Integrating UAV Observations and Atmospheric Modeling for Local-Scale Climate Adaptation and Governance

The increasing variability of local climatic conditions and the growing complexity of adaptation governance demand robust, data-driven tools for near-surface atmospheric monitoring. This paper investigates the integration of unmanned aerial vehicle (UAV) observations with atmospheric modeling for localized climate adaptation planning. By combining high-resolution meteorological measurements with open-source mesoscale models, the study proposes a multi-layered framework for linking atmospheric data with land-use, hydrological, and socio-environmental indicators. UAVs were used to collect microclimatic data—including surface temperature, relative humidity, and aerosol density—across heterogeneous agricultural and peri-urban landscapes. These datasets were assimilated into Weather Research and Forecasting (WRF) and openLISEM models to assess short-term variability in evapotranspiration, flood susceptibility, and atmospheric stability. The findings demonstrate that UAV-enabled atmospheric data improve local-scale model accuracy by up to 25%, significantly enhancing predictive reliability for adaptation-related decision-making. The paper further contextualizes these advancements within the Paris Agreement’s Article 7 on adaptation and Article 13 on transparency, suggesting that UAV-based atmospheric monitoring can strengthen compliance and evidence-based governance. By merging climate modeling, digital sensing, and legal-policy perspectives, this research contributes to a new paradigm of integrated atmospheric governance—where technology serves as both a diagnostic and regulatory instrument for resilience and sustainable development. Ultimately, this integration reinforces the adaptive capacity of the Global South, advancing the Sustainable Development Goals (SDGs 9, 11, and 13) by fostering resilient infrastructure, sustainable urban–rural systems, and climate-informed governance.