The experience was not pleasant.
My perspective, coming from a hydrometric background, was quite different than the other two anonymous reviewers who, I would guess, probably have academic backgrounds. I strongly objected to the use of discharge data that were clearly unfit for purpose. Key conclusions depended on these data to separate hydrologic from hydraulic influences on stage response. My objections led to two major revisions of the paper while the other reviewers found the paper to be either acceptable, or at worst, only requiring minor revision.
I was unsuccessful in my attempt to force the authors to either disregard the disinformative data in making their conclusions or to provide a clear statement about the uncertainty of the data (hence the uncertainty of their conclusions). The circumstances that a) the journal has no policy on data quality and b) the other reviewers expressed little, or no, concern through two major re-writes of the manuscript were troubling for me. The end result of the review process was less bad than if I had not been a reviewer but nonetheless the paper could have been much better had the authors actually understood the uncertainties inherent in their data.
The standards for data quality of the hydrometric monitoring community are much higher than those of the academic community.
Modern monitoring technology is much easier to use and more foolproof than it used to be. Hardware vendors try to differentiate themselves in the marketplace by providing promotional material that makes it seem as if getting good data is as simple as buying the right technology. Buy some toys, stick them in the river, write your thesis.
If it were that easy there wouldn’t be a problem.
The hydrographer needs to choose the methods, techniques and technologies that will work best for any given combination of monitoring objective and local conditions. Then the work really starts. It is not as simple as connecting a power supply to the electronics. Unfortunately, graduate students are almost never given the training in hydrometric principles and practices needed to collect good data.
Data from even the most reputable agencies have limitations in terms of how the data should be interpreted. It is deeply disturbing that many academics accept such data as being the ‘truth’ rather than with the healthy skepticism that separates good science from junk science.
There are really two issues here, one is the ability to collect data fit for the purpose of scientific investigation and the other is evaluating the fitness of 3rd party data for use in scientific investigation.
Much of the hydrometric data in the public domain has been collected to meet a broad variety of societal needs. Collecting data with the precision required to reliably isolate hydrological processes is more expensive than collecting data with a lower precision. All monitoring agencies operate to the highest affordable standard. This optimization of the trade-off between affordability and uncertainty is fundamentally an exercise in risk management. The risk of an unknown researcher coming to a false conclusion does not carry much weight in a manager’s assessment of how much money should be spent on technology, training, field work and other direct expenses of the hydrometric program. Caveat emptor.
In our attempts to understand the black box that is hydrology we look at system inputs and system outputs and then make inference about the processes in between. We can readily explain the majority of the relation between inputs and outputs with some relatively simplistic descriptions of process. The effects of these processes are large with respect to the uncertainties inherent in most sources of hydrometric data.
The easy work has already been done.
Getting data with the precision and accuracy to expand our knowledge of hydrology will require much more care and attention to how the data are collected.
Improving the sophistication of our hydrological understanding requires data that are fit for purpose. For a researcher to a) not know what the uncertainty is in their data and b) not care what the uncertainties in the data are tells me that we, the hydrometric community, have to do a better job of explaining our craft to the academic community.
“Quality is never an accident; it is always the result of intelligent effort.” – John Ruskin
To learn more about how to create quality in hydrometric data please read:
The 5 Essential Elements of a Hydrological Monitoring Program
Best practices, standards, and technologies for hydrometric monitoring have changed. Learn how modern approaches improve the availability, reliability, and accuracy of water information.