Linganisha mbinu

	Peaks-Over-Threshold Flood Analysis	Regional Flood Frequency Analysis
Nyanja	Disaster Studies	Disaster Studies
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1999	1997
Mwanzilishi	M. Lang, T. B. M. J. Ouarda & B. Bobée (operational POT guidelines); Pickands–Balkema–de Haan theory	J. R. M. Hosking & J. R. Wallis (L-moments regional frequency analysis)
Aina	Threshold-exceedance extreme-value frequency pipeline	Pooled (index-flood) extreme-value frequency estimation pipeline
Chanzo asilia	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.	Hosking, J. R. M., & Wallis, J. R. (1997). Regional Frequency Analysis: An Approach Based on L-Moments. Cambridge University Press, Cambridge. ISBN: 9780521430456
Majina mbadala	POT Flood Analysis, Partial Duration Series Analysis, Generalized Pareto Flood Modeling, Threshold Exceedance Flood Frequency	Regional Frequency Analysis, Index-Flood Method, L-Moments Regionalization, Pooled Flood Frequency Analysis
Zinazohusiana	3	3
Muhtasari	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.	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.