Lundberg, Viktor2024-06-142024-06-142024-06-14https://hdl.handle.net/2077/81727The expected climate change will increase the heat stress that our population will experience in the future. Heat waves can have serious consequences, especially for vulnerable groups of people like children and the elderly. The mortality rate in Sweden increased by roughly 10% during the summer of 2018, when Sweden experienced a record-breaking heat wave. To mitigate the effects of future heat waves, both immediate and preventative measures must be taken on a local scale to protect vulnerable groups of people living in Sweden. One of the municipalities needing information in order to make decisions regarding heat stress mitigation is Alingsås. This kind of information is currently not readily available. With the help of GIS and the SOLWEIG model, this report aims to create a mean radiant temperature map and a risk classification over Alingsås municipality in order to show where high mean radiant temperatures and the settlement of vulnerable groups coincide. IPCCs seventh report claims that the current greenhouse gas emissions will have an impact on our climate for many decades to come, which will contribute to more frequent and extreme weather events such as heat waves. Highly urbanized societies will be more severely affected than rural populations since the local temperatures in urban areas often exceed those of the municipality average during heat waves due to lack of vegetation, impermeable surfaces and reflective properties of building facades among others. Since Sweden has a very large number of urban citizens (nearly 90%), our population will arguably be more affected by future heat waves than most other countries on Earth. In 2022, Sweden experienced the warmest summer since 1947 and the third warmest day since 1858. The creation of a mean radiant temperature map was done through QGIS and the SOLWEIG model, which required pre-processing of meteorological data, LiDAR-data and landcover data in order to be used by the SOLWEIG model. LiDAR-data and landcover data from Lantmäteriet and meteorological data from ERA5 and SMHI (based on IPCC) were used. To create a risk classification, population data from SCB was combined with the mean radiant temperature map. With help from the risk classification and the mean radiant temperature map, risk zones were selected which were studied further. Histograms containing mean radiant temperature over these zones were created and compared to the municipality average, and scatterplots containing the correlation between vegetation and radiant temperatures were made. Results show that five areas in the municipality have both high radiant temperatures and high concentrations of vulnerable groups of people. These are Stockslycke, southern Noltorp, Brogården, Ingared and Sollebrunn. Three of these zones lie within the borders of Alingsås city. Further examination of the increase in radiant temperatures over time show that single trees will have a lessened effect on the radiant temperatures in the future, while forests and parks will retain their capability to decrease radiant temperatures on the ground. The correlation between normalized vegetation volume and radiant temperature is strong, and the optimal tree height for suppression of high radiant temperatures is between 10-15 meters. Therefore, planting small parks or large groups of trees together in areas with high radiant temperatures will both prevent heat stress and remain effective in future scenarios. A quantitative solution was presented since the identified risk zones consist of large open areas. Hence, they do not share the space limitation that highly urban areas have, which often require a qualitative solution.swe