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1
Pre-harvest Calibration of a Paired and Nested Watershed Study to Evaluate Event-Based
Suspended Sediment Export
The Little Creek StudySwanton Pacific Ranch
Davenport, CA
Michael C. GaedekeAugust, 2007
Project DirectorBrian Dietterick
Presentation Outline
• Location overview• Management history• Little creek monitoring project
• Goals• Study design• General timeline• Water quality data collected and
stat analysis
Management history
Looking Downstream in North Fork ~1910
Looking Upstream in North Fork ~2000
Little Creek study goals and current analysis
• Scientifically document water quality and channel conditions before, during, and after single-tree and small group selection harvests
• Evaluate the effectiveness of current forest practice rules and best management practices for timber harvesting activities in maintaining existing water quality and channel conditions
• Current analysis– Analyze the storm event water quality data from the Little Creek
watershed to assess the calibration (pre-treatment phase) of the Little Creek Study.
– Describe the existing variability– Determine the magnitude of change capable of being detected in
the post-treatment period
2
Paired Watershed Design•Control watershed: South Fork•Treatment watershed: North Fork
Nested Watershed Design•Control Watershed: Upper North Fork•Treatment watershed: North Fork
Timeline and study designCalibration period 2001-2008
•Measure existing water qualityTreatment in 2008
•Harvest portion of watershed between the North Fork and Upper North Fork stations
Post-treatment 2008-2011+•Measure for potential change in water quality
Field data collection – stage and streamflow
NF Stag e (ft)
NF
Flo
w (
ft^
3/s)
1.00.90.80.70.60. 50.40.30.2
25
20
15
10
5
0
S 0.573547R-Sq 99.6%R-Sq(adj) 99.5%
Fitted Line PlotNF Flow = - 1 .476 - 1.2 21 NF Stage
+ 29.10 NF S tage**2
S F Stag e (ft)
SF F
low
(ft̂
3/s)
1.21.11.00.90.80. 7
12
10
8
6
4
2
0
S 0.133903R- Sq 99.9%
R- Sq(adj) 99.9%
Fitted Line PlotS F Flow = 16.7 2 - 51.81 SF Stage
+ 40.2 0 SF St ag e* *2
A. Flow Meter
B. Pressure
Transducer
C. FW-1 Stage
Recorder
D. Staff Gage
Stage
B
C
D
A
Field data collection – water quality samples
• 1-hour interval samples• Collection of 24 bottles
before swap
• Lab analysis of 1 hour samples
• Turbidity• Units: nephlometric turbidity
units (NTUs)
• Suspended Sediment Concentration (SSC)
• Units: mg/L
Lab Water Quality Testing
A. Turbidimeter B. Scale
AB
Defining the dataset – Predicting SSC from turbidity
• Need for SSC to be predicted from turbidity– Turbidity shown to be a better predictor than flow
• Previous research has indicated SSC versus turbidity is a variable relationship that is best defined on an event basis
• Regression analysis used to establish relationship.– Regressions assessed based on r2, p-value, residual
plots, and fits– Data transformations when necessary
3
Defining the dataset – storm events
• Define storm events based on the hydrograph using Hewlett and Hibbard (1967) 0.05 slope method– Separates storm flow from base flow
• Minimum storm event size based on turbidity– Peak must be greater than 20 NTUs– Only samples >20 NTUs analyzed for SSC
• Storm event ends when turbidity drops below 20 NTUs or the 0.05 slope line intersects the hydrograph
End event
Event load calculation
• Calculate individual hourly loads to determine event loads– High temporal variability requires hourly sums
• Event loads establish the calibration dataset– Changes in the relationship used to detect
change
• Events with complete SSC and flow datasets used for analysis– Must have both datasets to calculate loads
Paired and nested analysis after transformations
Paired
Nested
South Fork load (ln[kg/ha])
No
rth
For
k l
oa
d (
ln[k
g/
ha]
)
543210-1-2-3
7
6
5
4
3
2
1
0
S 1. 00230R -Sq 64.8%
R -Sq( adj) 63.4%
Regression95% CI
Fitted Line PlotNorth Fork load (ln[kg/ha]) = 2.023 + 0.7657 South Fork load (ln[kg/ha])
Upper N. Fork load (ln[kg/ha])
No
rth
Fo
rk l
oa
d (
ln[k
g/
ha]
)
6543210-1-2
6
5
4
3
2
1
0
-1
S 0.382495
R -Sq 95.5%R -Sq( adj) 95.3%
Regression95% C I
Fitted Line PlotNorth Fork l oad (ln[kg/ha]) = 0.5090 + 0.8396 Upper N. Fork load (ln[k g/ha])
Assessing detectable magnitude of change using confidence intervals
• Back-transformation of confidence interval into non-logarithmic numbers not valid
• Generate a synthetic dataset representing percentage increases over the original dataset
• Transform the new dataset, perform regression, and compare new regression line to original confidence interval
New regression line comparison to existing confidence intervals for NF versus SF
South Fork load (ln[kg/ha])
No
rth
Fo
rk l
oa
d (
ln[k
g/
ha
])
543210-1-2-3
7
6
5
4
3
2
1
0
Existing conditionsNF load increased 30%
Associated RegressionsData Points and
30% Increase in Existing NF Conditions, NF v SF
95% C.I. for existing conditions-------
South Fork load (ln[kg/ha])
No
rth
Fo
rk l
oa
d (
ln[k
g/
ha
])
543210-1-2-3
7
6
5
4
3
2
1
0
Existing conditionsNF load increased 50%
Associated RegressionsData Points and
50% Increase in Existing NF Conditions, NF v SF
95% C.I. for existing conditions-------
South Fork load (ln[kg/ha])
No
rth
Fo
rk l
oa
d (
ln[k
g/
ha
])
543210-1-2-3
7
6
5
4
3
2
1
0
Existing conditionsNF load increased 70%
Associated RegressionsData Points and
70% Increase in Existing NF Conditions, NF v SF
95% C.I. for existing conditions-------
South Fork load (ln[kg/ha])
No
rth
Fo
rk l
oa
d (
ln[k
g/h
a])
543210-1-2-3
7
6
5
4
3
2
1
0
-1
Existing conditionsNF load increased 90%
Associated RegressionsData Points and
90% Increase in Existing NF Conditions, NF v SF
95% C.I. for existing conditions-------
4
New regression line comparison to existing confidence intervals for NF versus UNF
Upper North Fork load (ln[kg/ha])
No
rth
Fo
rk l
oa
d (
ln[k
g/
ha
])
6543210-1-2
6
5
4
3
2
1
0
Existing conditionsNF load increased 10%
Associated RegressionsData Points and
95% C.I. for existing conditions
10% Increase in Existing NF Conditions, NF v UNF
-------
Upper North Fork load (ln[kg/ha])
No
rth
Fo
rk l
oa
d (
ln[k
g/h
a])
6543210-1-2
6
5
4
3
2
1
0
Existing conditionsNF load increased 30%
Associated RegressionsData Points and
95% C.I. for existing conditions
30% Increase in Existing NF Conditions, NF v UNF
-------
Conclusions• Suspended sediment versus turbidity relationship
has allowed for prediction of suspended sediment data– Established on an event basis
• Data thus far has yielded a sufficient number of event-specific suspended sediment loads to enable simple linear regression analysis– Nested (NF versus UNF) relationship indicates less
variability than paired (NF versus SF) relationship– Narrower confidence intervals for magnitude of change
detection for nested relationship
Other Little Creek Study Components
• Annual geomorphic surveys– Longitudinal profiles and cross sections to
detect potential sediment source/sink areas
• LIDAR mapping analysis (Russ White)– Stream channel and road features under forest
canopy– Comparison with conventional surveys
Questions?