Nature-based Solutions as Building Blocks for coastal flood risk reduction: a model-based ecosystem service assessment

Abstract

Nature-based Solutions (NbS) are increasingly recognized as effective measures for mitigating flood risks and enhancing climate change adaptation. However, evaluating their efficacy in delivering flood risk reduction ecosystem service (FRR-ESS) is usually limited by reliance on qualitative, expert-based "quick-scan" scoring methods. While already challenging for present-day evaluations, this limitation becomes even more significant when addressing future climate scenarios, introducing deep uncertainties in the evaluation. The present study introduces a model-based framework to quantify FRR-ESS provided by coastal NbS, which integrates expert-based assessments with quantitative results from an eco-hydro-morphodynamic numerical model. The model enables a comparative evaluation of individual and combined effects of NbS following a Building Blocks approach. By integrating habitat map change prediction in the evaluation, NbS flood reduction response to present and future storm scenarios (i.e. wave climate and sea level rise) are investigated. The methodology is applied to a Mediterranean coastal lagoon in Sicily (Italy), and can be easily adapted to diverse coastal ecosystems. Our findings underscore the significant role of coastal habitats in reducing flood risk and highlight the importance of integrating physically-based modelling into FRR-ESS evaluation. This approach provides a robust and flexible tool for policymakers and stakeholders to make informed decisions that support both ecological sustainability and disaster risk reduction.

Fig. 1

Satellite image and overview of the test site, with bathimetries and wave rose (a), aerial view of inundation in the residential area of Granelli due to Helios storm (11th February 2023) (b), coastal erosion in front of the Granelli village (c). Satellite image in subpanel (a) was obtained from Planet.com ($\mathrm{\copyright }$ Planet Labs PBC, CC BY-NC-SA 2.0) and the figure was created by QGIS v3.22 software (https://download.qgis.org/downloads/).

Fig. 2

Map showing the present habitat conditions at the test site and the proposed NbS-BB. Satellite image was obtained from Planet.com ($\mathrm{\copyright }$Planet Labs PBC, CC BY-NC-SA 2.0) and the figure was created by QGIS v3.22 software (https://download.qgis.org/downloads/).

Fig. 3

Modelling chain applied to our case study (a): domain and grid of the SWAN model (b), domain and grid of the XBeach model (c), vegetation map for the No-NbS scenario (d).

Fig. 4

Methodology to correct variation of FRR-ESS rank scores $ES{S}_{\mathit{rs}}$ due to NbS-BB implementation based on NbS efficacy $E_{\mathit{NbS}}$ (a); computation of the ESS sigma score ${\sigma }_{\mathit{ESS}}$ depending on the relative ESS score $ES{S}_{\mathit{rel}}$ (b).

Fig. 5

Inundation maps for the hydrodynamic scenario S4.5-2100, with $H_S$ = 7.21 m, $T_p$ = 11.16 s, SLR = 0.57 m and $\theta$ = 262.5$_{}^{\circ }$N, for three different NbS-BB scenarios: no-NbS, NN (a); seagrass meadow reconstruction, SR (b); combination of seagrass meadow reconstruction and beach nourishment, SR+BN (c). City flooded surface $A_{\mathit{fc}}$ (d) and flooding reduction efficacy $E_{\mathit{fc}}$ for all investigated hydrodynamic scenarios (in shades of blue) (e).

Fig. 6

Habitat map for present state (a), S4.5-2070 SLR scenario (b) and S8.5-2100 scenario (c), corresponding ESS total scores; overview of sigma-scores ${\sigma }_{\mathit{ESS}}$ for all investigated scenarios.