Flood Prediction Using Satellite Soil Moisture Data Assimilation
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Solution Overview
Problem
Current methods for predicting landslide and flood disasters using soil water and basin water indices are insufficient in prediction accuracy, as they rely solely on rainfall data and face challenges in measuring soil moisture content, especially in areas where sensor placement is difficult, leading to inadequate wide-area coverage and accuracy.
Innovation Solution
A disaster prediction system that includes soil moisture acquisition, ground surface moisture acquisition, and estimation means to calculate moisture content at specified and freely-selected sites, using a combination of local measurements, data assimilation, and wide-area observations to correct and estimate soil moisture content across a wide area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If soil moisture content is measured using sensors at specific locations, then measurement precision is improved, but device complexity and cost increase, and wide-area coverage becomes difficult
Solution Approach 1:
The patent introduces satellite remote sensing as an intermediary tool to measure soil moisture content over wide areas without requiring extensive sensor deployment. The satellite-based observation system acts as a mediator between the need for precise moisture measurement and the constraint of limited sensor placement capability, enabling area-wide monitoring through remote sensing technology.
Solution Approach 2:
The patent replaces the mechanical sensor-based measurement system with a satellite remote sensing system. Instead of using physical sensors that require installation and maintenance on the ground, the system uses satellite-based electromagnetic radiation detection to measure soil moisture content, thereby eliminating the complexity associated with deploying and maintaining extensive sensor networks.
2Area of stationary object
If soil water index and basin water index are used for disaster prediction, then wide-area coverage is achieved, but prediction accuracy deteriorates due to reliance solely on rainfall data
Solution Approach 1:
The patent merges satellite-based soil moisture content observation data with rainfall data to create an enhanced disaster prediction system. By combining these two data sources, the system achieves both wide-area coverage from the satellite observations and improved prediction accuracy from the integrated multi-parameter analysis, overcoming the limitations of using rainfall data alone.
Solution Approach 2:
The patent incorporates real-time satellite observation data as feedback into the disaster prediction model. The system continuously updates soil moisture content measurements from satellite observations and feeds this information back into the prediction system, allowing for dynamic adjustment of disaster risk assessments and improving overall prediction accuracy through continuous data validation.
Data Source
AI summary
Moisture content information in soil at a specified site is acquired. Moisture content information a ground surface within a given range that includes the specified site is acquired. An amount of runoff or storage volume of water on the ground surface or in a ground at a freely-selected site in the given range is calculated based on the moisture content information in soil and the moisture content information at the ground. A degree of risk of flood disaster at the freely-selected site within the given range is calculated based on the amount of runoff or storage volume of water on the ground surface. A point where there is the risk of flood disaster within the given range is determined and highlighted based on the degree of risk of flood disaster.