Acknowledgement

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Acknowledgement

• The author would like to acknowledge
  contributions from Jiquan Chen, Asko Noormets,
  and Steve McNulty, PIs for an ongoing Carbon and
  Water Flux in Eastern US and China.
• Data presented have not been published unless
  referenced. Do not cite without permission from
  the PIs.

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Estimating Forest Ecosystem Evapotranspiration at
Multiple Scales
Ge Sun (Ge_Sun_at_ncsu.edu) Southern Global Change
Program, USDA Forest Service, Raleigh, NC
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Outline

• Why Evapotranspiration (ET) is important?
• Challenges in quantifying ET.
• Examples in estimating ET at multiple scales
  using simulation models SE US, Northern
  Wisconsin and Toledo

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Importance of ET

• Second largest hydrologic component
• Linked to ecosystem productivity
• Linked to biodiversity
• Sensitive to landuse and climate changes

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What is ET?
Vegetation and forest floor (It)
E
ET E It T
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The Hydrologic Cycle
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ET- the second largest flux of the water budget
USA
Global
Pg 760 mm ET 530 mm (70 of Pg) Q 230
mm (30 of Pg) Delta S 0 mm
Pg 730 mm ET 470 mm (64 of Pg) Q 260 mm
(36 of Pg) Delta S 0 mm
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ET -- a necessary Evil
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Why ET is Important (Law et al, 2002 Agr. For.
Meteorology)?
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Ecosystem Productivity ( from M.L. Rosenzweig,
1968. Net Primary Productivity of Terrestrial
Community Prediction from Climate Data)
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The Coupled Water and Energy Balance
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Future Climate Change
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Temperature Trends (19011998)
3o C
- 3o C
Source The Impact of Climate Change on
Americas Forests, Joyce Birdsey, 2000
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Precipitation Trends (1901 1998)
Source The Impact of Climate Change on
Americas Forests, Joyce Birdsey, 2000
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Ozone Effects on Tree Transpiration and Lowflow
Aug-Oct Baseflow 52.8265 0.11099 Ppt810
0.9376 O3avDmx1.61061 O3G60 1.35898 PDSI810
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Methods for Estimating ET

• Water Budget
• Eddy Covariance
• Remote Sensing
• Mathematical modeling

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Estimating ET Using the Evaporation Pan (Class A
Pan)
- 10 inches deep, 48 inches in diameter sit on a
wooden platform - Filled with water to 8
inches - Lake evaporation 0.7 Pan E -
Actual ET Reference Crop Coefficient Epan
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Estimating ET Using the Water Balance Method
For short period such as daily, monthly ?S
varies for several years, ?S 0 then annual
ET Pg - Q
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Measuring Streamflow at a Gaging Station
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Estimated Annual ET from Forested Watersheds, NC
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Forested Wetlands Water Balance
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Groundwater/Surface Water/Forest Interactions
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Spatial Distribution of ET
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Methods for Estimating ET

• Water Budget
• Eddy Covariance
• Mathematical modeling

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Study area
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Net Exchange of VaporTheory
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Energy Balances (RnSLEG)
Year Ecosystems Regression equation R2 N (days)
2002 Energy balance MHW SLEG 0.493 0.568 Rn 0.63 171
2002 Energy balance MRP SLEG 3.5121.272 Rn 0.82 163
2002 Energy balance YHW SLEG 0.2890.566 Rn 0.85 169
2002 Energy balance YRP SLEG -0.001420.557 Rn 0.72 179
2002 Energy balance PB SLEG 0.3670.603 Rn 0.80 178
2003 Energy balance MHW SLEG0.167 0.598 Rn 0.35 114
2003 Energy balance MRP SLEG1.611 0.479 Rn 0.66 121
2003 Energy balance IHW SLEG1.181 0.533 Rn 0.62 107
2003 Energy balance IRP SLEG0.750 0.618 Rn 0.64 110
2003 Energy balance PB SLEG -0.752 0.505 Rn 0.85 145
2002 LE MHW LE0.903 0.260 Rn 0.29 170
2002 LE MRP LE1.869 0.205 Rn 0.28 163
2002 LE YHW LE1.210 0.258 Rn 0.43 169
2002 LE YRP LE1.323 0.240 Rn 0.29 179
2002 LE PB LE1.323 0.221 Rn 0.31 178
2003 LE MHW LE0.914 0.341 Rn 0.15 146
2003 LE MRP LE1.790 0.175 Rn 0.19 134
2003 LE IHW LE1.055 0.272 Rn 0.23 154
2003 LE IRP LE2.193 0.170 Rn 0.11 124
2003 LE PB LE0.469 0.235 Rn 0.32 149
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Methods for Estimating ET

• Mathematical modeling
• at multiple temporal (annual, monthly, daily )
  and spatial scales (regional, stand, tree)

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Estimating ET using Penman-Montheith Equation

• Penman-Montheith Method Radiation based, most
  accurate prediction of Etp from forest ecosystem

Where, Etp potential ET (cm/s) L latent heat
(cal/g) Rn net solar radiation (ly/s) ? slope
of saturated vapor pressure-temperature curve,
mb/oC ?a air density (g/cm3) Cpa specific
heat of dry air (0.000242 cal/g oC) esz
saturated vapor pressure at height z (mb) ez
vapor pressure at height z (mb) ? psychmetric
constant (0.0657 mb/oC) ra atmospheric
diffusion resistance (s/cm) rs stomatal
resistance (s/cm)
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AET General model (Zhang et al., 2001, Water
Resources Research)
For mixed landuse watershed
P annual precip, PETpotential ET DefaultW 2.0
for forests, 0.5 for grass land
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Watershed distribution
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The PnET Model
WATER
CARBON/NITROGEN
2
1
17
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FOLIAR CANOPY
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4
3
13
WOOD C/N
PLANT C/N
7
8
5
SNOW
BUD C/N
10
14
9
6
23
16
15
FINE ROOT
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WOOD
NH4 NO3
SOIL WATER
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20
19
22
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SOIL
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Simulated Monthly Forest ET by 13 Ecosystem
Models
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The MIKE SHE Watershed Model (DHI, 2004)
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Transpiration
Precip
Interception
Evaporation
Unsaturated water flow
Groundwater flow
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Evapotranspiration

• Kristensen and Jensen Method
• Reference ET based on FAO Penman-Monteith model
• AETCanopy InterceptionSoil E Plan T
• Canopy Interceptionf(LAI)
• Plan T f(LAI, Rooting Depth, Soil Moisture)
• Soil Ef(LAI,PET, S Moisture)

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Unsaturated Water Movement (3 options)

• 1-D Richards Equation
• Require soil moisture release data
• Gravity flow (Vertical unit gradient)
• Require soil moisture release data
• Simple two-layer water balance
• Does not require soil moisture release data
• Require field capacity, porosity, wilting points

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Saturated Water Movement

• 3-D Finite Difference Method
• Geological layering
• Hydraulic conductivity, Specific yield (storage
  coefficients)

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Modeled Daily ET, Red PineNorthern Wisconsin
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Modeled Daily ET, Mature HardwoodNorthern
Wisconsin
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Modeled Daily ET, Pine BarrenNorthern Wisconsin
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Simulated Monthly ET of Three Ecosystems Northern
Wisconsin
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Oak Opening Ecosystem, Toledo, OHEstablished in
Winter 2003
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Simulated Groundwater TableOak Opening, Toledo
(April-Oct 2004)
Ground surface
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Simulated Soil Moisture ContentOak Opening,
Toledo (April-Oct 2004)
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Simulated Daily EvapotranspirationOak Opening,
Toledo (April-Oct 2004)
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Concluding Remarks

• ET is one important variable that links the
  atmospheric, the biological, ecological, and the
  hydrological processes at multiple scales.
• Quantifying ET remains challenging physical and
  biological control on ET.
• Combining field measurements and mathematical
  modeling appears to be powerful in estimating ET
  at multiple scales.