Spatio-temporal tracer variability in the glacier melt end-member - How does it affect hydrograph separation results?

Geochemical and isotopic tracers were often used in mixing models to estimate glacier melt contributions to streamflow, whereas the spatio‐temporal variability in the glacier melt tracer signature and its influence on tracer‐based hydrograph separation results received less attention. We present novel tracer data from a high‐elevation catchment (17 km2, glacierized area: 34%) in the Oetztal Alps (Austria) and investigated the spatial, as well as the subdaily to monthly tracer variability of supraglacial meltwater and the temporal tracer variability of winter baseflow to infer groundwater dynamics. The streamflow tracer variability during winter baseflow conditions was small, and the glacier melt tracer variation was higher, especially at the end of the ablation period. We applied a three‐component mixing model with electrical conductivity and oxygen‐18. Hydrograph separation (groundwater, glacier melt, and rain) was performed for 6 single glacier melt‐induced days (i.e., 6 events) during the ablation period 2016 (July to September). Median fractions (±uncertainty) of groundwater, glacier melt, and rain for the events were estimated at 49±2%, 35±11%, and 16±11%, respectively. Minimum and maximum glacier melt fractions at the subdaily scale ranged between 2±5% and 76±11%, respectively. A sensitivity analysis showed that the intraseasonal glacier melt tracer variability had a marked effect on the estimated glacier melt contribution during events with large glacier melt fractions of streamflow. Intra‐daily and spatial variation of the glacier melt tracer signature played a negligible role in applying the mixing model. The results of this study (a) show the necessity to apply a multiple sampling approach in order to characterize the glacier melt end‐member and (b) reveal the importance of groundwater and rainfall–runoff dynamics in catchments with a glacial flow regime

No influence of CO2 on stable isotope analyses of soil waters with OA-ICOS

Acknowledgements We are thankful for the support by Audrey Innes with the stable isotope, LOI, and GWC analysis. We thank Jonathan Dick for suggesting that we use sparkling water to generate different CO2 concentrations in the headspace and Claire Tunaley for proof reading. We further highly appreciate the help of David Galloway and Michael Mcgibbon from the School of Biological Sciences, University of Aberdeen, with the CO2 analysis. We are also thankful for the support by Robert Provencal and Doug S. Baer regarding the technical aspects of the isotope analyzer. We would also like to thank the European Research Council (ERC, Project No. GA 335910 VeWa) and the Natural Environment Research Council (NERC, Project No. NE/K000268/1) for funding. We thank three anonymous reviewers for their valuable feedback that helped to improve the manuscript.Peer reviewedPublisher PD

ESCIMO.spread (v2) : parameterization of a spreadsheet-based energy balance snow model for inside-canopy conditions

This article describes the extension of the ESCIMO.spread spreadsheet-based point energy balance snow model by (i) an advanced approach for precipitation phase detection, (ii) a method for cold content and liquid water storage consideration and (iii) a canopy sub-model that allows the quantification of canopy effects on the meteorological conditions inside the forest as well as the simulation of snow accumulation and ablation inside a forest stand. To provide the data for model application and evaluation, innovative low-cost snow monitoring systems (SnoMoS) have been utilized that allow the collection of important meteorological and snow information inside and outside the canopy. The model performance with respect to both, the modification of meteorological conditions as well as the subsequent calculation of the snow cover evolution, are evaluated using inside- and outside-canopy observations of meteorological variables and snow cover evolution as provided by a pair of SnoMoS for a site in the Black Forest mountain range (southwestern Germany). The validation results for the simulated snow water equivalent with Nash–Sutcliffe model efficiency values of 0.81 and 0.71 and root mean square errors of 8.26 and 18.07 mm indicate a good overall model performance inside and outside the forest canopy, respectively. The newly developed version of the model referred to as ESCIMO.spread (v2) is provided free of charge together with 1 year of sample data including the meteorological data and snow observations used in this study

The importance of snowmelt spatiotemporal variability for isotope-based hydrograph separation in a high-elevation catchment

Seasonal snow cover is an important temporary water storage in high-elevation regions. Especially in remote areas, the available data are often insufficient to accurately quantify snowmelt contributions to streamflow. The limited knowledge about the spatiotemporal variability of the snowmelt isotopic composition, as well as pronounced spatial variation in snowmelt rates, leads to high uncertainties in applying the isotope-based hydrograph separation method. The stable isotopic signatures of snowmelt water samples collected during two spring 2014 snowmelt events at a north- and a south-facing slope were volume weighted with snowmelt rates derived from a distributed physicsbased snow model in order to transfer the measured plotscale isotopic composition of snowmelt to the catchment scale. The observed δ$^{18}$O values and modeled snowmelt rates showed distinct inter- and intra-event variations, as well as marked differences between north- and south-facing slopes. Accounting for these differences, two-component isotopic hydrograph separation revealed snowmelt contributions to streamflow of 35±3 and 75±14% for the early and peak melt season, respectively. These values differed from those determined by formerly used weighting methods (e.g., using observed plot-scale melt rates) or considering either the north- or south-facing slope by up to 5 and 15 %, respectively

The importance of snowmelt spatiotemporal variability for isotope-based hydrograph separation in a high-elevation catchment

Seasonal snow cover is an important temporary water storage in high-elevation regions. Especially in remote areas, the available data are often insufficient to accurately quantify snowmelt contributions to streamflow. The limited knowledge about the spatiotemporal variability of the snowmelt isotopic composition, as well as pronounced spatial variation in snowmelt rates, leads to high uncertainties in applying the isotope-based hydrograph separation method. The stable isotopic signatures of snowmelt water samples collected during two spring 2014 snowmelt events at a north- and a south-facing slope were volume weighted with snowmelt rates derived from a distributed physics-based snow model in order to transfer the measured plot-scale isotopic composition of snowmelt to the catchment scale. The observed 18O values and modeled snowmelt rates showed distinct inter- and intra-event variations, as well as marked differences between north- and south-facing slopes. Accounting for these differences, two-component isotopic hydrograph separation revealed snowmelt contributions to streamflow of 35+/-3 and 75+/-14% for the early and peak melt season, respectively. These values differed from those determined by formerly used weighting methods (e.g., using observed plot-scale melt rates) or considering either the north- or south-facing slope by up to 5 and 15%, respectively.(VLID)3024679Version of recor

ESCIMO.spread (v2): parameterization of a spreadsheet-based energy balance snow model for inside-canopy conditions

This article describes the extension of the ESCIMO.spread spreadsheet-based point energy balance snow model by (i) an advanced approach for precipitation phase detection, (ii) a method for cold content and liquid water storage consideration and (iii) a canopy sub-model that allows the quantification of canopy effects on the meteorological conditions inside the forest as well as the simulation of snow accumulation and ablation inside a forest stand. To provide the data for model application and evaluation, innovative low-cost snow monitoring systems (SnoMoS) have been utilized that allow the collection of important meteorological and snow information inside and outside the canopy. The model performance with respect to both, the modification of meteorological conditions as well as the subsequent calculation of the snow cover evolution, are evaluated using inside- and outside-canopy observations of meteorological variables and snow cover evolution as provided by a pair of SnoMoS for a site in the Black Forest mountain range (southwestern Germany). The validation results for the simulated snow water equivalent with Nash–Sutcliffe model efficiency values of 0.81 and 0.71 and root mean square errors of 8.26 and 18.07 mm indicate a good overall model performance inside and outside the forest canopy, respectively. The newly developed version of the model referred to as ESCIMO.spread (v2) is provided free of charge together with 1 year of sample data including the meteorological data and snow observations used in this study

Illuminating hydrological processes at the soil-vegetation-atmosphere interface with water stable isotopes

Funded by DFG research project “From Catchments as Organised Systems to Models based on Functional Units” (FOR 1Peer reviewedPublisher PDFPublisher PD

The ScaleX campaign: scale-crossing land-surface and boundary layer processes in the TERENO-preAlpine observatory

Augmenting long-term ecosystem-atmosphere observations with multidisciplinary intensive campaigns aims at closing gaps in spatial and temporal scales of observation for energy- and biogeochemical cycling, and at stimulating collaborative research. ScaleX is a collaborative measurement campaign, co-located with a long-term environmental observatory of the German TERENO (TERrestrial ENvironmental Observatories) network in mountainous terrain of the Bavarian Prealps, Germany. The aims of both TERENO and ScaleX include the measurement and modeling of land-surface atmosphere interactions of energy, water, and greenhouse gases. ScaleX is motivated by the recognition that long-term intensive observational research over years or decades must be based on well-proven, mostly automated measurement systems, concentrated on a small number of locations