Precipitation extremes will increase in a warming climate, but the response of flood magnitudes to heavier precipitation events is less clear. Historically, there is little evidence for systematic increases in flood magnitude despite observed increases in precipitation extremes. Here we investigate how flood magnitudes change in response to warming, using a large initial-condition ensemble of simulations with a single climate model, coupled to a hydrological model. The model chain was applied to historical (1961–2000) and warmer future (2060–2099) climate conditions for 78 watersheds in hydrological Bavaria, a region comprising the headwater catchments of the Inn, Danube and Main River, thus representing an area of expressed hydrological heterogeneity. For the majority of the catchments, we identify a ‘return interval threshold’ in the relationship between precipitation and flood increases: at return intervals above this threshold, further increases in extreme precipitation frequency and magnitude clearly yield increased flood magnitudes; below the threshold, flood magnitude is modulated by land surface processes. We suggest that this threshold behaviour can reconcile climatological and hydrological perspectives on changing flood risk in a warming climate.

The C-band Sentinel-1 satellite constellation enables the continuous monitoring of the Earth’s surface within short revisit times. Thus, it provides Synthetic Aperture Radar (SAR) time series data that can be used to detect changes over time regardless of daylight or weather conditions. Within this study, a time series classification approach is developed for the extraction of the flood extent with a focus on temporary flooded vegetation (TFV). This method is based on Sentinel-1 data, as well as auxiliary land cover information, and combines a pixel-based and an object-oriented approach. Multi-temporal characteristics and patterns are applied to generate novel times series features, which represent a basis for the developed approach. The method is tested on a study area in Namibia characterized by a large flood event in April 2017. Sentinel-1 times series were used for the period between September 2016 and July 2017. It is shown that the supplement of TFV areas to the temporary open water areas prevents the underestimation of the flood area, allowing the derivation of the entire flood extent. Furthermore, a quantitative evaluation of the generated flood mask was carried out using optical Sentinel-2 images, whereby it was shown that overall accuracy increased by 27% after the inclusion of the TFV.

Accurate spatial and temporal representation of precipitation is of utmost importance for hydrological applications. Uncertainties in available data sets increase with spatial resolution due to small-scale processes over complex terrain. As previous studies revealed high regional differences in the performance of gridded precipitation data sets, it is important to assess the related uncertainties at the catchment scale, where these data sets are typically applied, e.g., for hydrological modeling. In this study, the uncertainty of eight gridded precipitation data sets from various sources is investigated over an alpine catchment. A high resolution reference data set is constructed from station data and applied to quantify the contribution of spatial resolution to the overall uncertainty. While the results demonstrate that the data sets reasonably capture inter-annual variability, they show large seasonal differences. These increase for daily indicators assessing dry and wet spells as well as heavy precipitation. Although the higher resolution data sets, independent of their source, show a better agreement, the coarser data sets showed great potential especially in the representation of the overall climatology. To bridge the gaps in data scarce areas and to overcome the issues with observational data sets (e.g., undercatch and station density) it is important to include a variety of data sets and select an ensemble for a robust representation of catchment precipitation. However, the study highlights the importance of a thorough assessment and a careful selection of the data sets, which should be tailored to the desired application.

This study assesses the change of the seasonal runoff characteristics in 98 catchments in central Europe between the reference period of 1981–2010, and in the near future (2011–2040), mid future (2041–2070) and far future (2071–2099). Therefore, a large ensemble of 50 hydrological simulations featuring the model WaSiM-ETH driven by a 50-member ensemble of the Canadian Regional Climate Model, version 5 (CRCM5) under the emission scenario Representative Concentration Pathway (RCP 8.5) is analyzed. A hierarchical cluster analysis is applied to group the runoff characteristics into six flow regime classes. In the study area, (glacio-)nival, nival (transition), nivo-pluvial and three different pluvial classes are identified. We find that the characteristics of all six regime groups are severely affected by climate change in terms of the amplitude and timing of the monthly peaks and sinks. According to our simulations, the monthly peak of nival regimes will occur earlier in the season and the relative importance of rainfall increases towards the future. Pluvial regimes will become less balanced with higher normalized monthly discharge during January to March and a strong decrease during May to October. In comparison to the reference period, 8% of catchments will shift to another regime class until 2011–2040, whereas until 2041–2070 and 2071–2099, 23% and 43% will shift to another class, respectively.

The monitoring of soil freezing and thawing states over large areas is very challenging on ground. In order to investigate the potential and the limitations of space-borne SAR polarimetry at C-band for soil state survey, analyses were conducted on an entire winter time series of fully polarimetric RADARSAT-2 data from 2011/2012 to identify freezing as well as thawing states within the soil. The polarimetric data were acquired over the Sodankylä test site in Finland together with in situ measurements of the soil and the snow cover. The analyses indicate clearly that the dynamics of the polarimetric entropy and mean scattering alpha angle are directly correlated to soil freezing and thawing states, even under distinct dry snow cover. First modeling attempts using the Extended Bragg soil scattering model justify the observed trends, which indicate surface-like scattering during frozen soil conditions and multiple/volume scattering for thawed soils. Hence, these first investigations at C-band foster motivation to work towards a robust polarimetric detection of soil freezing and thawing states as well as their transition phase.

This study introduces a holistic approach for the hydrological modelling of peak flows for the major Bavarian river basins, referred to as Hydrological Bavaria. This approach, intended to develop a robust modelling framework to support water resources management under climate change conditions, comprises a regionalized parameterization of the water balance simulation model (WaSiM) for 98 catchments in high temporal (3 h) and spatial (500 m) resolution using spatially coherent information and an automatized calibration (dynamically dimensioned search–simulated annealing, DDS-SA) for storage components. The performance of the model was examined using common metrics (Nash & Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE)). The simulations provided the means for the calculation of a level of trust (LOT) by comparing observed and simulated high flows with a five, ten, and 20-year return period. These estimates were derived by the Generalized Pareto Distribution (GPD) applying the peak over threshold (POT) sampling method. Results show that the model overall performs well with regard to the selected objective measures, but also exhibits regional disparities mainly due to the availability of meteorological inputs or water management data. For most catchments, the LOT shows moderate to high confidence in the estimation of return periods with the hydrological model. Therefore, we consider the holistic modelling approach applicable for climate change impact studies concerned with dynamic alterations in peak flows.