Local Climate Change Induced by Urbanization on a South China Sea Island

The South China Sea is a hotspot for regional climate research.Over the past 40 years,considerable improvement has been made in the development and utilization of the islands in the South China Sea,leading to a substantial change in the land-use of the islands.However,research on the impact of human development on the local climate of these islands is lacking.This study analyzed the characteristics of local climate changes on the islands in the South China Sea based on data from the Yongxing Island Observation Station and ERA5 re-analysis.Furthermore,the influence of urbanization on the local climate of the South China Sea islands was explored in this study.The findings revealed that the 10-year average temperature in Yongxing Island increased by approximately 1.11℃from 1961 to 2020,and the contribution of island development and urbanization to the local warming rate over 60 years was approximately 36.2%.The linear increasing trend of the annual hot days from 1961–2020 was approximately 14.84 days per decade.The diurnal temperature range exhibited an increasing trend of 0.05℃per decade,whereas the number of cold days decreased by 1.06days per decade.The rapid increase in construction on Yongxing Island from 2005 to 2021 led to a decrease in observed surface wind speed by 0.32 m s^(-1)per decade.Consequently,the number of days with strong winds decreased,whereas the number of days with weak winds increased.Additionally,relative humidity exhibited a rapid decline from 2001 to 2016 and then rebounded.The study also found substantial differences between the ERA5 re-analysis and observation data,particularly in wind speed and relative humidity,indicating that the use of re-analysis data for climate resource assessment and climate change evaluation on island areas may not be feasible.

Spatiotemporal Evolution Characteristics of Urban Land Surface Temperature Based on Local Climate Zones in Xi’an Metropolitan,China

Local climate zones(LCZs)are an effective nexus linking internal urban structures to the local climate and have been widely used to study urban thermal environment.However,few studies considered how much the temperature changed due to LCZs transformation and their synergy.This paper quantified the change of urban land surface temperature(LST)in LCZs transformation process by combining the land use transfer matrix with zonal statistics method during 2000–2019 in the Xi’an metropolitan.The results show that,firstly,both LCZs and LST had significant spatiotemporal variations and synchrony.The period when the most LCZs were converted was also the LST rose the fastest,and the spatial growth of the LST coincided with the spatial expansion of the built type LCZs.Secondly,the LST difference between land cover type LCZs and built type LCZs gradually widened.And LST rose more in both built type LCZs transferred in and out.Finally,the Xi’an-Xianyang profile showed that the maximum temperature difference between the peaks and valleys of the LST increased by 4.39℃,indicating that localized high temperature phenomena and fluctuations in the urban thermal environment became more pronounced from 2000 to 2019.

An Observational Study on the Local Climate Effect of the Shangyi Wind Farm in Hebei Province

Zhangjiakou is an important wind power base in Hebei Province,China.The impact of its wind farms on the local climate is controversial.Based on long-term meteorological data from 1981 to 2018,we investigated the effects of the Shangyi Wind Farm(SWF)in Zhangjiakou on air temperature,wind speed,relative humidity,and precipitation using the anomaly or ratio method between the impacted weather station and the non-impacted background weather station.The influence of the SWF on land surface temperature(LST)and evapotranspiration(ET)using MODIS satellite data from 2003 to 2018 was also explored.The results showed that the SWF had an atmospheric warming effect at night especially in summer and autumn(up to 0.95℃).The daytime air temperature changes were marginal,and their signs were varying depending on the season.The annual mean wind speed decreased by 6%,mainly noted in spring and winter(up to 14%).The precipitation and relative humidity were not affected by the SWF.There was no increase in LST in the SWF perhaps due to the increased vegetation coverage unrelated to the wind farms,which canceled out the wind farm-induced land surface warming and also resulted in an increase in ET.The results showed that the impact of wind farms on the local climate was significant,while their impact on the regional climate was slight.

Impact of Wetland Change on Local Climate in Semi-arid Zone of Northeast China

Wetlands are sensitive to climate change, in the same time, wetlands can influence climate. This study analyzed the spa- rio-temporal characteristics of wetland change in the semi-arid zone of Northeast China from 1985 to 2010, and investigated the impact of large area of wetland change on local climate. Results showed that the total area of wetlands was on a rise in the study area. Although natural wetlands （marshes, riparians and lakes） decreased, constructed wetlands （rice fields） increased significantly, and the highest in- crease rate in many places exceeded 30%. Anthropogenic activities are major driving factors for wetland change. Wetland change pro- duced an impact on local climate, mainly on maximum temperature and precipitation during the period of May-September. The increase （or decrease） of wetland area could reduce （or increase） the increment of maximum temperature and the decrement of precipitation. The changes in both maximum temperature and precipitation corresponded with wetland change in spatial distribution. Wetland change played a more important role in moderating local climate compared to the contribution of woodland and grassland changes in the study area. Cold-humid effect of wetlands was main way to moderating local climate as well as alleviating climatic wanning and drying in the study area, and heterogeneity of underlying surface broadened the cold-humid effect of wetlands.

Simulated Influence of Mountainous Wind Farms Operations on Local Climate

Renewable energy sources,especially wind power,were believed to be able to slow down global warming;however,evidence in recent years shows that wind farms may also induce climate change.With the rapid development of wind power industry,the number of wind farms installed in mountains has gradually increased.Therefore,it is necessary to study the impact of wind farms in mountainous areas on local climate.The Suizhou and Dawu wind farms in northern Hubei Province were chosen for the present study on the impact of wind farm operations on the local climate in mountainous areas.The mesoscale meteorological numerical model Weather Research and Forecasting Model(WRF)and the Fitch model,together with turbulence correction factor,were used to simulate wind farm operations and study their effects on local climate.The results showed the characteristics of wind speed attenuation in mountainous wind farms:the amplitude and range of wind speed attenuation were stronger in the nighttime than in the daytime,and stronger in summer than in winter.The surface temperature increased and became more significant in summer.However,a cooling variation was observed above the surface warming center.The height of this center was higher in the daytime than it was in the nighttime.The latent heat flux in the wind farms decreased at night,accompanied by an increase in sensible heat flux.However,these changes were not significant.Some differences were observed between the impact of wind farms on the climate in the plains and the mountains.Such differences are more likely to be related to complex terrain conditions,climate conditions,and the density of wind turbines.The present study may provide support for the development and construction of wind farms in mountainous areas.

Impact of Concentration Levels of Atmospheric Pollutants on Local Climate of Delta State, Nigeria

Studies in various regions of the world have revealed that air pollution can have a significant influence on local climate. This study therefore considers the impact of concentration levels of atmospheric pollutants on local climate of Delta state, Nigeria. Monthly and annual averaging of the daily pollutant concentrations and meteorological parameters within the period of investigation was carried out. Descriptive Statistics, correlation analysis, coefficient of determination (R2) analysis and least squares regression analysis of the selected meteorological parameters with CH4 and O3 concentrations for the period of 2003 to 2012 and NO2 and CO2 concentrations for the period of 2011 to 2014 were carried out. The regression relationship was then used to obtain predicted values for the meteorological parameters within the period of investigation. The results of the descriptive statistics of annual averages of CH4, O3, NO2 and CO2 concentrations within the period of investigation revealed that the emission levels breached FEPA and EGASPIN limits. The results of the correlation analysis indicated that CO2 had a strong significant positive correlation with temperature with a correlation coefficient of 0.962, while a moderate negative correlation coefficient of 0.549 was obtained for CH4, and very weak correlation coefficients of -0.167 and 0.077 were obtained for O3 and NO2 respectively. CH4, O3 and CO2 had a moderately significant positive correlation with solar radiation with correlation coefficients of 0.661, 0.571 and 0.656 respectively, while a weak negative correlation coefficient of 0.106 was obtained for NO2. CH4 had a strong significant positive correlation with relative humidity with a correlation coefficient of 0.859, while moderate correlation coefficients of -0.516 and 0.646 were obtained for NO2 and CO2 respectively, and a weak correlation coefficient of 0.345 was obtained for O3. CO2 and CH4 had a strong significant correlation with wind speed with correlation coefficients of 0.951 and -0.906 respectively, while a moderate negative correlation coefficient of 0.518 was obtained for O3, and a weak negative correlation coefficient of 0.317 was obtained for NO2. The predicted values of the meteorological parameters showed a significant level of agreement with their measured values. Therefore, among the atmospheric pollutants postulated as influencing meteorological parameters, CO2 appears to be the most strongly significant in explaining temperature variations in this region of Niger Delta, with correlation coefficient of 96.2% and coefficient of determination (R2) of 0.926, implying that CO2 influenced 92.6% variation in temperature in this part of Niger Delta within the period of investigation.

Impact of Local Climate Zones on the Urban Heat and Dry Islands in Beijing:Spatial Heterogeneity and Relative Contributions

Based on the building height and density data on a 100-m resolution,hourly 2-m temperature and humidity data at83 automatic weather stations,and gridded local climate zone(LCZ)data on a 120-m resolution in urban Beijing in2020,this study first employs the semivariogram combined with building parameters to calculate spatial correlations and has identified an LCZ grid resolution of 500 m suitable for best usage of the available observation data.Then,how the spatially heterogeneous LCZs affect and contribute to the canopy urban heat island intensity(UHII)and urban dry island intensity(UDII)are quantitatively investigated.It is found that UHII is high in winter and low in summer with a unimodal diurnal variation while UDI is low in winter but high in summer with a bimodal diurnal variation.The LCZ with compact mid-rise(open high-rise)buildings exhibits the highest UHII(UDII),followed by the compact high-rise(compact low-rise),while the LCZ of scattered trees presents both the lowest UHII and the lowest UDII.The most significant difference in the UHII(UDII)among the nine LCZ types in the urban area of Beijing is2.62℃(1.1 g kg^(-1)).Area-weighted averaging analysis reveals that the open mid-rise LCZ is the most significant contributor to the UHII(UDII),immediately followed by compact mid-rise(open low-rise),with the least contribution from bare rock or paved(scattered trees).The results also indicate that beyond the intrinsic physical properties of the LCZs of a city,their area proportions cannot be overlooked in evaluating their impact on the UHI and UDI.These quantitatively findings could help urban planners to create a livable urban climate and environment by adjusting the relevant land use.

Mapping urban carbon emissions in relation to local climate zones:Case of the building sector in Bangkok Metropolitan Administration,Thailand

This study focuses on carbon emissions of the building sector in relation to local climate zone(LCZ)classification,concentrating on two major parts.First,we estimated carbon emissions in the building sector,which were cal-culated for weekdays and weekends real-time daily energy consumption patterns.The estimations were divided into direct(from petroleum products consumption)and indirect emissions(from electricity consumption).Sec-ond,we examined urban carbon emissions mapping in relation to LCZ.Bangkok Metropolitan Administration(BMA)was used as the case study and 2016 as the base year for examination.The results illustrate that indirect emissions in Bangkok can be up to ten times higher than direct emissions.The analysis indicates that LCZ,such as compact high-rise,large low-rise,light industry,and warehouse zones had a relatively higher carbon emission intensity than others.Additionally,we identified that the compact high-rise zone has the highest indirect emission intensity,while the light industry and warehouse zone have the greatest direct emission intensity.These results provide insights into the dynamics of carbon emission characteristics in the building sector and the methodology purported here can be used to support low carbon city planning and policymaking in Bangkok.

Local climate zone mapping using remote sensing:a synergetic use of daytime multi-view Ziyuan-3 stereo imageries and Luojia-1 nighttime lightdata

The local climate zone(LCZ)scheme has been widely utilized in regional climate modeling,urban planning,and thermal comfort investigations.However,existing LCz classification methods face challenges in characterizing complex urban structures and human activities involving local climatic environments.In this study,we proposed a novel LCZ mapping method that fully uses space-borne multi-view and diurnal observations,i.e.daytime Ziyuan-3 stereo imageries(2.1 m)and Luojia-1 nighttime light(NTL)data(130 m).Firstly,we performed land cover classification using multiple machine learning methods(i.e.random forest(RF)and XGBoost algorithms)and various features(i.e.spectral,textural,multi-view features,3D urban structure parameters(USPs),and NTL).In addition,we developed a set of new cumulative elevation indexes to improve building roughness assessments.The indexes can estimate building roughness directly from fused point clouds generated by both along-and across-track modes.Finally,based on the land cover and building roughness results,we extracted 2D and 3D USPs for different land covers and used multi-classifiers to perform LCZ mapping.The results for Beijing,China,show that our method yielded satisfactory accuracy for LCZ mapping,with an overall accuracy(OA)of 90.46%.The overall accuracy of land cover classification using 3D USPs generated from both along-and across-track modes increased by 4.66%,compared to that of using the single along-track mode.Additionally,the OA value of LCZ mapping using 2D and 3D USPs(88.18%)achieved a better result than using only 2D USPs(83.83%).The use of NTL data increased the classification accuracy of LCZs E(bare rock or paved)and F(bare soil or sand)by 6.54%and 3.94%,respectively.The refined LCZ classification achieved through this study will not only contribute to more accurate regional climate modeling but also provide valuable guidance for urban planning initiatives aimed at enhancing thermal comfort and overall livabillity in urban areas.Ultimately,this study paves the way for more comprehensive and effective strategies in addressing the challenges posed by urban microclimates.

Enhanced geographic information system-based mapping of local climate zones in Beijing, China

The vague urban-rural dichotomy severely restricts effective comparisons and communications among urban heat island studies.A local climate zone(LCZ) scheme has therefore been developed to classify urban and natural landscapes in a standardized and universal manner. Despite LCZ mapping efforts in worldwide cities, this study attempts to propose an enhanced geographic information system-based workflow to enable the hierarchical classification of LCZs with fewer indicators but higher accuracies while considering supplementary classes and subclasses. Specifically, five morphological and coverage indicators that were easily obtained and well differentiated among LCZs were derived from a city street map and satellite images, and 25 LCZs(including 16 standard, 3 supplementary, and 6 sub-classified zones) were determined at a block-level according to the indicator hierarchy and criteria. The method was performed over Beijing, China, and evaluations by field surveys and google earth images showed a high accuracy with little noise and sharp boundaries, outperforming the widely-used remote sensing-based method of the World Urban Database and Access Portal Tools, particularly in terms of building height and heavy industry. Results also demonstrate that the Beijing core was dominated by open(including extremely open) mid-rise buildings(28.7%) and open lowrise buildings(12.8%), forming an inner-low-middle-high-outer-low annular building-height pattern. Significant land surface temperature differences were detected among the LCZs, where the low-rise and compact LCZs had higher temperatures than the mid-/high-rise and open LCZs during daytime, and subclasses LCZ XB/C/D(LCZ XE/F) generated lower(higher) temperatures than their parent classes in May. This method was proposed to augment the LCZ mapping system and further support applications(e.g., urban planning/management and climate/weather modeling) in high-density cities similar to Beijing.

Numerical Simulation Experiment of Land Surface Physical Processes and Local Climate Effect in Forest Underlying Surface

Based on the basic principles of atmospheric boundary layer and plant canopy micrometeorology, a forest underlying surface land surface physical process model and a two-dimensional atmospheric boundary layer numerical model are developed and numerical simulation experiments of biosphere and physiological processes of vegetation and soil volumetric water content have been done on land surface processes with local climate effect. The numerical simulation results are in good agreement with realistic observations, which can be used to obtain reasonable simulations for diurnal variations of canopy temperature, air temperature in canopy, ground surface temperature, and temporal and spatial distributions of potential temperature and vertical wind velocity as well as relative humidity and turbulence exchange coefficient over non-homogeneous underlying surfaces. It indicates that the model developed can be used to study the interaction between land surface process and atmospheric boundary layer over various underlying surfaces and can be extended to local climate studies. This work will settle a solid foundation for coupling climate models with the biosphere.

Mapping and analyzing the local climate zones in China’s 32 major cities using Landsat imagery based on a novel convolutional neural network

The Local Climate Zone(LCZ)scheme provides researchers with a standard method to monitor the Urban Heat Island(UHI)effect and conduct temperature studies.How to generate reliable LCZ maps has therefore become a research focus.In recent years,researchers have attempted to use Landsat imagery to delineate LCZs and generate maps worldwide based on the World Urban Database and Access Portal Tools(WUDAPT).However,the mapping results obtained by the WUDAPT method are not satisfactory.In this paper,to generate more accurate LCZ maps,we propose a novel Convolutional Neural Network(CNN)model(namely,LCZ-CNN),which is designed to cope with the issues of LCZ classification using Landsat imagery.Furthermore,in this study,we applied the LCZ-CNN model to generate LCZ mapping results for China’s 32 major cities distributed in various climatic zones,achieving a significantly better accuracy than the traditional classification strategies and a satisfactory computational efficiency.The pro-posed LCZ-CNN model achieved satisfactory classification accuracies in all 32 cities,and the Overall Accuracies(OAs)of more than half of the cities were higher than 80%.We also designed a series of experiments to comprehensively analyze the proposed LCZ-CNN model,with regard to the transferability of the network and the effectiveness of multiseasonal information.It was found that the first convolutional stage,corresponding to low-level features,shows better transferability than the second and third convolutional stages,which extract high-level and more image-or task-oriented features.It was also confirmed that the multi-seasonal information can improve the accuracy of LCZ classifica-tion.The thermal characteristics of the different LCZ classes were also analyzed based on the mapping results for China’s 32 major cities,and the experimental results confirmed the close relationship between the LCZ classes and the magnitude of the Land Surface Temperature(LST).

Deep learning-based local climate zone classification using Sentinel-1 SAR and Sentinel-2 multispectral imagery

As a newly developed classification system,the LCZ scheme provides a research framework for Urban Heat Island(UHI)studies and standardizes the worldwide urban temperature observa-tions.With the growing popularity of deep learning,deep learning-based approaches have shown great potential in LCZ mapping.Three major cities in China are selected as the study areas.In this study,we design a deep convolutional neural network architecture,named Residual combined Squeeze-and-Excitation and Non-local Network(RSNNet),that consists of the Squeeze-and-Excitation(SE)block and non-local block to classify LCZ using freely available Sentinel-1 SAR and Sentinel-2 multispectral imagery.Overall Accuracy(OA)of 0.9202,0.9524 and 0.9004 for three selected cities are obtained by applying RSNNet and training data of individual city,and OA of 0.9328 is obtained by training RSNNet with data from all three cities.RSNNet outperforms other popular Convolutional Neural Networks(CNNs)in terms of LCZ mapping accuracy.We further design a series of experiments to investigate the effect of different characteristics of Sentinel-1 SAR data on the performance of RSNNet in LCZ mapping.The results suggest that the combination of SAR and multispectral data can improve the accuracy of LCZ classification.The proposed RSNNet achieves an OA of 0.9425 when integrat-ing the three decomposed components with Sentinel-2 multispectral images,2.44%higher than using Sentinel-2 images alone.

Climate,Ecosystem and Migration:The Three Gorges Dam Study

The Three Gorges Dam,known as one of the biggest project items throughout the history,is a milestone for the development of China.Since it was completed in 2006,the dam has been persistently supplying ample electricity to the southern and eastern part of China.Despite its impressive output and benefits notwithstanding,we have to objectively pay our attention to the long-term impact that the Three Gorges Dam leaves to the place where we live,especially the influence on the climate,the ecosystem and the migration pattern of the reservoir area.

Climate Change and Geochemistry May Explain the Finding of Marine Cyanobacteria in a Continental Saline Lake

1 Introduction Alkaline Lake Khilganta located in a steppe zone of South Siberia(N 50°42’535,E 115°06’086)is unique among adjacent lakes because of a development of a thick(up to 3 cm)cyanobacterial mat dominated by

Perception of Post-Secondary Students on Environmental Practices in Selected Communities in the Philippines: Implications to Climate Change Action

Climate change contributes to disasters in the Philippines. Most human activities have had negative consequences on the environment, exacerbating global warming. Humans contribute to climate change and global warming by burning fossil fuels, cutting down trees, engaging in improper waste disposal, using electricity, and driving a car. This study assessed the environmental practices of communities in Nueva Ecija, Philippines, and their implications for climate change. Respondents were selected using convenient sampling. A questionnaire delivered online was used to elicit their responses then analyzed the data using SPSS. This study revealed that most post-secondary students do not litter but sometimes burn their trash in an open dump. Most of them used LPG as their primary source of fuel for cooking. Rice production is always the same as producing food waste in food production. Garden waste was sometimes produced. Plastic containers are commonly produced as recyclable wastes. They often dispose of wastes in controlled and regulated open dumpsites by their municipality or city. Due to the pandemic, special wastes like face masks and face shields are disposed of daily. Generally, despite no littering behavior, the respondents never burned their trash or threw it on any body of water. They perceived to disagree that these daily community activities contribute to climate change. Lastly, less than half of them affirmed that there are initiative programs at the barangay level to lessen and eliminate community activities that cause climate change.

Changing Characteristics of Urban Heat Island Effect in Weihai City

Based on the local climate zoning theory and the observation data of hourly temperature of 22 automatic weather stations from 2012 to 2021, K-means clustering algorithm was used to analyze the daily, monthly, seasonal, annual and spatial variation characteristics of urban heat island effect in Weihai City in the past 10 years. The results showed that in recent 10 years, the average urban heat island intensity was 1.24 ℃, showing a gradual weakening trend of -0.169 3 ℃/10 a;the summer average heat island intensity was 0.86 ℃, showing a gradual weakening trend of -0.047 5 ℃/10 a. The heat island intensity had obvious diurnal variation characteristics, that is, "it was weak in the day and strong at night". In terms of seasonal variation, heat island effect was the weakest in summer, stronger in spring and autumn, and the strongest in winter. The diurnal, seasonal and annual changes of heat island intensity showed a reverse trend to those of temperature. The high-value area of urban heat island was highly consistent with human residential activity areas and industrial and commercial intensive areas, and the extension trend of heat island intensity was the same as the direction of urban development and construction. The "cold island phenomenon" in some offshore areas was related to the geographical location, terrain and the southeast monsoon trend in summer. The adverse effects of urban heat island effect can be reduced by optimizing the types and distribution of vegetation communities, rationally planning and constructing urban water body, promoting green building materials and adjusting shape design, etc.

Five different types of lowered snow line in alpine valleys

The snow line for the same precipitation event in alpine valleys can vary greatly from valley to valley, even when the valleys are close together. Often this is not due to a difference in air mass, but to a different interaction of topography and topographyrelated meteorological conditions. By knowing the causes of a lowered snow line, we can better understand the differences in snow cover in alpine valleys at the same elevation over short distances or the differences in snow cover between alpine valleys and areas outside the valleys. As different types of lowered snow line respond differently to climate change, we can also understand future changes in snow conditions in alpine valleys. To better understand the process, we observed the temperature conditions during winter precipitation events on the territory of Eastern Julian Alps(Slovenia) over a period of two years, focusing on the height of the zero isotherm. Depending on atmospheric conditions, especially changes in the temperature profile and precipitation intensity during precipitation events, five causes of lowered snow line have been identified. These types can occur individually, but during multi-day snowfall events they occur frequently or in combination.

Cold-humid effect of Baiyangdian wetland

Data support for wetland protection function evaluation can be provided by quantitatively analyzing the ability of regulating the regional climate of the wetland ecosystem. In this study, the cold-humid effect of the Baiyangdian wetland was analyzed by comparing the meteorological conditions of the Baiyangdian wetland and its surrounding areas. Meanwhile, the regulatory functions of the Baiyangdian wetland for the processes and magnitudes of temperature and relative humidity from August to October 2008 were evaluated. The results show that daily mean temperatures in the Baiyangdian wetland were lower than in the surrounding areas, and that temperature differences mainly occurred in the daytime but were not obvious at night. Diurnal temperature ranges in the Baiyangdian wetland were lower than in the surrounding areas, and the higher the diurnal temperature range in the surrounding areas was, the stronger the regulation ability of the Baiyangdian wetland. Compared with the surrounding areas, the decline of the daily minimum temperature in the Baiyangdian wetland was gentler, and the mean relative humidity there was higher. The present findings indicate that effects of the Baiyangdian wetland on climate and humidity regulation are significant.

Mapping the urban natural ventilation potential by hydrological simulation

Urban wind environments are closely related to air pollution and outdoor human comfort.The urban natural ventilation potential(NVP)is an important factor in urban planning and design.However,for ventilation studies on urban scales,neither macroscale numerical simulations(i.e.,WRF,MM5,etc.)nor microscale computational fluid dynamics(CFD)simulations can conduct efficient analyses.Based on the similarity between water flows and airflows,an efficient approach is proposed in this paper to map the urban NVP.Through integrating the urban terrain model,urban form model,and prevailing wind pressure model,an airflow digital elevation model(AF-DEM),which represents the resistance to airflow and can be used for a hydrological simulation,is generated and applied to evaluate the urban airflow patterns under different terrain,urban form and ambient wind conditions.The objective was to develop a simulation platform that can efficiently predict the distribution of natural ventilation corridor and NVP.The stream network calculated through the simulation is regarded as potential ventilation corridors within the city,and an index calculated from the coverage rate of wind corridors(CRW)is proposed for evaluating the relative NVP.Taking Nanjing as a case study,8 AF-DEMs based on different wind directions and wind speed conditions are generated,and their corresponding ventilation corridor maps are constructed.The results are in good agreement with the empirical evidence,indicating that the hydrological model,though a rudimentary approximation of the actual airflows,was effective in revealing the natural ventilation corridor and characterize the relative NVP.Moreover,the implementation of this novel method is simple and convenient,and it has great application potential and value in urban design and management.