scholarly journals Homogeneity of Gridded Precipitation Datasets for the Colorado River Basin

2010 ◽  
Vol 49 (12) ◽  
pp. 2404-2415 ◽  
Author(s):  
Galina Guentchev ◽  
Joseph J. Barsugli ◽  
Jon Eischeid

Abstract Inhomogeneity in gridded meteorological data may arise from the inclusion of inhomogeneous station data or from aspects of the gridding procedure itself. However, the homogeneity of gridded datasets is rarely questioned, even though an analysis of trends or variability that uses inhomogeneous data could be misleading or even erroneous. Three gridded precipitation datasets that have been used in studies of the Upper Colorado River basin were tested for homogeneity in this study: that of Maurer et al., that of Beyene and Lettenmaier, and the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) dataset of Daly et al. Four absolute homogeneity tests were applied to annual precipitation amounts on a grid cell and on a hydrologic subregion spatial scale for the periods 1950–99 and 1916–2006. The analysis detects breakpoints in 1977 and 1978 at many locations in all three datasets that may be due to an anomalously rapid shift in the Pacific decadal oscillation. One dataset showed breakpoints in the 1940s that might be due to the widespread change in the number of available observing stations used as input for that dataset. The results also indicated that the time series from the three datasets are sufficiently homogeneous for variability analysis during the 1950–99 period when aggregated on a subregional scale.

2021 ◽  
pp. 1-47
Author(s):  
Siyu Zhao ◽  
Rong Fu ◽  
Yizhou Zhuang ◽  
Gaoyun Wang

AbstractWe have developed two statistical models for extended seasonal predictions of the Upper Colorado River Basin (UCRB) natural streamflow during April–July: a stepwise linear regression (reduced to a simple regression with one predictor) and a neural network model. Monthly, basin-averaged soil moisture, snow water equivalent (SWE), precipitation, and the Pacific sea surface temperature (SST) are selected as potential predictors. Pacific SST Predictors (PSPs) are derived from a dipole pattern over the Pacific (30°S–65°N) that is correlated with the lagging streamflow. For both models, the correlation between the hindcasted and observed streamflow exceeds 0.60 for lead times less than four months using soil moisture, SWE, and precipitation as predictors. This correlation is higher than that of an autoregression model (correlation ~0.50). Since these land-surface and atmospheric variables have no statistically significant correlations with the streamflow, PSPs are then incorporated into the models. The two models have a correlation of ~0.50 using PSPs alone for lead times from six to nine months, and such skills are probably associated with stronger correlation between SST and streamflow in recent decades. The similar prediction skills between the two models suggest a largely linear system between SST and streamflow. Four predictors together can further improve short-lead prediction skills (correlation ~0.80). Therefore, our results confirm the advantage of the Pacific SST information in predicting the UCRB streamflow with a long lead time, and can provide useful climate information for water supply planning and decisions.


2016 ◽  
Author(s):  
John R. Bargar ◽  
◽  
Sharon Bone ◽  
Kristin Boye ◽  
Emily Cardarelli ◽  
...  

2021 ◽  
Vol 21 ◽  
pp. 100206
Author(s):  
Connie A. Woodhouse ◽  
Rebecca M. Smith ◽  
Stephanie A. McAfee ◽  
Gregory T. Pederson ◽  
Gregory J. McCabe ◽  
...  

Fisheries ◽  
2018 ◽  
Vol 43 (4) ◽  
pp. 194-206 ◽  
Author(s):  
Brian G. Laub ◽  
Gary P. Thiede ◽  
William W. Macfarlane ◽  
Phaedra Budy

Sign in / Sign up

Export Citation Format

Share Document