Reconstructed summertime (June–July) streamflow dating back to 1788 CE in the Kazakh Uplands as inferred from tree rings
Ishim-Tobor River was carried out using random forest (RF), K-nearest neighbor (KNN) and multiple linear regression (MLR) models. The reliability of the ensemble reconstruction was verified by a comparison with other regional reconstructions and historical records. A correlation analysis and vapor f...
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ftdatacite:10.17632/t7g73cgxhp 2023-05-15T15:07:49+02:00 Reconstructed summertime (June–July) streamflow dating back to 1788 CE in the Kazakh Uplands as inferred from tree rings Feng Chen 2022 https://dx.doi.org/10.17632/t7g73cgxhp https://data.mendeley.com/datasets/t7g73cgxhp unknown Mendeley Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY Dataset dataset 2022 ftdatacite https://doi.org/10.17632/t7g73cgxhp 2022-02-09T13:20:43Z Ishim-Tobor River was carried out using random forest (RF), K-nearest neighbor (KNN) and multiple linear regression (MLR) models. The reliability of the ensemble reconstruction was verified by a comparison with other regional reconstructions and historical records. A correlation analysis and vapor fluxes were applied to visualize the significant influence of atmospheric circulation on the study area. The cumulative distribution functions (CDFs) examined the distribution of the high (low) flows highlighted by the reconstruction. New hydrological insights for the region: Our study analyzes the application of machine learning algorithms and a traditional MLR model to hydrological reconstruction. The single model reconstruction contained information and results on streamflow variability were not sufficient. Consequently, we integrated the three models into the ensemble reconstruction. The extended streamflow record reveals the basin's hydrological changes over the past 229 years. From 1788–2016, the reconstructed streamflow was perennially below the mean value, which indicates more prominent drought and water deficit conditions within the basin. This phenomenon was significantly influenced by water vapor transport from the North Atlantic and Arctic Oceans. If future climate scenarios lead to drought in the basin, then surface water demand will not be satisfied for 7 out of 10 years. Dataset Arctic North Atlantic DataCite Metadata Store (German National Library of Science and Technology) Arctic |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
description |
Ishim-Tobor River was carried out using random forest (RF), K-nearest neighbor (KNN) and multiple linear regression (MLR) models. The reliability of the ensemble reconstruction was verified by a comparison with other regional reconstructions and historical records. A correlation analysis and vapor fluxes were applied to visualize the significant influence of atmospheric circulation on the study area. The cumulative distribution functions (CDFs) examined the distribution of the high (low) flows highlighted by the reconstruction. New hydrological insights for the region: Our study analyzes the application of machine learning algorithms and a traditional MLR model to hydrological reconstruction. The single model reconstruction contained information and results on streamflow variability were not sufficient. Consequently, we integrated the three models into the ensemble reconstruction. The extended streamflow record reveals the basin's hydrological changes over the past 229 years. From 1788–2016, the reconstructed streamflow was perennially below the mean value, which indicates more prominent drought and water deficit conditions within the basin. This phenomenon was significantly influenced by water vapor transport from the North Atlantic and Arctic Oceans. If future climate scenarios lead to drought in the basin, then surface water demand will not be satisfied for 7 out of 10 years. |
format |
Dataset |
author |
Feng Chen |
spellingShingle |
Feng Chen Reconstructed summertime (June–July) streamflow dating back to 1788 CE in the Kazakh Uplands as inferred from tree rings |
author_facet |
Feng Chen |
author_sort |
Feng Chen |
title |
Reconstructed summertime (June–July) streamflow dating back to 1788 CE in the Kazakh Uplands as inferred from tree rings |
title_short |
Reconstructed summertime (June–July) streamflow dating back to 1788 CE in the Kazakh Uplands as inferred from tree rings |
title_full |
Reconstructed summertime (June–July) streamflow dating back to 1788 CE in the Kazakh Uplands as inferred from tree rings |
title_fullStr |
Reconstructed summertime (June–July) streamflow dating back to 1788 CE in the Kazakh Uplands as inferred from tree rings |
title_full_unstemmed |
Reconstructed summertime (June–July) streamflow dating back to 1788 CE in the Kazakh Uplands as inferred from tree rings |
title_sort |
reconstructed summertime (june–july) streamflow dating back to 1788 ce in the kazakh uplands as inferred from tree rings |
publisher |
Mendeley |
publishDate |
2022 |
url |
https://dx.doi.org/10.17632/t7g73cgxhp https://data.mendeley.com/datasets/t7g73cgxhp |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic North Atlantic |
genre_facet |
Arctic North Atlantic |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.17632/t7g73cgxhp |
_version_ |
1766339231561547776 |