Taohidi Alamou Lamidi^1,, Cossi Télesphore Nounangnonhou¹, Bienvenu Macaire Agbomahena¹

One of today's research challenges is to predict and anticipate the continuing effects of climate change, so as to be able to react and adapt to future developments. Global warming and weather forecasts indicate a growing risk of climate change-related events, which are not without consequences for ecosystems, including the Ouémé river basin. Water availability is strongly influenced by the variability of meteorological parameters. Precipitation and temperature are important parameters that have been side-lined in many water resource management projects. Daily rainfall and temperature data from 1982 to 2022 were collected at three representative sites in the Ouémé river basin: Bétérou, Savè and Kétou. Time series analysis, and more specifically trend analysis, was used as a first approach to describe the evolution of the various parameters over time. In this study, the ARIMA seasonal model (SARIMA) was used and forecasts were made for the next 30 years (2015-2045). The auto-regressive (p) integrated (d) moving average (q) model (ARIMA) is based on the Box Jenkins approach, which predicts future trends by making the data stationary and removing seasonality. The results of the study show that temperatures are high during the dry season, when precipitation is low. In addition, the multiplicative seasonal models that best fit precipitation and temperature are represented by ARIMA (5, 1, 0)(2, 0, 0)₁₂ and ARIMA (2, 1, 1)(1, 0, 1)₁₂ respectively. The information on patterns and trends can be used as a forecasting tool for the planning and procurement of water resource projects, as well as for the development of better water management practices in the study area.

ARIMA Models, forecasting, precipitation, temperature, Ouémé river