Reconstructing long-term gully dynamics in Mediterranean agricultural areas

Abstract

Abstract. Gully erosion is an important erosive process, especially in Mediterranean basins. However, the longterm dynamics of gully networks and the variation of sediment production in gullies is not well known. Available studies are often done over a few years only, while many gully networks form, grow, and change in response to environmental and land use or management changes over a long period. In order to clarify the effect of these changes, it is important to analyze the evolution of the gully network with a high temporal resolution. This study aims at analyzing gully morphodynamics over a long time scale (1956–2013) in a large Mediterranean area in order to quantify gully erosion processes and its contribution to overall sediment dynamics. A gully network of 20 km2 located in SW Spain, has been analyzed using a sequence of 10 aerial photographs in the period 1956–2013. The extension of the gully network both increased and decreased in the study period. Gully drainage density varied between 1.93 km km−2 in 1956, with a minimum of 1.37 km km−2 in 1980 and a maximum of 5.40 km km−2 in 2013. The main controlling factor of gully activity appeared to be rainfall, while land use changes were found to have only an indirect effect. A new Monte Carlo-based approach was proposed to reconstruct gully erosion rates from orthophotos. Gully erosion rates were found to be relatively stable between 1956–2009, with a mean value of 11.2 ton ha−1 yr−1, while in the period 2009–2011, characterized by extreme winter rainfalls, this value increased significantly, to 591 ton ha−1 yr−1. These results show that gully erosion rates are highly variable and that a simple interpolation between the start and end date would highly underestimate gully contribution during certain years, such as for example between 2009–2011. This illustrates the importance of the applied methodology using a high temporal resolution of orthophotos.

Rainfall and Vegetation Effects on Temporal Variation of Topographic Thresholds for Gully Initiation in Mediterranean Cropland and Olive Groves

“…Although there are some differences depending on whether the analysis is limited to a single land use class or by considering all three land use classes together, and depending on the rainfall index used, in general, rainfall characteristics perform well in explaining the variability of TT. Previous studies on the dynamics of gullies and its relation with precipitation have been mainly focussing on gully‐head retreat rates (Rieke‐Zapp & Nichols, ; Vanmaercke et al ., ) and the gully erosion rates (Oostwoud Wijdenes et al ., ; Martínez‐Casasnovas et al ., ; Hayas et al ., ). The linkage between the dynamics of the TT for gully head development for a given land use type and temporal variations in precipitation characteristics in this study is novel.…”

“…Hayas et al . () attributed this mainly to highly erosive rainfall events in 2009–2011, possibly aggravated by important land use changes.…”

“…The resolution of the photos limits the width of the gully channels that can be identified to 0·5–1 m. More details about the photointerpretation process can be found in Hayas et al . ().…”

Mapping and spatial‐temporal assessment of gully density in the Middle Volga region, Russia

“…Rossi et al, 2015;Vanmaercke et al, 2016;Zhao et al, 2016). By consequence, understanding the importance of climate and land use for gully erosion rates, typically requires a large number of observations, preferably over a sufficiently long measuring period (Torri and Poesen, 2014;Vanmaercke et al, 2016;Hayas et al, 2017). Only then, robust and statistically meaningful results can be obtained.…”

“…In order to better understand the magnitude and controlling factors of gully erosion, several studies have mapped and monitored active gullies and their densities, using aerial photographs and high-resolution satellite images (e.g. Mitchel, 1981;Ries and Marzolff, 2003;Vrieling et al, 2007;Bouaziz et al, 2009;Shruthi et al, 2011Shruthi et al, , 2014Torri and Poesen, 2014;Vanmaercke et al, 2016;Hayas et al, 2017). Recent studies use object-oriented analyses, methods of pixel identification and self-organizing neural networks for the detection of ravines and gully networks (Vrieling, 2006;Vrieling et al, 2007;Bouaziz et al, 2009;Desprats et al, 2013;Johansen, 2010).…”