Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows

Derived flood frequency analysis allows the estimation of design floods with hydrological modeling for poorly observed basins considering change and taking into account flood protection measures. There are several possible choices regarding precipitation input, discharge output and consequently the calibration of the model. The objective of this study is to compare different calibration strategies for a hydrological model considering various types of rainfall input and runoff output data sets and to propose the most suitable approach. Event based and continuous, observed hourly rainfall data as well as disaggregated daily rainfall and stochastically generated hourly rainfall data are used as input for the model. As output, short hourly and longer daily continuous flow time series as well as probability distributions of annual maximum peak flow series are employed. The performance of the strategies is evaluated using the obtained different model parameter sets for continuous simulation of discharge in an independent validation period and by comparing the model derived flood frequency distributions with the observed one. The investigations are carried out for three mesoscale catchments in northern Germany with the hydrological model HEC-HMS (Hydrologic Engineering Center's Hydrologic Modeling System). The results show that (I) the same type of precipitation input data should be used for calibration and application of the hydrological model, (II) a model calibrated using a small sample of extreme values works quite well for the simulation of continuous time series with moderate length but not vice versa, and (III) the best performance with small uncertainty is obtained when stochastic precipitation data and the observed probability distribution of peak flows are used for model calibration. This outcome suggests to calibrate a hydrological model directly on probability distributions of observed peak flows using stochastic rainfall as input if its purpose is the application for derived flood frequency analysis

Spatial interpolation of hourly rainfall – effect of additional information, variogram inference and storm properties

Hydrological modelling of floods relies on precipitation data with a high resolution in space and time. A reliable spatial representation of short time step rainfall is often difficult to achieve due to a low network density. In this study hourly precipitation was spatially interpolated with the multivariate geostatistical method kriging with external drift (KED) using additional information from topography, rainfall data from the denser daily networks and weather radar data. Investigations were carried out for several flood events in the time period between 2000 and 2005 caused by different meteorological conditions. The 125 km radius around the radar station Ummendorf in northern Germany covered the overall study region. One objective was to assess the effect of different approaches for estimation of semivariograms on the interpolation performance of short time step rainfall. Another objective was the refined application of the method kriging with external drift. Special attention was not only given to find the most relevant additional information, but also to combine the additional information in the best possible way. A multi-step interpolation procedure was applied to better consider sub-regions without rainfall. <br><br> The impact of different semivariogram types on the interpolation performance was low. While it varied over the events, an averaged semivariogram was sufficient overall. Weather radar data were the most valuable additional information for KED for convective summer events. For interpolation of stratiform winter events using daily rainfall as additional information was sufficient. The application of the multi-step procedure significantly helped to improve the representation of fractional precipitation coverage

Statistical approaches for identification of low-flow drivers: temporal aspects

The characteristics of low-flow periods, especially regarding their low temporal dynamics, suggest that the dimensions of the metrics related to these periods may be easily related to their meteorological drivers using simplified statistical model approaches. In this study, linear statistical models based on multiple linear regressions (MLRs) are proposed. The study area chosen is the German federal state of Lower Saxony with 28 available gauges used for analysis. A number of regression approaches are evaluated. An approach using principal components of local meteorological indices as input appeared to show the best performance. In a second analysis it was assessed whether the formulated models may be eligible for application in climate change impact analysis. The models were therefore applied to a climate model ensemble based on the RCP8.5 scenario. Analyses in the baseline period revealed that some of the meteorological indices needed for model input could not be fully reproduced by the climate models. The predictions for the future show an overall increase in the lowest average 7-day flow (NM7Q), projected by the majority of ensemble members and for the majority of stations.</p

Impact‐based forecasting for pluvial floods

Pluvial floods in urban areas are caused by local, fast storm events with very high rainfall rates, which lead to inundation of streets and buildings before the storm water reaches a watercourse. An increase in frequency and intensity of heavy rainfall events and an ongoing urbanization may further increase the risk of pluvial flooding in many urban areas. Currently, warnings for pluvial floods are mostly limited to information on rainfall intensities and durations over larger areas, which is often not detailed enough to effectively protect people and goods. We present a proof-of-concept for an impact-based forecasting system for pluvial floods. Using a model chain consisting of a rainfall forecast, an inundation, a contaminant transport and a damage model, we are able to provide predictions for the expected rainfall, the inundated areas, spreading of potential contamination and the expected damage to residential buildings. We use a neural network-based inundation model, which significantly reduces the computation time of the model chain. To demonstrate the feasibility, we perform a hindcast of a recent pluvial flood event in an urban area in Germany. The required spatio-temporal accuracy of rainfall forecasts is still a major challenge, but our results show that reliable impact-based warnings can be forecasts are available up to 5 min before the peak of an extreme rainfall event. Based on our results, we discuss how the outputs of the impact-based forecast could be used to disseminate impact-based early warnings

Klimafolgenabschätzungen in der Wasserwirtschaft und deren Nutzen für die Praxis

KlimafolgenClimate ImpactsDer globale Klimawandel kann regional unterschiedliche Auswirkungen haben. Während sich die wissenschaftliche Forschung vor allem mit der Analyse der Daten beschäftigt, ist die fachliche Praxis darum bemüht, die Ergebnisse zu interpretieren und Handlungsempfehlungen daraus abzuleiten. Im Zuge des Projektes KliBiW (Globaler Klimawandel – Wasserwirtschaftliche Folgenabschätzung für das Binnenland) wurden die Auswirkungen des Klimawandels auf die Hochwasser- und Niedrigwasserverhältnisse in Niedersachsen untersucht. Hierzu wurden die Daten von zwei regionalen Klimamodellen (WETTREG2006 und REMO), beide angetrieben durch das Globalmodell ECHAM5/MPI-OM, räumlich interpoliert und die Niederschläge zum Teil zeitlich disaggregiert, um hoch aufgelöste Klimainformationen bereitzuhalten. Anschließend erfolgte die Kopplung mit einem hydrologischen Modellsystem (PANTA RHEI), das bereits in der Hochwasservorhersagezentrale des NLWKN im Einsatz ist. Über Langzeitsimulationen wurden zukünftige Veränderungen in den Abflussverhältnissen räumlich und zeitlich differenziert für das Aller-Leine Gebiet identifiziert. Als Betrachtungszeiträume dienten eine nahe Zukunftsphase (2021 – 2050) und eine ferne Zukunftsphase (2071 – 2100). Die Veränderungen verschiedener hydrologischer Hoch- und Niedrigwasser-Kenngrößen wurden gegenüber einem Kontrollzeitraum (1971 – 2000) aufgezeigt. Die Auswertungen an 8 Pegeln in Einzugsgebieten >1.000 km² auf Tageswertbasis und an 6 Pegeln in Einzugsgebieten <1.000 km² auf Stundenwertbasis zeigten, dass sich die Hochwassersituation zukünftig verschärfen kann. Während kleinere Hochwässer häufiger auftreten können, nehmen die Scheitelabflüsse insbesondere in der fernen Zukunft zu. Aussagen zu größeren Ereignissen sind aufgrund der großen Bandbreite der Ergebnisse jedoch mit erheblichen Unsicherheiten behaftet. Die Niedrigwasserverhältnisse zeigten eine Abnahme der Abflüsse, speziell im Sommer, sowie eine Zunahme der Dauer undnder Volumendefizite bei Trockenperioden. Hierbei erschien die Variabilität und Ausprägung der Trockenheit in kleineren Einzugsgebieten etwas größer. Die Nutzung dieser Erkenntnisse stellt die fachliche Praxis vor die Herausforderung, die Ergebnisse zu interpretieren und zu kommunizieren. Unsicherheiten in den Modellketten müssen berücksichtigt und, wenn möglich, quantifiziert werden. Die abgeleiteten hydrologischen Konsequenzen des Klimawandels können z.B. Anwendung finden in der gesetzlich geforderten Berücksichtigung der Auswirkungen des Klimawandels auf die Risikogebiete entsprechend der Hochwasserrisikomanagement-Richtlinie (2007/60/EG). Dieser Beitrag gibt einen Überblick über wasserwirtschaftlich relevante Auswertungen von Klimamodelldaten auf unterschiedlichen räumlichen Skalen und zeigt anhand ausgewählter Beispiele auf, wie primär im wissenschaftlichen Kontext erhobene Ergebnisse effektiv für praxisrelevante Fragestellungen genutzt werden können

Changes over time in characteristics, resource use and outcomes among ICU patients with COVID-19-A nationwide, observational study in Denmark

BACKGROUND: Characteristics and care of intensive care unit (ICU) patients with COVID‐19 may have changed during the pandemic, but longitudinal data assessing this are limited. We compared patients with COVID‐19 admitted to Danish ICUs in the first wave with those admitted later. METHODS: Among all Danish ICU patients with COVID‐19, we compared demographics, chronic comorbidities, use of organ support, length of stay and vital status of those admitted 10 March to 19 May 2020 (first wave) versus 20 May 2020 to 30 June 2021. We analysed risk factors for death by adjusted logistic regression analysis. RESULTS: Among all hospitalised patients with COVID‐19, a lower proportion was admitted to ICU after the first wave (13% vs. 8%). Among all 1374 ICU patients with COVID‐19, 326 were admitted during the first wave. There were no major differences in patient's characteristics or mortality between the two periods, but use of invasive mechanical ventilation (81% vs. 58% of patients), renal replacement therapy (26% vs. 13%) and ECMO (8% vs. 3%) and median length of stay in ICU (13 vs. 10 days) and in hospital (20 vs. 17 days) were all significantly lower after the first wave. Risk factors for death were higher age, larger burden of comorbidities (heart failure, pulmonary disease and kidney disease) and active cancer, but not admission during or after the first wave. CONCLUSIONS: After the first wave of COVID‐19 in Denmark, a lower proportion of hospitalised patients with COVID‐19 were admitted to ICU. Among ICU patients, use of organ support was lower and length of stay was reduced, but mortality rates remained at a relatively high level

Evaluation of presumably disease causing SCN1A variants in a cohort of common epilepsy syndromes

Objective: The SCN1A gene, coding for the voltage-gated Na+ channel alpha subunit NaV1.1, is the clinically most relevant epilepsy gene. With the advent of high-throughput next-generation sequencing, clinical laboratories are generating an ever-increasing catalogue of SCN1A variants. Variants are more likely to be classified as pathogenic if they have already been identified previously in a patient with epilepsy. Here, we critically re-evaluate the pathogenicity of this class of variants in a cohort of patients with common epilepsy syndromes and subsequently ask whether a significant fraction of benign variants have been misclassified as pathogenic. Methods: We screened a discovery cohort of 448 patients with a broad range of common genetic epilepsies and 734 controls for previously reported SCN1A mutations that were assumed to be disease causing. We re-evaluated the evidence for pathogenicity of the identified variants using in silico predictions, segregation, original reports, available functional data and assessment of allele frequencies in healthy individuals as well as in a follow up cohort of 777 patients. Results and Interpretation: We identified 8 known missense mutations, previously reported as pathogenic, in a total of 17 unrelated epilepsy patients (17/448; 3.80%). Our re-evaluation indicates that 7 out of these 8 variants (p.R27T; p.R28C; p.R542Q; p.R604H; p.T1250M; p.E1308D; p.R1928G; NP-001159435.1) are not pathogenic. Only the p.T1174S mutation may be considered as a genetic risk factor for epilepsy of small effect size based on the enrichment in patients (P = 6.60 7 10-4; OR = 0.32, fishers exact test), previous functional studies but incomplete penetrance. Thus, incorporation of previous studies in genetic counseling of SCN1A sequencing results is challenging and may produce incorrect conclusions

Evaluation of presumably disease causing SCN1A variants in a cohort of common epilepsy syndromes

Objective: The SCN1A gene, coding for the voltage-gated Na+ channel alpha subunit NaV1.1, is the clinically most relevant epilepsy gene. With the advent of high-throughput next-generation sequencing, clinical laboratories are generating an ever-increasing catalogue of SCN1A variants. Variants are more likely to be classified as pathogenic if they have already been identified previously in a patient with epilepsy. Here, we critically re-evaluate the pathogenicity of this class of variants in a cohort of patients with common epilepsy syndromes and subsequently ask whether a significant fraction of benign variants have been misclassified as pathogenic. Methods: We screened a discovery cohort of 448 patients with a broad range of common genetic epilepsies and 734 controls for previously reported SCN1A mutations that were assumed to be disease causing. We re-evaluated the evidence for pathogenicity of the identified variants using in silico predictions, segregation, original reports, available functional data and assessment of allele frequencies in healthy individuals as well as in a follow up cohort of 777 patients. Results and Interpretation: We identified 8 known missense mutations, previously reported as path

Natural clusters of tuberous sclerosis complex (TSC)-associated neuropsychiatric disorders (TAND): new findings from the TOSCA TAND research project.

BACKGROUND: Tuberous sclerosis complex (TSC)-associated neuropsychiatric disorders (TAND) have unique, individual patterns that pose significant challenges for diagnosis, psycho-education, and intervention planning. A recent study suggested that it may be feasible to use TAND Checklist data and data-driven methods to generate natural TAND clusters. However, the study had a small sample size and data from only two countries. Here, we investigated the replicability of identifying natural TAND clusters from a larger and more diverse sample from the TOSCA study. METHODS: As part of the TOSCA international TSC registry study, this embedded research project collected TAND Checklist data from individuals with TSC. Correlation coefficients were calculated for TAND variables to generate a correlation matrix. Hierarchical cluster and factor analysis methods were used for data reduction and identification of natural TAND clusters. RESULTS: A total of 85 individuals with TSC (female:male, 40:45) from 7 countries were enrolled. Cluster analysis grouped the TAND variables into 6 clusters: a scholastic cluster (reading, writing, spelling, mathematics, visuo-spatial difficulties, disorientation), a hyperactive/impulsive cluster (hyperactivity, impulsivity, self-injurious behavior), a mood/anxiety cluster (anxiety, depressed mood, sleep difficulties, shyness), a neuropsychological cluster (attention/concentration difficulties, memory, attention, dual/multi-tasking, executive skills deficits), a dysregulated behavior cluster (mood swings, aggressive outbursts, temper tantrums), and an autism spectrum disorder (ASD)-like cluster (delayed language, poor eye contact, repetitive behaviors, unusual use of language, inflexibility, difficulties associated with eating). The natural clusters mapped reasonably well onto the six-factor solution generated. Comparison between cluster and factor solutions from this study and the earlier feasibility study showed significant similarity, particularly in cluster solutions. CONCLUSIONS: Results from this TOSCA research project in an independent international data set showed that the combination of cluster analysis and factor analysis may be able to identify clinically meaningful natural TAND clusters. Findings were remarkably similar to those identified in the earlier feasibility study, supporting the potential robustness of these natural TAND clusters. Further steps should include examination of larger samples, investigation of internal consistency, and evaluation of the robustness of the proposed natural clusters