Flood Frequency Analysis and Regional Hydrologic Modeling
Summary
Flood frequency analysis (FFA) seeks to estimate the probability and magnitude of extreme flood events by fitting statistical models to observed peak flow series. Traditionally, annual maximum series are employed to derive return periods for design floods, yet stationarity assumptions often fail under changing climate and land‐use conditions. Regional hydrologic modelling extends site‐specific analyses to ungauged catchments by grouping basins with similar hydroclimatic characteristics or by applying spatial interpolation and machine‐learning techniques. Such regional approaches may employ region-of-influence methods, which form dynamic pooling groups for each target site, or process-based models that link rainfall–runoff dynamics with catchment descriptors. Recent advances address nonstationarity by incorporating covariates related to precipitation intensity and watershed climate, and by exploiting large-scale datasets of annual maxima. Integration of L-moment theory with flexible distribution selection has enhanced the robustness of extreme quantile estimates. Concurrently, data-driven models—including decision-tree ensembles and neuro-fuzzy systems—have demonstrated superior performance in predicting peak flows in poorly gauged basins. Together, these developments improve the global applicability of FFA and regional modelling, informing infrastructure design, flood risk management and adaptation strategies under evolving hydroclimatic regimes.
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Flood Frequency Analysis and Regional Hydrologic Modeling publication trend
The graph below shows the total number of articles in flood frequency analysis and regional hydrologic modeling across all publications each year (not limited to Nature Index journals).
Technical terms
Annual maximum series (AMS): A record of the highest flood peak each year used for statistical frequency analysis.
Return period: The average interval of time between events of equal or greater magnitude.
Nonstationarity: Variation in the statistical properties of hydrological processes over time due to climatic or anthropogenic changes.
L-moments: Statistical measures derived from probability-weighted moments, used to characterise distribution shape and variability.
Generalised extreme value distribution (GEV): A three-parameter family of distributions commonly applied to model the extremes of annual maxima.
Random forest model: A machine-learning ensemble method using multiple decision trees for predictive modelling of flood frequency in ungauged catchments.
Adaptive neuro-fuzzy inference system (ANFIS): A hybrid machine-learning technique combining neural networks and fuzzy logic for modelling complex hydrological relationships.
References
- HYADES - A Global Archive of Annual Maxima Daily Precipitation. Scientific Data (2024).
- Accounting for hydroclimatic properties in flood frequency analysis procedures. Hydrology and Earth System Sciences (2024).
- Regional flood frequency analysis using data-driven models (M5, random forest, and ANFIS) and a multivariate regression method in ungauged catchments. Applied Water Science (2023).
- Region-of-influence approach to a frequency analysis of heavy precipitation in Slovakia. Hydrology and Earth System Sciences (2008).
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