Inflow Forecasting Research Articles

Machine-learning algorithms for forecast-informed reservoir operation (FIRO) to reduce flood damages

Water is stored in reservoirs for various purposes, including regular distribution, flood control, hydropower generation, and meeting the environmental demands of downstream habitats and ecosystems. However, these objectives are often in conflict with each other and make the operation of reservoirs a complex task, particularly during flood periods. An accurate forecast of reservoir inflows is required to evaluate water releases from a reservoir seeking to provide safe space for capturing high flows without having to resort to hazardous and damaging releases. This study aims to improve the informed decisions for reservoirs management and water prerelease before a flood occurs by means of a method for forecasting reservoirs inflow. The forecasting method applies 1- and 2-month time-lag patterns with several Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Artificial Neural Network (ANN), Regression Tree (RT), and Genetic Programming (GP). The proposed method is applied to evaluate the performance of the algorithms in forecasting inflows into the Dez, Karkheh, and Gotvand reservoirs located in Iran during the flood of 2019. Results show that RT, with an average error of 0.43% in forecasting the largest reservoirs inflows in 2019, is superior to the other algorithms, with the Dez and Karkheh reservoir inflows forecasts obtained with the 2-month time-lag pattern, and the Gotvand reservoir inflow forecasts obtained with the 1-month time-lag pattern featuring the best forecasting accuracy. The proposed method exhibits accurate inflow forecasting using SVM and RT. The development of accurate flood-forecasting capability is valuable to reservoir operators and decision-makers who must deal with streamflow forecasts in their quest to reduce flood damages.

Adaptive ensemble models for medium-term forecasting of water inflow when planning electricity generation under climate change

Medium-term forecasting of water inflow is of great importance for small hydroelectric power plants operating in remote power supply areas and having a small reservoir. Improving the forecasting accuracy is aimed at solving the problem of determining the water reserve for the future generation of electricity at hydroelectric power plants, taking into account the regulation in the medium term. Medium-term regulation is necessary to amplify the load in the peak and semi-peak portions of the load curve. The solution to such problems is aggravated by the lack of sufficiently reliable information on water inflow and prospective power consumption, which is of a stochastic nature. In addition, the mid-term planning of electricity generation should consider the seasonality of changes in water inflow, which directly affects the reserves and the possibility of regulation. The paper considers the problem of constructing a model for medium-term forecasting of water inflow for planning electricity generation, taking into account climatic changes in isolated power systems. Taking into account the regularly increasing effect of climate change, the current study proposes using an approach based on machine learning methods, which are distinguished by a high degree of autonomy and automation of learning, that is, the ability to self-adapt. The results showed that the error (RMSE) of the model based on the ensemble of regression decision trees due to constant self-adaptation decreased from 4.5 m3/s to 4.0 m3/s and turned out to be lower than the error of a more complex multilayer recurrent neural network (4.9 m3/s). The research results are intended to improve forecasting reliability in the planning, management, and operation of isolated operating power systems.

AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment

Abstract. Streamflow forecasts are traditionally effective in mitigating water scarcity and flood defense. This study developed an artificial intelligence (AI)-based management methodology that integrated multi-step streamflow forecasts and multi-objective reservoir operation optimization for water resource allocation. Following the methodology, we aimed to assess forecast quality and forecast-informed reservoir operation performance together due to the influence of inflow forecast uncertainty. Varying combinations of climate and hydrological variables were input into three AI-based models, namely a long short-term memory (LSTM), a gated recurrent unit (GRU), and a least-squares support vector machine (LSSVM), to forecast short-term streamflow. Based on three deterministic forecasts, the stochastic inflow scenarios were further developed using Bayesian model averaging (BMA) for quantifying uncertainty. The forecasting scheme was further coupled with a multi-reservoir optimization model, and the multi-objective programming was solved using the parameterized multi-objective robust decision-making (MORDM) approach. The AI-based management framework was applied and demonstrated over a multi-reservoir system (25 reservoirs) in the Zhoushan Islands, China. Three main conclusions were drawn from this study: (1) GRU and LSTM performed equally well on streamflow forecasts, and GRU might be the preferred method over LSTM, given that it had simpler structures and less modeling time; (2) higher forecast performance could lead to improved reservoir operation, while uncertain forecasts were more valuable than deterministic forecasts, regarding two performance metrics, i.e., water supply reliability and operating costs; (3) the relationship between the forecast horizon and reservoir operation was complex and depended on the operating configurations (forecast quality and uncertainty) and performance measures. This study reinforces the potential of an AI-based stochastic streamflow forecasting scheme to seek robust strategies under uncertainty.

Real-time optimization of a large-scale reservoir operation in Thailand using adaptive inflow prediction with medium-range ensemble precipitation forecasts

Study regionThe Sirikit Dam in the Nan River Basin is located on a main tributary of the Chao Phraya River in Thailand. Study focusThis study investigates forecasting river flows and real-time optimization of dam release using a distributed hydrological model with ensemble weather forecasting for reservoir operations which provide hydropower and irrigation facilities in Thailand during a case study of the 2019 drought event. Medium-range ensemble precipitation forecasts were employed using a hydrological model to predict the real-time reservoir inflow. Real-time optimization of the water release strategy determined a week in advance with an effective initial condition for hydropower generation and irrigation was conducted with different scenarios using dynamic programming considering inflow predictions. New hydrological insights for the regionThe medium-range ensemble precipitation forecast conducted by the European Centre for Medium Range Weather Forecasts was used to quantify precipitation for the study basin. The ensemble precipitation forecast with the hydrological model was employed for inflow prediction of the study basin which was located in a tropical climate with a distinct wet and dry season. The initial conditions of the hydrological model largely influenced the real-time inflow forecast. To determine the initial conditions of the model, the empirical data assimilation considering a drainage area factor was utilized and observed precipitation data were used for model input forcing data during the initial analysis period. This method improved the reservoir inflow prediction and real-time reservoir optimization using dynamic programming with considering ensemble forecasts provided more efficient operating decisions than employing historical data. The resulting information will be useful for water resource management, which may be adapted to other basins in the study region.

Assessing the energy potential of modernizing the European hydropower fleet

About 50% of all hydropower plants (HPPs) worldwide were originally commissioned more than 40 years ago, so that the advanced age of the fleet is a major concern across all continents, and especially in Europe. The modernization of HPPs can generate several benefits in terms of generation, flexibility, safety, operation, and may have neutral or even positive implications for the environment. In this work, we appraise several options for the modernization of existing plants, with the exclusion of measures expected to increase the hydro-morphological pressure on water bodies (e.g. increase of withdrawals or new parallel waterways): dam heightening, head loss reduction in waterways, increase of weighted efficiency of electro-mechanical equipment, digitalization and inflow forecast, and floating photovoltaic (evaporation reduction). We provide an indicative estimation of the additional power and annual generation that could be obtained compared to the current condition. We estimate that the overall energy generation could be increased by 8.4% for European Union and 9.4% for the whole Europe by implementing the above-mentioned strategies. The additional energy gain achievable by increasing the inflow was discussed but not included in the above mentioned overall indicator, because it is very site-specific. The additional energy storage achievable by reservoir interconnection and coordinated operation has been estimated in literature as 169 TWh. This suggests that the modernization of HPPs can generate significant benefits in terms of energy, and should be considered as an important element of energy policy, also considering the additional benefits in terms of reliability and flexibility of the energy system that it may deliver. The modernization options considered here, insofar as not entailing a worsening of the hydro-morphological alterations, are also expected to cause limited or no conflict with the environmental objectives of water policies in Europe.

The water dispatching of river basins during dry periods under the most stringent water management system in China

Abstract Since 2011, China has implemented its most stringent water management system to effectively protect water resources and guarantee socioeconomic development. More basin-scale water division schemes have been developed to act as references for basin-scale water resources management. Water dispatching during dry periods is an effective way to guarantee the water supply for the river basin, and is also an important component of basin-scale water resources management. Given this, the present study proposes a framework for the water dispatching of river basins during dry periods under the most stringent water management system in China. This framework mainly consists of the analysis and forecasting of rainfall and inflow, the dispatching requirements for the main water users, major reservoirs, and sections, as well as safeguard measures. The Jian River Basin in South China is presented as a case study. The total discharge of the Gaozhou Reservoir in 2017 was 25 million m3 more than the target discharge specified in the water dispatching scheme, and the total water storage utilization ratio during the dispatch period was 4.7% higher than the target utilization ratio. These factors demonstrate the effectiveness and applicability of the proposed framework.

PREDIKSI INFLOW DAN OUTFLOW UANG KARTAL DI PROVINSI BALI DENGAN METODE NEURO-FUZZY

In this paper, we present a novel approach to data-driven neuro-fuzzy modeling, which aims to create accurate monthly inflow and outflow forecast of money (M0) in Bali Province. The data is monthly time series included some religious ceremony identification variables and a monthly dummy variable from January 2011 to March 2019. Well known, Bali Province has unique cultures, the only one province which Hinduism majority religion in Indonesia, and listed as top tourism destination in the world. The neuro-fuzzy models were created using ANFIS architecture and sliding window time series analysis, then simulated using walk forward validation, interpreted using MAPE, and NRMSE. Based on the simulation of the last 24 months, the model of inflow obtained MAPE 23.33% (worth considering) and NRMSE 18.68% (accurate). Meanwhile, the model of outflow obtained MAPE 19.24% (accurate) and NRMSE 8.71% (very accurate). These models and their pieces of information could assist the central bank in Bali Province to prepare cash for money (M0) outflow and managed technic for counting money (M0) inflow.

Stochastic reservoir operation with data-driven modeling and inflow forecasting

This work applied implicit stochastic optimization (ISO) refined by long-term mean inflow forecasting and instance-based learning for the operation of the Sobradinho reservoir, Brazil. For efficiency assessment, the reservoir was also operated by perfect-forecast deterministic optimization, the standard operating policy, stochastic dynamic programming and two parameterization-simulation-optimization models, which were compared in terms of vulnerability, reliability and resilience found in each of the 100 synthetic inflow scenarios they were applied to. Evidence of long-term persistence was found in Sobradinho's records and this was replicated in the scenarios. The ISO model was employed with forecast horizons of 0, 1, 3, 6, 9, 12, 18 and 24 months. The operations demonstrated that the model with forecast horizons of 3 months or more was less vulnerable than all other models, revealing that it may be used efficiently for reservoir operation.

Long-term Reservoir Inflow Forecasts: Enhanced Water Supply and Inflow Volume Accuracy Using Deep Learning

Machine-learning algorithms have shown promise for streamflow forecasts, reservoir operations, and scheduling, but have exhibited lower accuracy in predicting extended time horizons for reservoir inflows. Newer deep learning algorithms exhibited improved inflow forecasting accuracy, but existing research has been mostly limited to real-time operation and short-term planning. We propose a new multi-step forecasting approach to improve long-term forecasting accuracy for both water supply and inflow volumes. This approach uses historical snow water equivalent (SWE) and reservoir inflow time-series data to train an Encoder-Decoder algorithm to predict the reservoir inflow of future time-steps during the April-July runoff period. The optimal model and hyperparameters are selected through five-fold time-series cross validation for variations between long short-term memory (LSTM) and convolutional neural network (CNN) Encoder-Decoder algorithms. We evaluated each algorithm using 30 years of reservoir inflow and SWE data at the Upper Stillwater Reservoir located in Utah. The optimal model was an LSTM Encoder-Decoder algorithm with 16 nodes per layer. Using this algorithm, we investigate the trade-off between model complexity and accuracy for long-term water supply, relative to a process-based Ensemble Streamflow Prediction (ESP) model as a baseline and simpler statistical methods traditionally used in forecasting (SARIMA, VAR, TBATS). Long-term water supply forecasts of the optimal deep learning algorithm proved superior to statistical methods and rivaled those of the ESP 50% exceedance probability forecast (i.e. the most probable forecast) evaluated over five consecutive hold-out periods.

Identifying the effect of forecast uncertainties on hybrid power system operation: A case study of Longyangxia hydro–photovoltaic plant in China

Power system operations are unavoidably influenced by forecast uncertainties. The forecast uncertainty's effect on operations was quantified in individual systems, however, was seldom investigated in a hybrid system. This study aims to quantify the effect of forecast uncertainties on the hybrid hydro–photovoltaic system operation. First, the operation model is built considering streamflow and photovoltaic forecast uncertainties with long- and short-time scales. Second, total release uncertainty is determined from the sum of squares for releases under different forecast scenarios. Finally, a variance partitioning method is used to divide the total release uncertainty into the uncertainties induced by individual forecast errors. Longyangxia hybrid hydro–photovoltaic power plant from China was selected as a case study. Results provide following insights: (1) long-term inflow forecast uncertainty accounts for 98.89% of the contribution to the long-term release uncertainty, while photovoltaic uncertainty accounts for 1.11%; (2) long-term inflow forecast uncertainty still dominates the short-term release uncertainty, with the contribution of 62.06%; and (3) the total release uncertainty decreases in long-term hybrid operation compared with the reservoir operation, indicating the complementary relationship between long-term hydropower and photovoltaic power. These implications highlight the importance of improving forecast accuracies for varied energy sources at different time scales.

Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia

Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia

A Method of Multi-Stage Reservoir Water Level Forecasting Systems: A Case Study of Techi Hydropower in Taiwan

Reservoirs in Taiwan often provide hydroelectric power, irrigation water, municipal water, and flood control for the whole year. Taiwan has the climatic characteristics of concentrated rainy seasons, instantaneous heavy rains due to typhoons and rainy seasons. In addition, steep rivers in mountainous areas flow fast and furiously. Under such circumstances, reservoirs have to face sudden heavy rainfall and surges in water levels within a short period of time, which often causes the water level to continue to rise to the full level even though hydroelectric units are operating at full capacity, and as reservoirs can only drain the flood water, this results in the waste of hydropower resources. In recent years, the impact of climate change has caused extreme weather events to occur more frequently, increasing the need for flood control, and the reservoir operation has faced severe challenges in order to fulfil its multipurpose requirements. Therefore, in order to avoid the waste of hydropower resources and improve the effectiveness of the reservoir operation, this paper proposes a real-time 48-h ahead water level forecasting system, based on fuzzy neural networks with multi-stage architecture. The proposed multi-stage architecture provides reservoir inflow estimation, 48-h ahead reservoir inflow forecasting, and 48-h ahead water level forecasting. The proposed method has been implemented at the Techi hydropower plant in Taiwan. Experimental results show that the proposed method can effectively increase energy efficiency and allow the reservoir water resources to be fully utilized. In addition, the proposed method can improve the effectiveness of the hydropower plant, especially when rain is heavy.

Modelling the Future of the Baltic Energy Systems: A Green Scenario

Abstract The electricity sector in Europe and in the world is undergoing rapid and profound changes. There is a sharp increase in the capacity of renewable energy sources, coal and nuclear power plants are being closed and new technologies are being introduced. Especially rapid changes are taking place in the energy systems of the Baltic States. Under these conditions, there is an emerging need for new planning tools particularly for the analysis of the power system properties in a long-term perspective. The main contribution of this article lies in the formulation and solution of optimization problems that arise when planning the development of power systems in the Baltic States. To solve this problem, it is necessary to use models of various power plants and make a number of assumptions, the justification of which requires the following actions: to briefly review the current situation of the production and demand of energy in the Baltic power systems; to conduct an overview of the Baltic interconnections and their development; to make forecasts of energy prices, water inflow, energy production and demand; to set and solve the problems of optimization of power plant operation modes; to demonstrate the possibility and limitations of the developed tools on the basis of real-life and forecast data. In this paper, a case study is performed using the main components of the overall modelling framework being developed. It focuses on the Baltic power systems in 2050 under the conditions of significant expansion in the installed capacity of renewable energy sources (RESs) and diminished fossil fuel power plant activity. The resulting electricity generation mix and trade balance with neighbouring countries is assessed, showing that even with significant RES expansion, the Baltic countries remain net importers and because of the intermittency of RESs, there are hours within the year when the demand cannot be met.

A procedure to forecast and manage water resources and to redistribute runoff water flow when passing floods

Economic losses from floods have become catastrophic due to the increase in the number and scale of their propagation. Existing procedures for passing floods and pre-preparing reservoirs for flood water acceptance are ineffective and need to be improved. Therefore, the task to devise a methodology that would eliminate these shortcomings was urgent. This paper has proposed a procedure for calculating the passage of floods based on the forecasts of water inflow, taking into consideration the characteristics of the flood wave and the mode of reservoir filling, which makes it possible to bring down (reduce) the maximum flow rate through a waterworks by accumulating floodwaters in the reservoir. The software package Mike 11 (Danish Institute, Denmark) was employed to build a hydrodynamic model of floodwater movement along the examined river section from a hydrological station to a waterworks, which makes it possible to determine the levels of water and the flow rate in a reservoir at any time in the form of free surface curves when passing floods of various range. Based on the devised methodology, recommendations have been compiled for the forced discharges of water through hydroelectric turbines (in m3/s) when passing floods of various probabilities (which is especially important for floods whose probability is 0.01 %). The constructed hydrodynamic model of floodwater movement through a reservoir has allowed the verification of the devised procedure. The procedure was devised in order to effectively pass floodwaters and bring down the maximum flow rate through a waterworks. The introduction of the methodology for calculating the passage of floods could make it possible to avoid idle water discharge through the water drains of waterworks to the lower pool and provide for the most efficient utilization of floodwater resources

A Comparative Analysis of Hidden Markov Model, Hybrid Support Vector Machines, and Hybrid Artificial Neural Fuzzy Inference System in Reservoir Inflow Forecasting (Case Study: The King Fahd Dam, Saudi Arabia)

The precise prediction of the streamflow of reservoirs is of considerable importance for many activities relating to water resource management, such as reservoir operation and flood and drought control and protection. This study aimed to develop and evaluate the applicability of a hidden Markov model (HMM) and two hybrid models, i.e., the support vector machine-genetic algorithm (SVM-GA) and artificial neural fuzzy inference system-genetic algorithm (ANFIS-GA), for reservoir inflow forecasting at the King Fahd dam, Saudi Arabia. The results obtained by the HMM model were compared with those for the two hybrid models ANFIS-GA and SVM-GA, and with those for individual SVM and ANFIS models based on performance evaluation indicators and visual inspection. The results of the comparison revealed that the ANFIS-GA model and ANFIS model provided superior results for forecasting monthly inflow with satisfactory accuracy in both training (R2 = 0.924, 0.857) and testing (R2 = 0.842, 0.810) models. The performance evaluation results for the developed models showed that the GA-induced improvement in the ANFIS and SVR forecasts was matched by an approximately 25% decrease in RMSE and around a 13% increase in Nash–Sutcliffe efficiency. The promising accuracy of the proposed models demonstrates their potential for applications in monthly inflow forecasting in the present semiarid region.

A hybrid framework for forecasting monthly reservoir inflow based on machine learning techniques with dynamic climate forecasts, satellite-based data, and climate phenomenon information

In this study, we developed and evaluated a hybrid framework for reservoir inflow forecast. This framework is unprecedented, which integrates new quasi-globally available observation-, satellite-, or model-based datasets using machine learing models to forecast inflow at the local scale. Under this framework, we compared random forests, gradient boosting machine, extreme learning machine, M5-cubist, elastic net, as well as their multi-model ensemble using Bayesian model averaging (BMA), and tested contributions from different input datasets, including retrospective forecast (reforecast) from florecast oriented low ocean resolution (FLOR) dynamic climate model, satellite-based hydrologic products, and climate phenomenon information. The performance was evaluated using Kling–Gupta efficiency (KGE) and correlation coefficient (R) in two headwater reservoirs, Harris reservoir in the humid Alabama–Coosa–Tallapoosa river basin and the Navajo reservoir in the arid Upper Colorado River Basin (UCRB). The results showed that for the Harris reservoir, the BMA combining five models with antecedent inflow and satellite-based hydrologic information as model inputs provided the best performance (KGE = 0.66, R = 0.76). For the Navajo reservoir, the gradient boosting machine model with all variables combined as input showed the best performance (KGE = 0.76, R = 0.83). Satellite-based soil moisture and evaporation consistently showed significant contributions to the inflow forecast. Benefits from climate indices and FLOR reforecast varied by locations, with more benefits coming from climate indices than FLOR potential evaporation reforecast at the Navajo reservoir in UCRB. Given the global coverage of the model inputs, our approach can be potentially applicable to improve reservoir inflow forecasts in different regions of the world.

A Scalable Earth Observations‐Based Decision Support System for Hydropower Planning in Africa

AbstractHydropower is a key part of the increasing shift in power production from nonrenewables to renewable energy. In regions such as Africa, hydropower reservoirs are vital for achieving several sustainable development goals, including clean water, energy, and poverty elimination. However, the operations of hydropower reservoirs are often suboptimal due to the lack of hydrologic data for generating reliable inflow forecasts. Here, we present a decision support system (DSS) framework for hydropower planning at daily to seasonal time scales by combining data from earth observation satellites (EOS) with ensemble climate forecasts from dynamical models and hydrologic modeling. The large uncertainty inherent in satellite‐based datasets is overcome by using a data validation framework which does not require ground‐based measurements. In addition, an EOS evapotranspiration product is used as a proxy for streamflow in calibrating hydrologic models. Compared to a DSS forced with a climatological forecast (zero‐skill), the hydropower production with the new DSS increased by 20%. The study highlights the advantage of using data from EOS in overcoming the issue of data scarcity in water resources applications, particularly in developing regions of the world such as Africa.

Risk Analysis for Short-Term Operation of the Power Generation in Cascade Reservoirs Considering Multivariate Reservoir Inflow Forecast Errors

In the short-term operation of the power generation of cascade reservoirs, uncertainty factors such as inflow forecast errors could cause various types of risks. The inflow to a downstream reservoir is not only affected by inflow forecast errors from upstream reservoirs but also the forecast errors associated with inflow to the stream segment between the reservoirs, such as from a tributary. The inflow forecast errors of different forecast periods may also be correlated. To address this multivariate problem, the inflow forecast error variables were jointly fitted in this study using the Gaussian mixture model (GMM) and a t-Copula function based on the analysis of the error distribution characteristics in different forecast periods. Therefore, a stochastic model that coupled with the GMM and t-Copula to calculate inflow forecast errors in multiple forecast periods was established. Furthermore, according to the simulation results of the stochastic model and the predicted runoff series, a set of simulated runoff processes were obtained. Then they were combined with the existing power generation plan to carry out the risk analysis for short-term operation of the power generation in a cascade reservoir. The approach was evaluated using the Jinguan cascade hydropower system within the Yalong River basin as a case study. For this case study, the risk analysis for short-term operation of the power generation was analyzed based on stochastic simulation of the inflow forecast errors; the results show the feasibility and effectiveness of the proposed methods.

Interval uncertainty analysis of a confined aquifer

Water inflow forecast is influenced by many factors and yields uncertain results. To more accurately predict the magnitude of water inflow and quantitatively define the corresponding response in the parameter change interval, this study combined a non-probabilistic set theory and uncertainty analysis to derive an equation for the confined water inflow. Using mining area data and comparing the calculation of upper and lower boundary limits obtained by a Monte Carlo method, results of the confined water inflow equation were calculated with relative errors of 5% and 10%. When corresponding to the rate of change of the variable parameter, the results showed that under the same error conditions, the allowable rate of change when calculating the minimum value using Eq. A was greater than when using Eq. B, and the maximum value using Eq. B yielded a greater allowable rate of change than the maximum value calculated by Eq. A. Thus, the obtained rate of change for Eq. A is indicative of the lower limit, and Eq. B is conducive to the calculation of the upper limit of mine water inflow.

Quantifying the Risks that Propagate from the Inflow Forecast Uncertainty to the Reservoir Operations with Coupled Flood and Electricity Curtailment Risks

Substantial uncertainty is inherent in reservoir inflow forecasting, which exerts a potential negative impact on reservoir risk. However, the risk propagation from the inflow forecast uncertainty (IFU) to reservoir operations remains elusive. Thus, a new integrated assessment framework was developed in this study to characterize the risk coupling with flood and electricity curtailment risks that propagate from the IFU to the reservoir operations. First, to incorporate the IFU, an improved Gaussian mixture distribution (IGMD) and Markov chain Monte Carlo (MCMC) algorithm were constructed to model the measured forecast errors and generate ensemble inflow forecasts, respectively. Next, to assess the reservoir risk, the flood risk induced by the IFU overestimation and the electricity curtailment risk related to the IFU underestimation were identified according to the reservoir operation rules. The sub-daily inflow forecast at the Jinping First Stage Hydropower Plant Reservoir of Yalong River, China (Jinping I Reservoir) was selected. The results indicated that the IGMD-based MCMC was capable of deriving robust ensemble forecasts. Furthermore, there was no flood risk (risk rate was zero) induced by the IFU when taking designed reservoir floods with a ≥10-year return period as the benchmark. In contrast, the electricity curtailment risk rate significantly increased up to 41% when considering the IFU. These findings suggested that compared with the flood prevention pressure, the IFU would more likely result in severe electricity curtailment risk at the Jinping I Reservoir.