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Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR -
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Page 1: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

City of Austin Water QualityMaster Planning - GIS Model

David MaidmentFrancisco Olivera

Mike Barrett Christine

DartiguenaveAnn Quenzer

CRWR - University of Texas

Page 2: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

OVERVIEW

The study areaDEM-based topographic

analysisGIS-based hydrologic analysis

Page 3: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

The Study Area

Page 4: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

LOCATION MAP

Page 5: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

LAND USE

Page 6: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

WATERSHEDS

Page 7: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

EDWARDS AQUIFER

Page 8: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

BMP’S

Page 9: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

CONTROL POINTS

Page 10: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

DEM-Based Topographic Analysis

Page 11: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

USGS 7.5’ QUADRANTS OF THE

AUSTIN AREA

Page 12: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

DIGITAL ELEVATION MODEL (DEM)

Page 13: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

HYDROLOGIC (RASTER-)GIS FUNCTIONS

1

8

4

4

1

16

4

8

1

1

2

4

128

64

1

?

64 N

128 NE

1 E

2 SE

4 S

8 SO

16 O

32 NO

0

1 1

5

0

0

0

0

0

9

12

0

0

0

0

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22

DEM Flowdirection Flow path

Flowaccumulation 8 directions

Page 14: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

FLOW DIRECTION

Water flows to one of its neighbor cells according to the direction of the steepest descent.

Flow direction takes one out eight possible values.

Page 15: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

FLOW ACCUMULATION

Flow accumulation is an indirect way of measuring drainage areas (in units of grid cells).

Page 16: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

STREAM DEFINITION

All grid cells draining more than 250 cells (user-defined threshold) are part of the stream network.

Page 17: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

STREAM SEGMENTATION

Stream segments (links) are the sections of a stream channel connecting two successive junctions, a junction and the outlet, or a junction and the drainage divide.

Page 18: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

WATERSHED DELINEATION

All grid cells flowing towards a specific stream segment (link) constitute its watershed or drainage area.

The watershed grid is then converted from raster into vector.

Page 19: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

BURNING-IN PROCESS

3.5.c Burnt in DEM

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145

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3.5.a DEM creek 3.5.b Digitized creek

Digitized creek Raised DEM Burnt in DEM

Page 20: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

DELINEATED STREAMS OF THE

AUSTIN AREA

Page 21: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

DELINEATED WATERSHEDS OF THE

AUSTIN AREA

Page 22: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

LOCATION OF CONTROL POINTS

Page 23: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

GIS-BasedHydrologic Analysis

Page 24: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

VECTOR AND RASTERREPRESENTATIONS OF THE

TERRAIN

Vector representation Raster representation

The parameter represented can be land use, impervious cover,runoff coefficient, EMC...

cell size

Page 25: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

IMPERVIOUS COVER VS. LAND USE

Land use Impervious cover (%) Category Code Urban Non urban

Single family 100, 113 40 30 Multi family 200 80 45 Commercial 300 95 60 Office 400 95 60 Industrial 500, 560 95 60 Civic 600 70 30 Park 700 15 5 Transportation 800, 870 100 85 Undeveloped 900, 999 15 5 Water 940 100 100

Page 26: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

CURRENT IMPERVIOUS COVER

Page 27: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

FUTURE IMPERVIOUS COVER

Page 28: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

EQUATION FOR ESTIMATINGANNUAL LOADS

For each land surface cell (30m x 30m):Load [M/T] = Precip [L/T] * Runoff Coeff * Mean Conc [M/L3] *Cell Area [L2]

Load = Direct Runoff Load + Baseflow Load

Use weighted flow accumulation to get downstream loads

In channel, load is adjusted for: groundwater recharge (flow and load decrease) channel erosion (load increase)

Overall loads are adjusted for BMP’s

Page 29: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

DIRECT RUNOFF COEFFICIENT VS. IMPERVIOUS COVER

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

Impervious Cover

Rv

Data obtained at small watersheds. One point per watershed per storm.

Page 30: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

DIRECT RUNOFF COEFFICIENT VS.

IMPERVIOUS COVER

0

0.2

0.4

0.6

0.8

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Impervious Cover

Rv

Non-Recharge Recharge

Data obtained at small watersheds. One point per watershed.

Page 31: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

BASEFLOW COEFFICIENT VS.IMPERVIOUS COVER

y = -0.0036x + 0.1904

R2 = 0.902

0.000.020.040.060.080.100.120.140.160.180.20

0 10 20 30 40 50

Impervious Cover (%)

Rs

Page 32: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

EMC’S VS. IMPERVIOUS COVER

Estimation of individual storm EMCEstimation of watershed EMCRelating EMC’s with impervious

cover

Page 33: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

NH3 CONCENTRATION VS.IMPERVIOUS COVER

y = 0.0024x + 0.1273

R2 = 0.695

0.000

0.100

0.200

0.300

0.400

0.500

0 20 40 60 80 100

Impervious Cover

NH

3 E

MC

, m

g/L

Page 34: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

TSS CONCENTRATION VS.IMPERVIOUS COVER

050

100150200250300350400

0.00 0.20 0.40 0.60 0.80 1.00

Impervious Cover

TS

S E

MC

(m

g/L

)

Page 35: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

EMC’S VS. IMPERVIOUS COVER

Constituent Direct runoffconcentration

(mg/l)*

Base flowconcentration in

undeveloped areas (mg/l)**

Base flowconcentration indeveloped areas

(mg/l) TSS 190 0 0 BOD C=14(IC)+3.5 0.45 0.8 COD C=98(IC)+18 12 20 TOC C=8.6(IC)+8 2 5 DP C=0.24(IC)+0.04 0.014 0.06 TP C=0.32(IC)+0.19 0.02 0.12

NH3 C=0.24(IC)+0.13 0.02 0.06 TKN C=1.53(IC)+0.13 0.28 0.46 NO3 0.82 0.15 0.6 TN 1.53(IC)+0.95 0.43 1.06 Cu C=0.016(IC)+0.006 NA NA Pb C=0.038(IC)+0.003 NA NA Zn C=0.19(IC) NA NA

Page 36: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

VOLUME OF WATERPRODUCED IN EACH CELL

Cell area

Runoff coefficient Precipitation (L/T) Volume of waterproduced by each

cell (L3/T)

Cell area (L2)

Page 37: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

MASS OF POLLUTANTPRODUCED IN EACH CELL

EMC (M/L3)Mass of pollutantproduced by

each cell (M/L3)

Volume of water produced by each cell (L3/T)

Page 38: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

VOLUME OF WATER LOST FROM EACH

CREEK CELL OF THE RECHARGE ZONE

L = cell size

recharge zone

Creek

LV2

(L/cell) length cell(L) zone recharge in creek of Length

/T)(L recharge creek Total

cell

/TLRecharge

33

Page 39: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

FLOW LOST IN THE RECHARGE ZONE

Creek Recharge (cfs)

Creek length (ft)

Recharge perUnit Length

(cfs/ft) Barton 20 37134 5.39E-04 Bear* 9 83758 1.08E-04 Onion 31 58156 5.33E-04 Slaughter 3.5 61333 5.71E-05 Williamson 1.9 57315 3.32E-05

Page 40: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

FLOW / LOAD

Volume of water ormass of pollutant

produced by each cell

Drainage area

station

The flow (L3/T) and load (M/T) are calculated with the weighted flowaccumulation function, as the sum of the contributions from theupstream cells.The same process is followed for direct runoff and baseflow.

Page 41: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

RunoffΣRechargeΣflow Observed

corrcoef

•For each gauged location (USGS stations), observed flow and predicted flow (after recharge zone correction) were compared.

•For each station, and its corresponding drainage area, a correction factor corrcoef was defined in the following way:

FLOW CALIBRATION

RechargeRunoffcorrcoefflow Observed

Page 42: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

FLOW CORRECTION COEFFICIENT

For the ungauged locations, the correction coefficient is extrapolated according to their impervious cover.

Flow correction coefficients

y = -0.0131x + 1.3015

R2 = 0.81690.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

0 10 20 30 40 50 60

ic

coef

Coefficient

Linear (Coefficient)

Page 43: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

•For each gauged location (USGS stations), observed load and predicted load (after recharge zone correction) were compared.

•For each station, it was assumed that the difference in load values was produced by channel erosion and a channel erosion coefficient was defined (Kg/yr/ft).

LOAD CALIBRATION

Erosion coefficient(observed load predicted load)

channel length upstream of the station

at the station

Page 44: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

LAND-GENERATEDPOLLUTANT

CONCENTRATION

BOD

0

4

8

12

16

20

0 4 8 12 16 20

Measured concentrations [mg/l]

Pre

dic

ted

co

nc

en

tra

tio

ns

[m

g/l]

TSS

0

500

1000

1500

2000

0 500 1000 1500 2000

Measured concentrations [mg/l]P

red

icte

d c

on

ce

ntr

ati

on

s [

mg

/l]

Page 45: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

CHANNEL EROSION

TSS Erosion vs. Impervious Cover

y = 2.2629x + 22.836

R2 = 0.6083

0

20

40

60

80

100

120

140

160

180

0 10 20 30 40 50 60

IC (%)

(Ob

se

rve

d l

oa

d -

Pre

dic

ted

Lo

ad

) /

U

ps

tre

am

ch

an

ne

l le

ng

th

•Apply the channel erosion equation to all ungauged watersheds.

• Add the channel erosion to the load at the stations.

Page 46: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

CONSTITUENTS THAT INVOLVE

CHANNEL EROSION

Pure land contribution: BOD, COD, DP, NH3, Cu, Pb, Zn.

Land and channel contribution: TSS, TOC, TP, TN.

Page 47: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

Construction Load and BMP Effect

Page 48: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

CONSTRUCTION LOAD

x % of the area has a development amount of 100%

100 % of the area has a development amount of x%

•EMC(TSS) = 600mg/L•Direct runoff coefficient = 0.5

Page 49: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

LOCATED BMP’SDEFINED BY LOAD REMOVAL

BMP grid Removed load grid

cell size

BMPi

Loadi

BMPI+1

LoadI+1

LoadI

LoadI + LoadI+1

WeightedFlowaccumulation

Page 50: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

LOCATED BMP’SDEFINED BY REMOVAL

EFFICIENCY

cell value =eff * loadBMP

no data

BMP grid

weightedflowaccumulation

Removed load grid

0

eff*loadBMP

Page 51: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

LOCATED BMP’SDEFINED BY REMOVAL

EFFICIENCY

initial loads at BMP’s

BMP2eff2

eff1 * load1

Load2

Total BMP removal

BMP1eff1

eff2 * (load2 - eff1 * load1)

BMP2eff2

BMP1 removal BMP2 removal

Load1 BMP1eff1

Page 52: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

NON-LOCATED BMP’S

Barton Springs Zone1 Non-Barton Springs zone Recharge

Zone Contributing

Zone COA Jurisdiction

BMP COA Non-COA

COA Non-COA

Urban2

EasternSuburban

3

WesternSuburban4

Non-COA

ZONE 1 2 3 4 5 6 7 8 NONE 100% 73% 14% 100% SED1 10% SAND2 44% 100% 34% 17% 24% 44% SAND3 10% 62% 56% COMP 36% 36% SOS 20% 20%

Page 53: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

NON-LOCATED BMP’S

Page 54: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

NON-LOCATED NON-DISCHARGE BMP’S

IC

DirectRunoff Coefficient

Direct Runoff Generated

Nondischarge

BMPs

Effective DirectRunoff

Effective Direct Runoff Coefficient

Effective IC

Effective IC is used for calculating channel erosion.

Page 55: City of Austin Water Quality Master Planning - GIS Model David Maidment Francisco Olivera Mike Barrett Christine Dartiguenave Ann Quenzer CRWR - University.

CONCLUSIONS

The goal of this research project was to determine current and future non-point source pollution loads in Austin streams.

The model aims at being as flexible as possible: The BMP parameters and the EMCs can be easily modified and they do not

require the analyst to recalibrate the model Modification of the current land use conditions, of the precipitation value used, or

of the impervious cover/runoff coefficient relationships will require recalibration of the model

The effects of both located and non-located BMP’s, and of construction activities were modeled.

Current flows matching observed flows at 17 USGS stations were determined

Loads were established for 122 sites (Environmental Integrity Index sites, USGS stations and mouths) within the study area.


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