Causes and Controlling Factors of Valley Bottom Gullies

Amare, Selamawit; Keesstra, Saskia; Ploeg, Martine van der; Langendoen, Eddy J.; Steenhuis, Tammo S.; Tilahun, Seifu A.

doi:10.3390/land8090141

Cited by 38 publications

(32 citation statements)

References 114 publications

(151 reference statements)

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“…The TWI determines soil saturation level which is important for gully erosion but it also does not have that much role to play in this region. Rainfall, lithology and drainage density have been depicted as major drivers of gully erosion by many researchers but in this region these parameters have low to moderate importance on gully erosion (Langendoen et al 2013;Amare et al 2019;Arabameri, Pradhan, and Rezaei 2019a). Therefore, it may be stated that although the key drivers of gully erosion are uniform for all regions some factors are more important for a particular region which may be insignificant for the others.…”

Section: Discussionmentioning

confidence: 99%

“…The location of the Inceptisols near the river induce a quick surface wash of the material. Amare et al (2019) has reported that run-off and sub-surface run-off are one of the main drivers of gully erosion citing examples from ample literatures (Moges and Holden 2008). Distance from road is the second most important variable of head-cut gully erosion here which indicates that anthropogenic interferences in terms of development and construction activity over the land leads to the weakening of soil and then surface run-off erodes it to form gully.…”

Section: Discussionmentioning

confidence: 99%

“…It suggests that tillage led to soil loosening and made the land suitable for gully erosion. Amare et al (2019) have depicted that valley bottom gully erosion are majorly associated with grazing land and crop land whereas Boardman (2014) and Boardman et al (2003) have shown that cultivation is mostly associated with gully erosion in semiarid climatic region. Soil erosion is highly related to the mismanagement of agricultural land (Cerd a et al 2020).…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Head-cut gully erosion susceptibility modelling based on ensemble Random Forest with oblique decision trees in Fareghan watershed, Iran

Pham

Mukherjee²,

Norouzi

et al. 2020

Geomatics, Natural Hazards and Risk

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Head-cut gully erosion susceptibility modelling based on ensemble Random Forest with oblique decision trees in Fareghan watershed, Iran

Pham

Mukherjee²,

Norouzi

et al. 2020

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

“…Furthermore, they frequently occur in alluvial deposits as a result of subsurface or saturation excess overland flow (e.g. Amare et al, 2019). We therefore also included the average distance of each site to a river as a potential predictor variable.…”

Section: Considered Predictor Variablesmentioning

confidence: 99%

“…For example, in the case of gully erosion, the topographic conditions leading to gully initiation will also depend on local soil and vegetation characteristics and the overall geomorphic setting (e.g. Torri and Poesen, 2014;Rossi et al, 2015;Amare et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Predicting gully densities at sub‐continental scales: a case study for the Horn of Africa

Vanmaercke

Chen

Haregeweyn

et al. 2020

Earth Surf Processes Landf

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Despite its environmental and scientific significance, predicting gully erosion remains problematic. This is especially so in strongly contrasting and degraded regions such as the Horn of Africa. Machine learning algorithms such as random forests (RF) offer great potential to deal with the complex, often non‐linear, nature of factors controlling gully erosion. Nonetheless, their applicability at regional to continental scales remains largely untested. Moreover, such algorithms require large amounts of observations for model training and testing. Collecting such data remains an important bottleneck. Here we help to address these gaps by developing and testing a methodology to simulate gully densities across Ethiopia, Eritrea and Djibouti (total area: 1.2 million km2). We propose a methodology to quickly assess the gully head density (GHD) for representative 1 km2 study sites by visually scoring the presence of gullies in Google Earth and then converting these scores to realistic estimates of GHD. Based on this approach, we compiled GHD observations for 1,700 sites. We used these data to train sets of RF regression models that simulate GHD at a 1 km2 resolution, based on topographic/geomorphic, land cover, soil and rainfall conditions. Our approach also accounts for uncertainties in GHD observations. Independent validations showed generally acceptable simulations of regional GHD patterns. We further show that: (i) model performance strongly depends on the amount of training data used, (ii) large prediction errors mainly occur in areas where also the predicted uncertainty is large and (iii) collecting additional training data for these areas results in more drastic model performance improvements. Analyses of the feature importance of predictor variables further showed that patterns of GHD across the Horn of Africa strongly depend on NDVI and annual rainfall, but also on normalized steepness index (ksn) and distance to rivers. Overall, our work opens promising perspectives to assess gully densities at continental scales. © 2020 John Wiley & Sons, Ltd.

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A review of the magnitude and response times for sediment yield reductions following the rehabilitation of gullied landscapes

Bartley

Poesen

Wilkinson

et al. 2020

Earth Surf Processes Landf

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Gully erosion is a major driver of elevated sediment yields across many areas of the globe, and considerable rehabilitation has occurred to reduce the amount of sediment eroded from gullies. However, compared to other forms of erosion, there has been little systematic review of the effectiveness of gully rehabilitation on reducing sediment yields. This study reviewed the global literature to provide an understanding of the potential sediment yield reductions that can occur following the rehabilitation of gullied landscapes. We focused on studies reporting a measured response on how gully and catchment sediment yield has changed since treatment. A total of 37 studies were found that met this criterion. The studies were partitioned into three broad categories, including those focused on: (i) treating the catchment above the gully; (ii) installing treatments in the actual gully channel; and (iii) a combination of approaches which include treating both the catchment and the gully channel. All the studies demonstrated a reduction in sediment yield following gully rehabilitation, with reported values ranging between 12 and 94%. The timeframes associated with the reductions in sediment yield varied considerably (2-80years). Applying a variety of rehabilitation measures, which generally includes treating both the hillslope above the gully, and trapping sediment within the gully, appears to result in shorter (median) timescales for sediment yield reduction. Overall, this review indicates that gully rehabilitation strategies combining both engineering and vegetation measures are often the most successful. Engineering measures such as check dams are important for stabilizing sites in the early phases to support the revegetation of gullies and adjacent hillslopes. However, vegetation is the key to the long-term success of gully rehabilitation. This is because many engineering structures eventually fail, or they have a limited life span as an active sediment trap.

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Causes and Controlling Factors of Valley Bottom Gullies

Cited by 38 publications

References 114 publications

Head-cut gully erosion susceptibility modelling based on ensemble Random Forest with oblique decision trees in Fareghan watershed, Iran

Head-cut gully erosion susceptibility modelling based on ensemble Random Forest with oblique decision trees in Fareghan watershed, Iran

Predicting gully densities at sub‐continental scales: a case study for the Horn of Africa

A review of the magnitude and response times for sediment yield reductions following the rehabilitation of gullied landscapes

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