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Assessments of climate change impacts on runoff regimes are essential to climate change adaptation and mitigation planning. Changing runoff regimes and thus changing seasonal patterns of water availability strongly influence various economic sectors such as agriculture, energy production, and fishery and also affect river ecology. In this study, we use new transient hydrological scenarios driven by the most up-to-date local climate projections for Switzerland, the Swiss Climate Change Scenarios. These provide detailed information on changes in runoff regimes and their time of emergence for 93 rivers in Switzerland under three Representative Concentration Pathways (RCPs): RCP2.6, RCP4.5, and RCP8.5. These transient scenarios also allow changes to be framed as a function of global mean temperature. The new projections for seasonal runoff changes largely confirm the sign of changes in runoff from previous hydrological scenarios with increasing winter runoff and decreasing summer and autumn runoff. Spring runoff is projected to increase in high-elevation catchments and to decrease in lower-lying catchments. Despite the increases in winter and some increases in spring, the annual mean runoff is projected to decrease in most catchments. Compared to lower-lying catchments, runoff changes in high-elevation catchments (above 1500 m a.s.l.) are larger in winter, spring, and summer due to the large influence of reduced snow accumulation and earlier snowmelt and glacier melt. The changes in runoff and the agreement between climate models on the sign of change both increase with increasing global mean temperatures and higher-emission scenarios. This amplification highlights the importance of climate change mitigation. The time of emergence is the time when the climate signal emerges significantly from natural variability. Under RCP8.5, times of emergence were found early, before the period 2036–2065, in winter and summer for catchments with mean altitudes above 1500 m a.s.l. Significant changes in catchments below 1500 m a.s.l. emerge later in the century. Not all catchments show significant changes in the distribution of seasonal means; thus, no time of emergence could be determined in these catchments. Furthermore, the significant changes of seasonal mean runoff are not persistent over time in some catchments due to nonlinear changes in runoff.

Future changes in river runoff will impact many sectors such as agriculture, energy production, or ecosystems. Here, we study changes in the seasonality, frequency, and magnitude of moderate low and high flows and their time of emergence. The time of emergence indicates the timing of significant changes in the flow magnitudes. Daily runoff is simulated for 93 Swiss catchments for the period 1981–2099 under Representative Concentration Pathway 8.5 with 20 climate model chains from the most recent transient Swiss Climate Change Scenarios. In the present climate, annual low flows typically occur in the summer half-year in lower-lying catchments ( m a.s.l.) and in the winter half-year in Alpine catchments (>1500 m a.s.l.). By the end of the 21st century, annual low flows are projected to occur in late summer and early autumn in most catchments. This indicates that decreasing precipitation and increasing evapotranspiration in summer and autumn exceed the water contributions from other processes such as snowmelt and glacier melt. In lower-lying catchments, the frequency of annual low flows increases, but their magnitude decreases and becomes more severe. In Alpine catchments, annual low flows occur less often and their magnitude increases. The magnitude of seasonal low flows is projected to decrease in the summer half-year in most catchments and to increase in the winter half-year in Alpine catchments. Early time of emergence is found for annual low flows in Alpine catchments in the 21st century due to early changes in low flows in the winter half-year. In lower-lying catchments, significant changes in low flows emerge later in the century. Annual high flows occur today in lower-lying catchments in the winter half-year and in Alpine catchments in the summer half-year. Climate change will change this seasonality mainly in Alpine catchments with a shift towards earlier seasonality in summer due to the reduced contribution of snowmelt and glacier melt in summer. Annual high flows tend to occur more frequent, and their magnitude increases in most catchments except some Alpine catchments. The magnitude of seasonal high flows in most catchments is projected to increase in the winter half-year and to decrease in the summer half-year. However, the climate model agreement on the sign of change in moderate high flows is weak.

To make sound decisions in the face of climate change, government agencies, policymakers and private stakeholders require suitable climate information on local to regional scales. In Switzerland, the development of climate change scenarios is strongly linked to the climate adaptation strategy of the Confederation. The current climate scenarios for Switzerland CH2018 - released in form of six user-oriented products - were the result of an intensive collaboration between academia and administration under the umbrella of the National Centre for Climate Services (NCCS), accounting for user needs and stakeholder dialogues from the beginning. A rigorous scientific concept ensured consistency throughout the various analysis steps of the EURO-CORDEX projections and a common procedure on how to extract robust results and deal with associated uncertainties. The main results show that Switzerland’s climate will face dry summers, heavy precipitation, more hot days and snow-scarce winters. Approximately half of these changes could be alleviated by mid-century through strong global mitigation efforts. A comprehensive communication concept ensured that the results were rolled out and distilled in specific user-oriented communication measures to increase their uptake and to make them actionable. A narrative approach with four fictitious persons was used to communicate the key messages to the general public. Three years after the release, the climate scenarios have proven to be an indispensable information basis for users in climate adaptation and for downstream applications. Potential for extensions and updates has been identified since then and will shape the concept and planning of the next scenario generation in Switzerland.

Heavy precipitation on the south side of the central Alps produced a catastrophic flood in October 1868. We assess the damage and societal impacts, as well as the atmospheric and hydrological drivers using documentary evidence, observations and novel numerical weather and runoff simulations.The greatest damage was concentrated close to the Alpine divide and Lago Maggiore. An atmospheric reanalysis emphasizes the repeated occurrence of streamers of high potential vorticity as precursors of heavy precipitation. Dynamical downscaling indicates high freezing levels (4000 m a.s.l.), extreme precipitation rates (max. 270 mm 24 h−1) and weather dynamics that agree well with observed precipitation and damage, and with existing concepts of forced low-level convergence, mid-level uplift and iterative northeastward propagation of convective cells. Simulated and observed peak levels of Lago Maggiore differ by 2 m, possibly because the exact cross section of the lake outflow is unknown. The extreme response of Lago Maggiore cannot be attributed to low forest cover. Nevertheless, such a paradigm was adopted by policy makers following the 1868 flood, and used to implement nationwide afforestation policies and hydraulic structures.These findings illustrate the potential of high-resolution, hydrometeorological models – strongly supported by historical methods – to shed new light on weather events and their socio-economic implications in the 19th century.

Model output statistics (MOS) methods can be used to empirically relate an environmental variable of interest to predictions from earth system models (ESMs). This variable often belongs to a spatial scale not resolved by the ESM. Here, using the linear model fitted by least squares, we regress monthly mean streamflow of the Rhine River at Lobith and Basel against seasonal predictions of precipitation, surface air temperature, and runoff from the European Centre for Medium-Range Weather Forecasts. To address potential effects of a scale mismatch between the ESM's horizontal grid resolution and the hydrological application, the MOS method is further tested with an experiment conducted at the subcatchment scale. This experiment applies the MOS method to 133 additional gauging stations located within the Rhine basin and combines the forecasts from the subcatchments to predict streamflow at Lobith and Basel. In doing so, the MOS method is tested for catchments areas covering 4 orders of magnitude. Using data from the period 1981–2011, the results show that skill, with respect to climatology, is restricted on average to the first month ahead. This result holds for both the predictor combination that mimics the initial conditions and the predictor combinations that additionally include the dynamical seasonal predictions. The latter, however, reduce the mean absolute error of the former in the range of 5 to 12 %, which is consistently reproduced at the subcatchment scale. An additional experiment conducted for 5-day mean streamflow indicates that the dynamical predictions help to reduce uncertainties up to about 20 days ahead, but it also reveals some shortcomings of the present MOS method.

Based on a hindcast experiment for the period 1982–2013 in 66 sub-catchments of the Swiss Rhine, the present study compares two approaches of building a regression model for seasonal streamflow forecasting. The first approach selects a single "best guess" model, which is tested by leave-one-out cross-validation. The second approach implements the idea of bootstrap aggregating, where bootstrap replicates are employed to select several models, and out-of-bag predictions provide model testing. The target value is mean streamflow for durations of 30, 60 and 90 days, starting with the 1st and 16th day of every month. Compared to the best guess model, bootstrap aggregating reduces the mean squared error of the streamflow forecast by seven percent on average. Thus, if resampling is anyway part of the model building procedure, bootstrap aggregating seems to be a useful strategy in statistical seasonal streamflow forecasting. Since the improved accuracy comes at the cost of a less interpretable model, the approach might be best suited for pure prediction tasks, e.g. as in operational applications.

A rain-on-snow flood occurred in the Bernese Alps, Switzerland, on 10 October 2011, and caused significant damage. As the flood peak was unpredicted by the flood forecast system, questions were raised concerning the causes and the predictability of the event. Here, we aimed to reconstruct the anatomy of this rain-on-snow flood in the Lötschen Valley (160 km2) by analyzing meteorological data from the synoptic to the local scale and by reproducing the flood peak with the hydrological model WaSiM-ETH (Water Flow and Balance Simulation Model). This in order to gain process understanding and to evaluate the predictability. The atmospheric drivers of this rain-on-snow flood were (i) sustained snowfall followed by (ii) the passage of an atmospheric river bringing warm and moist air towards the Alps. As a result, intensive rainfall (average of 100 mm day-1) was accompanied by a temperature increase that shifted the 0° line from 1500 to 3200 m a.s.l. (meters above sea level) in 24 h with a maximum increase of 9 K in 9 h. The south-facing slope of the valley received significantly more precipitation than the north-facing slope, leading to flooding only in tributaries along the south-facing slope. We hypothesized that the reason for this very local rainfall distribution was a cavity circulation combined with a seeder-feeder-cloud system enhancing local rainfall and snowmelt along the south-facing slope. By applying and considerably recalibrating the standard hydrological model setup, we proved that both latent and sensible heat fluxes were needed to reconstruct the snow cover dynamic, and that locally high-precipitation sums (160 mm in 12 h) were required to produce the estimated flood peak. However, to reproduce the rapid runoff responses during the event, we conceptually represent likely lateral flow dynamics within the snow cover causing the model to react "oversensitively" to meltwater. Driving the optimized model with COSMO (Consortium for Small-scale Modeling)-2 forecast data, we still failed to simulate the flood because COSMO-2 forecast data underestimated both the local precipitation peak and the temperature increase. Thus we conclude that this rain-on-snow flood was, in general, predictable, but requires a special hydrological model setup and extensive and locally precise meteorological input data. Although, this data quality may not be achieved with forecast data, an additional model with a specific rain-on-snow configuration can provide useful information when rain-on-snow events are likely to occur.

Strain collections are a treasure chest of numerous valuable and taxonomically validated bioresources. The Leibniz Institute DSMZ is one of the largest and most diverse microbial strain collections worldwide, with a long tradition of actinomycetes research. Actinomycetes, especially the genus Streptomyces , are renowned as prolific producers of antibiotics and many other bioactive natural products. In light of this, five Streptomyces strains, DSM 40971 T , DSM 40484 T , DSM 40713 T , DSM 40976 T , and DSM 40907 T , which had been deposited a long time ago without comprehensive characterization, were the subject of polyphasic taxonomic studies and genome mining for natural compounds based on in vitro and in silico analyses. Phenotypic, genetic, and phylogenomic studies distinguished the strains from their closely related neighbors. The digital DNA-DNA hybridization and average nucleotide identity values between the five strains and their close, validly named species were below the threshold of 70% and 95%-96%, respectively, determined for prokaryotic species demarcation. Therefore, the five strains merit being considered as novel Streptomyces species, for which the names Streptomyces kutzneri sp. nov., Streptomyces stackebrandtii sp. nov. , Streptomyces zähneri sp. nov. , Streptomyces winkii sp. nov., and Streptomyces kroppenstedtii sp. nov. are proposed. Bioinformatics analysis of the genome sequences of the five strains revealed their genetic potential for the production of secondary metabolites, which helped identify the natural compounds cinerubin B from strain DSM 40484 T and the phosphonate antibiotic phosphonoalamide from strain DSM 40907 T and highlighted strain DSM 40976 T as a candidate for regulator-guided gene cluster activation due to the abundance of numerous " Streptomyces antibiotic regulatory protein" (SARP) genes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Nouioui, Zimmermann, Hennrich, Xia, Rössler, Makitrynskyy, Pablo Gomez-Escribano, Pötter, Jando, Döppner, Wolf, Neumann-Schaal, Hughes and Mast.)

Climate change affects both water resources and agricultural production. With rising temperatures and decreasing summer precipitation, it is expected that agricultural production will be increasingly limited by drought. Where surface- or groundwater resources are available for irrigation, an increase in water withdrawals for irrigation is to be expected. Therefore, quantitative approaches are required to anticipate and manage the expected conflicts related to increased water abstraction for irrigation. This project aims to investigate how agricultural production, water demand for irrigation, runoff and groundwater dynamics are affected by future climate change and how climate change impacts combined with changes in agricultural water use affect groundwater dynamics. To answer these research questions, a comprehensive, loosely coupled model approach was developed, combining models from three disciplines: an agricultural plant growth model, a hydrological model and a hydrogeological model. The model coupling was implemented and tested for an agricultural area located in Switzerland in which groundwater plays a significant role in providing irrigation water. Our suggested modelling approach can be easily adapted to other areas. The model results show that yield changes are driven by drought limitations and rising temperatures. However, an increase in yield may be realized with an increase in irrigation. Simulation results show that the water requirement for irrigation without climate protection (RCP8.5) could increase by 40% by the end of the century with an unchanged growing season and by up to 80% with varietal adaptations. With climate change mitigation (RCP2.6) the increase in water demand for irrigation would be limited to 7%. The increase in irrigation (+12 mm) and the summer decrease in recharge rates (~20 mm/month) with decreasing summer precipitation causes a lowering of groundwater levels (40 mm) in the area in the late summer and autumn. This impact may be accentuated by an intensification of irrigation and reduced by extensification., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Information on possible changes in future flood risk is essential for successful adaptation planning and risk management. However, various sources of uncertainty arise along the model chains used for the assessment of flood risk under climate change. Knowledge on the importance of these different sources of uncertainty can help to design future assessments of flood risk, and to identify areas of focus for further research that aims to reduce existing uncertainties. Here we investigate the role of four sources of epistemic uncertainty affecting the estimation of flood loss for changed climate conditions for a meso-scale, pre-alpine catchment. These are: the choice of a scenario-neutral method, climate projection uncertainty, hydrological model parameter sets, and the choice of the vulnerability function. To efficiently simulate a large number of loss estimates, a surrogate inundation model was used. 46,500 loss estimates were selected according to the change in annual mean precipitation and temperature of an ensemble of regional climate models, and considered for the attribution of uncertainty. Large uncertainty was found in the estimated loss for a 100-year flood event with losses ranging from a decrease of loss compared to estimations for present day climate, to more than a 7-fold increase. The choice of the vulnerability function was identified as the most important source of uncertainty explaining almost half of the variance in the estimates. However, uncertainty related to estimating floods for changed climate conditions contributed nearly as much. Hydrological model parametrisation was found to be negligible in the present setup. For our study area, these results highlight the importance of improving vulnerability function formulation even in a climate change context where additional major sources of uncertainty arise., (Copyright © 2019 Elsevier B.V. All rights reserved.)

The Tambora volcano erupted in April 1815 caused many direct and indirect impacts on the climate system, as well as ecosystems and societies around the world. In Switzerland, the eruption contributed to the 1816 "Year Without a Summer", which is considered to be a key factor in generating the highest flooding ever documented of the Lake Constance (7th July 1817) and the flood of the Rhine in Basel. Snow was reported to remain during the summer of 1816, which laid the basis for a massive snow accumulation in the spring of 1817. The meltwater together with a triggering event led to the reported flooding. We aim to create a hydro-meteorological reconstruction of the 1816/1817 period in Switzerland to verify and quantify the historical sources and place them into present-day context. We used an analogue method that was based on historical measurements to generate temperature and precipitation fields for 1816/1817. These data drove a hydrological model that covers the Rhine Basin to Basel. We reproduced the reported features of the hydroclimate, especially in regards to the temperature and snow storage. We showed that the snow storage in spring 1816 and 1817 was substantial and attained the magnitude of a recent extreme, snow-rich winter (1999). However, simulations suggest that the snowfall alone in the spring of 1817, rather than the enduring snow from 1815/1816, led to the meltwater produced from the snow pack that contributed to the flooding in Lake Constance and Basel. These events were strongly underestimated, as the triggering rainfall event was reconstructed too weak. Artificial scenarios reveal that a precipitation amount with a magnitude higher than the largest recent flood (2005) was necessary to generate the documented flood levels. We conclude that these Tambora-following flood events were a product of an adverse combination of extreme weather within an extreme climate., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Lymph node size affects lymph node retrieval in surgical specimen and is used as criterion for pre-operative radiological estimation of metastatic disease. However, factors determining lymph node size remain to be established. Therefore, the association between lymph node size and presence of metastatic cancer deposits as well as different primary tumor characteristics was analyzed in a prospective cross-sectional study. Visible and palpable nodes were harvested, and conventional histology, immunohistochemistry, and molecular analysis were performed. The study cohort comprised 148 patients (median age 69 years, range 36-92). Lymph node dissection rendered 4167 nodes. Mean lymph node count was 28 (median 26, range 9-67). Metastatic disease was detected in 320 (8%) nodes and was associated with lymph node size (P<0.001). Positive nodes measuring ≤2 mm caused upstaging within the N category in one third of cases, but did not identify patients as node-positive as all patients also had positive larger nodes. Large tumor size (P=0.001), right tumor location (P<0.001), and deep tumor penetration (P=0.024) were all independently associated with lymph node size, whereas high lymphocytic antitumor reaction just missed statistical significance (P=0.053) in multivariable analysis. Microsatellite instability had no influence on lymph node size when analysis was restricted to right-sided tumors. In conclusion, analysis of small lymph nodes may lead to upstaging within the N category, but they do not identify a patient as node-positive and do therefore not influence clinical decision-making in the adjuvant setting. The majority of enlarged lymph nodes, including those measuring >1 cm, are not involved by cancer. Different tumor characteristics, such as large primary tumor size, right tumor location, and deep tumor penetration are independently associated with lymph node size and need to be considered when interpreting enlarged nodes detected by radiological imaging.

Background: A growing number of rapid Helicobacter pylori antibody tests are commercially available now, however, some of these tests are often used without sufficient evaluation. The aim of this study was to evaluate the performance of a commercially available rapid whole-blood immunoassay (gabControl(®) H. pylori; gabmed GmbH, Köln, Germany), for the qualitative detection of IgG antibodies against H. pylori with the (13)C-urea breath test ((13)C-UBT) serving as a reference method., Methods: A total of 108 consecutive outpatients, who were referred for (13)C-UBT by general practitioners and specialists, were also tested for H. pylori infection by the gabControl(®) H. pylori immunoassay. The clinical performance of this rapid whole-blood test was evaluated by determining the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) compared to the (13)C-UBT. The agreement between the two tests was calculated using Cohen's Kappa (κ) with 95 % confidence intervals (CI)., Results: The agreement between the gabControl(®) H. pylori assay and the (13)C-UBT was 0.62 [95 % confidence intervals (CIs) 0.47-0.76; P < 0.001]. With the (13)C-UBT serving as the non-invasive gold standard method of H. pylori diagnosis, the gabControl(®) H. pylori assay demonstrated a sensitivity and specificity of 91.4 and 76.7 %, respectively, with a PPV of 65.3 % and a NPV of 94.9 %. Seventeen (15.7 %) individuals with a positive H. pylori anamnesis showed a negative (13)C-UBT and were typed positive by the gabControl(®) H. pylori assay. Of these, 13 (76.5 %) and 3 individuals (17.6 %) had completed one and two eradication therapies, respectively., Conclusions: The gabControl(®) H. pylori immunoassay is a rapid and easy to use first line screening tool for H. pylori IgG antibody detection in daily clinical practice. However, this assay should not be used for confirmation of the successful H. pylori eradication after antibiotic treatment.