Modelling soil losses in Southern Africa

Elwell, H.A.

doi:10.1016/0021-8634(78)90043-4

Cited by 56 publications

(33 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results are typical of soil loss under vegetation covers obtained by other researchers (Elwell and Stocking, 1976;Morgan, 1996). The exponents of the curves are close to those obtained in many studies (Elwell, 1978;Brown et al, 1989). The results obtained in this study indicate that natural vegetation such as grass and forest are the optimum covers for controlling soil erosion in the catchment.…”

Section: Impacts Of Vegetation Cover On Soil Losssupporting

confidence: 81%

Soil erosion modelling with EUROSEM at Embori and Mukogodo catchments, Kenya

Mati

Morgan

Quinton

2006

Earth Surf Processes Landf

View full text Add to dashboard Cite

The applicability of the European Soil Erosion Model (EUROSEM) in Kenya was tested on two subcatchments: Embori, which lies within a large-scale commercial wheat-and barleygrowing region on the slopes of Mt Kenya; and Mukogodo, a dry rangeland region used for communal grazing, which lies at lower altitudes. For both catchments, EUROSEM was used to simulate interrill erosion from single storm events on plots of 10 m by 2 m. The model was calibrated and validated for a barley crop at Embori and three treatments of bare, grass and shrub covers at Mukogodo. The results indicated that EUROSEM was applicable to the Embori catchment, as correlation coefficients of 0·91 and 0·84 were obtained between observed and simulated runoff and soil loss, respectively. However, for Mukogodo, the model did not adequately predict soil loss from vegetated plots having grass or shrubs, although it could predict runoff from bare plots and grass quite well (R 2 = = = = = 0·97, R 2 = = = = = 0·60, respectively) as well as soil loss from bare plots (R 2 = = = = = 0·58), but the standard errors of estimate at 4·67 mm were rather high (P = = = = = 0·95). Thus, EUROSEM requires improvements in terms of being able to predict runoff and soil loss in semi-arid regions of Kenya, so that it can accommodate multiple vegetation covers, rangeland conditions and soils with surface sealing properties. In addition, the model requires further testing to sample a wider range of climatic and land use types for it to be applicable to Kenya and rangeland conditions generally.

show abstract

Section: Impacts Of Vegetation Cover On Soil Losssupporting

confidence: 81%

Soil erosion modelling with EUROSEM at Embori and Mukogodo catchments, Kenya

Mati

Morgan

Quinton

2006

Earth Surf Processes Landf

View full text Add to dashboard Cite

show abstract

“…The design of the temporal monitoring framework was primarily influenced by the well-established fact that SS yield is dominated by flood events (Gordon et al, 2004;Elwell, 1978;Horowitz, 2013). Sampling programme design therefore emphasised hydrologybased sampling (i.e.…”

Section: Flood Samplingmentioning

confidence: 99%

Design and implementation of a citizen technician–based suspended sediment monitoring network: Lessons from the Tsitsa River catchment, South Africa

Bannatyne¹,

Rowntree²,

Waal³

et al. 2017

WSA

View full text Add to dashboard Cite

Locally resident citizen technicians using basic equipment and Open Data Kit-enabled smartphones have collected flood-focused suspended sediment (SS) samples from 11 sites on the Tsitsa River and its tributaries, in the Eastern Cape Province of South Africa. In the highly degraded and gullied Tsitsa River catchment, existing modelled SS data were unverified and at odds with the results of studies based on dam sedimentation rates. Suspended sediment concentration (SSC), flux and yield data were required at subcatchment scale to support the prioritisation of community-based land rehabilitation initiatives in rural communal areas and to determine the relative contributions of subcatchments to SS yield at the site of the proposed Ntabelanga Dam. Approaches relying on researcher presence and/or installed instrumentation were precluded by cost, study area size, and the risk of equipment theft, vandalism and damage during high flows. Analysis of the quantitative data collected by the citizen technicians allows high-resolution SSC, flux, and yield data to be produced at subcatchment scale, which will be benchmarked by an acoustic SSC probe at a downstream Department of Water and Sanitation gauging weir. Qualitative descriptive and photographic data allows distant researchers to gain a real-time, catchment-wide overview of river and SS levels. This paper outlines the method, benefits and challenges of a direct-sampling approach that has the potential to address spatial and temporal challenges commonly experienced during SS sampling campaigns.

show abstract

“…The models tested, which are incorporated in the SEAGIS application (DHI, 1999), are the soil erosion models USLE (Wischmeier and Smith, 1978), SLEMSA (Elwell, 1978) and MMF (Morgan et al, 1984). The models were developed for erosion modelling at field-size plots.…”

Section: Seagismentioning

confidence: 99%

“…The extension contains tools to calculate distributed soil erosion rates using the models USLE/RUSLE (Wischmeier and Smith, 1978), SLEMSA (Elwell, 1978) or the Morgan, Morgan and Finney (MMF) method (Morgan et al, 1984) and to determine a distributed delivery ratio based on the CALSITE method (Bradbury, 1994).…”

Section: Introductionmentioning

confidence: 99%

A test of three soil erosion models incorporated into a geographical information system

Svorin

2003

Hydrological Processes

View full text Add to dashboard Cite

Abstract:An examination is carried out of the application of three soil erosion models, USLE/RUSLE, SLEMSA and the Morgan, Morgan and Finney method, to the Simeto catchment in Sicily, and of the differences in the results using the three models. The models are incorporated into a grid-based geographical information system, and the results are validated qualitatively and quantitatively. The significance of the choice of method to estimate the distributed input parameters and the method to validate the quality of the models are discussed.

show abstract

Modelling soil losses in Southern Africa

Cited by 56 publications

References 4 publications

Soil erosion modelling with EUROSEM at Embori and Mukogodo catchments, Kenya

Soil erosion modelling with EUROSEM at Embori and Mukogodo catchments, Kenya

Design and implementation of a citizen technician–based suspended sediment monitoring network: Lessons from the Tsitsa River catchment, South Africa

A test of three soil erosion models incorporated into a geographical information system

Contact Info

Product

Resources

About