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Flood Forecasting — A National Overview for Great Britain
Abstract
Great Britain has witnessed a new era in flood forecasting capabilities over the past decade. The severe flooding during the summer of 2007 and the subsequent Pitt Review have driven changes to organizational arrangements in flood forecasting, with much closer collaboration between meteorologists and hydrologists through dedicated flood forecasting centers.
... In the United Kingdom, this type of rainfall typically occurs in the summer months but can occur at any time of the year (Blenkinsop, Lewis, Chan, & Fowler, 2016;Hand, Fox, & Collier, 2004). Flash floods resulting from rapidly developing convection systems or alignment of storm cells can be particularly dangerous (Pilling, Dodds, Cranston, Price, & How, 2016) and affect large numbers of people if they occur over urban areas. For example, in England, there are more properties at risk of surface water flooding than river and coastal flooding combined (Environment Agency, 2009). ...
... In the United Kingdom, the first system for surface water flooding was the extreme rainfall alert (ERA) system introduced by the UK Met Office and the Environment Agency in 2009 (Hurford, Priest, Parker, & Lumbroso, 2012;Priest et al., 2011). The service was based on the likelihood of exceeding depth-duration thresholds for a 30-year return period event, but it did not consider surface-subsurface processes or vulnerability (Pilling et al., 2016). In 2010, the Flood Forecasting Centre (FFC) launched the Surface Water Flooding Decision Support Tool (SWFDST) for England and Wales. ...
... In 2010, the Flood Forecasting Centre (FFC) launched the Surface Water Flooding Decision Support Tool (SWFDST) for England and Wales. Details of the SWFDST are given by Pilling et al. (2016) and Ochoa-Rodríguez, Wang, Thraves, Johnston, F I G U R E 3 Schematic of a local-observed empirical-based system for surface water flood forecasting F I G U R E 4 Regional ensemble forecast empirical-based system for surface water flood forecasting and Onof (2018). The SWFDST is a more targeted system than the ERA as it takes account of urbanization and antecedent conditions. ...
Surface water (or pluvial) flooding is caused by intense rainfall before it enters rivers or drainage systems. As the climate changes and urban populations grow, the number of people around the world at risk of surface water flooding is increasing. Although it may not be possible to prevent such flooding, reliable and timely flood forecasts can help improve preparedness and recovery. Unlike riverine and coastal flooding where forecasting methods are well established, surface water flood forecasting presents a unique challenge due to the high uncertainties around predicting the location, timing, and impact of what are typically localized events. Over the past 5 years, there has been rapid development of convection‐permitting numerical weather prediction models, ensemble forecasting, and computational ability. It is now theoretically feasible to develop operational surface water forecasting systems. This paper identifies three approaches to surface water forecasting utilizing state‐of‐the‐art meteorological forecasts: empirical‐based scenarios, hydrological forecasts linked to presimulated impact scenarios, and real‐time hydrodynamic simulation. Reviewing operational examples of each approach provides an opportunity to learn from international best practice to develop targeted, impact‐based, surface water forecasts to support informed decision‐making. Although the emergence of new meteorological and hydrological forecasting capabilities is promising, there remains a scientific limit to the predictability of convective rainfall. To overcome this challenge, we suggest that a rethink of the established role of flood forecasting is needed, alongside the development of interdisciplinary solutions for communicating uncertainty and making the best use of all available data to increase preparedness.
This article is categorized under:
• Engineering Water > Engineering Water
Abstract
Recent improvements in forecasting intense rainfall mean it is now possible to forecast surface water flooding. However, operational practices need to adapt to deal with short lead times and high uncertainty in decision‐making.
... Surface water flooding presents a challenge for forecasters as events are often very localised, develop quickly and only last for short periods of time. Pilling et al. (2016) discussed the key future challenges for the flood forecasting community in Britain, with the most dangerous floods including those that result from rapidly developing convective systems or the organisation or alignment of storm cells that may result in flash flooding over large urban areas. This risk in Scotland is particularly high with over 100,000 properties identified as being at risk in the SEPA National Flood Risk Assessment. ...
... Surface water forecasting presents a unique challenge in Scotland due to the high uncertainties around predicting the location and timing of events. Pilling et al. (2016) discussed the key future challenges for the flood forecasting community in Britain, with the most dangerous floods including those that result from rapidly developing convection systems or the organisation or alignment of storm cells that may result in flash flooding over large urban areas. The risk in Scotland is particularly high with over 100,000 properties identified at risk from surface water flooding. ...
... In the UK, there is a long history of using rainfall threshold systems for surface water flooding starting with the Extreme Rainfall Alert (ERA) system introduced by the Met Office and the Environment Agency in 2009. The service was based on the likelihood of exceeding depth-duration thresholds for a 30-year return period event, but it took no account of surface-subsurface processes or vulnerability (Pilling et al., 2016). ...
In 2019, the Scottish Environment Protection Agency (SEPA) commissioned RAB in partnership with the University of Strathclyde to conduct a review of the current state of the science relevant for surface water flood forecasting in Scotland, specifically looking at both precipitation observations and forecasts, and hydrological and hydraulic modelling applications. This is the final report from this review and has been presented to inform SEPA of the best practice options available to improve surface water flood forecasting in Scotland.
... End-to-end forecasting has been around for some time and many meteorological or hydrological centres have some form of operational end-to-end flood forecast (e.g., [5,6]). The more sophisticated end-to-end forecasts go further down the chain, i.e., they include hydraulic components either through a look-up library of static flood maps (e.g., [7,8]) or by running hydraulic models (e.g., [4,9]). ...
... The discussion of the UK flood forecasting practice prior to the start of FFIR is because many aspects of the FFIR programme have become part of the current operational system, or aspects of FFIR were designed to run alongside with the proposed developments at the time. Recent updates to the operational flood forecasting practice for the FFC and SFFS can be found in [6,8], respectively. ...
... The FFIR proposed end-to-end forecasting system ( Figure 1b) is superficially similar to that of the current operational system (Figure 1a). This similarity arises in part due to the first stages of the forecasting chain being a tried and tested method [6] and thus a strong starting point for such a system. The additions, apart from the added capability of being able to produce warnings for flooding from intense rainfall, of the FFIR system to the operational system described (Section 3.1) are set out below • improved quality of radar-derived rainfall. ...
Recent surface-water and flash floods have caused millions of pounds worth of damage in the UK. These events form rapidly and are difficult to predict due to their short-lived and localised nature. The interdisciplinary Flooding From Intense Rainfall (FFIR) programme investigated the feasibility of enhancing the integration of an end-to-end forecasting system for flash and surface-water floods to help increase the lead time for warnings for these events. Here we propose developments to the integration of an operational end-to-end forecasting system based on the findings of the FFIR programme. The suggested developments include methods to improve radar-derived rainfall rates and understanding of the uncertainty in the position of intense rainfall in weather forecasts; the addition of hydraulic modelling components; and novel education techniques to help lead to effective dissemination of flood warnings. We make recommendations for future advances such as research into the propagation of uncertainty throughout the forecast chain. We further propose the creation of closer bonds to the end users to allow for an improved, integrated, end-to-end forecasting system that is easily accessible for users and end users alike, and will ultimately help mitigate the impacts of flooding from intense rainfall by informed and timely action.
... A major challenge is translating short-period heavy rainfall into surface water flooding impacts. Pilling et al. (2016) describe a Surface Water Flooding Decision Support Tool (SWFDST) used by the Flood Forecasting Centre (FFC), a partnership between the Met Office and the EA that aims to forecast the flooding impact of severe rainfall events. The work described here is contributing to that tool by improving the forecasts of short-period rainfall associated with convective events that are fed into the SWFDST. ...
... The severity of possible impacts is assessed based on the deduced hourly totals often expressed as the BE and RWC in FFC forecasts. Based on Halcrow (2008), 20 mm is a broad threshold for minor impacts and 30 mm for significant (or greater) impacts taking into account local sensitivities such as large urban areas or pre-existing saturated ground (Pilling et al., 2016). The deduced values are compared with the NWP model forecasts. ...
A major forecasting challenge is predicting the likely intensity of rainfall in convective situations in which intense short‐period rainfall can lead to surface water flooding. The study demonstrates a useful correlation between precipitable water and 1 hr gauge‐derived rainfall accumulations in situations with deep instability and relatively slack flow in depth. The correlation forms the basis of a forecasting technique, independent of numerical weather prediction (NWP) model forecasts of rainfall, being applied at the UK's Met Office Flood Forecasting Centre (FFC). Best‐fit lines from Figures 3 and 5 with the largest R2 values plotted on one graph. Grey top 10%, blue top 1% and purple top 0.5% of gauges. The dashed green line shows “extreme totals” derived from the highest rates in each of the 5 mm precipitable water (PW) ranges shown in Figure 5. The forecasting technique derived from the graphs relates the PW (horizontal axis) to widespread (grey line), isolated (blue line), maximum (purple line) and extreme (dashed green line) hourly rainfall totals. For a more detailed interpretation of the lines, see the text.
... Using all available deterministic and ensemble forecast products alongside expert assessment from the chief forecaster they will decide what the reasonable worst case is likely to be. These outputs are used to inform the flood guidance statement and the Environment Agency uses these scenarios to run their catchment models (Pilling et al., 2016). The speed of data-driven approaches in 435 comparison with these more traditional physics-based modelling approaches could prove beneficial for users wishing to run various scenarios quickly. ...
Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology and Earth system models – into a final prediction product. They are recognised as a promising way of enhancing prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at sub-seasonal to decadal scales, a better appreciation of the strengths of machine learning, plus expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally-expensive offline land model, can minimize the effect of biases that exist within dynamical outputs without explicit bias correction and downscaling, benefit from the strengths of machine learning models, and can learn from large datasets, while combining different sources of predictability with varying time-horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities. These include obtaining physically-explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating modelled initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.
... To date, gradual digitalization of the water sector and especially of river management has been achieved in an implicit way: in some cases, extreme events such as droughts or inundations associated with massive losses have led to the need for more efficient forecasts (Finley et al., 2020;Pilling et al., 2016;Sharma et al., 2021) and a new monitoring architecture. On the other hand, recurrent operations for irrigation require accurate evaluation of the diverted discharges and water quality monitoring of the environmental flows maintained within the riverbed (Jain, 2012;Lothrop et al., 2018;Lu et al., 2021) or ultimately, implementation of new policies such as the European Union (EU) Water Framework Directive for the good ecological status of European rivers (Directive 2000/ 60/EC) and groundwater resources (Directive 2006/118/ EC). ...
The growing complexity of the competition among uses and the emerging understanding of the synergy effects within the catchments and the rivers have underlined the need for information on various aspects of water: precipitations, discharges, velocities, and water depths are some of the basic characteristics that need to be studied for developing a management strategy that can identify and balance the various uses, ensure conservation of resources, and mitigate the potential effect of extreme events. At present, technical innovation provides the possibility to perform measurements on the field, to produce the needed information, and to move to a new paradigm for the management of rivers. This study analyzes the concept of smart river management that emerged recently with the wide spread of Information Technologies (IT) solutions and development of a new generation of sensors based on internet of things architecture. The analysis demonstrates that a holistic approach is needed for efficient management of rivers. The use of Information and Communication Technologies solutions can provide a real added value, but important efforts must be engaged for identifying key activities in the various domains that can benefit from the digital transformation. At the same time, this study introduces the paradigm of a river information system that encompasses the various activities taking place around the rivers and ensures gradual integration of the various IT components in a consistent environment that provides support to the many users of rivers. To achieve this objective, the current situation requires formalization of a roadmap and the need to address the various activities in a systematic way. Within this long‐term process, the digital twins concept represents a step toward the target water information system and has generated interest among professional communities.
... In the scope of fluvial and coastal floods, operational flood forecasting is an important element of modern flood risk management (Bachmann et al., 2016;Jain et al., 2018;Pilling et al., 2016). Based on short-term forecasts, warnings are issued, so that appropriate emergency mitigation measures can be implemented, for example, setting sand sack barriers or evacuation measures (Figure 11), which reduces damage to people and assets. ...
In recent years, the issue of high groundwater levels has caught attention. Unfavorable consequences of high groundwater levels are especially damage to buildings, infrastructure, and the environment. Processes that lead to high groundwater levels are hydrological (heavy or extended rainfall and flood events), or anthropogenic (reduced groundwater extractions, interaction with sewer networks, hydraulic engineering measures, structural interventions in the water balance, and mining activities). Several different map products have been prepared for the information of inhabitants and for planning purposes, and also methods for damage and risk analysis related to high groundwater levels have been developed. Groundwater management measures and structural measures are available to reduce the risk related to high groundwater levels. An operational management system could be combined from existing components, but operational forecasting systems for high groundwater levels are—different to flood forecasting systems—not yet common practice. A better understanding of the processes and the development of integrated approaches for modeling, design, planning, forecasting, and warning, as well as improvement of interdisciplinary collaboration between different organizations, are recommendations for the future. This article is categorized under: Engineering Water > Engineering Water Water and Life > Conservation, Management, and Awareness Science of Water > Hydrological Processes Science of Water > Water Extremes Pumping water from a basement during the Neiße flood 2010 in Saxony. The clear water indicates that the basement flooding originates from groundwater (photo: Reinhard Schinke).
... However, the accuracy of forecasts is likely to remain low at lead times beyond a few hours (Speight et al. 2018). The development of the hydrological model has advanced over the years with the distributed hydrological model providing more reliable results (Hand et al. 2004;Cuo et al. 2011;Pilling et al. 2016;Flack et al. 2019). There are many models that can be used to generate forecasts for a catchment (Speight et al. 2021). ...
The absence of a viable flood early warning system for the Sebeya River catchment continues to impede government efforts towards improving community preparedness, the reduction of flood impacts and relief. This paper reports on a recent study that used satellite data, quantitative precipitation forecasts and the rainfall–runoff model for short-term flood forecasting in the Sebeya catchment. The global precipitation measurement product was used as a satellite rainfall product for model calibration and validation and forecasted European Centre Medium-Range Weather Forecasts (ECMWF) rainfall products were evaluated to forecast flood. Model performance was evaluated by the visual examination of simulated hydrographs, observed hydrographs and a number of performance indicators. The real-time flow forecast assessment was conducted with respect to three different flood warning threshold levels for a 3–24-h lead time. The result for a 3-h lead time showed 72% of hits, 7.5% of false alarms and 9.5% of missed forecasts. The number of hits decreased, as the lead time increased. This study did not consider the uncertainties in observed data, and this can influence the model performance. This work provides a base for future studies to establish a viable flood early warning system in the study area and beyond. HIGHLIGHTS
Potential of the Hydrologiska Byråns Vattenbalansavdelning model for flood forecasting in the Sebeya catchment.;
Evaluation of European Centre Medium-Range Weather Forecasts (ECMWF) rainfall data.;
Graphical evaluation of flood forecasts based on the ECMWF.;
Categorical statistics indicated that the probability of detection was high for a short lead time, showing that a short lead-time forecast gives a higher skill score than forecasts for a longer lead time.;
... Britain (Pilling et al., 2016), Israel (Givati et al., 2016), Russia (Borsch & Simonov, 2016), and United States (Adams, 2016). In addition, Jain et al. (2018) provide an overview of flood forecasting systems in some other countries such as Nepal, India, Pakistan. ...
Floods are one of the most devastating natural disasters that can cause large economic damage and endanger human lives. Flood forecasting is one of the flood risk mitigation measures serving to protect human lives and social estate. The Danube River Basin (DRB) is the world's most international river basin, flowing through the territory of 19 countries, covering more than 800,000 km 2. The frequency of floods in the DRB increased in the last decades, urging the need for a more effective and harmonized regional and cross-border cooperation in the field of flood forecasting. Reliable and comprehensive hydrologic data are the basis of flood forecasting. This paper provides an overview of the national flood forecasting systems in the DRB. Detailed information about meteorological and hydrological measurements, flood modelling, forecasting, and flood warnings is provided for 12 countries that cover almost 95% of the total DRB area. Notably, significant differences exist among the countries in terms of the measuring network density, the models used as well as forecasting and warnings methodology. These differences can be attributed to the geographical and climatological setting, political situation, historical forecasting development, etc. It can be seen that there is still much room left for improvements of measurement networks (e.g., density, measured parameters) and models used that could be improved to enhance the flood forecasting in the DRB.
... • Global level (Alfieri et al., 2013;Arheimer et al., 2020;Pappenberger et al., 2010) • Continental level (Arduino et al., 2005;Emerton et al., 2016) (e.g., the European Flood Forecasting System (EFFS) and the European Flood Awareness System (EFAS; Alfieri et al., 2014; Bartholmes et al., 2009;Demeritt et al., 2013;; the Cooperation in Science and Technology (COST-731 actions), which was a European initiative for the quantification of uncertainty in hydrometeorological forecasting systems Rossa et al., 2011;Zappa et al., 2010); the EUROflood research project (Parker & Fordham, 1996); the Mesoscale Alpine Program Demonstration of Probabilistic Hydrological and Atmospheric Simulation of flood Events in the Alpine region (MAP D-PHASE; Zappa et al., 2008) • National level (e.g., the United States Adams, 2016; the River Forecast Centers-RFCs: Demargne et al., 2009; the NOAA/NWS Hydrologic Ensemble Forecast Service-HEPS: Brown et al., 2014;Great Britain Bell et al., 2013, 2017Pilling et al., 2016;Werner et al, , 2013France Thirel et al., 2010;Australia Pagano, Elliot et al., 2016; the national ensemble seasonal streamflow forecasting system- Feikema et al., 2018;Zhao et al., 2016; the ensemble 7-days streamflow forecasting service- Bennett et al., 2014;Li et al., 2020;Shrestha et al., 2015); China Liu, 2016;Brazil Fan et al., 2016, Israel Givati et al., 2016, and Russia Borsch & Simonov, 2016) • Regional level (e.g., Germany: Demuth and Rademacher, 2016) • Basin level (trans-boundary) (e.g., Amarnath et al., 2016;Artinyan et al., 2016;Awwad et al., 1994;Khavich & Ben-Zvi, 1995;Lin et al., 2010;Plate, 2007;Renner et al., 2009;Shi et al., 2015;Tshimanga et al., 2016;Werner et al., 2005;Younis et al., 2008;Yuan et al., 2016). ...
Ensemble forecasting applied to the field of hydrology is currently an established area of research embracing a broad spectrum of operational situations. This work catalogues the various pathways of ensemble streamflow forecasting based on an exhaustive review of more than 700 studies over the last 40 years. We focus on the advanced state of the art in the model‐based (dynamical) ensemble forecasting approaches. Ensemble streamflow prediction systems are categorized into three leading classes: statistics‐based streamflow prediction systems, climatology‐based ensemble streamflow prediction systems and numerical weather prediction‐based hydrological ensemble prediction systems. For each ensemble approach, technical information, as well as details about its strengths and weaknesses, are provided based on a critical review of the studies listed. Through this literature review, the performance and uncertainty associated with the ensemble forecasting systems are underlined from both operational and scientific viewpoints. Finally, the remaining key challenges and prospective future research directions are presented, notably through hybrid dynamical ‐ statistical learning approaches, which obviously present new challenges to be overcome in order to allow the successful employment of ensemble streamflow forecasting systems in the next decades. Targeting students, researchers and practitioners, this review provides a detailed perspective on the major features of an increasingly important area of hydrological forecasting.
... The pre-calculated impact maps are made up of a combination of data sources, including the Updated Flood Map for Surface Water (uFMfSW), Environment Agency National Receptor Database and National Population Database and are developed for 9 scenarios that is, 1 in 30, 100, 1,000 year and 1, 3, 6 hr rainfall duration design storms (Aldridge, Gunawan, Moore, Cole, & Price, 2016). The selected impact maps are then converted into county-level red/amber/yellow/ green risk areas (on the basis of risk = probability x impact) and delivered to a range of users including Local Authorities and emergency responders via the Flood Guidance Statement (Pilling et al., 2016). ...
The risk of surface water flooding (SWF) in England is already high and its frequency and severity is projected to increase in the future. SWF generally occurs due to intense, highly localised rainfall, which is challenging to forecast with sufficient accuracy to take proactive action ahead of flood events. Being able to manage the risk effectively lies in improved rainfall and flood forecast products, better communication of uncertainty and building the capacity of local responders. This study utilises state‐of‐the‐art high‐resolution ensemble rainfall forecasts and hydraulic modelling tools alongside a novel post‐processing method to develop and trial new SWF forecast products within an incident workshop attended by forecast producers and regional forecast users. Twenty‐two of 24 workshop participants reported that the new information would be useful to their organisation but more product development and training in its interpretation is required. Specific recommendations to improve SWF forecast provision include increased support for local government through a single government organisation responsible for SWF, making more use of existing static SWF mapping in a real‐time context and employing the process of user‐based consultation, as outlined in this study, to guide the future development of future SWF forecast information and processes.
... Fast expanding urban developments and continuously changing climate conditions are stress factors leading to an increasing risk of pluvial flood events [8]. According to recent hydrologic studies in the United States, every 1 percentage point increase in impervious surface causes a 3.3-4.7% ...
The effective forecast and warning of pluvial flooding in real time is one of the key elements and remaining challenges of an integrated urban flood risk management. This paper presents a new methodology for integrating risk-based solutions and 2D hydrodynamic models into the early warning process. Whereas existing hydrodynamic forecasting methods are based on rigid systems with extremely high computational demands, the proposed framework builds on a multi-model concept allowing the use of standard computer systems. As a key component, a pluvial flood alarm operator (PFA-Operator) is developed for selecting and controlling affected urban subcatchment models. By distributed computing of hydrologic independent models, the framework overcomes the issue of high computational times of hydrodynamic simulations. The PFA-Operator issues warnings and flood forecasts based on a two-step process: (1) impact-based rainfall thresholds for flood hotspots and (2) hydrodynamic real-time simulations of affected urban subcatchments models. Based on the open-source development software Qt, the system can be equipped with interchangeable modules and hydrodynamic software while building on the preliminary results of flood risk analysis. The framework was tested using a historic pluvial flood event in the city of Aachen, Germany. Results indicate the high efficiency and adaptability of the proposed system for operational warning systems in terms of both accuracy and computation time.
... On 1 April 2009, the EA launched the FFC to eradicate regional differences in flood forecasting (Alexander et al. 2016) and to set up an integrated flood risk monitoring platform which allows simulations linking meteorology, hydrology and flooding (HM Government 2016b). The FFC was established in partnership with the Met Office to allow for better prediction of the scale and timing of flooding events and better monitoring (Pilling et al. 2016), combining meteorology and hydrology expertise to provide a comprehensive, 24/7 forecasting service for flood risk. These organisations are major components of the flood risk communication system. ...
Part of a comprehensive review of flood risk management in mature economies, this report looks at the system in England, where insurance take-up is high and climate change is taken into account. It finds that the current system is in transition, with focus shifting towards flood resilience; however, progress is slow and more needs to be done to incentivise risk reduction and avoid over-reliance on structural protection and the future availability of insurance.
... Electrical Load (EL) forecasting is a fundamental and vital task for economically efficient operation and controlling of power systems [1][2][3][4][5]. It has often been employed for energy management, unit commitment and load dispatch [6][7][8][9][10][11][12][13][14]. The high accuracy of load forecasting guarantees the safe and stable operation of power systems [15][16][17]. ...
The process of modernizing smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems, and, in order to develop a more reliable, flexible, efficient and resilient grid, electrical load forecasting is not only an important key but is still a difficult and challenging task as well. In this paper, a short-term electrical load forecasting model, with a unit for feature learning named Pyramid System and recurrent neural networks, has been developed and it can effectively promote the stability and security of the power grid. Nine types of methods for feature learning are compared in this work to select the best one for learning target, and two criteria have been employed to evaluate the accuracy of the prediction intervals. Furthermore, an electrical load forecasting method based on recurrent neural networks has been formed to achieve the relational diagram of historical data, and, to be specific, the proposed techniques are applied to electrical load forecasting using the data collected from New South Wales, Australia. The simulation results show that the proposed hybrid models can not only satisfactorily approximate the actual value but they are also able to be effective tools in the planning of smart grids.
... It is useful to consider concurrent river and coastal flooding. As Pilling et al. [17] describe, a typical scenario could be a protracted stormy period bringing successive intense low pressure systems across Britain. This could result in a storm surge coinciding with high tides and large waves being driven onshore concurrent with saturated river catchments and high flows and further heavy rainfall. ...
Moments of rainfall spatial variability, which quantify how flood response time scales are affected when spatially variable rainfall is considered, compared to when rainfall is spatially uniform, have been suggested as a useful tool for forecasters to guide their choice between lumped or distributed rainfall information for runoff modelling. However, the approaches used to evaluate the validity of moments suffer from limitations. Hence, we adopt a novel approach for their evaluation by comparing moments to the relationship between observed hydrograph characteristics generated by spatially variable and by uniform rainfall events in the same catchment. We further investigate the usefulness of moments by testing whether the performance of a lumped hydrological model for events classified by moments as spatially variable is lower than for uniform events. Results confirmed that moments can identify spatially variable events and characterize differences in hydrograph features compared to uniform events, providing a useful tool for forecasters.
A regional coupled approach to water cycle prediction is demonstrated for the 4-month period from November 2013 to February 2014. This provides the first multi-component analysis of precipitation, soil moisture, river flow and coastal ocean simulations produced by an atmosphere-land-ocean coupled system focussed on the United Kingdom (UK), running with horizontal grid spacing of around 1.5 km across all components. The Unified Model atmosphere component, in which convection is explicitly simulated, reproduces the observed UK rainfall accumulation (r² of 0.95 for water day accumulation), but there is a notable bias in its spatial distribution – too dry over western upland areas and too wet further east. The JULES land surface model soil moisture state is shown to be in broad agreement with a limited number of cosmic-ray neutron probe observations. A comparison of observed and simulated river flow shows the coupled system is useful for predicting broad scale features, such as distinguishing high and low flow regions and times during the period of interest but are less accurate than optimised hydrological models. The impact of simulated river discharge on NEMO model simulations of coastal ocean state is explored in the coupled modelling framework, with comparisons provided relative to experiments using climatological river input and no river input around the UK coasts. Results show that the freshwater flux around the UK contributes of order 0.2 psu to the mean surface salinity, and comparisons to profile observations give evidence of an improved vertical structure when applying simulated flows. This study represents the first assessment of the coupled system performance from a hydrological perspective, with priorities for future model developments and challenges for evaluation of such systems discussed.
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By showing the uncertainty surrounding a prediction,
probabilistic forecasts can give an earlier indication of potential upcoming
floods, increasing the amount of time available to prepare. However, making
a decision based on probabilistic information is challenging. As part of the
UK-wide policy's move towards forecast-based flood risk management, the
Environment Agency (EA), responsible for managing risks of flooding in
England, is transitioning towards the use of probabilistic fluvial forecasts
for flood early warning. While science and decision-making are both
individually progressing, there is still a lack of an ideal framework for the incorporation of new and probabilistic science into decision-making practices,
and, respectively, the uptake of decision-makers' perspectives in the design
of scientific practice. To address this, interviews were carried out with EA
decision-makers (i.e. Duty Officers), key players in the EA's flood warning
decision-making process, to understand how they perceive this transition
might impact on their decision-making. The interviews highlight the complex
landscape in which EA Duty Officers operate and the breadth of factors that
inform their decisions, in addition to the forecast. Although EA Duty Officers already account for uncertainty and communicate their confidence in
the forecast they currently use, the interviews revealed a decision-making
process which is still very binary and linear to an extent, which appears at
odds with probabilistic forecasting. Based on the interview results, we make
recommendations to support a successful transition to probabilistic
forecasting for flood early warning in England. These recommendations include the new system's co-design together with Duty Officers, the preparation of clear guidelines on how probabilistic forecast should be
used for decision-making in practice, EA communication with all players in the decision-making chain (internal and external) that this transition
will become operational practice and the documentation of this transition to
help other institutes yet to face a similar challenge.
We believe that this paper is of wide interest for a range of sectors at the
intersection between geoscience and society. A glossary of technical terms
is highlighted by asterisks in the text and included in Appendix A.
Power load forecasting has an influence of great signification on improving the operational efficiency and economic benefits of the power grid system. Aiming at improving forecast performance, a substantial number of load forecasting models are proposed. However, these models have disregarded the limits of individual prediction models and the necessity of data preprocessing, resulting in poor prediction accuracy. In this article, a novelty hybrid model which combines data preprocessing technology, individual forecasting algorithm and weight determination theory is presented for obtaining higher accuracy and forecasting ability. In this model, an effective data preprocessing method named SSA is adopted to extract the load data characteristics and further improve the prediction performance. In addition, a combined forecasting mechanism composed of BP, SVM, GRNN and ARIMA is successfully established using the weight determination theory, which exceeds the limits of individual prediction models and comparatively improves prediction accuracy. And the thought of combine linear and nonlinear model together can further take the advantage of two kinds of models to forecast power load more effectively. To assess the validity of the combined model, four datasets of 30-minutes power load from Australia are selected for research. The experimental results show that the established model not only has obvious advantages over other individual models, but also can be applied as an available technology for electrical system programming.
Until recently, long range forecast systems showed only modest levels of skill in predicting surface winter climate around the Atlantic basin and associated fluctuations in the North Atlantic Oscillation at seasonal lead times. Here we use a new forecast system to assess seasonal predictability of winter north Atlantic climate. We demonstrate that key aspects of European and North American winter climate and the surface North Atlantic Oscillation are highly predictable months ahead. We demonstrate high levels of prediction skill in retrospective forecasts of the surface North Atlantic Oscillation, winter storminess, near surface temperature and wind speed; all of which have high value for planning and adaptation to extreme winter conditions. Analysis of forecast ensembles suggests that while useful levels of seasonal forecast skill have now been achieved, key sources of predictability are still only partially represented and there is further untapped predictability.
As the societal impacts of hazardous weather and other environmental pressures grow, the need for integrated predictions that can represent the numerous feedbacks and linkages between sub-systems is greater than ever. This was well illustrated during winter 2013/2014 when a prolonged series of deep Atlantic depressions over a 3 month period resulted in damaging wind storms and exceptional rainfall accumulations. The impact on livelihoods and property from the resulting coastal surge and river and surface flooding was substantial. This study reviews the observational and modelling toolkit available to operational meteorologists during this period, which focusses on precipitation forecasting months, weeks, days and hours ahead of time. The routine availability of high-resolution (km scale) deterministic and ensemble rainfall predictions for short-range weather forecasting as well as weather-resolving seasonal prediction capability represent notable landmarks that have resulted from significant progress in research and development over the past decade. Latest results demonstrated that the suite of global and high-resolution UK numerical weather prediction models provided excellent guidance during this period, supported by high-resolution observations networks, such as weather radar, which proved resilient in difficult conditions. The specific challenges for demonstrating this performance for high-resolution precipitation forecasts are discussed. Despite their good operational performance, there remains a need to further develop the capability and skill of these tools to fully meet user needs and to increase the value that they deliver. These challenges are discussed, notably to accelerate the progress towards understanding the value that might be delivered through more integrated environmental prediction.
[1] There are significant uncertainties inherent in precipitation forecasts and these uncertainties can be communicated to users via large ensembles that are generated using stochastic models of forecast error. The Met Office and the Australian Bureau of Meteorology developed the Short Term Ensemble Prediction System (STEPS) was developed to address these user requirements and has been operational for a number of years. The initial formulation of Bowler et al. (2006) has been revised and extended to improve the performance over large domains, to include radar observation errors, and to facilitate the combination of forecasts from a number of sources. This paper reviews the formulation of STEPS, discusses those aspects of the formulation that have proved most problematic and presents some solutions. The performance of STEPS nowcasts is evaluated using a combination of case study examples and statistical verification from the UK. Routine forecast verification demonstrates that STEPS is capable of producing near optimal blends of a rainfall nowcast and high resolution NWP forecast. It also shows that the spread of STEPS nowcast ensembles are a good predictor of the error in the control member (unperturbed) nowcast.
Following previous work on an inherently mass-conserving semi-implicit (SI) semi-Lagrangian (SL) discretization of the two-dimensional (2D) shallow-water equations and 2D vertical slice equations, that approach is here extended to the 3D deep-atmosphere, non-hydrostatic global equations. As with the reduced-dimension versions of this model, an advantage of the approach is that it preserves the same basic structure as a standard, non-mass-conserving, SISL version of the model. Additionally, the model is simply switchable to hydrostatic and/or shallow-atmosphere forms. It is also designed to allow simple switching between various geometries (Cartesian, spherical, spheroidal). The resulting mass-conserving model is applied to a standard set of test problems for such models in spherical geometry and compared with results from the standard SISL version of the model. This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.
Flooding due to coastal storm surges presents a significant threat to life and property. The UK has long had a storm surge forecasting system based on a single ‘deterministic’ simulation. This was augmented with an operational storm surge ensemble in December 2009. By producing several simulations sampling the forecast uncertainty, the ensemble estimates the probability of reaching critical water levels and thus supports a more risk-based approach to civil protection.
The original storm surge ensemble provided forecasts out to T + 54 h, limited by the forecast range of the driving MOGREPS-R atmospheric ensemble. Longer-range forecasts could provide advance notice of the potential for a significant event, allowing suitable preparatory actions to be taken. This study investigates the possibility of extending the storm surge ensemble to between 5 and 7 days using atmospheric data from the lower-resolution Met Office 15-day ensemble (MOGREPS-15).
Both case studies and statistical verification indicate the potential for useful forecasts out to the full 7.25 days tested. The best performance is obtained by extending the existing surge ensemble products with output from separate runs of the storm surge model, which have been driven by MOGREPS-15 meteorology from T + 0 h. An attempt to create a single surge history for each member by switching from MOGREPS-R to MOGREPS-15 input at T + 54 h led to spurious oscillations in some cases, and poorer performance on several statistical measures. These issues might be improved by smoothing the discontinuity in atmospheric forcing.
Following this successful trial, the separate-runs extension to the surge ensemble was implemented operationally in summer 2011. The study also demonstrates the benefit of online bias correction and ‘dressing’ the forecast members to account for errors which the system does not otherwise sample. The operational implementation of these features is left for future work. Copyright © 2012 British Crown copyright, the Met Office Published by John Wiley & Sons Ltd.
The probability-distributed principle in basin-scale hydrology considers the frequency of occurrence of hydrological variables (model inputs, parameters or elements) of certain magnitudes over the basin without regard to the location of a particular occurrence within the basin. The random assemblage of different parts is considered more important than the relation of the parts, one to another. Rainfall-runoff models based on probability-distributed infiltration capacity and storage capacity concepts, and which generate runoff according to Hortonian and saturation overland flow mechanisms respectively, are distinguished. Two types of probability-distributed storage capacity model are identified, one based on an assumption that storage elements at points in the basin respond independently of their neighbours, and the other where storage elements interact so as to equalize the depth of stored water over the basin. Allowing redistribution of water leads to simplification of the model equations. The probability-distributed principle is also used to represent the process of water translation through the basin. Interpretation of the instantaneous unit hydrograph as a probability density function of translation time is demonstrated and the inverse Gaussian density proposed as a suitable functional form on physical grounds.
Rainfall totals with this event exceeded long-term records by some 25%, which is a significant margin when those records have stood for over 100 years. Issued forecast guidance that utilised climatological tools alongside Global numerical weather prediction models over the preceding couple of days suggested totals close to previous record rainfall totals, and in light of this the event was reasonably well forecast, although the coarser models on their own did not capture rainfall as well, partly due to present resolution restrictions. However, the finerresolution models with a shorter lead-time were forecasting totals close to previous record values, although not the extreme values recorded.
This paper reports on work that demonstrates that weather radar can yield remarkably accurate rainfall measurements in Scotland. Analysis of data during 1999 and 2000 shows Nimrod radar data to have no consistent error bias and a 24% mean error in storm rainfall totals. For rivers such as the Ruchill Water in the Perthshire Highlands this highlights the fact that the observations and quality control corrections utilised by the Met Office Nimrod system may offer particular benefits in certain flood warning applications. Assessment of wind data indicates that smaller errors occur in Nimrod radar observations during lower wind speeds (less than 15 knots) and with an easterly airflow. However, errors may also be attributed to occasions when the Numerical Weather Prediction model fails to represent the wind conditions correctly at ground level. Copyright © 2006 Royal Meteorological Society.
This paper discusses developments in the last five to six years in the provision of operational flood forecasting in England, Wales, and Scotland. Before the formation of the Environment Agency (EA) in England and Wales and the Scottish Environment Protection Agency (SEPA), flood forecasting capabilities were fragmented. Just over a decade ago both organisations received governmental mandates for the provision of flood forecasting and warning nationally, and have as a result in recent years established systems providing national coverage: the National Flood Forecasting System, and Flood Early Warning System (FEWS) Scotland. These have facilitated a rapid expansion of catchments for which forecasts are provided, and the common framework used has enabled a more rapid introduction of scientific advances in forecasting techniques. This paper gives an overview of some of these recent developments, as well as providing an outlook to further developments to be undertaken in the near future. Copyright © 2009 Royal Meteorological Society
