Urban Water-Demand (UWD) forecasting is crucial for efficient water management, improving distribution, and supporting environmental sustainability. In tourist destinations with significant seasonal variations in number of inhabitants (water consumers), accurate water-demand forecasting becomes particularly important. This work evaluates two statistical models for short-term UWD forecasting, namely, Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Two different strategies for model deployment and comparison are considered: (i) a sliding window (SW) approach with one-year (1Y) and two-year (2Y) windows for training and; (ii) a expanding window (EW) approach. The ARIMA model ployed with a Sliding Window (SW) with a two-year (2Y) resolution achieved the best overall results, followed by SARIMA considering Expanding Window (EW) model. To place these outcomes in perspective, we performed a comparison with results from related work that took into account Machine Learning methods for regression for the same data. This comparison suggests that statistical methods provide results that are both competitive and robust in terms of quality for short-term forecasts.