Estimation of Flood Thresholds for Hydrological Warning Purposes Using Sentinel-1 SAR Imagery-Based Modeling in the Tumbes River Basin (PERU)

Abstract

Flood monitoring in dry tropical basins, such as the Tumbes River (Peru), faces critical challenges due to persistent cloud cover that restricts the operability of optical sensors during extreme events, coupled with the operational gap between satellite products and conventional hydrological monitoring. To overcome these limitations, this research developed a comprehensive methodological framework in Google Earth Engine that unifies automated image thresholding and Sentinel-1 SAR time series analysis for flood detection and the estimation of early warning thresholds. The Bmax Otsu and Edge Otsu algorithms were evaluated, previously calibrated using high-resolution imagery (PlanetScope) as reference data, topographically constrained by the HAND (Height Above the Nearest Drainage) model, and validated against established change detection algorithms. The analysis of seven hydrological events between 2017 and 2024 confirmed the statistical superiority of Bmax Otsu; although both methods achieved high overall accuracy (Bmax 95.8% versus Edge 95.7%), Bmax Otsu outperformed Edge Otsu in spatial consistency (Kappa 66.1% vs. 63.7%; IoU 45.6% vs. 45.0%). Based on this, a time series analysis was applied to discriminate permanent water bodies and isolate flood dynamics. Subsequently, the functional discharge–impact response was evaluated by linking the instantaneous flood extent captured by the SAR overpasses to their corresponding peak discharges. Validated against official INDECI damage reports, it was determined that significant impacts begin at an activation threshold of 743.49 m3/s (151 flooded ha, 157 affected inhabitants) and scale linearly up to extreme peak events of 1629.02 m3/s, compromising 1234 agricultural ha and 749 inhabitants. This methodology provides a validated, low-cost tool to translate SAR observations into critical thresholds for early warning systems in data-scarce regions.