Impacts of Permeability Uncertainty in a Coupled Surface‐Subsurface Flow Model Under Perturbed Recharge Scenarios - Engdahl, Nicholas B. (Water resources research, 2024)
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 of changes in 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 of relative changes in watersheds, even when uncertainty in specific simulation outputs may be high. Plain Language Summary Watershed simulations that consider stream‐groundwater interactions require the specification of lots of parameters to define the model but there is often little data to base those parameters on. Consequently, simulation results have uncertainty and if it is not quantified then any predictions made by these models have no “plus‐or‐minus” factors, giving an illusion of high confidence. The catch is that quantifying uncertainty is computationally expensive, which is why it is seldom done, yet at some point the uncertainty must be explored if these models are to be used for predictions. The work in this paper assesses the variability of predictions for a “typical” watershed simulation under three different rainfall scenarios using an ensemble of 250 random versions of the system. The results show that groundwater has higher variability than surface flows, meaning the latter is simulated with higher confidence. However, an important finding is that the relative changes due to the changes in the rainfall were generally consistent across the ensemble. The main point is that even with limited confidence in what an output will be, the relative change of that output (e.g., a 10% increase in recharge increases streamflow by 10%) can likely be inferred with much greater confidence. Key Points Ensemble simulations of coupled surface‐subsurface flows in a high mountain watershed were performed using the numerical model ParFlow Uncertainty throughout the simulation domain was significant and was dominated by the subsurface domain Relative changes in the surface‐ and groundwater values due to a perturbation had smaller confidence intervals than the base‐case values