Technical Note: Spectral correction for cavity ringdown isotope analysis of plant and soil waters

Abstract. The development of laser spectroscopic analysers has revolutionized isotope hydrology, dramatically increasing accessibility and reducing the cost of sample analysis. Despite their substantial benefits, these instruments are known to suffer from spectral interferences caused by small organic molecules that can bias measurements of some samples. Previous research has characterized this problem and tested a range of solutions for eliminating, detecting, or correcting influence in experimental or natural samples, yet interlaboratory comparisons show that affected data are still being reported. Here, we use paired spectroscopic (Picarro L2130-i; CRDS) and mass spectrometric (IRMS) data from a diverse suite of soil and plant xylem water samples to characterize spectral interference effects on CRDS δ²H and δ¹⁸O data. Interference is minimal for soil water but widespread in plant samples, with 13 % and 54 % of samples exhibiting biases larger than 8 ‰ for δ²H and 1 ‰ for δ¹⁸O, respectively. We develop multivariate statistical models that use analyser-reported spectral features to correct for interference. These models account for 57 % of the observed δ²H bias and 99 % of the δ¹⁸O bias, and after correction the standard deviation of the CRDS-IRMS differences for plant samples (4.1 ‰ for δ²H and 0.4 ‰ for δ¹⁸O) was similar to that for soil samples. Applying the models to CRDS measurements of water extracted from 1176 plants and 693 soils collected across diverse ecosystems improves the correspondence between plant and source soil water values and shows strong taxonomic differences in the prevalence of spectral interference. Our results show that spectral interference remains a significant concern in ecohydrology, particularly for plant water extracted from many woody species. The success of our spectral correction models across a wide range of taxa and data generated from two different CRDS analysers suggests that post-hoc correction of these data may be a viable solution to the problem.