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Monitoring Choices
Affect Our Discernment
of Watershed Processes
and Weather Controls on
Conservation Effectiveness
Mark Tomer
USDA/ARS
National Laboratory for Agriculture and the Environment
Driving questions
How can monitoring designs be chosen to provide the right information?
How do we manage tradeoffs among contaminants (e.g., NO3-N vs. P)? Can monitoring of multiple contaminants help answer this, or must we always choose the lesser of two pollutants?
Along what key pathways are contaminants being transported? How can monitoring efforts help identify transport pathways and sources?
Can monitoring results indicate what types of conservation practices could achieve water quality improvement and where to place them?
What is the role of (so called) extreme events in transport of agricultural pollutants? What monitoring duration do we need to discern this?
Can we use monitoring data to characterize conservation performance beyond a simple ‘% removal’ metric?
Elevation
384m
285m
10 Km
Gauge Grab
station site
Stream
Tile
Field
South Fork Iowa River
Watershed
Two Case Studies
Field Flume TC101 (10.6 ha)
Upper Tipton watershed - Tile
drained, farmed wetlands (potholes)
Phosphorus
concentrations in
two tiles and
Tipton Cr. outlet,
2005-2007
Tipton Creek
0
0.5
1
1.5
2
2.5
3
3.5
4
Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08
Date
To
tal P
, m
g L
-1
0
0.5
1
1.5
2
2.5
3
3.5
4
Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08
Date
To
tal P
, m
g/L
Large tile Small tile
Monitoring Sept. 2006 event
Three scales – field runoff, two tile outfalls, watershed outlet.
Automated sampling
Samples measured for NO3-N, Total P, and E. coli and monitoring of hydrologic discharge at all three scales.
Sediment at the watershed outlet with 7Be:210Pb nuclide analyses to estimate sediment source (channel vs. sheet & rill)
Context of event
Dry antecedent conditions
Late summer, full cover of mature crops
Large event, but small hydrologic
response
Peak discharge was about one half of
bank full discharge
"
"
Rainfall (mm)
47.9 - 57.6
57.7 - 67.2
67.3 - 76.9
77.0 - 86.6
" Rain gauge
Tile
Field
Rainfall event, Sept. 10-11 2006
0
20
40
60
80
100
120
140
9/10/06
0:00
9/10/06
12:00
9/11/06
0:00
9/11/06
12:00
9/12/06
0:00
Date & time
Ra
infa
ll, m
m
Tile Field
Hydrologic response to rainfall
event at three scales
0.0001
0.001
0.01
0.1
1
10
10-Sep 11-Sep 12-Sep 13-Sep 14-Sep 15-Sep 16-Sep 17-Sep
Date (2006)
Q, m
m h
r-1
Stream Field Tile
Note double peak;
First for runoff, then
for tile flow
Field flume: discharge, nutrients,
and E. coli
0.0
2.0
4.0
6.0
8.0
10.0
12.0
10-Sep 11-Sep 12-Sep
Date (2006)
NO
3-N
& to
tal P, m
g L
-1
ln (
E. co
li),
mp
n 1
00
mL
-1
0
10
20
30
40
50
60
Q, L s
-1
NO3-N total P ln E. coli Q
Tile outfalls:
discharge,
nutrients,
and E. coli
0
5
10
15
20
25
10-Sep 11-Sep 12-Sep 13-Sep 14-Sep 15-Sep 16-Sep 17-Sep
Date (2006)
NO
3-N
& to
tal P
, m
g L
-1
ln E
.co
li m
pn
10
0m
L-1
0
100
200
300
400
500
600
700
Q, L
s-1
NO -N total P ln E. coli Q
0
5
10
15
20
10-Sep 11-Sep 12-Sep 13-Sep 14-Sep 15-Sep 16-Sep 17-Sep
Date (2006)
NO
3-N
& to
tal P
, m
g L
-1
ln E
.co
li m
pn
10
0m
L-1
0
5
10
15
20
25
30
35
Q, L
s-1
Nitrate-N Total P ln E. coli Outlet Q
Large tile (TC240)
Small tile (TC242)
Hydrograph
separations at
tile outlets
based on
NO3-N
mixing model
0
100
200
300
400
500
600
700
10-Sep 11-Sep 12-Sep 13-Sep 14-Sep 15-Sep 16-Sep 17-Sep
Date (2006)
Q, L
s-1
Q Q (tile)
0
5
10
15
20
25
30
35
10-Sep 11-Sep 12-Sep 13-Sep 14-Sep 15-Sep 16-Sep 17-Sep
Date (2006)
Q,
L s
-1
Q Q (tile)
Large tile (TC240)
Small tile (TC242)
Stream outlet: discharge,
nutrients, and E. coli
0
5
10
15
20
25
10-Sep 11-Sep 12-Sep 13-Sep 14-Sep 15-Sep 16-Sep 17-Sep
Date (2006)
NO
3-N
& to
tal P, m
g L
-1
ln (E
.co
li),
mp
n 1
00
mL-1
0
1
2
3
4
5
6
Q, m
3s
-1
NO3-N Total P ln (E. coli) Q
Hydrograph separation – Stream outlet
0
1
2
3
4
5
6
10-Sep 11-Sep 12-Sep 13-Sep 14-Sep 15-Sep 16-Sep 17-Sep
Date (2006)
Q, m
3 s
-1
Q Q (tile) Q (ground water)
Cumulative NO3-N loads at tile
and stream gauges
0
100
200
300
400
500
600
700
800
900
1000
9/10 9/11 9/12 9/13 9/14 9/15 9/16 9/17
NO
3-N
lo
ad
, g h
a-1
Stream Tile
0
100
200
300
400
500
600
700
800
900
1000
9/10 9/11 9/12 9/13 9/14 9/15 9/16 9/17
NO
3-N
lo
ad
, g h
a-1
Stream Tile
Sediment response:
78% from channel sources*
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
10-Sep 11-Sep 12-Sep 13-Sep 14-Sep 15-Sep 16-Sep 17-Sep
Date (2006)
Se
dim
en
t, k
g m
-3
Fra
ctio
n s
ed
ime
nt
0
1
2
3
4
5
6
Dis
ch
arg
e, m
3 s
-1
Sediment concentrationg/m3
Fraction sediment fromfield erosion
Stream discharge
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
10-Sep 11-Sep 12-Sep 13-Sep 14-Sep 15-Sep 16-Sep 17-Sep
Date (2006)
Se
dim
en
t, k
g m
-3
Fra
ctio
n s
ed
ime
nt
0
1
2
3
4
5
6
Dis
ch
arg
e, m
3 s
-1
Sediment concentrationg/m3
Fraction sediment fromfield erosion
Stream discharge
*estimated on 7Be/210Pb nuclide ratios
Note: peak sediment concentration
occurred before hydrograph peak
Cumulative total P loads at tile,
field and stream gauges
0
10
20
30
40
50
60
70
10-Sep 11-Sep 12-Sep 13-Sep 14-Sep 15-Sep 16-Sep 17-Sep
Date (2006)
To
tal P
lo
ad
, g
ha-1
Stream Field Tile
Cumulative E. coli loads at tile, field
and stream gauges
0
2000
4000
6000
8000
10000
12000
14000
10-Sep 11-Sep 12-Sep 13-Sep 14-Sep 15-Sep 16-Sep 17-Sep
Date (2006)
E. co
li -
10
6 c
fu h
a-1
Stream Tile Field
Major
sources and
pathways:
Highly erodible crop land
Okoboji/Harps soils
Surface
Drainage districts
Sub-surface
• Subsurface (tile)
• Surface
• Channel
Study One - Conclusions NO3-N was dominantly sourced from tiles (>90%).
Sediment was dominantly (78%) sourced from stream banks.
Surface intakes draining depressions found an important source of P, along with stream sediments.
E. coli was dominated by near- and in-channel sources, although runoff and tile intake sources also contributed.
Conservation emphases on erosion control and nutrient management in this watershed should be expanded to include vegetative practices that stabilize/restore streams and buffer surface intakes that drain potholes.
This single event analysis helped clarify source pathways of key contaminants, helping to inform a more comprehensive approach to water quality management.
Driving questions
How can monitoring designs be chosen to provide the right information?
How do we manage tradeoffs among contaminants (e.g., NO3-N vs. P)? Can monitoring of multiple contaminants help answer this, or must we always choose the lesser of two evils?
Along what key pathways are contaminants being transported? Can monitoring efforts help identify transport pathways and sources?
Can monitoring results indicate what types of conservation practices could achieve water quality improvement and where to place them?
What is the role of (so called) extreme events in transport of agricultural pollutants? What monitoring duration do we need to discern this?
Can we use monitoring data to characterize conservation performance beyond a simple ‘% removal’ metric?
Transition to study two
Study one comprised detailed and nested
monitoring (at 3 scales) of multiple
contaminants during a single rainfall runoff
event (seven days)
Study two compared two fields for total P
transport and runoff amounts during
eleven years.
Elevation
384m
285m
10 Km
Gauge Grab
station site
Stream
Tile
Field
South Fork Iowa River
Watershed
SF101
SF102
Second Case Study
A tale of two fields: HOW DO RUNOFF AND NUTRIENT LOADS DIFFER BETWEEN THEM?
one manured: SF101 one not: SF102
SF101
(manured)
SF102
(not manured)
Rainfall /
runoff
record
(daily)
0
20
40
60
80
100
120
140
160
180
2000
5
10
15
20
25
30
35
40
45
50
Jan-0
0
Ju
l-00
Jan-0
1
Jul-01
Jan-0
2
Jul-02
Jan-0
3
Ju
l-03
Jan-0
4
Jul-04
Jan-0
5
Ju
l-05
Jan-0
6
Ju
l-06
Jan-0
7
Jul-07
Jan-0
8
Ju
l-08
Jan-0
9
Jul-09
Jan-1
0
Ju
l-10
Runoff
-pro
ducin
g p
recip
itation,
mm
Surf
ace
runoff
, m
m
Date
Runoff Rainfall
0
20
40
60
80
100
120
140
160
180
2000
5
10
15
20
25
30
35
40
45
50
Jan-0
0
Jul-00
Jan-0
1
Jul-01
Jan-0
2
Jul-02
Jan-0
3
Jul-03
Jan-0
4
Jul-04
Jan-0
5
Jul-05
Jan-0
6
Jul-06
Jan-0
7
Jul-07
Jan-0
8
Jul-08
Jan-0
9
Jul-09
Jan-1
0
Jul-10
Runoff
-pro
ducin
g p
recip
itation,
mm
Surf
ace runoff
, m
m
Date
Runoff Rainfall
SF101
SF102
Similarity in amounts of
rainfall and runoff per event
y = 1.01xR² = 0.83
0
20
40
60
80
100
120
0 20 40 60 80 100 120
SF10
2 ra
in (m
m)
SF101 rain (mm)
y = 1.16xR² = 0.77
0
5
10
15
20
25
30
35
40
0 5 10 15 20 25 30SF
102
run
off
(mm
)SF101 runoff (mm)
Significant difference in runoff – P load relationship
y = 0.018x0.947
R² = 0.870
y = 0.018x1.118
R² = 0.858
0.001
0.01
0.1
1
10
0 10 20 30 40
P lo
ss d
uri
ng
eve
nt (k
g/h
a)
Amount of runoff (mm/event)
Not Manured Manured
In-Field Conservation Practices Impact on
Runoff-P Load Relationship Could Improve
Effectiveness of Edge of Field Practices
0.001
0.01
0.1
1
10
0.01 0.1 1 10 100
P lo
ss d
uring
eve
nt (k
g/h
a)
Amount of runoff (mm/event)
Reduce runoff amounts
PL = aQb
Study two: Conclusions
Eleven years of monitoring provided data for >90 rainfall
runoff events in two field-sized watersheds differing in
manure application.
Long periods with little or no runoff were punctuated with
flashy runoff events.
Half the cumulative runoff observed in 11 yrs occurred in
<48 hours.
The two watersheds were similar in rainfall and runoff
amounts.
Study Two Conclusions: P losses
P losses characterized:
P losses averaged about 1.80 kg/ha.yr in the manured watershed
and 1.05 kg/ha.yr in the non-manured watershed.
Differences in the relationship between runoff and P losses were
observed – implications for assessment of practices, and on the
performance of additional practices placed below the field edge.
Large events placed in context:
Storms <60 mm resulted in 84-88% of the observed P load; more
than half the P load was associated with 30-60 mm rainfall events
in both watersheds.
Conservation practices that limit runoff from <60 mm storms should
also limit P losses from these soils.
Driving questions Can monitoring designs be chosen to provide the right information?
Yes, consider goals and options for TIMING, FREQUENCY, NESTING and DURATION of monitoring.
How do we manage tradeoffs among contaminants (e.g., NO3-N vs. P)? Can monitoring of multiple contaminants help answer this, or must we always choose the lesser of two pollutants?
Along what key pathways are contaminants being transported? Can monitoring efforts help identify transport pathways and sources?Contaminants may have unique sources and pathways, which nested, detailed monitoring can help to characterize.
Can monitoring results indicate what types of conservation practices could achieve water quality improvement and where to place them?YES, information on contaminant sources and pathways can help identify appropriate practices to address multiple contaminants, at least in a general way.
What is the role of (so called) extreme events in transport of agricultural pollutants? What monitoring duration do we need to discern this?Decade or more of monitoring needed to place large events in context.
Can we use monitoring data to characterize conservation performance beyond a simple ‘% removal’ metric?Suggestion to characterize runoff – nutrient load relationship when evaluating practice effectiveness.
ThanksCo-Authors
Kevin Cole
Tom Moorman
Tom Isenhart
John Kovar
Dave Heer
Chris Wilson
Technical supportKelly Barnett
Beth Douglass
Amy Morrow
Jeff Nichols
PartnersSouthfork Watershed Alliance
USDA-NRCS