Adaptive Approaches for Real‐Time Estimation of Kostiakov–Lewis Furrow Infiltration Parameters to Field Conditions

Abstract

ABSTRACT Real‐time Kostiakov–Lewis infiltration parameters estimation in furrow irrigation is challenging. The Direct VB (volume balance) approach, using an iterative volume balance method with direct field data and regression‐based curve fitting, produced inconsistent estimates. This study introduces three adaptive approaches (Storage‐VB adjustment, Time‐based VB and Advance‐simulating) that consider field irregularities such as cracks and undulation. The adaptive approaches were tested on 22 furrows (28–64 m) with real‐time data and 12 furrows (235 m) with experimental data. All approaches achieved high performance in advance distance simulation. However, the Direct VB showed inconsistency across furrows in parameter a (dimensionless exponent of infiltration decline over time), reaching a 73% coefficient of variation (CV), whereas the adaptive approaches resulted in a CV below 36% in all fields. The adaptive approaches also showed better agreement than the Direct VB approach during independent validation, making them preferable despite no statistical bias. The Advance‐simulating outperformed for the parameter f o , whereas for k , no single approach outperformed. The model's cross‐validation identifies both outlier and representative furrows, supporting reliable real‐time parameter estimates. The Furrow_Inf tool contributes to improved irrigation water management by translating these advances into practice through real‐time input and statistical evaluation to generate field‐representative parameters.