| Abstrakt: |
In the eastern region of the North American Continental Divide in the upper Colorado Rockies, this study demonstrates that enhancing streamflow predictability from May to July in the Yellowstone River Basin is enabled. This streamflow improvement is achieved by employing a land surface hydrology model in the watershed, coupled with an updated winter precipitation weather forcing dataset. Utilizing 13 snowpack telemetry stations from the US Department of Agriculture in the Yellowstone River Basin, the paper calculates ratios between a baseline simulated snowpack from the initial land surface model application and the observed snowpack. The average ratio serves as a constant multiplier for the existing snowfall weather forcing applied in the second land surface model simulation. As a result of the second simulation, the streamflow predictability reaches a Nash-Sutcliffe Efficiency (NSE) of 0.91, in contrast to the baseline simulation's 0.73 NSE during peak streamflow periods. The study also explores cold land hydrological processes, particularly those related to snowmelt-driven streamflow. In addition to streamflow, two land surface variables such as snowpack and soil moisture are assessed against insitu snowpack and satellite-based soil moisture observations in the Yellowstone River Basin. The comparisons reveal that the peak of soil moisture is mainly driven by springtime snowmelt and diminishes in the summer. The findings are confirmed by both land surface model simulations and satellite-borne soil moisture observations. Another noteworthy discovery is that soil infiltration properties in the Yellowstone River Basin are wetter than the western Continental Divide in North America, resulting in amplified streamflow in the eastern side despite similar levels of snowmelt runoff on either side of the Continental Divide in the upper Colorado Rockies in the United States. [ABSTRACT FROM AUTHOR] |
