Combining snow, streamflow, and precipitation gauge observations to infer basin‐mean precipitation

摘要: Precipitation data in mountain basins is typically sparse and subject to uncertainty due difficulties measurement capturing spatial variability. Streamflow provides indirect information about basin-mean precipitation, but inferring precipitation from streamflow requires assumptions hydrologic model structure that influence amounts. In this study, we test the extent which using both snow observations reduces differences inferred annual total compared inference alone. The case study area upper Tuolumne River basin Sierra Nevada of California, where distributed water equivalent (SWE) estimates have been made LiDAR as part NASA Airborne Snow Observatory (ASO). To reconstruct SWE for years prior ASO campaign, a robust relationship between courses pillows, longer record. Relative ASO's observations, point measurements tend overestimate at given elevation, under-sample high-elevation areas. We then infer streamflow, obtained multiple structures. When included inference, reduce by up one third standard deviations year structures, improve consistency structures terms yearly variability precipitation. reiterate previous findings types modeled physical processes help identify most appropriate This article protected copyright. All rights reserved.