Determining the spatio-temporal response of downstream coarse sediment sorting process in the Chel river (North Bengal,
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ORIGINAL ARTICLE
Determining the spatio‑temporal response of downstream coarse sediment sorting process in the Chel river (North Bengal, India) using cluster analysis Debarshi Ghosh1 · Snehasish Saha2 Received: 23 July 2020 / Accepted: 17 October 2020 © Springer Nature Switzerland AG 2020
Abstract The fluvial transportation efficiency related to rainstorm events is important to understand the variability of sediment sorting process in the channel. The supply limitation of different size of sediment grades in downstream reveals the nature of geomorphological scale of response (spatio-temporal). Pebble count method has been attempted here for determination of d50 (median) particle size associated with frequency distribution of sediment samples from 13 study reaches on the basis of standard sediment grade scale of Wentworth and modified Udden-Wentworth grain scale seasonally. Intra temporal variability (IATV) and inter temporal variability (IETV) results were categorized under hierarchical cluster analysis to view the sediment movement downstream from minimum distance algorithm. On the piedmont, the bed configuration is mostly boulder infested of heavier size class (> 256 mm) and that remains greater than 86% in pre-monsoon condition. It comes out on the basis of random mixing of sediment load (> 2 mm) while transporting as discharge. In Chel basin, the process of sediment load dispersal indicates less heterogeneity in the sediment sorting process towards downstream. The haphazard distribution of very coarse to medium boulders (4096–512 mm) up to reach 6 indicates the limit of channel competency. The paper seeks to know the seasonal variability in the sediment dispersal process i.e. finding the reach wise variability of the sediment mixing process downstream and the sediment supply limits of median particle size. Keywords Sediment sorting · Frequency distribution curve · Normal probability distribution · Hierarchical cluster · ANOVA
Introduction The main goal of cluster analysis is to build homogeneous groups from a geographical area according to certain variables (De Carvalho and Lechevallier 2009; Dutta et al. 2019, Dutta and Das 2019). Statistical parameters like the mean, median, standard, deviation, kurtosis, and skewness have been widely used to characterize grain size distribution * Debarshi Ghosh [email protected] Snehasish Saha [email protected] 1
Department of Geography, Dhupguri Girls’ College (Affiliated to University of North Bengal), Dhupguri, West Bengal, India
Departmentof Geography and Applied Geography, University Of North Bengal, Darjeeling, West Bengal, India
2
within the channel bed and clustering finds the similar distributions. Most of the studies have employed the method to group a limited number of grain size distribution parameters like the mean and standard deviation, and few studies have taken advantage of the entire distribution (Zhou et al. 2015; Nelson et al. 2014; Ordóñez et al. 2016). Thus, both the process helps to infer variations in hydrodynamic conditi
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