Separating physical and meteorological controls of variable transit times in zero‐order catchments

Abstract

[1] We observed water fluxes and isotopic compositions within the subsurface of six small nested zero-order catchments over the course of three North American monsoon seasons and found that mean transit times (mTTs) were variable between seasons and different spatial patterns of mTTs emerged each year. For each monsoon season, it was possible to correlate mTTs with a different physical catchment property. In 2007, mTTs correlated best with mean soil depth, in 2008 soil hydraulic conductivity gained importance in explaining the variability and in 2009 planform curvature showed the best correlation. Differences in meteorological forcing between the three monsoon seasons explained the temporal variability of mTTs. In 2007, a series of precipitation events caused the storage capacity of the soils of some of the zero-order catchments to be exceeded. As a result those catchments started producing quick runoff (overland and macropore flow). In 2008, precipitation events were more evenly distributed throughout the season, soils did not saturate, runoff coefficients decreased because more water left the catchment via evapotranspiration and soil hydraulic conductivity became a stronger control since matrix flow dominated. The 2009 monsoon was unusually dry, the soil storage became depleted and water flowed mainly through bedrock pathways. Therefore, topographic parameters gained importance in determining how quickly water arrived at the catchment outlet. In order to improve our understanding of what controls mTTs we suggest a dimensionless number that helps identifying partitioning thresholds and sorts precipitation events into one of the three response modes that were observed in our zero-order catchments.

Lessons learned from monitoring the stable water isotopic variability in precipitation and streamflow across a snow-dominated subarctic catchment

“…Based on the sampling available, the similarity in the isotopic composition of high elevation snow and early season rain makes it difficult to fully isolate the potential impact of a shift in the regions that experience snow accumulation and melt. Further, this similarity limits our ability to describe input isotopic signatures in order to characterize and quantify the extent of mixing within the catchment (e.g., Birkel et al 2012;Heidbüchel, Troch, and Lyon 2013;van der Velde et al 2015) or the timing of snowmelt-water movement through the system across scale (Lyon et al 2010a). Regardless of such limitations, we can still distill relevant first-order information about catchment processes from the systematic isotopic variation exhibited by this initial sampling.…”

Time series of tritium, stable isotopes and chloride reveal short-term variations in groundwater contribution to a stream

“…As discussed previously, a modification in storage due to a change in recharge dynamics may have activated different groundwater flowpaths and hence water parcels with different RTs (Heidbüchel et al, 2013;Cartwright and Morgenstern, 2015). When the rate of recharge was highest, flushing out of waters located in the deeper, older bedrock aquifer may have been triggered by the resulting pressure wave propagation.…”

“…One of the reasons is that this non-stationarity is not accounted for in the models commonly used in catchment TT research. In the last 5 years, an ever-growing number of studies has transferred its focus to assessing dynamic TT distributions (Hrachowitz et al, 2010(Hrachowitz et al, , 2013Roa-García and Weiler, 2010;Rinaldo et al, 2011;Cvetkovic et al, 2012;Heidbüchel et al, 2012Heidbüchel et al, , 2013McMillan et al, 2012;Tetzlaff et al, 2014;Birkel et al, 2015;Benettin et al, 2015;Harman, 2015;Klaus et al, 2015a;Kirchner, 2015). Most of these studies agreed on the importance of considering storage dynamics, because the RT distribution of storage water and the TT distribution of water transiting at the outlet of the catchment are likely to be very different.…”

Characteristics and controls of variability in soil moisture and groundwater in a headwater catchment

“…Variability controls which parts of the catchment are generating runoff and controlling water partitioning: it therefore controls uncertainty in flow predictions, depending on our knowledge or lack of knowledge about those water stores or fluxes. Similarly, variability controls how quickly water flows through a catchment, as the different response modes direct water into flow paths with different transit times (Heidbuechel et al, 2013). Variability also provides clues into unmeasured fluxes which are important for catchment response; for example, areas with more rapid water table movement suggest locations of preferential flow paths, either vertical or horizontal.…”