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Detecting H2O in exoplanet atmospheres is the first step on the path todetermining planet habitability. Coronagraphic design currently limits theobserving strategy used to detect H2O, requiring the choice of specificbandpasses to optimize abundance constraints. In order to examing the optimalobserving strategy for initial characterization of habitable planets usingcoronagraph-based direct imaging, we quantify the detectability of H2O as afunction of signal-to-noise ratio (SNR) and molecular abundance across 25bandpasses in the visible wavelength range (0.5-1 micron). We use apre-constructed grid consisting of 1.4 million geometric albedo spectra acrossa range of abundance and pressure, and interpolate the produce forward modelsfor an efficient nested sampling routine, PSGnest. We first test thedetectability of H2O in atmospheres that mimix a modern-Earth twin, and thenexpand to examine a wider range of H2O abundances; for each abundance value, weconstrain the optimal 20% bandpasses based on the effective signal-to-noiseratio (SNR) of the data. We present our findings of H2O detectability asfunctions of SNR, wavelength, and abundance, and discuss how to use theseresults for optimizing future coronographic instrument design. We find thatthere are specific points in wavelength where H2o can be detected down to 0.74micron with moderate-SNR data for abundances at the upper end of Earth'spresumed historical values, while at 0.9 micron, detectability is possible withlow-SNR data at modern Earth abundances of H2O.

Bayesian Analysis for Remote Biosignature Identification on exoEarths  (BARBIE) I: Using Grid-Based Nested Sampling in Coronagraphy Observation  Simulations for H2O