Importance of regional rainfall data in homogeneous clustering of data-sparse areas: a study in the upper Brahmaputra valley region

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. Further, 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 2 parameters (latitude, longitude and elevation) need to be taken into account for getting a definite conclusion.

Uncertainty Analysis of Regional Rainfall Frequency Estimates in Northeast India

“…Methods such as Regional Frequency Analysis (RFA) have been frequently employed in such situations that transfer data from gauged locations to places with little or no data [1,2]. Several studies can be found with the application of the RFA technique in the region [3][4][5][6][7]. Regional frequency analysis of extreme rainfall for any region aims to provide a detailed description of the distribution of rainfall events and predict probable estimates for a given return time.…”

“…Notable recent studies on the comparison of uncertainty analysis of rainfall from regional and at-site analysis can be found in the studies of [14][15][16], but very scarce studies are available for north east India. The available studies are limited to only selection of probability distribution and estimation of quantiles [3,5]. Resulting quantiles from RFA with small sample sizes are speculative, and constructing confidence intervals has been the simplest and most widely used approach to evaluate the uncertainty.…”