From Empirical Assumptions to Data-Informed Decisions: A Reliable Water Storage Soil Depth Estimation Method

Efficient water use in agriculture is crucial for sustainable water resource management, especially in areas experiencing increasing water scarcity. A critical yet often oversimplified component of irrigation planning is the estimation of water storage soil profile depth, commonly assumed to be 1-1.5 m as the root-zone depth based on practitioner experience rather than validated soil-water dynamics. Such assumptions introduce uncertainty and limit the reliability of irrigation scheduling decisions. This study presents a novel framework for estimating soil profile depth to store maximum water by integrating Richards’ equation, geotechnical soil column concepts, and the Soil Conservation Service Curve Number (SCS-CN) technique to derive an optimal soil profile depth that maximizes storage capacity based on measurable hydraulic and retention soil properties. By linking the water storage soil column depth with the SCS-CN parameter, for practical field applications such as irrigation scheduling and planning. The proposed framework improves model reliability and interpretability by replacing fixed-depth assumptions with soil-specific storage behaviour, thereby reducing uncertainty in irrigation water estimation. It enables consistent evaluation of field capacity, average soil moisture content, and maximum storage potential across soil types, leading to improved irrigation efficiency. By emphasizing physically constrained model selection, data-informed parameterization, and transparent decision-making metrics, this work enhances the reliability of hydrologic modeling and supports robust irrigation management under water-scarce conditions.
Keywords:  Water storage soil profile depth, Richards’ equation, Irrigation water management, Data-informed parameterization, SCS-Curve Number method.