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Click here to read Part 1 - background about measurement accuracy and error, definitions and more This series of discussions are to first give you a scientific picture of hydrological measurement errors and then open the interesting discussion of how to automatically detect, validate and correct erroneous sensor data given the observations from Data Acquisition System (DAS) and field visit information. Let’s now have a closer look...

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Ed Quilty looking through a water glass.

September 23, 2011 - Article by Stephen Hui, web and technology editor for the Georgia Straight Edward Quilty is the founder, president, and CEO of Vancouver-based Aquatic Informatics. Edward Quilty says his Vancouver-based company is the “best in the world at determining how much water is in a river”. The 39-year-old West Vancouver resident is the founder, president, and CEO of Aquatic Informatics. Aquatic Informatics is the company...

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Environmental agencies and organizations invest huge amount of money to build the required hardware and software infrastructure for collecting and storing data from field sensors in order to extract valuable information hidden in the time series numbers about the environment. If the sensor measurements could not accurately represent the environmental parameter of interest, the extracted information will be misleading and making decisions based upon false...

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Numbers on a red wall.

Advances in communications and data management technologies have allowed data providers to 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...

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Congo River.

High quality data are accurate, timely, meaningful and complete. Fitness-for-purpose is achieved if the stated or implied needs, or expectations, of the end-user of the data are met. The design of a quality management system starts with specification of end-user needs and expectations. These expectations are inter-dependent. Consider the situational irony of the sign stating: “Our data are timely, affordable and accurate – pick any two out...

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Black and White photo of Scientist Collecting Data.

This blog forum is your opportunity to voice your opinions about what can, and should be, done now to build toward a desired future for hydrometric data and water resource decision-making. As a regular contributor I personally invite an interactive exchange of knowledge and promotion of ideas on how to advance the science and practice to hydrometry. With this blog forum, we can identify opportunities...

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