Urban Database Research Articles

Digital-twin-based multi-scale simulation supports urban emergency management: a case study of urban epidemic transmission

ABSTRACT Improving emergency management capabilities is vital for sustainable cities. However, urban emergency management is a complex, giant system that interacts between urban virtual and real spaces, requiring simulation in the virtual space and feedback to the real space. The digital twin model can potentially play an important role in this process, but still faces significant challenges in terms of modeling, simulation, and visualization. In this study, we propose a digital-twin-based multi-scale simulation method and take epidemic transmission as a case study. First, an entity-oriented unified urban database integrating multi-source urban data was constructed as the digital twins of urban human settlements for emergency modeling. Then, a multi-scale urban emergency simulation method in 3D geographic environment was proposed to support emergency simulation for multi-level urban management units. Moreover, an adaptive fusion rendering of emergency information and 3D scene was proposed to support the refinement of emergency visualization. An experimental case study on epidemic risk estimation was conducted through thousands of outbreak scenarios. The results demonstrate that the proposed method can establish the digital twins of urban human settlements, where modeling, simulation, and visualization of emergency at multi-scales can be carried out, thus enhancing the efficacy and efficiency of emergency management.

Assessing the Relationship between Urban Heat Islands and Local Climate Zones during a Winter Period in the Coastal City of Balneário Camboriú/SC, Brazil

This research seeks to understand the link between urban heat island and urban cool island, which are the Local Climatic Zones (LCZ) and atmospheric systems during the winter season in the city of Balneário Camboriú, Southern Brazil. First, meteorological data on the urban environment was collected at 11 permanent points in the Balneário Camboriú metropolitan region. Next, a synoptic analysis of the dates was performed to understand the atmospheric systems operating in the region. Finally, the LCZs map created for the city in the World Urban Database and Access Portal Tools was used to correlate the magnitudes of the heat and cool islands found in Balneário Camboriú in the winter period. The results indicate that the increasing verticalization as a result of the construction of skyscrapers in Balneário Camboriú has a significant influence on local conditions for the occurrence of heat and cold islands. The findings indicate that LCZs with sparsely distributed buildings (LCZs 6, 8 and 9) and LCZs with dense vegetation (LCZ A) have lower intensity magnitudes of heat and cool conditions. The biggest magnitudes of heat and cool islands were reported in LCZs 1 and 3 during the timeframe. The synoptic analysis supports earlier research that points to atmospheric stability (Anticyclonic domain) as a favorable atmospheric setting for the emergence of urban heat and coolness islands.

Mapping local climate zones and its applications at the global scale: A systematic review of the last decade of progress and trend

The concept of local climate zone (LCZ) was proposed in 2012 to meet the needs of refinement and standardization of urban climate research. As an important contribution of the World Urban Database and Access Portal Tool (WUDAPT), which is an initiative to collect and share urban data globally to facilitate urban-focused climate researches, LCZ research has shown a booming trend in the past decade. As of the end of October 2023, more than 400 related papers, which involve about 250 cities in the world, have been published. At this juncture (decadal anniversary of the WUDAPT), a review from a global perspective and outlooks of the LCZ framework, from mapping to application and its coordinated development with WUDAPT, are needed to support the popularity and further progress of LCZ and WUDAPT. In this review, statistical and metadata analysis methods were used to summarize and analyze a sample of LCZ-related papers published since 2012, focusing on two aspects: mapping and application. The mapping methods were divided into three categories, namely, remote sensing-based, geographic information system-based, and hybrid methods. The application topics were grouped into two categories, namely, WUDAPT initiative and novel WUDAPT application. Two relevant themes originated from the above summary were further discussed, and then the future was forecasted on the basis of current research limitations. From this review, researchers can benefit from the recent progress in mapping and application of the LCZ system and future research directions, thereby further promoting the development in this field.

High-resolution urban climate simulations for heat and health applications in Philadelphia

Stakeholders need high-resolution urban climate information for city planning and adaptation to climate risks. Climate models have too coarse a spatial resolution to properly represent cities at the relevant scale, and downscaled products often fail to account for urban effects. We propose here a methodological framework for producing high-resolution urban databases that are used to drive the SURFEX-TEB land surface and urban canopy models. A historical simulation is carried out over the period 1991–2020, based on a reanalysis of the city of Philadelphia (Pennsylvania, USA). The simulation is compared with observations outside and inside the city, as well as with a field campaign. The results show good agreement between the model and observations, with average summer biases of only −1 °C and + 0.8 °C for daily minimum and maximum temperatures outside the city, and almost none inside. The simulation is used to calculate the maximum daily heat index (HIX) and to study emergency heat alerts. The HIX is slightly overestimated and, consequently, the model simulates too many heat events if not bias corrected. Overall, HIX conditions at Philadelphia International Airport are found to be suitable proxies for city-wide summer conditions, and therefore are appropriate to use for emergency heat declarations.

Urbanization impact on meteorological condition and O3 concentration under past and future climates scenarios over the Greater Bay Area in Southern China

Cities face twin challenges of changing climate and urban heat island (UHI) effect, with the environmental implications of urbanization still unclear. This study employs the Weather Research and Forecasting (WRF) model to examine the impacts of urbanization in the Greater Bay Area (GBA) during warm and cold seasons in past (2000) and near-future (2030) scenarios [i.e., under shared socioeconomic pathways (SSP): SSP1-26, SSP2-45, and SSP5-85]. Datasets from the World Urban Database and Access Portal Tools (WUDAPT) were incorporated into the WRF model to improve the representation of urbanization effects on weather patterns. Our findings indicate that urban expansion significantly increases urban temperatures and decreases wind speed across selected climate change scenarios. The spatially averaged urban temperatures in the fine resolution domain for 2030 could rise by 0.9 °C (warm season) and 2.4 °C (cold season), respectively, and urban wind speed decreases by ∼ 3 m/s under the SSP5-85 scenario. In cold season, the UHI effect could last over 20 h, while urban area may experience cooler nighttime temperatures than rural areas in warm season. Moreover, the future scenario predicts higher daytime O3 levels due to the warming effects of climate change and urbanization, but lower nighttime levels in warm season, attributed to intensified south-easterly sea breeze and background winds, when compared to the past scenario. This study highlights the importance of incorporating urbanization and climate change in future urban atmospheric environment studies, and underscores that urban climate change adaptation and mitigation should consider extra impacts on built-environment caused by urbanization.

Aerial-terrestrial data fusion for fine-grained detection of urban clues

Those who work in the design, development, and management of cities are often limited by the scarcity of data. Particularly in the Global South, urban databases may be insufficient, out of date, or simply not available. However, digital technology is making it possible to fill gaps and build substantial datasets using “urban clues,” or attributes, gathered in high-resolution imagery by sky- and street-based cameras. Aided by machine learning, it is possible to detect specific building characteristics (purpose, condition, size, material, and construction)—yielding an array of geolocated details about the built environment. The resulting composite view can be made available, as we have done, in an open-source portal for use in urban management. The insights gained in this way may help address common urban management challenges, such as locating homes vulnerable to hazards such as flooding or earthquakes, identifying urban sprawl and informal housing, prioritizing infrastructure investments, and guiding public program support. This approach has been applied in Colombia, Guatemala, Indonesia, Mexico, Paraguay, Peru, St Lucia, and St Maarten.

Urban growth’s implications on land surface temperature in a medium-sized European city based on LCZ classification

The study determined the influence of changes in land use and land cover (LULC) on land surface temperature (LST) over a 33-year period based on a medium-sized European city (Poznań, Poland). The LST was estimated from Landsat 5, 8 and Terra (MOD11A2v6) satellites. The local estimation of climate patterns was based on the Local Climate Zones (LCZ) classification utilised with the methodology proposed by the World Urban Database and Access Portal Tools (WUDAPT). Moreover, the Copernicus’ imperviousness density product (IMD) was used. Between 2006 and 2018 the area with IMD of 41–100% increased by 6.95 km2, 0–20% decreased by 7.03 km2. The contribution of built-up LCZs increased by 7.4% (19.21 km2) between 1988 and 2021 reaching 13% (34 km2) within open mid-rise LCZ. Due to urbanisation and reforestation, low plants LCZ shrunk by 12.7%. For every 10% increase in IMD, LST increases by up to 0.14 °C. Between 1988 and 2021 the LSTm in specific LCZs rose from 1.52 up to 2.97 °C. As per LST models LCZ change from natural to built-up led up to 1.19 °C LST rise. The increase of the LSTm was registered even when the LCZ remained unchanged.

Numerical investigation of thermal patterns and local wind circulations to characterize Urban Heat Island during a heatwave in Turin

Heatwaves are annually increasing in terms of intensity and frequency. In urban areas, the impact could be further exacerbated by the Urban Heat Island (UHI) effect. This work carries out an urban microclimate numerical analysis to evaluate the behavior of the UHI in the city of Turin (Italy) during a heatwave event. Turin is located in the North-West region of Italy, boarded by the Alps mountain ranges in the west and hills in the East. The study utilizes the WRF/MLUCM model and considers the heatwave period in June 2019. The high-resolution urban land use/land cover data is generated from local climate zone (LCZ) generator tool provided by the World Urban Database and Access Portal Tools (WUDAPT) portal. The simulation is validated with the data provided by ARPA (Regional Environmental Protection Agency) meteorological stations located over the region. The lower root mean squared error of air temperature and higher index of the agreement show that the simulation is in good agreement with the observational data. The simulation results are then used to analyze the rural-urban temperature distributions over the diurnal cycle and local wind circulations. According to the simulated results, the city of Turin has higher near-surface UHI intensity during the night and early morning (with maximum intensity greater than 5 °C), whereas the intensity is diminishing during the mid-day hours. The simulated wind patterns show a significant reduction in wind intensity over the urban areas. This reveals the potential of creating wind ventilation inside the city to minimize UHI effect and enhance air quality. Ultimately, this research contributes valuable insights into addressing the challenges posed by heatwaves and urban heat islands in Turin and offers a foundation for the development of sustainable urban climate strategies.

Classifying heterogeneous urban form into local climate zones using supervised learning and greedy clustering incorporating Landsat dataset

The concept of Local climate zone (LCZ) is used to generate LCZ maps worldwide as a part of World Urban Database and Access Portal Tools (WUDAPT) project utilizing Landsat imagery. Though WUDAPT has become the standard for LCZ mapping, it has certain limitations: 1) its outcome for heterogeneous cities is less accurate. 2) Due to fine granularity and huge-sized imagery, validating WUDAPT outcome becomes difficult for an urban expert. To overcome these, we have presented the LCZ classification and greedy clustering framework for heterogeneous cities (Nagpur (India)). Three classification schemes based on random forest, deep neural network, 1D-convolutional neural network and, subset ensemble are proposed. The overall accuracies are respectively 88%, 86%, 82%, 88%. Contextual information (mean, minimum, maximum, median, 25th, and 75th quantile values) at 90 m resolution is adopted while classification to handle spatial heterogeneity. Adding contextual information increases the overall accuracy by 4–6%, urban and mixed classes' accuracy by 4–7% and, 3–4% respectively. Further, clustering groups the spatially connected but similar pixels (classified Landsat) using breadth-first search concept and deque data structure creating LCZ map. Such LCZ map is helpful for an urban expert to validate correctness, select suitable level of granularity, visualize heterogeneity and suggest improvements.

Cooling wisdom of ‘water towns’: How urban river networks can shape city climate?

‘Water town’, a city design idea featuring buildings sitting along rivers and their associated riparian vegetation, serves as natural refuges for citizens to escape the dual challenges of heatwaves and urban heat islands. We investigated the multi-scale cooling effect of river networks in Shanghai, a typical modern city widely known as an assemblage of water towns. At the neighborhood scale, we conducted a series of field experiments along various rivers with different characteristics during the 2022 heatwave periods in central Shanghai. At the city scale, we first quantified the cooling impact of urban river networks by integrating remotely sensed data and fine-resolution urban river network maps. We found that the width of rivers, coverage ratio, density, and morphology of river networks are the key factors affecting the cooling potential. The confluence or proximity of river tributaries can also bring an enhanced cooling effect than standalone ones. Rivers were found to be all-day cool spots compared to adjacent city streets, with greater daytime cooling intensity (1–3 °C) than at night (0–0.6 °C). Compared to vegetation which has more spatial coverage within the city, rivers are constrained by a linear shape along certain paths but possess the ability to flow, which is beneficial for cooling by carrying cool water from upstream parks and offering wind corridors. The atmospheric cooling capability of 20% of river coverage is equivalent to 30% of tree canopy coverage in a neighborhood. The city-scale cooling effect yields a 36.9% cooling area with a maximum cooling extent of 5.5 °C. The disparities in cooling effect between river networks and standalone rivers primarily manifest in confluence areas and the reinforced cooling within dense river networks. Each additional confluence in local river networks leads to a mean increase of 3% in cooling area and 0.01 °C cooling intensity. Our study implies that urban watercourses cannot be neglected in urban climatic studies and should be incorporated into a new conceptualization of water-included urban local climate zone classifications in the world urban database.

Remote Sensing-Based Outdoor Thermal Comfort Assessment in Local Climate Zones in the Rural-Urban Continuum of eThekwini Municipality, South Africa.

Due to the need to continuously monitor and understand the thermal environment and its socioeconomic implications, this study used remotely sensed data to analyze thermal comfort variation in LCZs, including along the rural to urban gradient of the eThekwini Municipality in KwaZulu-Natal province of South Africa. LCZs were mapped using multi-temporal and multi-spectral Landsat 8 and Landsat 9 data using the approach by World Urban Database and Access Portal Tools (WUDAPT), while thermal data were used to retrieve land surface temperatures (LSTs). Data for training classification of LCZs and accuracy assessment were digitized from GoogleEarth guided by knowledge gained and data collected during a field survey in March 2022 as well as pre-existing maps. LCZs were mapped using the random forest classifier in SAGA GIS software while a single channel algorithm based on band 10 was used to compute LST for different scenes. The LSTs were adjusted and further used to derive thermal comfort based on the Universal Thermal Comfort Index (UTCI) categories as an indicator for outdoor thermal comfort on the extremely low- and extremely high-temperature periods in the cool and hot seasons, respectively. LCZs were mapped with high accuracy (overall accuracy of 90.1% and kappa of 0.88) while inter-class separability was high (>1.5) for all LCZ pairs. Built-up LCZs dominate the eastern parts of the municipality, signifying the influence of the sea on development within the area. Average LST was coolest in the dense forest, open low-rise and water LCZs in the cool and hot seasons, respectively. The compact high-rise LCZ was the warmest in both the hot (36 °C) and the cool (23 °C) seasons. The sea sands were among coolest regions in both seasons due to their high water content, attributed to their high water table and close proximity to the ocean. There was no thermal stress during the cool season, while most areas recorded moderate to strong heat stress in the hot season. Some areas in the densely built-up LCZs recorded very strong heat stress in the hot season. The findings suggest that policies and strategies should enhance heat mitigation capacities in strong-heat-stress areas during the hot season. Municipal authorities and citizens must work together to build strategies to minimize temperature extremes and associated socioeconomic pressures. Urban development policies, plans and strategies should consider implications on the thermal environment as well as the value of conservation of LCZs with high-heat mitigation value such as dense forests and expansion of built-up LCZs with low-heat absorption levels such as open low-rise. The study was based mainly on remotely sensed temperatures with some ground data used to validate results, which may limit the assessment. Overall, the study provides insights towards achievement of global sustainable and climate-smart development targets.

Mapping Local Climate Zones in Lausanne (Switzerland) with Sentinel-2 and PRISMA imagery: comparison of classification performance using different band combinations and building height data

ABSTRACT The Local Climate Zone (LCZ) concept has emerged as a valuable classification system for climate-related studies. The World Urban Database and Access Portal Tools (WUDAPT) protocol provides a framework for generating a LCZ segmentation which relies on the supervised classification of multispectral imagery. However, since LCZ is based on the physical and thermal properties of the urban surfaces, more insightful information on the surface reflectivity characteristics – which is provided by hyperspectral sensors – may be beneficial for improving the LCZ classification. This assumption is investigated in this study by comparing the classification performance of a supervised algorithm applied to multispectral (Sentinel-2) and hyperspectral (PRISMA) satellite imagery. The study area is the city of Lausanne (Switzerland). Experiments are performed considering these sensors and different band combinations, including the building height layer as an additional band. Preliminary outcomes show that PRISMA imagery yields satisfying results in terms of classification accuracy while not outperforming Sentinel-2. An improvement is achieved by leveraging the first 10 PRISMA Principal Components which allows to retain the uncorrelated information out of the original bands. These first results will be validated in future investigations by improving image pre-processing and exploiting a larger number of seasonal PRISMA acquisitions.

A Multi-Level Grid Database for Protecting and Sharing Historical Geographic Urban Data: A Case Study of Shanghai

Historical geographic data play an important supporting role in the study of long-term geographic studies, such as climate change, urban expansion and land-use and land-cover change. These data vary in source, format and accuracy and are widely found in historical documents, old maps, produced vector data, aerial photographs, old photographs, etc. The complex nature of data makes it difficult for researchers to organize, store and manage in a unified manner. Thus, GIS practitioners and social scientists will collectively face the challenge of integrating historical data into spatial databases. Herein, we introduced the concept of a multi-level spatial grid, selecting Shanghai as the study area, to construct the Shanghai historical geographic database and give the conceptual model and processing method. The experiment was performed using the China Historical Geographic Information System (CHGIS), which showed the historical evolution of Shanghai more conveniently. Meanwhile, we simulated one million rows of historical geographic data in Shanghai and compared the retrieval efficiency of the encoding method with the latitude–longitude and geometric object indexing methods, which demonstrated that our method was very effective. This research is important for the construction of a historical urban database, which can better preserve historical resources and promote urban culture with information science and technology.

Comparative Analysis of the Surface Urban Heat Island (SUHI) Effect Based on the Local Climate Zone (LCZ) Classification Scheme for Two Japanese Cities, Hiroshima, and Sapporo

The Local Climate Zone (LCZ) classification system is used in this study to analyze the impacts of urban morphology on a surface urban heat island (SUHI). Our study involved a comparative analysis of SUHI effects in two Japanese cities, Sapporo and Hiroshima, between 2000 to 2022. We used geographical-information-system (GIS) mapping techniques to measure temporal LST changes using Landsat 7 and 8 images during the summer’s hottest month (August) and classified the study area into LCZ classes using The World Urban Database and Access Portal Tools (WUDAPT) method with Google Earth Pro. The urban thermal field variance index (UTFVI) is used to examine each LCZ’s thermal comfort level, and the SUHI heat spots (HS) in each LCZ classes are identified. The research findings indicate that the mean LST in Sapporo only experienced a 0.5 °C increase over the time, while the mean LST increased by 1.8 °C in Hiroshima City between 2000 and 2022. In 2000, open low-rise (LCZ 6) areas in Sapporo were the hottest, but by 2022, heavy industry (LCZ 10) became the hottest. In Hiroshima, compact mid-rise (LCZ 2) areas were the hottest in 2000, but by 2022, heavy-industry areas took the lead. The study found that LCZ 10, LCZ 8, LCZ E, and LCZ 3 areas in both Dfa and Cfa climate classifications had unfavorable UTFVI conditions. This was attributed to factors such as a high concentration of heat-absorbing materials, impervious surfaces, and limited green spaces. The majority of the SUHI HS and areas with the highest surface temperatures were situated near industrial zones and large low-rise urban forms in both cities. The study offers valuable insights into the potential long-term effects of various urban forms on the SUHI phenomenon.

Developing Comprehensive Local Climate Zone Land Use Datasets for Advanced High-Resolution Urban Climate and Environmental Modeling

The Local Climate Zone (LCZ) classification scheme is a vital method of building a category dataset for high-resolution urban land. For the development of urban meteorology, air pollution and related disciplines, the high-resolution classification data of urban buildings are very important. This study aims to create LCZ datasets with detailed architectural characteristics for major cities and urban agglomerations in China, and obtain more accurate results. We constructed 120 m resolution land use datasets for 63 cities (mainly provincial capitals, municipalities directly under the Central Government, important prefecture-level cities and special administrative regions) and 4 urban agglomerations in China based on the local climate zone (LCZ) classification scheme using the World Urban Database and Access Portal Tools method (WUDAPT). Nearly 100,000 samples were used, of which 76,000 training samples were used to provide spectral signatures and 23,000 validation samples were used to ensure accuracy assessments. Compared with similar studies, the LCZ datasets in this paper were generally of good quality, with an overall accuracy of 71–93% (mean 82%), an accuracy for built classifications of 57–83% (mean 72%), and an accuracy for natural classifications of 70–99% (mean 90%). In addition, 35% of 63 Chinese cities have construction areas of more than 5%, and the plateaus northwest of Chengdu and Chongqing are covered with snow all year round. Therefore, based on the original LCZ classification system, the construction area (LZC H) and the snow cover (LCZ I) were newly added as the basic classifications of urban LCZ classification in China. Detailed architectural features of cities and urban agglomerations in China are provided by the LCZ datasets in this study. It can be applied to fine numerical models of the meteorological and atmospheric environment and improve the prediction accuracy.

Investigating the Spatial Heterogeneity of Urban Heat Island Responses to Climate Change Based on Local Climate Zones

Global warming and the urban heat island (UHI) phenomenon have significant impacts on human activities, against which it is necessary to develop effective coping strategies. Based on the local climate zone (LCZ) system, this study used the land-cover and surface temperature data on the Chang–Zhu–Tan (CZT) urban agglomeration in China in 2006, 2010, 2016, and 2020 to analyze the impact of climate change on the land surface temperature (LST) under different land-cover types. The results illustrate that the LCZ map generated on the basis of the improved World Urban Database and Access Portal Tools (WUDAPT) is more accurate and efficient than the traditional method. The accuracy is increased by more than 15%. From 2006 to 2020, the main built-up types in the CZT urban agglomeration were the sparsely built, the large low-rise, and the compact mid-rise types. The low-plant type represents the most significant proportion of the natural types, followed by the water and the dense-tree types. The built-up types in the CZT urban agglomeration tend to be the high-rise, dense, and industrial types. Urban construction land is taken mainly from the sparsely built type of land. The average LST of the large low-rise and heavy-industry zones is significantly higher than the average LST of the three cities. The average LST values for the water and dense-tree zones are significantly lower than the other average LST values. The LST is stable in each LCZ, showing little correlation with the size of the LCZ area. Compact low-rise land use is ineffective against climate warming and inhibits economic growth. Compact high-rise and open high-rise land can not only effectively deal with climate warming but can also significantly stimulate economic growth. This paper helps us to understand the effect of land cover on climate warming and the economic benefits of LCZs in the CZT urban agglomeration and provides strategies to optimize the use of land resources.

Effect of local climate zone (LCZ) and building category (BC) classification on the simulation of urban climate and air-conditioning load in Hong Kong

Reliable energy models are required to predict urban-scale air-conditioning (AC) load under extreme outdoor temperatures which is crucial to power security. In this study, we proposed a new landuse/landcover (LULC) scheme for these models by combining the local climate zones (LCZs) from World Urban Database Access Portal Tools (WUDAPT) and building category (BC) map. Apart from evaluating against the conventional LULC of Moderate-resolution Imaging Spectrometer (MODIS) and WUDAPT, the impact of urban canopy parameters and BC on the simulated meteorological conditions and AC load during the 2016 heatwave in Hong Kong was examined by the Weather Research and Forecasting (WRF) model. Incorporating LCZs (BC) reduced the bias of 2-m temperature (T2), 2-m relative humidity (RH2), and AC load intensity by 0.4 °C (2.0 °C), 1.98% (0.68%) and 6.78 W m−2 (13.09 W m−2), respectively. It captured the heterogeneous, intra-urban T2 and turbulence kinetic energy (TKE). Further sub-classifying the BC in LCZs led to WUDAPT/BC which performed even better and significantly improved WRF output. It simulated well the daytime wind speed, mitigated the overprediction (underprediction) of T2 (RH2), and rectified the underpredicted AC load in WUDAPT. Its benefit was prominent in daytime as well as in compact high-rise/mid-rise and open high-rise areas. (200 words).

Load quantification and effect evaluation of urban non-point source pollution in the Licun River based on SWAT model.

With the gradual control of point source pollution, the impact of urban nonpoint source pollution on river water quality is becoming more prominent. Regarding the current problem that nonpoint source pollution loads in urban basins are difficult to quantify and the impact on water quality is difficult to analyze, the Licun River basin in Qingdao was selected as the research object. Through the field survey and surface accumulation sampling analysis of the basin, the evaluation model of urban nonpoint source pollution was constructed by revising the land type data of the basin and the urban database of the SWAT model. The results showed that concentration of nitrate in precipitation was most sensitive to the simulation of nitrogen loading; organic P in baseflow was most sensitive to the simulation of phosphorus loading. The Nash-Sutcliffe efficiency coefficient (ENS) and the coefficients of determination (R2) of the SWAT model for runoff, total phosphorus (TP), and total nitrogen (TN) in the simulation validation period meet the model requirements,indicating a good model fit. In addition, the spatial and temporal distribution characteristics of urban nonpoint source pollution of TN and TP in 2021 were analyzed. In July, rainfall-runoff from the Licun River basin was the most polluted.

Local Climate Zones to Identify Surface Urban Heat Islands: A Systematic Review

The Land Surface Temperature (LST) obtained by remote sensing data is an essential indicator for analyzing the Surface Urban Heat Island (SUHI). A factor contributing to its occurrence is the reflections of the different materials in urban and rural areas, which significantly affect the energy balance near the surface. Therefore, recent studies have increasingly used the Local Climate Zones (LCZs) classification system to discriminate those urban areas. Therefore, our study aims to perform a systematic review using the PRISMA method of LCZ classification applied to understand the LST and the SUHI phenomenon. At first, 10,403 related articles were found in the scientific literature which, after passing through filtering stages, resulted in 51 that were further analyzed. Our results showed that these articles were very recent, beginning in 2016, and present an increasing trend. Even though all articles reviewed somehow try to understand the Urban Heat Island phenomenon, in more than half of them, the SUHI calculation method was not mentioned. China was the country with the most studies. Images from Landsat and TERRA/AQUA sensors appeared in 82% of the studies. The method that appears the most to LCZ definitions is from the World Urban Database. Finally, we conclude that LCZ mapping methodologies were accurate enough for SUHI studies considering the current climatic changes. Therefore, this systematic review can help new studies on SUHI identification through LCZ in different world areas using remote sensing data to estimate the LST.

Deep learning-based urban morphology for city-scale environmental modeling.

Herein, we introduce a novel methodology to generate urban morphometric parameters that takes advantage of deep neural networks and inverse modeling. We take the example of Chicago, USA, where the Urban Canopy Parameters (UCPs) available from the National Urban Database and Access Portal Tool (NUDAPT) are used as input to the Weather Research and Forecasting (WRF) model. Next, the WRF simulations are carried out with Local Climate Zones (LCZs) as part of the World Urban Data Analysis and Portal Tools (WUDAPT) approach. Lastly, a third novel simulation, Digital Synthetic City (DSC), was undertaken where urban morphometry was generated using deep neural networks and inverse modeling, following which UCPs are re-calculated for the LCZs. The three experiments (NUDAPT, WUDAPT, and DSC) were compared against Mesowest observation stations. The results suggest that the introduction of LCZs improves the overall model simulation of urban air temperature. The DSC simulations yielded equal to or better results than the WUDAPT simulation. Furthermore, the change in the UCPs led to a notable difference in the simulated temperature gradients and wind speed within the urban region and the local convergence/divergence zones. These results provide the first successful implementation of the digital urban visualization dataset within an NWP system. This development now can lead the way for a more scalable and widespread ability to perform more accurate urban meteorological modeling and forecasting, especially in developing cities. Additionally, city planners will be able to generate synthetic cities and study their actual impact on the environment.