Linganisha mbinu

	Regional Flood Frequency Analysis	Peaks-Over-Threshold Flood Analysis
Nyanja	Disaster Studies	Disaster Studies
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1997	1999
Mwanzilishi	J. R. M. Hosking & J. R. Wallis (L-moments regional frequency analysis)	M. Lang, T. B. M. J. Ouarda & B. Bobée (operational POT guidelines); Pickands–Balkema–de Haan theory
Aina	Pooled (index-flood) extreme-value frequency estimation pipeline	Threshold-exceedance extreme-value frequency pipeline
Chanzo asilia	Hosking, J. R. M., & Wallis, J. R. (1997). Regional Frequency Analysis: An Approach Based on L-Moments. Cambridge University Press, Cambridge. ISBN: 9780521430456	Lang, M., Ouarda, T. B. M. J., & Bobée, B. (1999). Towards operational guidelines for over-threshold modeling. Journal of Hydrology, 225(3-4), 103-117.
Majina mbadala	Regional Frequency Analysis, Index-Flood Method, L-Moments Regionalization, Pooled Flood Frequency Analysis	POT Flood Analysis, Partial Duration Series Analysis, Generalized Pareto Flood Modeling, Threshold Exceedance Flood Frequency
Zinazohusiana	3	3
Muhtasari	Regional flood frequency analysis estimates flood quantiles by pooling data across many hydrologically similar sites rather than relying on a single short record, which sharply reduces the uncertainty of rare-flood estimates and—crucially—allows estimation at ungauged sites. The dominant framework, codified by Hosking and Wallis in their 1997 book Regional Frequency Analysis: An Approach Based on L-Moments, rests on the index-flood assumption: within a homogeneous region, the flood frequency distributions at all sites are identical apart from a site-specific scale factor, the index flood. The method uses L-moments — linear combinations of order statistics that are far more robust than conventional moments for small samples and heavy tails (building on Hosking, Wallis, and Wood's earlier probability-weighted-moment work) — to test regional homogeneity, choose a common distribution, and fit a dimensionless regional growth curve that is then rescaled by each site's index flood. It is the standard approach for design-flood estimation where individual records are short or absent.	Peaks-over-threshold (POT) flood analysis models every independent flood peak that exceeds a chosen high threshold, rather than only the single largest peak in each year. The number of exceedances in time is treated as a Poisson process and the amounts by which peaks exceed the threshold are modeled with the Generalized Pareto distribution — the extreme-value limit for threshold exceedances given by the Pickands-Balkema-de Haan theorem. Because a wet year may contain several damaging floods and a dry year none, POT (also called the partial duration series) uses the data more efficiently than the annual-maximum approach, which is why Lang, Ouarda, and Bobée's 1999 operational guidelines and USGS Bulletin 17C both treat it as a key complement to annual-maximum frequency analysis. The method delivers the same design-flood quantiles for chosen return periods, often with lower variance at short return periods.