An integrative nature-based planning model for sustainable and resilient environments

Abstract

Despite increasing policy recognition of Nature-based Solutions (NbS), a persistent gap remains between conceptual sustainability frameworks and reproducible, perfor mance-based planning applications supported by spatial modeling and ecosystem service quantification. This study addresses this gap by developing a spatially explicit NbS planning and evaluation framework for the Shenks Property along the Severn River within the Chesapeake Bay watershed, Virginia, USA. The research integrates GIS-based hydrological modeling, ecosystem-service assessment using the InVEST platform, and resilience evaluation under NOAA 2050 intermediate sea-level rise projections.Multiple spatial datasets, including land-use/land-cover classifications, LiDAR-derived elevation models, soil characteristics, biodiversity inventories, and tidal observations, were analyzed within an integrated analytical workflow. Results indicate measurable ecological and hydrological improvements under NbS-based scenarios. Predictive simulations demonstrate an approximately 40% (± 5%) reduction in annual surface runoff and an approximately 20 m retraction of projected flood boundaries under future sea-level rise conditions. Carbon-storage capacity increased by 18–24%, while habitat-quality index values improved by 15–22% within rehabilitated wetland zones. Increased vegetation density additionally contributed to localized microclimatic cooling of approximately 2–3 °C. The findings demonstrate that NbS can simultane ously support climate adaptation, ecological restoration, and long-term spatial resil ience while potentially reducing maintenance demands relative to conventional gray infrastructure systems. Methodologically, the study advances the operationalization of NbS by integrating ecosystem-service modeling, hydrological simulations, and adap tive spatial planning into a transferable performance-based planning framework. How ever, while the study offers robust predictive insights, its findings are based on mode ling and scenario-based simulations. Therefore, long-term post-occupancy monitoring is essential to validate ecological performance and system resilience.