Leveraging machine learning and satellite precipitation data to overcome latency challenges in operational hydrology

Accurate and timely representation of spatiotemporal precipitation patterns is critical for monitoring and predicting hydrological extremes, particularly in operational hydrology and early warning systems. In regions with limited in-situ precipitation data, satellite precipitation products (SPPs) offer an accessible solution. However, the latency of these datasets—the delay between data collection and availability—remains a key challenge for real-time applications. This study developed a machine learning model based on the Random Forest (RF) algorithm to predict precipitation using low-latency data from GOES-16 Advanced Baseline Imager (ABI) bands. The model was applied to the Jubones River basin (3,391 km²) in southern Ecuador, a region characterized by complex terrain and hosting a key hydropower project. Leveraging hourly data over a five-year period, the RF model addressed the five-hour latency of traditional SPPs by generating near-real-time precipitation maps with a latency of only 10 minutes. The model’s performance was evaluated using quantitative and qualitative metrics across temporal scales, demonstrating progressive accuracy improvements with larger temporal aggregations. Root Mean Square Error (RMSE) values decreased from 0.48 to 0.05 mm/h, while Pearson’s Cross-Correlation (PCC) improved from 0.59 to 0.87 for scales ranging from hourly to monthly. Qualitative metrics, including Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI), further validated the approach. These findings highlight the potential of integrating advanced hydroinformatics techniques with remote sensing for managing hydrological extremes in diverse basins. The study underscores the importance of leveraging low-latency satellite data and machine learning to enhance real-time forecasting and operational hydrology. Future work will focus on refining the model for improved detection of extreme precipitation events and exploring its integration into stakeholder-driven decision-making frameworks.