The projected effects of urbanization and climate change on summer thermal environment in Guangdong-Hong Kong-Macao Greater Bay Area of China

Wang, Ziqian; Xiao, Zunqi; Tam, Chi‐Yung; Wei-juan, Pan; Chen, Ji‐Long; Hu, Chenxi; Ren, Chao; Wei, Wei; Yang, Song

doi:10.1016/j.uclim.2021.100866

Cited by 42 publications

(11 citation statements)

References 48 publications

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“…We hypothesize that Nanling's subtropical forests, characterized by relatively high temperatures (e.g., mean annual temperature=17 • C in our study versus 9.2 • C reported by 82) and intense radiation sit on the right side of the aforementioned peak threshold, thus implying uniform negative relationships between aboveground biomass and temperature and radiation. This particular finding could have significant repercussions for the future dynamics of Nanling's subtropical forests considering that future temperature increases brought by climate change according to global climate models' predictions for this region [85] could imply a reduction in forest basal area and aboveground biomass. However, other past studies in the region (i.e., Guangdong province and the Greater Bay Area) have found seemingly opposite results regarding future climate-forest biomass, carbon storage and productivity relationships, although these results might not be directly comparable to ours given the differences in target variables and input data sources.…”

Section: Discussionmentioning

confidence: 85%

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

González-Rodríguez,

2023

Preprint

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Understanding the spatial variations of forest features and their underlying drivers is crucial for managing and preserving forest ecosystems. Remote sensing techniques, due to their vast spatial coverage, are proving an essential tool for mapping and analyzing the complex spatial patterns of forests characteristics. In the current study, we use high-resolution Gaofen 1 satellite imagery to map features, such as biomass, biodiversity, and habitat type, of the subtropical forests in the Nanling area, a major biodiversity hot spot in the south of China; for subsequently analyzing the association between forest features and environmental drivers. We based our modeling approach on a semi-supervised fully convolutional neural network consisting of a ResNet architecture and a masking and aggregation module for computing predictions for forest survey plots of varied sizes. The fitted convolutional models yielded very high performance statistics, with test R2 values above 0.90 for basal area, aboveground biomass and Shannon diversity index regressions, slightly lower values for the regression of species richness (R2=0.83), and average test classification accuracy of forest types of 0.89, with lower performance for infrequent forest types, such as dwarf forests. The association analysis between predicted maps of forest features and environmental drivers revealed significant correlations being the strongest drivers the altitude, nitrogen content and climate variables, such as mean annual precipitation and temperature. Altitude was strongly associated with dwarf forests and high basal area and high aboveground biomass locations. Basal area and aboveground biomass were also positively influenced by annual rainfall and soil nitrogen and carbon, and negatively by temperature and radiation variables. Species richness was associated with high nitrogen and carbon contents, while the Shannon diversity index showed overall mild correlations with most drivers, with no remarkable patterns. We concluded that our semi-supervised convolutional approach using Gaofen 1 imagery proved useful for modeling the spatial distribution of the subtropical forests in Nanling; however, more research and data collection is necessary to improve the performance of species richness modeling and segmentation of infrequent forest types. Moreover, the strong associations found between climate, and predicted maps of biomass and forest types, provided key insights into the potential repercussions of climate change on Nanling forests.

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Section: Discussionmentioning

confidence: 85%

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

González-Rodríguez,

2023

Preprint

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“…Secondly, the Hong Kong Observatory has recorded daily climate data of the Hong Kong Administrative Region from 1884 to 2022 (the period from 1940 to 1946 is missing), which can analyse the long-term temperature change trend of the Hong Kong Administrative Region. [2,3,4] The International Coupled Model Intercomparison Project (CMIP) was initiated and organised by the Working Group on Coupled Models (WGCM) of the World Climate Research Program (WCRP). To date, WGCM has organised six Model Intercomparison Programs (CMIP1-6).…”

Section: Introductionmentioning

confidence: 99%

Daily temperature prediction with CMIP6 in the historical period of Hong Kong

Chen

2023

Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023)

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An ensemble forecast of historical climate simulations consisting of CMIP6 models was used to project years during World War II for which temperature data were missing for Hong Kong. In multi-model ensemble prediction, since the deep learning in this paper considers that GCM has dependencies between continuous values, the overall effect of prophecy may be better than machine learning. The daily temperature recorded by the Hong Kong Observatory and the extreme temperature index are used to evaluate the daily temperature forecast effect of CMIP6 in the historical period. In the 139-year record span of the Hong Kong Observatory, comparing the forecast results with and without CMIP6, the results show that except for the TN10p and TX10p cold indicators, all other indicators are on the rise, indicating that the temperature is rising.

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“…Generally, the impervious surface in urban areas leads to higher surface temperature and lower evaporation (Bornstein, 1968; Oke, 1988), while complex building structure results in higher surface roughness, elevated temperature, and weaker low‐level wind speed compared to natural surface (Hou et al., 2013; Soltani & Sharifi, 2017). Moreover, the anthropogenic heat (AH) released from buildings, traffic, and human populations can also strengthen the formation of the urban heat island (UHI) effect (Shahmohamadi et al., 2011), which can strongly enhance the urban temperature (Mohajerani et al., 2017; Z. Wang et al., 2021) and leads to lower atmospheric stability (Han & Baik, 2008). This creates an environment conducive to triggering extreme rainfall events (Fung et al., 2021; Holst et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

Urbanization Impacts on Tropical Cyclone Rainfall Extremes‐Inferences From Observations and Convection‐Permitting Model Experiments Over South China

Hu,

Tam,

Loi

et al. 2023

JGR Atmospheres

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How urbanization affects tropical cyclone (TC) rainfall is inferred from station observations over the Great Bay Area (GBA, located on the South China coast) and numerical model experiments. Observations from 41 TCs indicate that surface wind is noticeably weaker in urban compared to rural stations during TC passages, while the urban heat island effect is considerably suppressed. Extreme (99th percentile of) hourly rainfall for these TC events during 2008–2017 is more intense over urban compared to rural stations. For eight selected TC cases, dynamical downscaling was carried out using the convection‐resolving Weather Research and Forecasting model, each with three parallel experiments: “Nourban” in which the urban area was replaced by cropland; “AH0” (“AH300”) in which the diurnal maximum anthropogenic heat (AH) was set to 0 (300 W/m2) in city locations. Both AH0 and AH300 show a significant increase in urban hourly rainfall intensity and probability in all ranges (most obvious for heavy rainfall >40 mm/hr), over the GBA mega‐city. Further diagnosis indicates increased moisture flux convergence in urban areas over a 2‐day period during the landfall, contributes to increased rainfall, likely induced by the surface roughness. The influence of AH, however, is found to be insignificant. The increase in accumulated rainfall due to urbanization is proportional to the storm residence time over the city, implying greater rainfall exacerbation for slower or larger TCs. Overall, urbanization intensifies extreme TC rainfall over the coastal GBA mega‐city mainly due to surface frictional convergence, with stronger intensification for storms residing longer over the city.

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The projected effects of urbanization and climate change on summer thermal environment in Guangdong-Hong Kong-Macao Greater Bay Area of China

Cited by 42 publications

References 48 publications

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

Daily temperature prediction with CMIP6 in the historical period of Hong Kong

Urbanization Impacts on Tropical Cyclone Rainfall Extremes‐Inferences From Observations and Convection‐Permitting Model Experiments Over South China

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