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Abstract. The effects of uncertainty in radar-estimated precipitation input on simulated runoff generation from a medium-sized (100-km 2) basin in northern Texas are investigated. The radar-estimated rainfall was derived from Next Generation Weather Radar (NEXRAD) Level II base reflectivity data and was supplemented by ground-based rain-gauge data. Two types of uncertainty in the precipitation estimates are considered: (1) those arising from the transformation of reflectivity to rainfall rate and (2) those due to the spatial and temporal representation of the "true" rainfall field. The study explicitly differentiates between the response of simulated saturation-excess runoff and infiltrationexcess runoff to these uncertainties. The results indicate that infiltration-excess runoff generation is much more sensitive than saturation-excess runoff generation to both types of precipitation uncertainty. Furthermore, significant reductions in infiltration-excess runoff volume occur when the temporal and spatial resolution of the precipitation input is decreased. A method is developed to relate this storm-dependent reduction in runoff volume to the spatial heterogeneity of the highest-intensity rainfall periods during a storm. IntroductionThe past decade has marked a new era in the field of hydrology, resulting from the installation of the U.S. National where the precipitation errors will be magnified in the conversion to runoff. While the above cited studies address the effects of rainfall errors on runoff simulations, the authors of this paper are aware of only one publication which has investigated how hydrologic predictions are affected by changes in the parameters of the reflectivity to rainfall transformation. In that paper, Pessoa et al. [1993] found that different widely accepted reflectivity-rainfall (Z-R) relationships resulted in significantly different simulated hydrographs. The paper suggests that identification of appropriate Z-R relationship parameters in real time is necessary in order to produce reliable hydrologic forecasts with radar-estimated precipitation. In the use of historical radar data for hydrologic simulations, there are many options available for the identification of proper Z-R parameters and subsequent precipitation bias correction. 2655

Evaluating the potential of alternative energy crops across large geographic regions, as well as over time, is a necessary component to determining if biofuel production is feasible and sustainable in the face of growing production demands and climatic change. Switchgrass (Panicum virgatum L.), a native perennial herbaceous grass, is a promising candidate for cellulosic feedstock production. In this study, current and future (from 2080 to 2090) productivity is estimated across the central and eastern United States using ALMANAC, a mechanistic model that simulates plant growth over time. The ALMANAC model was parameterized for representative ecotypes of switchgrass. Our results indicate substantial variation in switchgrass productivity both within regions and over time. States along the Gulf Coast, southern Atlantic Coast, and in the East North Central Midwest have the highest current biomass potential. However, these areas also contain critical wetland habitat necessary for the maintenance of biodiversity and agricultural lands necessary for food production. The southern United States is predicted to have the largest decrease in future biomass production. The Great Plains are expected to experience large increases in productivity by 2080-2090 due to climate change. In general, regions where future temperature and precipitation are predicted to increase are also where larger future biomass production is expected. In contrast, regions that show a future decrease in precipitation are associated with smaller future biomass production. Switchgrass appears to be a promising biofuel crop for the central and eastern United States, with local biomass predicted to be high (>10 Mg/ha) for approximately 50% of the area studied for each climate scenario. In order to minimize land conversion and loss of biodiversity, areas that currently have and maintain high productivity under climate change should be targeted for their long-term growth potential.

that hydrologic simulations are much more sensitive to rainfall measurement errors when runoff is generated by the Horton mechanism than when ranoff is generated by saturation excess (which occurs after the soil profile becomes saturated by a rising water table). This suggests that rainfall-runoff forecasts 1405