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.  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 Overview

3.  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 Background

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 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-river- area.html http://www.lifeinlakehavasu.com/lower-colorado-river- area.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/)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/)http://www.cbrfc.noaa.gov/

9. Operations Forecast Gila River watershed from CBRFC website. Accessed July 2013 from CBRFC (http://www.cbrfc.noaa.gov/)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/cgi- bin/cgiwrap/zinger/basinStatus.cgi?gilariver)http://198.17.86.43/cgi- bin/cgiwrap/zinger/basinStatus.cgi?gilariver

11.  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 Visual Analytics

12.  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 Visual “Pipeline” Taken from Ward et. al., (2010). © 2010 by A.K. Peters, Ltd. All rights reserved. Reproduced here for educational purposes only.

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

14.  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 Goals and Objectives

15.  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 Proposed Methodology

16.  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/)http://www.wunderground.com/wundermap/ Proposed Mash-up

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

18. Proposed Mashup

19.  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) Project Timeline

20.  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 Summary

21.  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/)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, 1- 8.  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. References

22.  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).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. References (cont)

23. Questions ?