13134

1117 دكتري

دكتري

آب

اصفهان: دانشگاه صنعتي اصفهان، دانشكده كشاورزي

۱۳۹۶

هفده، [۲۴۴]ص.:‌ مصور، جدول، نمودار

سعيد اسلاميان

رضا مدرس

علي طالبي، حميدرضا صفوي، مهدي قيصري

1396/10/30

كتابنامه

كشاورزي

مهندسي كشاورزي

ID1117 دكتري

Frequency Analysis of Maximum 24 h Rainfall Using Region of Influence Approach Case Study Urmia Lake Basin Zohreh Dehghan z dehghan@ag iut ac ir Date of Submission January 1 2018 Department of Water Engineering College of Agriculture Isfahan University of Technology Isfahan 84156 83111 Iran 1st Supervisor Dr Saeid Eslamian saeid@cc iut ac ir 2nd Advisor Dr Reza Modarres reza modarres@cc iut ac ir Collage Graduate Coordinator Dr M SHirvani Abstract Homogenous regions are commonly regionalized based on different methods andwith consideration of a category of attributes relating to basin or stations of interest Inthis study a region of influence ROI and clustering approaches were used tofrequency analysis occurring on discontinuous boundaries as well as evaluation of theperformance of these regionalization models The goals of this study are to consider thedegree of importance for each of the defined attributes including statistical climatic and geographical attributes of rainfall stations as well as determine an appropriateweight for each of these attributes in each of the defined groups that can allocate tothemselves In this study the maximum 24 hour rainfall of the Urmia Lake Basin ULB for 63 rainfall selected stations for the period 30 years 1979 2008 was used For this purpose seven groups of attributes including climatic geographical statisticalattributes and their combinations along with five defined weighting scenarios usingprincipal component analysis PCA and correlation coefficients matrix methods wereapplied The results showed that each of the defined groups of attributes has differentperformance in terms of a number of clusters a scattering of stations and spatial patternof regions Furthermore the results of weighting scenarios indicate that the performanceof weighted regionalization models is better than non weighted models in the estimationof extreme quantiles based on different attributes and defined weights especially forWard clustering method Through the two regionalization methods Ward and ROIclustering methods the ROI approach demonstrated the much better performance and itcan certainly be a very suitable and more effective alternative for the clustering method In the ROI approach the estimation results of the extreme amounts of maximum 24 hour rainfall were obtained with more accuracy higher reliability and less error Keywords Frequency analysis Regional of influence Ward clustering Principalcomponents analysis correlation coefficient regionalization Professor at Department of Water Engineering College of Agriculture Isfahan University ofTechnology Isfahan Iran Tel 98 31 33913447 Assistant Professor at Watershed Management Department of Range and Watershed Management College of Natural Resources Isfahan University of Technology Isfahan Iran Tel 98 31 33913582

سعيد اسلاميان

رضا مدرس

علي طالبي، حميدرضا صفوي، مهدي قيصري