Feasibility of Multi-Year Forecast for the Colorado River Water Supply: Time Series Modeling

Abstract

The future of the Colorado River water supply (WS) affects millions of people and the US 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, a tendency that is confirmed by updated WS observation. Such information could aid in management decision-making in the face of near-future water shortages.

Colorado River water supply is predictable on multi-year timescales owing to long-term ocean memory

“…Because of deficiencies in current climate models simulating observed land hydrology, these results suggest that enhanced predictive skill could be achieved by improving model performance in simulating soil water variability. The dynamical prediction presented here supports earlier statistical predictions of Colorado River water supply years ahead 21,22,29,30 .…”

“…These results lead to a hypothesis proposed herein that skillful multi-year predictions of the Colorado River water supply are possible by utilizing long-term ocean memory, its associated atmospheric teleconnections, and the natural filtering effect in the land system altogether. A similar concept has been applied to develop seasonal drought forecasts based on El Niño Southern Oscillation (ENSO) predictions 20 and a statistical model for multi-year water supply predictions 21,22 , yet its application to the water supply forecast beyond seasonal timescales remains unknown due to the signal-to-noise paradox 7 .…”

Research on the Impact of Tourism Development on the Sustainable Development of Reservoir Headwater Area Using China’s Tingxi Reservoir as an Example

“…Furthermore, construction is a type of change and should be a change for the better, to better meet the needs of human beings and to make the existing natural landscape and ecology more sustainable. Therefore, from the perspective of conservation measures, leisure environment, tourism facilities, landscape and ecological environment, we can look into issues such as public transportation, parking and open space, Internet communication, monuments and buildings, residents' environmental awareness, visitors' environmental quality, garbage volume, forest and ecological habitat, motor vehicle fumes, water source, and air quality [14][15][16][17][18][19][20][21], which is helpful to understand the impact of development on the existing environment.…”

“…By conducting a survey based on environmental issues such as the community environment, the villagers' environmental literacy, preservation of historical sites, awareness of ecological conservation, coordination of conservation policies, maintenance of tourist trails and bicycle lanes, tourism transportation planning, Wi-Fi network speed, bicycle rental, community modernization and scale, rest and parking space, experience of tourism activities, water and air quality, fumes from steam and locomotives, mountain slope development, etc. can be determined [14][15][16][17][18][19][20]. Social issues can be identified such as tourism visibility, quality of services and activities, content of community activities, friendly treatment of village culture, tourism indicators or descriptions, tourism and leisure facilities, human resources, DIY activities, hardware and software facilities, tourism environment and space quality, indigenous cultural traditions and historical relics, the image of tourism companies or organizations, the promotion of traditional cultural activities, the interaction mechanism between villagers and tourists, the number and popularity of traditional culture and characteristic industries, the management and safety maintenance of activities, the allocation of service or management personnel, the sense of travel security, the willingness to travel again, etc.…”

Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques

“…Many authors [14,15] have considered hydro-meteorological data, but alternative input data for prediction are also available, such as images from video cameras [16] or satellite-based information [17,18], among other possibilities. Regarding the computational methods applied in dammed water level prediction problems, there have been different attempts using time series processing algorithms [19], empirical orthogonal functions [20], error correction-based forecasting [21], multivariate approaches [22], or ensemble-based algorithms [23]. However, the use of Machine Learning (ML) algorithms in this problem has been massive in the last years, including many different types of learning algorithms such as neural networks, support vector machines, and different hybrid approaches.…”