  Mock catalogues are a crucial tool in the analysis of galaxy surveys data,
both for the accurate computation of covariance matrices, and for the
optimisation of analysis methodology and validation of data sets. In this
paper, we present a set of 1800 galaxy mock catalogues designed to match the
Dark Energy Survey Year-1 BAO sample (Crocce et al. 2017) in abundance,
observational volume, redshift distribution and uncertainty, and redshift
dependent clustering. The simulated samples were built upon HALOGEN (Avila et
al. 2015) halo catalogues, based on a $2LPT$ density field with an exponential
bias. For each of them, a lightcone is constructed by the superposition of
snapshots in the redshift range $0.45<z<1.4$. Uncertainties introduced by
so-called photometric redshifts estimators were modelled with a
\textit{double-skewed-Gaussian} curve fitted to the data. We also introduce a
hybrid HOD-HAM model with two free parameters that are adjusted to achieve a
galaxy bias evolution $b(z_{\rm ph})$ that matches the data at the 1-$\sigma$
level in the range $0.6<z_{\rm ph}<1.0$. We further analyse the galaxy mock
catalogues and compare their clustering to the data using the angular
correlation function $ w(\theta)$, the comoving transverse separation
clustering $\xi_{\mu<0.8}(s_{\perp})$ and the angular power spectrum $C_\ell$.
