ALBERT

Beschreibung: Coastal defences have long been employed to halt or slow coastal erosion. Their impact on local sediment flux and ecology has been studied in detail through field studies and numerical simulations. The non-local impact of a modified sediment flux regime on mesoscale erosion and accretion has received less attention. Morphological changes at this scale due to defended structures can be difficult to quantify or identify with field data. Engineering scale numerical models, often applied to assess the design of modern defences on local coastal erosion, tend not to cover large stretches of coast and are rarely applied to assess the impact of older structures. We extend previous work to explore the influences of sea walls on the evolution and morphological sensitivity of a pinned, soft-cliff, sandy coastline under a changing wave climate. The Holderness coast of East Yorkshire, UK, is used as a case study, represented both as a defended example with major sea walls included and a natural example where no sea defences exist. Using a mesoscale numerical coastal evolution model, stochastic wave climate data are perturbed gradually to assess the sensitivity of the coastal morphology to changing wave climate for both the defended and natural scenarios. Comparative analysis of the simulated output suggests that sea walls in the south of the region have a greater impact on sediment flux due to the increased sediment availability along this part of the coast. Multiple defended structures, including those separated by several kilometres, were found to interact with each other, producing a complex imprint on coastal morphology under a changing wave climate. Although spatially and temporally heterogeneous, sea walls generally slowed coastal recession and accumulated sediment on their up-drift side.

Beschreibung: The impact of future sea level rise on coastal erosion as a result of a changing climate has been studied in detail over the past decade. The potential impact of a changing wave climate on erosion rates, however, is not typically considered. We explore the effect of changing wave climates on a pinned, soft-cliff, sandy coastline, using as an example the Holderness coast of East Yorkshire, UK. The initial phase of the study concentrates on calibrating a model to recently measured erosion rates for the Holderness coast using an ensemble of geomorphological and shoreface parameters under an observed offshore wave climate. Stochastic wave climate data are perturbed gradually to assess the sensitivity of the coastal morphology to changing wave climate. Forward-modelled simulations indicate the nature of the morphological response of the coast to changes in wave climate over the next century. Results indicate that changes to erosion rates over the next century will be spatially and temporally heterogeneous, with a variability of up to ±25% in the erosion rate relative to projections under constant wave climate. The heterogeneity results from the current coastal morphology and the sediment transport dynamics consequent on differing wave climate regimes.

Beschreibung: Linking landslide size and frequency is important at both human and geological time-scales for quantifying both landslide hazards and the effectiveness of landslides in the removal of sediment from evolving landscapes. Landslide inventories are usually compiled following a particular triggering event such as an earthquake or storm, and their statistical behavior is typically characterized by an inflected power-law relationship. The occurrence of landslides is expected to be influenced by the material properties of rock and/or regolith in which failure occurs. Here we explore the statistical behavior and the controls of a secular landslide inventory (SLI) (i.e. events occurring over an indefinite time period) consisting of mapped landslide deposits and their underlying lithology (bedrock or superficial) across the United Kingdom. The magnitude-frequency distribution of this secular inventory exhibits an inflected power law relationship, well approximated by an inverse Gamma or double Pareto model. The scaling exponent for the power-law relationship is α = −1.76. The small-event rollover occurs at a significantly higher magnitude than observed in single-event landslide records, which we interpret as evidence of “landscape annealing” at these relatively short length-scales, noting the corollary that a secular dataset will tend to underestimate the frequency of small landslides. This is supported by a subset of data where a complete landslide inventory was recently mapped. Large landslides also appear to be under-represented relative to model predictions, which we interpret as a non-linear or transient landscape response as the UK emerged from the last glacial maximum and through relatively volatile conditions toward a generally more stable late Holocene climate.

Beschreibung: Linking landslide size and frequency is important at both human and geological timescales for quantifying both landslide hazards and the effectiveness of landslides in the removal of sediment from evolving landscapes. The statistical behaviour of the magnitude-frequency of landslide inventories is usually compiled following a particular triggering event such as an earthquake or storm, and their statistical behaviour is often characterised by a power-law relationship with a small landslide rollover. The occurrence of landslides is expected to be influenced by the material properties of rock and/or regolith in which failure occurs. Here we explore the statistical behaviour and the controls of a secular landslide inventory (SLI) (i.e. events occurring over an indefinite geological time period) consisting of mapped landslide deposits and their underlying lithology (bedrock or superficial) across the United Kingdom. The magnitude-frequency distribution of this secular inventory exhibits an inflected power-law relationship, well approximated by either an inverse gamma or double Pareto model. The scaling exponent for the power-law scaling of medium to large landslides is α = −1.71 ± 0.02. The small-event rollover occurs at a significantly higher magnitude (1.0–7.0 × 10−3 km2) than observed in single-event landslide records (~ 4 × 10−3 km2). We interpret this as evidence of landscape annealing, from which we infer that the SLI underestimates the frequency of small landslides. This is supported by a subset of data where a complete landslide inventory was recently mapped. Large landslides also appear to be under-represented relative to model predictions. There are several possible reasons for this, including an incomplete data set, an incomplete landscape (i.e. relatively steep slopes are under-represented), and/or temporal transience in landslide activity during emergence from the last glacial maximum toward a generally more stable late-Holocene state. The proposed process of landscape annealing and the possibility of a transient hillslope response have the consequence that it is not possible to use the statistical properties of the current SLI database to rigorously constrain probabilities of future landslides in the UK.