Results from the 2012 Report – Global Hydrological Monitoring Industry Trends published by Aquatic Informatics Inc.
The first Geostationary Operational Environmental Satellite (GOES) was launched in October 1975.
The GOES launch initiated a sequence of events leading to a major re-design of hydrometric programs throughout North America. The relatively cheap and reliable data communication provided by GOES provided an immediate benefit for hydrometric operators: to monitor station health and optimize timing for field trips.
The introduction of the first graphical web browser the mid 1990’s sparked a revolution in information sharing.
Advances in communications and data management technologies have allowed data providers to better meet the latent demand for hydrometric data in support of adaptive management of our water resources. Discharge is a derived variable for which considerable care is required to ensure reliable results. The hydrometric data production process has historically been managed on an annual production cycle to ensure that all information relevant to the final result has been obtained and reviewed. This meant that, until recently, even preliminary water level data were generally unavailable until well after any given event had passed. Even if available, such preliminary data would carry a high uncertainty because, without lengthy review process, there was no systematic way to identify and control for the many sources of potential error that could affect the data. Sources of error in hydrometric monitoring include bias, trends, transients and flat lines. Bias is a consistent additive error that is usually a result of a departure between the true values of the zero reference offset correction from the assumed value. Trends are departures between the true values of the water level time-series from the sensed values. Trends may be strictly a time-linear decay in sensor performance but may also include an amplification of error with magnitude. Transients are short duration excursions from the true value of the water level by the sensor, typically as a result of a recursive fault in the sensing system. Flat line errors are a result of insensitivity of the sensor to variability in the true water level; insensitivity can be a result of electro-mechanical malfunction of the sensing system or as a result of hydraulic isolation (e.g. plugged stilling well intakes). These sources of error must be addressed before water level information can reliably be used as an input for discharge derivation.
Today’s advanced data management systems have the ability to identify and treat most errors automatically, ensuring that the best possible data is readily available.
Hydrometric data are now widely available, in near-real-time, on the internet. Commercial hydrological data management systems, like AQUARIUS, automatically identify and treat most errors, allowing environmental agencies to produce timely and high quality data. Any further modifications to the data are immediately propagated as soon as errors are discovered and corrected. Automation changes the role of stream hydrographer from one of manipulating tens of thousands of points of data to one of managing a handful of rule-set parameters. These parameters become increasingly refined through the continuous learning provided by retrospective analysis and review. Real-time hydrometric information enables adaptive decision-making in all aspect of water resource management. Water resource managers can now monitor conditions continuously and intervene to prevent adverse consequences of water excess or insufficiency.
This is the topic that will be discussed in the upcoming hydrology webinar: 7 Ways to Quality Control Water Data in Real-Time on January 31st. My friend, Derek Forsbloom will share practical, tested, and refined controls that have been automated at Water Survey Canada to correct the continuous water data published from 2,500 gauging stations.
Derek Forsbloom shares how Water Survey of Canada systematically corrects continuous water data from 2,500 gauging stations. Learn how to elevate the quality of hydrometric data in real-time.