Volume 14, Issue 52 (sumer 2010)
jwss 2010, 14(52): 27-37 |
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Motamednia M, Sadeghi S, Moradi H, Asadi H. Evaluation of Different Modeling Procedures in Unit Hydrograph-Hyetograph Relationship . jwss 2010; 14 (52) :27-37
URL: http://jstnar.iut.ac.ir/article-1-1221-en.html
URL: http://jstnar.iut.ac.ir/article-1-1221-en.html
, sadeghi@modares.ac.ir
Abstract: (33196 Views)
An extensive data collection on precipitation and runoff is required for development and implementation of soil and water projects. The unit hydrograph (UH) is an appropriate base for deriving flood hydrographs and therefore provides comprehensive information for planners and managers. However, UH derivation is not easy job for whole watersheds. The development of UH by using easily accessible rainfall data is then necessary. Besides that, the validity evaluation of different statistical modeling methods in hydrology and UH development has been rarely taken into account. Towards the attempt, the present study was planned to compare the efficiency of different modeling procedures in hydrograph and 2-h representative UH relationship in Kasilian watershed with concentration time of some 10h. The study took place by using 23 storm events occurred during four seasons within 33 years and applying two and multivariable regression models and 36 variables. According to the results, the median of estimated errors in estimation of 2-h UH dependent variables for verification stage varied from 37 to 88%. The results verified the better performance of cubic and linear bivariate models and logarithm-transformed data in multivariable model as well. The efficiency of multivariable models decreased when they were subjected to principle component analysis. The performance of backward method was frequently proved for estimation of dependent variables based on evaluation criteria, whereas the forward was found to be more efficient for time-dependent factors estimation.
Keywords: Regression model, Unit hydrograph simulation, Kasilian watershed, Principle component analysis, Regression models.
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