TITLE:
Evaluate the Accuracy of Fargas and BLM Models for Identification of Erosion Intensity
AUTHORS:
Naser Abdi
KEYWORDS:
Erosion, Fargas, GIS, BLM, Sediment
JOURNAL NAME:
Open Journal of Geology,
Vol.6 No.11,
November
28,
2016
ABSTRACT: Erosion process not only changes the land use to badlands, but also produces sediments that are dumped in the dam reservoirs, reduces the reservoir volume and finally makes it useless. So, it is necessary to do study on erosion intensity and the sediment production evaluation for which there are some methods and models. The experimental Fargas and BLM models are largely used for this issue, in Iran as well as many other countries, separately or together based on the data availability. These studies results are as data for sediment supply estimation in different watershed management studies. So, the result accuracy is important for determination of sediment occurrence. This study evaluates these models’ results accuracy, in order to find the limitations and any solutions. Therefore, these two models were used and run in the same area, Aghbolagh drainage basin, Iran; the results were compared and evaluated. These models are based on some factors like rock type, drainage density, surface erosion and litter cover. The study includes field and laboratory analysis. The data were combined in GIS software and processed. The results reveal that, Fargas model predicts 3.67%, 14.26% and 81.06% of the area susceptible for high, severe and very severe erosion respectively; whilst, referring to BLM model outcome, 42.96% area has high sensitivity; 42.96% and 24.94% of the area have high and severe sensitivity for erosion, respectively. Furthermore, both models show same severity for around 18% of the study area. So, for these two models results very low similarities are concluded, which could be an indication of low reliability of the results, especially when they are used separately without any combination or comparison. Finally, it is recommended to use both of them together, or use another method beside each mentioned models.