Long-Lead Seasonal Prediction of Streamflow over the Upper Colorado River Basin: The Role of the Pacific Sea Surface Temperature and Beyond

2021 ◽  
pp. 1-47
Author(s):  
Siyu Zhao ◽  
Rong Fu ◽  
Yizhou Zhuang ◽  
Gaoyun Wang
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
River Basin ◽  
Colorado River ◽  
Sea Surface ◽  
Colorado River Basin ◽  
Lead Times ◽  
Upper Colorado River Basin ◽  
The Pacific

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.

scholarly journals A Comparison of in Situ, Reanalysis, and Satellite Water Budgets over the Upper Colorado River Basin

2013 ◽  
Vol 14 (3) ◽  
pp. 888-905 ◽  
Author(s):  
Rebecca A. Smith ◽  
Christian D. Kummerow
Keyword(s):  
Soil Moisture ◽  
Interannual Variability ◽  
River Basin ◽  
Water Budget ◽  
Colorado River ◽  
Colorado River Basin ◽  
Upper Colorado River Basin ◽  
Precipitation Estimates

Abstract Using in situ, reanalysis, and satellite-derived datasets, surface and atmospheric water budgets of the Upper Colorado River basin are analyzed. All datasets capture the seasonal cycle for each water budget component. For precipitation, all products capture the interannual variability, though reanalyses tend to overestimate in situ while satellite-derived precipitation underestimates. Most products capture the interannual variability of evapotranspiration (ET), though magnitudes differ among the products. Variability and magnitude among storage volume change products widely vary. With regards to the surface water budget, the strongest connections exist among precipitation, ET, and soil moisture, while snow water equivalent (SWE) is best correlated with runoff. Using in situ precipitation estimates, the Max Planck Institute (MPI) ET estimates, and accumulated runoff, changes in storage are calculated and compare well with estimated changes in storage calculated using SWE, reservoir, and the Climate Prediction Center’s soil moisture. Using in situ precipitation estimates, MPI ET estimates, and atmospheric divergence estimates from the European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim) results in a long-term atmospheric storage change estimate of −73 mm. Long-term surface storage estimates combined with long-term runoff come close to balancing with long-term atmospheric convergence from ERA-Interim. Increasing the MPI ET by 5% leads to a better balance between surface storage changes, runoff, and atmospheric convergence. It also brings long-term atmospheric storage changes to a better balance at +13 mm.

scholarly journals Causal effect of the tropical Pacific sea surface temperature on the Upper Colorado River Basin spring precipitation

2021 ◽  
Author(s):  
Siyu Zhao ◽  
Jiaying Zhang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Models ◽  
Colorado River ◽  
Causal Effect ◽  
Tropical Pacific ◽  
Sea Surface ◽  
Spring Precipitation ◽  
Upper Colorado River Basin ◽  
The Tropical Pacific

AbstractThe Colorado River is one of the most important rivers in the southwestern U.S., with ~ 90% of the total flow originating from the Upper Colorado River Basin (UCRB). The UCRB April–July streamflow is well-correlated to the UCRB spring precipitation. It is known that the UCRB precipitation is linked to an El Niño-like sea surface temperature (SST) pattern, but the causal effect of the tropical Pacific SST on the UCRB spring precipitation is still uncertain. Here, we apply a Granger causality approach to understand the causal effect of the tropical Pacific averaged SST in previous three seasons (winter, fall, and summer) on the UCRB averaged precipitation in spring in observations and two climate models. In observations, only the winter SST has Granger causal effect (with p-value ~ 0.05) on spring precipitation, while historical simulations of the two climate models overestimate the causal effect for winter and fall (with p-value < 0.01 and < 0.05, respectively) due to model biases. Moreover, future projections of the two climate models show divergent causal effects, especially for the scenario with high anthropogenic emissions. The divergent projections indicate that (1) there are large uncertainties in model projections of the causal effect of the tropical Pacific SST on UCRB spring precipitation and (2) it is uncertain whether climate models can reliably capture changes in such causality. These uncertainties may result in large uncertainties in seasonal forecasts of the UCRB hydroclimate under global climate change.

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
Keyword(s):  
Time Series ◽  
River Basin ◽  
Colorado River ◽  
Meteorological Data ◽  
Grid Cell ◽  
Colorado River Basin ◽  
Variability Analysis ◽  
Upper Colorado River Basin ◽  
The Pacific ◽  
Rapid Shift

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.

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