Reservoir Operation Research Articles

Impacts of climate change and reservoir operation on droughts: a case study in the Upper Part of Dong Nai River Basin, Vietnam

ABSTRACT This study aims to investigate the impacts of climate change and reservoir operation on drought in the Upper Part of Dong Nai (UPDN) River Basin. Climate change scenarios for the UPDN River Basin were portrayed according to the Coupled Model Intercomparison Project Phase 6 (CMIP6) based on an ensemble of five general circulation models (GCMs), namely, EC-Earth3-Veg, CanESM5, EC-Earth3, HadGEM3-GC31-LL, and CNRM-CM6-1-HR for two Shared Socioeconomic Pathway scenarios, SSP2-4.5 and SSP5-8.5. The historical climate monitoring data at five weather stations and three hydrological gauges related to upstream reservoirs in the period 1990–2020 were collected. The streamflow for the period 2021–2099 was simulated by applying the Soil and Water Assessment Tool (SWAT) model. Two meteorological and hydrological drought indices of the 6-month time scale, namely, standardized precipitation index (SPI6) and streamflow drought index (SDI6), were calculated through designed modules integrated in the Drought Index Calculator (DrinC) software. The results show that climate change coupled with reservoir operation has seasonally changed the runoff, which has changed the drought situation in the entire basin. The lag time variations of hydrological drought in response to meteorological drought were significant in the main basin of the UPDN River Basin. These findings provide useful information for managers and policymakers in sustainable water resources management and development adapting to climate change.

Rule-mediated connectivity in social-ecological-technological systems: A comparative network analysis of reservoir operation rules in Coyote Valley Dam (United States) and Ameghino Dam (Argentina).

In the management of reservoirs, different forms of infrastructure (such as dams, hydropower units, information) are functionally interdependent and often managed by different types of actors to form a social-ecological-technological system. Such interdependence also occurs because institutions (understood as rules that guide and constrain actor behavior) exist to indicate how infrastructures should be managed. We apply institutional analysis and social network analysis to identify how functionally interdependent infrastructures and actors are connected by formal rules created to manage reservoir operations in Argentina (Ameghino Dam, Chubut) and the United States (Coyote Valley Dam, California). Using Exponential Random Graph Models and motif analysis we develop and test hypotheses about which types of patterns of rule-mediated interdependence are more likely to occur in the management of reservoir operations as well as how contextual features, such as the socio-political environment and the types of actors involved, influence rule-mediated interdependence in social-ecological-technological systems. We find that the type of actors involved and the socio-political context in which rules are designed shape the patterns of rule-mediated interdependencies. These findings shed light on and call for more attention to the role that formal rules play in shaping infrastructure management across socio-political contexts.

Discussion on pore-to-field scale evolution of invasive pattern in immiscible two-phase flow with pseudoplastic behavior in porous media

The immiscible two-phase flow with non-Newtonian behavior presents many challenges for geoscience engineering applications. Due to the non-Darcy flow with pseudoplasticity in bitumen or heavy oil reservoir, the variable and velocity-affected viscous force leads to unstable and unpredictable evolution of invasive patterns in porous media. Primarily, drainage experiments, computational fluid dynamics, and analytical model analyses were conducted to track morphological variations of invasive interface, revealing the evolution mechanisms of invasive patterns with pseudoplasticity. Subsequently, the field-scale two-phase flow simulation, established by the blocked-centered finite difference method, quantitively investigates the impact of invasive patterns on the extraction of pseudoplastic oil. Ultimately, given previous methods for evaluation of invasive patterns, capillary number, and mobility ratio play a communicating role in the pore-scale and the field-scale study. Through the dual-scale framework, the capillary number and mobility ratio, serving as driving force and resistance, respectively, predictably improve the evolutionary diagram pore-scale invasive patterns and accurately evaluate the field-scale potential of pseudoplastic oil extraction. From both pore and field perspectives, our work holds promising application for optimizing invasive patterns by injection operation in the pseudoplastic oil reservoirs.

Ditch rotation irrigation coupled with multi-scale ecological reservoir operation

Ditch rotation irrigation coupled with multi-scale ecological reservoir operation

Forecast-Informed Reservoir Operation within an Actively Managed Distributed Storage Network Using the High-Resolution Rapid Refresh Product

Forecast-Informed Reservoir Operation within an Actively Managed Distributed Storage Network Using the High-Resolution Rapid Refresh Product

An explicit robust optimization framework for multipurpose cascade reservoir operation considering inflow uncertainty

An explicit robust optimization framework for multipurpose cascade reservoir operation considering inflow uncertainty

Rule Curve of Meninting Reservoir in West Lombok Regency, West Nusa Tenggara

Meninting Reservoir in West Lombok Regency is essential for addressing uneven water distribution on Lombok Island. It reallocates water from the Sesaot, Penimbung, and Ketapang Irrigation Areas, covering 1,490 hectares, and supplies 150 liters/second of raw water while generating 2×0.4 MW of electricity. To ensure equitable water distribution, an optimal rule curve is needed. This study analyzes water demand and availability using the continuity equation. Water availability was assessed based on discharge data from the Orong Atas AWLR station, while demand was evaluated under various scenarios, including different planting schedules and Micro Hydro Power Plant (MHP) integration. Results show that irrigation demand ranges from 1,082,648.92 m³ to 1,496,415.21 m³ per half-month period, while raw water and MHP demands are 197,100 m³ and 2,857,950 m³, respectively. The optimal rule curve is achieved with the October II planting schedule without MHP, ensuring 100% reliability. However, with MHP, the best rule curve occurs under the November I schedule, reducing reliability to 54.2% and increasing failure risk to 45.8% in dry years. These findings emphasize the need for strategic planting schedules and reservoir management to balance irrigation, raw water supply, and hydropower generation. The study provides a scientific basis for optimizing reservoir operations to ensure sustainable water use in West Lombok Regency.

Analysis of carbon dioxide reservoir performance and optimization strategies: a case study of a carbon dioxide plant

This study aimed at developing an empirical model for estimating and analyzing CO2 reservoir performance. This was achieved by using decline curve analysis (DCA) to determine an accurate empirical decline model for reservoir performance prediction and optimal producing, i.e., time at which the plant recovery build-up (RBU) efficiency has declined to an approximate value of 44.5%. MATLAB R2013b was used for calculating raw gas density whereas Ms Excel 2007 was used in plotting and performing all calculations. Through an examination of real production data, a harmonic decline model with an Arps’ decline constant (d) of 1.0 and a nominal decline rate of approximately 0.035572337/year was identified as the most suitable for characterizing reservoir performance. The study identifies the year 2048 as an optimal opportunity for implementing production optimization strategies to maximize returns while ensuring efficient resource recovery. Acknowledging the limitations of DCA, the study recommends exploring alternative forecasting models such as type curve analysis (TCA), material balance equation (MBE), and well simulation techniques to mitigate uncertainties in reservoir performance prediction. In addition, the study proposes a 5-year model verification and improvement program to refine reservoir descriptions and enhance data acquisition practices, aiming to optimize production efficiency and minimize uncertainties in CO2 reservoir operations.

Predicting Post-Wildfire Stream Temperature and Turbidity: A Machine Learning Approach in Western U.S. Watersheds

Wildfires significantly impact water quality in the Western United States, posing challenges for water resource management. However, limited research quantifies post-wildfire stream temperature and turbidity changes across diverse climatic zones. This study addresses this gap by using Random Forest (RF) and Support Vector Regression (SVR) models to predict post-wildfire stream temperature and turbidity based on climate, streamflow, and fire data from the Clackamas and Russian River Watersheds. We selected Random Forest (RF) and Support Vector Regression (SVR) because they handle non-linear, high-dimensional data, balance accuracy with efficiency, and capture complex post-wildfire stream temperature and turbidity dynamics with minimal assumptions. The primary objectives were to evaluate model performance, conduct sensitivity analyses, and project mid-21st century water quality changes under Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. Sensitivity analyses indicated that 7-day maximum air temperature and discharge were the most influential predictors. Results show that RF outperformed SVR, achieving an R2 of 0.98 and root mean square error of 0.88 °C for stream temperature predictions. Post-wildfire turbidity increased up to 70 NTU during storm events in highly burned subwatersheds. Under RCP 8.5, stream temperatures are projected to rise by 2.2 °C by 2050. RF’s ensemble approach captured non-linear relationships effectively, while SVR excelled in high-dimensional datasets but struggled with temporal variability. These findings underscore the importance of using machine learning for understanding complex post-fire hydrology. We recommend adaptive reservoir operations and targeted riparian restoration to mitigate warming trends. This research highlights machine learning’s utility for predicting post-wildfire impacts and informing climate-resilient water management strategies.

Multilayer entropy-weighted TOPSIS method and its decision-making in ecological operation during the subsidence period of the Three Gorges Reservoir

Coordinating the downstream ecological demand and the power generation demand of hydropower stations is an important task in the operation of reservoirs, and how to evaluate the ecological satisfaction of the scheduling process is a difficult problem that needs to be solved urgently. A multi-objective optimal reservoir scheduling model was constructed to coordinate the spawning flow demand of " Four Major Chinese Carps"; The model takes the maximum power generation and the maximum membership degree of downstream river ecological water demand as the objective functions, and uses the dynamic programming multi-objective solution algorithm based on penalty factors to solve the problem, and obtains the non-inferior solution set in each scenario. The multilayer entropy-weighted TOPSIS method was used to study the non-inferior solution of the multi-objective scheduling model of the Three Gorges Reservoir, and the satisfactory solution ranking of the river flow rise process, ecological flow-related requirements, and power generation water requirements was obtained under the four schemes including 4d ~ 7d, which improved the reliability of the evaluation results and made up for the shortcomings of the traditional TOPSIS method in terms of hierarchy and weight science. The research results of this paper can provide a reference for decision-making on the ecological management of the spawning period of the " Four Major Chinese Carps" in the Three Gorges Reservoir of the Yangtze River.

Deep reinforcement learning for multiple reservoir operation planning in the Chao Phraya River Basin

This study demonstrates application of Deep Deterministic Policy Gradient (DDPG)-based algorithm to provide comprehensive and flexible plans for reservoir operation planning of the multiple reservoir system in the Chao Phraya River Basin (CPYRB), Thailand aiming to mitigate flood and drought risks in the region. The multi-agent-based Deep Reinforcement Learning (DRL) model is accordingly constructed considering 7-D predicted inflow, reservoir water released from adjacent reservoir, downstream flow condition, and changes in reservoir water storage, as state variables. The desired goal is to increase water storage levels in all reservoirs by 10–15% to ensure higher potential in supplying water for crop cultivation over the dry seasons and preventing flood occurrences during wet season. Simulation results from 2009 to 2022 indicate that DRL–DDPG-based algorithm can perform well in solving sequential decision problems for optimal operation of multiple reservoir system to achieve the desired water storage goal. It can offer realistic simulation results of seasonal and annual release schemes and reservoir release ratios among reservoirs in the system compared to actual operation and Fmincon and ANFIS optimizations. Importantly, DRL model demonstrates a significant advantage in view of increasing the long-term water storage levels in all reservoirs as targeted in the modelling process while maintaining the similar and consistent release schemes in the reservoir system. For the multipurpose multiple reservoir system operation, adjusting the dynamic desired goals within multi-agent-based RL model is advisable to attain the specific desired outcomes and address various water scenarios.

A Reservoir Dam Monitoring Technology Integrating Improved ABC Algorithm and SVM Algorithm

A reservoir dam is a water conservancy project with large investment and high social and economic benefits, which plays an irreplaceable role in flood control, power generation, water storage, and urban water supply. There is a risk of accidents in the process of reservoir dams, so dam monitoring is an important means to achieve the safe operation of reservoirs. In this paper, taking advantage of the high-dimensional and nonlinear characteristics of dam monitoring data samples, the fusion-improved ABC (artificial bee colony) algorithm is introduced, and the SVM (support vector machine) algorithm is used to optimize the penalty factor and kernel function parameters. The test results of the ABC and SVM algorithm are relatively stable, with small fluctuation amplitude, which can continuously monitor water level, pore water pressure, dam deformation, temperature, humidity, vibration, and other indicators is less than 10%, which is significantly lower than the standard ABC algorithm, the standard ANN algorithm, and the standard SVM algorithm. The independence and characteristics of the ABC–SVM algorithm are significantly higher, and the correlation is 0.03, the RMS (root mean square) is 0.2334, which is lower than that of the standard ABC algorithm of 0.09, and the standard ANN algorithm of 0.8. The stability of the results and performance stability are analyzed, which is greater than 90%. The ABC and SVM is used to predict the displacement and deformation law of the reservoir dam.

Efficient optimisation of physical reservoir computers using only a delayed input

Reservoir computing is a machine learning algorithm for processing time dependent data which is well suited for experimental implementation. Tuning the hyperparameters of the reservoir is a time-consuming task that limits is applicability. Here we present an experimental validation of a recently proposed optimisation technique in which the reservoir receives both the input signal and a delayed version of the input signal. This augments the memory of the reservoir and improves its performance. It also simplifies the time-consuming task of hyperparameter tuning. The experimental system is an optoelectronic setup based on a fiber delay loop and a single nonlinear node. It is tested on several benchmark tasks and reservoir operating conditions. Our results demonstrate the effectiveness of the delayed input method for experimental implementation of reservoir computing systems.

A Methodology to Improving the Performance of MOAHA Optimization Algorithm using Chaos Theory; Principle and Application in Optimal Reservoir Operation

A Methodology to Improving the Performance of MOAHA Optimization Algorithm using Chaos Theory; Principle and Application in Optimal Reservoir Operation

Investigation of casing failure mechanisms induced by hydraulic fracture dynamic penetration in heterogeneous shale gas reservoirs

The occurrence of casing failure in multi-lithology layered shale reservoirs is significantly influenced by the behavior of hydraulic fracture penetration. However, the mechanisms underlying casing failure during the dynamic penetration of hydraulic fractures in heterogeneous reservoirs remain largely unexplored. In order to study how the dynamic expansion law of hydraulic fractures in heterogeneous reservoirs affects casing stress, a Hydraulic Fracture-Heterogeneous Reservoir-Cement Sheath-Casing (HHCC) model has been developed based in the cohesive zone model framework. Furthermore, the reliability of the model is verified by comparing the simulation results with the experimental results. The focus of this study encompasses the analysis of casing stress distribution during both the initiation and dynamic penetration phases of hydraulic fracturing in heterogeneous reservoirs. Additionally, it investigates the effects of bedding surface bonding strength and temporary plugging technology on casing stress. Furthermore, mitigation strategies are proposed based on these findings, and the model is used to evaluate their effectiveness in reducing the risk of casing failure, particularly in the Weiyuan Block. The outcomes of this research yield critical insights aimed at preventing casing failure during hydraulic fracturing operations in heterogeneous shale gas reservoirs.

Numerical analysis of the hydraulic fracture propagation behavior encountering gravel in conglomerate reservoirs

Hydraulic fracturing, which forms complex fracture networks, is a common technique for efficiently exploiting low-permeability conglomerate reservoirs. However, the presence of gravel makes conglomerate highly heterogeneous, endowing the deformation, failure, and internal micro-scale fracture expansion mechanisms with uniqueness. The mechanism of fracture expansion when encountering gravel in conglomerate reservoirs remains unclear, challenging the design and effective implementation of hydraulic fracturing. This paper utilizes the Abaqus platform and adopts the maximum circumferential strain fracture criterion to establish a two-dimensional fluid-solid-structure coupled fracture expansion model. The study investigates the impact of permeability, Young’s modulus, in-situ stress difference, and incidence angle on the fracture expansion behavior in the presence of gravel. The results indicate that low-permeability gravel induces pressure changes near the fracture tip, like 13% pore pressure increase and stress reductions. Shear stress on the gravel surface rises by 28.3%, and choosing layers with a smaller permeability gap between matrix and gravel can reduce complex near-well fractures. High Young’s modulus gravel has a stress shielding effect around fractures, which is direction-dependent, with over tenfold stress variations. It causes a 60.7% increase in peak shear stress on the gravel surface, exacerbating fracture complexity and reducing extension distance, and a greater strength disparity between gravel and matrix enhances this effect. A high in-situ stress difference (over 8 MPa) weakens gravel-caused stress shielding, leading to stable fracture propagation with a single morphology and increased extension distance, though not favoring complex network formation. As the incidence angle rises, the gravel’s impact on stress and seepage fields weakens, but the risk of shear failure on the cementation interface increases. The findings provide a basis for optimizing operations in such reservoirs considering the various properties of gravel and their effects on fluid flow and fracture behavior.

Water-Level Fluctuations Rather than Water Content Changes Induced by Reservoir Operation Impact Bacterial Functioning for Nitrogen Transformation in Riparian Zones

Water-Level Fluctuations Rather than Water Content Changes Induced by Reservoir Operation Impact Bacterial Functioning for Nitrogen Transformation in Riparian Zones

Evaluation of future hydropower potential for mountainous headwaters in response to climate change

Snowmelt runoff and hydropower generation performance together with reservoir storages were evaluated for climate projections in two mountainous headwaters in Türkiye’s north (Black Sea Region) and south (Mediterranean Region). The EU-CORDEX database was used to obtain an ensemble of different Global Climate Models under two scenarios. Moreover, evaluations were performed regarding the Global Warming Levels. The results indicate that a dramatic increase in temperatures and alterations in precipitation patterns will bring significant decreases in snow variables and inflows, which will be particularly pronounced in the southern basin. HBV-Light and WEAP model were used to estimate future reservoir inflows and hydropower generation projections through reservoir operations, respectively. The hydropower generation is anticipated to decline up to 35% and 9% in the southern and northern basins, respectively, for far future (2075-2099). Four management scenarios (including increased irrigation and operational hydropower capacity) were assessed for reconsideration of operation strategies.

Enhanced NSGA-II algorithm based on novel hybrid crossover operator to optimise water supply and ecology of Fenhe reservoir operation

Reservoir-operation optimisation is a crucial aspect of water-resource development and sustainable water process management. This study addresses bi-objective optimisation problems by proposing a novel crossover evolution operator, known as the hybrid simulated binary and improved arithmetic crossover (SBAX) operator, based on the simulated binary cross (SBX) and arithmetic crossover operators, and applies it to the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) algorithm to improve the algorithm. In particular, the arithmetic crossover operator can obtain an optimal solution more precisely within the solution space, whereas the SBX operator can explore a broader range of potential high-quality solutions. Considering the advantages of both operators, this study introduces an improved arithmetic operator to reduce the risk of local convergence inherent in conventional arithmetic operators. Subsequently, two strategies for the SBAX operator are discussed: SBX operator + new arithmetic operator and new arithmetic operator + SBX operator. The convergence of the bi-objective Pareto solution set is evaluated based on the generation and inverted generational distances. This method is used for the collaborative optimisation of the water supply and ecological operation of the Fenhe Reservoir, where its effectiveness is demonstrated. A comparative analysis of the bi-objective optimisation schemes obtained using different crossover operators indicates the following: (1) the NSGA-II algorithm based on the SBAX operator achieves a convergence efficiency that is 14.25–41.95% higher than that of the conventional NSGA-II algorithm; (2) the reservoir operation indices of the scheduling scheme derived from the NSGA-II algorithm based on the SBAX operator significantly outperform those obtained using the conventional NSGA-II algorithm. The optimal strategy reduces the annual average water abandonment by 11.2–14.52 million m3. This study provides a novel approach for bi-objective optimisation and sustainable reservoir management.

BULANGO ULU RESERVOIR OPERATIONAL PATTERN MODEL

The Bulango Ulu Reservoir is located in Gorontalo Province, the construction of the Bulango Ulu Reservoir is to meet the needs of raw water, irrigation water, and hydropower, to ensure whether the need and availability of water in the rainy and dry seasons can meet the initial planning goals. In this study, the method used is to simulate the operation pattern of the reservoir with variations in the wet season with the probability of discharge (66.67%), the normal season with the probability of discharge (50%), and the dry season with the probability of discharge (33.33%) so as to determine the availability of the Bulango Ulu Reservoir during the rainy season and dry season whether the needs can be met. The results obtained can be used as input for the regulation of the Bulango Ulu Reservoir operation pattern.