\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{Visualization of Distributed Temperature Sensing shows fine-scale water
temperature dynamics in a subarctic stream over melt season}



\author[1]{Danny Croghan}%
\author[2]{Kieran Khamis}%
\author[2]{David Hannah}%
\author[3]{Ilkka Martinkauppi}%
\author[1]{Pertti Ala-Aho}%
\author[1]{Hannu Marttila}%
\affil[1]{Oulun yliopisto}%
\affil[2]{University of Birmingham}%
\affil[3]{Geologian tutkimuskeskus}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
As far as I can tell, no abstract is necessary for HPEye submissions%
\end{abstract}%



\sloppy


\emph{Visualization of Distributed Temperature Sensing shows fine-scale
water temperature dynamics in a subarctic stream over melt season}

Danny Croghan\textsuperscript{1}, Kieran Khamis\textsuperscript{2},
David M. Hannah\textsuperscript{2}, Ilkka
Martinkauppi\textsuperscript{3}, Pertti Ala-Aho\textsuperscript{1}, and
Hannu Marttila\textsuperscript{1}

(1) University of Oulu, Oulu, Finland

(2) University of Birmingham, Birmingham, United Kingdom

(3) Geological Survey of Finland (GTK), Finland

\emph{Description:}

Rising global air temperatures are altering thermal regimes of streams
with major implications for physical, chemical and biological processes
(Hannah \& Garner, 2015; Michel et al., 2022; Van Vliet \& Zwolsman,
2008) Stream water temperature increases in high latitude regions are
more pronounced and rapid than anywhere else in the world (Post et al.,
2019), hence thermal regimes in polar streams are currently undergoing
rapid perturbations that require urgent study (Blaen et al., 2013;
Docherty et al., 2019; Park et al., 2017).

Technological advances in high-resolution monitoring offer tools to
improve processes understanding, feed hydrological-thermal models and
guide more targeted management strategies (Ouellet et al., 2020). The
development of Distributed Temperature Sensing (DTS) allows
high-resolution spatial (centimeter to meter scale) and temporal
(minutes to hours) continuous measurement of water temperature to enable
identification of patterns and processes at finer-scales than possible
previously (Matheswaran et al., 2014; Mohamed et al., 2021). However,
the use of DTS to explore in-stream temperature dynamics in
high-latitude regions over seasonal scales has been poorly documented to
date. Ploum et al. (2018) visualized DTS data in a boreal stream, where
the stream thermal regime was heavily influenced by an upstream lake. In
this article, for the first time, we visualize spatially resolved DTS
measurements of water temperature changes across the melt season in a
subarctic stream.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\selectlanguage{ngerman}\emph{\textbf{Figure 1} -- Map of Pallas catchment and location in
Finland. The location of the DTS cable, stream, and ditch systems are
shown. Classification of vegetation in the catchment is derived from
Räsänen et al. (2021).}

The video (Video S1) shows daily mean water temperature at a 10m
resolution for a 2 km reach in a headwater subarctic stream in Pallas,
Northern Finland (68°02\selectlanguage{english}'N, 24deg16\selectlanguage{english}'W; Figure 1). For context, moving
graphs of precipitation, air temperature, and snow depth are also shown.
A Halo DTS system (Sensornet, UK) was installed into the stream and made
double-ended measurements. The DTS measured water temperature at 2 m
spatial resolution and at a 30-minute temporal resolution from 01 May
2021 to 16 Sep 2021. The data collection captured the onset of snowmelt
period and continued to early autumn which comprises the period where
the stream shows the strongest connectivity with the wider catchment in
Pallas (Marttila et al., 2021). DTS temperature records were calibrated
against a reference HOBO Pendant water temperature logger (Onset, MA,
USA) located in an upstream spring. The DTS data were then cleaned, with
areas the cable that were exposed to air temperature due to dewatering
filtered out using cross-correlation analysis, and 10 m averages
calculated (n = 5). To facilitate assessment of spatial and seasonal
dynamics daily mean water temperature records are presented. All data
processing and visualization was completed using R version 4.04 (R Core
Team, 2022). Graphs were created using the ``\emph{ggplot} '' package
and ``\emph{gganimate} '' was used to generate moving images.

Our visualization shows snowmelt to be a dominant control on the thermal
regime of the whole Lompolonjanganoja stream system. Throughout the
snowmelt period, water temperature remains relatively low and constant
at all locations along the cable, likely because of cold meltwater
runoff from the catchment dominates temperature dynamics at all points
during this period. Thus, length of snowmelt season is highlighted as a
crucial control on stream temperature dynamics (Slemmons et al., 2013).
After snowmelt, localized fluctuations in water temperature regime
become apparent. The 500-650 m segment of the stream had consistently
above average stream temperature during the summer period. Thus, it may
be inferred that localized inputs of warm surface water occur here,
creating a ``hotspot'' of potential increased biogeochemical activity
(Marruedo Arricibita et al., 2018). GIS investigation revealed drainage
ditches may be a possible source of these warm water inputs, a prominent
feature in Nordic peatlands due to historical drainage practices
(Hasselquist et al., 2018; Nieminen et al., 2018).

The cable terminates in a large natural spring (located at the 1900-2000
m portion of the cable), which is seen as a very constant temperature in
the spring throughout the study period. Interestingly, while DTS has
previously been used to identify groundwater inputs into streams
(Marruedo Arricibita et al., 2018; Matheswaran et al., 2014), and
despite numerous other springs visible in the channel, the impact of
springs on stream temperature appears minimal during the summer period.
Only one area of the cable (\textasciitilde{}800m) appeared consistently
cooler than the rest of the stream during the warmest period of early
July. While cold water refugia have previously been found to be
important refugia from high temperatures in other systems (Fullerton et
al., 2018), the low impact visible for this subarctic stream suggests
that the downstream influence of springs provides minimal mitigation to
high temperature events. By autumn (September), water temperature across
the cable once again became relatively spatially homogenous despite air
temperatures remaining elevated. This indicates the hydrological
connectivity providing localized `hotspots' in water temperature become
disconnected from the main channel by early Autumn. Thus, it is notable
the most dynamic period of water temperature response to catchment
behavior appears constrained to the post-melt summer months.

Our dataset has potential to investigate dominant controlling processes
for water temperature at high spatial resolution in logistically
challenging high latitude environments. Identification of patterns in
the resolved spatial and temporal data become apparent through animation
and the great potential of DTS to assess seasonal drivers of water
temperature can be observed.

\emph{Acknowledgements}

Funding for these studies was provided by a grant from Maa- ja
Vesitekniikan Tuki ry. We also thank the Finnish Meteorological
Institute (FMI) for allowing the instrument to be installed into an FMI
hut. We acknowledge Mirka Hatanpaa, Metsahallitus for assisting in DTS
sensor maintenance, and Asmo Huusko, GTK for assisting in planning and
installation of the DTS sensor. Writing was supported by Arctic
Interactions Profi 4 (ArcI) program in University of Oulu, and
Hydrological RDI partnership network funded by Academy of Finland
(project code 337523), and national Freshwater Competence Center.

\emph{References}

Blaen, P. J., Hannah, D. M., Brown, L. E., \& Milner, A. M. (2013).
Water temperature dynamics in High Arctic river
basins.\emph{Hydrological Processes} , \emph{27} (20), 2958--2972.
https://doi.org/10.1002/HYP.9431

Docherty, C. L., Dugdale, S. J., Milner, A. M., Abermann, J., Lund, M.,
\& Hannah, D. M. (2019). Arctic river temperature dynamics in a changing
climate. \emph{River Research and Applications} , \emph{35} (8),
1212--1227. https://doi.org/10.1002/RRA.3537

Fullerton, A. H., Torgersen, C. E., Lawler, J. J., Steel, E. A.,
Ebersole, J. L., \& Lee, S. Y. (2018). Longitudinal thermal
heterogeneity in rivers and refugia for coldwater species: effects of
scale and climate change. \emph{Aquatic Sciences} , \emph{80} (3), 1.
https://doi.org/10.1007/S00027-017-0557-9

Hannah, D. M., \& Garner, G. (2015). River water temperature in the
United Kingdom: Changes over the 20th century and possible changes over
the 21st century. \emph{Progress in Physical Geography} , \emph{39} (1).
https://doi.org/10.1177/0309133314550669

Hasselquist, E. M., Lidberg, W., Sponseller, R. A., Agren, A., \&
Laudon, H. (2018). Identifying and assessing the potential hydrological
function of past artificial forest drainage. \emph{Ambio} , \emph{47}
(5), 546--556. https://doi.org/10.1007/S13280-017-0984-9/FIGURES/3

Marruedo Arricibita, A. I., Krause, S., Gomez-Velez, J., Hannah, D. M.,
\& Lewandowski, J. (2018). Mesocosm experiments identifying hotspots of
groundwater upwelling in a water column by fibre optic distributed
temperature sensing. \emph{Hydrological Processes} , \emph{32} (2),
185--199. https://doi.org/10.1002/HYP.11403

Marttila, H., Lohila, A., Ala-Aho, P., Noor, K., Welker, J. M., Croghan,
D., Mustonen, K., Merio, L.-J., Autio, A., Muhic, F., Bailey, H.,
Aurela, M., Vuorenmaa, J., Penttila, T., Hyoky, V., Klein, E., Kuzmin,
A., Korpelainen, P., Kumpula, T., \ldots{} Klove, B. (2021). Subarctic
catchment water storage and carbon cycling -- leading the way for future
studies using integrated datasets at Pallas, Finland. \emph{Hydrological
Processes} . https://doi.org/10.1002/HYP.14350

Matheswaran, K., Blemmer, M., Rosbjerg, D., \& Boegh, E. (2014).
Seasonal variations in groundwater upwelling zones in a Danish lowland
stream analyzed using Distributed Temperature Sensing
(DTS).\emph{Hydrological Processes} , \emph{28} (3), 1422--1435.
https://doi.org/10.1002/HYP.9690

Michel, A., Schaefli, B., Wever, N., Zekollari, H., Lehning, M., \&
Huwald, H. (2022). Future water temperature of rivers in Switzerland
under climate change investigated with physics-based
models.\emph{Hydrology and Earth System Sciences} , \emph{26} (4),
1063--1087. https://doi.org/10.5194/HESS-26-1063-2022

Mohamed, R. A. M., Gabrielli, C., Selker, J. S., Selker, F., Brooks, S.
C., Ahmed, T., \& Carroll, K. C. (2021). Comparison of fiber-optic
distributed temperature sensing and high-sensitivity sensor spatial
surveying of stream temperature. \emph{Journal of Hydrology} ,\emph{603}
, 127015. https://doi.org/10.1016/J.JHYDROL.2021.127015

Nieminen, M., Palviainen, M., Sarkkola, S., Lauren, A., Marttila, H., \&
Finer, L. (2018). A synthesis of the impacts of ditch network
maintenance on the quantity and quality of runoff from drained boreal
peatland forests. \emph{Ambio} , \emph{47} (5), 523--534.
https://doi.org/10.1007/S13280-017-0966-Y/FIGURES/6

Ouellet, V., St-Hilaire, A., Dugdale, S. J., Hannah, D. M., Krause, S.,
\& Proulx-Ouellet, S. (2020). River temperature research and practice:
Recent challenges and emerging opportunities for managing thermal
habitat conditions in stream ecosystems. \emph{Science of The Total
Environment} , \emph{736} , 139679.
https://doi.org/10.1016/J.SCITOTENV.2020.139679

Park, H., Yoshikawa, Y., Yang, D., \& Oshima, K. (2017). Warming Water
in Arctic Terrestrial Rivers under Climate Change. \emph{Journal of
Hydrometeorology} , \emph{18} (7), 1983--1995.
https://doi.org/10.1175/JHM-D-16-0260.1

Ploum, S. W., Leach, J. A., Kuglerova, L., \& Laudon, H. (2018). Thermal
detection of discrete riparian inflow points (DRIPs) during contrasting
hydrological events. \emph{Hydrological Processes} , \emph{32} (19),
3049--3050. https://doi.org/10.1002/HYP.13184

Post, E., Alley, R. B., Christensen, T. R., Macias-Fauria, M., Forbes,
B. C., Gooseff, M. N., Iler, A., Kerby, J. T., Laidre, K. L., Mann, M.
E., Olofsson, J., Stroeve, J. C., Ulmer, F., Virginia, R. A., \& Wang,
M. (2019). The polar regions in a 2degC warmer world. \emph{Science
Advances} , \emph{5} (12). https://doi.org/10.1126/SCIADV.AAW9883

R Core Team. (2022). \emph{R: A Language and Environment for Statistical
Computing} (1.1.416). https://www.r-project.org/

Rasanen, A., Manninen, T., Korkiakoski, M., Lohila, A., \& Virtanen, T.
(2021). Predicting catchment-scale methane fluxes with multi-source
remote sensing. \emph{Landscape Ecology} , \emph{36} (4), 1177--1195.
https://doi.org/10.1007/S10980-021-01194-X/FIGURES/4

Slemmons, K. E. H., Saros, J. E., \& Simon, K. (2013). The influence of
glacial meltwater on alpine aquatic ecosystems: a
review.\emph{Environmental Science: Processes \& Impacts} , \emph{15}
(10), 1794--1806. https://doi.org/10.1039/C3EM00243H

Van Vliet, M. T. H., \& Zwolsman, J. J. G. (2008). Impact of summer
droughts on the water quality of the Meuse river. \emph{Journal of
Hydrology} , \emph{353} (1--2), 1--17.
https://doi.org/10.1016/J.JHYDROL.2008.01.001

\selectlanguage{english}
\FloatBarrier
\end{document}

