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Are diurnal fluctuations in streamflow real?

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  • Fundacion para los Estudios Patrimoniales Pleistocenos (FEPPO) Foundation

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Diurnal variations in streamflow (DVS) have been studied in detail by underwater pressure loggers. Some of this equipment requires barometric compensation with a logger or sensor located outside the water. Studies related to this topic have not offered a critical report of the validity of patterns inferred with these instruments. While studying a forested watershed in Southern Chile (40° S), we detected a DVS when the external logger was placed 1.5 m above ground, under a marked diurnal fluctuation in air temperature (amplitude 12.4 °C) and a dampened fluctuation in stream temperature (amplitude 1.4 °C). Synchronization was apparent between air and stream temperature in a direct relationship, but the synchronization between air/stream temperature and streamflow was negative, with some hours of lag time. In laboratory experiments, when the external logger is considerably warmer than the water-level logger, depth measurements can be underestimated by up to 1.5 cm. When the opposite occurs, water depths can be overestimated by up to 0.9 cm and are large instrumental/methodological artifacts compared to the field water diurnal variation of 1.3 cm. Finally, we relocated the external logger in front of the water-level logger and inside a weir, but exposed to the air. Results confirmed the pattern previously detected in the field, but streamflow fluctuations were 19% less accentuated. We conclude that the incorrect placement of the external logger, along with an instrumental artifact, can intensify a DVS pattern.
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J. Hydrol. Hydromech., 58, 2010, 3, 149–162
DOI: 10.2478/v10098-010-0014-0
149
ARE DIURNAL FLUCTUATIONS IN STREAMFLOW REAL?
JAIME G. CUEVAS1*), MATÍAS CALVO2), CHRISTIAN LITTLE3,4),
MARIO PINO2), PAUL DASSORI2)
1)Instituto de Investigaciones Agropecuarias, Oficina Técnica Valdivia, Campus Isla Teja s/n, Casilla 567, Valdivia, Chile; *
Mailto: jxcuevas@inia.cl.
2)Instituto de Geociencias, Universidad Austral de Chile, Campus Isla Teja s/n, Valdivia, Chile; Mailto: matiascalvot@gmail.com.
3)Escuela de Graduados, Facultad de Ciencias Forestales y Recursos Naturales, Universidad Austral de Chile, Campus Isla Teja s/n,
Valdivia, Chile.
4)Fundación Centro de los Bosques Nativos, FORECOS, Chile.
Diurnal variations in streamflow (DVS) have been studied in detail by underwater pressure loggers.
Some of this equipment requires barometric compensation with a logger or sensor located outside the
water. Studies related to this topic have not offered a critical report of the validity of patterns inferred with
these instruments. While studying a forested watershed in Southern Chile (40° S), we detected a DVS
when the external logger was placed 1.5 m above ground, under a marked diurnal fluctuation in air
temperature (amplitude 12.4 °C) and a dampened fluctuation in stream temperature (amplitude 1.4 °C).
Synchronization was apparent between air and stream temperature in a direct relationship, but the
synchronization between air/stream temperature and streamflow was negative, with some hours of lag time.
In laboratory experiments, when the external logger is considerably warmer than the water-level logger,
depth measurements can be underestimated by up to 1.5 cm. When the opposite occurs, water depths can
be overestimated by up to 0.9 cm and are large instrumental/methodological artifacts compared to the field
water diurnal variation of 1.3 cm. Finally, we relocated the external logger in front of the water-level
logger and inside a weir, but exposed to the air. Results confirmed the pattern previously detected in the
field, but streamflow fluctuations were 19% less accentuated. We conclude that the incorrect placement
of the external logger, along with an instrumental artifact, can intensify a DVS pattern.
KEY WORDS: Instrumental Analysis, Methodological Artifacts, Southern Rainforests, Stream and Air
Temperatures.
Jaime G. Cuevas, Matías Calvo, Christian Little, Mario Pino, Paul Dassori: SÚ DENNÉ FLUKTUÁCIE
PRIETOKOV V TOKOCH REÁLNE? J. Hydrol. Hydromech., 58, 2010, 3; 43 lit., 7 obr., 2 tab.
Počas dňa sme podrobne sledovali zmeny prietokov v tokoch tlakovými snímačmi, umiestnenými pod
vodou. Niektoré z týchto zariadení vyžadujú barometrickú kompenzáciu snímačov, ktoré nie sú umiestnené
vo vode. Štúdie z tejto oblasti obyčajne nehodnotia kriticky správnosť výsledkov meraní týmito
zariadeniami. Počas meraní v zalesnenom povodí na juhu Chile (40° S), sme zistili zmeny prietokov
v tokoch počas dňa, ak bol externý tlakový snímač (logger) uložený 1,5 m nad zemou, počas výrazných
denných zmien teploty vzduchu (amplitúda 12.4 °C) a stlmené fluktuácie teploty vody v toku (amplitúda
1.4 °C). Synchronizácia medzi teplotami vody a vzduchu bola zrejmá, ale synchronizácia medzi teplotami
vody v toku a prietokmi bola negatívna, s hodinovými posunmi voči sebe. V experimentoch v laboratóriu,
keď bol externý snímač podstatne teplejší ako snímač vo vode, meranie hĺbok bolo podhodnotené až o 1,5
cm. V opačnom prípade, hĺbka hladiny vody bola nadhodnotená až o 0,9 cm a boli zistené významné
inštrumentálne/metodologické artefakty v porovnaní s poľnými meraniami až o 1,3 cm. Nakoniec, externý
snímač bol umiestnený pred snímač s údajmi o vodnej hladine a dovnútra prepadu, ale bol vo vzduchu.
Výsledky meraní potvrdili chody prietokov namerané v teréne, ale fluktuácie prietokov boli nižšie o 19 %.
Z toho vyplýva, že nesprávne umiestnenie externého snímača, spolu s prístrojovými artefaktmi, môžu
intenzifikovať zmeny prietokov v tokoch počas dňa.
KĽÚČOVÉ SLOVÁ: inštrumentálna analýza, metodologické artefakty, južné dažďové pralesy, teploty
vzduchu a vody.
J. G. Cuevas, M. Calvo, Ch. Little, M. Pino, P. Dassori
150
1. Introduction
Streamflows are well known to vary seasonally
(Ward, Trimble, 2003; Cuevas et al., 2006; Little et
al., 2008; Lara et al., 2009), between years (Ward,
Trimble, 2003; Lara et al., 2008; Little et al., 2009),
and with the highest and lowest flows occurring in
rainy and dry seasons or years, respectively. These
variations, in addition to between-day fluctuations
(daily), are mainly related to precipitation control
(snow, rain; Graham, 1999; Tesař et al., 2008).
Streamflow variations experienced in time periods
of minutes or hours (diurnal) are not as well under-
stood. The pattern of higher streamflow at dawn
and lower streamflow during the afternoon was
noted several decades ago (Troxell, 1936; Wicht,
1941), but it was only with the advent of automatic
datalogger systems that these variations could be
studied in detail. In this way, diurnal variations
have been linked to a chain of causal factors,
namely, solar radiation increases air temperature
which then increases evapotranspiration (Monteith
1965), while decreasing water recharge towards
groundwater (Hughes, 2010). Thus, the water table
decreases, causing a diminution in streamflows
(Szilágyi et al., 2008; Gribovszki et al., 2008).
Some studies have shown that trees are mainly re-
sponsible for evapotranspiration in a watershed
(Bren, 1997) given that diurnal fluctuations have
been observed in tree sapflows (Bond et al., 2002;
Střelcová et al., 2009). Moreover, diurnal stream-
flow variations can also occur in losing reaches as a
consequence of increased streambed infiltration
rates during the afternoon (Constantz, 1998).
Lastly, snowmelt and freeze-thaw cycles can also
contribute to diurnal variation in streamflows, espe-
cially in alpine environments (Jordon, 1983; Caine,
1992). All cited processes have widely varying lag
times (Constantz, 1998; Bond et al., 2002; Szilágyi
et al., 2008).
In spite of the accepted concept of diurnal fluc-
tuation of streamflows and ongoing research of this
topic, related studies do not offer a critical report of
the validity of patterns measured with different
electronic devices (Constantz, 1998; Wondzell et
al., 2007; Gribovszki et al., 2008). Common water-
level loggers are pressure sensors that measure
hydrostatic pressure in addition to atmospheric
pressure, making it necessary to deduct the latter by
putting another logger outside the water (baromet-
ric compensation). In spite of the availability of
differential water level sensors, which do not need a
second sensor as they have vented cables allowing
compensation for barometric pressure changes
(Acworth, Brain 2008; Cockburn, Lamoureux
2008a, 2008b), their use is not as common as non-
vented water-level loggers. In the latter case, any
error in separated instrumental records can have
consequences on inferred water depths. Moreover,
many electronic devices are sensitive to tempera-
ture (Boylestad, Nashelsky, 1999; Cook, 1999;
Paynter, 1999), which should not cause a problem
if equipment is used to register temperature. How-
ever, problems are likely to arise when other vari-
ables, such as conductivity, pH, pressure, etc., are
measured as a good temperature compensation for
the circuits is required. If the thermal regime of
both sensors is not comparable, their differential
response to temperature may result in misleading
measurements. Thus, we propose that this deduc-
tion with the use of separate sensor records can
generate a measurement error which is greater if the
external logger is not correctly located.
The aim of this study was to evaluate the validity
of diurnal water level variations in three situations:
i) field measurements with an underwater logger
and another located 10 m from it at 1.5 m above
ground; ii) a detailed laboratory evaluation of log-
ger behaviour under a controlled water level with
known pressure and temperature variations; and iii)
a re-evaluation of field measurements by relocating
the barometric compensation logger in front of the
submerged logger, inside a weir, but exposed to the
air, and taking into account the patterns detected in
ii). Thus, we aim to provide evidence that baromet-
ric compensations that are not carefully carried out
can generate errors in streamflow estimations, es-
pecially when water discharges and fluctuations are
relatively low.
2. Materials and methods
2.1 Study site
We studied a small watershed (42,600 m2) lo-
cated in the Valdivia Rainforest Ecoregion of
Southern Chile (40 °S, 73.5° W) (Fig. 1). This wa-
tershed (RC6) consisted of native riparian vegeta-
tion (26-m wide), surrounded by an exotic forest
plantation of fast-growing Eucalyptus globulus
trees. Common native species in the riparian strip
were the Amomyrtus luma and Drimys winteri trees;
Chusquea quila and Tepualia stipularis shrubs;
Blechnum chilense and Lophosoria quadripinnata
ferns; and the Lapageria rosea vine.
Are diurnal fluctuations in streamflow real?
151
Fig. 1. Watershed studied in Los Ríos Region in Southern Chile.
Obr. 1. Povodie, ktoré bolo predmetom štúdia v oblasti Los Ríos na juhu Čile.
The watershed is 450 m long, 100 m wide, and
has an 80-m elevation difference between the high-
est and lowest points (Fig. 1). The bedrock belongs
to the local basement composed of micaschist. The
upper 2 m of the metamorphic rocks are deeply
weathered and fractured under the soil, whereas it is
compact and impermeable with some unconnected
joints below the basement.
According to our data for the period January-
December 2009, mean air temperature at 1.5 above
J. G. Cuevas, M. Calvo, Ch. Little, M. Pino, P. Dassori
152
ground was 10.5 °C and mean runoff was 8.9 m3 h-1
(range 0.02–161.7). Stations close-by showed an
annual rainfall of 1526 to 2046 mm, thus precipita-
tion for RC6 is probably within these limits.
RC6 is neither inhabited nor used by humans, for
example, there is no diversion of water for irriga-
tion. Patterns reported in this study are representa-
tive of six other watersheds examined in the same
zone.
2.2 Field monitoring 1
We measured three variables: air temperature,
streamflow, and stream temperature. Streamflow
was determined by a V-notch weir (90°) and a pres-
sure/temperature logger (HOBO U20-001-01, On-
set Computer Corporation, Bourne, USA) to record
data every 15 minutes. The logger was placed into a
polyvinyl chloride (PVC) tube open at the bottom
and perforated at intermediate levels to allow quick
water flow inside the tube. The equipment was
hung from a steel chain connected to the top cap of
the tube.
The weir bottom was cleaned periodically of
sediments, and the accumulation of leaves and
branches on the V-notch was prevented by placing
a metallic mesh in front of the notch, and arranged
as a semicircle.
Streamflows were calibrated from logger records
to make a linear regression with data from a staff
gauge installed in the weir. Water levels were later
converted to streamflows by calibrating flows di-
rectly measured with a jar and a chronometer, at
varying water depths. The regression model was a
power function between water level and stream-
flow.
According to the manufacturer, logger operation
range is 0 to 207 kPa (approx. 0–9 m water depth at
sea level), and from –20 to 50 °C. The typically
reported error is 0.5 cm for water level and 0.37 °C
(at 20 °C) for temperature, with a resolution of 0.02
kPa (0.21 cm water) and 0.1 °C (at 20 °C). Those
errors should be kept in mind for all measurements
reported in this paper.
We installed another logger of the same model
for barometric compensation at 1.50 m above
ground, 10 m apart and 3.5 m above the weir water
level. The 1.50 m height is within the standard
range for meteorological stations (1.2–2.0 m, WMO
2008). This instrument registered air temperature
and atmospheric pressure and was protected in a
gill-type radiation shield with 8 plates (Vaisala,
Helsinki, Finland) to avoid solar radiation. This
meteorological station was also used to characterize
the thermal environment within the forest, and to
calculate the adiabatic lapse rate of air temperature
comparing its records with another station located
at a higher altitude in the zone (results not re-
ported). The study period was from 8 December
2008 (spring) to 14 April 2009 (autumn).
2.3 Laboratory experiments
We put a logger into a test tube filled with water
up to different depths depending on the experiment
(22–39 cm; the water level range in logger field
measurements was 19.0–22.5 cm). The test tube
was closed, though not airtight, with a piece of
plastic or aluminium film to prevent water evapora-
tion. This seal was placed as an inverted cone to
facilitate water return to the main body of water in
case of evaporation. The test tube was cooled or
warmed by using a freezer or oven, respectively, or,
in some cases, ambient temperature was used when
a difference existed between water and air tempera-
ture. Data capture was every 15 s to 5 min depend-
ing on experiment duration.
Several thermal and pressure regimes were tes-
ted, for example, with nearly constant atmospheric
pressure, enabling the assessment of water logger
behaviour by only taking into account the water
temperature variation.
2.4 Field monitoring 2
In another field monitoring from 17 December
2009 to 25 March 2010, we relocated the external
station by placing the logger inside the weir, in
front of the water-level logger, and both below the
water table. However, the first logger was placed in
a PVC tube that was closed at the bottom and open
at the top to allow air and not water to enter. Thus,
the logger inside responded mainly to atmospheric
pressure variations, given that stream and air tem-
perature variations were very narrow (see Results).
We validated data in both field studies by dis-
carding rainy periods which could mislead possible
associations between streamflow, air, and stream
temperature. The anomalous patterns registered by
logger readings were not considered to be attribut-
able to watershed behaviour (e.g., when sensors
were offloaded or when weirs were cleaned of sedi-
ments).
Are diurnal fluctuations in streamflow real?
153
2.5 Statistical analyses
We used simple statistics, such as parametric lin-
ear regression and Pearson correlation coefficients
(r). Assumptions of regression analyses were
checked by inspecting the normal residual distribu-
tion. When these assumptions were not met, we
used Kendall’s robust line-fit method, a non-
parametric linear regression (Sokal, Rohlf, 1995)
(the same method is also known as Theil-Sen re-
gression; Theil, 1950; Sen, 1968). The non-
parametric Gamma statistic (Г) was used as a
measure of correlation, adequate when data contain
many tied observations (Siegel, Castellan, 1988).
All statistical analyses were carried out by means of
the Statistica 6.0 software (StatSoft, Inc., Tulsa,
Oklahoma, USA), except for the non-parametric
regression performed by the R-software (R Devel-
opment Core Team, 2009) and the ‘mblm’ package
by Komsta (2007).
3. Results
3.1 Field monitoring 1
A representative record is shown in Fig. 2, with
the compensation logger located 1.5 m above
ground. Data exhibited a clear fluctuation in air
temperatures with a mean maxima occurring at
13:35 h (range: 11:30–18:00 h)1) and minima at
08:47 h (range: 00:15–08:00 and 21:30–23:45 h).
Maximum stream temperatures (see below) oc-
curred at 14:34 h (range: 00:00–03:15 and 08:00–
–23:45 h) and minimum temperatures at 06:57 h
(range: 00:00–10:30 and 22:00–23:45 h). On the
other hand, streamflow maxima (see below) oc-
curred at 07:13 h (range: 00:00–08:00 and 20:15–
–23:45 h) and minima at 13:42 h (range: 09:15–
–18:15 h).
Tab. 1 shows stream temperature as never higher
than 15.3 °C, with an absolute minimum of 10.8 °C
(accuracy ± 0.37 °C). This caused diurnal fluctua-
tions to be especially dampened with a mean of
1.4 °C throughout the day (range 0.2–2.2 °C). The
opposite occurred for air temperature reaching val-
ues as low as 3.8 °C and as high as 33.2 °C. How-
1)Hours are expressed according to the official Chilean hour
during winter, which is GMT minus 4 hours. However, it
should be GMT minus 5 hours according to the time zone
where Chile is located. Thus, hours can be corrected
discounting 1 h for a more accurate expression.
ever, daily thermal amplitude was generally 12.4 °C
(range 2.9–23.9 °C).
Streamflow varied diurnally by a 1.9 factor
(maximum streamflow/minimum streamflow,
Qmax/Qmin) (range 1.43–3.22), with a mean
minimum value of 1.73 m3 h
-1 (range 0.84–2.85),
and a mean maximum value of 3.29 m3 h-1 (range
1.87–4.81), equivalent to a 1.3 cm (range 0.7–2.4
cm) diurnal water level variation (measured with
the logger).
Synchronization seems to be apparent between
air and stream temperature cycles in a direct rela-
tionship. Moreover, temperature maxima
(air/stream) correspond to streamflow minima, and
vice versa, with some hours of lag time (Fig. 2).
3.2 Laboratory experiments
Experiment 1. This experiment was carried out
with a fairly constant air temperature (22.3–23.3 °C),
an atmospheric pressure variation of 83 Pa (Coeffi-
cient of variation, CV = 0.02%), and lasted 68 min.
Water was initially colder than air and warmed by
placing the test tube on top of an oven (Fig. 3). The
water level record is nearly constant above 7 °C
with fluctuations attributable to instrumental error.
However, below 7 °C water depth strongly and
artificially decreased because this variable was kept
constant in the experiment, and resulted in a 1.5 cm
variation for the whole experiment. This sharp
diminution occurs when air and water loggers differ
more than 15 °C. If we project the range of stream
temperature variation (10.8 + 2.2 °C, 15.3 – 2.2 °C)
in this figure, we conclude that maximum mea-
surement error is 0.41–0.43 cm and within instru-
mental error (0.5 cm). If the true value is the mid-
point of that range, the error would only be ± 0.2
cm. Thus, we can be confident that the logger is
producing precise results in the above-mentioned
range. Moreover, the deviation between true water
depth and logger records is irrelevant due to the
calibration between both variables when measured
in the field (Materials and Methods).
Experiment 2. This experiment lasted ca. 3 days.
Air and water temperatures fluctuated passively in
the laboratory (amplitude: 4.8 and 5.6 °C, respec-
tively), as with the water depth records (Fig. 4A).
When air and water temperature differences were
higher (i.e., positive), water level records showed
lower values than when such differences were small
or even negative, with one possible exception to-
wards the end of the experiment. The pattern is
seen more clearly in Fig. 4B: as the difference
J. G. Cuevas, M. Calvo, Ch. Little, M. Pino, P. Dassori
154
Time (hours)
00:00 12:00 00:00 12:00 00:00 12:00 00:00 12:00 00:00 12:00 00:00 12:00 00:00 12:00 00:00
Temperature (oC)
0.0
5.0
10.0
15.0
20.0
25.0
30.0
Streamflow (m3 h-1)
0.0
2.0
4.0
6.0
8.0
10.0
12.0
Air temperature
Stream temperature
Streamflow
Fig. 2. Daily records of streamflow and air/stream temperature during a representative week in December 2008 (Field monitoring
1). Logger for barometric compensation was placed 1.5 m above ground.
Obr. 2. Denné záznamy prietokov a teplôt vody a vzduchu počas reprezentatívneho týždňa v decembri 2008 (poľné meranie 1).
Snímač pre barometrickú kompenzáciu bol umiestnený 1,5 m nad povrchom.
T a b l e 1. Diurnal fluctuations in stream and air temperature from December 2008 to April 2009.
T a b u ľ k a 1. Denné fluktuácie teplôt vody a vzduchu od decembra do apríla 2009.
Maximum temperature Minimum temperature Diurnal amplitude
Stream
Mean [°C] 13.5 12.2 1.4
Range [°C] 12.3–15.3 10.8–13.6 0.2–2.2
Air
Mean [°C] 21.5 9.1 12.4
Range [°C] 13.8–33.2 3.8–15.7 2.9–23.9
between air and water temperature increased, the
depth registered by the logger was lower (Gamma
statistic, Г = –0.554, probability P < 0.001), in spite
of the great dispersion of points around the curve.
This dispersion is probably due to instrumental
error for each specific temperature difference (0.4
to 0.6 °C).
Experiment 3. This experiment is a good exam-
ple of a situation where both loggers did not experi-
ence the same thermal and pressure regimes be-
cause one logger was in the test tube inside the
refrigerator, and another was outside the refrigera-
tor. Atmospheric pressure and air temperature were
very stable in the compensation logger (variation of
53 Pa, CV = 0.009%; and 1.8 °C, respectively).
Decreasing water temperature was associated with
a 1.2 cm diminution in water level records (Fig.
5A). As the difference between air and water tem-
perature increased, the depth registered by the log-
ger was lower (Fig. 5B; Pearson correlation coeffi-
cient, r = –0.90, P < 0.001). Dispersion of points
around the curve was close to 0.4 °C.
Experiment 4. When both loggers were placed in
the refrigerator, temperature curves followed a con-
vergent trend. Greater differences between air and
water temperatures (i.e., more negative) were asso-
ciated with higher water depth records (Fig. 6A).
When these differences were less negative, water
depth records decreased up to 15:00 h, and then
increased again. No clear relationship is seen be-
tween water level and the difference in air and wa-
ter temperature (Fig. 6B). Overall range of variation
over the entire experiment was 0.69 cm.
Overall underestimation and overestimation. Us-
ing results from all our experiments, both those
shown and not shown, we determined that underes-
timation in water depth in relation to the mean of a
particular experiment can be as high as 1.5 cm
(minimum 0.3). On the other hand, overestimation
ranges between 0.3 and 0.9 cm.
Are diurnal fluctuations in streamflow real?
155
Time (hours)
15:00 15:10 15:20 15:30 15:40 15:50 16:00
Temperature (°C)
0.0
5.0
10.0
15.0
20.0
25.0
Water depth (cm)
33.5
34.0
34.5
35.0
35.5
36.0
36.5
37.0
Air temperature
Water temperature
Water depth
Fig. 3. Experiment 1: Water in a test tube was first cooled and then warmed on top of an oven. Water depths correspond to pressure
logger records. The black rectangle indicates the time lapse when water temperature was below 7 °C (see text).
Obr. 3. Experiment 1: Voda v testovacej trubici bola najskôr ochladená a potom zohriata na peci. Hĺbka hladiny vody korešponduje
so záznamom snímača. Čierna hrubá čiara reprezentuje časový úsek, kedy bola teplota vody nižšia, ako 7 °C (pozri text).
Time (hours)
18:00 06:00 12:00 18:0 0 06:00 12:00 18:00 06:00 00:00 00:00 00:00
Temperature (°C)
-5
0
5
10
15
20
Water depth (cm)
38.2
38.4
38.6
38.8
39.0
39.2
Air temperature
Water temperature
Water depth
Air temperature minus water temperature (°C)
-2-1012345678
Water depth (cm)
38.2
38.4
38.6
38.8
39.0
39.2
Y = -0.074X + 38.96
Γ = -0.55
A
B
Fig. 4. Experiment 2: A. Water and air fluctuated freely in temperature during a 3-day monitoring. B. The same experiment shows a
relationship between the difference in air and water temperature in relation to the depth recorded by the water-level logger.
Obr. 4. Experiment 2: A. Teplota vody a vzduchu sa voľne menila počas trojdňového monitoringu. B. Ten istý experiment ukazuje
vzťah medzi rozdielom teplôt vzduchu a vody vo vzťahu k hĺbke meranej snímačom umiestneným na úrovni hladiny vody.
J. G. Cuevas, M. Calvo, Ch. Little, M. Pino, P. Dassori
156
Time (hours)
17:10 17:30 17:50 18:10 18:30 18:50
Temperature (°C)
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
Water depth (cm)
32.0
32.5
33.0
33.5
34.0
34.5
Air temperature
Water temperature
Water depth
Air temperature minus water temperature (°C)
-10-8-6-4-20246810
Water depth (cm)
32.6
32.8
33.0
33.2
33.4
33.6
33.8
34.0
34.2
Y = -0.048X + 33.2
r = -0.90
A
B
Fig. 5. Experiment 3: A. Logger in the water was cooled in a freezer while the external barometer was placed outside the freezer
(missing data points correspond to moments when the freezer was opened to monitor the experiment). B. The same experiment
shows a relationship between the difference in air and water temperature in relation to the depth recorded by the water-level logger.
Obr. 5. Experiment 3: A. Snímač vo vode bol chladený v chladničke (chýbajúce údaje zodpovedajú času, keď bola chladnička
otvorená, aby bolo možné experiment monitorovať). B. Ten istý experiment ukazuje vzťah medzi rozdielom teplôt vzduch/voda v
závislosti od hĺbky vody registrovanej snímačom na hladine vody.
3.3 Field monitoring 2
When we relocated the external logger in front of
the water-level logger, air/stream temperatures had
about the same magnitudes and followed the same
temporal trends. Tab. 2 shows that air/stream tem-
perature ranged between 10.3 and 14.2 °C with
mean maximum and minimum values of 12.8 and
11.8 °C, respectively. Mean difference for every
moment of data capture was only 0.05 °C (range:
0–0.391 °C). Mean diurnal amplitude was close to
1 °C. Maximum temperatures occurred at 15:15–
–15:22 h (range: 00:00–01:15 h and 11:30–23:45
h), and minimum temperatures at 07:06–07:16 h
(range: 00:00–10:45 h and 21:15–23:45 h), for
stream and air, respectively (Fig. 7).
On the other hand, streamflow maxima occurred
at 06:29 h (range: 00:00–11:00 h), and minima at
16:26 h (ranging mostly between 13:45 and 23:15
h). Qmax/Qmin varied diurnally by a 1.6 factor
(range 1.35–1.82) with a mean minimum value of
1.28 m3 h -1 (range 0.65–2.56) and a mean maxi-
mum value of 1.97 m3 h -1 (range 1.16–3.58),
equivalent to a diurnal water level variation (mea-
sured with the logger) of 0.8 cm (range 0.6–1.0
cm).
Once again, inverse synchronization seems ap-
parent between air/stream temperature and stream-
flow cycles (Fig. 7).
4. Discussion
4.1 Field measurements
Our first evaluation showed a marked variation
in air temperature and streamflow while the stream
temperature fluctuation was especially dampened.
Temperature results are not surprising because air
Are diurnal fluctuations in streamflow real?
157
Time (hours)
12:00 13:00 14:00 15:00 16:00 17:00 18:00
Temperature (°C)
0
5
10
15
20
25
30
35
40
Water depth (cm)
33.0
33.2
33.4
33.6
33.8
34.0
34.2
Air tremperature
Water temperature
Water depth
Air temperature minus water temperature (°C)
-14-12-10-8-6-4-2 0
Water depth (cm)
33.4
33.5
33.6
33.7
33.8
33.9
34.0
34.1
34.2
A
B
Fig. 6. Experiment 4: A. Same setting as in Fig. 5, except that the barometer was placed inside the freezer. B. The same experiment
shows the relationship between the difference in air and water temperature in relation to the depth recorded by the water-level
logger.
Obr. 6. Experiment 4: A. To isté, ako na obr. 5, s výnimkou toho, že barometer bol umiestnený v chladničke. B. Ten istý experi-
ment znázorňuje vzťah medzi teplotami vzduchu a vody v závislosti od hĺbky vody registrovanej snímačom na hladine vody.
T a b l e 2. Diurnal temperature variations for RC6 watershed from December 2009 to March 2010.
T a b u ľ k a 2. Denné fluktuácie teplôt vody a vzduchu pre povodie RC6 od decembra do marca 2009.
Maximum temperature Minimum temperature Diurnal amplitude
Stream
Mean [°C] 12.8 11.8 1.1
Range [°C] 11.2–14.0 10.3–13.1 0.4–1.8
Air
Mean [°C] 12.9 11.7 1.2
Range [°C] 11.2–14.2 10.3–13.1 0.4–2.1
temperature at every height is more variable than
the temperature of a body of water (Geiger, 1957;
pp. 73, 157). When we placed the logger for baro-
metric compensation immediately in front of the
other, results confirmed the streamflow pattern
previously detected in the field, but streamflow
fluctuations were 19% less accentuated. Since air
and stream temperatures were very similar and had
the same temporal trend, streamflow pattern can no
longer be explained by a possible methodologi-
cal/instrumental artifact.
Our results support a tight and inverse coupling
between both air and stream temperature with
streamflow. Other studies have found similar diur-
nal fluctuations in streamflow, expressed as maxi-
mum Q/minimum Q ratios, that do not surpass the
value 2.0 (Constantz, 1998; Bond et al., 2002;
Wondzell et al., 2007; Szilágyi et al., 2008). The
reported association can be mediated by evapotran-
J. G. Cuevas, M. Calvo, Ch. Little, M. Pino, P. Dassori
158
Time (hours)
00:00 12:00 00:00 12:00 00:00 12:00 00:00 12:00 00:00 12:00 00:00 12:00 00:00 12:00 00:00
Temperature (oC)
-4.0
0.0
4.0
8.0
12.0
16.0
Streamflow (m3 h-1)
0.0
2.0
4.0
6.0
8.0
10.0
12.0
Air temperature
Stream temperatu re
Streamflow
0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00
0.6
0.8
1.0
1.2
1.4
1.6
1.8
Streamflow (m3 h-1)
Time (hours)
Fig. 7. Diurnal variation in streamflow and air/stream temperature after relocation of the external barometer (inside the weir, but
exposed to the air), for a representative week in January 2010 (Field monitoring 2). Streamflow is shown on the same scale as in
Fig. 2, as well as an expanded scale (insert graph).
Obr. 7. Denné variácie v prúdení vody a teplôt vzduch/voda po zmene umiestnenia externého barometra (vo vnútri priepadu, ale
exponovaného na vzduchu), pre reprezentatívny týždeň v januári 2010. (Monitorovanie 2). Prietok je znázornený v tej istej mierke,
ako je to na obr. 2, ako aj v rozšírenej mierke (vložený obrázok).
spiration which increases with air temperature
(Monteith, 1965), a phenomenon especially intense
in trees (Bren, 1997; Bond et al., 2002; Střelcová et
al., 2009). When evapotranspiration decreases wa-
ter recharge to the soil (Hughes, 2010), the ground-
water level is also expected to decrease. Moreover,
Szilágyi et al. (2008) and Gribovszki et al. (2008)
showed a direct coupling between groundwater
level and streamflow, both phenomena associated
to riparian vegetation in Hungary. We also showed
that air temperature positively influences stream
temperatures, an outcome already discussed in de-
tail by Mohseni and Stefan (1999). Other explica-
tive mechanisms for diurnal fluctuations in stream-
flow are unlikely in our watershed because the
schist under the stream is not permeable, thus mak-
ing our stream a probable gaining stream rather
than a losing reach that could lose water as a func-
tion of stream temperature (Constantz, 1998).
Lastly, snowmelt and freeze-thaw cycles do not
occur in summer, and it does not snow during win-
ter in our study site.
Streamflow maxima generally occurred early in
the morning, as other studies have documented
(Wondzell et al., 2007; Gribovszki et al., 2008),
although Constantz (1998) showed maxima after 10
a.m. Regarding streamflow minima, our mean re-
sults (13:42–16:26 h) are close to the findings of
the abovementioned studies, especially the later
hour. Stream temperature maximum was at 14:34–
–15:15 h and the minimum was at 06:57–07:06 h,
both within the range reported by Constantz (1998).
Maximum and minimum air temperatures oc-
curred at 13:35 and 08:47 h, respectively, times
close to midday and sunrise which were reported as
typical hours by Geiger et al. (2003, p. 73) (if we
consider that Chilean time should be one hour less
than the official hour). It is to be noted that in our
second field monitoring, times for extreme air tem-
peratures diverged from the first field monitoring,
approaching corresponding times for stream tem-
perature. This fact emphasized stream influence on
the air surrounding the water table (Geiger et al.,
2003, p. 157).
4.2 Generation of a methodological/
instrumental artifact
Water level is calculated by deducting atmos-
pheric pressure from the pressure measured below
water with both measurements coming from inde-
pendent instruments; thus, any error in separated
instrumental records can have consequences on
inferred water depths. Experiment 1 showed that
when air and atmospheric pressure are fairly stable,
water level records are reliable in the realistic range
Are diurnal fluctuations in streamflow real?
159
for stream temperatures in our first field study
(10.8–15.3 °C). Error is about 0.4 cm depth (0.5 cm
according to logger manufacturer).
Problems arise when either of the loggers experi-
ence strong differences in temperature with the
other (Experiments 2 and 3), or there is a large
temporal temperature fluctuation (Experiment 4).
Large temperature differences between both in-
struments in Experiment 3 were produced because
one was placed in a freezer and the other left out-
side. Therefore, air pressure in the freezer probably
decreased when the air cooled following Gay Lus-
sac’s second law (Maiztegui, Sábato, 1973) or the
ideal gas equation (Resnick, Halliday, 1970, p.
754), when the systems are closed or semi-closed,
like a freezer in our laboratory. On the contrary, the
variation in both variables was not detected by the
external logger. Thus, barometric compensation
was flawed. Though this is an extreme case, which
is unlikely in a well-designed experiment, it can be
similar to one in the field: since stream temperature
has a narrow variation, large temperature discrep-
ancies between the air and water logger are caused
by the external logger which faces warmer or
colder temperatures than the submerged logger
throughout the day (Fig. 2). The range of tempera-
tures tested in the laboratory is realistic for the log-
ger exposed to air, but, of course, streams are not as
warm as this range suggests. However, this thermal
regime served to stress and thus understand the
sensor responses under extreme situations.
Even when both loggers are placed in the same
conditions, such as in Experiment 4, water depth
records are not stable, suggesting that large tempo-
ral fluctuations in temperature are also associated to
imprecise measurements. The pressure sensor is
temperature compensated over the range 0–40 °C
(Onset Computer Corporation, 2005, 2006), but
rapid fluctuations in the laboratory (more than 5
°C/h) impose a severe challenge to this compensa-
tion (see next section). In the field, on the other
hand, the problem would be slighter because air
temperature varies on the average 12.4 °C in 5
hours from sunrise to midday (2.5 °C/h).
Pressure measured by the external logger is the
most probable explanation for the methodologi-
cal/instrumental artifact caused by large tempera-
ture fluctuations in the air. Onset Computer Corpo-
ration (2010) reports that both water level and pres-
sure accuracy depend directly on temperature, with
maximum accuracy at 16 °C. Over this temperature,
the measurements are overestimated, and vice versa
when temperatures are below 16 °C. Thus, sharp
streamflow patterns (unreal) found in our first field
monitoring compared with the dampened curves of
the second monitoring (real) can be explained as
follows: when air temperatures are high at noon, the
external logger records an overestimated air pres-
sure, phenomenon that does not occur with the
submerged logger. Therefore, barometrically com-
pensated pressure is lower than the real value and
water depth is underestimated. This contributes to
the creation of a low streamflow pattern. When air
temperatures are low at dawn, lower than stream
temperatures, the external logger records an under-
estimated air pressure, phenomenon that does not
occur with the submerged logger. Therefore, com-
pensated pressure is higher than the real value and
water depth is overestimated. This contributes to
the creation of a high streamflow pattern.
Both over- and underestimations can be very
high when compared to the normal diurnal water
level variation. Since they are operating in the same
direction as the reported pattern in streamflow, the
methodological and instrumental artifact makes a
weak phenomenon stronger.
4.3 External logger position
Placement of the external logger 1.5 m above
ground proved incompatible for both barometric
compensation and characterization of the forest
thermal regime, as well as the calculation of the
adiabatic lapse rate of air temperature. In fact, On-
set Computer Corporation (2005, 2006) recom-
mends placing external loggers in an observation
well “several feet below ground level” to minimize
the temperature fluctuation rate. When these fluc-
tuations are high, logger accuracy is degraded since
thermal response time is 10 min to achieve full
temperature compensation of the pressure sensor. A
minimum of 30 min is recommended to reach tem-
perature equilibrium for the highest accuracy level.
Unfortunately, literature consulted about diurnal
fluctuations in streamflow did not give sufficient
details about the external logger position (Con-
stantz, 1998; Bond et al., 2002; Wondzell et al.,
2007; Szilágyi et al., 2008; Gribovszki et al., 2008).
The placement that we used in our second field
measurement is near the location proposed by the
manufacturer although it disagrees with some of its
other recommendations: “barometric readings can
be taken within 15 km of the water logger or more
without significantly degrading the accuracy of the
compensation”. As we have seen, conditions faced
by both loggers must be as similar as possible, a
J. G. Cuevas, M. Calvo, Ch. Little, M. Pino, P. Dassori
160
requirement difficult to meet at 15 km when con-
sidering different continentality, altitude, microcli-
mate, etc.
Relocation of the external logger is the most
convenient and accurate strategy to detect diurnal
patterns in streamflow. Thermal fluctuations of
both loggers are especially dampened and have
virtually the same magnitude and temporal trend.
Thus, they are sensing temperature and atmospheric
pressure in the same way. Another alternative
would be to develop a correction factor for the
measurements already taken. However, different
experiments produce different slopes and intercepts
of the depth-temperature difference curve making it
difficult to choose which correction factor to use.
5. Conclusions
We conclude that diurnal fluctuations in stream-
flow are real but can be intensified by an incorrect
location of the barometric compensation logger.
Our results are relevant depending on the range of
the water level fluctuation and the frequency of
data capture. For instance, Kröger et al. (2008) and
Ostrander et al. (2008), with the same logger model
that we used, measured streamflows and tide ampli-
tudes, and placing the external logger at 1.5 or 4 m
above ground, respectively. Both the purpose of
those research studies and the temporal scale of
measured water fluctuations did not show any of
the possible instrumental and methodological un-
certainties. Thus, no putative artifact is important in
those cases. The reported artifact has great impor-
tance when the objective is either to document
small signals in streamflow, to know the exact
value of streamflow, or for the assessment of statis-
tical relationships between variables.
Finally, our field detection of diurnal streamflow
variation is striking, considering that we used a
water-level logger designed for the 0–9 m range
and with a 0.21 cm resolution. There are other log-
gers (e.g., U20-001-04, Onset Computer Corpora-
tion; BaroDiver 11.11.55.01, Schlumberger) that
have a narrow range (0–4 m; 0–1.5 m, respec-
tively), with a better resolution (0.14–0.10 cm, re-
spectively), and a lower typical error (0.3–0.45 cm,
respectively). However, the dependency of mea-
surements on air/water temperature must be tested
as set forth in this study.
Acknowledgement. We thank the FONDECYT
grant no. 1085024 for founding this research. We
also thank the FORECOS Foundation and Val-
divian Coastal Reserve for providing equipment
and facilities for field work; Dr. Hamil Uribe for
warning us about a possible methodologi-
cal/instrumental artifact in our data; Anton Huber,
José Luis Arumí, Robert Brümmer, Carlos Oyarzún
and Diego Rivera for providing literature and help-
ful advice; Miguel López and Marcel Fuentes who
facilitated our data analyses work. We greatly ap-
preciate the valuable comments that we received
from two anonymous referees. A preliminary ver-
sion of this manuscript was presented as a lecture at
the 2nd International Biohydrology Conference
2009: “A Changing Climate for Biology and Soil
Hydrology Interactions” held in Bratislava, Slova-
kia from 21 September to 24 September 2009. We
thank the organizing committee for inviting us to
submit this paper to the journal.
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Accepted 23 June 2010
SÚ DENNÉ FLUKTUÁCIE PRIETOKOV
V TOKOCH REÁLNE?
Jaime G. Cuevas, Matías Calvo, Christian Little,
Mario Pino, Paul Dassori
Z výsledkov štúdia vyplýva reálnosť denných fluk-
tuácií prietokov v tokoch, tieto však môžu byť zintenzív-
nené nevhodnou lokalizáciou barometricky kompen-
zovaného snímača. Význam týchto výsledkov závisí od
rozsahu fluktuácií vodných hladín a od frekvencie zápisu
údajov. Napríklad Kröger et al. (2008) a Ostrander et al.
(2008) použili ten istý snímač ako my na meranie prieto-
J. G. Cuevas, M. Calvo, Ch. Little, M. Pino, P. Dassori
162
kov v tokoch a amplitúdy prílivu, s umiestnením ex-
terného snímača 1,5 a 4 m nad povrchom terénu. Účel a
mierka času meraných fluktuácií vody v týchto štúdiách
nenaznačovali možné metodické a inštrumentalistické
nejasnosti. V tomto prípade nebol zistený žiadny
všeobecne známy artefakt. Tu opísané artefakty sú
významné, ak je cieľom meraní dokumentovať malé
zmeny hladín v toku, získať presné hodnoty prietokov,
alebo určiť štatistické závislosti medzi premennými.
Nakoniec, detekcia denných zmien prietokov v
(malých) tokoch je prekvapujúca, berúc do úvahy fakt,
že bol použitý snímač na meranie hladín vody s rozsa-
hom 0–9 m a rozlíšením 0,21 cm. Existujú aj iné snímač
(napr., U20-001-04, Onset Computer Corporation;
BaroDiver 11.11.55.01, Schlumberger) s užšími
rozsahmi (0–4 m; 0–1,5 m), s lepším rozlíšením (0,14–
–0,10 cm), a s nižšou chrakteristickou chybou merania
(0,3–0,45 cm). Samozrejme, závislosť medzi meraním
teplôt vzduchu a vody musí byť testovaná tak, ako to
bolo urobené v tejto štúdii.
... A barometric pressure compensation logger was placed in a sheltered garage at a 500 m distance to prevent vandalism. However, in the last months of this study the barometer was relocated to a PVC dry well surrounded by wetland water, about 20 m from the submerged sensor, following the recommendations of Cuevas et al. (2010). The device employed was a U20L-04 sensor from the same manufacturer of the other pressure transducer. ...
... The raw pressure accuracies are 0.62 kPa and 0.43 kPa, respectively. Assuming independent errors, the precision error associated with the difference between the two records is determined as follows (Farrance and Frenkel, 2012): a) Typical error: = √0.5 + 0.4 = 0.6 cm b) Maximum error: = √1 + 0.8 = 1.3 cm Cuevas et al. (2010) experimentally determined that the error in the water level estimated from two HOBO U20-001-01 pressure loggers was 0.4 cm. We also tested the two loggers in controlled conditions in the laboratory, exposed to the air, to determine the possible differences in records between them. ...
... However, in our study, we proved that this is an instrumental artifact (see the Very small variations section under Results). This artifact is attributed to the difference in exposure temperature between atmospheric and water pressure transducers, and the dependence of pressure transducer measurements on temperature (Cuevas et al., 2010;McLaughlin and Cohen, 2011;Rau et al., 2019). ...
Article
Full-text available
Coastal wetlands are transitional ecosystems between land and sea. Participants of citizen science programs have detected frequent floods in wetlands, as well as small pools that appear and then disappear. Considering that it is not clear whether their main hydrologic drivers are of marine or continental origin, we studied the El Culebrón wetland located in the Chilean semi-arid zone. El Culebrón is strongly influenced by extreme rain events. This wetland also experiences seasonal changes in its water stage (WS). A high mean sea level agreed with 41% of the WS rises. High intensity storm surges coincided with 53% of WS peaks. A small tsunami in 2022 impacted the WS, and another very intense tsunami flooded it in 2015. An apparent diurnal cycle in the WS was discarded due to an instrumental artifact. The combination of the aforementioned factors provided an explanation for 91% of the WS rises. The probable and novel mechanism for sea level and storm surge influence on WS is the formation of a sand barrier between the coastal lagoon and the sea. As a whole, El Culebrón receives varied influences from both the sea and the mainland, but it seems to be more dependent on freshwater sources.
... Freeman et al. (2004) highlighted water temperature gradients and rapid temperature fluctuations as potential sources of error, while Cain, Davis, Loheide II, and Butler (2004) (2015) and Moore et al. (2016) went on to suggest that all transducers be tested for inaccuracies over the range of temperature measurements and individual correction equations be developed to apply to collected data. That conclusion is supported by others who have found that air temperature-induced errors can be as high as 19% in streamflow monitoring (Cuevas et al., 2010) To preempt these errors for absolute transducers, it is recommended as 'best practice' deployment of barometric and water level pressure transducers to place both in similar thermal regimes (i.e. within a dry well at the same depth) (McLaughlin and Cohen, 2011). ...
... Therefore, quantifying and correcting temperature-induced errors is essential to ensure that the observed data trends are real and not entirely or partially artifacts. While there are some attempts to provide that validation through high-frequency manual readings (Cuevas et al., 2010;Gribovszki, Kalicz, & Szilágyi, 2013), it is often impractical or impossible to capture the range of potential temperature and water level values, leaving laboratory-derived correction equations as the best alternative, as recommended by Moore et al. (2016) and Liu and Higgins (2015). ...
... To confidently attribute diurnal or seasonal signals to true variation rather than artificial errors requires additional validation. One source of validation is using additional instrumentation that is not susceptible to the same error source, such as frequent manual measurements of stage/level (e.g., Cuevas et al., 2010;Gribovszki et al., 2013). Where this is not practical or possible, we have shown that correction equations can quantify the additional uncertainty of environmentally-induced errors and rule them out as the cause of the signal of interest. ...
Article
For more than a decade intensive research on the ecohydrology of black ash wetland ecosystems has been performed to understand these systems before they are drastically altered by the invasive species, emerald ash borer (EAB). In that time there has been little research aimed at the scale and persistence of the alterations. Three distinct but related research articles will be presented to demonstrate a method for moderate resolution mapping of black ash across its entire range, understand the relative impacts of EAB and climate change on probable future wetland conditions, and develop an experimental and modeling approach to quantify and reduce uncertainty around water level measurements that underpin much of our understanding in these systems. Results from this research demonstrate that the scale and persistence of these impacts will be dependent not only on the immediate impacts of EAB, but also on vegetative response, the true extent of black ash wetlands on the landscape, and the compounding influence of a changing climate. Major findings from this research include 1) the effects of EAB and climate in the study area are counteracting, generally with a larger drying climate impact, 2) across its range black ash can be distinguished from other forest types using a combination of unsupervised and supervised learning on satellite imagery, and 3) over larger spatial scales and time periods uncertainty of our results is critical for interpretation and should be considered at the lowest level of data collection. At a higher level, this research is intended to serve as a bridge between study-site level changes and the spatial and temporal extent of those changes, opening new research questions to better understand these relatively rapid shifts in regional forested wetlands.
... Daily variations in surface water level have been observed in several studies (Bond et al., 2002;Wondzell et al., 2010;McLaughlin and Cohen, 2011;Gribovszki et al., 2010Gribovszki et al., , 2013. While this phenomenon has been attributed to a set of confounding factors (Cuevas et al., 2010), including evapotranspiration of riparian vegetation (White, 1932), it may also be the consequence of an inappropriate monitoring design and operation (Freeman et al., 2004), for instance, external sensors, loggers, or cables directly exposed to solar radiation or precipitation. To minimise these issues, the barometric and submerged sensors of the absolute pressure transducer systems were installed in temperature conditions that were as similar and as buffered as possible. ...
... To minimise these issues, the barometric and submerged sensors of the absolute pressure transducer systems were installed in temperature conditions that were as similar and as buffered as possible. Vented tube sensors, in principle, do not require additional barometric compensation (Cuevas et al., 2010); nonetheless, when using them, the vented tube should be free of obstructions, dry and isolated from direct solar radiation to avoid potential issues as recommended (Freeman et al., 2004;Cain et al., 2004;Gribovszki et al., 2010). Uncertainty in the measurement of water level time series can be approximated by sampling from a uniform distribution using the nominal sensor accuracy as the estimated error (Table 1.2). ...
Thesis
Full-text available
Water is the backbone of human development. However, a major impediment for sustainable development is the limited amount of data available to support evidence-based decision making on water resources and catchment management. Recently, participatory approaches to environmental monitoring have become more popular, and are being promoted as a potential pathway to address long-standing data gaps. I hypothesised that such a participatory approach to monitoring water resources is an efficient way to reduce data scarcity and to support decision making in the context of catchment management. To test this hypothesis, I studied one of the largest bottom-up initiatives of participatory environmental monitoring in the world: The Regional Initiative for Hydrological Monitoring of Andean Ecosystems (iMHEA). iMHEA is a partnership of academic and non-governmental institutions who instrumented a participatory hydrological monitoring network of headwater catchments in the tropical Andes. After a rigorous quality control of the generated data, I used them to analyse the impacts of land-use changes on the hydrological response of different Andean biomes, to develop a statistical model to predict such impacts in ungauged basins, and to evaluate a particular catchment intervention, i.e. pre-Inca artificial infiltration systems. Lastly, I put my findings in a broader context, testing similar approaches in the Ethiopian Highlands, and developing a simple method for the hydro-economic evaluation of nature-based solutions for water. I find that the data generated using such a participatory approach meets quality standards comparable to those of scientific research and, furthermore, stakeholders are more incentivised to provide open access to their data and to participate in a process of knowledge co-creation and its further assimilation. Strengthening the evidence body over which decisions are based can contribute to the improvement of water resources management as well as to the sustainable development of local communities.
... More recently, Sorensen and Butcher (2011) examined the accuracy and drift of different brands of PTs and found that the manufacturers' specifications were not met during field deployment. The effect of temperature on sensor performance has also received some attention (Cuevas et al., 2010;McLaughlin and Cohen, 2011;Liu and Higgins, 2015). These studies concluded that strong temperature fluctuations such as those that occur under field conditions affect PTs of all types. ...
... Manual dips are indicated by blue dots. thermal effects into water levels during barometric compensation (Cuevas et al., 2010;McLaughlin and Cohen, 2011), which is demonstrated in Fig. 6. The graph of water level versus time shows a clear diurnal variation in the time series recorded by a non-vented and barometric PT pair. ...
Article
Full-text available
Hydraulic head and gradient measurements underpin practically all investigations in hydrogeology. There is sufficient information in the literature to suggest that head measurement errors can impede the reliable detection of flow directions and significantly increase the uncertainty of groundwater flow rate calculations. Yet educational textbooks contain limited content regarding measurement techniques, and studies rarely report on measurement errors. The objective of our study is to review currently accepted standard operating procedures in hydrological research and to determine the smallest head gradients that can be resolved. To this aim, we first systematically investigate the systematic and random measurement errors involved in collecting time-series information on hydraulic head at a given location: (1) geospatial position, (2) point of head, (3) depth to water, and (4) water level time series. Then, by propagating the random errors, we find that with current standard practice, horizontal head gradients <10-4 are resolvable at distances ⪆170 m. Further, it takes extraordinary effort to measure hydraulic head gradients <10-3 over distances <10 m. In reality, accuracy will be worse than our theoretical estimates because of the many possible systematic errors. Regional flow on a scale of kilometres or more can be inferred with current best-practice methods, but processes such as vertical flow within an aquifer cannot be determined until more accurate and precise measurement methods are developed. Finally, we offer a concise set of recommendations for water level, hydraulic head and gradient time-series measurements. We anticipate that our work contributes to progressing the quality of head time-series data in the hydrogeological sciences and provides a starting point for the development of universal measurement protocols for water level data collection.
... More recently, 25 Sorensen and Butcher (2011) examined the accuracy and drift of different brands of PTs and found that the manufacturers' specifications were not met during field deployment. The effect of temperature on sensor performance has also received some attention (Cuevas et al., 2010;McLaughlin and Cohen, 2011;Liu and Higgins, 2015). These studies concluded that strong temperature fluctuations such as those that occur under field conditions affect PTs of all types. ...
... Sorensen and Butcher (2011) noted that temperature compensation often significantly compromises the accuracy of pressure readings. 20 For non-vented PTs, it is especially important to consider placement of the barometric PT to prevent adding noise from thermal effects into water levels during barometric compensation (Cuevas et al., 2010;McLaughlin and Cohen, 2011), which is demonstrated in Figure 6. The graph of water level versus time shows a clear diurnal variation in the time series recorded by a non-vented/barometric PT pair. ...
Preprint
Hydraulic head and gradient measurements underpin practically all investigations in hydro(geo)logy. There is sufficient information in the literature to suggest that head measurement errors may be so large that flow directions can not be inferred reliably, and that their magnitude can have as great an effect on the uncertainty of flow rates as the hydraulic conductivity. Yet, educational text books contain limited content regarding measurement techniques and studies rarely report on measurement errors. The objective of our study is to review currently-accepted standard operating procedures in hydrological research and to determine the smallest head gradients that can be resolved. To this aim, we first systematically investigate the systematic and random measurements errors involved in collecting time series information on hydraulic head at a given location: (1) geospatial position, (2) point of head, (3) depth to water, and (4) water level time series. Then, by propagating the random errors, we find that with current standard practice, horizontal head gradients < 10 −4 are resolvable at distances 170 m. Further, it takes extraordinary effort to measure hydraulic head gradients < 10 −3 over distances < 10 m. In reality, accuracy will be worse than our theoretical estimates because of the many possible systematic errors. Regional flow on a scale of kilometres or more can be inferred with current best-practice methods, but processes such as vertical flow within an aquifer cannot be determined until more accurate and precise measurement methods are developed. Finally, we offer a concise set of recommendations for water level, hydraulic head and gradient time series measurements. We anticipate that our work contributes to progressing the quality of head time series data in the hydro(geo)logical sciences, and provides a starting point for the development of universal measurement protocols for water level data collection. Copyright statement.
... Generally, vented pressure transducers require correction for barometric pressure, as well as ambient temperature and water temperature to determine the hydrostatic load more accurately [31,32]. Therefore, a data logger pressure transducer (Baro-Diver DI500) was installed to measure barometric pressure and ambient temperature, while the pressure transducer under water (Mini-Diver DI501) records the hydrostatic pressure and water temperature. ...
Article
This study presents a new method of installing non-vented pressure transducers with dataloggers to monitor river/streams stage in mountain areas. The installation was performed in the bedrock of the streams using a protective copper casing, next to streams natural control and without affecting their characteristics since it does not require infrastructure. This allowed the installation of 18 pressure transducers in areas with mining influence, urban and natural protected areas, among others. The results obtained show that the risks of damage to the pressure transducer due to extreme floods or vandalism are significantly reduced, and that it is possible to build temporary monitoring stations using the natural section/channel controls or structure controls in urban environments. The proposed method considerably reduces infrastructure and maintenance costs for stage monitoring, making it feasible to install in mountain rivers and streams with similar characteristics to those presented in this study.
... Composite cubic spline interpolation, threshold intensities, and bias correction 43,[49][50][51]70 Spatial interpolation uncertainty Subsampling from the available rain gauges in the network 67 Streamflow Water level measurement uncertainty, barometric and temperature pressure compensation Sampling from uniform or normal distributions with standard error of ±5mm or using sensor nominal accuracy 67,74,75,79,82,91 Rating curve uncertainty, calibration and extrapolation Constraining uncertainty by using a discharge control structure; voting point likelihood; heteroscedastic maximum likelihood model 56,81,83,85,92 Streamflow time discretisation Comparing indices calculated at different time steps 5,92 Seasonality in hydrological response Using total length of time series or divided by season 38,93 Hydrological index calculation method, e.g. baseflow separation method ...
Article
Full-text available
This article presents a hydrometeorological dataset from a network of paired instrumented catchments, obtained by participatory monitoring through a partnership of academic and non-governmental institutions. The network consists of 28 headwater catchments (o 20 km 2) covering three major biomes in 9 locations of the tropical Andes. The data consist of precipitation event records at 0.254 mm resolution or finer, water level and streamflow time series at 5 min intervals, data aggregations at hourly and daily scale, a set of hydrological indices derived from the daily time series, and catchment physiographic descriptors. The catchment network is designed to characterise the impacts of land-use and watershed interventions on the catchment hydrological response, with each catchment representing a typical land use and land cover practice within its location. As such, it aims to support evidence-based decision making on land management, in particular evaluating the effectiveness of catchment interventions, for which hydrometeorological data scarcity is a major bottleneck. The data will also be useful for broader research on Andean ecosystems, and their hydrology and meteorology.
... There was, however, a general decrease in the amplitude of the flow cycles which could be due to an increase in the counteracting effect of the ET induced cycle. One concern regarding diel discharge is that the cycles are caused by temperature sensitivity of the probes resulting in erroneous water level measurements (Cuevas et al., 2010). However, the diver used to measure discharge in HW1 was compensated for temperature effects. ...
Article
Sub-daily variations in the rates and dominance of the main controls of stream dissolved organic carbon (DOC) concentration (production, mobility and instream processes) have the potential to create a subtle sub-daily rhythm of DOC variation in streams. We used high-frequency data, covering the spring-summer-autumn period, which included discharge, specific conductivity, pH, groundwater levels, temperature, evapotranspiration and solar radiation to investigate the interplay between factors potentially driving diel DOC cycles in northern catchments. We focused on a peatland dominated 1st order stream (0.65 km²) before investigating the propagation of the signals downstream to a 2nd order stream (3.2 km²), with a lower percentage of peat fringing the stream channel. DOC cycles in the 1st order stream had a median peak time of 14:00 h and temporally varying amplitude, with a median of 0.61 mg l⁻¹. Results supported the hypothesis that diel DOC cycles at the site are driven by hydrological processes, specifically the viscosity-effect theory: viscosity-driven increases in flow from the riparian area in the afternoon flush DOC from the peat to the stream. The temporal variability in the amplitude of the diel DOC cycle was controlled by antecedent temperature. Downstream, the diel DOC signal was weaker, with around 4-fold lower amplitudes and minima in the afternoon. The lower proportion of riparian peat downstream appeared to reduce the influence of terrestrial processes on DOC cycles. In-stream photodegradation and decomposition likely became more dominant as connectivity between DOC sources and stream reduced. The study highlighted that even in climates such as the Scottish Highlands, where energy input is relatively low and precipitation frequent, sub-daily hydrological and biogeochemical rhythms occur. Unravelling the intricacy of such diel cycles is fundamental to fully understanding stream functioning and the global carbon cycle.
Article
Continuous water level monitoring using absolute pressure transducers with onboard datalogging is common practice in hydrologic studies. While there has been some discussion and study of temperature-derived error (TDE), there has not been a systematic evaluation of the problem. We sought to answer three questions: 1) are current best practices enough to avoid these errors, 2) can laboratory correction be used to correct field data from varying conditions, and 3) what is the scale of the additional uncertainty of the correction procedure? We evaluated the magnitude of such errors under laboratory conditions that mimicked common monitoring scenarios. Using field data, we also demonstrated the impact of TDEs on calculated daily mean water level and diurnal signal decomposition to estimate evapotranspiration (ET). To address instrument and model uncertainty, we fit 1000 possible correction models using a double-bootstrap approach. Correction models fit expected error as a function of water and air temperature and rate of change of air temperature. TDEs were a significant source of error, resulting in recorded data outside of manufacturer-stated instrument uncertainty, with 45% of bootstrap models showing significant but small TDEs under best-practice deployment. Correction equations did introduce additional error, often on a much smaller scale than instrument uncertainty. When tested against a validation dataset, correction equations effectively reduced total measurement uncertainty below instrument uncertainty by up to 65%. The effects of TDEs on case-study field data resulted in 56% of daily mean values outside of instrument error bounds (errors: -1.5–4.2 cm). Our results suggest that a single laboratory correction equation can be used across monitoring scenarios, though we suggest matching deployment conditions as closely as practical. Identification and correction of TDEs is essential to avoid erroneous conclusions, downstream analyses, and water resources management. This article is protected by copyright. All rights reserved.
Article
With continuing growth of urban population worldwide, high-resolution hydrologic forecasting is an increasingly important hydroinformatics service for large urban areas. In the Dallas-Fort Worth (DFW) area, the Collaborative Adapting Sensing of Atmosphere (CASA) WX program has been providing real-time hydrologic products, such as rainfall and streamflow, at 1 min–500 m resolution using the NWS Research Hydrologic Distributed Model forced by the Quantitative Precipitation Estimate from a network of X-band weather radars. There is an increasing demand, however, for even higher-spatial resolution hydrologic products. In this paper, we assess the ability of the current streamflow product to capture the hydrologic response of urban catchments in the DFW area, the utility of ultrasonic distance sensors for real-time sensing of water level in urban streams, and the feasibility of higher-resolution operation using parallel processing and cloud computing. We show that the CASA WX streamflow product skillfully captures the stage and streamflow response from rainfall for the majority of the nine catchments studied, but that timing errors significantly deteriorate the quality of streamflow prediction for certain basins. Comparative evaluation of different computing models shows that a reduction in runtime of up to 34% is possible with parallel processing at 1 min–250 m resolution.
Article
Full-text available
Diel variations in stream discharge have long been recognized, but are relatively little studied. Here we demonstrate that these diel fluctuations can be used to investigate both streamflow generation and network routing. We treat evapo-transpiration (ET) as a distributed impulse function in an advection model and analyze the effect of ET on diel fluctuations in discharge. We show that when flow velocity is high during high baseflow, discharge fluctuations tend to be in phase and constructive interference reinforces ET-generated signals resulting in strong diel fluctuations measured at a gauging station at the mouth of the watershed. As flow velocity slows with baseflow recession, ET-generated signals are increasingly out of phase so that fluctuations in discharge are masked by destructive interference. These results demonstrate that naturally produced fluctuations in discharge constitute discrete impulse functions that can be used to analyze eco-hydrologic behavior of whole-watersheds during baseflow periods.
Article
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Gribovszki et al. (Gribovszki, Z., Kalicz, P., Szilagyi, J., Kucsara, M., 2008. Riparian zone evapotranspiration estimation from diurnal groundwater-level fluctuations. J. Hydrol.) recently reported a peculiar phase-shift in diurnal streamflow and riparian zone groundwater-level fluctuations during streamflow recession for a small forested watershed. Employing high-frequency (10 min) groundwater-level and streamflow measurements in a wooded riparian zone of a gaining stream they demonstrated that groundwater-level changes lagged behind that of streamflow changes by about 1–1.5 h. To check the validity of their claim, a 2D finite element numerical model of the coupled system of vadose and saturated zones was employed with diurnal fluctuations in evapotranspiration. The model successfully reproduced the reported phase-shift and produced diurnal groundwater and streamflow fluctuations similar in their characteristics to what was reported. This finding demonstrates that by observing a similar phase-shift in diurnal fluctuations between high-frequency streamflow rate (or stream stage) and groundwater-level measurements during recession flow conditions, does not, in itself, indicate that the stream section is a losing one.
Article
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The effect on removal of lower, mid, and upper slope vegetation on the diurnal variation in streamflow from a 46-ha catchment was observed. The diurnal variation in streamflow of the small stream was measurable during the late-spring-to-late-autumn period. The amplitude in streamflow variation reached a maximum in early summer and declined during autumn. Observation of diurnal variations during the periods of higher flow in winter and spring showed that they may occur but were masked by much larger variations associated with storm runoff. Simulation of the characteristics of the flow measurement system showed that diurnal variations can only be studied using V-notch weirs and float recorders during periods of low flow. No effect of the clearing of slope vegetation on the phase of the outflow could be found. However, there was evidence of a significant increase in amplitude, probably due to increased groundwater outflow from the slopes. It was concluded that the diurnal variation is due to transpiration by the riparian and near-riparian vegetation only, and that the lower to mid slope vegetation plays little role in this variation. Simulations suggested that increased amplitude is associated with increased flow rates, and that the amplitude is not directly affected by water use of vegetation on the catchment slopes. It was concluded that the amplitude of the variation is insensitive to changes in slope hydrology and cannot be used to provide insight into deep slope processes.
Article
Anyone who merely turns the pages of this new edition will find that 48 percent of the fig­ ures are familiar to him from the third edition. But whoever reads it will discover that no three consecutive pages of text have been transferred unaltered. The enormous development that has taken place since 1950, particularly the surprising extension in the practical applica­ tions of micrometeorology, have made it necessary to rewrite the book. In producing this work, I had in mind two aims which were linked more closely to each other than I had at first dared to hope. The new edition was to be a clear and vivid textbook for those who were just taking up the study of microclimatology, and at the same time a ref­ erence work for those already familiar with the subject. For the fIrst task, I had in mind the students who would recoil with horror at the insurmountable barrier of an apparently unlim­ ited and ever-increasing pile of literature and thus were in need of assistance. In addition, I was thinking of colleagues working in related sciences, who have no time to study our liter­ ature.
Article
A large-scale hydrologic model of macroscale dimension for total daily runoff to the Baltic Sea has been developed using 25 subbasins ranging from 21 000 to 144 000 km2. Daily synoptic input was calibrated against monthly recorded river flows. Reasonable model results for the water balance were obtained while keeping the level of detail to a minimum with a proven conceptual modeling approach. Important elements of the modeled water balance are presented for the five main Baltic Sea drainage basins. The model is used for cooperative research with both meteorological and oceanographic modeling within the Baltic Sea Experiment (BALTEX) and the Swedish Regional Climate Modelling Programme (SWECLIM). It provides off-line analysis for coupled model development and fills a needed role until truly coupled models become available. Furthermore, the model is suitable for operational applications and will be used to extend runoff records, fill in missing data, and perform quality checks on new observations.
Article
In the time alloted for this subject it will be impossible to discuss, in its entirety, all phases of the methods used in computing the loss of water by transpiration from native plant‐life along the Santa Ana River. The results of this work are published in Bulletin 44 of the Division of Water Resources, State of California. The present paper is confined to a discussion of the diurnal fluctuations that occur in the flow of the Santa Ana River and the adjacent ground‐water. On Figure 1 have, been plotted the gage‐height records for a short period (July 5–8, 1935) for eight southern California streams. These records have been selected at random from the large number of records filed in the local office of the United States Geological Survey. Some of these streams have a large drainage‐area, others are small; some are short and steep, others flat and long. The points of measurement range from 15 to 2950 feet above sea‐level. One of the streams (Mojave River) is in the desert‐region; the others drain into the Pacific Ocean. The object of Figure 1 is to demonstrate, that the diurnal fluctuation exists and is, to a large degree, similar at most points of measurement. As a rule, the maximum discharge occurs about 10 o'clock in the morning, and the minimum late in the afternoon. It is readily recognized that these fluctuations are in the main caused by the evaporation and transpiration‐loss in or adjacent to the stream‐channel.
Article
Riparian vegetation typically has a great influence on groundwater level and groundwater-sustained stream baseflow. By modifying the well-known method by White [White, W.N., 1932. Method of estimating groundwater supplies based on discharge by plants and evaporation from soil – results of investigation in Escalante Valley, Utah – US Geological Survey. Water Supply Paper 659-A, 1–105] an empirical and hydraulic version of a new technique were developed to calculate evapotranspiration (ET) from groundwater level readings in the riparian zone. The method was tested with hydrometeorological data from the Hidegvíz Valley experimental catchment, located in the Sopron Hills region at the western border of Hungary. ET rates of the proposed method lag behind those of the Penman–Monteith method but otherwise the two estimates compare favorably for the day. At nights, the new technique yields more realistic values than the Penman–Monteith equation. On a daily basis the newly-derived ET rates are typically 50% higher than the ones obtainable with the original White method. Sensitivity analysis showed that the more reliable hydraulic version of our ET estimation technique is most sensitive (i.e., linearly) to the laboratory- and/or slug-test derived values of the saturated hydraulic conductivity and specific yield taken from the riparian zone.
Article
The flow of meltwater through a snowpack can be described by Darcy's law, and by a set of equations relating conductivity, capillary pressure, and liquid water content which are similar to the equations governing unsaturated groundwater flow. Field observations were conducted which were designed to test the applicability of the unsaturated flow equations to the description of meltwater movement at the scale of a small watershed. Simple instrumentation was used to measure capillary pressure and meltwater flux rates; this was supplemented with energy and water balance measurements for the study watershed. Observations of surface snowmelt, meltwater flux, and streamflow for the snowmelt period confirm the nature of meltwater flow as described by the unsaturated flow equations, and illustrate the controlling influence of snowpack hydraulics on the streamflow hydrograph. Observed speeds of the meltwater wave front in the snowpack average about 0.22 m h−1 and capillary pressures and kinematic wave speeds observed are comparable to those reported in other deep-snow environments. The difficulty of obtaining reliable liquid water content measurements is identified as a major limitation in the determination of hydraulic properties of the snowpack.