A cloud-native modelling framework to quantify the multiple benefits of urban tree planting

Planting of trees in cities can have several benefits: they can store carbon, increase biodiversity, reduce urban heat and pollution, and mitigate flooding. To inform investment into tree planting as a climate change adaptation solution, it is vitally important that all these potential benefits can be outlined and understood by relevant stakeholders. Here we present a fully integrated, scalable, cloud-based modelling framework to provide such insights, built using open-source models and datasets.

Within this framework, the Green Urban Scenarios (GUS) model simulates tree growth and attempts to quantify several of these impacts including carbon storage, annual water storage, and air pollution. In order to measure the impacts of tree planting and growth scenarios on surface water (pluvial) flooding we combine the GUS model with a design storm model which allows us to quantify the impact of different tree planting scenarios on individual rainfall events, including future climate change scenarios. We then input the adjusted rainfall into a pluvial simulation flood model, the IBM Integrated Flood Model (IFM) to produce maps of estimated flood depth. Finally, we combine flood depth with OpenStreetMap data to estimate the impact to assets such as buildings, transport networks and energy infrastructure.  

The models are integrated into a complete end-to-end workflow using a cloud-native, scalable modelling framework based on Kubernetes and OpenShift. Open datasets for England are used to obtain tree locations, historical rainfall data, climate projections, soil data, elevation models and land cover data and the workflow can be run for where the input data are available. We provide examples for several cities and towns in England, demonstrating how the framework enables users to quickly and easily summarise the potential benefits of tree planting scenarios for different regions, and for current and future climate change scenarios.