Evaluation of Hydrologic Models forAlternative Covers

Charles D. ShackelfordColorado State University

Craig H. BensonUniversity of Wisconsin-Madison

What are Alternative Covers?

Soil covers consisting of one or more layers used inplace of a prescribed cover and having equivalenthvdroloaic performance.

Replace clay caps & composite caps (RCRA C & D)

Significant cost savings (~$50 to $75k per acre)

Employ natural materials and principles... fit well withnature and likely are to have a long service life.

How do alternative covers work?

Exploit (i) natural water storage capacity of finer soils and (ii)water removal capabilities of evaporation and transpiration.

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Storage capacity of cover

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Key: Design for sufficient storage capacity to retain water thataccumulates during winter with limited drainage

Conventional Alternative Cover Profiles

Capillary Barrier

150mmTopsoJI

460 mm Si It

600 mm Sand

Interim Cover

Monolithic Barrier

150mmTopsoil

1200 mm Sandy Clay

300 mm Interim Cover

Design Methodology

Characterize unsaturated hydraulicproperties of borrow soils and growingcharacteristics of plants

Preliminary sizing of cover profile by handcalculations

Demonstrate performance standard is met(e.g., < 1 mm/yr percolation) by numericalmodeling or field demonstration.

Advantages of Modeling

Assess a wide range of scenarios that mayaffect cover during service life

Fast and inexpensive (relative to field studies)

Disadvantages of Modeling

Accuracy of models has not been assessed.,critical since performance criteria limitpercolation to small quantities (~ 1 mm/yr)

Potential to mislead by careful input selection

Models Being Evaluated

Name

HELP

HYDRUS-

Vadose/W

LEACHM

Source

USEPA

Type

Unit GradientBucket Model

UNSAT-H PNNL/USDOE Richards' Equation

USDASalinityLaboratoryMEND and

Geo-Slope Ltd.Cornell Univ.

Richards' Equation

Richards' Equation

Richards' Equation

Tasks

I: Baseline Assessment of Hydrologic Models

: Comparison of Field Data and ModelPredictions

: Model Improvement

IV: Algorithms for Long-Term PerformanceAssessment

I: Baseline Assessment of Hydrologic Models

- Comparison of model predictions for a suite ofcontrasting climates (Las Vegas, NV; Richland, WA;Albuquerque, NM; Denver, CO; Columbus, OH; andSavannah, GA)

- Identify range of predictions obtained for a given set ofinput conditions.

- Identify algorithms having greatest impact on predictions

: Comparison of Field Data and Model Predictions

- Unbiased assessment of model accuracy

- Identify changes needed to improve accuracy

- Guides work in Task

Sources of Field Data

University of Wisconsin Field Study (Khire etal. 1997, 1999)

Sandia Alternative Landfill CoverDemonstration (Steve Dwyer of Sandia)

USEPA's Alternative Cover AssessmentProgram (Bill Albright of Desert ResearchInstitute and Craig Benson of UW)

ACAP Test Sites:

• Twenty-four test covers at eleven sites in sevenstates.

• Ten conventional covers (seven composite andthree clay)

• Fourteen alternative covers (eight monolithicbarriers and six capillary barriers) — used in thisstudy

• Eight sites with side-by-side comparison ofconventional and alternative covers

ACAP Field SitesPolson.MT

Boardman, OR

Sacramento, CA

Altamont, CA

Monterey.CA

Apple Valley, CA Albany.GA

DRIDesert fteewth totirte WtSCXXdN

20

SRODiversjon

Berm

20

Alldimensionsin meters

0.6

10

DownSlope

-0.6

ACAPTest Section

Plan View

Large bathtubso with cover soil

instruments

Typical Lysimater Cross-Section

20

InterimCover Soil

LLDPECutoff

Earthen^Berm—^ LLDPE

Geomembrane'ercolationPipe

Existing Slope (>2%)

Geocomposite Drain

arthenBerm

dozer at Altamont CA site.

Anti-Seep CoiSar Detail

Root Barrier

Wires

Cable Tire

Bentonite

PolyethyleneExcavation

Anti-Seep Collar(see Detail)

Heat Dissipation UnitThermocoupleWater Content Reflectometer

Heat Dissipation UnitThermocoupleWater Content Reffectometer

GeocompositeDrain

Geornembrane

weatherstation &

dataloggereel

cover SOIL «^

Sample of below ground

density distribution...

0.0

0.2

0.4

f 0-6CDQ

0.8

1.0

1.2

Relative Root Density

0.0 0.2 0.3 0.5 0.6

A •

R = 0.61 e'107z +0.007

j»-» Kiefer Landfill 'Sacramento, CA

June '01

• Sample A* Sample 8

Aerial view of completed test sections at Kiefer

-

Kiefer Site - Sacramento, CAWarm semi-arid climate (precipitation ~ 434 mm/yr)

1080-mm-thick monolithic covers

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(a) Thin AlternativePrecipitation

Evapotranspiration

SoilWater Storage

Percolation

Surface Runoff

f

• ' •

MissingData

i . . .

500

400 V)o

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73

HYDRUS-2D

Uncoupled mass & heat transfer. For liquidflow (Richards' Eq.):

eedt

d

dx.,K(K A

I ij ^dXj

where KjjAand KizA are anisotropy factors

Boundary ConditionsVariable flux — upper boundary foratmospheric interaction (evaporation)

Prescribed head (constant or variable)

Prescribed flux

Seepage face (requires saturation) - lowerboundary for lysimeter

Deep drainage (unit gradient) - conventionallower boundary for cover modeling

Evaporation in HYRUS-2D

Potential Flux = Precipitation - Potential Evaporation (PE)

Precipitation < PE,calculate actual evaporation (AE)

Precipitation > PE,No evaporation

Solve Richards' Equationw/ interchangeable surface head/flux BC

Flux BC based onhydraulic gradient

Head BC if> hsat)

Transpiration in HYDRUS-2D

Read Input forPotential Transpiration

(PT)

Read Input forRoot Length Density Function

(RLD)

Obtain Plant Stress factor (a) corresponding toPressure heads in the root zone

Calculate Actual Transpiration (AT) as a sink termf(PT,a,RLD)

Example of HYDRUS-2DPredictions: Marina, CA sit

120mmTopsoi I

Costal sub-humid climate with466 mm/yr. Seasonal, but nosnow or freezing

-. H™ Capillary barrier with ~ 1.2 m1100mm , ,T / , , .. rSandy Clay °^fme textured soil for storage

300mmSand

Four primary water balancequantities: runoff,evapotranspiration, soil waterstorage, and percolation

odel Predictions and Field Data300

200

•ocCD

.0

100Q.'o0

T I

Marina Alternative Cover

Precipitation

Runoff

QL5/1/00 7/1/00 9/1/00 11/1/00 1/1/01 3/1/01 5/1/01 7/1/01

300

£200cg

Q.(A

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/ HYDRUS-2D

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-co

500

400

300

200

100

Field Capacity-

Storage HYDRUS-2D

Field

Wilting Point' •

60

E 40

o

20

0

PercolationField

HYDRUS-2D

J I

5/1/00 7/1/00 9/1/00 11/1/00 1/1/01 3/1/01 5/1/01 7/1/01 5/1/00 7/1/00 9/1/00 11/1/00 1/1/01 3/1/01 5/1/01 7/1/01

Predicted and Measured Runoff: HYDRUS-2D

Prediction

Over-prediction

Under-prediction

Almostmatch

Test Site

Poison, MT

Boardman, OR(thin)

Field PredictedSRO SRO(mm) (mm)

Albany, GA 14.9

Altamont, CA 1.34

Marina, CA 0.00

Sacramento, Qn .CA(thin) yu>4

Sacramento, fiCA (thick) b -b

Cedar Rapids, oc 0IA 26'8

17.7

0.00

420

26.5

47.4

289

318

0.07

SurfaceLayer(cm/d)

0.017

0.24

0.0058

0.077

0.077

0.46

4.20

1.68

Boardman, OR(thick)

0.00 0.06 1.21

MeanIntensity

I(cm/d)

4.26

1.65

2.79

2.24

2.24

4.13

1.23

1.39

1.39

255

Surface Runoff Calibration bySimultaneous Adjustment of Ksat and a

300

£co

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Q_

200

100

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Marina Alternative Cover

Field

K = 10"8 cm/s, cc=0.0004 1/cmS

K = 10~7cm/s,a=0.01 1/cm

K =10"D cm/s, a=0.04 1/cm

K =1CTcm/s, a=0.1 1/cms

,-5.Field SRO &K=10'°cm/s

Precipitation

Ks

(cm/s)-

10-8

rr*»

D" •

I.

---10....... 10'

-7

-6

300

200CO

8

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100

05/1/00 7/1/00 9/1/00 11/1/00 1/1/01 3/1/01 5/1/01 7/1/01

Scaling to field condition is a key issue!

Model Calibration to Runoff400

1"

c300

I8. 200

UJ

• 100CD-̂>'g.'oo

0

Marina Alternative Cover

FieldEvapotranspiration'

HYDRUSEvapotranspiration HYDRUS

Surface RunoffField Surface Runoff

to

o 3-3 8

2 I

05/1/00 6/28/00 8/24/00 10/21/00 12/18/00 2/14/01 4/13/01 6/10/01

500

E

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200

100

0

.-SWS-fc 9

Field Soil Water Storage

HYDRUSSoil Water Storage

-SWS-wp

200

150

JS Percolation / i

' ^/

TJ

Sensitivity AnalysisMost influential factors:

rCat, a and n parameters (affect infiltration & storage)

Growing season (affects water removal)

Lower boundary condition (opposite of expected)

Errors in meteorological data (precipitation errors)

Less influential factors:

Pore interaction term, hysteresis

Wilting point, root depth, and root density distribution

Example - Effect of n Parameter300

§200

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I 100Q_

0

Marina Alternative Cover

Precipitation

Field SRO

30

20

ZJO

10

5/1/00 7/1/00 9/1/0011/1/001/1/01 3/1/01 5/1/01 7/1/01

0)•a

500

400

300

200

100

•Field Capacity

Field

Wilting Point

I I I I I I

500

400 i:

CD

300

COCD

200

100

c_o

c

400

300

20°

100

0

n-«K

= 1.3

400

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3

100

05/1/00 7/1/00 9/1/0011/1/001/1/01 3/1/01 5/1/01 7/1/01

60

40

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20

0

Field

n = 1.3,1.4, and 1.5

1 J I

60

40

Accomplishments to DateTasks:

• Task I - 25% complete (on schedule)

Task II - 50% complete (on schedule)

Key Findings:

• Model predictions can deviate greatly from field condition(critical issue for regulatory acceptance of designs)

Hydraulic property scaling is a key issue (pedogenesis willbe critical)

Growing season is a key issues (affect long-termpredictions)

Near Future Work

Complete comparisons between field data and modelpredictions (HYDRUS-2D, UNSAT-H, HELP complete,Vadose/W and LEACHM in progress)

Complete sensitivity analysis with all models (HYDRUS-2D, UNSAT-H, HELP complete, Vadose/W and LEACHMin progress)

Complete baseline comparisons (all models about 25%complete)