https://harvest.usask.ca/handle/10388/14623

Baron, A. (2023) Water source, climate, and water chemistry combine to influence DOC concentration and DOM quality in Buffalo Pound Lake, Saskatchewan.

Flow management has the potential to significantly affect ecosystem condition. Shallow lakes in arid regions are especially susceptible to flow management changes, which can have important implications for the formation of cyanobacterial blooms. Here, we reveal water quality shifts associated with changing source water inflow management. Using in situ monitoring data, we studied a seven-year time span during which inflows to a shallow, eutrophic drinking water reservoir transitioned from primarily natural landscape runoff (2014–2015) to managed flows from a larger upstream reservoir (Lake Diefenbaker; 2016–2020) and identified significant changes in cyanobacteria (as phycocyanin) using generalized additive models to classify cyanobacterial bloom formation. We then connected changes in water source with shifts in chemistry and the occurrence of cyanobacterial blooms using principal components analysis. Phycocyanin was greater in years with managed reservoir inflow from a mesotrophic upstream reservoir (2016–2020), but dissolved organic matter (DOM) and specific conductivity, important determinants of drinking water quality, were greatest in years when landscape runoff dominated lake water source (2014–2015). Most notably, despite changing rapidly, it took multiple years for lake water to return to a consistent and reduced level of DOM after managed inflows from the upstream reservoir were resumed, an observation that underscores how resilience may be hindered by weak resistance to change and slow recovery. Environmental flows for water quality are rarely defined, yet we show that trade-offs exist between poor water quality via elevated conductivity and DOM and higher bloom risk, depending on water source. Our work highlights the importance of source water quality, not just quantity, to water security, and our findings have important implications for water managers who must protect ecosystem services while adapting to projected hydroclimatic change.

The partitioning of precipitation between rainfall and snowfall is a crucial component of the evolution of the snowpack in mountains. Most snowpack models use the air temperature and humidity near the surface to derive the precipitation phase. However, the phase at the surface is strongly influenced by processes such as melting and refreezing of falling hydrometeors that occur above the surface. Atmospheric models simulate these processes and the corresponding phase at the surface. However, snowpack models rarely use this information. In this study, we considered two estimates of precipitation phase from an atmospheric model and tested them with a physically-based snow model over the mountains of southwestern Canada and northwestern United States. The results were compared with traditional approaches using the air temperature and humidity near the surface to derive the precipitation phase. Our results showed that the precipitation phase associated with the snow level obtained from the atmospheric model improved snowfall estimate and snowpack prediction compared to the traditional approaches. In contrast, the cloud/precipitation scheme of the atmospheric model decreased performance in phase estimate and snow simulations due to missing physical processes. Our study highlights that snowpack predictions in the mountains can be improved if valuable information is obtained from atmospheric models.

Economic-Evaluation, Refereed Publications

https://harvest.usask.ca/handle/10388/14623