Meeting 03/24/2017 Short Overview UH-DAIS Lab Research

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Presentation transcript:

Meeting 03/24/2017 Short Overview UH-DAIS Lab Research Waterlevel Prediction and Flood Early Warning Systems Flood Risk Mapping (in collaboration with UT Center for Research in Water Resources) Expert System for Flood Risk Mapping Flooding Funding Opportunities NSF Planning Proposal Flooding People 8. March 16-20 Virginia Travel

Predicting, Mapping, and Understanding Flooding Research Tasks: Predict Water Levels Flood Risk Mapping Early Warning System Understand Flooding People: Christariny Hutapea, Yue Cao, Chong Wang, and Yongli Zhang. http://www.harriscountyfws.org/ UH-DAIS

2. A Generic DAG-Based Chaining Approach for WLP R(t),R(t-1),…(Rainfall) W(t), W(t-1),…(Water-level) V(t),V(t-1) (Stream Velocity) S(t), S(t-1)… (Soil Moisture) D(t), D(t-1),…(Discharge) Raw Data DAG of Measuring Points Prediction Scenario Mapping Tool currently under development at UH Model Execution Framework Data Sets (one for each Measuring Point) Single Target Prediction System uses f2 f1 f3 f4 DAG Models (one for each Measuring Point) off the shelf

Training Challenges for Data Driven WLP Approaches Need to extract historic water level, discharge(?? https://water.usgs.gov/edu/streamflow2.html ) and rainfall data from USGS National Water Information System, Austin Flood Early Warning System, HydroMet, HCFCD EWS, Seattle… Extract historical soil moisture data from NASA NALDS !?! Creating stream velocity data: use USGS, use the National Water Model predictions to create velocity data in training sets,…?!? Remark: Creating a Challenging Water Level Prediction Benchmark to Test and Compare Various WLP Systems is important! W3,t=f3( W1,t, W3,t-1, R3,t, V3,t-1, S3,t-1) Prediction Scenario f2 f1 f3 f4 DAG UH-DAIS

Using Learned Models to Make Water Level Predictions Rain Forecast has to be fed into the system; we do not plan to develop a rain forecasting system… Soil Moisture has also be fed into the system or alternatively be predicted; there might be some useful work at NASA SMAP Stream Velocity has also be fed into the system or alternatively predicted Those data have to be available in real-time or “almost real-time” W3,t=f3( W1,t, W3,t-1, R3,t, V3,t-1, S3,t-1) Prediction Scenario f2 f1 f3 f4 DAG UH-DAIS

Model Fusion and Meta Learning USGS Models (http://water.usgs.gov/nrp/software.php) What model works best under which circumstances? Model Fusion and Meta-Learning Data-driven Models for the Watersheds NOAA Models (http://water.noaa.gov/about/nwm ) Our Project UH-DAIS

3. Automated Flood Risk Mapping Austin Fire First Responder Vehicle Flood Risk Map Creating these maps is a lot work and requires GIS and hydrology knowledge.

Flood Risk Mapping Wharton County, Texas http://abc13.com/news/wharton-mayor-calls-for-voluntary-evacuation/744958/ Wharton County Fire Chief Wharton County April 2016 Flooding

Height Above Nearest Drainage (HAND) developed by: UT Austin Center for Research in Water Resources Flood HAND Normal

Texas Hand Map (7,600,000 Address Points) 237 counties mapped 16 CSEC counties geocoded 1 non-CSEC county geocoded

Hand-based Flood Risk Assessment Austin Metropolitan Area (Williamson, Travis, and Hays County)

Automated Flood Risk Mapping

Polygon Analysis for Automated Flood Risk Mapping FEMA Warning Zones Find Correspondence Find Agreement, Combine, … Hand Value Multi-Contour Maps Austin Fire First Response Vehicle Flood Risk Map UH-DAIS

Analyzing Different Polygonal Data that Describe Levels of Flood Risks Benefits and Objectives: Flood Risk Map Validation Flood Risk Map Standardization Creating Flood Risk Maps Automatically Mapping Agreement/Disagreement with Respect to Different Flood Risk Models. Understanding the weakness/strength of particular methods Creating “combined” flood risk models Dynamic Flood Risk Maps that adapt their appearance based on contextual information UH-DAIS

4. Expert Systems for Flood Risk Mapping Local Knowledge Expert System creates Flood Risk Mapping Knowledge Base

5. Flooding Funding Oppurtunities TWDB funded 17 projects most of which develop flood early warning systems, but there are other funding sources. NSF and other funding agencies provide funding opportunities for flooding related research; funding amounts range from $100,000 to $1,500,000. Development activities, such as those described earlier, can be funded and the City of Austin Flood Early Warning System and the Austin Fire Department could participate as stakeholders in these projects, and hopefully benefit from project results. Proposals usually require a social scientist to be on the proposal to investigate social, behavioral or HCI aspects of the product developed in this research. For example, on Feb. 16, 2017 Dr. Eick and S. Camille Peres (Texas A&M University) submitted NSF Planning proposal for $100,000 titled “Design of a Generic Water Level Prediction and Flood Early Warning System” to NSF 16-610 UH-DAIS

6. NSF Planning Proposal (Eick&Peres) Design of a Generic Water Level Prediction and Flood Early Warning System Overview: Flood is the most hazardous natural disasters in the world …Consequently, many US cities developed sensor based early warning systems that collect water level and other data in real-time…One major research goal of the proposal is the development of a flood early warning system that provides the capability to predict future water levels. To accomplish this goal novel data-driven water level prediction techniques that interpolate the past into the future will be investigated, compared and fused with classical hydrological models. However, for a flood early warning system to be useful, it is critical to ensure that the needs of the stakeholders interacting with such a system are considered in the system design. Investigating the behavioral aspects and human-computer interaction aspects of stakeholders using flood warning systems is the second major research goal of this proposal. To accomplish these two goals, we plan to assemble a strong research team that consists of surface hydrologists and members of organizations, such as NOAA, that develop water level prediction models and representatives of flood control districts and emergency management departments of several US cities. UH-DAIS

7. Flooding People: Collaborators S. Camille Peres (Texas A&M Univesity)---Behavioral and HCI aspects of flood warning systems: https://sph.tamhsc.edu/eoh/faculty/peres.html Jian Chen (University of Louisiana at Lafayette)---Hydrology models and their use for flood prediction, GIS: https://www.researchgate.net/profile/Jian_Chen20 Marian Muste (University of Iowa)---instrumentation, maybe visualization and web-platforms for early warning systems: http://www.iihr.uiowa.edu/mmuste/ Bruce Race (College of Architecture, University of Houston)---community resilience; architectural aspects of flooding: https://aiahouston.org/v/member-detail/Bruce-Race/slak7/ David Maidment (Director, UT Center for Research for in Water Resources): National Water Model, Flood Inundation Mapping, Flood Risk Assessment,… http://www.ce.utexas.edu/prof/maidment/gishydro/maidment/maidment.html David Arctur ((Research Scientist, UT Center for Research for in Water Resources) Time Whiteaker (Research Scientist, UT Center for Research for in Water Resources) Harry Evans (Former Austin Fire Chief, UT Center for Research for in Water Resources) Xing Zheng (PhD Student, UT Center for Research for in Water Resources) Christine Thies (Austin Fire Department): Head of the GIS Department, Liaison to Fire Fighters Jorge Urquidi (Austin Flood Early Warning System): Flood Risk Map Generation Maybe, Will Merrell (Houston-Galveston Area Research Council) Will.Merrell@h-gac.com: Environmental Planner, planning aspects of flooding UH-DAIS

More Flooding People (Houston-based) Gary Bezemek is a Precinct Coordinator for the Harris County Flood Control District.  Mr. Bezemek has been with the Flood Control District since April 2004, and is a professional engineer licensed to practice engineering in the state of Texas. In his current capacity, Mr. Bezemek is the liaison between Precinct 4 and the Flood Control District. Prior to assuming the Precinct Coordinator roll, Mr. Bezemek was the program manager for the Flood Control District’s Watershed Master Plan Studies program. Prior to coming to the District, Mr. Bezemek worked for more than 9 years in the private sector as a consulting engineer for two local Harris County engineering firms. Mr. Bezemek holds a Bachelor of Science in Civil Engineering degree from the University of Texas and a Master of Science in Civil Engineering degree from Stanford University. Michael F. Bloom, Manager, Sustainability Practice at R. G. Miller Engineers, Inc., Steering Committee Member at Houston Land and Water Sustainability Forum,...before: Chair, Watershed Management Committee at Water Environment Association of Texas, Instructor for Advanced Sustainable Design (CEVE 323-523)... Education: Drexel University, Syracuse University Specialty: Sustainability consultant who helps public infrastructure and land development clients reduce detention requirements, reduce drainage... Bret Jordan, PhD has 10 years of experience in hydrology, fluvial geomorphology, open channel hydraulics, storm water management, erosion control, sediment transport and stream restoration design in the academic and private consulting sectors. He has worked on over 30 different river systems ranging from steep mountain headwater streams to low gradient sand bed streams and coastal marshlands in the Pacific Northwest, Inter-Mountain West, Southeast and Gulf Coast regions. These projects have ranged from watershed scale analysis of sediment and nutrient transport, reach scale stream and coastal marshland restoration designs and site specific analysis of hydraulic structures. Brett has also has taught graduate level courses in the Civil Engineering Department at Colorado State University and short courses to government agencies focusing on field data collection and analysis of river systems. brett@hydrogeodesigns.com UH-DAIS

More Out of Town Flooding People Edward Clark (Director of the NOAA National Water Center’s (NWC) Geo-Intelligence Division NWC) .The NWC Geo-Intelligence Division is responsible for providing centralized and consistent data services, geospatial analyses, and cartographic expertise to support science and engineering development, systems implementation, and water resources operations at local, regional, national, and global scales. GID collaborates with partners and supports NWC and field operations, external partners, customers and stakeholders, and corporate knowledge management. Prior to joining the NWC, Ed has served in NWS Headquarters as the National Flash Flood Service Leader in the Analyze, Forecast, and Support Office. Prior to his tenure at NWS HQ, Ed had over seven years of experience as an operational hydrologic forecaster, working as a Senior Hydrologist at the Colorado Basin River Forecast Center in Salt Lake City. Ed’s background is in civil engineering with an emphasis on water resources and hydrology. Edward.Clark@noaa.gov Marian Muste (Research Engineer, IIHR Hydroscience and Engineering (http://www.iihr.uiowa.edu/about/ ); Additional Titles: Researcher, Iowa Flood Center, Center for Global and Regional Environmental Research) My most recent research area is hydroinformatics (a.k.a. cyber infratructure for water-related investigations over large-scales). Special efforts are focused on modern field data acquisition systems, internet-based geodatabases and ancillary cyber-tools for harvesting, storing, handling data, and conduct of uncertainty, risk, and reliability analyses (http://hydroinfo.uiowa.edu).  The practical goals of this research are related with development of eco-hydrologic watershed-scale data and information interactive repositories (http://iowadis.org). The observatories are virtual (digital) environments that aggregate large-scale datasets acquired by sensor networks with results of numerical simulations for conduct of research, education, and supporting the decision making process. These cyber-platforms enable transformative processes for extraction of data and knowledge from the raw data garnered from observatory information and numerical simulations for the benefit of science, practice and society. IIHR—Hydroscience & Engineering 302E C. Maxwell Stanley Hydraulics Laboratory, Iowa City, IA 52242-1585, U.S.A.Phone: (319) 384 0624Fax:       (319) 335 5238Email: marian-muste@uiowa.edu; UH-DAIS

Great Dismal Swamp, Virginia 8. Virginia Travel Great Dismal Swamp, Virginia Data Analysis and Intelligent Systems Lab

South Virginia: almost everything is NAVY UH-DAIS

Virginia Aquarium https://www.virginiaaquarium.com/ UH-DAIS

Virginia Museum of Contemporary Art http://www.virginiamoca.org/ UH-DAIS

Viriginia Beach Shamrock Weekend March 17-19, 2017 UH-DAIS

Virginia Beach Shamrock Races March 18, 2017 trishula trident March 18, 2000