Floods are the most common and widely distributed natural hazard, threatening life and property worldwide. Governments worldwide are facing significant challenges associated with flood hazard, specifically: increasing urbanization; against the background of uncertainty associated with increasing climate variability under climate change. Thus, flood hazard assessments need to consider climate change uncertainties explicitly. This paper explores the role of climate change uncertainty through uncertainty analysis in flood modelling through a probabilistic framework using a Monte Carlo approach and is demonstrated for case study catchment. Different input, structure and parameter uncertainties were investigated to understand how important the role of a non-stationary climate may be on future extreme flood events. Results suggest that inflow uncertainties are the most influential in order to capture the range of uncertainty in inundation extent, more important than hydraulic model parameter uncertainty, and thus, the influence of non-stationarity of climate on inundation extent is critical to capture. Topographic controls are shown to create tipping points in the inundation–flow relationship, and these may be useful and important to quantify for future planning and policy. Full Monte Carlo analysis within the probabilistic framework is computationally expensive, and there is a need to explore more time-efficient strategies which may result in a similar estimate of the full uncertainty. Simple uncertainty quantification techniques such as Latin hypercube sampling approaches were tested to reduce computational burden.
Flood events are the most commonly occurring natural disaster, with over 5 million properties at risk in the UK alone. Changes in the global climate are expected to increase the frequency and magnitude of flood events. Flood hazard assessments, using climate projections as input, guide policy decisions and engineering projects to reduce the impact of large return period events. Probabilistic flood modeling is required to take into account uncertainties in climate model projections. However, the dichotomous relationship between probabilistic modeling, computational cost and model resolution limits the applicability of such techniques. This paper examines improvements to traditional Monte Carlo methods using Latin hypercube sampling (LHS) and Multi‐level Monte Carlo (MLMC) to quantify the uncertainty in flood extent resulting from input hydrograph uncertainty. The results demonstrate that MLMC is a more efficient modeling strategy than current methods (i.e., traditional Monte Carlo) with high resolution outputs produced in less time than previously possible. The novel application of MLMC technique to three Scottish case studies, demonstrating a variety of river characteristics, domain sizes and computational costs, using a high resolution 5 m grid resulted in a 99.2% reduction in computational cost compared to traditional Monte Carlo methods and up to 2.3 times speedup over Latin Hypercube Sampling.
With evidence suggesting that climate change is resulting in changes within the hydrologic cycle, the ability to robustly model hydroclimatic response is critical. This paper assesses how extreme runoff—1:2- and 1:30-year return period (RP) events—may change at a regional level across the UK by the 2080s (2069–2098). Capturing uncertainty in the hydroclimatic modelling chain, flow projections were extracted from the EDgE (End-to-end Demonstrator for improved decision-making in the water sector in Europe) multi-model ensemble: five Coupled Model Intercomparison Project (CMIP5) General Circulation Models and four hydrological models forced under emissions scenarios Representative Concentration Pathway (RCP) 2.6 and RCP 8.5 (5 × 4 × 2 chains). Uncertainty in extreme value parameterisation was captured through consideration of two methods: generalised extreme value (GEV) and generalised logistic (GL). The method was applied across 192 catchments and aggregated to eight regions. The results suggest that, by the 2080s, many regions could experience large increases in extreme runoff, with a maximum mean change signal of +34% exhibited in East Scotland (1:2-year RP). Combined with increasing urbanisation, these estimates paint a concerning picture for the future UK flood landscape. Model chain uncertainty was found to increase by the 2080s, though extreme value (EV) parameter uncertainty becomes dominant at the 1:30-year RP (exceeding 60% in some regions), highlighting the importance of capturing both the associated EV parameter and ensemble uncertainty.
The COVID-19 pandemic has impacted public health, the economy and society—both directly and indirectly. Few approaches exist to understand these complex impacts in a way that (1) acknowledges cross-sectoral interdependencies; (2) models how short-term shocks translate into impacts on longer-term outcomes; (3) builds in local, contextual variation; and (4) recognises a wide set of priorities. The Urban Systems Abstraction Hierarchy (USAH) is proposed as an approach with these capabilities, and applied to Edinburgh (UK) between March-October 2020 to identify city-level impacts of the pandemic and associated policy responses. Results show changing priorities in the system and suggest areas which should be targeted for future urban resilience planning in Edinburgh for both short-term shocks and long-term recovery. This makes both methodological contributions (in the form of testing a new complex systems approach) and practical contributions (in the form of city-specific results which inform different aspects of resilience) to urban science.
Human-induced changes in climatic behavior and variations in future river flows has been at the fore-front of recent academic and political discourse. Future climate projections are a vital tool in tackling climate change and supporting future adaptation, however until recently models have been viewed individually with a lack of uncertainty quantification. A multi-model ensemble (MME) with a wide range of general circulation models, regional climate models and emissions scenarios, EURO-CORDEX provides climate projections as well as flow series projections across the European domain from 1950 to 2100. This paper explores the validity of the 68 chain MME flow projections by investigating its ability to match observed flow records in the UK over the period 1975–2004. The work explores magnitude through quantile matching and seasonality matching by time-series decomposition of trends. Two statistical tests [Mann-Whitney, and Mean Average Arctangent Percentage Error (MAAPE)] were used to compare EURO-CORDEX flow projections to observed river flows recorded by the National River Flow Archive (NRFA) across 1,436 UK river catchments. Results indicate a high degree of similarity justifying the application of this dataset for assessing future hydrological changes across a regional scale. Discretizing the flow projections into regional and hydrometric areas highlights the variability in performance between neighboring domains and the strong influence local features may have on climate model performance. The validation of EURO-CORDEX flow projection data regionally enables a wide range of applications including the exploration of future changes in local and national river flows.
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