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 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.