Effects of temporal resolution of input precipitation on the performance of hydrological forecasting

Flood prediction systems rely on good quality precipitation input data and forecasts to drive hydrological models. Most precipitation data comes from daily stations with a good spatial coverage. However, some flood events occur on sub-daily time scales and flood prediction systems could benefit from using models calibrated on the same time scale. This study compares precipitation data aggregated from hourly stations (HP) and data disaggregated from daily stations (DP) with 6-hourly forecasts from ECMWF over the time period 1 October 2006–31 December 2009. The HP and DP data sets were then used to calibrate two hydrological models, LISFLOOD-RR and HBV, and the latter was used in a flood case study. The HP scored better than the DP when evaluated against the forecast for lead times up to 4 days. However, this was not translated in the same way to the hydrological modelling, where the models gave similar scores for simulated runoff with the two datasets. The flood forecasting study showed that both datasets gave similar hit rates whereas the HP data set gave much smaller false alarm rates (FAR). This indicates that using sub-daily precipitation in the calibration and initiation of hydrological models can improve flood forecasting

A global evaluation of multi-model ensemble tropical cyclone track probability forecasts

At the Met Office, dynamic ensemble forecasts from the Met Office Global and Regional Ensemble Prediction System (MOGREPS-G), the European Centre for Medium-Range Weather Forecasts Ensemble (ECMWF ENS) and National Centers for Environmental Prediction Global Ensemble Forecast System (NCEP GEFS) global ensemble forecast models are post-processed to identify and track tropical cyclones. The ensemble members from each model are also combined into a 108-member multi-model ensemble. Track probability forecasts are produced for named tropical cyclones showing the probability of a location being within 120km of a named tropical cyclone at any point in the next 7-days, and also broken down in to each 24-hour forecast period. This paper presents the verification of these named-storm track probabilities over a two-year period across all global tropical cyclone basins, and compares the results from basin to basin. The combined multi-model ensemble is found to increase the skill and value of the track probability forecasts over the best-performing individual ensemble (ECMWF ENS), for both overall 7-day track probability forecasts and 24-hour track probabilities. Basin-based and storm-based verification illustrates that the best performing individual ensemble can change from basin to basin and from storm to storm, but that the multi-model ensemble adds skill in every basin, and is also able to match the best performing individual ensemble in terms of overall probabilistic forecast skill in several high-profile case studies. This study helps to illustrate the potential value and skill to be gained if operational tropical cyclone forecasting can continue to migrate away from a deterministic-focused forecasting environment to one where the probabilistic situation-based uncertainty information provided by the dynamic multi-model ensembles can be incorporated into operational forecasts and warnings

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

Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System

Operational global-scale hydrological forecasting systems are used to help manage hydrological extremes such as floods and droughts. The vast amounts of raw data that underpin forecast systems and the ability to generate information on forecast skill have, until now, not been publicly available. As part of the Global Flood Awareness System (GloFAS; https://www.globalfloods.eu/, last access: 3 December 2022) service evolution, in this paper daily ensemble river discharge reforecasts and real-time forecast datasets are made free and openly available through the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). They include real-time forecast data starting on 1 January 2020 updated operationally every day and a 20-year set of reforecasts and associated metadata. This paper describes the model components and configuration used to generate the real-time river discharge forecasts and the reforecasts. An evaluation of ensemble forecast skill using the continuous ranked probability skill score (CRPSS) was also undertaken for river points around the globe. Results show that GloFAS is skilful in over 93 % of catchments in the short (1 to 3 d) and medium range (5 to 15 d) against a persistence benchmark forecast and skilful in over 80 % of catchments out to the extended range (16 to 30 d) against a climatological benchmark forecast. However, the strength of skill varies considerably by location with GloFAS found to have no or negative skill at longer lead times in broad hydroclimatic regions in tropical Africa, western coast of South America, and catchments dominated by snow and ice in high northern latitudes. Forecast skill is summarised as a new headline skill score available as a new layer on the GloFAS forecast Web Map Viewer to aid user interpretation and understanding of forecast quality.</p

Verification of heat stress thresholds for a health-based heatwave definition

Heatwaves represent a threat to human health and excess mortality is one of the associated negative effects. A health-based definition for heatwaves is therefore relevant, especially for early warning purposes, and it is here investigated via the Universal Thermal Climate Index (UTCI). The UTCI is a bioclimate index elaborated via an advanced model of human thermo-regulation that estimates the thermal stress induced by air temperature, wind speed, moisture and radiation on the human physiology. Using France as a testbed, the UTCI was computed from meteorological reanalysis data to assess the thermal stress conditions associated to heat-attributable excess mortality in five cities. UTCI values at different climatological percentiles were defined and evaluated in their ability to identify periods of excess mortality (PEMs) over 24 years. Using verification metrics such as the probability of detection (POD), the false alarm ratio (FAR) and the frequency bias (FB), daily minimum and maximum heat stress levels equal or above corresponding UTCI 95th percentiles (15±2°C and 34.5±1.5°C, respectively) for 3 consecutive days are demonstrated to correlate to PEMs with the highest sensitivity and specificity (0.69 ≤ POD ≤ 1, 0.19 ≤ FAR ≤ 0.46, 1 ≤ FB ≤ 1.48) than minimum, maximum and mean heat stress level singularly and other bioclimatological percentiles. This finding confirms the detrimental effect of prolonged, unusually high heat stress at day and night time and suggests the UTCI 95th percentile as a health meaningful threshold for a potential heat health watch warning system

HESS Opinions: On forecast (in)consistency in a hydro-meteorological chain: curse or blessing?

Flood forecasting increasingly relies on numerical weather prediction forecasts to achieve longer lead times. One of the key difficulties that is emerging in constructing a decision framework for these flood forecasts is what to dowhen consecutive forecasts are so different that they lead to different conclusions regarding the issuing of warnings or triggering other action. In this opinion paper we explore some of the issues surrounding such forecast inconsistency (also known as "Jumpiness", "Turning points", "Continuity" or number of "Swings"). In thsi opinion paper we define forecast inconsistency; discuss the reasons why forecasts might be inconsistent; how we should analyse inconsistency; and what we should do about it; how we should communicate it and whether it is a totally undesirable property. The property of consistency is increasingly emerging as a hot topic in many forecasting environments

The monetary benefit of early flood warnings in Europe

Effective disaster risk management relies on science-based solutions to close the gap between prevention and preparedness measures. The consultation on the United Nations post-2015 framework for disaster risk reduction highlights the need for cross-border early warning systems to strengthen the preparedness phases of disaster risk management, in order to save lives and property and reduce the overall impact of severe events. Continental and global scale flood forecasting systems provide vital early flood warning information to national and international civil protection authorities, who can use this information to make decisions on how to prepare for upcoming floods. Here the potential monetary benefits of early flood warnings are estimated based on the forecasts of the continental-scale European Flood Awareness System (EFAS) using existing flood damage cost information and calculations of potential avoided flood damages. The benefits are of the order of 400 Euro for every 1 Euro invested. A sensitivity analysis is performed in order to test the uncertainty in the method and develop an envelope of potential monetary benefits of EFAS warnings. The results provide clear evidence that there is likely a substantial monetary benefit in this cross-border continental-scale flood early warning system. This supports the wider drive to implement early warning systems at the continental or global scale to improve our resilience to natural hazards

Proposal for a new meteotsunami intensity index

Atmospherically generated coastal waves labelled as meteotsunami are known to cause destruction, injury, and fatality due to their rapid onset and unexpected nature. Unlike other coastal hazards such as tsunami, there exist no standardised means of quantifying this phenomenon, which is crucial to understand shoreline impacts and to enable researchers to establish a shared language and framework for meteotsunami analysis and comparison. In this study, we present a new five-level Lewis Meteotsunami Intensity Index (LMTI) trialled in the United Kingdom (UK) but designed for global applicability. A comprehensive dataset of meteotsunami events recorded in the UK was utilised, and the index's effectiveness was evaluated, with intensity level and spatial distribution of meteotsunami occurrence derived. Results revealed a predominant occurrence of Level 2 moderate intensity meteotsunami (69 %) in the UK, with distinct hotspots identified in south-western England and Scotland. Further trial implementation of the LMTI in a global capacity revealed its potential adaptability to other meteotsunami-prone regions, facilitating the comparison of events and promoting standardisation of assessment methodologies.</p

Reducing inconsistencies in point observations of maximum flood inundation level

Flood simulation models and hazard maps are only as good as the underlying data against which they are calibrated and tested. However, extreme flood events are by definition rare, so the observational data of flood inundation extent are limited in both quality and quantity. The relative importance of these observational uncertainties has increased now that computing power and accurate lidar scans make it possible to run high-resolution 2D models to simulate floods in urban areas. However, the value of these simulations is limited by the uncertainty in the true extent of the flood. This paper addresses that challenge by analyzing a point dataset of maximum water extent from a flood event on the River Eden at Carlisle, United Kingdom, in January 2005. The observation dataset is based on a collection of wrack and water marks from two postevent surveys. A smoothing algorithm for identifying, quantifying, and reducing localized inconsistencies in the dataset is proposed and evaluated showing positive results. The proposed smoothing algorithm can be applied in order to improve flood inundation modeling assessment and the determination of risk zones on the floodplain