Sumari Neema SimonMaginga Theofrida J.2026-06-172026-06-172026-06-15https://www.mdpi.com/2220-9964/15/6/268https://www.suaire.sua.ac.tz/handle/20.500.14820/7672International Journal of Geo-InformationFlood hazards are intensifying across Africa due to rapid urban expansion and hydro- climatic variability. This study develops a multi-metric geospatial framework combining extreme value analysis, hydrograph-based metrics, and dependence modelling to quan- tify flood magnitude, frequency, timing, and joint risk dynamics. Daily precipitation and streamflow reanalysis data (1985–2025) were analyzed for two contrasting Tanzanian catchments: the large Rufiji basin (RU) and the smaller Mirongo catchment (MW). An- nual maxima were modelled using the Generalized Extreme Value (GEV) distribution, complemented by flow duration curves, peak-over-threshold detection, and regression- copula dependence analysis. Results reveal strong hydrological contrasts. RU exhibits amplified rare-event growth (design floods from ~2850 to 11,770 m3/s), extended recession persistence (>100 days), low flashiness, and long rainfall-runoff lags (~15 days), indicating storage-dominated behavior. MW shows smaller design floods (~80 to 370 m3/s), higher flashiness, and short lags (~4 days), reflecting rapid, rainfall-driven response. Gaussian copula parameters indicate moderate dependence in both basins (0.32 and 0.34), suggesting that joint dependence alone does not distinguish flood mechanisms without complementary metrics. The proposed framework improves basin-specific flood risk profiling and supports geospatial early-warning system design in data-scarce Sub-Saharan environments.enFlood hazard characterizationExtreme value analysisRainfall-runoff dependenceHydrological response dynamicsGeospatial flood risk profilingFlow regime analysisPeak-over-threshold modellingTanzaniaArticle