Evidence map›Paper›PMID 24415132›Full record

ArticleEnvironmental monitoring and assessment2014

Assessing the effects of severe rainstorm-induced mixing on a subtropical, subalpine lake.

Nobuaki Kimura, Wen-Cheng Liu, Chih-Yu Chiu, T K Kratz

Abstract read
PubMed Publisher
In one paragraph

Article in Environmental monitoring and assessment, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. The time of concentration application in studies around the world: a review.Environmental science and pollution research international · 2022
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Nobuaki KimuraDepartment of Civil Disaster Prevention Engineering, National United University, Miao-Li, 36003, Taiwan.
Wen-Cheng Liu
Chih-Yu Chiu
T K Kratz

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Severe rainstorms cause vertical mixing that modifies the internal dynamics (e.g., internal seiche, thermal structure, and velocity filed) in warm polymictic lakes. Yuan Yang Lake (YYL), a subtropical, subalpine, and seasonally stratified small lake in the north-central region of Taiwan, is normally affected by typhoons accompanied with strong wind and heavy rainfall during the summer and fall. In this study, we used the field data, statistical analysis, spectral analysis, and numerical modeling to investigate severe rainstorm-induced mixing in the lake. Statistical determination of the key meteorological and environmental conditions underlying the observed vertical mixing suggests that the vertical mixing, caused by heat loss during severe rainstorms, was likely larger than wind-induced mixing and that high inflow discharge strongly increased heat loss through advection heat. Spectral analysis revealed that internal seiches at the basin scale occurred under non-rainstorm meteorological conditions and that the internal seiches under the rainstorm were modified on the increase of the internal seiche frequencies. Based upon observed frequencies of the internal seiches, a two-dimensional model was simulated and then appropriate velocity patterns of the internal seiches were determined under non-rainstorm conditions. Moreover, the model implemented with inflow boundary condition was conducted for rainstorm events. The model results showed that the severe rainstorms promoted thermal destratification and changed vertical circulation of the basin-scale, internal seiche motion into riverine flow.

Indexed as

Environmental MonitoringModels, TheoreticalRainLakesSeasonsTaiwanWater PollutantsWindWater Pollutants

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.