The document proposes developing a prototype mash-up using visual analytics concepts to enhance river operations forecasting for the Gila-Salt-Verde River System in Arizona. The goals are to provide a one-stop source for web-based forecasts and increase efficiency for operators. The methodology involves creating an interactive map with the Google Maps API that integrates stream gauge, dam and hydrologic data. Operators will test the prototype versus existing tools to evaluate if it reduces time to access forecast data. If successful, the concepts could be expanded to other river systems for improved emergency management and decision making.

1. The Gila-Salt-Verde River System:
Improving River Forecasts and
Emergency Management through
Visualization
Douglas Blatchford, PE
Pennsylvania State MGIS Program
Advisors: Dr. Miller, Dr. Reed, Dr. Kollat
Geography 596A, Summer 2013

2. Overview
 Background
 Colorado River/Gila River Systems
 Operation and emergency management forecast web apps in
the Phoenix metropolitan area
 Visual Analytics
 Goals and objectives
 Enhance existing web-based tools through visualization
 Proposed methodology
 Develop mash-up based on Google Map API
 Project timeline

3. Background
 Gila is a major tributary to the Colorado River System
 Flows through southern Arizona
 Dams provide water supply and flood protection for
Phoenix metro area
 Dams operated by Salt River Project (SRP) and the
United States Army Corp of Engineers (USACE)
 Operations forecast is key to managing water
resources

4. Colorado River Basin
-Colorado River supplies water to
municipalities and irrigators
throughout the West
-Dams along the river are operated to
meet water user demands in Arizona
California , Nevada and Mexico
-Accessed from the Colorado River Water Users Association
(CRWUA), July 2013 from
http://crwua.org/ColoradoRiver/RiverMap.aspx

5. Gila River Basin
-Gila-Salt-Verde River system, New
Mexico and Arizona as related to the
Colorado River
-Flow along the Gila at Yuma affects
major operations of Colorado River
and water levels in Lake Mead,
behind Hoover Dam
Accessed July 2013 from
http://www.lifeinlakehavasu.com/lower-colorado-riverarea.html

6. Facilities
Gila-Salt-Verde River system drains
through Phoenix metropolitan area.
SRP facilities provide water supply
and flood control protection.
Dams include :
-Gila:
-Painted Rock and Coolidge
-Salt:
-Stewart Mountain, Mormon Flat,
Horse Mesa and Roosevelt
-Verde:
-Bartlett and Horse shoe

7. Operations Forecast
The Colorado River Basin Forecast Center
(CBRFC) issues forecasts online for the
Gila-Salt-Verde River system as well as
other major rivers in the basin.
Accessed July 2013 from the CBRFC (http://www.cbrfc.noaa.gov/)

8. Operations Forecast
River operators and emergency
managers typically access existing
and future gage data.
Accessed July 2013 from
CBRFC (http://www.cbrfc.noaa.gov/)

9. Operations Forecast
Gila River watershed from CBRFC
website.
Accessed July 2013 from
CBRFC (http://www.cbrfc.noaa.gov/)

10. Operations Forecast
USACE provides a webpage with links
to Gila-Salt-Verde River system and
USGS gage sites.
-Provides easier access to existing data
-No forecast modeling like CBRFC
Accessed July 2013 from:
USACE (http://198.17.86.43/cgibin/cgiwrap/zinger/basinStatus.cgi?gilariver)

11. Visual Analytics
Visual analytics is an outgrowth of the fields of:
Information visualization
Scientific visualization
Analytical reasoning
Facilitated by interactive visual interfaces
Goal is to process data for analytic discourse
Constructive evaluation, correction, and rapid
development of processes and models
This ultimately improves our knowledge and decisions

12. Visual “Pipeline”
Ward (2009) describes a visual pipeline as:
The process of starting with data
Generating an image or model via computer
Sequence of stages studied in terms of
Algorithms
Data Structures
Coordinate systems
Taken from Ward et. al., (2010). © 2010 by A.K. Peters, Ltd. All rights reserved. Reproduced here for
educational purposes only.

13. Visual Pipeline
Data modeling
Data selection
Data to visual settings
Scene parameter setting (view transformations)
Rendering or generation of the visualization
Taken from Ward et. al., (2010). © 2010 by A.K. Peters, Ltd. All rights reserved. Reproduced here for
educational purposes only.

14. Goals and Objectives
 Develop a “one-stop” source of web-based forecasts
for the Gila-Salt-Verde system
 Implement web-based prototype and tool using the
concepts of visual analytics
 Increase SRP and USACE operational efficiency

15. Proposed Methodology
 Develop mash-up to be tested by operators at USACE
and SRP
 Use a Google Map API, with Fusion Tables
 Includes gage, dam and other hydrographic information
 Visual analysis will be tested by timing access to
information
 Questionnaire developed for operators, asking to
access gage data from the prototype or from the
CBRFC or USACE websites

16. Proposed Mash-up
Geographic extent:
Painted Rock Dam on the west to Coolidge on the east
Designed as a prototype specific to SRP/USACE
Further work will be necessary to offer more capabilities
Anticipated outcome: will take less time to access forecast
data
Existing mash-ups already use National Climatic Database
(NCDB) such as WunderMap
(http://www.wunderground.com/wundermap/)

17. Existing Products
Taken from Weather Underground (accessed July 2013 from
http://www.wunderground.com/wundermap)

18. Proposed Mashup

19. Project Timeline
August 14 to September 30 – Complete Mash-up
October 1 to October 23 – Complete testing and
capstone report
May 12-14, 2014 - Present paper at American Water
Resources Association Spring Specialty Conference in
GIS and Water Resources, Decision Support in Water
Resources, Salt Lake City (permission pending)

20. Summary
Propose to develop prototype Mash-up based on
Google API
The intent is to enhance river operations forecasting
along the Gila-Salt-Verde River System
Concepts of visual analytics will be used to develop
the prototype

21. References
Andrienko, G., Andrienko, N., Dykes, J., Kraak, M., & Schumann, H. (2010). GeoVA(t)-geospatial visual analytics: focus on time.
International Journal of Geographical Information Science, 24:10, 1453-1457.
Andrienko, G., Andrienko, N., Ursaka, D., Dransch, D., Jason, D., Fabrikant, S.I., Mikail, J., Kraak, M., Schumann, H. & Tominski, C.
(2010). Space, time, and visual analytics. International Journal of Geographical Information Science, 24:10, 1577-1600.
CBRFC (accessed July 2013 from http://www.cbrfc.noaa.gov/)
Kasprzyk, J., Nataraj, S., Reed, P.M., & Lempert, R.J. (2013). Many objective robust decision making for complex environmental
systems undergoing change. Environmental Modeling & Software, 42, 55-71.
Keim, D.A. (2002). Information visualization and visual data mining. IEEE transactions on visualization and computer graphics, 8:1, 18.
Keim, D., Andrienko, G., Fekete, J., Carsten, G., Kohlhammer, J., & Melancon, G. (2008). Visual analytics: definition, process, and
challenges. Information Visualization, LNCS 4950, 154-175.
Kollat, J. B., & Reed, P.M. (2006). Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater
monitoring design. Advances in Water Resources, 29, 792-807.
Kollat, J.B., & Reed, P. (2007). A framework for visually interactive decision-making and design using evolutionary multi-objective
optimization (VIDEO). Environmental Modeling & Software, 22, 1691-1704.
MacEachren, A.M., & Kraak, M. (1997). Exploratory cartographic visualization: advancing the agenda. Computers & Geosciences,
23:4, 335-343.

22. References (cont)
Reed, P.M., Hadka, D., Herman, J.D., Kasprzyk, J.R., & Kollat, J.B. (2013). Evolutionary multiobjective optimization in water
resources: the past, present, and future. Advances in Water Resources, 51, 438-456.
Tang, Y., Reed, P.M., & Kollat, J. B. (2007). Parallelization strategies for rapid and robust evolutionary multiobjective optimization
in water resources applications. Advances in Water Resources 30, 335-353.
USACE (accessed July 2013 from http://198.17.86.43/cgi-bin/cgiwrap/zinger/basinStatus.cgi?gilariver).
Ward, M., Grinstein, G., & Keim, D. (2010). Interactive data visualization: Foundations, techniques, and applications. Natick, MA: A K
Peters, Ltd.
Xie, X., & Zhang, D. (2010). Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter. Advances
in Water Resources, 33, 678-690.

23. Questions ?