the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Leveraging gauge networks and strategic discharge measurements to aid development of continuous streamflow records
Michael J. Vlah
Matthew R. V. Ross
Spencer Rhea
Emily S. Bernhardt
Abstract. Streamflow, or discharge, is an essential measure in the study of rivers and streams. However, quantifying continuous discharge can be difficult, especially for nascent monitoring efforts, due to the challenges of establishing gauging locations, sensor protocols, and installations. Here, we investigate the potential for both simple and complex models to accurately estimate continuous discharge (at least daily estimates), using only discrete manual measurements of streamflow. We were inspired to do this work because some continuous discharge series generated by the National Ecological Observatory Network (NEON) during its pre- and early-operational phases (2015–present) are marked by anomalous data due to sensor drift, gauge movement, and incomplete rating curves. Using field-measured discharge as truth, we reconstructed continuous discharge for all 27 NEON stream gauges over this period via linear regression on nearby donor gauges and/or prediction from neural networks trained on a large corpus of established gauge data. Top reconstructions achieved median efficiencies of 0.83 (Nash-Sutcliffe, or NSE) and 0.81 (Kling-Gupta, or KGE) across all sites, and improved KGE at 11 sites versus published data. Estimates from this analysis inform ~199 site-months of missing data in the official record, and can be used jointly with NEON data to enhance the descriptive and predictive value of NEON’s stream data products. We provide 5-minute composite discharge series for each site that combine the best estimates across modeling approaches and NEON’s published data.
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Michael J. Vlah et al.
Status: final response (author comments only)
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RC1: 'Comment on egusphere-2023-1178', Roy Sando, 16 Aug 2023
General comments –
This manuscript documents work to use a “donor-gauge” concept to predict continuous daily streamflow discharge time series by developing OLS equations using a limited number of discrete, manual discharge measurements coupled with overlapping timeseries of streamflow observed at one or more nearby donor gauges. Additionally, the manuscript describes results from training multiple Neural Network models to fill in gaps or improve data that are inaccurate training on basin characteristics or basin characteristics and a process-based model, and finetuned on partial streamflow records meeting certain quality criteria.
Overall, this work is important in that it shows the feasibility of creating synthetic time series of streamflow discharge using only discrete manual measurements as long as there is at least one nearby continuous streamgage. The paper is very well written, and the analyses are thorough and well described. I believe the paper could benefit from some reorganization and clarifying text.
I suggest that the results and discussion be separated into two separate sections. There is much to discuss from this work. I added comments for specific text that I thought could go into the discussion section.
The LSTMs, while thoroughly and expertly developed, are limited by a lack of transferability. This is ok, given that the primary contribution of this paper is to show that continuous streamflow discharge can be predicted at high temporal resolution by developing simple models using a limited number of concurrent measurements. That said, a lot of value could be added by holding out data at the NEON sites and showing the performance of the models on the holdout data. I understand the authors’ point that this approach is not always optimal for producing the best predictions, but it is quite useful for understanding the performance of the model at ungauged sites. I don’t think this analysis is required for the work to published, but I would suggest at least adding this into a subsection of the discussion that discusses future work or ideas.
In my opinion, the paper is a bit misleading in that both the simple (OLS) and complex (LSTMs) models are put in the context of leveraging the donor gauge information to predict at “virtual gage” sites (for example, line 26 of the abstract). However, the LSTMs are really only being used to essentially correct the NEON time series and fill gaps. I don’t think this is a big deal, as the work and results are still important, but I do think that there are some points in the manuscript that could be written more clearly. I address these in the specific comments below.
Specific comments –
Line 26-27. If by “simple and complex” you mean OLS and LSTM-RNN, I don’t think this statement is entirely true. If I understood the paper correctly, weren’t the discrete manual measurements only used in the development of the OLS models? It was my understanding that the NNs were trained on the continuous timeseries data published as part of the NEON data that met quality assurance criteria.
Line 89. I’m having a hard time following the sentence that begins with “Where co-location…”. I assume the author is referring to co-location of the study (or site of interest), but because the previous paragraph is discussing gauge placement, I could also see this referring to co-location of low-cost monitoring equipment. Depending on which meaning the authors intend, I think the implications and direction of this paragraph are changed. I suggest adding a bit of text to clarify. Possible rewrite:
“Where co-location of the site of interest with an existing stream gauge…” or “Where co-location of low-cost, rapid-deployment monitoring gauges with an existing stream gauge…”
Line 110. “wide range of flows” is subject to interpretation. I think it would be helpful if the authors provided more information on what is meant by “wide range of flows”. I think this information would also provide valuable insight into how transferrable this method is to other locations. If laid out thoroughly, I think this could allow readers to have a greater understanding of how well sampled a location needs to be to use the methods described in this manuscript to reconstruct or predict streamflows. For example, how important is it that the flows used to develop the equations or train the models captures the annual extremes? How does predictive performance change as a function of the relative distribution of flows represented by the training data? At the very least, I think some text is required to discuss the implications of the representativeness of the training data.
Line 150. Figure 1. The resolution of this figure is poor. I suggest replacing this figure with a higher resolution figure. I also think that this figure would benefit greatly from having the NEON sites labeled to give the reader some geographical context.
Lines 164-173. Suggest moving this text up into a new section titled “2.3 Donor-gauge Selection” as it is a crucial component of the analysis and risks being overlooked by including it in the section on model selection.
Lines 169-170.
- Are the three criteria provided in parentheses the only criteria used to determine donor-gauge candidacy? If so, suggest removing the “e.g.”, as you are not providing examples of criteria, but the exhaustive list of criteria. If so, I suggest listing all the criteria used and not just providing examples.
- For the criteria used in determining geographic similarity: what data and methods were used to determine whether a site was in an urban area or downstream from a reservoir?
Additionally, see the technical comment for lines 167-170 provided below.
Line 268. Authors state that the models were “trained, validated, and tested”, but the methods only describe training and validation. Were the LSTMs test on holdout data? If not, suggest removing “tested”.
Lines 362-365. Great that the authors noted the limitations of the evaluation metrics due to potentially limited representation of flow in the evaluation data. If possible, providing more information (beyond the statement on disproportionate representation of low flows) would be very helpful to the reader in understanding where there might be more or less uncertainty in the model predictions.
Line 374. Figure 2. Do the bars show the mean KGE of the 27 sites? The figure caption does not state what the value represented by the bars is. Please include that in the caption.
Line 385. Figure 3. This is a great figure! A great presentation of a lot of info. Adding an x-axis showing time, even if the interval is in years, would add a lot of value. Additionally, if the stations could be ordered in the same manner as they are on the x-axis of Figure 2, it would help in the assessment of performance at individual sites and add to the flow of the paper.
Lines 419-426. This text could go into a discussion section.
Lines 436-441. This text could go into a discussion section.
Lines 455-474. This text could go into a discussion section.
Line 515. Figure A1. Do the bars show the mean KGE of the 27 sites? The figure caption does not state what the value represented by the bars is. Please include that in the caption.
Technical comments –
Line 99. “site location selection” is redundant. Suggest changing to “site-selection issues”.
Line 100. Suggest changing “high-quality” to “higher quality”.
Line 105. Suggest removing the comma after “series,”
Line 108. Change “virtual gauges.” to “virtual gauges” by removing the period.
Lines 167-170. For conciseness and clarity, suggest the following rewrite: “For each target site, up to four donor gauge candidates were selected based on spatial proximity (within 50km) and geographic similarity (not in an urban area; not downstream of a reservoir). Generally, no more than four gauges met these criteria, but for one target site (MCRA) there were 10 nearby candidate gauges to select from, all of which were associated with the Andrews Experimental Forest in western Oregon, USA.”
Lines 172-173. Suggested rewrite: “…chose three candidate sites representing A) a catchment upstream of the target site (GSWS08), B) downstream of the target site on the MCRA mainstem (GSLOOK), and C) downstream on a tributary of MCRA (GSWS01).”
Line 198. Change “glmnet” to “Ridge-regression”.
Line 455. Change “low flow conditions” to “low-flow conditions”
Citation: https://doi.org/10.5194/egusphere-2023-1178-RC1 -
RC2: 'Comment on egusphere-2023-1178', Anonymous Referee #2, 26 Aug 2023
This paper describes an effort to reconstruct and correct streamflow data series from NEON (National Ecological Observatory Network) stream gaging sites, based on limited field measurements at the NEON sites and information from nearby stream gages.
For anyone who has attempted to use NEON streamflow data, this contribution is a gift. The methods are clearly described, code is provided, and the resulting datasets are made available. Thanks to the authors for their efforts.
Comments:
- Abstract: please describe how the linear models performed compared to the LSTM models, and why.
- A number of datasets and models are used in the analysis. It would be helpful to provide a table of these and their hyperlinks with a description of each, in the Appendix. For example, CAMELS, NHM, etc. And in addition, can you provide links to the “donor” datasets that you used? Perhaps this is provided and I missed it.
- Results/Discussion. A number of products including code, model results, and datasets are produced. It would be helpful to provide a table of the results created by the analysis and their hyperlinks with a description of each, in the text.
- Results/Discussion. I could not find metadata in the materials provided at the hyperlinks. Please provide measurement units (are these unit area flows in mm?) and other metadata for the composite series linked at https://figshare.com/articles/dataset/Composite_discharge_series_for_each_NEON_stream_river_site_/23206592
- Results/discussion (l. 390-400) and conclusions: (l. 495-500). Please provide a slightly expanded explanation for why the LSTM models performed considerably less well than the linear models. It appears that it was not the model structures themselves but their data requirements that limited the performance of the LSTM models relative to the regression models. Can you help readers better understand these limitations more generally? Were watershed characteristics or other data shown in Table 2 relevant to the performance of the linear vs. LSTEM models? How did the differences in drainage areas of the CAMELS data set relative to these watersheds (l. 25-251) influence model performance?
- Results/discussion. For each site, could you summarize the record lengths over which you were able to reconstruct the 5-minute data vs. developing a daily estimate? And if the daily estimates were based on the LSTM models, which have a median NSE of 0.32 to 0.47, why are the confidence intervals for daily discharge in the resulting composite data series so narrow?
- Conclusions. Do you have any recommendations for NEON to improve their stream gaging procedures, data collection, and information management processes? Will it be necessary for users of NEON data to use your code in the future?
Citation: https://doi.org/10.5194/egusphere-2023-1178-RC2 -
CC1: 'Comment on egusphere-2023-1178', Nick Harrison, 30 Aug 2023
The manuscript “Leveraging gauge networks and strategic discharge measurements to aid development of continuous streamflow records” uses data from the National Ecological Observatory Network (NEON) to develop models for predicting continuous streamflow from discrete measurements. The authors also provide a dataset that combines published NEON data with gap-filled and corrected data derived from their models. Both the manuscript and dataset will be extremely useful for NEON data users and their process may be useful for other researchers attempting to build continuous streamflow records at other sites. We have some substantive and editorial comments outlined below that will strengthen the manuscript and increase the reproducibility of their analyses.
Substantive comments:
Abstract:
The authors do an excellent job of summarizing the challenges to developing continuous streamflow records and highlighting the importance of overcoming these challenges for the research community. The abstract is a bit narrowly focused on the implications of their analyses for the NEON dataset when they may be useful for a broader group of researchers.
Methods Section:
Section 2.1: Based on the text in the Methods section, it appears that the authors applied the tier classifications from the outputs produced by the analysis in Rhea et al. (2023). That manuscript used a combination of RELEASE-2022 and Provisional NEON data available at that time. For best alignment, we suggest that the authors apply the classification framework from Rhea et al. (2023) to the RELEASE-2023 and provisional NEON data used in this manuscript. These new outputs should be included in the archived dataset associated with this manuscript for completeness. If this is already done, the authors should update the Methods section with additional detail to make this clear. Also, any Provisional data used in the analysis should be archived, if possible, by the authors to maintain reproducibility.
Section 2.5: The authors should provide citations and/or a rationale for the NSE and KGE benchmarks that they apply to the various datasets since those are different for each model. Some potential examples include Knoben et al. (2019), Moriasi et al. (2015), Golmohammadi et al. (2014), and Chiew and McMahon (1993).
Results and Discussion Section:
Line 455: While practical considerations such as site access, timing, and safety do indeed limit the collection of higher-flow discharge measurements, low-to-baseflow conditions are typically present throughout the majority of annual water year, particularly in smaller stream systems. Stream monitoring networks that include small streams with unstable low-flow hydrologic controls must balance the accurate characterization of the low to mid-flow regime with measuring discharge during high-flow events, which are relatively infrequent. Streams with unstable low-flow hydraulic controls (e.g., those with unconsolidated bed material) exhibit highly variable stage-discharge relationships, both within a given water year and across longer timescales. As such, multiple gaugings are required each year in order to accurately characterize the stage-discharge relationship in the low-to-middle segments of the rating curve. In contrast, high-flow gaugings can often be used in rating curves over longer periods of time due to the relative stability of high-flow controls (e.g., banks, valley walls, etc.).
The Discussion section focused a lot on NEON, without much discussion of how the methods could be applied more generally. This was an important point brought up in the Introduction, but it was not extensively revisited in the Discussion. Limiting most of how the results apply specifically to NEON to the Results section, and discussing how the methodology could be applied to un-gaged watersheds or watersheds with incomplete records more generally, would likely increase the amount of interested readers beyond just those who are using NEON data.
Editorial comments (primarily about citing NEON data to improve reproducibility):
Throughout the text of the paper: it is not clear from the citations whether NEON provisional data (see definition at https://www.neonscience.org/data-samples/data-management/data-revisions-releases) were used in assessment of the linear regression and LSTM models (e.g. lines 116-117). Please clarify in the text and citations in terms of which data were used in which part of the analysis.
Line 106: The readme for the macrosheds repository linked here reports an incorrect data product ID (reports DP4.00230.001, should be DP4.00130.001).
Line 117: Separate citations are needed for released and provisional datasets of the Discharge field collection data product (DP1.20048.001). These are two separate datasets. See https://www.neonscience.org/data-samples/guidelines-policies/citing for NEON citation guidelines.
Lines 118, 126: The date the data were accessed should be in the references entry, not the inline citation, unless specifically requested by the journal. Regardless, the accessed date should be included in the references on lines 671 and 669, respectively. See https://www.neonscience.org/data-samples/guidelines-policies/citing for NEON citation guidelines.
Lines 669-671: NEON data citations need to be updated to match the NEON citation guidelines detailed in https://www.neonscience.org/data-samples/guidelines-policies/citing.
Line 326: Please change ‘2023-release’ to ‘RELEASE-2023’ to be consistent with NEON terminology.
Line 509: Please change ‘DP4.00230.001’ to ‘DP4.00130.001’ as the incorrect data product ID is currently being used.
Line 579: In the acknowledgements section, please include the standard NEON data acknowledgement (see https://www.neonscience.org/data-samples/guidelines-policies/citing).
Citation: https://doi.org/10.5194/egusphere-2023-1178-CC1
Michael J. Vlah et al.
Data sets
Composite discharge series for all NEON river/stream sites, plus figures and all input/output data associated with Vlah, Ross, Rhea, Bernhardt. 2023. "Virtual gauges: the surprising potential to reconstruct continuous streamflow from strategic measurements" Michael J. Vlah, Matthew R. V. Ross, Spencer Rhea, Emily S. Bernhardt https://doi.org/10.6084/m9.figshare.c.6488065.v1
Model code and software
vlahm/neon_q_sim: Preprint phase Michael J. Vlah https://zenodo.org/record/7976251
Michael J. Vlah et al.
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