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Trinity River Integrated Information Management System

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Trinity River Integrated Information Management System Development Team: Trinity River Restoration Program (Andreas Krause) ESSA Technologies (Philip Bailey) – PowerPoint PPT presentation

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Title: Trinity River Integrated Information Management System


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
Western United States
Tributary
3
Existing Information Systems
Western United States
IIMS
Tributary
4
Decision Support
5
Data Types
6
Philosophy and Approach
7
Information flow
8
Database Structure
9
Database Structure
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
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14
Adult fish Module
15
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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
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21
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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
IT
Functionality
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
Tools and Advanced Options
Multiple Plot Types
Summary Statistics and Plot Options
Station and Variable Selection
Date Range Restriction
26
Time Series Analyst
27
Desktop Architecture
28
Data Web Portal Architecture
29
(No Transcript)
30
(No Transcript)
31
GIS Concept
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