Improving real-time reservoir operation during flood season by making the most of streamflow forecasts

2021 ◽  
Vol 595 ◽  
pp. 126017
Author(s):  
Jiabiao Wang ◽  
Tongtiegang Zhao ◽  
Jianshi Zhao ◽  
Hao Wang ◽  
Xiaohui Lei
Keyword(s):  
Real Time ◽  
Reservoir Operation ◽  
Flood Season ◽  
Streamflow Forecasts

Real-Time Reservoir Operation by Tree-Based Model Predictive Control Including Forecast Uncertainty

2020 ◽  
Author(s):  
Gokcen Uysal ◽  
Rodolfo-Alvarado Montero ◽  
Dirk Schwanenberg ◽  
Aynur Sensoy
Keyword(s):  
Water Supply ◽  
Real Time ◽  
Predictive Control ◽  
Reservoir Operation ◽  
Lead Time ◽  
Flood Control ◽  
Closed Loop ◽  
Multi Stage ◽  
Streamflow Forecasts ◽  
Related Technique

<p>Streamflow forecasts include uncertainties related with initial conditions, model forcings, hydrological model structure and parameters. Ensemble streamflow forecasts can capture forecast uncertainties by having spread forecast members. Integration of these forecast members into real-time operational decision models which deals with different objectives such as flood control, water supply or energy production are still rare. This study aims to use ensemble streamflows as input of the recurrent reservoir operation problem which can incorporate (i) forecast uncertainty, (ii) forecasts with a higher lead-time and (iii) a higher stability. A related technique for decision making is multi-stage stochastic optimization using scenario trees, referred to as Tree-based Model Predictive Control (TB-MPC). This approach reduces the number of ensemble members by its tree generation algorithms using all trajectories and then proper problem formulation is set by Multi-Stage Stochastic Programming. The method is relatively new in reservoir operation, especially closed-loop hindcasting experiments and its assessment is quite rare in the literature. The aim of this study is to set a TB-MPC based real-time reservoir operation with hindcasting experiments. To that end, first hourly deterministic streamflows having one single member are produced using an observed flood hydrograph. Deterministic forecasts are tested with conventional deterministic optimization setup. Secondly, hourly ensemble streamflow forecasts having a lead-time up to 48 hours are produced by a novel approach which explicitly presents dynamic uncertainty evolution. Produced ensemble members are directly provided to input to related technique. Uncertainty becomes much larger when managing small basins and small rivers. Thus, the methodology is applied to the Yuvacik dam reservoir, fed by a catchment area of 258 km<sup>2</sup> and located in Turkey, owing to its challenging flood control and water supply operation due to downstream flow constraints. According to the results, stochastic optimization outperforms conventional counterpart by considering uncertainty in terms of flood metrics without discarding water supply purposes. The closed-loop hindcasting experiment scenarios demonstrate the robustness of the system developed against biased information. In conclusion, ensemble streamflows produced from single member can be employed to TB-MPC for better real-time management of a reservoir control system.</p>

scholarly journals Benchmarking Real-Time Streamflow Forecast Skill in the Himalayan Region

Forecasting ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 230-247
Author(s):  
Ganesh R. Ghimire ◽  
Sanjib Sharma ◽  
Jeeban Panthi ◽  
Rocky Talchabhadel ◽  
Binod Parajuli ◽  
...  
Keyword(s):  
Real Time ◽  
Water Policy ◽  
Low Complexity ◽  
Critical Issue ◽  
Forecast Skill ◽  
Streamflow Forecasting ◽  
Streamflow Forecast ◽  
Time Flow ◽  
Wide Range ◽  
Streamflow Forecasts

Improving decision-making in various areas of water policy and management (e.g., flood and drought preparedness, reservoir operation and hydropower generation) requires skillful streamflow forecasts. Despite the recent advances in hydrometeorological prediction, real-time streamflow forecasting over the Himalayas remains a critical issue and challenge, especially with complex basin physiography, shifting weather patterns and sparse and biased in-situ hydrometeorological monitoring data. In this study, we demonstrate the utility of low-complexity data-driven persistence-based approaches for skillful streamflow forecasting in the Himalayan country Nepal. The selected approaches are: (1) simple persistence, (2) streamflow climatology and (3) anomaly persistence. We generated the streamflow forecasts for 65 stream gauge stations across Nepal for short-to-medium range forecast lead times (1 to 12 days). The selected gauge stations were monitored by the Department of Hydrology and Meteorology (DHM) Nepal, and they represent a wide range of basin size, from ~17 to ~54,100 km2. We find that the performance of persistence-based forecasting approaches depends highly upon the lead time, flow threshold, basin size and flow regime. Overall, the persistence-based forecast results demonstrate higher forecast skill in snow-fed rivers over intermittent ones, moderate flows over extreme ones and larger basins over smaller ones. The streamflow forecast skill obtained in this study can serve as a benchmark (reference) for the evaluation of many operational forecasting systems over the Himalayas.

Inflow Forecast for Real-Time Reservoir Operation

1980 ◽  
Vol 13 (3) ◽  
pp. 427-432
Author(s):  
G. Ambrosino ◽  
G. Fronza ◽  
F. Garofalo
Keyword(s):  
Real Time ◽  
Reservoir Operation

scholarly journals The role of retrospective weather forecasts in developing daily forecasts of nutrient loadings over the southeast US

2014 ◽  
Vol 18 (8) ◽  
pp. 2885-2898
Author(s):  
J. Oh ◽  
T. Sinha ◽  
A. Sankarasubramanian
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Daily Precipitation ◽  
Coefficient Of Determination ◽  
Climatic Data ◽  
Weather Forecasts ◽  
Nutrient Loadings ◽  
Daily Streamflow ◽  
Streamflow Forecasts ◽  
Southeast Us

Abstract. It is well known in the hydrometeorology literature that developing real-time daily streamflow forecasts in a given season significantly depends on the skill of daily precipitation forecasts over the watershed. Similarly, it is widely known that streamflow is the most important predictor in estimating nutrient loadings and the associated concentration. The intent of this study is to bridge these two findings so that daily nutrient loadings and the associated concentration could be predicted using daily precipitation forecasts and previously observed streamflow as surrogates of antecedent land surface conditions. By selecting 18 relatively undeveloped basins in the southeast US (SEUS), we evaluate the skill in predicting observed total nitrogen (TN) loadings in the Water Quality Network (WQN) by first developing the daily streamflow forecasts using the retrospective weather forecasts based on K-nearest neighbor (K-NN) resampling approach and then forcing the forecasted streamflow with a nutrient load estimation (LOADEST) model to obtain daily TN forecasts. Skill in developing forecasts of streamflow, TN loadings and the associated concentration were computed using rank correlation and RMSE (root mean square error), by comparing the respective forecast values with the WQN observations for the selected 18 Hydro-Climatic Data Network (HCDN) stations. The forecasted daily streamflow and TN loadings and their concentration have statistically significant skill in predicting the respective daily observations in the WQN database at all 18 stations over the SEUS. Only two stations showed statistically insignificant relationships in predicting the observed nitrogen concentration. We also found that the skill in predicting the observed TN loadings increases with the increase in drainage area, which indicates that the large-scale precipitation reforecasts correlate better with precipitation and streamflow over large watersheds. To overcome the limited samplings of TN in the WQN data, we extended the analyses by developing retrospective daily streamflow forecasts over the period 1979–2012 using reforecasts based on the K-NN resampling approach. Based on the coefficient of determination (R2Q-daily) of the daily streamflow forecasts, we computed the potential skill (R2TN-daily) in developing daily nutrient forecasts based on the R2 of the LOADEST model for each station. The analyses showed that the forecasting skills of TN loadings are relatively better in the winter and spring months, while skills are inferior during summer months. Despite these limitations, there is potential in utilizing the daily streamflow forecasts derived from real-time weather forecasts for developing daily nutrient forecasts, which could be employed for various adaptive nutrient management strategies for ensuring better water quality.

A real-time operation of the Three Gorges Reservoir with flood risk analysis

2015 ◽  
Vol 16 (2) ◽  
pp. 551-562 ◽  
Author(s):  
Yixiang Sun ◽  
Deshan Tang ◽  
Huichao Dai ◽  
Pan Liu ◽  
Yifei Sun ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Flood Risk ◽  
Reservoir Operation ◽  
Three Gorges Reservoir ◽  
Three Gorges ◽  
Hydropower Generation ◽  
Inference System ◽  
The Three Gorges Reservoir ◽  
Flood Season ◽  
The Three Gorges

Risk analysis is essential to reservoir operation. In this study, a new analysis for reservoir operation is proposed to enhance the utilization rate of the flood water from the Three Gorges Reservoir (TGR) during the flood season. Based on five scenarios of hydrology forecasting with the adaptive neuro-fuzzy inference system (ANFIS), a multi-objective optimum operation was implemented employing the risk control constraints of the genetic algorithm (GA) for the TGR. The results of this analysis indicated that the optimum hydropower generation was 5.7% higher than the usual operating hydropower generation, which suggested that, during flood season, it would be beneficial to increase hydropower generation from reservoirs, while maintaining a safe degree of flood risk.

scholarly journals Design and implementation of an operational multimodel multiproduct real-time probabilistic streamflow forecasting platform

2017 ◽  
Vol 19 (6) ◽  
pp. 911-919 ◽  
Author(s):  
Tirthankar Roy ◽  
Aleix Serrat-Capdevila ◽  
Juan Valdes ◽  
Matej Durcik ◽  
Hoshin Gupta
Keyword(s):  
Real Time ◽  
Modular Design ◽  
Model Averaging ◽  
River Basins ◽  
Streamflow Forecasting ◽  
Hydrologic Models ◽  
Design And Implementation ◽  
Probabilistic Forecasts ◽  
Streamflow Forecasts ◽  
Monitoring And Forecasting

Abstract The task of real-time streamflow monitoring and forecasting is particularly challenging for ungauged or sparsely gauged river basins, and largely relies upon satellite-based estimates of precipitation. We present the design and implementation of a state-of-the-art real-time streamflow monitoring and forecasting platform that integrates information provided by cutting-edge satellite precipitation products (SPPs), numerical precipitation forecasts, and multiple hydrologic models, to generate probabilistic streamflow forecasts that have an effective lead time of 9 days. The modular design of the platform enables adding/removing any model/product as may be appropriate. The SPPs are bias-corrected in real-time, and the model-generated streamflow forecasts are further bias-corrected and merged, to produce probabilistic forecasts that are computed via several model averaging techniques. The platform is currently operational in multiple river basins in Africa, and can also be adapted to any new basin by incorporating some basin-specific changes and recalibration of the hydrologic models.

Sign in / Sign up

Export Citation Format

Share Document