Probabilistic forecast models for hydro-environmental characteristics and risk-based adaptive reservoir operation

This study was motivated by the desire to improve risk-based decision making and adaptive management of large-scale water resources systems. The overall objective of the research was to develop a methodology for managing a water resources system in an adaptive manner accounting for risks and uncertainties of the hydro-environmental characteristics. The characteristics considered in this research are the stage-discharge relationship, reservoir inflow, and water qualities in terms of biological oxygen demand (BOD) and total phosphorus (TP).; First, stage-discharge ratings were developed and assessed using both deterministic and probabilistic. For deterministic approaches, nonlinear programming (NLP), fuzzy rule-based modeling, and a one-dimensional hydrodynamic model were used. For the probabilistic approach, a Bayesian Markov chain Monte Carlo (MCMC) technique was employed. Based upon a comparison of the methods, a hybrid methodology which combines NLP and Bayesian MCMC was proposed as the appropriate alternative.; Second, monthly inflow forecast systems were developed using stochastic artificial neural networks and nonparametric modeling. To determine whether or not a k-nearest neighbor (k-NN) bootstrapping method might be used in practice for daily inflow forecasts aimed at short term reservoir system operation, a daily forecast model was developed. It was concluded that the k-NN method was preferred due to its ease of application. In addition, it was demonstrated that this method can be applied successfully for daily inflow forecasting.; Third, probabilistic BOD and TP models were developed using Bayesian networks. The relationships between reservoir release and risk of violating the water quality standards were derived. The case study clearly demonstrated that the probabilistic models overcome the weaknesses of deterministic water quality models by offering information about risks of violation of standards.; Fourth, instead of relying on the classical rule curves for reservoir system operation, an adaptive sampling implicit optimization (ASISO) model was developed that considered multiple objectives of energy production, water supply, and water quality management in terms of BOD and TP. The ASISO based decision support system demonstrated an alternative for reservoir system operation by combining simulation and optimization algorithms and incorporating the risk of water quality standard violation and adaptive sampling of the inflow series.