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Abstract
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Article Information:
An Investigation Survey on MPSIAC Model to Predict Sediment Yield in Iran
Arash Parehkar, Najmeh Behnam and Mehran Shokrabadi
Corresponding Author: Arash Parehkar
Submitted: March 09, 2013
Accepted: April 05, 2013
Published: June 20, 2013 |
Abstract:
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Lack of information to prepare erosion maps for quantitative and qualitative sediment evaluation is a major need in watershed management. MPSIAC (Modified Pacific South West International Agency Committee) Model is apparently known as an appropriate method to measure sediment yield of watersheds in Iran. In this study the results obtained from the research on Ivanaki’s watershed and sixteen different research projects, which conducted by others, were selected and analyzed. The purposes of this study were to compare works done in the country and show the limitation of the model, usage of it in different conditions and possibility of calculating errors if the input data were not gathered carefully. A sensitivity analysis was conducted to find the sensitive parameters in different watersheds. A database for the model was prepared from six watersheds using Geographic Information System (GIS). By evaluation of nine main factors it was cleared that Land Use, Upland Erosion and Ground Cover were the most sensitive factors respectively and the Climate and Runoff factors were the least, while observation of Runoff and sediment yield did not show this matter. According to the results, each factor which had more input quantity had the highest sensitivity. Finally, the research indicated that the usage of MPSIAC model for watersheds with sediments more than 2.2 ton/ha/yr must not be used, since the model is so sensitive in this status and possible errors may get over 50%
Key words: MPSIAC Model, Prediction, Sediment, Sensitivity Analysis, Watershed, ,
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Cite this Reference:
Arash Parehkar, Najmeh Behnam and Mehran Shokrabadi, . An Investigation Survey on MPSIAC Model to Predict Sediment Yield in Iran. Research Journal of Environmental and Earth Sciences, (06): 342-349.
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ISSN (Online): 2041-0492
ISSN (Print): 2041-0484 |
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