based on satellite observations was consequently developed to derive the global distribution of land surface temperature and wetness (Williams et al. 2000 and Basist et al. 2001).

The Defense Department has maintained a set of polar orbiting satellites that pass over the entire earth at 6AM and 6PM every day. These orbits are particularly attractive because they pass over the same location at the same time daily. These satellites are equipped with sensors that detect microwaves that can pass through clouds. The Special Sensor Microwave Imager (SSM/I) can detect both surface temperature (McFarland et al. 1990, Neale et al. 1990, Njoku 1994, Weng and Grody 1998) and surface wetness (Basist et al. 1998).

A major difficulty in deriving surface temperature from passive microwave measurements is the variable emissivity associated with different surfaces. For the microwave spectrum the emissivity of soil depends on its water and/or mineral content, as well as the effects of vegetation and surface roughness. Since the microwave emissivity is variable, the brightness temperature is not a function of surface temperature alone. Therefore, any algorithm that attempts to estimate surface temperature must first infer the particular surface condition for a microwave measurement, and either make appropriate emissivity adjustments to the microwave measurement, or filter the measurement if reliable adjustments are not possible. The approach used here assumes no a priori information about the surface conditions, allowing the satellite observations to provided a dynamic assessment of the surface type and current emissivity. The Basist Wetness Index (BWI) is simply the emissivity adjustment associated with water in the radiating surface. Surface wetness has strong correspondence with the upper level surface wetness and we rely on surface wetness as our measure of surface wetness throughout this paper. Wetness originates from multiple sources (i.e. precipitation, snow melt, and irrigation).

The products used in this study are monthly climatologies for surface wetness and temperature from the period January 1988 to 2002 for the United States, Brazil and India. We use both the average value of each measure in each month and the interannual variance. The spatial resolution is 1/3 degree (approximately 30 Km) for both data products. The centroid of each pixel is associated with the centroid of counties in the United States, districts in India, and municipios in Brazil. All of these divisions have approximately the same resolution, although the municipios are slightly smaller.

The three countries were selected for several reasons. First, they encompass large land masses and thus allow spatial variation in both climate and other variables (e.g., soil and adaptation techniques). Second, the three countries are placed in various locations around the globe, so that the results could be extrapolated to other countries with similar climatic conditions. Third, these countries represent various development levels (Mendelsohn et al 2001), which allow us to predict what could be the different impacts and adaptation measures most likely to be effective in countries with different levels of development.

Data concerning farms for the United States were collected from US Census of Agriculture surveys in 1997. Soil data were collected from the National Resource Inventory for each county (see Mendelsohn et al 1994 for details). US socioeconomic 63