Hydrologic Processes Controlling Stream Water Chemistry in a Claypan Watershed in Missouri

Transcription

1 Hydrologic Processes Controlling Stream Water Chemistry in a Claypan Watershed in Missouri Fengjing Liu 1, Robert Lerch 2, John Yang 1, and Claire Baffaut 2 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

2 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).

3 Research Objectives To determine the sources of stream flow in a claypan watershed; To understand the controls of stream water chemistry in this watershed.

4 Goodwater Creek Experimental Watershed (GCEW) Fact of GCEW Wells W1 Active Weir Inactive Weir Rain Gauge Stream Elevation (m) High: 295 N Low: m Drainage Area = 72 km 2 ; Slope = 0 3%; Mean Annual Precipitation = 965 mm; Soil Hydrology Group = C-D; Soil Surface Texture = Silt Loam; Claypan Restrictive Layer = cm;

5 Soils and Land Uses at GCEW Hydrologic Soil Group 8% B/D 25.3% C/D 66.4% D 0.3% Water ² Kilometers Land Uses 72.9% Cropland 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 2.2% Woody-Dominated Wetland

6 Geologic Profile

7 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.

8 Data Analysis and Modeling Isotopic & Chemical Data Conservative + Nonconservative Process Diagnosis Diagnostic Tool of Mixing Models (Hooper, 2003) Determining & Evaluating Endmembers Endmember Mixing? Y (1) # of Endmembers (2) Conservative Tracers Endmember Contributions N Probably Fractal Behavior as of Kirchner (2000) Endmember Mixing Analysis (Christophersen et al., 1992) Liu et al., 2008

9 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? Eigenvectors, V, extracted by PCA with all chemical solutes in stream; Projection of stream water chemistry using eigenvectors, V; Xˆ * = X * V ( VV ) T T 1 V Residuals (R) calculated; X* for measured concentrations in stream; R = Xˆ * X * Residuals: a random pattern vs. measured concentrations in stream?

10 Stream Flow & Electric Conductivity (EC) EC (μs cm -1 ) EC /10/2010 2/26/2011 9/14/2011 4/1/ /18/2012 Stream flow (m 3 s -1 )

11 EC, μs cm -1 Concentrations of Major and Trace Elements Ca, ppm Fe, ppm PPT GW W11 W9 W1 Zn, ppm PPT GW W11 W9 W1

12 Determination of Conservative Tracers D 2-D Residuals Calculated by DTMM EC, μs cm -1 Sr, ppm R 2 = 0.55 R 2 = R 2 = 0.13 R 2 = Measured Concentrations, with the same unit as ordinates

13 Conservative Tracers Elements 1-D 2-D 3-D EC Ca Mg Na K Al Sr Fe Zn S P Mn Ba The distribution of residuals against the measured chemical concentrations in stream water becomes a random pattern in 2- D for Ca, Mg, Na, K, Al, and Sr. Ca, Mg, Na, K, Al, and Sr are conservative; Three endmembers.

14 Mixing Diagram 4 2 Rainwater (Surface runoff) Mixing Diagram for Goodwater Creek Groundwater Stream at W1 Stream at W9 Stream at W11 Rainwater U2 (PC2) 0-2 Groundwater Seep flow -4 Seep Flow (Shallow subsurface flow) U1 (PC1)

15 Endmember Contributions to Stream Flow (%) Contributions of surface runoff and groundwater are almost equal (~40-50%) during dry seasons; Contribution of groundwater appears to be greater at smaller catchment scales; Contribution of shallow subsurface flow is ~15-20%.

16 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%.

17 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.

18 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), Hooper, R. P. (2003), Diagnostic tools for mixing models of streamflow chemistry, Water Resources Research, 39(3), 1055, doi: /2002WR 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, 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: /2007wr Lory J. A. (1999), Agricultural Phosphorus and Water Quality, USEPA (2006), Human Health Issues: Pesticides,

19 Acknowledgements Funding was provided by the USDA-NIFA through Capacity Building Grant Program (# ); An Evans-Allen Grant (# ); and a USDA-NIFA s award for Establishing 1890 Land Grant Universities Water Center (# ). 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.

20 More data We have other studies in the same and adjacent watersheds showing much more data in two posters below at this conference: Poster #2 by Omar Al-qudah and Fengjing Liu: Assessment of Nitrate Concentrations in Groundwater in a Claypan Watershed in Missouri; 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.