Machine Learning Modeling of Climate Variability Impact on River Runoff
The hypothesis of this study was one of existence of spatially organized links between the time series of river runoff and climate variability indices, describing the oscillations in the atmosphere–ocean system: ENSO (El Niño–Southern Oscillation), PDO (Pacific Decadal Oscillation), AMO (Atlantic Mu...
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ftdoajarticles:oai:doaj.org/article:faaddecaddbf4c79954347b1a366219c 2023-05-15T17:33:48+02:00 Machine Learning Modeling of Climate Variability Impact on River Runoff Mateusz Norel Krzysztof Krawiec Zbigniew W. Kundzewicz 2021-04-01T00:00:00Z https://doi.org/10.3390/w13091177 https://doaj.org/article/faaddecaddbf4c79954347b1a366219c EN eng MDPI AG https://www.mdpi.com/2073-4441/13/9/1177 https://doaj.org/toc/2073-4441 doi:10.3390/w13091177 2073-4441 https://doaj.org/article/faaddecaddbf4c79954347b1a366219c Water, Vol 13, Iss 1177, p 1177 (2021) river runoff climate variability machine learning Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 article 2021 ftdoajarticles https://doi.org/10.3390/w13091177 2022-12-31T06:09:26Z The hypothesis of this study was one of existence of spatially organized links between the time series of river runoff and climate variability indices, describing the oscillations in the atmosphere–ocean system: ENSO (El Niño–Southern Oscillation), PDO (Pacific Decadal Oscillation), AMO (Atlantic Multidecadal Oscillation), and NAO (North Atlantic Oscillation). The global river flow reconstructions (ERA-20-CM-R) for 18 study areas on six continents and climate variability indices for the period 1901–2010 were used. The split-sample approach was applied, with the period 1901–2000 used for training and 2001–2010 used for testing. The quality measures used in this paper were mean absolute error, dynamic time warping, and top extreme events error. We demonstrated that a machine learning approach (convolution neural network, CNN) trained on climate variability indices can model the river runoff better than the long-term monthly mean baseline, both in univariate (per-cell) and multivariate (multi-cell, regionalized) settings. We compared the models to the baseline in the form of heatmaps and presented results of ablation experiments (test time ablation, i.e., jackknifing, and training time ablation), which suggested that ENSO is the primary determinant among the considered indices. Article in Journal/Newspaper North Atlantic North Atlantic oscillation Directory of Open Access Journals: DOAJ Articles Pacific Water 13 9 1177 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
river runoff climate variability machine learning Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 |
spellingShingle |
river runoff climate variability machine learning Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 Mateusz Norel Krzysztof Krawiec Zbigniew W. Kundzewicz Machine Learning Modeling of Climate Variability Impact on River Runoff |
topic_facet |
river runoff climate variability machine learning Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 |
description |
The hypothesis of this study was one of existence of spatially organized links between the time series of river runoff and climate variability indices, describing the oscillations in the atmosphere–ocean system: ENSO (El Niño–Southern Oscillation), PDO (Pacific Decadal Oscillation), AMO (Atlantic Multidecadal Oscillation), and NAO (North Atlantic Oscillation). The global river flow reconstructions (ERA-20-CM-R) for 18 study areas on six continents and climate variability indices for the period 1901–2010 were used. The split-sample approach was applied, with the period 1901–2000 used for training and 2001–2010 used for testing. The quality measures used in this paper were mean absolute error, dynamic time warping, and top extreme events error. We demonstrated that a machine learning approach (convolution neural network, CNN) trained on climate variability indices can model the river runoff better than the long-term monthly mean baseline, both in univariate (per-cell) and multivariate (multi-cell, regionalized) settings. We compared the models to the baseline in the form of heatmaps and presented results of ablation experiments (test time ablation, i.e., jackknifing, and training time ablation), which suggested that ENSO is the primary determinant among the considered indices. |
format |
Article in Journal/Newspaper |
author |
Mateusz Norel Krzysztof Krawiec Zbigniew W. Kundzewicz |
author_facet |
Mateusz Norel Krzysztof Krawiec Zbigniew W. Kundzewicz |
author_sort |
Mateusz Norel |
title |
Machine Learning Modeling of Climate Variability Impact on River Runoff |
title_short |
Machine Learning Modeling of Climate Variability Impact on River Runoff |
title_full |
Machine Learning Modeling of Climate Variability Impact on River Runoff |
title_fullStr |
Machine Learning Modeling of Climate Variability Impact on River Runoff |
title_full_unstemmed |
Machine Learning Modeling of Climate Variability Impact on River Runoff |
title_sort |
machine learning modeling of climate variability impact on river runoff |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doi.org/10.3390/w13091177 https://doaj.org/article/faaddecaddbf4c79954347b1a366219c |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Water, Vol 13, Iss 1177, p 1177 (2021) |
op_relation |
https://www.mdpi.com/2073-4441/13/9/1177 https://doaj.org/toc/2073-4441 doi:10.3390/w13091177 2073-4441 https://doaj.org/article/faaddecaddbf4c79954347b1a366219c |
op_doi |
https://doi.org/10.3390/w13091177 |
container_title |
Water |
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13 |
container_issue |
9 |
container_start_page |
1177 |
_version_ |
1766132429836255232 |