Comparing three spatial modeling tools for assessing urban ecosystem services

The (re)integration of nature into cities has been a progressively promoted strategy to foster sustainable urbanization. To quantify the ecosystem services (ES) provided by urban nature, spatially explicit ES modeling is a key method. However, particularly in absence of independent validation data, there is a clear need for multi model assessments in order to better understand uncertainties in model outcomes. Here we applied three commonly used open-source ES models (i.e., InVEST, LUCI, NC-Model) to quantify three key urban ES (i.e., local temperature regulation, flood protection and global climate regulation) using the city of The Hague (The Netherlands) as a case study. We quantified the three ES for the current situation and under two hypothetical scenarios representing changes in the amount of vegetation within the city. We found mostly positive correlations between the estimates for a given ES (Spearman's rho from 0.11 to 0.84). Yet, our comparison also revealed systematic differences in the ES indicator values between the ES models, as well as different responses to the scenarios. These differences may stem from differences in model structure (i.e., differences in biophysical processes accounted for) and model parameterization (i.e., differences in the value used to quantify a given biophysical process). To further advance urban ES modeling, we recommend i) to improve the representation of urban nature (e.g., green roofs, bioswales, gardens) and urban-specific conditions and processes (e.g., drainage systems, building patterns, soil characteristics) in urban ES models and ii) to systematically account for uncertainty in (urban) ES assessments (e.g., through multi-model assessments).

Automated summary

The integration of nature into cities promotes sustainable urbanization. To quantify the ecosystem services provided by urban nature, spatially explicit modeling is crucial. However, multi model assessments are needed to understand uncertainties in model outcomes, especially when independent validation data is lacking.