Flash flood simulations for an Egyptian city - mitigation measures and impact of infiltration

Within this work, the impact of mitigation measures and infiltration on flash floods is investigated by using a 2D robust shallow water model including infiltration with the Green-Ampt model. The results show the combined effects of infiltration and mitigation measures as well as the effectiveness of bypass channels in addition to retention basins. Retention basins at appropriate locations could reduce the maximum water depth at critical locations by 23%, while the additional implementation of drainage channels lead to a reduction of 75%, considering also infiltration lead to a further reduction of 97%. If infiltration was considered without mitigation measures, the peak water depth was reduced by 67%. For an exceptional extreme event the measures lead to a reduction of 73% at some locations, while at other locations the overflow from retention basins due to overstraining generated even higher inundations with an increase of 58%

On the processes of renewal of the North Atlantic deep water in the Irminger Sea

Observations of temperature and electrical conductivity by a recording in situ salinometer are discussed in respect oo the physical processes connected with the renewal of North Atlantic deep water. The measured fine structure of the layering suggests that the downward movement of cooled surface water is combined with horizontal mixing down to more than 1000 m depth. This is confirmed by the existence of water elements which have slightly different temperature and salinity. Curves of temperature, conductivity, and salinity and T-S diagrams are shown

Upscaling the shallow water model with a novel roughness formulation

This study presents a novel roughness formulation to conceptually account for microtopography and compares it to four existing roughness models from literature. The aim is to increase the grid size for computational efficiency, while capturing subgrid scale effects with the roughness formulation to prevent the loss in accuracy associated with coarse grids. All roughness approaches are implemented in the Hydroinformatics Modeling System and compared with results of a high resolution shallow water model in three test cases: rainfall-runoff on an inclined plane with sinewave shaped microtopography, ow over an inclined plane with random microtopography and rainfall-runoff in a small natural catchment. Although the high resolution results can not be reproduced exactly by the coarse grid model, e.g. local details of ow processes can not be resolved, overall good agreement between the upscaled models and the high resolution model has been achieved. The proposed roughness formulation generally shows the best agreement of all compared models. It is further concluded that the accuracy increases with the number of calibration parameters available, however the calibration process becomes more difficult. Using coarser grids results in significant speedup in comparison with the high resolution simulation. In the presented test cases the speedup varies from 20 up to 2520, depending on the size and complexity of the test case and the difference in cell sizes.The authors thank the Alexander von Humboldt-Foundation for the Humboldt Research Fellowship granted to Dr. Dongfang Liang.This is the accepted manuscript. The final version is available at http://link.springer.com/article/10.1007%2Fs12665-015-4726-7

Model Integration and Coupling in A Hydroinformatics System

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

A Mathematical Framework for Agent Based Models of Complex Biological Networks

Agent-based modeling and simulation is a useful method to study biological phenomena in a wide range of fields, from molecular biology to ecology. Since there is currently no agreed-upon standard way to specify such models it is not always easy to use published models. Also, since model descriptions are not usually given in mathematical terms, it is difficult to bring mathematical analysis tools to bear, so that models are typically studied through simulation. In order to address this issue, Grimm et al. proposed a protocol for model specification, the so-called ODD protocol, which provides a standard way to describe models. This paper proposes an addition to the ODD protocol which allows the description of an agent-based model as a dynamical system, which provides access to computational and theoretical tools for its analysis. The mathematical framework is that of algebraic models, that is, time-discrete dynamical systems with algebraic structure. It is shown by way of several examples how this mathematical specification can help with model analysis.Comment: To appear in Bulletin of Mathematical Biolog

Signatures of polaronic excitations in quasi-one-dimensional LaTiO3.41_{3.41}

The optical properties of quasi-one-dimensional metallic LaTiO$_{3.41}$ are studied for the polarization along the $a$ and $b$ axes. With decreasing temperature modes appear along both directions suggestive for a phase transition. The broadness of these modes along the conducting axis might be due to the coupling of the phonons to low-energy electronic excitations across an energy gap. We observe a pronounced midinfrared band with a temperature dependence consistent with (interacting) polaron models. The polaronic picture is corroborated by the presence of strong electron-phonon coupling and the temperature dependence of the dc conductivity.Comment: 5 pages, 5 figure

Characterization of Reachable Attractors Using Petri Net Unfoldings

International audienceAttractors of network dynamics represent the long-term behaviours of the modelled system. Their characterization is therefore crucial for understanding the response and differentiation capabilities of a dynamical system. In the scope of qualitative models of interaction networks, the computation of attractors reachable from a given state of the network faces combinatorial issues due to the state space explosion. In this paper, we present a new algorithm that exploits the concurrency between transitions of parallel acting components in order to reduce the search space. The algorithm relies on Petri net unfoldings that can be used to compute a compact representation of the dynamics. We illustrate the applicability of the algorithm with Petri net models of cell signalling and regulation networks, Boolean and multi-valued. The proposed approach aims at being complementary to existing methods for deriving the attractors of Boolean models, while being %so far more generic since it applies to any safe Petri net

Reports of the AAAI 2019 spring symposium series

Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must "build machines that make sense of what goes on in their environment," a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data; self-driving cars have already been involved in fatalities, including a pedestrian; and yet machine learning is unable to explain the contexts within which it operates