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In groundwater applications, knowledge about a range of hydraulically relevant parameters is beneficial to counteract data scarcity and enhance parameter robustness. The present study provides a novel procedure to reverse engineer representative grain diameters and effective porosity from values of hydraulic conductivity. A large data set comprising more than 500 sieve curves and more than 1000 values of hydraulic conductivity was analyzed for interparameter dependencies between characteristic grain sizes, hydraulic conductivity and effective porosity. Based on that analysis, a mathematical model consisting of a set of empirical and physically based equations was developed. The procedure was applied on another large data set and its accuracy, deviations and error propagation were quantified. The method works best for well‐sorted sediments. Practical examples are given in order to demonstrate the range of applicability of the procedure. The input is interchangeable, such that different input data enable calculating characteristic grain sizes, hydraulic conductivity and effective porosity. A free, open‐source stand‐alone GUI of the procedure is available for download.

Pluvial floods in urban areas are caused by local, fast storm events with very high rainfall rates, which lead to inundation of streets and buildings before the storm water reaches a watercourse. An increase in frequency and intensity of heavy rainfall events and an ongoing urbanization may further increase the risk of pluvial flooding in many urban areas. Currently, warnings for pluvial floods are mostly limited to information on rainfall intensities and durations over larger areas, which is often not detailed enough to effectively protect people and goods. We present a proof‐of‐concept for an impact‐based forecasting system for pluvial floods. Using a model chain consisting of a rainfall forecast, an inundation, a contaminant transport and a damage model, we are able to provide predictions for the expected rainfall, the inundated areas, spreading of potential contamination and the expected damage to residential buildings. We use a neural network‐based inundation model, which significantly reduces the computation time of the model chain. To demonstrate the feasibility, we perform a hindcast of a recent pluvial flood event in an urban area in Germany. The required spatio‐temporal accuracy of rainfall forecasts is still a major challenge, but our results show that reliable impact‐based warnings can be forecasts are available up to 5 min before the peak of an extreme rainfall event. Based on our results, we discuss how the outputs of the impact‐based forecast could be used to disseminate impact‐based early warnings.

Abstract. In this case study, we present the implementation of a FEM-based numerical pore-scale model that enables to track and quantify the propagating fluid-fluid interfacial area on highly complex μ-CT obtained geometries. Special focus is drawn to the reservoir specific capillary pressure (pc)- wetting phase saturation (Sw)- interfacial area (awn)- relationship. The basis of this approach are high resolution μ-CT images representing the geometrical characteristics of a georeservoir sample. The successfully validated two-phase flow model is based on the Navier-Stokes equations, including the surface tension force in order to consider capillary effects for the computation of flow and the phase field method for the emulation of a sharp fluid-fluid interface. In combination with specialized software packages, a complex high resolution modeling domain could be obtained. A numerical workflow based on REV-scale pore size distributions is introduced. This workflow aims at the successive modification of model and model setup for simulating such a type of two-phase problem on asymmetric μ-CT-based model domains. The geometrical complexity is gradually increased starting from idealized pore geometries until complex μ-CT-based pore network domains, whereas all domains represent geostatistics of the REV-scale core sample pore size distribution. Finally, the model could be applied on a complex μ-CT-based model domain and the pc-Sw-awn relationship could be computed.

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Abstract. In this case study, we present the implementation of a finite element method (FEM)-based numerical porescale model that is able to track and quantify the propagating fluid-fluid interfacial area on highly complex microcomputed tomography (µ-CT)-obtained geometries. Special focus is drawn to the relationship between reservoir-specific capillary pressure (p c ), wetting phase saturation (S w ) and interfacial area (a wn ). The basis of this approach is highresolution µ-CT images representing the geometrical characteristics of a georeservoir sample. The successfully validated 2-phase flow model is based on the Navier-Stokes equations, including the surface tension force, in order to consider capillary effects for the computation of flow and the phase-field method for the emulation of a sharp fluid-fluid interface.In combination with specialized software packages, a complex high-resolution modelling domain can be obtained. A numerical workflow based on representative elementary volume (REV)-scale pore-size distributions is introduced. This workflow aims at the successive modification of model and model set-up for simulating, such as a type of 2-phase problem on asymmetric µ-CT-based model domains. The geometrical complexity is gradually increased, starting from idealized pore geometries until complex µ-CT-based pore network domains, whereas all domains represent geostatistics of the REV-scale core sample pore-size distribution. Finally, the model can be applied to a complex µ-CT-based model domain and the p c -S w -a wn relationship can be computed.