distributed ecohydrological modeling: the potential for water resources management larry band, unc...

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Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

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Page 1: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

Distributed ecohydrological modeling: the potential for water resources

management

Larry Band, UNC

Christina Tague, SDSU

Page 2: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

Characteristics of watersheds regulating biogeochemical cycling and export

• In situ cycling of carbon and nutrients

• Ecosystem Processes: deposition/fixation, assimilation, uptake, decomposition, mineralization, nitrification, immobilization, denitrification, and all that….

• Transport within hydrologic flowpaths

• Hydrologic Processes: overland flow, shallow throughflow, groundwater flow,….

•Distribution of net source/sink strength along flowpaths

Page 3: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

Plot based ecosystem models

• water, carbon, nutrient flux computed in 1-d

• long time step (one day to one month)

• long time domain (decades to centuries) incorporating long term feedbacks to ecosystem state

• typically run without consideration of spatial heterogeneity

• no incorporation of spatial dependency along flowpaths

Page 4: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

Dynamic BGC

Page 5: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

Nitrogen saturation hypothesis:• Definition: Terrestrial ecosystems have a maximum rate of nitrogen uptake determined by the net ecosystem productivity and other (plot) N sinks, input exceeding this rate may leach to ...

• Streamwater chemistry diagnostics:

• ecosystem saturation stage diagnosed by streamwater chemistry, particularly in growing season

• Based on plot paradigm: assumes leaching below rooting zone is contributed directly to streams

Page 6: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

Watershed model:

• e.g. HSPF - works well for discharge based on land use

• also typically fix nutrient loading from land surface classes

• lumped models cannot address downslope divergence (variable nutrient source/sink strength along flowpaths)

• adjust for BMPs with reduction factor

• no feedback to ecosystem processes controlling source quality

Page 7: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

Oregon Ridge

5 0 5 10 Kilometers

N

Baltimore Ecosystem Study: Urban LTER

Page 8: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU
Page 9: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU
Page 10: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

Infrastructure impacts on flowpaths

Page 11: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

Characteristics of watersheds regulating biogeochemical cycling and export:

anthropogenic alteration

• direct addition or abstraction of material

• irrigation, fertilization, spills

• wastewater treatment and disposal

• alteration of hydrologic flowpaths

• impermeable surfaces

• street drainage

• storm and sanitary sewers

• vegetation management

Page 12: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

RIPARIAN ZONES

• Critical interface between terrestrial and aquatic components of a watershed.

• Demonstrated ability to prevent pollutant movement from upland land uses into streams.

• Most work on groundwater nitrate, in agricultural watersheds.

Page 13: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

Denitrification

NO3- NO2

- NO N2O N2

- Anaerobic

- Heterotrophic (requires organic C)

• Expect high rates in wetland soils.

• Key component of the water quality maintenance function of riparian zones.

Page 14: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

Model and data flow structure

Page 15: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

RHESSys Object Hierarchy

Page 16: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

Basin/hillslope/patch hierarchy

Page 17: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

Major carbon flux processes: patch level

• Photosynthesis: Farquhar algorithm combining conductance and enzymatic limitations

• Stomatal physiology: Conductance uses Jarvis method f(temp, LWP, PAR, VPD)

• Respiration: Organ (foliage, root, stem) specific rates (massC/massC) modified by temperature Q10

• Allocation: Root, stem, foliar allocation of net photosynthate determined by Waring/Landsberg function on water, nutrient stress, and carbon supply

• Litter decomposition: litter/soil moisture, T, quality determines rates from different soil pools

Page 18: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU
Page 19: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU
Page 20: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

Daily carbon flux (g.m-2day-1): Upland plot, Pond Branch

-6

-4

-2

0

2

4

6

8

1 31 61 91 121 151 181 211 241 271 301 331 361

Series1

Page 21: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

Pondbranch Nitrate Concentration

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

14-O

ct-9

8

8-N

ov-9

8

3-D

ec-9

8

28-D

ec-

98

22-J

an-9

9

16-F

eb-9

9

13-M

ar-9

9

7-A

pr-

99

2-M

ay-9

9

27-M

ay-9

9

21-J

un-9

9

16-J

ul-99

10-A

ug-

99

4-S

ep-9

9

Date

Nit

rate

Co

nc (

mg

/L)

Observed Nitrate Conc. Simulated Nitrate Conc.

Nitrate export from Pond Branch: Note large increase in concentrations during summer - partially derived from riparian zones

Page 22: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

Denitrification rates during summer and winter for hillslope 1

Page 23: Distributed ecohydrological modeling: the potential for water resources management Larry Band, UNC Christina Tague, SDSU

NO3 export concentrations as f(hydrology)

• riparian patches typically near saturation, anaerobic, active denitrifying zones

• significant reduction of [NO3] from upslope

• low decomposition, accumulates carbon, nutrient rich material

• during dry-downs riparian patches

• may disconnect from uplands, WFPS drops

• become aerobic, strongly nitrify

• flip from sink to source of NO3

• note: growing season NO3 export not related to catchment nitrogen saturation