SPH modeling of water-related natural hazards

This paper collects some recent smoothed particle hydrodynamic (SPH) applications in the field of natural hazards connected to rapidly varied flows of both water and dense granular mixtures including sediment erosion and bed load transport. The paper gathers together and outlines the basic aspects of some relevant works dealing with flooding on complex topography, sediment scouring, fast landslide dynamics, and induced surge wave. Additionally, the preliminary results of a new study regarding the post-failure dynamics of rainfall-induced shallow landslide are presented. The paper also shows the latest advances in the use of high performance computing (HPC) techniques to accelerate computational fluid dynamic (CFD) codes through the efficient use of current computational resources. This aspect is extremely important when simulating complex three-dimensional problems that require a high computational cost and are generally involved in the modeling of water-related natural hazards of practical interest. The paper provides an overview of some widespread SPH free open source software (FOSS) codes applied to multiphase problems of theoretical and practical interest in the field of hydraulic engineering. The paper aims to provide insight into the SPH modeling of some relevant physical aspects involved in water-related natural hazards (e.g., sediment erosion and non-Newtonian rheology). The future perspectives of SPH in this application field are finally pointed out

Computational Methods and Applications to Simulate Water-Related Natural Hazards

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High-performance simulation technologies for water-related natural hazards

PhD ThesisWater-related natural hazards, such as flash floods, landslides and debris flows, usually happen in chains. In order to better understand the underlying physical processes and more reliably quantify the associated risk, it is essential to develop a physically-based multi-hazard modelling system to simulate these hazards at a catchment scale. An effective multi-hazard modelling system may be developed by solving a set of depth-averaged dynamic equations incorporating adaptive basal resistance terms. High-performance computing achieved through implementation on modern graphic processing units (GPUs) can be used to accelerate the model to support efficient large-scale simulations. This thesis presents the key simulation technologies for developing such a novel high-performance water-related natural hazards modelling system. A new well-balanced smoothed particle hydrodynamic (SPH) model is first presented for solving the shallow water equations (SWEs) in the context of flood inundation modelling. The performance of the SPH model is compared with an alternative flood inundation model based on a finite volume (FV) method in order to select a better numerical method for the current study. The FV model performs favourably for practical applications and therefore is adopted to develop the proposed multi-hazard model. In order to more accurately describe the rainfallrunoff and overland flow process that often initiates a hazard chain, a first-order FV Godunovtype model is developed to solve the SWEs, implemented with novel source term discretisation schemes. The new model overcomes the limitations of the current prevailing numerical schemes such as inaccurate calculations of bed slope or friction source terms and provides much improved numerical accuracy, efficiency and stability for simulating overland flows and surface flooding. To support large-scale simulation of flow-like landslides or debris flows, a new formulation of depth-averaged governing equations is derived on the Cartesian coordinate system. The new governing equations take into account the effects of non-hydrostatic pressure and centrifugal force, which may become significant over terrains with steep and curved topography. These equations are compatible with various basal resistance terms, effectively leading to a unified mathematical framework for describing different type of water-related natural hazards including surface flooding, flow-like landslides and debris flows. The new depthaveraged governing equations are then solved using an FV Godunov-type framework based on the second-order accurate scheme. A flexible and GPU-based software framework is further designed to provide much improved computational efficiency for large-scale simulations and ease the future implementation of new functionalities. This provides an effective codebase for the proposed multi-hazard modelling system and its potential is confirmed by successfully applying to simulate flow-like landslides and dam break floods.Newcastle University and China Scholarship Council, Henry Lester Trust and Great Britain China Education Trus

The KULTURisk Regional Risk Assessment methodology for water-related natural hazards – Part 1: Physical–environmental assessment

Abstract. In recent years, the frequency of catastrophes induced by natural hazards has increased, and flood events in particular have been recognized as one of the most threatening water-related disasters. Severe floods have occurred in Europe over the last decade, causing loss of life, displacement of people and heavy economic losses. Flood disasters are growing in frequency as a consequence of many factors, both climatic and non-climatic. Indeed, the current increase of water-related disasters can be mainly attributed to the increase of exposure (elements potentially at risk in flood-prone area) and vulnerability (i.e. economic, social, geographic, cultural and physical/environmental characteristics of the exposure). Besides these factors, the undeniable effect of climate change is projected to strongly modify the usual pattern of the hydrological cycle by intensifying the frequency and severity of flood events at the local, regional and global scale. Within this context, the need for developing effective and pro-active strategies, tools and actions which allow one to assess and (possibly) to reduce the flood risks that threatens different relevant receptors becomes urgent. Several methodologies to assess the risk posed by water-related natural hazards have been proposed so far, but very few of them can be adopted to implement the last European Flood Directive (FD). This paper is intended to introduce and present a state-of-the-art Regional Risk Assessment (RRA) methodology to appraise the risk posed by floods from a physical–environmental perspective. The methodology, developed within the recently completed FP7-KULTURisk Project (Knowledge-based approach to develop a cULTUre of Risk prevention – KR) is flexible and can be adapted to different case studies (i.e. plain rivers, mountain torrents, urban and coastal areas) and spatial scales (i.e. from catchment to the urban scale). The FD compliant KR-RRA methodology is based on the concept of risk being function of hazard, exposure and vulnerability. It integrates the outputs of various hydrodynamic models with site-specific bio-geophysical and socio-economic indicators (e.g. slope, land cover, population density, economic activities etc.) to develop tailored risk indexes and GIS-based maps for each of the selected receptors (i.e. people, buildings, infrastructure, agriculture, natural and semi-natural systems, cultural heritage) in the considered region. It further compares the baseline scenario with alternative scenarios, where different structural and/or non-structural mitigation measures are planned and eventually implemented. As demonstrated in the companion paper (Part 2, Ronco et al., 2014), risk maps, along with related statistics, allow one to identify and classify, on a relative scale, areas at risk which are more likely to be affected by floods and support the development of strategic adaptation and prevention measures to minimizing flood impacts. In addition, the outcomes of the RRA can be eventually used for a further socio-economic assessment, considering the tangible and intangible costs as well as the human dimension of vulnerability

Basic features of the predictive tools of early warning systems for water-related natural hazards: examples for shallow landslides

Abstract. To manage natural risks, an increasing effort is being put in the development of early warning systems (EWS), namely, approaches facing catastrophic phenomena by timely forecasting and alarm spreading throughout exposed population. Research efforts aimed at the development and implementation of effective EWS should especially concern the definition and calibration of the interpretative model. This paper analyses the main features characterizing predictive models working in EWS by discussing their aims and their features in terms of model accuracy, evolutionary stage of the phenomenon at which the prediction is carried out and model architecture. Original classification criteria based on these features are developed throughout the paper and shown in their practical implementation through examples of flow-like landslides and earth flows, both of which are characterized by rapid evolution and quite representative of many applications of EWS

WCSPH for modelling multiphase flows and natural hazards

Among the numerous types of meshless particle methods, SPH is successfully applied to simulate complex multiphase flows with impact involving fluids with high-density ratio as well as non-Newtonian fluids. These problems are concern in the applied engineering dealing with water related natural hazards, such as landslide induced tsunami in artificial reservoir, intense rainfall induced shallow landslides. This contribution aims at providing an overview on the recent applications of the standard weakly compressible WCSPH for modelling these kinds of multiphase flows. The relevant aspects related with the interface treatment and numerical stability in high density multiphase flow will be discussed. Advanced modelling aspects connected with the SPH simulation of non-Newtonian fast dense granular flows and the interaction with pore water. The aspect of tuning model parameters is discussed

KULTURisk regional risk assessment methodology for water-related natural hazards - Part 2: Application to the Zurich case study

The aim of this paper is the application of the KULTURisk regional risk assessment (KR-RRA) methodology, presented in the companion paper (Part 1, Ronco et al., 2014), to the Sihl River basin, in northern Switzerland. Flood-related risks have been assessed for different receptors lying on the Sihl River valley including Zurich, which represents a typical case of river flooding in an urban area, by calibrating the methodology to the site-specific context and features. Risk maps and statistics have been developed using a 300-year return period scenario for six relevant targets exposed to flood risk: people; economic activities: buildings, infrastructure and agriculture; natural and semi-natural systems; and cultural heritage. Finally, the total risk index map has been produced to visualize the spatial pattern of flood risk within the target area and, therefore, to identify and rank areas and hotspots at risk by means of multi-criteria decision analysis (MCDA) tools. Through a tailored participatory approach, risk maps supplement the consideration of technical experts with the (essential) point of view of relevant stakeholders for the appraisal of the specific scores weighting for the different receptor-relative risks. The total risk maps obtained for the Sihl River case study are associated with the lower classes of risk. In general, higher (relative) risk scores are spatially concentrated in the deeply urbanized city centre and areas that lie just above to river course. Here, predicted injuries and potential fatalities are mainly due to high population density and to the presence of vulnerable people; flooded buildings are mainly classified as continuous and discontinuous urban fabric; flooded roads, pathways and railways, most of them in regards to the Zurich central station (Hauptbahnhof) are at high risk of inundation, causing severe indirect damage. Moreover, the risk pattern for agriculture, natural and semi-natural systems and cultural heritage is relatively less important mainly because the scattered presence of these assets. Finally, the application of the KR-RRA methodology to the Sihl River case study, as well as to several other sites across Europe (not presented here), has demonstrated its flexibility and the possible adaptation of it to different geographical and socioeconomic contexts, depending on data availability and particulars of the sites, and for other (hazard) scenarios

A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers

The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the high-flow season (HFS) and the low-flow season (LFS) as the 3-month and the 1-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 rivers from six European countries spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in the frequency estimation of high and low flows, we fit a bi-variate meta-Gaussian probability distribution to the selected flow signatures and average flow in the antecedent months in order to condition the distribution of high and low flows in the HFS and LFS, respectively, upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the frequency distribution of high and low flows one season in advance in a real-world case. Our findings suggest that there is a traceable physical basis for river memory which, in turn, can be statistically assimilated into high- and low-flow frequency estimation to reduce uncertainty and improve predictions for technical purposes