Detection of runoff timing changes in pluvial, nival, and glacial rivers of western Canada

Abstract

[1] Changes in air temperature, precipitation, and, in some cases, glacial runoff affect the timing of river flow in watersheds of western Canada. We present a method to detect streamflow phase shifts in pluvial, nival, and glacial rivers. The Kendall-Theil robust lines yield monotonic trends in normalized sequent 5-day means of runoff in nine river basins of western Canada over the period . In comparison to trends in the timing of the date of annual peak flow and the center of volume, two other less robust metrics often used to infer streamflow timing changes, our approach reveals more detailed structure on the nature of these changes. For instance, our trend analyses reveal extension of the warm hydrological season in nival and glacial rivers of western Canada. This feature is marked by an earlier onset of the spring melt, decreases in summer streamflow, and a delay in the onset of enhanced autumn flows. Our method provides information on streamflow timing changes throughout the entire hydrological year, enhancing results from previous methods to assess climate change impacts on the hydrological cycle.

Attribution of streamflow trends in snow and glacier melt‐dominated catchments of the Tarim River, Central Asia

“…For glacierized basins, the analysis of concurrent trends in temperature and precipitation can be complemented by considering glacier mass balance time series [Li et al, 2010;Pellicciotti et al, 2010]. Streamflow trend analyses at a high temporal resolution, e.g., using annual time series of streamflow of each day of the year, are useful to investigate temporal shifts of the streamflow regime, which may then further be related to catchment properties [Dery et al, 2009;Kormann et al, 2015].…”

Glacier Meltwater Contributions and Glaciometeorological Regime of the Illecillewaet River Basin, British Columbia, Canada

“…The retreat and thinning of glaciers affects both water availability and water temperature in glacierized catchments (Moore, 2006). Much of the long-term storage (glacier ice) gets depleted in late summer and early fall (Fountain & Tangborn, 1985), and ice-melt runoff helps to maintain streamflows after the seasonal snow has melted (e.g., Déry et al, 2009;Huss, Farinotti, Bauder, & Funk, 2008;Moore & Demuth, 2001). Glacier melt also has a cooling effect on streams because of the colder meltwater and higher flows; this reduces the sensitivity to energy inputs and maintains habitat for cold-water species in downstream rivers (Moore, 2006).…”

Seasonal flows of international British Columbia-Alaska rivers: The nonlinear influence of ocean-atmosphere circulation patterns

“…Data gaps were not interpolated, and all data within any window position of the filter including a data gap were in turn transformed to data gaps; this conservative approach favors using slightly less data in the analysis over making assumptions during interpolation. The filter gave a 50% response at a timescale of 3m 1/2 ∼ 9 days, roughly comparable to the 5-day sequent means used by Whitfield et al [78] and Déry et al [13], for example, in hydroclimatic modeling and analysis. This combination of fine sampling interval and low-pass filter thus suppresses very high-frequency noise while maintaining a high seasonal resolution.…”

“…This high seasonal resolution, which is becoming widely used in hydroclimatological analyses (e.g., [13,19,28,29,78]), facilitates the study of seasonally transient physical hydrologic processes (such as the onset of the spring snowmelt freshet, or late-summer peak glacier meltwater production following exhaustion of the seasonal snowpack), which may last only a few weeks and can be easily obscured in monthly or seasonal averages or totals. It also expedites the identification of nonlinear effects which can similarly be lost to such temporal aggregation [13,34,53,62,66]. Previous work has empirically demonstrated that the specific data processing and analysis methods used here enable detection of statistically and physically significant hydrologic teleconnections that cannot be identified using more temporally coarse-grained datasets [19].…”