Regional soil erosion risk mapping in Lebanon

Kheir, Rania Bou; Cerdan, Olivier; Abdallah, Chadi

doi:10.1016/j.geomorph.2006.05.012

Cited by 83 publications

(49 citation statements)

References 10 publications

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“…It should be noted that the RUSLE model was selected because of the relatively limited data required and its simplicity, as already stated by other authors [68,69]. The RUSLE model is well studied and it has been widely applied at different scales to estimate soil erosion loss, and to plan erosion control for different land cover categories such as croplands, rangelands, and disturbed forest lands [21,31,41,70,71].…”

Section: Advantages and Uncertainties Of The Rusle Modelmentioning

confidence: 99%

Soil Erosion Risk Assessment in Uganda

Karamage

Zhang

Liu

et al. 2017

Forests

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Land use without adequate soil erosion control measures is continuously increasing the risk of soil erosion by water mainly in developing tropical countries. These countries are prone to environmental disturbance due to high population growth and high rainfall intensity. The aim of this study is to assess the state of soil erosion by water in Uganda at national and district levels, for various land cover and land use (LCLU) types, in protected areas as well to predict the impact of support practices on soil loss reduction. Predictions obtained using the Revised Universal Soil Loss Equation (RUSLE) model indicated that the mean rate of soil loss risk in Uganda's erosion-prone lands was 3.2 t·ha −1 ·y −1 , resulting in a total annual soil loss of about 62 million tons in 2014. About 39% of the country's erosion-prone lands were comprised of unsustainable mean soil loss rates >1 t·ha −1 ·y −1 . Out of 112 districts in Uganda, 66 districts were found to have unsustainable estimated soil loss rates >1 t·ha −1 ·y −1 . Six districts in Uganda were found to have mean annual soil loss rates of >10 t·ha −1 ·y −1 : Bududa (46.3 t·ha −1 ·y −1 ), Kasese (37.5 t·ha −1 ·y −1 ), Bundibugyo (28.9 t·ha −1 ·y −1 ), Bulambuli (20.9 t·ha −1 ·y −1 ), Sironko (14.6 t·ha −1 ·y −1 ) and Kotido (12.5 t·ha −1 ·y −1 ). Among the LCLU types, the highest soil loss rates of 11 t·ha −1 ·y −1 and 10.6 t·ha −1 ·y −1 were found in moderate natural forest and dense natural forest, respectively, mainly due to their locations in highland areas characterized by steep slopes ranging between 16% to 21% and their high rainfall intensity, ranging from 1255 mm·y −1 to 1292 mm·y −1 . Only five protected areas in Uganda were found to have high mean estimated mean soil loss rates >10 t·ha −1 ·y −1 : Rwenzori Mountains (142.94 t·ha −1 ·y −1 ), Mount Elgon (33.81 t·ha −1 ·y −1 ), Bokora corridor (12.13 t·ha −1 ·y −1 ), Matheniko (10.39 t·ha −1 ·y −1 ), and Nangolibwel (10.33 t·ha −1 ·y −1 ). To manage soil erosion in Uganda's protected areas, there is an urgent need to control wildfires and human-induced disturbances such as timber harvesting and soil compaction from domestic animals. Our study analysis revealed that well-established terraces and strip-cropping could significantly reduce soil loss rates in Uganda's croplands by 80% (from 1.5 t·ha −1 ·y −1 to 0.3 t·ha −1 ·y −1 ) and by 47% (from 1.5 t·ha −1 ·y −1 to 0.8 t·ha −1 ·y −1 ), respectively, well below the sustainable soil erosion tolerance rate (1 t·ha −1 ·y −1 ) for land and water conservation.

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Section: Advantages and Uncertainties Of The Rusle Modelmentioning

confidence: 99%

Soil Erosion Risk Assessment in Uganda

Karamage

Zhang

Liu

et al. 2017

Forests

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“…Olley et al, 1993;Prosser and Abernethy, 1996;Beavis, 2000), and around the world (e.g. Graf, 1979;Harvey, 1992;Kennedy, 2001;Li et al, 2003;Bacellar et al, 2005;Kheir et al, 2006). Hillslope gullies are those that erode into colluvium, aeolium, saprolite, weak sedimentary rock, or other weathered rock, and have also been defi ned as valley-side or valley-head gullies (Brice, 1966;Schumm, 1999).…”

Section: Hillslope and Colluvial Gulliesmentioning

confidence: 99%

Alluvial gully erosion: an example from the Mitchell fluvial megafan, Queensland, Australia

Brooks

Shellberg

Knight

et al. 2009

Earth Surf Processes Landf

133

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Considerable attention has been focused on the role of gullies as a contributor to contemporary sediment loads of rivers in Australia. In southern Australia rapid acceleration of hillslope gully erosion has been widely documented in the postEuropean period (~ last 200 years). In the northern Australian tropics, however, gully erosion processes operating along alluvial plains have not been well documented and can differ substantially from those gullies eroding into colluvium on hillslopes. Aerial reconnaissance surveys in 2004 along 13 500 km of the main stem rivers that drain into the Gulf of Carpentaria (GoC), identifi ed extensive areas of alluvial lands that have been impacted by a pervasive form of gully erosion. More detailed remote sensing based mapping within the 31 000 km 2 Mitchell River fl uvial megafan has identifi ed that active gullying into alluvium occupies ~ 0·4% (129 km 2 ) of the lower Mitchell catchment. These alluvial gullies are concentrated along main drainage channels and their scarp heights are highly correlated to the local relief between the fl oodplain and river thalweg. While river incision into the megafan since the Pleistocene has developed the relief potential for erosion, other factors such as fl oodplain hydrology, soil dispersibility, and vegetation also infl uence the distribution of gullies. In this paper we present a conceptual model of alluvial gullies, and contend that they represent a distinct end member in the continuum of gully forms that have been described in the geomorphic literature. An understanding of the processes driving this form of alluvial gullying can only be gained when they are differentiated from widely described colluvial hillslope gully models and theories. We present evidence of type examples of alluvial gullying in the Mitchell, and through analysis of their distribution and morphology at different scales, highlight some of the key mechanisms that are potentially initiating these features and driving their expansion.

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“…In addition, its selection was dependent on its inclusion of local sites that have experienced major soil-erosion problems (Bou Kheir et al, 2001a, 2001b, 2006aKhawlie et al, 2002), its suffering from the absence of quantitative measures of soil loss (SL) rates (t ha À1 ) and the lack of governmental solutions and resource management plans.…”

Section: The Study Areamentioning

confidence: 99%

“…Three vegetation cover density classes were distinguished: (1) high vegetation cover (>95 per cent) mainly with oak trees with persisting leaves (Q. calliprinos), (2) medium vegetation cover (35-95 per cent) including grasslands, degraded coniferous forests as well as plantations of lemons or bananas and (3) low to no vegetation cover (0-35 per cent) including horticulture/ agricultural lands, olive yards, vineyards, deciduous fruit trees, bare rocks, badlands and urban areas. Soil and rock infiltration, and erodibility/movement were derived by classifying soil units and lithological formations shown in the soil (Gèze, 1956) and geological (Dubertret, 1945) maps at 1:200 000 and 1:50 000, respectively (Bou Kheir et al, 2006a).…”

Section: Construction Of Thematic Erosion Factorial Mapsmentioning

confidence: 99%

A conditional GIS‐interpolation‐based model for mapping soil‐water erosion processes in Lebanon

Kheir

2007

Land Degrad Dev

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Soil erosion by water is a major cause of landscape degradation in Mediterranean environments, including Lebanon. This paper proposes a conditional decision-rule interpolation-based model to predict the distribution of multiple erosion processes (i.e. sheet, mass and linear) in a representative area of Lebanon from the measured erosion signs in the field (root exposure, earth pillars, soil etching and drift and linear channels). First, erosion proxies were derived from the structural OASIS classification of Landsat thematic mapper (TM) imageries combined with the addition of several thematic erosion maps (slope gradient, aspect and curvature, drainage density, vegetal cover, soil infiltration and erodibility and rock infiltration/movement) under a geographic information systems (GIS) environment. Second, erosion signs were measured in the field, and interpolated by the statistical moments (means and variance) in the defined erosion proxies, thus producing quantitative erosion maps (t ha À1 ) at a scale of 1:100 000. Seven decision rules were then generated and applied on these maps in order to produce the overall decisive erosion map reflecting all existing erosion processes, that is, equality (ER), dominance (DOR), bimodality (BR), masking (MR), aggravating (AR), dependence (DER) and independence (IR). The produced erosion maps are divided into seven classes ranging between 0 and more than 1Á8 t ha À1 for sheet erosion, and 0 and more than 10Á5 t ha À1 for mass and linear erosion. They are fairly matching with coincidences values equal to 43 per cent (sheet/linear), 48 per cent (sheet/mass) and 49 per cent (linear/mass). The overall accuracies of these maps were estimated to be 76 per cent (sheet erosion), 78 per cent (mass erosion) and 78Á5 per cent (linear erosion). The overall decisive erosion map with 15 classes corresponds well to land management needs. The model used is relatively simple, and may also be applied to other areas. It is particularly useful when GIS database on factors influencing erosion is limited.

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Regional soil erosion risk mapping in Lebanon

Cited by 83 publications

References 10 publications

Soil Erosion Risk Assessment in Uganda

Soil Erosion Risk Assessment in Uganda

Alluvial gully erosion: an example from the Mitchell fluvial megafan, Queensland, Australia

A conditional GIS‐interpolation‐based model for mapping soil‐water erosion processes in Lebanon

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