2013 cayuga lake study technical briefing (cltac) … cayuga lake study technical briefing (cltac)...

2013 Cayuga Lake Study

Technical Briefing (CLTAC)

Cayuga Lake Modeling Project

Nov. 5, 2014

Nov. 5, 2014 Upstate Freshwater Institute 1

Outline1. Goals and Analysis Components

2. Monitoring

3. Constituent Concentrations

4. Concentration-Driver Relationships

5. Load Estimation Techniques and Best Methods

6. 2013 April – October Load Estimates

7. P bioavailability and Loads

8. 2000-2012, Loading and Magnitudes of Interannual Variability

9. Inlet Channel Effects: Case for Inlet Load Adjustments

10. Summary



2. Monitoring








10. Summary


Goals and Analysis Components

1. review of tributary program

2. provide critical analyses of relationships

3. develop constituent loads that will support the water quality model

May 19, 2014 Upstate Freshwater Institute 4


2. Monitoring








10. Summary


Monitoring: Sample Counts

Tributary TP(µg/L)

PP(µg/L)

TDP(µg/L)

SRP(µg/L)

SUP(µg/L)

Tn(NTU)

TSS(mg/L

FSS(mg/L

VSS(mg/L

t-NH3

(µg/L)NOX

(µg/L)DOC

(mg/L)Si

(mg/L

FallCreek

97 96 97 88 87 93 63 63 63 66 62 57 57

Cayuga InletCreek

72 72 73 63 63 71 47 47 47 47 46 47 47

SalmonCreek

96 96 97 87 87 91 56 56 56 61 62 54 54

TaughannockCreek

18 18 19 19 19 19 10 10 10 10 10 10 10

Six MileCreek

79 79 80 70 70 78 46 46 46 49 50 48 47

Totals 362 361 366 327 326 352 222 222 222 233 230 216 215


meets or exceeds QAPP requirements

- according to constituent and tributary

0.1

1

10

100

flow

biweekly sample

event sample

0.1

1

10

100F

low

(m

3/s

)

0.1

1

10

2013

0.1

1

10

2013

0.1

1

10

MAM J J A S O N

MAM J J A S O N

Monitoring: Temporal Coverage

Fall Cr., Cayuga In. Cr., Salmon Cr., Six Mile Cr.

biweekly and event samples

12-22% of days monitored during study interval (constituent dependent)

32-44% of tributary inflow monitored


Fall Cr. Cayuga In. Cr.

Salmon Cr.

Six Mile Cr.

Taugh. Cr.

robustmonitoring

coverage


2. Monitoring








10. Summary


Data Quality for Constituent Concentrations triplicate water quality

samples collected at Salmon Cr. mouth

precision of triplicates (as coefficients of variation, cv) used as a metric of data quality

average cv for all constituents were less than 15% (shown for TP, TDP,TSS)


Coeff. of Variation (cv)

0 4 8 12 16 20

0.0

2.5

5.0

7.5

>20

0.0

2.5

5.0

7.5

10.0

Count

0.0

2.5

5.0

7.5

0.0

2.5

5.0

7.5

n=11mean=13.3%median=4.1%

n=18mean=6.9%

median=3.9%

n=19mean=6.7%

median=2.9%

n=11mean=8.3%

median=8.1%

TP

TDP

TSS

t-NH3

good data quality supports analyses

with stream concentration data

Constituent Concentrations: Fall Creek Time Series for TP, PP

TP dominated by PP

PP increased dramatically during periods of high flow (see three largest events monitored : June 30, July 21, and August 8-9)

peak TP and PP concentrations were 996 and 927 µg/L, respectively


M A M J J A S O N

P(µ

g/L

)

0

250

500

750 TP

PP

Flo

w

(m3/s

)

0

25

50

75

100

2013

Fall Creek During High Flow


Constituent Concentrations: Fall Creek Time Series for Dissolved P


SRP, SUP demonstrated some seasonality

SRP, SUP were generally lowest during periods of low flow and increased during runoff events

increases observed were less substantial than for particulate constituents

M A M J J A S O N

P(µ

g/L

)

0

25

50

75 TDP

SRP

SUP

Flo

w

(m3/s

)

0

25

50

75

100

2013

Constituent Concentrations:Fall Creek Time Series for Suspended Solids (SS) and Tn

forms of SS were generally low during low flow

TSS dominated by FSS

TSS was lowest in spring and fall, increased during periods of high flow

70-fold increase from Aug 6 to Aug 8

patterns of Tn similar to SS


M A M J J A S O N

Tn

(NT

U)

0

200

400

SS

(m

g/L

)

0

250

500

750 TSS

FSS

Flo

w

(m3/s

)

0

25

50

75

100

2013

Constituent Concentrations: Time Series for Fall Creek

other dissolved constituents (DOC, t-NH3, Si) similar to dissolved P

generally lowest during periods of low flow and increased during runoff events

increases during high flow, less substantial than for particulates

NOX highest in spring and fall

dominant form of dissolved N


M A M J J A S O N

DO

C(m

g/L

)

0

4

8

t-N

H3

(µg/L

)

0

50

100

150

NO

X

(µg/L

)

0

2500

5000

7500

Si

(mg/L

)

0

2

4

6

Flo

w

(m3/s

)

0

25

50

75

100

2013

Constituent Concentrations: Example Box-Whiskers for Low Flow vs. High Flow for Fall Creek

large increases in mean concentration between low and high flow for particulates

dissolved constituents showed increases from low-to-high flow but less substantial than for particulates

Fall Creek patterns (not necessarily magnitudes) were similarly demonstrated in other streams


LOW HIGHP

P (

µg

/L)

0

100

200

300

400

500

LOW HIGH

SR

P (

µg

/L)

0

10

20

30

40

LOW HIGH

SU

P (

µg

/L)

0

10

20

30

40

LOW HIGH

Tn

(N

TU

)

0

50

100

150

200

250

300

LOW HIGH

TS

S (

mg

/L)

0

100

200

300

400

500

LOW HIGH

t-N

H3 (

µg

/L)

0

20

40

60

80

100

LOW HIGH

NO

X (

µg

/L)

0

1000

2000

3000

4000

LOW HIGH

DO

C (

mg

/L)

0

5

10

15

LOW = 25.9HIGH = 109.6

LOW = 6.4HIGH = 9.8

LOW = 7.2HIGH = 8.6

LOW = 10.2HIGH = 72.4

LOW = 10.4HIGH = 141.6

LOW = 23.4HIGH = 26.6

LOW = 1,237HIGH = 2,205

LOW = 5.2HIGH = 6.3

median

mean

75th

percentile

25th

percentile

Key:

90th

percentile

95th

percentile

10th

percentile

5th

percentile

Constituent Concentrations: Comparisons Between Tributaries


TP

(µg/L

)

0

20

40

60

PP

(µg/L

)

0

20

40

FC CI SC TC 6M

SR

P(µ

g/L

)

0

10

20

0

100

200

300

400

0

100

200

300

FC CI SC TC 6M

0

10

20

At low flow:

TP, PP highest average in Fall Creek

Tn, SS similar to PP

SRP highest for Salmon Creek

Low Flow High Flow

At high flow:

TP, PP highest average in Cayuga In. Creek

SRP highest for Salmon Creek

Constituent Concentrations: Comparisons Between Tributaries


Low Flow High Flow

At high flow:

high flow conditions similar to low flow conditions

NOX >> t-NH3

DO

C(m

g/L

)

0

2

4

6

8

t-N

H3

(µg/L

)

0

20

40

FC CI SC TC 6M

NO

X

(µg/L

)

0

2000

4000

6000

0

2

4

6

8

0

20

40

FC CI SC TC 6M

0

2000

4000

6000

At low flow:

DOC highest in Fall Cr. and Salmon Cr.

t-NH3 similar for all streams

NOX highest for Salmon Creek

Constituent Concentrations:Phosphorus Ratios TP

dominated by PP for Fall, Cayuga In. Cr., Six Mile Cr., and Taugh Cr. at low and high flow

at low flow Salmon Cr. had the lowest PP:TP ratio (i.e., Salmon Cr. TP dominated by TDP)

PP:TP ratio increased with increasing flow for all tribs

TDP dominated by SUP for Fall Cr. and

Cayuga In. Cr. dominated by SRP for Salmon Cr.

and Six Mile Cr. bioavailability implications SRP:TDP increased slightly with

increasing flow (all but Six Mile Cr.)



2. Monitoring








10. Summary


Concentration-Driver Relationships: Concentration-Flow

conc.-Q relationships were stronger for particulates than for dissolved constituents

significance, strength of fit varied between parameters, streams


Q (m3/s)

0.1 1 10 100 1000

PP

(µ

g/L

)

1

10

100

1000

Q (m3/s)

0.1 1 10 100 1000

SR

P (

µg/L

)

0.1

1

10

100

Q (m3/s)

0.1 1 10 100 1000

NO

X (

µg/L

)

100

1000

10000

PP=8.20·Q0.768

r2=0.39

p<0.001

SRP=2.64·Q0.251

r2=0.04

p=0.19

NOx=1023·Q0.01

r2=0.00

p=0.96

TSS=2.03·Q1.194

r2=0.50

p<0.001

Q (m3/s)

0.1 1 10 100 1000

TS

S (

mg/L

)

0.1

1

10

100

1000

Fall Creek

Q (m3/s)

1 10 1 10 1 10 1001 10

PA

Vm

(1/m

)

0.01

0.1

1

10

100

Concentration-Driver Relationships: Flow-Projected Area Minerogenic Particles per Unit Volume (PAVm)

valuable metric for P and optical impacts of inorganic (mineral) particles

4 size classes support modeling of fate of minerogenic particle inputs to lake


Class 1 Class 2 Class 3 Class 4

positive dependency on Q

Fall Creek

0.1 1 10 30

0

10

20

30

40

Particle size (m)

Cu

mu

lati

ve P

AV

m (

cm2 /L

)

Size-class-2 PAVm

Size-class-3 PAVm

4 size classes:Class 1: 2 mClass 2: 25.6 mClass 3: 5.611 mClass 4: 11 m

Concentration-Driver Relationships: Concentration-Air Temperature

conc.-air T relationships not significant for particulates exception Fall Cr.

conc.-air T significance, strength of fit better for dissolved constituents than for particulates better predictor

than flow for most parameters, streams


Fall Creek

Air T (°F)

20 30 40 50 607080

PP

(µ

g/L

)

1

10

100

1000

Air T (°F)

20 30 40 50 607080

SR

P (

µg/L

)

0.1

1

10

100

Air T (°F)

20 30 40 50 607080

NO

X (

µg/L

)

100

1000

10000

PP=0.07·T1.536

r2=0.12

p=0.02

SRP=0.002·Q2.450

r2=0.33

p<0.001

NOx=57.5E4·Q

1.596

r2=0.24

p=0.001

TSS=0.02·Q1.711

r2=0.09

p=0.04

Air T (°F)

20 30 40 50 607080

TS

S (

mg/L

)

0.1

1

10

100

1000

Concentration-Driver Relationships: Concentration-Turbidity

exploratory

conc.-Tn relationships very strong for particulates r2>0.8

p<0.001

conc.-Tn results varied between parameters, streams for dissolved constituents good predictor for

some parameters, streams


Fall Creek

Tn (NTU)

1 10 100 1000

PP

(µ

g/L

)

1

10

100

1000

Tn (NTU)

1 10 100 1000

SR

P (

µg/L

)

0.1

1

10

100

Tn (NTU)

1 10 100 1000

NO

X (

µg/L

)

100

1000

10000

PP=3.13·Q0.852

r2=0.93

p<0.001

SRP=1.28·Q0.409

r2=0.22

p<0.001

NOx=1538·Q-0.127

r2=0.04

p=0.24

TSS=0.73·Q1.168

r2=0.92

p<0.001

Tn (NTU)

1 10 100 1000

TS

S (

mg/L

)

0.1

1

10

100

1000


2. Monitoring








10. Summary


Load Estimation Techniques Adopted FLUX32 (F32) Load Estimation Software

Regression Methods 5 methods

Non-regression methods 3 methods

Manual Methods Multiple linear regression with flow, air T

C/Q regression using instantaneous flow (Fall Cr. only)

C/Q regression using instantaneous flow with event load estimates (Fall Cr. only)

Monthly mean method


Load Estimation Results for Fall Creek:All Methods, April-October


Method PP Load (kg) SUP Load (kg) SRP Load (kg)

F32 method 6 C/Q interpolated 8,010 742 855

F32 method 6 C/Q 11,289 770 1,202

F32 method 5 C/Q adj. 8,156 755 893

F32 method 4 C/Q flow wtd. adj. 8,300 759 907

F32 Rising/Falling limb 8,008 739 850

Manual regr. 10,251 746 1,108

Manual regr. with events 10,223 645 1,153

Multiple Linear Regression 8,896 784 971

F32 method 3 Beale’s Ratio Estimator 10,778 802 1,095

F32 method 2 flow weighted conc. 10,223 794 1,055

F32 method 1 average load 12,425 964 1,298

Monthly mean 12,259 828 1,085

---regression methods (range) 8,008-11,289 739-770 850-1,202

---non-regression methods (range) 10,223-12,259 794-828 1,055-1,095

Load Estimation Results for Fall Creek Using Different Methods, April-October


PP

L (

mt)

0

5

10

15

20

25

SU

PL (

mt)

0.0

0.5

1.0

1.5

F3

2 M

6 R

eg

r.,

Inte

rp.

F3

2 M

6 R

eg

r.

F3

2 M

5 R

eg

r.

F3

2 M

4 R

eg

r.

F3

2 H

yd

ro S

ep

Ma

nu

al R

eg

r.

Ma

nu

al R

eg

r.,

Eve

nts

Ma

nu

al M

LR

F3

2 M

3

F3

2 M

2

F3

2 M

1

Mo

nth

ly M

ean

SR

PL (

mt)

0.0

0.5

1.0

1.5

2.0

Avg. All

Methods

w/ 95% CI

BestEstimate

Avg.

Regr.

Methods

w/ 95% CI

PPL

okay agreement, regression methods within 10-22% of best method

SUPL

strong agreement, regression methods within 1-6% of best methods

SRPL

okay agreement, regression methods within 7-20% of best method

this level of uncertainty is not

uncommonin load estimation

Best Methods for Load Estimation Particulate Constituents

FLUX32 C/Q Regression, Interpolation daily time step significant relationships between concentration and flow stratified by flow regime supported by differences in C/Q slopes between low and

high flow allows smooth interpolation between observations using defined C/Q

relationship estimates air T generally not significantly related to particulate concentration usually less uncertainty than non-regression methods

Dissolved Constituents Multiple Linear Regression with Q, air T as drivers

daily time step flow generally not strong, rarely significant predictor of concentration concentration-air T significant in most cases multiple regression model better than C/Q alone


Best Methods for Load Estimation:Unmonitored Tributaries

Monitored Watershed 5 largest tribs, ~60% of watershed area assumed 60% of hydrologic inflow FC, CI, SC, TC, 6M (yellow)

Unmonitored Watershed 40 % of lake’s watershed many small streams (each < 3.5% of

lake’s watershed) assumed unmonitored area ∝ to

unmonitored inflow

Unmonitored Load Estimates prorated monitored loads to whole-lake

watershed loads based on ratio of monitored:unmonitored watershed areas

consistent with land use information


SC

FC

6MCI

TC

~40% unmonitored


2. Monitoring



5. Load Estimation Techniques and Best Methods6. 2013 April – October Load Estimates




10. Summary


2013 April-October Loading Estimates:Primary Constituents


Tributary

TP

(kg)

PP

(kg)

TDP

(kg)

SRP

(kg)

SUP

(kg)

Tn

(NTU·m3)

Fall Creek 9,765 8,010 1,755 971 784 5.86E+09

Cayuga Inlet Creek 9,564 9,208 356 167 189 1.04E+10

Salmon Creek 5,792 4,171 1,621 1,234 387 3.02E+09

Taugh. Creek 4,931 3,639 1,291 813 479 4.96E+09

Six Mile Creek 2,500 2,036 464 321 143 2.77E+09

Unmonitored 22,157 18,422 3,735 2,387 1,349 1.84E+10

Point Sources 1,415 745 669 302 368 -

Total Watershed 56,124 46,232 9,892 6,194 3,698 4.54E+10

sum of daily estimates

2013 April-October Loading Estimates:Secondary Constituents


Tributary

TSS

(kg)

FSS

(kg)

VSS

(kg)

t-NH3

(kg)

NOX

(kg)

DOC

(kg)

Si

(kg)

Fall Creek 11,196,214 10,040,965 1,155,249 2,755 110,606 551,300 343,068

Cayuga Inlet Creek 10,429,918 9,640,675 789,243 889 15,656 154,949 230,061

Salmon Creek 2,866,144 2,493,251 372,893 1,179 225,205 266,865 231,276

Taugh. Creek 3,227,723 3,003,859 223,864 1,514 65,289 264,981 343,492

Six Mile Creek 2,738,443 2,456,535 281,908 584 6,663 109,585 125,778

Unmonitored 20,731,759 18,810,156 1,921,602 4,711 288,203 917,309 866,936

Point Sources - - - - - - -

Total Watershed 51,190,201 46,445,441 4,744,760 11,631 711,621 2,264,989 2,140,611

sum of daily estimates

Non-point Source

Point Source

2013 April-October Loading Estimates: Phosphorus

TPL dominated by PPL

Fall Cr. and Cayuga In. – largest individual contributors of PPL

Fall Cr. – 8.0 mt (17.3%) Cayuga In. – 9.2 mt (20%)

Fall Cr. and Salmon Cr. – largest contributors of TDPL

Fall Cr. – 1.8 mt (17.7%) Salmon Cr. – 1.6 mt (16.4%)

Cayuga Lake PPL and TDPL is dominated by non-point sources


PPL

93%

TDPL

7%1.6%

98.4%

FC CI SC TC 6M UM PTS

P (

mt)

0

5

10

15

20

25

PP

SUP

SRP

2013 April-October Loading Estimates: Turbidity and Forms of SS

TSSL dominated by FSSL (minerogenic particles) 87-93%

Fall Cr. and Cayuga Inlet Cr. – largest individual sources

TnL patterns were similar to TSSL


FC CI SC TC 6M UM

SS

(10

3 m

t)

0

5

10

15

20

25

FSS

VSS

FC CI SC TC 6M UM

Tn (

10

6 N

TU

m3)

0.0

5.0e+3

1.0e+4

1.5e+4

2.0e+4

2.5e+4

2013 April-October Loading Estimates: PAVm

apportioned according to 4 size classes

size class 2 contributes the most (34%)

smallest size class (PAV1) contributes the least

general similarity to SS and Tn patterns


FC CI SC 6M UnMon.

PA

Vm

Load

(10

6·m

2)

0

2000

4000

6000

PAV1

PAV2

PAV3

PAV4

Total PAVm Load (m2)

PA

Vm

Load

(10

6·m

2)

0

5000

10000

15000

10%34%

30%

26%

2013 April-October Loading Estimates: NOX

Salmon and Fall Cr. largest individual sources

NOXL >> than t-NH3L


FC CI SC TC 6M UM

t-N

H3 (

mt)

0

1

2

3

4

5

6

FC CI SC TC 6M UMN

OX (

mt)

0

100

200

300

400

TD

P (

mt)

0

1

2

3

4

DO

C (

mt)

0

250

500

750

FC CI SC TC 6M UMN

OX (

mt)

0

100

200

300

PP

(m

t)

0

5

10

15

20

High flow load

Low flow load

FC CI SC TC 6M UM

Tn

(10

6 N

TU

·m3)

0.0

5.0e+3

1.0e+4

1.5e+4

TS

S

(10

3 m

t)

0

5

10

15

20

2013 April-October Loading Estimates:Low vs High Flow

delivered during high flow 18% of days for Fall Cr. during study

91% of PPL and TnL

97% of TSSL

other particulates similar

delivered during high flow 70% of TDPL

61 % of DOCL

65 % of NOXL


Particulates Dissolved

PP

(m

t)

0

5

10

15

20

High flow load

Low flow load

FC CI SC TC 6M UM

Tn

(10

6 N

TU

·m3)

0.0

5.0e+3

1.0e+4

1.5e+4

TS

S

(10

3 m

t)

0

5

10

15

20

majority of constituent loads delivered during

high flow

2013 April-October Loading Estimates:As Yields (kg/km2)

yield = load ÷ watershed area

to indicate potency of individual sources

TP yield was dominated by PP

Cayuga Inlet had the largest TP yield 40 kg/km2

TDP yield was dominated by SRP Taugh Cr. and Salmon Cr.

~ 7 kg/km2


TP

(kg/k

m2)

0

10

20

30

40

50

PP

TDP

FC CI SC TC 6M UM

TD

P

(kg/k

m2)

0

2

4

6

8 SUP

SRP

2013 April-October Loading Estimates:As Yields (kg/km2)


TSS yield was dominated by FSS

Cayuga Inlet had the largest TSS yield 43.3·103 kg/km2

Fall Cr. was second highest for TSS yield 33.8·103 kg/km2

Tn similar to TSS patterns Taugh. Cr. and Six Mile

Creek ranked 2nd and third

20.4 and 18.7 kg/km2FC CI SC TC 6M UM

Tn

(10

6 N

TU

·m3/k

m2)

0

10

20

30

40

TS

S

(10

3 k

g/k

m2)

0

10

20

30

40

50

FSS

VSS


2. Monitoring




6. 2013 April – October Load Estimates7. P bioavailability and Loads



10. Summary


Phosphorus Bioavailability

Tributarymean

PPfBAP (%)

CVmeanSUP

fBAP (%)CV

Fall Cr. 10.6 60 92.7 7

Cayuga Inlet Cr. 5.7 64 59.7 24

Salmon Cr. 19.2 70 84.4 20

Six Mile Cr. 6.0 69 65.5 49

CHWWTP 25.4 11 62.7 16

IAWWTP 1.2 83 73.2 29

LSC - - 8.2 141

fBAP found to be watershed specific, correlated to % watershed in agriculture

%Ag-PP fBAP, r =0.97

%Ag-SUP fBAP, r = 0.78


fBAP – fraction of P bioavailable

PP was least bioavailable

SUP was mostly bioavailable

SRP (not shown) was nearly completely available (> 93%)

Percent Land Use in Ag. (%)

0 25 50 75

fB

AP

(%

)

0

25

50

75

100

SC

SC

FC

FC

CI

CI6M

6M

PP

SUP

2013 Tributary Loading Estimates: Bioavailable P, April – October


TPL

(56.1 mt)

FCSC

6M

TC

CI

Unmon.

Total LoadsP Fractions

~ 73% reduction

BAPL

(14.9 mt)

Bioavailable LoadsP Fractions

Apportionment of BAP Loads, 2013


Percent Contribution (%)

Source BAPL

Fall Cr. 16.9

Cayuga In. 5.3

Salmon Cr. 15.8

Six Mile Cr. 3.5

Taugh. Cr. 10.7

Unmon. Tribs. 43.4

summed (%) 95.7

IAWWTP 1.4

CHWWTP 0.5

minor WWTP 0.9

LSC 1.5

summed (%) 4.3

summed (%) 100

non-pointsources

pointsources


2. Monitoring





7. P bioavailability and Loads8. 2000-2012, Loading and Magnitudes of Interannual

Variability


10. Summary


BA

PL (

mt)

0

10

20

30

40

50

2000-201212y

no 201113y tribs SC PtS

2013

14.9

2.34 0.65

29.625.1

Load Estimates for the 2002-2012 Period: Magnitudes and Interannual Variability 2013 conc.-driver relationships applied

comparison to 2013 estimates

total, Salmon Cr., summed point sources


• 2013 close to long-term mean

• interannual variability large (± 1 std. dev.)

• >> Salmon Cr.

• >> summed point sources

• important for reasonable expectations for management actions

interannualvariability


2. Monitoring








10. Summary


Adjustments in P/Suspended Solids Loading from Effects of Inlet Channel Necessary

strong evidence that the P and SS loads delivered to the lake from the inlet channel are lower than the sum of tributary inputs (2013 example) in-channel deposition

instrument deployments together with conc-Tn relationships will be used to estimate the effective loading (subsequently)

development of adjusted loads is necessary to support realistic predictions for the shelf


Tn (

NT

U)

1

10

100

1000

10000

Q (

cfs

)

0

1000

2000

3000

4000Cay. Inlet

6 Mile Crk.

2013

05/01 07/01 09/01 11/01

TS

S (

mg/L

)

1

10

100

1000

10000

large differences

large differences

IL

Cayuga In. Cr.

6Mile

Fall Cr.

Estimation of Constituent Concentration Using In Situ Deployments


TD

P (

µg/L

)1

10

100

PP

(µ

g/L

)

1

10

100

1000

In Situ Tn (NTU)

1 10 100 1000

SR

P (

µg/L

)

0.1

1

10

100

In Situ Tn (NTU)

1 10 100 1000

TS

S (

mg/L

)

1

10

100

1000[PP] =9.29·Tn

0.517

r ²=0.71

[TDP] =5.25·Tn0.366

r ²=0.67

[SRP] =1.21·Tn0.583

r ²=0.60

[TSS] =1.93·Tn0.801

r ²=0.84

Instrument deployments

Frequent (>> daily) Tn (driver)measurements

M J J A S O

In S

itu

Tn (

NT

U)

1

10

100

1000

Concentration-driver (Tn) relationships

frequent estimates of concentrations and daily

flows used to estimate daily loads

advantage in load estimation

and certainty


2. Monitoring







9. Inlet Channel Effects: Case for Inlet Load Adjustments10. Summary


Summary comprehensive event-based

monitoring of major tributaries conducted in 2013

constituent concentrations increased during high flow, particularly metrics of particulates

concentration-driver relationships developed to estimate conditions on non-monitored days Q and T, relationships of varied

strengths

loading estimates made (daily time step) to support modeling for multiple constituents multiple protocols, reasonable closure majority of loads delivered during high

flow intervals sediment loads mostly inorganic apportioned according sources


Summary adjustments of constituent loading

from tributaries entering the Inlet Channel are required because of depositional losses

bioavailability of P forms and development of BAPL’s PP – limited, SUP – mostly, SRP ~

completely

BAPL ~ 25% of TPL for the lake

point source contributions are minor (< 5%)

interannual variations (driven by Q) are large relative to individual source contributions


Questions


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