About GRRFGS:
GRRFGS is an automated high resolution distributed hydrological model leveraging remote sensing data for flood prediction by using advance analytics solution. The introduced technology seamlessly integrates smart algorithms with satellite based high resolution information. Traditional hydrological models often suffer from data scarcity and calibration complexities. This new model addresses these challenges by integrating readily available remote sensing datasets, enabling efficient and accurate simulations.
Model Concept
The model employs a pixel based, at high resolution of 250m, data-driven approach, utilizing diverse remote sensing products as primary inputs. It automates key hydrological processes (interception, abstractions, evaporation, infiltration, overland flow, discharge and velocity), minimizing manual intervention and enabling real-time or near-real-time simulations. The core concept is to translate remotely sensed observations into accurate estimates of hydrological states and fluxes at pixel level.

Key Features:
• Automated Data Ingestion: Seamless integration of multi-source remote sensing data (e.g., precipitation, soil moisture, evapotranspiration, land cover, elevation).
• Spatially Distributed Modelling: Gridded approach to capture spatial variability in hydrological processes.
• Machine Learning Integration: Utilization of machine learning algorithms for parameter estimation, data assimilation, and enhancing accuracy and efficiency.
• Modular Design: Flexible framework allowing integration of new remote sensing products and hydrological algorithms.
• Real-Time or Near-Real-Time Capabilities: Designed for rapid processing and output generation, enabling timely decision-making.
• Cloud-Based Architecture: Scalable and accessible platform for data processing and model execution.
Data Processing
• Satellite-based precipitation estimates calibrated with local in-situ data.
• Evapotranspiration quantification by applying solar radiation algorithm and incorporating satellite products from thermal infrared sensors.
• Intercepted rainfall is calculated by Leaf Area Index approach.
• Soil losses – infiltration rates are calculated using modified Green- Amp loss method
• Overland flow/surface runoff quantification is based on modified manning n roughness scheme.
• Integration of terrain information - Digital Elevation Model (DEM) for discharge and velocity estimation at sub-catchment level.
Benefits:
• Reduced reliance on ground-based observations. Efficient for data scarce areas.
• Improved spatial and temporal resolution of hydrological simulations.
• Enhanced accuracy and efficiency through automation.
• Increased accessibility and scalability through cloud-based architecture.
• Real-time or near-real-time monitoring and forecasting capabilities.
• Reduced cost of model implementation and running.
Implementation:
The model will be developed using a modular and open-source framework, facilitating collaboration and future enhancements. Implementation will involve:
• Data acquisition and pre-processing.
• Development and integration of hydrological algorithms.
• Integration of machine learning algorithms.
• Cloud-based deployment and testing.
• Validation and calibration against observed data.
• User interface development.
Sustainability:
Long-term sustainability will be ensured through continuous data updates, model refinement, and community engagement. Regular validation and performance evaluations will be conducted to maintain model accuracy and reliability.

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