Abstract
Delineation of homogeneous regions has found its way into many hydrological applications as it helps in addressing the challenges in understanding the behavior of rainfall distribution and its variability at a local scale. In the present study, rainfall data recoded by 83 tea gardens in the upper Brahmaputra valley region of Assam have been used to identify homogeneous rainfall regions by using fuzzy clustering analysis. Furthermore, seven different cluster validity indices (CVs) were utilized to find out the optimum clustering in the fuzzy c-means (FCM) algorithm. The clusters thus formed were assessed for statistical homogeneity by performing homogeneity tests based on L-moment. Three different combinations of feature vectors were employed in FCM algorithm and the outputs were compared for attaining best solutions to regionalization. The results were further compared with previous regionalization studies. The analysis and comparison conclude that if regionalization needs to be done at a local scale, further sub-clustering of a larger clustered region to smaller regions may be required. Local rainfall data can be used for the purpose provided a good dataset with large number of station points are available within the region. Along with rainfall data, geographical location parameters (latitude, longitude, and elevation) need to be taken into account for getting a definite conclusion.
Highlights
Rainfall is one of the most important hydrological parameters that requires to be studied scrupulously, both in spatial and temporal scale
Three different combinations of feature vectors were employed in fuzzy c-means (FCM) algorithm to attain the best solutions to regionalization
Seven different cluster validity indices (CVs) were used to determine the optimal partition in the fuzzy c-means (FCM) algorithm, out of which Extended Xie and beni index (VXB) and Kwon index (VK) were opted for clustering of rainfall regions owing to their satisfactory performance
Summary
Rainfall is one of the most important hydrological parameters that requires to be studied scrupulously, both in spatial and temporal scale. Challenge comes in understanding the behavior of rainfall distribution pattern when applied on a regional scale. Its frequency and magnitude may drastically vary depending upon the orography (Venkatesh and Jose 2007), large-scale synoptic and convective precipitation types (Karaca et al 2000, Unal et al 2012; Baltacı et al 2015; Efe et al 2019) of the region. Scarcity of sufficient data at many sites of interest, may further complicate the investigation. To address this issue, the region may be classified into few homogeneous rainfall regions of similar rainfall distribution, termed as regionalization. The variations in the orographic arrangements and altitude differences in the region give rise to irregular and complex rainfall patterns at a local scale, which eventually amplifies the need of regionalization
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