Subarctic catchment water storage and carbon cycling – leading the way for future studies using integrated datasets at Pallas, Finland
About: This article is published in Hydrological Processes.The article was published on 2021-08-14 and is currently open access. It has received 9 citations till now.
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TL;DR: In this paper , a detailed characterization of spatiotemporal variations of stable water 18O and 2H isotopes in both snowpack and meltwater in a subarctic catchment was provided.
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.
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3 citations
TL;DR: In this article , a novel evolutionary model (JBGP) was developed to detect the most dominant ecohydrological parameters affecting the occurrence of Arctic charr across tributaries within the large subarctic Teno River catchment, in northernmost Finland and Norway.
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TL;DR: In this article , the authors measured high-frequency dissolved organic carbon (DOC) and in-stream carbon dioxide (pCO2) in a subarctic, peatland influenced headwater catchment in Pallas, Northern Finland.
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.
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TL;DR: In this paper , the authors 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.
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.
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References
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TL;DR: In this paper, the isotopic fractionation of water in simple condensation-evaporation processes is considered quantitatively on the basis of the fractionation factors given in section 1.2.
Abstract: In chapter 2 the isotopic fractionation of water in some simple condensation-evaporation processes are considered quantitatively on the basis of the fractionation factors given in section 1.2. The condensation temperature is an important parameter, which has got some glaciological applications. The temperature effect (the δ's decreasing with temperature) together with varying evaporation and exchange appear in the “amount effect” as high δ's in sparse rain. The relative deuterium-oxygen-18 fractionation is not quite simple. If the relative deviations from the standard water (S.M.O.W.) are called δ D and δ 18 , the best linear approximation is δ D = 8 δ 18 . Chapter 3 gives some qualitative considerations on non-equilibrium (fast) processes. Kinetic effects have heavy bearings upon the effective fractionation factors. Such effects have only been demonstrated clearly in evaporation processes, but may also influence condensation processes. The quantity d = δ D −8 δ 18 is used as an index for non-equilibrium conditions. The stable isotope data from the world wide I.A.E.A.-W.M.O. precipitation survey are discussed in chapter 4. The unweighted mean annual composition of rain at tropical island stations fits the line δ D = 4.6 δ 18 indicating a first stage equilibrium condensation from vapour evaporated in a non-equilibrium process. Regional characteristics appear in the weighted means. The Northern hemisphere continental stations, except African and Near East, fit the line δ D = 8.0 δ 18 + 10 as far as the weighted means are concerned (δ D = 8.1 δ 18 + 11 for the unweighted) corresponding to an equilibrium Rayleigh condensation from vapour, evaporated in a non-equilibrium process from S.M.O.W. The departure from equilibrium vapour seems even higher in the rest of the investigated part of the world. At most stations the δ D and varies linearily with δ 18 with a slope close to 8, only at two stations higher than 8, at several lower than 8 (mainly connected with relatively dry climates). Considerable variations in the isotopic composition of monthly precipitation occur at most stations. At low latitudes the amount effect accounts for the variations, whereas seasonal variation at high latitudes is ascribed to the temperature effect. Tokyo is an example of a mid latitude station influenced by both effects. Some possible hydrological applications are outlined in chapter 5. DOI: 10.1111/j.2153-3490.1964.tb00181.x
7,081 citations
TL;DR: It is demonstrated how phase angle statistics can be used to gain confidence in causal relation- ships and test mechanistic models of physical relationships between the time series and Monte Carlo methods are used to assess the statistical significance against red noise backgrounds.
Abstract: Many scientists have made use of the wavelet method in analyzing time series, often using popular free software. However, at present there are no similar easy to use wavelet packages for analyzing two time series together. We discuss the cross wavelet transform and wavelet coher- ence for examining relationships in time frequency space be- tween two time series. We demonstrate how phase angle statistics can be used to gain confidence in causal relation- ships and test mechanistic models of physical relationships between the time series. As an example of typical data where such analyses have proven useful, we apply the methods to the Arctic Oscillation index and the Baltic maximum sea ice extent record. Monte Carlo methods are used to assess the statistical significance against red noise backgrounds. A software package has been developed that allows users to perform the cross wavelet transform and wavelet coherence (http://www.pol.ac.uk/home/research/waveletcoherence/). As we are interested in extracting low s/n ratio signals in time series we discuss only CWT in this paper. While CWT is a common tool for analyzing localized intermittent os- cillations in a time series, it is very often desirable to ex- amine two time series together that may be expected to be linked in some way. In particular, to examine whether re- gions in time frequency space with large common power have a consistent phase relationship and therefore are sug- gestive of causality between the time series. Many geophys- ical time series are not Normally distributed and we suggest methods of applying the CWT to such time series. From two CWTs we construct the Cross Wavelet Transform (XWT) which will expose their common power and relative phase in time-frequency space. We will further define a measure of Wavelet Coherence (WTC) between two CWT, which can find significant coherence even though the common power is low, and show how confidence levels against red noise back- grounds are calculated. We will present the basic CWT theory before we move on to XWT and WTC. New developments such as quanti- fying the phase relationship and calculating the WTC sig- nificance level will be treated more fully. When using the methods on time series it is important to have solid mecha- nistic foundations on which to base any relationships found, and we caution against using the methods in a "scatter-gun" approach (particularly if the time series probability density functions are modified). To illustrate how the various meth- ods are used we apply them to two data sets from meteo- rology and glaciology. Finally, we will provide links to a MatLab software package.
4,586 citations
"Subarctic catchment water storage a..." refers background in this paper
...Further details of coherence calculation can be found in Grinsted et al. (2004)....
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TL;DR: The Structure-from-Motion (SfM) method as mentioned in this paper solves the camera pose and scene geometry simultaneously and automatically, using a highly redundant bundle adjustment based on matching features in multiple overlapping, offset images.
2,901 citations
"Subarctic catchment water storage a..." refers methods in this paper
...The basic approach has been to utilize UAS and Structure from Motion photogrammetry (Westoby et al., 2012) to produce a time series of digital surface models (DSM) throughout winter and spring, and then compare winter DSM with snow-free summer DSM to extract snow depth....
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17 Dec 2014
1,367 citations
"Subarctic catchment water storage a..." refers background in this paper
...…cover reductions continue to drive broad-scale and long-term change across the northern areas (Bailey et al., 2021; Bekryaev et al., 2010; Bintanja & Selten, 2014; Buchwall et al., 2020; Cohen et al., 2014; Ernakovich et al., 2014; Lee et al., 2011; Liston & Hiemstra, 2011; Neumann et al., 2019)....
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TL;DR: In this paper, the authors show that the rapid Arctic warming has contributed to dramatic melting of Arctic sea ice and spring snow cover, at a pace greater than that simulated by climate models.
Abstract: The Arctic region has warmed more than twice as fast as the global average — a phenomenon known as Arctic amplification. The rapid Arctic warming has contributed to dramatic melting of Arctic sea ice and spring snow cover, at a pace greater than that simulated by climate models. These profound changes to the Arctic system have coincided with a period of ostensibly more frequent extreme weather events across the Northern Hemisphere mid-latitudes, including severe winters. The possibility of a link between Arctic change and mid-latitude weather has spurred research activities that reveal three potential dynamical pathways linking Arctic amplification to mid-latitude weather: changes in storm tracks, the jet stream, and planetary waves and their associated energy propagation. Through changes in these key atmospheric features, it is possible, in principle, for sea ice and snow cover to jointly influence mid-latitude weather. However, because of incomplete knowledge of how high-latitude climate change influences these phenomena, combined with sparse and short data records, and imperfect models, large uncer - tainties regarding the magnitude of such an influence remain. We conclude that improved process understanding, sustained and additional Arctic observations, and better coordinated modelling studies will be needed to advance our understanding of the influences on mid-latitude weather and extreme events.
1,199 citations