Most fi eld erosion studies in agricultural areas provide little information on the probable errors involved. Here, for the fi rst time, we compare the accuracy, time and cost of conventional and new methodologies for gully surveying, and provide a model to estimate the effort required to achieve a specifi ed accuracy. Using a terrestrial LiDAR survey of a 7.1-m-long gully reach as a benchmark data set, the accuracies of different measurement methods (a new 3D photo-reconstruction technique, total station, laser profi lemeter, and pole) are assessed for estimating gully erosion at a reach scale. Based on further fi eld measurements performed over nine gullies (>100 m long), a simulation approach is derived to model the expected volume errors when 2D methods are used at the gully scale. All gullies considered were located near Cordoba, Spain. At the reach scale, the fi eld measurements using 3D photo-reconstruction and total station techniques produced cross-sectional area error values smaller than 4%, with other 2D methods exceeding 10%. For volume estimation, photo-reconstruction proved similar to LiDAR data, but 2D methods generated large negative volume error (EV) values (<–13% for laser profi lemeter and pole). We show that the proposed error expressions derived from the model are in line with the reach-scale fi eld results. A measurement distance factor (MDF) is defi ned that represents the ratio between cross-section distance and the gully length, and thus refl ects relative survey effort. We calculate the required MDF for specifi ed values of EV, illustrating how MDF decreases with increasing gully length and sinuosity.

Comparing the accuracy of several field methods for measuring gully erosion

Exploring the multiple land degradation pathways across the planet

Abstract In the Highlands of Northern Ethiopia soil and water conservation (SWC) practices, including construction of check dams in gullies, have been widely implemented for the last three decades. Despite this extensive installation of check dams, their effects on runoff response are not well understood as compared to those of other SWC practices. Hence, this study examines the effects of check dams on runoff response in gully channels. 90 degree V-notch weirs were installed to measure a wide range of runoff discharges at the upper and lower sections of five gully reaches: two channel cut in sandstone (a gully with check dams and vegetation (SCV) and an untreated gully (S)) and three cut in limestone (an untreated gully (L), a gully with check dams but no vegetation (LC) and a gully with check dams and vegetation (LCV)). Automatic sensors were installed to monitor runoff depth during two rainy seasons (29/08/14 – 17/09/14 and 24/7/15 – 14/09/15). All runoff characteristics at the lower section of each gully reaches were calculated for a gully reaches length of 50 m. In the sandstone area, the results show longer lag times of runoff to reach the lower section of the channel reach in the treated gully (SCV) compared to the untreated gully: difference in time lag equal lag to production of runoff equals 51% for runoff initiation, 61% for peak runoff and 44% for runoff end. An increase of hydraulic roughness by check dams and water transmission losses in deposited sediments are responsible for the delay of runoff to reach the lower part of the gully channels. In the limestone area, different time lags were recorded in different gully reaches regardless of the treatment effects (lag to runoff initiation, lag to peak flow and lag to runoff end were larger at LC, L and LCV, respectively). The reduction of peak runoff discharge between the upper and lower gully sections was larger in the gullies with check dam and vegetation (8–17%) than in gullies without treatment (5–6%). Reduction of runoff volume between these 2 gully sections was also larger in treated gullies than in untreated gullies: i.e. 18%, 9% and 8% in SCV, LCV and LC, respectively while it was only 4% in S and 6% in L. This study shows that the implementation of check dams combined with vegetation reduced peak flow discharge and runoff volume as large sections of runoff infiltrated in the sediments deposited behind the check dams. As gully check dams are implemented in a large areas of the North Ethiopia Highlands, this contributes to groundwater recharge and increased river base flow.

https://biblio.ugent.be/publication/8518957/file/8518969.pdf

Effects of check dams on runoff characteristics along gully reaches, the case of Northern Ethiopia

Soil erosion assessment in the Blue Nile Basin driven by a novel RUSLE-GEE framework

Gully erosion is the most severe type of water erosion and is a major land degradation process. Gully erosion susceptibility mapping (GESM)’s efficiency and interpretability remains a challenge, especially in complex terrain areas. In this study, a WoE-MLC model was used to solve the above problem, which combines machine learning classification algorithms and the statistical weight of evidence (WoE) model in the Loess Plateau. The three machine learning (ML) algorithms utilized in this research were random forest (RF), gradient boosted decision trees (GBDT), and extreme gradient boosting (XGBoost). The results showed that: (1) GESM were well predicted by combining both machine learning regression models and WoE-MLC models, with the area under the curve (AUC) values both greater than 0.92, and the latter was more computationally efficient and interpretable; (2) The XGBoost algorithm was more efficient in GESM than the other two algorithms, with the strongest generalization ability and best performance in avoiding overfitting (averaged AUC = 0.947), followed by the RF algorithm (averaged AUC = 0.944), and GBDT algorithm (averaged AUC = 0.938); and (3) slope gradient, land use, and altitude were the main factors for GESM. This study may provide a possible method for gully erosion susceptibility mapping at large scale.

Josef Krása

Papers

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