Reply on RC1

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> Groundwater is an essential part of the water supply worldwide and the demands on this water source can be expected to increase in the future. To satisfy the needs and ensure sustainable use of resources, increasingly detailed knowledge of groundwater systems is necessary. However, it is difficult to directly map groundwater with well-established geophysical methods, as these are sensitive to both lithology and pore fluid. Surface nuclear magnetic resonance (SNMR) is the only method with direct sensitivity to water and it is capable of non-invasively quantifying water content and porosity in the subsurface. Despite these attractive features SNMR has not been widely adopted in hydrological research, the main reason being an often-poor signal-to-noise ratio, which leads to long acquisition time and high uncertainty on results. Recent advances in SNMR acquisition protocols based on a novel steady-state approach has demonstrated the capability of acquiring high quality data much faster than previously possible. In turn, this have enabled high-density groundwater mapping with SNMR. We demonstrate the applicability of the new steady-state scheme in three field campaigns in Denmark where more than 100 SNMR soundings with approximately 30 m depth of investigation were conducted. We show how the SNMR soundings enables us to track water level variations at the regional scale and we demonstrate a high correlation between water levels obtained from SNMR data and water levels measured in boreholes. We also interpret the SNMR results jointly with independent transient electromagnetics (TEM) data, which allows us to identify regions with water bound in small pores. Field practice and SNMR acquisition protocols where optimized during the campaigns, and we now routinely measure high-quality data on eight to ten sites per day with a two-person field crew. Together, the results from the three surveys demonstrate that with steady-state SNMR it is now possible to map regional variations in water levels with high quality data and short acquisition times.