The role of antecedent conditions in translating precipitation events into extreme floods at the catchment scale and in a large-basin context

In this study, we analyze how precipitation, antecedent conditions, and their spatial patterns and interactions lead to extreme floods in a large catchment. The analysis is based on 10 000 years of continuous simulations from a hydro-meteorological modelling chain for a large catchment, the Aare River basin, Switzerland. To account for different flood-generating processes, we based our work on simulations with hourly time resolution. The hydro-meteorological modelling chain consisted of a stochastic weather generator (GWEX), a bucket-type hydrological model (HBV), and a routing system (RS MINERVE), providing the hydrological basis for flood protection management in the Aare River basin. From the long continuous simulations of runoff, snow, soil moisture, and dynamic storage, we were able to assess which combinations of antecedent conditions and triggering precipitation lead to extreme floods in the sub-basins of the Aare catchment. We found that only about 18 % to 44 % (depending on the sub-catchment) of annual maximum precipitation (AMP) and simulated annual maximum flood (AMF) events occurred simultaneously, highlighting the importance of antecedent conditions for the generation of large floods. For most sub-catchments in the 200–500 km2 range, after return periods greater than 500 years we found only AMF caused by triggering AMP, which is notably higher than the return periods typically used for design floods. Spatial organization within a larger area is complicated. After routing the simulated runoff, we analyzed the important patterns and drivers of extreme flooding at the outlet of the Aare River basin using a random forest. The different return period classes had distinct key predictors and showed specific spatial patterns of antecedent conditions in the sub-catchments, leading to different degrees of extreme flooding. While precipitation and soil moisture conditions from almost all sub-catchments were important for more frequent floods, for rarer events only the conditions in specific sub-catchments were important. Snow conditions were important only from specific sub-catchments and for more frequent events

Hochwasserereignisse aus kontinuierlicher Langzeitsimulation zur Überprüfung der Sicherheit der Stauanlagen. Schlussbericht vom 17.03.2021

Die in diesem Projekt entwickelte Methodik erlaubt es, auf der Basis von kontinuierlichen Langzeitsimulationen verschiedene Abflussverläufe von Hochwassern mit gegebenen Wiederkehrperioden durch realistische Ganglinien wiederzugeben. Der vorliegende Bericht beschreibt zum einen die Entwicklung dieser Methodik und zum anderen erste Auswertungen der Resultate aus dem Projekt „Extremhochwasser an der Aare“ (EXAR) für 19 Stauanlagen unter Bundesaufsicht im Einzugsgebiet der Aare. Der Vorteil der entwickelten Methodik ist, dass sich realitätsnahe repräsentative Ganglinien für eine Sicherheitsabschätzung zu definierten Jährlichkeitsbereichen ergeben. Dies kann zu realistischeren Abschätzungen führen als die sonst häufig verwendeten synthetischen Ganglinien, welche typischerweise nur durch einen oder zwei Parameter definiert werden. In einem ersten Schritt wurden aus den vorliegenden EXAR-Daten bivariate Jährlichkeiten bezüglich Abflussspitze und Hochwasservolumen berechnet und die entsprechenden Hochwasserganglinien bestimmten Jährlichkeitsbereichen (z.B. HQ100, HQ1’000, HQ5’000) zugeordnet. Innerhalb jedes Jährlichkeitsbereiches wurden dann die Ganglinien über funktionelles Clustering gruppiert. Dieses Clustering basiert auf einer Beschreibung der Ganglinien durch Funktionen, was bedeutet, dass die Ganglinien nicht nur nach bestimmten Charakteristika wie Abflussspitze oder Hochwasservolumen gruppiert werden, sondern die gesamte Form der Ganglinien in den Clustering-Prozess miteinbezogen wird. Aus jedem Cluster wurde anschliessend ein funktioneller Boxplot konstruiert, welcher wiederum die Form der Ganglinien im Cluster statistisch aggregiert darstellt. Die sich daraus ergebenden repräsentativen Ganglinien sollen den jeweils gewählten Jährlichkeitsbereich gut abdecken. Die Mittellinie des funktionellen Boxplots (was in etwa einem Median eines klassischen Boxplots entspricht) dient dann als repräsentative Ganglinie und entspricht einer tatsächlichen Ganglinie des Ausgangsdatensatzes. Um die Methode hinsichtlich ihrer Eignung als Grundlage für die Beurteilung der Hochwassersicherheit von Stauanlagen zu evaluieren, wurden zwei unterschiedliche Fälle betrachtet: 1) Stauanlagen mit beweglichen Organen zur Hochwasserentlastung und 2) Stauanlagen mit einem freien Überfall ohne zusätzliche bewegliche Organe zur Hochwasserentlastung. Für beide Fälle wurde jeweils der maximale Pegelanstieg im Stauraum berechnet und mit dem Volumen und der Abflussspitze der eingehenden Ereignisganglinien verglichen. In der Evaluation zeigte sich, dass die Mittellinie der funktionellen Ganglinien nicht immer am besten für eine Beurteilung der Hochwassersicherheit der Stauanlage geeignet ist. Deshalb wurden aus den funktionellen Boxplots jeweils weitere Ganglinien extrahiert. Zum einen waren dies repräsentative Ganglinien für Ereignisse mit sehr grossem Volumen innerhalb des Clusters, zum anderen repräsentative Ganglinien für Ereignisse mit grosser Abflussspitze innerhalb des Clusters. Diese zusätzlich ausgewählten Ganglinien decken den Bereich ungünstiger Pegelanstiege für die untersuchten Stauanlagen gut ab. Zusätzlich wurde die Methode univariat auf Anlagen angewendet, welche als Wehre betrachtet werden können. Der Fokus lag dabei auf der Abflussspitze. Für alle Anlagen ergaben sich mit der univariaten Methode enge funktionelle Boxplots, bei welchen die Mittellinie repräsentativ für die Kurvenschar der Jährlichkeitsbereiche war. Das 2020 angelaufene Projekt „Extremhochwasser Schweiz“ wird weiterentwickelte Langzeitsimulationen für grosse Einzugsgebiete (≥ 1‘000 km²) in der gesamten Schweiz bereitstellen und auch kleine (ca. 10–1‘000 km²) Einzugsgebiete abdecken können. Mit der hier entwickelten Methode und ersten Tests für hypothetische Anlagen mit freiem Überfall wurde eine gute Grundlage geschaffen, mit welcher diese Simulationen ebenfalls im Hinblick auf die Stauanlagensicherheit ausgewertet werden können

Ambient temperature and kidney function in primary care patients

Introduction Exposure to high ambient temperatures is associated with a risk of acute kidney injury. However, evidence comes from emergency departments or extreme weather exposures. It is unclear whether temperature-related adverse kidney outcomes can also be detected at a community level in a temperate climate zone. Methods In a 9.5-year retrospective cohort study we correlated estimated glomerular filtration rate (eGFR) values of Swiss adult primary care patients from the FIRE cohort (Family medicine Research using Electronic medical records) with same-day maximum local ambient temperature data. We investigated 5 temperature groups (< 15 °C, 15–19 °C, 20–24 °C, 25–29 °C and  ≥ 30 °C) as well as possible interactions for patients with increased kidney vulnerability (chronic heart failure, diabetes, chronic kidney disease, therapy with renin–angiotensin–aldosterone-system (RAAS) inhibitors, diuretics or non-steroidal anti-inflammatory drugs). Results We included 18,000 primary care patients who altogether provided 132,176 creatinine measurements. In the unadjusted analysis, higher ambient temperatures were associated with lower eGFR across all age and vulnerability groups. In the adjusted models, we did not find a consistent association.The highest ambient temperature differences (> 25 or > 30 versus < 15 °C) were associated with marginally reduced kidney function only in patients with ≥ 3 risk factors for kidney vulnerability, with a maximum estimated glomerular filtration rate reduction of −2.9 ml/min/1.73m$^{2}$ (SE 1.0), P 0.003. Discussion In a large primary care cohort from a temperate climate zone, we did not find an association between ambient temperatures and kidney function. A marginal inverse association in highly vulnerable patients is of unclear clinical relevance

Importance of maximum snow accumulation for summer low flows in humid catchments

Winter snow accumulation obviously has an effect on the following catchment runoff. The question is, however, how long this effect lasts and how important it is compared to rainfall inputs. Here we investigate the relative importance of snow accumulation on one critical aspect of runoff, namely the summer low flow. This is especially relevant as the expected increase of air temperature might result in decreased snow storage. A decrease of snow will affect soil and groundwater storages during spring and might cause low streamflow values in the subsequent warm season. To understand these potential climate change impacts, a better evaluation of the effects of inter-annual variations in snow accumulation on summer low flow under current conditions is central. The objective in this study was (1) to quantify how long snowmelt affects runoff after melt-out and (2) to estimate the sensitivity of catchments with different elevation ranges to changes in snowpack. To find suitable predictors of summer low flow we used long time series from 14 Alpine and pre-Alpine catchments in Switzerland and computed different variables quantifying winter and spring snow conditions. In general, the results indicated that maximum winter snow water equivalent (SWE) influenced summer low flow, but could expectedly only partly explain the observed inter-annual variations. On average, a decrease of maximum SWE by 10 % caused a decrease of minimum discharge in July by 6–9 % in catchments higher than 2000 m a.s.l. This effect was smaller in middle- and lower-elevation catchments with a decrease of minimum discharge by 2–5 % per 10 % decrease of maximum SWE. For higher- and middle-elevation catchments and years with below-average SWE maximum, the minimum discharge in July decreased to 70–90 % of its normal level. Additionally, a reduction in SWE resulted in earlier low-flow occurrence in some cases. One other important factor was the precipitation between maximum SWE and summer low flow. When only dry preceding conditions in this period were considered, the importance of maximum SWE as a predictor of low flows increased. We assessed the sensitivity of individual catchments to the change of maximum SWE using the non-parametric Theil–Sen approach as well as an elasticity index. Both sensitivity indicators increased with increasing mean catchment elevation, indicating a higher sensitivity of summer low flow to snow accumulation in Alpine catchments compared to lower-elevation pre-Alpine catchments

COVID-19 and lessons from multi-hazard early warning systems

Having a common framework for early action to cope with complex disasters can make it easier for authorities and other stakeholders, including populations at risk, to understand the full spectrum of secondary and tertiary effects and thus where to focus preparedness efforts, and how best to provide more targeted warnings and response services. Meteorological and hydrological services world-wide have developed and implemented Multi-Hazard Early Warning Systems (MHEWS) for weather and climate related hazards that are now being expanded and transitioned towards Multi-Hazard Impact-based Early Warning Systems (MHIEWS). While it is still early days it is becoming clear that there are useful lessons from this approach in the COVID-19 global pandemic, and some valuable insight to be gained in risk communication, risk analysis and monitoring methodologies and approaches. The ability to understand and respond effectively to warnings through appropriate behaviours and actions is central to resilient societies and communities. By avoiding physical, societal and economic harm to the greatest extent possible, recovery from a hazard is likely to be faster, less costly and more complete. MHIEWS can be a common approach for all hazards and therefore more likely to become a trusted tool that everyone can understand and use as a basic element of their national disaster risk management system. The interconnectedness of hazards and their impacts is a strong motivator for a common approach. One of the lessons from the COVID-19 pandemic and extreme weather events is the need to understand the vulnerability of individuals, communities and societies so as to provide reliable, targeted guidance and warnings and the willingness and capacity to prepare for a reasonable worst-case scenario based on informed long-term planning. Meteorology and hydrology are making good progress in this direction and the process can be readily applied to health and other sectors

Comprehensive space–time hydrometeorological simulations for estimating very rare floods at multiple sites in a large river basin

Estimates for rare to very rare floods are limited by the relatively short streamflow records available. Often, pragmatic conversion factors are used to quantify such events based on extrapolated observations, or simplifying assumptions are made about extreme precipitation and resulting flood peaks. Continuous simulation (CS) is an alternative approach that better links flood estimation with physical processes and avoids assumptions about antecedent conditions. However, long-term CS has hardly been implemented to estimate rare floods (i.e. return periods considerably larger than 100 years) at multiple sites in a large river basin to date. Here we explore the feasibility and reliability of the CS approach for 19 sites in the Aare River basin in Switzerland (area: 17 700 km2) with exceedingly long simulations in a hydrometeorological model chain. The chain starts with a multi-site stochastic weather generator used to generate 30 realizations of hourly precipitation and temperature scenarios of 10 000 years each. These realizations were then run through a bucket-type hydrological model for 80 sub-catchments and finally routed downstream with a simplified representation of main river channels, major lakes and relevant floodplains in a hydrologic routing system. Comprehensive evaluation over different temporal and spatial scales showed that the main features of the meteorological and hydrological observations are well represented and that meaningful information on low-probability floods can be inferred. Although uncertainties are still considerable, the explicit consideration of important processes of flood generation and routing (snow accumulation, snowmelt, soil moisture storage, bank overflow, lake and floodplain retention) is a substantial advantage. The approach allows for comprehensively exploring possible but unobserved spatial and temporal patterns of hydrometeorological behaviour. This is of particular value in a large river basin where the complex interaction of flows from individual tributaries and lake regulations are typically not well represented in the streamflow observations. The framework is also suitable for estimating more frequent floods, as often required in engineering and hazard mapping

Comprehensive space-time hydrometeorological simulations for estimating very rare floods at multiple sites in a large river basin

Estimates for rare to very rare floods are limited by the relatively short streamflow records available. Often, pragmatic conversion factors are used to quantify such events based on extrapolated observations, or simplifying assumptions are made about extreme precipitation and resulting flood peaks. Continuous simulation (CS) is an alternative approach that better links flood estimation with physical processes and avoids assumptions about antecedent conditions. However, long-term CS has hardly been implemented to estimate rare floods (i.e. return periods considerably larger than 100 years) at multiple sites in a large river basin to date. Here we explore the feasibility and reliability of the CS approach for 19 sites in the Aare River basin in Switzerland (area: 17 700 km2) with exceedingly long simulations in a hydrometeorological model chain. The chain starts with a multi-site stochastic weather generator used to generate 30 realizations of hourly precipitation and temperature scenarios of 10 000 years each. These realizations were then run through a bucket-type hydrological model for 80 sub-catchments and finally routed downstream with a simplified representation of main river channels, major lakes and relevant floodplains in a hydrologic routing system. Comprehensive evaluation over different temporal and spatial scales showed that the main features of the meteorological and hydrological observations are well represented and that meaningful information on low-probability floods can be inferred. Although uncertainties are still considerable, the explicit consideration of important processes of flood generation and routing (snow accumulation, snowmelt, soil moisture storage, bank overflow, lake and floodplain retention) is a substantial advantage. The approach allows for comprehensively exploring possible but unobserved spatial and temporal patterns of hydrometeorological behaviour. This is of particular value in a large river basin where the complex interaction of flows from individual tributaries and lake regulations are typically not well represented in the streamflow observations. The framework is also suitable for estimating more frequent floods, as often required in engineering and hazard mapping

The Unexploited Treasures of Hydrological Observations Beyond Streamflow for Catchment Modeling

While measured streamflow is commonly used for hydrological model evaluation and calibration, an increasing amount of data on additional hydrological variables is available. These data have the potential to improve process consistency in hydrological modeling and consequently for predictions under change, as well as in data‐scarce or ungauged regions. Here, we show how these hydrological data beyond streamflow are currently used for model evaluation and calibration. We consider storage and flux variables, namely snow, soil moisture, groundwater level, terrestrial water storage, evapotranspiration, and altimetric water level. We aim at summarizing the state‐of‐the‐art and providing guidance for the use of additional hydrological variables for model evaluation and calibration. Based on a review of the current literature, we summarize observation methods and uncertainties of currently available data sets, challenges regarding their implementation, and benefits for model consistency. The focus is on catchment modeling studies with study areas ranging from a few km 2 to ~500,000 km 2 . We discuss challenges for implementing alternative variables that are related to differences in the spatio‐temporal resolution of observations and models, as well as to variable‐specific features, for example, discrepancy between observed and simulated variables. We further discuss advancements required to deal with uncertainties of the hydrological data and to integrate multiple, potentially inconsistent datasets. The increased model consistency and improvement shown by most reviewed studies regarding the additional variables often come at the cost of a slight decrease in streamflow model performance

The Unexploited Treasures of Hydrological Observations Beyond Streamflow for Catchment Modeling

While measured streamflow is commonly used for hydrological model evaluation and calibration, an increasing amount of data on additional hydrological variables is available. These data have the potential to improve process consistency in hydro- logical modeling and consequently for predictions under change, as well as in data-scarce or ungauged regions. Here, we show how these hydrological data beyond streamflow are currently used for model evaluation and calibration. We consider storage and flux variables, namely snow, soil moisture, groundwater level, terrestrial water storage, evapotranspiration, and altimetric water level. We aim at summarizing the state-of-the-art and providing guidance for the use of additional hydrological variables for model evaluation and calibration. Based on a review of the current literature, we summarize observation methods and un- certainties of currently available data sets, challenges regarding their implementation, and benefits for model consistency. The focus is on catchment modeling studies with study areas ranging from a few km 2 to ~500,000 km 2 . We discuss challenges for implementing alternative variables that are related to differences in the spatio-temporal resolution of observations and models, as well as to variable-specific features, for example, discrepancy between observed and simulated variables. We further discuss advancements required to deal with uncertainties of the hydrological data and to integrate multiple, potentially inconsistent datasets. The increased model consistency and improvement shown by most reviewed studies regarding the additional variables often come at the cost of a slight decrease in streamflow model performance

Multi-phasic life-threatening anaphylaxis refractory to epinephrine managed by extracorporeal membrane oxygenation (ECMO): A case report

We present a case of a 52-year-old patient suffering from multi-phasic life-threatening anaphylaxis refractory to epinephrine treatment. Extracorporeal membrane oxygenation (ECMO) therapy was initiated as the ultima ratio to stabilize the patient hemodynamically during episodic severe bronchospasm. ECMO treatment was successfully weaned after 4 days. Mastocytosis was diagnosed as the underlying condition. Although epinephrine is recommended as a first-line treatment for anaphylaxis, this impressive case provides clear evidence of its limited therapeutic success and emphasizes the need for causal therapies