In this commentary, we summarize and build upon discussions that emerged during the workshop “Isotope-based studies of water partitioning and plant–soil interactions in forested and agricultural environments” held in San Casciano in Val di Pesa, Italy, in September 2017. Quantifying and understanding how water cycles through the Earth's critical zone is important to provide society and policymakers with the scientific background to manage water resources sustainably, especially considering the ever-increasing worldwide concern about water scarcity. Stable isotopes of hydrogen and oxygen in water have proven to be a powerful tool for tracking water fluxes in the critical zone. However, both mechanistic complexities (e.g. mixing and fractionation processes, heterogeneity of natural systems) and methodological issues (e.g. lack of standard protocols to sample specific compartments, such as soil water and xylem water) limit the application of stable water isotopes in critical-zone science. In this commentary, we examine some of the opportunities and critical challenges of isotope-based ecohydrological applications and outline new perspectives focused on interdisciplinary research opportunities for this important tool in water and environmental science.

This study evaluated between-sample memory in isotopic measurements of δ²H and δ¹⁸O in water samples by laser spectroscopy. Ten isotopically depleted water samples spanning a broad range of oxygen and hydrogen isotopic compositions were measured by three generations of off-axis integrated cavity output spectroscopy and cavity ring-down spectroscopy instruments. The analysis procedure encompassed small (less than 2‰ for δ²H and 1‰ for δ¹⁸O) and large (up to 201‰ for δ²H and 25‰ for δ¹⁸O) differences in isotopic compositions between adjacent sample vials. Samples were injected 18 times each, and the between-sample memory effect was quantified for each analysis run. Results showed that samples adversely affected by between-sample isotopic differences stabilised after seven–eight injections. The between-sample memory effect ranged from 14% and 9% for δ²H and δ¹⁸O measurements, respectively, but declined to negligible carryover (between 0.1% and 0.3% for both isotopes) when the first ten injections of each sample were discarded. The measurement variability (range and standard deviation) was strongly dependent on the isotopic difference between adjacent vials. Standard deviations were up to 7.5‰ for δ²H and 0.54‰ for δ¹⁸O when all injections were retained in the computation of the reportable δ-value, but a significant increase in measurement precision (standard deviation in the range 0.1‰–1.0‰ for δ²H and 0.05‰–0.17‰ for δ¹⁸O) was obtained when the first eight injections were discarded. In conclusion, this study provided a practical solution to mitigate between-sample memory effects in the isotopic analysis of water samples by laser spectroscopy.

Rain gauges, weather radars, satellite sensors and modelled data from weather centres are used operationally for estimating the spatial-temporal variability of rainfall. However, the associated uncertainties can be very high, especially in poorly equipped regions of the world. Very recently, an innovative method, named SM2RAIN, that uses soil moisture observations to infer rainfall, has been proposed by Brocca et al. (2013) with very promising results when applied with in situ and satellite-derived data. However, a thorough analysis of the physical consistency of the SM2RAIN algorithm has not been carried out yet. In this study, synthetic soil moisture data generated from a physically-based soil water balance model are employed to check the reliability of the assumptions made in the SM2RAIN algorithm. Next, high quality and multiyear in situ soil moisture observations, at different depths (5-30 cm), and rainfall for ten sites across Europe are used for testing the performance of the algorithm, its limitations and applicability range. SM2RAIN shows very high accuracy in the synthetic experiments with a correlation coefficient, R, between synthetically generated and simulated data, at daily time step, higher than 0.940 and an average Bias lower than 4%. When real datasets are used, the agreement between observed and simulated daily rainfall is slightly lower with average R-values equal to 0.87 and 0.85 in the calibration and validation periods, respectively. Overall, the performance is found to be better in humid temperate climates and for sensors installed vertically. Interestingly, algorithms of different complexity in the reproduction of the underlying hydrological processes provide similar results. The average contribution of surface runoff and evapotranspiration components amounts to less than 4% of the total rainfall, while the soil moisture variations (63%) and subsurface drainage (30%) terms provide a much higher contribution. Overall, the SM2RAIN algorithm is found to perform well both in the synthetic and real data experiments, thus offering a new and independent source of data for improving rainfall estimation, and consequently enhancing hydrological, meteorological and climatic studies.