Dataset Information

Spatial and temporal changes of outdoor thermal stress: influence of urban land cover types.

ABSTRACT: Green infrastructure (GI) has emerged as a feasible strategy for promoting adaptive capacities of cities to climate change by alleviating urban heat island (UHI) and thus heat stress for humans. However, GI can also intensify the winter cold stress. To understand the extent of UHI within a city as well as the link between outdoor thermal stress both diurnally and seasonally, we carried out an empirical study in Würzburg, Germany from 2018 to 2020. At sub-urban sites, relative humidity and wind speed (WS) was considerably higher and air temperature (AT) lower compared to the inner city sites. Mean AT of inner city sites were higher by 1.3 °C during summer and 5 °C during winter compared to sub-urban sites. The magnitude followed the spatial land use patterns, in particular the amount of buildings. Consequently, out of 97 hot days (AT > 30 °C) in 3 years, 9 days above the extreme threshold of wet bulb globe temperature of 35 °C were recorded at a centre location compared to none at a sub-urban site. Extreme heat stress could be halved with 30-40% cover of greenspaces including grass lawns, green roofs, and green walls with little compromise in increasing winter cold stress.

SUBMITTER: Rahman MA

PROVIDER: S-EPMC8758735 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Research on spatial non-stationarity of land-cover classification accuracy has been ongoing for over two decades with most of the work focusing on single date maps. We extend the understanding of thematic map accuracy spatial patterns by: (1) quantifying spatial patterns of map-reference agreement for class-specific land-cover change rather than class-specific land cover for both omission and commission expressions of map error; (2) reporting goodness-of-fit estimates for the empirical models, which have been lacking in previous assessments, and; (3) using the empirical model results to map the locations of the relative likelihoods of map-reference agreement for specific land-cover change classes. We evaluated 10 map-based explanatory variables in single and multivariable logistic regression models to predict the likelihood of agreement between map and reference land-cover change (2001-2011) labels using the National Land Cover Database (NLCD) 2011 land cover and accuracy data. Logistic models for omission error had better goodness-of-fit estimates than models for commission error. For the omission error models, the explanatory variable, density of the mapped class-specific change in the immediate neighbourhood surrounding the sample pixel, produced the best model fit results (Tjur coefficient of discrimination, D, ranged from 0.59 to 0.98) compared to multivariable models and all other single explanatory variable models. Maps of the predicted likelihood of map-reference agreement produced from the best fitting omission error models provide a spatially explicit description of spatial variation of classification uncertainty at both local and regional scales. Application of the models indicated higher likelihoods of agreement (>50%) comprised a greater proportion of the land-cover change class area than the proportion of the land-cover change class with lower likelihoods of agreement. NLCD users can apply reported equations to map land-cover change uncertainty.

Dataset Information

Spatial and temporal changes of outdoor thermal stress: influence of urban land cover types.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets