63 research outputs found
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Cartograms for use in forecasting weather driven natural hazards
This study evaluates the potential of using cartograms to visualise, and aid interpretation of, forecasts of weather driven natural hazards in the context of global weather forecasting and early warning systems. The use of cartograms is intended to supplement traditional cartographic representations of the hazards in order to highlight the severity of an upcoming event. Cartogrammetric transformations are applied to forecasts of floods, heatwaves, windstorms and snowstorms taken from the European Centre for Medium-range Weather Forecasts (ECMWF) forecast archive. Key cartogram design principles of importance in standard weather forecast visualisation are tested in terms of the tasks needed to visualise and interpret the forecast maps. These design principles include the influence of spatial autocorrelation of the variable mapped, the minimum and maximum values of a variable, the value of the sea, the addition of geographic features and the geographic extent used. Results show that the utility of the cartograms is dependent on these design principles, but the optimal cartogram transformation is dependent on geographical features (such as coastlines) and forecast features (such as snowstorm intensity). The importance of forecaster familiarisation training is highlighted. It was found in particular that for highly spatially autocorrelated weather variables used in analysing several upcoming natural hazards such as 2m temperature anomaly, the visualisation of the distortion provides a promising addition to standard forecast visualisations for highlighting upcoming weather driven natural hazards
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Evaluation of the consistency of ECMWF ensemble forecasts
An expected benefit of ensemble forecasts is that a sequence of consecutive forecasts valid for the same time will be more consistent than an equivalent sequence of individual forecasts. Inconsistent (jumpy) forecasts can cause users to lose confidence in the forecasting system. We present a first systematic, objective evaluation of the consistency of the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble using a measure of forecast divergence that takes account of the full ensemble distribution. Focusing on forecasts of the North Atlantic Oscillation and European Blocking regimes up to two weeks ahead, we identify occasional large inconsistency between successive runs, with the largest jumps tending to occur at 7-9 days lead. However, care is needed in the interpretation of ensemble jumpiness. An apparent clear flip-flop in a single index may hide a more complex predictability issue which may be better understood by examining the ensemble evolution in phase space
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What is the most useful approach for forecasting hydrological extremes during El Niño?
In the past, efforts to prepare for the impacts of El Niño-driven flood and drought hazards have often relied on seasonal precipitation forecasts as a proxy for hydrological extremes, due to a lack of hydrologically relevant information. However, precipitation forecasts are not the best indicator of hydrological extremes. Now, two different global scale hydro-meteorological approaches for predicting river flow extremes are available to support flood and drought preparedness. These approaches are statistical forecasts based on large-scale climate variability and teleconnections, and resource-intensive dynamical forecasts using coupled ocean-atmosphere general circulation models. Both have the potential to provide early warning information, and both are used to prepare for El Niño impacts, but which approach provides the most useful forecasts?
This study uses river flow observations to assess and compare the ability of two recently-developed forecasts to predict high and low river flow during El Niño: statistical historical probabilities of ENSO-driven hydrological extremes, and the dynamical seasonal river flow outlook of the Global Flood Awareness System (GloFAS-Seasonal). Our findings highlight regions of the globe where each forecast is (or is not) skilful compared to a forecast of climatology, and the advantages and disadvantages of each forecasting approach. We conclude that in regions where extreme river flow is predominantly driven by El Niño, or in regions where GloFAS-Seasonal currently lacks skill, the historical probabilities generally provide a more useful forecast. In areas where other teleconnections also impact river flow, with the effect of strengthening, mitigating or even reversing the influence of El Niño, GloFAS-Seasonal forecasts are typically more useful
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Robust algorithm for detecting floodwater in urban areas using Synthetic Aperture Radar images
Flooding is a major hazard in both rural and urban areas worldwide, but it is in urban areas that the impacts are most severe. High resolution Synthetic Aperture Radar (SAR) sensors are able to detect flood extents in urban areas during both day- and night-time. If obtained in near real-time, these flood extents can be used for emergency flood relief management or as observations for assimilation into flood forecasting models. In this paper a method for detecting flooding in urban areas using near real-time SAR data is developed and extensively tested under a variety of scenarios involving different flood events and different images. The method uses a SAR simulator in conjunction with LiDAR data of the urban area to predict areas of radar shadow and layover in the image caused by buildings and taller vegetation. Of the urban water pixels visible to the SAR, the flood detection accuracy averaged over the test examples was 83%, with a false alarm rate of 9%. The results indicate that flooding can be detected in the urban area to reasonable accuracy, but that this accuracy is limited partly by the SAR’s poor visibility of the urban ground surface due to shadow and layover
Influence of ENSO and tropical Atlantic climate variability on flood characteristics in the Amazon basin
Flooding in the Amazon basin is frequently attributed to modes of large-scale climate variability, but little attention is paid to how these modes influence the timing and duration of floods despite their importance to early warning systems and the significant impacts that these flood characteristics can have on communities. In this study, river discharge data from the Global Flood Awareness System (GloFAS 2.1) and observed data at 58 gauging stations are used to examine whether positive or negative phases of several Pacific and Atlantic indices significantly alter the characteristics of river flows throughout the Amazon basin (1979–2015). Results show significant changes in both flood magnitude and duration, particularly in the north-eastern Amazon for negative El Niño–Southern Oscillation (ENSO) phases when the sea surface temperature (SST) anomaly is positioned in the central tropical Pacific. This response is not identified for the eastern Pacific index, highlighting how the response can differ between ENSO types. Although flood magnitude and duration were found to be highly correlated, the impacts of large-scale climate variability on these characteristics are non-linear; some increases in annual flood maxima coincide with decreases in flood duration. The impact of flood timing, however, does not follow any notable pattern for all indices analysed. Finally, observed and simulated changes are found to be much more highly correlated for negative ENSO phases compared to the positive phase, meaning that GloFAS struggles to accurately simulate the differences in flood characteristics between El Niño and neutral years. These results have important implications for both the social and physical sectors working towards the improvement of early warning action systems for floods.Campus Lima Centr
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Mapping combined wildfire and heat stress hazards to improve evidence-based decision making
Heat stress and forest fires are often considered highly correlated hazards as extreme temperatures play a key role in both occurrences. This commonality can influence how civil protection and local responders deploy resources on the ground and could lead to an underestimation of potential impacts, as people could be less resilient when exposed to multiple hazards. In this work, we provide a simple methodology to identify areas prone to concurrent hazards, exemplified with, but not limited to, heat stress and fire danger. We use the combined heat and forest fire event that affected Europe in June 2017 to demonstrate that the methodology can be used for analysing past events as well as making predictions, by using reanalysis and medium-range weather forecasts, respectively. We present new spatial layers that map the combined danger and make
suggestions on how these could be used in the context of a Multi-Hazard Early Warning System. These products could be particularly valuable in disaster risk reduction and emergency response management, particularly for civil protection, humanitarian agencies and other first responders whose role is to identify priorities during pre-interventions and emergencies
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Assimilating high resolution remotely sensed soil moisture into a distributed hydrologic model to improve runoff prediction
The susceptibility of a catchment to flooding during an extreme rainfall event is affected by its soil moisture condition prior to the event. A study to improve the state vector of a distributed hydrologic model by assimilating high resolution remotely sensed soil moisture is described. The launch of Sentinel-1 has stimulated interest in measuring soil moisture at high resolution suitable for hydrological studies using Synthetic Aperture Radars (SARs). The advantages of using SAR soil moisture in conjunction with land cover data are considered. These include the ability to reduce contamination of the surface soil signal due to vegetation, radar artefacts, mixed pixels and land cover classes not providing meaningful soil moistures. Results for 2008 using ASAR data showed
that the assimilation of ASAR soil moisture values improved the predicted flows for all images. The improvement was less marked for 2007, probably because the antecedent soil moisture conditions were of reduced importance during the extreme flooding that occurred then. Particularly for 2008, the higher resolution of ASAR data improved predicted flows compared to low resolution ASCAT data that were not disaggregated and limited to the temporal frequency of ASAR. The method is likely to give better results with Sentinel-1 rather than ASAR data due to its higher temporal resolution
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Floodwater detection in urban areas using Sentinel-1 and WorldDEM data
Remote sensing using Synthetic Aperture Radar (SAR) is an important tool for emergency flood incident management. At present operational services are mainly aimed at flood mapping in rural areas, as mapping in urban areas is hampered by the complicated backscattering mechanisms occurring there. A method for detecting flooding at high resolution in urban areas that may contain dense housing is presented. This largely uses remotely sensed data sets that are readily available on a global basis, including open-access Sentinel-1 SAR data, the WorldDEM Digital Surface Model (DSM), and open-access World Settlement Footprint data to identify urban areas. The method is a change detection technique that estimates flood levels in urban areas locally. It searches for increased SAR backscatter in the post-flood image due to double scattering between water (rather than unflooded ground) and adjacent buildings, and reduced SAR backscatter in areas away from high slopes. Areas of urban flooding are detected by comparing an interpolated flood level surface to the DSM. The method was tested on two flood events that occurred in the UK during the storms of Winter 2019-20. High urban flood detection accuracies were achieved for the event in moderate density housing. The accuracy reduced for the event in dense housing, when street widths became comparable to the DSM resolution, though would still be useful for incident management. The method has potential for operational use for detecting urban flooding in near real-time on a global basis
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The fate of the Caspian Sea under projected climate change and water extraction during the 21st century
The Caspian Sea (CS) delivers considerable ecosystem services to millions of people. It experienced water level variations of 3 m during the 20th century alone. Robust scenarios of future CS level are vital to inform environmental risk management and water-use planning. In this study we investigated the water budget variation in the CS drainage basin and its potential impact on CS level during the 21st century using projected climate from selected climate change scenarios of shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs), and explored the impact of human extractions. We show that the size of the CS prescribed in climate models determines the modelled water budgets for both historical and future projections. Most future projections show drying over the 21st century. The moisture deficits are more pronounced for extreme radiative forcing scenarios (RCP8.5/SSP585) and for models where a larger CS is prescribed. By 2100, up to 8 (10) m decrease in CS level is found using RCP4.5 (RCP8.5) models, and up to 20 (30) m for SSP245 (SSP585) scenario models. Water extraction rates are as important as climate in controlling future CS level, with potentially up to 7 m further decline, leading to desiccation of the shallow northern CS. This will have wide-ranging implications for the livelihoods of the surrounding communities; increasing vulnerability to freshwater scarcity, transforming ecosystems, as well as impacting the climate system. Caution should be exercised when using individual models to inform policy as projected CS level is so variable between models. We identify that many climate models either ignore, or do not properly prescribe, CS area. No future climate projections include any changes in CS surface area, even when the catchment is projected to be considerably drier. Coupling between atmosphere and lakes within climate models would be a significant advance to capture crucial two-way feedbacks
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Skilful seasonal forecasts of streamflow over Europe?
This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the Ensemble Streamflow Prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to seven months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40% of Europe. Patterns in the EFAS seasonal streamflow hindcasts skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim to improve climate-model based seasonal streamflow forecasting
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