1 Trinity River Integrated Information Management System Development Team: Trinity River Restoration Program (Andreas Krause) ESSA Technologies (Philip.

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Presentation transcript:

1 Trinity River Integrated Information Management System Development Team: Trinity River Restoration Program (Andreas Krause) ESSA Technologies (Philip Bailey) Utah State University (David Stevens)

2 Existing Information Systems Basin United Western United States Tributary SIAM USGS NWIS KRIS Isolated. Repetitive. Hero mentality. Poorly Documented. Isolated. Well Documented. Remote. Complex. Poorly coordinated. Online. High Level. Data Rich. Functionally Poor.

3 Existing Information Systems Basin United Western United States IIMS Tributary

4 Decision Support

5 Data Types

6 Philosophy and Approach Smolts produced / spawner Spawners km -1 X X X X X X X After Before

7 Information flow

8 Database Structure

9

10 Data Sets Current  Stream flow  Reservoir operations  Water Temperature  Sediment » Transport and PSA Bedload Suspended sediment  Adult Salmon Returns » Hatchery vs. natural » Species » Cohorts (age class)  GIS Standard Layers » Aerial Photos » Project Construction » Inundation mapping » Riparian Vegetation » Geomorphology Upcoming  Salmon Spawning Redds  Salmon Juvenile Out-migration  Cross sections » Transects » Pin history » Overlays e.g. W.U.A.  Sediment » Bulk samples » Pebble counts

11 Architecture

12 IIMS Desktop

13

14 Adult fish Module

15

16 Correlation Plots Multiple Time Series Plots

17 Time Series Analyst

18 Number of Observations for each variable Time Distribution of Observations for Each Variable

19 GeoIIMS Toolbar

20

21

22 Upcoming Features  Cross Section Module  Depth Related data  Profiles » Longitudinal » Depth  Model results  Photo Library  Flow Scheduler

23 Issues with large DB initiatives  User Experience » Data owners feel threatened  Raw data » Disparate formats » Expensive to assimilate  Technical » Interaction between developers and IT bureaucracy Data Functionality IT

24 Challenges  Agreeing on common database: » Naming / location » Functionality  Keeping it broadly applicable » Focus on frequent / high value data & function » Careful with automation  Information flow » Data stewardship » Developers / users / IT specialists

25 Time Series Analyst Multiple Plot Types Station and Variable Selection Date Range Restriction Summary Statistics and Plot Options Tools and Advanced Options

26 Time Series Analyst

27 Desktop Architecture

28 Data Web Portal Architecture

29

30

31 GIS Concept