Abstract Effective flood risk management depends on methods for estimating hazard and an appraisal of the dominant uncertainties in analysis. Typically, hydraulic models are used to simulate extent flooding estimate flow a particular reach chosen probability exceedance. However, this definition causes problems at river confluences where flows derive from multiple sources. Here, model‐based approach was adopted describe multisite joint distribution three rivers that converge city Carlisle...

Abstract The January 2005 flood event in the Eden catchment (UK) has focused considerable research effort towards strengthening and extending operational forecasting region. become a key study site within remit of phase two Flood Risk Management Research Consortium. This paper presents synthesis results incorporating model uncertainty analysis, computationally efficient real‐time data assimilation/forecasting algorithms, two‐dimensional (2D) inundation modelling, visualization for decision...

In an assessment of how Arctic sea ice cover could be remediated in a warming world, we simulated the injection SO2 into stratosphere making annual adjustments to rates. We treated one climate model realization as surrogate "real world" with imperfect "observations" and no rerunning or reference control simulations. rates were proposed using novel predictive regime which incorporated second simpler forecast "optimal" decision pathways. Commencing simulation 2018, was by 2043 maintained until...

Solar geoengineering has been proposed as a method of meeting climate objectives, such reduced globally averaged surface temperatures. However, because incomplete understanding the effects on system, its implementation would be in presence substantial uncertainties. In our study, we use two fully coupled atmosphere–ocean general circulation models: one which strategy is designed, and implemented (a real-world proxy). We show that regularly adjusting amount solar response to departures...

Abstract In this study, we utilise Artificial Neural Network (ANN) models to estimate the 100- and 1500-year return levels for around 900,000 ungauged catchments in contiguous USA. The were trained validated using 4,079 gauges several selected catchment descriptors out of a total 25 available. study area was split into 15 regions, which represent major watersheds. ANN developed each region evaluated by calculating performance metrics such as root-mean-squared error (RMSE), coefficient...

Abstract. This paper considers the correction of deterministic forecasts given by a flood forecasting model. A stochastic based on evolution an adaptive, multiplicative, gain is presented. number models for are considered and quality resulting probabilistic assessed. The techniques presented offer computationally efficient method providing existing system output.

Abstract The Geoengineering Model Intercomparison Project (GeoMIP) has been designed as a method to compare set of benchmark geoengineering interventions across modelling groups within the World Climate Research Program (WCRP) Coupled (CMIP). While we agree with objectives GeoMIP, this paper describes how present experimental design could be extended by adding simple control component. Using model predictive framework show provides an automated solution for problem balancing radiative...

Abstract. The Delft Flood Early Warning System provides a versatile framework for real-time flood forecasting. UK Environment Agency has adopted the to deliver its National Forecasting System. system incorporates new forecasting models very easily using an "open shell" framework. This paper describes how we added data-based mechanistic modelling approach model inventory and presents case study Eden catchment (Cumbria, UK).

Response function models are often used to represent the behaviour of complex, high order global carbon cycle (GCC) and climate in applications which require short model run times. Although apparently black-box, these response need not necessarily be entirely opaque, but instead may also convey useful insights into properties parent or process. By exploiting a transfer (TF) framework analyse Lenton GCC model, this paper attempts demonstrate that representations can sometimes provide...

Abstract Operational flood forecasting has become a complex and multifaceted task, increasingly being treated in probabilistic ways to allow for the inherent uncertainties process. This paper reviews recent applications of data‐based mechanistic ( DBM ) models within operational UK N ational F lood orecasting S ystem. The position chain is considered along with their offline calibration validation. online adaptive implementation assimilation water level information as used outlined. Two...

Abstract. This paper considers the correction of deterministic forecasts given by a flood forecasting model. A stochastic based on evolution an adaptive, multiplicative, gain is presented. number models for are considered and quality resulting probabilistic assessed. The techniques presented offer, in certain situations, effective computationally efficient method providing existing system output.

Climate change will impact the probabilities of different weather conditions and make new possible, with implications for societal exposure to extreme hazards. While there is agreement that frequency intensity many hazards increase at global scale, uncertainty in spatial distribution changes, which needs be considered assessments future risk. Typically, this quantified by exploring range hazard intensities across a climate model ensemble given forcing. An alternative approach consider...

Climate science and policy making are currently dominated by model-led forecasting as a means of informing decision-making. However, given the very significant uncertainties surrounding our understanding both climate socio-economic systems their interactions, it appears more reasonable to view decision-making recursive problem led updates based on unfolding observed state these systems. Not surprisingly, many aspects current decision machinery already possess this attribute, embedded is in...