Data about water quantity and water quality are fundamental to some of the most important decisions made by engineers and in choices made by societies. Abundance and quality of water are critical factors in many aspects of our economy, our environment, and our social and physical well-being. It is the case than multiple water resources objectives must be simultaneously managed. The costs of sub-optimal water resources choices can be substantial. Uncertainty is antagonistic to optimization.
There is little in the previous statements that won’t resonate with the experience of many people in the water resources industry. However, if I rearrange these statements in a sequence: “water is valuable; decisions control net valuation; data control decisions; data quality and decision quality are positively correlated,” then one might logically infer that data are inherently valuable and that trusted data are more valuable than untrusted data.
This conclusion, that trustworthy hydrometric data are valuable, is what I ‘believe’ to be true.
It is a case I have argued many times and in various ways in previous blog posts. It provides the rationale for incremental investment in quality management by the best hydrometric data producers in the world.
I am an empiricist. I would like to support my ‘belief’ in the value of data with knowledge that the value is ‘observable’ and hence quantifiable. Hydrometric data, and its trustworthiness, are not ‘normal’ commodities in the sense that the ‘true’ value can be readily observed by transactions in the marketplace. Economists can be quite clever in their ability to quantify value for all manner of assets and I am curious whether there has been any economic analysis on the incremental value of trustworthy hydrometric data.
How can a decision be justified to invest in increasing station density if there is no way of ‘knowing’ the value of the new source of data? How can a decision be justified to invest in telemetry if there is no way of ‘knowing’ the value of data timeliness? How can a decision for implementing redundancy be justified if there is no way of ‘knowing’ the value of data reliability? How can a decision for implementing a robust quality management framework be justified if there is no way of ‘knowing’ the value of data trustworthiness? Once any of these things are done is it not possible to retrospectively quantify the net benefit?
There is a relative abundance of literature on the concept of ‘expected value of information,’ which is an economic approach to resolving the relative benefit of investing in information prior to decision-making. This type of analysis seems like it would be useful for the task but I am unable to find any studies that would allow for a valuation of hydrometric ‘best practices.’
Many hydrologists are clearly uncomfortable with estimating the value of their data.
They can speak with clarity about the risks and consequences of either inadequate monitoring design or data that are missing, late, or inaccurate. They also understand the variability of water availability and of the risks and consequences of inadequate information about this variability.
They know what their data cost but not what it is worth. We can therefore measure that ‘better’ data will usually cost more but we are unable to measure that ‘better’ data are worth more.
Connecting value to data is not a skill in the hydrology domain. Assigning worth to an asset requires sophisticated economic analysis if the asset is a public good and not a freely traded commodity. Given the role of timely, reliable, and trustworthy data in resolving water conflicts and in the implementation of beneficial management to avoid hardship and conflict, one would expect that economists would have been working hard to ensure that investments in water data are properly valued.
This does not seem to be the case. Perhaps I am missing some important studies or investigations on this subject. If you know of any relevant research please contact me directly at email@example.com or reply to this post.