  Photometric galaxy surveys probe the late-time Universe where the density
field is highly non-Gaussian. A consequence is the emergence of the
super-sample covariance (SSC), a non-Gaussian covariance term that is sensitive
to fluctuations on scales larger than the survey window. In this work, we study
the impact of the survey geometry on the SSC and, subsequently, on cosmological
parameter inference. We devise a fast SSC approximation that accounts for the
survey geometry and compare its performance to the common approximation of
rescaling the results by the fraction of the sky covered by the survey,
$f_\mathrm{SKY}$, dubbed 'full-sky approximation'. To gauge the impact of our
new SSC recipe, dubbed 'partial-sky', we perform Fisher forecasts on the
parameters of the $(w_0,w_a)$-CDM model in a 3x2 points analysis, varying the
survey area, the geometry of the mask and the galaxy distribution inside our
redshift bins. The differences in the marginalised forecast errors, with the
full-sky approximation performing poorly for small survey areas but excellently
for stage-IV-like areas, are found to be absorbed by the marginalisation on
galaxy bias nuisance parameters. For large survey areas, the unmarginalised
errors are underestimated by about 10% for all probes considered. This is a
hint that, even for stage-IV-like surveys, the partial-sky method introduced in
this work will be necessary if tight priors are applied on these nuisance
parameters.
