Search

Abstract: Representation and quantification of uncertainty in climate change impact studies are a difficult task. Several sources of uncertainty arise in studies of hydrologic impacts of climate change, such as those due to choice of general circulation models (GCMs), scenarios and downscaling methods. Recently, much work has focused on uncertainty quantification and modeling in regional climate change impacts. In this paper, an uncertainty modeling framework is evaluated, which uses a generalized uncertainty measure to combine GCM, scenario and downscaling uncertainties. The Dempster–Shafer (D–S) evidence theory is used for representing and combining uncertainty from various sources. A significant advantage of the D–S framework over the traditional probabilistic approach is that it allows for the allocation of a probability mass to sets or intervals, and can hence handle both aleatory or stochastic uncertainty, and epistemic or subjective uncertainty. This paper shows how the D–S theory can be used to represent beliefs in some hypotheses such as hydrologic drought or wet conditions, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The D–S approach has been used in this work for information synthesis using various evidence combination rules having different conflict modeling approaches. A case study is presented for hydrologic drought prediction using downscaled streamflow in the Mahanadi River at Hirakud in Orissa, India. Projections of n most likely monsoon streamflow sequences are obtained from a conditional random field (CRF) downscaling model, using an ensemble of three GCMs for three scenarios, which are converted to monsoon standardized streamflow index (SSFI-4) series. This range is used to specify the basic probability assignment (bpa) for a Dempster–Shafer structure, which represents uncertainty associated with each of the SSFI-4 classifications. These uncertainties are then combined across GCMs and scenarios using various evidence combination rules given by the D–S theory. A Bayesian approach is also presented for this case study, which models the uncertainty in projected frequencies of SSFI-4 classifications by deriving a posterior distribution for the frequency of each classification, using an ensemble of GCMs and scenarios. Results from the D–S and Bayesian approaches are compared, and relative merits of each approach are discussed. Both approaches show an increasing probability of extreme, severe and moderate droughts and decreasing probability of normal and wet conditions in Orissa as a result of climate change. [Copyright &y& Elsevier]

• A new implementation of the FP method for storage systems leads to a more accurate objective function evaluation especially in nonlinear cases and to a vectorized unconstrained optimization problem solved many times faster. • FP results as compared to stochastic dynamic programming and novel two-stage stochastic programming results show its overall superiority. • A multi-reservoir application of the FP model is developed with corrective simulations to estimate moments of spillway flows and all other covariance terms. The Fletcher-Ponnambalam (FP) method is an explicit stochastic optimization method for design and operations management of real-world storage systems including surface water reservoir and groundwater management problems. The FP method faces no curse of dimensionality and no need for scenario generation. The paper introduces a novel implementation for the FP method, named FP-2022 here for clarity, by removing the need for nonlinear constraints and by decreasing the number of decision variables to just one third of its original value, significantly reducing solving time (∼27 times faster than the original formulation). Additionally, new expressions derived for the first and second moments of both reservoir release deficit and surplus variables and the already-derived expression for the second moments of reservoir storages are incorporated into the FP-2022 formulation enabling the method to reach an improved optimality for a nonlinear objective function. The enhanced procedure is applied to solving a reservoir operation optimization problem for a major dam in Brazil. The result comparisons made with other methods along with a thorough analysis of release operation policies prove the optimality of this highly numerically efficient and convenient-to-use FP-2022 method. Finally, a multi-reservoir application of the model is also tested with corrective simulations for improved estimates of some additional variables of interest. A specific constraint-handling approach regarding reservoir release lower and upper bounds is also presented. Satisfactory results are obtained for solving the Parambikulam-Aliyar reservoir system, a real world five-reservoir operation optimization problem from India. [ABSTRACT FROM AUTHOR]

Abstract: Fletcher–Ponnambalam presented a new model for considering the balance equation of the storage volume of the reservoir using indicator functions. For stochastic inflows, the two storage moments of this balance equation, namely, the mean and variance, calculated using a random release policy were found to be quite accurate unlike any known models. Significantly, this model required no discretization of storage volumes or releases. In this paper, this work has been extended to two new cases: for multireservoir systems which require further consideration of the stochastic releases and for arbitrary distribution of inflows using the Beta-equivalent Kumaraswamy distribution which has a simpler form than Beta. The randomized release policies are easy to use even in a multireservoir problem. The Parambikulm-Aliyar Project from India is used as a case study. Results show accurate predictions of mean of storages in the multireservoir case but show the need for further improvement in the standard deviations of storages. The optimal benefits and the policies obtained are shown to be at least as good as obtained using Monte Carlo-based methods. [ABSTRACT FROM AUTHOR]