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| Created: | Sep 10, 2026 at 1:54 p.m. (UTC) | |
| Last updated: | Sep 10, 2026 at 2:10 p.m. (UTC) | |
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| Sharing Status: | Public |
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Abstract
This repository provides the Python code developed to analyze the spatiotemporal organization of extreme precipitation events (EPEs) using event synchronization and complex network theory. The workflow identifies EPEs from gridded daily precipitation using grid-specific percentile thresholds, constructs event time series, quantifies synchronization between spatial locations, and determines statistically significant network connections through surrogate-based testing. The resulting climate networks are characterized using complementary network metrics, including degree centrality, clustering coefficient, betweenness centrality, mean geographic distance, and long-range directedness. The workflow also incorporates boundary correction to reduce geometric biases associated with finite spatial domains. The code supports analyses over user-defined temporal periods and seasons, enabling comparison of changes in EPE frequency, synchronization, and network organization through time. It was developed for analyzing summer and winter extreme precipitation across Texas from 1980 to 2024 but provides a general framework that can be adapted to other gridded precipitation datasets, geographic domains, and analysis periods.
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This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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