It is challenging to quantify representative long-term variability of streamflow and its possible low-frequency climate drivers from observed streamflow data available, which is usually limited. To address this issue, a hierarchical, multilevel Bayesian regression (HBR) with the partially pooled method was developed to reconstruct the 1489-2006 annual streamflow data at six Athabasca River Basin (ARB) gauging stations based on 14 tree ring chronologies. Seven nested models were developed to maximize the availability of tree ring predictors. A leave-m-out cross validation method was used to verify the model performance. The reconstruction model was demonstrated to be skillful and seems to better capture low flow than high flow scenarios. More droughts in the premeasurement proxy record with great severity and duration were found from the reconstructed data, which shows that instrumental records are deficient in representing the variability of streamflow accurately, especially at multidecadal scales. Results obtained from wavelet analysis, partial wavelet coherence, and composite analysis show the reconstructed streamflow of ARB has two statistically significant modes, one at interannual time scale (2-8 year) strongly teleconnected to ENSO and a low-frequency mode (~80 year period) which may be teleconnected to PDO and AMO. The AMO index is shown to be negatively correlated with paleo streamflow data of ARB at multidecadal time scale. The long-term streamflow reconstructions and the relationships with ENSO, PDO, and AMO provide useful information on the long-term changes in the hydrological regime of ARB.
Abstract:The uncertainty of forecasted runoffs brings risks of water shortages to water users in the intake area of long-distance water transfer projects, and the uncertainty of spot market prices may cause them to buy water at high prices. In order to hedge these risks, this paper proposes a risk hedging model for decision-making in water option trading from the viewpoint of water users. With the objective of maximizing the expected revenue of water users, the proposed model was solved by an analytical method and an optimal water option strategy was obtained for the users. The proposed model is applied to an intake area of an inter-basin water transfer project in China. The results show that the proposed water option trading model can provide water users with an optimal option strategy. The optimal options trading strategy can effectively reduce the risk caused by the uncertainties of forecasted runoffs and water prices. We also explored the influence of the uncertainty degree of the forecasted runoffs and water price on the option trading strategy. The results show that the expected revenue of water users increases as the variances of the errors of forecasted runoffs and water prices increase.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.