Southeastern Austria as part of the southeastern Alpine forelands experiences an increase of temperature and a tendency of decreasing precipitation. Especially in summer, the temperature strongly increased by about 0.7 °C per decade since the 1970s. Drought vulnerability under climate change is therefore a key question in this region. Here, we address this question by exploring the hydrological sensitivity of the Raab catchment in Austria (area 987 km²), a typical catchment in these Alpine forelands. Using the process-oriented Water Flow and Balance Simulation Model (WaSiM) over 1982–2011, we focus on low-flow conditions during extended summer (May–September) and analyze the catchment’s runoff sensitivity to climate change, but also land use and water management change. We find that climate change drivers dominate the summertime runoff response (decrease > 40/> 70%), based on moderate and strong climate change cases in the region (temperature + 2/+ 4 K, precipitation − 15/− 30%). Land use changes towards more dry and sealed areas enhance surface runoff and thus may lead to somewhat increased flood peaks. In contrast, water withdrawal for irrigation reduces runoff during low-flow periods in the summer when the irrigation demand is high. Although the impact of these non-climatic drivers on runoff generally is lower than that of the climate change considered, their interactive effects may reinforce the catchment’s tendency of running drier during summer. While more detailed scenario-based assessments are needed to further assess drought risks, this initial study provides clear evidence for the vulnerability of Alpine foreland catchments to increasing summer dryness under climate change.

Precipitation is the most important input to hydrological models, and its spatial variability can strongly influence modeled runoff. The highly dense station network WegenerNet (0.5 stations per km²) in southeastern Austria offers the opportunity to study the sensitivity of modeled runoff to precipitation input. We performed a large set of runoff simulations (WaSiM model) using 16 subnetworks with varying station densities and two interpolation schemes (inverse distance weighting, Thiessen polygons). Six representative heavy precipitation events were analyzed, placing a focus on small subcatchments (10–30 km²) and different event durations. We found that the modeling performance generally improved when the station density was increased up to a certain resolution: a mean nearest neighbor distance of around 6 km for long-duration events and about 2.5 km for short-duration events. However, this is not always true for small subcatchments. The sufficient station density is clearly dependent on the catchment area, event type, and station distribution. When the network is very dense (mean distance < 1.7 km), any reasonable interpolation choice is suitable. Overall, the station density is much more important than the interpolation scheme. Our findings highlight the need to study extreme precipitation characteristics in combination with runoff modeling to decompose precipitation uncertainties more comprehensively.

Precipitation is a key input to hydrological models. While rain gauges provide the most direct precipitation measurements, their accuracy in capturing rain patterns highly depends on the spatial variability of rainfall events and the gauge network density. In this study, we employ a high-resolution meteorological station network (mean station distance of 1.4 km), the WegenerNet in southeastern Austria, to investigate the impact of station density and interpolation schemes on runoff simulations. We first simulate runoff during heavy precipitation (three short-duration and three long-duration events) using a physically based hydrological model with precipitation input obtained from a full network of 158 stations. The same simulations are then repeated with precipitation inputs from subnetworks of 5, 8, 16, 32, and 64 stations, using three different interpolation schemes – Inverse Distance Weighting with a weighting power of 2 and of 3, respectively, and Thiessen polygon interpolation. We find that the performance of runoff simulations is greatly influenced by the spatial variability of precipitation input, especially for short-duration rainfall events and in small catchments. For long-duration events, reliable runoff simulations in the study area can be obtained with a subnetwork of 16 or more well-distributed gauges (mean station distance of about 6 km). We find a clear effect of interpolation schemes on runoff modeling as well, but only for low-density gauge networks. The sensitivity to the precipitation input is smaller for long-duration heavy precipitation events and bigger catchments. As a next step we suggest to study an ensemble of precipitation datasets in combination with runoff modeling to be able to decompose the effects of precipitation measurement uncertainties and its spatial variability.