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Calculating Runoff Coefficient of Urmia Lake Basin by Empirical Models and Remot Sensing (RS) Technology

Akbari, Mahdi | 2016

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 49087 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Tajrishy, Masoud; Arasteh, Peyman
  7. Abstract:
  8. Estimation of runoff in the ungauged basins is a challenge for hydrologists. The main objective of this research is to produce runoff coefficient map using SCS-CN (1972) and Kennessey (1930) as empirical models to for Urmia Lake basin between for 2006-2011.Both SCS-CN and Kennessey methods use slope, land use, and soil permeability data to estimate surface runoff. Accuracy of each model is tested along with the observed runoff using the Root Mean Square Error (RMSE).Urmia Lake basin includes about 400,000 hectares irrigated land, which constitutes around 10 percent of the entire basin area. To exclude the anthropological activities from the estimations, methods were applied only for 28 upstream subbasins. In these subbasins,there are less agricultural activities, so the observed runoff data from hydrometry stations is equivalent to the natural runoff generation potential of the region.The mean annual runoff coefficient ,in the selected 28 subbasins, is 0.2 based on 2006 observed data. The mean annual runoff coefficient by Kennessy was also 0.2. However,SCS-CN method was extremely overestimating the annual surface runoff by the mean runoff coefficient of 0.6.Overall, Kennessey method was more accurate than SCS-CN in estimating the annual runoff in the Urmia Lake basin. This model has three default partial runoff coefficients based on slope, land cover and soil permeability. In this research Kennessey model is calibrated by observed data. The mean error (RMSE devided to mean of annual observed runoff coefficient) of the modeling during 2006-2011 is 135%. After calibration of Kennessey method, the accuracy of modeling was improved by 70% and correlation between observed and model runoff coefficients for validation years was boosted from 20% to 50%
  9. Keywords:
  10. Geographic Information System (GIS) ; Remote Sensing ; Lake Urmia Watershed ; Runoff Coefficient ; Soil Conservation Service Curve Number (SCS-CN)Method ; Platelet Rich Plasma (PRP) ; Wet Electrospining

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