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National Science Foundation [EAR-1344552, EAR-1344664]; Philecology Foundation; CUAHSI Pathfinder Fellowship; Science Without Borders Program; CAPES Foundation, Brazil

Transit times through hydrologic systems vary in time, but the nature of that variability is not well understood. Transit times variability was investigated in a 1 m3 sloping lysimeter, representing a simplified model of a hillslope receiving periodic rainfall events for 28 days. Tracer tests were conducted using an experimental protocol that allows time-variable transit time distributions (TTDs) to be calculated from data. Observed TTDs varied with the storage state of the system, and the history of inflows and outflows. We propose that the observed time variability of the TTDs can be decomposed into two parts: ‘internal' variability associated with changes in the arrangement of, and partitioning between, flow pathways; and ‘external' variability driven by fluctuations in the flow rate along all flow pathways. These concepts can be defined quantitatively in terms of rank StorAge Selection (rSAS) functions, which is a theory describing lumped transport dynamics. Internal variability is associated with temporal variability in the rSAS function, while external is not. The rSAS function variability was characterized by an ‘inverse storage effect', whereby younger water is released in greater proportion under wetter conditions than drier. We hypothesize that this effect is caused by the rapid mobilization of water in the unsaturated zone by the rising water table. Common approximations used to model transport dynamics that neglect internal variability were unable to reproduce the observed breakthrough curves accurately. This suggests that internal variability can play an important role in hydrologic transport dynamics, with implications for field data interpretation and modeling. This article is protected by copyright. All rights reserved.

[1] Despite the availability of weather radar data at high spatial (1 km2) and temporal (5–15 min) resolution, ground-based rain gauges continue to be necessary for accurate estimation of storm rainfall input to catchments during flash flood events, especially in mountainous catchments. Given economical considerations, a long-standing problem in catchment hydrology is to establish optimal placement of a small number of rain gauges to acquire data on both rainfall depth and spatiotemporal variability of intensity during extreme storm events. Using weather radar observations and a dense network of 40 tipping bucket rain gauges, this study examines whether it is possible to determine a reliable “best” set of rain gauge locations for the Sabino Canyon catchment near Tucson, Arizona, USA, given its complex topography and dominant storm track pattern. High-quality rainfall data are used to evaluate all possible configurations of a “practical” network having from one to four rain gauges. A multicriteria design strategy is used to guide rain gauge placement, by simultaneously minimizing the residual percent bias and maximizing the coefficient of correlation between the estimated and true mean areal rainfall and minimizing the normalized spatial mean squared error between the estimated and true spatiotemporal rainfall distribution. The performance of the optimized rain gauge network was then compared against randomly designed network ensembles by evaluating the quality of streamflows predicted using the Kinematic Runoff and Erosion (KINEROS2) event-based rainfall-runoff model. Our results indicate that the multicriteria strategy provided a robust design by which a sparse but accurate network of rain gauges could be implemented for semiarid basins such as the one studied.