Regional flood frequency analysis and prediction in ungauged basins including estimation of major uncertainties for mid-Norway

Abstract

Study region: 26 boreal catchments (mid-Norway). Study focus: We performed regional flood frequency analysis (RFFA) using the L-moments method and annual maximum series (AMS) of mean daily streamflow observations for reliable prediction of flood quantiles. We used similarity in at-site and regional parameters of distributions, high flow regime and seasonality, and runoff response from precipitation-runoff models to identify homogeneous catchments, bootstrap resampling for estimation of uncertainty and regression methods for prediction in ungauged basins (PUB). New hydrological insights for the region: The rigorous similarity criteria are useful for identification of catchments. Similarity in runoff response has the least identification power. For the PUB, a linear regression between index-flood and catchment area (R2 = 0.95) performed superior to a power-law (R2 = 0.80) and a linear regression between at-site quantiles and catchment area (e.g. R2 = 0.88 for a 200 year flood). There is considerable uncertainty in regional growth curves (e.g. −6.7% to −13.5% and +5.7% to +24.7% respectively for 95% lower and upper confidence limits (CL) for 2–1000 years return periods). The peaks of hourly AMS are 2–47% higher than that of the daily series. Quantile estimates from at-site flood frequency analysis (ASFFA) for some catchments are outside the 95% CL. Uncertainty estimation, sampling of flood events from instantaneous or high-resolution observations and comparative evaluation of RFFA with ASFFA are important.

Keywords

• Regional flood frequency
• Boreal catchments
• L-moments
• Uncertainty
• Bootstrap resampling
• Prediction in ungauged basins