Integrating the measurement of Soil Water Content by proximal Cosmic-Rays Neutron Sensors in the assessment of wildfire susceptibility

It is widely recognised that the ability of measure Soil Water Content (SWC) is crucial to improve early warning systems for environmental hazards like floods, droughts, landslides, avalanches and wildfires. However, hydrological variables are notably more difficult to measure than meteorological variables. Common technologies to measure SWC are invasive point-scale probes, which are hardly representative of a wider area, unsuitable for coarse-textured soils and easy to be broken or lost. The main alternative is remote sensing, which suffers limits related to spatial resolution, measurement depth and continuity.

As an attempt to compensate for the lack of measurements of hydrological variables, computational models are widely used to derive them from meteorological ones. For example, the Canadian-developed Fire Weather Index (FWI) relies mainly on precipitations and temperature to evaluate the dryness of the soil. Indeed models still need to be validated and improved using measured data.

Proximal sensors based on the concept of Cosmic Rays Neutrons Sensing (CRNS) emerged as a reliable option for non-invasive measurement of SWC, within a large footprint (hectares), in depth (tens of cm) and with sub-daily resolution. CRNS is based on the detection of neutrons, which are generated in the atmosphere by the interaction of cosmic rays (high energy particles naturally flowing from space), then backscattered by the soil and effectively absorbed by water, due to their strong interaction with hydrogen. CRNS systems can easily be integrated in meteorological stations and operate autonomously also in remote areas, while transmitting the data for a real-time monitoring.

In the framework of the MOSAIC Project*, six CRNS systems manufactured by Finapp were installed in sites selected to span different altitudes vegetation types and exposures, integrating them into pre-existent meteorological stations. Computation of the FWI is also available for the same sites. We will compare the information provided by the CRNS with the output of the FWI and discuss how the model can be improved by integrating the SWC measurement.

*This work is part of the MOSAIC Project (Managing prOtective foreSt fAcIng clImate Change compound events), co-funded by the European Union through the Interreg Alpine Space programme (Project ID: ASP0100014), and it involves the use of data provided by courtesy of ARPAV (Dipartimento Regionale per la Sicurezza del Territorio).