Plucinski, B.; Sun, Y.; Wang, S.-Y.; Gillies, R.R.; Eklund, J.; Wang, C.-C. Feasibility of Multi-Year Forecast for the Colorado River Water Supply: Time Series Modeling. Water2019, 11, 2433.
Plucinski, B.; Sun, Y.; Wang, S.-Y.; Gillies, R.R.; Eklund, J.; Wang, C.-C. Feasibility of Multi-Year Forecast for the Colorado River Water Supply: Time Series Modeling. Water 2019, 11, 2433.
Plucinski, B.; Sun, Y.; Wang, S.-Y.; Gillies, R.R.; Eklund, J.; Wang, C.-C. Feasibility of Multi-Year Forecast for the Colorado River Water Supply: Time Series Modeling. Water2019, 11, 2433.
Plucinski, B.; Sun, Y.; Wang, S.-Y.; Gillies, R.R.; Eklund, J.; Wang, C.-C. Feasibility of Multi-Year Forecast for the Colorado River Water Supply: Time Series Modeling. Water 2019, 11, 2433.
Abstract
The future of the Colorado River water supply (WS) affects millions of people and the U.S. economy. A recent study suggested a cross-basin correlation between the Colorado River and its neighboring Great Salt Lake (GSL). Following that study, the feasibility of using the previously developed multi-year prediction of the GSL water level to forecast the Colorado River WS was tested. Time-series models were developed to predict the changes in WS out to 10 years. Regressive methods and the GSL water level data were used for the depiction of decadal variability of the Colorado River WS. Various time-series models suggest a decline in the 10-year-averaged WS since 2013 before starting to increase around 2020. Comparison between this WS prediction and the WS projection published in a 2012 government report (derived from climate models) reveals a widened imbalance between supply and demand by 2020. Further research to update similar multi-year prediction of the Colorado River WS is needed. Such information could aid in management decision making in the face of future water shortages.
Keywords
time series; Colorado River; water supply; cross-validation; decadal prediction
Subject
Physical Sciences, Mathematical Physics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
