This work discusses a 1D fluid dynamics arterial network model with fourteen vessel segments, which was constructed to assimilate ex vivo pressure-area data recorded in seven vessel segments from eleven male Merino sheep. The 1D fluid dynamics model incorporated a two-parameter elastic model and a four-parameter viscoelastic Kelvin model to predict vessel deformation. The wall model was calibrated and relations predicting vessel sti?ffness and unstressed vessel radii were developed using nonlinear optimization to estimate model parameters that minimized the least squares error between model predictions and measured data. Moreover, to accurately predict flow, pressure, and area dynamics within the network, the inflow waveform was estimated along with outflow boundary conditions. The latter was done using a single vessel segment representing the ascending aorta. Results demonstrate that it is possible to estimate parameters in simpler settings (0D for wall parameters and a single vessel for the in- and outflow parameters), and apply these to the network model.