Urban Model Research Articles

Multi-scale correlation analysis between geometric parameters and solar radiation in high density urban environment——Case study in Nanjing

This study explores the impact of multi-scale urban morphology on solar radiation in high-density environments, focusing on Nanjing as a case study. Using Grasshopper, the research decomposed urban models into four scales—district, block group, block, and plot—and analyzed eight morphological indicators, including site coverage (SC), floor area ratio (FAR), and block surface ratio (BSR). Solar radiation was simulated for the entire year, the summer solstice, and the winter solstice. Key findings reveal that different scales exhibit varying influences on solar radiation. At the district scale, BSR shows the highest correlation with annual solar radiation (R2 = 0.96), while at the neighborhood cluster scale, the minimum distance between buildings (DBmin) is most correlated (R2 = 0.7). At the block scale, BSR significantly correlates with summer solar radiation (R2 = 0.55). At the plot scale, no single indicator strongly correlates with solar radiation, but a combination of indicators is more relevant. These findings enable rapid solar performance assessments in urban design, promoting efficient solar energy use.

Adjoint-based sensitivity analysis and assimilation of multi-source data for the inference of spatio-temporal parameters in a 2D urban flood hydraulic model

This contribution presents a novel approach for the calibration of distributed parameters in a 2D urban flood hydraulic model. It focuses on the challenging issue of inferring distributed friction parameters from multi-source heterogeneous spatio-temporal observations of their hydraulic signatures in the context of an urban flash flood in a complex street network. A variational data assimilation algorithm is used to infer high-dimensional multi-variate parameters (spatialized friction and inflow discharge time series) using multi-source observations. This method relies on a differentiable 2D shallow water hydraulic model which enables to generate high-resolution sensitivity maps of local gradients and Derivative-based Global Sensitivity Measures (DGSM), enabling to guide adequate definition of parameter spatialization for the data assimilation process. Assimilated data include real local limnigraphic measurements and high-water marks collected after a major flood event, as well as modeled flow velocity used in twin experiments setups. This study is the first to leverage high-water marks with a variational method for the calibration of distributed parameters in an urban flood model. The multi-source data is used to infer inflow hydrographs and distributed friction parameters in setups of varying complexity. In the main setup, the complex structure of the street network, along with the sensitivity maps and hydraulics expertise led to define a model configuration with 45 friction patches. A high-dimensional parameter vector composed of these friction values and an upstream inflow is inferred simultaneously by assimilating real limnigraphic data and high-water marks. This leads to an increase in model fit to observations and satisfying parameter estimates.

Urban land use simulation and carbon-related driving factors analysis based on RF-CA in Shanghai, China

As global climate change intensifies, climate protection is important for the sustainable development of human society. In the process of urbanization and industrialization, carbon dioxide emissions are an important factor contributing to global warming. Therefore, modelling projections of future urban land use under low-carbon scenarios are essential for sustainability policy development. Few studies have focused on the impact of carbon emissions on urban land use change in Shanghai, as current research has primarily concentrated on developing methods for urban land use modelling. This paper utilizes a cellular automata (CA) simulation model based on the Random Forest (RF) algorithm to select various spatial variables of carbon emissions as the driving factors that affect urban land use changes. These variables include traffic location factors, economic development factors, electricity consumption, and population density. In this study, remote sensing imagery of urban nighttime lighting is also used to construct a simulation model of land use types in Shanghai. The model is then used to analyze the contribution of carbon emission constraints to urban land use changes. Actual historical land use data from 2013 and 2019 are used for validation, and the prediction model is used to predict land use outcomes under different low-carbon scenarios in 2025. The model is validated by simulating multiple intra-city land use maps for 2019 (kappa = 0.88, OA=92.71 %). The method of out-of-bag error from the random forest is used to evaluate the significance of carbon emission constraints. Using the validated model, the constraints in the CA model are changed to predict the land use simulation results of Shanghai in 2025 under different low-carbon scenarios. In terms of significance, factors such as distance to power plants, distance to major roads, real GDP, and population density can all have a significant impact on changes in urban land use. By selecting the low-carbon scenario with the most appropriate thresholds for each driver, it is possible to obtain the land use simulation results of Shanghai in 2025 under the optimal low-carbon scenario, while ensuring the high accuracy of the RF-CA model and simultaneously reducing the impact of factors on the city’s overall carbon emissions. This paper provides a scientific base for urban planners and scholars to thoughtfully design urban land use while cutting down on carbon emissions. Furthermore, it can aid government agencies in establishing associated planning approaches.

Nonlinear impacts of landscape and climatological interactions on urban thermal environment during a hot and rainy summer

Investigating the nonlinear impacts of urban landscape and climatic parameters on urban temperatures, a critical issue within urban climatology. Chengdu, characterized by its hot, rainy summers and rapid urban development, serves as an ideal model to illustrate these dynamics. Our investigation utilizes advanced analytical methods such as Random Forests (RF), SHapley additive explanation (SHAP), and Partial Dependence Plots (PDP) to analyze how landscape and climatic factors influence air temperature (AT) and land surface temperature (LST). Significant findings reveal profound thermal heterogeneity across Chengdu’s urban fabric, underscored by spatially distinct phenomena where some regions exhibit strong contrasts in temperature impacts due to varying climatic and landscape factors. The study identifies relative humidity and rainfall as key drivers of temperature variations during the summer months, reflecting Chengdu’s specific climatic idiosyncrasies. These insights are critical, as they highlight how urban planning and green infrastructure can be strategically used to mitigate adverse thermal effects. The research not only enhances understanding of the complex interplays within urban microclimates but also offers new perspectives on urban heat management. It contributes to the scientific community by providing evidence-based strategies for urban planners to counter the urban heat island effect and enhance urban resilience against climate change. This comprehensive analysis underscores the importance of incorporating multiple variables into urban climate models, lays the groundwork for more refined urban environmental policies and practices.

From urban modelling to city digital twins – Reflections from the applied urban modelling (AUM) symposia

From urban modelling to city digital twins – Reflections from the applied urban modelling (AUM) symposia

Clarifying new urban mobility services based on a threefold business model framework

Market entrants have brought a variety of new urban mobility services over the past years, which are rooted in the sharing economy and have their origin in digitalization. Digital data serve as key resource of a business model and, accordingly, digital technologies are the basis of key activities. Building our analysis on the resource-based view and on the business model debate, we ask: what degrees of digitalization do urban mobility service business models exhibit? We perform a systematic literature review and a qualitative content analysis. As a result, we identify a continuum of highly and lowly digitalized business models. We derive a threefold business model framework, substantiated in conventional mobility, hybrid, and data-driven business models. (1) Conventional mobility business models are dominated by mobility as a key resource, digitalization is low and performed by key partners, (2) hybrid models contain both conventional mobility and data-driven key resources, and (3) data-driven models take digital data as key resources, while conventional mobility is carried out by key partners. As a first main contribution, we conceptualize conventional versus purely data-driven business models along the continuum of data-driven business model components. New urban mobility services are the group of both hybrid and data-driven business models, while conventional urban mobility stands on its own. As a second contribution, we clarify the Mobility-as-a-Service (MaaS) concept by corroborating it as a purely data-driven business model with key partners provisioning mobility.

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.

The impact of roof systems on cooling and building energy efficiency

The excessive warming in the built environment, due to urbanization and anthropogenic heat emissions, has adverse effects on building energy consumption. Diverse technology using, e.g., vegetated roofs or innovative roof materials, have been proposed and implemented to ameliorate both indoor and outdoor thermal environments and reduce energy consumption. In this study, we apply a state-of-the-art urban canopy model to simulate the thermal performance of multiple roof technology, viz. the white, green, and hybrid roofs, in the contrasting urban environments of Princeton, NJ and Phoenix, AZ, USA. In addition, we estimate the combined energy-water saving potential for green roofs with five different irrigation schemes. It is found that green roofs can achieve a combined energy-water saving of $9.68 m−2 roof area in Phoenix with moisture-controlled irrigation, and $5.23 m−2 in Princeton without irrigation. These results can help to promote building energy efficiency by adapting to flexible and sustainable roof technology for heat mitigation.

Climate-responsive urban planning through generative models: Sensitivity analysis of urban planning and design parameters for urban heat island in Singapore's residential settlements

The Urban Heat Island (UHI) effect exacerbates the sustainability and well-being challenges of extreme heat events. While city planning and design measures have been shown to mitigate UHI severity, the complex interaction among these measures has limited the ability of previous research to assess their impact holistically and across urban scales. To investigate the cross-scalar effectiveness of multiple UHI mitigation measures, this study applies sensitivity analysis (SA) to nine parameters in an urban generative model. Previously unstudied planning parameters, land parcel area and road network density, are included in the analysis. From the SA of 21,000 model solutions for a 100 ha case study site in Singapore, building density, podium density, and land parcel area are found to have greatest impacts on UHI. This finding supports a hypothesis that urban planning parameters have a high potential for UHI mitigation. Key findings include that a high green plot ratio (>50 %) combined with a low site coverage ratio (<50 %) permits even high-density model solutions (gross plot ratio >4) to maintain annual UHI below 0.89 °C. The conclusion discusses the implications of the findings for heat-resilient city planning and demonstrates that performance-based evaluation of generative urban models can improve upon prescriptive planning approaches.

Will there be water? Climate change, housing needs, and future water demand in California

Climate change in California is expected to alter future water availability, impacting water supplies needed to support future housing growth and agriculture demand. In groundwater-dependent regions like California's Central Coast, new land-use related water demand and decreasing recharge is already stressing depleted groundwater basins. We developed a spatially explicit state-and-transition simulation model that integrates climate, land-use change, water demand, and groundwater gain-loss to examine the impact of future climate and land use change on groundwater balance and water demand in five counties along the Central Coast from 2010 to 2060. The model incorporated downscaled groundwater recharge projections based on a Warm/Wet and a Hot/Dry climate future from a spatially explicit hydrological process-based model. Two urbanization projections from a parcel-based, regional urban growth model representing 1) recent historical and 2) state-mandated housing growth projections were used as alternative spatial targets for future urban growth. Agricultural projections were based on recent historical trends from remote sensing data. Annual projected changes in groundwater balance were calculated as the difference between land-use related water demand, based on historical estimates, and climate-driven recharge plus agriculture return flows. Results indicate that future changes in climate-driven groundwater recharge, coupled with cumulative increases in agricultural water demand, result in overall declines in future groundwater balance, with a Hot/Dry future resulting in cumulative groundwater decline in all but Santa Cruz County. Cumulative declines by 2060 are especially prominent in San Luis Obispo (−2.9 to −5.1 Bm3) and Monterey counties (−6.5 to −8.7 Bm3), despite limited changes in agricultural water demand over the model period. These two counties show declining groundwater reserves in a Warm/Wet future as well, while San Benito and Santa Barbara County barely reach equilibrium. These results suggest future groundwater supplies may not be able to keep pace with regional demand and declining climate-driven recharge, resulting in a potential reduction in water security in the region. However, our county-scale projections showed new housing and associated water demand does not conflict with California's groundwater sustainability goals. Rather, future climate coupled with increasing agricultural groundwater demand may reduce water security in some counties, potentially limiting available groundwater supplies for new housing.

A GIS-Based Approach for Urban Building Energy Modeling under Climate Change with High Spatial and Temporal Resolution

The energy demand and associated greenhouse gas (GHG) emissions of buildings are significantly affected by the characteristics of the building and local climate conditions. While energy use datasets with high spatial and temporal resolution are highly needed in the context of climate change, energy use monitoring data are not available for most cities. This study introduces an approach combining building energy simulation, climate change modeling, and GIS spatial analysis techniques to develop an energy demand data inventory enabling assessment of the impacts of climate change on building energy consumption in Shanghai, China. Our results suggest that all types of buildings exhibit a net increase in their annual energy demand under the projected future (2050) climate conditions, with the highest increase in energy demand attributed to Heating, Ventilation, and Cooling (HVAC) systems. Variations in building energy demand are found across building types. Due to the large number of residential buildings, they are the main contributor to the increases in energy demand and associated CO2 emissions. The hourly residential building energy demand on a typical hot summer day (29 July) under the 2050 climate condition at 1 p.m. is found to increase by more than 40%, indicating a risk of energy supply shortage if no actions are taken. The spatial pattern of total annual building energy demand at the individual building level exhibited high spatial heterogeneity with some hotspots. This study provides an alternative method to develop a building energy demand inventory with high temporal resolution at the individual building scale for cities lacking energy use monitoring data, supporting the assessment of building energy and GHG emissions under both current and future climate scenarios at minimal cost.

Urban flash flood hazard mapping using machine learning, Bahir Dar, Ethiopia

ABSTRACT Increased frequency and magnitude of flooding pose a significant natural hazard to urban areas worldwide. Mapping flood hazard areas are crucial for mitigating potential damage to human life and property. However, conventional hydrodynamic approaches are hindered by their extensive data requirements and computational expenses. As an alternative solution, this paper explores the use of machine learning (ML) techniques to map flood hazards based on readily available geo-environmental variables. We employed various ML classifiers, including decision tree (DT), random forest (RF), XGBoost (XGB), and k-nearest neighbor (kNN), to assess their performance in flood hazard mapping. Model evaluation was conducted using the area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). Our results demonstrated promising outcomes, with AUC values of 93% (DT), 97% (RF), 98% (XGB), and 91% (kNN) for the validation dataset. RF and XGB have slightly higher performance than DT and kNN and distance to river was the most important factor. The study highlights the potential of ML for urban flood modeling, offering reasonable accuracy and supporting early warning systems. By leveraging available geo-environmental variables, ML techniques provide valuable insights into flood hazard mapping, aiding in effective urban planning and disaster management strategies.

Observatory of Town and Biomimetics For Urban Modeling and Architectural Design of Scientific Space in Landscaped Site.

Some modern architectural designs face difficulties in completion ,andthe dificult and complexe shapes lead to an increase in the cost of the project. The irregular urban planning of town creates an imbalance in the urban fabric and causes the functional problems. In this searches using biomimetics , we practice the urban planning of scientific space through projects of scientific use in several branches in a pivotal urban area whose scientific space extends to the eastern suburb of Ouzera Town. After a brief presentation of an urban observatory from Ouzera Town that enhances the scientifc project, the scientific spaces are disgnated for the rapid exucution in scientific space. The architectural design derived from the plants and biological structures facilitates the urban realization insuring easy practices and achieving and urban form through landscape architecture that is consistent in ecologicaland landscaped area of Ouzera Terriory.

Dynamic predictions for the composition and efficiency of heating, ventilation and air conditioning systems in urban building energy modeling

The composition and efficiency of heating, ventilation and air conditioning (HVAC) systems are crucial inputs for urban building energy modeling (UBEM). However, in current studies, the heterogeneity of HVAC systems is often ignored, leading to huge modeling errors. To address these issues, this study developed a three-step prediction approach that can dynamically forecast the composition of HVAC systems (SystemID), the efficiency of heat sources and the efficiency of entire systems at the urban scale. The performance of the prediction models was evaluated through the ten-fold cross-validation. The results showed that the accuracy in predicting the composition could reach 0.87, and the coefficient of determination (R2) was greater than 0.93 in efficiency forecasts. The accuracy of the developed approach was further evaluated through the testing set in two situations, i.e., with the predicted and actual SystemID data. In the first situation, the overall accuracy in predicting the composition reached 78.2 %, and the R2 for forecasting the system efficiencies was over 0.6. Then, with the SystemID assumed to be accurate, the R2 increased to more than 0.87. To analyze the performance of the developed method in energy use predictions, two case regions in Changzhou and Nanjing were used, respectively. The simulation results were compared with the scenario in which the system efficiencies were determined based on the national standard. The results showed that the developed method could enhance the accuracy of total energy use predictions by approximately 10 % and HVAC energy use predictions by roughly 40 % at both urban and building scales. This helps policy-makers craft energy-saving strategies more reasonably.

Extraction of the essential elements for urban systems modelling – A word-to-vector approach

Due to its ever-evolving nature, urbanisation continues to escalate in complexity, further exacerbating the urban sustainability challenges. This necessitates the need for evidence-based policymaking enabled by modelling approaches, to facilitate informed decisions, and propel and gravitate towards urban sustainability. The major constraint is that of identifying the essential characteristics for consideration when modelling cities as complex systems, in a structured manner that integrates these characteristics, cognisant of their relative importance. The distinctive urban systems, corresponding system characteristics and interdependencies impacting the modelling of cities as complex systems, can be identified from peer-reviewed literature. The limiting constraint is, although there is widely available information on cities in research databases, the ability to use this literature for a quantitative model has not been proven, presenting a research gap. This approach results in significant complexities. In order to resolve these complexities, this study seeks a systems-based approach including a 2-tier structured protocol, leveraging qualitative-to-quantitative techniques to automatically extract the key systems which impact the development of city models. Through a systematic literature review, data on 13 key systems is qualitatively extracted from research databases such as Scopus and ScienceDirect, for the duration 2014 – 2024. Through word2vector analysis, machine learning techniques are utilised to perform the quantitative mapping of each urban system into corresponding system characteristics, and quantitatively illustrate them based on relative importance. The results illustrate that this proposed method is significant to characterize the essential systems that constitute a city as a complex system, based on machine learning and text analytics.

The role of technology belief, perceived risk and initial trust in users’ acceptance of urban air mobility: An empirical case in China

Urban Air Mobility (UAM) is poised to revolutionize transportation, necessitating an assessment of public acceptance before broad commercial adoption. This study presents the Urban Air Mobility Acceptance Model (UAM-AM), which draws from the Technology Acceptance Model (TAM) and underscores the crucial role of initial trust, technology belief and perceived risk. The UAM-AM is validated using Structural Equation Modeling (SEM) based on 544 questionnaires for the first time in China. The findings highlight the significant impact of perceived ease of use and perceived usefulness on acceptance, uncovering a complex interplay with the intention to utilize UAM services. Notably, initial trust emerges as a foundational factor, influencing attitudes directly or indirectly through perceived ease of use and perceived usefulness. Moreover, the research identifies technology belief and perceived risk as fundamental drivers of initial trust. Examination of demographic segments reveals a heightened technology belief among individuals with backgrounds in science, indicative of a more favorable attitude towards UAM adoption. In closing, the paper presents recommendations for policymakers, service providers, and eVTOL manufacturers to formulate effective strategies that promote public acceptance during the initial phases of UAM deployment.

The food-water-climate nexus of green infrastructure: Examining ecosystem services trade-offs of peri-urban agriculture

Emission reduction, heat mitigation, and improved access to water and food provision are increasingly critical challenges for urban areas in the context of global climate change adaptation and mitigation. The revival of local agricultural production is often lauded as a potential nature-based solution. However, an expansion of peri-urban agriculture (peri-UA) may entail significant ecosystem trade-offs. This study explores the impacts on the food-water-climate nexus of different scenarios of peri-urban agricultural expansion in a semi-arid, Mediterranean climate, addressing local food provision, freshwater use, local temperature regulation, global climate change mitigation, and the trade-offs thereof. We estimate food provision and irrigation water requirements based on a georeferenced urban metabolism approach along with atmospheric and biosphere models to examine four land-use scenarios in the Metropolitan Area of Barcelona.Our study reveals that a 31 % (+17.27 km2) and 115 % (+64.25 km2) increase in the current peri-UA in the AMB, results in an increase in local food production of 24 % (+16,503 tons year−1) and 86 % (+58,940 tons year−1), and irrigation water requirements by 10.0 % (+3.2 hm3) and 43.5 % (+14.1 hm3), respectively. The expansion of irrigated peri-UA potentially reduces near-surface temperatures by 0.7 °C, albeit temperature reductions in the densest urban areas are minimal. Since the additional peri-UA is achieved by replacing natural non-forested and forest areas, the simulations predict reductions in the net ecosystem productivity of up to 18.5 % and total carbon stocks by 3.3 %. This integrated approach combining urban metabolism and atmospheric modelling to determine the trade-offs appears to be a promising tool for informing land-use decision-making in the context of urban climate adaptation and mitigation.

A conjugate method for simulating the dynamics of stochastic urban spatial network models

Urban networks are integral components of urban systems, contributing to their functioning and shaping the overall dynamics of urban areas. They are characterized by their complexity, interdependence, and dynamic nature. The construction, analysis and understanding of urban network models is therefore essential to address complex urban challenges, fostering sustainable development, and improving the overall quality of life in systems like cities and regions. In this work, we present and analyze the properties of a stochastic spatial-interaction model of urban structures. In addition, we devise a suitable time-stepping integrator that allows analyzing the evolution of this stochastic system at large times intervals, providing information of the dynamical behavior of the system in its equilibrium state. Numerical simulation studies are carried out to illustrate the practical effectiveness of the proposed approach.

Isotope-based source assessment of water flowing from storm sewer systems to a receiving river during dry weather periods

Urban stormwater management systems, particularly storm sewers, are critical for managing runoff in urban areas. These systems are designed to function during wet weather events; however, field-based observations of these systems suggest that they may also be active flow pathways in dry weather conditions, ultimately contributing to streamflow. Unlike dry weather flow in wastewater systems, storm sewer dry weather flow has not been thoroughly explored. This research used stable isotopes of oxygen and hydrogen in water to examine the sources of dry weather flow from storm sewers in a highly urban catchment. A stable isotope mixing model was applied at the outfalls of two stormwater catchments and the receiving Black Creek, located in Toronto, Canada. Findings suggest that during dry periods, storm sewers receive non-stormwater inputs from tap water, wastewater, and groundwater, along with some precipitation, and that these sources may constitute up to 19 % of Black Creek's flow at the watershed scale. Seasonal patterns in flow and water sources were observed for the Black Creek and outfalls. At one outfall, dry weather flow was predominantly from the water distribution system (i.e., tap water and/or wastewater) throughout spring, summer, and fall. In contrast, at the second outfall, groundwater dominated in spring and summer, and groundwater and water distribution were equally proportioned in fall. Black Creek baseflow comprises a dynamic mix of water sources that at times are similar to the sources observed at the stormwater outfalls. Considering these findings, future work should incorporate strategic sampling of additional outfalls, and multiple years of data collection to explore inter-annual variability in these processes and focus on replicating a similar study in other urban watersheds with different climates and/or water infrastructure design. The study findings highlight that our understanding of dry weather flow from storm sewers is relatively limited, emphasizing the need for further exploration of this phenomenon to inform urban hydrological modelling, water quality studies, and urban water management.

Towards an Urban Planning Scenario Model System—A Tool for Exploring Urban Uncertainty: A Case Study of Diaozhen, China

The ‘Urban Interaction’ project aims to develop an urban planning model system at the scale of towns or small cities consisting of three modules: growth forecast, land-use decision, and evaluation. This paper presents the framework of the model system to identify and discuss the assumptions and theoretical basis of the model system. The model system will be driven by scenario planning theory and sustainable urban development principles. It will export land-use planning based on selected urban development scenarios and urban planning theories. This paper takes Diao Town in Jinan as an example. Applying GIS spatial analysis and hierarchical analysis, this paper determines the suitability of land use and the weights of different influencing factors, combined with the land-use conflict identification model, for land-use decision-making. Finally, the assessment module verifies whether the planning scheme complies with laws and regulations to achieve an active, reactive response to uncertainty. The paper discusses the ‘uncertainty’ of urban planning and proposes a creative, flexible, and timely planning platform that allows planners and other participants to model and visualize their scenarios.