tokens: excitation temperatures) owing to atmospheric transmission and telluric lines, and would limit the detections to some energetic rotational transitions such as the 12.4 m blend observed in Pontoppidan et al. (2010a) . 5. Conclusions From the outcome of our model, we have provided a scenario that can explain the observations in the mid-IR towards a Herbig star: we considered some limitations in our description of disks around Herbig stars, and we think that these would not be problematic for the correct physical description of the mid-IR spectroscopy. Observations have been performed up to now with facilities affected by low spectral resolution and lower sensitivity. These two elements limit any possible firm conclusion about the nature of disks around Herbig stars, which can be intrinsically different from the ones surrounding T Tauri stars. Low signal-to-noise ratio (S/N) is the most likely explanation for the low detection rates of mid-IR water lines around early type PMSs. This problem also partially affects later type PMSs. T Tauri stars spectra are less noisy; however, mid-IR water line fluxes are lower, and the detection rate is only a factor two more than for Herbig stars. Better performing instruments with resolution 3000 and longer integration times are definitively needed to detect water successfully in the 1–20 m range in protoplanetary disks. Appendix A: Sensitivity limits Our analysis sample includes a selection of T Tauri and Herbig Spitzer IRS high resolution observations. BCD data products were retrieved from the Spitzer archive 4 . The data were reduced with the c2d IRS science pipeline using the PSF extraction method (see Lahuis et al. 2007 ; Lahuis 2007 , for details), which provides higher S/N spectra than the standard tapered aperture extraction used to populate the Spitzer archive. After extraction, additional defringing ( Lahuis Boogert 2003 ) was applied to remove any low-level fringe residuals. Sensitivity limits were determined by calculating the line flux for synthetic lines with an amplitude equivalent to the 1 noise level in the IRS spectrum at the respective blend wavelengths and a width comparable to the blend width at a spectral resolution of 600. Appendix B: Spectral convolution We applied Gaussian convolution and rebinning to the theoretical spectra from our models. For this task we used the ISO spectral analysis package ( Kessler et al. 1996 ), considering the three resolutions of Spitzer IRS LH/SH modules (; Houck et al. 2004 ), VLT VISIR (; Lagage et al. 2000 ). For JWST MIRI we split the range into four spectral windows: for 7.63–11.71 m, for 11.71–13.59 m, for 13.59–18.14 m, for 18.14–28.43 m, accounting for the variable spectral resolution ( Clampin 2011 ). We add noise through a random generator according to \newpage where is the computed noise and a function both of the continuum flux () multiplied for a random generator between 0 and 1 () and of the selected S/N. Here we consider 100 as claimed in
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