Assessing impacts of climate change on Lake Victoria Basin, Africa

The Nile River is mainly sourced by the Lake Victoria basin catchment. Potable water is vital in this region. Greenhouse gases induced climate change is believed to affect the water resources system leading to alteration in planning and management. Previous studies had been carried out in focus on the pollution, fisheries, wetland of the Lake Victoria basin, while limited research in view of the effects of climate change on hydrological regime. In view of the importance of water in that region, assessing the potential climate change impacts is necessary. Factors such as downscaling of climate scenarios obtain from general circulation models and climate scenarios should be taken into consideration

Flood estimation at ungauged sites using artificial neural networks

Artificial neural networks (ANNs) have been applied within the field of hydrological modelling for over a decade but relatively little attention has been paid to the use of these tools for flood estimation in ungauged catchments. This paper uses data from the Centre for Ecology and Hydrology's Flood Estimation Handbook (FEH) to predict T-year flood events and the index flood (the median of the annual maximum series) for 850 catchments across the UK. When compared with multiple regression models, ANNs provide improved flood estimates that can be used by engineers and hydrologists. Comparisons are also made with the empirical model presented in the FEH and a preliminary study is made of the spatial distribution of ANN residuals, highlighting the influence that geographical factors have on model performance

Ideal point error for model assessment in data-driven river flow forecasting

When analysing the performance of hydrological models in river forecasting, researchers use a number of diverse statistics. Although some statistics appear to be used more regularly in such analyses than others, there is a distinct lack of consistency in evaluation, making studies undertaken by different authors or performed at different locations difficult to compare in a meaningful manner. Moreover, even within individual reported case studies, substantial contradictions are found to occur between one measure of performance and another. In this paper we examine the ideal point error (IPE) metric – a recently introduced measure of model performance that integrates a number of recognised metrics in a logical way. Having a single, integrated measure of performance is appealing as it should permit more straightforward model inter-comparisons. However, this is reliant on a transferrable standardisation of the individual metrics that are combined to form the IPE. This paper examines one potential option for standardisation: the use of naive model benchmarking

A non-Linear neural network technique for updating river flow forecasts

forecast

A real-time combination method for the outputs of different rainfall-runoff models

Application of the concept of combining the estimated forecast output of different rainfall-runoff models to yield an overall combined estimated output in the context of real-time river flow forecasting is explored. A Real-Time Model Output Combination Method (RTMOCM) is developed, based on the structure of the Linear Transfer Function Model (LTFM) and utilizing the concept of the Weighted Average Method (WAM) for model output combination. A multiple-input single-output form of the LTFM is utilized in the RTMOCM. This form of the LTFM model uses synchronously the daily simulation-mode model-estimated discharge time series of the rainfall-runoff models selected for combination, its inherent updating structure being used for providing updated combined discharge forecasts. The RTMOCM is applied to the daily data of five catchments, using the simulation-mode estimated discharges of three selected rainfall-runoff models, comprising one conceptual model (Soil Moisture Accounting and Routing Procedure-SMAR) and two black-box models (Linear Perturbation Model-LPM and Linearly-Varying Variable Gain Factor Model-LVGFM). In order to get an indication of the accuracy of the updated combined discharge forecasts relative to the updated discharge forecasts of the individual models, the LTFM is also used for updating the simulation-mode discharge time series of each of the three individual models. The results reveal that the updated combined discharge forecasts provided by the RTMOCM, with parameters obtained by linear regression, can improve on the updated discharge forecasts of the individual rainfall-runoff-models

Comparison of different forms of the multi-layer feed-forward neural network method used for river flow forecast combination

The multi-layer feed-forward neural network (MLFFNN) is applied in the context of river flow forecast combination, where a number of rainfall-runoff models are used simultaneously to produce an overall combined river flow forecast. The operation of the MLFFNN depends on the neuron transfer function, which is non-linear. These models, each having a different structure to simulate the perceived mechanisms of the runoff process, utilise the information carrying capacity of the model calibration data indifferent ways. Hence, in a discharge forecast combination procedure, the discharge forecasts of each model provide a source of information different from that of the other models used in the combination. In the present work, the significance of the choice of the transfer function type in the overall performance of the MLFFNN, when used in the river flow forecast combination context is critically investigated. Five neuron transfer functions are used in this investigation, namely, the logistic function, the bipolar function, the hyperbolic function, the arctan function and the scaled arctan function. The results indicate that the logistic function yields the best model forecast combination performance.Not applicableti vo is st en SB. 20/7/1

Comparison of different forms of the multi-layer feed-forward neural network method used for river flow forecast combination

The multi-layer feed-forward neural network (MLFFNN) is applied in the context of river flow forecast combination, where a number of rainfall-runoff models are used simultaneously to produce an overall combined river flow forecast. The operation of the MLFFNN depends on the neuron transfer function, which is non-linear. These models, each having a different structure to simulate the perceived mechanisms of the runoff process, utilise the information carrying capacity of the model calibration data indifferent ways. Hence, in a discharge forecast combination procedure, the discharge forecasts of each model provide a source of information different from that of the other models used in the combination. In the present work, the significance of the choice of the transfer function type in the overall performance of the MLFFNN, when used in the river flow forecast combination context is critically investigated. Five neuron transfer functions are used in this investigation, namely, the logistic function, the bipolar function, the hyperbolic function, the arctan function and the scaled arctan function. The results indicate that the logistic function yields the best model forecast combination performance.Not applicableti vo is st en SB. 20/7/1