4.6 Article

Human impact on sediment fluxes within the Blue Nile and Atbara River basins

Journal

GEOMORPHOLOGY
Volume 180, Issue -, Pages 231-241

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.geomorph.2012.10.013

Keywords

Sediment yield; Soil erosion; BQART; Blue-Nile/Atbara system; Regional scale; Human footprint

Funding

  1. FRIEND/Nile project of UNESCO through the Flanders in Trust Fund of the Flemish Government of Belgium
  2. Belgian Federal Science Policy Programme BELSPO [Stereo II SR/00/133]

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A regional assessment of the spatial variability in sediment yields allows filling the gap between detailed, process-based understanding of erosion at field scale and empirical sediment flux models at global scale. In this paper, we focus on the intrabasin variability in sediment yield within the Blue Nile and Atbara basins as biophysical and anthropogenic factors are presumably acting together to accelerate soil erosion. The Blue Nile and Atbara River systems are characterized by an important spatial variability in sediment fluxes, with area-specific sediment yield (SSY) values ranging between 4 and 4935 t/km(2)/y. Statistical analyses show that 41% of the observed variation in SSY can be explained by remote sensing proxy data of surface vegetation cover, rainfall intensity, mean annual temperature, and human impact. The comparison of a locally adapted regression model with global predictive sediment flux models indicates that global flux models such as the ART and BQART models are less suited to capture the spatial variability in area-specific sediment yields (SSY), but they are very efficient to predict absolute sediment yields (SY). We developed a modified version of the BQART model that estimates the human influence on sediment yield based on a high resolution composite measure of local human impact (human footprint index) instead of countrywide estimates of GNP/capita. Our modified version of the BQART is able to explain 80% of the observed variation in SY for the Blue Nile and Atbara basins and thereby performs only slightly less than locally adapted regression models. (C) 2012 Elsevier B.V. All rights reserved.

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