Search

Meltwater runoff from the Greenland Ice Sheet (GrIS) is an important contributor to global sea level rise, but substantial uncertainty exists in its measurement and prediction. Common approaches for estimating ice sheet runoff are in situ gauging of proglacial rivers draining the ice sheet, and surface mass balance (SMB) modeling. To obtain hydrological and meteorological datasets suitable for both runoff characterization and SMB model validation, we established an automated weather station (AWS) and cluster of traditional and experimental river stage sensors on the Minturn River, the largest proglacial river draining Inglefield Land, NW Greenland. Secondary installations measuring river stage were installed in the Fox Canyon River and North River at Thule Air Base, NW Greenland. Proglacial runoff at these sites is dominated by supraglacial processes only, uniquely advantaging them for SMB studies. The three installations provide rare hydrological time-series and an opportunity to evaluate experimental measurements of river stage from a harsh, little-studied polar region. The installed instruments include submerged vented and non-vented pressure transducers, a bubbler sensor, experimental bank-mounted laser rangefinders, and time-lapse cameras. The first three years of observations (2019 to 2021) from these stations indicate a) a meltwater runoff season from late June to late August/early September, roughly synchronous throughout the region; b) early onset (~ June 23 to July 8) of a strong diurnal runoff signal in 2019 and 2020, suggesting minimal meltwater storage in snow/firn; c) one-day lagged air temperature displays the strongest correlation with river stage; d) river stage correlates more strongly with ablation zone albedo than with net radiation; and e) late-summer rain-on-ice events appear to trigger the region’s sharpest and largest floods. The new gauging stations provide valuable in situ hydrological observations from a little-studied, rapidly changing area and are freely available through the PROMICE network (https://promice.org/weather-stations/).

Supraglacial meltwater channels that flow on the surfaces of glaciers, ice sheets, and ice shelves connect ice surface climatology with subglacial processes, ice dynamics, and eustatic sea level changes. Their important role in transferring water and heat across and into ice is currently absent from models of surface mass balance and runoff contributions to global sea level rise. Furthermore, relatively little is known about the genesis, evolution, hydrology, hydraulics, and morphology of supraglacial rivers, and a first synthesis and review of published research on these unusual features is lacking. To that end, we review their ( a) known geographical distribution; ( b) formation, morphology, and sediment transport processes; ( c) hydrology and hydraulics; and ( d) impact on ice sheet surface energy balance, heat exchange, basal conditions, and ice shelf stability. We conclude with a synthesis of key knowledge gaps and provide recommendations for future research. ▪ Supraglacial streams and rivers transfer water and heat on glaciers, connecting climate with subglacial hydrology, ice sliding, and global sea level. ▪ Ice surface melting may expand under a warming climate, darkening the ice surface and further increasing melt.

The Surface Water and Ocean Topography (SWOT) satellite mission aims to improve the frequency and accuracy of global observations of river water surface elevations (WSEs) and slopes. As part of the SWOT mission, an airborne analog, AirSWOT, provides spatially-distributed measurements of WSEs for river reaches tens to hundreds of kilometers in length. For the first time, we demonstrate the ability of AirSWOT to consistently measure temporal dynamics in river WSE and slope. We evaluate data from six AirSWOT flights conducted between June 7–22, 2015 along a ~90 km reach of the Tanana River, AK. To validate AirSWOT measurements, we compare AirSWOT WSEs and slopes against an in situ network of 12 pressure transducers (PTs). Assuming error-free in situ data, AirSWOT measurements of river WSEs have an overall root mean square difference (RMSD) of 11.8 cm when averaged over 1 km2 areas while measurements of river surface slope have an RMSD of 1.6 cm/km for reach lengths >5 km. AirSWOT is also capable of recording accurate river WSE changes between flight dates, with an RMSD of 9.8 cm. Regrettably, observed in situ slope changes that transpired between the six flights are well below AirSWOT's accuracy, limiting the evaluation of AirSWOT's ability to capture temporal changes in slope. In addition to validating the direct AirSWOT measurements, we compare discharge values calculated via Manning's equation using AirSWOT WSEs and slopes to discharge values calculated using PT WSEs and slopes. We define or calibrate the remaining discharge parameters using a combination of in situ and remotely sensed observations, and we hold these remaining parameters constant between the two types of calculations to evaluate the impact of using AirSWOT versus the PT observations of WSE and slope. Results indicate that AirSWOT-derived discharge estimates are similar to the PT-derived discharge estimates, with an RMSD of 13.8%. Additionally, 42% of the AirSWOT-based discharge estimates fall within the PT discharge estimates' uncertainty bounds. We conclude that AirSWOT can measure multitemporal variations in river WSE and spatial variations in slope with both high accuracy and spatial sampling, providing a compelling alternative to in situ measurements of regional-scale, spatiotemporal fluvial dynamics.

The Greenland Ice Sheet’s contribution to global sea-level rise is accelerating1 due to increased melting of its bare-ice ablation zone2–6, but there is growing evidence that climate models overestimate runoff from this critical area of the ice sheet7–12. Current climate models assume all bare ice runoff escapes to the ocean, unlike snow covered areas where some fraction of runoff is retained and/or refrozen in porous firn13–15. Here we use in situ measurements and numerical modeling to reveal extensive retention and refreezing of liquid meltwater in bare glacial ice, explaining chronic runoff overestimation by climate models. From 2009–2018, refreezing of liquid meltwater in bare, porous glacial ice reduced meltwater runoff by 11–23 Gt a-1 in southwest Greenland alone, equivalent to 10–20% of annual meltwater production. This mass retention is commensurate with current estimates of climate model ice sheet meltwater runoff uncertainty, and may represent an overlooked buffer on projected runoff increases for the coming century16. Inclusion of bare-ice retention and refreezing processes in climate models therefore has immediate potential to improve forecasts of ice sheet runoff and its contribution to global sea-level rise.

Concurrent ice sheet surface runoff and proglacial discharge monitoring are essential for understanding Greenland ice sheet meltwater release. We use an updated, well-constrained river discharge time series from the Watson River in southwest Greenland, with an accurate, observation-based ice sheet surface mass balance model of the ∼ 12 000 km2 ice sheet area feeding the river. For the 2006–2015 decade, we find a large range of a factor of 3 in interannual variability in discharge. The amount of discharge is amplified ∼ 56 % by the ice sheet's hypsometry, i.e., area increase with elevation. A good match between river discharge and ice sheet surface meltwater production is found after introducing elevation-dependent transit delays that moderate diurnal variability in meltwater release by a factor of 10–20. The routing lag time increases with ice sheet elevation and attains values in excess of 1 week for the upper reaches of the runoff area at ∼ 1800 m above sea level. These multi-day routing delays ensure that the highest proglacial discharge levels and thus overbank flooding events are more likely to occur after multi-day melt episodes. Finally, for the Watson River ice sheet catchment, we find no evidence of meltwater storage in or release from the en- and subglacial environments in quantities exceeding our methodological uncertainty, based on the good match between ice sheet runoff and proglacial discharge.

Greenland’s snowline exhibits large fluctuations and is a primary amplifier of ice sheet surface melt and runoff., Greenland Ice Sheet mass loss has recently increased because of enhanced surface melt and runoff. Since melt is critically modulated by surface albedo, understanding the processes and feedbacks that alter albedo is a prerequisite for accurately forecasting mass loss. Using satellite imagery, we demonstrate the importance of Greenland’s seasonally fluctuating snowline, which reduces ice sheet albedo and enhances melt by exposing dark bare ice. From 2001 to 2017, this process drove 53% of net shortwave radiation variability in the ablation zone and amplified ice sheet melt five times more than hydrological and biological processes that darken bare ice itself. In a warmer climate, snowline fluctuations will exert an even greater control on melt due to flatter ice sheet topography at higher elevations. Current climate models, however, inaccurately predict snowline elevations during high melt years, portending an unforeseen uncertainty in forecasts of Greenland’s runoff contribution to global sea level rise.

Extensive, complex supraglacial river networks form on the southwest Greenland ice sheet (GrIS) surface each melt season. These networks are the dominant pathways for surface meltwater transport on this part of the ice sheet, but their fluvial morphometry has received little study. This paper utilizes high-resolution (2 m) WorldView-1/2 images, digital elevation models, and GIS tools to present a detailed morphometric characterization (river number, river length, Strahler stream order, width, depth, bifurcation ratio, braiding index, drainage density, slope, and relief ratio) for 523 GrIS supraglacial river networks. A new algorithm is presented to determine Strahler stream order in supraglacial environments. Results show that (1) Supraglacial river networks are broadly similar to terrestrial landscapes in that they follow Horton’s laws (river number, mean river length, and slope versus stream order), widen downstream, and have comparable mean bifurcation ratios (3.7 ± 1.9) and braiding indices; (2) unlik...

AirSWOT is an experimental airborne Ka-band radar interferometer developed by NASA-JPL as a validation instrument for the forthcoming NASA Surface Water and Ocean Topography (SWOT) satellite mission. In 2017, AirSWOT was deployed as part of the NASA Arctic Boreal Vulnerability Experiment (ABoVE) to map surface water elevations across Alaska and western Canada. The result is the most extensive known collection of near-nadir airborne Ka-band interferometric synthetic aperture radar (InSAR) data and derivative high-resolution (3.6 m pixel) digital elevation models to produce water surface elevation (WSE) maps. This research provides a synoptic assessment of the 2017 AirSWOT ABoVE dataset to quantify regional WSE errors relative to coincident in situ field surveys and LiDAR data acquired from the NASA Land, Vegetation, and Ice Sensor (LVIS) airborne platform. Results show that AirSWOT WSE data can penetrate cloud cover and have nearly twice the swath-width of LVIS as flown for ABoVE (3.2 km vs. 1.8 km nominal swath-width). Despite noise and biases, spatially averaged AirSWOT WSEs can be used to estimate sub-seasonal hydrologic variability, as confirmed with field GPS surveys and in situ pressure transducers. This analysis informs AirSWOT ABoVE data users of known sources of measurement error in the WSEs as influenced by radar parameters including incidence angle, magnitude, coherence, and elevation uncertainty. The analysis also provides recommended best practices for extracting information from the dataset by using filters for these four parameters. Improvements to data handing would significantly increase the accuracy and spatial coverage of future AirSWOT WSE data collections, aiding scientific surface water studies, and improving the platform’s capability as an airborne validation instrument for SWOT.

River systems in remote environments are often challenging to monitor and understand where traditional gauging apparatus are difficult to install or where safety concerns prohibit field measurements. In such cases, remote sensing, especially terrestrial time-lapse imaging platforms, offer a means to better understand these fluvial systems. One such environment is found at the proglacial Isortoq River in southwestern Greenland, a river with a constantly shifting floodplain and remote Arctic location that make gauging and in situ measurements all but impossible. In order to derive relevant hydraulic parameters for this river, two true color (RGB) cameras were installed in July 2011, and these cameras collected over 10 000 half hourly time-lapse images of the river by September of 2012. Existing approaches for extracting hydraulic parameters from RGB imagery require manual or supervised classification of images into water and non-water areas, a task that was impractical for the volume of data in this study. As such, automated image filters were developed that removed images with environmental obstacles (e.g., shadows, sun glint, snow) from the processing stream. Further image filtering was accomplished via a novel automated histogram similarity filtering process. This similarity filtering allowed successful (mean accuracy 79.6 %) supervised classification of filtered images from training data collected from just 10 % of those images. Effective width, a hydraulic parameter highly correlated with discharge in braided rivers, was extracted from these classified images, producing a hydrograph proxy for the Isortoq River between 2011 and 2012. This hydrograph proxy shows agreement with historic flooding observed in other parts of Greenland in July 2012 and offers promise that the imaging platform and processing methodology presented here will be useful for future monitoring studies of remote rivers.

Significance Meltwater runoff is an important hydrological process operating on the Greenland ice sheet surface that is rarely studied directly. By combining satellite and drone remote sensing with continuous field measurements of discharge in a large supraglacial river, we obtained 72 h of runoff observations suitable for comparison with climate model predictions. The field observations quantify how a large, fluvial supraglacial catchment attenuates the magnitude and timing of runoff delivered to its terminal moulin and hence the bed. The data are used to calibrate classical fluvial hydrology equations to improve meltwater runoff models and to demonstrate that broad-scale surface water drainage patterns that form on the ice surface powerfully alter the timing, magnitude, and locations of meltwater penetrating into the ice sheet., Meltwater runoff from the Greenland ice sheet surface influences surface mass balance (SMB), ice dynamics, and global sea level rise, but is estimated with climate models and thus difficult to validate. We present a way to measure ice surface runoff directly, from hourly in situ supraglacial river discharge measurements and simultaneous high-resolution satellite/drone remote sensing of upstream fluvial catchment area. A first 72-h trial for a 63.1-km2 moulin-terminating internally drained catchment (IDC) on Greenland’s midelevation (1,207–1,381 m above sea level) ablation zone is compared with melt and runoff simulations from HIRHAM5, MAR3.6, RACMO2.3, MERRA-2, and SEB climate/SMB models. Current models cannot reproduce peak discharges or timing of runoff entering moulins but are improved using synthetic unit hydrograph (SUH) theory. Retroactive SUH applications to two older field studies reproduce their findings, signifying that remotely sensed IDC area, shape, and supraglacial river length are useful for predicting delays in peak runoff delivery to moulins. Applying SUH to HIRHAM5, MAR3.6, and RACMO2.3 gridded melt products for 799 surrounding IDCs suggests their terminal moulins receive lower peak discharges, less diurnal variability, and asynchronous runoff timing relative to climate/SMB model output alone. Conversely, large IDCs produce high moulin discharges, even at high elevations where melt rates are low. During this particular field experiment, models overestimated runoff by +21 to +58%, linked to overestimated surface ablation and possible meltwater retention in bare, porous, low-density ice. Direct measurements of ice surface runoff will improve climate/SMB models, and incorporating remotely sensed IDCs will aid coupling of SMB with ice dynamics and subglacial systems.

We document the density and hydrologic properties of bare, ablating ice in a mid-elevation (1215 m a.s.l.) supraglacial internally drained catchment near Kangerlussuaq, southwest Greenland. We find water saturated, low-density (474–725 kg m −3 , μ = 688 kg m −3 ) ice to at least 1.1 m depth below the ice sheet surface. This near surface, low-density ice consists of alternating fractured porous ice and clear solid ice lenses, overlain by a thin ( −3 , μ = 455 kg m −3 ) unsaturated weathering crust. Ice-density data from 10 shallow (0.9–1.1 m) ice cores along an 800 m transect suggest an average 15–22 cm of liquid meltwater storage within this low-density ice. Water saturation of this ice is confirmed through measurable water levels (1–29 cm, μ = 10 cm) in 84 % of cryoconite holes and rapid infilling of 83 % of 1 m drilled holes sampled along the transect. Though preliminary, these findings are consistent with descriptions of shallow, depth-limited aquifers in weathering crusts of temperate and polythermal glaciers worldwide, and confirm the potential for substantial transient meltwater storage within porous low-density ice on the Greenland Ice Sheet ablation zone surface. A conservative estimate for the ~ 63 km 2 catchment yields 0.010–0.014 km 3 of liquid meltwater storage in near-surface, low-density ice. Further work is required to determine whether these findings are representative of broader areas of the Greenland Ice Sheet ablation zone, and to assess the implications for sub-seasonal surface mass balance calculations, surface lowering observations from airborne and satellite altimetry, and supraglacial runoff processes.

Shade-grown coffee (shade coffee) is an important component of the forested tropics, and is essential to the conservation of forest-dependent biodiversity. Despite its importance, shade coffee is challenging to map using remotely sensed data given its spectral similarity to forested land. This paper addresses this challenge in three districts of northern Nicaragua, here leveraging cloud-based computing techniques within Google Earth Engine (GEE) to integrate multi-seasonal Landsat 8 satellite imagery (30 m), and physiographic variables (temperature, topography, and precipitation). Applying a random forest machine learning algorithm using reference data from two field surveys produced a 90.5% accuracy across ten classes of land cover, with an 82.1% and 80.0% user’s and producer’s accuracy respectively for shade-grown coffee. Comparing classification accuracies obtained from five datasets exploring different combinations of non-seasonal and seasonal spectral data as well as physiographic data also revealed a trend of increasing accuracy when seasonal data were included in the model and a significant improvement (7.8–20.1%) when topographical data were integrated with spectral data. These results are significant in piloting an open-access and user-friendly approach to mapping heterogeneous shade coffee landscapes with high overall accuracy, even in locations with persistent cloud cover.

Choice of watershed delineation technique is an important source of uncertainty for cryo-hydrologic studies of the Greenland Ice Sheet (GrIS), with different methods yielding different watersheds for a common pour point. First, this paper explores this uncertainty for the Akuliarusiarsuup Kuua River Northern Tributary, Western Greenland. Next, a standardized, semi-automated modeling framework for generating land-ice watersheds for GrIS land-terminating ice (henceforth referred to as CryoSheds) using geographic information systems (GIS) hydrologic modeling tools is presented. The framework uses ArcGIS and the ArcPy geoprocessing library to delineate two types of land-ice watersheds, namely those defined by: (1) a hydraulic pressure potential with varying water to ice overburden pressure ratios (k-value), which determines theoretical flow paths from the hydrostatic equation, using surface and bedrock digital elevation models (DEMs) and (2) a surface topography DEM alone. Lastly, a demonstration of the CryoSheds method is presented for seven remotely sensed proglacial pour points along the Aussivigssuit River (AR), Western Greenland, and its largest tributaries. GrIS meltwater runoff from these seven nested land-ice watersheds is estimated using Modele Atmospherique Regional (MAR) v.3.2 and runoff uncertainties due to watershed delineation parameter selection is estimated.

River systems in remote environments are often challenging to monitor and understand where traditional gauging apparatus are difficult to install or where safety concerns prohibit field measurements. In such cases, remote sensing, especially terrestrial time lapse imaging platforms, offer a means to better understand these fluvial systems. One such environment is found at the proglacial Isortoq River in southwest Greenland, a river with a constantly shifting floodplain and remote Arctic location that make gauging and in situ measurements all but impossible. In order to derive relevant hydraulic parameters for this river, two RGB cameras were installed in July of 2011, and these cameras collected over 10 000 half hourly time-lapse images of the river by September of 2012. Existing approaches for extracting hydraulic parameters from RGB imagery require manual or supervised classification of images into water and non-water areas, a task that was impractical for the volume of data in this study. As such, automated image filters were developed that removed images with environmental obstacles (e.g. shadows, sun glint, snow) from the processing stream. Further image filtering was accomplished via a novel automated histogram similarity filtering process. This similarity filtering allowed successful (mean accuracy 79.6%) supervised classification of filtered images from training data collected from just 10% of those images. Effective width, a hydraulic parameter highly correlated with discharge in braided rivers, was extracted from these classified images, producing a hydrograph proxy for the Isortoq River between 2011 and 2012. This hydrograph proxy shows agreement with historic flooding observed in other parts of Greenland in July 2012 and offers promise that the imaging platform and processing methodology presented here will be useful for future monitoring studies of remote rivers.

Thermally incised meltwater channels that flow each summer across melt-prone surfaces of the Greenland ice sheet have received little direct study. We use high-resolution WorldView-1/2 satellite mapping and in situ measurements to characterize supraglacial water storage, drainage pattern, and discharge across 6,812 km(2) of southwest Greenland in July 2012, after a record melt event. Efficient surface drainage was routed through 523 high-order stream/river channel networks, all of which terminated in moulins before reaching the ice edge. Low surface water storage (3.6 ± 0.9 cm), negligible impoundment by supraglacial lakes or topographic depressions, and high discharge to moulins (2.54-2.81 cm⋅d(-1)) indicate that the surface drainage system conveyed its own storage volume every

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 dataset of meltwater river discharge for the vicinity of Kangerlussuaq, Southwest Greenland. The dataset contains measurements of river water level and discharge for three sites along the Akuliarusiarsuup Kuua (Watson) River's northern tributary, with 30 min temporal resolution between June 2008 and August 2010. Additional data of water temperature, air pressure, and lake water level and temperature are also provided. Discharge data were measured at sites with near-ideal properties for such data collection. Regardless, high water bedload and turbulent flow introduce considerable uncertainty. These were constrained and quantified using statistical techniques, which revealed that the greatest discharge data uncertainties are associated with streambed elevation change and measurements. Large portions of stream channels deepened according to statistical tests, but poor precision of streambed depth measurements also added uncertainty. Data will periodically be extended, and are available in Open Access at doi:10.1594/PANGAEA.762818.