Enhancing Resilience Through Low-Cost Sensors and High-Resolution Coastal Predictions for Early Warning Systems

Coastal regions are increasingly vulnerable to extreme weather events and rising sea levels, posing significant risks to human lives, infrastructure, and ecosystems. Enhancing resilience in these areas demands innovative and cost-effective solutions for early warning systems. This study explores the integration of low-cost and do-it-yourself (LC+DIY) sensor devices and networks with high-resolution coastal prediction models to improve early warning capabilities for at-risk coastal communities. By leveraging advancements in IoT technology, the proposed sensor networks can monitor key environmental parameters, such as water levels, waves, and water temperature and salinity, in real time. Several examples of sensors, along with their applications across various continents, highlight the suitability of low-cost sensors, particularly in scenarios requiring extensive data collection and in geographically diverse contexts such as developing countries. These LC+DIY examples range from laboratory experiment comparisons to the development of monitoring system networks in Mozambique (eastern Africa). The system's affordability and scalability make it accessible to resource-constrained regions, addressing gaps in traditional (i.e. commercial) monitoring systems. This approach underscores the potential of integrating low-cost technologies with advanced modelling to safeguard coastal communities and ecosystems against climate-related hazards. Moreover, these initiatives also present significant opportunities, including fostering citizen science through collaborative approaches, such as integrating open-source platforms. The next steps include conducting further inter-comparisons with commercial devices, empowering local communities through an open science approach, and the ongoing development and refinement of LC+DIY prototypes to enhance their functionality and accessibility.

 

Acknowledgments

This work is funded by ECOBAYS (PID2020-115924RB-I00) from the Agencia Estatal de Investigación - Spain