Eliška K. Lorencová (Czech Republic) 1; Jan Geletič (Czech Republic) 2; Timon Mcphearson (United States of America) 3; Petr Bašta (Czech Republic) 1; Vojtěch Cuřín (Czech Republic)
1 - Global Change Research Institute of the Czech Academy of Sciences (CzechGlobe); 2 - Institute of Computer Science of the Czech Academy of Sciences; 3 - Urban Systems Lab, The New School
Cities are complex and dynamic systems and understanding of these complexities requires new methods and approaches. Moreover, specific information on climate change risks (such as surface overheating and longer heatwaves) in the cities is needed to support decision-making regarding future urban adaptation planning. Stakeholder involvement through public participation and knowledge co-production is gradually gaining attention in finding sustainable urban solutions. In case of climate change impacts and adaptation, citizen science approaches, such as crowd-sourced mapping, can provide valuable finer scale information based on local knowledge and perspectives of the citizens. However, there is a very limited use of citizen science and crowd-sourcing information for mapping climate-related risks and adaptation planning in urban environments.
The aim of this paper is to illustrate, based on a case study of climate change adaptation in the city of Prague (Prague 6 district), a novel approach to integrate citizen science data on climate change-related heat risk with modelling approaches to simulate heatwaves in urban settings.In this study were utilized data from the crowd-sourcing heat risk mapping exercise as well as suggestions for particular adaptation measures and integrated these outcomes with the results of urban climate model simulations in order to support the local scale adaptation planning.In total, 504 respondents identified 1,220 locations of extreme heat and proposed 712 adaptation measures using a web-based mapping tool. Within the modelling approach, MUKLIMO_3 urban climate model was used for meteorological simulations as well as spatio-temporal analysis of patterns during heatwave period in the Prague 6. The hourly time-step modelling results for air temperature in the heatwave between 16.00 and 2.00 CEST (31 July to 1 August 2017) were analyzed. We identified heat related hot-spots, in which the air temperature reached upper 30%, respectively 40% from the range of minimum and maximum temperatures.
In the next step, we integrated the crowd-sourcing spatially explicit data of perceived extreme heat locations (by stakeholders) with heat hot-spots from modelling results. The results show a very strong match, in case of upper 30% heat modelling hot-spot, there is a 79% overlap between the citizen data and the actual heat simulation results. The upper 40% heat modelling hot-spot shows even stronger intersection, 96% of data acquired from citizens overlaps with heat modelling hot-spots. Outcomes of the participatory crowd-source mapping indicate further opportunities for mainstreaming these approaches into adaptation planning as well as use of the data for validation of the heat modelling results.