1. Acknowledgments We thank I. Güor for laboratory assistance, Mary Juana for seeds, Herb Isside for greenhouse care, and M.I. Menter for questionable statistical advice. Funding for this project was provided by the Swarthmore College Department of Biology, a Merck summer stipend, and my mom. [Note that people’s titles are omitted.] Conclusions You can, of course, start your conclusions in column #3 if your results section is “data light.” Conclusions should not be mere reminders of your results. Instead, you want to guide the reader through what you have concluded from the results. What is the broader significance? Would anyone be mildly surprised? Why should anyone care? This section should refer back, explicitly, to the “burning issue” mentioned in the introduction. If you didn’t mention a burning issue in the introduction, go back and fix that -- your poster should have made a good case for why this experiment was worthwhile. A good conclusion will always refer to the literature on the topic -- how does your research add to what is already published on the topic? Blah, blah, blah. Blah, blah, blah. Blah, blah, blah. Blah, blah, blah. Blah, blah, blah. Blah, blah, blah. Blah, blah, blah. Blah, blah, blah. Blah, blah, blah. Blah, blah, blah. Blah, blah, blah. Blah, blah, blah. Blah, blah, blah. Blah, blah, blah. Shraddhanand Shukla, Andrew W. Wood, Department of Civil and Enviornmental Engineering, University of Washington Literature cited Bender, D.J., E.M Bayne, and R.M. Brigham. 1996. Lunar condition influences coyote (Canis latrans) howling. American Midland Naturalist 136:413-417. Brooks, L.D. 1988. The evolution of recombination rates. Pages 87-105 in The Evolution of Sex, edited by R.E. Michod and B.R. Levin. Sinauer, Sunderland, MA. Scott, E.C. 2005. Evolution vs. Creationism: an Introduction. University of California Press, Berkeley. Society for the Study of Evolution. 2005. Statement on teaching evolution.. Accessed 2005 Aug 9. Remember: no period after journal name. Ever (unless you use abbreviation). Abutting these last sections can save you a little space, and subtly indicates to viewers that the contents are not as important to read. Application of LDAS-era Land Surface Models for Drought Characterization And Prediction In Washington State Abstract Accurate appraisal of the current and future status of drought is still a major challenge for scientists and water managers. No ubiquitous definition of drought exists and different indices of meteorological and hydrological elements yield different perspectives on drought. Although traditional drought indices are based on meteorological inputs, hydrologic variables such as soil moisture and runoff, the by- products of the hydro- meteorological process affecting a watershed, can be used to derive indicators of drought status, and are arguably more closely related to the societal impacts of drought than the drought indices based on the meteorological variables only. This presentation compares drought metrics based on modeled soil moisture and runoff with the conventional drought indices and other independent measures, including observed or naturalized streamflow and reservoir levels. Hydrologic fields used for this analysis are simulated by a physically- based, semi-distributed hydrologic model, the Variable Infiltration Capacity model, for Washington State We also show that ensemble hydrologic predictions of these fields can be used to extend both traditional and model- based drought indices into the future and provide uncertainty estimates for the future evolution of a drought. The significant similarities between the model-based metrics and the traditional indicators of drought suggest that the hydrologic models are at least as capable of characterizing drought as traditional meteorological indices, and offer a way forward toward developing a capacity for drought prediction. 2007 Drought is mainly driven by the lack of precipitation, but its severity and duration depends on the antecedent moisture condition, hydrologic and socio-economic conditions of the region. Most of the drought indices being used, however, are either merely dependent on climate variable or incorporate an extremely simplified water balance model. Retrospective Simulation VIC run for the period 1915-2006 was done at 1/16th degree and daily time-step over the Washington State domain, to simulate the soil-moisture and runoff data used in the present study. Soil-moisture Percentiles Daily soil-moisture was averaged to monthly values for the period 1950-2006. These historical monthly values of soil-moisture for each grid were sorted for each month and empirical climatological distributions were developed using the Weibull plotting positions Standardized Precipitation and Runoff Index Standardized Precipitation Index (McKee et al., 1993) is a probability index based on the precipitation only. Computation of the SPI involves fitting a gamma probability distribution function of precipitation total for a station. In this study we also devise a Standardized Runoff Index (Shukla and Wood, 2007) based on the same analogy as SPI but using the simulated runoff data. SRI is an indicator of the hydrologic drought. Spatial Resolution vs 10 Climate Divisions 62 Water Resources Inventory Agencies NOAA PDSI smoothed SM %-ile Statewide 2005 Drought : Warm winter with below average precip Owing to the extremely low snowpack a statewide drought was declared on March10, 2005. As of April 1 st Mountain snow pack was 26% of average Precipitation was between 51 and 76% of average (Source: WSDA, http://agr.wa.gov/Environment/Drought/) Stream flows were between 22 and 90% of average (Source: WSDA, http://agr.wa.gov/Environment/Drought/) Statewide 1976-77 Drought : Warm winter with below average precip