https://macsphere.mcmaster.ca/handle/11375/28030

Tang, W. (2022) Identifying streamflow changes in western North America from 1979 to 2021 using Deep Learning approaches. PhD Thesis, McMaster University. https://macsphere.mcmaster.ca/handle/11375/28030

Streamflow in Western North America (WNA) has been experiencing pronounced changes in terms of volume and timing over the past century, primarily driven by natural climate variability and human-induced climate changes. This thesis advances on previous work by revealing the most recent streamflow changes in WNA using a comprehensive suite of classical hydrometric methods along with novel Deep Learning (DL) based approaches for change detection and classifica- tion. More than 500 natural streams were included in the analysis across western Canada and the United States. Trend analyses based on the Mann-Kendall test were conducted on a wide selection of classic hydrometric indicators to represent varying aspects of streamflow over 43 years from 1979 to 2021. A general geograph- ical divide at approximately 46◦N degrees latitude indicates that total streamflow is increasing to the north while declining to the south. Declining late summer flows (July–September) were also widespread across the WNA domain, coinciding with an overall reduction in precipitation. Some changing patterns are regional specific, including: 1) increased winter low flows at high latitudes; 2) earlier spring freshet in Rocky Mountains; 3) increased autumns flows in coastal Pacific North- west; and 4) dramatic drying in southwestern United States. In addition to classic hydrometrics, trend analysis was performed on Latent Features (LFs), which were extracted by Variation AutoEncoder (VAE) from raw streamflow data and are considered “machine-learned hydrometrics”. Some LFs with direct hydrological implications were closely associated with the classical hydrometric indicators such as flow quantity, seasonal distribution, timing and magnitude of freshet, and snow- to-rain transition. The changing patterns of streamflows revealed by LFs show direct agreement with the hydrometric trends. By reconstructing hydrographs from select LFs, VAE also provides a mechanism to project changes in streamflow patterns in the future. Furthermore, a parametric t-SNE method based on DL technology was developed to visualize similarity among a large number of hydro- graphs on a 2-D map. This novel method allowed fast grouping of hydrologically similar rivers based on their flow regime type and provides new opportunities for streamflow classification and regionalization.

https://macsphere.mcmaster.ca/handle/11375/28030