The 2023 Hedge Pact aims to plant 50,000 km of hedgerows across France by 2030, highlighting their multifunctional role. In particular, hedgerows—composed of woody and shrubby plants—can increase infiltration and reduce runoff, thereby modifying watershed hydrology. While their effects on surface flows are well documented, their impact on deeper hydrological processes (such as percolation, recharge, and evapotranspirative uptake) remains largely unexplored, especially in the deeper compartments of the critical zone (vadose zone, groundwater table). These questions are particularly important in the context of climate change, which disrupts hydrological dynamics. Hedgerows also influence contaminant transport, such as pesticides, although it is unclear whether this effect is beneficial (enhanced infiltration and higher organic matter content promoting contaminant retention) or detrimental (accelerated transfer via macroporosity induced by root networks). Distinguishing the relative importance of these processes is key to quantifying the impact of these landscape features on both the quantity and quality of water resources in agricultural environments, depending on their agro-pedoclimatic context.

This PhD project aims to quantify the impact of hedgerows on the dynamics of hydrological flows at various spatial and temporal scales—from the local scale around the hedge to the watershed scale, and from event-based to seasonal or annual scales. To achieve this, a combined approach of field observations and numerical hydrological modeling is implemented. Observations mainly rely on Electrical Resistivity Tomography (ERT), conducted continuously beneath a hedgerow using the OhmPi resistivity meter developed by Clément et al. (2020) at REVERSAAL, INRAE (Figure 1).

This method provides detailed imaging of natural moisture evolution within the soil profile and root zone at different depths. Frequency Domain Reflectometry (FDR) probes also continuously measure soil moisture and temperature, providing complementary data. The triggering of ERT measurements is optimized by the OhmPi system, which links on-site data (moisture and precipitation) to automatically adjust acquisition frequency, allowing relevant rapid hydrological variations to be captured according to environmental conditions without unnecessary data collection.

Hydrological modeling is then used to identify the processes behind the observations, particularly the temporal evolution of soil water content (Figure 2), and to link these with observed changes in electrical resistivity (Figure 3). This helps to better understand the hedge’s influence on flows and quantify related fluxes (infiltration and evapotranspiration). The study site is a hedgerow located in the Yzeron watershed (Mercier sub-basin) near Lyon.

The methodology is structured around three main steps:

1. Study of infiltration during a rainfall event: The objective is to analyze how hedgerows influence deep infiltration during rainfall events by examining the mobilized compartments (pore matrix, macropores, bioturbation) and their effects on transfer times. This approach builds on previous studies, notably Kowalski (2020), who tracked water flows under a hedge using a tracer in short-term forced infiltration conditions.
2. Detailed analysis of infiltration and evapotranspiration at the seasonal scale: This phase integrates post-rainfall dynamics by quantifying recharge and evapotranspiration beneath the hedge over time. It will provide insights into hedgerow influence during dry periods when evapotranspiration is dominant.
3. Seasonal dynamics at the watershed scale: The analysis is extended to the watershed scale to study the impact of meteorological chronologies on hydrological fluxes. Simulations will incorporate results from the previous steps to quantify the partitioning of fluxes (recharge, evapotranspiration, lateral flows) in a hedgerow-integrated landscape under various climatic conditions.

Expected results of this PhD will improve understanding of how hedgerows regulate water fluxes both locally and at the watershed scale, accounting for seasonal effects and climate change. This knowledge should contribute to better hedgerow management in agricultural landscapes and enhance their role in preserving water quality and contaminant management.

Funding :

• 50% H2O’Lyon, University of Lyon
• 50% AQUA Department, INRAE

References :

• Clement, Rémi, Yannick Fargier, Vivien Dubois, Julien Gance, Emile Gros, and Nicolas Forquet. 2020. “OhmPi: An Open Source Data Logger for Dedicated Applications of Electrical Resistivity Imaging at the Small and Laboratory Scale.” HardwareX 8: e00122. doi:10.1016/j.ohx.2020.e00122.
• Kowalski, Elia. 2020. Etude de l’influence des haies et talus sur les transferts de contaminants réactifs : apport de la géophysique. INRAE: INRAE.