Selected Presentation from the INSTAAR Monday Noon Seminar Series.

Institute of Arctic and Alpine Research, University of Colorado at Boulder.http://instaar.colorado.edu

http://instaar.colorado.edu/other/seminar_mon_presentations

This seminar presentation has been posted to the internet to foster communication with the science community and thepublic.

Most of the INSTAAR presentations were originally given in PowerPoint format; they were converted to Adobe PDF forposting. You may need to install the free Adobe Acrobat Reader to view these files.

These presentations are "works in progress". They are not peer reviewed. They should not be referenced for any kind ofpublication. Contact the author for proper references and additional information before any use, even for unpublishedworks such as your own presentations.

LICENSING AGREEMENT.Free use of these presentations is limited to a nonprofit educational or private non-commercial context and requires thatyou contact the author, give credit to the author, and display the copyright notice. All rights to reproduce thesepresentations are retained by the copyright owner. Images remain the property of the copyright holder. By accessingthese presentations, you are consenting to our licensing agreement.

21 Apr. 2003 Irina Overeem, INSTAAR. Email: Irina.Overeem@colorado.edu" Quantifying stratigraphic variability; a case-study of the New Jersey shelf over the last 21,000 years."Seminar given at INSTAAR, University of Colorado. Copyright 2003 Irina Overeem. All Rights Reserved. Overeem presentation (1.4 Mb PDF). .

Selected Presentation from the INSTAAR Monday Noon Seminar Series.

Institute of Arctic and Alpine Research, University of Colorado at Boulder.http://instaar.colorado.edu

http://instaar.colorado.edu/other/seminar_mon_presentations

21 Apr. 2003 Irina Overeem, INSTAAR. Email: Irina.Overeem@colorado.edu" Quantifying stratigraphic variability; a case-study of the New Jersey shelf over the last 21,000 years."Seminar given at INSTAAR, University of Colorado. Copyright 2003 Irina Overeem. All Rights Reserved. Overeem presentation (1.4 Mb PDF). .

Abstract

This talk presents a numerical modeling study using the New Jersey shelf development since the Last Glacial Maximumas an example. HydroTrend and SedFlux are run to mimic the rather complex history of the shelf, where rapid sea levelrise, glacial melting and climate change interact. We set up a ‘best-guess’ scenario and compared the simulatedstratigraphy against available seismic data and found a reasonable match.Subsequently, the ranges in key climatological boundary conditions and their effect on the stratigraphy predictions havebeen evaluated. It was found that the dynamics of the Laurentide Ice Sheet control the drainage area of the rivers draining towards theNJ shelf and as such form an important control on discharge and sediment supply variability and consequently on thedepositional geometry. Temperature and precipitation scenarios were based on CCM model output. Considerable variability especially as aresult of changing precipitation conditions, is associated with these apparently sophisticated inputs.Even more uncertain is the effect of changing storm conditions, both due to limited data input as well as a more primitiveprocess description. Uncertainty in long-term climatological boundary conditions certainly affects our ability to make quantitativestratigraphic predictions. But careful mapping of the effects of the uncertainty provides an associated variability attributethat makes the model prediction more valuable.

Quantifying stratigraphic

variability due to climatological

boundary conditions

Irina Overeem

INSTAAR noon seminar, April 2003

Outline

• Geological setting: New Jersey Shelf

• Numerical models: Hydrotrend and SedFlux

• Base-case 21,000 year simulation for New Jersey

• High-resolution sensitivity experiments (focus on climate)

SENS 1) paleo drainage area

SENS 2) paleo temperature and precipitation

SENS 3) storm climate

• Conclusions

A = drainage basin area 34,210 km2

Extra due to low sea level 24,000 km2

A = drainage basin area 59,000 km2

HUDSON

RIVER

Dyke et al., 2002

Projected Upper Hudson

drainage extends far north

A = 120,000 km2 ?

21,000 years ago

Numerical models: Hydrotrend and SedFlux

HydroTrend

INPUT(t)

T, P, A, H, ELA +

statistical properties

ENGINE

Hydrological mass balance (daily)

Qi =Qsurf +Qniv + Qgw+Qice

Empirical relation Qs ~ A, H, T

Qs ~ _ (Qi /Qmean)c

OUTPUT (t)- Q, Qs, Qb (daily)

for 5 grain-size classes

2DSedFlux

INPUT(t)

sea level(t), bathymetry (t-0)

Q, Qs, Qb

ENGINE

River: avulsion, floodplain SR

Marine: delta plume, storm-diffusion

Basin: compaction

OUTPUT (x,z,t)- 2D-geometry

- grain, perm, bulk, dens, age

(per bin)

- sedimentation rates

SedFlux simulation; 21 000 year rundaily time-step, 10 cm depth resolution

7 EPOCHS with varying input parameters

- sea level rise (Bard et al., 1990; Chappell et al., 1996 , Shackleton, 2000 )

-140

-120

-100

-80

-60

-40

-20

0

0500010000150002000025000

time in years BP

se

a l

ev

el

in m

Epoch 1 2 3 4 5 6 7

7 EPOCHS with varying input parameters

- drainage area

0

20000

40000

60000

80000

100000

120000

140000

160000

0510152025

i i k BP

Are

a i

n k

m2

sudden decoupling

glacial area

sea level rise

sea level rise

Epoch 1 2 3 4 5 6 7

7 EPOCHS with varying input parameters

- temperature and precipitation forced by relative climate factor based

on Deuterium content Vostok ice-core (Jouzel et al., 1996 )

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25

time in kBP

rela

tive C

F

-25

-20

-15

-10

-5

0

5

10

15

20

25

jan feb mar apr may jun jul aug sept oct nov dec

21 kBP

16 kBP

14 kBP

13.9 kBP

11.5 kBP

11 kBP

6 kBP

PD

CCM1 temperature at 21 kBP

Present-Day (PD)temperature

Community Climate Model – Temperature output generated at daily resolution

7 EPOCHS with varying parameters

- run-off induced by basal glacial melt

- estimate from glaciological model

(after Marshall, 1999; 2002)

0

200

400

600

800

1000

1200

0500010000150002000025000

time in years BP

Q i

n m

3/s

base flow estimates due to LIS melt

Base case SedFlux: 21,000 year run

170 km profile

20

140

60

100

Water

Depth

in m

Base case SedFlux; simulated bulk density profile

outer Shelf

wedge

mid shelf wedge

Thickness distribution: SedFlux 21 000 yr run

well positions

Age Grain-size

SENS1: 4 paleo drainage area scenarios

0

20000

40000

60000

80000

100000

120000

140000

160000

0510152025

time in kyrs BPA

rea

in

km

2

sudden decoupling

glacial area

sea level rise

sea level rise

PD-A = 35 *103 km2

PD-A + SL-A = 59 *103 km2

PD-A+ GLAC-A = 120* 103 km2

PD-A + GLAC-A+ SL-A= 140 *103 km2

Range in input parameters

(increased uncertainty

the further back in time)

low confidence

high

confidence

Relative changes in HydroTrend output

relative change in HT output due to change in paleo area and

elevation

0

0.2

0.4

0.6

0.8

1

1 2 3 4

scenario no

ch

an

ge

re

lati

ve

to

sc

1

delta A

delta H

delta Q

delta Qs

SENS 1 SedFlux output - sea floor properties

Sea floor properties differ tremendously between the 2 scenarios,

both in volume and GSD (x)

SedFlux output- GSD(x)

300 year run

- coarsening due to

enlargement of the

estimated drainage basin

SedFlux output - thickness distribution

300 y runTotal volume of

deposited sediment

increases with

increasing drainage

area:

∆∑TH / ∆A =0.00076

(between s2 –s3, only

area change)

Elevation increase adds

to that effect:

∆∑TH / ∆A = 0.00146

(between s1 - s2 = area

SENS 2; Paleo Temperature and Precipitation:

4 scenarios

Sc1: present-day T minus T-trend of Vostok ice core

Sc2: based on Community Climate Model (CCM1) T and P at the Hudson rivermouth

Sc3: based on Community Climate Model (CCM1)T at the Hudson rivermouth, P in thedrainage basin

Sc4: P based on glaciological modeling (Marshall, 2002)

Concluding ‡ input parameters T and P have huge uncertainty

paleo precipitation 21KBP (CCM data)

0

20

40

60

80

100

120

140

160

180

200

jan feb mar apr may jun jul aug sept okt nov dec

P i

n m

m

Hudson rivermouth

projected Upper Hudson

P based on Glacial model

paleo temperature 21 KBP

-30

-20

-10

0

10

20

30

jan feb mar apr may jun jul aug sept okt nov dec

T i

n g

rd C

Hudson rivermouth

PD-Vostok

PD

Paleo Temperature and Precipitation:

HydroTrend output 300 yr run

Cooling

Effect of lower T (∆T =10 C)

- ∆Q /∆T = 9.6 m3/s / C

- ∆Qs /∆T = 17 kg/s / C

Wettening

Effect of higher P (∆P= 0.5 m)

Higher Q and Qb

∆Q /∆P = 3242 m3/s / m rain

∆Qb /∆P = 65 kg/s / m rain

- No change in Qs

SedFlux output: thickness distribution

- total deposited volume

decreases slightly with T

- total deposited volume

increases with P

- deposited volume is

dominated by P changes as

compared to T changes:

∆TH /∆P = 3777 m / m

∆TH /∆T = -109 m / C

- increase in P mainly

increases floodplain and

bedload sedimentation.

SedFlux output: GSD(t)

change in T estimate (~ 10 C)only results in limited GSD(t) change

change in P estimate (~ 0.5 m)results in more pronounced GSD(t) change

GSD(t)- relative changes

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 5 10 15 20 25 30

distance from rivermouth in km

de

lta

GS

D/d

elt

a P

-0.04

-0.03

-0.02

-0.01

0

0.01

0 5 10 15 20 25 30

distance from river mouth in km

delt

a G

SD

/delt

a T

Precipitation

Temperature

The mean grainsize deposited

over time is more strongly influenced

by changes in P input (~40 %)

than by changes in T input (~ 3%)

SENS 3: Storm experiments

New Jersey Shelf at present-day heavily influenced by storm ‡quantify what variability this introduces

Experiments:

Storm 0 = no storms

Storm 1 = Kdiff 0.75, Storm Mag 4

Storm 2 = Kdiff 1.5, Storm Mag 8

Storm 3 = Kdiff 3, Storm Mag 8

Storm 4 = Kdiff 10, Storm Mag 8

Storm 5 = Kdiff 20, Storm Mag 8

Storm 6 = Kdiff 50, Storm Mag 8

SENS 3: storm scenarios

No storms Extreme storms

SedFlux output: X-section bulk density

SedFlux output; geometric attribute

Thickness distribution

Extreme storms

No storms

∆Th prediction

++++180-460%10% -----------0-1200Ocean storms

+22%26%

Q = 210%

Qs = -15%0.5 mPrecipitation

+/--15% - 22%21%

Q = 8%

Qs = 45%10 CTemperature

+/ -

++

< 5%

10-20%

16%

36%

14%

18%

35k +/- 5k

140k +/- 50k

Area

due LIS/SL

influence

SEDFLUX

Pseudowells

SEDFLUX

Variability

_ GSD(t)

SEDFLUX

Geometry

mean TH

HydroTrend

(Qs)

Input -

Range

CLIMATIC

BOUNDARY

CONDITION

SUMMARIZING TABLE

SedFlux output; volume attribute GSD(x)

5cm-bins

dep

thd

ep

th

var GSD in phi var GSD in phi

50 cm

Entire sediment column

(5m)

10 cm

GSD-variance along the longitudinal

profile for different depth-zones

Thick storm beds

resolved

bedload deposition zone resolved

Conclusions

• SedFlux is able to predict long-term stratigraphic patterns

(geometry and thickness at 100m longitudinal resolution and physicalproperties at 10 cm depth resolution).

• To make relevant predictions for acoustic properties HydroTrend andSedFlux need to be integrated and used at daily time-step to capturepeak-flows and storms.

• High-resolution input data are increasingly online available on globalscale (e.g. present-day climate, paleo from CCM) but the associateduncertainties can be significant. This does not necessarily propagateinto the sea floor properties prediction.

• SedFlux provides a method to quantify variability due to uncertaintiesin the boundary conditions by running different input scenarios.

A high-resolution output matrix can be associated with differentresolution layer models of variance in data (e.g. variance of GSD in

50 l )