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.
Perceptive the rainfall pattern is tough for the solution of several regional environmental issues of water resources management, with implications for agriculture, climate change, and natural calamity such as floods and droughts. Statistical computing, modeling and forecasting data are key instruments for studying these patterns. The study of time series analysis and forecasting has become a major tool in different applications in hydrology and environmental fields. Among the most effective approaches for analyzing time series data is the ARIMA (Autoregressive Integrated Moving Average) model introduced by Box and Jenkins. In this study, an attempt has been made to use Box-Jenkins methodology to build ARIMA model for monthly rainfall data taken from Dibrugarh for the period of 1980- 2014 with a total of 420 points. We investigated and found that ARIMA (0, 0, 0) (0, 1, 1)12 model is suitable for the given data set. As such this model can be used to forecast the pattern of monthly rainfall for the upcoming years, which can help the decision makers to establish priorities in terms of agricultural, flood, water demand management etc.
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 parameters (latitude, longitude and elevation) need to be taken into account for getting a definite conclusion.
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