Filip Muhic

Bio: Filip Muhic is an academic researcher from University of Oulu. The author has co-authored 1 publications.

The Spatiotemporal Variability of Snowpack and Snowmelt Water 18O and 2H Isotopes in a Subarctic Catchment

Abstract: This study provides a detailed characterization of spatiotemporal variations of stable water 18O and 2H isotopes in both snowpack and meltwater in a subarctic catchment. We performed extensive sampling and analysis of snowpack and meltwater isotopic compositions at 11 locations in 2019 and 2020 across three different landscape features: (a) forest hillslope, (b) mixed forest, and (c) open mires. The vertical isotope profiles in the snowpack's layered stratigraphy presented a consistent pattern in all locations before snowmelt, and isotope profiles homogenized during the peak melt period; represented by a 1–2‰ higher δ $\delta $18O value than prior to melting. Our data indicated that the liquid‐ice fractionation was the prime reason that caused the depletion of heavy isotopes in initial meltwater samples prior to the peak melt period. The liquid‐ice fractionation was influenced by snowmelt rate, with higher fractionation during slow melt. The kinetic liquid‐ice fractionation was evident only in close examination of meltwater lc‐excess values, not δ $\delta $18O values alone. Meltwater was isotopically heavier and more variable than the depth‐integrated snowpack; the weighted mean of meltwater isotope values was higher by 0.62–1.33‰ δ $\delta $18O than the weighted mean of snowpack isotope values in forest hillslope and mixed forest areas, and 1.51–6.37‰ δ $\delta $18O in open mires. Our results reveal close to 3.1‰ δ $\delta $18O disparity between the meltwater and depth‐integrated snowpack isotope values prior to the peak melt period, suggesting that proper characterization of meltwater δ $\delta $18O and δ $\delta $2H values is vital for tracer‐based ecohydrological studies and models.

Coupling of Water‐Carbon Interactions During Snowmelt in an Arctic Finland Catchment

Abstract: Snowmelt spring floods regulate carbon transport from land to streams. However, these coupled processes are rarely documented through high‐resolution measurements focused on water‐carbon interactions. We collated a state‐of‐the‐art high‐frequency data set throughout a snowmelt and early post snowmelt period, alongside regular samples of stream water, precipitation, and snowmelt isotopes (δ18O). Our study was conducted during the 2019 snowmelt and initial post snowmelt season in a subarctic, peatland influenced headwater catchment in Pallas, Northern Finland. We measured high‐frequency dissolved organic carbon (DOC), and in‐stream carbon dioxide (pCO2). We identified a change in hydrological processes as the snowmelt season progressed and the post snowmelt season began. We found (a) Overland flow dominated stream DOC dynamics in early snowmelt, while increased catchment connectivity opened new distal pathways in the later snowmelt period; (b) CO2 processes were initially driven by rapid bursts of CO2 from the meltwaters in snowmelt, followed by dilution and source limitation emerging post snowmelt as deep soil pathways replaced the snowpack as the main source of CO2; (c) stream carbon concentration shifted from being relatively balanced between CO2 and DOC during the early snowmelt period to being increasingly DOC dominated as snowmelt progressed due to changes in DOC and CO2 source supply. The study highlights the importance of using high‐frequency measurements combined with high‐frequency data analyses to identify changes in the processes driving water‐carbon interactions. The degree to which water‐carbon interactions respond to the continuation of Arctic water cycle amplification is central to delineating the evolving complexity of the future Arctic.

Measuring the spatiotemporal variability of snow depth in subarctic environments using unmanned aircraft systems (UAS) – Part 2: Snow processes and snow-canopy interactions

Abstract: . Detailed information on seasonal snow cover and depth is essential to the understanding of snow processes, 10 operational forecasting, and as input for hydrological models. Recent advances in unmanned aircraft systems (UASs) and structure from motion (SfM) techniques have enabled low-cost monitoring of spatial snow depth distribution in resolutions up to a few centimeters. Here, we study the spatiotemporal variability of snow depth and interactions between snow and vegetation in different subarctic landscapes consisting of a mosaic of conifer forest, mixed forest, transitional woodland/shrub, and peatland areas. To determine the spatiotemporal variability of snow depth, we used high-resolution (50 cm) snow depth maps 15 generated from repeated UAS-SfM surveys in the winter of 2018/2019 and a snow-free bare ground survey after snowmelt. Due to poor sub-canopy penetration with the UAS-SfM method, tree masks were utilized to remove canopy areas and the area (36 cm) immediately next to the canopy before analysis. Snow depth maps were compared to the in-situ snow course and a single-point continuous ultrasonic snow depth measurement. Based on the results, the difference between the UAS-SfM survey median snow depth and single-point measurement increased for all land cover types during the snow season, from +5 cm at 20 the beginning of the accumulation to -16 cm in coniferous forests and -32 cm in peatland during the melt period. This highlights the poor representation of point measurements even on the sub-catchment scale. The high-resolution snow depth maps agreed well with the snow course measurement, but the spatial extent and resolution of maps were substantially higher. The snow depth variability (5–95 percentiles) within different land cover types increased from 17 cm to 42 cm in peatlands and from 33 cm to 49 cm in the coniferous forest from the beginning of the snow accumulation to the melt period. Both the median snow 25 depth and its variability were found to increase with canopy density; this increase was greatest in the conifer forest area, followed by mixed forest, transitional woodland/shrub, and open peatlands. Using the high spatial resolution data, we found a systematic increase (2–20 cm), then a decline of snow depth near the canopy with increasing distance (from 1 m to 2.5 m) of the peak value through the snow season. This study highlights the potential of the UAS-SfM in high-resolution monitoring of snow depth in multiple land cover types and snow-vegetation interactions in subarctic and remote areas where field data is not 30 available.