EFFECT OF EXTREME STORMS ON CHESAPEAKE BAY SUBMERGED

AQUATIC VEGETATION Presentation to the Modeling Subcommittee

February 8, 2005

Lewis Linker & Ping Wang

Hurricane Agnes - June 1972:• “…all [SAV] decreased significantly through 1973…eelgrass decreased the most (89%)…For all species combined the decrease was 67%.” (The Effect of Tropical Storm Agnes on the Chesapeake Bay Estuarine System CRC Publication No 54. November 1976.)

• “Hurricane Agnes in 1972 was the biggest shock to the grass beds, causing sharp Baywide declines.” (Bay Journal, v.14(4), June 2004. )

Extreme events, like hurricanes, have unique characteristics with regard to strength, path, & timing:

Susquehanna Meltdown, January 1996: • A warm front brought rain to the Susquehanna winter snow pack, and an ice dam formed in the lower reaches of the river. (Mike Langland, USGS.)

• Similar magnitude of flow and sediment loads as Agnes (30 MMT Agnes sediment, 10 MMT Meltdown sediment). No significant effect on SAV reported.

Observations On Extreme Event Timing - continued:

Hurricane Juan - November 1985: • Although only a category 1 hurricane, Juan ranks as the 8th costliest hurricane to strike the U.S. mainland.

• In the Chesapeake, Hurricane Juan was a 100 year storm primarily affecting the Potomac and James. No significant effect on SAV reported.

Use of Chesapeake Bay Water Quality Model (WQM) and Watershed Model (WSM):

•Ten year simulation period from 1985 to 1994 includes Hurricane Juan (November 1985).

• Hurricane Juan, a 100 year storm is well simulated by watershed loading model and WQM.

• Gives us a chance to say, “That was a nice hurricane, let’s do it again!”

Simulation of SAV Shoots in WQM:

d SH

-------- = [P - (1-Fpsr) - R - SL] * SH + Krps * RT

d t

Where:

SH = shoot biomass;

Fspr = fraction of gross production routed from shoot to roots;

P = production; R = respiration; SL = sloughing; RT = root biomass;

Krps = rate at which root biomass produces shoots.

Growth of SAV is temperature, light, and nutrient-dependent. Roots and shoots simulated for interannual simulation of biomass. SAV presence increases simulated TSS settling rate.

.

Source: Cerco et al. (2002). Figure 9. Modeled (mean [solid line] and interval encompassing 95 percent of computations [dashed line]) and observed (mean [dot] and 95 percent confidence interval [vertical line through dot]) mesohaline SAV community (Ruppia, above ground shoot biomass only). Observations from Moore et al. (2000). Model simulation from Eastern Bay using the 10,000-cell 1998 version of the Chesapeake Bay Water Quality Model.

The WQM simulates light extinction (Ke) from water, dissolved organic material (DOM), volatile suspended sediment (VSS), and inorganic suspended solids (ISS):

Ke = a1 + a2 * ISS + a3 * VSS

where:a1 = background attenuation from water and color (DOM)a2 = attenuation from inorganic solids

a3 = attenuation from organic suspended solids

0

50

100

150

200

250

300

144 87

130

173

216

259

302

345

388

431

474

517

560

603

646

689

732

775

818

861

904

947

990

1033

1076

Days from Jan 1, 1985

Obs

erve

d Fl

ow (t

hous

and

cubi

c fe

et/s

econ

d)

Potomac Fall-line

0

50

100

150

200

1

52

103

154

205

256

307

358

409

460

511

562

613

664

715

766

817

868

919

970

1021

1072

Days from Jan 1, 1985

Ob

serv

ed F

low

(th

ou

san

d c

ub

ic f

eet/

seco

nd

) James Fall-line

0

50

100

150

200

250

300

350

400

1

52

103

154

205

256

307

358

409

460

511

562

613

664

715

766

817

868

919

970

1021

1072

Days from Jan 1, 1985

Ob

serv

ed F

low

(th

ou

san

d c

ub

ic f

eet/

seco

nd

)

Susquehanna Fall-line

Nov.1**** May 8 Sept 3July 6

Nov.1**** May 8 Sept 3July 6

November storm condition in one neap-spring tide cycle.

September low flow condition in one neap-spring tide cycle.

May-Storm Scenario: Use storm condition for corresponding days in May; and use September low flow condition for the November storm period.

No-Storm Scenario: Use September low flow condition for the November storm period.

0

2

4

6

8

10

12

14

16

18

20

1

53 105

157

209

261

313

365

417

469

521

573

625

677

729

781

833

885

937

989

1041

1093

Day elasped since January 1, 1985

Ke

(1/m

)

May-Storm

July-Storm

Sept-Storm

Nov-Storm

No-Storm

upto near 100/m

Potomac Tidal Fresh

0

200

400

600

800

1000

1200

1

53

10

5

15

7

20

9

26

1

31

3

36

5

41

7

46

9

52

1

57

3

62

5

67

7

72

9

78

1

83

3

88

5

93

7

98

9

10

41

10

93

Day elasped since January 1, 1985

TS

S (

mg

/L)

May-Storm

July-Storm

Sept-Storm

Nov-Storm

No-Storm

Potomac Tidal Fresh

0

5000

10000

15000

20000

25000

30000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Months elapsed from January 1985

SA

V B

iom

as

s (

ton

)

May Storm

July Storm

Sept Storm

Nov. Storm

No Storm

Potomac Tidal Fresh

Simulated and observed SAV shoot biomass for a tidal fresh SAV community. Modeled (mean [solid line] and interval encompassing 95 percent of computations [dashed line]) and observed (mean [dot] and 95 percent confidence interval [vertical line through dot]) freshwater SAV community (above ground shoot biomass only). Observations from Moore et al. Model simulation from the Susquehanna Flats (Segment CB1TF) using the 10,000-cell 1998 version of the Water Quality Model. Source: Cerco et al.

0

100

200

300

400

500

600

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Months elapsed from January 1985

SA

V B

iom

ass

(to

n)

May Storm

July Storm

Sept Storm

Nov. Storm

No Storm

James Tidal Fresh

0

4000

8000

12000

16000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Months elapsed from January 1985

SA

V B

iom

ass

(to

n)

May Storm

July Storm

Sept Storm

Nov. Storm

No Storm

Lower Potomac

Most of the effect seen in the first simulated year. By the second simulated year, an echo of the previous year’s extreme event remains. No carry-over of the extreme event is observed by the third simulated year.

0

1000

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6000

7000

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9000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

Months elapsed from January 1985

SA

V B

iom

ass

(to

n)

May Storm

July Storm

Sept Storm

Nov. Storm

No Storm

Lower James SAV Biomass (tons carbon)

Simulated and observed SAV shoot biomass for a polyhaline SAV community. Modeled (mean [solid line] and interval encompassing 95 percent of computations [dashed line]) and observed (mean [dot] and 95 percent confidence interval [vertical line through dot]) polyhaline SAV community (Zostera above ground shoot biomass only). Observations from Moore et al. Model simulation from the Mobjack Bay (segment MOBPH, formerly WE4) using the 10,000-cell 1998 version of the Chesapeake Bay Water Quality Model. Source: Cerco et al.

• Extreme events like hurricanes deliver high sediment loads and reduce clarity below what’s needed to support SAV, often over a period of weeks.

• Extreme storm events during the SAV growing season are detrimental, especially so in periods before peak shoot biomass. • Extreme storm events during the SAV growing season but after the shoot biomass peak are estimated to be less detrimental. In the simulated tidal fresh SAV community the November extreme event has no effect on SAV shoot biomass as the shoot biomass during this time is already in a normal, natural decline leading to an absence of SAV shoot biomass by December.

Conclusions:

• In tidal fresh SAV communities the degree of diminution of SAV shoot biomass carries over as an “echo” of decreased SAV biomass in the second year. This is due to the decrease in simulated shoot biomass carried forward by a decrease in simulated shoot biomass. By the third simulated year of SAV response, the effect of extreme storms on SAV biomass is generally unobserved. • In the simulated polyhaline James SAV community, the SAV response to extreme storms was similar to that of the tidal fresh with the exception of the November storm. In this case, the simulated November storm is close to the secondary October peak in the polyhaline SAV community shoot biomass, and the resulting decreased SAV shoot biomass in November carried over to a noticeable SAV decrease in the second year.

Conclusions: