Evidence map›Paper›PMID 42106380›Full record

ArticleScientific reports2026

Impact of urban blue spaces on urban surface temperatures - A seasonal perspective.

Lukas Fricke, Nadja Kabisch

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Lukas FrickeInstitute of Earth System Sciences, Physical Geography and Landscape Ecology Section, Leibniz University Hannover, Schneiderberg 50, 50167, Hannover, Germany. fricke@phygeo.uni-hannover.de.
Nadja KabischInstitute of Earth System Sciences, Physical Geography and Landscape Ecology Section, Leibniz University Hannover, Schneiderberg 50, 50167, Hannover, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Assessments on the cooling potential of natural environments in urban areas has largely focussed on green spaces at very specific spatial and temporal dimensions. Less has been studied so far about the surface thermal heat mitigation effect of urban blue spaces across an entire city and for different seasons and years. This study investigates the impact of urban blue spaces, such as lakes and ponds, on surface temperature for the city of Hannover, Germany. Using remote sensing data, we assess how different lake sizes and surrounding urban land use structures influence water surface temperature, surface temperature cooling intensity and distance across seasons and for three consecutive years. Our results indicate that larger lakes consistently exhibit the coolest water surface temperatures and provide the highest surface cooling intensities. The study identifies a case-specific Threshold Value of Efficiency (TVoE) of 0.70 ha for surface cooling intensity in the hottest months. We found that high proportions of impervious areas and buildings are associated with increased water surface temperatures and reduced surface temperature cooling effects, while high vegetation close to urban blue spaces show significantly lower surface temperature values. Given our limitations in data availability for particular environmental condition indicators (e.g. wind regime, air humidity) and water body characteristics (shape and depth) probably also influencing the thermal effect of blue spaces, we suggest a multi-method approach combining remote sensing and in-situ based environmental measurements to assess the thermal regulation of urban environments.

Indexed as

Remote SensingUrban blue spaceUrban planning

Identifiers

PMID42106380
PMCPMC13157505

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Registered trials

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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.