Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/9930
Title: STUDY OF SPATIAL VARIABILITY OF RAINFALL IN UTTARAKHAND
Authors: Chaturvedi, Shailja
Keywords: HYDROENERGY
SPATIAL VARIABILITY
RAINFALL
UTTARAKHAND
Issue Date: 2008
Abstract: Rainfall 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.
URI: http://hdl.handle.net/123456789/9930
Other Identifiers: M.Tech
Appears in Collections:MASTERS' DISSERTATIONS (Hydrology)

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