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 km² 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 <2 d to the bed. Moulin discharges mapped inside ∼52% of the source ice watershed for Isortoq, a major proglacial river, totaled ∼41–98% of observed proglacial discharge, highlighting the importance of supraglacial river drainage to true outflow from the ice edge. However, Isortoq discharges tended lower than runoff simulations from the Modèle Atmosphérique Régional (MAR) regional climate model (0.056–0.112 km³⋅d⁻¹ vs. ∼0.103 km³⋅d⁻¹), and when integrated over the melt season, totaled just 37–75% of MAR, suggesting nontrivial subglacial water storage even in this melt-prone region of the ice sheet. We conclude that (i) the interior surface of the ice sheet can be efficiently drained under optimal conditions, (ii) that digital elevation models alone cannot fully describe supraglacial drainage and its connection to subglacial systems, and (iii) that predicting outflow from climate models alone, without recognition of subglacial processes, may overestimate true meltwater export from the ice sheet to the ocean. Significance Meltwater runoff from the Greenland ice sheet is a key contributor to global sea level rise and is expected to increase in the future, but it has received little observational study. We used satellite and in situ technologies to assess surface drainage conditions on the southwestern ablation surface after an extreme 2012 melting event. We conclude that the ice sheet surface is efficiently drained under optimal conditions, that digital elevation models alone cannot fully describe supraglacial drainage and its connection to subglacial systems, and that predicting outflow from climate models alone, without recognition of subglacial processes, may overestimate true meltwater release from the ice sheet.

Greenland’s snowline exhibits large fluctuations and is a primary amplifier of ice sheet surface melt and runoff.

Fine‐scale, subseasonal fluctuations in Arctic‐Boreal surface water reflect regional water balance and modulate trace gas emissions to the atmosphere but have eluded detection using traditional satellite remote sensing. We use high‐resolution (~3–5 m), high‐frequency CubeSat sensors to measure near‐daily changes in lake surface area through an object‐based tracking method that incorporates machine learning to overcome notable limitations of CubeSat imagery. From ~76,000 images we obtain >2.2 million individual observations of changing surface areas for 85,358 lakes in Northern Canada and Alaska between 1 May and 1 October 2017. We find broad‐scale lake area declines across diverse climatic, hydrologic, and physiographic terrains. Localized exceptions reveal lowland flooding and aquatic vegetation phenology cycles. Cumulative small shoreline changes of abundant lakes on the Canadian Shield exceed total inundation variations of better‐studied lowland environments, revealing a surprisingly dynamic landscape with respect to subseasonal variations in surface water extent and trace gas emissions.

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. 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.

We document the density and hydrologic properties of bare, ablating ice in a mid-elevation (1215 m a.s.l.) supraglacial internally drained catchment in the Kangerlussuaq sector of the western Greenland ice sheet. We find low-density (0.43–0.91 g cm⁻³, μ = 0.69 g cm⁻³) ice to at least 1.1 m depth below the ice sheet surface. This near-surface, low-density ice consists of alternating layers of water-saturated, porous ice and clear solid ice lenses, overlain by a thin (< 0.5 m), even lower density (0.33–0.56 g cm⁻³, μ = 0.45 g cm⁻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 14–18 cm of specific meltwater storage within this low-density ice. Water saturation of this ice is confirmed through measurable water levels (1–29 cm above hole bottoms, μ = 10 cm) in 84 % of cryoconite holes and rapid refilling of 83 % of 1 m drilled holes sampled along the transect. These findings are consistent with descriptions of shallow, depth-limited aquifers on the weathered surface of 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 km² supraglacial catchment yields 0.009–0.012 km³ of liquid meltwater storage in near-surface, porous ice. Further work is required to determine if these findings are representative of broader areas of the Greenland ice sheet ablation zone, and to assess the implications for sub-seasonal mass balance processes, 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.