Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern California

Adams, Stephen; Bledsoe, Brian; Stein, Eric

doi:https://doi.org/10.5194/hess-2021-553

Preprints

https://doi.org/10.5194/hess-2021-553

Preprints

10 Jan 2022

| 10 Jan 2022

Status: a revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern California

Stephen Adams, Brian Bledsoe, and Eric Stein

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.

Received: 04 Nov 2021 – Discussion started: 10 Jan 2022

Stephen Adams et al.

Status: closed

RC1:
'Comment on hess-2021-553', Anonymous Referee #1, 20 Mar 2022

Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern

California

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.

Specific comments:

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. , 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. , (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. , (9), 4389-4411.

Beven, K., & Smith, P. (2015). Concepts of information content and likelihood in parameter calibration for hydrological simulation models. , (1), A4014010.

Citation: https://doi.org/10.5194/hess-2021-553-RC1
- AC1: 'Reply on RC1', Stephen Adams, 12 Jun 2022
  
  Thank you RC1 for the excellent feedback and opportunnity to improve our manuscript. We have accepted and responded to all comments. In response to general feedback, we have bolstered the background with additional literature on hydrology and stream classification of flashy and ephemeral streams (revised revised lines 36-38; 100-101). We have also included additional language highlighting flashy and ephemeral streams (revised lines 16-17; 18; 45). We more clearly articulated differences between modeling general hydrology and modeling environmental flows (revised lines 81-83). We clarify “best current practices” (revised line 105). Additionally, we strengthened the background and discussion related to hydrologic model error and uncertainty (revised lines 196-198; 415-416).
  For specific comments, we have corrected our interpretation of Lane et al. (2017) (revised lines 60-61). We changed “separate” to “separation” (revised line 65). We defined all acronyms prior to using (revised line 39, 45, 58, etc.). We substantially changed Figure 1 to eliminate county delineations, added the cities of Santa Barbara, Los Angeles, and San Diego, and changed the inset to show the western US. We specified “daily average streamflow” (revised line 161). We rewrote the final paragraph in Section 2.2.1 to provide more detail about methods and scripts for stream classification (revised lines 165-171). We also removed mention of specific R packages from the main text. The following paragraph in Section 2.2.2 was rewritten to provide further discussion of how highly correlated metrics were removed and to remove mention of specific R packages (revised lines 175-178). We removed mention of specific R packages from the following paragraph in Section 2.2.3 (revised line 183). Figure 2 was completely redeveloped into a more intuitive flowchart to provide a better visual tool for understanding this complex process. Finally, we reworded the reporting of our results to clarify model calibration was accurate, not “successful” (revised lines 336-337).
  
  Citation: https://doi.org/10.5194/hess-2021-553-AC1
RC2:
'Comment on hess-2021-553', Anonymous Referee #2, 31 Mar 2022

Dear Authors,

thank you very much for this work. I think the work is interesting but i have some suggestions that i hope help to improve the manuscript. My main concern is about the recent literature review in ungauged catchments. I miss some recent references. For example, the authors might want to have a look at the work from Almeida et al., Le Vine et al., Addor et al., Kratzert

Also, there are some instances that requires clarification in the text and i provide a few more comments:

page 3: "singular streamflow metrics". Please, clarify/re-word

page 3: what about transferring hydrological index?

page 3: "network of models". Please, clarify/reword

page 3: "process-based". Just a suggestion, but you might want to specify conceptual instead of process based.

page 6: equations (1) and (2) and the remaining equations in the manuscript. Please, use proper mathematical notation (eg matrices in bold, etc)

page 6: "1cfs". Could you please explain a bit why this specific number is choosen

page 6: "weighted classical (metric) multidimensional scaling" could you please provide some brief description, same for the c-index, Dunn, McClain and Silhouette.

page 7: "calibrated paramters inherentely have greater uncertainty than directly calculated parameters". Please, explain a bit explicitly what you are trying to say.

page 7: "reciprocating model accuracy". Please, explain a bit explicitly what you are trying to say.

page 9: I think a flow chart with the specific steps would be helpful

page 13: "succesful". I suggest you to elaborate this more, what do you mean by succesful? why 1% whas choosen and how?

references: please, check for the DOIs

In case that the editor asked for a revised manuscript, i will be happy to serve as reviewer of the revised manuscript

Best,

Reviewer

Citation: https://doi.org/10.5194/hess-2021-553-RC2
- AC2: 'Reply on RC2', Stephen Adams, 12 Jun 2022
  
  Thank you RC2 for your great review of our paper. We have accepted and responded to all your comments, which have improved our manuscript. We addressed your general feedback by augmenting our background literature related to ungaged catchments (revised lines 91-93; 94-96). For specific comments, we clarified “singular streamflow metrics” (revised line 76) and “network of models” (revised lines 85-86). We added transferring hydrologic index (revised line 87). We specified that we used “conceptual” models and not just process-based (revised line 76-77; 415-416). We ensured all equations use proper mathematical notation required by HESS. We provided more detail on why we chose 1 cfs as the threshold for a dry stream (revised line 155). We enhanced our description of “weighted classical (metric) multidimensional scaling (revised lines 166-167) and clustering indices (revised lines 168-169). We clarified our statement that “calibrated parameters inherently have greater uncertainty than directly calculated parameters” (revised lines 196-198). We also clarified “reciprocating model accuracy” (revised lines 193-194). We completely redeveloped Figure 2, which was also requested by RC1. We clarified “successful” model calibration (revised lines 336-337), which was also requested by RC1. Finally, we added DOIs to citations.
  
  Citation: https://doi.org/10.5194/hess-2021-553-AC2

Status: closed

RC1:
'Comment on hess-2021-553', Anonymous Referee #1, 20 Mar 2022

Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern

California

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.

Specific comments:

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. , 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. , (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. , (9), 4389-4411.

Beven, K., & Smith, P. (2015). Concepts of information content and likelihood in parameter calibration for hydrological simulation models. , (1), A4014010.

Citation: https://doi.org/10.5194/hess-2021-553-RC1
- AC1: 'Reply on RC1', Stephen Adams, 12 Jun 2022
  
  Thank you RC1 for the excellent feedback and opportunnity to improve our manuscript. We have accepted and responded to all comments. In response to general feedback, we have bolstered the background with additional literature on hydrology and stream classification of flashy and ephemeral streams (revised revised lines 36-38; 100-101). We have also included additional language highlighting flashy and ephemeral streams (revised lines 16-17; 18; 45). We more clearly articulated differences between modeling general hydrology and modeling environmental flows (revised lines 81-83). We clarify “best current practices” (revised line 105). Additionally, we strengthened the background and discussion related to hydrologic model error and uncertainty (revised lines 196-198; 415-416).
  For specific comments, we have corrected our interpretation of Lane et al. (2017) (revised lines 60-61). We changed “separate” to “separation” (revised line 65). We defined all acronyms prior to using (revised line 39, 45, 58, etc.). We substantially changed Figure 1 to eliminate county delineations, added the cities of Santa Barbara, Los Angeles, and San Diego, and changed the inset to show the western US. We specified “daily average streamflow” (revised line 161). We rewrote the final paragraph in Section 2.2.1 to provide more detail about methods and scripts for stream classification (revised lines 165-171). We also removed mention of specific R packages from the main text. The following paragraph in Section 2.2.2 was rewritten to provide further discussion of how highly correlated metrics were removed and to remove mention of specific R packages (revised lines 175-178). We removed mention of specific R packages from the following paragraph in Section 2.2.3 (revised line 183). Figure 2 was completely redeveloped into a more intuitive flowchart to provide a better visual tool for understanding this complex process. Finally, we reworded the reporting of our results to clarify model calibration was accurate, not “successful” (revised lines 336-337).
  
  Citation: https://doi.org/10.5194/hess-2021-553-AC1
RC2:
'Comment on hess-2021-553', Anonymous Referee #2, 31 Mar 2022

Dear Authors,

thank you very much for this work. I think the work is interesting but i have some suggestions that i hope help to improve the manuscript. My main concern is about the recent literature review in ungauged catchments. I miss some recent references. For example, the authors might want to have a look at the work from Almeida et al., Le Vine et al., Addor et al., Kratzert

Also, there are some instances that requires clarification in the text and i provide a few more comments:

page 3: "singular streamflow metrics". Please, clarify/re-word

page 3: what about transferring hydrological index?

page 3: "network of models". Please, clarify/reword

page 3: "process-based". Just a suggestion, but you might want to specify conceptual instead of process based.

page 6: equations (1) and (2) and the remaining equations in the manuscript. Please, use proper mathematical notation (eg matrices in bold, etc)

page 6: "1cfs". Could you please explain a bit why this specific number is choosen

page 6: "weighted classical (metric) multidimensional scaling" could you please provide some brief description, same for the c-index, Dunn, McClain and Silhouette.

page 7: "calibrated paramters inherentely have greater uncertainty than directly calculated parameters". Please, explain a bit explicitly what you are trying to say.

page 7: "reciprocating model accuracy". Please, explain a bit explicitly what you are trying to say.

page 9: I think a flow chart with the specific steps would be helpful

page 13: "succesful". I suggest you to elaborate this more, what do you mean by succesful? why 1% whas choosen and how?

references: please, check for the DOIs

In case that the editor asked for a revised manuscript, i will be happy to serve as reviewer of the revised manuscript

Best,

Reviewer

Citation: https://doi.org/10.5194/hess-2021-553-RC2
- AC2: 'Reply on RC2', Stephen Adams, 12 Jun 2022
  
  Thank you RC2 for your great review of our paper. We have accepted and responded to all your comments, which have improved our manuscript. We addressed your general feedback by augmenting our background literature related to ungaged catchments (revised lines 91-93; 94-96). For specific comments, we clarified “singular streamflow metrics” (revised line 76) and “network of models” (revised lines 85-86). We added transferring hydrologic index (revised line 87). We specified that we used “conceptual” models and not just process-based (revised line 76-77; 415-416). We ensured all equations use proper mathematical notation required by HESS. We provided more detail on why we chose 1 cfs as the threshold for a dry stream (revised line 155). We enhanced our description of “weighted classical (metric) multidimensional scaling (revised lines 166-167) and clustering indices (revised lines 168-169). We clarified our statement that “calibrated parameters inherently have greater uncertainty than directly calculated parameters” (revised lines 196-198). We also clarified “reciprocating model accuracy” (revised lines 193-194). We completely redeveloped Figure 2, which was also requested by RC1. We clarified “successful” model calibration (revised lines 336-337), which was also requested by RC1. Finally, we added DOIs to citations.
  
  Citation: https://doi.org/10.5194/hess-2021-553-AC2

Stephen Adams et al.

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Short summary

Managing for environmental streamflows involve the practice of prioritizing healthy stream ecosystems while distributing water resources. Classifying similar streams is a useful step in developing environmental streamflows. Environmental streamflows are often needed on streams that must be modeled because they do not contain flow data. This paper has developed a new method of classification that prioritizes model accuracy. The new method advances environmental streamflow management.


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