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[1] Microalgae are receiving increased global attention as a potential sustainable "energy crop" for biofuel production. An important step to realizing the potential of algae is quantifying the demands commercial-scale algal biofuel production will place on water and land resources. We present a high-resolution spatiotemporal assessment that brings to bear fundamental questions of where production can occur, how many land and water resources are required, and how much energy is produced. Our study suggests that under current technology, microalgae have the potential to generate 220 × 10 9 L yr −1 of oil, equivalent to 48% of current U.S. petroleum imports for transportation. However, this level of production requires 5.5% of the land area in the conterminous United States and nearly three times the water currently used for irrigated agriculture, averaging 1421 L water per liter of oil. Optimizing the locations for microalgae production on the basis of water use efficiency can greatly reduce total water demand. For example, focusing on locations along the Gulf Coast, southeastern seaboard, and Great Lakes shows a 75% reduction in consumptive freshwater use to 350 L per liter of oil produced with a 67% reduction in land use. These optimized locations have the potential to generate an oil volume equivalent to 17% of imports for transportation fuels, equal to the Energy Independence and Security Act year 2022 "advanced biofuels" production target and utilizing some 25% of the current irrigation demand. With proper planning, adequate land and water are available to meet a significant portion of the U.S. renewable fuel goals.

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A subfactor-based regression technique for estimating hydraulic roughness coefficients for shallow overland flow was developed from simulated rainfall/runoff plots originallycollected for erosion studies. The data were collected from 14 different native rangeland areas in the western United States. Rainfall was appliedat a constantintensity of 65mm/hrfrom a rotating-boom rainfall simulator. Surfaces evaluated ranged from smooth bare soil to gravelly bare soil and sparsely to densely vegetated rangeland areas. A reference table of "effective roughness" coefficients for shallow overland flow is presented with a description of site char acteristics. The derived roughness regression equations predict an "effective Darcy-Wiesbach roughness coefficient" for nativerangeland (r2 = 0.70)that incorporates the effect of raindrop impact, soil texture, random roughness, rocks, litter, and canopy and basal plant cover. The sites evaluated in the paper covered a wide range of vegetation types and included short-, mid-, and tallgrass prairies; desert shrubsand sagebrush; and oak and pinyon-juniper woodlands. No trend in effective roughness coefficient associated with type of vegetation (grass or shrub) or soil texture was apparent.

The hydrologic component of the CREAMS model is described and discussed in terms of calculating a surface water balance for shallow land burial systems used for waste disposal. Parameter estimates and estimation procedures are presented in detail in the form of a user's guide. Use of the model is illustrated with three examples based on analysis of data from Los Alamos, New Mexico and Rock Valley, Nevada. Use of the model in design of trench caps for shallow land burial systems is illustrated with the example applications at Los Alamos. 42 48 49 low 39 39 39 45 49 49 38 40 46 40 46

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