Selected Presentation from the INSTAAR Monday Noon Seminar Series.

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1 Selected Presentation from the INSTAAR Monday Noon Seminar Series. Institute of Arctic and Alpine Research, University of Colorado at Boulder. This seminar presentation has been posted to the internet to foster communication with the science community and the public. Most of the INSTAAR presentations were originally given in PowerPoint format; they were converted to Adobe PDF for posting. 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 They are not peer reviewed.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 of publication. Contact the author for proper references and additional information before any use, even for unpublished works such as your own presentations.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 that you contact the author, give credit to the author, and display the copyright notice. All rights to reproduce these presentations are retained by the copyright owner. Images remain the property of the copyright holder. By accessing these presentations, you are consenting to our licensing agreement. 21 Apr Irina Overeem, INSTAAR. 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)..

2 Selected Presentation from the INSTAAR Monday Noon Seminar Series. Institute of Arctic and Alpine Research, University of Colorado at Boulder Apr Irina Overeem, INSTAAR. " 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 Maximum as an example. HydroTrend and SedFlux are run to mimic the rather complex history of the shelf, where rapid sea level rise, glacial melting and climate change interact. We set up a best-guess scenario and compared the simulated stratigraphy against available seismic data and found a reasonable match. Subsequently, the ranges in key climatological boundary conditions and their effect on the stratigraphy predictions have been evaluated. It was found that the dynamics of the Laurentide Ice Sheet control the drainage area of the rivers draining towards the NJ shelf and as such form an important control on discharge and sediment supply variability and consequently on the depositional geometry. Temperature and precipitation scenarios were based on CCM model output. Considerable variability especially as a result 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 primitive process description. Uncertainty in long-term climatological boundary conditions certainly affects our ability to make quantitative stratigraphic predictions. But careful mapping of the effects of the uncertainty provides an associated variability attribute that makes the model prediction more valuable.

3 Quantifying stratigraphic variability due to climatological boundary conditions Irina Overeem INSTAAR noon seminar, April 2003

4 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

5 HUDSON RIVER A = drainage basin area 34,210 km 2 Extra due to low sea level 24,000 km 2 A = drainage basin area 59,000 km 2

6 21,000 years ago Dyke et al., 2002 Projected Upper Hudson drainage extends far north A = 120,000 km 2?

7 Numerical models: Hydrotrend and SedFlux HydroTrend INPUT(t) T, P, A, H, ELA + statistical properties 2DSedFlux INPUT(t) sea level(t), bathymetry (t-0) Q, Qs, Qb 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 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

8 SedFlux simulation; year run daily 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 ) sea level in m Epoch time in years BP -140

9 7 EPOCHS with varying input parameters - drainage area sea level rise sudden decoupling glacial area sea level rise Area in km 2 Epoch

10 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 ) relative CF time in kbp Present-Day (PD) temperature jan feb mar apr may jun jul aug sept oct nov dec CCM1 temperature at 21 kbp 21 kbp 16 kbp 14 kbp 13.9 kbp 11.5 kbp 11 kbp 6 kbp PD

11 Community Climate Model Temperature output generated at daily resolution

12 7 EPOCHS with varying parameters - run-off induced by basal glacial melt - estimate from glaciological model (after Marshall, 1999; 2002) base flow estimates due to LIS melt Q in m3/s time in years BP

13 Base case SedFlux: 21,000 year run 170 km profile Water Depth in m

14 Base case SedFlux; simulated bulk density profile mid shelf wedge outer Shelf wedge

15 Thickness distribution: SedFlux yr run

16 well positions Age Grain-size

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18 SENS1: 4 paleo drainage area scenarios low confidence sea level rise sudden decoupling glacial area sea level rise Area in km time in kyrs BP high confidence PD-A = 35 *10 3 km 2 PD-A + SL-A = 59 *103 km 2 PD-A+ GLAC-A = 120* 10 3 km 2 PD-A + GLAC-A+ SL-A= 140 *10 3 km 2 Range in input parameters (increased uncertainty the further back in time)

19 Relative changes in HydroTrend output relative change in HT output due to change in paleo area and elevation change relative to sc delta A delta H delta Q delta Qs scenario no

20 SENS 1 SedFlux output - sea floor properties Sea floor properties differ tremendously between the 2 scenarios, both in volume and GSD (x)

21 SedFlux output- GSD(x) 300 year run - coarsening due to enlargement of the estimated drainage basin

22 SedFlux output - thickness distribution 300 y run Total volume of deposited sediment increases with increasing drainage area: TH / A = (between s2 s3, only area change) Elevation increase adds to that effect: TH / A = (between s1 - s2 = area

23 SENS 2; Paleo Temperature and Precipitation: 4 scenarios T in grd C paleo temperature 21 KBP jan feb mar apr may jun jul aug sept okt nov dec Hudson rivermouth PD-Vostok PD Sc1: present-day T minus T-trend of Vostok ice core P in mm paleo precipitation 21KBP (CCM data) Hudson rivermouth projected Upper Hudson P based on Glacial model jan feb mar apr may jun jul aug sept okt nov dec 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 the drainage basin Sc4: P based on glaciological modeling (Marshall, 2002) Concluding input parameters T and P have huge uncertainty

24 Paleo Temperature and Precipitation: HydroTrend output 300 yr run Cooling Effect of lower T ( T =10 C) - Q / T = 9.6 m 3 /s / C - Qs / T = 17 kg/s / C Wettening Effect of higher P ( P= 0.5 m) Higher Q and Qb Q / P = 3242 m 3 /s / m rain Qb / P = 65 kg/s / m rain - No change in Qs

25 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.

26 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

27 GSD(t)- relative changes delta GSD/delta T Temperature distance from river mouth in km The mean grainsize deposited over time is more strongly influenced by changes in P input (~40 %) than by changes in T input (~ 3%) delta GSD/delta P Precipitation distance from rivermouth in km

28 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

29 SENS 3: storm scenarios No storms Extreme storms

30 SedFlux output: X-section bulk density

31 SedFlux output; geometric attribute Thickness distribution No storms Extreme storms Th prediction

32 SUMMARIZING TABLE CLIMATIC Input - HydroTrend SEDFLUX SEDFLUX SEDFLUX BOUNDARY CONDITION Range (Qs) Geometry mean TH Variability _ GSD(t) Pseudowells Area 35k +/- 5k 14% 16% < 5% +/ - due LIS/SL influence 140k +/- 50k 18% 36% 10-20% ++ Q = 8% Temperature 10 C Qs = 45% 21% -15% - 22% +/- Q = 210% Precipitation 0.5 m Qs = -15% 26% 22% + Ocean storms % % ++++

33 SedFlux output; volume attribute GSD(x) 5cm-bins

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36 var GSD in phi depth depth var GSD in phi

37 10 cm 50 cm Entire sediment column (5m)

38 GSD-variance along the longitudinal profile for different depth-zones

39 Thick storm beds resolved

40 bedload deposition zone resolved

41 Conclusions SedFlux is able to predict long-term stratigraphic patterns (geometry and thickness at 100m longitudinal resolution and physical properties at 10 cm depth resolution). To make relevant predictions for acoustic properties HydroTrend and SedFlux need to be integrated and used at daily time-step to capture peak-flows and storms. High-resolution input data are increasingly online available on global scale (e.g. present-day climate, paleo from CCM) but the associated uncertainties can be significant. This does not necessarily propagate into the sea floor properties prediction. SedFlux provides a method to quantify variability due to uncertainties in the boundary conditions by running different input scenarios. A high-resolution output matrix can be associated with different resolution layer models of variance in data (e.g. variance of GSD in 50 l )