Integration of Distributed Streamflow Measurement Metadata for Improved Water Resource Decision-Making
Round 1
Reviewer 1 Report
The paper reports on efforts to fill in streamflow data gaps in the Pacific Northwest using stakeholder engagement to identify data sources and explore challenges. The problem addressed is old and largely intractable, although advances in the technologies involved in data handling have made inroads. The paper canvasses the challenges and points to solutions. It is very well written, clear and potentially useful. The description of the methodology is sketchy but probably sufficient for the purposes of the paper.
Author Response
We have added multiple paragraphs to add details to the methods and a workflow figure.
Reviewer 2 Report
Document Attached
Comments for author File: 
Comments.docx
Author Response
Thank you for your comments, we have addressed them in the attached file.
Author Response File: Author Response.docx
Reviewer 3 Report
Although this is not a research article and mostly similar to a short report that explains the streamflow stations in the US, its contribution to the science is valuable where different sources of data are given which help researches to find good quality streamflow data for their researches. Manuscript can be published after addressing following comment:
- Please add a section describing about the streamflow measurement techniques. It would be quite helpful.
Author Response
We added various points in the discussion highlighting that this information was rarely provided, and when it was provided it was not detailed enough for us to report additional details there. For example, the only information provided was generally just “stage-discharge”, with no details on those specific methods.
“Very few of the data providers included information about their streamflow measurement techniques. Several organizations operating the largest networks provide data collection protocols through their online data portals [25].(L282,285)”
“Metadata about streamflow measurement methods are important for characterizing uncertainty [29,30] and confirming the applicability of data for its intended purpose. Streamflow data collection methods were reported for only 8% of the continuously monitored gage locations. (L305-308)”
