Changes in land surface processes with land cover change:
An analysis of the energy and water balance over a simulated
woody encroachment gradient.
Ann Thijs
Homework 5
Physical Climatology
Land Surface Modeling Project
Changes in land surface processes with land cover change:
An analysis of the energy and water balance over a simulated
woody encroachment gradient.
Abstract
The sensitivity of land surface processes to a change in vegetation composition was
assessed. The simulated change in vegetation cover constitutes “woody encroachment”, the
gradual replacement of grasslands by shrubs and trees. The effect of this woody encroachment
on the energy and water balance was assessed for the Sleepers River watershed, using a 5 year
time series of forcing data.
Results of the sensitivity analysis show that a total shift from grass cover to woody cover
results in a yearly increase in net shortwave radiation (36%) and sensible heat flux (253%), and a
decrease in net longwave radiation (- 15%), latent heat flux (-16%) and ground heat flux (-21%).
The yearly surface runoff and evapotranspiration are respectively 1% and 16% lower
with complete woody cover, while the yearly subsurface runoff is 8% larger. The decreased net
longwave radiation can be explained by the lower vegetation temperature, ground temperature
and soil temperature with 100% woody cover. The unexpected result for the decrease in latent
heat and evapotranspiration can be explained by the low photosynthetic capacity and stomatal
conductance for the plant functional type (PFT9) chosen to represent the encroaching species.
Page 2/15
Ann Thijs
Homework 5
Physical Climatology
Land Surface Modeling Project
Introduction
Human impacts on the environment are manifold and the human impact on land surface
processes can manifest itself either through altered climatic conditions or through human induced
changes in land cover. Changes in land cover type are either direct, e.g. agriculture, forest
clearing; or indirect as a result of altered disturbance processes, e.g. altered fire cycles, cattle
grazing and so forth.
Woody encroachment has been defined as the addition of woody canopies without major
losses of herbaceous cover, although herbaceous production might decrease” (Asner et al, 2004).
Proposed mechanisms for this change in land cover pertain to chronic overgrazing by domestic
animals, disturbance of the natural fire cycle, rising CO2 levels, altered precipitation regimes and
nitrogen pollution (e.g. Van Auken, 2000). This change in ecosystem structure implies a change
in ecosystem functioning as well and forms the main focus of my dissertation research.
Approach
The effect of woody encroachment as a land cover change on land surface processes was
modeled using the Community Land Model (CLM2.0). The forcing data was kept as in the initial
dataset given for the Sleepers River watershed in Vermont. The same forcing variables are being
obtained at an encroaching central Texas savanna site, but due to the relative shortness of time
series of forcing variables (16 months), I decided to use the 5 year dataset from Sleepers river
watershed instead. The climatic conditions in Vermont are hardly comparable to the climatic
conditions for Central Texas, and as thus are the results of this sensitivity analysis not applicable
to a Central Texas savanna ecosystem.
Page 3/15
Ann Thijs
Homework 5
Physical Climatology
Land Surface Modeling Project
The different plant functional types considered to represent the different land covers over
the encroachment gradient are broadleaf evergreen temperate shrub (PFT 9), C3 non arctic grass
(PFT 13) and C4 grass (PFT 14). Plant functional type 9 was chosen over other possible
candidates (PFT 1, needleleaf temperate evergreen tree and PFT 5, broadleaf evergreen
temperate tree) to represent the encroaching species. At my study site in Central Texas, the main
encroaching species is Ashe juniper, and it is not clear whether the cylindrical leaves of Ashe
juniper should be considered as needleleaf and broadleaf. Furthermore, the photosynthetic
characteristic for PFT 9 (Vmax 25 = 17) closely resembles Ashe juniper.
The root distribution for PFT9 was altered (parameter b was set to 1.0 instead of 1.5) to
allow for the deeper rooting system of the encroaching species in comparison with grasses
(Figure 1).
Root Distribution of different plant functional types
0
Soil Depth [m]
1
2
PFT9, BES temperate original
3
PFT9, BES temperate modified
4
PFT13, C3 grass
5
PFT14, C4 grass
6
0
0.2
0.4
0.6
0.8
1
1.2
Cumulative Root Distribution
Figure 1: Root distribution of the different plant functional types.
The seasonal changes in the grassland component of the modeled land cover types is
represented in Figure 2 and Table 1, with low leaf area indices in the winter time and higher leaf
Page 4/15
Ann Thijs
Homework 5
Physical Climatology
Land Surface Modeling Project
area indices for C3 in the spring and for C4 grasses in the summer time. One paper (Hicks and
Dugas, 1998) gives an empirical relationship for the leaf area of Ashe juniper as:
Tree leaf area [m2] = -18.5 [m2] + 12.4 [m2/m2] * canopy area [m2]
with the intercept not significantly different from zero. Using this equation, the derived leaf area
index for the encroaching species would be 12.4, which seems extremely high. Instead of using
this value, I opted to set the leaf area index for the encroaching species at 5 for the entire year.
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
LAI C3 grass
LAI C4 grass
Ja
n
F e uar
br y
ua
r
M y
ar
ch
Ap
ril
M
ay
Ju
ne
Ju
A ly
S e ug
pt ust
em
b
O er
ct
N ob
ov er
e
D mb
ec e
em r
be
r
LAI [m2/m2]
Seasonal change in grass LAI
Figure 2: Seasonal changes in grass leaf area index.
Table 1: Seasonal changes in LAI
January
February
March
April
May
June
July
August
September
October
November
December
LAI C3 grass
(PFT 13)
0.5
1.0
1.5
1.5
1.5
1.5
1.0
1.0
1.0
1.0
0.5
0.5
Page 5/15
LAI C4 grass
(PFT 14)
0.5
0.5
1.0
1.0
1.0
1.5
1.5
1.5
1.5
1.0
0.5
0.5
LAI tree
(PFT 9)
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
Ann Thijs
Homework 5
Physical Climatology
Land Surface Modeling Project
A sensitivity analysis of the energy and water balance components to fractional cover of
trees (PFT9) was performed. The leaf area indices for the different plant functional types
remained the same throughout the sensitivity analysis, only the fractional cover of the different
plant function types was altered. Table 2 and Figure 3 give the different tested scenarios, an
encroachment gradient that ranges from pure grassland to complete tree canopy cover.
Table 2: Fractional cover of the different plant functional types used in the sensitivity
analysis.
Scenario 1
Scenario 2
Scenario 3
Scenario 4
Scenario 5
Scenario 6
No trees
20% trees
40% trees
60% trees
80% trees
100% trees
fraction C3 grass
(PFT 13)
0.49
0.4
0.3
0.2
0.1
1
fraction C4 grass
(PFT 14)
0.49
0.4
0.3
0.2
0.1
1
fraction trees
(PFT 9)
2
0.2
0.4
0.6
0.8
98
Figure 3: Representation of different land covers over the encroachment gradient : 0, 20%,
40%, 60%, 80%, 100% tree cover.
Page 6/15
Ann Thijs
Homework 5
Physical Climatology
Land Surface Modeling Project
Results and Discussion
The CLM2.0 model was run for each of the scenarios. Differences between the different
scenarios were assessed visually. For the energy and water balance components, the yearly
cumulative sums were calculated, averaged and compared for the different scenarios. Next to the
yearly values, the summer conditions (June 21 – September 20) were assessed separately. For the
state variables, the yearly averages and summer averages were calculated and compared.
The outcomes of scenario 1 and scenario 6 are compared and a summary of the results is
given in table 3.
Table 3: Summary of model output comparison between scenario 1 and scenario 6. Positive
values indicate that the component/variable was larger for scenario 6, negative values indicate
the opposite.
Yearly
Summer
conditions
Energy Balance Components
Net Shortwave Radiation
Net Longwave Radiation
Sensible Heat Flux
Latent Heat Flux
Ground Heat Flux
- relative differences [%] 36.36
11.38
-15.33
-29.54
253.19
153.60
-16.10
-30.84
-21.01
-21.34
Water Balance Components
Surface Runoff
Subsurface Runoff
Evapotranspiration
- relative differences [%] -1.01
9.49
8.75
31.24
-16.10
-30.84
State Variables
Vegetation temperature [K]
Ground temperature [K]
Soil temperature (1) [K]
Soil temperature (10) [K]
Soil moisture (1) [%]
Soil moisture (10) [%]
- absolute differences -1.23
-1.77
-3.97
-8.79
-4.98
-8.79
-4.54
6.07
0.22
0.94
0.03
0.04
Page 7/15
Ann Thijs
Homework 5
Physical Climatology
Land Surface Modeling Project
For each variable the different scenarios are represented on one graph in the figures
below. When the outcomes between the different scenarios were not much different, only
scenario 1 (no tree cover) and scenario 6 (100% tree cover) are represented for clarity.
Sensitivity of the energy balance components
All energy balance components (net short wave radiation, net longwave radiation, latent
heat, sensible heat, ground heat) show maximum values in the summer time. The responses to
the land cover change are component specific and are discussed separately.
The net shortwave radiation increased with increased tree cover (Figure 4). The
difference between scenario 1 and scenario 6 amounted to 31095 W/m2 on a yearly basis
(36.36%), but the difference was smaller in the summertime (only 11.38%). The increase in net
shortwave radiation can probably be explained by the higher LAI values with increasing tree
canopy cover and a decrease in the albedo of the ecosystem. The smaller difference in the
summertime might be due to the higher grass LAI values in the summertime.
scenario 1
250000
200000
150000
100000
50000
scenario 1
scenario 2
scenario 3
scenario 4
scenario 5
scenario 6
0
11
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0
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1
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11
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3
scenario 6
sum daily net
shortwave radiation
400
350
300
250
200
150
100
50
0
Cumulative net shortwave radiation
11
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9
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3
daily net
shortwave radiation
Net shortwave radiation
Figure 4: Daily and cumulative net shortwave radiation [W/m2] for the different scenarios.
Page 8/15
Ann Thijs
Homework 5
Physical Climatology
Land Surface Modeling Project
The net longwave radiation showed an opposite trend from the net shortwave radiation
and decreased with increase in tree canopy cover (Figure 5), probably due to the lower
temperature of vegetation, ground and soil in scenario 6 (Figures 12-13, Table 3). The total
yearly difference amounted to 6271 W/m2 (-15%) between scenario 1 and scenario 6 and the
differences were even more pronounced in the summer time (-29%) when also the temperature of
vegetation, soil and ground were more different between the different scenarios (Table 3).
Cumulative net longwave radiation
100
50
scenario 1
scenario 6
0
90000
80000
70000
60000
50000
40000
30000
20000
10000
0
scenario 1
scenario 2
scenario 3
scenario 4
scenario 5
scenario 6
11
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9
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97
0
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1
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2
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3
-50
sum daily net
longwave radiation
150
11
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96
9
11
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97
0
11
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97
1
11
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2
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3
daily net
longwave radiation
Net longwave radiation
Figure 5: Daily and cumulative net longwave radiation [W/m2] for the different scenarios.
The sensible heat flux showed the strongest response to a change in land cover with a
strong increase in sensible heat flux with an increased tree canopy cover (Figure 6). The 235%
relative increase between scenario 1 and scenario 6 seems large, especially because the
vegetation, ground and soil temperature are lower in scenario 6 than in scenario 1. An increase
in sensible heat can be expected due to the higher leaf area index and the larger roughness length
for scenario 6, but it seems odd that this is not reflected in the latent heat flux as well.
There is a decrease (-16 % annually) in the latent heat flux with increase in tree canopy
cover (Figure 7). The higher leaf area index and higher roughness length in scenario 6 would let
one expect a higher latent heat flux with an increase in tree cover, but the photosynthetic
Page 9/15
Ann Thijs
Homework 5
Physical Climatology
Land Surface Modeling Project
characteristics of PFT 9 (low Vmax,25 and concurrently low stomatal conductance) might be
responsible for the lack of such a response.
To test this hypothesis, scenario 6 was altered into scenario 7 *** with a different Vmax, 25
for PFT9 (69 instead of 17, 69 is the Vmax, 25 value for PFT 5, a broadleaf evergreen temperate
tree). The results for scenario 7*** are given as a black line in Figure 6 and 7. When Vmax, 25 is
altered, a completely different result shows up; the sensible heat flux is for scenario 7*** is still
higher than for scenario 1, but is well below the sensible heat flux of scenario 6 (Figure 6). As
expected, the latent heat flux for scenario 7*** is much larger than for scenario 6 and scenario 1.
The conclusion of this extra simulation is that we don’t see the expected increase in latent heat
flux in scenario 6, because the low photosynthetic capacity of PFT 6 prevents high
photosynthetic rates and high transpiration rates, despite the high leaf area index. Since the
photosynthetic capacity of the encroaching species, Ashe juniper, at my study site resembles
more PFT9 than PFT5, I opt to keep using the normal parameterization for PFT9.
Cumulative sensible heat
scenario 1
scenario 1
120000
100000
80000
60000
40000
20000
0
-20000
11
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9
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0
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scenario 6
sum daily sensible heat
250
200
150
100
50
0
-50
-100
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daily sensible heat
Sensible heat
Figure 6: Daily and cumulative sensible heat [W/m2] for the different scenarios.
Page 10/15
scenario 2
scenario 3
scenario 4
scenario 5
scenario 6
scenario 7 ***
Ann Thijs
Homework 5
Physical Climatology
Land Surface Modeling Project
Latent heat
Cumulative latent heat
daily latent heat
90
70
scenario 1
50
30
scenario 6
10
-10
11
9
96
/1
/1
11
0
97
/1
/1
11
1
97
/1
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11
2
97
/1
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11
sum daily latent heat
scenario 1
110
3
97
/1
/1
80000
70000
60000
50000
40000
30000
20000
10000
0
11
9
96
/1
/1
scenario 2
scenario 3
scenario 4
scenario 5
scenario 6
11
0
97
/1
/1
11
1
97
/1
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11
2
97
/1
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11
3
97
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scenario 7 ***
Figure 7: Daily and cumulative latent heat [W/m2] for the different scenarios.
The ground heat flux is reduced with an increase in tree canopy cover (-21%), as can be
expected due to lower ground temperature (Figure 12) and the denser canopy in scenario 6 that
shields the ground from large fluctuations.
200
150
100
scenario 1
50
scenario 6
0
-50
10000
scenario 1
8000
scenario 2
6000
scenario 3
4000
scenario 4
2000
scenario 5
0
scenario 6
-2000
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-100
sum daily ground heat
Cumulative ground heat
11
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9
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0
11
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97
1
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2
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3
sum daily ground heat
Ground heat
Figure 8: Daily and cumulative ground heat [W/m2] for the different scenarios.
Sensitivity of the water balance components
The decrease of latent heat (Figure 7) with increase of tree canopy cover mirrors in the
decrease of evapotranspiration (Figure 9). This unexpected result, again, is probably due to the
low photosynthetic capacity and stomatal conductance of the trees and this also finds some
support in the higher soil moisture values in scenario 6 (Figure 14).
Page 11/15
Ann Thijs
Homework 5
Physical Climatology
Land Surface Modeling Project
Cumulative evapotranspiration
4
3.5
3
2.5
2
1.5
1
0.5
0
-0.5
-1
scenario 1
11
/1
/1
96
9
11
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97
0
11
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1
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2
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3
scenario 6
sum daily evapotranspiration
11
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9
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0
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1
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3
daily evapotranspiration
evapotranspiration
1800
1600
1400
1200
1000
800
600
400
200
0
scenario 1
scenario 2
scenario 3
scenario 4
scenario 5
scenario 6
Figure 9: Daily and cumulative evapotranspiration [mm/day] for the different scenarios.
An increase in tree cover has only a slight negative effect on the yearly surface runoff (1%, Figure 10), and a positive effect on the yearly subsurface runoff (+ 8.75 %, Figure 11). Both
for surface and subsurface runoff, the peak flows seems to be earlier for no tree cover. The lower
interception due to the lower LAI is probably responsible for this.
Cumulative surface runoff
sum daily surface runoff
11
/1
/1
96
9
11
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0
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3
45
40
35
30
25
20
15
10
5
0
1600
1400
1200
1000
800
600
400
200
0
scenario 1
scenario 6
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3
daily surface runoff
Surface runoff
scenario 1
scenario 6
Figure 10: Daily and cumulative surface runoff [mm/day] for the different scenarios.
8
7
6
5
4
3
2
1
0
Cumulative subsurface runoff
scenario 1
scenario 6
sum daily subsurface runoff
11
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3
daily subsurface runoff
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2
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3
Subsurface runoff
4000
3500
3000
2500
2000
1500
1000
500
0
scenario 1
scenario 6
Figure 11: Daily and cumulative subsurface runoff [mm/day] for the different scenarios.
Page 12/15
Ann Thijs
Homework 5
Physical Climatology
Land Surface Modeling Project
Changes in state variables
Some of the state variables have already helped to explain the sensitivity of the energy
and water balance components to a change in land cover. Here these changes are summarized
shortly. The vegetation temperature (Figure 12) is on average 1.23 K lower when there is a
complete tree cover. The ground temperature (Figure 12) shows less fluctuations in temperature
with a complete tree cover, and is also on average 3.97 K lower. Soil temperature 1 (Figure 13)
is reflective of the ground temperature when there is no snow cover and is higher than the ground
temperature when there is snow cover. Soil temperature 1 is about 5 K lower with complete tree
cover than with complete grass cover. The soil temperature at depth 10 is a smooth sinusoidal in
function of the seasons and is also on average about 4.5 K lower with complete tree cover.
Ground temperature
Vegetation temperature
310
300
290
280
270
260
250
240
230
320
310
300
290
280
270
260
250
240
230
scenario 1
scenario 1
scenario 6
11
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9
11
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0
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0
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1
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2
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3
scenario 6
Figure 12: Vegetation and ground temperature [K] for the different scenarios.
Soil temperature 1
295
320
315
310
305
300
295
290
285
280
275
270
290
scenario 1
scenario 1
285
scenario 6
scenario 6
280
275
3
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2
1
11
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0
97
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11
11
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9
3
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2
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0
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11
11
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9
270
96
/1
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11
Soil temperature 10
Figure 13: Soil temperature at two depths [K] for the different scenarios.
Page 13/15
Ann Thijs
Homework 5
Physical Climatology
Land Surface Modeling Project
Soil moisture 1 (Figure 14) was on average 0.03 % higher under complete tree cover than
under complete grassland. The lower ground and soil temperature will lead to lower evaporation
rates. This is also in accordance with the lower evapotranspiration rates (Figure 9).
Soil moisture 10
Soil moisture 1
0.5
0.5
0.45
0.45
scenario 1
3
2
97
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11
11
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97
1
0
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11
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96
0
0.3
0.25
9
0.3
/1
/1
scenario 6
0.35
0.25
11
scenario 1
0.4
scenario 6
11
0.4
0.35
Figure 14: Soil moisture at two depths [m3/m3] for the different scenarios.
Snow water content and snow depth were slightly larger with complete tree cover than
with complete grassland cover (Figure 15), probably because of the lower ground temperatures
with complete tree cover.
SWE
Snow depth
scenario 1
3
97
2
11
/1
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1
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97
97
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0
scenario 6
9
3
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2
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1
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0
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11
11
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97
9
scenario 6
11
scenario 1
96
/1
/1
11
1.4
1.2
1
0.8
0.6
0.4
0.2
0
11
500
450
400
350
300
250
200
150
100
50
0
Figure 15: Snow water [kg/m2] and snow depth [m] for the different scenarios.
Page 14/15
Ann Thijs
Homework 5
Physical Climatology
Land Surface Modeling Project
Conclusion
The simulated sensitivities of land surface processes with change in land cover are
largely what one would expect with an increase in tree cover. The unexpected result for the
sensitivities of the sensible heat flux and the latent heat flux to an increased tree cover can be
explained by the low photosynthetic capacity and stomatal conductance of the encroaching tree
species.
It will be interesting to see whether the low photosynthetic capacity of Ashe juniper
actually results in lower evapotranspiration rates, because this is opposite to popular belief. As
stated before, the results of this simulation can not directly be translated to central Texas savanna
ecosystems. A longterm dataset of forcing variables and a more intense parameterization of the
ecosystem is needed before such question can be answered in a modeling context.
Cited Literature
Asner G.P., Elmore A.J., Olander L.P., Martin R.E., and Harris A.T. (2004) Grazing
systems, ecosystem responses, and global change. Annu. Rev. Environ. Resour., 29, 261-299.
Hicks R.A. and Dugas W.A. (1998) Estimating Ashe juniper leaf area from tree and
stem characteristics. Journal of Range Management, 51, 633-637.
Van Auken O.W. (2000) Shrub invasions of North American Semiarid grasslands.
Annual review of ecology and systematics, 31, 197-221.
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