Efficient meltwater drainage through supraglacial streams and rivers on the southwest Greenland ice sheet.

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 <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 km(3)⋅d(-1) vs. ∼0.103 km(3)⋅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.

Spectrally driven classification of high spatial resolution, hyperspectral imagery: a tool for mapping in-stream habitat.

Streams represent an essential component of functional ecosystems and serve as sensitive indicators of disturbance. Accurate mapping and monitoring of these features is therefore critical, and this study explored the potential to characterize aquatic habitat with remotely sensed data. High spatial resolution, hyperspectral imagery of the Lamar River, Wyoming, USA, was used to examine the relationship between spectrally defined classes and field-mapped habitats. Advantages of this approach included enhanced depiction of fine-scale heterogeneity and improved portrayal of gradational zones between adjacent features. Certain habitat types delineated in the field were strongly associated with specific image classes, but most included areas of diverse spectral character; spatially buffering the field map polygons strengthened this association. Canonical discriminant analysis (CDA) indicated that the ratio of the variability among groups to that within a group was an order of magnitude greater for spectrally defined image classes (20.84) than for field-mapped habitat types (1.82), suggesting that unsupervised image classification might more effectively categorize the fluvial environment. CDA results also suggested that shortwave-infrared wavelengths were valuable for distinguishing various in-stream habitats. Although hyperspectral stream classification seemed capable of identifying more features than previously recognized, the technique also suggested that the intrinsic complexity of the Lamar River would preclude its subdivision into a discrete number of classes. Establishing physically based linkages between observed spectral patterns and ecologically relevant channel characteristics will require additional research, but hyperspectral stream classification could provide novel insight into fluvial systems while emerging as a potentially powerful tool for resource management.