Supplementary Material s66

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; September-

November). 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

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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 (%) Madeira RE(%) = (Control/Dataset-1)*100 Dataset mm/day RE (%) Sheffield 4.4 7.3 Dataset Time span Spatial Resolution (°) 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

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 Station Observed Simulated Relative Agência number mean river mean river Error Station name Nacional de (Fig. 1, discharge discharge (RE) Águas paper) (m3/s) (m3/s) (%) Juruá River Eirunepé - 12550000 6 1833.9 1799.2 -2 montante 12680000 5 Envira 1296.8 1310.4 1 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. Purus Juruá CTL LCC_CF Precip. CTL LCC_CF Precip. (mm/day) (mm/day) Control ED2020 BAU2030 BAU2050 Control ED2020 BAU2030 BAU2050 Jan 8.9 8.7 8.5 8.0 Jan 9.3 9.2 9.4 8.9 Feb 9.4 8.7 8.6 7.9 Feb 9.9 9.4 9.6 8.9 Mar 9.6 9.2 9.0 8.2 Mar 9.7 9.6 9.6 9.0 Apr 7.5 7.4 7.2 6.9 Apr 7.3 7.0 7.0 6.5 May 4.6 4.4 4.4 4.4 May 4.3 4.0 4.1 4.1 Jun 2.5 2.4 2.4 2.4 Jun 1.9 1.8 1.7 1.7 Jul 1.9 1.9 1.8 1.8 Jul 1.2 0.9 0.8 0.7 Aug 2.5 2.2 2.1 1.9 Aug 1.8 1.2 1.0 0.8 Sep 4.3 3.4 2.3 1.6 Sep 4.0 3.2 2.3 1.8 Oct 6.3 5.6 4.8 4.0 Oct 5.7 4.9 3.9 3.2 Nov 7.3 7.8 7.8 7.3 Nov 7.7 8.2 8.2 7.4 Dec 8.5 8.8 8.6 8.3 Dec 8.5 8.7 8.7 8.1

Madeira CTL LCC_CF Precip. (mm/day) Control ED2020 BAU2030 BAU2050 Jan 8.2 8.1 8.1 8.4 Feb 8.5 8.0 8.3 8.2 Mar 7.1 6.8 6.8 6.7 Apr 4.3 4.1 4.1 4.0 May 2.6 2.5 2.5 2.5 Jun 1.5 1.4 1.4 1.3 Jul 1.0 0.9 0.9 0.9 Aug 1.3 1.1 0.9 0.9 Sep 2.5 2.0 1.7 1.6 Oct 4.2 3.5 3.2 3.1 Nov 5.7 5.3 4.7 4.9 Dec 7.4 7.1 7.0 7.1

175 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).