TY - Generic T1 - An Assessment of Differences in Gridded Precipitation Datasets in Complex Terrain T2 - 84th Annual Western Snow Conference Y1 - 2016 A1 - Brian Henn A1 - Andrew J. Newman A1 - Ben Livneh A1 - Christopher Daly A1 - Jessica D. Lundquist AB -

Hydrologic modeling and other geophysical applications are sensitive to precipitation forcing data quality, and there are known challenges in spatially distributing gauge-based precipitation over complex terrain. We conduct a comparison of six high-resolution, daily and monthly gridded precipitation datasets over the Western United States. We compare the long-term average spatial patterns, and interannual variability of water-year total precipitation, as well as multi-year trends in precipitation across the datasets. We find that the greatest absolute differences among datasets occur in high-elevation areas and in the maritime mountain ranges of the Western United States, while the greatest percent differences among datasets relative to annual total precipitation occur in arid and rain-shadowed areas. Differences between datasets in some high-elevation areas may exceed 200 mm yr-1 on average, and relative differences range from 5-60% across the Western United States. (KEYWORDS:  orographic precipitation, precipitation uncertainty, hydrologic modeling, distributed datasets)

JF - 84th Annual Western Snow Conference CY - Seattle, Washington UR - /files/PDFs/2016Henn.pdf ER - TY - Generic T1 - Snow Pillows, LiDAR, and Streamgauges: Incorporating Snow and Streamflow Observations in the Basin Water Balance T2 - 83rd Annual Western Snow Conference Y1 - 2015 A1 - Brian Henn A1 - Martyn P. Clark A1 - Dmitri Kavetski A1 - Bruce McGurk A1 - Thomas H. Painter A1 - Jessica Lundquist KW - basin hydrology KW - hydrologic modeling KW - orographic precipitation KW - remote sensing AB -

Prior studies have suggested that point measurements of SWE may not be spatially representative of the basin snowpack. The development of remotely-sensed snow observations, such as the Airborne Snow Observatory (ASO) and MODIS-based snow cover estimates, allows for relating point and distributed estimates of snow. We examine how SWE at courses and pillows compares with distributed ASO SWE in water year 2014 in a highelevation basin in Yosemite National Park. We find that peak basin-mean ASO SWE was less than that found by averaging regional snow pillows, but that it melted at a slower rate than pillow SWE during the ablation season. Based on this, we develop an approach for bias-correcting historical snow pillow indices of SWE to better represent the basin mean. We then calibrate an ensemble of lumped hydrologic models to infer basin-mean precipitation from streamflow and SWE. Models calibrated to only streamflow observations have substantial uncertainty in inferred precipitation. Including appropriate SWE observations in the calibration is found to reduce this uncertainty. However, calibrating to fractional snow cover in addition to streamflow did not improve the consistency of the models. We suggest that this approach can improve understanding of water balance components in high-elevation, sparsely-measured basins.

 

Presentation in PDF

JF - 83rd Annual Western Snow Conference T3 - Proceedings of the Western Snow Conference CY - Grass Valley, California UR - /files/PDFs/2015Henn.pdf ER - TY - Generic T1 - Doing Hydrology Backward: Estimating Mountain Precipitation Patterns from Streamflow T2 - 82nd Annual Western Snow Conference Y1 - 2014 A1 - Brian Henn A1 - Martyn P. Clark A1 - Dmitri Kavetski A1 - Jessica D. Lundquist KW - Bayes KW - precipitation KW - streamflow KW - water balance AB -

Estimation of basin-mean precipitation in mountainous areas may be difficult due to high spatial variability and time-varying precipitation patterns. Streamflow offers additional information about the water balance of the basin with which to estimate precipitation. We apply a methodology for inferring basin-mean precipitation from streamflow using Bayes’ Theorem, and adapt this approach to snow-dominated basins in the Sierra Nevada mountain range of California. As part of this approach, we develop and couple a temperature-index snow model to an existing lumped hydrologic model. We infer 1981-2006 annual average precipitation across four basins and compare the results to those obtained from similar approaches based on climatological precipitation patterns. We also use the approach to identify an example of year-to-year variability in precipitation patterns, finding that the inferred precipitation patterns generally match other observations from two anomalous water years. The method offers the potential for inferring spatial precipitation patterns at a level of precision that could improve upon current methods. Future work on this approach will focus on identifying spatial patterns of precipitation across a more extensive collection of basins and water years.

JF - 82nd Annual Western Snow Conference T3 - Proceedings of the Western Snow Conference CY - Durango, Colorado UR - sites/westernsnowconference.org/PDFs/2014Henn.pdf ER -