This thesis focuses on optimization techniques for multi-reservoir hydropower systems operation, with a particular concern with the representation and impact of uncertainty. The thesis reports on three research investigations: 1) examination of the impact of uncertainty representations, 2) efficient solution methods for multi-reservoir stochastic dynamic programming (SDP) models, and 3) diagnostic analyses for hydropower system operation. The first investigation explores the value of sophistication in the representation of forecast and inflow uncertainty in stochastic hydropower optimization models using a sampling SDP (SSDP) model framework. SSDP models with different uncertainty representation ranging in sophistication from simple deterministic to complex dynamic stochastic models are employed when optimize a single reservoir systems [similar to Faber and Stedinger, 2001]. The effect of uncertainty representation on simulated system performance is examined with varying storage and powerhouse capacity, and with random or mean energy prices. In many cases very simple uncertainty models perform as well as more complex ones, but not always. The second investigation develops a new and efficient algorithm for solving multi-reservoir SDP models: Corridor SDP. Rather than employing a uniform grid across the entire state space, Corridor SDP efficiently concentrates points in where the system is likely to visit, as determined by historical operations or simulation. Radial basis functions (RBFs) are used for interpolation. A greedy algorithm places points where they are needed to achieve a good approximation. In a four-reservoir test case, Corridor DP achieves the same accuracy as spline-DP and linear-DP with approximately 1/10 and 1/1100 the number of discrete points, respectively. When local curvature is more pronounced (due to minimum-flow constraints), Corridor DP achieves the same accuracy as spline-DP and linear-DP with approximately 1/30 and 1/215 the number of points, respectively. The third investigation explores three diagnostic approaches for analyzing hydropower system operation. First, several simple diagnostic statistics describe reservoir volume and powerhouse capacity in units of time, allowing scale-invariant comparisons and classification of different reservoir systems and their operation. Second, a regression analysis using optimal storage/release sequences identifies the most useful hydrologic state variables . Finally spectral density estimation identifies critical time scales for operation for several single-reservoir systems considering mean and random energy prices. Deregulation of energy markets has made optimization of hydropower operations an active concern. Another development is publication of Extended Streamflow Forecasts (ESP) by the National Weather Service (NWS) and others to describe flow forecasts and their precision; the multivariate Sampling SDP models employed here are appropriately structured to incorporate such information in operational hydropower decisions. This research contributes to our ability to structure and build effective hydropower optimization models.