Streamflow impacts of biofuel policy-driven landscape change.

Likely changes in precipitation (P) and potential evapotranspiration (PET) resulting from policy-driven expansion of bioenergy crops in the United States are shown to create significant changes in streamflow volumes and increase water stress in the High Plains. Regional climate simulations for current and biofuel cropping system scenarios are evaluated using the same atmospheric forcing data over the period 1979-2004 using the Weather Research Forecast (WRF) model coupled to the NOAH land surface model. PET is projected to increase under the biofuel crop production scenario. The magnitude of the mean annual increase in PET is larger than the inter-annual variability of change in PET, indicating that PET increase is a forced response to the biofuel cropping system land use. Across the conterminous U.S., the change in mean streamflow volume under the biofuel scenario is estimated to range from negative 56% to positive 20% relative to a business-as-usual baseline scenario. In Kansas and Oklahoma, annual streamflow volume is reduced by an average of 20%, and this reduction in streamflow volume is due primarily to increased PET. Predicted increase in mean annual P under the biofuel crop production scenario is lower than its inter-annual variability, indicating that additional simulations would be necessary to determine conclusively whether predicted change in P is a response to biofuel crop production. Although estimated changes in streamflow volume include the influence of P change, sensitivity results show that PET change is the significantly dominant factor causing streamflow change. Higher PET and lower streamflow due to biofuel feedstock production are likely to increase water stress in the High Plains. When pursuing sustainable biofuels policy, decision-makers should consider the impacts of feedstock production on water scarcity.

Representativity of a mesoscale network for weather-related factors governing pollen dispersal.

The cultivation of transgenic crops, such as maize, requires successful gene isolation in field environments. Five spatial statistical techniques are used to evaluate the use of a regional mesoscale observation network (Iowa Environmental Mesonet) as a means to drive field-scale pollen dispersion modeling. The Nearest Neighbor Index, Fractal Dimension, Morisita Index, Thiessen Polygons, and Coefficient of Representativity are computed showing the positive and negative impacts of sequential addition of observation networks into a mesonet framework (a collection of pre-existing networks). While it is shown that the arbitrary combination of disparate observing networks increases spatial resolution, this improvement is often at the expense of increased clustering due to co-location of observation sites near urban areas. Network composition in terms of density and degree of clustering was evaluated with a grid analysis using the Barnes scheme as a means to mitigate clustering and improve prediction accuracies when mesonet data are applied to modeling. This paper shows the importance of understanding and accounting for the spatial characteristics of an observational network before applying it to a modeling effort such as field scale pollen dispersion.