Hydrologic Processes Controlling
Stream Water Chemistry in a
Claypan Watershed in Missouri
Fengjing Liu1, Robert Lerch2, John
Yang1, and Claire Baffaut2
1 Department of Agriculture & Environmental Science &
Cooperative Research Programs, Lincoln University of
Missouri, Jefferson City, MO
2 Cropping Systems and Water Quality Research Unit,
Agricultural Research Service, USDA, Columbia, MO
Motivations
• Runoff from agricultural watersheds remains a
critical concern of stream water quality in the
Midwest (USEPA, 2006).
• Water quality has declined in some parts of
Missouri in the 1990s (Lory, 1999).
• Processes controlling agrochemical transport
are still poorly understood for claypan
watersheds in the U.S. Midwest (Lerch and
Blanchard, 2003).
Research Objectives
• To determine the sources of stream flow in a
claypan watershed;
• To understand the controls of stream water
chemistry in this watershed.
Goodwater Creek Experimental
Watershed (GCEW)
Fact of GCEW
W1
Active Weir
Inactive Weir
Rain Gauge
Stream
Elevation (m)
High: 295
Low: 243
N
Wells
0
5000 m
• Drainage Area = 72 km2;
• Slope = 0 – 3%;
• Mean Annual
Precipitation = 965 mm;
• Soil Hydrology Group =
C-D;
• Soil Surface Texture =
Silt Loam;
• Claypan Restrictive
Layer = 15 – 45 cm;
Soils and Land Uses at GCEW
Land Uses
72.9% Cropland
Hydrologic Soil Group
8% B/D
25.3% C/D
66.4% D
0.3% Water
0 1.25 2.5
²
5.2% Deciduous Forest
0.1% Deciduous Woody/Herbaceous
0.0% Evergreen Forest
14.3% Grassland
0.1% Herbaceous-Dominated Wetland
0.2% High Intensity
2.0% Impervious
2.3% Low Intensity
0.7% Open Water
5 Kilometers
2.2% Woody-Dominated Wetland
Geologic Profile
Sample Collection and Analysis
• Samples have been collected since summer
2011 from rain gauges, groundwater wells, seep
flows, and streams at three weirs.
• Stream samples were taken biweekly to
monthly and groundwater samples bimonthly.
• Samples were analyzed for pH, electric
conductivity (EC), major and trace elements
using ICP-OES at our laboratory at Lincoln
University.
Data Analysis and Modeling
Isotopic & Chemical Data
Conservative + Nonconservative
Diagnostic Tool of
Mixing Models (Hooper, 2003)
Process Diagnosis
Endmember
Mixing?
N
Probably Fractal Behavior
as of Kirchner (2000)
Y
(1) # of Endmembers
(2) Conservative Tracers
Determining & Evaluating
Endmembers
Endmember Mixing Analysis
(Christophersen et al., 1992)
Endmember Contributions
Liu et al., 2008
Diagnostic Tools of Mixing Models
•
Chemical equilibrium is a non-linear process, while mixing
is a linear one.
•
Eigenvectors of principal component analysis (PCA) on
stream water data can be used to determine solutes that
are caused by mixing, along with the number of
endmembers without using any information from the
endmembers (Hooper, WRR, 2003).
How?
¾ Eigenvectors, V, extracted by PCA with all chemical solutes in stream;
¾ Projection of stream water chemistry using eigenvectors, V;
Xˆ * = X *V T (VV T ) −1V
¾ Residuals (R) calculated; X* for measured concentrations in stream;
R = Xˆ * − X *
¾ Residuals: a random pattern vs. measured concentrations in stream?
Stream Flow & Electric Conductivity (EC)
450
EC
35
EC (μS cm-1)
350
30
300
25
250
20
200
15
150
100
10
50
5
0
8/10/2010
2/26/2011
9/14/2011
4/1/2012
0
10/18/2012
Stream flow (m3 s-1)
400
40
Concentrations of Major and Trace
Elements
20
15
Ca, ppm
600
400
10
200
5
0
0
10
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
8
Zn, ppm
Fe, ppm
EC, μS cm-1
800
6
4
2
0
PPT GW W 11 W 9 W 1
PPT GW W 11 W 9 W 1
Determination of Conservative Tracers
1-D
2-D
EC, μS cm-1
100
0
-100
R2 = 0.55
R2 = 0.53
-200
0
100 200 300 400 500 0
100 200 300 400 500
0.04
R2 = 0.13
Sr, ppm
Residuals Calculated by DTMM
200
R2 = 0.08
0.00
-0.04
0.0
0.1
0.2
0.3 0.0
0.1
0.2
0.3
Measured Concentrations, with the same unit as ordinates
Conservative Tracers
Elements
1-D
2-D
3-D
EC
0.55
0.53
0.20
Ca
0.16
0.12
0.09
Mg
0.13
0.10
0.07
Na
0.04
0.04
0.04
K
0.17
0.01
0.02
Al
0.15
0.05
0.04
Sr
0.13
0.08
0.07
Fe
0.90
0.34
0.28
Zn
0.97
0.84
0.84
S
0.87
0.86
0.46
P
0.77
0.36
0.36
Mn
0.81
0.68
0.38
Ba
0.74
0.54
0.53
• The distribution of
residuals against the
measured chemical
concentrations in
stream water becomes
a random pattern in 2D for Ca, Mg, Na, K, Al,
and Sr.
• Ca, Mg, Na, K, Al, and
Sr are conservative;
• Three endmembers.
Mixing Diagram
4
Rainwater
(Surface runoff)
2
Mixing Diagram for
Goodwater Creek
Stream at W1
Stream at W9
Groundwater
Stream at W11
U2 (PC2)
Rainwater
0
Groundwater
Seep flow
-2
-4
Seep Flow
(Shallow subsurface flow)
-6
-10
-5
0
U1 (PC1)
5
10
Endmember
Contributions to
Stream Flow (%)
• Contributions of surface
runoff and groundwater
are almost equal (~4050%) during dry seasons;
• Contribution of
groundwater appears to
be greater at smaller
catchment scales;
• Contribution of shallow
subsurface flow is ~1520%.
Summaries
• Stream flow was primarily controlled by surface
runoff from rain events, groundwater below the
claypan, and shallow subsurface water above the
claypan;
• During dry seasons, the contribution of
groundwater to stream flow becomes important,
but still usually less than 50%;
• Shallow subsurface flow above claypan contributed
only 15-20%.
Implications
• Though groundwater contributed less than 50% to
stream flow, the impact of groundwater quality to
stream water quality cannot be ignored, as
groundwater has been highly contaminated by
nitrate (see poster from Dr. Omar Al-Qudah at this
conference);
• Herbicides primarily persist in shallow soils after
they are applied; the impact of shallow subsurface
water to herbicide concentrations in stream water
during dry seasons need to be re-examined.
References
•
Christophersen, N. and R. P. Hooper (1992), Multivariate analysis of streamflow
chemical data: the use of principal components analysis for the end-member mixing
problem, Water Resources Research, 28(1), 99-107.
•
Hooper, R. P. (2003), Diagnostic tools for mixing models of streamflow chemistry,
Water Resources Research, 39(3), 1055, doi: 10.1029/2002WR001528.
•
Lerch, R. N. and P. E. Blanchard, (2003), Watershed vulnerability to herbicide transport
in northern Missouri and southern Iowa streams. Environmental Sciences and
Technology, 37, 5518-5527.
•
Liu, F., R. C. Bales, M. H. Conklin, and M. E. Conrad (2008a), Streamflow generation
from snowmelt in semi-arid, forested and seasonally snow-covered catchments, Valles
Caldera, New Mexico, Water Resources Research, 44, W12443,
doi:10.1029/2007WR006278.
•
Lory J. A. (1999), Agricultural Phosphorus and Water Quality,
http://muextension.missouri.edu/xplor).
•
USEPA (2006), Human Health Issues: Pesticides,
http://www.epa.gov/opp00001/health/human.htm.
Acknowledgements
• Funding was provided by the USDA-NIFA through
– Capacity Building Grant Program (#2011-38821-30956);
– An Evans-Allen Grant (#0225140);
– and a USDA-NIFA’s award for Establishing 1890 Land Grant
Universities Water Center (#2010-38821-21614).
• Field instrumentation, sampling and lab analysis were
assisted by Mr. Mark Olson, Mr. Romel Lewis, Mr. Greg
Peters, Dr. Omar Al-Qudah and Ms. Dandan Huang.
More data …
• We have other studies in the same and adjacent
watersheds showing much more data in two
posters below at this conference:
¾ Poster #2 by Omar Al-qudah and Fengjing Liu:
Assessment of Nitrate Concentrations in
Groundwater in a Claypan Watershed in Missouri;
¾ Poster #13 by Dandan Huang and Fengjing Liu:
Impacts of Land Use and Land Cover Changes on
Hydrology and Water Quality in Hinkson Creek
Watershed in Missouri.