Low-cost Efficient Wireless Intelligent Sensors (LEWIS) IoT-Based Rainfall Monitoring

Rainfall measurement with high spatial and temporal resolution is critical for flood forecasting, drought mitigation, and disaster preparedness. Rainfall patterns are highly variable, both geographically and over time. This variability presents a significant challenge for monitoring, as rain gauges can accurately capture temporal patterns only at a single location. Furthermore, the high cost of commercial instruments restricts their widespread deployment, and rain gauge networks often fail to adequately capture the spatial heterogeneity of precipitation patterns. To address these limitations, this study introduces a low-cost IoT-based rainfall monitoring system developed upon the Low-cost Efficient Wireless Intelligent Sensor (LEWIS) platform. The contribution of this research is a pilot development and preliminary field validation to test its potential. Four rainfall sensors were designed, developed, and deployed at different locations across the semi-arid region of the United States, in the State of New Mexico, to capture localized precipitation variability. Each sensor node integrates a rainfall detection module with an LTE-enabled microcontroller and is powered by a compact solar-battery system, ensuring autonomous and self-sufficient operation. Real-time precipitation data are transmitted to a cloud server for continuous access and visualization. During a 5-month field trial covering the New Mexico monsoon season, co-located LEWIS 6 sensors achieved high repeatability at a fraction of the cost of conventional gauges. Comparisons with volunteer observations, model-based precipitation data, and radar-derived rainfall estimates highlight the important observational gap that IoT systems, such as LEWIS 6, can fill.