The wetlands of the Okavango Delta accommodate a multitude of ecosystems with a large diversity in fauna and flora. They not only provide the traditional livelihood of the local communities but are also the basis of a tourism industry that generates substantial revenue for the whole of Botswana. For the global community, the wetlands retain a tremendous pool of biodiversity. As the upstream states Angola and Namibia are developing, however, changes in the use of the water of the Okavango River and in the ecological status of the wetlands are to be expected. To predict these impacts, the hydrology of the Delta has to be understood. This article reviews scientific work done for that purpose, focussing on the hydrological modelling of surface water and groundwater. Research providing input data to hydrological models is also presented. It relies heavily on all types of remote sensing. The history of hydrologic models of the Delta is retraced from the early box models to state-of-the-art distributed hydrological models. The knowledge gained from hydrological models and its relevance for the management of the Delta are discussed.

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Construction of small hydropower plants (<10 megawatts) is booming worldwide, exacerbating ongoing habitat fragmentation and degradation, and further fueling biodiversity loss. A systematic approach for selecting hydropower sites within river networks may help to minimize the detrimental effects of small hydropower on biodiversity. In addition, a better understanding of reach‐ and basin‐scale impacts is key for designing planning tools. We synthesize the available information about (1) reach‐scale and (2) basin‐scale impacts of small hydropower plants on biodiversity and ecosystem function, and (3) interactions with other anthropogenic stressors. We then discuss state‐of‐the‐art, spatially explicit planning tools and suggest how improved knowledge of the ecological and evolutionary impacts of hydropower can be incorporated into project development. Such tools can be used to balance the benefits of hydropower production with the maintenance of ecosystem services and biodiversity conservation. Adequate planning tools that consider basin‐scale effects and interactions with other stressors, such as climate change, can maximize long‐term conservation.

Abstract. Reliable real-time forecasts of the discharge can provide valuable information for the management of a river basin system. For the management of ecological releases even discharge forecasts with moderate accuracy can be beneficial. Sequential data assimilation using the Ensemble Kalman Filter provides a tool that is both efficient and robust for a real-time modelling framework. One key parameter in a hydrological system is the soil moisture, which recently can be characterized by satellite based measurements. A forecasting framework for the prediction of discharges is developed and applied to three different sub-basins of the Zambezi River Basin. The model is solely based on remote sensing data providing soil moisture and rainfall estimates. The soil moisture product used is based on the back-scattering intensity of a radar signal measured by a radar scatterometer. These soil moisture data correlate well with the measured discharge of the corresponding watershed if the data are shifted by a time lag which is dependent on the size and the dominant runoff process in the catchment. This time lag is the basis for the applicability of the soil moisture data for hydrological forecasts. The conceptual model developed is based on two storage compartments. The processes modeled include evaporation losses, infiltration and percolation. The application of this model in a real-time modelling framework yields good results in watersheds where soil storage is an important factor. The lead time of the forecast is dependent on the size and the retention capacity of the watershed. For the largest watershed a forecast over 40 days can be provided. However, the quality of the forecast increases significantly with decreasing prediction time. In a watershed with little soil storage and a quick response to rainfall events, the performance is relatively poor and the lead time is as short as 10 days only.

Abstract-During the last decade, researchers have proposed a number of model transformations enabling performance predictions. These transformations map performance-annotated software architecture models into stochastic models solved by means of analytical or numerical analysis or by system simulation. However, so far, a detailed quantitative evaluation of the accuracy and efficiency of different transformations is missing, making it hard to select an adequate transformation for a given context. This paper provides an in-depth comparison and quantitative evaluation of representative model transformations to, e.g., Queueing Petri Nets and Layered Queueing Networks. The semantic gaps between typical source model abstractions and the different analysis techniques are revealed. The accuracy and efficiency of each transformation are evaluated by considering four case studies representing systems of different size and complexity. The presented results and insights gained from the evaluation help software architects and performance engineers to select the appropriate transformation for a given context, thus significantly improving the usability of model transformations for performance prediction.

Synchronous spike discharge of cortical neurons is thought to be a fingerprint of neuronal cooperativity. Because neighboring neurons are more densely connected to one another than neurons that are located further apart, near-synchronous spike discharge can be expected to be prevalent and it might provide an important basis for cortical computations. Using microelectrodes to record local groups of neurons does not allow for the reliable separation of synchronous spikes from different cells, because available spike sorting algorithms cannot correctly resolve the temporally overlapping waveforms. We show that high spike sorting performance of in vivo recordings, including overlapping spikes, can be achieved with a recently developed filter-based template matching procedure. Using tetrodes with a three-dimensional structure, we demonstrate with simulated data and ground truth in vitro data, obtained by dual intracellular recording of two neurons located next to a tetrode, that the spike sorting of synchronous spikes can be as successful as the spike sorting of nonoverlapping spikes and that the spatial information provided by multielectrodes greatly reduces the error rates. We apply the method to tetrode recordings from the prefrontal cortex of behaving primates, and we show that overlapping spikes can be identified and assigned to individual neurons to study synchronous activity in local groups of neurons.

Performance predictions early in the software development process can help to detect problems before resources have been spent on implementation. The Palladio Component Model (PCM) is an example of a mature domain-specific modeling language for component-based systems enabling performance predictions at design time. PCM provides several alternative model solution methods based on analytical and simulation techniques. However, existing solution methods suffer from scalability issues and provide limited flexibility in trading-off between results accuracy and analysis overhead. Queueing Petri Nets (QPNs) are a general-purpose modeling formalism, at a lower level of abstraction, for which efficient and mature simulation-based solution techniques are available. This paper contributes a formal mapping from PCM to QPN models, implemented by means of an automated model-to-model transformation as part of a new PCM solution method based on simulation of QPNs. The limitations of the mapping and the accuracy and overhead of the new solution method compared to existing methods are evaluated in detail in the context of five case studies of different size and complexity. The new solution method proved to provide good accuracy with solution overhead up to 20 times lower compared to PCM's reference solver.

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