Proglacial River Stage, Discharge, and Temperature Datasets from the Akuliarusiarsuup Kuua River Northern Tributary, Southwest Greenland, 2008-2011

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Proglacial river stage, discharge, and temperature datasets from the Akuliarusiarsuup Kuua River northern tributary, Southwest Greenland, 2008-2011

Rennermalm, Asa K.; Smith, Laurence C.; Chu, V. W.; et.al. https://scholarship.libraries.rutgers.edu/discovery/delivery/01RUT_INST:ResearchRepository/12643424100004646?l#13643533170004646

Rennermalm, A. K., Smith, L. C., Chu, V. W., Forster, R. R., Box, J. E., & Hagedorn, B. (2012). Proglacial river stage, discharge, and temperature datasets from the Akuliarusiarsuup Kuua River northern tributary, Southwest Greenland, 2008-2011. Earth System Science Data, 4, 1–12. https://doi.org/10.7282/T3DV1H3C

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Open Access Open Geography Proglacial river stage, discharge, and temperature datasets from the Akuliarusiarsuup Kuua River northern tributary, Southwest Greenland, 2008–2011

A. K. Rennermalm1, L. C. Smith2, V. W. Chu2, R. R. Forster3, J. E. Box4, and B. Hagedorn5 1Department of Geography, Rutgers, The State University of New Jersey, 54 Joyce Kilmer Avenue, Piscataway, NJ 08854-8045, USA 2Department of Geography, University of California, Los Angeles, 1255 Bunche Hall, P.O. Box 951524, Los Angeles, CA 90095-1524, USA 3Department of Geography, University of Utah, 260 S. Central Campus Dr., Salt Lake City, UT 84112, USA 4Department of Geography, The Ohio State University, 1036 Derby Hall, 154 North Oval Mall, Columbus, OH 43210-1361, USA 5Environment and Natural Resources Institute, University of Alaska Anchorage, 3211 Providence Drive, Anchorage, AK 99508, USA

Correspondence to: A. K. Rennermalm ([email protected])

Received: 8 July 2011 – Published in Earth Syst. Sci. Data Discuss.: 14 September 2011 Revised: 21 February 2012 – Accepted: 19 March 2012 – Published: 12 May 2012

Abstract. Pressing scientific questions concerning the Greenland ice sheet’s climatic sensitivity, hydrology, and contributions to current and future sea level rise require hydrological datasets to resolve. While direct observations of ice sheet meltwater losses can be obtained in terrestrial rivers draining the ice sheet and from lake levels, few such datasets exist. We present a new hydrologic dataset from previously unmonitored sites in the vicinity of Kangerlussuaq, Southwest Greenland. This dataset contains measurements of river stage and discharge for three sites along the Akuliarusiarsuup Kuua (Watson) River’s northern tributary, with 30 min temporal resolution between June 2008 and July 2011. Additional data of water temperature, air pressure, and lake stage are also provided. Flow velocity and depth measurements were collected at sites with incised bedrock or structurally reinforced channels to maximize data quality. However, like most proglacial rivers, high turbulence and bedload transport introduce considerable uncertainty to the derived discharge estimates. Eleven propagating error sources were quantified, and reveal that largest uncertainties are associated with flow depth observations. Mean discharge uncertainties (approximately the 68 % confidence interval) are two to four times larger (±19 % to ±43 %) than previously published estimates for Greenland rivers. Despite these uncertainties, this dataset offers a rare collection of direct measurements of ice sheet runoff to the global ocean and is freely available for scientific use at doi:10.1594/PANGAEA.762818.

1 Introduction but estimated to be as much as twice the ice flow discharge losses between 2000 and 2008 (van den Broeke et al., 2009). Mean annual air temperatures over the Greenland ice sheet Continued and increased mass losses from the Greenland ice have warmed 1.8 ◦C between 1840 and 2007 (Box et al., sheet may have far reaching consequences, contributing per- 2009). This trend has continued since 2007, with large sur- haps 17–54 cm to global sea level rise by 2100 (Table 3, line face air temperature anomalies along Greenland’s coast in 3 in Pfeffer et al., 2008), while influencing regional meteorol- 2010 (Box et al., 2010), accompanied by a near-tripling of ogy (Dethloff et al., 2004) and ocean circulation in the North overall ice sheet mass balance losses since the 1960s (Rignot Atlantic (Driesschaert et al., 2007; Fichefet et al., 2003; et al., 2008). Meltwater runoff losses are poorly constrained, Jungclaus et al., 2006). However, improved projections of

Published by Copernicus Publications. 2 A. K. Rennermalm et al.: Proglacial river stage, discharge, and temperature datasets future mass losses and their associated global and regional 2 Site description impacts require a better understanding of Greenland ice sheet hydrologic processes and runoff. Three continuous river monitoring sites (Sites AK2, AK3, Where available, time series of proglacial river discharge and AK4, Fig. 1, Table 1), one lake, and one additional river can reveal insights about hydrologic connections between site with shorter period observations (Sites AK1, and AK5, ice sheet surface melting and riverine meltwater losses (van Fig. 1, Table 1) were selected along the Akuliarusiarsuup de Wal and Russell, 1994), englacial water storage dynam- Kuua River’s northern tributary. The Akuliarusiarsuup Kuua ics (Mathews, 1963), meltwater travel time (Elliston, 1973), River is the northern branch of the Watson River that dis- seasonal changes in subglacial drainage systems (when charges into Kangerlussuaq Fjord near the town of Kanger- combined with dye tracer analysis, Nienow et al., 1998), lussuaq. All sites are located in the ice sheet proglacial supraglacial lake drainages and the hydrologic drainage sys- zone, within 2 km of the ice edge and 27–30 km northeast tem (Bartholomew et al., 2011), catastrophic drainage events of Kangerlussuaq. Watersheds of Sites AK3 and AK4 are in the proglacial environment (Mernild and Hasholt, 2009; 7.8 and 64.2 km2, respectively, and are both sub-watersheds Mernild et al., 2008; Russell et al., 2011; Russell, 1989, nested within Site AK2’s larger 101.4 km2 watershed (Fig. 1 2009), and channel erosion and sediment transport processes lower inset). Watersheds were delineated using ice sheet sur- (Russell et al., 1995; Russell, 2007). They are also valu- face elevation data from the Advanced Spaceborne Thermal able for estimating ice sheet meltwater losses, and for valida- Emission and Reflection Radiometer (ASTER) global digi- tion/calibration of ice sheet runoff models that estimate total tal elevation map of surface topography (METI and NASA, meltwater losses for Greenland (Mernild et al., 2010, 2011) 2009) using ESRI’s ArcGIS hydrology tool. While delin- and elsewhere (Baker et al., 1982; Fountain and Tangborn, eations based only on surface topography have precedence 1985; Hock and Noetzli, 1997; Klok et al., 2001; Verbunt et (Mernild et al., 2010), accuracy of watershed delineations al., 2003). To date, only four peer-reviewed articles present improves with inclusion of basal topography owing to the ef- analyses of such measurements in Greenland (Mernild and fect of hydrostatic pressure on the hydraulic potentiometric Hasholt, 2009; Russell et al., 1995; Russell, 2007; van de surface (Cuffey and Paterson, 2010; Lewis and Smith, 2009). Wal and Russell, 1994), owing to logistical inaccessibility Pending availability of such data these delineations based on and difficulties in measuring river discharge in proglacial surface topography only can be considered a first approxi- gravel-bed braided river systems (Ashmore and Sauks, 2006; mation. Meltwater runoff from the Greenland ice sheet is Smith et al., 1996). transported to these sites (AK2, AK3, and AK4) via different Here we present a new dataset (June 2008 to July 2011) of proglacial pathways, with varying degrees of upstream im- in situ hydrologic measurements collected at multiple, pre- poundment in lakes (Fig. 1). Prior to reaching Site AK4, ice viously unmonitored proglacial sites in the upper Akuliaru- sheet meltwater is routed through one lake, whereas flows to siarsuup Kuua River drainage, Southwest Greenland. These Site AK3 first pass through 3–4 lakes. Site AK2 integrates include time series of river stage recorded every 30 min, all water flow discharging from Lake A (Fig. 1), which in- together with occasional in-channel measurements of flow cludes flows from both Sites AK3 and AK4, plus a third inlet width, depth, and velocity to establish empirical rating curves connecting Lake A and B at the southern side of the drainage relating continuously recorded river stage to in situ discharge basin. Upstream of this third inlet are two lakes (B and C), estimates. Other measurements include: lake stages, stream of which Lake C is usually ice dammed, causing meltwa- temperatures, and barometric air pressure. While three pre- ter to be routed to Lake B via an intermittent stream (Rus- vious studies have monitored discharge and stages in the sell et al., 2011). Catastrophic drainage of the ice dammed middle and lower parts of the Akuliarusiarsuup Kuua River Lake C occurred in 1984, 1987, 2007 and 2008 (Mernild and drainage (Mernild and Hasholt, 2009; Russell et al., 1995; Hasholt, 2009; Mernild et al., 2008; Russell et al., 2011; Rus- van de Wal and Russell, 1994), this is the first study to per- sell, 1989, 2007, 2009). When the ice dam bursts, Lake C form a comprehensive error assessment. Although discharge no longer drains via the intermittent stream but instead dis- measurements were made at sites with near-ideal geomor- charges to Lake B along the ice margin (Russell et al., 2011). phology (incised bedrock and structurally reinforced channels) strongly turbulent flows and high sediment bedloads de- 3 Methods grade precision of the derived discharge estimates. To assess data precision, eleven error sources were considered, includ- River stage (e.g. water level), temperature, and discharge ing uncertainties associated with changing streambed eleva- measurements were collected at Sites AK2, AK3, and AK4 tion, measurement errors, and rating curve fitting. Streambed (Table 1) between June 2008 and July 2011. Uncorrected elevation trends and variability were studied in depth with stage, Lw-uc, (Fig. 2) was determined as the difference be- statistical techniques. All errors were factored in to estab- tween measured water pressure from submerged Solinst Lev- lish rating curves for upper and lower discharge ranges that elogger pressure transducers (nominal precision 0.3 cm), and represented approximately the 68 % confidence interval of barometric air pressure was recorded simultaneously with discharge estimates. Solinst Barologger pressure transducers (nominal precision

Earth Syst. Sci. Data, 4, 1–12, 2012 www.earth-syst-sci-data.net/4/1/2012/ A. K. Rennermalm et al.: Proglacial river stage, discharge, and temperature datasets 3

Figure 1. Map of site locations in the proglacial environment and watershed boundaries upstream Sites AK2, AK3, and AK4, inset of Greenland map with study area (lower right corner).

Table 1. Study sites and dataset variables. Dataset variables are: surface pressure (ps), total pressure measured with solinst levelogger in stream, (pt), water pressure (pw), uncorrected stage (Lw-uc), stage (Lw), water temperature (T), discharge (Q), upper range of discharge (Qu), and lower range of discharge (Ql). Dataset also contain ﬂags when dataset were augmented during data gaps.

Site Full site name Latitude Longitude Elevation (m) Measured parameters DOI

AK1 AK-001-001 67.078031 −50.276525 150 ps doi:10.1594/PANGAEA.762816 AK2 AK-002-001 67.132282 −50.138113 340 pt, pw, Lw-uc, Lw, T, Q, Qu, Ql doi:10.1594/PANGAEA.762817 AK2 AK-002-002 67.131681 −50.137597 340 ps doi:10.1594/PANGAEA.762890 AK3 AK-003-001 67.143023 −50.122732 340 pt, pw, Lw-uc, Lw, T, Q, Qu, Ql doi:10.1594/PANGAEA.762895 AK4 AK-004-001 67.146558 −50.106616 340 pt, pw, Lw-uc, Lw, T, Q, Qu, Ql doi:10.1594/PANGAEA.762897 AK5 AK-005-001 67.149556 −50.125860 340 pt, pw, Lw doi:10.1594/PANGAEA.762898

0.1 cm) on land every 30 min. Leveloggers were enclosed tions probably due to freezing, and were discarded from the in perforated steel boxes at the end of 1.5–3 m steel rods at- dataset. Brief data gaps occurring during logger retrieval and tached to bedrock or rocks, thus allowing direct emplacement data downloading were filled using a second, temporary sen- in the stream without resting on the channel bed (Fig. 2, fore- sor placed in the river. Estimated true stage at Levelogger ground). These boxes were maintained at a fixed depth rela- installation sites, Lw, was calculated by adding Lw-uc to the tive to a datum plane and located within 1–30 m of the cross distance from logger steelbox to the stream bed, dbox (Ta- section used for in situ discharge measurements. Barolog- ble 3, Fig. 2), dbox was 65 cm at Site AK2, and mean dbox gers measuring air pressure were installed 150 m from Site and standard deviation was 15.4 ± 2.7 cm and 15.4 ± 2.8 cm AK2 (Table 1) and assumed representative for all three gaug- for Sites AK3 and AK4. Additionally, Lw time series were ing sites given the close proximity of these sites (less than (1) corrected for minor discontinuities in Lw-uc due to slight 2 km) and the limited elevation differences between the sites. differences in Levelogger placement in steel box following Data were retrieved from loggers in early June and late Au- Levelogger retrieval for data downloading by matching time gust of each year. From August to June, Leveloggers were series before and after these discontinuities; (2) forced to placed in rubber balloons filled with antifreeze solution to zero during prolonged periods with subzero temperatures un- protect sensors from extreme pressures during freezing con- less constant subzero temperatures persisted for days, which ditions (Solinst Inc., 2011). Despite this precaution, some indicate phase changes. At Site AK4, occasional sensor ex- wintertime recordings displayed extreme pressure fluctua- posure to air revealed an offset from zero Lw-uc values. These

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Figure 2. Schematic figure showing installation setup at Site AK2. A Solinst Levelogger is installed in a perforated steel box attached to an iron rod bolted to bedrock (foreground) where the top of the iron rod defines the datum plane. The vertical distance from datum to Levelogger is denoted dd, stage (estimated true distance from stream bed to water surface) is denoted Lw, distance from Levelogger to streambed is denoted dbox, and uncorrected stage recorded by Leveloggers corrected for barometric pressure fluctuations, Lw-uc, is distance from Levelogger to time varying water surface. Upstream of Levelogger installation (30 m) is the stream cross section where in situ discharge measurements are conducted at 21 measurement verticals (background). At each vertical, measurements are made of water depth (dw) and stream velocity at 0.6 dw (2010), or 0.2 dw and 0.8 dw (2011). Distance from streambed to datum plane is also estimated at each vertical (dc). Lengths of dc and dw are illustrated for interval 15 and 17 respectively, but are measured for all verticals 1 to 21. Installation setups at Sites AK3 and AK4 are the same, except that datum planes are defined by a bridge, 31 or 16 verticals were used, and the Levelogger installation are in closer proximity (1 m) to measurement verticals.

offsets were removed from Lw values during and before sen- intervals dividing the cross section into a 21, 31, and 16 sor exposure. Offsets were quantified as median deviations, measurement verticals at Sites AK2, AK3, and AK4, re- which were 8.1 cm, 5.4 cm, and −0.059 cm, for periods 18 to spectively, using the bridges at Sites AK3 and AK4, and a 22 October 2008, 12 to 23 September 2009, 9–13 Septem- suspended rope at Site AK2 (Fig. 2, background). Horizon- ber 2010. tal spacing between verticals was chosen to ensure adequate Discharge, Q, was determined using the standard mid- numbers of measurement points during low flow conditions. section method (WMO, 2010b), which involved collection At each vertical, measurements of dw and velocity were col- of average velocity and water depth, dw, at 21, 33, and lected using measurement rod/tape, and Price-type AA cur- 16 measurement verticals in stream cross sections at Sites rent meter. At Site AK2, velocity measurements were made AK2, AK3 and AK4. Observations were made in chan- at 0.6 dw in 2010, and at 0.2 dw and 0.8 dw in 2011, which typ- nels with near-ideal geomorphologic properties compared to ically provides average velocity along a vertical (e.g. WMO, standards stated by the World Meteorological Organization 2010b). At Sites AK3 and AK4, velocity was measured ap- (WMO, 2010b) (Table 2). Stream cross sections consisted proximately 0.1–0.3 m below the water surface. Here, stream of one incised bedrock channel (Site AK2) and two struc- flows were highly turbulent, shallow, and well mixed with turally reinforced bridge crossings (Site AK3, and AK4), a near-vertical velocity profile above the bed. Duplicate which are likely to remain stable even during high flow (Ta- measurements conducted confirmed reproducibility of these ble 2). Stream banks were sufficiently high to prevent over- near-surface velocity measurements at Sites AK3 and AK4. topping, and free of vegetation (Table 2). Channels were Thus, velocities determined at Sites AK3 and AK4 satisfied relatively straight for near-ideal distance both upstream and the measurement goals of determining average stream veloc- downstream, and confined all flow through Sites AK2 and ity for each vertical. AK3 and the majority of flow through Site AK4 (Table 2). Each cross section was marked at 1.0 m, 0.25 m, and 0.5 m

Earth Syst. Sci. Data, 4, 1–12, 2012 www.earth-syst-sci-data.net/4/1/2012/ A. K. Rennermalm et al.: Proglacial river stage, discharge, and temperature datasets 5

Table 2. Criteria for selection of ideal discharge and water stage monitoring sites deﬁned by World Meteorological Organization (WMO, 2010b), and characteristics of observation sites in this study.

WMO characteristics of ideal Site AK2 Site AK3 Site AK4 monitoring sites Sites with natural channels Near-ideal site at bedrock in- Near-ideal site at bridge Near-ideal site at bridge should be stable to ensure va- cised channel and large boul- crossing reinforced with crossing reinforced with lidity of stage-discharge rela- ders large boulders large boulders tionship Stream channel should be Ideal downstream conditions, Ideal upstream conditions, Ideal conditions straight 10 times the stream upstream channel is straight downstream channel is width both upstream and approximately 5 times the straight but widens into a downstream the monitoring stream width lake site Flow should be confined to Ideal conditions Ideal conditions Some flow bypass the rein- one channel with no flow by- forced channel passing the site as subsurface flow Stream bed should be free of Ideal conditions Ideal conditions Ideal conditions vegetation and have no scour and fill Banks should be permanent, Ideal conditions Near-ideal conditions. While High flow may reach the bot- high enough for floods, and the bridge set an upper limit tom of the bridge, but this oc- vegetation free for high flow observations, curs rarely observed stage has never reach this limited Discharge gauging site Ideal conditions Ideal conditions Ideal conditions should be in proximity of stage monitoring site Gauging site must not be af- Ideal conditions Influence from downstream Ideal conditions fected by downstream con- lake has been detected during fluence of other streams extreme high lake stage

To assess changes and variability in streambed elevation measured cross sectional stream depth correlated strongly over time relative to a fixed point, cross sectional stream (r = 1.0); hence, modeled dc were used at Site AK2. The depths (dc) were determined relative to a datum plane de- most accurate dc and dw measurements were made in 2011 fined as the bottom edge of a steel beam supporting a bridge when sturdy rods and heavy sounding weights limited the in- crossing the river (Sites AK3, and AK4) or the top of the fluence of turbulence on depth measurements. iron rod installation (Site AK2, Fig. 2). At Sites AK3 and Time series of Q were derived by the typically used AK4, dc was measured from the bridges, simultaneous with method of fitting first-degree power functions to occasional dw, and velocity. At Site AK2, physical separation of da- in situ measurements of Q and true stage (Lw −d0) at corre- tum plane and channel cross section made simultaneous mea- sponding times (e.g. WMO, 2010a), where d0 is the water surements impossible (Fig. 2). Instead, dc was determined depth at zero discharge determined as minimum Lw. Power with a linear regression model constructed by relating oc- function best-fit parameters were determined with a non- casional datum plane measurements to Solinst water depth linear least squares method. To fit the rating curve, 11, 25, recordings: dc = dw + dd − c1Lw − c2; where dc is the cross and 29 in situ Q measurements were used for Sites AK2, sectional stream depth from datum plane at a vertical, dw is AK3 and AK4, respectively. At Site AK2, an eleventh the cross section water depth, dd is vertical distance between point was added to represent peak conditions during the datum and Solinst Levelogger enclosure, Lw is estimated ver- 31 August 2008 jokulhlaup triggered by drainage of the ice tical distance between water surface and stream bed at Solinst dammed lake (Lake C) by matching the recorded maximum Levelogger site. The coefficients c1 and c2 were determined Lw (384 cm) with an independent estimate of peak discharge through linear regression (c1 = 1.0, c2 = 0.2 m). Modeled and (416 m3 s−1) from the ice dammed lake (Russell et al., 2011). www.earth-syst-sci-data.net/4/1/2012/ Earth Syst. Sci. Data, 4, 1–12, 2012 6 A. K. Rennermalm et al.: Proglacial river stage, discharge, and temperature datasets

Indeed, calculated jokulhluap volume (11.5 × 106 m3) was was established as the percent error resulting from fitting the similar to a previous volume estimate based on detailed field rating curve with maximum standard error of matching time surveying (12.9×106 m3) (Russell et al., 2011). series before and after data downloading, and Solinst Level- In addition to pressure, Solinst Levelogger instrument ogger precision added and subtracted from Lw. Efit was de- records ambient water temperatures with a nominal preci- termined as the root mean square error from fitting discharge sion of +0.05 ◦C. These temperature time series are sup- measurement to a power-law rating curve expressed in per- plied for Sites AK2, AK3, and AK4. The majority of winter cent of discharge. As most error terms were calculated as stream temperature recordings at Site AK2 were discarded standard errors, Eq can be interpreted as approximately the from the dataset because they were impacted by river ice in 68 % confidence interval of discharge observations. some years, and water in other years. Winter stream temper- Uncertainty ranges for continuous Q time series derived atures at Site AK3 and AK4 were preserved in the dataset from Lw were quantified with rating curves specifying upper despite shifting influence by water, ice, and air conditions and lower discharge ranges, Qu and Ql, respectively. Rat- because they provided insights into occasional wintertime ing curve parameters were determined with non-linear least- water phase changes. Lake stage and water temperature ob- square method by fitting power law functions to in situ dis- servations were collected at Site AK5 (Table 1) for a single charge with added/subtracted Eq. Furthermore, fitting was year between 2 June 2007 and August 19, 2008. Baromet- constrained to ensure proportionality between confidence in- ric pressure was collected at Site AK1 between 2 June 2007 terval and discharge magnitude. Ql and Qu envelope approx- and 21 August 2009 (Table 1). These observations are also imately the 68 % confidence interval of discharge (because included in the presented dataset. maximum error is used for ELw it may be higher than 68 %), meaning that the true discharge value is within the upper and 3.1 Discharge uncertainty and stream bed stability lower Q boundaries at least 68 % of the time. assessment Derivation of continuous Q time series from rating curves implicitly assumes stable dc. This was examined by calcu- Although observations were made at sites with near-ideal lating the slope of depth changes at each vertical between channel properties (Table 2), precise discharge estimates are June 2008 and July 2011 using linear regression analysis. typically difficult in high-bedload proglacial rivers owing to Statistical significance of these slopes was established with high flow velocities, turbulence, and bedload transport rates two-sided t-tests testing the null hypothesis that these slopes (Ashmore and Sauks, 2006; Smith et al., 1996). Therefore, were statistically similar to zero (α = 0.1). Thus, rejecting the discharge uncertainty ranges were quantified. An error prop- null hypothesis indicates dc changes between June 2008 and agation methodology similar to Sauer and Meyer (1992) was July 2011. Two sources of flow depth error propagate uncer- applied and extended to include errors associated with rat- tainty to derived discharge estimates: (1) temporal changes ing curve fitting and Solinst Levelogger water depth obser- in bed elevation, which may increase or decrease the distance vations: between the suspended pressure transducer and the channel q E = E2 + E2 + E2 + E2 + E2 + E2 + E2 + E2 + E2 + E2 + E2 (1) floor (dbox); and (2) human measurement error associated q d t i s h v sb sd sv Lw fit with dw observatons in turbulent flow using steel rods and where Eq is discharge estimate error (%), determined tapes. by the percent error from: Ed = depth observation er- To further explore dc variability, a one-way ANOVA anal- ror, Et = velocity pulsation error, Ei = current meter instru- ysis was employed to test the null hypothesis that deployment ment error, Es = velocity distribution error in the vertical, mean dc was statistically similar during all field deployments. Eh = flow angle error, Ev = horizontal distribution error of Separate ANOVA tests were applied for each measurement velocity and depth, Esb = systematic error in width mea- vertical. Accepting the null hypothesis suggests that dc is surements, Esd = systematic error in depth measurements, statistically similar over time, and it can be inferred that ob-

Esv = systematic error in velocity measurements, ELw = Lw served temporal variability is likely due to measurement er- uncertainty due to Solinst instrument precision and retrieval, rors. This interpretation hinges on the assumptions that dc Efit = rating curve fitting error. variability within each field deployment represents measure- Ed was determined as mean measurement vertical standard ment uncertainty. This is a reasonable assumption given that ∗ error of dc −dc 0.01dw, where dc is vertical datum depth, field deployments were short (<7 days) and no major flood- ∗ dc is mean vertical datum depth in 2011 deployment, and ing occurred that could change streambed morphology. In dw is vertical water depth. Et was determined as the mean contrast, different dc means, and significant slopes are sug- standard error using seven pairs of area-weighted velocity gestive of changing bed elevation, but could also be a result measurements made occasionally at Site AK4. Eh was set of measurement bias from different methods, and stream con- to 1 %, which allows 5 degrees uncertainty of flow angles on ditions over time. Finally, ANOVA tests include calculation to the measurement (WMO, 2010b). Ei, Es, Ev, Esb, Esd, of between-, σB, and within-group variability, σW, which Esv were identified using relationships and guidelines pro- were used to examine relative magnitudes dc error disper- vided by Sauer and Meyer (Sauer and Meyer, 1992). ELw sions during and between deployments.

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Site AK2 Site AK3 Site AK4 t−test t−test t−test 0.4 0.3 0.2 −1 −1 0 −1 0 0 m yr m yr m yr change −0.2 −0.3 −0.2 2 0 0.5

4 1 1 1.5

(m) 6 (m) (m) c c c

d d d 2 2 8 2.5 10 3 3 0 10 20 1 3 5 7 1 3 5 7 Stream bank distance (m) Stream bank distance (m) Stream bank distance (m)

Data range Deployment averages

Figure 3. Streambed elevation changes, and depth measurement variability. Top panels show outcome of t-test (gray shaded region show rejected null hypothesis indicating signiﬁcant change) suggesting changes in the majority of Site AK3 channel, but limited changes at Sites AK2 and AK4. Middle panels show streambed changes over time at each vertical in cross sections determined with linear regression models. Bottom panels show dc measurement range (gray), and mean dc in ﬁeld deployments (black). Large dc range is observed at all sites, but most variability in mean dc is at Site AK2.

3.2 Comparison of discharge magnitudes between sites AK3 is also the only site with a large fraction of measurement in the AK-river system verticals with significant dc trends (Table 3, Fig. 3). Signifi- cant trends for sites are both positive and negative, with mean Discharge from the three monitored sites, and two additional change between 8 cm yr−1 and 32 cm yr−1 (Table 3). These sites reported in literature (Russels and Watson Sites, 9 km trends may reflect actual channel morphology change as well and 32 km downstream of Site AK2) were analyzed for con- as dc observation uncertainty due to differences in methodol- sistency with a back-of-the-envelope method. The water- ogy and flow conditions between deployments. Regardless sheds of these five sites are all within the AK and Watson of trend magnitude, a majority of measurement verticals un- river systems and are subjected to similar synoptic weather derwent changes in mean dc between 2008 and 2011, verified conditions. However, differences in ice sheet watershed size by rejecting the null hypothesis of stable mean with one-way and elevation should result in varying absolute and specific ANOVA tests (Table 3). ANOVA tests also confirm that dc discharge (= discharge/watershed area) between sites. Ab- variability between deployments were greater than dc mea- solute and specific discharge were compared between the surements made within deployments (σ2 /σ2 < 1, Table 3), sites with the following expected outcomes: (1) absolute dis- W B and that greatest relative variability in mean deployment dc charge should be proportional to area, because smaller wa- were at Site AK2 (lowest σ2 /σ2 < 1, Table 3). tersheds are sub watersheds in the larger watersheds (the W B Empirical stage-discharge rating curves satisfactorily ex- only exception is Site AK3, which is not a sub watershed of plain discharge variability at Site AK2 and AK4 (large R2, Site AK4); (2) specific discharge should be inversely propor- Table 4), but less well at Site AK3 (small R2, Table 4). tional to area because larger watersheds extend further onto These rating curves were parameterized using in situ dis- higher ice sheet elevations where runoff production is smaller charge measurements from Sites AK2, AK3, and AK4 span- (e.g. Mernild et al., 2010, 2011). ning between 52 % and 97 % of observed above-zero Lw −d0 (Table 4, Fig. 4). Most of Lw −d0 outside the range covered 4 Results by in situ discharge measurements at Site AK2 and AK3 oc- cur during low flow conditions (Fig. 4). Thus, if low-flow Q Even using near-ideal channel cross sections, surveyed dc data are realized and sampled in future years at Site AK2 show considerable variability. Large data ranges, and differ- and AK3, these rating curves may become slightly different deployment mean dc are observed at all sites particularly ent. Residuals between observed and estimated discharge at Site AK2 (Fig. 3). While largest absolute variation is at lack structured bias at Site AK3 and AK4 suggesting that rat- ¯∗ Site AK2 (shown byσ ¯ (dc −dc ), Table 3), depth measurement ing curve relationships are robust, and useful despite channel variability influences discharge most strongly at Site AK3. deepening trends at Site 3. In contrast, Site AK2 residuals At Site AK3, shallow mean water depths results in large rela- are positive in 2010 deployment and negative in 2011 deploy- ¯ ¯∗ tive variation (shown by dw and Cv(dc −dc ,dw), Table 3). Site ment (Fig. 4). Because Site AK2 channel bed topography is www.earth-syst-sci-data.net/4/1/2012/ Earth Syst. Sci. Data, 4, 1–12, 2012 8 A. K. Rennermalm et al.: Proglacial river stage, discharge, and temperature datasets

∗ Table 3. Parameters describing channel depth variability and change. In the table dc is datum to stream bed distance, dc is mean datum to stream bed distance in 2011.

Parameter Symbol AK2 AK3 AK4 ¯∗ Mean cross section standard deviation of σ¯ (dc −dc ) 0.45 0.17 0.15 ¯∗ dc −dc (m) ¯ ¯∗ Mean cross section coeﬃcient of variation Cv(dc −dc ,dw) 0.09 0.65 0.18 (standard deviation of dc dc − d¯c divided by mean dw)

Mean cross section water depth (m) d¯w 4.76 0.26 0.92 Fraction of cross section with significant – 0.05 0.84 0.25 dc trends (t-test, α = 0.1) Mean significant trends (cm yr−1) – 0.32 0.08 0.10 Fraction of cross section with different de- – 0.82 0.77 0.39 ployment mean dc (ANOVA, α = 0.1)

2 2 Ratio of within- and between-group vari- σW/σB 0.17 0.11 0.28 ance determined with ANOVA

Site AK2 Site AK3 Site AK4 ) ) ) 0 0 0 −d −d −d w w w P(L P(L P(L 50 4 20

) 40 ) )

−1 −1 3 −1 15 s s s 3 30 3 3 2 10 20

1 5 Discharge (m 10 Discharge (m Discharge (m

0 0 0 10 1 5 0 0 0

Residual −10 Residual −1 Residual −5 0 100 200 0 50 0 50 100 L −d (cm) L −d (cm) L −d (cm) w 0 w 0 w 0

Figure 4. Probability distribution of non-zero Lw − d0 determined with kernel density functions (top panels), and discharge rating curves (solid black line), including observations (black points), and conﬁdence interval (grey shaded) determined from upper and lower discharge rating curves (middle panels), and residuals between observed and estimated discharge (bottom panels). The extreme discharge estimate at maximum Lw − d0 observed at Site AK2 on 31 August 2008 is not shown. Minimum and maximum stage (Lw − d0) associated with in situ discharge observations contain between 50 and 99 % of observed above-zero stage, and observational points are largely enveloped by conﬁdence intervals; rating curves and derived discharge uncertainties and can therefore be considered adequately representative of true conditions at these sites.

stable with no trends, AK2 residual are more likely due to dw ter instrument errors (Ei), rating curve fit (Efit), and velocity uncertainties, and a switch from 0.6 to 0.2/0.8 velocity area pulsation errors (Et) (Fig. 5). Errors are particularly large method from 2010 to 2011. at Site AK3, which propagate to Qu and Ql time series so that average uncertainty is 43 % of Q, which may explain the The largest source of uncertainty for estimated discharge poor rating curve fit of this site. At Sites AK2 and AK4, Q derives from dw measurements (Ed), followed by current me-

Earth Syst. Sci. Data, 4, 1–12, 2012 www.earth-syst-sci-data.net/4/1/2012/ A. K. Rennermalm et al.: Proglacial river stage, discharge, and temperature datasets 9

Table 4. Model parameters and diagnostics for discharge rating curves. Rating curves are parameterized as ﬁrst degree power functions: β Q = C(Lw −d0) , where Q is discharge, C is a multiplier, Lw is stage at levelogger installation, d0 is estimated water depth at zero discharge, and β is the exponent, N is the number of Q observations used for ﬁtting, fLw is fraction of Lw observation gaps between 5 June 2008 and 23 July 2011.

Parameter Symbol Site AK2 Site AK3 Site AK4 Rating curve multiplier C 4.29×10−7 7.69×10−4 1.05×10−1

Water depth at zero discharge (cm) d0 101 0 0 Rating curve exponent β 3.48 1.89 1.04 Number of observations used for ﬁtting N 11 25 29

Fractional Lw data range for observa- – 0.52 0.55 0.97 tions used for ﬁtting Coeﬃcient of determination R2 1.00 0.32 0.94

Fraction of data gaps in Q time series fLw 0.58 0.02 0.22 between 5 June 2008 and 23 July 2011

40 Site AK2 Site AK2 40 Site AK3 ) −1

s 30 30 Site AK4 3

20 10 Discharge (m Error (%) 0 10 2008−07−01 2009−07−01 2010−07−01 2011−07−01

6 C) 0 ° E E E E E E E E E E E 4 d i fit t s h v sb sd sv Lw

2 Figure 5. Components of total discharge error (%), includ- Stream channel temperature ( 0 ing: Ed = depth observation error, Ei = current meter instru- 2008−07−01 2009−07−01 2010−07−01 2011−07−01 ment error, Efit = rating curve error, Et = velocity pulsation error, Es = velocity distribution error in the vertical, Eh = flow an- Figure 6. Site AK2 time series of retrieved discharge and mea- gel error, Ev = horizontal distribution error of velocity and depth, sured stream temperature every 30 min (blue and black lines). Up- Esb = systematic error in width measurements, Esd = systematic er- per and lower ranges of discharge retrievals are shown in light ror in depth measurement, Esv = systematic error in velocity mea- blue. Vertical axes are restricted to show interannual variability; surements, ELw = Lw uncertainty due to Solinst instrument precision and retrieval. The largest errors are observed at Site AK3, but all hence, the discharge outlier peak on 31 August 2008 is not shown 3 −1 three sites have prominent errors associated with depth observation (Qpeak = 416 m s ). Majority of wintertime discharge was not cal- and current meter instrument. culated at Site AK2 due to unrealistic Levelogger recordings at subzero temperatures at this site. Majority of wintertime stream temperatures were also removed as they were controlled by interannual variability in river ice thickness rather than water temperatures. is better constrained with mean uncertainties 30 % and 19 % of Q. In other words, true discharge is on average within ±0.3 Q, ±0.43 Q, and ±0.19 Q at Sites AK2, AK3, and AK4 winter data retrievals were discarded, owing to implausible approximately 68 % of the time. pressure variations, at sub zero temperatures, attributed to Stream discharge at all sites displays strong seasonal, and river ice processes (Fig. 6). Winter discharge retrievals for interannual variability (Figs. 6, 7 and 8). Daily stream tem- Site AK3 and AK4 include minor perturbations, and large perature variability is generally low, except during very low discharge anomalies near the end of the year in 2008, 2009 flow at Site AK4 when the sensor was exposed between 18 and 2010 (only 2008 for Site AK4). Extreme discharge was to 23 October in 2008, between 12 to 22 September 2009, recorded on 31 August 2008 at Site AK2 and AK3 coinciding and between 9 to 13 September 2010 (verified by compar- with catastrophic drainage of the upstream ice dammed lake. ing Levelogger temperatures with air temperatures). Some Extreme discharges were also recorded on 6 and 7 September www.earth-syst-sci-data.net/4/1/2012/ Earth Syst. Sci. Data, 4, 1–12, 2012 10 A. K. Rennermalm et al.: Proglacial river stage, discharge, and temperature datasets

Table 5. Discharge diagnostics for study sites, and AK river sites reported in literature. A is area, QS is approximate average summer −8 −1 discharge, Qs/A is speciﬁc discharge in 10 m s .

2 3 −1 −8 −1 Site A (km ) QS (m s ) Qs/A (10 m s ) Reference AK3 7.8 11 13 This study AK4 64.2 121 18 This study AK2 101.4 151 15 This study Russell glacier 364–670 502 7.5–14 Van de Wal and Russell (1994) Watson River 6279 200–3003 3.2–4.8 Mernild and Hasholt (2009)

1 Median discharge between 15 July and 15 August in 2008–2011, 2 Approximate average derived from Fig. 9 in van de Wal and Russel (1994), 3 Approximate summer averages derived from Fig. 2 in Mernild and Hasholt (2009).

Site AK3 Site AK4 25 ) 8 ) −1 −1 20 s s 3 3 6 15 4 10

2 5 Discharge (m Discharge (m 0 0 2008−07−01 2009−07−01 2010−07−01 2011−07−01 2008−07−01 2009−07−01 2010−07−01 2011−07−01 15 15 10 C) C) ° ° 10 5

5 0 −5 0 Stream channel Stream channel temperature ( −10 temperature (

−5 −15 2008−07−01 2009−07−01 2010−07−01 2011−07−01 2008−07−01 2009−07−01 2010−07−01 2011−07−01

Figure 7. Site AK3 time series, same as in Fig. 6. Vertical axes Figure 8. Site AK4 time series, same as in Fig. 7. Vertical are restricted to show interannual variability; hence, discharge out- axes are restricted to show interannual variability; hence, the dis- lier peaks on 31 August 2008 and 9 September 2010 is not shown charge outlier peak between 6 to 7 September 2010 is not shown 3 −1 3 −1 3 −1 (Qpeak = 31 m s , and Qpeak = 85 m s , respectively). Subzero (Qpeak = 56 m s ). Similar to Site AK3, subzero stream tempera- stream temperatures indicate frozen conditions and zero stream tures indicate frozen conditions and zero stream flow, except during flow, except during prolonged periods with unchanged temperatures prolonged periods with unchanged temperatures indicative of phase indicative of phase change and flowing water. change and flowing water. Additionally, sensor air exposure between 18 to 22 October 2008, 12 to 23 September 2009, and 9 to 13 September 2010 resulted in subzero and highly variable stream 2010 at Site AK4, and on 11 September at Site AK3 (Site temperatures. AK2 records data gap begin 11 September 2010). Site AK3 discharge anomalies may be a result of backflow, as this stream branch is upstream of the input of the ice dammed 5 Discussion lake into the AK river system but yet in proximity to the lake where the ice dammed lake discharges into (Lake B, Fig. 1). Discharge uncertainties for the three study sites are consid- Summer discharge at five AK-river sites are proportional erable, but discharge magnitudes are internally consistent to ice sheet drainage area, and specific discharge inversely within the AK-river basin. The average ∼68 % confidence proportional to area with the exception of Site AK3 (Table 5). interval varies between ±19 % and ±46 % of Q estimates. At Site AK3, specific discharge is less than at the next larger These error magnitudes are much larger than previously as- site (Site AK4). This may be due to larger discharge uncer- sumed in this river system, e.g. 10 % (van de Wal and Rus- tainty at Site AK3, but also due to real differences in runoff sell, 1994). The largest error source is channel depth ob- production intensity as these watersheds do not overlap spa- servations. At Sites AK2 and AK4, channel depth errors tially. are probably dominated by measurement uncertainties, because stream channels were stable, and unchanging, owing to structural reinforcement and bedrock incision. In contrast, Site AK3 channel cross section profile changed, suggesting that dynamic changes in channel morphology at Site AK3

Earth Syst. Sci. Data, 4, 1–12, 2012 www.earth-syst-sci-data.net/4/1/2012/ A. K. Rennermalm et al.: Proglacial river stage, discharge, and temperature datasets 11 added to depth observation uncertainty here. However, Site uncertainty analysis to discharge measurements in the Aku- AK3 also has the lowest discharge, which is disproportion- liarusiarsuup Kuua river system. ally influenced by depth measurement errors. Thus, it is pos- The resulting stage, discharge, and temperature dataset is sible that measurement uncertainties dominate at Site AK3 useful for understanding riverine conditions in the Southwest as well. Greenland proglacial environment, the response of Green- While majority of discharge at subzero stream tempera- land ice sheet meltwater production to climatic variables, and tures have been discarded for Site AK2, sub zero tempera- possibly hydrologic processes operating within the ice sheet tures at Site AK3 and AK4 suggest that these streams are itself. Ongoing data collections at these sites are planned un- mostly frozen during winter. Exceptions are 1–3 large Lw til at least 2013 so that the utility of these time series may be anomalies sustained for several days at Sites AK3 and AK4 extended further. which suggest the possibility of occasional water discharge Data are available in Open Access at: events at these sites even during winter. However, further in- doi:10.1594/PANGAEA.762818. vestigation is needed to confirm this possibility. Regardless, Acknowledgements. This research was funded by the NASA estimated winter discharge magnitudes should be considered Cryosphere Program managed by Thomas Wagner, grants particularly uncertain given that specialized techniques are NNG05GN89G and NNX11AQ38G. needed to determine discharge during winter ice conditions (Pelletier, 1990; WMO, 2010a, b). These techniques are not Edited by: H. Grobe applied here because they require in situ discharge measurements to be collected in winter, whereas all field deployments References were in summer. Although all three monitored sites along the Akuliaru- Ashmore, P. and Sauks, E.: Prediction of discharge from wa- siarsuup Kuua River’s northern tributary have near-ideal ter surface width in a braided river with implications for at-a- station hydraulic geometry, Water Resour. Res., 42, W03406, geomorphological properties, and similar data collection doi:10.1029/2005WR003993, 2006. methodologies, uncertainty ranges identified in this study Baker, D., Escher-Vetter, H., Moser, H., Oerter, H., and Reinwarth, cannot be transferred to downstream sites or to other river- O.: A glacier discharge model based on results from field stud- ine observational sites in Greenland. This is due to strong ies of energy balance, water storage and flow, in: Hydrological site dependency as illustrated by large uncertainty variation Aspects of Alpine and High Mountain Areas (Proceedings of the between Sites AK2, AK3 and AK4. Regardless, this study Exeter Symposium, July 1982), 103–112, 1982. highlights large uncertainties in this river system, primarily Bartholomew, I., Nienow, P., Sole, A., Mair, D., Cowton, T., Palmer, introduced by channel depth observations. This study also S., and Wadham, J.: Supraglacial forcing of subglacial drainage shows how discharge errors can be determined and used to in the ablation zone of the Greenland ice sheet, Geophys. Res. constrain confidence intervals of Q time series. Given the Lett., 38, L08502, doi:10.1029/2011GL047063, 2011. large errors encountered in this study, comprehensive uncer- Box, J., Yang, L., Bromwich, D., and Bai, L.: Greenland Ice Sheet Surface Air Temperature Variability: 1840–2007, J. Climate, 22, tainty analysis should be considered more widely in similar 4029–4049, doi:10.1175/2009JCLI2816.1, 2009. data collections efforts. Box, J. E., Cappelen, J., Decker, D., Fettweis, X., Mote, T. L., Tedesco, M., and van de Wal, R.: 2010 Greenland, in: Arctic Report Card 2010, available at: www.arctic.noaa.gov/reportcard, 6 Conclusions 2010. Cuffey, K. and Paterson, W. S. B.: The Physics of Glaciers, 4th Edn., Elsevier Inc., 2010. Here, half-hourly hydrologic datasets of stage, temperature, Dethloff, K., Dorn, W., Rinke, A., Fraedrich, K., Junge, M., Roeck- and derived discharge are presented for previously unmon- ner, E., Gayler, V., Cubasch, U., and Christensen, J. H.: The im- itored sites in proglacial streams and lakes draining the pact of Greenland’s deglaciation on the Arctic circulation, Geo- Greenland ice sheet near Kangerlussuaq. The dataset adds phys. Res. Lett., 31, L19201, doi:10.1029/2004GL020714, 2004. to a small collection of hydrologic datasets for Greenland, Driesschaert, E., Fichefet, T., Goosse, H., Huybrechts, P., Janssens, which are particularly rare for streams and rivers draining I., Mouchet, A., Munhoven, G., Brovkin, V., and Weber, the ice sheet near its edge. Encountered limitations associ- S. L.: Modeling the influence of Greenland ice sheet melt- ated with turbulent water flow, high sediment bedloads, in- ing on the Atlantic meridional overturning circulation dur- struments, and observations are mitigated through quantita- ing the next millennia, Geophys. Res. Lett., 34, L10707, doi:10.1029/2007GL029516, 2007. tive error assessment. Previous studies present discharge es- Elliston, G. R.: Water movement through the Gornergletscher, Int. timated from stage observations at three sites situated 3 km, Assoc. Sci. Hydrol., 95, 79–84, 1973. 9 km, and 32 km downstream of Site AK2 (Mernild and Fichefet, T., Poncin, C., Goosse, H., Huybrechts, P., Janssens, I., Hasholt, 2009; Russell et al., 1995; Russell, 2007; van and Treut, H. L.: Implications of changes in freshwater flux from de Wal and Russell, 1994), but do not include uncertainty the Greenland ice sheet for the climate of the 21st century, Geo- ranges. Thus, this study is the first to apply a statistical data phys. Res. Lett., 30, 1911, doi:10.1029/2003GL017826, 2003. www.earth-syst-sci-data.net/4/1/2012/ Earth Syst. Sci. Data, 4, 1–12, 2012 12 A. K. Rennermalm et al.: Proglacial river stage, discharge, and temperature datasets

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