Changing channels

Scientists agree: flood damages will increase dramatically across Europe over the coming decades. And as extreme rainfall events happen again and again, some places will be hit harder than others. So why is it that some areas are becoming more prone to flooding, and others not? Louise Slater explains why collecting data on river flows is so important in our efforts to find out

Deploying teaching assistants to support learning: from models to typologies

The deployment of Teaching Assistants (TAs) to support learning has been the subject of much critical debate, including the particular concern that TAs too often becomes a less skilled replacement for the teacher rather than acting as an additional source of support. Despite efforts to encapsulate the TAs contribution to learning within specific models of deployment, wide variations in practices make the role and its contribution to learning difficult to define. Drawing on data gathered in four secondary schools in England, this paper explores TA deployment practices through six typologies: The Island; The Container; The Separate Entity; The Conduit for Learning; The Partner and The Expert. Illustrated graphically, these bring key elements together in a more contextualised and dynamic way. The paper concludes that the spatial and relational dimensions of deployment warrant more nuanced treatment and that more emphasis on partnership and mutuality and rather less on difference and hierarchy might be productiv

Regulation of Human Rhinovirus Induced Type I Interferon-beta, Type III Interferon-lambda and Pro-Inflammatory Cytokine Gene Expression in Normal Human Bronchial Epithelial Cells

Asthma is an economically important disease, with exacerbations causing significant morbidity and morality. Viral infections cause ~80% of asthma exacerbations; the majority of which are attributed to rhinovirus infection. How rhinovirus infection leads to an acute asthma exacerbation is incompletely understood. The up-regulation of proinflammatory cytokines/chemokines from rhinovirus infected bronchial epithelial cells and an impaired ability of rhinovirus infected asthmatic bronchial epithelial cells to produce type I interferon-β and type III interferon-λs, are believed to contribute. This study aimed to investigate differences in the signalling requirements of rhinovirusinduced pro-inflammatory cytokines/chemokines from those of IFN-β/IFN-λ. The viral pattern recognition receptors, signalling intermediates and transcription factors required by rhinovirus to induce pro-inflammatory cytokine/chemokine and IFN-β/IFN-λ expression were explored using short interfering RNA, constitutive activation/overexpression of signalling molecules and IFN-β promoter reporter mapping experiments. The viral pattern recognition receptors RIG-I, MDA5 and TLR3 were required for rhinovirus-induced pro-inflammatory cytokines IL-8/CXCL8, ENA-78/CXCL5, IL-6 RANTES/CCL5, IP-10/CXCL10 and IFN-β expression. Only MDA5 and TLR3 are required for rhinovirus-induced IFN-λ expression. Whilst having common signalling intermediates, the adaptor protein TRAF6, the kinases JNK2 and PI3Kα and the transcription factor NF-kB p65 were not required for rhinovirus-induced IFN-β/λ expression, but were required for rhinovirus induction of some/all of the proinflammatory cytokines measured. IRF3 was the only transcription factor identified to be commonly required for rhinovirus-induced expression of IFN-β and IFNλ-s. These findings support the hypothesis that the induction of IFN-β/λ and pro-inflammatory cytokines/chemokines, by rhinovirus, requires one or more distinct signalling molecules, and/or transcription factors. TRAF6, JNK2, PI3Kα and NF-kB p65 are potential novel therapeutic targets for rhinovirus-induced asthma exacerbations, the inhibition of which may suppress the detrimental actions of pro-inflammatory cytokines/chemokines without inhibiting IFN-β/λ production in asthmatic bronchial epithelial cells

Evaluating the drivers of seasonal streamflow in the U.S. Midwest

Streamflows have increased notably across the U.S. Midwest over the past century, fueling a debate on the relative influences of changes in precipitation and land cover on the flow distribution. Here we propose a simple modeling framework to evaluate the main drivers of streamflow rates. Streamflow records from 290 long-term USGS stream gauges were modeled using five predictors: precipitation, antecedent wetness, temperature, agriculture, and population density. We evaluated which predictor combinations performed best for every site, season and streamflow quantile. The goodness-of-fit of our models is generally high and varies by season (higher in the spring and summer than in the fall and winter), by streamflow quantile (best for high flows in the spring and winter, for low flows in the fall, and good for all flow quantiles in summer), and by region (better in the southeastern Midwest than in the northwestern Midwest). In terms of predictors, we find that precipitation variability is key for modeling high flows, while antecedent wetness is a crucial secondary driver for low and median flows. Temperature improves model fits considerably in areas and seasons with notable snowmelt or evapotranspiration. Last, in agricultural and urban basins, harvested acreage and population density are important predictors of changing streamflow, and their influence varies seasonally. Thus, any projected changes in these drivers are likely to have notable effects on future streamflow distributions, with potential implications for basin water management, agriculture, and flood risk management

Climatology of flooding in the United States

Flood losses in the United States have increased dramatically over the course of the past century, averaging US$7.96 billion in damages per year for the 30-year period ranging from 1985 to 2014. In terms of human fatalities, floods are the second largest weather-related hazard in the United States, causing an average of 82 deaths per year between 1986 and 2015. Given the wide-reaching impacts of flooding across the United States, the evaluation of flood-generating mechanisms and of the drivers of changing flood hazard are two areas of active research. Flood events can be driven by a variety of physical mechanisms, including rain and snowmelt, frontal systems, monsoons, intense tropical cyclones, and more generic cyclonic storms. However, flood frequency analysis has traditionally been based on statistical analyses of the observed flood distributions that rarely distinguish among these physical flood-generating processes. In reality, flood frequency distributions are often characterized by ‘mixed populations’ arising from multiple flood-generating mechanisms, which can be challenging to disentangle. Temporal changes in the frequency and magnitude of flooding have also been the subject of a large body of work in recent decades. The science has moved from a focus on the detection of trends and shifts in flood peak distributions towards the attribution of these changes, with particular emphasis on climatic and anthropogenic factors, including urbanisation and changes in agricultural practices. A better understanding of these temporal changes in flood peak distributions, as well as of the physical flood-generating mechanisms, will enable us to move forward with the estimation of future flood design values in the context of both climatic and anthropogenic change

Examination of changes in annual maximum gage height in the continental United States using quantile regression

This study focuses on the detection of temporal changes in annual maximum gage height (GH) across the continental United States and their relationship to changes in short- and long-term precipitation. Analyses are based on 1805 U.S. Geological Survey records over the 1985-2015 period and are performed using quantile regression. Trends were significant only at a limited number of sites, with a higher number of detections at the tails of the distribution. Overall, we found only weak evidence that the annual maximum GH records have been changing over the continental United States during the past 30 years, possibly due to a weak signal of change, large variability, and limited record length. In addition to trend detection, we also assessed to what extent these changes can be attributed to storm total rainfall and long-term precipitation. Our findings indicate that temporal changes in GH maxima are largely driven by storm total rainfall across large areas of the continental United States (east of the 100th meridian, U.S. West Coast). Long-term precipitation accumulation, on the other hand, is a strong flood predictor in regions where snowmelt is an important flood generating mechanism (e.g., northern Great Plains, Rocky Mountains), and is overall a relatively less important predictor of extreme flood events

Enhancing the predictability of seasonal streamflow with a statistical-dynamical approach

Seasonal streamflow forecasts facilitate water allocation, reservoir operation, flood risk management, and crop forecasting. They are generally computed by forcing hydrological models with outputs from general circulation models (GCMs) or using large-scale climate indices as predictors in statistical models. In contrast, hybrid statistical-dynamical forecasts (combining statistical methods with dynamical climate predictions) are still uncommon and their skill is largely unknown. Here, we conduct systematic forecasting of seasonal streamflow using eight GCMs from the North-American Multi-Model Ensemble, 0.5-9.5 months ahead, at 290 streamgauges in the U.S. Midwest. Probabilistic forecasts are developed for low to high streamflow using predictors that reflect climatic and anthropogenic influences. Results indicate that GCM forecasts of climate and antecedent climatic conditions enhance seasonal streamflow predictability; while land cover and population density predictors decrease biases or enhance skill in certain catchments. This paper paves the way for novel forecasting approaches using dynamical GCM predictions within statistical frameworks

On the impact of gaps on trend detection in extreme streamflow time series

Streamflow time series often contain gaps of varying length and location. However, the influence of these gaps on trend detection is poorly understood and cannot be estimated a priori in trend-detection studies. We simulated the effects of varying gap size (1, 2, 5, and 10 years) and location (one quarter, one third, and half of the way) on the detection rate of significant monotonic trends in annual maxima and peaks-over-threshold, based on the most commonly-used trend tests in time series of varying length (from 15 to 150 years) and trend magnitude (β1). Results show that, in comparison with the complete time series, the loss in trend detection rate tends to grow with (i) increasing gap size, (ii) increasing gap distance from the middle of the time series, (iii) decreasing β1 slope, and (iv) decreasing time series length. Based on these findings, we provide objective recommendations and cautionary remarks for maximal gap allowance in trend detection in extreme streamflow time series

Measuring the changing pulse of rivers

River flood risks are expected to rise as climate change intensifies the global hydrological cycle and more people live in floodplains (1). Changing risk may be revealed by trends in flood frequency, magnitude, or seasonality, as well as by shifts in the mechanisms that generate inundations (2). However, detection and attribution of climate signals in flood records is often hampered by brief, incomplete, or poor-quality flood data (3). Additionally, it can be difficult to disentangle the effects of changing climate, land cover, channel morphology, and human activities (2, 4). On page 588 of this issue, Blöschl et al. (5) overcome these problems through a consistent pan-European assessment of observed flood seasonality trends between 1960 and 2010. They thus provide the first evaluation of how climatic changes are influencing flood regimes at the continental scale

Recent trends in U.S. flood risk

Flooding is projected to become more frequent as warming temperatures amplify the atmosphere’s water holding capacity and increase the occurrence of extreme precipitation events. However, there is still little evidence of regional changes in flood risk across the USA. Here, we present a novel approach assessing the trends in inundation frequency above the National Weather Service’s four flood level categories in 2,042 catchments. Results reveal stark regional patterns of changing flood risk that are broadly consistent above the four flood categories. We show that these patterns are dependent on the overall wetness and potential water storage, with fundamental implications for water resources management, agriculture, insurance, navigation, ecology, and populations living in flood-affected areas. Our findings may assist in a better communication of changing flood patterns to a wider audience compared with the more traditional approach of stating trends in terms of discharge magnitudes and frequencies