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Abstract Modern integrated hydrologic models (IHMs) are powerful tools for investigating coupled hydrologic system dynamics. The tradeoff for this realism is a high computational burden and large numbers of parameters in each cell, few of which can be specified with a high degree of confidence. These factors combined make uncertainty quantification (UQ) a problem for IHM‐based simulations, yet without rigorous UQ, it is not clear how much confidence can be placed on conclusions made with IHMs. Previous work evaluated steady‐state cases where the permeability field was a random variable, and a logical continuation is to consider transient conditions. This work assesses the confidence of an IHM representation of a first‐order basin in central Idaho, USA, using an ensemble of 250 permeability realizations under three different recharge forcing signals. The results show that surface water is simulated with high confidence across all the permeability realizations, but the groundwater system and changes to it have lower confidence. However, uncertainty in changes to the groundwater system decrease with time since an increase in the recharge, meaning that the farther one gets from a “peak” in the flow (of any size) the more confident one can be in the response (i.e., smaller inter‐quartile range). The ensemble was also used to assess how many realizations were needed to capture expected behaviors of the ensemble and their range of variability. Unsurprisingly, groundwater requires larger ensembles than surface flows, but the size of the ensembles necessary for convergence were smaller than initially expected. 

Abstract Coupled simulations of surface and variably saturated subsurface flow, termed integrated hydrologic models (IHMs), can provide powerful insights into the complex dynamics of watersheds. The system of governing equations solved by an IHM is non‐linear, making them a significant computational burden and challenging to accurately parameterize. Consequently, a large fraction of the IHM studies to date have been “numerical hypothesis testing” studies, but, as parallel computing continues to improve, IHMs are approaching the point where they might also be useful as predictive tools. For this to become reality, the predictive uncertainty of such highly parameterized simulations must be considered. However, uncertainty is seldom considered in the IHM literature, likely due to the long runtimes of the complex simulations. The questions considered herein are how much uncertainty is there in an IHM for a common watershed simulation scenario, and how likely is it that any one realization of a system will give the same relative change as any other due to a perturbation in recharge? A stochastic ensemble of 250 permeability field realizations was used to show that uncertainty in a high‐mountain headwaters systems is dominated by the subsurface. Recharge perturbation scenarios echo these results, but the uncertainty ofchangesin streamflow or groundwater pressure heads were significantly smaller than the uncertainty in their base‐case values. The main finding is that IHMs do provide confident, predictive estimates ofrelativechanges in watersheds, even when uncertainty in specific simulation outputs may be high.