Abstract. Simulations of flow for a discrete fracture model in fractured porous rocks have gradually become more practical, as a consequence of increased computer power and improved simulation and characterization techniques. Discrete fracture models can be formulated in a lower-dimensional framework, where the fractures are modeled in a lower dimension than the matrix, or in an equi-dimensional form, where the fractures and the matrix have the same dimension.When the velocity of the flow field is needed explicitly, as in streamline simulation of advective transport, only the equi-dimensional approach can be used directly. The velocity field for the lower-dimensional model can then be recovered by post-processing which involves expansion of the lower-dimensional fractures to equi-dimensional ones.In this paper, we propose a technique for expanding lower-dimensional fractures and we compare two different discretization methods for the pressure equation; one vertex-centered approach which can be implemented as either a lower-or an equi-dimensional method, and a cellcentered method using the equi-dimensional formulation. The methods are compared with respect to accuracy, convergence, condition number, and computer efficiency.

Abstract. Japan has experienced several catastrophic flood events causing extensive damage to property and the national economy due to its topography, geography, and climate. Steep and short rivers, frequent typhoons and torrential rains, extremely high concentration of people and assets in flood-prone areas, and intensive human intervention subject the country to frequent flood disasters. Risk Management Solutions (RMS) has developed a stochastic inland flood model as part of its Japan Typhoon Model to assess flood risk due to typhoon for the (re)insurance industry. The RMS flood risk model consists of i) a precipitation-driven flood hazard module, ii) a building-level exposure module, iii) a component-based vulnerability module and iv) a financial module. The flood model is driven by 105,000 years of continuously simulated precipitation accounting for typhoon and non-typhoon precipitation. Rainfall-runoff and routing models, fluvial-and pluvial-inundation models, and probabilistic defence failures are included in the flood hazard module to obtain a realistic view of flood risk. By combining a large, country-level stochastic dataset with a high-resolution grid (~40m) for flood inundation modeling, and building level exposure data and hundreds of unique component-based vulnerability types, a comprehensive view of flood risk is provided on both local and aggregate levels, The financial module accounts for insured risk from different financial contracts.

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<p>Large scale climatic patterns and river network topology have an important impact on the space-time structure of floods. For example, in a recent study we showed that the effect of the North Atlantic Oscillation (NAO) is visible in the structure of economic losses at the European scale. The analysis revealed that in Northern Europe the majority of historic winter floods occurred during a positive NAO state, whereas the majority of summer floods occurred during a negative state. Through the application of a state-of-the-art flood catastrophe model, we also observed that there exists a statistically significant relationship between economic flood losses and the NAO. In this study we further advance the analysis by exploring the correlation structure of flood losses in Europe during different seasons and for different NAO states.  Flood loss correlation is measured in terms of “loss synchrony scale” (LSC), a metric formalized for this study following the definition of “flood synchrony scale” in Berghuijs et al. (2019). For an individual event and an individual CRESTA region, the LSC is defined as the maximum radius around the CRESTA, within which at least half of the other CRESTA regions experience a loss due to the same event. We analyse the LSC across Europe, as produced by the loss model, and check   for consistency with the data-based flood synchrony scale in Berghujs et al. (2019). We further explore how the LSC changes between different seasons, and between NAO states. This analysis can help improve financial preparedness to catastrophic floods as a better understanding of the correlation structure of the flood events allows for a better distribution of resources as well as a more efficient application of mitigation measures.</p><p>Berghuijs W R, Allen S T, Harrigan S and Kirchner J W 2019 Growing spatial scales of synchronous river flooding in Europe Geophys. Res. Lett. 46 1423–8</p>

<p>We study the impact of climate change on European flood economic losses under 1.5°C global warming scenario. Climate scenarios were generated with the Community Atmospheric Model (CAM) version 5 under the protocols of the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) experiment. Present climate scenario corresponding to the years 2006-2015 includes observed forcing conditions for sea surface temperatures (SSTs) and sea-ice cover. The future 1.5°C scenario was constructed following SST warming according to the response to the RCP2.6 in CMIP5 model simulations. Each scenario comprises five 10-year long simulations that differ in the initial weather state. For each scenario we generated a 1000 years long stochastic set of precipitation based on the main modes of variability of gridded precipitation data through Principal Component Analysis applied to the monthly precipitation fields of the combined 50 simulated years. The other variables were obtained through an analogue month approach. Stochastic monthly fields were subsequently disaggregated in space and time to 3-hourly, 6 km resolution grids, and these were finally fed to a well-calibrated flood-loss model. The flood-loss model comprises a rainfall-runoff component, a flood routing scheme, an inundation component and a financial module that integrates flood hazard, buildings vulnerability, and economic exposure at location level. Prior to model evaluation, the stochastic meteorological forcing was bias-corrected with the stochastic set (based on observations) employed in the construction and calibration of the flood-loss model. The method for bias-correction preserves the ratio of quantiles of the future scenario to the present and preserves the correlation structure of the forcing variables. Average annual loss for Europe with the current-climate scenario generated by CAM is within 10-15% of the current industry estimate (based on observations), which suggests the applicability of the proposed approach. For the future scenario the model suggests a significant increase in loss (> 4 times) with respect to the present, which is in line with other studies for similar future global warming pathways.</p>