Arctic Mackenzie Deltachannel planform evolution during 1983–2013 utilisingLandsatdata and hydrological time series

Abstract

AbstractArctic deltas, such as the Mackenzie Delta, are expected to face major climate change and increased human influence in the near future. Deltas are characterised by highly dynamic fluvial processes, and changing climate will cause considerable evolution of the riverine environment. The changes are difficult to predict with existing knowledge and data. This study quantified channel planform change of the Mackenzie Delta (1983–2013), analysing its temporal and spatial patterns. We addressed the main obstacle of research on large remote areas, the lack of data, by developing a unique work flow that utilised Landsat satellite imagery, hydrological time series, remote sensing‐based change analysis, and automatic vectorisation of channels. Our results indicate that the Mackenzie Delta experienced constant evolution but at a highly varying rate over the 30 years. The study demonstrates that the magnitude and duration of flood peaks and the presence of spring ice breakup floods determine the rate of Arctic delta planform change. Changing winter conditions and spring flood magnitudes may therefore affect the stability of Arctic deltas. However, no clear trends towards decreased recurrence or magnitude of spring floods or increased instability of the delta plain have yet been observed in the Mackenzie Delta. The delta plain was most dynamic at the beginning and at the end of the examined period, corresponding to intense flooding, whereas the rates of change were subtle during the low‐flood period 1994–2007. The largest changes have occurred along the wide Middle Channel and in the outermost delta. Relative to their size, however, smaller meandering channels have been highly dynamic. Hotspots of change in the delta plain are located in anastomosing and braiding channel segments and, at the local scale, in point bars and cut‐banks along meandering channels. Our study describes how Landsat satellite data can be utilised for advancing fluvial geomorphological research in remote areas. However, cloudiness in the delta restricts production of dense time series with simultaneous coverage of the whole area and requires manual preprocessing.