Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/13947
Authors: Karn, Arun Lal
Keywords: SCS-CN Inspired Mathod;Hemawati;Kalu;Watersheds/plots;WRDM
Issue Date: 2016
Abstract: The present study is carried out to evaluate the accuracy of different CN determination methods. The data of 12 experimental plots having various land uses (sugarcane, maize, black gram, fallow land) and slopes (5, 3, and 1%), and two watersheds namely Hemawati and Kalu were chosen for analysis. In present study, a strong non linear relation was observed between rainfall and runoff with coefficient of determination (R2) varies from 0.411 to 0.943. The plots of maize land uses were found to show highest amount of initial abstraction (Ia) followed by sugarcane, black gram and fallow land plots. The CN derived by Least Square Method were significantly different (p<0.05) with geometric mean, median and mean methods, but latter ones did not significantly differed among each others. The present study analysis however indicates that no single method found to performed best for all watersheds/plots, but based on rank of the performance, the geometric mean method was the best method followed by arithmetic mean, median and least square method. This study evaluates 8 different SCS-CN inspired models including the existing SCS-CN method for comparison using the rainfall-runoff data measured from plot scale agricultural watersheds in India. It was observed that M2 with λ=0.05 performed better than the original SCS-CN model M1 with λ=0.2 and also model M8 incorporating the parameters of non-linear Ia-S relation, in which rainfall (P) is an implicit function of climatic/meteorological characteristics and antecedent moisture is a function of 5-day antecedent rainfall, M = ϒ P5. It performed the best of all models followed by M7 in terms of its lower RMSE and higher NSE and n(t) values whereas, based on the ranks and scores of each individual model for each performance index, model M1 was the poorest, and model M7 was the best followed by M8, M5, M6, M3, M4 and M2.
Other Identifiers: M.Tech
Appears in Collections:DOCTORAL THESES (WRDM)

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