Accurate estimation of spatiotemporal precipitation dynamics is crucial for flash flood forecasting; however, it is still a challenge in Andean-Amazon sub-basins due to the lack of suitable rain gauge networks. This study proposes a framework to improve hourly precipitation estimates by integrating multiple satellite-based precipitation and soil-moisture products using random forest modeling and bias correction techniques. The proposed framework is also used to force the GR4H model in three Andean-Amazon sub-basins that suffer frequent flash flood events: upper Napo River Basin (NRB), Jatunyacu River Basin (JRB), and Tena River Basin (TRB). Overall, precipitation estimates derived from the framework (BC-RFP) showed a high ability to reproduce the intensity, distribution, and occurrence of hourly events. In fact, the BC-RFP model improved the detection ability between 43% and 88%, reducing the estimation error between 72% and 93%, compared to the original satellite-based precipitation products (i.e., IMERG-E/L, GSMAP, and PERSIANN). Likewise, simulations of flash flood events by coupling the GR4H model with BC-RFP presented satisfactory performances (KGE* between 0.56 and 0.94). The BC-RFP model not only contributes to the implementation of future flood forecast systems but also provides relevant insights to several water-related research fields and hence to integrated water resources management of the Andean-Amazon region.

In response to the growing need for effective hydrological hazard forecasting under increasing climate variability, this chapter presents the Global Water Level Forecast System (GWLFS), an open-source web platform that integrates Earth Observation data with ensemble hydrological forecasts to support real-time flood risk monitoring worldwide. The system combines satellite-derived water level observations from the Hydroweb project (over 20 000 virtual stations) with GEOGLOWS ECMWF streamflow simulations, applying a discharge-to-water level transformation (DWLT) method to generate a retrospective water level simulation and a 15 day 52-ensemble water level forecast. Using monthly duration curves, DWLT statistically links simulated discharge to satellite altimetry, enabling the estimation of synthetic water level time series and high-flow thresholds based on Gumbel-distributed return periods. Transformed outputs are visualized through an interactive web application built on the Tethys Platform, offering dynamic colour-coded warnings, forecast animations, historical comparisons and performance metrics. The system architecture utilizes high-performance tools such as Polars and a microservices backend to enable scalability and rapid processing across more than 25 000 monitoring stations. Validation using the Kling–Gupta efficiency (KGE) metric demonstrates the model's reliability across diverse hydroclimatic regions.

Accurate streamflow information is critical for early flood and drought warning. However, global hydrological forecasting systems are affected by residual errors in meteorological forcing, model structure, and routing, which propagate into simulated streamflow. Within the GEOGLOWS River Forecast System (RFS), ERA5 runoff biases are routed into streamflow simulations. The most effective operational bias-correction method, MFDC-QM, requires local discharge observations and cannot be applied consistently in ungauged basins. This study evaluates a pre-routing, grid-scale runoff bias-correction framework that adjusts ERA5 runoff before routing by combining Flow Duration Curve (FDC) mapping and Sparse Cumulative Distribution Function (CDF) matching, using GSCD as a spatially distributed reference runoff data. Baseline GEOGLOWS RFS, pre-routing correction, and MFDC-QM were compared for 1980–2025 using 16,517 gauging stations, Kling–Gupta Efficiency (KGE), and paired significance tests. Globally, the median KGE increased modestly from 0.16 to 0.22, compared with 0.48 for MFDC-QM. Results demonstrate a clear regional dependence: pre-routing correction produced statistically significant gains in South America and Africa (p < 0.05), where ERA5 runoff exhibits stronger residual biases, but had limited effects in Europe and North America, where dense hydrometeorological networks likely impose stronger observational constraints on the underlying reanalysis. These patterns show that pre-routing correction is most valuable where residual forcing bias is large and observational constraints are limited, complementing observation-based post-processing in ungauged, data-limited regions.