Supplementary Material
Feedbacks between deforestation, climate, and hydrology in the Southwestern Amazon:
implications for the provision of ecosystem services
5
Letícia S. Lima, Michael T. Coe, Britaldo S. Soares Filho, Santiago V. Cuadra, Lívia C. Dias, Marcos H.
Costa, Leandro S. Lima, Hermann O. Rodrigues
Validation
Simulations using dynamic vegetation models – e.g. IBIS (Kucharik et al. 2000), and atmospheric
10
general circulation models, AGCMs – e.g. CCM3 (Kiehl et al. 1998), have been applied in several studies
of the Amazon basin and our results are broadly consistent with results from studies with deforestation
scenarios (Lean and Warrilow 1989; Shukla et al. 1990; Salati and Nobre 1991; Hahmann and Dickinson
1997; Costa and Foley 2000; Sampaio et al. 2007; Ramos da Silva et al. 2008; Coe et al. 2009).
Coupled CCM3-IBIS. CCM3-IBIS model was globally validated (Delire et al., 2002) and
15
validated for the Amazon (Senna et al., 2009; Coe et al., 2009). It has been used in several studies to
simulate the interactions among ecosystems and atmosphere in the Amazon basin to predict climate and
hydrologic impacts of land use changes in this region (e.g., Costa and Foley, 2000; Coe et al., 2009). The
Terrestrial Hydrology Model with Biogeochemistry – THMB, was validated for the Amazon basin by
Coe et al. (2007, 2009).
20
The simulations of land cover changes in the coupled CCM3-IBIS and in IBIS stand-alone are
performed replacing natural vegetation by tropical grasses (C4 species) in deforested areas, as in Costa et
al. (2007). These grasses have lower leaf area indices (LAI) and shallower root systems, resulting in a
lower transpiration rate per unit of leaf area when compared to Amazon tropical rain forest (dominated by
C3 tree species).
25
Precipitation dataset. In order to evaluate the uncertainties in the precipitation dataset used in this
study (CRU3.0, Mitchell and Jones, 2005), we compared the mean precipitation as simulated by the
model against 4 different precipitation datasets: Cramer & Leemans (2001); Legates and Willmott (1990);
Sheffield (2006) and CMAP (Xie and Arkin, 1997). The relative differences between the Control
simulation (CTL), using CRU 3.0, and the average for all datasets were less than 4%; the greatest
30
difference was found for the Madeira river basin (Table S1). The greatest difference between CTL and the
datasets (up to 11.1%) were found for the comparison with CMAP dataset (Xie and Arkin, 1997) in the
Madeira basin.
Evapotranspiration. In CTL simulation, average evapotranspiration (ET) values were ~1147
mm/year (Madeira basin), ~1331 mm/year (Purus basin) and ~1356 mm/year (Juruá basin). Madeira basin
35
is partially covered by savanna biome which could explain lower values of simulated ET for this basin
when compared to the other basins. These values are in good agreement with previous studies (Costa and
Foley 1999). Field campaigns in central Amazon found evaporation values of ~1368 mm/year
(Shuttleworth et al. 1988). Estimated ET based on analysis of eddy covariance sites data (Fisher et al.
2009) found values of ~1370 mm/year for the entire Amazon.
40
Discharge. The simulated mean annual river discharge (CTL simulation) was compared against
observations from three different stations in the mainstream of each basin. Although the simulated
discharge of Juruá and Purus Rivers were in good agreement with the observed values, we found an
almost constant underestimation (about 30%) for the Madeira River. In order to find a reason for this
specific bias, we calculated the differences between the observed and simulated discharge for different
45
river segments. We found that the major discharge deficit came from outside the Brazilian border (more
than 25%, upstream station number 15320002). The same pattern was not found for Juruá and Purus river
basins, whose areas are almost entirely inside Brazilian borders. In fact, almost 60% of Madeira basin
area is located outside the Brazilian border. As the precipitation used as input data in our simulations are
based on rain gauges measurements, we concluded that most of the Madeira discharge bias were related
50
with the CRU3.0 precipitation data set, once this area have a very low rain gauge density. The same
underestimation of precipitation were found on the other precipitation datasets (Table S1). Similar bias
were also reported in previous studies (e.g., Costa and Foley, 1997; Coe et al., 2002). In order to correct
this bias, we applied a constant correction of an additional 30% on the runoff data calculated by IBIS for
the Madeira basin (similar to Coe et al., 2002; 2009). The discharge results and relative error (RE)
55
considering this correction are shown in Table S2.
The simulated mean discharge is in good agreement with the observed data. The overall discharge
underestimation is about 6% (Table S2) with the Purus basin having the greatest difference from the
observations. Despite the uncertainties associated with the precipitation datasets, other sources of factors
may explain the model deviations: (i) During the period of the observed data deforestation occurred but in
60
the CTL simulation the Amazon land cover was constant and did not include any human interference. (ii)
The IBIS and THMB models may have unknown sources of bias as a result of model representation of
biophysics, parameter uncertainties and those caused by spatial resolution.
Analysis
Mean values of P and ET. In the simulations with climate feedback (LCC_CF) the
65
evapotranspiration and precipitation changes for the period of simulation were evaluated in terms of mean
values for each basin. The same was done for the evapotranspiration in the simulations without climate
feedback (LCC_NoCF). The comparison between scenarios was done considering the average values for
the entire period of simulation (1950-1999), although the first two years of simulation (1950 and 1951)
were excluded from the analysis as it is the period required to enable the hydrologic model (THMB) to
70
reach the equilibrium.
Changes in precipitation seasonality. In the LCC_CF simulation, we analyzed the changes in
precipitation for each scenario for each season (Figure 3, paper). Firstly, we calculated the spatial average
value for each basin and each month. Secondly, we calculated the monthly average values considering the
entire period of simulation (excluding the first two years), presented in Table S3. Then we calculated the
75
average for every group of three months (December-February; March-May; June-August; SeptemberNovember). The resulted values of every scenario were then compared to the CTL simulation in terms of
the relative difference (%).
Water deficit period. The water deficit period is analyzed in the LCC_CF simulation. It is defined
as the period in which the values of precipitation minus evapotranspiration are less than zero (P-ET < 0).
80
First, we calculated the monthly mean of each of these variables (P and ET) for the simulation period,
averaged for the basin area. We discarded the first two years as they were used by THMB to reach the
equilibrium. We calculated the difference (P-ET) for each monthly mean, and after that, the results for all
scenarios were plotted in the same graph in order to compare the simulations.
River discharge. Observed values of river discharge was obtained from the Agência Nacional de
85
Águas (ANA) website (http://hidroweb.ana.gov.br/) for the stations listed in Table S2. In order to
compare simulated versus observed values we considered the initial year of observations in each station
calculating the monthly average values, and then annual average values. For the results presented in the
paper, we considered the values obtained in the closest stations to the mouth of the main rivers, which
was the stations: “Gavião” in Juruá river (ANA station code 12840000); “Arumã-jusante” in Purus river
90
(ANA station code 13962000); and “Manicoré” in Madeira river (ANA station code 15700000).
95
References
1.
Coe MT, Costa MH, Botta A, Birkett C (2002) Long-term simulations of discharge and floods in the
Amazon basin. J Geophys Res 107, 17.
2.
Coe MT, Costa MH, Howard EA (2007) Simulating the surface waters of the Amazon River basin:
impacts of new river geomorphic and flow parameterizations. Hydrol Process 22: 2542-2553.
100
3.
Coe MT, Costa MH, Soares-Filho BS (2009) The influence of historical and potential future
deforestation on the stream flow of the Amazon river – land surface processes and atmospheric
feedbacks. J Hydrol 369:165-174.
4.
Costa MH, Foley JA (1997) Water balance of the Amazon Basin: dependence on vegetation cover
and canopy conductance. J Geophys Res 102, 17.
105
5.
Costa MH, Foley JA (1999) Trends in the hydrologic cycle of the Amazon basin. J Geophys Res
104:14189-14198.
6.
Costa MH, Foley JA (2000) Combined effects of deforestation and doubled atmospheric CO2
concentrations on the climate of Amazonia. J Clim 13:18-34.
7.
110
Costa MH, Oliveira CHC, Andrade G, Bustamante TR, Silva FA, Coe MT (2002) A macroscale
hydrological data et of river flow routing parameters for the Amazon basin. J Geophys Res 107, D20.
8.
Costa MH, Yanagi SNM, Souza PJOP, Ribeiro A, Rocha EJP (2007) Climate change in Amazonia
caused by soybean cropland expansion, as compared to caused by pastureland expansion. Geophys
Res Lett 34:L07706.
9.
115
Cramer WP, Leemans R (2001) Global 30-Year Mean Monthly Climatology, 1930-1960, version 2.1.
Data set available at http://www.daac.ornl.gov from Oak Ridge National Laboratory Distributed
Active Archive Center, Oak Ridge, USA. Accessed August, 15, 2011.
10. Delire C, Foley JA, Thompson S (2004) Long-term variability in a coupled atmosphere-biosphere
model. J Clim 17:3947-3959.
11. Fisher JB, Malhi Y, Bonal D, Rocha HR, Araújo AC, et al. (2009) The land-atmosphere water flux in
120
the tropics. Glob Change Biol 15:2694-2714.
12. Hahmann AN, Dickinson RE (1997) RCCM2-BATS model over Tropical South America:
applications to tropical deforestation. J Clim 10:1944-1964.
13. Kiehl JT, Hack JJ, Bonan GB, Boville BA, Williamson DL, Rasch PJ (1998) The National Center
For Atmospheric Research Community Climate Model: CCM3. J Clim 11:1131-1149.
125
14. Kucharik CJ, Foley JA, Delire C, Fisher VA, Coe MT, Lenters JD, Young-Molling C, Ramankutty N
(2000) Testing the performance of a Dynamic Global Ecosystem Model: water balance, carbon
balance, and vegetation structure. Glob Biogeochem Cycles 14:795-825.
15. Lean J, Warrilow DA (1989) Simulation of the regional climatic impact of Amazon deforestation.
Nature 342:411-413.
130
16. Legates DR, Willmott CJ (1990) Mean seasonal and spatial variability in gauge-corrected, global
precipitation. Int J Climatol 10:111-127.
17. Mitchell TD, Jones PD (2005) An improved method of constructing a database of monthly climate
observations and associated high-resolution grids. Int J Climatol 25:693-712.
18. Ramos da Silva R, Werth D, Avissar R (2008) Regional impacts of future land-cover changes on the
135
Amazon basin wet-season climate. J Clim 21:1153-1170.
19. Salati E, Nobre CA (1991) Possible climatic impacts of tropical deforestation. Clim Change 19:177196.
20. Sampaio G, Nobre C, Costa MH, Sayamurty P, Soares-Filho BS, Cardoso M (2007) Regional climate
change over eastern Amazonia caused by pasture and soybean cropland expansion. Geophys Res Lett
140
34:L17709.
21. Senna MCA, Costa MH, Pinto LIC, Imbuzeiro HMA, Diniz LMF, Pires GF (2009) Challenges to
Reproduce Vegetation Structure and Dynamics in Amazonia Using a Coupled Climate–Biosphere
Model. Earth Interact 13:1-28.
22. Sheffield J, Goteti G, Wood EF (2006) Development of a 50-Year high-resolution global dataset of
145
meteorological forcings for land surface modeling. J Clim 19:3088-3111.
23. Shukla J, Nobre C, Sellers P (1990) Amazon Deforestation and Climate Change. Science 247:13221325.
24. Shuttleworth WJ (1988) Evaporation from Amazonian Rainforest. Proc R Soc Lond B
233(1272):321-346.
150
25. Xie P, Arkin PA (1997) Global precipitation: A 17-year monthly analysis based on gauge
observations, satellite estimates, and numerical model outputs. Bull Am Meteorol Soc 78:2539-2558.
155
Fig. S1: Deforestation scenarios for the Amazon used in the simulations (original spatial resolution). (a)
End of Deforestation by 2020 (ED202020) (Nepstad et al., 2009); (b) Business as Usual by 2030
(BAU2030) (Soares-Filho et al., 2006), and (c) Business as Usual by 2050 (BAU2050) (Soares-Filho et
al., 2006). The original dataset was resampled to model’s spatial resolution (CCM3: 2.81°; IBIS:
0.0833°).
160
Juruá
Purus
Dataset
mm/day
RE (%)
Dataset
mm/day
RE (%)
Sheffield
6.2
-1.6
Sheffield
6.0
-0.7
Cramer
6.0
2.3
Cramer
6.0
-0.8
CMAP
5.6
9.8
CMAP
5.4
10.0
Legates
6.6
-7.6
Legates
6.1
-2.7
Average
6.1
0.0
Average
5.9
0.9
Control (CRU)
6.1
0.0
Control (CRU) 5.9
0.0
Relative Error (%)
RE(%) = (Control/Dataset-1)*100
Madeira
Dataset
mm/day
RE (%)
Sheffield
4.4
7.3
Dataset
Time span
Cramer
4.6
3.5
Sheffield
1970-1999
1.0
CMAP
4.3
11.1
Cramer
1930-1960
0.5
Legates
4.8
-0.9
CMAP
1979-2000
2.5
Average
4.6
3.6
Legates
1920-1980
2.5
Control (CRU)
4.7
0.0
Control (CRU)
1950-1999
0.5
Spatial Resolution (°)
Table S1 – comparison of mean values (first column) and relative errors (second column) between
different precipitation datasets and the CTL simulation using CRU 3.0 (Mitchell and Jones, 2005). The
165
gray box depicts the time span and spatial resolution of each dataset.
Station code
Agência
Nacional de
Águas
Station
number
(Fig. 1,
paper)
Station name
Observed
mean river
discharge
(m3/s)
Simulated
mean river
discharge
(m3/s)
Relative
Error
(RE)
(%)
1833.9
1799.2
-2
1296.8
1310.4
1
Juruá River
12550000
6
12680000
5
Eirunepé montante
Envira
12840000
9
Gavião
4780.3
4368.7
-9
Purus River
13650000
3
Floriano Peixoto
590.4
576.2
-2
13600002
1
Rio Branco
344.2
370.7
8
13880000
7
Canutama
6537.9
5574.7
-15
13962000
10
Arumã-jusante
10469
9244.8
-12
Madeira River
15320002
2
Abunã
18099.2
16233.1
-10
15630000
4
Humaitá
21829.3
20367.7
-7
15700000
8
Manicoré
24726
22395
-9
Average relative error (%)
-6
Table S2 – Observed versus simulated mean river discharge (CTL simulation) and relative error (RE) for
170
ten stations in the study area.
Juruá
Precip.
(mm/day)
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Madeira
Precip.
(mm/day)
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
175
CTL
Control
8.9
9.4
9.6
7.5
4.6
2.5
1.9
2.5
4.3
6.3
7.3
8.5
LCC_CF
ED2020 BAU2030
8.7
8.7
9.2
7.4
4.4
2.4
1.9
2.2
3.4
5.6
7.8
8.8
CTL
Control
8.2
8.5
7.1
4.3
2.6
1.5
1.0
1.3
2.5
4.2
5.7
7.4
8.5
8.6
9.0
7.2
4.4
2.4
1.8
2.1
2.3
4.8
7.8
8.6
BAU2050
8.0
7.9
8.2
6.9
4.4
2.4
1.8
1.9
1.6
4.0
7.3
8.3
Purus
Precip.
(mm/day)
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
CTL
LCC_CF
Control
ED2020 BAU2030 BAU2050
9.3
9.9
9.7
7.3
4.3
1.9
1.2
1.8
4.0
5.7
7.7
8.5
9.2
9.4
9.6
7.0
4.0
1.8
0.9
1.2
3.2
4.9
8.2
8.7
9.4
9.6
9.6
7.0
4.1
1.7
0.8
1.0
2.3
3.9
8.2
8.7
8.9
8.9
9.0
6.5
4.1
1.7
0.7
0.8
1.8
3.2
7.4
8.1
LCC_CF
ED2020 BAU2030
8.1
8.0
6.8
4.1
2.5
1.4
0.9
1.1
2.0
3.5
5.3
7.1
8.1
8.3
6.8
4.1
2.5
1.4
0.9
0.9
1.7
3.2
4.7
7.0
BAU2050
8.4
8.2
6.7
4.0
2.5
1.3
0.9
0.9
1.6
3.1
4.9
7.1
Table S3: Average values of precipitation for each basin in each scenario of the LCC_CF simulations,
averaged for the entire period of simulation (1950-1999).