New approach for obtaining the C-factor of RUSLE considering the seasonal effect of rainfalls on vegetation cover

Macedo, Pietro Menezes Sanchez; Oliveira, Paulo Tarso Sanches de; Antunes, Mauro Antônio Homem; Durigon, Valdemir Lúcio; Fidalgo, E. C. C.; Carvalho, Daniel Fonseca de

doi:10.1016/j.iswcr.2020.12.001

Cited by 21 publications

(15 citation statements)

References 27 publications

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“…Among sampling methods, equal-width increment (EWI) is the most used for sampling suspended sediment due to its simplicity and the greater hydro-sedimentological detail of the cross section [15]. Entrained solid discharge can also be determined from samples, but when equipment, human resources, and finances are limited, all sediment discharge can be estimated and simulated by models consisting of values representing the physical and hydraulic characteristics of the channel [5,16] and through the use of geo-technological tools and resources [17,18].…”

Section: Introductionmentioning

confidence: 99%

Dynamics of Sediment Transport in the Teles Pires River Basin in the Cerrado-Amazon, Brazil

et al. 2022

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The Teles Pires River basin is experiencing significant water challenges due to recent urban growth, expansion of irrigated agriculture, and the rise of hydroelectric power plants in Brazil’s forest and savanna regions, impacting water availability and sediment production. This study evaluated and estimated the production of suspended sediment (Qss) and total sediment (Qst) in rivers and streams of the Teles Pires River basin, using different sampling methods for suspended-solid discharge: vertical sampling (reference), composite sampling (section), sampling along the standard vertical, and sampling along three verticals, collected using the equal-width increment method. The Qss and Qst values varied from 0.31 to 39.35 metric tons (t) per day (d−1) and from 0.32 to 43.70 t d−1, respectively. The average percentages of the entrained solid discharge varied from 3 to 5%, between the dry and rainy seasons, and across all hydrological sections. The different sampling methods of Qss resulted in similar Qst in each of the monitoring sections. The statistical performance of the simple linear regression model was satisfactory with Willmott index of agreement greater than 0.8234 and 0.9455 for estimates of Qss and Qst, respectively. The dynamics of sediment production and transport was influenced by land use and cover, drainage area, and the hydrological seasonality of the region. The different sampling methods of Qss are compatible with obtaining suspended and total solid discharge; however, the standard vertical sampling is the most simplified and can be applied in a hydrological section with uniform hydraulic conditions.

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Section: Introductionmentioning

confidence: 99%

Dynamics of Sediment Transport in the Teles Pires River Basin in the Cerrado-Amazon, Brazil

et al. 2022

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“…The overall conclusion is that while the NDVI approach is useful in terms of identifying spatial and temporal variations in the C value, the actual estimation equation (Equation ( 1)) requires some revisions. Revisions may also focus on the method of NDVI calculation, as proposed by [5]. It is likely that these revisions may be site-specific.…”

Section: Discussionmentioning

confidence: 99%

“…The understanding of the effects of vegetation on erosion ensured that it was listed as one of the parameters within the Universal Soil Loss Equation (USLE) [3]. However, representing vegetation spatial and temporal changes within models remains a challenge and recent studies refer to attempts to find an appropriate temporal scale for parameterising the vegetation cover management (C) parameter, typically using remote-sensing techniques [4][5][6]. Look-up tables for the USLE to guide the derivation of C values for specific vegetation coverage categories is provided by [3].…”

Section: Introductionmentioning

confidence: 99%

Temporal Influences of Vegetation Cover (C) Dynamism on MUSLE Sediment Yield Estimates: NDVI Evaluation

Gwapedza

Hughes

Slaughter

et al. 2021

Water

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Vegetation cover is an important factor controlling erosion and sediment yield. Therefore, its effect is accounted for in both experimental and modelling studies of erosion and sediment yield. Numerous studies have been conducted to account for the effects of vegetation cover on erosion across spatial scales; however, little has been conducted across temporal scales. This study investigates changes in vegetation cover across multiple temporal scales in Eastern Cape, South Africa and how this affects erosion and sediment yield modelling in the Tsitsa River catchment. Earth observation analysis and sediment yield modelling are integrated within this study. Landsat 8 imagery was processed, and Normalised Difference Vegetation Index (NDVI) values were extracted and applied to parameterise the Modified Universal Soil Loss Equation (MUSLE) vegetation (C) factor. Imagery data from 2013–2018 were analysed for an inter-annual trend based on reference summer (March) images, while monthly imagery for the years 2016–2017 was analysed for intra-annual trends. The results indicate that the C exhibits more variation across the monthly timescale than the yearly timescale. Therefore, using a single month to represent the annual C factor increases uncertainty. The modelling shows that accounting for temporal variations in vegetation cover reduces cumulative simulated sediment by up to 85% across the inter-annual and 30% for the intra-annual scale. Validation with observed data confirmed that accounting for temporal variations brought cumulative sediment outputs closer to observations. Over-simulations are high in late autumn and early summer, when estimated C values are high. Accordingly, uncertainties are high in winter when low NDVI leads to high C, whereas dry organic matter provides some protection from erosion. The results of this study highlight the need to account for temporal variations in vegetation cover in sediment yield estimation but indicate the uncertainties associated with using NDVI to estimate C factor.

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“…This index translates vegetation reflectance into a percentage of vegetation cover (Khunrattanasiri, 2023). However, in response to seasonal canopy variations, our study employs an alternative method, replacing the traditional C-factor with the NDVI vegetation index (Macedo et al 2021).…”

Section: Vegetation Cover Factor (C)mentioning

confidence: 99%

Assessing Soil Erosion Dynamics in the Rmel Watershed, Northern Morocco by Using the Rusle Model, Gis, and Remote Sensing Integration

Ezzahraa BOUAYAD,

EL IDRYSY,

OUALLALI

et al. 2023

AgricultForest

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New approach for obtaining the C-factor of RUSLE considering the seasonal effect of rainfalls on vegetation cover

Cited by 21 publications

References 27 publications

Dynamics of Sediment Transport in the Teles Pires River Basin in the Cerrado-Amazon, Brazil

Dynamics of Sediment Transport in the Teles Pires River Basin in the Cerrado-Amazon, Brazil

Temporal Influences of Vegetation Cover (C) Dynamism on MUSLE Sediment Yield Estimates: NDVI Evaluation

Assessing Soil Erosion Dynamics in the Rmel Watershed, Northern Morocco by Using the Rusle Model, Gis, and Remote Sensing Integration

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