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Weather radar provides rainfall estimates at high resolution in both space and time, which is useful for many hydrological applications. Despite this, the radar rainfall estimation process introduces many sources of error, impacting the reliability of results obtained from the radar rainfall estimates. Key error sources include signal attenuation, radar calibration issues, ground clutter contamination, variability in the drop-size distribution and variation in the vertical profile of reflectivity. To gain an improved understanding of potential limitations, and the corresponding uncertainty of rainfall rates, the impact of these errors has been systematically investigated, developing a radar error model by inverting the rainfall estimation process.To this end, an ensemble of realistic rainfall events is simulated, and working backwards in a stochastic manner gives an ensemble of weather radar images, corresponding to each rainfall event, at each time step. The radar error model includes random noise effects, drop-size distribution errors, sampling estimation variance and importantly, attenuation effects. To allow for direct comparisons, standard radar processing methods are applied to each radar image, to obtain corrected ‘best guess’ rainfall estimates which would be obtained from each weather radar ensemble member in real world applications. The difference between the simulated and corrected rainfall for each ensemble member is then treated as the uncertainty corresponding to the radar rainfall estimation process.A simple measure is introduced, to help understand how often errors result in a rainfall signal completely irretrievable, referred to as ‘rainfall shadow’. Areas of rainfall that are ‘shadowed’ are defined as pixels where the simulated ‘true’ rainfall rate is significant, but the ensemble member has less than 10% of the original signal. This is equivalent to considering where a significant rainfall rate has been completely lost, and would therefore be irretrievable using standard correction methods, to quantify the frequency of occurrence in real-world radar rainfall applications. The impact of location of rainfall within images is considered, by introducing the second moment of area for radar images, in order to quantify the proximity of intense rainfall to the radar transmitter.Results show relationships between rainfall shadows and high bias and uncertainty in rainfall estimates, related to the amount of rainfall (i.e. proportion and rates) in images. More central rainfall also results in higher errors and higher variability. The minimum likelihood of occurrence of rainfall shadows showed that 50% of event images have at least 3% of significant rainfall shadowed. In addition, 25% of images had a shadowed area of over 45km2, with the minimum largest shadow in one area for 5% of images exceeding an area of 50km2. This gap would result in an underestimation of the impact of potential floods, showing that weather radar has potential for important information to be lost. A model framework for representing this uncertainty in the radar rainfall estimation process provides methodology for assessing the impacts of radar rainfall errors on hydrological applications.

Flows in rivers can be strongly affected by obstacles to flow or artificial structures such as bridges, weirs and dams. This is especially true during floods, where significant backwater effects or diversion of flow out of bank can result. However, within contemporary industry practice, linear features such as bridges are often modelled using coarse approximations, empirically based methods or are omitted entirely. Presented within this paper is a novel Riemann solver which is capable of modelling the influence of such features within hydrodynamic flood models using finite volume schemes to solve the shallow water equations. The solution procedure represents structures at the interface between neighbouring cells and uses a combination of internal boundary conditions and a different form of the conservation laws in the adjacent cells to resolve numerical fluxes across the interface. Since the procedure only applies to the cells adjacent to the interface at which a structure is being modelled, the method is therefore potentially compatible with existing hydrodynamic models. Comparisons with validation data collected from a state of the art research flume demonstrate that the solver is suitable for modelling a range of flow conditions and structure configurations such as bridges and gates., Paper submitted for publication in Applied Mathematics and Computation special issue titled 'Hyperbolic PDE in computational physics: advanced mathematical models and structure-preserving numerics'. 17 pages, 13 figures

Investigating the nature of trends in time series is one of the most common analyses performed in hydro-climate research. However, trend analysis is also widely abused and misused, often overlooking its underlying assumptions, which prevent its application to certain types of data. A mechanistic application of graphical diagnostics and statistical hypothesis tests for deterministic trends available in ready-to-use software can result in misleading conclusions. This problem is exacerbated by the existence of questionable methodologies that lack a sound theoretical basis. As a paradigmatic example, we consider the so-called Şen’s ‘innovative’ trend analysis (ITA) and the corresponding formal trend tests. Reviewing each element of ITA, we show that (1) ITA diagrams are equivalent to well-known two-sample quantile-quantile (q–q) plots; (2) when applied to finite-size samples, ITA diagrams do not enable the type of trend analysis that it is supposed to do; (3) the expression of ITA confidence intervals quantifying the uncertainty of ITA diagrams is mathematically incorrect; and (4) the formulation of the formal tests is also incorrect and their correct version is equivalent to a standard parametric test for the difference between two means. Overall, we show that ITA methodology is affected by sample size, distribution shape, and serial correlation as any parametric technique devised for trend analysis. Therefore, our results call into question the ITA method and the interpretation of the corresponding empirical results reported in the literature.

Using data from 520 gauging stations in Britain and gridded rainfall datasets, the seasonality of storm rainfall and flood runoff is compared and mapped. Annual maximum (AMAX) daily rainfall occurs predominantly in summer, but AMAX floods occur most frequently in winter. Seasonal occurrences of annual daily rainfall and flood maxima differ by more than 50% in dry lowland catchments. The differences diminish with increasing catchment wetness, increase with rainfalls shorter than daily duration and are shown to depend primarily on catchment wetness, as illustrated by variations in mean annual rainfall. Over the whole dataset, only 34% of AMAX daily flood events are matched to daily rainfall annual maxima (and only 20% for 6-hour rainfall maxima). The discontinuity between rainfall maxima and flooding is explained by the consideration of coincident soil moisture storage. The results have serious implications for rainfall-runoff methods of flood risk estimation in the UK where estimation is based on a depth–duration–frequency model of rainfall highly biased to summer. It is concluded that inadequate treatment of the seasonality of rainfall and soil moisture seriously reduces the reliability of event-based flood estimation in Britain.

Downscaling is usually necessary for robust hydrological impact assessments. This may be undertaken using a wide range of methods, including a combination of dynamical and statistical-stochastic downscaling. This study uses the Spatial–Temporal Neyman-Scott Rectangular Pulses model—RainSimV3, the precipitation-conditioned daily weather generator—ICAAM-WG, and the change factor approach for downscaling synthetic climate scenarios for robust hydrological impact assessment at middle-sized basins. The ICAAM-WG was developed based on the concept of the Climate Research Unit daily weather generator (CRU-WG), motivated by the need for improved representation of heat waves by downscaling methods given the positive feedback between low soil moisture and high air temperature. We demonstrated the validity of the proposed methodology in the 705-km2 Mediterranean climate basin in southern Portugal. The results show that, for the control period 1980–2010, both RainSimV3 and ICAAM-WG reproduced not only the mean climatology, but also extreme wet and low precipitation events, as well as the extremes of temperature and heat waves. We found that downscaling with ICAAM-WG (SIM6), which uses second-order autoregressive processes for the simulation of temperature during consecutive dry and wet days, outperformed ICAAM-WG (SIM4), which used only first-order autoregressive processes, leading to improved simulation of heat waves. ICAAM-WG (SIM6) well reproduced observed heatwave extremes with return periods of up to 30 years; however, ICAAM-WG (SIM4) overestimated these extremes substantially. This indicates the importance of incorporating second-order autoregressive processes in the simulation of heatwave length. In the context of climate warming, the proposed methodology provides a tool to improve downscaled projections of future extremes with confidence intervals for not only wet events but also dry spells and heat waves.

Reanalysis datasets are increasingly used to drive flood models, especially for continental and global analysis and in areas of data scarcity. However, the consequence of this for risk estimation has not been fully explored. We investigate the implications of four reanalysis products (ERA-5, CFSR, MERRA-2 and JRA-55) on simulations of historic flood events in five basins in England. These results are compared to a benchmark national gauge-based product (CEH-GEAR1hr). The benchmark demonstrated better accuracy than reanalysis products when compared with observations of water depth and flood extent. All reanalysis products predicted fewer buildings would be inundated by the events than the national dataset. JRA-55 was the worst by a significant margin, underestimating by 40 % compared with 14 %–18 % for the other reanalysis products. CFSR estimated building inundation the most accurately, while ERA-5 demonstrated the lowest error in terms of river stage (29.4 %) and floodplain depth (28.6 %). Accuracy varied geographically, and no product performed best across all basins. Global reanalysis products provide a useful resource for flood modelling where no other data are available, but they should be used with caution due to the underestimation of impacts shown here. Until a more systematic international strategy for the collection of rainfall and flood impact data ensures more complete global coverage for validation, multiple reanalysis products should be used concurrently to capture the range of uncertainties.

Rainfall-runoff models produce outputs which differ from observations due to uncertainties in process description, process parametrization, uncertainties in observations and changing spatio-temporal variability of input and state variables. Traditionally, attention has been focused mostly on process parameters to quantify runoff uncertainty using e.g. GLUE.Here we have focused on the role of precipitation uncertainty relating to discharge. For this purpose, we used an inverse model approach. We generated time series of daily precipitation with high spatial resolution using a modified version of Random Mixing and the Shannon-Whittaker interpolation to improve simulated runoff using the SHETRAN (physically-based) and HBV (conceptual) models, both spatially distributed for various sub-catchments of the Neckar River in Germany. HBV was initially calibrated using interpolated precipitation, while SHETRAN uses pre-defined parameters. The modelling goal was to find a spatio-temporal series of precipitation which improved the predicted runoff, under the constraints that the precipitation values be the same at the measurement locations and share their spatial variability with the observations at a given step. Care was taken to select subsequent days for improvement such that the previously improved step considered the effect of the previous steps.We asked the questions: i) does improving precipitation inputs for one sub-catchment bring runoff improvement for the others? ii) Can the improved precipitation using SHETRAN be used for HBV and still get runoff improvements as compared to the interpolated precipitation and vice versa?Results showed that overall runoff errors were reduced by 40 to 50% for all sub-catchments. For the peaks, a reduction of 70 to 90% was observed. As compared with the interpolated fields, new fields showed similar overall distribution but different details at finer spatial scales. Swapping improved precipitations between SHETRAN and HBV showed improvement as compared with the discharge from interpolated precipitation.

Dynamical model skill in forecasting extratropical precipitation is limited beyond the medium-range (around 15 d), but such models are often more skilful at predicting atmospheric variables. We explore the potential benefits of using weather pattern (WP) predictions as an intermediary step in forecasting UK precipitation and meteorological drought on sub-seasonal timescales. Mean sea-level pressure forecasts from the European Centre for Medium-Range Weather Forecasts ensemble prediction system (ECMWF-EPS) are post-processed into probabilistic WP predictions. Then we derive precipitation estimates and dichotomous drought event probabilities by sampling from the conditional distributions of precipitation given the WPs. We compare this model to the direct precipitation and drought forecasts from the ECMWF-EPS and to a baseline Markov chain WP method. A perfect-prognosis model is also tested to illustrate the potential of WPs in forecasting. Using a range of skill diagnostics, we find that the Markov model is the least skilful, while the dynamical WP model and direct precipitation forecasts have similar accuracy independent of lead time and season. However, drought forecasts are more reliable for the dynamical WP model. Forecast skill scores are generally modest (rarely above 0.4), although those for the perfect-prognosis model highlight the potential predictability of precipitation and drought using WPs, with certain situations yielding skill scores of almost 0.8 and drought event hit and false alarm rates of 70 % and 30 %, respectively.

Achieving urban flood resilience at local, regional and national levels requires a transformative change in planning, design and implementation of urban water systems. Flood risk, wastewater and stormwater management should be re-envisaged and transformed to: ensure satisfactory service delivery under flood, normal and drought conditions, and enhance and extend the useful lives of ageing grey assets by supplementing them with multi-functional Blue-Green infrastructure. The aim of the multidisciplinary Urban Flood Resilience (UFR) research project, which launched in 2016 and comprises academics from nine UK institutions, is to investigate how transformative change may be possible through a whole systems approach. UFR research outputs to date are summarised under three themes. Theme 1 investigates how Blue-Green and Grey (BG + G) systems can be co-optimised to offer maximum flood risk reduction, continuous service delivery and multiple co-benefits. Theme 2 investigates the resource capacity of urban stormwater and evaluates the potential for interoperability. Theme 3 focuses on the interfaces between planners, developers, engineers and beneficiary communities and investigates citizens’ interactions with BG + G infrastructure. Focussing on retrofit and new build case studies, UFR research demonstrates how urban flood resilience may be achieved through changes in planning practice and policy to enable widespread uptake of BG + G infrastructure.

City Catchment Analysis Tool – CityCAT-is a novel software system for rapid assessment of combined pluvial and fluvial flood risk using a unique combination of: efficient software architecture throughout and especially in the numerical part; use of standard, readily available data sets; efficient algorithms for grid generation; and robust and accurate solutions of the flow equations. It is based on advanced software architecture and accurate solutions for complex free-surface flow over the terrain distinguishing between permeable and impermeable surfaces and taking into account effects of man-made features such as buildings as obstacles to flow. The software is firstly rigorously validated with demanding test cases based on analytical solutions and laboratory studies. Then the unique capability for assessment of the effectiveness of specific flood alleviation interventions across large urban domains, such as roof storage on buildings or introduction of permeable surfaces, is demonstrated.

Duration is a key characteristic of floods influencing the design of protection infrastructures for prevention, deployment of rescue resources during the emergency, and repartition of damage costs in the aftermath. The latter financial aspect mainly relies on the insurance industry and allows the transfer of damage costs from the public sector to the private capital market. In this context, the cost of catastrophes affecting a large amount of insured properties is partly or totally transferred from insurance companies to reinsurance companies by contracts that define the portion of transferred costs according to the temporal extent of the flood events synthesized in the so-called hours clause. However, hours clauses imply standard flood event durations, such as 168 h (1 week), regardless of the hydrological properties characterizing different areas. In this study, we firstly perform a synoptic-scale exploratory analysis to investigate the duration and magnitude of large flood events that occurred around the world and in Europe between 1985 and 2016, and then we present a data-driven procedure devised to compute flood duration by tracking flood peaks along a river network. The exploratory analysis highlights the link of flood duration and magnitude with flood generation mechanism, thus allowing the identification of regions that are more or less prone to long-lasting events exceeding the standard hours clauses. The flood tracking procedure is applied to seven of the largest river basins in Central and Eastern Europe (Danube, Rhine, Elbe, Weser, Rhone, Loire, and Garonne). It correctly identifies major flood events and enables the definition of the probability distribution of the flood propagation time and its sampling uncertainty. Overall, we provide information and analysis tools readily applicable to improve reinsurance practices with respect to spatiotemporal extent of flooding hazard.

The impact of stormwater drainage and detention ponds on flooding is assessed using statistical analysis and physically based computer simulation of a 45-year case study for a peri-urban catchment. In 1978, the 54 km2 Ouseburn catchment in Newcastle upon Tyne was impacted by the connection of a new 2.1 km2 residential development, directly to the Ouseburn River, via a stormwater drain, which reduced the time to peak and increased flood risk. Further residential developments of 1.6 km2 have been built since 2004, again with separated sewer systems, but this time linked to stormwater detention ponds before draining into the Ouseburn River. Detailed analysis of the data, confirmed with computer simulation, shows that in contrast with the 1978 intervention, these new developments had only a minimal effect on the flows in the Ouseburn River, in fact achieving a small reduction in peak flows for large events. This study assesses the post-construction efficiency of such systems, and we show that the stormwater detention ponds are working as designed.

Flooding in urban areas during heavy rainfall, often characterized by short duration and high-intensity events, is known as "surface water flooding." Analyzing surface water flood risk is complex as it requires understanding of biophysical and human factors, such as the localized scale and nature of heavy precipitation events, characteristics of the urban area affected (including detailed topography and drainage networks), and the spatial distribution of economic and social vulnerability. Climate change is recognized as having the potential to enhance the intensity and frequency of heavy rainfall events. This study develops a methodology to link high spatial resolution probabilistic projections of hourly precipitation with detailed surface water flood depth maps and characterization of urban vulnerability to estimate surface water flood risk. It incorporates probabilistic information on the range of uncertainties in future precipitation in a changing climate. The method is applied to a case study of Greater London and highlights that both the frequency and spatial extent of surface water flood events are set to increase under future climate change. The expected annual damage from surface water flooding is estimated to be to be £171 million, £343 million, and £390 million/year under the baseline, 2030 high, and 2050 high climate change scenarios, respectively.

Weather-pattern, or weather-type, classifications are a valuable tool in many applications as they characterize the broad-scale atmospheric circulation over a given region. This study analyses the aspects of regional UK precipitation and meteorological drought climatology with respect to a new set of objectively defined weather patterns. These new patterns are currently being used by the Met Office in several probabilistic forecasting applications driven by ensemble forecasting systems. Weather pattern definitions and daily occurrences are mapped to Lamb weather types (LWTs), and parallels between the two classifications are drawn. Daily precipitation distributions are associated with each weather pattern and LWT. Standardized precipitation index (SPI) and drought severity index (DSI) series are calculated for a range of aggregation periods and seasons. Monthly weather-pattern frequency anomalies are calculated for SPI wet and dry periods and for the 5% most intense DSI-based drought months. The new weather-pattern definitions and daily occurrences largely agree with their respective LWTs, allowing comparison between the two classifications. There is also broad agreement between weather pattern and LWT changes in frequencies. The new data set is shown to be adequate for precipitation-based analyses in the UK, although a smaller set of clustered weather patterns is not. Furthermore, intra-pattern precipitation variability is lower in the new classification compared to the LWTs, which is an advantage in this context. Six of the new weather patterns are associated with drought over the entire UK, with several other patterns linked to regional drought. It is demonstrated that the new data set of weather patterns offers a new opportunity for classification-based analyses in the UK.

Water scarcity is a critical issue in Iberia but few studies have investigated future drought over the region. The few that do are continental or global studies and do not report the range of possible future projections. In this paper, historical drought and future projections of drought were examined, using observed and downscaled rainfall data from climate models, for the main Iberian international basins (Douro, Tagus and Guadiana). Two drought indices were used, the standardized precipitation index (SPI) and the drought severity index (DSI). However, problems were found in the distribution fitting required for the calculation of SPI which render this index inadequate for assessing droughts in Iberia. The skill of CMIP5 climate models in representing historical drought for the Douro, Tagus and Guadiana is very variable with some not showing enough persistence of dry conditions, and others simulating droughts that are too long and too severe. Nevertheless, the observed range of drought conditions for these basins was encompassed by the ensemble members. Furthermore, we found no relationship between performance in simulating mean large-scale circulation and model ability to reproduce the historical drought characteristics for the region. All models project an intensification of drought conditions for the three basins. However, some only project small increases, while most project extreme multi-year droughts by the end of the century. We found that climate model future projections are neither related to their performance in simulating historical drought nor to their large-scale circulation patterns. This result emphasises the need for climate change impact studies to take into account the whole range of climate model projections in order to provide the best information for robust adaptation. Choosing one or a subset of climate models based on historical performance would not lead to a credible and justified reduction in uncertainty.

A robust hydrological impact assessment is indispensable for mitigation and adaptation planning. This study presents an integrated modelling methodology for evaluating climate change impacts on water availability, sediment yield and extreme events at the catchment scale. We propose the use of the spatial–temporal Neyman–Scott Rectangular Pulses (STNSRP) model—RainSim V3 and the rainfall conditioned daily weather generator—ICAAM-WG, as well as the physically based spatially distributed hydrological model—SHETRAN. The change factor approach was applied for obtaining unbiased rainfall and temperature statistics. The ICAAM-WG was developed based on the modified Climate Research Unit daily Weather Generator (CRU-WG). The methodology is proposed to generate synthetic series of hourly precipitation, daily temperature and potential evapotranspiration, hourly runoff and hourly sediment discharge. We demonstrated a possible application in a 705-km2 Mediterranean climate basin in southern Portugal. The case study showed the evaluation of future climate change impacts on annual and monthly water balance components and sediment yield, annual and seasonal flow duration curves, empirical extreme value distributions and the theoretical fits. It did not consider the possible uncertainty due to the limit of computational resources. The methodology can be well justified as follows: (a) the use of synthetic hourly instead of daily precipitation enables SHETRAN to be more capable of reproducing reliable storm runoff processes and the consequent sediment transport processes; (b) the use of SHETRAN makes possible the impact assessment to be accessible for any model grid square within the study basin; (c) the use of a statistical–stochastic downscaling method facilitates the generation of the synthetic series with unlimited length. It makes possible robust hydrological impact assessments if uncertainties related to the global climate model, regional climate model, greenhouse gas emission scenario, downscaling method, hydrological model and observational data are considered.

The expansion of built-up areas within urban and peri-urban river catchments has the potential to increase flooding. Hydrological modelling of these catchments is an important tool to analyse and manage the increasing flood hazard, but it requires an understanding of the areas contributing to runoff from the urban parts of the catchment. This is difficult as in many catchments fine resolution data is not available and the connectivity of the impervious areas, sewer networks and the drainage network are not well understood. In this research, we present a novel approach for calculating the separate and combined sewer fractions for an urban river catchment and use these fractions to improve hydrological modelling of the catchment. The fractions are calculated using monthly runoff coefficients from paired rural/urban catchments, with the procedure carried out for 20 highly urbanized catchments within the UK. The calculated fractions have been compared with the OS MasterMap high-resolution land use data set and also verified against results obtained with the standard method of using hourly rainfall-runoff data in an event-based approach. Hydrological modelling using the physically-based Shetran system demonstrates improved results using the approach developed here. For urban and peri-urban catchments without fine resolution sewer and hydrological data, it is recommended that this new method should be applied before detailed hydrological modelling of the catchments is undertaken in order to evaluate the flows to the drainage network and determine the impact of urbanisation on the water balance.

Two approaches are presented for using the UKCP09 weather generator (WG) to assess changes in extreme short-duration rainfall. The first provides change factors for design storms of varying duration and frequency, building on established hydrological techniques and with uncertainty estimation. The second approach characterises and facilitates the selection of WG timeseries for use in impact models. The methods have been tested at ten case study sites around the UK. The results show an increase in the intensity of short-duration (1 h and 12 h) events in winter but a reduction in summer, although the latter is subject to high levels of uncertainty. The number of intense events increases slightly and more significantly for some locations, especially in winter. Generally, it is hard to establish any conclusions geographically, although increases in rainfall intensity and events in the Lake District site stand out. The methods provide practical techniques for utilising the UKCP09 WG to assess the impacts of climate change, in particular for urban drainage and sewer modelling.

Summary Floods pose multi-dimensional hazards to critical infrastructure and society and these hazards may increase under climate change. While flood conditions are dependent on catchment type and soil conditions, seasonal precipitation extremes also play an important role. The extreme precipitation events driving flood occurrence may arrive non-uniformly in time. In addition, their seasonal and inter-annual patterns may also cause sequences of several events and enhance likely flood responses. Spatial and temporal patterns of extreme daily precipitation occurrence are characterized across the UK. Extreme and very heavy daily precipitation is not uniformly distributed throughout the year, but exhibits spatial differences, arising from the relative proximity to the North Atlantic Ocean or North Sea. Periods of weeks or months are identified during which extreme daily precipitation occurrences are most likely to occur, with some regions of the UK displaying multimodal seasonality. A Generalized Additive Model is employed to simulate extreme daily precipitation occurrences over the UK from 1901 to 2010 and to allow robust statistical testing of temporal changes in the seasonal distribution. Simulations show that seasonality has the strongest correlation with intra-annual variations in extreme event occurrence, while Sea Surface Temperature (SST) and Mean Sea Level Pressure (MSLP) have the strongest correlation with inter-annual variations. The north and west of the UK are dominated by MSLP in the mid-North Atlantic and the south and east are dominated by local SST. All regions now have a higher likelihood of autumnal extreme daily precipitation than earlier in the twentieth century. This equates to extreme daily precipitation occurring earlier in the autumn in the north and west, and later in the autumn in the south and east. The change in timing is accompanied by increases in the probability of extreme daily precipitation occurrences during the autumn, and in the number of days with a very high probability of an extreme event. These results indicate a higher probability of several extreme occurrences in succession and a potential increase in flooding.

Globally, water resources management faces significant challenges from changing climate and growing populations. At local scales, the information provided by climate models is insufficient to support the water sector in making future adaptation decisions. Furthermore, projections of change in local water resources are wrought with uncertainties surrounding natural variability, future greenhouse gas emissions, model structure, population growth, and water consumption habits. To analyse the magnitude of these uncertainties, and their implications for local-scale water resource planning, we present a top-down approach for testing climate change adaptation options using probabilistic climate scenarios and demand projections. An integrated modelling framework is developed which implements a new, gridded spatial weather generator, coupled with a rainfall-runoff model and water resource management simulation model. We use this to provide projections of the number of days and associated uncertainty that will require implementation of demand saving measures such as hose pipe bans and drought orders. Results, which are demonstrated for the Thames Basin, UK, indicate existing water supplies are sensitive to a changing climate and an increasing population, and that the frequency of severe demand saving measures are projected to increase. Considering both climate projections and population growth, the median number of drought order occurrences may increase 5-fold by the 2050s. The effectiveness of a range of demand management and supply options have been tested and shown to provide significant benefits in terms of reducing the number of demand saving days. A decrease in per capita demand of 3.75 % reduces the median frequency of drought order measures by 50 % by the 2020s. We found that increased supply arising from various adaptation options may compensate for increasingly variable flows; however, without reductions in overall demand for water resources such options will be insufficient on their own to adapt to uncertainties in the projected changes in climate and population. For example, a 30 % reduction in overall demand by 2050 has a greater impact on reducing the frequency of drought orders than any of the individual or combinations of supply options; hence, a portfolio of measures is required.

Classic extreme value theory provides asymptotic distributions of block maxima (BM) Y or peaks over threshold (POT). However, as BM and POT are relatively small subsets of the values assumed by the parent process Z, alternative approaches have been proposed to make inferences on extremes by using intermediate values and non-asymptotic models. In this study, we investigate the finite sample theory of extremes based on order statistics, and present a set of results enabling the analysis of the properties of non-asymptotic distributions of BM in finite-size blocks of data under the assumption that the parent process Z has stationary temporal dependence. In particular, we suggest the beta-binomial distribution ( F β B ) as a suitable approximation of the marginal distribution of order statistics and BM under dependence, thus generalizing the theoretical results available under independence. We demonstrate the usefulness of the F β B distribution in three conceptual applications of hydrological interest. Firstly, we review the so-called Complete Time-series Analysis (CTA) framework, showing that the differences between FY and FZ are due to the inherent theoretical nature of the processes Y and Z and the different probabilistic failure scenarios described by FY and FZ. Secondly, we provide a theoretical rule to select the number of the largest maxima (LM) and over-threshold exceedances (OT) required to approximate the desired portion of the upper tail of FZ with specified accuracy. Finally, we discuss how the F β B distribution offers an interpretation and generalization of the so-called metastatistical extreme value (MEV) framework and its simplified version (SMEV), avoiding the use of high-dimensional joint distributions and preliminary data thresholding and declustering. All methodological results are validated by Monte Carlo simulations involving widely used stochastic processes with persistence. Real-world stream flow data are also analyzed as proof of concept.

Over the last two decades, the frequency of water resource drought in the UK, coupled with the more recent pan-European drought of 2003, has increased concern over changes in climate. Using the UKCIP02 Medium-High (SRES A2) scenario for 2070–2100, this study investigates the impact of climate change on the operation of the Integrated Resource Zone (IRZ), a complex conjunctive-use water supply system in north-western England. The results indicate that the contribution of individual sources to yield may change substantially but that overall yield is reduced by only 18%. Notwithstanding this significant effect on water supply, the flexibility of the system enables it to meet modelled demand for much of the time under the future climate scenario, even without a change in system management, but at significant expense for pumping additional abstraction from lake and borehole sources. This research provides a basis for the future planning and management of the complex water resource system in the north-west of England.

This study investigated the impact of fluvial flooding on bridges. A high-resolution flood model is coupled with damage and transport modelling, to assess structural vulnerability and critical functionality of bridges subjected to flooding. The study involves integrated investigation of riverine bridges, devises a systematic methodology and practical implementation in computer-based decision support tools. The research draws on the principles of a risk-based approach to assess the hydrodynamic effects of floods at bridges, and moves these forward by advancing a deep analysis over the whole UK territory. This research will fill the gap of current guidance for design and assessment of bridges relevance within the overall transport system, highly inadequate for evaluating these risks in light of the increasing external pressures.

Stochastic weather generators (WGs), which provide long synthetic time series of weather variables such as rainfall and potential evapotranspiration (PET), have found widespread use in water resources modelling. When conditioned upon the changes in climatic statistics (change factors, CFs) predicted by climate models, WGs provide a useful tool for climate impacts assessment and adaption planning. The latest climate modelling exercises have involved large numbers of global and regional climate models integrations, designed to explore the implications of uncertainties in the climate model formulation and parameter settings: so called ‘perturbed physics ensembles’ (PPEs). In this paper we show how these climate model uncertainties can be propagated through to impact studies by testing multiple vectors of CFs, each vector derived from a different sample from a PPE. We combine this with a new methodology to parameterise the projected time-evolution of CFs. We demonstrate how, when conditioned upon these time-dependent CFs, an existing, well validated and widely used WG can be used to generate non-stationary simulations of future climate that are consistent with probabilistic outputs from the Met Office Hadley Centre's Perturbed Physics Ensemble. The WG enables extensive sampling of natural variability and climate model uncertainty, providing the basis for development of robust water resources management strategies in the context of a non-stationary climate.

Trends in the locations and technologies of UK electricity generation plant suggest that demand for cooling water abstractions from rivers will decrease in the coming decades, unless there is widespread uptake of carbon dioxide capture and storage (CCS). CCS may prove to be essential if the UK is to achieve its carbon dioxide and greenhouse gas emission targets. ‘Decarbonisation’ strategies that rely on CCS are therefore potentially at risk of not having sufficient cooling water in periods of low river flows. In this paper, regional freshwater demands for cooling water are assessed against regional freshwater availability at low flows in a scenario of medium climate change. In the strategy with high CCS, demands for water greatly exceed current and future availability in the north-west (NW) England, Humber, East (E) Midlands and Thames regions. These risks can be mitigated by increasing the penetration of hybrid cooling systems or shifting generating capacity to estuaries or the coast. The former could reduce national water use by up to 35%, whereas applying the latter in the NW England, Humber and E Midlands regions offers nationwide reductions from 30 to 50%.

Clouds play a key role in hydroclimatological variability by modulating the surface energy balance and air temperature. This study utilizes MODIS cloud cover data, with corroboration from global meteorological reanalysis (ERA-Interim) cloud estimates, to describe a cloud climatology for the upper Indus River basin. It has specific focus on tributary catchments in the northwest of the region, which contribute a large fraction of basin annual runoff, including 65% of flow originating above Besham, Pakistan or 50 km3 yr−1 in absolute terms. In this region there is substantial cloud cover throughout the year, with spatial means of 50%–80% depending on the season. The annual cycles of catchment spatial mean daytime and nighttime cloud cover fraction are very similar. This regional diurnal homogeneity belies substantial spatial variability, particularly along seasonally varying vertical profiles (based on surface elevation). Correlations between local near-surface air temperature observations and MODIS cloud cover fraction confirm the strong linkages between local atmospheric conditions and near-surface climate variability. These correlations are interpreted in terms of seasonal and diurnal variations in apparent cloud radiative effect and its influence on near-surface air temperature in the region. The potential role of cloud radiative effect in recognized seasonally and diurnally asymmetrical temperature trends over recent decades is also assessed by relating these locally observed trends to ERA-Interim-derived trends in cloud cover fraction. Specifically, reduction in nighttime cloud cover fraction relative to daytime conditions over recent decades appears to provide a plausible physical mechanism for the observed nighttime cooling of surface air temperature in summer months.

Recent developments in urban drainage modelling allow for a more realistic coupling of the two-dimensional (2D) surface and one-dimensional (1D) sub-surface drainage domain exchanging water through storm drain inlets instead of a sub-catchment approach based on manholes. Experience has shown, however, that comprehensive records of storm drain inlet locations are often missing or incomplete, preventing users accessing the full benefit of these modelling capabilities. Therefore, this study developed a GIS routine to generate synthetic storm drain inlet locations for the purpose of urban flood modelling. Hydrodynamic model results for a synthetically generated and surveyed storm drain inlet network were obtained using the CityCAT 1D/2D system. On a catchment scale the flow field (surface and flow captured by inlets) simulated by the network of synthetic storm drainage inlets shows satisfactory results when compared with that simulated using the actual network. The results also highlight the sensitivity of the inflows to relatively small changes in terms of the location of storm drain inlets and the effectiveness of storm drain inlets in ponding areas.

Hot weather events, ventilation assets, changing passenger demand and service expectations have all caused increased attention on thermal comfort on London’s Tube. This study provides estimates of the future number of days when passengers travelling on sections of the Tube could be subjected to thermal discomfort under future scenarios of climate change, and the potential number of passengers dissatisfied. A risk based methodology is presented, integrating a spatial weather generator modified for urban areas and a thermal comfort model. The study provides an initial assessment of adaptation options by considering the implications of lowering train temperatures by 2 °C and 4 °C to represent saloon cooling. Median results under a 2050 high scenario indicate that all Tube lines assessed could experience near-complete passenger dissatisfaction with the thermal environment in trains in the unlikely event that nothing else were to change. Adaptation aimed at lowering train temperatures has the potential to provide tangible improvements in thermal comfort. However, this was not projected to be sufficient to maintain comfortable thermal conditions for many of the lines in the 2050s under high emission scenarios, requiring a combination of other infrastructure cooling measures to be implemented in parallel.

Wrapt (Weather generator rainfall analysis processing tool) is a software tool created as part of a UK Water Industry Research project on the impact of climate change on sewer networks. It allows users to generate design storm change factors and select timeseries from output of the UKCP09 (UK climate projections 2009) weather generator without the need for manual processing or detailed knowledge of weather generators. Wrapt has been developed to assess changes in extremes of short-duration rainfall events – relevant to assessing the performance of sewer networks – but also has potential for applications elsewhere (e.g. water quality modelling).

This paper proposes a non-negative depth reconstruction method for improving the numerical performance of a partial inertial model (PIM) for applications involving steep-slope and low-friction conditions. The PIM solves the continuity equation of two-dimensional (2D) shallow water equations (SWEs) with the interface fluxes evaluated by a simplified momentum equation that partially restores the inertial terms. In applying the PIM to flood simulations, a practical challenge is to represent complex topography and to track the moving wet–dry interface without resulting in negative water depths. Another challenge is to avoid the numerical issue caused by the lack of physical diffusive terms when it is applied to low-friction cases. To cope with these difficulties, the PIM is improved by introducing a non-negative depth reconstruction method, featuring two different ways for calculating the interface fluxes. The performance of the improved PIMs is investigated through applications to several theoretical and practical benchmark test cases. The comparison of the numerical results against analytical solutions or predictions from the original PIM and a full 2D finite-volume hydrodynamic model shows that the proposed reconstruction method can avoid non-negative water depth predictions, and improve the numerical performance of the original PIM when applied to steep-slope and low-friction conditions.

High temperatures and heatwaves can cause large societal impacts by increasing health risks, mortality rates, and personal discomfort. These impacts are exacerbated in cities because of the Urban Heat Island (UHI) effect, and the high and increasing concentrations of people, assets and economic activities. Risks from high temperatures are now widely recognised but motivation and implementation of proportionate policy responses is inhibited by inadequate quantification of the benefits of adaptation options, and associated uncertainties. This study utilises high spatial resolution probabilistic projections of urban temperatures along with projections of demographic change, to provide a probabilistic risk assessment of heat impacts on urban society. The study focuses on Greater London and the surrounding region, assessing mortality risk, thermal discomfort in residential buildings, and adaptation options within an integrated framework. Climate change is projected to increase future heat-related mortality and residential discomfort. However, adjusting the temperature response function by 1–2 °C, to simulate adaptation and acclimatisation, reduced annual heat related mortality by 32–69 % across the scenarios tested, relative to a no adaptation scenario. Similar benefits of adaptation were seen for residential discomfort. The study also highlights additional benefits in terms of reduced mortality and residential discomfort that mitigating the urban heat island, by reducing albedo and anthropogenic heat emissions, could have.

Integrated flood management is essential in urban planning to align flood protection and mitigation with the complex social and physical infrastructure in cities, and involves managing surface water by retaining and transferring it along its pathway across multiple infrastructure systems. However, despite many potential flood management solutions (natural and engineered), spatial prioritization to implement these solutions from a catchment perspective remains difficult. A transferable, source-to-impact flood analysis is developed to identify locations with high surface flooding (impact) and locations contributing to this flooding (source), to define spatial prioritization criteria for flood management intervention. The analysis was applied to Newcastle-upon-Tyne (UK) and combined a spatial rainfall cell dependency analysis with the hydrodynamic flood model CityCAT. Locations within the study area were then classified based on four priority criteria: contribution to (i) total flood extent; (ii) maximum flood depths; (iii) flooded green spaces and roads; and (iv) likelihood of flood exposure. The results illustrate the importance of considering the catchment holistically and identify hydrological linkages between flood source and impact areas, and the corresponding impact on (and interaction with) different infrastructure systems. Different criteria lead to different spatial prioritization information, which stresses the importance of combining criteria that address the specific needs and targets of the desired flood management strategy (e.g. Blue-Green infrastructure). The concept offers a basis for developing a systematic, high-level approach to inform catchment-scale prioritization for flood management intervention, which can be applied prior to developing flood alleviation schemes. In doing so, the approach will help identify opportunities to combine multiple water management solutions and allocate resources more efficiently.

Automated easy-to-use tools capable of generating spatial-temporal weather scenarios for the present day or downscaled future climate projections are highly desirable. Such tools would greatly support the analysis of hazard, risk and reliability of systems such as urban infrastructure, river catchments and water resources. However, the automatic parameterization of such models to the properties of a selected scenario requires the characterization of both point and spatial statistics. Whilst point statistics, such as the mean daily rainfall, may be described by a map, spatial properties such as cross-correlation vary according to a pair of sample points, and should ideally be available for every possible pair of locations. For such properties simple automatic representations are needed for any pair of locations.To address this need simple empirical models are developed of the lag-zero cross-correlation-distance (XCD) properties of United Kingdom daily rainfall. Following error and consistency checking, daily rainfall timeseries for the period 1961–1990 from 143 raingauges are used to calculate observed XCD properties. A three parameter double exponential expression is then fitted to appropriate data partitions assuming isotropic and piecewise-homogeneous XCD properties. Three models are developed: 1) a national aseasonal model; 2) a national model partitioned by calendar month; and 3) a regional model partitioned by nine UK climatic regions and by calendar month. These models provide estimates of lag-zero cross-correlation properties of any two locations in the UK.These cross-correlation models can facilitate the development of automated spatial rainfall modelling tools. This is demonstrated through implementation of the regional model into a spatial modelling framework and by application to two simulation domains (both ∼10,000 km2), one in north-west England and one in south-east England. The required point statistics are generally well simulated and a good match is found between simulated and observed XCD properties.The models developed here are straightforward to implement, incorporate correction of data errors, are pre-calculated for computational efficiency, provide smoothing of sample variability arising from sporadic coverage of observations and are repeatable. They may be used to parameterise spatial rainfall models in the UK and the methodology is likely to be easily adaptable to other regions of the world.

[1] The information contained in hyetographs and hydrographs is often synthesized by using key properties such as the peak or maximum value Xp, volume V, duration D, and average intensity I. These variables play a fundamental role in hydrologic engineering as they are used, for instance, to define design hyetographs and hydrographs as well as to model and simulate the rainfall and streamflow processes. Given their inherent variability and the empirical evidence of the presence of a significant degree of association, such quantities have been studied as correlated random variables suitable to be modeled by multivariate joint distribution functions. The advent of copulas in geosciences simplified the inference procedures allowing for splitting the analysis of the marginal distributions and the study of the so-called dependence structure or copula. However, the attention paid to the modeling task has overlooked a more thorough study of the true nature and origin of the relationships that link , and I. In this study, we apply a set of ad hoc bootstrap algorithms to investigate these aspects by analyzing the hyetographs and hydrographs extracted from 282 daily rainfall series from central eastern Europe, three 5 min rainfall series from central Italy, 80 daily streamflow series from the continental United States, and two sets of 200 simulated universal multifractal time series. Our results show that all the pairwise dependence structures between , and I exhibit some key properties that can be reproduced by simple bootstrap algorithms that rely on a standard univariate resampling without resort to multivariate techniques. Therefore, the strong similarities between the observed dependence structures and the agreement between the observed and bootstrap samples suggest the existence of a numerical generating mechanism based on the superposition of the effects of sampling data at finite time steps and the process of summing realizations of independent random variables over random durations. We also show that the pairwise dependence structures are weakly dependent on the internal patterns of the hyetographs and hydrographs, meaning that the temporal evolution of the rainfall and runoff events marginally influences the mutual relationships of , and I. Finally, our findings point out that subtle and often overlooked deterministic relationships between the properties of the event hyetographs and hydrographs exist. Confusing these relationships with genuine stochastic relationships can lead to an incorrect application of multivariate distributions and copulas and to misleading results.

Extreme rainfall events pose considerable threats to society and critical infrastructure yet, by definitions, these events are rare. Reliable estimates of the likelihood of such events are required to assist with impact quantification and risk management. Regional frequency analysis of pooled extreme rainfall to estimate the likely impacts of a changing climate is a well established method to assess the probability of these extremes. Previous analyses of country-wide changes to UK extreme daily rainfall have used the 9 Hadley UK Precipitation (HadUKP) regions (Alexander and Jones, 2000), which are not appropriate for use with extremes. While these characterize well the mean daily intensity and frequency within each region, extreme rainfall climatology differs from that of the mean characteristics. As a result the existing HadUKP regions do not represent extreme rainfall behaviour adequately, with regard to the frequency, intensity or timing of extreme events. Focussing on many extreme rainfall characteristics, such as the seasonal timing and magnitude of maxima, this article presents a formal representation of extreme regions which specifically describe extreme spatial and temporal characteristics. The article concludes with a brief exploration of improved event estimation using these extreme rainfall regions. The new regional definitions provide clearer upward trends in annual maxima over the period 1961–2010 than those estimated using the HadUKP regional pools. © 2013 Royal Meteorological Society