Sediment transport mechanisms influencing spatiotemporal resuspension patterns in a shallow, polymictic reservoir

Figures & data

Table 1 Morphometric and watershed characteristics of Lake Waco.

Characteristic	Value
Conservation pool elevation (m a.s.l.)	141
Surface area (×10^6 m^2)	35.3
Mean depth (m)	6.4
Maximum depth (m)	21.9
Volume (×10^8 m^3)	2.26
Water residence time (yr)*	0.37
Perimeter (×10^5 m)	1.12
Lake watershed area (×10^5 ha)	4.24
N. Bosque River watershed area (×10^5 ha)	3.19
Watershed/lake area	120
*Calculated as 20-yr average (1980–2001 excluding 1990 and 1991 when river discharge was not recorded).

Figure 1 Lake Waco bathymetric map indicating North Bosque River (NBR), South Bosque River (SBR), deep-water upwelling (DWU), and lacustrine (LAC) sampling stations. Contour lines represent 5 ft (1.5 m) depth intervals and become deeper near dam. Bathymetry data were post-processed from original source (TWDB 2003). Inset map indicates McLennan County, TX

Figure 2 Local wind patterns during study (NOAA 2009). Data were temporally grouped from (A) Nov 2003 to May 2004 and (B) Jun to Oct 2004 to show contrasting wind patterns.

Figure 3 North Bosque River discharge (solid line; USGS 2008) and Lake Waco water surface elevation (broken line; USACE 2008) during study.

Table 2 Annual averages (± standard deviations) for TSS net and gross sedimentation rates (g/m²/d), resuspension rates (gross−net sedimentation rates, g/m²/d), and percent resuspension (resuspen-
sion rate/gross sedimentation rate, %) by sampling station.

Station	Net Sed. Rate	Gross Sed. Rate	Resusp. Rate	% Resusp.
NBR	30.8 ± 19.4	91.9 ± 51.3	64.7 ± 53.0	60.4 ± 22.8
SBR	26.3 ± 17.4	80.6 ± 42.9	53.7 ± 36.9	64.6 ± 17.0
DWU	25.3 ± 19.8	102.9 ± 89.8	77.5 ± 75.7	71.2 ± 14.7
LAC	29.2 ± 19.7	71.6 ± 34.6	47.1 ± 22.7	66.4 ± 10.9

Table 3 Inorganic and organic percent composition of TSS in photic and bottom sediment traps and resuspended materials by sampling station. Values reported as annual averages ± standard deviations.

	Inorganic			Organic
Station	Photic	Bottom	Resusp.	Photic	Bottom	Resusp.
NBR	81 ± 6	85 ± 4	88 ± 7	19 ± 6	15 ± 4	12 ± 7
SBR	79 ± 7	84 ± 4	88 ± 3	21 ± 7	16 ± 4	12 ± 3
DWU	80 ± 5	86 ± 3	89 ± 4	20 ± 5	14 ± 3	12 ± 4
LAC	81 ± 8	84 ± 5	85 ± 5	20 ± 8	16 ± 5	15 ± 5

Figure 4 TSS resuspension rate (bottom trap sedimentation rate − photic trap sedimentation rate) through time by sampling station.

Figure 5 TSS percent resuspension (resuspension rate/bottom trap sedimentation rate) through time by sampling station.

Table 4 Linear regression models of TSS percent resuspension (arcsine squareroot transformed) as a function of average mixing depth (log₁₀ transformed) based on daily resultant wind speeds and directions. Models are presented for significant regressions (α= 0.05) only.

	Slope
Station	Value	SE*	r^2	p-value	SEE**
Whole-lake	−0.21	0.06	0.13	0.002	0.11
DWU	−0.24	0.08	0.30	0.010	0.08

Table 5 Linear regression models of TSS percent resuspension (arcsine squareroot transformed) as a function of mixing depth-to-station depth ratio (z_mix:z_site; log₁₀ transformed) based on daily resultant wind speeds and directions. Models are presented for significant regressions (α= 0.05) only.

	Slope
Station	Value	SE*	r^2	p-value	SEE**
Whole-lake	−0.24	0.06	0.17	<0.001	0.11
SBR	−0.27	0.12	0.20	0.040	0.11

Table 6 Linear regression models of TSS percent resuspension (arcsine squareroot transformed) as a function of average North Bosque River discharge (log₁₀ – log₁₀ transformed). Models are presented for significant regressions (α= 0.05) only.

	Slope
Station	Value	SE*	r^2	p-value	SEE**
Whole-lake†	0.16	0.04	0.19	<0.001	0.11
NBR	0.29	0.08	0.44	0.003	0.12
SBR	0.18	0.07	0.30	0.011	0.10

Table 7 Multiple linear regression model of TSS percent resuspension (arcsine squareroot transformed) as a function of average North Bosque River discharge (log₁₀ – log₁₀ transformed) and z _mix :z _site (log₁₀ transformed) based on daily resultant wind speeds and directions for the South Bosque River station.

	Slope
Variable	Value	SE*	p-value
MLR model (r ^2= 0.47, p-value = 0.003, SEE** = 0.09)
NB discharge	0.17	0.06	0.008
zmix:zsite	−0.25	0.10	0.026

Figure 6 Central Texas reservoirs (triangles; TWDB 2009) and large world natural lakes (circles; ILEC 2009) characterized by mean depth and surface area (modified from Havens et al. 2007). Trendline represents dynamic ratio (DR =√surface area (in km²)/avg. depth (in m); Håkanson 1982) of 0.8 with lakes below this trendline (DR > 0.8) being increasingly affected by wind–wave mixing. Central Texas reservoirs include Aquilla (AQ), Belton (BE), Buchanan (BU), Cedar Creek (CC), Eagle Mountain (EM), Granbury (GB), Grapevine (GP), Lewisville (LW), Limestone (LS), Lyndon B. Johnson (LJ), Possum Kingdom (PK), Ray Hubbard (RH), Richland-Chambers (RC), Somerville (SM), Stillhouse Hollow (SH), Travis (TR), Waco (WC), and Whitney (WH). World natural lakes include Baikal (BA), Biwa (BI), Balaton (BT), Chad (CD), Champlain (CH), Constance (CO), Chapala (CP), Dianchi (DI), Dong-hu (DU), Erie (ER), Gatiun (GA), Geneva (GE), George (GO), Kasumiguara (KA), Kinneret (KI), Malawi (MA), Mendota (ME), Maggiore (MG), Neagh (NE), Nicaragua (NI), Okeechobee (OK), Ontario (ON), Peipsi (PE), Poyang (PY), St. Clair (SC), Simcoe (SI), Tanganyika (TA), Tahoe (TH), Tai-hu (TU), Taupo (TZ), Vattern (VA), Valencia (VL), Washington (WA), and Zurich (ZU).

Havens , K E , Jin , K R , Iricanin , N and James , R T . 2007 . Phosphorus dynamics at multiple time scales in the pelagic zone of a large shallow lake in Florida, USA . Hydrobiologia , 581 : 25 – 42 .

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