ABSTRACT The Matanuska lowland north of Anchorage, Alaska, was episodically glaciated during the Pleistocene by the merged westward flow of the Matanuska and Knik glaciers. During the late Wisconsin glaciation, glacial Lake Atna filled the Copper River Basin, impounded by an ice dam blocking the Matanuska drainage divide at Tahneta Pass and the adjacent Squaw Creek headwaters and ice dams at other basin outlets, including the Susitna and Copper rivers. On the Matanuska lowland floor upvalley from the coalesced glacier’s late-Wisconsin terminus, a series of regularly spaced, symmetrical ridges with 0.9-km wavelengths and heights to 36 m are oriented normal to oblique to the valley and covered by smaller subparallel ridges with wavelengths typically ~80 m and amplitudes to 3 m. These and nearby drumlins, eskers, and moraines were previously interpreted to be glacial in origin. Borrow-pit exposures in the large ridges, however, show sorting and stratification, locally with foreset bedding. A decade ago we reinterpreted such observations as evidence of outburst flooding during glacial retreat, driven by water flushing from Lake Atna through breaches in the Tahneta Pass and Squaw Creek ice dam. In this view, the ridges once labeled Rogen and De Geer moraines were reinterpreted as two scales of fluvial dunes. New observations in the field and from meter-scale light detection and ranging (LiDAR) and interferometric synthetic aperture radar (IfSAR) digital elevation models, together with grain-size analyses and ground-penetrating radar profiles, provide further evidence that portions of the glacial landscape of the Matanuska lowlands were modified by megaflooding after the Last Glacial Maximum, and support the conclusion that the Knik Glacier was the last active glacier in the lowland.

ABSTRACT During the past 50 years, geomorphology has become progressively more quantitative, with increasing emphasis on nondimensional metrics that facilitate comparison across field sites, on quantitative conceptual models, on quantification and rigorous dating of geomorphic history, and on exploration of how to quantify physical processes responsible for producing and redistributing sediment. These shifts in emphasis have been facilitated by development of new techniques for collecting and analyzing data, including advances in remote-sensing technology and geochronologic and isotopic methods. During the past half century, the geomorphic community has become more diverse with respect to gender, geographic representation, and disciplinary background, and this has facilitated interdisciplinary approaches to understanding planetary surfaces.

Spatial variability in precipitation has received little attention in the study of connections between climate, erosion, and tectonics. However, long-term precipitation patterns show large variations over spatial scales of ∼10 km and are strongly controlled by topography. We use precipitation rate estimates from Tropical Rainfall Measuring Mission (TRMM) satellite radar data to approximate annual precipitation over the Himalaya at a spatial resolution of 10 km. The resulting precipitation pattern shows gradients across the range, from east to west along the range, and fivefold differences between major valleys and their adjacent ridges. Basin-wide average precipitation estimates correlate well with available measured mean runoff for Himalayan rivers. Estimated errors of 15%–50% in TRMM-derived annual precipitation are much smaller than the spatial variability in predicted totals across the study area. A simple model of orographic precipitation predicts a positive relationship between precipitation and two topographically derived factors: the saturation vapor pressure at the surface and this pressure times the slope. This model captures significant features of the pattern of precipitation, including the gradient across the range and the ridge-valley difference, but fails to predict the east-west gradient and the highest totals. Model results indicate that the spatial pattern of precipitation is strongly related to topography and therefore must co-evolve with the topography, and suggest that our model may be useful for investigation of the relationships among the coupled climate-erosion-tectonic system.