Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes

Abstract

Dominant processes in a watershed are those that most strongly control hydrologic function and response. Estimating dominant processes enables hydrologists to design physically realistic streamflow generation models, design management interventions, and understand how climate and landscape features control hydrologic function. A recent approach to estimating dominant processes is through their link to hydrologic signatures, which are metrics that characterize the streamflow timeseries. Previous authors have used results from experimental watersheds to link signature values to underlying processes, but these links have not been tested on large scales. This paper fills that gap by testing signatures in large sample data sets from the U.S., Great Britain, Australia, and Brazil, and in Critical Zone Observatory (CZO) watersheds. We found that most inter‐signature correlations are consistent with process interpretations, that is, signatures that are supposed to represent the same process are correlated, and most signature values are consistent with process knowledge in CZO watersheds. Some exceptions occurred, such as infiltration and saturation excess processes that were often misidentified by signatures. Signature distributions vary by country, emphasizing the importance of regional context in understanding signature‐process links and in classifying signature values as “high” or “low.” Not all signatures were easily transferable from single, small watersheds to large sample studies, showing that visual or process‐based assessment of signatures is important before large‐scale use. We provide a summary table with information on the reliability of each signature for process identification. Overall, our results provide a reference for future studies that seek to use signatures to identify hydrological processes.

How do hydrologists perceive watersheds? A survey and analysis of perceptual model figures for experimental watersheds

“…Number of instances of each store and flux found in the review of perceptual model figures, overlain on the taxonomy of hydrological processes reproduced from McMillan et al (2022). Areas of black‐edged circles are proportional to the number of instances of each process.…”

“…We used the first 500 results from this search, ordered by relevance. The following pre-compiled lists were added to the results: reference list for taxonomy of hydrological processes (McMillan, 2022), reference list of process descriptions in critical zone observatories (McMillan et al, 2022), reference lists from experimental watersheds in the Experimental hydrology wiki (https:// experimental-hydrology.net), and papers contained in the Hydrological Processes special issue on 'Research and Observatory Catchments: the Legacy and the Future'.…”

National‐Scale Detection of Reservoir Impacts Through Hydrological Signatures

“…The statistical and dynamical properties of streamflow timeseries can be described by quantitative metrics, called hydrological signatures (see review by McMillan, 2020). For natural and near‐natural catchments, hydrological signatures have been shown to provide useful insights into catchment behavior, and have been widely used to assess underlying processes and evaluate model structure and parameterization (McMillan et al., 2022). For example, event runoff ratios are used to explore the partitioning of fast and slow runoff processes, whilst the variability of flow can be connected to higher water storage (Estrany et al., 2010; McMillan et al., 2014).…”

When good signatures go bad: Applying hydrologic signatures in large sample studies

“…In such watersheds, hourly data and careful parameter choice are needed to avoid conflating separate events. We used hourly data, but large sample studies may use daily values (e.g., McMillan et al, 2022), which would be insufficient to identify convective rainstorms.…”

“…For example, recession analysis signatures are influenced by parameters for recession extraction and fitting (Dralle et al, 2017). Large sample studies often use constant parameter values (Addor et al, 2018; McMillan et al, 2022), but these may be unsuitable for some watersheds. Strategies to select parameter values include using sensitivity analysis to explore how parameter choices affect study conclusions (Tashie et al, 2020), conducting in‐depth checks for representative watersheds such as one watershed per climate region where signature parameters are sensitive to climate, or deriving parameters from the flow regime (Stoelzle et al, 2020).…”