OGC graphic.

WaterML 2.0 Part 2 – Ratings, Gaugings and Cross Sections

I am writing this over the Atlantic Ocean on my way home from the ratings, gaugings and cross sections intensive workshop in Reading, England.

This Open Geospatial Consortium (OGC) workshop marks the launch of a project to develop WaterML2.0 part 2.

In a concurrent meeting in Exeter, the OGC formally adopted WaterML2.0 part 1, the part that deals with time-series data, as an official international standard.

The use-cases for WaterML 2.0 part 1 are relatively self-evident. The ability to search for data based on geographic features (e.g. watersheds or rivers), discover the metadata attributes of the data, and access the data using a common inter-change format should yield huge efficiencies for any water management or water science objective. The use-cases for WaterML 2.0 part 2 require a more progressive outlook on the future of hydrometry.

Presently, the primary use-case for exchange of ratings is for time-sensitive requirements such as flood forecasting where the forecaster has independent access to the real-time water level information and has a requirement to transform the water level into discharge in advance of the discharge information being published conventionally. In this case, there is typically a hard-coded communication link with the data provider that does not require a global inter-change format. The potential for mis-interpretation of ratings is very high for unsophisticated users so, by far, the preferred method for dissemination of discharge information is for the discharge to be produced and published by the experts who are responsible for the results.

In spite of this I would argue there are three compelling arguments in favour of development and implementation of inter-operability standards for ratings, gaugings and cross sections.

  1. Fitness for purpose investigations: In the absence of objective measures of discharge uncertainty the best way to evaluate the potential for error is to examine the residuals of the rating for the period in question. I would personally never subject a discharge dataset to a hypothesis test without first looking at the distribution and scatter of rating observations around the curve. If ratings and gaugings were available to the research community a higher standard could be achieved in hydrological research. Peer review of research findings could evaluate whether the data are robust to the conclusions made from the data.
  2. Investigations into techniques for quantification of hydrometric uncertainty: Most current research into advancing the science for quantification of hydrometric uncertainty is based on the assumption that there are sufficient degrees of freedom to characterize the error distribution in each segment of the rating. This is almost never the case. Until such a time that researchers have access to large enough numbers of operational ratings and gaugings we will not be able to advance the science to yield a pragmatic solution for quantification of uncertainty.
  3. Fluvial geomorphologic research: the study of cross-sections and ready access the data needed to construct specific discharge plots should yield improved understanding of sediment and bedload transport at a regional scale. This, in turn, should yield advancements in the science of aquatic ecology.

The technological barriers to sharing of ratings gaugings and cross sections will soon be resolved.

The real challenges lie in the cultural barriers. There is little motivation for data providers to expose this information that may be subject to mis-interpretation, or re-interpretation, by unqualified critics. However we are at an impasse. We can’t develop robust techniques to objectively quantify uncertainty so that there will be no more need to share ratings for subjective evaluation of uncertainty until we start to share ratings, gaugings and cross sections.

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