Huaming Yao

Assessment of Folsom Lake response to historical and potential future climate scenarios: 2. Reservoir management

An integrated forecast–decision system for Folsom Lake (California) is developed and used to assess the sensitivity of reservoir performance to various forecast–management schemes under historical and future climate scenarios. The assessments are based on various combinations of inflow forecasting models, decision rules, and climate scenarios. The inflow forecasting options include operational forecasts, historical analog ensemble forecasts, hydrologic ensemble forecasts, GCM-conditioned hydrologic ensemble forecasts, and perfect forecasts. Reservoir management is based on either heuristic rule curves or a decision system which includes three coupled models pertinent to turbine load dispatching, short-range energy generation scheduling, and long/mid-range reservoir management. The climate scenarios are based on historical inflow realizations, potential inflow realizations generated by General Circulation Models assuming no CO2 increase, and potential inflow realizations assuming 1% CO2 annual increase. The study demonstrates that (1) reliable inflow forecasts and adaptive decision systems can substantially benefit reservoir performance and (2) dynamic operational procedures can be effective climate change coping strategies.

Impacts of climate variability on the operational forecast and management of the Upper Des Moines River Basin

Data from the regulated 14,000 km2 upper Des Moines River basin and a coupled forecast-control model are used to study the sensitivity of flow forecasts and reservoir management to climatic variability over scales ranging from daily to interdecadal. Robust coupled forecast-control methodologies are employed to minimize reservoir system sensitivity to climate variability and change. Large-scale hydrologic-hydraulic prediction models, models for forecast uncertainty, and models for reservoir control are the building blocks of the methodology. The case study concerns the 833.8 × 106 m3 Saylorville reservoir on the upper Des Moines River. The reservoir is operated by the U.S. Corps of Engineers for flood control, low-flow augmentation, and water supply. The total record of 64 years of daily data is divided into three periods, each with distinct characteristics of atmospheric forcing. For each climatic period the coupled forecast-control methodology is simulated with a maximum forecast lead time of 4 months and daily resolution. For comparison, the results of operation using current reservoir control practices were obtained for the historical periods of study. Large differences are found to exist between the probabilistic long-term predictions of the forecast component when using warm or cool and wet or dry initial conditions in the spring and late summer. Using ensemble input corresponding to warm or cool and wet or dry years increases these differences. Current reservoir management practices cannot accommodate historical climate variability. Substantial gain in resilience to climate variability is shown to result when the reservoir is operated by a control scheme which uses reliable forecasts and accounts for their uncertainty. This study shows that such coupled forecast-control decision systems can mitigate adverse effects of climatic forcing on regional water resources.

Medium-range flow prediction for the Nile : a comparison of stochastic and deterministic methods

Abstract Due to its great importance, the availability of long flow records, contemporary as well as older, and the additional historical information of its behaviour, the Nile is an ideal test case for identifying and understanding hydrological behaviours, and for model development. Such behaviours include the long-term persistence, which historically has motivated the discovery of the Hurst phenomenon and has put into question classical statistical results and typical stochastic models. Based on the empirical evidence from the exploration of the Nile flows and on the theoretical insights provided by the principle of maximum entropy, a concept newly employed in hydrological stochastic modelling, an advanced yet simple stochastic methodology is developed. The approach is focused on the prediction of the Nile flow a month ahead, but the methodology is general and can be applied to any type of stochastic prediction. The stochastic methodology is also compared with deterministic approaches, specifically an analogue (local nonlinear chaotic) model and a connectionist (artificial neural network) model based on the same flow record. All models have good performance with the stochastic model outperforming in prediction skills and the analogue model in simplicity. In addition, the stochastic model has other elements of superiority such as the ability to provide long-term simulations and to improve understanding of natural behaviours.

Integrating climate‐hydrology forecasts and multi‐objective reservoir management for northern California

The Integrated Forecast and Reservoir Management (INFORM) Project was conceived to demonstrate increased water-use efficiency in Northern California water resources operations through (1) the innovative application of climate, hydrologic, and decision science, and (2) reciprocal technology transfer activities between the INFORM scientists and the staff of federal and state agencies with an operational forecast and management mandate in Northern California. Toward achieving this goal, INFORM objectives include implementing a prototype integrated forecast-management system for primary Northern California reservoirs, for individual reservoirs as well as system-wise. Project objectives also include demonstrating the utility of climate and hydrologic forecasts through near-real-time tests of the integrated system with actual data and management input.

Control models for hydroelectric energy optimization

The optimization of hydroelectric energy is addressed via a new multilevel control model, which is used to derive estimates of system firm energy with or without dependable capacity commitments. The model is able to optimize individual turbine operation as well as overall system operation on an hourly and daily basis. The mechanism by which the various models are linked and exchange information ensures full compatibility among the control levels and guarantees operational consistency across all timescales. The model is applied to the Lanier-Allatoona-Carters system, located in the southeastern United States, and is suitable for planning as well as operational applications.

A control model for dependable hydropower capacity optimization

In this article a control model that can be used to determine the dependable power capacity of a hydropower system is presented and tested. The model structure consists of a turbine load allocation module and a reservoir control module and allows for a detailed representation of hydroelectric facilities and various aspects of water management. Although this scheme is developed for planning purposes, it can also be used operationally with minor modifications. The model is applied to the Lanier-Allatoona-Carters reservoir system on the Chattahoochee and Coosa River Basins, in the southeastern United States. The case studies demonstrate that the more traditional simulation-based approaches often underestimate dependable power capacity. Firm energy optimization with or without dependable capacity constraints is taken up in a companion article [Georgakakos et al., this issue].

New control concepts for uncertain water resources systems: 2. Reservoir management

Reservoir management decisions continuously strive to balance conflicting risks and benefits. Hydrologic uncertainty is a major complicating factor, especially during extreme events when data are sparse and probabilistic characterizations are less reliable. The set control approach offers an alternative by assuming that inputs are unknown but bounded. The solution provides a set of control actions which guarantee that the system will operate within the stated constraints for a certain time horizon. The method is applied to a three-reservoir system in the southeastern United States.

New control concepts for uncertain water resources systems: 1. Theory

A major complicating factor in water resources systems management is handling unknown inputs. Stochastic optimization provides a sound mathematical framework but requires that enough data exist to develop statistical input representations. In cases where data records are insufficient (e.g., extreme events) or atypical of future input realizations, stochastic methods are inadequate. This article presents a control approach where input variables are only expected to belong in certain sets. The objective is to determine sets of admissible control actions guaranteeing that the system will remain within desirable bounds. The solution is based on dynamic programming and derived for the case where all sets are convex polyhedra. A companion paper (Yao and Georgakakos, this issue) addresses specific applications and problems in relation to reservoir system management.