Observations of enhanced rainfall variability in Kenya, East Africa.

Susan M Kotikot, Erica A H Smithwick, Helen Greatrex

Abstract read

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. 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

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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.

Integration of infectious diseases in climate governance in the Horn of Africa: A document review of climate strategies, policies, and action plans across four countries.The journal of climate change and health
Review

4 · The record

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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

3 authors.

Susan M KotikotDepartment of Geography, Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, PA, USA. smk6598@psu.edu.

Erica A H SmithwickDepartment of Geography, Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, PA, USA.

Helen GreatrexDepartment of Geography, Department of Statistics, Institute for Computational and Data Sciences, The Pennsylvania State University, University Park, PA, USA.

Funding

National Geographic Society early career grant EC-86758R-21National Science Foundation BCS Award 2149244National Science Foundation DGE Award 1828822

6 · The paper itself

Abstract

Understanding local patterns of rainfall variability is of great concern in East Africa, where agricultural productivity is dominantly rainfall dependent. However, East African rainfall climatology is influenced by numerous drivers operating at multiple scales, and local patterns of variability are not adequately understood. Here, we show evidence of substantial variability of local rainfall patterns between 1981 and 2021 at the national and county level in Kenya, East Africa. Results show anomalous patterns of both wetting and drying in both the long and short rainy seasons, with evidence of increased frequency of extreme wet and dry events through time. Observations also indicate that seasonal and intraseasonal variability increased significantly after 2013, coincident with diminished coherence between ENSO (El Nino Southern Oscillation) and rainfall. Increasing frequency and magnitude of rainfall variability suggests increasing need for local-level climate change adaptation strategies.

Indexed as

Climate changeKenyaRainfall variabilitySelf-organizing mapsWavelet analysis

Identifiers

PMID38839907

DOI10.1038/s41598-024-63786-2

PMCPMC11153539

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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.