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  1. Proceedings of the 3rd International Workshop on Cyber-Physical Systems for Smart Water Networks (CySWATER '17)
  2. Enhancing precipitation models by capturing multivariate and multiscale climate dynamics
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Enhancing precipitation models by capturing multivariate and multiscale climate dynamics

Content Provider ACM Digital Library
Author Ding, Ruizhou Stamoulis, Dimitrios Bhardwaj, Kartikeya Marculescu, Diana Marculescu, Radu
Abstract To improve precipitation predictions and enable accurate rainfall models for smart water networks, it is imperative to account for multivariate and multiscale precipitation dynamics with long-memory temporal relationships, long-range spatial dependencies, and low-frequency variability. While prior art has motivated the use of complex networks to capture these trends, existing work is limited to specific climate phenomena (such as El NiƱo) and regions. In this paper we employ a comprehensive assessment of complex networks with respect to multivariate dynamics across multiple temporal and spatial scales (multiscale). Our work is the first to incorporate both carbon emissions and precipitation anomalies data into a multivariate analysis. By effectively substantiating the ability of complex networks to capture multivariate and multiscale climate dynamics, we postulate their potential as reanalysis and assessment tools to enhance regional water models of arbitrary range and granularity, and to eventually enable reliable smart water systems.
Starting Page 39
Ending Page 42
Page Count 4
File Format PDF
ISBN 9781450349758
DOI 10.1145/3055366.3055374
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2017-04-21
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Climate anomalies Precipitation Complex networks Complex network properties Carbon emissions
Content Type Text
Resource Type Article
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