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Quantification of node importance in rain gauge network: influence of temporal resolution and rain gauge density

Rain gauge network is important for collecting rainfall information effectively and efficiently. Rain gauge networks have been studied for several decades from a range of hydrological perspectives, where rain gauges with unique or non-repeating information are considered as important. However, the p...

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Autores principales: Tiwari, Shubham, Jha, Sanjeev Kumar, Singh, Ankit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7300113/
https://www.ncbi.nlm.nih.gov/pubmed/32555387
http://dx.doi.org/10.1038/s41598-020-66363-5
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author Tiwari, Shubham
Jha, Sanjeev Kumar
Singh, Ankit
author_facet Tiwari, Shubham
Jha, Sanjeev Kumar
Singh, Ankit
author_sort Tiwari, Shubham
collection PubMed
description Rain gauge network is important for collecting rainfall information effectively and efficiently. Rain gauge networks have been studied for several decades from a range of hydrological perspectives, where rain gauges with unique or non-repeating information are considered as important. However, the problem of quantification of node importance and subsequent identification of the most important nodes in rain gauge networks have not yet been extensively addressed in the literature. In this study, we use the concept of the complex networks to evaluate the Indian Meteorological Department (IMD) monitored 692 rain gauge in the Ganga River Basin. We consider the complex network theory-based Degree Centrality (DC), Clustering Coefficient (CC) and Mutual Information (MI) as the parameters to quantify the rainfall variability associated with all the rain gauges in the network. Multiple rain gauge network scenario with varying rain gauge density (i.e. Network Size (NS) = 173, 344, 519, and 692) and Temporal Resolution (i.e. TR = 3 hours, 1 day, and 1 month) are introduced to study the effect of rain gauge density, gauge location and temporal resolution on the node importance quantification. Proxy validation of the methodology was done using a hydrological model. Our results indicate that the network density and temporal resolution strongly influence a node’s importance in rain gauge network. In addition, we concluded that the degree centrality along with clustering coefficient is the preferred parameter than the mutual information for the node importance quantification. Furthermore, we observed that the network properties (spatial distribution, DC, Collapse Correlation Threshold (CCT), CC Range distributions) associated with TR = 3 hours and 1 day are comparable whereas TR = 1 month exhibit completely different trends. We also found that the rain gauges situated at high elevated areas are extremely important irrespective of the NS and TR. The encouraging results for the quantification of nodes importance in this study seem to indicate that the approach has the potential to be used in extreme rainfall forecasting, in studying changing rainfall patterns and in filling gaps in spatial data. The technique can be further helpful in the ground-based observation network design of a wide range of meteorological parameters with spatial correlation.
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spelling pubmed-73001132020-06-22 Quantification of node importance in rain gauge network: influence of temporal resolution and rain gauge density Tiwari, Shubham Jha, Sanjeev Kumar Singh, Ankit Sci Rep Article Rain gauge network is important for collecting rainfall information effectively and efficiently. Rain gauge networks have been studied for several decades from a range of hydrological perspectives, where rain gauges with unique or non-repeating information are considered as important. However, the problem of quantification of node importance and subsequent identification of the most important nodes in rain gauge networks have not yet been extensively addressed in the literature. In this study, we use the concept of the complex networks to evaluate the Indian Meteorological Department (IMD) monitored 692 rain gauge in the Ganga River Basin. We consider the complex network theory-based Degree Centrality (DC), Clustering Coefficient (CC) and Mutual Information (MI) as the parameters to quantify the rainfall variability associated with all the rain gauges in the network. Multiple rain gauge network scenario with varying rain gauge density (i.e. Network Size (NS) = 173, 344, 519, and 692) and Temporal Resolution (i.e. TR = 3 hours, 1 day, and 1 month) are introduced to study the effect of rain gauge density, gauge location and temporal resolution on the node importance quantification. Proxy validation of the methodology was done using a hydrological model. Our results indicate that the network density and temporal resolution strongly influence a node’s importance in rain gauge network. In addition, we concluded that the degree centrality along with clustering coefficient is the preferred parameter than the mutual information for the node importance quantification. Furthermore, we observed that the network properties (spatial distribution, DC, Collapse Correlation Threshold (CCT), CC Range distributions) associated with TR = 3 hours and 1 day are comparable whereas TR = 1 month exhibit completely different trends. We also found that the rain gauges situated at high elevated areas are extremely important irrespective of the NS and TR. The encouraging results for the quantification of nodes importance in this study seem to indicate that the approach has the potential to be used in extreme rainfall forecasting, in studying changing rainfall patterns and in filling gaps in spatial data. The technique can be further helpful in the ground-based observation network design of a wide range of meteorological parameters with spatial correlation. Nature Publishing Group UK 2020-06-17 /pmc/articles/PMC7300113/ /pubmed/32555387 http://dx.doi.org/10.1038/s41598-020-66363-5 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Tiwari, Shubham
Jha, Sanjeev Kumar
Singh, Ankit
Quantification of node importance in rain gauge network: influence of temporal resolution and rain gauge density
title Quantification of node importance in rain gauge network: influence of temporal resolution and rain gauge density
title_full Quantification of node importance in rain gauge network: influence of temporal resolution and rain gauge density
title_fullStr Quantification of node importance in rain gauge network: influence of temporal resolution and rain gauge density
title_full_unstemmed Quantification of node importance in rain gauge network: influence of temporal resolution and rain gauge density
title_short Quantification of node importance in rain gauge network: influence of temporal resolution and rain gauge density
title_sort quantification of node importance in rain gauge network: influence of temporal resolution and rain gauge density
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7300113/
https://www.ncbi.nlm.nih.gov/pubmed/32555387
http://dx.doi.org/10.1038/s41598-020-66363-5
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