The Isotope based Hydrograph Separation (IHS) has been instrumental in understanding the partitioning of streamflow sources and processes. However, uncertainties persist in the accuracy of IHS estimations and the appropriate definition and sampling of endmembers. To address these uncertainties, we used field data of snowpack, snowfall, and snow meltwater isotopes (δ18O) from Pallas, Northern Finland to estimate the total meltwater contribution during the snowmelt period. We investigated the biases resulting from the application of different sampling strategies for event water endmember. The total meltwater contribution to streamflow was 59.6 % (±2% uncertainty) using the time-variant rolling runoff-corrected melt flux-weighted meltwater 18O isotope value. However, replacing it with either snowfall or winter snowpack 18O isotope weighted average values underestimated the meltwater contribution by 17.8 % or 22.6 %, respectively. Conversely, using time-variant instantaneous meltwater 18O isotope values overestimated the meltwater contribution by only 1.5 %. These discrepancies highlight the importance of choosing the appropriate endmember isotopes in IHS. The large differences in meltwater contribution for a 2-week peak discharge period based on different endmembers can lead to different interpretations of hydrological, ecohydrological, and biogeochemical processes. Thus, to better understand streamflow generation processes, we suggest using rolling runoff-corrected meltwater 18O or 2H isotope values in the IHS. In the absence of meltwater samples, the 18O or 2H isotope values of snowpack samples during the peak melt season may provide reasonable estimates of the meltwater contribution, with some minor underestimations. Our study highlights the importance of appropriate event meltwater endmember selection and sampling methodology for the IHS.
Pan-Arctic Precipitation Isotope Network (PAPIN) was established in 2018 to coordinate precipitation sampling at 19 stations across key tundra, subarctic, maritime, and continental climate zones. Here, we present a first assessment of rainfall samples collected in summer 2018.
Abstract Stream ecosystems are affected by multiple abiotic stressors, and species responses to simultaneous stressors may differ from those predicted based on single‐stressor responses. Using 12 semi‐natural stream channels, we examined the individual and interactive effects of flow level (low or high flow) and addition of fine sediments (grain size <2 mm) on key ecosystem processes (leaf breakdown, algal biomass accrual) and benthic macroinvertebrate and fungal communities. Both stressors had mostly independent effects on biological responses, with sand addition being the more influential of the two. Sand addition decreased algal biomass and microbe‐mediated leaf breakdown significantly, whereas invertebrate shredder‐mediated breakdown only responded to flow level. Macroinvertebrate community composition responded significantly to both stressors. Fungal biomass decreased and shredder abundance increased when sand was added; thus, organisms at different trophic levels can exhibit highly variable responses to the same stressor. Terrestrial endophytic fungi were abundant in low‐flow flumes where leaf mass loss was also highest, indicating that terrestrial endophytes may contribute importantly to leaf decomposition in the aquatic environment. Leaf breakdown rates depended on the identity and abundance of the dominant decomposer species, suggesting that the effects of anthropogenic activities on ecosystem processes may be driven by changes in the abundance of a few key species. The few observed interactive effects were all antagonistic (i.e., less than the sum of the individual effects); for example, increased flow stimulated algal biomass accumulation but this effect was largely cancelled by sand. While our finding that sand and stream flow did not have strong synergistic effects can be considered reassuring for management, future experiments should manipulate these and other human stressors in experiments that run for much longer periods, thus focusing on the long‐term impacts of multiple simultaneously operating stressors.
Calibrated water vapor isotope and mixing ratio data from Pallas-Yllastunturi National Park, Finland. Site Name: Sammaltunturi Station, Finland (Finnish Meteorological Institute) Site Location: 67.973°N; 24.116°E Site Elevation: 565 m above sea level Instrumentation: Picarro L2130-i Isotope and Gas Concentration Analyser Parameters: δ18O water vapor, δ2H water vapor, deuterium (d)-excess water vapor, mixing ratio (5-minute averages) Date/Time start: 20/12/2017 05:45 EET Date/Time end: 31/03/2018 23:55 EET
<p>Stable isotopes of oxygen and hydrogen in precipitation (&#948;<sup>18</sup>O<sub>P</sub>, &#948;<sup>2</sup>H<sub>P</sub>, d-excess) are valuable hydrological tracers linked to ocean-atmospheric processes such as moisture source, storm trajectory, and seasonal temperature cycles. However, characteristics of &#948;<sup>18</sup>O<sub>P</sub>, &#948;<sup>2</sup>H<sub>P</sub> and d-excess and the processes governing them are yet to be quantified across the Arctic due to a lack of long-term empirical data. The Pan-Arctic Precipitation Isotopes Network (PAPIN) is a new coordinated network of 24 stations aimed at the direct sampling, analysis, and synthesis of precipitation isotope geochemistry in the north. Our ongoing event-based sampling provides a rich spatial dataset during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (&#8220;MOSAiC&#8221;) expedition and new insight into coupled climate processes operating in the Arctic today. To date, precipitation &#948;<sup>18</sup>O and &#948;<sup>2</sup>H data (2018-2019) exhibit pronounced spatial and seasonal variability that broadly conforms to theoretical and observed understanding: (1) decreasing &#948;<sup>18</sup>O<sub>P</sub>/ &#948;<sup>2</sup>H<sub>P</sub> with increasing latitude and elevation, (2) decreasing &#948;<sup>18</sup>O<sub>P</sub>/ &#948;<sup>2</sup>H<sub>P</sub> with increasing continentality, and (3) increasing &#948;<sup>18</sup>O<sub>P</sub>/ &#948;<sup>2</sup>H<sub>P</sub> with increasing SAT. However, event-based sampling reveals remarkable variability among these relationships. For example, our observed Arctic mean summer -latitude slope of -0.3&#8240;/degree of latitude is 50% smaller than the annual latitude effect in the mid-latitudes (-0.6&#8240;/degree). This rate decreases to -0.1&#8240;/degree of latitude in Finland and Russia, while in Alaska and northern Canadian a -0.7&#8240;/degree latitudinal rate is observed. Similarly, we observe marked spatial differences in mean &#948;<sup>18</sup>O-temperature coefficients. Using back-trajectory analysis, we attribute these nuances to divergent moisture sources and transport pathways into, within, and out of the Arctic, and demonstrate how atmospheric circulation processes drive changes in isotope geochemistry and climate that are linked to sea ice concentration. For example, Alaska moisture derived from the North Pacific Ocean, Sea of Okhotsk, and the Bering Sea remains relatively enriched in <sup>18</sup>O<sub>P</sub>/<sup>2</sup>H due to higher sea surface temperatures, whereas moisture originating from ice-covered seas to the north is characterized by relatively depleted values. This is the first coordinated network to quantify the spatial patterns of isotopes in precipitation, simultaneously, across the entire Arctic. In combination with a Pan-Arctic network of continuous water vapor isotope analyzers, our process-level studies will resolve the patterns and processes governing the &#948;<sup>18</sup>O, &#948;<sup>2</sup>H and d-excess values of the Arctic water cycle during the MOSAiC expedition and beyond.</p>
Abstract We conducted a series of tracer test experiments in 12 outdoor semi-natural flumes to assess the effects of variable flow conditions and sand addition on hyporheic zone conditions in gravel beds, mimicking conditions in headwater streams under sediment pressure. Two tracer methods were applied in each experiment: 2–5 tracer-pulse tests were conducted in all flumes and pulses were monitored at three distances downstream of the flume inlet (0 m, 5 m and 10 m, at bed surface), and in pipes installed into the gravel bed at 5 m and 10 m distances. The tracer breakthrough curves (total of 120 tracer injections) were then analysed with a one-dimensional solute transport model (OTIS) and compared with data from the gravel pipes in point-dilution pulse tests. Sand addition had a strong negative effect on horizontal fluxes (qh), whereas the fraction of the median travel time due to transient storage (F200) was determined more by flow conditions. These results suggest that even small additions of sand can modify the hyporheic zone exchange in gravel beds, thus making headwater streams with low sediment transport capacity particularly vulnerable to sediments transported into the stream from catchment land use activities.
Natural abundance variations in stable isotope ratios of hydrogen and oxygen are important environmental tracers with a significant range of applications  (e.g., the exploration of the present water cycle, paleoclimate reconstructions, ecology, and food authenticity). These applications and research themes are often based on spatially explicit predictions of precipitation isotopic variations obtained from point sample collections and measurements through various interpolation techniques. The derivation of spatially continuous and georeferenced isotope databases, known as isotopic landscapes (isoscapes), has been considered most effective through regression kriging for precipitation beginning in the early 2000s. However, the number of interpolation methods used in geostatistics has increased rapidly in recent decades, with new machine learning algorithms becoming increasingly important and proving more successful than conventional methods for certain isotopic parameters. In the present research we present a monthly 10 x 10 km European isoscape based on state-of-the art hybrid machine learning method that combines LASSO Regression and Random Forest (Zhang et al., 2019) for spatial predictions for 1973-2022. Data were retrieved from the IAEA/WMO Global Network of Isotopes in Precipitation (no. of stations: 329) and other national datasets from about 10 countries (no. of stations: ~150). A pilot study (for 2008-2017; Erdélyi et al. 2023) indicated the highest prediction error for the northern premises. This suggested the incorporation of sea ice as an additional predictor, since a Pan-Arctic precipitation stable isotope study pointed out that sea ice cover change is a key driver of oceanic moisture sources (Mellat et al., 2021). Results indicate an overwhelming importance of minimum temperature with the variable representing sea ice cover, ranking among the least influential parameters. The analysis fails to consider moisture source effects, transport distances, and secondary processes of recycling associated with evaporation and transpiration from landscapes across Europe. These results provide a more refined prediction due to the higher station density compared to previous models and thanks to the hybrid model, a more accurate prediction of monthly precipitation stable isotope compositions is expected for the critical areas including the latitudinal margins as well as the mountainous zones. Activities for this presentation were supported by the IAEA (CRP F31006, CRP F33024, TC-project RER7013, Contract 23550/R0) and WATSON Cost Action 19120. This research was also funded by UEFISCDI Romania, grants number PN-III-P2-2.1-PED-2019-4102, PN-III-P4-ID-PCE-2020-2723 and ARIS (Grants P1-0143, N1-0054, N1-0309, J6-3141, J6-50214).   Erdélyi, D., Kern, Z., Nyitrai, T., et al. (2023). Predicting the spatial distribution of stable isotopes in precipitation using a machine learning approach: a comparative assessment of random forest variants. International Journal of Geomathematics, 14:14. doi:10.1007/s13137-023-00224-x Mellat, M., Bailey, H., Mustonen, K-R., Marttila, H., Klein, E. S., Gribanov, K., ... Welker, J. M. (2021). Hydroclimatic Controls on the Isotopic (δ18 O, δ2 H, d-excess)  Traits of Pan-Arctic Summer Rainfall Events. Frontiers in Earth Science, 9:651731. doi:10.3389/feart.2021.651731 Zhang, H., Nettleton, D., & Zhu, Z. (2019). Regression-enhanced random forests. arXiv preprint arXiv:1904.10416.
Abstract. Dissolved organic carbon (DOC) dynamics are evolving in the rapidly changing Arctic and a comprehensive understanding of the controlling processes is urgently required. For example, the transport processes governing DOC dynamics are prone to climate-driven alteration given their strong seasonal nature. Hence, high-resolution and long-term studies are required to assess potential seasonal and interannual changes in DOC transport processes. In this study, we monitored DOC at a 30 min resolution from September 2018 to December 2022 in a headwater peatland-influenced stream in northern Finland (Pallas catchment, 68° N). Temporal variability in transport processes was assessed using multiple methods: concentration–discharge (C–Q) slope for seasonal analysis, a modified hysteresis index for event analysis, yield analysis, and random forest regression models to determine the hydroclimatic controls on transport. The findings revealed the following distinct patterns: (a) the slope of the C–Q relationship displayed a strong seasonal trend, indicating increasing transport limitation each month after snowmelt began; (b) the hysteresis index decreased post-snowmelt, signifying the influence of distal sources and DOC mobilization through slower pathways; and (c) interannual variations in these metrics were generally low, often smaller than month-to-month fluctuations. These results highlight the importance of long-term and detailed monitoring to enable separation of inter- and intra-annual variability to better understand the complexities of DOC transport. This study contributes to a broader comprehension of DOC transport dynamics in the Arctic, specifically quantifying seasonal variability and associated mechanistic drivers, which is vital for predicting how the carbon cycle is likely to change in Arctic ecosystems.
