Prediction of sediment yield of the Inxu River catchment (South Africa) using the MUSLE

Gwapedza, David; Nyamela, Namso; Hughes, D.; Slaughter, Andrew; Mantel, Sukhmani; Waal, Benjamin van der

doi:10.1016/j.iswcr.2020.10.003

Cited by 34 publications

(23 citation statements)

References 38 publications

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“…The other two catchments subsequently served to validate the outcome of the T35C analysis based on observed sediment data. The sediment yield calculations were conducted using the MUSLE, as applied in [50,51] in the form:…”

Section: Sediment Yield Calculationsmentioning

confidence: 99%

“…K, LS, C and P are the soil erodibility, slope length and steepness, cover management, and soil erosion control practice factors, respectively. The MUSLE factors were calculated similarly to [51] from readily available spatial and hydrological datasets. Discharge data for the greater Tsitsa and T35C catchments were accessed from the Department of Water and Sanitation (DWS; http://www.dwa.gov.za/Hydrology/Verified/hymain.aspx, last accessed 20 August 2020).…”

Section: Sediment Yield Calculationsmentioning

confidence: 99%

“…Discharge data for the greater Tsitsa and T35C catchments were accessed from the Department of Water and Sanitation (DWS; http://www.dwa.gov.za/Hydrology/Verified/hymain.aspx, last accessed 20 August 2020). Observed sediment data and discharge for the Inxu and Tsitsa catchments were measured under the Tsitsa Project (see [51,52]),but only a few months of daily observed data were available in both catchments. Seven months of data were available for the Inxu (October 2016 to April 2017).…”

Section: Sediment Yield Calculationsmentioning

confidence: 99%

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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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Section: Sediment Yield Calculationsmentioning

confidence: 99%

Section: Sediment Yield Calculationsmentioning

confidence: 99%

Section: Sediment Yield Calculationsmentioning

confidence: 99%

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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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“…defined as a fourth-order catchment in a hierarchal classification system in which a primary catchment is the major unit [20]. A total of 11 hillslope seep wetlands were selected for the study; these are the dominant type of wetland in the catchment.…”

Section: Plos Onementioning

confidence: 99%

“…The study was conducted in two quaternary catchments (T35D and T35E) situated in the Tsitsa River catchment, in the Eastern Cape of South Africa ( Fig 1 ). A quaternary catchment is defined as a fourth-order catchment in a hierarchal classification system in which a primary catchment is the major unit [ 20 ]. A total of 11 hillslope seep wetlands were selected for the study; these are the dominant type of wetland in the catchment.…”

Section: Introductionmentioning

confidence: 99%

The evaluation of biological indices to assess the condition of hillslope seep wetlands in the Tsitsa River Catchment, South Africa

2021

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The increase in the degradation of wetlands globally has highlighted the need to assess their ecological condition. Hillslope seep wetlands are among the least studied wetland types, yet they the most vulnerable because of their small size and steep slopes. Human pressure and the vulnerable nature of these wetlands requires wetland assessment tools to assess their condition. This study sought to evaluate the performance of the Floristic Quality Assessment Index for all species (FQAIall), the FQAI for dominant species (FQAIdom), and the Floristic Assessment Quotient for Wetlands (FAQWet) in response to the Anthropogenic Activity Index (AAI) and WET-Health in eleven hillslope seep wetlands and used these indices to assess the degree and intensity of disturbance. Vegetation samples were collected in summer 2016 and winter 2017. All assessment indices, FQAIall, FQAIdom, FAQWet and WET-Health, showed that hillslope seep wetlands were impacted by human activities. FQAIall showed the strongest response to AAI in winter, while FAQWet showed the strongest response to WET-Health. To the best of our knowledge, researchers in South Africa have used only WET-Health to assess wetland condition, and this is the first study to assess the condition of hillslope seep wetlands using a combination of indices (FQAIall, FQAIdom, FAQWet, and WET-Health). Overall, the findings of this study suggest that FQAIall and FAQWet are potentially better tools for assessing the biological condition of hillslope seep wetlands in South Africa.

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Suspended sediment load estimation in a severely eroded and data poor catchment

Bannatyne

Foster

Rowntree

et al. 2022

Hydrological Processes

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Soil erosion rates are high in many parts of Southern Africa, and are likely to rise because of climate change. Suspended sediment loads (SSL) and yields (SSY) are used to measure and benchmark soil erosion and/or sediment transport rates and determine trajectories of change. Some modelled SSY are available for Southern African catchments, but there is a dearth of contemporary observed data. Northern hemi-

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Prediction of sediment yield of the Inxu River catchment (South Africa) using the MUSLE

Cited by 34 publications

References 38 publications

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

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

The evaluation of biological indices to assess the condition of hillslope seep wetlands in the Tsitsa River Catchment, South Africa

Suspended sediment load estimation in a severely eroded and data poor catchment

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