U-Surf: a global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling DOI Creative Commons
Yifan Cheng, Lei Zhao, TC Chakraborty

и другие.

Earth system science data, Год журнала: 2025, Номер 17(5), С. 2147 - 2174

Опубликована: Май 21, 2025

Abstract. High-resolution urban climate modeling has faced substantial challenges due to the absence of a globally consistent, spatially continuous, and accurate dataset represent spatial heterogeneity surfaces their biophysical properties. This deficiency long obstructed development urban-resolving Earth system models (ESMs) ultra-high-resolution modeling, over large domains. Here, we present U-Surf, first-of-its-kind 1 km resolution present-day (circa 2020) global continuous surface parameter dataset. Using canopy model (UCM) in Community System Model as base for satisfying requirements, U-Surf leverages latest advances remote sensing, machine learning, cloud computing provide most relevant parameters, including radiative, morphological, thermal properties, UCMs at facet level. Generated using systematically unified workflow, ensures internal consistency among key making it first coherent significantly improves representation land both within across cities globally; provides essential, high-fidelity constraints ESMs; enables detailed city-to-city comparisons globe; supports next-generation kilometer-resolution scales. parameters can be easily converted or adapted various types UCMs, such those embedded weather regional models, well air quality models. The fundamental provided by also used features learning have other broad-scale applications socioeconomic, public health, planning contexts. We expect advance research frontier science, climate-sensitive design, coupled human–Earth systems future. is publicly available https://doi.org/10.5281/zenodo.11247598 (Cheng et al., 2024).

Язык: Английский

Uncovering the Impacts of 2D and 3D Urbanization on Urban Heat Islands in 384 Chinese Cities DOI
Jian Sun,

Zezhuang Liu,

Fan Xia

и другие.

Environmental Science & Technology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 7, 2025

Rapid urbanization in China has exacerbated the urban heat island (UHI) effect, posing considerable challenges to sustainability and public health. Most UHI studies have focused on impacts of two-dimensional (2D) urbanization, which involves outward city expansion increased built-up area. However, as cities mature, they typically transition from horizontal vertical densification (3D urbanization), leading material stock density. The implications this shift for effect remain underexplored. This study compared 2D 3D urbanization-induced across 384 Chinese 2000 2020, using impervious surface gridded stocks. Our results surprisingly indicated that lost explanatory power intensity when area percentage exceeded 87%. Relative importance analysis utilizing a random forest algorithm revealed population, vegetation abundance, precipitation significantly moderated effects emphasizing crucial role green spaces mitigating thermal stress. examined spatiotemporal dynamics China, key urbanization. findings highlight urgent need incorporate characteristics devising mitigation strategies.

Язык: Английский

Процитировано

0
Was global urbanization from 1985 to 2015 efficient in terms of land consumption? DOI Creative Commons
Hannes Taubenböck, Johannes Mast, Richard Lemoine-Rodríguez

и другие.

Habitat International, Год журнала: 2025, Номер 160, С. 103397 - 103397

Опубликована: Апрель 11, 2025

Язык: Английский

Процитировано

0
Characterizing dynamics of built-up height in China from 2005 to 2020 based on GEDI, Landsat, and PALSAR data DOI
Peimin Chen, Huabing Huang, Peng Qin

и другие.

Remote Sensing of Environment, Год журнала: 2025, Номер 325, С. 114776 - 114776

Опубликована: Апрель 26, 2025

Язык: Английский

Процитировано

0
Enhancing Climate-Driven Urban Tree Cooling with Targeted Nonclimatic Interventions DOI
Zhaowu Yu, Siheng Li, Wenjun Yang

и другие.

Environmental Science & Technology, Год журнала: 2025, Номер unknown

Опубликована: Май 2, 2025

Urban trees play a pivotal role in mitigating heat, yet the global determinants and patterns of their cooling efficiency (CE) remain elusive. Here, we quantify diel CE 229 cities across four climatic zones employ machine-learning model to assess influence variables on CE. We found that for every 10% increase tree cover, surface temperatures are reduced by 0.25 °C during day 0.04 at night. Trees humid regions exhibit highest daytime CE, while those arid demonstrate greatest effect This can be explained difference canopy density between zones. During day, high zone converts more solar radiation into latent heat flux. At night, low intercepts less longwave radiation, which favors cooling. While factors contribute nearly twice as much nonclimatic ones, our findings suggest optimizing is possible managing within specific thresholds due nonlinear effects. For instance, revealed regions, an impervious coverage approximately 60% optimal, whereas areas, reducing it around 40% maximizes benefits. These insights underscore need targeted management sustain benefits offer practical guidance designing climate-resilient, nature-based urban strategies.

Язык: Английский

Процитировано

0
U-Surf: a global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling DOI Creative Commons
Yifan Cheng, Lei Zhao, TC Chakraborty

и другие.

Earth system science data, Год журнала: 2025, Номер 17(5), С. 2147 - 2174

Опубликована: Май 21, 2025

Abstract. High-resolution urban climate modeling has faced substantial challenges due to the absence of a globally consistent, spatially continuous, and accurate dataset represent spatial heterogeneity surfaces their biophysical properties. This deficiency long obstructed development urban-resolving Earth system models (ESMs) ultra-high-resolution modeling, over large domains. Here, we present U-Surf, first-of-its-kind 1 km resolution present-day (circa 2020) global continuous surface parameter dataset. Using canopy model (UCM) in Community System Model as base for satisfying requirements, U-Surf leverages latest advances remote sensing, machine learning, cloud computing provide most relevant parameters, including radiative, morphological, thermal properties, UCMs at facet level. Generated using systematically unified workflow, ensures internal consistency among key making it first coherent significantly improves representation land both within across cities globally; provides essential, high-fidelity constraints ESMs; enables detailed city-to-city comparisons globe; supports next-generation kilometer-resolution scales. parameters can be easily converted or adapted various types UCMs, such those embedded weather regional models, well air quality models. The fundamental provided by also used features learning have other broad-scale applications socioeconomic, public health, planning contexts. We expect advance research frontier science, climate-sensitive design, coupled human–Earth systems future. is publicly available https://doi.org/10.5281/zenodo.11247598 (Cheng et al., 2024).

Язык: Английский

Процитировано

0