1D-3D hybrid modeling—from multi-compartment models to full resolution models in space and time

Investigation of cellular and network dynamics in the brain by means of modeling & simulation has evolved into a highly interdisciplinary field, that uses sophisticated modeling & simulation approaches to understand distinct areas of brain function. Depending on the underlying complexity, these models vary in level of detail to cope with the attached computational cost. Hence for large network simulations, single neurons are typically reduced to time-dependent signal processors, dismissing spatial aspects of the cells. For single cell or small-world networks, general purpose simulators allow for space and time-dependent simulations of electrical signal processing, based on the cable equation theory. An emerging field in Computational Neuroscience encompasses a new level of detail by incorporating the 3D morphology of cells and organelles into 3D space and time-dependent simulations. Every approach has its advantages and limitations, such as computational cost, integrated and methods-spanning simulation approaches, depending on the network size could establish new ways to investigate the brain. We present a hybrid simulation approach, that makes use of reduced 1D-models using e.g. the NEURON which couples to fully resolved models for simulating cellular and sub-cellular dynamics, including the detailed 3D-morphology of neurons and organelles. To couple 1D- & 3D-simulations, we present a geometry and membrane potential mapping framework, with which graph-based morphologies, e.g. in swc-/hoc-format, are mapped to full surface and volume representations of the neuron; membrane potential data from 1D-simulations are used as boundary conditions for full 3D simulations. Thus, established models and data, based on general purpose 1D-simulators, can be directly coupled to the emerging field of fully resolved highly detailed 3D-modeling approaches. The new framework is applied to investigate electrically active neurons and their intracellular spatio-temporal Calcium Dynamics

Automatische Codegenerierung für Massiv Parallele Applikationen in der Numerischen Strömungsmechanik

Solving partial differential equations (PDEs) is a fundamental challenge in many application domains in industry and academia alike. With increasingly large problems, efficient and highly scalable implementations become more and more crucial. Today, facing this challenge is more difficult than ever due to the increasingly heterogeneous hardware landscape. One promising approach is developing domain‐specific languages (DSLs) for a set of applications. Using code generation techniques then allows targeting a range of hardware platforms while concurrently applying domain‐specific optimizations in an automated fashion. The present work aims to further the state of the art in this field. As domain, we choose PDE solvers and, in particular, those from the group of geometric multigrid methods. To avoid having a focus too broad, we restrict ourselves to methods working on structured and patch‐structured grids. We face the challenge of handling a domain as complex as ours, while providing different abstractions for diverse user groups, by splitting our external DSL ExaSlang into multiple layers, each specifying different aspects of the final application. Layer 1 is designed to resemble LaTeX and allows inputting continuous equations and functions. Their discretization is expressed on layer 2. It is complemented by algorithmic components which can be implemented in a Matlab‐like syntax on layer 3. All information provided to this point is summarized on layer 4, enriched with particulars about data structures and the employed parallelization. Additionally, we support automated progression between the different layers. All ExaSlang input is processed by our jointly developed Scala code generation framework to ultimately emit C++ code. We particularly focus on how to generate applications parallelized with, e.g., MPI and OpenMP that are able to run on workstations and large‐scale cluster alike. We showcase the applicability of our approach by implementing simple test problems, like Poisson’s equation, as well as relevant applications from the field of computational fluid dynamics (CFD). In particular, we implement scalable solvers for the Stokes, Navier‐Stokes and shallow water equations (SWE) discretized using finite differences (FD) and finite volumes (FV). For the case of Navier‐Stokes, we also extend our implementation towards non‐uniform grids, thereby enabling static mesh refinement, and advanced effects such as the simulated fluid being non‐Newtonian and non‐isothermal

A combined modelling approach for simulating channel–wetland exchanges in large African river basins

In Africa, many large and extensive wetlands are hydrologically connected to rivers, and their environmental integrity, as well as their influence on downstream flow regimes, depends on the prevailing channel–wetland exchange processes. These processes are inherently complex and vary spatially and temporally. Understanding channel–wetland exchanges is therefore, indispensable for the effective management of wetlands and the associated river basins. However, this information is limited in most of the river basins containing large wetlands in Africa. Furthermore, it is important to understand the links between upstream and downstream flow regimes and the wetland dynamics themselves, specifically where there are water resource developments that may affect these links (upstream developments), or be affected by them (downstream developments). Hydrological modelling of the entire basin using basin-scale models that include wetland components in their structures can be used to provide the information required to manage water resources in such basins. However, the level of detail of wetland processes included in many basin-scale models is typically very low and the lack of understanding of the wetland dynamics makes it difficult to quantify the relevant parameters. Detailed hydraulic models represent the channel-wetland exchanges in a much more explicit manner, but require relatively more data and time resources to establish than coarser scale hydrological models. The main objective of this study was, therefore, to investigate the use of a detailed hydraulic wetland model to provide a better understanding of channel–wetland exchanges and wetland dynamics, and to use the results to improve the parameterisation of a basin-scale model. The study focused on improving the water resource assessments modelling of three data-scarce African river basins that contain large wetlands: the floodplains of the Luangwa and Upper Zambezi River basins and the Usangu wetland in the Upper Great Ruaha River basin. The overall objective was achieved through a combined modelling approach that uses a detailed high-resolution LISFLOOD-FP hydraulic model to inform the structure and parameters of the GW Pitman monthly hydrological model. The results from the LISFLOOD-FP were used to improve the understanding of the channel–wetland exchange dynamics and to establish the wetland parameters required in the GW Pitman model. While some wetland parameters were directly quantified from the LISFLOOD-FP model results, others, which are highly empirical, were estimated by manually calibrating the GW Pitman wetland sub-model implemented in excel spreadsheets containing the LISFLOOD-FP model results. Finally, the GW Pitman model with the inclusion of the estimated wetland parameters was applied for each basin and the results compared to the available downstream observed flow data. The two models have been successfully applied in southern Africa, with the GW Pitman model being one of the most widely applied hydrological models in this region. To address the issue of data scarcity, during setup of these models, the study mainly relied on the global datasets which clearly adds to the overall uncertainty of the modelling approach. However, this is a typical situation for most of the data scarce regions of the continent. A number of challenges were, however, faced during the setup of the LISFLOOD-FP, mainly due to the limitations of the data inputs. Some of the LISFLOOD-FP data inputs include boundary conditions (upstream and downstream), channel cross-sections and wetland topography. In the absence of observed daily flows to quantify the wetland upstream boundary conditions, monthly flow volumes simulated using the GW Pitman monthly model (without including the wetland sub-model) were disaggregated into daily flows using a disaggregation sub-model. The simulated wetland inflows were evaluated using the observed flow data for downstream gauging stations that include the wetland effects. The results highlighted that it is important to understand the possible impacts of each wetland on the downstream flow regime during the evaluations of the model simulation results. Although the disaggregation approach cannot be validated due to a lack of observed data, it at least enables the simulated monthly flows to be used in the daily time step hydraulic model. One of the recommendations is that improvements are required in gauging station networks to provide more observed information for the main river and the larger tributary inflows into these large and important wetland systems. Even a limited amount of newly observed data would be helpful to reduce some of the uncertainties in the combined modelling approach. The SRTM 90 m DEM (used to represent wetland topography) was filtered to reduce local variations and noise effects (mainly vegetation bias), but there were some pixels that falsely affect the inundation results, and the recently released vegetation-corrected DEMs are suggested to improve the simulation results. Channel cross-section values derived from global datasets should be examined because some widths estimated from the Andreadis et al. (2013) dataset were found to be over-generalised and did not reflect widths measured using high-resolution Google Earth in many places. There is an indication that channel cross-sections digitised from Google Earth images can be successfully used in the model setup except in densely vegetated swamps where the values are difficult to estimate, and in such situations, field measured cross-section data are required. Small channels such as those found in the Usangu wetland could play major role in the exchange dynamics, but digitising them all was not straightforward and only key ones were included in the model setup. Clearly, this inevitably introduced uncertainties in the simulated results, and future studies should consider applying methods that simplify extractions of most of these channels from high-resolution images to improve the simulated results. The study demonstrated that the wetland and channel physical characteristics, as well as the seasonal flow magnitude, largely influence the channel–wetland exchanges and wetland dynamics. The inundation results indicated that the area–storage and storage–inflow relationships form hysteretic curves, but the shape of these curves vary with flood magnitude and wetland type. Anticlockwise hysteresis curves were observed in both relationships for the floodplains (Luangwa and Barotse), whereas there appears to be no dominant curve type for the Usangu wetlands. The lack of well-defined hysteretic relationships in the Usangu could be related to some of the difficulties (and resulting uncertainties) that were experienced in setting up the model for this wetland. The storage–inflow relationships in all wetlands have quite complex rising limbs due to multiple flow peaks during the main wet season. The largest inundation area and storage volume for the Barotse and Usangu wetlands occurred after the peak discharge of the wet season, a result that is clearly related to the degree of connectivity between the main channel and those areas of the wetlands that are furthest away from the channel. Hysteresis effects were found to increase with an increase in flood magnitudes and temporal variations in the wetland inflows. Overall, hysteresis behaviour is common in large wetlands and it is recommended that hysteresis curves should be reflected in basin-scale modelling of large river basins with substantial wetland areas. At a daily time scale, inflow–outflow relationships showed a significant peak reduction and a delayed time to peak of several weeks in the Barotse and Usangu wetlands, whereas the attenuation effects of the Luangwa floodplain are minimal. To a large extent, the LISFLOOD-FP results provided useful information to establish wetland parameters and assess the structure of Pitman wetland sub-model. The simple spreadsheet used to estimate wetland parameters did not account for the wetland water transfers from the upstream to the next section downstream (the condition that is included in the LISFLOOD-FP model) for the case when the wetlands were distributed across more than one sub-basin. It is recommended that a method that allows for the upstream wetland inflows and the channel inflows should be included in the spreadsheet. The same is true to the Pitman model structure, and a downstream transfer of water can be modelled through return flows to the channel. The structure of the wetland sub-model was modified to allow an option for the return flows to occur at any time during the simulation period to provide for types of wetlands (e.g. the Luangwa) where spills from the channel and drainage back to the channel occur simultaneously. The setup of the GW Pitman model with the inclusion of wetland parameters improved the simulation results. However, the results for the Usangu wetlands were not very satisfactory and the collection of additional field data related to exchange dynamics is recommended to achieve improvements. The impacts of the Luangwa floodplain on the flow regime of the Luangwa River are very small at the monthly time scale, whereas the Barotse floodplain system and the Usangu wetlands extensively regulate flows of the Zambezi River and the Great Ruaha River, respectively. The results highlighted the possibilities of regionalising some wetland parameters using an understanding of wetland physical characteristics and their water exchange dynamics. However, some parameters remain difficult to quantify in the absence of site-specific information about the water exchange dynamics. The overall conclusion is that the approach implemented in this study presents an important step towards the improvements of water resource assessments modelling for research and practical purposes in data-scarce river basins. This approach is not restricted to the two used models, as it can be applied using different model combinations to achieve similar study purpose