1

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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59

Thank you!

• Liang Yang

• (512) 471-3824

• liang@jsg.utexas.edu

• http://www.geo.utexas.edu/climate