Spatial Correlation between Altitude & other Climatic Variables : A Climatic Model of Kashmir Region using Regression Analysis
Zubair Malik1, Sachikanta Nanda2, R. Annadurai3
1Zubair Malik, Student , Masters, Remote Sensing & GIS in SRM Institute of Science and Technology.
2Dr. Sachikanta Nanda (Ph.D) Assistant Professor (Sr.G) Department of Civil Engineering, Kattankulathur Campus, SRM Institute of Science and Technology. (formerly known as SRM University).
3Dr. R. Annadurai (Ph.D) Professor Department of Civil Engineering, Kattankulathur Campus, SRM Institute of Science and Technology (formerly known as SRM University).
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4388-4391 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9391038620/2020©BEIESP | DOI: 10.35940/ijrte.F9391.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Kashmir has been encountering expanded temperature and change in precipitation system, which may antagonistically influence the significant environments in the nation differentially. In this study a effort has been put forward to deduce a relationship between altitude & other climatic variables such as Annual average temperature, Annual average precipitation, Annual average wind speed, Annual average solar radiation, Annual average max temperature, Annual average min temperature & Annual average vapor pressure. The data used for this study was provided by worldclim (version2.0), the spatial resolution of the data was about 1 km². The large study area & fine spatial resolution produced a vast amount of data points, which were analyzed with a significance level of the tests at 5%. Further for analysis the data was divided into zones categorized according to the elevation zones, this was mainly done to ease the work flow as the observation points were vast and also to reduce the standard error for each of the variables during analysis. Based on the climate data and the DEM (Digital Elevation Model) used for this study we deduced four multiple linear regression equations for four elevation zones, which describe the relationship between altitude & climatic variables. Although deducing a climate model which can precisely define relationship between the climatic variables & altitude is a challenge although an effort has been made to describe the relationship between elevation and climatic variables.
Keywords: DEM (Digital Elevation Model), World Clim, Correlation Analysis, Global and Local Spatial Auto correlation Analysis, Principal Component Analysis.
Scope of the Article: Regression and prediction.