Over the past two decades, southeastern Nigeria has witnessed huge economic losses due to severe flooding caused by climate change. Understanding the contribution of climate change to flooding is critical for runoff utilization and control, especially in the tropics. This study is focused on quantifying the amount of flooding contributed by climate change effects in the River Basin of southeastern Nigeria. First, the Climate-Flood model equation was expressed using the Time-Series Autoregressive model technique. Using Minitab software, mean annual values of contributing climate variables from prolonged periods of recorded data were investigated. Runoff predictions made from climate-flood models using only the annual records of climatic variables were filtered from the resultant of the respective annual climatic variables, and mean change in flood due to climate change. The output was analyzed considering geology, infiltration capacity, drainage lines and digital elevation model (DEM) of the area. The result shows the obvious trend in climatic variables consistently generates significant impact on the flood. Owerri had the highest positive change in evaporation of 0.11% with a rainfall of 0.03%. Awka had the highest change in sunshine hours and rainfall of 0.41%. Umuahia had the highest change in solar radiation of 0.08, while Enugu had the highest change in air temperature of 0.12. When the contribution of climate change to runoff variation was quantified, Enugu shows highest with 1.68 m³/s while Owerri has 0.04 m³/s. This knowledge is necessary in designing hydrological structures and formulating policies that will help prevent/mitigate flood disasters in the region.

Insufficient length of streamflow record poses challenges in capturing extreme events, frequencies, and sequences crucial for effective water resources systems design. Consequently, simulation and application of statistically similar streamflow values becomes imperative. In this study, two Markov-based models, namely the Thomas-Fiering (T-F) model and the Autoregressive (AR) model, were developed based on a log-normal probability distribution to synthesize monthly and annual flows using a decade of historical data (1980–1989) collected from the Umulokpa gauging station in the Adada River catchment of Enugu State, Nigeria. Except for the month of December (p = 0.03), no significant difference (p > 0.05) was observed between the monthly and annual model-predicted streamflows. However, both models demonstrated limitations in fully capturing the characteristics of the observed flows, particularly regarding Kurtosis. Further accuracy assessment from computed correlation coefficients (0.018 for T-F and 0.122 for AR), along with root mean square error (RMSE) values (12.84 for T-F and 11.95 for AR), indicate that while both models deviated from the observed flows, the AR model demonstrated superior performance compared to the T-F model. However, to apply these models reliably, for future flow prediction in the Adada catchment, streamflow data from rivers with comparable catchment characteristics is essential.