Subfield Moisture Modeling Using Shallow Water Overland Flow
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Solution Overview
Problem
Current computer-based tools for monitoring soil nutrient levels in agricultural fields rely on low-granularity topographical maps, which fail to capture subfield-specific hydrologic differences and nutrient variations, leading to inadequate crop yield predictions.
Innovation Solution
A system and method for generating high-granularity subfield-based soil moisture models using advanced mathematical equations and high-resolution elevation data, incorporating overland flow and shallow water models to predict hydrologic fluxes and nutrient levels at the field-pixel level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If zone-based low-granularity topographical maps are used, then device complexity is reduced, but measurement precision of soil moisture and nutrient levels deteriorates
Solution Approach 1:
The field is divided into multiple subfields based on elevation data and hydrologic boundaries. Each subfield is modeled independently with its own moisture and nutrient characteristics, allowing high-granularity predictions without requiring complex zone-based mapping systems. The segmentation is based on natural hydrologic boundaries identified through shallow water flow modeling.
Solution Approach 2:
The system applies local quality by providing subfield-specific soil moisture and nutrient level predictions rather than uniform zone-based estimates. Each subfield receives customized agricultural recommendations based on its unique hydrologic and topographic characteristics, improving measurement precision for localized conditions.
2Ease of operation
If zone-based mapping is used, then ease of operation is improved, but loss of information about subfield-specific hydrologic differences increases
Solution Approach 1:
The field is automatically segmented into hydrologic subfields using shallow water flow modeling and elevation data. This segmentation preserves subfield-specific hydrologic information while maintaining ease of operation through automated boundary detection and subfield identification, eliminating the need for manual zone delineation.
Solution Approach 2:
The system performs self-service by automatically identifying hydrologic boundaries and subfield configurations through shallow water flow modeling. This automated process preserves subfield-specific information without requiring manual intervention, maintaining ease of operation while preventing information loss.
3Measurement precision
If high-granularity subfield-based models are implemented, then measurement precision of soil moisture predictions is improved, but device complexity increases
Solution Approach 1:
The system extracts only the essential hydrologic parameters needed for subfield modeling from elevation data, such as flow directions, accumulation areas, and watershed boundaries. By taking out only the critical information needed for moisture predictions rather than modeling all field characteristics, the system achieves high measurement precision without excessive complexity.
Solution Approach 2:
The system uses shallow water flow modeling as an intermediary process to translate elevation data into meaningful hydrologic boundaries and subfield configurations. This intermediary step simplifies the complexity by providing a clear mathematical framework for deriving subfield parameters from raw elevation data, enabling high-granularity predictions without direct complex modeling.
4Loss of information
If subfield-level nutrient monitoring is implemented, then loss of information about nitrogen washout variations is reduced, but productivity requirements increase
Solution Approach 1:
The system performs preliminary action by pre-calculating hydrologic fluxes and water flow patterns through shallow water modeling before conducting nutrient analysis. This preliminary hydrologic characterization enables subsequent nitrogen washout predictions to be made more efficiently, reducing computational productivity requirements by avoiding repeated full-scale hydrologic-nutrient coupling calculations.
Data Source
AI summary
Subfield moisture model improvement in generating overland flow modeling using shallow water calculations and kinematic wave calculations is disclosed. In an embodiment, a computer-implemented data processing method comprises: receiving precipitation data and infiltration data for an agricultural field; obtaining surface water depth data, surface water velocity data, and surface water discharge data for the same agricultural field; determining subfield geometry data for the agricultural field; executing a plurality of water calculations and wave calculations using the subfield geometry data to generate an overland flow model that includes moisture levels for the agricultural field; based on, at least in part, the overland flow model, generating and causing displaying a visual graphical image of the agricultural field comprising a plurality of color pixels having color values corresponding to the moisture levels determined for the agricultural field. Output of the overland flow model is provided to control computers of seeders, planters, fertilizer spreaders, harvesters, or combines to control seeding, planting, fertilizing or irrigation activities in the field.