A new time‐space accounting scheme to predict stream water residence time and hydrograph source components at the watershed scale

Abstract

[1] Hydrograph source components and stream water residence time are fundamental behavioral descriptors of watersheds but, as yet, are poorly represented in most rainfall-runoff models. We present a new time-space accounting scheme (T-SAS) to simulate the pre-event and event water fractions, mean residence time, and spatial source of streamflow at the watershed scale. We use a physically based hydrologic model together with field data from the well-studied Maimai M8 watershed and HJ Andrews WS10 watershed to explore how catchment properties, particularly soil depth, controls the age and source of streamflow. Our model simulates unsaturated, saturated subsurface, and surface rainfall-runoff processes. We first demonstrate the ability of the model to capture hydrograph dynamics and compare the model flow component and age simulations against measured values at the two sites. We show that the T-SAS approach can capture flow and transport dynamics for the right dominant process reasons. We then conduct a series of virtual experiments by switching soil depths between the two watersheds to understand how soil depth and its distribution control water age and source. Results suggest that thicker soils increase mean residence time and damp its temporal dynamics in response to rainfall inputs. Soil depth influenced the geographic source of streamflow, whereas pre-event water sources became more concentrated to near stream zones as soil depth increased. Our T-SAS approach provides a learning tool for linking the dynamics of residence time and time-space sources of flow at the watershed scale and may be a useful framework for other distributed rainfall-runoff models.

Controls on snowmelt water mean transit times in northern boreal catchments

“…As recently observed by Sayama and McDonnell (2009), there are limitations in estimating time-invariant transit-time distributions with no ability to quantify the dynamics of the distributions with flow conditions and rainfall regimes. This echoes the empirical findings of Tetzlaff et al (2007) and Hrachowitz et al (2009b) with respect to variations in long-term catchment MTTs.…”

Modeling of extreme freshwater outflow from the north-eastern Japanese river basins to western Pacific Ocean

“…(Sasaki, 2014;Luo et al, 2014;Apip et al, 2011;Sayama and McDonell, 2009;Tachikawa et al, 2004;Sayama et al, 2003, Kojima et al, 1998 for simulating river discharge, depending upon basin properties and rainfall. We simulated total freshwater outflow at river mouths from all of the nine first class river basins flowing from the north- For two rivers (Takase and Tone) the simulation was not made at the river mouth but on the most downstream available discharge stations Ueno and Nunokawa (afterwards renamed to Fukawa) respectively, because we did not have any discharge data available close to the river mouth.…”

What can flux tracking teach us about water age distribution patterns and their temporal dynamics?

“…Botter et al, 2010; der Velde et al, 2012). In one of these rare attempts, Sayama and McDonnell (2009) analyzed the spatio-temporal patterns in two contrasting catchments. They found significant differences in the variability of MTT in response to rainfall events, and concluded that both storage depth and rainfall pattern control the spatio-temporal pattern of flow path distributions.…”