Sediment Data Reconstruction Using Multi-Method Segmentation
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Solution Overview
Problem
Existing methods for reconstructing long-series sediment data in data-lacking areas are inadequate, requiring large volumes of existing data and often unable to provide daily sediment concentration processes, thus failing to fully meet the needs of river governance, management, and protection.
Innovation Solution
A comprehensive reconstruction method involving data collection from hydrological and sediment stations, using flow-sediment content annual relationship curves, correlation methods between water quality and sediment data, adjacent station flow-sediment content relationships, and multi-year average flow-sediment content curves to calculate daily sediment data in data-rich and data-lacking years.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional reconstruction methods (correlation between flow and sediment transport rate, analogy method) are used, then some sediment data can be obtained, but the methods require large volumes of existing data and cannot provide daily sediment concentration processes
Solution Approach 1:
The patent segments the reconstruction process into five distinct methods based on data availability: (1) flow-sediment content annual relationship curve method for data-rich years, (2) correlation method between water quality and sediment data for partial data years, (3) adjacent station same year flow-sediment content relationship curve method for data-lacking years, (4) multi-year average flow-sediction content relationship curve method for remaining years, and (5) combination of methods. This segmentation allows selection of appropriate methods based on specific data conditions, resolving the contradiction between data volume requirements and precision needs.
2Loss of information
If conventional reconstruction methods are used, then some characteristic values of sediment transport can be inferred, but the methods cannot obtain the daily sediment concentration process
Solution Approach 1:
The patent employs dynamic method selection based on data availability conditions. The system dynamically switches between different reconstruction methods: using flow-sediment annual relationship curves when data is rich, correlation methods when partial data exists, adjacent station relationships when data is lacking, and multi-year averages for remaining cases. This dynamic approach ensures daily sediment concentration processes are obtained while maintaining reconstruction efficiency.
3Quantity of substance
If correlation method between flow and sediment transport rate is used, then sediment data can be interpolated and extended, but the method has high requirements for the volume of existing data
Solution Approach 1:
The patent merges multiple reconstruction methods into a comprehensive system. It combines flow-sediment annual relationship curve method, correlation method between water quality and sediment data, adjacent station flow-sediment relationship curve method, and multi-year average method. This merging allows the system to overcome the limitation of high data volume requirements by providing alternative pathways through different methods, thereby ensuring reconstruction reliability.
Data Source
AI summary
A comprehensive reconstruction method for long-series sediment data in data-lacking areas includes steps of: collecting hydrological and sediment data of a target river section; calculating sediment data in data-rich years with a flow-sediment content annual relationship curve method; calculating sediment data in only water quality and sediment test years with a correlation method between water quality and sediment data and hydrological station sediment data; calculating sediment data in data-lacking years with an adjacent station same year flow-sediment content relationship curve method; and calculating sediment data in remaining years with a multi-year average flow-sediment content relationship curve method. The method comprehensively adopts four methods to reconstruct the long-series sediment data based on sediment actual observation and characteristics in the data-lacking areas, which can make up for the limitations and deficiencies between the four methods, and the required data is easier to collect than those in the conventional methods.