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Summary: Conventional techniques for quantifying and then managing flood risks are invalid under ‘non-stationary’ climate conditions. Trend detection and attribution are problematic given that the outcome depends on the start and end date of the record, choice of index and test statistic, assumed behaviour of the system, and many non-climatic confounding factors. Analyses are further hampered by short and non-homogeneous flow records. In this paper, we use an objective weather classification scheme to reconstruct the atmospheric drivers of fluvial flood occurrence and magnitude in England, Scotland and Wales since the 1870s. We demonstrate the index using long (>50year) annual maximum (AMAX) and peak over threshold (POT) flood records for 114 stations. Synoptic indices show modest skill at hindcasting multi-decadal variations in flood frequency at national, regional and catchment scales, but not for flood magnitudes. Flood rich episodes are identified in the periods 1908–1934, 1977–1988 and from 1998 onwards. We find that five weather types account for 68% of flood occurrence, and just three types were linked to the most widespread winter floods. These flood-generating systems generally show no sustained changes in frequency, persistence, relative contribution, or rain-bearing properties since the 1930s. However, there are emergent patterns in the day-to-day persistence (declining) and mean precipitation yield (rising) of anticyclonic weather types that merit further investigation. Based on our evaluation, we recommend use of objective weather indices derived from observed atmospheric pressure patterns when interpreting fluvial flood risk linked to climate drivers. [Copyright &y& Elsevier]

Summary: This paper introduces an approach to route discharge with a variable river flow velocity based on the Manning–Strickler formula within large scale hydrological models. The approach has been developed for the global scale hydrological model WaterGAP and model results have been analyzed focusing on Europe. The goal was to find a method that is simple enough to derive the required parameters from globally available data while being sophisticated enough to deliver realistic flow velocity estimates for a large variety of environmental conditions. The river bed roughness (Manning’s n) is approximated in a spatially explicit way based on topography, the location of urban population, and river sinuosity. The hydraulic radius is estimated from actual river discharge, and river bed slope is derived by combining a high resolution DEM, a 5arc min drainage direction map, and river sinuosity. The modeled river flow velocity has been validated against data of US gauging stations. The representation of lateral transport has clearly been improved compared to the constant flow velocity applied in older model versions. The effect of incorporating variable flow velocities as compared to a constant flow velocity is largest on flood discharge, which generally increases in large rivers. The impact on monthly discharge hydrographs is marginal only. WaterGAP has been driven by three climate change projections for the 2050s to assess climate change impacts on flow velocity, and on the residence time of water in the European river system. Results indicate a decrease in residence times for Northern Europe and an increase for parts of the Mediterranean. [Copyright &y& Elsevier]

Summary: Several studies have shown that statistics of streamflow time series, in particular empirical moments, scale with physical properties of the drainage basin, such as the catchment area. Those scaling laws have been extensively used to estimate statistics of streamflow series at ungauged sites. The role of climate variability and change has not been considered in such models. Further, most studies are based on classical statistics, where parameter uncertainties are usually neglected or not formally considered. In this paper we develop and apply hierarchical Bayesian models, to both assess regional and at-site trends in time in a spatial scaling framework, and simultaneously provide a rigorous framework for assessing and reducing parameter and model uncertainties. The models are tested with reconstructed natural inflow series from over 40 hydropower sites in Brazil with catchments areas varying from 2588 to 823,555km2. Both annual maximum flood series and monthly streamflow are considered. Cross-validated results show that the Hierarchical Bayesian models are able to skillfully estimate monthly and flood flow probability distribution parameters for sites that were not used in model fitting. The models developed can be used to provide record augmentation at sites that have short records, or to estimate flow at ungauged sites, even in the absence of an assumption of time stationarity. Since model uncertainties are accounted for, the precision of the estimates can be quantified and hypotheses tests for regional and at-site trends can be formally made. A formal inclusion of climate predictors to facilitate seasonal forecasting or climate change scenario development is also feasible. This is indicated, but not developed here. [Copyright &y& Elsevier]