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Design flood values give estimates of flood magnitude within a given return period and are essential to making adaptive decisions around land use planning, infrastructure design, and disaster mitigation. Many hydrologic applications where flood retention is important, e.g. floodplain management and reservoir design, need design flood values for different durations. Flood–Duration–Frequency (QDF) models extend the standard statistical flood frequency analysis framework to multiple flood durations and are analogous to intensity–duration–frequency models for precipitation. Implementations of QDF models commonly assume simple scaling, where only the magnitude of the index flood is assumed to change with duration, despite empirical analyses showing a more complex dependence structure. We propose a multiscaling extension to existing QDF models where the magnitude of the index flood and the slope of the growth curve may scale independently with duration. In an application to 12 locations in Norway, we assess how three different QDF models capture relationships between floods of different duration. Incorporating duration dependency independently in both the index flood and the growth curve (extended QDF model) improves modeling of both short-duration events and events with long return periods. This model extension further expands the models' ability to simultaneously model a wide range of durations. As measured by the integrated quadratic distance, the extended QDF model performs better than the original QDF model in 83% of the out of sample subdaily durations studied. Additionally, we find that the choice of durations used to fit QDF models is a highly influential aspect of the modeling process. • Flood–Duration–Frequency (QDF) models produce flood statistics at multiple durations. • We extend QDF models so that different quantiles can have different slopes. • This improves estimates and allows for a wider range of durations to be modeled. • Additionally, we introduce a Bayesian framework for QDF estimation. [ABSTRACT FROM AUTHOR]

Hydrological impact assessments are increasingly performed at fine spatial and temporal resolutions in order to resolve local-scale changes under a future climate. Apart from the uncertainty represented by different climate models, emission scenarios and post-processing methods, the local-scale internal variability of the climate can be a major source of uncertainty for hydrological projections. To assess the latter at the catchment scale, this paper presents a methodology which is particularly suitable for spatially distributed hydrological models. An ensemble of daily precipitation and daily mean temperature realizations on a high-resolution grid is simulated from stochastic weather generators (WGs) trained on historical data and equipped with climate change information obtained from a regional climate model. Based on the resulting simulated daily runoff data, the significance of changes in the runoff regime is assessed using a statistical hypothesis test, and the variability contributed by the two input variables is quantified using the analysis of variance (ANOVA). As a proof of concept, simulations on a 1-km grid over a period of 19 years are carried out for nine catchments in central Norway. Significant changes in runoff regimes are found, indicating that the trends introduced in the WGs are not overwhelmed by the local-scale internal variability. Variability in the runoff simulations varies substantially throughout the year; it is highest in periods with potential snowmelt and lowest during winter. Temperature is the dominant source of variability in the colder months (November–March) due to its influence on rainfall and snowmelt. High variability in May–June is contributed comparably by both temperature and precipitation. In summer and early autumn the runoff variability is precipitation dominated. The results are in line with findings in the literature where the runoff variability is driven by the large-scale internal climate variability. This indicates that ignoring the local-scale internal variability may yield an underestimation of the overall variability in runoff projections and projected changes. • Gridded climate input data simulated using stochastic weather generators. • Significance of changes in runoff regime assessed using a statistical test. • Runoff variability due to input variables quantified using ANOVA. • Results in line with findings based on large-scale internal climate variability. • Runoff projections need to consider local-scale internal variability of climate. [ABSTRACT FROM AUTHOR]

• All GCMs except MIROC-ESM simulate a higher mean temperature in Medieval Climate Anomaly (MCA) period than Little Ice Age (LIA) period, while simulated annual precipitation varies a lot in different GCMs and catchments. • No significant change of flood characteristics during the last millennium from ensemble-mean results. • In future, we will have an extraordinary shift in flood seasonality and generating processes, and flood frequency increase in most of the study catchments in Norway. • Climate projections represent the largest contribution to overall uncertainty in the projected changes in hydrological extremes for most of the catchments. With the recent warming trend over Europe and the Arctic, the Nordic regions have experienced more frequent and damaging extreme hydrological events which are anticipated to increase towards the end of the 21st century. Despite explicit trends, large variations have been observed across basins and regions when it comes to precipitation and floods hinting at a strong natural hydroclimatic variability that further complicates any assessment of potential future changes. In this study, we aim to better understand how climate variability links with the current extremes and future projections of floods in Norway in the context of the last millennium and the future. Specifically, we simulate over 1000 years (850–2099) daily discharge and floods at 34 catchments over five regions of Norway from the last millennium (including a warm period and a cold period; 850–1849) to the end of the 21st century by an ensemble model-chain method including four global climate models (GCMs), two bias-correction methods and two hydrological models. The modelling results show (i) all GCMs except MIROC-ESM simulate a higher mean temperature in Medieval Climate Anomaly (MCA) period than Little Ice Age (LIA) period, while simulated annual precipitation varies a lot in different GCMs and catchments, (ii) no significant change of flood characteristics during the last millennium from ensemble-mean results, (iii) in future, we will have an extraordinary shift in flood seasonality and generating processes, and flood frequency increase in most of the study catchments in Norway, and (iv) climate projections represent the largest contribution to overall uncertainty in the projected changes in hydrological extremes for most of the catchments. [ABSTRACT FROM AUTHOR]