Soil loss estimation using RUSLE and GIS in the Córrego Rico watershed

Abstract

In recent decades, agricultural practices and urbanization, combined with inadequate conservation measures, have likely intensified soil erosion in the headwaters of the Córrego Rico watershed (HCRW), located in northeastern São Paulo State, Brazil. The HCRW represents an important water source for Jaboticabal and surrounding municipalities; however, it remains vulnerable to erosion driven by intensive agricultural use. This study aimed to estimate soil erosion and its spatial distribution in the HCRW using the Revised Universal Soil Loss Equation (RUSLE) integrated with a geographic information system (GIS). Spatial layers were developed in GIS for each RUSLE factor: average annual soil loss (A), rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover-management (C), and support practice (P). Estimated average annual soil loss (A) reached 68.61 t ha⁻¹ year⁻¹, with values ranging from 22.82 to 414.15 t ha⁻¹ year⁻¹. Approximately 77.40% of the watershed exceeded the tolerable soil loss limit of 8.6 t ha⁻¹ year⁻¹, indicating significant risks to soil productivity and ecosystem services, with potential implications for food and water security in the HCRW.

Keywords:
GIS; soil conservation; soil erosion risk; rainfall erosivity; soil erodibility

Introduction

Spatial variability of soil erosion within watersheds is highly relevant for soil and water management, as it responds to changes in land use and management, influences sediment production and transport, and degrades surface water quality (Kebede et al., 2021; Lopes et al., 2021; Bogale et al., 2023). Accurate assessment of erosion processes requires reliable estimates of erosion rates and identification of key driving factors, including intense rainfall, soil properties, and management practices (Tangi et al., 2022; Shmilovitz et al., 2023).

Among available approaches, the Revised Universal Soil Loss Equation (RUSLE) remains the most widely applied model for estimating soil erosion across diverse regions, as it integrates climatic, soil, topographic, vegetation, and management factors (Singh et al., 2023; Gao et al., 2023). Integration of RUSLE with remote sensing and geographic information systems (GIS) enables spatial modeling of erosion and identification of priority areas for conservation, while streamlining complex spatial analyses (Saadi et al., 2022; Abdelsamie et al., 2023). For instance, in the Samambaia River watershed (GO), 28% of the area exhibited soil losses more than twice the tolerable limit, highlighting critical zones for implementation of soil and water conservation practices (Botelho et al., 2018).

In northeastern São Paulo State, Brazil, headwaters of the Córrego Rico watershed (HCRW) constitute an important water source for Jaboticabal (SP) and surrounding municipalities; however, they remain threatened by erosion associated with intensive agricultural use (Britto et al., 2025; Bovério et al., 2025). Therefore, this study aimed to estimate soil erosion and its spatial distribution in the HCRW using the RUSLE model integrated with remote sensing data and GIS.

Material and Methods

The study was conducted in the headwaters of the Córrego Rico watershed (HCRW), located in the municipalities of Monte Alto and Taquaritinga, within the Ribeirão Preto administrative region, northeastern São Paulo State, Brazil (Figure 1).

Figure 1
Location of the headwaters of the Córrego Rico watershed (HCRW).

The HCRW belongs to the Córrego Rico sub-basin and covers an area of approximately 50.23 km². It is located between latitudes 21°10′ S and 21°27′ S and longitudes 48°08′ W and 48°33′ W, with elevation ranging from 410 to 740 m. The climate is classified as Aw according to the Köppen system, with mean annual rainfall between 1,100 and 1,700 mm. Mean temperature reaches 22 °C in the warmest month and 18 °C in the coldest month. The area lies within geomorphological province V, corresponding to the Western Plateau of São Paulo State.

In upper portions of the watershed, predominant soils are Red-Yellow Argisols (PVA), developed from sandstones with calcareous cement of the Bauru Formation. In lower portions, Red Latosols (LV) originate from basic effusive rocks of the Serra Geral Formation (São Paulo, 1974).

Natural vegetation consists of tropical broadleaf forest and Brazilian savanna (Cerrado). Current land use is dominated by sugarcane, perennial crops (mainly citrus), and areas with sparse vegetation or weeds. These land uses exert significant anthropogenic pressure on soil and local water resources.

QGIS 3.24.2 (QGIS Development Team, 2022) was used for visualization, management, and analysis of geospatial data. The digital elevation model (DEM) was derived from PALSAR sensor data onboard the ALOS satellite, with spatial resolution of 12.5 × 12.5 m, referenced to the SIRGAS 2000 datum and projected in the UTM coordinate system (zone 22K, Southern Hemisphere).

Average annual soil loss (A) was estimated using the Revised Universal Soil Loss Equation (RUSLE) [eq. (1)], as described by Singh et al. (2023). Calculations were performed on a pixel-by-pixel basis. Spatial distribution of soil loss in the HCRW was generated using map algebra (Grid Math tool) in Surfer 13 (Golden Software, 2015).

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

Where:

A represents the soil loss (in t ha^-1 year^-1);

R stands for the rainfall erosivity factor (in MJ mm ha^-1 h^-1 year^-1);

K is the soil erodibility factor (in t ha h ha^-1 MJ^-1 mm^-1);

LS is the slope length and steepness factor (dimensionless);

C is the vegetation cover and soil management factor (dimensionless), and

P is the conservation practices factor (dimensionless).

Rainfall erosivity (R) was estimated using the netErosivity SP software (Moreira et al., 2006). Geographic coordinates of 53 Hydrological Response Units (HRUs) within the HCRW were used as input. HRUs were delineated using the SWAT hydro-sedimentological model (Arnold et al., 2012), considering soil type, land use, management, and slope.

Soil erodibility (K) represents susceptibility of soil to erosion under rainfall impact. A mean value of 0.0425 Mg h MJ⁻¹ mm⁻¹ was adopted, reflecting soil composition of the HCRW, which includes 99.29% Argisols, 0.41% Latosols, and 0.30% Litholic Neosols (Rossi, 2017).

GIS-based techniques enable generation and management of digital elevation models (DEMs), allowing estimation of topographic effects on soil erosion processes. In RUSLE applications, these approaches support calculation of the LS factor, which is inherently difficult to determine. We adopted the LS-TOOLMFD approach (Zhang et al., 2017), because it combines multiple flow direction (MFD) and single flow direction (SFD) algorithms to improve accuracy in estimating slope length (λ) and LS factor. Moreover, this method has been validated in watersheds of variable slopes. Due to the geomorphological complexity in the HCRW, LS calculations were performed using LS_TOOL version 3.1 (Zhang et al., 2017).

The cover-management factor (C) was determined using land use and land cover data for years 2010, 2020, 2021, and 2022. Land use classes (Table 1) included: (1) sugarcane replanting/onion cultivation; (2) weeds; (3) sugarcane in early growth (sprouting); (4) sugarcane in full development; (5) forest; (6) citrus orchards; (7) sugarcane straw; (8) degraded areas with low vegetation cover; (9) exposed soil with terraces (sugarcane replanting); and (10) Cerrado vegetation with shrub cover. These classes were used to assign C factor values based on literature (Table 2).

Table 1
Land use and management classes and their respective areas in the HCRW.

Land use and management class	HRU	Area (%)
Forest (Cerrado shrub vegetation); woodland; citrus orchards; herbaceous vegetation (weeds); onion cultivation; degraded pasture.	1, 2, 3, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 21, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49 e 50.	52.5
Citrus orchards; sugarcane in full development; onion cultivation; exposed soil with terraces (sugarcane replanting); low vegetation cover, pasture, and degraded areas; herbaceous vegetation (weeds); forest.	20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 51, 52 e 53.	38.3
Forest (Cerrado shrub vegetation); woodland; citrus orchards; herbaceous vegetation (weeds); onion cultivation; degraded pasture; sugarcane in full development; exposed soil; low vegetation cover, pasture, and degraded areas.	4, 5, 6, 7 and 40	9.2

• HRU – hydrological response unit.

Table 2
Cover-management factor (C) values for land use classes in the HCRW.

Main use	Factor C
Sugarcane	0.115
Herbaceous vegetation (weeds)	0.013
Forest	0.015
Citrus orchards	0.088
Low vegetation cover, pasture, and degraded areas	0.507
Forest (Cerrado shrub vegetation)	0.020

• Source: Cardoso (2023).

Satellite imagery indicates that soil conservation practices (SCP) in the study area are predominantly contour-based. However, limited research quantifies the effects of different SCP measures on the P factor at a regional scale (Tian et al., 2021). Therefore, the P factor was estimated using an empirical approach based on slope gradient (%), assuming contour-based practices. Accordingly, the conservation practice factor (P) for cultivated areas was calculated using [eq. (2)], as proposed by Demarchi et al. (2019).

$P=0.69947-0.08991D+0.01184D^2-0.000335D^3$ (2)

Where:

D is the slope (%).

Soil loss tolerance was set at 8.60 t ha^-1 year^-1, according to Cardoso (2023).

Results and Discussion

Soil loss can be estimated using modeling approaches, among which the Revised Universal Soil Loss Equation (RUSLE) is widely applied. The factors of this equation are described below for the HCRW.

Rainfall erosivity factor (R)

In this study, R factor values ranged from 7,768 to 7,969 MJ mm ha⁻¹ h⁻¹ year⁻¹ (Figure 2a). Higher values were concentrated in the northwest and southern portions of the watershed, whereas lower values occurred in the northeastern sector of the HCRW. Overall, these values indicate high rainfall erosivity across the basin. According to Ricardi & Lima (2021), erosivity values in São Paulo State ranging from 7,357 < R ≤ 9,810 MJ mm ha⁻¹ h⁻¹ year⁻¹ are classified as high.

Figure 2
Spatial distribution of RUSLE factors: (a) rainfall erosivity (R), (b) soil erodibility (K), (c) topographic factor (LS), (d) cover-management factor (C), (e) support practice factor (P), and (f) potential soil loss.

A spatial pattern was observed, with lower values in downstream areas (HRUs 11, 12, 13, 14, 17, 18, 19, 24, 25, and 28) and higher values in upstream areas (HRUs 1, 2, 3, 5, 6, 10, 48, and 49), likely influenced by elevation, as higher altitudes were associated with greater erosivity. Reported erosivity values for São Paulo State range from 5,968 to 8,540 MJ mm ha⁻¹ h⁻¹ year⁻¹ (Ricardi & Lima, 2021), and the values obtained in this study fall within this range.

Although rainfall erosivity is high in the HCRW, erosion processes are also strongly controlled by interactions among climate, topography, soil properties, and land use and management practices, which together can intensify soil loss in watersheds (Xiao et al., 2021; Wang et al., 2022; Ebabu et al., 2022; Firoozi & Firoozi, 2024).

Soil erodibility factor (K)

Soil erodibility (K factor) in the Córrego Rico Watershed was estimated at 0.0425 Mg h MJ⁻¹ mm⁻¹, indicating high erodibility for Argisols, Latosols, and Litholic Neosols, according to Rossi (2017). Demarchi et al. (2019) also report similar values as characteristic of Argisols. Values exceeding 0.030 Mg h MJ⁻¹ mm⁻¹ are classified as high; therefore, the value observed in this study falls within this category (Rossi, 2017).

A comparable K value (0.0464 Mg h MJ⁻¹ mm⁻¹) was reported by Silva et al. (2022) for Argisols in the Jaboatão River basin (PE), Brazil. Argisols are inherently susceptible to erosion due to the increase in clay content in the textural B horizon (Bt), which creates an infiltration gradient, with higher infiltration in the A horizon than in the B horizon.

In RUSLE, the K factor represents intrinsic soil susceptibility to erosion and is largely controlled by aggregate stability and soil texture (Gupta et al., 2023). Therefore, quantifying soil erodibility and identifying its controlling variables are essential for erosion risk assessment, mitigation strategies, and ecological restoration (Han et al., 2023). Spatial representation of soil erodibility further enables identification of erosion-prone areas and supports delineation of management zones for soil and water conservation.

Topographic factor (LS)

The LS factor influences soil erosion dynamics through slope length (L) and slope steepness (S), which are key parameters controlling surface runoff processes (Li et al., 2024). Higher LS values are associated with increased erosion intensity. Thus, the LS factor shows a direct relationship with soil erosion, as runoff-driven erosion increases with greater slope length and steepness (Xiao et al., 2021).

The highest LS values were observed in HRUs 1, 2, 3, 4, 5, 6, 8, 10, 20, 23, 26, 35, 42, 48, and 49, classified as high (10–20) and very high (>20) (Figure 2c). These values correspond to areas with the steepest slopes (Figure 3). A slope gradient of 15% is considered steep and requires careful management to prevent instability and drainage issues. This class represents 0.37% of the HCRW area. In these locations, slope length and steepness increase runoff velocity, thereby enhancing erosion intensity.

Figure 3
Slope distribution of HRUs (1–53) in the HCRW.

Moderate LS values (5–10) occur across most HRUs, both upstream and downstream, except HRUs 14 and 19. These areas also contribute to runoff dynamics and flow velocity and account for 4.19% of the HCRW area.

Low (<5) LS values, including very low (<1) and low (1–5) classes, dominate the watershed, covering 95.44% of the HCRW area. These values are widely distributed, mainly in areas with gentle slopes and near drainage channels, where topographic influence on runoff is reduced due to lower slope gradients (Figure 3).

LS values between 0 and 5 largely coincide with slopes ranging from 0 to 20%, corresponding to flat to gently undulating terrain. In contrast, LS values >5 generally align with slopes >20%, associated with strongly undulating, mountainous, or steep terrain (Figures 2c and 3).

Approximately 24.06% of the HCRW exhibits strongly undulating relief, including HRUs 7, 9, 11, 12, 13, 14, 17, 27, 35, 36, and 42 (Figure 3). Mountainous areas account for 12.99% of the watershed (HRUs 2, 16, 20, 23, 28, and 49), while steep areas represent 15.86% (HRUs 1, 3, 4, 5, 6, 8, 10, 26, and 48). Overall,

54.25% of the HCRW presents terrain with moderate to high slope, which may constrain agricultural use. Steep slopes in agricultural landscapes are typically defined as those exceeding 7° (≈12.3%), where runoff and erosion risks are significantly increased, particularly in soils with high erodibility (Wang et al., 2022). These conditions highlight critical concerns for the HCRW.

Areas with the highest erosion susceptibility coincide with steep slopes, particularly in strongly undulating (20–45%) and mountainous (>45%) terrain. As slope increases, runoff velocity also increases, intensifying soil erosion processes (Xiao et al., 2021).

Cover and management factor (C)

The magnitude and spatial distribution of the cover-management factor (C) are presented in Figure 2d, reflecting land use and management composition in each HRU (Tables 1 and 2). In the study area, C values ranged from 0.013 to 0.115, with a mean of 0.064. Higher C values were concentrated in the northwestern, central, and southeastern regions of the HCRW.

The highest C-factor values (0.099–0.115) cover 38.3% of the study area (Figure 2d), mainly concentrated near the central drainage network in areas with slopes <5% (Figure 3), including HRUs 20, 22–35, and <1% slopes in HRUs 51–53. In contrast, the lowest C-factor values (0.013–0.030) occur across 52.5% of the HCRW, encompassing HRUs 1, 2, 3, 8–10, 12–19, 21, 36–39, 41–50 (Figure 2d). In these HRUs, dominant land use classes include citrus orchards, sugarcane in full development, exposed soil with terraces (sugarcane replanting/onion cultivation), low vegetation cover, pasture and degraded areas, herbaceous vegetation (weeds), and forest (Table 1). Intermediate C values (0.082–0.098) account for 9.2% of the watershed area.

Conservation practice factor (P)

The spatial distribution of the P factor is shown in Figure 2e. In the study area, P values ranged from 0.5071 to 0.8567, with a mean of 0.6019. Typical P values range from 0.2 (inverted terraces) to 1.0 (absence of conservation practices). In this study, P values were estimated based on contour farming conditions and slope gradient. The results are consistent with those reported by Ebabu et al. (2022).

Variability in P values among HRUs reflects differences in the effectiveness of support practices, largely associated with variations in runoff generation and sediment retention capacity. Similar patterns have been reported at regional and global scales, highlighting substantial variability in the performance of conservation practices in reducing runoff and soil loss (Xu et al., 2021; Ebabu et al., 2022).

Higher P values were observed in HRUs 1–6, 8, 10, 16, 20, 23, 26, 28, 48, and 49, corresponding to areas with steeper slopes (Figures 2e and 3), representing 26.37% of the watershed. Lower P values dominate most of the area (49.57%), including HRUs 15, 18, 19, 21, 22, 25, 29–34, 37–47, and 50–53. HRUs 7, 9, 11–14, 17, 27, 35, 36, and 42 exhibit intermediate P values and account for 24.06% of the HCRW area.

P values between 0.16 and 0.20 indicate effective soil conservation practices (Ebabu et al., 2022). In contrast, values between 0.51 and 0.86 (Figure 2e) suggest limited effectiveness of current practices in reducing runoff and controlling erosion, as they exceed the threshold of 0.34 reported by Ebabu et al. (2022).

Annual soil erosion estimation and prioritization for soil conservation planning

Integrating RUSLE with geospatial techniques and satellite data enabled pixel-based estimation of average annual soil loss and identification of erosion-prone areas. Total estimated soil loss in the HCRW reached 344,650.53 t year⁻¹. Soil loss rates ranged from 0.002 to 3,023.97 t ha⁻¹ year⁻¹ (Figure 2f), with a mean of 68.61 t ha⁻¹ year⁻¹. This value is 7.98 times higher than the tolerable soil loss limit (T = 8.60 t ha⁻¹ year⁻¹) for soils in the HCRW.

Soil loss was classified into six severity classes: very low (0–4.3 t ha⁻¹ year⁻¹), low (4.3–8.6 t ha⁻¹ year⁻¹), moderate (8.6–17.2 t ha⁻¹ year⁻¹), high (17.2–43 t ha⁻¹ year⁻¹), very high (43–86 t ha⁻¹ year⁻¹), and severe (>86 t ha⁻¹ year⁻¹) (Figure 4a). Very low and low classes account for 16.52% and 6.08% of the watershed area, respectively (Table 3). The remaining classes total 77.40% of the HCRW: 10.46% moderate, 27.74% high, 19.23% very high, and 19.97% severe. This proportion indicates that most of the watershed exceeds the tolerable soil loss threshold of 8.60 t ha⁻¹ year⁻¹.

Figure 4
Spatial distribution of soil erosion: (a) average annual soil loss rate and (b) erosion severity classes.

Table 3
Soil erosion risk classes and corresponding area coverage in the HCRW.

Range of soil erosion risk (SER)	Soil erosion risk class (SERC)	Area (ha)	Area (%)
t ha-1 year-1		ha	%
0 - 4.3	Very-low	829.77	16.52
4.3 - 8.6	Low	305.48	6.08
8.6 - 17.2	Moderate	525.61	10.46
17.2 - 43	High	1393.36	27.74
43 - 86	Very-high	965.84	19.23
>86	Severe	1003.37	19.97
Total		5023.44	100.00

The average annual soil loss for HRUs in the HCRW ranged from 22.82 to 414.15 t ha⁻¹ year⁻¹ (Table 4). Based on these values, each HRU was classified according to soil erosion risk classes (SERC), and a priority ranking was established (Figure 4b; Table 4). HRU 26 received the highest priority (414.15 t ha⁻¹ year⁻¹), whereas HRU 38 received the lowest priority (22.82 t ha⁻¹ year⁻¹), for implementation of soil and water conservation measures. Notably, all HRUs were classified within high, very high, or severe erosion risk classes (Figure 4b). This widespread severity is consistent with findings reported for other river basins (Atoma et al., 2020; Singh et al., 2023).

Table 4
Prioritization of hydrological response units (HRUs) in the HCRW based on soil erosion severity classes.

HRU	Very low	Low	Moderate	High	Very high	Severe	E	Area	A	SERC	Priority rank
	(0 - 4.3) (4.3 - 8.6) (8.6 - 17.2) (17.2 - 43)				(43 - 86)	(> 86)
	t year-1	t year-1	t year-1	t year-1	t year-1	t year-1	t year-1	ha	t ha-1 year-1
1	24.83	42.58	149.13	569.04	963.07	10,776.21	12,524.85	107.17	116.87	Severe	6.00
2	29.55	67.86	122.92	670.41	710.01	2,215.62	3,816.37	85.67	44.55	Very High	28.00
3	7.90	27.46	49.98	257.94	304.08	4,547.12	5,194.48	42.67	121.73	Severe	5.00
4	5.75	36.93	141.36	616.07	359.66	1,725.77	2,885.52	59.53	48.47	Very High	25.00
5	25.33	46.23	145.65	1,326.33	2,724.50	15,941.84	20,209.87	189.61	106.59	Severe	8.00
6	7.86	10.92	47.38	322.06	652.16	12,785.98	13,826.36	67.67	204.31	Severe	3.00
7	6.68	13.12	73.10	200.45	358.16	389.82	1,041.34	28.48	36.56	High	38.00
8	7.98	17.11	78.18	791.68	1,692.11	1,414.22	4,001.29	80.56	49.67	Very High	24.00
9	5.26	19.18	49.04	429.26	709.28	792.33	2,004.35	46.14	43.44	Very High	30.00
10	5.25	6.11	65.23	428.89	559.55	15,692.40	16,757.42	64.80	258.61	Severe	2.00
11	20.09	49.82	256.06	1,389.61	3,492.56	6,506.87	11,715.02	187.92	62.34	Very High	19.00
12	2.94	0.00	19.11	237.47	414.29	982.56	1,656.36	32.44	51.06	Very High	23.00
13	4.96	9.81	35.96	485.99	1,112.90	1,194.61	2,844.24	54.81	51.89	Very High	22.00
14	14.53	30.54	104.96	741.23	694.59	1,484.96	3,070.82	79.03	38.86	High	36.00
15	29.01	56.23	181.77	1,284.79	1,175.05	2,280.10	5,006.95	136.09	36.79	High	37.00
16	24.91	23.35	117.15	536.48	625.37	734.65	2,061.92	59.41	34.71	High	40.00
17	22.36	130.20	223.75	957.51	1,937.52	5,748.47	9,019.81	160.25	56.29	Very High	21.00
18	6.37	22.57	124.81	814.34	1,486.61	3,010.11	5,464.81	97.33	56.15	Very High	20.00
19	1.61	6.23	18.38	97.46	128.67	311.04	563.40	11.72	48.08	Very High	26.00
20	9.84	16.48	37.56	434.48	1,285.72	14,553.31	16,337.39	91.06	179.41	Severe	4.00
21	38.42	63.18	186.92	1,049.85	1,329.76	1,600.11	4,268.24	124.28	34.34	High	41.00
22	12.16	31.70	105.28	672.08	1,027.20	5,847.72	7,696.14	96.27	79.95	Very High	12.00
23	29.54	55.70	261.86	1,467.59	2,407.15	9,270.41	13,492.26	192.53	70.08	Very High	16.00
24	0.04	0.06	0.22	1.17	2.97	13.29	17.75	0.25	71.02	Very High	15.00
25	12.39	16.00	99.78	358.24	559.76	1,161.28	2,207.44	52.33	42.18	High	32.00
26	3.82	9.49	32.23	158.28	384.06	29,949.16	30,537.04	73.73	414.15	Severe	1.00
27	1.77	2.75	8.58	109.25	219.77	619.79	961.92	14.47	66.48	Very High	17.00
28	8.11	35.23	130.71	681.18	1,887.13	9,157.28	11,899.64	116.77	101.91	Severe	9.00
29	9.65	32.17	138.78	859.55	839.76	798.97	2,678.88	77.55	34.55	High	39.00
30	5.31	7.66	37.07	243.26	489.79	2,219.42	3,002.51	37.55	79.97	Very High	11.00
31	13.23	29.80	84.06	291.95	438.52	1,341.35	2,198.91	49.88	44.09	Very High	29.00
32	12.76	23.44	79.16	484.25	609.67	3,435.36	4,644.63	64.70	71.78	Very High	14.00
33	33.24	83.59	282.08	1,715.11	1,329.77	1,461.32	4,905.10	165.36	29.66	High	48.00
34	31.22	56.03	194.48	1,104.85	836.05	1,388.58	3,611.22	112.22	32.18	High	43.00
35	33.84	74.53	253.09	2,287.93	4,246.42	27,352.43	34,248.24	313.86	109.12	Severe	7.00
36	10.48	26.04	84.38	681.48	791.30	526.47	2,120.14	64.78	32.73	High	42.00
37	12.24	20.18	100.98	757.74	510.34	92.79	1,494.27	60.22	24.81	High	51.00
38	3.99	8.68	57.87	195.36	143.40	50.96	460.28	20.17	22.82	High	53.00
39	16.31	27.73	114.16	704.51	535.60	367.75	1,766.06	67.95	25.99	High	50.00
40	32.03	55.48	231.64	959.45	1,007.05	1,205.41	3,491.05	115.78	30.15	High	47.00
41	53.70	160.96	420.76	3,458.25	2,778.31	2,858.75	9,730.74	313.94	31.00	High	44.00
42	28.35	58.30	210.22	1,364.82	3,063.15	15,775.49	20,500.33	226.42	90.54	Severe	10.00
43	9.47	18.51	82.78	627.23	903.15	1,373.91	3,015.07	67.00	45.00	Very High	27.00
44	10.55	26.67	88.68	485.25	586.83	65.12	1,263.09	51.39	24.58	High	52.00
45	7.92	15.50	58.10	283.38	470.97	597.59	1,433.46	35.84	39.99	High	33.00
46	3.35	12.59	20.50	200.08	464.13	1,774.70	2,475.34	31.83	77.77	Very High	13.00
47	7.83	19.09	43.44	503.36	1,138.94	2,734.33	4,446.99	68.16	65.25	Very High	18.00
48	11.67	58.57	142.37	836.97	1,184.45	1,850.89	4,084.93	93.63	43.63	Very High	31.00
49	23.78	47.38	206.36	978.86	1,472.33	1,525.94	4,254.64	107.31	39.65	High	34.00
50	75.87	116.08	384.74	2,017.95	2,484.43	4,638.71	9,717.78	246.16	39.48	High	35.00
51	22.31	61.18	167.05	1,035.60	1,190.39	801.27	3,277.79	105.77	30.99	High	45.00
52	8.69	13.72	114.70	603.84	596.67	235.55	1,573.16	51.73	30.41	High	46.00
53	25.40	58.38	241.90	1,214.11	1,204.99	428.14	3,172.90	121.25	26.17	High	49.00
Total	872.48	1,959.08	6,706.37	40,984.29	58,520.07	235,608.24	344,650.53	5,023.44	-		-

• HRU - hydrological response unit; ( ) - t ha^-1 year^-1; E - total soil loss; A - average soil erosion rate; SERC - soil erosion risk classes.

Results shown in Figure 4 indicate substantial anthropogenic pressure on water bodies within the HCRW. Globally, river systems are increasingly affected by multiple human-induced stresses, including direct interventions (e.g., channel modification) and indirect impacts (e.g., land use and land cover changes), which alter hydrological and sediment dynamics (Tangi et al., 2022). In many

basins, sediment transport is often dominated by critical source areas, as demonstrated by Lemos et al. (2022) and Yaekob et al. (2022). From a soil perspective, such areas are defined as those where erosion rates exceed tolerable soil loss limits (Arega et al., 2024). In the HCRW, all HRUs exceed the tolerable soil loss threshold (Table 4; Figure 4b), indicating that the entire watershed can be considered critical with respect to soil erosion.

Integration of RUSLE with GIS proved effective for estimating SERC and supporting watershed-scale planning for sustainable soil management. In the HCRW, priority classes for soil and water conservation (SWC) were defined based on soil loss (A) and SERC (Table 4). Prioritization of HRUs provides a practical framework for environmental monitoring and soil and water management. HRUs classified as high to severe risk correspond predominantly to agricultural areas, where C and P factor values are highest (Figures 2d and 2e). These areas are therefore expected to generate the highest sediment loads, with potential impacts on water bodies in the HCRW.

In HRUs classified as high to severe SERC, protection of water bodies is not a dominant feature in the HCRW (Rodrigues & Pissarra, 2014). This condition raises concerns regarding water quality, as the HCRW supplies public water to municipalities such as Jaboticabal (SP). Valera et al. (2019) demonstrated that riparian buffers of 10, 30, or even 50 m are insufficient to maintain water resource protection within environmental protection areas (APA) in the Uberaba River basin (MG). These authors also suggested that limits established by the current Brazilian Forest Code should be expanded. Therefore, water quality in the HCRW requires urgent attention, particularly given the high sediment loads generated by erosion processes identified in this study.

Regarding water quality impacts, Lopes et al. (2021) showed that substantial inputs of sediment and nutrients from eroded upland areas can severely degrade reservoir conditions, especially in the absence of native riparian vegetation, as observed in the Córrego Olaria watershed (Pindorama, SP). In the HCRW, several water bodies exhibit limited riparian vegetation cover, reinforcing the need for improved landscape organization and implementation of soil and water conservation practices, as highlighted by Valera et al. (2019) and Lopes et al. (2021).

The Aquatic Life Protection Index (ALPI) for Córrego Rico in Jaboticabal, measured at the dirt road bridge near Barrinha/São Carlos Mill, was classified as fair in 2010 and predominantly poor from 2012 to 2023, except in 2013, when it was rated as fair (CBH-MOGI, 2024). Similarly, the Trophic State Index (TSI) indicated mesotrophic conditions, with total phosphorus exceeding legal thresholds and serving as the primary indicator of water quality impairment, likely associated with surrounding agricultural activities (CBH-MOGI, 2024).

To further support these findings, HRU 30 was analyzed in detail regarding soil erosion, land use, and land cover (Figure 5). Intensive soil disturbance occurs during crop preparation and harvesting under irrigated onion cultivation (Figure 5, points 1y and 2y). Soil loss in these locations ranged from 43 to 86 t ha⁻¹ year⁻¹ (Figure 5a). Field observations conducted in 2024 identified rill erosion, confirming consistency between RUSLE estimates and observed erosion patterns.

Figure 5
Images of HRU 30: land use and land cover reference points (1y and 2y, onion cultivation; 3y, permanent preservation areas; 4y, sugarcane); (b) July 2020; (c) September 2022; (d) May 2024; and (e) rill erosion observed at points 1y and 2y in May 2024. Source: Google Earth Pro (2020, 2022, 2024); imagery © 2025 Maxar Technologies and Airbus. Accessed January 30, 2026.

The presence of rill erosion indicates progression of erosive processes associated with onion cultivation in HRU 30 during the study period (2010–2022). Soil loss estimates for the Córrego Rico watershed reported by Costa et al. (2016) ranged from 0 to 75 t ha⁻¹ year⁻¹, with extensive areas between 10 and 15 t ha⁻¹ year⁻¹, which corroborates the results obtained in this study.

Reduced erosion in sugarcane systems is associated with contour cultivation and mechanized harvesting that maintains straw cover (Figure 5, point 4y). Martins Filho et al. (2009) reported that 50% and 100% residue cover in Argisols reduce soil loss by up to 68% and 89%, respectively. When residue cover is ≤50%, erosion increases, and sediments become enriched in organic matter and nutrients. Given the high erodibility of Argisols, conservation practices such as drainage terraces, channels, and diversion structures are recommended for erosion-prone areas within the watershed (De Maria et al., 2016; Hassen et al., 2022). However, these practices are not widely implemented in the HCRW. Therefore, technical training and rural extension are needed to support proper design and adoption of soil and water conservation (SWC) practices.

The present results also indicate that recommendations made by Costa et al. (2016), Britto et al. (2025), and Bovério et al. (2025) regarding the need for public policies to mitigate land degradation in the Córrego Rico watershed have not been consistently implemented. Continued expansion of agricultural areas has likely intensified degradation processes in the HCRW.

In Permanent Preservation Areas (PPA) of the HCRW, such as point 3y (Figure 5), surface water is not visible, and vegetation is sparse, dominated by grasses and low-stature plants. This condition indicates reduced ecological function and increased vulnerability of the watershed, particularly regarding habitat provision for local wildlife (Rodrigues et al., 2022). The limited effectiveness of these areas highlights failure to maintain essential ecosystem services, especially biodiversity conservation in the HCRW.

Conclusions

This study aimed to estimate soil loss due to water erosion and its spatial distribution in the headwaters of the Córrego Rico watershed (HCRW) using RUSLE and GIS techniques. The average soil loss estimated by RUSLE was 68.61 t ha⁻¹ year⁻¹, corresponding to a total annual loss of 344,650.53 t year⁻¹.

The spatial variability of soil erosion in the study area showed that 22.06% falls within very low and low erosion classes, with rates below 8.60 t ha⁻¹ year⁻¹ (tolerable soil loss). The moderate class represents 10.46% of the watershed, while the high, very high, and severe classes account for 27.74%, 19.23%, and 19.97%, respectively. These results indicate negative impacts on soil productivity, ecosystem services, and food and water security in the HCRW.

Hydrological response units (HRUs) were identified based on estimated erosion severity and classified according to priority for planning and implementation of soil and water conservation measures. In this way, the results provide useful information for policymakers, land use planners, and decision-makers in defining strategies for soil and water conservation in critical HRUs of the HCRW.

Management practices in agricultural areas, such as no-till, minimum tillage, mulching, cover crops, contour farming, intercropping, and the use of organic fertilizers, should be prioritized across HRUs. In addition, restoration of vegetation in permanent preservation areas is necessary.

This study confirms that integration of the RUSLE model with GIS is an effective approach for assessing spatial variability of soil loss in the HCRW.

Acknowledgments

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

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• Data Availability Statement:
  The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Edited by

• Area Editor:
  Alexandre Barcellos Dalri

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Publication Dates

• Publication in this collection
  20 July 2026
• Date of issue
  2026

History

• Received
  8 July 2025
• Accepted
  26 Mar 2026