Water and energy fluxes to the Kara Sea based on a combination of observational data and hydrological modeling.

Abstract:

Some of the largest and most consequential uncertainties in the trajectory of the Arctic climate are associated with the hydrology of the Arctic terrestrial system. The challenge facing the research community is to provide a scientifically based analysis to the fundamental questions: How do various Arctic landscapes and river basins respond to climate change and are the trends in streamflow from them increasing or decreasing? How does the magnitude and timing of river and thermal flux to the Arctic Ocean change? To obtain answers from these research questions we applied a combination of in situ observations and hydrological modeling using the UNH Water Balance Model (WBM) and estimated long-term changes in water and energy fluxes to the Kara Sea which is mostly impacted in the Arctic by contributing river flows.

We used the newly collected observational data for river discharge and water temperature for all main downstream gauges in the river systems draining into the Kara Sea basin and those modeled by a new version of University of New Hampshire Water Balance Model (WBM) to provide detailed information about fluxes of water and energy along the Kara Sea coast and evaluate changes in their magnitude and timing over the last 50 years. To identify and quantify contributions of individual drivers/factors to changes in river flux to the Kara Sea we used a new water source tracking capability recently developed in WBM. It allows “fingerprinting” of water at all stages of surface and sub-surface fluxes and storages within the water cycle including soils, groundwater pools, lakes, reservoirs, and fluxes such as river flows, and runoff originated from snowmelt, rain, glaciers, and baseflow. These results are critical to understanding changes in terrestrial runoff generation and to characterize the contribution of changes in river fluxes to oceanic circulation and sea ice formation in the Arctic Ocean.

This work was supported by NSF grants: 1913962, 1917515 and Russian Foundation for Basic Research, grants: 18-05-60192, 18-05-60240.