GIS in Environmental and Water Resources Engineering
Research Progress Report Jan 15, 1999
Research Areas• Texas data and water
modeling: Hudgens, Mason, Davis Jonsdottir, Gu, Azagra, Niazi
• Environmental Risk Assessment: Hay-Wilson, Romanek, Kim
• Global runoff: Asante, Lear
• Nonpoint source pollution: Melancon, Osborne
• Flood hydrology and hydraulics: Ahrens, Perales, Tate
• Internet: Favazza,Wei
Research Areas• Texas data and water
modeling: Hudgens, Mason, Davis Jonsdottir, Gu, Azagra, Niazi
• Environmental Risk Assessment: Hay-Wilson, Romanek, Kim
• Global runoff: Asante, Lear
• Nonpoint source pollution: Melancon, Osborne
• Flood hydrology and hydraulics: Ahrens, Bigelow, Perales, Tate
• Internet: Favazza,Wei
Brad Hudgens
Geospatial Data Development for Water Availability Modeling
Determining Watershed Properties
• Need to know at many points on a stream network: the upstream drainage area, average precipitation and SCS CN value, and the downstream flow length
• Grids of these variables are computed using the flow accumulation function
• An attribute table is obtained using the Combine function
Weighted Flow AccumulationAvgCN=flowaccumulation(fdr, CN)+CN
flowaccumulation(fdr)+1
Combine Grids
GRID : “combine”
David Mason
Geospatial Data Development for Water Availability Modeling
Control Point Status
• FINALLY, Acquired all control points for Nueces and Guadalupe River basins
• STILL, Waiting for control points on the San Antonio River basin
Meanwhile…..• Finished development of a single-line stream
network for all basins• Attached control points with ID numbers to line
network• Obtained more clearly defined project goals
– Which watershed parameters are needed?• Worked on streamlining database development
– Develop tools to automate the process
Trinity River TMDL
Subtask on Network Analyst
Kim Davis
Jona Finndis Jonsdottir
Geospatial Data for Total Maximum Daily Loads
New Tool Development for Water Modeling
Richard Gu
Rainfall Runoff in the Guadalupe River BasinRainfall Runoff in the Guadalupe River Basin
Esteban Azagra
Objectives
• Run HEC-PrePro and HMS programs for a sample area.
• Comparison of the runoff with field data.
• Calibration of the modeling system.
What have I done?
• Run HEC-PrePro and HMS.
• Analysis of parameters.
• Comparison of the model with field data
Analyzing Parameters• For Vx constant: X =
flow
• For X constant: VX
flow
• Use of Manning to change the values of VX
Comparison and Future work
• Precipitation data used for HMS showed big differences between the model and the field data.
• The use of NEXRAD Precipitation could help for a more detailed comparison.
Surface/Subsurface Modeling
By: Shiva Niazi1/15/99
GMS Model
Argus ONE Model
Argus ONE vs. GMS• Argus ONE
• Can create interface within software- inc. built-in functions
• Must manually create boundary, river arcs?
• GMS• Supports more
MODFLOW packages• Time consuming
Research Areas• Texas data and water
modeling: Hudgens, Mason, Davis Jonsdottir, Gu, Azagra, Niazi
• Environmental Risk Assessment: Hay-Wilson, Romanek, Kim
• Global runoff: Asante, Lear
• Nonpoint source pollution: Melancon, Osborne
• Flood hydrology and hydraulics: Ahrens, Bigelow, Perales, Tate
• Internet: Favazza,Wei
Lesley Hay Wilson
Spatial Environmental Risk Assessment
Current Research Status• Completed dissertation proposal defense on
Dec 11th• Objective is to develop the spatial risk
assessment methodology with emphasis on application to large, complex sites
• Working on the site conceptual model and linkages between Access and ArcView
Risk Assessment Data Model
SourceReceptor
Geographicpathways
Cross-mediapathways
Human, Ecological
Forward Risk Estimation
Target Level Calculation
Research ApproachSpatial Site Conceptual Model• Spatial representations of the site conceptual
model elements (e.g., sources, receptors)• Individual data layers for each element• Supported by
– database of exposure pathway components– spreadsheet of transport and transfer algorithms– grid-based models
• Implemented in a tiered approach
Connection of SCM Database and RBSL Spreadsheets
ODBC
Identify COC Pathway Segments
Source Concentrations
Link
Pathway Endpoint Concentrations
Access Site Conceptual
Model Database
Excel Spreadsheet
Perform simple fate and
transport calculations
Other Activities• Marcus Hook Project team meetings completed
Jan 11-13th (team)• EWRE seminar presentation of dissertation
proposal scheduled for Jan 20th
Andrew Romanek
Surface Representation of the Marcus Hook Refinery
Activities• 3 day meeting with BP, Langan, UT, and
others (Mon. - Wed.)– Update of progress– Delineation of future tasks
• COC Transport Extension
• Thesis
COC Tranport Extension• Surface water model extension to
predict concentrations
• Steady state, conservative, mixing model (only decreases in concentration from additional flow)
• Initial attempt yielded a maximum benzene concentration of 0.26 mg/L
Thesis
• Intro to risk assessment and project
• Digital Facility Description– Spatial and Tabular Databases– Data development (Photogrammetry)– Connection between Spatial and Tabular
• Map-Based Modeling– Surface and Groundwater models
Spatial Analysis of Sources and Source Areas on Marcus
HookProgress report by Julie KimFriday, November 20, 1998
Research Areas• Texas data and water
modeling: Hudgens, Mason, Davis Jonsdottir, Gu, Azagra, Niazi
• Environmental Risk Assessment: Hay-Wilson, Romanek, Kim
• Global runoff: Asante, Lear
• Nonpoint source pollution: Melancon, Osborne
• Flood hydrology and hydraulics: Ahrens, Bigelow, Perales, Tate
• Internet: Favazza,Wei
Global Runoff RoutingEstimating Flow Velocity
Kwabena Asante
Methods
• Lag Between Runoff Stations
• Lag Between Rainfall and Runoff
• Empirical Methods
Rainfall Distribution in November
Normalized Observed and Simulated Hydrographs
0
10000
20000
30000
40000
50000
60000
9 10 11 12 1 2 3 4 5 6 7 8
month of the year
obse
rved
flow
in m
3/s
simulated flowobserved
Empirical Equations:Generally of the form: P = a * Q b
Leopold and Maddock (1953): a = 1.3, b = 0.1Matalas (1969): a = 1, b = 0.155
River Q observed Q observed V leopold V matalasUnits m3/s cfs m/s m/sNiger 8500 300175 1.40 2.15Nile 2322 82001 1.23 1.76Congo 44893 1585384 1.65 2.79Zambesi 3378 119293 1.28 1.87
River Qobserved
Qobserved
Sleopold
DLeopold
Vleopold
VManning
Units m3/s cfs slope depth, m m/s m/sNiger 8500 300175 4.348E-05 7.57 1.40 1.40Nile 2322 82001 8.212E-05 4.50 1.23 1.36Congo 44893 1585384 1.924E-05 14.73 1.65 1.45Zambesi 3378 119293 6.834E-05 5.23 1.28 1.37
Grid Cell Translation from High to Low Resolution
Mary LearNovember 20, 1998
Research Areas• Texas data and water
modeling: Hudgens, Mason, Davis Jonsdottir, Gu, Azagra, Niazi
• Environmental Risk Assessment: Hay-Wilson, Romanek, Kim
• Global runoff: Asante, Lear
• Nonpoint source pollution: Melancon, Osborne
• Flood hydrology and hydraulics: Ahrens, Bigelow, Perales, Tate
• Internet: Favazza,Wei
Patrice Melancon
Pollutant Loading Model for Tillamook Bay
Flow ContributionBay Inflow Contribution by Watershed
7%
14%
36%
29%
9%
5%
Miam i
Kilchis
Wilson
Trask
Tillam ook
Other
Distribution matches values reported for the watershed
Flow vs Load Contribution by LanduseTillamook River Flow Contribution by Land Use
2%
1%
2%
11%
83%
1%
0%
Urban
Rural Res
Rural Ind
AgLand
CAFO
Forest
Water
Wilson River Flow Contribution by Land Use
99%
0%
1%0%
0%
0%0%
0%
Urban
Rural Res
Rural Ind
AgLand
CAFO
Forest
Water
Wetlands
Total Bacteria Load Contribution - Tillamook River
6%
2%
89%
1%
1%1%
Urban
Rur Res
Rur Ind
AgLand
CAFO
Forest
Total Bacteria Load Contribution - Wilson River7%
1%
1%
1%
78%
12%
Urban
Rural Res
Rural Ind
AgLand
CAFO
Forest
Concentration ProfilesBacteria Concentration Profile - Trask River with E&S Sample Pts Indicated
Reflects Current Level of BMP Implementation
0.0
50.0
100.0
150.0
200.0
250.0
300.0
350.0
400.0
450.0
0.0 20000.0 40000.0 60000.0 80000.0 100000.0 120000.0 140000.0 160000.0 180000.0
Distance Along River (ft)
Con
cent
ratio
n (fc
/100
ml)
TRA-HAT TRA-BP1
TRA-BP2
TRA-BP3
TRA-BPS TRA-TEF
TRA-STP
HOQ-CON
TRA-RM0
Katherine Osborne
Water Quality Master Planning for Austin
Research Areas• Texas data and water
modeling: Hudgens, Mason, Davis Jonsdottir, Gu, Azagra, Niazi
• Environmental Risk Assessment: Hay-Wilson, Romanek, Kim
• Global runoff: Asante, Lear
• Nonpoint source pollution: Melancon, Osborne
• Flood hydrology and hydraulics: Ahrens, Bigelow, Perales, Tate
• Internet: Favazza,Wei
Seth Ahrens
Flood Forecasting in Houston
Rainfall Data: Benefits of MATLAB over Visual Basic
Lat. Lon. Rf.
Time interval isinconsistent.Each time intervalin own file.
Program A
Time (min) Rf.(mm)
All data in one grid in ten-minute intervals.
Program B
Final output is an ArcViewASCII grid in the proper projection.
Benefits: Can now more efficiently prepare rainfall data. Original technique incorporated Visual Basicin Excel. Though it worked, the method proved to be cumbersome, error-prone (relied too much onuser), and time-consuming.
Creating Animated Rainfall Maps
• Program available from www.ulead.com or ganges\ahrens\research\bin\animation\
• Install ga20tu program on c:\temp.• Animation program only requires frames (i.e.
Gif files) and the time interval between frames.
• Full directions on my web site after CE server is fixed.
Sample Animation MapTime
Incremental Cumulative
Incremental (left) data give insight as to how much rain has fallen in a particular area in the ten minutes prior to the time in the lower-left-hand corner.
The cumulative (right) information, meanwhile, allows the user to get a better idea how much total rain fell over the area of interest.
N.B: The incremental data range from about0.5 in/hr to 6.0 in/hr while the cumulativedata range from 0.5 in to 8.0 in.
Jerry Perales
GIS-Based Infiltration Modeling
Eric Tate
Mapping Flood Water Surface Elevation
Map-Based Hydrology and Hydraulics
ArcViewInput Data
DEM
HEC-HMSFlood
discharge
HEC-RASWatersurfaceprofiles
ArcViewFlood
plain maps
CRWR-PrePro AvRAS
Flood Plain Mapping
Real-time flood emergency mapping
Flood hydrologyanalysis system
Nexrad radarrainfall input
Precomputedflood map
library
Real time
Offline
Research Areas• Texas data and water
modeling: Hudgens, Mason, Davis Jonsdottir, Gu, Azagra, Niazi
• Environmental Risk Assessment: Hay-Wilson, Romanek, Kim
• Global runoff: Asante, Lear
• Nonpoint source pollution: Melancon, Osborne
• Flood hydrology and hydraulics: Ahrens, Bigelow, Perales, Tate
• Internet: Favazza,Wei
David Favazza
Map-Based Modeling on the Internet
Kevin Wei
Displaying Environmental Maps on the Internet
Research Review
Next Research Progress Report Friday Dec18, 1998, 2PM, ECJ 9.236