Evidence map›Paper›PMID 38365984›Full record

ArticleScientific reports2024

Evolutionary analysis of rainstorm momentum and non-stationary variating patterns in response to climatic changes across diverse terrains.

Chien-Lin Huang, Nien-Sheng Hsu

RetractedAbstract readRetracted Publication
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Chien-Lin HuangDepartment of Civil Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan.
Nien-Sheng HsuDepartment of Civil Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan. nsshue@ntu.edu.tw.

Funding

National Science and Technology Council NSTC 111-2811-M-002-127
6 · The paper itself

Abstract

This study aims to analyze time-series measurements encompassing rainstorm events with over a century of datasets to identify rainstorm evolution and dimensional transitions in non-stationarity. Rainstorm events are identified using partial duration series (PDS) to extract changes in rainstorm characteristics, namely maximum intensity (MAXI), duration (D), total rainfall (TR), and average rainfall intensity (ARI), in response to climate change. Ensemble empirical mode decomposition is used for trend filtering and non-stationary identification to explore spatiotemporal insight patterns. Trend models for the first-second-order moments of rainstorm characteristics are used to formulate the identified mean-variance trends using combined multi-dimensional linear-parabolic regression. Best-fitting combinations of various distributions (probability density functions) and trend models for multiple characteristic series are identified based on the Akaike information criterion. We analyze the dimensional transition in rainfall non-stationarity based on sensitivity analysis using PDS to determine its natural geophysical causes. The integrated methodology was applied to the data retrieved from nine weather stations in Taiwan. Our findings reveal rainstorm characteristics of "short D but high rainfall intensity" or "lower MAXI but high TR" across multiple stations. The parabolic trend of the first-order moment (i.e., mean) of ARI, D, and TR appears at the endpoint of the mountain ranges. Areas receiving monsoons and those on the windward plain show a rising parabolic trend in the first- and second-order moments (i.e., mean-variance) characterizing MAXI, implying that the occurrence frequency and magnitude of extreme MAXI increases. Non-stationary transitions in MAXI appear for mountain ranges exposed to the monsoon co-movement effect on both windward and leeward sides. Stations in the plains and rift valleys show upgraded and downgraded transitions in the non-stationary dimensions for D, respectively.

Identifiers

PMID38365984
PMCPMC11297036

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