Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern California
Abstract. Environmental streamflow management can improve the ecological health of streams by returning modified flows to more natural conditions. The Ecological Limits of Hydrologic Alteration (ELOHA) framework for developing regional environmental flow criteria has been implemented to reverse hydromodification across the heterogenous region of coastal southern California (So. CA) by focusing on two elements of the flow regime: streamflow permanence and flashiness. Within ELOHA, classification groups streams by hydrologic and geomorphic similarity to stratify flow-ecology relationships. Analogous grouping techniques are used by hydrologic modelers to facilitate streamflow prediction in ungaged basins (PUB) through regionalization. Most watersheds, including those needed for stream classification and environmental flow development, are ungaged. Furthermore, So. CA is a highly heterogeneous region spanning a gradient of urbanization, which presents a challenge for regionalizing ungaged basins. In this study, we develop a novel classification technique for PUB modeling that uses an inductive approach to group regional streams by modeled hydrologic similarity followed by deductively determining class membership with hydrologic model errors and watershed metrics. As a new type of classification, this “Hydrologic Model-based Classification” (HMC) prioritizes modeling accuracy, which in turn provides a means to improve model predictions in ungaged basins, while complementing traditional classifications and improving environmental flow management. HMC is developed by calibrating a regional catalog of process-based rainfall-runoff models, quantifying the hydrologic reciprocity of calibrated parameters that would be unknown in ungaged basins, and grouping sites according to hydrologic and physical similarity. HMC was applied to 25 USGS streamflow gages in the south coast region of California and was compared to other hybrid PUB approaches combining inductive and deductive classification. Using an Average Cluster Error metric, results show HMC provided the most hydrologically similar groups according to calibrated parameter reciprocity. Hydrologic Model-based Classification is relatively complex and time-consuming to implement, but it shows potential for advancing ungaged basin management. This study demonstrates the benefits of thorough stream classification using multiple approaches, and suggests that Hydrologic Model-based Classification has advantages for PUB and building the hydrologic foundation for environmental flow management.
Stephen Adams et al.
Stephen Adams et al.
Stephen Adams et al.
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Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern
Thank you for the opportunity to review this paper, it was an engaging exercise. Overall this is a well-written paper that presents a seemingly novel approach to stream classification based on hydrologic model error. My remaining concern lies in the lack of literature/background on the physical hydrology and stream classification of flashy and ephemeral streams. While the authors covered the recent classifications of CA including their region of interest, given the focus of this journal and their study aims (specifically the connection they try to make with the PUB initiative), I encourage the authors to include some discussion of the distinct hydrologic processes and rapidly increasing research focus on ephemeral streams more broadly. Further, while modeling ungaged basins and facilitating development of e-flow criteria are related, the aim of the models and specific flow characteristics / processes handled may differ and this should be clearly articulated. At this point the selection of the 2 flow metrics used in the inductive classification feels insufficiently justified as ‘best current practices’, given the numerous metrics that have been successfully used to describe various aspects of ephemeral/flashy stream flow regimes (See Merrit et al 2021 and references therein as a starting point). This then sets up a somewhat uneven comparison, although I am convinced by the end that the HMC approach is complementary. I would also like to see some background and discussion on past studies tacking hydrologic model error and parameter clustering/regression techniques as a way to handle data limitations and infer watershed similarities and differences to place this method in context (e.g. Ehret et al 2020; De Vos et al 2010; Knoben et al 2020; Beven et al 2020). In summary, this paper would be more compelling if framed in terms of the existing literature on arid stream hydrology and hydrologic modeling/ flow regionalization and the study region were introduced later on in the context of an application, rather than as a singular case study.
L55: Not quite accurate. Suggest to change to: Lane et al. (2017) grouped unimpaired gages based on their natural streamflow regime before using watershed characteristics to predict the flow type of ungaged reaches.
L60 – Define acronyms (CA, So. CA) before using; Change separate to separation.
Fig 1 – the county delineations in this map seem unnecessary, and it may help to instead add a few major cities as landmarks for the gage locations. Furthermore, to be accessible to a non-CA or -US audience, having the inset map above just show a floating CA rather than the western US seems confusing.
L153 – Suggest to specify that these studies used ‘daily average streamflow’
L155 – This paragraph could benefit from some description of what each of these methods/codes actually do, and why this approach was selected. Simply stating a list of indices and packages feels insufficient, as multivariate data analysis is a complex and nuanced process. I also recommend to remove mention of the specific R packages used and simply share a link to the code repository at the end of the paper to support repeatability, a critical part of hydrologic modeling, and make the paper easier to read.
L160 – Similarly to the above comment, ‘removing highly correlated metrics’ is a subjective undertaking and some discussion of how this was done would increase transparency/repeatability.
Figure 2 – I challenge the authors to try to develop a more intuitive flowchart to visually depict this somewhat complex process, including a visual of the example provided in the text below. See Figure 1 in Ehret et al 2020 as one example.
L320 – Do you provide the model performance values to justify ‘successful calibration’
Merritt, A. M., Lane, B., & Hawkins, C. P. (2021). Classification and prediction of natural streamflow regimes in arid regions of the USA. Water, 13(3), 380.
Knoben, W. J., Freer, J. E., Peel, M. C., Fowler, K. J. A., & Woods, R. A. (2020). A brief analysis of conceptual model structure uncertainty using 36 models and 559 catchments. Water Resources Research, 56(9), e2019WR025975.
Ehret, U., van Pruijssen, R., Bortoli, M., Loritz, R., Azmi, E., & Zehe, E. (2020). Adaptive clustering: reducing the computational costs of distributed (hydrological) modelling by exploiting time-variable similarity among model elements. Hydrology and Earth System Sciences, 24(9), 4389-4411.
Beven, K., & Smith, P. (2015). Concepts of information content and likelihood in parameter calibration for hydrological simulation models. Journal of Hydrologic Engineering, 20(1), A4014010.