Synthetic Simulation of Spatially‐Correlated Streamflows: Weighted‐Modified Fractional Gaussian Noise
Abstract Stochastic methods have been typically used for the design and operations of hydraulic infrastructure. They allow decision makers to evaluate existing or new infrastructure under different possible scenarios, giving them the flexibility and tools needed in decision making. In this paper, we...
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| Format: | Article |
| Language: | English |
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Wiley
2024-02-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2023WR035371 |
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| author | Cristián Chadwick Frederic Babonneau Tito Homem‐de‐Mello Agustín Letelier |
| author_facet | Cristián Chadwick Frederic Babonneau Tito Homem‐de‐Mello Agustín Letelier |
| author_sort | Cristián Chadwick |
| collection | DOAJ |
| description | Abstract Stochastic methods have been typically used for the design and operations of hydraulic infrastructure. They allow decision makers to evaluate existing or new infrastructure under different possible scenarios, giving them the flexibility and tools needed in decision making. In this paper, we present a novel stochastic streamflow simulation approach able to replicate both temporal and spatial dependencies from the original data in a multi‐site basin context. The proposed model is a multi‐site extension of the modified Fractional Gaussian Noise (mFGN) model which is well‐known to be efficient to maintain periodic correlation for several time lags, but presents shortcomings in preserving the spatial correlation. Our method, called Weighted‐mFGN (WmFGN), incorporates spatial dependency into streamflows simulated with mFGN by relying on the Cholesky decomposition of the spatial correlation matrix of the historical streamflow records. As the order in which the decomposition steps are performed (temporal then spatial, or vice‐versa) affects the performance in terms of preserving the temporal and spatial correlation, our method searches for an optimal convex combination of the resulting correlation matrices. The result is a Pareto‐curve that indicates the optimal weights of the convex combination depending on the importance given by the user to spatial and temporal correlations. The model is applied to a number of river basins in Chile, where the results show that the WmFGN approach maintains the qualities of the single‐site mFGN, while significantly improving spatial correlation. |
| format | Article |
| id | doaj-art-4a5ce06f67ae488180e1cb3eb3f1d0e8 |
| institution | OA Journals |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-4a5ce06f67ae488180e1cb3eb3f1d0e82025-08-20T02:09:29ZengWileyWater Resources Research0043-13971944-79732024-02-01602n/an/a10.1029/2023WR035371Synthetic Simulation of Spatially‐Correlated Streamflows: Weighted‐Modified Fractional Gaussian NoiseCristián Chadwick0Frederic Babonneau1Tito Homem‐de‐Mello2Agustín Letelier3Faculty of Engineering and Sciences Universidad Adolfo Ibáñez Santiago ChileKedge Business School Talence FranceSchool of Business Universidad Adolfo Ibáñez Santiago ChileFaculty of Engineering and Sciences Universidad Adolfo Ibáñez Santiago ChileAbstract Stochastic methods have been typically used for the design and operations of hydraulic infrastructure. They allow decision makers to evaluate existing or new infrastructure under different possible scenarios, giving them the flexibility and tools needed in decision making. In this paper, we present a novel stochastic streamflow simulation approach able to replicate both temporal and spatial dependencies from the original data in a multi‐site basin context. The proposed model is a multi‐site extension of the modified Fractional Gaussian Noise (mFGN) model which is well‐known to be efficient to maintain periodic correlation for several time lags, but presents shortcomings in preserving the spatial correlation. Our method, called Weighted‐mFGN (WmFGN), incorporates spatial dependency into streamflows simulated with mFGN by relying on the Cholesky decomposition of the spatial correlation matrix of the historical streamflow records. As the order in which the decomposition steps are performed (temporal then spatial, or vice‐versa) affects the performance in terms of preserving the temporal and spatial correlation, our method searches for an optimal convex combination of the resulting correlation matrices. The result is a Pareto‐curve that indicates the optimal weights of the convex combination depending on the importance given by the user to spatial and temporal correlations. The model is applied to a number of river basins in Chile, where the results show that the WmFGN approach maintains the qualities of the single‐site mFGN, while significantly improving spatial correlation.https://doi.org/10.1029/2023WR035371stochastic hydrologyhydrologystreamflows |
| spellingShingle | Cristián Chadwick Frederic Babonneau Tito Homem‐de‐Mello Agustín Letelier Synthetic Simulation of Spatially‐Correlated Streamflows: Weighted‐Modified Fractional Gaussian Noise Water Resources Research stochastic hydrology hydrology streamflows |
| title | Synthetic Simulation of Spatially‐Correlated Streamflows: Weighted‐Modified Fractional Gaussian Noise |
| title_full | Synthetic Simulation of Spatially‐Correlated Streamflows: Weighted‐Modified Fractional Gaussian Noise |
| title_fullStr | Synthetic Simulation of Spatially‐Correlated Streamflows: Weighted‐Modified Fractional Gaussian Noise |
| title_full_unstemmed | Synthetic Simulation of Spatially‐Correlated Streamflows: Weighted‐Modified Fractional Gaussian Noise |
| title_short | Synthetic Simulation of Spatially‐Correlated Streamflows: Weighted‐Modified Fractional Gaussian Noise |
| title_sort | synthetic simulation of spatially correlated streamflows weighted modified fractional gaussian noise |
| topic | stochastic hydrology hydrology streamflows |
| url | https://doi.org/10.1029/2023WR035371 |
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