Hydrological transit times in nested urban and agricultural watersheds in the Greater Toronto Area, Canada

Abstract

Watershed mean transit times (MTTs) are used to characterize the hydrology of watersheds. Watershed MTTs could have important implications for water quality, as relatively long MTTs imply lengthier water retention, thereby allowing more time for pollutant transformation and more moderate release of pollutants into streams. Although estimates of MTTs are common for undisturbed watersheds, only a few studies to date have applied MTT models to urbanized watersheds. In the present study, we use δ18O to compare estimates of MTTs for paired suburban‐industrial and agricultural watersheds in Toronto, Canada. Although differences in precipitation δ18O between the two watersheds were negligible, there were significant differences in stream δ18O, suggesting differences in water transport pathways. Less damping between input precipitation δ18O and output stream δ18O in the suburban‐industrial watershed indicated a larger streamflow contribution from quick‐flow transport pathways. We applied three transit time models to quantify MTTs. Considering overall model fit, root mean square error, and uncertainty in model parameters, the exponential model performed the best of the three models. Optimized MTTs using this distribution across the suburban‐industrial subwatersheds ranged from 2.1 to 2.9 months, whereas those in the agricultural subwatersheds ranged from 2.7 to 7.5 months. The relatively small difference between urban and agricultural MTTs coincides with observations elsewhere. Model efficiencies could potentially be improved, and MTTs estimated more reliably, with a higher sampling frequency that captures a greater volume of overall discharge. Overall, this work provides a distinct first glimpse into the separation of MTTs between paired watersheds with such a large contrast in their land use.

Assessing basin storage: Comparison of hydrometric‐ and tracer‐based indices of dynamic and total storage

“…SD ratios were similar to values for basins in Scotland and the Western Cascades of Oregon (Tetzlaff, Seibert, McGuire, et al, ) and fell within the range (0.04–0.40) for basins across Switzerland with similar areas to those examined here (Seeger & Weiler, , Figure a). However, they were smaller than those observed by Parajulee et al () for two basins in the ORM region (Figure b), one of which had its headwaters on the ORM. Parajulee et al () sampled a greater range of flow conditions compared to this study, and inclusion of high flow samples would likely have increased the variability of streamflow isotopic composition and thus SD ratios relative to values given here.…”

“…However, they were smaller than those observed by Parajulee et al () for two basins in the ORM region (Figure b), one of which had its headwaters on the ORM. Parajulee et al () sampled a greater range of flow conditions compared to this study, and inclusion of high flow samples would likely have increased the variability of streamflow isotopic composition and thus SD ratios relative to values given here. Greater SD ratios from Parajulee et al () may also reflect the larger extent of urban and agricultural areas in their basins compared to those we examined.…”

“…Mean transit time versus the ratio of the standard deviation of δ 18 O in streamflow to the standard deviation of δ 18 O in precipitation for basins in Switzerland ((a) – data from Seeger & Weiler, ) and southern Ontario ((b) – data from Parajulee, Wania, & Mitchell, ), and unweighted and flow‐weighted estimates of the fraction of young water ( F yw ) versus the ratio of the standard deviation of δ 18 O in streamflow to the standard deviation of δ 18 O in precipitation for basins in Switzerland ((c) – F yw data from von Freyberg et al () and isotopic ratio data for corresponding basins from Seeger and Weiler ())…”

Travel times for snowmelt‐dominated headwater catchments: Influences of wetlands and forest harvesting, and linkages to stream water quality

“…The most common approach has been spatially lumped and time averaged convolution integral models (McGuire & McDonnell, 2006). These models have been employed to estimate mean travel times since the 1980s (Małoszewski & Zuber, 1982; Rodhe, Nyberg, & Bishop, 1996) and continue to be used because of their parsimony (Lane et al, 2020; Parajulee, Wania, & Mitchell, 2019), despite recognized limitations in accounting for variable flow conditions and catchment heterogeneity (Kirchner, 2016a). To tackle some of these limitations, time variant models have been developed (Benettin et al, 2017; Harman, 2015; Klaus, Chun, McGuire, & McDonnell, 2015; Rinaldo et al, 2015).…”

“…Mean travel times have been related to flowpath distance, hillslope gradient, soil characteristics, catchment area and meteorological conditions such as rainfall intensity (Broxton, Troch, & Lyon, 2009; Cartwright, Irvine, Burton, & Morgenstern, 2018; Heidbüchel, Troch, & Lyon, 2013; Hrachowitz, Soulsby, Tetzlaff, Dawson, & Malcolm, 2009; Hrachowitz, Soulsby, Tetzlaff, & Speed, 2010; McGuire et al, 2005; Tetzlaff et al, 2009). Most studies are from rain‐dominated systems although there has been an increase in the number of studies focused on catchments influenced by snow (Ala‐Aho, Tetzlaff, McNamara, Laudon, & Soulsby, 2017; Broxton et al, 2009; Fang et al, 2019; Lane et al, 2020; Lyon et al, 2010; Parajulee et al, 2019; Peralta‐Tapia et al, 2016). Concerns have been raised about comparing travel times estimates across space and time using the common time averaged models.…”