Predicting gully width and widening rates from upstream contribution area and rainfall: A case study in SW Spain

Hayas, Antonio; Peña, Adolfo; Vanwalleghem, Tom

doi:10.1016/j.geomorph.2019.05.017

Cited by 26 publications

(17 citation statements)

References 32 publications

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“…Oostwoud Wijdenes et al, 1999), gully headcut retreat (Vanmaercke et al, 2016), gully widening and deepening (e.g. Hayas et al, 2019), mass movements (e.g. Zegeye et al, 2020), fluting (Poesen et al, 2002), piping or tunnel erosion (Bernatak-Jakiel and Poesen, 2018), sediment transport and sediment deposition (e.g.…”

Section: The Challenge Of Assessing Gully Erosion At Regional To Continental Scalesmentioning

confidence: 99%

“…The top-widths may be significantly larger than the gully floor-widths, especially for older gullies or gullies formed in soils with little cohesion (e.g. Hayas et al, 2019). Nonetheless, gully floor-width and hydraulic radius are often of greater geomorphic relevance as they relate more directly to the maximum runoff discharges passing through the gully (e.g.…”

Section: Assessing Gully Propertiesmentioning

confidence: 99%

“…from a rectangular to a more trapezoidal shape (e.g. Vanwalleghem et al, 2005b;Hayas et al, 2019). Hence, applying such a (time-specific) relationship to assess gully volumes over longer time periods may induce further uncertainties and biases.…”

Section: Assessing Gully Propertiesmentioning

confidence: 99%

See 2 more Smart Citations

Measuring, modelling and managing gully erosion at large scales: A state of the art

Vanmaercke

Panagos

Vanwalleghem

et al. 2021

Earth-Science Reviews

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155

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Section: The Challenge Of Assessing Gully Erosion At Regional To Continental Scalesmentioning

confidence: 99%

Section: Assessing Gully Propertiesmentioning

confidence: 99%

See 1 more Smart Citation

Measuring, modelling and managing gully erosion at large scales: A state of the art

Vanmaercke

Panagos

Vanwalleghem

et al. 2021

Earth-Science Reviews

Self Cite

155

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“…However, high temporal and spatial resolution field and laboratory data regarding EG widening are currently very limited (Wells et al, 2013). Needed improvements to width algorithms are dependent upon observed measurements that contain spatially and temporally evolving EG with a range of soil, land use, and climatic considerations (Hayas, Peña, & Vanwalleghem, 2019). For this, it would be important to have more EG experimental datasets to evaluate and enhance the model's utility in predicting soil losses due to ephemeral gully development in other conditions.…”

Section: Global Sensitivity Analysismentioning

confidence: 99%

Model prediction capacity of ephemeral gully evolution in conservation tillage systems

Luquin

Campo‐Bescós

Muñoz‐Carpena

et al. 2021

Earth Surf Processes Landf

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Ephemeral gully (EG) erosion has an important impact on agricultural soil losses and increases field surface hydrology connectivity and transport of pollutants to nearby water bodies. Watershed models including an EG component are scarce and not yet properly evaluated. The objective of this study is to evaluate the capacity of one such tool, AnnAGNPS, to simulate the evolution of two EG formed in a conservation tillage system. The dataset for model testing included runoff measurements and EG morphological characteristics during 3 years. Model evaluation focused on EG evolution of volume, width, and length model outputs, and included calibration and testing phases and a global sensitivity analysis (GSA). While the model did not fully reproduce width and length, the model efficiency to simulate EG volume was satisfactory for both calibration and testing phases, supporting the watershed management objectives of the model. GSA revealed that the most sensitive factors were EG depth, critical shear stress, headcut detachment exponent coefficient b, and headcut detachment leading coefficient a. For EG outputs the model was additive, showing low sensitivity to interactions between the inputs. Prediction of EG spatial evolution on conservation tillage systems requires improved development of gully erosion components, since many of the processes were developed originally for traditional tillage practices or larger channel systems. Our results identify the need for future research when EG form within conservation tillage systems, in particular to study gully headcut, soil erodibility, and width functions specific to these practices.

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“…The development of techniques for gully mapping in large areas has been investigated from several perspectives [3], [32]. Following a rough classification, traditional methods focus on the estimation of gully risk, density or susceptibility using empirical model-based approximations [33], [34]. More recent draw on direct measurements, generally based on combinations of remote sensing methods [29], [35] - [37], Geographic Information Systems (GIS) [38] and Machine Learning (ML) [39] - [41].…”

Section: Introductionmentioning

confidence: 99%

Use of TanDEM-X and Sentinel Products to Derive Gully Activity Maps in Kunene Region (Namibia) Based on Automatic Iterative Random Forest Approach

Orti

Winiwarter

Corral-Pazos-de-Provens

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Gullies are landforms with specific patterns of shape, topography, hydrology, vegetation, and soil characteristics. Remote sensing products (TanDEM-X, Sentinel-1 and Sentinel-2) serve as inputs into an iterative algorithm, initialized using a micro-mapping simulation as training data, to map gullies in the northwestern of Namibia. A Random Forest Classifier examines pixels with similar characteristics in a pool of unlabeled data and gully objects are detected where high densities of gully pixels are enclosed by an alpha shape. Gully objects are used in subsequent iterations following a mechanism where the algorithm uses the most reliable pixels as gully training samples. The gully class continuously grows until an optimal scenario in terms of accuracy is achieved. Results are benchmarked with manually tagged gullies (initial gully labelled area <0.3% of the total study area) in two different watersheds (408 km 2 and 302 km 2 , respectively) yielding total accuracies of >98%, with 60% in the gully class, Cohen Kappa>0.5, Matthews Correlation Coefficient>0.5, and ROC Area Under the Curve>0.89. Hence, our method outlines gullies keeping low false-positive rates while the classification quality has a good balance for the two classes (Gully/No Gully). Results show the most significant gully descriptors as the high temporal radar signal coherence (22.4%) and the low temporal variability in Normalized Difference Vegetation Index (NDVI) (21.8%). This research builds on previous studies to face the challenge of identifying and outlining gully-affected areas with a shortage of training data using global datasets, which are then transferable to other large (semi-) arid regions.

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Predicting gully width and widening rates from upstream contribution area and rainfall: A case study in SW Spain

Cited by 26 publications

References 32 publications

Measuring, modelling and managing gully erosion at large scales: A state of the art

Measuring, modelling and managing gully erosion at large scales: A state of the art

Model prediction capacity of ephemeral gully evolution in conservation tillage systems

Use of TanDEM-X and Sentinel Products to Derive Gully Activity Maps in Kunene Region (Namibia) Based on Automatic Iterative Random Forest Approach

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