1. Flooding Research Initiatives Christoph F. Eick
Great Dismal Swamp, Virginia
Data Analysis and Intelligent Systems Lab

2. Meeting 06/06/2017 with Arturo S. Leon
Flooding Expertise Arturo
Short Overview UH-DAIS Lab Research
UH-DAIS Flooding Work Overview
Waterlevel Prediction and Flood Early Warning Systems
Submitted NSF Planning Proposal
Automated Flood Risk Mapping (in collaboration with UT Center for Research in Water Resources)
Expert System for Flood Risk Mapping (Austin Fire and Austin Flood Early Warning System)
Helping First Responders with DS/GIS/HCI to Deal with Flooding
Flooding People

3. 1. Arturo Leon
Welcome to the research group of Dr. Arturo S.Leon. Our main research interests are on the areas of hydraulic transients (single- and two-phase flows), resilient approaches to flood control, optimal reservoir operation under uncertainty, sustainable stormwater management and modeling, real-time control of complex hydraulic systems, computational hydraulics (CFD ) and physical modeling of hydraulic structures.

4. 3. Predicting, Mapping, and Understanding Flooding
Research Tasks:
Predict Water Levels
Flood Risk Mapping
Early Warning System
Automated Planning
Tools for Dealing with
and Recovering from
Flooding
Understand Flooding
People: Christariny Hutapea,
Yue Cao, Romita Banerjee,
Qian Qiu, Priyal Kulkarni and Yongli Zhang.
UH-DAIS

5. 4. 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

6. Training Challenges for Data Driven WLP Approaches
Need to extract historic water level, discharge(?? ) 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 NLADS 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

7. 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

8. Model Fusion and Meta Learning
USGS Models (
What model works best under which
circumstances?
Model Fusion
and
Meta-Learning
Data-driven Models
for the Watersheds
NOAA Models ( )
Our Project
UH-DAIS

9. 5. 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

10. 5. 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.

11. Flood Risk Mapping Wharton County, Texas
Wharton County
Fire Chief
Wharton County
April 2016 Flooding

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

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

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

15. Automated Flood Risk Mapping

16. FEMA Flood Maps

17. Polygon Analysis for Better Flood Risk Mapping
Knowledge of Flooded Areas
from Past Floods
FEMA Flood
Risk Zones
Find Correspondence
Find Agreement,
Combine, Validate
Hand Value
Multi-Contour
Maps
DEM (Digital
Elevation Maps)
HEC-RAS Generated
Polygons
Austin Fire
First Response
Vehicle Flood
Risk Map
UH-DAIS

18. FEMA and Austin Fire Maps

19. 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 that take knowledge from different sources (e.g. HEC-RAS generated, elevation maps, knowledge of past flooding)
Dynamic Flood Risk Maps that adapt their appearance based on contextual information
UH-DAIS

20. UH-DAIS Research to Address these Problems
Computational Methods to create flood risk maps from point-wise or grid-based flood risk assessment (e.g. hand value maps or elevation maps). We investigated in the past and are currently investigating:
graph-based approaches
Continuous function based approaches
Similarity Assessment Methods to find Agreement Between Different Polygonal Flood Risk Maps
Correspondence Contouring Methods (e.g. find a sequence of elevation thresholds so that the obtained contour polygons best match with FEMA flood risk zones)
Computing Agreement Maps between Different Methods
Creating “better” flood risk maps by combining information from different sources.
UH-DAIS

21. 4. Expert Systems for Flood Risk Mapping
Local
Information (Watersheds, Elevation maps,…)
User
Expert
System
creates
(Not an expert
in GIS & hydrology)
Flood Risk
Mapping
Knowledge
Base

22. 8. Helping First Responders with DS/GIS/HCI to Deal with Flooding
How do First Responders use Flood Risk Maps? How is feedback from First Responders used to Enhance Flood Risk Maps?
What other information is important to communicate to first responders when dealing with flood events? What is the current approach communicating this information?
What role does GIS contribute play in helping first responders do deal with flood events?
Human Computer Interaction (HCI): How to present flooding related information to first responders? What formats are the best to display information? What are the best communication strategies for first responders?
Can crowd sourcing help to alleviate the effects of flooding? If yes, what should it be used for?
What is needed in the future? What should future research focus on the enhance the ‘state of the art’ of dealing with flooding?
What about planning procedure to deal with flooding events?
UH-DAIS

23. 9. Flooding People: (Potential) Collaborators
S. Camille Peres (Texas A&M Univesity)---Behavioral and HCI aspects of flood warning systems:
Jian Chen (University of Louisiana at Lafayette)---Hydrology models and their use for flood prediction, GIS:
Marian Muste (University of Iowa)---instrumentation, maybe visualization and web-platforms for early warning systems:
Bruce Race (College of Architecture, University of Houston)---community and infra-stucture resilience; architectural and policy aspects of flooding:
David Maidment (Director, UT Center for Research for in Water Resources): National Water Model, Flood Inundation Mapping, Flood Risk Assessment,…
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) Environmental Planner, planning aspects of flooding
UH-DAIS

24. 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 )... 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.
Phil Bedient (Rice University; )
UH-DAIS

25. 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.
Marian Muste (Research Engineer, IIHR Hydroscience and Engineering ( ); 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 ( The practical goals of this research are related with development of eco-hydrologic watershed-scale data and information interactive repositories ( 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 , U.S.A.Phone: (319) Fax:       (319)
UH-DAIS

26. 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
UH-DAIS