Do food banks help? Food insecurity in the UK.

Belgium Herbarium image of Meise Botanic Garden

What is going wrong with community engagement? How flood communities and flood authorities construct engagement and partnership working

In this paper, we discuss the need for flood risk management in England that engages stakeholders with flooding and its management processes, including knowledge gathering, planning and decision-making. By comparing and contrasting how flood communities experience ‘community engagement’ and ‘partnership working’, through the medium of an online questionnaire, with the process’s and ways of working that the Environment Agency use when ‘working with others’, we demonstrate that flood risk management is caught up in technocratic ways of working derived from long-standing historical practices of defending agricultural land from water. Despite the desire to move towards more democratised ways of working which enable an integrated approach to managing flood risk, the technocratic framing still pervades contemporary flood risk management. We establish that this can disconnect society from flooding and negatively impacts the implementation of more participatory approaches designed to engage flood communities in partnership working. Through the research in this paper it becomes clear that adopting a stepwise, one-size-fits-all approach to engagement fails to recognise that communities are heterogenous and that good engagement requires gaining an understanding of the social dimensions of a community. Successful engagement takes time, effort and the establishment of trust and utilises social learning and pooling of knowledge to create a better understanding of flooding, and that this can lead to increasing societal connectivity to flooding and its impacts

Trends in the GloFAS-ERA5 river discharge reanalysis

The main objective of this study is to analyse the GloFAS-ERA5 river discharge reanalysis for any noticeable change (including gradual trends or discontinuities) in the annual mean time series across the 1979-2018 (40-year) period, and to evaluate how realistic these are compared with available observed river discharge time series. These variabilities are quantified by linear regression in order to highlight any concerning features in the GloFAS-ERA5 time series. This work is particularly important for GloFAS, as large trends, discontinuities or other similar features could have a major consequence on the GloFAS flood thresholds in around 50% of catchments, which are based on GloFAS-ERA5, and thus subsequently on the issuing of flood warnings. In addition, this study also contributes to the understanding of the water cycle variable behaviour in ERA5 (driver of GloFAS-ERA5) and ERA5-Land (higher resolution land reanalysis forced by ERA5, produced offline) by exploring the linear trends in river discharge and related hydrological variables. In exploring the stability of the time series in ERA5, we seek to trigger potential further discussions and research studies, which subsequently should help with the planning and development for the next generation ECMWF reanalysis, ERA6

The impact of uncertain precipitation data on insurance loss estimates using a flood catastrophe model

Catastrophe risk models used by the insurance industry are likely subject to significant uncertainty, but due to their proprietary nature and strict licensing conditions they are not available for experimentation. In addition, even if such experiments were conducted, these would not be repeatable by other researchers because commercial confidentiality issues prevent the details of proprietary catastrophe model structures from being described in public domain documents. However, such experimentation is urgently required to improve decision making in both insurance and reinsurance markets. In this paper we therefore construct our own catastrophe risk model for flooding in Dublin, Ireland, in order to assess the impact of typical precipitation data uncertainty on loss predictions. As we consider only a city region rather than a whole territory and have access to detailed data and computing resources typically unavailable to industry modellers, our model is significantly more detailed than most commercial products. The model consists of four components, a stochastic rainfall module, a hydrological and hydraulic flood hazard module, a vulnerability module, and a financial loss module. Using these we undertake a series of simulations to test the impact of driving the stochastic event generator with four different rainfall data sets: ground gauge data, gauge-corrected rainfall radar, meteorological reanalysis data (European Centre for Medium-Range Weather Forecasts Reanalysis-Interim; ERA-Interim) and a satellite rainfall product (The Climate Prediction Center morphing method; CMORPH). Catastrophe models are unusual because they use the upper three components of the modelling chain to generate a large synthetic database of unobserved and severe loss-driving events for which estimated losses are calculated. We find the loss estimates to be more sensitive to uncertainties propagated from the driving precipitation data sets than to other uncertainties in the hazard and vulnerability modules, suggesting that the range of uncertainty within catastrophe model structures may be greater than commonly believed

Quantifying co-benefits and disbenefits of Nature-based Solutions targeting Disaster Risk Reduction

Nature-based Solutions function (NBS) as an umbrella concept for ecosystem-based approaches that are an alternative to traditional engineering solutions for Disaster Risk Reduction. Their rising popularity is explained partly by their entailing additional benefits (so-called co-benefits) for the environment, society, and economy. The few existing frameworks for assessing cobenefits are lacking guidance on co-benefit pre-assessment that is required for the NBS selection and permission process. Going beyond these, this paper develops a comprehensive guidance on quantitative pre-assessment of potential co-benefits and disbenefits of NBS tackling Disaster Risk Reduction. It builds on methods and frameworks from existing NBS literature and related disciplines. Furthermore, this paper discusses the evaluation of the quantified results of the pre-assessment. In particular, the evaluation focuses on the significance of change of the estimated co-benefits and dis-benefits as well as the sustainability of the NBS. This paper will support decision-making in planning processes on suitability and sustainability of Nature-based Solutions and assist in the preparation of Environmental Impact Assessments of projects

How do I know if my forecasts are better? Using benchmarks in Hydrological ensemble prediction

The skill of a forecast can be assessed by comparing the relative proximity of both the forecast and a benchmark to the observations. Example benchmarks include climatology or a naïve forecast. Hydrological ensemble prediction systems (HEPS) are currently transforming the hydrological forecasting environment but in this new field there is little information to guide researchers and operational forecasters on how benchmarks can be best used to evaluate their probabilistic forecasts. In this study, it is identified that the forecast skill calculated can vary depending on the benchmark selected and that the selection of a benchmark for determining forecasting system skill is sensitive to a number of hydrological and system factors. A benchmark intercomparison experiment is then undertaken using the continuous ranked probability score (CRPS), a reference forecasting system and a suite of 23 different methods to derive benchmarks. The benchmarks are assessed within the operational set-up of the European Flood Awareness System (EFAS) to determine those that are ‘toughest to beat’ and so give the most robust discrimination of forecast skill, particularly for the spatial average fields that EFAS relies upon. Evaluating against an observed discharge proxy the benchmark that has most utility for EFAS and avoids the most naïve skill across different hydrological situations is found to be meteorological persistency. This benchmark uses the latest meteorological observations of precipitation and temperature to drive the hydrological model. Hydrological long term average benchmarks, which are currently used in EFAS, are very easily beaten by the forecasting system and the use of these produces much naïve skill. When decomposed into seasons, the advanced meteorological benchmarks, which make use of meteorological observations from the past 20 years at the same calendar date, have the most skill discrimination. They are also good at discriminating skill in low flows and for all catchment sizes. Simpler meteorological benchmarks are particularly useful for high flows. Recommendations for EFAS are to move to routine use of meteorological persistency, an advanced meteorological benchmark and a simple meteorological benchmark in order to provide a robust evaluation of forecast skill. This work provides the first comprehensive evidence on how benchmarks can be used in evaluation of skill in probabilistic hydrological forecasts and which benchmarks are most useful for skill discrimination and avoidance of naïve skill in a large scale HEPS. It is recommended that all HEPS use the evidence and methodology provided here to evaluate which benchmarks to employ; so forecasters can have trust in their skill evaluation and will have confidence that their forecasts are indeed better

How do I know if I’ve improved my continental scale flood early warning system?

Flood early warning systems mitigate damages and loss of life and are an economically efficient way of enhancing disaster resilience. The use of continental scale flood early warning systems is rapidly growing. The European Flood Awareness System (EFAS) is a pan-European flood early warning system forced by a multi-model ensemble of numerical weather predictions. Responses to scientific and technical changes can be complex in these computationally expensive continental scale systems, and improvements need to be tested by evaluating runs of the whole system. It is demonstrated here that forecast skill is not correlated with the value of warnings. In order to tell if the system has been improved an evaluation strategy is required that considers both forecast skill and warning value. The combination of a multi-forcing ensemble of EFAS flood forecasts is evaluated with a new skill-value strategy. The full multi-forcing ensemble is recommended for operational forecasting, but, there are spatial variations in the optimal forecast combination. Results indicate that optimizing forecasts based on value rather than skill alters the optimal forcing combination and the forecast performance. Also indicated is that model diversity and ensemble size are both important in achieving best overall performance. The use of several evaluation measures that consider both skill and value is strongly recommended when considering improvements to early warning systems

Migrant care workers at the intersection of rural belonging in small English communities

Shortage of staff in the private care sector brought migrant participants of this study to rural communities in northwest England. The care workers, fourteen highly skilled first-generation migrants, described experiences of feeling unsettled, despite residing in these communities for an average of nine years. Social divisions, such as their race, ethnicity, and gender, intersected in rural England to create an overwhelming, at times, feeling of being othered. We use intersectionality as a framework to examine the advantageous and disadvantageous positionings of migrant workers, alongside their strategies of resistance and adaptation, filling in the gaps that acculturation theory glosses over