Including Regional Knowledge Improves Baseflow Signature Predictions in Large Sample Hydrology

Abstract

A catchment's hydrological response is controlled by climatic forcing and by the landscape through which water moves. Yet when we compare large samples of catchments, we often find climate to be the only good predictor of the hydrological response and a lot of variability is left unexplained. This contradicts extensive evidence from field and regional studies which shows the importance of catchment form (e.g., geology) on catchment hydrological processes, particularly on baseflow processes. We hypothesize that this is due to limitations in (a) the catchment attributes we use to inform our analyses and (b) the hydrological signatures we use to describe the hydrological response. To test these hypotheses, we use a large sample of catchment data across the contiguous United States. By reviewing literature from several U.S. regions, we show that region‐specific knowledge is underutilized in large sample studies. To organize the findings from these regions, we propose and apply a framework based on standardized perceptual models. Informed by these perceptual models, we use both available and newly calculated catchment attributes to show that baseflow signature predictions can be improved regionally. Multiple baseflow signatures are needed to better distinguish between different baseflow sources, such as the subsurface, surface water bodies, and snow. We conclude with pointing at potential future directions and argue that we should aim at a more systematic and hydrologically motivated selection of catchment attributes and hydrological signatures.

A review of hydrologic signatures and their applications

“…The weak relationship between watershed descriptors and signatures contradicts extensive field evidence that shows how watershed features control streamflow responses. Therefore, there is great potential to create new watershed descriptors that better characterize hydrologic behavior and flow signatures (Gnann, McMillan, Woods, & Howden, 2020). In turn, this would allow for better predictions of the flow regime in ungauged watersheds.…”

A Hydrologic Functional Approach for Improving Large‐Sample Hydrology Performance in Poorly Gauged Regions

“…These theoretical studies collectively emphasized the importance of soil thickness, the vertical pattern of soil saturated hydraulic conductivity, water leakage along the soil‐bedrock interface, hillslope geometry (e.g., length, curvature), land surface slope, and water input characteristics, in controlling how a catchment drains the stored water. Field‐based hydrology studies further depicted the importance of the slope of shallow weathered bedrock, micro‐topography along the bedrock surface and the land surface, the conductivity contrast between soil and shallow bedrock, and the presence of sink‐holes within deep aquifers and macro‐pores within shallow soil, in controlling hillslope‐scale threshold‐like behavior of hydrologic connectivity (Appels et al., 2015; Beven & Germann, 2013; Gnann et al., 2021; Hopp & McDonnell, 2009; Jackson et al., 2016; McDonnell, 1990; Thompson et al., 2010). However, globally available datasets do not exist for most of the features suggested above.…”

“…Specifically, this study responds to calls made by Oudin et al (2010), Troch, Berne, et al (2013), and more recently by Knoben et al (2018), Gnann et al (2021) and Wu et al (2021), to create new process-based descriptors of catchment functional behaviors for applications in large-sample or comparative hydrology, in order to improve the estimation of (dis)similarity in hydrologic behaviors among catchments and to improve the predictions of streamflow signatures in poorly gauged regions. In doing so, we introduce three interactive functional indices to assess whether the addition of the functional indices to climatic indices, or the use of the interactive functional indices instead of their individual climatic and physical components, as catchment descriptors, could:…”

“…The importance of the interaction amongst catchments' climatic and physical attributes in controlling streamflow generation mechanisms and shape-based streamflow signatures were widely examined and emphasized in theoretical and field-based hydrology experiments (Ameli et al, 2015;Hopp & McDonnell, 2009;Kelleher et al, 2015), but not yet sufficiently acknowledged in LSH methodologies. In this context, Gnann et al (2021) urged for the development of new process-based catchment descriptors to improve LSH methodologies through sufficiently including the hydrologically relevant role of underground catchment attributes in different environmental settings. But how can new process-based interactive catchment descriptors be created; preferably based on globally available catchment data?…”

Perceptual perplexity and parameter parsimony

“…As a community we have avoided thinking deeply about how that particular conundrum might be resolved. In fact, when we know that we would ideally want models that are robust in calibration (and which should therefore, be parsimonious in their parameterization), it is far easier not to think about the problem at all, but rather take a more top‐down approach to defining hydrological functioning as represented by model structures from observed behavior at the catchment scale (e.g., Chappell et al, 2006; Gnann et al, 2021; Ockenden & Chappell, 2011; Sivapalan et al, 2003; Wagener et al, 2021; Young, 2003, 2013; Young & Beven, 1994). An example of such an approach is the study of Wrede et al (2015) who defined different models within SuperFLEX for three subcatchments of the Attert catchment in Luxembourg where the geological characteristics suggested quite different perceptual models.…”