Gully erosion processes, disciplinary fragmentation, and technological innovation

Bennett, Sean J.; Wells, Robert R.

doi:10.1002/esp.4522

Cited by 65 publications

(40 citation statements)

References 95 publications

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“…Peak discharge and critical rainfall duration are often central variables in gully models (Foster and Lane, 1983;Watson and Laflen, 1986;Hairsine and Rose, 1992;Sidorchuk, 1999;Gordon et al, 2007) and are related to erosion initiation parameters and thresholds, such as shear stress and stream power. This second factor has frequently been reported in literature as being more correlated with both laminar and linear erosion (Torri and Borselli, 2003;de Araújo, 2007;Nazari Samani et al, 2016;Bennett and Wells, 2019). Figure 9a shows the performance of the tested intensities.…”

Section: Foster and Lane Model (Flm)supporting

confidence: 63%

“…and Wells (2019), for instance, no model has ever been presented to clearly explain the process of gully formation. Among the models that do consider gully erosion, the use of empirical approach prevails (Thompson, 1964;Watson and Laflen, 1986;Woodward, 1999;Nachtergaele et al, 2001Nachtergaele et al, , 2002Poesen et al, 2002;Yao et al, 2008;Wells et al, 2013); whereas others focus primarily on physically-based algorithms (Foster and Lane, 1983;Storm et al, 1990;Hairsine and Rose, 1992;Ascough et al, 1997;Sidorchuk, 1999;Alonso et al, 2002;Sander et al, 2007;Dabney et al, 2015).…”

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confidence: 99%

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Physically-based model for gully simulation: application to the Brazilian Semiarid Region

Alencar¹,

Araújo²,

Teixeira³

2019

Preprint

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Abstract. Gullies are one of the most relevant erosion processes, connected to land degradation and desertification, in special in arid and semiarid regions. Despite its role, gully erosion is neglected by many models and researches. This study presents a physically-based model for small permanent gullies, typical in the Brazilian Semiarid Region. The model consists of coupling two previous models, those by Foster and Lane (1983) and Sidorchuk (1999). As both models require input data of peak discharge and duration, different rain intensities were tested. The rain intensity that suited gully erosion modelling best was the 30-minute intensity. The Foster and Lane model supplied a better response for smaller areas, where bed-channel erosion is more pronounced. The Sidorchuk model presented a better performance in larger sections, where wall erosion becomes more prominent. The experimental area is located in the semiarid State of Ceará, Brazil, in which the land use is characterised by agriculture and livestock. We measured and modelled three gullies ageing almost six decades. The threshold between the prevailing domains of each process (channel bed or wall erosion) is based on the cross-section area; and it is intrinsically connected to wall erosion: for the case study, the threshold area was approximately 2 m2. The final model, hereby called FL-SM (Foster & Lane and Sidorchuk Model) performed very well, with Nash–Sutcliffe coefficient of 0.846.

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Section: Foster and Lane Model (Flm)supporting

confidence: 63%

mentioning

confidence: 99%

Physically-based model for gully simulation: application to the Brazilian Semiarid Region

Alencar¹,

Araújo²,

Teixeira³

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Gully erosion consists of a process that erodes one (or a system of) channel(s) which mainly begins due to the concentration of surface water discharge erosion during intense rainfall events (Bernard et al, 2010). The concentrated flow causes a deep topsoil incision and may reach the groundwater table and sustain the process (Starkel, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Despite their relevance to hydro-sedimentological processes, gullies are often neglected in models (Poesen, 2018) and should be directly addressed (Paton et al, 2019). However, gully erosion is a process that involves the interaction of many variables, with several of them being difficult to assess (Bernard et al, 2010;Castillo and Gómez, 2016). According to Bennett and Wells (2019), for instance, no model has ever been presented to clearly explain the process of gully formation.…”

Section: Introductionmentioning

confidence: 99%

Physically based model for gully simulation: application to the Brazilian semiarid region

Alencar

Araújo²,

Teixeira

2020

Hydrol. Earth Syst. Sci.

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Abstract. Gullies lead to land degradation and desertification as well as increasing environmental and societal threats, especially in arid and semiarid regions. Despite this fact, there is a lack of related research initiatives. In an effort to better understand soil loss in these systems, we studied small permanent gullies, which are a recurrent problem in the Brazilian northeastern semiarid region. The increase in sediment connectivity and the reduction of soil moisture, among other deleterious consequences, endanger this desertification-prone region and reduce its capacity to support life and economic activities. Thus, we propose a model to simulate gully-erosion dynamics, which is derived from the existing physically based models of Foster and Lane (1983) and Sidorchuk (1999). The models were adapted so as to simulate long-term erosion. A threshold area shows the scale dependency of gully-erosion internal processes (bed scouring and wall erosion). To validate the model, we used three gullies that were over 6 decades old in an agricultural basin in the Brazilian state of Ceará. The geometry of the channels was assessed using an unmanned aerial vehicle and the structure from motion technique. Laboratory analyses were performed to obtain soil properties. Local and regional rainfall data were gauged to obtain sub-daily rainfall intensities. The threshold value (cross-section area of 2 m2) characterizes when erosion in the walls, due to loss of stability, becomes more significant than sediment detachment in the wet perimeter. The 30 min intensity can be used when no complete hydrographs from rainfall are available. Our model could satisfactorily simulate the gully-channel cross-section area growth over time, yielding a Nash–Sutcliffe efficiency of 0.85 and an R2 value of 0.94.

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“…As early as 1998, Poesen et al [8] highlighted the need to monitor gullies through their location, length, and cross section. Poesen [9] subsequently emphasized the need for improvement in the automatic measurement of gullies in large areal extents, where geospatial technology should play a significant role and the technology be transferable [10].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Three Algorithms for the Evaluation of TanDEM-X Data for Gully Detection in Krumhuk Farm (Namibia)

Orti

Negussie

Corral-Pazos-de-Provens

et al. 2019

Remote Sensing

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Namibia is a dry and low populated country highly dependent on agriculture, with many areas experiencing land degradation accelerated by climate change. One of the most obvious and damaging manifestations of these degradation processes are gullies, which lead to great economic losses while accelerating desertification. The development of standardized methods to detect and monitor the evolution of gully-affected areas is crucial to plan prevention and remediation strategies. With the aim of developing solutions applicable at a regional or even national scale, fully automated satellite-based remote sensing methods are explored in this research. For this purpose, three different algorithms are applied to a Digital Elevation Model (DEM) generated from the TanDEM-X satellite mission to extract gullies from their geomorphological characteristics: (i) Inverted Morphological Reconstruction (IMR), (ii) Smoothing Moving Polynomial Fitting (SMPF) and (iii) Multi Profile Curvature Analysis (MPCA). These algorithms are adapted or newly developed to identify gullies at the pixel level (12 m) in our study site in the Krumhuk Farm. The results of the three methods are benchmarked with ground truth; specific scenarios are observed to better understand the performance of each method. Results show that MPCA is the most reliable method to identify gullies, achieving an overall accuracy of approximately 0.80 with values of Cohen Kappa close to 0.35. The performance of these parameters improves when detecting large gullies (>30 m width and >3 m depth) achieving Total Accuracies (TA) near to 0.90, Cohen Kappa above 0.5, and User Accuracy (UA) and Producer Accuracy (PA) over 0.50 for the gully class. Small gullies (<12 m wide and <2 m deep) are usually neglected in the classification results due to spatial resolution constraints within the input DEM. In addition, IMR generates accurate results for UA in the gully class (0.94). The MPCA method developed here is a promising tool for the identification of large gullies considering extensive study areas. Nevertheless, further development is needed to improve the accuracy of the algorithms, as well as to derive geomorphological gully parameters (e.g., perimeter and volume) instead of pixel-level classification.

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Gully erosion processes, disciplinary fragmentation, and technological innovation

Cited by 65 publications

References 95 publications

Physically-based model for gully simulation: application to the Brazilian Semiarid Region

Physically-based model for gully simulation: application to the Brazilian Semiarid Region

Physically based model for gully simulation: application to the Brazilian semiarid region

Comparison of Three Algorithms for the Evaluation of TanDEM-X Data for Gully Detection in Krumhuk Farm (Namibia)

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