Linking Terrestrial and Aquatic Ecosystem Models ... - EM-1 Project

Linking Terrestrial and Aquatic
Ecosystem Models: Issue, Challenge,
and Opportunity
Changhui Peng, Nigel Roulet, Tim Moore, Youngil
Kim, Haibin Wu, Dong Hua et EM_1 project
members

Estimated global fluxes of organic carbon from terrestrial
to aquatic systems:
25%
50%
(Summarized from Lal, 2003; Schimel et al., 2001)

Great Wall
(Jenerette and Lal, 2005)

Remove Great Wall
(Jenerette and Lal, 2005)

Uncertainty in Ecosystem Carbon Budget

Leaching
leaching of dissolved organic carbon (DOC) and
dissolved inorganic carbon (DIC) to groundwater and
stream is an important avenue of carbon loss from some
terrestrial ecosystems.
Abut 20% of CO2 produced in artic soils, for example,
leaches to groundwater and is released from lakes and streams
(Kling et al. 1991, Science).
Despite their importance. Leaching losses of carbon to groundwater
are seldom measured and therefore frequently ignored in ecosystems
carbon budgets

DOC is still missing in current
ecosystem carbon budget
DOC is poorly represented in most
terrestrial carbon models
Within forested ecosystem, DOC leaching from the forest floor
and organic soil horizons ranges from 10 to 85 g m-2 yr -1
(Neff and Asner, 2000)

Disturbance
Disturbance is an episodic cause of carbon loss
from many ecosystems
Fire and harvest of plants or peat can be the dominant
Avenues of carbon losses from ecosystems:
Carbon losses during fires in the Canadian boreal forests
= 10 to 30% of average NPP (Harden et al., 2000, GCB)

(fromY. Kim)
Flooded Soil

( From Youngil. Kim)

Modelling objectives:
• To simulate terrestrial and aquatic ecosystem CO2 and
CH4 dynamics under human activity, fire disturbance and
climate change
• To assess the impacts of reservoirs on the GHG
emission of EM_1 region

How Challenge
From empirical model
To process-based model

Model Framework
Empirical model + Process-based model
Coupled model
TRIPLEX Model
Terrestrial
carbon
simulation
TRIPLE-DOC Model
River
River Model
Hanson Lake Model
Lake
carbon
simulation
Flooded soil Model

TRIPLEX1.0 Framework
Pool:
Process:
Precipitation
Atmospheric CO2
Solar radiation
Moisture
N limitation
GPP
N
Store
C Allocation
Increment
Leafs
C N
Roots Wood
C N C N
Height
Diameter
Soil
water
Temperature
Litter fall
C N
Volume
Basal Area
Structure
Metabolic
Disturbance
N mineralization
C N C N
Mortality
Active (C, N)
Thinning
Runoff
Decomposition
(C, N)
Slow (C, N)
Harvesting
Tree number
Leaching
Passive (C, N)
Wood
production
(Peng et al. 2002)

TRIPLEX Model
C dynamics (monthly)
TRIPLEX-Flux
CO2 Flux (daily)
CO2
Sunlit leaf
Stoma Cell
O2
CO2
Energy
Light
reactions
Calvin
cycle
H2O
Sugar
C
N
Shaded leaf
Water

(Peng, Moore, Hua et al.)

Respiration
Hanson’s s Lake Model
Atmospheric CO 2
Exchange
Respiration
DIC
Photosynthesis
GPP
DOC
Exudation
POC L
Death
POC D
Sedimentation
C S
(Hanson et al., 2004, Global Change Biology)

Lake sub-Model Functions
Primary production:
ln GPP = 0.883 ln TP
Problem: Empirical
(Hanson et al., 2004, Global Change Biology)

Phytoplankton Kinetics
Si Si
NO3
Light
NH3
Phyt
O C:N:P
∂C
∂t
P
= R −R −R C
( )
Where:
G D S P
C p
R G
R D
R S
= growth rate constant (per day)
= death rate constant (per day)
= settling rate constant (per day)
PO 4 PO 4
= phytoplankton carbon concentration (mg/L)
W2 model
(Cole &Wells, 2000)

Phytoplankton
• The growth rate of a population of phytoplankton in a
natural environment:
– is a complicated function of the species of
phytoplankton present
– involves differing reactions to solar radiation,
temperature, and the balance between nutrient
availability and phytoplankton requirements
• Due to the lack of information to specify the growth
kinetics for individual algal species in a natural
environment,
– this model characterizes the population as a whole by
the total biomass of the phytoplankton present

River sub-Model
STRUCTURE OF LAND-FLUVIAL SYSTEMS
Land Surface Processes (Grid)
River Routing and Sediment
Transport Network
Gas Exchange
Upland
Surface Water
Riparian/Floodplain
Deposition
Erosion
Floodplain
mineral soil
Water and dissolved
Fresh OM
Particulate
River Routing
Reservoirs
Free-Flowing
Estuary/Delta

Respiration
Lake/Reservoir Model
Atmospheric CO 2
Exchange
Watershed
DOC, POC,
DIC
Inflow
Reservoir
Respiration
Lake
DOC
Terrestrial
DIC
Exudation
Flooded soil
DIC
Photosynthesis
Lake
DIC
GPP
Terrestrial
DOC
Terrestrial
POC
Lake
POC L
Outflow
Degradation
Flooded soil
DOC
Flooded soil
POC
Erosion
Death
Lake
POC D
Sedimentation
DOC, POC,
DIC
Flooded
soil
Passive
C pool
Active
C pool
Slow
C pool
Sediment
C pool
(from Haibin Wu, Peng et al)

Regional model: spatial and temporal
heterogeneity and connectivity at
multiple scales
Forest
DOC
Lake

Flooded soil sub-Model structure
(Sebastian et al., Ecological modeling, submitted)

Another GHG: methane emissions from
lakes
Global emission
is 8-48 Tg/yr,
corresponds to 6-
16% of total
natural methane
emissions and
greater than
oceanic emission.
(Bastviken et al., 2004, Global Biogeochemical Cycles)

DSS for HQ
Regional Assessments of GHG
(Ongoing Efforts)
Visualization
ArcGIS and RS
Prediction of Net GHG Emission
and Their Responses to Land Use,
Fire and Climate Change
Sensitivity
Analysis
Analysis
Projected Changes
Over Space and Time:
CO2 and CH4
Manag.
Options
Ecosystem Models
Driving
( TRIPLEX, McWM, ICC )
Variables
GIS-Based
Database
Transfer
Government
HQ
Monitoring
Results
Validation
Parameterization
GIS/Oracle
Client Group
Flux Tower, Field Measurements
and Site Data
Climate and Soil Data
His. Land-Use Records
Figure 1. Flowchart of a Decision -Support System (DSS) for Assessing the Effect of Land Cover
Changes, Fires and Creation of Reservoirs on GHG Emission in Eastmain Region

Summary
Issues:
Leaching –DOC, Fire , Creation of Reservoir, Flooded
Soil
Challenges:
- from empirical to process-based models
-Coupling terrestrial with aquatic models
- spatial and temporal heterogeneity and connectivity
-scaling up and data-model fusion
Opportunities:
-Eastmain project
-DSS
-Others

Acknowledgement:
This project has been supported by
CFCAS and HydroQuebec

Model Inputs and outputs:
•Inputs
(stand, soil, and climate conditions)
• Monthly temperature
• Monthly precipitation
• Monthly relative humidity
• Forest age structure
•Outputs
(Terrestrial ecosystem: biomass, litter, G&Y,
soilC&N, soil water, C release, GPP, NPP;
Aquatic ecosystem: DOC, POC, DIC, CO 2 , CH4)

Lake/Reservoir Model input variables:
Climate data: daily temperature, precipitation, and cloud.
Diving variables: Total phosphorus (TP, unit: μg L -1 );
Watershed inflow DOC, POC, and PIC (unit: g C m -2 yr -1 );
Outflow DOC, POC, and DIC (unit: g C m -2 yr -1 );
Acid-neutralizing capacity (ANC, unit: μEq L -1 );
Watershed area (unit: m 2 );
Lake area to watershed area;
Mean water depth (unit: m).

Lake/Reservoir Model Validation data:
Gross primary production (GPP);
Respiration (R);
Daily-measured NEP (Net ecosystem production of
carbon) of flux;
DOC, POC, and PIC in lake;
Percent of DOC, POC, and PIC from the terrestrial and
phytoplankton carbon;
Ration of fooled soil C decomposition (R d
);
Ration of fooled soil C erosion (R e
);
Conversion of POC to DIC (f pd
);
Conversion of DOC to DIC (f dd
);
Death of algae (D).

Soil DOC model Validation data:
• The volumetric soil water content;
• The maximum canopy water storage;
• Soil water flux (vertical, downward);
• DOC concentration in leaching water;
• Sorption and desorption rate of DOC.
(Measurements will be preferable if they can be done at soil
layers with the depth of 10, 30, 60, 100 cm, respectively.)

Methane emissions from hydroelectric
reservoirs in tropics
But it is
an empirical
model!
(Corinne et al., 1999, Global Biogeochemical Cycles)

Atmospheric deposition to land surface: 0.1-0.3 Pg DOC yr -1 (Willey et al., 2000)
DOC contained in soil water: 1.1Pg
DOC contained in groundwater: 8.0 Pg
DOC contained in streams and lake: 1.1 Pg
Export to the ocean: 0.2-0.6 Pg DOC yr-1
( Ludwig et al., 1996; Moore, 1998)

Model Testing for 2 Flux tower sites
(Fluxnet-Canada)
110 yrs black spruce 75 yrs mixedwood

Model Validation – OBS Flux Tower
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
Field data from NSA-OBS-FLXTR in Jul 1996
NEP
-0.4
183
184
185
186
187
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
214
g C m -2 30 min -1
Day of year
Daily Simulation using TRIPLEX-Flux

Model Validation – Using OBS Flux Tower
0.4
Simulated NEP (gC /m 2 /30min)
0.2
0
-0.2
R 2 = 0.7304
-0.4
-0.4 -0.2 0.0 0.2 0.4
Observed NEP (gC /m 2 /30min)
Daily Simulation using TRIPLEX-Flux

Boreal Mixedwood Site (Ontario)
0.40
May
0.20
0.00
-0.20
122
123
125
127
129
130
132
134
136
137
139
141
143
144
146
148
150
151
0.4
0.3
0.2
0.1
0
R 2 = 0.77
-0.1
-0.2
-0.2 -0.1 0.0 0.1 0.2 0.3 0.4
Observed NEE (gC m -2 30m in -1 )
Ontario station in 2004
EC (OM W )
Sim ulated NEE
Day of year
NEE (g C m -2 30min -1 )
Simulated NEE (gC m -2 30min -1 )

Comparing NPP Spatial Distribution at Landscape Level
(a) TRIPLEX
(Zhou et al, 2005)
(b) Remote Sensing
(Liu et al, 2002)
Fig. 4 The comparison
between NPP (t C ha-1 yr-
1) simulations at
landscape (a) and remote
sensing (b) levels for the
LAMF in 1994. (a) was
based on the TRIPLEX
model simulation for 1994
(averaged 3.28 tC ha-1 yr-
1, SD=0.79), and (b) was
converted using spatial
data from Liu et al. (2002)
for 1994 (averaged 3.08 tC
ha-1 yr-1, SD=1.15). The
grid size is 3x3 km.
Kappa Statistic (k) = 0.55
Good agreement if 0.55