi.org/10.1007/s10531-006-9017-x
.
Sarker
S.
2021
Investigating Topologic and Geometric Properties of Synthetic and Natural River Networks Under Changing Climate
.
PhD thesis
. University of Central Florida, p.
114
.
Sarker
S.
2022
Fundamentals of climatology for engineers: Lecture note
.
Eng
3
,
573
595
.
https://doi.org/10.3390/eng3040040
.
Sarker
S.
,
Veremyev
A.
,
Boginski
V.
&
Singh
A.
2019
Critical nodes in river networks
.
Scientific Reports
9
,
11178
.
https://doi.org/10.1038/s41598-019-47292-4
.
Scarano
F. R.
&
Ceotto
P.
2015
Brazilian Atlantic Forest: Impact, vulnerability, and adaptation to climate change
.
Biodiversity and Conservation
24
,
2319
2331
.
Schick
J.
,
Bertol
I.
,
Cogo
N. P.
&
González
A. P.
2014
Erodibilidade de um Cambissolo Húmico sob chuva natural
.
Revista Brasileira de Ciencia do Solo
38
,
1906
1917
.
https://doi.org/10.1590/S0100-06832014000600025
.
Schröder
J. J.
,
Schulte
R. P. O.
,
Creamer
R. E.
,
Delgado
A.
,
van Leeuwen
J.
,
Lehtinen
T.
,
Rutgers
M.
,
Spiegel
H.
,
Staes
J.
,
Tóth
G.
&
Wall
D. P.
2016
The elusive role of soil quality in nutrient cycling: A review
.
Soil Use and Management
32
(
4
),
476
486
.
https://doi.org/10.1111/sum.12288</otherinfo
.
Schroth
G.
,
Faria
D.
,
Araujo
M.
,
Bede
L.
,
Van Bael
S. A.
,
Cassano
C. R.
,
Oliveira
L. C.
&
Delabie
J. H. C.
2011
Conservation in tropical landscape mosaics: The case of the cacao landscape of southern Bahia, Brazil
.
Biodiversity and Conservation
20
,
1635
1654
.
https://doi.org/10.1007/s10531-011-0052-x
.
Schürz
C.
,
Mehdi
B.
,
Kiesel
J.
,
Schulz
K.
&
Herrnegger
M.
2020
A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda
.
Hydrology and Earth System Sciences
24
,
4463
4489
.
https://doi.org/10.5194/hess-24-4463-2020
.
Sidi Almouctar
M. A.
,
Wu
Y.
,
Zhao
F.
&
Dossou
J. F.
2021
Soil Erosion Assessment Using the RUSLE Model and Geospatial Techniques (Remote Sensing and GIS) in South-Central Niger (Maradi Region)
.
Water
13
,
3511
.
https://doi.org/10.3390/w13243511
.
SOS Mata Atlântica & INPE
.
2017
Atlas dos remanescentes florestais da Mata Atlântica: período 2015–2016
.
SOS Mata Atlântica and INPE
,
São Paulo
.
Sud
A.
,
Sajan
B.
,
Kanga
S.
,
Singh
S. K.
,
Singh
S.
,
Durin
B.
,
Kumar
P.
,
Meraj
G.
,
Sahariah
D.
,
Debnath
J.
&
Chand
K.
2024
Integrating RUSLE model with cloud-based geospatial analysis: A Google Earth engine approach for soil erosion assessment in the Satluj Watershed
.
Water
16
,
1073
.
https://doi.org/10.3390/w16081073
.
Tabarelli
M.
,
Pinto
L. P.
,
Silva
J. M. C.
,
Hirota
M.
&
Bede
L.
2005
Challenges and opportunities for biodiversity conservation in the Brazilian Atlantic Forest
.
Conservation Biology
19
,
695
700
.
https://doi.org/10.1111/j.1523-1739.2005.00694.x
.
Torres
M. L. M.
,
Rego
N. C.
,
Geuz
F.
,
Levy
M. C.
&
Moreau
M.
2001
Programa de recuperação das bacias dos rios Cachoeira e Almada -Diagnóstico Regional
.
Núcleo de bacias hidrográficas da UESC, Superintendência de Recursos Hídricos do Estado da Bahia
.
Trindade
M. E. D. J.
,
Cetra
M.
&
Jucá-Chagas
R.
2010
Ictiofauna do Ribeirão Limoeiro, Bacia do Rio Cachoeira, BA
.
Biota Neotropica.
10
,
111
117
.
https://doi.org/10.1590/S1676-06032010000400015
.
Udayakumara
E. P. N.
,
Shrestha
R. P.
,
Samarakoon
L.
&
Schmidt-vogt
D.
2010
People's perception and socioeconomic determinants of soil erosion: A case study of Samanalawewa watershed
.
Sri Lanka International Journal of Sediment Research
25
,
323
339
.
Wang
G.
,
Gertner
G.
,
Singh
V.
,
Shinkareva
S.
,
Parysow
P.
&
Anderson
A.
2002
Spatial and temporal prediction and uncertainty of soil loss using the revised universal soil loss equation: A case study of the rainfall–runoff erosivity R factor
.
Ecological Modelling
153
,
143
155
.
https://doi.org/10.1016/S0304-3800(01)00507-5
.
Wischmeier
W. H.
&
Smith
D. D.
1965
Predicting Rainfall Erosion Losses From Cropland East of the Rocky Mountains
.
United States Department of Agriculture
,
Washington DC
(Handbook no. 282)
.
Wischmeier
W. H.
&
Mannering
J. V.
1969
Soil and water management and conservation. Relation of soil properties to its erodibility
.
Soil Science Society of American Proceedings
33
,
131
137
.
Wischmeier
W. H.
&
Smith
D. D.
1978
Predicting Rainfall Erosion Losses; A Guide to Conservation Planning
, Vol.
537
.
U.S. Department of Agriculture
,
Washington
, pp.
58
(Agriculture Handbook)
.
Yao
X.
,
Yu
J.
,
Jiang
H.
,
Sun
W.
&
Li
Z.
2016
Roles of soil erodibility, rainfall erosivity and land use in affecting soil erosion at the basin scale
.
Agricultural Water Management
174
,
82
92
.
https://doi.org/10.1016/j.agwat.2016.04.001
.
Zwiener
V. P.
,
Padial
A. A.
,
Marques
M. C. M.
,
Faleiro
F. V.
,
Loyola
R.
&
Peterson
A. T.
2017
Planning for conservation and restoration under climate and land use change in the Brazilian Atlantic Forest
.
Diversity and Distributions
23
,
955
966
.
https://doi.org/10.1111/ddi.12588
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC-ND 4.0), which permits copying and redistribution for non-commercial purposes with no derivatives, provided the original work is properly cited (http://creativecommons.org/licenses/by-nc-nd/4.0/).