Reservoir Inflow Forecasting Research Articles

확률론적 중장기 댐 유입량 예측 (II) 앙상블 댐 유입량 예측을 위한 GDAPS 활용

This study develops ESP (Ensemble Streamflow Prediction) system by using medium-term numerical weather prediction model which is GDAPS(T213) of KMA. The developed system forecasts medium- and long-range exceedance Probability for streamflow and RPSS evaluation scheme is used to analyze the accuracy of probability forecasts. It can be seen that the daily probability forecast results contain high uncertainties. A sensitivity analysis with respect to forecast time resolution shows that uncertainties decrease and accuracy generally improves as the forecast time step increase. Weekly ESP results by using the GDAPS output with a lead time of up to 28 days are more accurately predicted than traditional ESP results because conditional probabilities are stably distributed and uncertainties can be reduced. Therefore, it can be concluded that the developed system will be useful tool for medium- and long-term reservoir inflow forecasts in order to manage water resources.

Improving Daily Reservoir Inflow Forecasts with Model Combination

A major issue in real-time management of water resources is the need for accurate and reliable hydrologic forecasts at least 24 or 48 h ahead. An experiment on improving the accuracy of a conceptual hydrologic model used for daily reservoir inflow forecasting, by resorting to model combination, is presented. A robust weighted-average method is used to take advantage of three dynamically different models: a nearest-neighbor model, a conceptual model, and an artificial neural network model. At each time step, the output of each of these three models is computed, and either the absolute best result is considered or the competitive results are combined using the improved weighted-average method. The latter approach has shown a significant forecast improvement for up to 4-day-ahead prediction. Moreover, it is found that with the model combination, there is no need for postcorrection of the conceptual model forecasts. It is also found that the prediction accuracy is mainly driven by the nearest-neighbor method for the 2-day-ahead forecasts, and relatively by each model afterwards. However, none of the three models appears significantly better than the combined model approach, whatever the prediction lead time.

Impact of meteorological predictions on real‐time spring flow forecasting

AbstractMeteorological predictions, such as precipitation and temperature, are commonly used to improve real‐time hydrologic forecasting, despite their inherent uncertainty and their absence in the model calibration stage. In this study, we quantify the effect of meteorological prediction errors on the accuracy of daily spring reservoir inflow forecasts using weather predictions in both the model calibration and testing phases. Different modelling experiments are compared using an operational conceptual model and nonlinear empirical models to assess the effects of using daily numerical weather predictions as opposed to the use of historical observations. It is found that, even with large prediction errors, meteorological forecasts can provide significant improvement of spring flow forecast for up to 7 days lead time, particularly for low flows. Spring flow prediction errors associated with the type of hydrological model used are significantly larger than those related to the meteorological predictions, particularly for 1 to 4 days ahead forecasts. The experimental results also indicate that multiple model‐based forecasting using an iterative prediction approach appears to be the most effective method for an adequate use of weather predictions. Copyright © 2003 John Wiley & Sons, Ltd.

Use of seasonal streamflow forecasts in water resources management

The interannual variability in streamflow presents challenges in managing the associated risks and opportunities of water resources systems. This paper investigates the use of seasonal streamflow forecasts to help manage three water resources systems in south-east Australia. The seasonal streamflow forecasts are derived from the serial correlation in streamflow and the relationship between El Nino/Southern Oscillation (ENSO) and streamflow. This paper investigates the use of ENSO and serial correlation in reservoir inflow to optimise water restriction rules for an urban township and the use of seasonal forecasts of reservoir inflow to help make management decisions in two irrigation systems. The results show a marginal benefit in using seasonal streamflow forecasts in the three management examples. The results suggest that although the ENSO–streamflow teleconnection and the serial correlation in streamflow are statistically significant, the correlations are not sufficiently high to considerably benefit the management of conservative low-risk water resources systems. However, the seasonal forecasts can be used in the system simulations to provide an indication of the likely increases in the available water resources through an irrigation season, to allow irrigators to make more informed risk-based management decisions.

Multivariate Reservoir Inflow Forecasting Using Temporal Neural Networks

An experiment on predicting multivariate water resource time series, specifically the prediction of hydropower reservoir inflow using temporal neural networks, is presented. This paper focuses on dynamic neural networks to address the temporal relationships of the hydrological series. Three types of temporal neural network architectures with different inherent representations of temporal information are investigated. An input delayed neural network (IDNN) and a recurrent neural network (RNN) with and without input time delays are proposed for multivariate reservoir inflow forecasting. The forecast results indicate that, overall, the RNN obtained the best performance. The results also suggest that the use of input time delays significantly improves the conventional multilayer perceptron (MLP) network but does not provide any improvement in the RNN model. However, the RNN with input time delays remains slightly more effective for multivariate reservoir inflow prediction than the IDNN model. Moreover, it is ...

Assessment of Folsom lake response to historical and potential future climate scenarios: 1. Forecasting

In collaboration with operational forecast and management Agencies, an integrated forecast-control system is designed and applied to a major reservoir in California to evaluate the potential benefits of climate information for flood control, hydroelectric energy production, and low flow augmentation. In addition to retrospective studies involving the historical period 1964–1993, system simulations were performed for the future period 2001–2030, under a control and a 1% greenhouse-gas-increase scenario. This paper presents the forecast component formulation and validates ensemble 30-day reservoir-inflow forecasts under a variety of situations. The control component formulation and corresponding reservoir management results are presented in Yao and Georgakakos, this issue. The forecast component is based on ensemble flow forecasting. Quantiles of the distribution of climate-model precipitation simulations are used to select catchment-scale historical daily precipitation time series for the generation of an ensemble of daily reservoir-inflow by hydrologic models. Ensemble generation takes into consideration both atmospheric-forcing and hydrologic-model uncertainties. Principal conclusions of this paper are that the integrated system provides reliable ensemble inflow volume forecasts for the majority of the deciles of forecast frequency, and that the use of climate model simulations is beneficial mainly during high flow periods. It is also found that to maintain reliability for future climate periods, generation of ensemble inflow forecasts should use input time series that reflect potential sharp changes in precipitation amount.

Daily reservoir inflow forecasting using artificial neural networks with stopped training approach

In this paper, an early stopped training approach (STA) is introduced to train multi-layer feed-forward neural networks (FNN) for real-time reservoir inflow forecasting. The proposed method takes advantage of both Levenberg–Marquardt Backpropagation (LMBP) and cross-validation technique to avoid underfitting or overfitting on FNN training and enhances generalization performance. The methodology is assessed using multivariate hydrological time series from Chute-du-Diable hydrosystem in northern Quebec (Canada). The performance of the model is compared to benchmarks from a statistical model and an operational conceptual model. Since the ultimate goal concerns the real-time forecast accuracy, overall the results show that the proposed method is effective for improving prediction accuracy. Moreover it offers an alternative when dynamic adaptive forecasting is desired.

Real-time monitoring, optimization and control of a hydroelectric generation complex

The authors describe the design, implementation, field installation, and testing of the new control system developed for the COLBUN/MACHICURA hydrogeneration complex in Chile. This control system features state-of-the-art production planning functions (reservoir inflow forecasting and generation scheduling), generation control functions (regulation adjustment and generator setpoint control), and hydraulic monitoring and control functions, plus a complete set of SCADA (supervisory control and data acquisition) functions. All the optimization algorithms have been formulated based on a unified methodology using linear programming, which provides a high level of effectiveness and cohesiveness among all the applications functions. >

Accuracy of reservoir inflow forecasts based on radar rainfall measurements

For efficient application of optimum operating procedures to flood protection reservoirs it is essential to forecast on line, i.e., during a storm, the relevant inflow hydrographs. This can be achieved with the aid of a weather radar linked to a computer in which the optimum reservoir operating program is stored as well as a program of a hydrologic rainfall‐runoff model producing the reservoir inflow hydrographs from radar‐measured rainfall data. These inflow hydrographs may be evaluated on the computer during each storm event and can be updated every 5 min, if necessary. The accuracy of these computed hydrographs was tested against the recordings of two river gages installed in catchments close to a weather radar at Hohenpeissenberg in Upper Bavaria. By using a linearly distributed mathematical catchment model it was shown that for the two rivers the synthetic hydrographs computed from radar‐measured rainfall were more accurate than those obtained from continuous measurements of the official point rain gage network of the German Weather Service (one recording gage in 500 km2) and that they were of the same accuracy as those obtained from a special very dense rain gage network (one recording gage in 25 km2) set up for research purposes in the same area.