Event-Based Hydrological Signatures to Quantify Soil Moisture Parameters in Agricultural Soils Under Contrasting Irrigation Practices in Humid Climates

<b><sc>Highlights</sc></b> <list list-type=bullet><list-item> Soil moisture time series can identify irrigation-relevant parameters. </list-item><list-item> An event-based approach accurately estimates FC and PEL. </list-item><list-item> Previous signature methods are less accurate in agricultural soils. </list-item><list-item> Stable deep soil moisture is a challenge to identifying different events. </list-item></list> <b><sc>ABSTRACT.</sc></b> Accurate estimation of soil moisture parameters, such as field capacity and permanent wilting point, is essential for improved irrigation management. However, these parameters can be challenging to accurately estimate in production conditions, and literature reference values are not always accurate for field-specific management. This study presents a technique for quantifying soil moisture parameters based on the identification of event-based hydrologic signatures in volumetric water content (θ) data. The data were collected from corn and cotton fields under different irrigation treatments (non-irrigated, full irrigation, precision irrigation) in Virginia during 2020 and 2021. The proposed method identifies various hydrologic events, including infiltration, gravity drainage, evapotranspiration, and rewetting, and estimates of field capacity and plant extraction limit values based on transitions and occurrence periods of these events. The results of the event-based analysis were compared to two existing hydrologic signature methods in terms of classification accuracy (determining whether soil moisture levels reached field capacity or plant extraction limit values for a given sensor) and estimation error (difference between signature and reference values). The event-based method resulted in higher classification accuracy (75.0%–79.2%) and lower estimation error (1.69%–1.87%) for field capacity determination across all sensors tested when compared to existing methods. Additionally, the event-based approach exhibited superior classification accuracy for plant extraction limit estimation, except for deep sensors (>30 cm) in 2021. By more accurately estimating hydrologic parameters using temporal patterns in volumetric water content data, this approach can ultimately support more efficient irrigation water management. More broadly, it contributes to a more thorough understanding of which signature methods are most suitable for different types of hydrologic, climatic, and analytical contexts.