Power training improves balance, particularly using a low load, high velocity regimen, in older adults with initial lower muscle power and slower contraction. Further studies are warranted to define the mechanisms underlying this adaptation, as well as the optimum power training intensity for a range of physiological and clinical outcomes in older adults with varying levels of health status and functional independence.

Peak muscle power may be improved similarly using light, moderate, or heavy resistances, whereas there is a dose-response relationship between training intensity and muscle strength and endurance changes. Therefore, using heavy loads during explosive resistance training may be the most effective strategy to achieve simultaneous improvements in muscle strength, power, and endurance in older adults.

Abstract:Given the structural shortcomings of conceptual rainfall-runoff models and the common use of time-invariant model parameters, these parameters can be expected to represent broader aspects of the rainfall-runoff relationship than merely the static catchment characteristics that they are commonly supposed to quantify. In this article, we relax the common assumption of time-invariance of parameters, and instead seek signature information about the dynamics of model behaviour and performance. We do this by using a temporal clustering approach to identify periods of hydrological similarity, allowing the model parameters to vary over the clusters found in this manner, and calibrating these parameters simultaneously. The diagnostic information inferred from these calibration results, based on the patterns in the parameter sets of the various clusters, is used to enhance the model structure. This approach shows how diagnostic model evaluation can be used to combine information from the data and the functioning of the hydrological model in a useful manner.

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Abstract. The application of Artificial Neural Networks (ANNs) in rainfall-runoff modelling needs to be researched more extensively in order to appreciate and fulfil the potential of this modelling approach. This paper reports on the application of multi-layer feedforward ANNs for rainfallrunoff modelling of the Geer catchment (Belgium) using both daily and hourly data. The daily forecast results indicate that ANNs can be considered good alternatives for traditional rainfall-runoff modelling approaches, but the simulations based on hourly data reveal timing errors as a result of a dominating autoregressive component. This component is introduced in model simulations by using previously observed runoff values as ANN model input, which is a popular method for indirectly representing the hydrological state of a catchment. Two possible solutions to this problem of lagged predictions are presented. Firstly, several alternatives for representation of the hydrological state are tested as ANN inputs: moving averages over time of observed discharges and rainfall, and the output of the simple GR4J model component for soil moisture. A combination of these hydrological state representers produces good results in terms of timing, but the overall goodness of fit is not as good as the simulations with previous runoff data. Secondly, the possibility of using multiple measures of model performance during ANN training is mentioned.