Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/9930
Full metadata record
DC FieldValueLanguage
dc.contributor.authorChaturvedi, Shailja-
dc.date.accessioned2014-11-21T07:48:02Z-
dc.date.available2014-11-21T07:48:02Z-
dc.date.issued2008-
dc.identifierM.Techen_US
dc.identifier.urihttp://hdl.handle.net/123456789/9930-
dc.guideArya, D. S.-
dc.description.abstractRainfall varies geographically and seasonally. It varies from one geographical location to another, from one watershed to another in. the same country, and from one point to another within the same watershed. ' This variation can be considerable, depending upon the atmospheric and topographic factors as well as their interactions. Elevation, slope, aspect and - - exposure are the main topographic factors affecting rainfall variability. The present study attempts to analyze the rainfall pattern in space over a region which forms a part of the world's loftiest mountain range with a very few number of rain gauge. stations. The .State of Uttarakhand lies in the lower middle Himalayan range and the rainfall is highly variable which is attributed to orography. In the present study, seasonal and annual rainfall records of 80 stations of Uttarakhand were used to study the spatial variablility. The broad objectives of the study were to applying various deterministic and geostatistical interpolation techniques to study the rainfall variability (annual as well as seasonal) and to estimating annual average rainfall values. In the present study geostatistical analyst of ARCGIS software used. Based on the analysis of data sets, following are the findings: i. Among all the techniques applied, the geostatistical (Kriging) techniques generate a more reasonable surface depicting the spatial variation of the rainfall pattern. ii. Co-Kriging with Hole Effect model is the best -predictor. to distribute the annual period rainfall over the entire region. With this generated surface the mean areal auL. rainfall in the state is 1587.32 mm. iii. The seasonal prediction are given below: a. For monsoon, winter and autumn rainfall, Co Kriging with Hole Effect model gives the best prediction surface. b. Co-Kriging with Spherical model gives the best prediction surface for spring rainfall.en_US
dc.language.isoenen_US
dc.subjectHYDROENERGYen_US
dc.subjectSPATIAL VARIABILITYen_US
dc.subjectRAINFALLen_US
dc.subjectUTTARAKHANDen_US
dc.titleSTUDY OF SPATIAL VARIABILITY OF RAINFALL IN UTTARAKHANDen_US
dc.typeM.Tech Dessertationen_US
dc.accession.numberG14307en_US
Appears in Collections:MASTERS' THESES (Hydrology)

Files in This Item:
File Description SizeFormat 
HYDG14307.pdf16.21 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.