Spatial and temporal rainfall patterns in Central Dry Zone, Myanmar - A hydrological cross-scale analysis

  • Author(s): Mya Thandar Toe, Mamoru Kanzaki, Tsung-Hsun Lien, and Ke-Sheng Cheng
  • DOI: 10.3319/TAO.2016.02.15.01(Hy)
  • Keywords: Climatological monsoon break, Orographic effect, Semivariogram, K-means cluster analysis, Principal component analysis
  • Citation: Toe, M. T., M. Kanzaki, T.-H. Lien, and K.-S. Cheng, 2017: Spatial and temporal rainfall patterns in Central Dry Zone, Myanmar - A hydrological cross-scale analysis. Terr. Atmos. Ocean. Sci., 28, 425-436, doi: 10.3319/TAO.2016.02.15.01(Hy)
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Abstract

Myanmar (Burma) is traditionally an agriculture-based country. However, irrigation is not available in most of its agricultural lands. This study focuses on the Central Dry Zone (CDZ), which is the driest part of Myanmar. Exploratory data analysis, semivariogram analysis and modeling, K-means cluster analysis and principal component analysis were conducted in this study to investigate the general CDZ climatology. The spatial and temporal rainfall variation patterns of different scales, including daily, event-scale and monthly rainfall are studied. A climatological monsoon break divides the wet season into two peaks. The monsoon break is the result of different climate dynamics – May-to-June period monsoon southwesterly and August-to-October period tropical cyclone vorticity. Rainfall stations in different clusters identified by the K-means cluster analysis reflect the orographic effect and different climate dynamics, which influence the spatial and temporal rainfall variation patterns in the CDZ. Principal component analysis results for average monthly rainfall reveals that the first principal component mainly explains the spatial variabilities in average monthly rainfall in the CDZ. The second principal component explains the seasonal (temporal) variation in average monthly rainfall. It was found that during the wet season, spatial rainfall variations in the CDZ are more significant than the seasonal (temporal) rainfall variation. Understanding the spatial and temporal variability in CDZ rainfall can provide valuable information on potential water availability in both the time and spatial domains, which will then enable making sound cropland planning and forest management decisions.

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