Simulation of river flow in the Thames over 120 years: evidence of change in rainfall-runoff response?

Study region: The Thames catchment in southern England, UK. Study focus: Modelling with 124 years of rainfall, potential evaporation (PE), temperature and naturalised flow data. Daily rainfall-runoff flow simulation using current and three historic land cover scenarios to determine the stationarity of catchment response examined through three time-frames of analysis – annual, seasonal and flow extremes. The criterion of response stationarity is often assumed in climate change impact studies. New hydrological insights: The generally close correspondence between observed and simulated flows using the same model parameter values for the whole period is indicative of the temporal stability of hydrological processes and catchment response, and the quality of the hydrometric data. Changes that have occurred are a decrease in flood peak response times, typically two to three days pre and post the early 1940s, from change in agricultural practices and channel conveyance, and an increase of about 15% in summer flow from increase in urban land cover between the first decade of the 20th and 21st centuries. The water balance was found to be sensitive to the PE data used, with care needed to avoid discontinuity between two parts of the data record using different methods for calculation. Long-term mean annual rainfall shows little change but contrasting patterns of variation in seasonal rainfall demonstrate a variable climate for which simulated flow is similar to observed flow

An investigation of the effect of transient climate change on snowmelt, flood frequency and timing in northern Britain

Climate change is almost certain to affect snow and ice processes. Even at lower latitudes, changes in snow cover at high altitudes can significantly affect catchment hydrology. This paper uses data from a transient Regional Climate Model projection (HadRM3Q0) for 1950-2099 (A1B emissions) to drive hydrological models for three nested catchments on the river Dee in north-east Scotland, to assess potential changes in flood frequency and timing using annual maxima and moving-window analyses. Some results are also shown for an upland catchment in northern England. Modelling is performed both with and without a snow module, to demonstrate the effects of snowfall/melt and how these change through time and vary between catchments. Modelled changes in flood magnitude and timing are non-linear, with most changes for daily mean flows not significant. For longer duration (30-day) flows with snow there are significant decreases in peak magnitude, particularly for the smaller higher altitude Dee catchments, with peaks occurring months earlier in future (changes without snow are generally not significant). There is a general convergence in results with and without snow later in the period, as snow processes become less important, but convergence occurs at different times for different catchments and occurs differently for daily and 30-day peak flows due to the differential effects of snow at different durations. This highlights the importance of including snow processes for such catchments, particularly for longer duration flows, but also highlights the complexity of interactions: Physical catchment properties, the balance between precipitation occurrence and temperature, and how this balance alters as the climate changes will each be critical in determining the impact on the magnitude and timing of peak flows, making it hard to generalise results

Regionalisation of climate impacts on flood flows to support the development of climate change guidance for Flood Management

Current Defra / Environment Agency guidance (FCDPAG3 supplementary note: http://www.defra.gov.uk/environ/fcd/pubs/pagn/climatechangeupdate.pdf) requires all flood management plans to allow for climate change by incorporating, within a sensitivity analysis, an increase in river flows of up 20% over the next 50 years, and beyond. This guidance is the same for all of England and Wales, making no allowance for regional variation in climate change or catchment type. This reflects the lack of scientific evidence to resolve the spatial distribution of potential impacts on flood flows with enough confidence to set such policy regionally. The 20% allowance was first raised in 1999 for MAFF and subsequently reviewed following the release of the UKCIP02 scenarios. Although the 20% figure is a memorable precautionary target, there is the risk that it leads to a significant under- or over-estimation of future flood risk in individual catchments. Defra and the Environment Agency procured project FD2020 (Regionalisation of climate change impacts on flood flows) to provide a more rigorous science base for refreshing the FCDPAG3: supplementary note guidance. The FD2020 approach is exploring the relationships between catchment characteristics and climate change impacts on peak flows in a “scenario neutral” way. This is done by defining a regular set of changes in climate that encompass all the current knowledge from the new scenarios available from the IPCC Fourth Assessment Report. For each of the 155 catchments included in the research, this broad approach will provide multiple scenarios to produce a “vulnerability surface” for change in the metrics of peak flows (e.g. the 20-year flood flow). Some of the UKCP09 products have also been used to understand what these projections may mean for changes to peak flow. The catchment-based analysis will be used to generalise to other gauged sites across Britain, using relationships with catchment characteristics, providing the scientific evidence for the development of regional guidance on climate change allowances. Specifically the project is: Investigating the impact of climate change on peak river flows in over 150 catchments across Britain to assess the suitability of the FCDPAG3 20% climate change allowance. Investigating catchment response to climate change to identify potential similarities such that the FCDPAG3 nationwide allowance could be regionalised. Investigating the uncertainty in changes to future peak river flows from climate change. Developing an approach that has longevity beyond the project timeframe and the lifetime of the latest generation of climate model results

Detector Description and Performance for the First Coincidence Observations between LIGO and GEO

For 17 days in August and September 2002, the LIGO and GEO interferometer gravitational wave detectors were operated in coincidence to produce their first data for scientific analysis. Although the detectors were still far from their design sensitivity levels, the data can be used to place better upper limits on the flux of gravitational waves incident on the earth than previous direct measurements. This paper describes the instruments and the data in some detail, as a companion to analysis papers based on the first data.Comment: 41 pages, 9 figures 17 Sept 03: author list amended, minor editorial change

Stochastic thermodynamics of holonomic systems

International audienc

Tocilizumab in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Background: In this study, we aimed to evaluate the effects of tocilizumab in adult patients admitted to hospital with COVID-19 with both hypoxia and systemic inflammation. Methods: This randomised, controlled, open-label, platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing several possible treatments in patients hospitalised with COVID-19 in the UK. Those trial participants with hypoxia (oxygen saturation <92% on air or requiring oxygen therapy) and evidence of systemic inflammation (C-reactive protein ≥75 mg/L) were eligible for random assignment in a 1:1 ratio to usual standard of care alone versus usual standard of care plus tocilizumab at a dose of 400 mg–800 mg (depending on weight) given intravenously. A second dose could be given 12–24 h later if the patient's condition had not improved. The primary outcome was 28-day mortality, assessed in the intention-to-treat population. The trial is registered with ISRCTN (50189673) and ClinicalTrials.gov (NCT04381936). Findings: Between April 23, 2020, and Jan 24, 2021, 4116 adults of 21 550 patients enrolled into the RECOVERY trial were included in the assessment of tocilizumab, including 3385 (82%) patients receiving systemic corticosteroids. Overall, 621 (31%) of the 2022 patients allocated tocilizumab and 729 (35%) of the 2094 patients allocated to usual care died within 28 days (rate ratio 0·85; 95% CI 0·76–0·94; p=0·0028). Consistent results were seen in all prespecified subgroups of patients, including those receiving systemic corticosteroids. Patients allocated to tocilizumab were more likely to be discharged from hospital within 28 days (57% vs 50%; rate ratio 1·22; 1·12–1·33; p<0·0001). Among those not receiving invasive mechanical ventilation at baseline, patients allocated tocilizumab were less likely to reach the composite endpoint of invasive mechanical ventilation or death (35% vs 42%; risk ratio 0·84; 95% CI 0·77–0·92; p<0·0001). Interpretation: In hospitalised COVID-19 patients with hypoxia and systemic inflammation, tocilizumab improved survival and other clinical outcomes. These benefits were seen regardless of the amount of respiratory support and were additional to the benefits of systemic corticosteroids. Funding: UK Research and Innovation (Medical Research Council) and National Institute of Health Research

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

Background: Many patients with COVID-19 have been treated with plasma containing anti-SARS-CoV-2 antibodies. We aimed to evaluate the safety and efficacy of convalescent plasma therapy in patients admitted to hospital with COVID-19. Methods: This randomised, controlled, open-label, platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]) is assessing several possible treatments in patients hospitalised with COVID-19 in the UK. The trial is underway at 177 NHS hospitals from across the UK. Eligible and consenting patients were randomly assigned (1:1) to receive either usual care alone (usual care group) or usual care plus high-titre convalescent plasma (convalescent plasma group). The primary outcome was 28-day mortality, analysed on an intention-to-treat basis. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936. Findings: Between May 28, 2020, and Jan 15, 2021, 11558 (71%) of 16287 patients enrolled in RECOVERY were eligible to receive convalescent plasma and were assigned to either the convalescent plasma group or the usual care group. There was no significant difference in 28-day mortality between the two groups: 1399 (24%) of 5795 patients in the convalescent plasma group and 1408 (24%) of 5763 patients in the usual care group died within 28 days (rate ratio 1·00, 95% CI 0·93–1·07; p=0·95). The 28-day mortality rate ratio was similar in all prespecified subgroups of patients, including in those patients without detectable SARS-CoV-2 antibodies at randomisation. Allocation to convalescent plasma had no significant effect on the proportion of patients discharged from hospital within 28 days (3832 [66%] patients in the convalescent plasma group vs 3822 [66%] patients in the usual care group; rate ratio 0·99, 95% CI 0·94–1·03; p=0·57). Among those not on invasive mechanical ventilation at randomisation, there was no significant difference in the proportion of patients meeting the composite endpoint of progression to invasive mechanical ventilation or death (1568 [29%] of 5493 patients in the convalescent plasma group vs 1568 [29%] of 5448 patients in the usual care group; rate ratio 0·99, 95% CI 0·93–1·05; p=0·79). Interpretation: In patients hospitalised with COVID-19, high-titre convalescent plasma did not improve survival or other prespecified clinical outcomes. Funding: UK Research and Innovation (Medical Research Council) and National Institute of Health Research

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Regionalised impacts of climate change on flood flows: uncertainty analysis. Milestone report 5. Revised November 2009

This milestone report for project FD2020 ‘Regionalised impacts of climate change on flood flows’ describes the analysis undertaken to assess the potential level of uncertainty, due to various assumptions and simplifications necessary to develop the project’s ‘scenario-neutral’ approach to regionalisation. It relies on three previous project milestone reports, describing the hydrological models, the catchments modelled and the model calibration (Crooks et al. 2009), the development of the sensitivity framework (Prudhomme and Reynard 2009), and the identification of flood response types (Prudhomme et al. 2009a). The scenario-neutral approach required that the monthly changes in precipitation and temperature suggested by current Global Climate Models (GCMs) were distilled down into a ‘simple’ sensitivity framework, using single harmonic functions (i.e. annual sine-curves with a single peak and trough). These 4200 ‘scenarios’ (525 precipitation x 8 temperature / potential evaporation) were then applied to baseline catchment time-series using the delta change method of downscaling, and run through the catchment hydrological models. This resulted in the production of flood response patterns, representing the response of each catchment to the prescribed sets of changes in precipitation and temperature / potential evaporation in terms of the percentage change in flood peaks at four return periods. The main aim of the uncertainty analysis is to assess whether values obtained from the flood response patterns will consistently over- or under-estimate the impact of climate change scenarios. The uncertainty analysis thus addresses the following factors: 1. Assumptions made for sensitivity framework development; 2. Use of a fitted harmonic instead of monthly factors; 3. Use of the simple delta change method of downscaling; 4. Natural variability. Due to the number of factors investigated, the analysis is performed on a small subset of catchments, chosen to be as representative as possible of the nine flood response types found in Great Britain (described as ‘Damped-Extreme’, ‘Damped-High’, ‘Damped-Low’, ‘Neutral’, ‘Mixed’, ‘Enhanced-Low’, ‘Enhanced-Medium’, ‘Enhanced-High’ and ‘Sensitive’). There is one catchment modelled with the PDM hydrological model (at a daily time step) for each of the nine flood response types, for which the full uncertainty analysis is performed. In addition, there are four catchments modelled with the CLASSIC hydrological model (at a daily time step), representing four of the flood response types, for which a subset of the analysis is performed. The results show that the level of uncertainty from different factors varies significantly between catchments. For some catchments the overall level of uncertainty varies little with return period, whilst for others it increases / decreases with return period. The four CLASSIC catchments show a similar pattern of uncertainty to that for the corresponding PDM catchments. However, each of the CLASSIC catchments has a higher level of uncertainty than the PDM catchment of the same flood response type. This probably reflects the larger catchment area of the CLASSIC catchments. Generalising the catchment results to their flood response types suggests that ‘Neutral’ catchments will have the lowest level of uncertainty and ‘Sensitive’ catchments will have the highest level of uncertainty. The different levels of uncertainty for the different catchment types are compatible with the underlying climatological and hydrological differences between their flood response types. Despite the small number of catchments investigated here, the fact that the results are physically reasonable, and the similarity of the results for comparable PDM and CLASSIC example catchments, gives confidence in the extension of the results to catchment type. The next step is to develop guidance on what level of uncertainty to allow, according to flood response type and return period. The potential effect of catchment area on the level of uncertainty will also have to be borne-in-mind