Hydrological drought is a serious issue globally, which is likely to be amplified by 21st century climate change. In the UK, the impacts of changes in river flow and groundwater drought severity in a future of climate change and higher water demand are potentially severe. Recent publication of a new nationally consistent set of river flow and groundwater level projections (the eFLaG dataset), based on state-of-the-art UKCP18 climate projections, offers a unique opportunity to quantitatively assess future UK hydrological drought susceptibility. The dataset includes a transient, multi-model ensemble of hydrological projections driven by a single regional climate model (RCM), with a 12-member perturbed-parameter ensemble, for 200 catchments and 54 boreholes spanning a period from 1961 to 2080. Assessment of a baseline period (1989–2018) shows that the RCM-driven projections adequately reproduce observed river flow and groundwater level regimes, improving our confidence in using these models for assessment of future drought. Across all hydrological models and most catchments, future low river flows are projected to decline consistently out to 2080. Drought durations, intensities and severities are all projected to increase in most (over 90 %, pooling across different drought characteristics) UK catchments. However, the trajectory of low groundwater levels and groundwater drought characteristics diverges from that of river flows. Whilst groundwater levels at most (> 85 %) boreholes are projected to decline (consistent with river flows), these declines are relatively modest (< 10 % reduction) in transient low groundwater levels by 2080, and, in fact, six show moderate increases. Groundwater drought characteristics in the far future (2050–2079) are often similar to those of the baseline (1989–2018), with only 33 % of boreholes showing an increase (towards worsening drought) of more than 10 % for drought severity (48 % of boreholes for drought intensity). Interestingly, for some boreholes, droughts are projected to be more prolonged and severe in the near future (2020–2049) before returning to shorter durations and lower severity in the far future. A number of explanatory factors for this divergence between river flow and groundwater are discussed. The sensitivity to seasonal changes in precipitation and potential evapotranspiration is proposed as a principal driver of divergence because low river flows are more influenced by shorter-term rainfall deficits in the summer half-year, whilst groundwater drought appears to be offset somewhat by the wetter winter signal in the RCM projections. Our results have important implications for water management, demonstrating a widespread increase in river flow drought severity and diminishing low flows that could have profound societal and environmental impacts unless mitigated. Furthermore, the divergence in projections of drought in river flows and groundwater levels brings into question the balance between surface and subsurface water resources. The projected contrast in fortunes of surface and subsurface water resources identified for the UK may be replicated in other parts of the world where climate projections suggest a shift towards drier summers and wetter winters.

This paper details the development and evaluation of the enhanced future FLows and Groundwater (eFLaG) dataset of nationally consistent hydrological projections for the UK, based on the latest UK Climate Projections (UKCP18). The projections are derived from a range of hydrological models. For river flows, multiple models (Grid-to-Grid, PDM (Probability Distributed Model) and GR (Genie Rural; both four- and six-parameter versions, GR4J and GR6J)) are used to provide an indication of hydrological model uncertainty. For groundwater, two models are used, a groundwater level model (AquiMod) and a groundwater recharge model (ZOODRM: zooming object-oriented distributed-recharge model). A 12-member ensemble of transient projections of present and future (up to 2080) daily river flows, groundwater levels and groundwater recharge was produced using bias-corrected data from the UKCP18 regional (12 km) climate ensemble. Projections are provided for 200 river catchments, 54 groundwater level boreholes and 558 groundwater bodies, all sampling across the diverse hydrological and geological conditions of the UK. An evaluation was carried out to appraise the quality of hydrological model simulations against observations and also to appraise the reliability of hydrological models driven by the regional climate model (RCM) ensemble in terms of their capacity to reproduce hydrological regimes in the current period. The dataset was originally conceived as a prototype climate service for drought planning for the UK water sector and so has been developed with drought, low river flow and low groundwater level applications as the primary objectives. The evaluation metrics show that river flows and groundwater levels are, for the majority of catchments and boreholes, well simulated across the flow and level regime, meaning that the eFLaG dataset could be applied to a wider range of water resources research and management contexts, pending a full evaluation for the designated purpose. Only a single climate model and one emissions scenario are used, so any applications should ideally contextualise the outcomes with other climate model-scenario combinations. The dataset can be accessed in Hannaford et al.

Observing, in a quantitative and robust way, the dynamic space-time pattern of precipitation in mountainous terrain presents a major challenge of great practical importance. The difficulties of this task are further exacerbated in mid to high latitudes where the typical melting layer for precipitation (i.e. the 0°C isotherm) is often close to the surface during winter months. One way to address this challenge is by improving observations made using networks of weather radars. Quantitative Precipitation Estimates (QPEs) derived from these instruments have many applications, for example as input to a hydrological model to simulate river flow for flood forecasting purposes. Here, a set of QPEs - obtained from an observation campaign using the National Centre for Atmospheric Science’s mobile X-band dual-polarisation Doppler weather radar (NXPol) in a mountainous area of Northern Scotland - are assessed with reference to observed river flows. Each form of QPE is used as an input to Grid-to-Grid (G2G), a distributed hydrological model used for flood forecasting across Great Britain, and the simulated river flows compared to observations. The location of the radar was specially chosen to infill an area of reduced coverage in the existing C-band radar network for the British Isles. Assessments of radar QPE often only examine a final precipitation “best estimate” product and typically with reference to raingauges at specific locations. Here, we exploit the processing capabilities of NXPol and the hydrological modelling framework to investigate the benefits of ten separate processing methods that increase with complexity and make differing use of dual-polarisation variables. The role of the radar beam elevation and distance from the radar is investigated, and NXPol QPEs are compared to that provided by the radar network. Additionally, a preliminary investigation is carried out into the role of the drop-size distribution on the relationship between radar-reflectivity and rain-rate using disdrometer data. The hydrological assessment reported on here has the benefit of integrating the precipitation over space and time which serves to complement and extend a previous meteorological assessment using raingauge data alone. The assessment proves to be insensitive to issues affecting both raingauges (e.g. representativity, wind-induced under-catch) and local artefacts in the space-time radar-rainfall field. It facilitates a direct assessment of whether potential benefits in the new QPEs are carried forward to an end-use such as flood forecasting, providing fresh insights for the development of new dual-polarisation radar QPE methods.

•Precipitation data from an X-band radar in northern Scotland are assessed.•Assessment against river flow using a distributed hydrological model brings fresh insights.•Lowest usable beam elevation is the main limit on performance in this mountainous area.•Use of dual-polarisation estimators improves performance during summer months.•The X-band radar is suited for filling gaps in the existing C-band radar network.

Abstract. This paper presents an ‘enhanced future FLows and Groundwater’ (eFLaG) dataset of nationally consistent hydrological projections for the UK, based on the latest UK Climate Projections (UKCP18). The hydrological projections are derived from a range of river flow models (Grid-to-Grid, PDM, GR4J and GR6J), to provide an indication of hydrological model uncertainty, as well as groundwater level (Aquimod) and groundwater recharge (ZOODRM) models. A 12-member ensemble of transient projections of present and future (up to 2080) daily river flows, groundwater levels and groundwater recharge were produced using bias corrected data from the UKCP18 Regional (12 km) climate ensemble. Projections are provided for 200 river catchments, 54 groundwater level boreholes and 558 groundwater bodies, all sampling across the diverse hydrological and geological conditions of the UK. An evaluation was carried out, to appraise the quality of hydrological model simulations against observations and also to appraise the reliability of hydrological models driven by the RCM ensemble, in terms of their capacity to reproduce hydrological regimes in the current period. The dataset was originally conceived as a prototype climate service for drought planning for the UK water sector, so has been developed with drought, low river flow and low groundwater level applications as the primary focus. The evaluation metrics show that river flows and groundwater levels are, for the majority of catchments and boreholes, well simulated across the flow and level regime, meaning that the eFLaG dataset could be applied to a wider range of water resources research and management contexts, pending a full evaluation for the designated purpose.

Forecasting the potential for flood-producing precipitation and any subsequent flooding is a challenging task; the process is highly non-linear and inherently uncertain. Acknowledging and accounting for the uncertainty in precipitation and flood forecasts has become increasingly important with the move to risk-based warning and guidance services which combine the likelihood of flooding with the potential impact on society and the environment. A standard approach to accounting for uncertainty is to generate ensemble forecasts. Here the national Grid-to-Grid (G2G) model is coupled to a Best Medium Range (BMR) ensemble which consists of three models spanning different time horizons: an ensemble nowcast for the first 6h, which is blended with the short-range 2.2 km Met Office Global Regional Ensemble Prediction System (MOGREPS-UK) ensemble up to 36h and the ~20 km global MOGREPS-G up to day 6. The G2G model is driven by 15-minute accumulations on a 1 km grid. 16-months of precipitation and river flow ensemble forecasts have been processed to develop and assess a joint verification framework which can facilitate the evaluation of the end-to-end forecasting chain. Analysis concluded the following: (1) daily precipitation accumulations provide the best guidance in terms of rain volume for hydrological impacts. One reason may be because it removes the impact of timing errors at the sub-daily scale. However, sub-daily precipitation can be more closely related to river flow on an ensemble member-by-member basis. (2) Observation uncertainty is important. The same forecasts verified against three different observed precipitation sources (raingauge, radar or merged) can provide markedly different results and interpretations. G2G river flow performance can also be affected, when driven by these datasets rather than forecasts. (3) The change in precipitation-intensity with model is evident and has an impact on downstream modelling and verification. (4) The period used for ensemble verification should be at least two years. The 16-month test period was sufficient for generating enough precipitation threshold-exceedances for the 95th percentile: but insufficient for higher thresholds and for river flow thresholds above half the median annual maximum flood at sub-regional scales. (5) A new method of presenting Time-Window Probabilities (TWPs) has been developed for precipitation thresholds that are hydrologically relevant. Verification of these shows that probabilities are larger, and more reliable so that users can have greater confidence in them. (6) Overall precipitation forecast skill was far more uniform than for river-flow, primarily because the atmosphere is a continuum whilst catchments are finite and subject to external, non-atmospheric factors including antecedent moisture. (7) Though G2G can be sensitive to precipitation outliers, the precipitation ensemble is generally under-spread and spread does not appear to amplify or propagate to enhance the river flow ensemble spread, so spread is reduced rather than increased in the downstream application.

The intensity of cosmic ray neutrons is inversely correlated with the amount of water present in the surrounding environment. This effect is already employed by around 50 neutron sensors in the COSMOS-UK network to provide daily estimates of soil moisture across the UK. Here, these same sensors are used to automatically provide estimates of snow water equivalent (SWE). Lying snow is typically ephemeral and of shallow depth for most parts of the UK. Moreover, soil moisture is usually high and variable, which acts to increase uncertainties in the SWE estimate. Nevertheless, even under such challenging conditions, both above ground and buried cosmic ray neutron sensors are still able to produce potentially useful SWE estimates. Triple collocation analysis suggests typical uncertainties of less than around 4 mm under UK snow conditions.

Flooding from rivers, surface water and the sea is a major hazard in many cities of the world and is expected to increase in the future due to climate change.With funding from the UK-China Urban Flooding Research Impact Programme, part of the UK-China Newton Fund, researchers and practitioners from both countries are collaborating on a project which focuses on surface water flooding and aims to enhance technical co-operation and knowledge-sharing in urban flood risk management.A comparison of current approaches to urban flood risk management in China and the UK is presented, and a case study of the benefits of Sponge City construction in the city of Wuhan, China is used by way of demonstration.Interventions to increase infiltration rates and on-site storage prove most effective for the 1-year return period, but have a more limited effect as flood event rarity increases.The paper discusses how similar results have been found in the UK for case studies of Sustainable Drainage Systems (SuDS), either installed in new urban developments or retrofitted in older ones.These studies also recognise the multiple benefits brought by the installation of green infrastructure, particularly in terms of community engagement and well-being.

Abstract During surface water flooding events, emergency responders require detailed information on the risks posed in order to provide an appropriate and effective response. Few early warning systems quantitatively estimate the risk and impacts of surface water flooding. Improvements in computational processing capability, availability of new datasets and developments in forecasting models means that the forecasting information currently being supplied by the Flood Forecasting Centre can be improved upon through the application of a timely, impact‐based model. This article presents a novel approach to collating receptor datasets into a pre‐calculated Impact Library for use in a Hazard Impact Model (HIM) that will operate using real‐time probabilistic rainfall and surface runoff forecasts for England and Wales. The HIM provides an approach suitable for modelling flood impacts. Initial results are presented for a case study covering the 2012 floods in the North East of England. Information generated by the HIM provides additional benefits beyond current methods. Features include operator access to 1 km 15 min spatial–temporal data, analysis of individual impact criteria and modular refinement of the Impact Library to suit different situations. The HIM has been developed in partnership via the Natural Hazards Partnership.

A framework for joint verification of river flow and precipitation ensembles is developed and demonstrated over Britain for eventual use in an operational flood forecasting setting. The river flow ensembles are obtained from a distributed hydrological model, the G2G model, using an ensemble of 15 min precipitation accumulations as input on a 1 km grid. The precipitation ensemble consists of operational Numerical Weather Prediction (NWP) forecasts from the Met Office Unified Model. Both hourly and daily precipitation accumulations are verified, and the relevance of different accumulation periods discussed in the context of timing errors and hydrological response. The implications of precipitation observation error are investigated by comparing verification results from raingauge- and radar-derived precipitation estimates. Challenges of verification using only a limited record of precipitation ensembles, from a system only relatively recently made operational, are addressed. Methods of obtaining more robust verification statistics, given the available ensembles, are presented and demonstrated for an example period in December 2015. For precipitation, percentile thresholds are used to ensure a given number of threshold crossing events for analysis using a contingency table and derived skill scores. For river flow, percentiles thresholds are of less relevance to operational flood guidance. Instead, exceedance of a flow threshold of given rarity (return-period) is used as a surrogate measure of flood severity. At the regional scale, both river flow and precipitation verification analyses are found to be dependent on the locations considered. This is linked to variations in precipitation amount. For river flows, catchment properties - and in particular catchment size - are found to be a key influence on verification. It is demonstrated how such behaviour can be used to obtain more-robust river flow verification statistics at sub-regional scales.

Existing surface water flood forecasting methods in Scotland are based on indicative depth‐duration rainfall thresholds with limited understanding of the likelihood of inundation or associated impacts. Innovative risk‐based solutions are urgently needed to advance surface water forecasting capabilities for improved flood resilience in urban centres. A new model‐based solution was developed for Glasgow, linking 24‐h ensemble rainfall predictions from the Met Office Global and Regional Ensemble Prediction System for the UK (MOGREPS‐UK) with static flood risk maps through the Grid‐to‐Grid hydrological model. This new forecasting capability was used operationally by the Scottish Flood Forecasting Service during the 2014 Commonwealth Games to provide bespoke surface water flooding guidance to responders. The operational trial demonstrated the benefits of being able to provide targeted information on real‐time surface water flood risk. It also identified the high staff resource requirement to support the service due to the greater uncertainty in surface water flood forecasting compared to established fluvial and coastal methods.

Operational rainfall and flood forecasting systems across the world are increasingly using ensemble approaches. Such systems are operated by the Flood Forecasting Centre (FFC) and Scottish Flood Forecasting Service (SFFS) across Great Britain producing ensemble gridded hydrological forecasts for the next 5-6 days. In order to maximise the practical day-to-day use of these systems for decision-making and warning, duty hydro-meteorologists require a sound understanding of both the meteorological and hydrological ensemble forecast skill. In this work, a common verification framework is defined and used in order to understand the relative levels of skill in both rainfall and river flow forecasting systems. A blended 24-member ensemble precipitation forecast, produced by the Met Office, is used to drive the operational distributed hydrological model in ensemble mode. The hydrological forecasts provide output every 15 minutes out to 6 days on a 1km grid. The blended rainfall forecast is a mixture of the 2.2 km MOGREPS-UK ensemble up to 36h and the 32 km global MOGREPS-G ensemble at longer lead-times. The forecasts are interpolated on to a common 2 km grid and the hydrological model used is the Grid-to-Grid model (G2G) developed by the Centre for Ecology & Hydrology. To establish an upper bound on skill, assessments over a daily lead-time interval are studied first, and will be the focus here. Spatial and regional variations in forecast skill are compared between the precipitation (e.g. daily accumulations) and the river flow forecasts. Also of interest is the impact of catchment size and how to pool and present the skill metrics in a meaningful way for end-users. For precipitation, the impact of observation type: gridded gauge-only analyses and a radar-derived (gauge calibrated) precipitation product, is compared to quantify the uncertainty that comes from the observations. Of particular interest is understanding how the spread in the precipitation forecast is modulated by the downstream hydrological model. Is it inflated, does it remain comparable, or is it reduced? The work aims to establish the basis for a real-time monitoring tool that can assist hydro-meteorologists in their interpretation of operational ensemble forecasts, and facilitate associated decision making processes.

It has been common for flood forecasting systems to be commissioned at a catchment or regional level in response to local priorities and hydrological conditions, leading to variety in system design and model choice. As systems mature and efficiencies of national management are sought, there can be a drive towards system rationalisation, gaining an overview of model performance and consideration of simplification through model-type convergence. Flood forecasting model assessments, whilst overseen at a national level, may be commissioned and managed at a catchment and regional level, take a variety of forms and be large in number. This presents a challenge when an integrated national assessment is required to guide operational use of flood forecasts and plan future investment in flood forecasting models and supporting hydrometric monitoring. This contribution reports on how a nationally consistent framework for flood forecasting model performance has been developed to embrace many past, ongoing and future assessments for local river systems by engineering consultants across England & Wales. The outcome is a Performance Summary for every site model assessed which, on a single page, contains relevant catchment information for context, a selection of overlain forecast and observed hydrographs and a set of performance statistics with associated displays of novel condensed form. One display provides performance comparison with other models that may exist for the site. The performance statistics include skill scores for forecasting events (flow/level threshold crossings) of differing severity/rarity, indicating their probability and likely timing, which have real value in an operational setting. The local models assessed can be of any type and span rainfall-runoff (conceptual and transfer function) and flow routing (hydrological and hydrodynamic) forms. Also accommodated by the framework is the national G2G (Grid-to-Grid) distributed hydrological model, providing area-wide coverage across the fluvial rivers of England and Wales, which can be assessed at gauged

Major UK floods over the last decade have motivated significant technological and scientific advances in operational flood forecasting and warning. New joint forecasting centres between the national hydrological and meteorological operating agencies have been formed that issue a daily, national Flood Guidance Statement (FGS) to the emergency response community. The FGS is based on a Flood Risk Matrix approach that is a function of potential impact severity and likelihood. It has driven an increased demand for robust, accurate and timely forecast and alert information on fluvial and surface water flooding along with impact assessments. The Grid-to-Grid (G2G) distributed hydrological model has been employed across Britain at a 1km resolution to support the FGS. Novel methods for linking dynamic gridded estimates of river flow and surface runoff with more detailed offline flood risk maps have been developed to obtain real-time probabilistic forecasts of potential impacts, leading to operational trials. Examples of the national-scale G2G application are provided along with case studies of forecast flood impact from (i) an operational Surface Water Flooding (SWF) trial during the Glasgow 2014 Commonwealth Games, (ii) SWF developments under the Natural Hazards Partnership over England & Wales, and (iii) fluvial applications in Scotland.

The growing demand for improved risk-based Surface Water Flooding (SWF) warning systems is evident in EU directives and in the UK Government’s Pitt Review of the 2007 summer floods. This paper presents a novel approach for collating receptor and vulnerability datasets via the concept of an Impact Library, developed by the Health and Safety Laboratory as a depository of pre-calculated impact information on SWF risk for use in a real-time SWF Hazard Impact Model (HIM). This has potential benefits for the Flood Forecasting Centre (FFC) as the organisation responsible for the issuing of flood guidance information for England and Wales. The SWF HIM takes a pixel-based approach to link probabilistic surface water runoff forecasts produced by CEH’s Grid-to-Grid hydrological model with Impact Library information to generate impact assessments. These are combined to estimate flood risk as a combination of impact severity and forecast likelihood, at 1km pixel level, and summarised for counties and local authorities. The SWF HIM takes advantage of recent advances in operational ensemble forecasting of rainfall by the Met Office and of SWF by the Environment Agency and CEH working together through the FFC. Results are presented for a case study event which affected the North East of England during 2012. The work has been developed through the UK’s Natural Hazards Partnership (NHP), a group of organisations gathered to provide information, research and analysis on natural hazards for civil contingencies, government and responders across the UK.

The preparation of routine flood guidance statements and formulation of incident management strategies requires national operating agencies to have a firm understanding of the performance of flood forecasting models. Studies of flood forecasting model performance are commonly evaluated on a groupedcatchment or local basis and can lack the analytical consistency required for integration into coherent national assessments. Here, the first nationally consistent analysis of flood forecasting model performance across England and Wales is presented. Application of the assessment framework, accounting for regional and model-type differences, yields a national overview of relative forecasting capability for models in current operational use. To achieve extensive site coverage, information from many existing local performance studies are pooled into a single structure for analysis under a national framework. The performance information spanning a variety of local models is also compared against the area-wide national G2G (Grid-to-Grid) distributed model. An integrated national assessment gives an evidence base of model performance useful for guiding strategic planning and investment in flood forecasting models. A concise single-page Performance Summary has been created for each site model that contains performance statistics, forecast hydrographs and catchment properties to aid operational use. A prototype web portal has been developed to make information on forecasting model performance more accessible and understandable for end-users.

Flooding events that impede railway infrastructure can cause severe travel delays for the general public and large fines in delayed minutes for the rail industry. Early warnings of flood inundation can give more time to implement mitigation measures which help reduce cancellations, delays and fines. Initial work is reported on the development of a real-time flood inundation forecasting and mapping system for the Cowley Bridge track area near Exeter, UK. This location is on one of the main access routes to South West England and has suffered major floods in the past resulting in significant transport impacts. Flood forecasting systems in the UK mainly forecast river level/flow rather than extent and depth of flood inundation. Here, the development of a chain of coupled models is discussed that link rainfall to river flow, river level and flood extent for the rail track area relating to Cowley Bridge. Historical events are identified to test model performance in predicting inundation of railway infrastructure. The modelling system will operate alongside a series of in-situ sensors chosen to enhance the flood mapping forecasting system. Sensor data will support offline model calibration/verification and real-time data assimilation as well as monitoring flood conditions to inform track closure decisions.

A national assessment of the performance of flood forecasting models - operated in real-time by the Environment Agency, Natural Resources Wales and the national Flood Forecasting Centre - is crucial to their informed use for flood guidance across England & Wales. It is also essential to guide future strategic investment in flood incident management. This report provides a nationwide analysis of flood forecasting model performance across implementations by local centres of the National Flood Forecasting System (NFFS). It considers regional and model-type differences and presents an overview of the current forecasting capability of models in operational use. Previous forecast performance studies of local models were performed on a grouped-catchment or regional basis and not necessarily using a consistent assessment framework. Spatial analysis of flood forecasting model performance in this report is based on Wales and the English geographical regions that align to the old Environment Agency region names. The report also extends the forecast model performance information available for the Grid-to-Grid (G2G) model, a distributed grid-based hydrological model with rainfall-runoff and flow routing elements. G2G is implemented within the NFFS for the Flood Forecasting Centre as an area-wide national model across England & Wales. The G2G model forecasts are compared with those from the local models which span a variety of model-types: rainfall-runoff models of conceptual and transfer function form, and channel flow routing models of hydrological and hydrodynamic form. The approach taken to performance assessment has been to gather “raw” data (river flow observations, flow forecasts and historical simulation of flows) from previous local model performance studies. While there are significant regional differences in how these data were gathered and in the methodologies used to generate flood forecast model outputs, collation of the underlying “project” datasets has allowed standardisation of the methods of assessment used here. This report presents the background to a flood forecasting model “Performance Summary”, as a template for reporting performance at any site from a given model-type, including the underlying performance measures employed. The Performance Summary takes the form of one A4 page for each model at each site, and contains a variety of different performance measures and graphical displays. Just over 2,000 Performance Summary pages have been produced for those working in an operational setting or in strategic planning. The results contained in the Performance Summary for each site and model combination have been brought together and used as the basis of a national analysis and summary. This constitutes an extensive national evidence-base of model performance, stratified by model-type, model-group, geographical region and lead-time. Where there is a choice of model forecast, it also includes information on comparative performance. This report provides an update to the evidence-base contained in R&D Report SC130006/R2. It includes performance analysis of a further 245 local site models, with a total of 921 now analysed along with the 829 sites at which G2G is assessed. The R&D report should be consulted for further background of a strategic nature and omitted here as not of such immediate operational relevance.