Günter Blöschl’s research while affiliated with Federal Agency for Water Management Austria and other places

Understanding how agricultural land management influences sediment transport is crucial for identifying critical source areas (CSAs) and improving erosion mitigation strategies. While numerous studies focus on in-stream sediment concentrations, fewer investigate overland flow on the hillslopes. We monitored streamflow and sediment fluxes at an overland flow station (E2) and an in-stream station (MW) across 55 runoff events (2011–2022) in the Hydrological Open Air Laboratory (HOAL), Austria. The catchment was segmented into four distinct areas (A, B, GW9, C) based on topography, hydrological connectivity, and proximity to the stream, allowing a spatially explicit assessment of erosion hotspots. Temporal patterns of sediment transport were analysed to infer spatial variability, and differences in sediment transport dynamics among areas were quantified using Kruskal-Wallis tests and effect size analysis. Results suggest that at E2 (hillslope scale), non-erosive cultivation significantly reduced peak turbidity (~9.5 times) and sediment load (~3.8 times) in flat agricultural areas (7.2 % slope, <500 m from the stream) but had no measurable effect in steep (10–12 % slope) or distant (>1000 m) agricultural areas. Across all field types, conversion to non-erosive cultivation did not affect peak flow. At MW (catchment scale), compared to E2, peak turbidity at MW decreased (~5.4–7.7 times) due to dilution from subsurface flow contributions, while peak flow increased (~2.8–11 times) due to additional inputs from wetlands, springs, and subsurface flows. Sediment load at MW was ~2.4–5.4 times higher than at E2, likely due to unmonitored diffuse overland flow and sediment inputs from tile drainages. Our findings indicate that non-erosive cultivation alone in steep terrains or distant agricultural areas is insufficient to effectively mitigate sediment transport. Effective sediment management in agricultural catchments requires spatially targeted erosion control strategies that account for topography, hydrological connectivity, and field proximity to streams.

A catchment's runoff response to precipitation largely depends on the antecedent soil moisture and on the characteristics of the precipitation event, but also on other hydro‐meteorological conditions, such as evapotranspiration. Studies investigating the effects of hydro‐meteorological variables on runoff characteristics in catchments with daily temporal resolution mostly used surrogate measures of soil moisture derived from hydrological models or remote sensing products. Here, we applied a time series‐based pattern search to up to 12 years of daily in situ measured soil moisture in three depths (5, 20 and 50 cm) in three headwater catchments, two of which are located in Germany (forest and grassland) and one in Austria (agriculture), to identify key variables influencing runoff characteristics under analogous soil moisture patterns. After detecting groups of analogous soil moisture, we split the corresponding runoff into similar and different patterns based on goodness‐of‐fit criteria and analysed their influencing hydro‐meteorological variables with descriptive statistics and Spearman rank correlation coefficients ( ρ ). Results showed that in the forest and in the grassland catchment, the antecedent soil moisture mainly influenced runoff characteristics for analogous soil moisture patterns. In the agricultural catchment in Austria, both the antecedent soil moisture and rainfall characteristics had an influence on runoff characteristics. The proposed method can be used to evaluate hydro‐meteorological drivers of event runoff characteristics under analogous soil moisture. In this way, hydrological processes that dominate in either group of similar or different runoff patterns can be differentiated, providing insights into the potential predictability of the respective runoff pattern.

Due to public health or environmental concerns, examining the effects of preferential flow processes on the transport of pathogenic microorganisms or contaminants of emerging concern must be studied in the laboratory. However, the resulting transport parameters cannot be directly applied to field-scale groundwater models. This research explores how an upscaling relationship, with removal as a function of distance, can be found using B. subtilis spores and microspheres as colloidal trac-ers in saturated flow-through experiments at three different scales. The study investigates transport processes in a 4-m-tall undisturbed gravel column and compares the results with previously published data. Results showed that colloidal removal (log-removal, attachment rate/efficiency) in heterogeneous porous media follows a power law as a function of travel distance, rather than an exponential relationship, which is normally assumed in removal equations. It was found that by using a power function, it was possible to decrease the difference between the attachment coefficients so that the meso-scale value was closer to the small-scale value. To the contrary, using a dual permeability model increased the difference between attachment rates at these two scales. Groundwater transport modeling may benefit from taking this power law relationship into account, instead of using a constant first-order removal rate, as most tracer tests are performed at a smaller scale than the scale that is being modeled. The meso-scale column provides insights into upscaling processes by incorporating an intermediate step when comparing groundwater transport at the column scale to the field scale.

Flood risk management institutions and practitioners need accurate and easy-to-use approaches that incorporate the changing climate conditions into flood predictions in ungauged basins. The present work aims at developing an operative procedure to include the expected variation in precipitation extremes in flood frequency analysis. We relate Flood Frequency Curves and Intensity-Duration-Frequency curves through quantile-quantile relationships. Assuming that the percentage variations of precipitation and flood quantiles are linked by the quantile-quantile relationship, we obtain modified Flood Frequency Curves accounting for the projected changes in precipitation extremes. The methodology is validated in a virtual world based on the Rational Formula approach where flood events are the result of the combination of two jointly distributed random variables: extreme precipitation and peak runoff coefficient. The proposed methodology is found to be reliable in basins where flood changes are dominated by precipitation changes rather than variations in the runoff generation process. To illustrate its practical usefulness, the procedure is applied to 227 catchments within the Po River basin in Italy using projected percentage changes of precipitation extremes from CMIP5 CORDEX simulations for the end of the century (2071-2100). With projected changes in 100-year precipitation ranging from 5 to 50\%, the corresponding variations in 100-year flood magnitudes are expected to span a broader range (10 to 90\%), reflecting substantial heterogeneity in catchment responses to rainfall changes. Powered by TCPDF (www.tcpdf.org)

Understanding bacterial dynamics in large river systems is crucial for predicting continental-scale ecological functioning under anthropogenic pressures. Here, two consecutive surveys 6-years apart along the 2600 km Danube River found that carbon incorporation per cell and hour decreased by 5000 atoms every kilometer and that cells multiplied five times during their travel down the entire river. Resolving these cell turnovers taxonomically revealed taxa with a hundredfold difference from these average numbers. Bacterial community turnover was due to replacement (phylotype turnover) and could be linked to species sorting. This was despite an overall decrease in diversity richness downstream. Using linear models, we were able to relate carbon, cell, phylotype and diversity turnover rates to water residence time and discharge with outliers associated with human impacts. As such the reproduceable macroecological models predict microbial changes from anthropogenic and climate alterations along a continental drainage system providing insights into their ecological consequences.

The most catastrophic floods in Ukraine occur in mountainous regions, where forecasting them based on existing global climate and hydrological models is ineffective. One of the alternative methods of Flood frequency analysis is the selection and use of the most suitable theoretical distributions for further modeling of extreme water flows, the so-called Peaks Above Threshold (РОТ). The research was carried out on the example of the Opir River (Carpathian Mountains, Ukraine) based on daily observations of water flow over a multi-year period (1961-2020). 10 theoretical distributions were examined using the Chi-square, Kolmogorov-Smirnov, Anderson-Darling criteria. The GEV distribution gave best goodness-of-fit values than other distributions. Based on the GEV distribution function, a statistical model was constructed, the parameters of which were estimated using the Monte Carlo method. The resulting model was used to simulate extreme floods on the Opir River. Two simulation procedures were performed using the Monte Carlo method to generate random data from the best fitting distributions while ignoring the correlations between variables and the Iman Conover method to generate random data from the best fitting distributions while preserving the rank correlation structure. An analysis of the obtained models shows that in the range of percentiles 0-60%, the model built using the Iman Conover methods demonstrates the best agreement of the simulated POT values. In the range of percentiles 60-80%, the model built by the Monte Carlo method demonstrates the best coincidence.

Below‐ground urban stormwater networks (BUSNs) significantly influence urban flood dynamics, yet their representation at the watershed or larger scales remains challenging. We introduce a scalable urban hydrologic framework that centers on a novel network‐level BUSN representation, balancing the needs for physical basis, parameter parsimony, and computational efficiency. Our framework conceptualizes an urban watershed into four interacting zones: hillslopes (natural), storm‐sewersheds (urban), a sub‐network channel (tributaries), and a main channel. We develop an innovative Graph Theory‐based algorithm to derive network‐level BUSN parameters from publicly available datasets, enabling efficient, scalable parameterization. We demonstrate this framework's applicability at nine representative watersheds in the Houston metropolitan region, USA, with urban imperviousness ranging from 0% to 64% and drainage areas ranging from 24 to 302 km2 ${\text{km}}^{2}$. Our model achieves satisfying computational efficiency, completing hourly time step simulations for 18 years in less than 5 sec per watershed on a standard PC. Validation against observed daily streamflow confirms that the model can capture small‐to‐large flood peaks and seasonal and annual water balance over these watersheds. Comparisons with the National Water Model show better performance in predicting flood peaks and overall water balance, underscoring the promises of our new framework for urban hydrologic modeling at large scales. Furthermore, analysis reveals nonlinear relationships between BUSNs' designed capacities and flood reduction effects. Our approach bridges the gap between detailed hydraulic and large‐scale hydrologic models, providing a valuable tool for urban flood prediction and management across broader spatial and temporal scales.

Climate change is expected to increase heavy rainfall with concomitant increases in flooding¹. Causes of increased heavy rainfall include the higher water-holding capacity of a warmer atmosphere and changes in atmospheric circulation patterns², which may translate into future heavy rainfall increases in most of Europe³. However, gathering evidence on the time evolution of past changes has been hampered by data limitations and measurement uncertainties, in particular for short rainfall durations, such as 1 h. Here we show an 8% increase in daily and 15% increase in hourly heavy rainfall over the last four decades by analysing a new dataset comprising 883 stations in Austria from 1900 to 2023. These increases are fully consistent between two independent networks and occurred after a retarding phase between 1960 and 1980. Hourly heavy rainfall changes are aligned with temperature increases with the sensitivity of a 7% increase per 1 °C of warming, in line with Clausius–Clapeyron scaling. Daily heavy rainfall changes, however, are aligned with atmospheric circulation indices with little correlation to air temperature, which suggests a bigger role of atmospheric circulation modes than previously thought. The daily heavy rainfall changes are remarkably consistent with observed flood increases of about 8% in large catchments. The hourly heavy rainfall changes are similarly consistent with flood changes in small catchments, although the flood increase is stronger (25% over the last four decades). Climate adaptation measures in flood management may therefore be more pressing for rivers draining smaller catchment areas than for large rivers.