Monthly land cover-specific evapotranspiration models derived from global eddy flux measurements and remote sensing data

Evapotranspiration (ET) is arguably the most uncertain ecohydrologic variable for quantifying watershed water budgets. Although numerous ET and hydrological models exist, accurately predicting the effects of global change on water use and availability remains challenging because of model deficiency and/or a lack of input parameters. The objective of this study was to create a new set of monthly ET models that can better quantify landscape-level ET with readily available meteorological and biophysical information. We integrated eddy covariance flux measurements from over 200 sites, multiple year remote sensing products from the Moderate Resolution Imaging Spectroradiometer (MODIS), and statistical modelling. Through examining the key biophysical controls on ET by land cover type (i.e. shrubland, cropland, deciduous forest, evergreen forest, mixed forest, grassland, and savannas), we created unique ET regression models for each land cover type using different combinations of biophysical independent factors. Leaf area index and net radiation explained most of the variability of observed ET for shrubland, cropland, grassland, savannas, and evergreen forest ecosystems. In contrast, potential ET (PET) as estimated by the temperature-based Hamon method was most useful for estimating monthly ET for deciduous and mixed forests. The more data-demanding PET method, FAO reference ET model, had similar power as the simpler Hamon PET method for estimating actual ET. We developed three sets of monthly ET models by land cover type for different practical applications with different data availability. Our models may be used to improve water balance estimates for large basins or regions with mixed land cover types. Copyright © 2015 John Wiley & Sons, Ltd

Federal policy mandating safer cigarettes: A hypothetical simulation of the anticipated population health gains or losses

If manufacturing a safer cigarette is technically possible-an open question-then mandating that tobacco manufacturers improve the safety of cigarettes would likely have both positive and negative implications for the nation's health. On the one hand, removing toxins may reduce the incidence of smoking-related diseases and premature mortality in smokers. On the other hand, smokers might be less inclined to quit, those who have quit might resume the habit, and youth who have never smoked will have one less reason to avoid tobacco use. To assess the expected population health impacts of a legislative or regulatory mandate, we created the Tobacco Policy Model, a system dynamics computer simulation model. The model relies on secondary data and simulates the U.S. population over time spans as long as 50 years. Our simulation results reveal that even if requiring cigarettes to be safer makes smoking more attractive and increases tobacco use, a net gain in population health is still possible. © 2004 by the Association for Public Policy Analysis and Management.