WATERs: Demonstration and Development of a Test-bed Digital Observatory for the

1. Page 1 WATERs: Demonstration and Development of a Test-bed Digital Observatory for the Susquehanna River Basin and Chesapeake Bay Michael Piasecki Drexel University Bill Ball Johns Hopkins University Pat Reed Penn State University Kevin Dressler Penn State University WATERs Workshop Austin, TX November 15, 2006

2. Page 4 Project Objectives • deploy HIS WorkGroup level Observations Database Model and ancillary packages like access portal, data loader, HIS Analyst, and hydro-objects library (Drexel U.) • identify, collect, extract, and prepare Susquehanna River and Chesapeake Bay data for ODM population (JHU for CB data, and PSU for SRB data) • perform a Gap Analysis of available data that would seek to identify the data requirements to support the scientific questions (PSU, JHU, Drexel U.) • use SPARROW (SPAtially Referenced Regressions On Watershed attributes) to identify "hot zones" in terms of nutrient contributions and/or their associated uncertainty. This information is then used to conceptualize where we should locate stream reach arrays (Figure 4). Issues that would be addressed include: network design for basin-scale stream reach arrays, (PSU) • provide direct link SPARROW and PIHM to ODM using HydroObjects library or webservices. Need to develop these specifically as they do not yet exist. (Drexel U.) • test metadata framework for ODM: can the metadata be supplied? How easy is it? Is the data loader a reasonable tool? Do we need additional metadata based on the number of data sources we have? (Drexel U., JHU, PSU) • Test functionality of cybercollaboratory for project communication, document repository, and provide feedback to development team (Drexel U., JHU, PSU)

3. Page 2 Susquehanna Watershed and Chesapeake Bay • The drainage basin for the Chesapeake Bay extends over 6 states (MD, VA, PA, NY, WVA, DE) and covers 166,000 km2 (65,000 mi2). • excessive nutrient influx causes algal blooms in CB subsequent hypoxic water cause shell fish decline seagrass dieback, adverse affect for bio diversity • Contributes 75% of nutrient fluxes to CB main stem • Nitrogen mostly in dissolved form 60% Agriculture contributions, 30% atmospheric • Phosphorus mostly in particulate form, associated with erosion and sediment fluxes, approx 2000 tons yearly trapped in 3 largest reservoirs. • Low load from upland forests, => disproportionate contribution from lower Susquehanna Basin • sources and fates of nutrients are poorly understood because of limited knowledge of fluxes, stores, and residence times of water

4. Page 9 Example Bay Region Model Spatial Outputs Relation of predicted and observed total nitrogen load after SPARROW (Preston and Brakebill, 1999) Example of delivered nitrogen yield in 1987, using the SPARROW Model (Preston and Brakebill, 1999)

5. Page 10 Spatial Network Design Tradeoffs Apply visualization and gap analysis tools to SPARROW Model spatial output -- Target future monitoring locations -- Reduce model uncertainty

6. Hand collection, preparation, and manual feed into applic. SPARROW_webservices NAWQA Input file Automatic ingestion NWIS WorkGroup HIS NARR Regional Subsets HODM Page 11 SPARROW & ODM …populate SPARROW PIHM

7. Metadata specifications Controlled vocabulary Metadata (ISO) about dataset Xkeyword = Stage Height thesaurusName = GCMD FGDC GCMD Mapper Metadata (FGDC) about dataset YTheme_Keyword = Gage Height Theme_Keyword_Thesaurus= USGS Mapper ISO USGS Page 12 Metadata Development and Semantic Mediation Need to resolve metadata heterogeneity by mapping vocabulary & metadata e.g. search for stage height Need to find all “hits” in data sets X and Y Metadata repository

8. Page 13 Questions? Contact:Michael Piasecki Michael.Piasecki@drexel.edu Bill Ball bball@jhu.edu Pat Reed preed@engr.psu.edu Kevin Dressler kxd11@psu.edu Project Website:soon to come …..

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11. Page 2 Drexel University, College of Engineering