Assessing The Local Spatial Variation In The Relationships Between Rainfall, Vegetation And Elevation

• Main Author: Narashid, Rohayu Haron
• Format: Thesis
• Language: English
• Published: 2018
• Subjects: G1-922 Geography (General)
• Online Access: http://eprints.usm.my/48261/1/ROHAYU%20HARON%20NARASHID_hj.pdf; http://eprints.usm.my/48261/

Rainfall varies spatially ranging from large to local scales. Spatial elements such as vegetation and topography are the contributing factors to local variations of rainfall. However, local spatial variation process in rainfall due to vegetation and topography is unidentified when using a global model. This study aims to assess the local spatial variation of rainfall in the relationships between rainfall, vegetation and elevation using a local modelling approach. The main data used consist of rainfall depths, vegetation index of Normalized Difference Vegetation Index (NDVI) from Landsat 7 ETM+ satellite images and the elevation data from 174 and 103 locations of rainfall stations within the Northern and East Coast Region of Peninsular Malaysia respectively. Based on the availability of NDVI datasets from the years 2000, 2009 and 2011, the local spatial variations of rainfall were determined. The small clustering patterns in rainfall, vegetation and elevation that were computed in Moran's Index with the value of 0.1 to 0.5 showed low values of the variables being clustered in the study areas. Thus, the spatial process in rainfall, vegetation and elevation demonstrated a potential for local variations. The spatial pattern of these variables led to the exploration of non-stationary relationships. In order to explore the local spatial variation of rainfall, the regression techniques of Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) were applied to determine three types of models i.e. : (1) the relationship between rainfall and vegetation; (2) the relationship between rainfall and elevation; and (3) the relationship between rainfall, vegetation and elevation. The statistical findings for all relationships had shown significant local variations when Akaike's Information Criterion (AICc) obtained from GWR were lower. The GWR R-squared (0.146 to 0.770) improved the OLS r-squared (0 to 0.176). The best GWR model with the highest AICc difference values ( AICc) for years 2000, 2011 and 2009 were found in Model 1(164.571), Model 3 (163.946) and Model 2 (147.605), respectively. Land use and vegetation changes are the possible reasons when the relationship between rainfall-elevation for year 2011 was found to be more significant. The significant location of local spatial variations of rainfall due to vegetation and elevation can also be demonstrated based on the findings. With the detailed capabilities provided in remotely sensed data, the local variations of the relationships are possible to be carried out. Therefore, the spatial relationship that exists between rainfall, vegetation and elevation at the local level are significantly contributing to the local variations in rainfall.