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Transcript of 1 Modeling the synergistic impacts of atmospheric and land-based influences on water quality and...
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Modeling the synergistic impacts of atmospheric and land-based
influences on water quality and quantity in linked upland and estuarine
ecosystems
Zong-Liang Yang (PI), David Maidment (co-PI), Paul Montagna (co-PI), James McClelland (co-PI), Hongjie Xie (co-PI)
Guo-Yue Niu, Seungbum Hong, Cédric David, Hae-Cheol Kim, Sandra Arismendez, Rae Mooney, Patty Garlough, Rachel Mills,
Beibei Yu, Ling Lu, Almoutaz El Hassan
11-13 May 2010
Outline
• Introduction• Models and tools• Climate change in Texas• Noah land surface model with multi-physics
options• River network model (RAPID)• Statistical modeling of nutrient outflow• Estuary model• Summary and accomplishments• New IDS
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Introduction: An incomplete water cycle
• Atmosphere Land– To atmospheric scientists:
• evapotranspiration (ET), space/time distribution; • runoff immediately lost to ocean, treated as a
residual of P – ET– To hydrologists:
• runoff, space/time; • river routing, ET is a residual (P – Runoff)
• Land Oceans– To terrestrial hydrologists: figures mask oceans– To marine scientists: figures mask land
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Enter the coastal environments!
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Zong-Liang Yang (UT Geological Sciences)Climate modelingLand surface modeling
Hongjie Xie (UTSA Geological Sciences)Remote sensing analysisLULC, NEXRAD
David Maidment (UT Civil Engineering)Hydrology, river routing
Land/ocean coupling, BGC cycles
Jim McClelland (UT Marine Science)
Paul Montagna (TAMU Corpus Christi)Estuary ecologyCoastal BGC
Modeling framework
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Framework for calculation
Atmospheric Model or Dataset
Vector River Network - High-Performance Computing
River Network Model
Land Surface Model
Modeling across spatial and temporal scales:
GlobalregionalwatershedcoastalCurrent and future: interannual to hourly
Area of interest for integration with bays
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San Antonio Bay
Mission Bay, Copano Bay and Aransas Bay
Guiding questions
• What is the effect of global climate change on complex coastal environment processes?
• What are the quantities of nutrient and organic matter export from land to sea?
• How does the timing of export events influence ecosystem structure and productivity in coastal waters?
• How do changes on land influence watershed export and ecosystem properties in coastal waters?
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Precipitation gradient across Precipitation gradient across TexasTexas
Texas is home to four of 10 largest cities in the nation. Chemical plants and oil refineries are located along the coast near Houston.
Precipitation increases from semi-arid west to humid east.
Temperature decreases from south to north.
Precipitation comparisonPrecipitation comparisonMonthly Precipitation
Pre
cipi
tatio
n (c
m)
0
2
4
6
8
10
12
14
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Seattle, WACorpus Christi, TXVictoria, TXRochester, NYMilwaukee, WIDes Moines, IADallas, TX
J F M A M J J A S O N D
Texas
double peaks of monthly precipitation: spring, fall
Models and tools
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Weather models and datasets
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WRF modelClimate modelsNASA datasets (NLDAS, satellite datasets of land cover and change)
Radar observationsNEXRAD data
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Land Surface Models forLand – atmosphere processes
First version in 1999 Noah is fully coupled with WRF in North America
Noah model
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NHDPlus – River and Catchment Network for the Nation
3 million river reaches
Integration of the National Hydrography Dataset, National Elevation Dataset and National Land Cover Dataset completed by EPA in 2006
5,175 river reaches26,000 km2
Guadalupe and San Antonio Basins, TXEntire dataset
Connectivity information
River gages and dam operation data
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Historical and real-time
measurements
Dam operation data
Parallel computing for river flow mapping
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Desktop computerParallel computer (UT’s Lonestar)
• Today’s computers are as powerful as supercomputers ten years ago
• Most computers come with multiple processors• High performance parallel computers are becoming increasingly
accessible
Climate change in Texas
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Data
• WCRP CMIP3 dataset
• 16 global climate models
• Three emission scenarios (A1B, A2, B1)
• A1B 39 simulations
• A2 37 simulations
• B1 36 simulations
• Precipitation and temperature (monthly)
• Statistical downscaling with bias-correction
Temperature projections
Projected probability distributions of surface temperature changes in the period of 2070–2099 relative to 1971-2000
means by different climate models over Texas
Projected precipitation changes
Projected monthly precipitation anomalies over individual five regions in Texas
Summary
• Texas is getting warmer (2-5ºC by the end of this century); more warming in the north than in the south.
• Overall decreasing trend of precipitation. Decreasing precipitation in the winter (5-15%), and increasing precipitation in the summer (5%).
• We have downscaled precipitation (and other variables) at 3-hourly and fine-spatial scales for hydrological studies.
• Bias correction is applied to precipitation before it is used to drive hydrological models.
Noah land surface model with multi-physics options
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Noah-MP with multi-physics options 1. Leaf area index (prescribed; predicted)2. Turbulent transfer (Noah; NCAR LSM)3. Soil moisture stress factor for transpiration (Noah; BATS; CLM)4. Canopy stomatal resistance (Jarvis; Ball-Berry)5. Snow surface albedo (BATS; CLASS)6. Frozen soil permeability (Noah; Niu and Yang, 2006)7. Supercooled liquid water (Noah; Niu and Yang, 2006)8. Radiation transfer: Modified two-stream: Gap = F (3D structure; solar zenith angle; ...) ≤ 1-GVF Two-stream applied to the entire grid cell: Gap = 0 Two-stream applied to fractional vegetated area: Gap = 1-GVF9. Partitioning of precipitation to snowfall and rainfall (CLM; Noah)10. Runoff and groundwater: TOPMODEL with groundwater TOPMODEL with an equilibrium water table (Chen and Kumar, 2001) Original Noah scheme BATS surface runoff and free drainage
More to be added
Niu et al. (2010a,b)
Maximum # of Combinations 1. Leaf area index (prescribed; predicted) 22. Turbulent transfer (Noah; NCAR LSM) 23. Soil moisture stress factor for transp. (Noah; BATS; CLM) 34. Canopy stomatal resistance (Jarvis; Ball-Berry) 25. Snow surface albedo (BATS; CLASS) 26. Frozen soil permeability (Noah; Niu and Yang, 2006) 27. Supercooled liquid water (Noah; Niu and Yang, 2006) 28. Radiation transfer: 3 Modified two-stream: Gap = F (3D structure; solar zenith angle; ...) ≤ 1-GVF Two-stream applied to the entire grid cell: Gap = 0 Two-stream applied to fractional vegetated area: Gap = 1-GVF9. Partitioning of precipitation to snow- and rainfall (CLM; Noah) 210. Runoff and groundwater: 4 TOPMODEL with groundwater TOPMODEL with an equilibrium water table (Chen and Kumar, 2001) Original Noah scheme BATS surface runoff and free drainage
Niu et al. (2010a,b)
2x2x3x2x2x2x2x3x2x4 = 4608 combinations
Ensemble-based process studies and quantification of uncertainties
Modeled Tskin (July 12th, 21:00 UTC, 2004)
Niu et al. (2010b)
Modeled Leaf Area Index (LAI) and Green Vegetation Fraction (GVF)
Niu et al. (2010b)
River network model: RAPID
Routing Application for Parallel computatIon of Discharge
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Framework for calculation
Atmospheric Model or Dataset
Vector River Network - High-Performance Computing
River Network Model
Land Surface Model
River network modeling
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RAPID•Uses mapped rivers•Uses high-performance parallel computing•Computes everywhere including ungaged locations
Streamflow map
33Guadalupe River at Victoria
Flow calculated everywhere
1) Optimize model parameters
2) Run model
Guadalupe River near Victoria, TX
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Animation: flow map (Jan-Jun 2004)
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Thank you to: Adam Kubach, Texas Advanced Computing Center
•01/01/2004 – 06/30/2004 every 6 hours (RAPID computes 3-hourly)•5,175 river reaches; 26,000 km2
•River model improves computations over lumped model, compared to observations at 36 gages
Transition of RAPID to other river basins
• Learning RAPID– Compile and run for an existing case (0-1
months) if basic knowledge of Linux and Fortran
• Adapting to new domain– Downloading NHDPlus data and preparing
input files (2 weeks)– Downloading and processing USGS data (1
week)– Getting gridded runoff information 36
Water chemistry measurements and statistical modeling of nutrient outflow
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San AntonioSan Antonio, Guadalupe, , Guadalupe, MissionMission and and AransasAransas Rivers Rivers
WatershedsWatersheds
Legend
Guadalupe
San Antonio
Mission
Aransas
Land Use/Land Cover Open Water
Developed
Rock/Sand/Clay
Forest/Shrub
Grassland/Pasture
Cultivated Crops
Wetland
0 40 80 120 16020
Kilometers
Legend
Guadalupe
San Antonio
Mission
Aransas
Land Use/Land Cover Open Water
Developed
Rock/Sand/Clay
Forest/Shrub
Grassland/Pasture
Cultivated Crops
Wetland
High resolution sampling
• Sampling targeted to high flow events that potentially carry more nutrients
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Measured concentrationsMeasured concentrationsSan Antonio River
10/0
7
2/08
6/08
10/0
8
2/09
6/09
10/0
9
Nitr
ate
conc
entr
atio
n (
M)
0
200
400
600
800
1000
Guadalupe River
10/0
7
2/08
6/08
10/0
8
2/09
6/09
10/0
9
Nitr
ate
conc
entr
aton
( M
)
0
20
40
60
80
100Guadalupe River
10/0
7
2/08
6/08
10/0
8
2/09
6/09
10/0
9
DO
N c
once
ntra
tion
(M
)
0
10
20
30
40
50
60
70
San Antionio River
10/0
7
2/08
6/08
10/0
8
2/09
6/09
10/0
9
DO
N c
once
ntra
tion
(M
)
0
10
20
30
40
50
60
70
concentration-runoff relationshipsconcentration-runoff relationships
Runoff (mm/day)
0.01 0.1 1 10
Nitr
ate
conc
entr
atio
n (
M)
0
50
100
150
200
250
300
TCEQUTMSI
Guadalupe River
Runoff (mm/day)
0.01 0.1 1 10
DO
N c
once
ntra
tion
(M
)
0
10
20
30
40
50
60
70Guadalupe River
Runoff (mm/day)
0.01 0.1 1 10
Nitr
ate
conc
entr
atio
n (
M)
0
200
400
600
800
1000
1200
TCEQUTMSI
San Antonio River
Runoff (mm/day)
0.01 0.1 1 10
DO
N c
once
ntra
tion
(M
)
0
10
20
30
40
50
60
70San Antonio River
DON: concentration-runoff relationshipsDON: concentration-runoff relationships
Runoff (mm/day)
0.01 0.1 1 10 100
DO
N c
once
ntra
tion
(M
)
0
10
20
30
40
50
60
70GuadalupeSan AntonioMissionAransas
LOADEST Regression modelsLOADEST Regression models
MODEL 2
Ln(Conc) = a0 + a1 LnQ + a2 LnQ^2
where: Conc = constituent concentration LnQ = Ln(Q) - center of Ln(Q)
MODEL 6
Ln(Conc) = a0 + a1 LnQ + a2 LnQ^2 + a3 Sin(2 pi dtime) + a4 Cos(2 pi dtime)
where: Conc = constituent concentration LnQ = Ln(Q) - center of Ln(Q) dtime = decimal time - center of decimal time
Model fit: RModel fit: R22 values (expressed as %) values (expressed as %)
San Antonio Guadalupe
Model 2 Model 6 Model 2 Model 6
Constituent Conc. Flux Conc. Flux Conc. Flux Conc. Flux
Nitrate 62 69 70 76 55 85 65 88
Ammonium 3 86 5 86 24 90 24 90
Phosphate 55 77 60 79 61 90 64 91
DON 40 98 54 99 48 85 50 86
Model output: concentration time seriesModel output: concentration time series
Guadalupe River
1/07
5/07
9/07
1/08
5/08
9/08
1/09
5/09
9/09
1/10
DO
N c
once
ntra
tion
(M
)
0
50
100
150
200Guadalupe River
1/07
5/07
9/07
1/08
5/08
9/08
1/09
5/09
9/09
1/10
Nitr
ate
conc
entr
aton
( M
)
0
200
400
600
800
1000
San Antonio River1/
07
5/07
9/07
1/08
5/08
9/08
1/09
5/09
9/09
1/10
Nitr
ate
conc
entr
atio
n (
M)
0
200
400
600
800
1000San Antionio River
1/07
5/07
9/07
1/08
5/08
9/08
1/09
5/09
9/09
1/10
DO
N c
once
ntra
tion
(M
)
0
50
100
150
200
Model output: flux time seriesModel output: flux time series
Guadalupe River
1/07
5/07
9/07
1/08
5/08
9/08
1/09
5/09
9/09
1/10
DO
N f
lux
(kg/
d)
0
20000
40000
60000
80000
100000
120000
140000
160000
San Antionio River
1/07
5/07
9/07
1/08
5/08
9/08
1/09
5/09
9/09
1/10
DO
N f
lux
(kg/
d)
0
2000
4000
6000
8000San Antonio River
1/07
5/07
9/07
1/08
5/08
9/08
1/09
5/09
9/09
1/10
Nitr
ate
flux
(kg/
d)
0
5000
10000
15000
20000
25000
30000
35000
Guadalupe River
1/07
5/07
9/07
1/08
5/08
9/08
1/09
5/09
9/09
1/10
Nitr
ate
flux
(kg/
d)
0
5000
10000
15000
20000
25000
30000
35000
Summary
• Collected and analyzed 2-yr sampling data in Guadalupe, San Antonio, Mission, and Aransas rivers.
• Noted that general patterns of DIN dilution and DON enrichment during storm flows are similar across all four rivers.
• Major differences in nitrate concentrations among rivers (during low flows) reflect differences in anthropogenic nitrogen sources.
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Estuary model
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Study Study AreaArea
Two River BasinsGuadalupeSan Antonio
Four HUCs in each basin
Guadalupe EstuaryCentrally located
along Texas coastMicrotidalSmall bay area but
large watershed relative to other Texas systems
A generic ecosystem model (3 components with 2 boundary conditions)
Mass-balance modelTwo boundaries: LGRW &
LSRWThree components: Nutrient
(DIN) –Phytoplankton – Zooplankton
Re-mineralization and implicit sinking (or horizontal exchange) were assumed to be 50%, respectively
Δ=1 hr & RK 4th order scheme
Model Results No loadings (both No loadings (both boundaries shut boundaries shut down):down): Initial nitrogen pool for DIN, Phyto and Zoo will get eventually depleted
When LSRW (2nd panel) or LGRW (3rd panel) were open: discharged DIN kept nitrogen pool for DIN, Phyto and Zoo to a certain level
LSRW and LGRW had a different timing, duration and magnitude in responses of DIN, Phyto and Zoo
Source: Arismendez et al. (2009) Ecol. Informatics 4: 243-253
Model Conclusions and Discussion
• Estuary response differs with respect to varying nutrient concentrations.
• Lower San Antonio River is delivering more nutrients and driving greater ranges of ecological response than the Lower Guadalupe River.
• Increases in nutrient concentrations due to human alterations of the landscape may result in future eutrophic conditions in the Guadalupe Estuary.
Conclusions
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Main thoughts
• Coastal processes are truly and highly interdisciplinary.
• Collaboration requires careful planning, frequent communication, and high-level patience.
• Training next-generation students and postdocs are rewarding and challenging.
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Accomplishments• Peer-reviewed Publications
– 10 published– 10 in press, submitted, or in preparation
• 2 Ph.D. Dissertations and 2 M.S. Theses; 4 Post-docs• Numerous conferences and Proceedings• An Integrated Framework of Models and Datasets
– Noah-MP for climate and hydrology studies– RAPID river flow model– Water chemistry data
• Organized 2nd NCEP/NOAA Workshop of Numerical Weather and Climate Modeling, Austin, Texas, April 19-21, 2010– Next-generation Land Surface Modeling; transition research to
operation– Beyond Land-Atmosphere Interactions
• River flow and nutrient exports coastal processes55
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New IDSZong-Liang Yang (UT Geological Sciences)
Climate modelingLand surface modeling
Hongjie Xie (UTSA Geological Sciences)Remote sensing analysisLULC, NEXRAD
Wei Min Hao (U.S. Forest Service)NASA satellite land datasets
David Maidment (UT Civil Engineering)Hydrology, river routing
Land/ocean coupling, BGC cycles
Collaborators:NCAR, USGS, …
Jim McClelland (UT Marine Science)
Paul Montagna (TAMU Corpus Christi)Estuary ecologyCoastal BGC
Study Domain
Community-based sampling program: recruiting/training volunteers living near downstream locations; primarily focus on storm-flow conditions, also collect quarterly samples during baseflow to compare with TCEQ or USGS.
Research Questions• What are the entire pathways of particulates and solutes
from the atmosphere through terrestrial and riverine environments to the coastal waters in the west Gulf of Mexico?
• How are these pathways affected by climate change and land use change?
• What are the effects of atmosphere dry and wet nitrogen deposition on riverine nitrogen exports and estuarine ecosystem functions?
• What are the effects of changing climate and land use on terrestrial runoff and associated nutrient export to and availability within ocean margin waters?
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Thank you!
• Liang Yang
• (512) 471-3824
• http://www.geo.utexas.edu/climate