A Bayesian Fuzzy Clustering Approach for Design of Precipitation Gauge Network Using Merged Remote Sensing and Ground-Based Precipitation Products

Sreeparvathy, V and Srinivas, VV (2022) A Bayesian Fuzzy Clustering Approach for Design of Precipitation Gauge Network Using Merged Remote Sensing and Ground-Based Precipitation Products. In: Water Resources Research, 58 (2).

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Abstract

A two-level clustering approach is proposed for optimal design/expansion of a ground-based precipitation monitoring network (GPN). It harnesses the advantages of Infinite Bayesian fuzzy clustering in the first level to partition the study area into homogeneous precipitation zones by considering structural/statistical characteristics and temporal variability of the observed precipitation. In the second level, an ensemble of hierarchical and partitional clustering techniques is considered in the time domain to effectively partition each zone into groups by considering weighted inter-site dissimilarities of precipitation. The dissimilarities account for correlation, temporal dynamics, and fuzzy mutual information of precipitation at existing stations and possible new gauge locations. Key station�s location in each group is identified by a proposed ranking procedure that accounts for population density, land-use/landcover, and fuzzy marginal entropy of precipitation. For use with the approach, information on precipitation was derived for fine resolution ungauged grids covering the study area using random forest-based regression relationships developed for gauged grids between merged multiple satellite-based precipitation products (CHIRPS, IMERG) and ground-based precipitation measurements. The potential of the proposed approach over other clustering-based procedures is illustrated through a case study on a GPN comprising 1,128 gauges in Karnataka state (191,791 km2) of India. Potential locations for installing new gauges and areas where there is scope for relocating existing stations are identified. The proposed methodology appears promising and could be extended to design networks monitoring various other hydrometeorological variables. © 2022. American Geophysical Union. All Rights Reserved.

Item Type:	Journal Article
Publication:	Water Resources Research
Publisher:	John Wiley and Sons Inc
Additional Information:	The copyright for this article belongs to John Wiley and Sons Inc
Keywords:	Fuzzy clustering; Land use; Location; Population statistics; Product design; Remote sensing, Bayesian; Bayesian clustering; Fuzzy entropy; Ground based precipitation; India; Infinite bayesian clustering; Merging satellite precipitation product; Rain gauge networks; Random forests; Satellite precipitation products, Decision trees
Department/Centre:	Division of Mechanical Sciences > Divecha Centre for Climate Change Division of Interdisciplinary Sciences > Interdisciplinary Centre for Water Research Division of Mechanical Sciences > Civil Engineering
Date Deposited:	16 Mar 2022 06:11
Last Modified:	16 Mar 2022 06:11
URI:	http://eprints.iisc.ac.in/id/eprint/71496

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