Water Resources Research, Volume 56, Issue 3

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American Geophysical Union (AGU)
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Where Is the Bottom of a Watershed?
Laura Condon | K. H. Markovich | Christa Kelleher | Jeffrey J. McDonnell | Grant Ferguson | Jennifer C. McIntosh

Watersheds have served as one of our most basic units of organization in hydrology for over 300 years (Dooge, 1988, https://doi.org/10.1080/02626668809491223; McDonnell, 2017, https://doi.org/10.1038/ngeo2964; Perrault, 1674, https://www.abebooks.com/first‐edition/lorigine‐fontaines‐Perrault‐Pierre‐Petit‐Imprimeur/21599664536/bd). With growing interest in groundwater‐surface water interactions and subsurface flow paths, hydrologists are increasingly looking deeper. But the dialog between surface water hydrologists and groundwater hydrologists is still embryonic, and many basic questions are yet to be posed, let alone answered. One key question is: where is the bottom of a watershed? Knowing where to draw the bottom boundary has not yet been fully addressed in the literature, and how to define the watershed “bottom” is a fraught question. There is large variability across physical and conceptual models regarding how to implement a watershed bottom, and what counts as “deep” varies markedly in different communities. In this commentary, we seek to initiate a dialog on existing approaches to defining the bottom of the watershed. We briefly review the current literature describing how different communities typically frame the answer of just how deep we should look and identify situations where deep flow paths are key to developing realistic conceptual models of watershed systems. We then review the common conceptual approaches used to delineate the watershed lower boundary. Finally, we highlight opportunities to trigger this potential research area at the interface of catchment hydrology and hydrogeology.

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Correlation Effects? A Major but Often Neglected Component in Sensitivity and Uncertainty Analysis
Nhu Cuong | Saman Razavi

Global sensitivity analysis (GSA) provides essential insights into the behavior of Earth and environmental systems models and identifies dominant controls of output uncertainty. Previous work on GSA, however, has typically been under the assumption that the controlling factors such as model inputs and parameters are independent, whereas, in many cases, they are correlated and their joint distribution follows a variety of forms. Although this assumption can limit the credibility of GSA and its results, very few studies in the field of water and environmental modeling address this issue. In this paper, we first discuss the significance of correlation effects in GSA and then propose a new GSA framework for properly accounting for correlations in input/parameter spaces. To this end, we extend the “variogram‐based” theory of GSA, called variogram analysis of response surfaces (VARS), and develop a new generalized star sampling technique (called gSTAR) to accommodate correlated multivariate distributions. We test the new gSTAR‐VARS method on two test functions, against a state‐of‐the‐art GSA method that handles correlation effects. We then apply gSTAR‐VARS to the HBV‐SASK model, calibrated via a Bayesian, Markov chain Monte Carlo approach, for design flood estimation in the Oldman River Basin in Canada. Results demonstrate that accounting for correlation effects can be critically important in GSA, especially in the presence of nonlinearity and interaction effects in the underlying response surfaces. The proposed method can efficiently handle correlations and different distribution types and simultaneously generate a range of sensitivity indices, such as total‐variogram effects, variance‐based total‐order effects, and derivative‐based elementary effects.

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Modeling the Fate of Pharmaceuticals in a Fourth‐Order River Under Competing Assumptions of Transient Storage
Yan Liu | Christiane Zarfl | Nandita B. Basu | Olaf A. Cirpka

Quantifying the degradation of micropollutants in streams is important for river‐water quality management. While biodegradation is believed to be enhanced in transient‐storage zones of rivers, it can also occur in the main channel. Photodegradation is restricted to the main channel and surface transient‐storage zones. In this study, we propose a transient‐storage model framework to address the transport and fate of micropollutants in different domains of a river. We fitted the model to nighttime and daytime measurements of a tracer and four pharmaceuticals in River Steinlach, Germany. We could separate the surface and subsurface fractions of the total transient‐storage zone by fitting fluorescein photodegradation at daytime versus conservative nighttime transport. In reactive transport, we tested two model variants, allowing biodegradation in the main channel or restricting it to the transient‐storage zones, obtaining similar model performances but different degradation rate coefficients. Carbamazepine is relatively conservative; photodegradation of metoprolol and venlafaxine can be quantitatively attributed to the main channel and surface transient‐storage zone; metoprolol, venlafaxine, and sulfamethoxazole undergo biodegradation. We projected a decrease of overall pollutant removal under higher flow conditions, regardless of attributing biodegradation to specific river compartments. Our study indicates that model‐based analysis of daytime and nighttime field experiments allows (1) distinguishing photodegradation and biodegradation, (2) reducing equifinality of surface and subsurface transient‐storage, and (3) estimating biodegradation in different domains under different assumptions. However, entirely reducing the equifinality of attributing biodegradation to different compartments is hardly possible in lowland rivers with only limited transient storage.