Introduction

1. Impacts of Land-Cover Change on the Hydrometeorology of the Amazon Renato Ramos da Silva and Roni Avissar Department of Civil & Environmental Engineering, Duke University, Durham, NC 27708 44.14 Introduction Hydroclimate change resulting from the replacement of natural forest by degraded vegetation in the Amazon has yet to be fully understood and quantified. Previous studies using coarse grid global circulation models (GCMs) had shown that continental deforestation reduces rainfall and can affect the climate of other regions, such as North America (Nobre et al., 1991; Werth and Avissar, 2002). In this study the Regional Atmospheric Modeling System (RAMS) is used to understand and quantify the impacts of deforestation on the hydrometeorology of the Amazon basin during the wet season based on realistic scenarios of deforestation. Three scenarios of land covers are considered (Fig. 1): (1) current vegetation; (2) degraded land cover as a result of deforestation expansion, as estimated by Soares Filho et al. (2004), for 2030; and (3) same as (2) but for 2050. • Results • RAMS simulated relatively well the rainfall accumulation for January and February 1999. In general results from the model as compared with observations show that: • The model simulates well the accumulation of rainfall in Rondonia as compared with observations from raingauges (Fig. 3). • The simulated spatial accumulation agrees relatively well with TRMM satellite derived data, but the model overestimates rainfall near the Andes and underestimates it near the coast of the state of Para (Fig. 4). • Empirical Orthogonal Function analysis shows that the model is able to capture the principal modes of rainfall variability (Fig. 5). Fig. 5. Empirical Orthogonal Function Analysis applied to daily rainfall accumulation for January and February 1999, as derived from the TRMM satellite (left), and simulated with RAMS (center), and their respective principal components (PCs) (right). The percentage of explained variance is provided for each EOF, and numbers in parenthesis are correlation coefficient between model and observation PCs. Observations Fig. 1: Land cover in the Amazon Basin: (a) current vegetation, (b) 2030, (c) 2050. Dark green represents forest, yellow is pasture and magenta is mixed woodland (cerrado). Scenarios are from Soares Filho et al. (2004). • Wind strengthens as a result of the replacement of forest by pasture in the state of Para and Mato Grosso, while it weakens in Tocantins and north of Amazonas, emphasizing complex land-atmosphere interactions that remained to be explained; • These results emphasize that the region west of Para’ has a high climatic susceptibility to land-cover change. In this study, deforested areas were assumed to be pasture. A more realistic scenario needs to recognize other non-natural land cover such as agricultural fields, artificial lakes and urban areas. To account for these landscape heterogeneities higher resolution simulations need to be performed and evaluated. (a) RAMS (b) Fig. 3. Simulated rainfall accumulation in Rondonia compared to observations from a raingauge network consisting of 40 stations. The bars represent the standard deviation in the observations. (c) Methodology The Regional Atmospheric Modeling System (RAMS) is used to simulate the hydrometeorology of the region for January and February 1999. Results are compared with observations from the LBA TRMM/Wet AMC (Avissar et al., 2002). The two-month numerical integration was conducted using a 20-km grid size covering the domain shown in Figs. 1-2. The NCEP Reanalysis was used as atmospheric boundary conditions (Kalnay et al. 1996). First, a control simulation using current land cover (Fig. 1a) was performed. Then, two other simulations were produced using the same boundary conditions as in the control case, but assuming projected land covers for 2030 and 2050 (Figs. 1b-c). Rainfall near the Andes is overestimated (Fig. 4). This is caused by the extreme gradient of topography, which is particularly challenging to simulate with current state-of-the-art numerical models. Literature cited Avissar R., P. L. Silva Dias, M. A. Silva Dias, and C. Nobre, 2002: The large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA): Insights and future research needs. J. Geophys. Res., 107, (doi 10.129/2002JD002704). Huffman, G.J., R.F. Adler, B. Rudolph, U. Schneider, and P. Keehn, 1995: Global Precipitation Estimates Based on a Technique for Combining Satellite-Based Estimates, Rain Gauge Analysis, and NWP Model Precipitation Information, J. Clim., 8, 1284-1295. Kalnay, E., et al., 1996: The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 77 (3), 437-471. Nobre, C. A., P. J. Sellers, and J. Shukla, 1991: Amazonian deforestation and regional climate change, J. Clim., 4(10), 957-987. Pielke, R. A., et al., 1992: A Comprehensive Meteorological Modeling System - Rams. Meteorology and Atmospheric Physics, 49,69-91. Soares Filho, B. S., H. Rodrigues, D. Nepstad, G. Cerqueira, E. Voll , A. Alencar, and P. Lefebvre, 2004: A spatially explicit simulation model of deforestation for the Amazon Basin. III LBA Scientific Conference (Brasilia, DF). Werth, D., and R., Avissar, 2002: The local and global effects of Amazon deforestation. J. Geophys. Res., 107, (doi 10.129/2001JD000717). Fig. 6. Rainfall anomaly (mm) relative to the control simulation for 2030 (top) and 2050 (bottom) and percentage change for some of the sub-regions Rainfall anomalies shown in Fig. 6 are associated with changes in wind speed and temperature (Fig. 7). Wind speed stronghtens in deforested areas such as the state of Para’ and Mato Grosso, but decreases in Tocantins and north of the Amazonas state. Temperatures increase in the deforested areas. Fig. 4. Accumulated rainfall for January and February 1999 simulated with RAMS (top) and derived from the TRMM satellite (bottom) (Huffman et al., 1995). Units are mm. Fig. 2. Topography of the simulated domain. The terrain changes abruptly from near sea level in the Amazon Basin to heights on the order of 6000 meters in the Andes, making this region particularly challenging to simulate with numerical models. Fig. 7. Wind speed anomalies (shaded, left), for 2030 (top) and 2050 (down). Vectors represent the mean wind. Surface temperature anomalies are shown in the right panel. The analysis of the first three principal components shows good correlation between model results and observations (Fig. 5). • Conclusions • These preliminary simulations presented here indicate that: • Rainfall gradually decrease as deforestation increase in the heavily deforested regions of the Amazon Basin; • Rainfall accumulation seem to significantly increase near the heavily deforested regions, e.g., in Bolivia and the state of Tocantins; • Comparing the simulations for 2030 and 2050 with the control simulations, we find that: • rainfall in the Amazon decreases by about 6% and 10% for 2030 and 2050, respectively (Fig. 6); • some regions such as Bolivia and Tocantins had higher accumulations of rainfall. For further information Please contact renato@duke.edu.