Importance of slope gradient and contributing area for optimal prediction of the initiation and trajectory of ephemeral gullies

Ephemeral gully erosion rates are controlled by various factors out of whic h topography plays an important role. This study investigates the possibili ty to predict the location of ephemeral gullies using two topographic attri butes only: i.e., local slope gradient (S) and upslope contributing area pe r unit length of contour (A(s)). An inverse relationship between S and A(s) , in which the relative importance of the area exponent was varied, was eva luated as to its performance in predicting the location of ephemeral gullie s for three intensively cultivated catchments in the Belgian loess belt. Th is model is easily applicable as it only uses information on topography, wh ich can be derived from a digital elevation model, and land use. Predicted locations of ephemeral gullies were confronted with the locations recorded over 5 years of field observations. For all catchments a high relative area exponent (relative to the slope exponent) was required to yield good resul ts. The optimal relative area exponent ranged from 0.7 to 1.5; however, the quality of the prediction did not vary significantly when changing the exp onent within this range. An average relative area exponent of ca. 1 is appr opriate for all three study areas and may therefore be proposed for similar areas under similar conditions. A striking discrepancy was found between t he high relative area exponent required to predict optimally the trajectory of the gullies and the low relative area exponent (0.2) required to identi fy the spots in the landscape where ephemeral gullies begin. This indicates that spots in the landscape where gullies start are more controlled by slo pe gradient, while the presence of concavities control the trajectory of th e gullies until the slope gradient is too low and sediment deposition start s. No relationship was found between the frequency of the occurrence of eph emeral gullies and the percentage of predicted gully pixels for each freque ncy class. This indicates that frequently occurring gullies at the same loc ation are not easier to predict than accidental gullies. (C) 1999 Elsevier Science B.V. All rights reserved.