Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/103235
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Type: Journal article
Title: Estimating mountain basin-mean precipitation from streamflow using Bayesian inference
Author: Henn, B.
Clark, M.
Kavetski, D.
Lundquist, J.
Citation: Water Resources Research, 2015; 51(10):8012-8033
Publisher: American Geophysical Union
Issue Date: 2015
ISSN: 0043-1397
1944-7973
Statement of
Responsibility: 
Brian Henn, Martyn P. Clark, Dmitri Kavetski, and Jessica D. Lundquist
Abstract: Estimating basin-mean precipitation in complex terrain is difficult due to uncertainty in the topographical representativeness of precipitation gauges relative to the basin. To address this issue, we use Bayesian methodology coupled with a multimodel framework to infer basin-mean precipitation from streamflow observations, and we apply this approach to snow-dominated basins in the Sierra Nevada of California. Using streamflow observations, forcing data from lower-elevation stations, the Bayesian Total Error Analysis (BATEA) methodology and the Framework for Understanding Structural Errors (FUSE), we infer basin-mean precipitation, and compare it to basin-mean precipitation estimated using topographically informed interpolation from gauges (PRISM, the Parameter-elevation Regression on Independent Slopes Model). The BATEA-inferred spatial patterns of precipitation show agreement with PRISM in terms of the rank of basins from wet to dry but differ in absolute values. In some of the basins, these differences may reflect biases in PRISM, because some implied PRISM runoff ratios may be inconsistent with the regional climate. We also infer annual time series of basin precipitation using a two-step calibration approach. Assessment of the precision and robustness of the BATEA approach suggests that uncertainty in the BATEA-inferred precipitation is primarily related to uncertainties in hydrologic model structure. Despite these limitations, time series of inferred annual precipitation under different model and parameter assumptions are strongly correlated with one another, suggesting that this approach is capable of resolving year-to-year variability in basin-mean precipitation.
Keywords: Precipitation; basin hydrology; Bayesian inference; streamflow; snow; model calibration
Rights: © 2015. American Geophysical Union. All Rights Reserved.
RMID: 0030040501
DOI: 10.1002/2014WR016736
Appears in Collections:Civil and Environmental Engineering publications

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