Future Climate Change Impact on Urban Heat Island in Two Mediterranean Cities Based on High-Resolution Regional Climate Simulations

Revealing the response of urban heat island effect to water body evaporation from main urban and suburb areas

Urban residents face increasing risk of heat stress due to the combined impact of climate change and intensification of the urban heat island (UHI) associated with urban growth. Considering the combined effect of urban growth and climate change is vital to understanding how temperatures in urban areas will change in the future. This study investigated the impact of urban growth and climate change on the UHI and heat stress in a subtropical city (Brisbane, Australia) in the present day (1991–2000) and medium term (2041–2050; RCP8.5) during summer. A control and urban growth scenario was used to compare the temperature increase from climate change alone with the temperature increase from climate change and urban growth. Average and minimum temperatures increased more with climate change and urban growth combined than with climate change alone, indicating that if urban growth is ignored, future urban temperatures could be underestimated. Under climate change alone, rural temperatures increased more than urban temperatures, decreasing the effect of the UHI by 0.4 °C at night and increasing the urban cool island by 0.8 °C during the day. With climate change, the number of hot days and nights doubled in urban and rural areas in 2041–2050 as compared to 1991–2000. The number of hot nights was higher in urban areas and with urban growth. Dangerous heat stress, defined as apparent temperature above 40 °C, increased with climate change and occurred on average 1–2 days every summer during 2041–2050, even in shaded conditions. There was higher temperature increases with urban growth and climate change than with climate change alone, indicating that reducing the effect of the UHI is vital to ensuring urban growth does not increase the heat stress risks that urban residents will face in the future.

A new parameterization of urban areas in the Community Climate System Model version 4 (CCSM4) allows for simulation of temperature in cities where most of the global population lives. CCSM4 Coupled Model Intercomparison Project phase 5 (CMIP5) simulations [Representative Concentration Pathway (RCP) 2.6, 4.5, and 8.5] are analyzed to examine how urban and rural areas might respond differently to changes in climate. The urban heat island (UHI), defined as the urban minus rural air temperature, is used as a metric. The average UHI at the end of the twenty-first century is similar to present day in RCP2.6 and RCP4.5, but decreases in RCP8.5. Both the daytime and nocturnal UHIs decrease in RCP8.5, but the decrease in the daytime UHI is larger and more uniform across regions and seasons than in the nocturnal UHI. This is caused by changes in evaporation that warm the rural surface more than the urban. There is significant spatial and seasonal variability in the response of the nocturnal UHI caused mainly by changes in the rural surface. In Europe, the response to climate change of rural leaf–stem area in summer and clouds and rural soil moisture in winter explains the majority of this variability. Climate change increases the number of warm nights in urban areas substantially more than in rural areas. These results provide evidence that urban and rural areas respond differently to climate change. Thus, the unique aspects of the urban environment should be considered when making climate change projections, particularly since the global population is becoming increasingly urbanized.

Quantitatively projecting the impact of future climate change on the socio-economy and exploring its internal mechanism are of great practical significance to adapt to climate change and prevent climate risks. Based on the economy-climate (C-D-C) model, this paper introduces a yield impact of climate change (YICC) model that can quantitatively project the climate change impact. The model is based on the YICC as its core concept and uses the impact ratio of climate change (IRCC) indicator to assess the response of the economic system to climate change over a long period of time. The YICC is defined as the difference between the economic output under changing climate condition and that under assumed invariant climate condition. The IRCC not only reflects the sensitivity of economic output to climate change but also reveals the mechanism of the nonlinear interaction between climate change and non-climatic factors on the socio-economic system. Using the main grain-producing areas in China as a case study, we use the data of the ensemble average of 5 GCMs in CMIP6 to project the possible impact of climate change on grain production in the next 15–30 years under three future scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5). The results indicate that the long-term climate change in the future will have a restraining effect on production in North region and enhance production in South region. From 2021 to 2035, climate change will reduce production by 0.60–2.09% in North region, and increase production by 1.80–9.01% in South region under three future scenarios. From 2021 to 2050, compared with the climate change impact in 2021–2035, the negative impact of climate change on production in North region will weaken, and the positive impact on production in South region will enhance with the increase in emission concentration. Among them, climate change will reduce grain output in North region by 0.52–1.99%, and increase output in South region by 1.35–9.56% under the three future scenarios. The combination of economic results and climate change research is expected to provide scientific support for further revealing the economic mechanism of climate change impacts.

The Standardized Precipitation Index (SPI), a method widely used to analyze droughts related to climate change, does not consider variables related to temperature and is limited because it cannot consider changes in hydrological balance, such as evapotranspiration from climate change. If we were to consider only the future increase in precipitation from climate change, droughts may decrease. However, because usable water can diminish from an increase in evapotranspiration, it is important to research on projected droughts considering the amount of evapotranspiration along with projecting and evaluating potential droughts considering the impact of climate change. As such, this study evaluated the occurrence of droughts using the Standardized Precipitation Evapotranspiration Index (SPEI) as a newly conceptualized drought index that is similar to SPI but includes the temperature variability. We extracted simulated future precipitation and temperature data (2011-2099) from the Representative Concentration Pathway (RCP) climate change scenario of IPCC AR5 to evaluate the impact of future climate change on the occurrence of droughts of South Korea. We analyzed the ratio of evapotranspiration to precipitation of meteorological observatories nationwide. In addition, we calculated the SPEI related to drought in the process to evaluate the future occurrence of droughts of South Korea. To confirm validity of SPEI results, extreme indices were analyzed. This resulted in the notion that as we go further into the future, the precipitation increases. But because of an increase in evapotranspiration also from a rise in temperature and continued dryness, the severity of droughts is projected to exacerbate.

Ecosystem services (ESs) in rapidly urbanizing agricultural basins are vulnerable to environmental changes. Adequately understanding the driving forces and the dynamics of ESs related to water quantity and quality can provide a basis for making sound management decisions on the development of basins. Here, we explored the impacts of future land use and climate changes on four ESs: nitrogen and phosphorous purification, water supply, and soil retention services in the Taihu Basin region of eastern China. Spatially explicit methods, a cellular automata-Markov (CA-Markov) model and the delta downscaling method were used to quantify the ESs, simulate land use changes, and project future climate changes, respectively. We built a business-as-usual land use scenario, representative concentration pathways (RCPs) scenarios for climate change, as well as a combined land use and climate change scenario to analyze the changes in the drivers and the responses of ESs. The results showed the following: (1) future land use changes would significantly enhance the nitrogen purification service while reducing the phosphorus purification service compared to other services; (2) climate change would have substantial effects on water supply and soil retention, but these impacts would vary with different RCPs scenarios during three future periods; and (3) the combined scenarios of both drivers would obviously influence all ESs and lead to a nitrogen purification service that was different from the other three services. Moreover, the policy implications of the results were discussed. The findings can help guide the creation of policies for land structure and patterns, climate change adaptation, and ecosystem-based management to promote the sustainable development of watersheds at the regional scale.

We live in an urbanizing world. Since 2008, more than half of humanity lives in cities, both large and small, and old and new.[...]

Despite research into the response of ammonia (NH3 ) volatilization in farmland to various meteorological factors, the potential impact of future climate change on NH3 volatilization is not fully understood. Based on a database consisting of 1063 observations across China, nonlinear NH3 models considering crop type, meteorological, soil and management variables were established via four machine learning methods, including support vector machine, multi-layer perceptron, gradient boosting machine and random forest (RF). The RF model had the highest R2 of 0.76 and the lowest RMSE of 0.82 kg NH3 -N ha-1 , showing the best simulation capability. Results of model importance indicated that NH3 volatilization was mainly controlled by total input of N fertilizer, followed by meteorological factors, human managements and soil characteristics. The NH3 emissions of China's cereal production (paddy rice, wheat and maize) in 2018 was estimated to be 3.3Mt NH3 -N. By 2050, NH3 volatilization will increase by 23.1-32.0% under different climate change scenarios (Representative Concentration Pathways, RCPs), and climate change will have the greatest impact on NH3 volatilization in the Yangtze river agro-region of China due to high warming effects. However, the potential increase in NH3 volatilization under future climate change can be mitigated by 26.1-47.5% through various N fertilizer management optimization options.

To protect the grassland ecosystem’s security, and coordinate the utilisation of grassland resources, explicitly estimating the response of Net Primary Productivity (NPP) of 10 grassland groups to future climate change is necessary. Based on the Comprehensive and Sequential Classification System (CSCS) and modified Carnegie Ames Stanford Approach (CASA) model, in conjunction with four Representative Concentration Pathways (RCP 2.6, RCP 4.5, RCP 6.0, RCP 8.5) of the Intergovernmental Panel on Climate Change (IPCC), the response of China’s grassland NPP to climate change was simulated and estimated. Results showed that: (1) the simulation accuracy of modified CASA model (R2 = 0.65) is 34% higher than that of CASA model, indicating that the modified CASA was suitable for estimating grassland NPP in China; (2) annual mean NPP and total NPP (2001–18) were 138.4 g C m−2 year−1 and 495.7 T g C, respectively; compared with the period 2001–18, NPP during the 2050 under RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5 were predicted with increases of 34.8%, 35.9%, 34.9% and 35.8%, respectively; and (3) from the present-day to the 2050, the NPP of Warm desert exhibited the largest increase (73.2–76.3%), while that of Tundra and alpine grassland the smallest (1.3–1.6%). These differences in NPP increase likely lead to differences in grassland carrying capacity. To respond to the impact of future climate change on grassland, grassland classification management strategies according to different groups should be implemented as grassland NPP changes differently in different grassland groups.

Evaluation of agricultural water demand under future climate change scenarios in the Loess Plateau of Northern Shaanxi, China

  The Urban Heat Island (UHI) phenomenon, characterized by higher temperatures in urban areas compared to surrounding rural areas, has been documented for over two centuries. Integral to the dynamics of urban socio-ecological systems, UHIs represent a prominent environmental repercussion of anthropogenic activities. As one of the most noticeable and concerning consequences of human activities within urban socio-ecological systems, UHIs significantly impact public health and economic development. This phenomenon serves as a quasi-experimental design, offering insights into the potential impacts of urbanization. Although previous studies have utilized remote sensing and ground monitoring technologies to observe UHIs globally and in various regions, little research has focused on the future patterns and risks of UHI intensity and its changes under socio-economic and climate change scenarios. In our study, we first quantified Beijing's UHI intensity at a 1km resolution using seamless near-surface air temperature and surface temperature data, 30 m land-cover data with a fine classification system (GLC_FCS30), and topographic information, employing the simplified urban extent algorithm. We then developed a predictive model for UHI intensity using the Extreme Gradient Boosting (XGBoost) algorithm, trained with 2020 data on land use types, landscape patterns, population, and topography. Finally, using the patch-generating land use simulation (PLUS) model, we simulated future land use in Beijing from 2020 to 2100 under four SSPs-RCPs scenarios (SSP 1-RCP 2.6, SSP2-RCP 4.5, SSP3-RCP 4.5, and SSP 4-RCP 6.0), and projected the corresponding UHI intensities. We also analyzed the potential risks of UHI by considering the level of population exposure. Our results indicate that, compared to the baseline level of 2020, Beijing's overall UHI level is expected to rise under any scenario, with a more significant increase in the area affected by UHIs, especially in the urban expansion scenario of SSP 4. Due to the considerable projected decline in China's future population, the number of people affected by UHI and the overall risk of UHI in Beijing shows a decreasing trend. However, the proportion of the population affected by higher degrees of UHI is gradually increasing. Our study not only identifies the characteristics of changes in UHI intensity and risk under different future scenarios but also provides insights for future urban planning and climate-economic goal setting by identifying key areas, thereby aiming to minimize the impact of localized climate change on urban residents.

<h3>Abstract</h3> Urban heat islands, where temperatures are elevated relative to non-urban surrounds, are near-ubiquitous in cities globally. Yet, the magnitude and form of urban heat islands in the tropics, where heat has a large morbidity and mortality burden, is less well understood, especially for socioeconomically diverse communities such as those living in urban informal settlements. We utilised 29 years of Landsat satellite-derived surface temperature, corroborated by <i>in situ</i> measurements, to provide a detailed spatial and temporal assessment of urban heat islands in Makassar, Indonesia, a city that is representative of rapidly growing urban settlements across the tropics. We did so with explicit consideration of vulnerable communities living informally. Our analysis identified surface urban heat islands of up to 9.2 °C in long-urbanised parts of the city and 6.3 °C in informal settlements, the seasonal patterns of which were driven by change in non-urban areas rather than in urban areas themselves. In recently urbanised areas, the majority of urban heat island increase occurred before areas became 50% urbanised. As tropical cities continue to expand we expect that urban heat islands will develop quickly as land is urbanised, whereas the established heat island in long-urbanised areas will remain stable in response to city expansion. Green and blue space protect some informal settlements from the worst urban heat islands and maintenance of such space will be essential to mitigate the growing heat burden from urban expansion and anthropogenic climate change. We advocate for green space to be prioritised in urban planning, redevelopment and informal settlement upgrading programs, with consideration of the unique environmental and socioeconomic context of tropical cities. <h3>Highlights</h3> Long-term, fine-scale data are essential to understand the dynamics of urban heat Surface heat islands reached 9.2 °C in the urban core, 6.3 °C in informal settlements <i>In situ</i> data support the use of remote sensing for heat island characterisation The majority of heat island growth occurred before land was 50% urbanised Green and blue space can mitigate heat in informal settlements

Global Warming is a major environmental problem that all kind of organisms has been affected at present. Urban Heat Island (UHI) is one of primary impacts of Global Warming. UHI is a phenomenon that the temperature of urban area is higher than surrounding rural areas or suburban areas. This increasing trend of temperature in urban areas affects many environmental entities such as air quality, water resources, habitats behaviors, climate changes. The most remarkable incident that relate with UHI, is the difference of thermal properties of the surfaces. Many countries experience the consequences of Urban Heat Islands in many aspects such as economic, health, social and environmental affects. Thus to mitigate such impacts of UHI, it is very important to identify the main reasons behind this. In this paper UHIs in Colombo, Gampaha Districts and the relationship between UHI and vegetation cover were analyzed based on Landsat 8, 30 m resolution data. Land Surface Temperature was derived from Landsat thermal Infrared band through several equations of United State Geological Surfay (USGS) guidelines using Arc GIS 10. Conversion of Digital Number (DN) values to Top of Atmosphere (TOA) Radiance, Conversion of TOA Radiance to Satellite Brightness temperature and final calculation of Land Surface Temperature considering land surface emissivity are the steps that had been done for the analysis. Vegetation cover was derived by using vegetation index with the Red and Near Infra Red bands. The result shows that the land high surface temperature directly relates with the urbanized regions where vegetation cover is very less. High temperature difference could be identified that cause to arise the urban heat island effects in Colombo & Gampaha districts. There is a strong linearly negative correlation with correlation coefficient value of -0.742 between land surface temperature and vegetation cover. 78.8 km2 (including water) of total area had been identified as NDVI value less than 0.1. And extent of high temperature area was 74.12 km2 where temperature more than 27˚C at 10.22 am. The area in temperature range of 25-27 was 464.95 km2 and area in NDVI value range 0.1-0.2 was 333.04 km2. 1471.1 km2 was identified as NDVI value between 0.3-0.4 and the area at low temperature was 1529 km2 where temperature less than 25˚C. According to this results, high temperature at non-vegetated areas and low temperature at vegetated areas could be noted very clearly. This is probably due to the ecological function of vegetation that lay down the surface temperature from high evapotranspiration. Vegetated areas are mostly sensed with surface temperature. Thus research output can be useful for policy-makers and planners of development projects such as Western province Megapolis project as well as for general public to understand the urban heat island effects and importance of vegetation cover to mitigate such impacts. Keywords: Urban heat island, Vegetation, NDVI, Arc GIS, Landsat, Sri Lanka

Urban Heat Island (UHI) effects adversely impact weather, air quality, and climate. Previous studies have attributed UHI effects to localized, surface processes. Based on an observational and modeling study of an extreme UHI (heat wave) episode in the Baltimore metropolitan region, we find that upstream urbanization exacerbates UHI effects and that meteorological consequences of extra‐urban development can cascade well downwind. Under southwesterly wind, Baltimore, MD, experienced higher peak surface temperatures and higher pollution concentrations than did the larger urban area of Washington, DC. Ultra‐high resolution numerical simulations with National Land Cover Data (NLCD) of 2001 show a nonlocal, dynamical contribution to UHI effects; when the upstream urban area is replaced by natural vegetation (in the model) the UHI effects could be reduced by more than 25%. These findings suggest that judicious land‐use and urban planning, especially in rapidly developing countries, could help alleviate UHI consequences including heat stress and smog.

The phenomenon of urban heat island (UHI) is based on the occurrence of elevated air temperature in urban areas, in relation to the surrounding urban, rural and forest areas. The aim of the study was the assessment of the UHI in Łódź in 2014-2019. Łódź is a large city with an area of 293.3 km2, located in the center of Poland. The UHI was defined as the difference of at least 1.5°C in daily minimum temperature at the point representing the commutative conditions (lower temperature) and daily minimum temperature at the point located in the city center (higher temperature). Based on data from the Weather Research and Forecasting (WRF)/CALMET models and the above criterium, the occurrence of UHI cases was analyzed. In summary the phenomenon of urban heat island in 2014-2019 was observed in every studied year, most often in 2018 with the highest UHI index almost 5°C, and increased over the years. The results proved that the highest UHI contrast was seen at night between 11 p.m. and 3 a.m. The study confirms that the UHI phenomenon in Łódź is persistent and has expanded in spatial extent over the years due to urban growth. Int J Occup Med Environ Health. 2024;37(6):581-90.