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Water Quality

Water Quality. First Day Report Amanda Vizedom, Josh Lieberman , Chuck Vardeman, Stan Skrobialowski, Kai Liu. Models, Ontologies and Data Reuse. HydroFeatures (OGC paper) WaterML OM Ontology Nwis:   http://waterdata.usgs.gov/nwis/qw

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Water Quality

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  1. Water Quality First Day Report Amanda Vizedom, Josh Lieberman , Chuck Vardeman, Stan Skrobialowski, Kai Liu

  2. Models, Ontologies and Data Reuse • HydroFeatures (OGC paper) • WaterML • OM Ontology • Nwis:  http://waterdata.usgs.gov/nwis/qw • Many sources identified with their limitations and relevance • Preliminary conclusion is that the conceptual models are not explicitly documented. Confusing taxonomies • What is a nutrient? For what? • Sample analysis includes varying categories/sub-categories and different data sets use different categories.

  3. Example of problems in the data • Lots of things grouped in Information category. • Really many are chemical constituents

  4. Initial Scenario Idea Goal: Gen some water quality data and enough about it to enable proper use & avoid misinterpretation • People have point measurements and want to construct a property of a larger “feature” from that data. • Ex. Lake measurements are grounding data • Have 4 measures and want to determine the lake salinity. • From a sample hydrologic feature (point), can I get to a hydrologic feature of interest?  (And if so, how?) • Suggests an ODP idea.

  5. Let’s do a Measurement Pattern • Model relation of sample measurements to properties of a larger entity. • We could connect this to hydrological features.

  6. Older Work Approach • Choose hydrologic features that are of particular interest (understood: for some purpose)  • Example is Quabbin Reservoir (water for Boston) • Resident and water quality authority each wants to know about their water • Form DB can get sample data for 3 points- surface, 20 and 80 ft. but only for 1984.

  7. Issues • Much of this already done in work but needs to read and incorporated • Samples vs aggregate • sample and everything you need to know to understand what it means • where taken • smallest granularity loc • feature(s) of interest • when taken • method of measurement • instruments • duration? • location  • sensitivity • conditions of measurement • flow (rate, direction) • Quality characteristics that are measured • Units of measure • tolerance?

  8. Missing Piece • Relationship between sample characteristics to feature of interest characteristics • e.g., using sample data to characterize a stream reach • What are the relationships? • What is the scope of the feature • Set them up for expert commentary on the relationships between them.

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