Urban Climate Research Articles

Evaluation and inter-comparison of twenty-three gridded rainfall products representing a typical urban monsoon climate in India

Accurate and reliable estimation of rainfall is crucial for scientific research and various applications. However, the observed rainfall data is often limited. With the advancements in technology, many global gridded rainfall products are now available, but their accuracy levels vary across the world. In this study, we comprehensively analyzed the reliability and effectiveness of 23 publicly available global rainfall datasets against the observed rainfall for Patna, representing a typical urban monsoon climate in India. Thirteen continuous and ten categorical statistical metrics were applied at daily, weekly, monthly, and annual intervals over 16 years (2000–2015). The results indicate that the reliability of all derived rainfall datasets varied on different temporal scales and reference datasets used. Overall, in continuous metrics, MERRA2 and MSWEP consistently outperformed in all the temporal scales whereas in categorical metrics for analyzing the rainfall detection ability, AIMERG, followed by MERRA2 demonstrated superior performance among others. Furthermore, IMD GRID, GSMAP, PCCS, AIMERG, and IMERG performed well in estimating different rainfall intensities. MERRA2 and MSWEP, which have not been widely considered for evaluation in a monsoon climate were found to be outstanding performers consistently. Therefore, we suggest broadening the selection of global rainfall products in the evaluation to fully utilize the potentiality of all available options. Furthermore, our approach offers a reliable framework to comprehensively assess the performance of different gridded rainfall products and assist in the selection of the best rainfall product for a particular region and purpose.

Capturing Urban Heterogeneity Enhances Tropical Cyclones Simulation in Houston

AbstractUrbanization in the coastal region has increased socioeconomic losses from landfalling tropical cyclones (TCs). Although previous studies have explored the broad impacts of urbanization on TCs, the effect of heterogeneity caused by local intra‐urban variability has not been examined. To address this gap, this study utilized the urban Local Climate Zone (LCZ) to capture the urban landscape heterogeneity and the impacts of this representation on the simulation of the post‐landfall TCs characteristics in Houston. Taking the case of two recent TCs: Hurricane Harvey (occurred in 2017) and Tropical Storm Imelda (occurred in 2019), the study evaluated the impact of urban heterogeneity on 10‐m winds, 2‐m temperature, land surface temperature, and precipitation across Houston. The consideration of intra‐urban heterogeneity using LCZ can improve the 10‐m winds, 2‐m temperature, and spatial pattern of land surface temperature. Although the cumulative rainfall remained largely similar within the experiments (with and without LCZ), incorporating intra‐urban heterogeneity notably modified the spatial structure of urban rainfall, particularly in simulating heavy rainfall hot spots. These findings are consistent for both TCs and demonstrate the positive impact of incorporating intra‐urban heterogeneity on the landfalling TC simulations over the Houston area.

Effects of Climate Change on the Future Attractiveness of Tourist Destinations in Greece

Climate change is a major challenge for the global tourism sector, affecting destinations worldwide. Greece, known for its scenery and abundant cultural history, is particularly vulnerable to these impacts. Tourism is a key driver of Greece’s economy, yet climate change threatens both natural environments and cultural sites. To evaluate the impacts of climate change on different types of Greek tourism (beach, sightseeing, winter tourism in mountainous areas), the widely used Holiday Climate Index (beach and urban versions) alongside three additional climatic indices customized for Greek climatic conditions, namely the Urban Climate Comfort Index, the Beach Utility Index, and the Mountainous Winter Climate Index were utilized for top tourist destinations of Greece. The results indicate that urban tourism may face challenges during peak summer months due to rising temperatures, but the shoulder seasons (April–May and September–October) will offer improved conditions, potentially extending the tourist season. For beach tourism, favorable conditions are expected to increase from April to October, with significant gains in June and September. Winter tourism in mountainous areas, especially snow-dependent activities like skiing, is at risk due to the declining snow availability. Overall, the study highlights both the challenges and opportunities posed by climate change for Greece’s tourism sector. It emphasizes the importance of adaptation strategies, including infrastructural improvements and promoting alternative activities, to minimize negative impacts and enhance the future attractiveness of Greek tourism.

Separation of the Urbanization Effects on Thermal Environment

Rapid urbanization leads to changes in the thermal characteristics of urban areas. Despite cities serving as natural laboratories to investigate responses to global change, it remains unclear how urbanization affects the thermal environment during global warming. Urbanization effects can be categorized as either the effect of urban climate (UCE) or land cover change (LCCE). This study proposes a conceptual framework for separating the urbanization effects on the thermal environment in 29 major Chinese cities. Results show that land surface temperature increases with impervious surface fractions in the urban area and decreases with vegetation fractions in rural areas, resulting in significant UCE and LCCE across cities, with LCCE approximately 1.3 to 5.7 times greater than UCE. These findings provide a comprehensive understanding of the thermal environment change caused by urbanization. To mitigate the negative effects of urbanization on the thermal environment, reducing land cover change would be more effective.

Development of a Multi‐Scale Meteorological Large‐Eddy Simulation Model for Urban Thermal Environmental Studies: The “City‐LES” Model Version 2.0

AbstractTo bridge the gaps between meteorological large‐eddy simulation (LES) models and computational fluid dynamics (CFD) models for microscale urban climate simulations, the present study has developed a meteorological LES model for urban areas. This model simulates urban climates across both mesoscale (city scale) and microscale (city‐block scale). The paper offers an overview of this LES model, which distinguishes itself from standard numerical weather prediction models by resolving buildings and trees at the microscale simulations. It also differs from standard CFD models by accounting for atmospheric stratification and physical processes. Noteworthy features of this model include: (a) the calculation of long‐ and short‐wave radiations in three dimensions, incorporating multiple reflections within urban canopy layers using the radiosity method, and accounting for building and tree shadows in the simulations; (b) the provision of various heat stress indices (Universal Thermal Climate Index, Wet Bulb Globe Temperature, MRT, THI); (c) the assessment of the efficacy of heat stress mitigation measures such as dry‐mist spraying, roadside trees, cool pavements, and green/cool roofs strategies; (d) the capability to run on supercomputers, with the code parallelized in a three‐dimensional manner, and the model can also run on a graphics processing unit cluster. Following the introduction of this model, the study confirms its basic performance through various numerical experiments, including simulations of thermals in the convective boundary layer, coherent structure of turbulence over urban canopy, and thermal environment and heat stress indices in urban districts. The model developed in this study is intended to serve as a community tool for addressing both fundamental and applied studies in urban climatology.

Comparison of Physically Based and Empirical Modeling of Nighttime Spatial Temperature Variability during a Heatwave in and around a City

Abstract The numerical weather prediction model HARMONIE-AROME and a multiple linear regression model (referred to in this article as the TURCLIM model after the local climate observation network) were used to model surface air temperature for 25–31 July 2018 in the City of Turku, Finland, to study their performance in urban areas and surrounding rural areas. The 0200 LT (local standard time) temperatures modeled by the HARMONIE-AROME and TURCLIM models were compared to each other and against the observed temperatures to find the model best suited for modeling the urban heat island effect and other spatial temperature variabilities during heatwaves. Observed temperatures were collected from 74 sites, representing both rural and urban environments. Both models were able to reproduce the spatial nighttime temperature variation. However, HARMONIE-AROME modeled temperatures were systematically warmer than the observed temperatures in stable conditions. Spatial differences between the models were mostly related to the physiographic characteristics: for the urban areas, HARMONIE-AROME modeled on average 1.4°C higher temperatures than the TURCLIM model, while for other land-cover types, the average difference was 0.51°C at maximum. The TURCLIM model performed well when the explanatory variables were able to incorporate enough information on the surrounding physiography. Respectively, systematic cold or warm bias occurred in the areas in which the thermophysically relevant physiography was lacking or was only partly captured by the model. Significance Statement As more and more people are living in an urban environment, the demand for accurate urban climate modeling is growing. This study aims to understand the differences between the numerical weather prediction and multiple linear regression modeling and their limitations in modeling surface air temperature in subkilometer scale. The case study shows that models are capable of predicting the spatial variation of 0200 LT nighttime temperature during a heatwave in a high-latitude coastal city. Both models are therefore valuable assets for city planners who need accurate information about the impacts of the physiography on the urban climate. The results indicate that to improve the performance of the models, more accurate physiographic description and higher spatial resolution of the models are needed.

Assessment of shift in GWPZs in Kashmir Valley of Northwestern Himalayas

Groundwater is a critical resource for the Kashmir Valley, which is increasingly pressured by urbanization and climate change. This study aims to delineate Groundwater Potential Zones (GWPZs) in the Kashmir Valley using the Analytical Hierarchy Process (AHP) and Geographical Information Systems (GIS). The research integrates eight thematic layers, including geology, geomorphology, slope, drainage density, land use/land cover (LULC), lineament density, rainfall, and topographic wetness index (TWI), to assess GWPZs for 1995, 2010, and 2020. Weights for each layer were assigned based on their influence on groundwater dynamics. The generated GWPZs were classified into five classes: very high, high, moderate, low, and very low. Over 25 years, significant changes in GWPZs were observed: very low potential zones decreased by 45.17%, low potential zones by 26.17%, and very high potential zones by 72.95%. Conversely, moderate potential zones increased by 50.87%, and high potential zones saw a slight increase of 9.00%. Results indicated that high and very high GWPZs are predominantly located in the valley's alluvial plains, primarily covered by agricultural and partially horticultural land. Validation of the generated GWPZs using bore well locations, discharge data, and Receiver Operating Characteristic (ROC) curves demonstrated high accuracy, with Area Under the Curve (AUC) values of 0.915, 0.898, and 0.890 for the respective years. These findings underscore the adverse impact of urbanization on groundwater potential zonation and emphasize the need for sustainable water management practices. This study provides valuable insights into the long-term shifts in groundwater potential in response to urban expansion and climate change. It highlights the importance of continuous monitoring and adaptive management to safeguard groundwater resources.

A novel approach to estimate land surface temperature from landsat top-of-atmosphere reflective and emissive data using transfer-learning neural network

Land Surface Temperature (LST) is a crucial parameter in studies of urban heat islands, climate change, evapotranspiration, hydrological cycles, and vegetation monitoring. However, conventional satellite-based approaches for LST retrieval often require additional data like land surface emissivity (LSE). Meanwhile, traditional machine learning (ML) techniques face challenges in acquiring representative training data and leveraging data from varied sources effectively. To address these issues, we introduce a novel transfer-learning (TL) neural network approach for LST retrieval using top-of-atmosphere (TOA) reflective and emissive data from Landsat. This method not only improves LST retrieval by integrating various data types but also demonstrates the potential of shortwave data in surrogating LSE information, thereby reducing dependence on explicit LSE data. Our TL approach utilized extensive simulations from the radiative transfer model (RTM) and measurements from the real world. The simulations are comprehensive, covering a wide range of atmospheric and surface scenarios, and the inclusion of real-world data mitigates the discrepancy between simulations and actual observations. When applied to a decade of Landsat-8 observations and ground measurements from 241 stations across diverse regions, our TL method significantly outperforms ML, single-channel (SC), and split-window (SW) algorithms in terms of root mean square error (RMSE), with improvements of 0.46 K, 0.84 K, and 0.57 K, respectively. This superiority underscores the advantage of integrating simulated and observed data, as well as the benefit of utilizing both reflective and emissive data without relying on uncertain LSE inputs. Our findings present a promising new TL framework for estimating LST directly from TOA data, offering a robust approach that we have made publicly available through Google Earth Engine (GEE) for broader use. The LST data retrieved by our proposed method can provide valuable insights for environmental research.

Modeled temperature, mortality impact and external benefits of cool roofs and rooftop photovoltaics in London

Population exposure to high temperatures poses health risks and increases mortality. ‘Cool roofs’ (high-albedo roofs) and rooftop photovoltaics (RPV) may reduce temperatures in urban areas. Here, using advanced urban climate modeling, we model impacts of these measures on air temperature and heat-related mortality in London during the record-breaking hot summer of 2018. We estimate changes in mean near-surface air temperature of −0.3 °C in the RPV scenario and −0.8 °C in the cool roof scenario. We find that the heat-related mortality in this period (estimated 655–920) could have been reduced by 96 (12%) by RPV, or 249 (32%) by cool roofs, in scenarios where all roofs have these measures. Monetized using value of statistical life, we estimate benefits for RPV and cool roofs of £237 M and £615 M, respectively. We estimate that up to 20 TWh of electrical energy would be generated in the full RPV scenario. We show that, for conditions such as in London June–August 2018, RPV or cool roofs may reduce near-surface air temperatures and associated heat-related mortality, with cool roofs having a larger effect.

A WRF-UCM-SOLWEIG framework for mapping thermal comfort and quantifying urban climate drivers: Advancing spatial and temporal resolutions at city scale

The capability to evaluate city-scale outdoor thermal comfort is crucial for understanding the public's experience of heat stress, especially during heat waves. Nevertheless, there remains a methodological gap in accurately mapping thermal comfort with high spatial and temporal resolutions in complex urban environment and in quantifying the contributions of various urban climate drivers, such as sky view factors and tree canopy fraction. To bridge this gap, we propose an innovative WRF-UCM-SOLWEIG framework that couples a mesoscale model with an urban microclimate model. Specifically, it integrates the Weather Research and Forecasting model coupled with the urban canopy model (WRF-UCM) and the Solar and Longwave Environmental Irradiance Geometry model (SOLWEIG). This framework is then applied to a densely populated tropical city in China, to quantify the impact of urban climate drivers on the Universal Thermal Climate Index (UTCI). The results reveal a significant diurnal variation in the impact of urban morphology. Notably, the most significant drivers affecting daytime UTCI are the impervious surface fraction and the tree canopy fraction, with maximum Pearson's correlation coefficients of 0.80 and -0.70, respectively. These findings suggest that urban heat mitigation strategies should prioritize on reducing impervious surfaces and enhancing shade management, alongside expanding urban forestry measures.

Investigating the efficacy of a fast urban climate model for spatial planning of green and blue spaces for heat mitigation

Problems caused by urban heat have prompted the exploration of urban greenery and blue spaces for heat mitigation. Various numerical models can simulate heat-related processes, but their use as support-tools to urban planners remains underexplored, particularly at the city-scale, due to high computational demand and complexity of such models. This study investigates the spatial relationships between urban heat, urban form and urban green and blue spaces with the fast climate model TARGET (The Air-temperature Response to Green/blue-infrastructure Evaluation Tool), which only requires minimal inputs of standard meteorological data, land cover and building geometry data. Using the City of Zurich as our case study, we: (i) validated the TARGET model against air temperature measurements from private sensor networks, (ii) performed a sensitivity analysis to identify key variables affecting urban heat, and (iii) investigated urban heat relationships with blue-green cover at locations frequented by pedestrians. Presence of urban green and blue spaces across the region shows potential for reducing local air temperatures by up to 5.2 °C (with urban forest). Investigating this relationship at different locations in the city revealed key districts that should potentially be targeted for reduction of pedestrian heat-impacts, due to their high pedestrian traffic, fewer green and blue spaces and high daytime air temperatures. Our results not only provide insights into the cooling effect of different amounts of green and blue features in the urban environment, but also demonstrates the application and integration potential of simplified models like TARGET to support the planning of more liveable future cities.

Assessing diurnal land surface temperature variations across landcover and local climate zones: Implications for urban planning and mitigation strategies on socio-economic factors

Rising temperatures and rapid urbanization globally reinforce the need to understand urban climates. We investigated the influence of land cover and local climate zones (LCZs) on diurnal land surface temperature (LST) in various seasons in greater Delhi region, India, and their implications on socio-economic factors. Day LST was the highest in the summer and night LST in the monsoon, which also had the lowest diurnal differences in LST. Higher height and density of built-up features contributed to greater heat at night. During the day, open built-up and vegetated areas experienced relatively less heat than their compact equivalents. The lowest diurnal difference was in medium height compact urban zones and tall vegetation. Social inequity in access to urban cooling was indicated by large low-income and heat-vulnerable populations inhabiting the hottest LCZs. This research highlighted that even in semi-arid and subtropical climates, spatial planning policy should consider both the seasonality and diurnal differences in temperature as much as appropriate morphologies for design of thermally comfortable and climate resilient urban spaces. These policies should address the evidenced social inequities in heat exposure to reduce the adverse health impacts on vulnerable groups and therefore contribute to wider societal and economic benefits of healthier populations.

Towards Defining Climate Adaptation Literacy in the Context of Urban Resilience

Abstract The climate crisis intensifies rapidly, straining the urban environment and society. Addressing climate change requires collaborative efforts, integrating physical resilience with knowledge empowerment. Formal and informal knowledge are vital for effective adaptation. In the updated Intergovernmental Panel on Climate Change (IPCC) AR6 Report an emphasis on knowledge and its role in reducing response risk was highlighted. At the urban scale, adaptation knowledge is essential, bridging social and technical systems. Here, community knowledge evolves from a passive component within a greater adaptation strategy to an active, integral element. This paper develops a unique definition of climate adaptation literacy for the urban built environment based on an extensive literature review on urban climate adaptation, guided by the PISA (Program for International Student Assessment) 2025 Framework. The results of this work highlight existing gaps in climate adaptation knowledge and present urban climate adaptation literacy as a means to measure the response risk. These insights aid the formulation of resilience policies and urban adaptation strategies and contribute to the safety, resilience, sustainability, and functionality of the material and non-material values of the urban environment

Lisbon Urban Climate: Statistical Analysis/Approach for Urban Heat Island Effect Based on a Pioneering Urban Meteorological Network

The urban heat island (UHI) effect is a widely recognized phenomenon consisting of heat accumulation by dense urban construction and human activities, resulting in higher temperatures across urban areas compared to their surroundings. This article aims to quantify the UHI effect on several areas throughout the city of Lisbon, Portugal, with the main goal of validating, evaluating, and reinforcing urban climate adaptation and resilience strategies proposed in the recent scientific literature. A set of nine quality-controlled weather stations from the “Lisboa Aberta” network that are compliant with the World Meteorological Organization (WMO) standards and installation requirements were used to characterize Lisbon’s UHI, in comparison to a reference weather station from the Instituto Português do Mar e da Atmosfera (IPMA), located at Lisbon Airport. By applying a principal component analysis (PCA) in an innovative way to 10 urban indexes, it is shown that the thermal inertia in Lisbon’s urban areas is positively correlated with the UHI intensity and urban density, regardless of the daily heating/cooling cycle. Furthermore, the results show that land use also has an impact on the UHI effect, with continuous, vertical building areas showing the greatest deviations in comparison to the reference, averaging +1.8 °C. Contrastingly, horizontal building areas reveal an average deviation of +1.3 °C, with sparse, discontinuously built areas representing an average UHI effect of +0.2 °C. Finally, through a climatope analysis, it is determined that, across Lisbon, high-density urban areas and ventilation corridors are responsible for inducing average UHI effects of +1.7 °C and +0.2 °C, respectively.

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.

Coupling MATSim and the PALM Model System—Large Scale Traffic and Emission Modeling with High-Resolution Computational Fluid Dynamics Dispersion Modeling

To effectively mitigate anthropogenic air pollution, it is imperative to implement strategies aimed at reducing emissions from traffic-related sources. Achieving this objective can be facilitated by employing modeling techniques to elucidate the interplay between environmental impacts and traffic activities. This paper highlights the importance of combining traffic emission models with high-resolution turbulence and dispersion models in urban areas at street canyon level and presents the development and implementation of an interface between the mesoscopic traffic and emission model MATSim and PALM-4U, which is a set of urban climate application modules within the PALM model system. The proposed coupling mechanism converts MATSim output emissions into input emission flows for the PALM-4U chemistry module, which requires translating between the differing data models of both modeling systems. In an idealized case study, focusing on Berlin, the model successfully identified “hot spots” of pollutant concentrations near high-traffic roads and during rush hours. Results show good agreement between modeled and measured NOx concentrations, demonstrating the model’s capacity to accurately capture urban pollutant dispersion. Additionally, the presented coupling enables detailed assessments of traffic emissions but also offers potential for evaluating the effectiveness of traffic management policies and their impact on air quality in urban areas.

Instant infrared: Estimating urban surface temperatures from street view imagery

As the impact of climate change on cities intensifies, the analysis and modeling of the urban microclimate are becoming increasingly important. A key parameter to this end is the estimation of urban surface temperatures. Traditional approaches for such estimates, that use urban micrometeorology models, are based on simulations that require heavy computations and complex inputs — including urban geometry, radiative parameters, and meteorological conditions. As a result, they cannot be applied extensively in all cities, where some of the input parameters might be missing and computational power might not be available. In this paper, we propose an alternative approach founded upon a deep learning framework, requiring markedly simplified inputs: street view imagery and meteorological conditions. We use our model to estimate building facade surface temperatures in the city of London (Ontario, Canada) and compare results both with simulated values obtained from an established simulation software TUF-3D and ground truth data collected through an onsite campaign with a FLIR thermal imager. Results substantiate the superiority of our proposed approach over TUF-3D, concurrently emphasizing its advantages in terms of input simplicity and computational resource efficiency. As street view imagery is becoming ubiquitous across the world – for instance, through platforms such as Google Street View – our approach lays the foundation for a new paradigm of fast, cost-effective, and highly scalable models, empowering urban designers and local authorities to better understand a changing urban climate.

Optimal representation of tree foliage for local urban climate modeling

Trees impact the local urban climate, notably at street level by intercepting solar radiation and providing shading. Evapotranspiration in foliage may reduce the air temperature although it may increase relative humidity and leaf drag may reduce wind speed, affecting thermal comfort. To document and quantify this impact, microclimate modeling with Computational Fluid Dynamics (CFD) simulations requires explicit information of the urban configuration, including trees. However, trees are complex individuals with a variety of shapes and a variety of foliage distribution. This study aims to investigate the sensibility to the tree modeling of the urban climate simulations. Starting with terrestrial LiDAR data from trees of different species, ages, and forms, we propose a systematic evaluation of the optimal representation of arboreal configurations in terms of local urban comfort. One way to represent the foliage of trees accurately is to apply Delaunay triangulation on the LiDAR data, which yields a convex envelope model. The resulting foliage shape is very close to the actual tree, but includes a high number of facets leading to complex objects to model numerically. Comparing four species and three maturity level of trees with this method, the paper shows that the size of the zone shadowed by a tree is the parameter with the largest impact on thermal comfort, as the ability of trees to absorb solar radiation is the main asset to improve thermal comfort. The UTCI could be up to 2.1°C lower for a mature ACPL than for a sapling, mainly because the zone covered by the tree is larger. In addition, polyhedron shape rhombicuboctahedron (RBC) produces accurate shadowed zones. Mostly, in literature, tree canopies are modeled with cubic representations while we see that they overestimate the size of the shadowed zone. To have reliable compromise between accuracy and time for conception and computational time, this paper shows that the RBC is the best alternative to common tree models. Despite requiring a good knowledge of the canopy geometry, RBC provides a strong capacity for accurately modelling complex canopy shapes of most tree species and offers large benefits in reduced complexity. We show that the RBC shape, thanks to its simple but flexible geometry, is an efficient and accurate methodological approach to model trees and allows savings in computational time (up to 15% faster than the convex envelope) and costs; and we expect that this method will improve the modeling of further parametric studies on vegetation impact on thermal urban comfort.

Developing a Chained Simulation Method for Quantifying Cooling Energy in Buildings Affected by the Microclimate of Avenue Trees

This paper introduces a methodology aimed at bridging the gap between building energy simulation and urban climate modeling. A coupling method was developed through the Building Control Virtual Test Bed (BCVTB) and applied to a case study in Taipei City, Taiwan, to address the microclimate factors of street trees crucial to cooling energy consumption. The use of the Urban Weather Generator for weather file modification revealed a 0.63 °C average air temperature disparity. The coupling method emphasized the importance of accurate wind speed and convective heat transfer coefficients (CHTCs) on building surfaces in determining cooling energy. The results indicated that elevated CHTC values amplify heat exchange, with higher wind velocities playing a crucial role in heat dissipation. The presence of street trees was found to significantly reduce heat flux penetration, leading to a reduction in building surface temperatures by as much as 9.5% during hot months. The cooling energy was lowered by 16.7% in the BCVTB simulations that included trees compared to those without trees. The EnergyPlus-only simulations underestimated the cooling energy needs by approximately 9.3% during summer months. This research offers valuable insights into the complex interactions between buildings and their environments. The results highlight the importance of trees and shading in mitigating the heat island effect and improving energy-efficient urban planning.

Designing a knowledge transfer model for accelerating urban climate adaptation in Malaysia

The urgency of adapting urban areas to the increasing impacts of climate change has prompted the scientific community to seek new approaches in partnership with public entities and civil society organizations. In Malaysia, Penang Island has developed a nature-based urban climate adaptation program (PNBCAP) seeking to increase urban resilience, reduce urban heat and flooding, strengthening social resilience, and build institutional capacity. The project includes a strong knowledge transfer component focused on encouraging other cities in the country to develop and implement adaptation policies, projects, and initiatives. This research develops a model adopting the most efficient processes to accelerate the transfer of knowledge to promote urban adaptation based on the PNBCAP. The methodology is developed based on a review of literature focused on innovation systems and change theories. The integration of success strategies in adaptation contributes to informing the creation of solutions around the alliance of local, state, and national government agencies, scientific institutions, and civil society organizations, in a new framework designated the Malaysian Adaptation Sharing Hub (MASH). MASH is structured in 3-steps and will function as an accelerator for the implementation of urban climate adaptation policies, with the target of creating 2 new adaptation-related policies to be adopted annually by each city member, based on knowledge gathered in the PNBCAP. It is concluded that, to speed up urban adaptation, it is necessary to reinforce and promote the sharing of knowledge resulting from or associated with pilot projects.