| 1. |
- Olsson, J., et al.
(författare)
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Neural Networks for rainfall forecasting by atmospheric downscaling
- 2004
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Ingår i: Journal of Hydrologic Engineering. - 1084-0699. ; 9:1, s. 1-12
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Tidskriftsartikel (refereegranskat)abstract
- Several studies have used artificial neural networks (NNs) to estimate local or regional recipitation/rainfall on the basis of relationships with coarse-resolution atmospheric variables. None of these experiments satisfactorily reproduced temporal intermittency and variability in rainfall. We attempt to improve performance by using two approaches: (1) couple two NNs in series, the first to determine rainfall occurrence, and the second to determine rainfall intensity during rainy periods; and (2) categorize rainfall into intensity categories and train the NN to reproduce these rather than the actual intensities. The experiments focused on estimating 12-h mean rainfall in the Chikugo River basin, Kyushu Island, southern Japan, from large-scale values of wind speeds at 850 hPa and precipitable water. The results indicated that (1) two NNs in series may greatly improve the reproduction of intermittency; (2) longer data series are required to reproduce variability; (3) intensity categorization may be useful for probabilistic forecasting; and (4) overall performance in this region is better during winter and spring than during summer and autumn.
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| 2. |
- Berndtsson, R., et al.
(författare)
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Solar-climatic relationship and implications for hydrology
- 2001
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Ingår i: Nordic Hydrology. - 0029-1277. ; 32:2, s. 65-84
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Tidskriftsartikel (refereegranskat)abstract
- Research during the latest years has indicated a significant connection between climate and solar activity. Specifically, a relationship between Northern Hemisphere air temperature and sunspot cycle length (SCL) has been shown. By using monthly SCL and land air temperature from 1753-1990 (238 years) we show that this relationship also holds for a single observation point in south of Sweden. Using data after 1850 yields a statistically significant linear correlation of 0.54 between SCL and mean temperature. Furthermore, we show that there are indications of a low-dimensional chaotic component in both SCL and the interconnected mean land air temperature. This has important implications for hydrology and water resources applications. By pure definition of chaos this means that it is virtually impossible to make long-term predictions of mean temperature. Similarly, because of the strong connection between temperature and many hydrological components, it is probable that also long-term water balance constituents may follow chaotic trajectories. Long-term projections of water resources availability may therefore be impossible. Repeated short-term predictions may, however, still be viable. We exemplify this by showing a technique to predict interpolated mean temperature 6 and 12 months ahead in real time with encouraging results. Improving the technique further may be possible by including information on the SCL attractor. To summarize, research into the possible existence of chaotic components in hydrological processes should be an important task for the next years to come.
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| 3. |
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| 4. |
- Merabtene, T, et al.
(författare)
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Risk assessment for optimal drought management of an integrated water resources system using a genetic algorithm
- 2002
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Ingår i: Hydrological Processes. - : Wiley. - 1099-1085 .- 0885-6087. ; 16:11, s. 2189-2208
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Tidskriftsartikel (refereegranskat)abstract
- A decision support system (DSS) is developed and applied to assess the susceptibility of water supply systems to droughts, and to aid decision-makers in determining optimal supply strategies. The DSS integrates three fundamental modules for water resources management: (1) a real time rainfall-runoff forecasting model enhanced by Kalman filtering; (2) a water demand forecast model; and (3) a reservoir operation model. Simulation and optimization procedures for the reservoir operation model are based on risk analysis to evaluate the system performance and to derive the most appropriate supply strategy of minimum risk, for the designed operating conditions. The optimization technique, based on genetic algorithms, introduces two new and distinct features, with the aim of minimizing the risks of drought damage and improving the convergence of the model toward practical solutions. Firstly, risk-based measures of system performance, termed reliability, resiliency and vulnerability, are combined into a global risk index, referred to as the drought risk index (DRI). The DRI, formulated as a weighted function of the risk measures, serves as the objective function to be minimized during the search for the optimal operation. Secondly, in the genetic algorithm search, each new generation of water supply solutions is created from solutions with risk levels clustered inside a defined 'acceptable risk space'. In other words, the convergence of the algorithm is improved by retaining only those solutions with DRI values smaller than the maximum acceptable risk. As a case study, the DSS is applied to the water resources system in Fukuoka City, western Japan. The DSS is believed to be an efficient tool for the assessment of a sequence of water supply scenarios, leading to the improved utilization of existing water resources during drought.
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| 5. |
- Sivakumar, B., et al.
(författare)
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Dynamics of monthly rainfall-runoff process at the Göta basin : A search for chaos
- 2000
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Ingår i: Hydrology and Earth System Sciences. - : Copernicus GmbH. - 1027-5606 .- 1607-7938. ; 4:3, s. 407-417
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Tidskriftsartikel (refereegranskat)abstract
- Sivakumar et al. (2000a), by employing the correlation dimension method, provided preliminary evidence of the existence of chaos in the monthly rainfall-runoff process at the Göta basin in Sweden. The present study verifies and supports the earlier results and strengthens such evidence. The study analyses the monthly rainfall, runoff and runoff coefficient series using the nonlinear prediction method, and the presence of chaos is investigated through an inverse approach, i.e. identifying chaos from the results of the prediction. The presence of an optimal embedding dimension (the embedding dimension with the best prediction accuracy) for each of the three series indicates the existence of chaos in the rainfall-runoff process, providing additional support to the results obtained using the correlation dimension method. The reasonably good predictions achieved, particularly for the runoff series, suggest that the dynamics of the rainfall-runoff process could be understood from a chaotic perspective. The predictions are also consistent with the correlation dimension results obtained in the earlier study, i.e. higher prediction accuracy for series with a lower dimension and vice-versa, so that the correlation dimension method can indeed be used as a preliminary indicator of chaos. However, the optimal embedding dimensions obtained from the prediction method are considerably less than the minimum dimensions essential to embed the attractor, as obtained by the correlation dimension method. A possible explanation for this could be the presence of noise in the series, since the effects of noise at higher embedding dimensions could be significantly greater than that at lower embedding dimensions.
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