The Significance of Model Structure in One- Dimensional Stream Solute Transport Models with Multiple...
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- Slide 1
- The Significance of Model Structure in One- Dimensional Stream
Solute Transport Models with Multiple Transient Storage Zones
Masters Defense of: Patrick Corbitt Kerr Advisor: Michael Gooseff 1
Committee Members: Peggy Johnson 1 Diogo Bolster 2 1 Department of
Civil and Environmental Engineering, The Pennsylvania State
University, State College, PA, USA 2 Department of Civil
Engineering and Geological Sciences, University of Notre Dame, IN,
USA 1
- Slide 2
- Motivation Low-order streams are at the head of the river
continuum and are the primary interface between the river network
and its drainage basin. These streams feature a strong connectivity
with the riparian ecosystem due to channel complexity and stream
gradient. 2 Vannote. R.L., G. W. Minshall, K. W. Cummins, J. R.
Sedell, and C. E. Cushing. 1980. The river continuum concept. Can.
J. Fish. Aquat. Sci. 37: 130-137 1 1
- Slide 3
- Motivation The hydraulic characteristics and biogeochemical
conditions of low-order streams are different than for high-order
streams. Biogeochemical processing is dependent on hydrodynamic
transport. Residence Time Travel Path Residence Conditions 3 Stream
Corridor Restoration: Principles, Processes, and Practices. 1998.
Federal Interagency Stream Restoration Working Group. 2 2
- Slide 4
- Motivation We seek to understand hydrodynamic and
biogeochemical processes, so we try to model it. Simulation of
hydrodynamic transport requires conceptual models to approximate
the complex geometry and physics. Tracer experiments are used to
populate parameters in the solute transport model as well as verify
model physics. 4
- Slide 5
- Motivation These models can provide insight into areas of the
stream difficult to observe. Interpretation of models can also lead
to metrics, a means to quantify biogeochemical and hydraulic
characteristics. These metrics can used at the local, reach, or
watershed scale to investigate processes such as nutrient cycling.
5 Preston, S.D., Alexander, R.B., Woodside, M.D., and Hamilton,
P.A., 2009, SPARROW MODELINGEnhancing Understanding of the Nations
Water Quality: U.S. Geological Survey Fact Sheet 20093019, 6 p. 3
3
- Slide 6
- Transient Storage Model 6 Thackston, E. L., and K. B. Schnelle,
J. (1970). "Predicting effects of dead zones on stream mixing." J.
Sanit. Eng. Div. Am. Soc. Civ. Eng., 96(SA2), 319-331. Hays, J. R.,
Krenkel, P. A., and K. B. Schnelle, J. (1966). Mass transport
mechanisms in open-channel flow, Vanderbilt Univer., Nashville,
Tenn. 4 5 4 5
- Slide 7
- Previous Work Bencala, K. E., and Walters, R. A. (1983).
"Simulation of solute transport in a mountain pool-and-riffle
stream: a transient storage model." Water Resources Research,
19(3), 718-724. Stream_Solute_Workshop. (1990). "Concepts and
methods for assessing solute dynamics in stream ecosystems."
Journal of the North American Benthological Society, 9, 95-119.
Runkel, R. L., and Broshears, R. E. (1991). "One dimensional
transport with inflow and storage (OTIS): A solute transport model
for small streams ", Center for Adv. Decision Support for Water
Environ. Syst., ed., Tech Rep. 91-01. D'Angelo, D. J., Webster, J.
R., Gregory, S. V., and Meyer, J. L. (1993). "Transient storage in
Appalachian and Cascade mountain streams as related to hydraulic
characteristics." Journal of the North American Benthological
Society, 12(3), 223-235. Choi, J., Harvey, J. W., and Conklin, M.
H. (2000). "Characterizing multiple timescales of stream and
storage zone interaction that affect solute fate and transport in
streams." Water Resources Research, 36(6), 1511-1518. Harvey, J.
W., Saiers, J. E., and Newlin, J. T. (2005). "Solute transport and
storage mechanisms in wetlands of the Everglades, south Florida."
Water Resources Research, W05009, doi:10.1029/2004WR003507.
Gooseff, M. N., McKnight, D. M., Runkel, R. L., and Duff, J. H.
(2004). "Denitrification and hydrologic transient storage in a
glacial meltwater stream, McMurdo Dry Valleys, Antarctica."
Limnology and Oceanography, 49(5), 1884-1895. Ensign, S. H., and
Doyle, M. W. (2005). "In-channel transient storage and associated
nutrient retention: Evidence from experimental manipulations "
Limnology and Oceanography. Lautz, L. K., and Siegel, D. I. (2007).
"The effect of transient storage on nitrate uptake lengths in
streams: an inter-site comparison." Hydrological Processes, 21(26),
3533-3548. Briggs, M. A., Gooseff, M. N., Arp, C. D., and Baker, M.
A. (2008). "Informing a stream transient storage model with
two-storage zones to discriminate in-channel dead zone and
hyporheic exchange." Water Resources Research, Vol. 45. 7
- Slide 8
- 1-SZ Inadequacy 1-SZ models lump the stream into only 2- zones,
mobile and immobile. Breakthrough Curves in the channel are not
uniform. Discrimination of immobile zones can lead to better
models. 8 June Slug
- Slide 9
- Multiple Storage Zones Surface Transient Storage (STS) Light,
Aerobic, Particulate, Diurnal Temperature Hyporheic Transient
Storage (HTS) Dark, Anaerobic , Dissolved, Temperate 9
- Slide 10
- Competing Model Structure 10
- Slide 11
- Nested Model Structure 11 HTS MC STS
- Slide 12
- Numerical Model Runkels OTIS was converted to Matlab, multiple
storage zones and a GUI were added. F.D. (Crank-Nicholson) 12
Runkel, R. L., and Broshears, R. E. (1991). "One dimensional
transport with inflow and storage (OTIS): A solute transport model
for small streams ", Center for Adv. Decision Support for Water
Environ. Syst., ed., Tech Rep. 91-01. 6 6
- Slide 13
- A :1.8 m A HTS :0.5 m A STS :1 m D :0.006 m/s Q :0.01 m/s STS
:0.00005 s -1 HTS :0.000005 s -1 U/S Boundary Condition: 1.0 g/m
Step 1-8hr @ 200m U/S 13 Conceptual Comparison of Competing versus
Nested Transient Storage Module Structure using Identical
Parameters
- Slide 14
- Study Site Laurel Run: 1 st order stream Study Reach: 460-m
Drainage Area is 4.66 km of valley-ridge topography, old-growth
deciduous trees and mountain laurel. Chesapeake Bay Watershed
14
- Slide 15
- Tracer Experiments Conservative Tracer: Cl- 3 Constant Rate
Injections: June, July, August High->Low Flow 3 Control
Sections: 0m, 75m, 460m Campbell Scientific CR- 1000 data loggers
with CS547A Cond/Temp Probes 2 Piezometers with Trutrack WT-HR
Capacitance Rods 15
- Slide 16
- MC/STS Parsing 2-SZ model requires 2 more parameters (A HTS,
HTS ) Solution: Second BTC in STS A STS Estimation Velocity
Transects A/A STS Ratio 16
- Slide 17
- Field Results 17 Breakthrough Curves of Solute in Main Channel
A) June, B) July, C) August ParameterJuneJulyAugust A/A STS 2.0 2.6
Q (x10-2 m/s) 5.762.900.87 Q lat (x10-6 m/s)6.3818.00.73
- Slide 18
- Optimization Process Global Optimization Algorithm: SCE-UA
(1992) (Shuffled Complex Evolution Method University of Arizona) 18
IndividualPoint1 Family/GroupSimplexN+1
Community/TribeComplexM=2.N+1 PopulationSampleS=P.(2N+1) N =
Dimension of Problem M = Size of Complex P = Number of Complexes S
= Size of Sample 1-SZ Parameters: D, A, A S, 2-SZ Parameters: D, A,
A STS, STS, A HTS, HTS
- Slide 19
- SCE-UA Optimization Process 19 Duan Q., Sorooshian, S., Gupta,
V. K. 1994. Optimal Use of the SCE-UA Global Optimization Method
for Calibrating Watershed Models. Journal of Hydrology, Vol. 158,
265-284. 6 6
- Slide 20
- SCE-UA Optimization Process 20 Duan Q., Sorooshian, S., Gupta,
V. K. 1994. Optimal Use of the SCE-UA Global Optimization Method
for Calibrating Watershed Models. Journal of Hydrology, Vol. 158,
265-284. 6 6
- Slide 21
- SCE-UA Optimization Process 21 Duan Q., Sorooshian, S., Gupta,
V. K. 1994. Optimal Use of the SCE-UA Global Optimization Method
for Calibrating Watershed Models. Journal of Hydrology, Vol. 158,
265-284. 6 6
- Slide 22
- SCE-UA Optimization Process 22 Duan Q., Sorooshian, S., Gupta,
V. K. 1994. Optimal Use of the SCE-UA Global Optimization Method
for Calibrating Watershed Models. Journal of Hydrology, Vol. 158,
265-284. 6 6
- Slide 23
- SCE-UA Optimization Process 23 Duan Q., Sorooshian, S., Gupta,
V. K. 1994. Optimal Use of the SCE-UA Global Optimization Method
for Calibrating Watershed Models. Journal of Hydrology, Vol. 158,
265-284. 6 6
- Slide 24
- Parameter Optimization 24 Color Coded Parameter Optimization
for July First Iteration - BLUE, Last Iteration - RED 1-SZCompeting
2-SZNested 2-SZ
- Slide 25
- Optimized Parameters Parameter JuneJulyAugust Conceptual
1-SZC-SZN-SZ1-SZC-SZN-SZ1-SZC-SZN-SZC-SZN-SZ D
(m/s)0.8310.9081.080.2010.2260.3200.5030.7450.8620.006 (x10^-5 s -1
)5.4111.63.73 STS (x10-5 s -1 )4385502102401602105.00 HTS (x10-5 s
-1 )9.1917.78.2714.94.7811.40.500 A
(m)0.6180.4110.4180.4710.3310.3390.1870.1340.1371.80 A S
(m)0.3300.1290.125 A STS (m)0.2060.2090.1650.1700.05150.05291.00 A
HTS (m)0.5890.6060.1450.1290.1510.1550.500
RMSE0.3060.3510.3530.4490.3510.3540.2760.4400.4635 Q (x10-2 m/s)
5.762.900.871.00 Q lat (x10-6 m/s)6.3818.00.730.00 25
- Slide 26
- Optimized Parameters Parameter JuneJulyAugust Conceptual
1-SZC-SZN-SZ1-SZC-SZN-SZ1-SZC-SZN-SZC-SZN-SZ D
(m/s)0.8310.9081.080.2010.2260.3200.5030.7450.8620.006 (x10^-5 s -1
)5.4111.63.73 STS (x10-5 s -1 )4385502102401602105.00 HTS (x10-5 s
-1 )9.1917.78.2714.94.7811.40.500 A
(m)0.6180.4110.4180.4710.3310.3390.1870.1340.1371.80 A S
(m)0.3300.1290.125 A STS (m)0.2060.2090.1650.1700.05150.05291.00 A
HTS (m)0.5890.6060.1450.1290.1510.1550.500
RMSE0.3060.3510.3530.4490.3510.3540.2760.4400.4635 Q (x10-2 m/s)
5.762.900.871.00 Q lat (x10-6 m/s)6.3818.00.730.00 26
- Slide 27
- Optimized Parameters Parameter JuneJulyAugust Conceptual
1-SZC-SZN-SZ1-SZC-SZN-SZ1-SZC-SZN-SZC-SZN-SZ D
(m/s)0.8310.9081.080.2010.2260.3200.5030.7450.8620.006 (x10^-5 s -1
)5.4111.63.73 STS (x10-5 s -1 )4385502102401602105.00 HTS (x10-5 s
-1 )9.1917.78.2714.94.7811.40.500 A
(m)0.6180.4110.4180.4710.3310.3390.1870.1340.1371.80 A S
(m)0.3300.1290.125 A STS (m)0.2060.2090.1650.1700.05150.05291.00 A
HTS (m)0.5890.6060.1450.1290.1510.1550.500
RMSE0.3060.3510.3530.4490.3510.3540.2760.4400.4635 Q (x10-2 m/s)
5.762.900.871.00 Q lat (x10-6 m/s)6.3818.00.730.00 27
- Slide 28
- Optimized Parameters Parameter JuneJulyAugust Conceptual
1-SZC-SZN-SZ1-SZC-SZN-SZ1-SZC-SZN-SZC-SZN-SZ D
(m/s)0.8310.9081.080.2010.2260.3200.5030.7450.8620.006 (x10^-5 s -1
)5.4111.63.73 STS (x10-5 s -1 )4385502102401602105.00 HTS (x10-5 s
-1 )9.1917.78.2714.94.7811.40.500 A
(m)0.6180.4110.4180.4710.3310.3390.1870.1340.1371.80 A S
(m)0.3300.1290.125 A STS (m)0.2060.2090.1650.1700.05150.05291.00 A
HTS (m)0.5890.6060.1450.1290.1510.1550.500
RMSE0.3060.3510.3530.4490.3510.3540.2760.4400.4635 Q (x10-2 m/s)
5.762.900.871.00 Q lat (x10-6 m/s)6.3818.00.730.00 28
- Slide 29
- Optimized Parameters Parameter JuneJulyAugust Conceptual
1-SZC-SZN-SZ1-SZC-SZN-SZ1-SZC-SZN-SZC-SZN-SZ D
(m/s)0.8310.9081.080.2010.2260.3200.5030.7450.8620.006 (x10^-5 s -1
)5.4111.63.73 STS (x10-5 s -1 )4385502102401602105.00 HTS (x10-5 s
-1 )9.1917.78.2714.94.7811.40.500 A
(m)0.6180.4110.4180.4710.3310.3390.1870.1340.1371.80 A S
(m)0.3300.1290.125 A STS (m)0.2060.2090.1650.1700.05150.05291.00 A
HTS (m)0.5890.6060.1450.1290.1510.1550.500
RMSE0.3060.3510.3530.4490.3510.3540.2760.4400.4635 Q (x10-2 m/s)
5.762.900.871.00 Q lat (x10-6 m/s)6.3818.00.730.00 29
- Slide 30
- BTC Comparisons 30 June July August
- Slide 31
- Single Storage Zone Metrics Main channel residence time Storage
zone residence time Mean travel time 31
- Slide 32