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Comparison CCA physiographical space based interpolation and linear moments methods for regional flood frequency analysis, case study: watersheds of Mazandaran Province | ||
| مهندسی و مدیریت آبخیز | ||
| Article 8, Volume 7, Issue 2, August 2015, Pages 195-210 PDF (1.18 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22092/ijwmse.2015.101262 | ||
| Authors | ||
| Zahra Sheikh* 1; Ahmad Pahlavanravi2 | ||
| 1MSc, Faculty of Natural Resources, University of Zabol, Iran | ||
| 2Associate Professor, Faculty of Natural Resources, University of Zabol, Iran | ||
| Abstract | ||
| The physiographical and climatological attributes of basins and maximum annual flood statics were obtained from 38 hydrometric stations of Mazandaran Province. The first, physiographical space based interpolation method was used for regional flood analysis with 10, 20, 50 and 100 year return periods. The Canonical Correlation Analysis was used to design physiographic space and regional analysis was obtained application of three interpolation methods include, ordinary Kriging, simple Kriging and IDW in the physiographic space. The results showed at-site flood quantiles have maximum correlation spatial in the physiographic space and their spatial structures following from Gaussian semi variogram. In order to evaluated the accuracy results using L-moments method for regional flood frequency analysis. In this method Ward clustering has been used to the determination of homogeneous regions. Homogeneity areas were adjusted by using homogenous and discordancy tests. Using goodness-of-fit Z DIST the Generalized Logistic distribution was selected for all clusters as the best regional distribution and regional estimates obtained by parameters selected distribution. Finally, performance methods evaluated using Jack Knife procedure and the five statistical indexes BIAS, BIASr, RMSE, RMSEr and NASH. Both methods give the same and desirable performance based on NASH; however results of other indexes showed that physiographical space based interpolation method provide better performance than linear moments and improving estimated by increasing the return periods, while the L-moments offers better performance for short return periods. The results of interpolation indicated that geostatistics estimates have more accuracy than deterministic estimates and ordinary Kriging has best performance. | ||
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