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River floods are among the most common natural disasters worldwide, with substantial economic and humanitarian costs. Despite enormous efforts, gauging the risk of extreme floods with unprecedented magnitude is an outstanding challenge. Limited observational data from very high-magnitude flood events hinders prediction efforts and the identification of discharge thresholds marking the rise of progressively larger floods, termed flood divides. Combining long hydroclimatic records and a process-based model for flood hazard assessment, here we demonstrate that the spatial organization of stream networks and the river flow regime control the appearance of flood divides and extreme floods. In contrast with their ubiquitous attribution to extreme rainfall and anomalous antecedent conditions, we show that the propensity to generate extreme floods is well predicted by intrinsic properties of river basins. Most importantly, it can be assessed prior to the occurrence of catastrophes through measurable metrics of these properties derived from commonly available discharge data, namely the hydrograph recession exponent and the coefficient of variation of daily flows. These results highlight the propensity of certain rivers for generating extreme floods and demonstrate the importance of using hazard mapping tools that, rather than solely relying on past flood records, identify regions susceptible to the occurrence of extreme floods from ordinary discharge dynamics.

Relationships between nitrate concentrations and discharge rates (C–Q) at the catchment outlet can provide insights into sources, mobilization and biogeochemical transformations of nitrate within the catchment. Nitrate C–Q relationships often exhibit considerable scatter that might be related to variable hydrologic conditions during runoff events at sampling time, corresponding to variable sources and flow paths despite similar discharge (Q) rates. Although previous studies investigated the origins of this scatter in individual or in a few catchments, the role of different runoff event types across a large set of catchments is not yet fully understood. This study combines a hydrological runoff event classification framework with low-frequency nitrate samples in 184 catchments to explore the role of different runoff events in shaping long-term C–Q relationships and their variability across contrasting catchments. In most of the catchments, snow-impacted events produce positive deviations of concentrations, indicating an increased nitrate mobilization compared to the long-term pattern. In contrast, negative deviations occur mostly for rainfall-induced events with dry antecedent conditions, indicating the occurrence of lower nitrate concentrations (C) in river flows than their long-term pattern values during this type of event. Pronounced differences in event runoff coefficients among different event types indicate their contrasting levels of hydrologic connectivity that in turn might play a key role in controlling nitrate transport due to the activation of faster flow paths between sources and streams. Using long-term, low-frequency nitrate data, we demonstrate that runoff event types shape observed scatter in long-term C–Q relationships according to their level of hydrologic connectivity. In addition, we hypothesize that the level of biogeochemical attenuation of catchments can partially explain the spatial variability of the scatter during different event types.

Conceptual hydrological models are irreplaceable tools for large-scale (i.e., from ­regional to global) hydrological predictions. Large-scale modeling studies typically strive to employ one single model structure regardless of the diversity of catchments under study. However, little is known on the optimal model complexity for large-scale applications. In a modeling experiment across 700 catchments in the contiguous United States, we analyze the performance of a ­conceptual (bucket style) distributed hydrological model with varying complexity (5 model versions with 11–45 parameters) but with exactly the same inputs and spatial and temporal resolution and implementing the same regional parameterization approach. The performance of all model versions compares well with those of contemporary large-scale models tested in the United States, suggesting that the applied model structures reasonably account for the dominant hydrological processes. Remarkably, our results favor a simpler model structure where the main hydrological processes of runoff generation and routing through soil, groundwater, and the river network are conceptualized in distinct but parsimonious ways. As long as only observed runoff is used for model validation, including additional soil layers in the model structure to better represent ­vertical soil heterogeneity seems not to improve model performance. More complex models tend to have lower model performance and may result in rather large uncertainties in simulating states and fluxes (soil moisture and groundwater recharge) in model ensemble applications. Overall, our results indicate that simpler model structures tend to be a more reliable choice, given the limited validation data available at large scale.

Flood frequency distributions with heavy-tailed indicate a sizable chance of the occurrence of extreme floods. When heavy-tailed flood behavior is reliably identified, flood hazards caused by the unexpected can be reduced. However, for cases with limited or varying record lengths it is challenging to robustly estimate tail behavior with currently used indices, which rely solely on the graphical or mathematical performance of limited observations and are regardless of the physical processes.In this work, we start by analyzing runoff generation processes and show that the hydrograph recession is a proper descriptor of the emergence of heavy-tailed behavior of flood frequency distributions. We then examine it in a large set of seasonal case studies, which encompasses a variety of climate and physiographic conditions across Germany. Our results show that the newly proposed approach can detect cases with heavy-tailed behavior, and compare severity across cases by evaluating their tail heaviness. Remarkably, it displays robust identification of heavy/nonheavy-tailed behavior for cases with short data records, benchmarked against two other frequently used metrics for heavy tails in hydrological studies, i.e., the upper tail ratio and the shape parameters of generalized extreme value distributions.We highlight that the proposed method leverages the information of common discharge dynamics for inferring heavy-tailed flood behavior, which addresses the main limitations of currently used metrics and provides information on the characteristic flood hazard of river basins.This study summarizes results of the DFG-funded project "Propensity of rivers to extreme floods: climate-landscape controls and early detection - PREDICTED" (Deutsche Forschungsgemeinschaft - German Research Foundation, Project Number 421396820).

Notwithstanding hundreds of years of efforts, flooding is still the most common natural disaster. A reliable assessment of the impending flood hazard is indeed an outstanding challenge with severe consequences. Mistaken estimates of the odds and magnitude of extreme floods especially result in huge economic losses due to widespread destruction of infrastructure and properties.We show here that we can infer the propensity of rivers to generate extreme floods by means of two hydroclimatic and geomorphic descriptors of watersheds, which embody the spatial organization of the stream network and the characteristic streamflow dynamics of the river basin. These features are main determinants of a sharp increase of the magnitude of the rarer floods and of the flood value for which this marked growth of magnitude occurs, which we term flood divide as it separates ordinary from extreme floods. Their relevance is suggested by a novel ecohydrological approach to flood hazard assessment and confirmed by observations from hundreds of watersheds in the USA and Germany.We first ascertained the capability of the method to distinguish between basins which do not and exhibit a flood divide, and its ability to dependably estimate its magnitude. We then applied a dimensional reduction tool to pinpoint key physioclimatic controls of the occurrence of flood divides, verifying our results against data. Finally, we utilized descriptors of these controls (namely the hydrograph recession exponent and streamflow variability) within binary logistic regression to predict the possible occurrence of flood divides and extreme floods in river basins. Repeated analyses for independent realizations of subsets of data indicate good prediction accuracy.The identified controls of the propensity of rivers to generate extreme floods are readily estimated from primary hydroclimatic variables. The tool thus allows for inferring cases where extreme events shall be expected from short records of ordinary events, providing valuable information to raise awareness of the peril of floods in river basins.This study summarizes results of the DFG-funded project "Propensity of rivers to extreme floods: climate-landscape controls and early detection - PREDICTED" (Deutsche Forschungsgemeinschaft - German Research Foundation, Project Number 421396820).

Conceptual hydrological models are irreplaceable tools for large-scale (i.e., from regional to global) hydrological predictions. Large-scale modeling studies typically strive to employ one single model structure regardless of the diversity of catchments under study. However, little is known on the optimal model complexity for large-scale applications. In a modeling experiment across 700 catchments in the contiguous United States, we analyze the performance of a conceptual (bucket style) distributed hydrological model with varying complexity (5 model versions with 11–45 parameters) but with exactly the same inputs and spatial and temporal resolution and implementing the same regional parameterization approach. The performance of all model versions compares well with those of contemporary large-scale models tested in the United States, suggesting that the applied model structures reasonably account for the dominant hydrological processes. Remarkably, our results favor a simpler model structure where the main hydrological processes of runoff generation and routing through soil, groundwater, and the river network are conceptualized in distinct but parsimonious ways. As long as only observed runoff is used for model validation, including additional soil layers in the model structure to better represent vertical soil heterogeneity seems not to improve model performance. More complex models tend to have lower model performance and may result in rather large uncertainties in simulating states and fluxes (soil moisture and groundwater recharge) in model ensemble applications. Overall, our results indicate that simpler model structures tend to be a more reliable choice, given the limited validation data available at large scale.

River floods are the most common natural hazards worldwide and accurate flood estimation is crucial for reducing flood risk. Traditional flood frequency analysis relies on the assumption of homogeneity of the analysed floods. However, floods arise from multiple generating mechanisms, such as rainfall on wet and dry soils, rain-on-snow and snow-melt events. Streamflow records may therefore comprise mixtures of events. Ignoring this may cause significant errors in the estimation of flood frequency. The problem is particularly evident in catchments with a discontinuity in the flood frequency distribution, where the rarest floods are significantly larger than the rest of the events in the record. These situations cannot be represented by traditional frequency analyses. Extreme floods may thus occur unexpectedly and produce disproportionate losses and casualties. Here, we propose a practical method to handle the problem of flood frequency estimation in catchments with strong discontinuities in the flood frequency curves.In this work, we focus on rivers among 160 case studies in Germany which show a marked discontinuity in the empirical flood frequency distribution and we use the simplified Metastatistical Extreme Value (SMEV) approach to separately include floods with different generating mechanisms in the estimation of the flood frequency distribution. We extract all the independent ordinary events from daily streamflow records and organize them into two groups according to the key runoff generation processes (rain-on-dry, mixture of rain-on-wet and snowmelt processes). We fit a statistical distribution (either power law or log normal based on the statistical properties of the ordinary events) to each group. Then, we use SMEV to calculate the emerging frequency distribution.Our results show that the proposed approach improves the estimation of the magnitude of floods with long return periods. Considering the mixture of generating processes allows to reproduce the observed discontinuities in the flood frequency curves. Comparison with the standard Generalized Extreme Value distribution shows that the proposed method reduces the estimation bias, especially for large quantiles.This study summarizes the results of the DFG-funded project "Propensity of rivers to extreme floods: climate-landscape controls and early detection - PREDICTED" (Deutsche Forschungsgemeinschaft - German Research Foundation, Project Number 421396820).

Investigating the influence of land-cover mosaics on water quality is vital for effective management aimed at mitigating the hazard of exceeding regulatory water quality thresholds. In particular, suspended sediments in rivers can easily jeopardize aquatic ecological functions, by transporting significant amounts of pollutants along flow paths. Nonetheless, a relationship between land-cover mosaics and suspended sediment dynamics remains unclear due to the complexity of interactions and feedbacks between geomorphological and hydrometeorological conditions in diverse river basins. Here, we aim to analyse the linkages of landscape metrics describing the spatial patterns of land-cover with the (power-law) rating exponent of suspended sediment concentration SSC and river discharge Q (i.e. SSC = aQb), as an integral property of sediment dynamics. For three major river basins in Germany (Elbe, Rhine, and Weser covering about 66 % of total German territory), the sediment rating parameters are extracted at high flows. Moreover, thoroughly selected descriptors are computed to characterize the composition and spatial configuration of land cover as well as topographic and hydrogeological conditions (e.g., temporal variability in Q and flashiness index). Preliminary results show the existence of correlations between the spatial organization of land cover along the river network and the rating exponent b at high flow regime. In particular, they indicate that high increase in suspended sediments at high flows is generated in catchments with more homogeneous land cover and sediment sources distributed near large streams. Our findings can aid policymakers and watershed managers in making informed decisions and taking necessary actions to improve current and future river water quality issues caused by suspended sediments.

Floods are often disastrous due to underestimation of the magnitude of rare events. Underestimation commonly happens when the occurrence of floods follow a heavy-tailed distribution, but this behavior is not recognized and thus neglected for flood hazard assessment. In fact, identifying heavy-tailed flood behavior is challenging because of limited data records and the lack of physical support for currently used indices. We address these issues by deriving a new index of heavy-tailed flood behavior from a physically-based description of streamflow dynamics. The proposed index, which is embodied by the hydrograph recession exponent, enables inferring heavy-tailed flood behavior from daily flow records, even of short length. We test the index in a large set of case studies across Germany encompassing a variety of climatic and physiographic settings. Our findings demonstrate that the new index enables reliable identification of cases with either heavy or nonheavy tailed flood behavior from daily flow records. Additionally, the index suitably estimates the severity of tail heaviness and ranks it across cases, achieving robust results even with short data records. The new index addresses the main limitations of currently used metrics, which lack physical support and require long data records to correctly identify tail behaviors, and provides valuable information on the tail behavior of flood distributions and the related flood hazard in river basins using commonly available discharge data.

Discontinuities in flood frequency curves, here referred to as flood divides, hinder the estimation of rare floods. In this paper we develop an automated methodology for the detection of flood divides from observations and models, and apply it to a large set of case studies in the USA and Germany. We then assess the reliability of the PHysically-based Extreme Value (PHEV) distribution of river flows to identify catchments that might experience a flood divide, validating its results against observations. This tool is suitable for the identification of flood divides, with a high correct detection rate especially in the autumn and summer seasons. It instead tends to indicate the emergence of flood divides not visible in the observations in spring and winter. We examine possible reasons of this behavior, finding them in the typical streamflow dynamics of the concerned case studies. By means of a controlled experiment we also re-evaluate detection capabilities of observations and PHEV after discarding the highest maxima for all cases where both empirical and theoretical estimates display flood divides. PHEV mostly confirms its capability to detect a flood divide as observed in the original flood frequency curve, even if the shortened one does not show it. These findings prove its reliability for the identification of flood divides and set the premises for a deeper investigation of physiographic and hydroclimatic attributes controlling the emergence of discontinuities in flood frequency curves.

Floods are often disastrous due to underestimation of the magnitude of rare events. When the occurrence of floods follows a heavy-tailed distribution the chance of extreme events is sizable. However, identifying heavy-tailed flood behavior is challenging because of limited data records and the lack of physical support for currently used indices. We address these issues by deriving a new index of heavy-tailed flood behavior from a physically-based description of streamflow dynamics. The proposed index, which is embodied by the hydrograph recession exponent, enables inferring heavy-tailed flood behavior from daily flow records. We test the index in a large set of case studies across Germany. Results show its ability to identify cases with either heavy- or nonheavy-tailed flood behavior, and to evaluate the tail heaviness. Remarkably, the results are robust also for decreasing the lengths of data records. The new index thus allows for assessing flood hazards from commonly available data.

Relationships between nitrate concentrations and discharge rates (C–Q) at the catchment outlet can provide insights into sources, mobilization and biogeochemical transformations of nitrate within the catchment. Nitrate C–Q relationships often exhibit considerable scatter that might be related to variable hydrologic conditions during runoff events at sampling time, corresponding to variable sources and flow paths despite similar discharge (Q) rates. Although previous studies investigated the origins of this scatter in individual or in a few catchments, the role of different runoff event types across a large set of catchments is not yet fully understood. This study combines a hydrological runoff event classification framework with low-frequency nitrate samples in 184 catchments to explore the role of different runoff events in shaping long-term C–Q relationships and their variability across contrasting catchments. In most of the catchments, snow-impacted events produce positive deviations of concentrations, indicating an increased nitrate mobilization compared to the long-term pattern. In contrast, negative deviations occur mostly for rainfall-induced events with dry antecedent conditions, indicating the occurrence of lower nitrate concentrations (C) in river flows than their long-term pattern values during this type of event. Pronounced differences in event runoff coefficients among different event types indicate their contrasting levels of hydrologic connectivity that in turn might play a key role in controlling nitrate transport due to the activation of faster flow paths between sources and streams. Using long-term, low-frequency nitrate data, we demonstrate that runoff event types shape observed scatter in long-term C–Q relationships according to their level of hydrologic connectivity. In addition, we hypothesize that the level of biogeochemical attenuation of catchments can partially explain the spatial variability of the scatter during different event types.

Anomalies in the frequency of river floods, i.e., flood-rich or -poor periods, cause biases in flood risk estimates and thus make climate adaptation measures less efficient. While observations have recently confirmed the presence of flood anomalies in Europe, their exact causes are not clear. Here we analyse streamflow and climate observations during 1960-2010 to show that shifts in flood generation processes contribute more to the occurrence of regional flood anomalies than changes in extreme rainfall. A shift from rain on dry soil to rain on wet soil events by 5% increased the frequency of flood-rich periods in the Atlantic region, and an opposite shift in the Mediterranean region increased the frequency of flood-poor periods, but will likely make singular extreme floods occur more often. Flood anomalies driven by changing flood generation processes in Europe may further intensify in a warming climate and should be considered in flood estimation and management.

BEaTriX is a unique facility developed at the INAF-Osservatorio Astronomico Brera (Merate, Italy) to test ATHENA’s X-ray mirror. It can perform the X-ray acceptance tests (PSF and Aeff) of the ATHENA Silicon Pore Optics Mirror Modules (MM) at their production rate (2 MM/day), at 4.51 and 1.49 keV. The X-ray beam approximates the one created by an astronomical source (collimated and large), re-created in a small lab (about 9 m x 18 m) thanks to an innovative design. A microfocus X-ray source produces a divergent beam which is conditioned (collimated, monochromatic, and expanded) by a parabolic mirror and a set of silicon crystals, one of which is asymmetrically cut with respect to the lattice planes. The first beam line, at the energy of 4.51 keV, has already been developed. The large monochromatic beam is collimated to < 3 arcsec, with a flux of 60 photons/s/cm2. Its size (170 mm x 60 mm) is sufficiently large to cover the entrance pupil of the MMs. The PSF and Aeff of the first optically representative MM were successfully measured., Grants: ESA contract # 4000123152/18/NL/BW, AHEAD grant #654215 and #871158, ASI accordo attuativo # 2019-27-HH.0, Internal INAF funds

River floods are the commonest natural disaster worldwide causing substantial monetary losses and death tolls yearly. Despite enormous efforts, gauging the peril of extreme floods is an outstanding challenge for varied economic sectors and the society at large. Appraisal of the flood hazard is especially difficult when the magnitude of the rarer floods strongly increases. Limited data records in fact hinder prediction of these instances and the identification of flood divides marking the rise of progressively larger floods. Here we demonstrate that the spatial organization of stream networks and the river flow regime control the appearance of flood divides and extreme floods. Unlike their ubiquitous attribution to anomalous hydrometeorological conditions, we show for the first time that the propensity to generate extreme floods is well predicted by intrinsic properties of river basins. Most importantly it can be assessed prior to the occurrence of catastrophes through measurable metrics of these properties derived from commonly available discharge data, with far-reaching implications for flood hazard assessment and preparedness.

Large scale modelling is becoming increasingly important in hydrology, particularly to characterize and quantify changes in the hydrological regime, whose drivers are typically large-scale phenomena, up to the global scale (e.g., climate change). This can be done with distributed models by estimating spatially consistent model parameters, that are parameters having a functional relationship with catchment characteristics. In this study we adopt the newly developed PArameter Set Shuffling (PASS) approach, based on a machine learning decision tree algorithm, for the regional calibration of the SALTO (SAme Like The Others) model. The method exploits observed patterns of locally calibrated parameters and catchment (climatic and geomorphological) descriptors, to derive functional relationships between the variables. The results, for around 100 catchments located in North-Western Italy, demonstrate that the use of regionally calibrated parameter sets results in similarly good performances as for local calibration. This suggests that the predicted parameter sets can be efficiently used for streamflow prediction in ungauged basins or under changing conditions.

Floods are generated by complex interactions between different processes that are still poorly understood especially at the large scale. Here we investigate 177,267 rainfall-runoff events across 373 German catchments to identify which spatial and temporal properties of precipitation events and wetness state of catchments lead to the emergence of floods by applying two machine learning approaches based on decision trees and random forests. We found that in wet mountainous catchments with rather shallow soils, rainfall characteristics play an ultimate role in modulating flood occurrence. Instead, in the drier catchments the occurrence of flood events is driven primarily by the pre-event wetness conditions and spatial interplay between soil moisture and rainfall within the catchment. Snow cover additionally enhances flood occurrence in all study catchments. The identified ingredients and regional flavors of flood events in Germany provide new insights on the spatial dynamics of hydro-meteorological processes leading to floods and might become an essential tool to foster regionally-coordinated flood management strategies and early warning systems.

Statistical distributions of flood peak discharges often show heavy tail behavior, i.e., extreme floods are more likely to occur than would be predicted by commonly used distributions that have exponential asymptotic behavior. This heavy tail behavior may surprise flood managers and citizens, as human intuition tends to expect light tail behavior, and the heaviness of the tails is very difficult to predict, which may lead to unnecessarily high flood damage. Despite its high importance, the literature on the heavy tail behavior of flood distributions is rather fragmented. In this review, we provide a coherent overview of the processes causing heavy flood tails and the implications for science and practice. Specifically, we propose nine hypotheses on the mechanisms causing heavy tails in flood peak distributions related to processes in the atmosphere, the catchment and the river system. We then discuss to which extent the current knowledge supports or contradicts these hypotheses. We also discuss the statistical conditions for the emergence of heavy tail behavior based on derived distribution theory and relate them to the hypotheses and flood generation mechanisms. We review the degree to which the heaviness of the tails can be predicted from process knowledge and data. Finally, we recommend further research towards testing the hypotheses and improving the prediction of heavy tails.

Heavy-tailed probability distributions of streamflow are frequently observed in river basins. In case of right skewed distributions, the larger probability assigned to relatively high flows translates into an unneglectable chance of occurrence of extreme floods in a long-term hydrological response. Although the spatial variability of rainfall has been identified as an impactful driver of flood events, delineating its effects for the emergence of heavy tails of streamflow distributions is still challenging.In this study we apply a simple stochastic approach to generate spatially various rainfall as the input of a well-established continuous hydrological model. The model embeds the soil water balance in hillslopes, the probability distributions of transit times in the hillslopes of subcatchments, and the response time distribution in channels derived from a geomorphological analysis of the river network. We investigate the role of spatially variable rainfall for the emergence of heavy tails in streamflow distributions by simulating a wide range of spatial rainfall variability in five catchments in Germany, and then put the modelling results into real world context by analyzing historical data in 175 catchments across the whole Germany.We find that increasing spatial variability of rainfall determines heavier streamflow tails only beyond a certain increase threshold which depends on physiographic features of catchments. Small and elongated catchments are less resilient to increasing spatial rainfall variability, i.e., their streamflow distributions begin to exhibit heavier tails for smaller increments of the spatial variability of rainfall. The distribution of runoff-routing pathway is suggested to be an effective attribute of catchments in this process.

Reliable assessment of the flooding hazard of river basins is crucial for many social and economic activities. We present here the Physically-based Extreme Value (PHEV) distribution of river discharges. PHEV is a process-based alternative to empirical estimates and statistical methods hitherto used to characterize extremes of hydrometeorological variables. It arises from a description of key hydro-meteorological processes driving runoff production (e.g., precipitation inputs, evapotranspiration rates, soil moisture states and catchment responses) through solution of the master equation for the probability distribution of streamflow in a catchment. PHEV pairs physical understanding of the mechanisms producing extreme events and defining their chance of occurrence with an easily tractable mathematical descriptions of them, thus providing a theoretical underpinning to the study of manifold flood-related issues, such as the emergence of heavy tails in streamflow and flood distributions, flood rich and poor periods, and the reasons leading to the occurrence of extreme flood events.In this work we benchmark capabilities of PHEV for predicting odds and magnitudes of floods against a standard distribution and the latest statistical approach for extreme estimation. The methods are first applied to an extensive dataset to compare their skills for predicting observed flood quantiles in a wide range of case studies. Synthetic time series of streamflow, generated for select river basins from contrasting hydro-climatic regions, are later used to assess performances for rare events. The analyses outline the domain of applicability of PHEV and reveal less biased capabilities to estimate flood magnitudes with return periods much longer than the sample size used for calibration. Results also show reduced prediction uncertainty of PHEV for rare floods, notably if the flood magnitude-frequency curve displays an inflection point.Such discontinuities typically hinder estimation of high streamflow quantiles. PHEV reveals itself as a reliable tool to foresee their occurrence in a large set of case studies from the US and Germany, also when using shortened data series where the highest observations were removed. Case studies for which PHEV predicts the occurrence of an inflection point which is not visible in the empirical flood magnitude-frequency curve mostly belong to river flow regimes characterized by values weakly oscillating around their mean, which rarely exhibit extreme flow values by their nature. The limited length of the available data series might be thus constraining the possibility to observe extreme floods that shall be expected. These results indicate the possibility to reliably appraise the propensity of rivers to generate extreme floods by means of a process-based description of watershed dynamics, thus laying the foundation for a better comprehension of their physio-climatic controls.

Large scale hydrological modelling requires the estimation of model parameters across a variety of different environments. To deal with this issue, robust parameter estimation procedures, able to exploit observed patterns of climate and geomorphological characteristics, must be considered. This contribution presents hydroPASS, a newly developed R package available in GitHub, which automatically implements the PArameter Set Shuffling (PASS) method in around 100 catchments over North-Western Italy. This was developed and tested for SALTO (SAme Like The Others) model, but in principle is valid for every distributed or semi-distributed catchment model. In particular, the package contains the function to run SALTO model (Fortran code), the function for PASS application (R code) and functions for data pre-processing and post-processing (R codes). To ease the repeatability and reproducibility of experiments, examples are provided with full documentation. An example of how to use PASS for the regional calibration of other models (e.g., TUW model), is also provided. From the source package, installation packages have been built for Windows, Linux and Mac operating systems and can be freely downloaded from GitHub.

Over the last decades, treatment of domestic wastewater promoted by environmental regulations have reduced human health risks and improved water quality. However, ecological risks caused by effluents of wastewater treatment plants (WWTPs) discharged into rivers still persist. Moreover, the evolution of these ecological risks in the future is intimately related to effects of changing climate, especially regarding streamflow in receiving rivers. Here, we present a systematic and transferable framework for assessing the ecological risks posed by individual WWTP-effluents at the catchment-scale. The framework combines the size-class k of WWTPs, which is a proxy for load, with the location of their outflows in river networks, represented by its stream-order ω. We identify ecological risks by using three proxy indicators: the urban discharge fraction and the local-scale concentrations of each total phosphorous and ammonium-nitrogen discharged from WWTPs. About 3,200 WWTPs over three large catchments (Rhine, Elbe, and Weser) in Central Europe were analyzed by incorporating simulated streamflow for the most extreme projected climate change scenario. We found that WWTPs causing ecological risks in future prevail in lower stream-orders, across almost all size-classes. Distinct patterns of ecological risks are identified in the k-ω framework for different indicators and catchments. We show that, as climate changes, intensified risks are especially expected in lower stream-orders receiving effluents of intermediate size WWTPs. We discuss implications of our findings for prioritizing WWTPs advancement and urging updates on environmental regulations. Further applications of the k-ω framework are discussed to help achieving global long-term commitments on freshwater security.

The BEaTriX (Beam Expander Testing X-ray) facility is now operative at the INAF-Osservatorio Astronomico Brera (Merate, Italy). This facility has been specifically designed and built for the X-ray acceptance tests (PSF and Effective Area) of the ATHENA Silicon Pore Optics (SPO) Mirror Modules (MM). The unique setup creates a parallel, monochromatic, large X-ray beam, that fully illuminates the aperture of the MMs, generating an image at the ATHENA focal length of 12 m. This is made possible by a microfocus X-ray source followed by a chain of optical components (a paraboloidal mirror, 2 channel cut monochromators, and an asymmetric silicon crystal) able to expand the X-ray beam to a 6 cm × 17 cm size with a residual divergence of 1.5 arcsec (vertical) × 2.5 arcsec (horizontal). This paper reports the commissioning of the 4.5 keV beam line, and the first light obtained with a Mirror Module.

The favorite solution foreseen for the realization of future very large x-ray mirror modules (diameters above 1 m) is the partition of the optics in azimuthal and radial modules. Even if this approach solves the initial problem of the procurement and the handling of very large substrates, it moves the difficulties in the second phase, when thousands of segments have to be aligned and assembled without degrading their optical performances. The usage of large monolithic shells would provide indubitable advantages with respect to the segmented approach, but poses challenging procurement problems. For example, in the case of Lynx, where superb imaging capabilities are combined with a very large effective area, a mirror module with 3mdiameter is foreseen. One of the three technologies considered by the Study Team for the realization of the mirrors is based on monolithic glass (fused silica) shells, figured with direct polishing technique. The simplicity of the full shell concept is quite attractive: the complete optics module could be composed of few hundreds of parts instead of several thousands of pieces. As a drawback, to be compliant with the mass budget, the shell thickness should be maintained very small even for larger mirror shells. Given that the glass is a brittle material, the procurement of such large raw shells, and of their spares, is considered the major critical issue of the process. In order to overcome the difficulties and the cost related to the assembly of thousands of segments, a straightforward way to realize very large monolithic raw mirrors shells is needed. In this paper we present the first results related to an alternative approach, based on light-weighted metal sheets, which could have an astonishing impact on the full shell concept, providing very large shells at negligible costs. In particular, we focus on spin forming technology, commonly used for the realization of axial-symmetric parts. This process makes use of a rotating machine to deform metal over a pre-shaped mold. As a replica approach, it allows the realization of several back up optics without major impacts on the costs, as it would be the case if the mirror shell realization starts from blanks machining. Aluminium is commonly used in plate turning. Given its low density and good mechanical properties, it may represent an effective and cheap alternative to more exotic low-density materials or to the glass itself. Metal optics, made of Aluminium 6061, are already widely used to fulfil the demands of a-thermal instrument design. Starting from aluminum substrates, diamond turning can be used for figuring the optic. An additional thin layer of electro-less nickel will allow final figuring and polishing. These further figuring processes are nowadays well proven on thick substrates, allowing the realization of optics with cutting-edge performances. Their successful employ on thin shell substrates would open new window on x-ray optics realization.

The ATHENA X-ray telescope will be the largest X-ray optics ever built. The ground calibration of this mirror assembly raises significant difficulties due to its unprecedented size, mass and focal length. The VERT-X project aims at developing an innovative calibration system which will be able to accomplish to this extremely challenging task.The design is based on an X-ray parallel beam produced by an X-ray source positioned in the focus of a highly performing X-ray collimator; the beam will be accurately moved by a raster-scan mechanism covering all the ATHENA optics at different off-axis angles. The main driving factor in the VERT-X design is the ATHENA calibration requirement on the accuracy in the HEW measure which is 0.1”, all over the field of view. The VERT-X project, started in January 2019, is financed by ESA and conducted by a consortium that includes INAF together with EIE, Media Lario, BCV Progetti and GPAP.

The Lynx X-ray Observatory, with superb imaging capabilities and with large throughput, is one of the four large strategic missions considered in the Astro2020 decadal survey. The realization of the mirror assembly within the desired tolerances is quite challenging and different mirror module concepts are proposed. The simplest mirror module design corresponds to less than a few hundred monolithic shells made of fused silica. The complete optomechanical design, compliant with the mass budget, foresees that the shell thickness ranges between 2 and 4 mm (for mirror shells between 0.4 and 3 m diameter). A technology development roadmap for this approach is funded in Italy by ASI and pursued out by INAF-OAB. In this paper, we present the advancements obtained in the procurement of new raw glass shells, in the development of the different phases of the process, and in the realization of a new single-reflection shell, representative of the final optical configuration suitable to validate the

A new type of x-ray facility, the Beam Expander Testing x-ray facility (BEaTriX), has been designed and is now under construction at INAF– Osservatorio Astronomico di Brera (Merate, Italy) to perform the acceptance tests of the silicon pore optics modules of the ATHENA X-ray telescope. Crystals of high perfection and large dimensions are needed in order to obtain a wide beam (20 × 6 cm2) with an X-ray divergence

This article describes BabyIAXO, an intermediate experimental stage of the International Axion Observatory (IAXO), proposed to be sited at DESY. IAXO is a large-scale axion helioscope that will look for axions and axion-like particles (ALPs), produced in the Sun, with unprecedented sensitivity. BabyIAXO is conceived to test all IAXO subsystems (magnet, optics and detectors) at a relevant scale for the final system and thus serve as prototype for IAXO, but at the same time as a fully-fledged helioscope with relevant physics reach itself, and with potential for discovery. The BabyIAXO magnet will feature two 10 m long, 70 cm diameter bores, and will host two detection lines (optics and detector) of dimensions similar to the final ones foreseen for IAXO. BabyIAXO will detect or reject solar axions or ALPs with axion-photon couplings down to $g_{a\gamma} \sim 1.5 \times 10^{-11}$ GeV$^{-1}$, and masses up to $m_a\sim 0.25$ eV. BabyIAXO will offer additional opportunities for axion research in view of IAXO, like the development of precision x-ray detectors to identify particular spectral features in the solar axion spectrum, and the implementation of radiofrequency-cavity-based axion dark matter setups., Comment: 77 pages, 49 figures. Prepared for submission to JHEP. Third version after referees comments

Two meteorite pieces have been recovered in Italy, near the town of Cavezzo (Modena), on 2020 January 4th. The associated fireball was observed on the evening of New Year’s Day 2020 by eight all-sky cameras of the PRISMA fireball network, a partner of FRIPON. The computed trajectory had an inclination angle of approximately 68° and a velocity at infinity of 12.8 km s−1. Together with the relatively low terminal height, estimated as 21.5 km, those values were indicating the significant possibility of a meteorite dropping event, as additionally confirmed by the non-zero residual total mass. The strewn-field was computed taking into account the presence of two bright light flashes, revealing that the meteoroid had been very likely subject to fragmentation. Three days after the event, two samples, weighing 3.1 and 52.2 g, were collected as a result of a dedicated field search and thanks to the involvement of the local people. The two pieces were immediately recognized as freshly fallen fragments of meteorite. The computed orbital elements, compared with the ones of known Near-Earth Asteroids from the NEODyS database, are compatible with one asteroid only; 2013 VC10. The estimated original mass of the meteoroid, 3.5 kg, and size, approximately 13 cm, is so far the smallest among the current 35 cases in which meteorites were recovered from precise strewn-field computation thanks to observational data. This result demonstrates the effectiveness of accurate processing of fireball network data even on challenging events generated by small size meteoroids.

The BEaTriX (Beam Expander Testing X-ray) facility, being completed at INAF-Brera Astronomical Observatory, will represent an important step in the acceptance roadmap of Silicon Pore Optics mirror modules, and so ensure the final angular resolution of the ATHENA X-ray telescope. Aiming at establishing the final angular resolution that can be reached and the respective fabrication/positioning tolerances, we have been dealing with a set of comprehensive optical simulations. Simulations based on wave optics were carried out to predict the collimation performances of the paraboloidal mirror, including the effect of surface errors obtained from metrology. Full-ray-tracing routines were subsequently employed to simulate the full beamline. Finally, wavefront propagation simulation allowed us assessing the sensitivity and the response of a wavefront sensor that will be utilized for the qualification of the collimated beam. We report the simulation results and the methodologies we adopted.

BEaTriX (Beam Expander Testing X-ray) is the X-ray facility under construction at the INAF-Osservatorio Astronomico Brera (Merate, Italy) to prove that it is possible to perform the X-ray acceptance tests (PSF and Aeff) of the ATHENA mirror modules at the required rate and with the required accuracy. The unique optical setup makes use of a micro-focus X-ray source with anode in Titanium, a paraboloidal mirror with small radius of curvature, and a set of crystals to monochromate and expand the beam to fully illuminate the entrance pupil of the ATHENA MMs. The quality of the optical components, and their precise alignment, guarantees the production of a parallel beam at 4.51 keV, to be extended in a second phase to 1.49 keV in order to complete the acceptance requirements for the ATHENA MMs. The completion of the facility is expected to occur in July this year, while the commissioning will start in September. In this paper, we present the current status.

Axions are a natural consequence of the Peccei-Quinn mechanism, the most compelling solution to the strong-CP problem. Similar axion-like particles (ALPs) also appear in a number of possible extensions of the Standard Model, notably in string theories. Both axions and ALPs are very well motivated candidates for Dark Matter, and in addition, they would be copiously produced at the sun's core. A relevant effort during the last decade has been the CAST experiment at CERN, the most sensitive axion helioscope to-date. The International Axion Observatory (IAXO) is a large-scale 4th generation helioscope. As its primary physics goal, IAXO will look for solar axions or ALPs with a signal to background ratio of about 5 orders of magnitude higher than CAST. Recently the IAXO collaboration has proposed and intermediate experimental stage, BabyIAXO, conceived to test all IAXO subsystems (magnet, optics, detectors and sun-tracking systems) at a relevant scale for the final system and thus serve as pathfinder for IAXO but at the same time as a fully-fledged helioscope with record and relevant physics reach in itself with potential for discovery. BabyIAXO was endorsed by the Physics Review committee of DESY last May 2019. Here we will review the status and prospects of BabyIAXO and its potential to probe the most physics motivated regions of the axion & ALPs parameter space., 6 pages, ICHEP 2020 Virtual conference