Reservoir Inflow Research Articles

An analytical two-stage risk analysis model in the real-time reservoir operation

Flood risk analysis is vital for real-time reservoir operation. The reservoir operation horizon (OH) is generally longer than the streamflow forecast horizon (FH), while the gap between the FH and the OH was seldom considered in analytical flood risk models. This study develops an analytical two-stage risk model covering the period within the FH (the first stage) and from the FH to the OH (the second stage). In the first stage, the errors in forecasted streamflow and reservoir water level-storage relationships are considered using the multinormal distribution within the FH. In the second stage, design flood hydrographs are used to estimate the flood risk by conducting reservoir routing. Finally, the total flood risk is calculated by using the copula method to combine the flood risks from the first and second stages. Results for a case study using China’s Three Gorges Reservoir indicate that an effective FH can be identified to minimize flood risk, and balance between the forecast accuracy and length of the horizon. The length of the effective FH is not fixed and depends on reservoir inflow. Moreover, larger errors will not always lead to greater flood risks. The proposed method provides useful information on flood risk for real-time reservoir operation.

Evaluating precipitation corrections to enhance high-alpine hydrological modeling

Gridded meteorological data products often fall short in accurately capturing the amount of precipitation and its patterns in regions characterized by high elevations and complex topography. However, realistic precipitation data is crucial for high-alpine hydrological modeling. To address these discrepancies, we analyze possible corrections for solid, liquid and total precipitation of the 1 km2 gridded meteorological INCA-product in the high-alpine catchment of the Kölnbrein hydropower reservoir operated by VERBUND Hydro Power GmbH in the Malta Valley in Austria. By leveraging information from a stereo-satellite-derived snow depth map with physically-based snowpack modeling with Alpine3D, we quantitatively adjust and spatially redistribute solid precipitation, complemented by a multiplicative, stepwise correction model for liquid precipitation. We compare and evaluate five approaches using the hydrological COSERO model to our a) baseline simulation with no corrections on INCA in contrast of correcting, b) the amount and distribution of solely solid precipitation, c) the amount of liquid and solid precipitation, d) the amount of liquid and solid precipitation and the spatial distribution of the latter, e) precipitation inversely by the inflow bias, and f) calibrating the precipitation correction factor. In evaluating these strategies to improve the accuracy of reservoir inflow predictions, we found that separately correcting solid and liquid precipitation yielded the best results (c &d), with a substantial increase of up to 65% over the study period (1.10.2015–30.9.2023), while the other correction variants ranged between 42 and 52%. The inflow predictions by COSERO showed an increase in Nash-Sutcliffe Efficiency (NSE) by 17% and in Kling-Gupta Efficiency by 57% and 59% for variants c and d, respectively, along with an almost complete elimination of model bias. The higher KGE values observed for variant d compared to c during spring, summer, and fall suggest that a more realistic snow distribution enhances the simulation of snowmelt-driven runoff dynamics. In contrast, using a global (i.e., spatially homogeneous) and uniform (i.e., not distinguishing between liquid and solid precipitation phase) correction factor, inversely derived from the inflow bias (e), or solely correcting solid precipitation (b), demonstrated less performance, with a KGE increase of 47% and 49%, respectively, compared to 59% for variant d. Conversely, the calibration of the global and uniform correction factor (f) resulted in significant performance metric improvements (17% NSE, 60% KGE and 90% pBias), similar to variant d, however also led to unrealistic simulations of evapotranspiration, sublimation and glacier net runoff. The simulated water balance components – evapotranspiration and sublimation, as well as glacier runoff – in variant d were deemed plausible based on our comparison with additional simulations using Alpine3D, as well as findings from other high-alpine catchments in Austria reported in the literature. Overall, our results underscore the importance of applying a dual correction strategy for both liquid and solid precipitation, particularly when significant deficiencies are present in meteorological datasets, and suggest that such corrections should be supplemented by a comprehensive analysis of the simulated high-alpine water balance components.

Hydropower Plant Available Energy Forecasting Using Artificial Neural Network and Particle Swarm Optimization

Accurate forecasting of the available energy portion that corresponds to the reservoir inflow of the month(s) ahead provides important decision support for hydropower plants in energy production planning for revenue maximization, as well as for environmental impact prevention and flood control upstream and downstream of a basin. Therefore, a reliable forecasting tool or model is deemed necessary and crucial. Considering the fluctuation and nonlinearity of data which significantly influence the forecasting results, this study develops an effective hybrid model by integrating an Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) called “PSO-ANN” model based on the hydrological and meteorological data pre-processed by cross-correlation function (CCF), autocorrelation function (AFC), and normalization techniques for predicting the available energy portion corresponding to the reservoir inflow mentioned above for a case study hydropower plant in Laos, namely, the Theun-Hinboun hydropower plant (THHP). The model was evaluated by using correlation coefficient (r), relative error (RE), root mean square error (RMSE), and Taylor diagram plots in comparison with popular single-algorithm approaches such as ANN, and NARX models. The results demonstrated the superiority of the proposed PSO-ANN approach over the other two models, in addition to being comparable to those proposed by previous studies.

Application of remote sensing and GIS techniques for monitoring water volume variations in inaccessible reservoirs

ABSTRACT Change detection processes using remote sensing and Geographic Information System (GIS) techniques were applied to monitor and estimate the water volume in the Mosul Dam reservoir during a period in which it was inaccessible due to ISIS occupation of the Province of Mosul. A total of 17 scenes of LANDSAT 8 images have been captured and the calculated areas have been used to estimate the water level (WL) and water volume (V) in the reservoir using the volume–area–elevation curve that was developed in 2011. The results indicated that WL ranged between a minimum of 308.5 m and a maximum of 321 m, while V ranged between 3.95 and 6.8 km3 for the minimum and maximum WL, respectively. For the blackout period with missing data, the monthly reservoir inflows (RIs) from the drainage watershed and the mean inflows were calculated and found to be compatible with the mean inflows of Mosul reservoir for the years 1986–2011.

Data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on multi-head attention mechanism

Accurate and reliable daily reservoir inflow forecast plays an essential role in several applications involving the management and planning of water resources, such as hydroelectric generation, flood control, water supply, and basin ecological dispatching. Runoff usually exhibits strong non-linearity, high uncertainty, and spatial and temporal variability. Existing techniques fail to capture complete dynamics change processes effectively. A data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on a multi-head attention mechanism was developed, referred to as the GWOCS-VMD-CNN-Transformer (GCVCT). First, the model utilize Grey Wolf Optimizer coupled with Cuckoo Search (GWO-CS) algorithms to optimize parameters in variational mode decomposition model (VMD). This approach helps obtain highly correlated intrinsic mode function (IMF) components, enhancing the frequency resolution of the input dataset. The proposed method overcomes the bottleneck of other available methods by decomposing the time series to capture the main long-term and short-term properties of hydrological processes. Second, the convolution neural network and Transformer (CNN-Transformer) are based on a multi-head attention mechanism as the objective predictive method. Finally, six evaluation indicators verify the performance of the proposed approach. The approach’s reliability was evaluated using the historical daily reservoir inflow data from the Xiluodu (XLD) and Wudongde (WDD) reservoirs in the Jinsha River Basin, China. Several single and hybrid models were developed for comparative analysis. The results indicate that the proposed ensemble approach fits better than other developed model methods. The GCVCT model showed excellent performance in forecasting the inflows of XLD and WDD reservoirs, with NSE values of 0.985 and 0.984, respectively. Furthermore, the GCVCT framework forecast capacity for peak inflow was further verified through discussion and analysis of the 48 peak flows during the validation period, consistently outperforming other models in predicting peak flow for both study reservoirs. This framework provides an effective method for the scientific optimal scheduling of hydropower reservoirs, enabling more sustainable and efficient management practices. It also demonstrates the potential of powerful deep-learning models in intelligent hydrological forecasting.

Analytical and descriptive study of the production behavior for pilot multi-stages hydraulic fracturing wells in southeast Iraq

The hydraulic fracturing technique is a widely used technique worldwide, making it interesting to study. This technique was applied for the first time in Iraq on a tight carbonate reservoir in the Halfaya oil field. In this field, the oil production rates, and flowing pressure were low during production periods reflecting a problem for the development plan designed to increase production using a hydraulic fracture technique. Initially, the hydraulic fracture well showed high oil-producing rates, but then, quickly experienced a high decline failing to keep a stable production rate. To address this problem, it is important to describe and analyze the behavior of pilot hydraulic fracturing wells during their production period, study the inflow performance relationship (IPR), and determine the optimum wellhead pressure above the bubble point pressure (Pb) to avoid loss of lifting energy. Furthermore, it identifies the allowable flow rate to keep stable production, investigates the effect of selecting internal tubing size, and reveals a future production procedure for hydraulic fracture wells. This study reveals that the transient inflow performance relationship observed in the production history of hydraulic fracture well in the tight carbonate reservoir and traditional inflow performance relationship concepts are not applicable. The optimum wellhead pressure for stability in production for Wells w-5 and w-55 is determined to be 750 and 580 psi with optimum rates of 800 and 450 Bbl/D, respectively. The results also showed that producing at a high flow rate may cause a depletion in the fracture potential storage without giving an opportunity for a reservoir to compensate for the produced fluid into fracturing potential storage. The internal tubing size has a passive effect on the hydraulic fracture well, as an increase in size causes an unstable flow zone. Lastly, future production procedures emphasize keeping wellhead pressures stable or increasing them if they drop. This can be accomplished by re-adjusting the choke size according to any changes observed.

Future Agricultural Water Availability in Mediterranean Countries under Climate Change: A Systematic Review

Warming and drying trends in the Mediterranean Basin exacerbate regional water scarcity and threaten agricultural production, putting global food security at risk. This study aimed to review the most significant research on future water availability for the Mediterranean agricultural sector under climate change (CC) scenarios published during 2009–2024. Two searches were performed in the Scopus and Web of Science databases, to which previously identified significant studies from different periods were also added. By applying a methodology duly protocoled in the PRISMA2020-based guideline, a final number of 44 particularly relevant studies was selected for review. A bibliometric analysis has shown that most of the published research was focused on Southwestern European countries (i.e., Spain, Italy, Portugal) and grapevine and olive tree crops. Overall, the reviewed studies state that future Mediterranean water reserves may not meet agricultural water demands, due to reduced reservoir inflows and higher irrigation demands under future CC and socioeconomic scenarios. Regarding adaptation measures to improve water-use management in agriculture, the majority of the reviewed studies indicate that the use of integrated modelling platforms and decision–support systems can significantly contribute to the development and implementation of improved water/land-management practices.

Reservoir Operation based Machine Learning Models: Comprehensive Review for Limitations, Research Gap, and Possible Future Research Direction

The operation of dams and reservoirs is critical for water resource management, including flood control, irrigation, hydropower generation, and environmental conservation. Traditional optimization techniques like Dynamic Programming (DP), Linear Programming (LP), and Nonlinear Programming (NLP) have been foundational in managing these operations. However, they often fall short in addressing the complexities of modern water management challenges posed by climate variability and increasing water demands. Machine learning (ML) techniques have emerged as powerful tools to enhance the efficiency and accuracy of dam and reservoir operations. This paper provides a comprehensive review of various ML models, including Neural Networks, Genetic Algorithms, Decision Trees, and Ensemble Methods, highlighting their applications in predicting reservoir inflows, optimizing water release schedules, and improving flood risk management. Notably, ML models like Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs) have shown significant improvements in forecasting accuracy and operational decision-making. Despite these advancements, several limitations and research gaps persist, including the need for real-time data integration, adaptive learning mechanisms, and models that consider socio-economic and climatic factors. This review underscores the importance of addressing these gaps to develop more robust and generalizable ML models. Future research directions are suggested to focus on hybrid models combining ML with traditional optimization techniques, comprehensive validation across diverse conditions, and the integration of ecological and economic considerations. By systematically identifying and addressing these limitations, this research aims to pave the way for more effective and sustainable dam and reservoir management practices. Leading towards suggesting that enhancing real-time data integration and developing adaptive learning mechanisms are in order to improve model responsiveness.

Utilizing Artificial Intelligence Techniques for a Long–Term Water Resource Assessment in the ShihMen Reservoir for Water Resource Allocation

Accurate long–term water resource supply simulation and demand estimation are crucial for effective water resource allocation. This study proposes advanced artificial intelligence (AI)–based models for both long–term water resource supply simulation and demand estimation, specifically focusing on the ShihMen Reservoir in Taiwan. A Long Short–Term Memory (LSTM) network model was developed to simulate daily reservoir inflow. The climate factors from the Taiwan Central Weather Bureau’s one–tiered atmosphere–ocean coupled climate forecast system (TCWB1T1) were downscaled using the K–Nearest Neighbors (KNN) method and integrated with the reservoir inflow model to forecast inflow six months ahead. Additionally, Multilayer Perceptron (MLP) and Gated Recurrent Unit (GRU) were employed to estimate agricultural and public water demand, integrating both hydrological and socio–economic factors. The models were trained and validated using historical data, with the LSTM model demonstrating a strong ability to capture seasonal variations in inflow patterns and the MLP and GRU models effectively estimating water demand. The results highlight the models’ high accuracy and robustness, offering valuable insights into regional water resource allocation. This research provides a framework for integrating AI–driven models with Decision Support Systems (DSSs) to enhance water resource management, especially in regions vulnerable to climatic variability.

Enhancing real-time flood forecasting and warning system by integrating ensemble techniques and hydrologic model simulations

ABSTRACT Flooding poses a severe threat to communities and infrastructure worldwide, which requires advanced flood forecasting warning systems. In this research paper, a real-time flood forecasting and warning system for the Dharoi Dam in the state of Gujarat, India is developed. This novel system combines ensemble techniques and hydrological modeling simulations to enhance flood prediction accuracy and provides a timely warning. The study focuses on critical gaps in the current flood forecasting capabilities, recognizes the need for improved flood management in the region, and builds upon the existing research conducted globally and in India. The real-time flood forecasting and warning system uses information from various sources such as rainfall, river flows, and water level observations. The system enhances the accuracy of flood forecasts with a 1–5-day lead time by utilizing ensemble techniques, which incorporate multiple models and their corresponding forecasts. The 2-day and 3-day lead times combined with postprocessing techniques yield excellent results, as evidenced by the reservoir inflow correlation value of 0.86 and the receiver operating characteristic-area under the curve (ROC-AUC) value of 0.93. This work aims to reduce the impact of floods in this region and can be used by decision-makers as a disaster management tool.

Reservoir Operating Rules to Create Effective Fish Migration Passages in a Reservoir

AbstractLow flow velocity disrupts the potential fish migration passages in a reservoir, and causes reservoirs to become labyrinths for migratory fish. Previous studies showed that fish migration passages in a reservoir can be restored by modifying reservoir operation. However, reservoir operating rules that are specifically designed to support fish migration have not been established, leading to low success for fish migration and high socioeconomic losses. In this research, we established new reservoir operating rules to improve the migration success rate while also reducing socioeconomic losses. Under the new rules, two parameters (reservoir water level and predicted reservoir inflows) were adapted to trigger water releases to open migration passages. The two parameters were optimized by considering hydrological processes, fish migration processes below the dam, the swimming ability of fish, and the socioeconomic benefits provided by the reservoir. We used data from China's Danjiangkou Reservoir to test the effectiveness of the proposed method. We found that the new method can not only improve the migration success rate (from 0.1% to 4.2%), but can also slightly reduce the reservoir's socioeconomic losses (by 3.7%–7.0%). The new method effectively balances the needs of fish migration with the need for socioeconomic development in the areas surrounding dammed rivers.

GCAM–GLORY v1.0: representing global reservoir water storage in a multi-sector human–Earth system model

Abstract. Reservoirs play a significant role in modifying the spatiotemporal availability of surface water to meet multi-sector human demands, despite representing a relatively small fraction of the global water budget. Yet the integrated modeling frameworks that explore the interactions among climate, land, energy, water, and socioeconomic systems at a global scale often contain limited representations of water storage dynamics that incorporate feedbacks from other systems. In this study, we implement a representation of water storage in the Global Change Analysis Model (GCAM) to enable the exploration of the future role (e.g., expansion) of reservoir water storage globally in meeting demands for, and evolving in response to interactions with, the climate, land, and energy systems. GCAM represents 235 global water basins, operates at 5-year time steps, and uses supply curves to capture economic competition among renewable water (now including reservoirs), non-renewable groundwater, and desalination. Our approach consists of developing the GLObal Reservoir Yield (GLORY) model, which uses a linear programming (LP)-based optimization algorithm and dynamically linking GLORY with GCAM. The new coupled GCAM–GLORY approach improves the representation of reservoir water storage in GCAM in several ways. First, the GLORY model identifies the cost of supplying increasing levels of water supply from reservoir storage by considering regional physical and economic factors, such as evolving monthly reservoir inflows and demands, and the leveled cost of constructing additional reservoir storage capacity. Second, by passing those costs to GCAM, GLORY enables the exploration of future regional reservoir expansion pathways and their response to climate and socioeconomic drivers. To guide the model toward reasonable reservoir expansion pathways, GLORY applies a diverse array of feasibility constraints related to protected land, population, water sources, and cropland. Finally, the GLORY–GCAM feedback loop allows evolving water demands from GCAM to inform GLORY, resulting in an updated supply curve at each time step, thus enabling GCAM to establish a more meaningful economic value of water. This study improves our understanding of the sensitivity of reservoir water supply to multiple physical and economic dimensions, such as sub-annual variations in climate conditions and human water demands, especially for basins experiencing socioeconomic droughts.

Fine-tuning inflow prediction models: integrating optimization algorithms and TRMM data for enhanced accuracy.

This research explores machine learning algorithms for reservoir inflow prediction, including long short-term memory (LSTM), random forest (RF), and metaheuristic-optimized models. The impact of feature engineering techniques such as discrete wavelet transform (DWT) and XGBoost feature selection is investigated. LSTM shows promise, with LSTM-XGBoost exhibiting strong generalization from 179.81 m3/s RMSE (root mean square error) in training to 49.42 m3/s in testing. The RF-XGBoost and models incorporating DWT, like LSTM-DWT and RF-DWT, also perform well, underscoring the significance of feature engineering. Comparisons illustrate enhancements with DWT: LSTM and RF reduce training and testing RMSE substantially when using DWT. Metaheuristic models like MLP-ABC and LSSVR-PSO benefit from DWT as well, with the LSSVR-PSO-DWT model demonstrating excellent predictive accuracy, showing 133.97 m3/s RMSE in training and 47.08 m3/s RMSE in testing. This model synergistically combines LSSVR, PSO, and DWT, emerging as the top performers by effectively capturing intricate reservoir inflow patterns.

Storing and managing water for the environment is more efficient than mimicking natural flows

Dams and reservoirs are often needed to provide environmental water and maintain suitable water temperatures for downstream ecosystems. Here, we evaluate if water allocated to the environment, with storage to manage it, might allow environmental water to more reliably meet ecosystem objectives than a proportion of natural flow. We use a priority-based water balance operations model and a reservoir temperature model to evaluate 1) pass-through of a portion of reservoir inflow versus 2) allocating a portion of storage capacity and inflow for downstream flow and stream temperature objectives. We compare trade-offs to other senior and junior priority water demands. In many months, pass-through flows exceed the volumes needed to meet environmental demands. Storage provides the ability to manage release timing to use water efficiently for environmental benefit, with a co-benefit of increasing reservoir storage to protect cold-water at depth in the reservoir.

Assessment and optimisation of a bywash channel at Slaithwaite reservoir

Improvement works to the bywash channel at Slaithwaite reservoir were undertaken in early 2022. An assessment, design and subsequent minor construction works were required to satisfy a measure in the interests of safety relating to the capacity of the bywash channel and its use in the context of an emergency at the dam. The on-site plan was revised and re-certified as a result of changes made including outline designs to enable reservoir inflows to be diverted into the bywash.

Development of a multi-objective reservoir operation model for water quality-quantity management

This study aims to develop a multi-objective quantitative-qualitative reservoir operation model (MOQQROM) by a simulation-optimization approach. However, the main challenge of these models is their computational complexity. The simulation-optimization method used in this study consists of CE-QUAL-W2 as a hydrodynamic and water quality simulation model and a multi-objective firefly algorithm-k nearest neighbor (MOFA-KNN) as an optimization algorithm which is an efficient algorithm to overcome the computational burden in simulation-optimization approaches by decreasing simulation model calls. MOFA-KNN was expanded for this study, and its performance was evaluated in the MOQQROM. Three objectives were considered in this study, including (1) the sum of the squared mass of total dissolved solids (TDS), (2) the sum of the squared temperature difference between reservoir inflow and outflow as water quality objectives, and (3) the vulnerability index as a water quantity objective. Aidoghmoush reservoir was employed as a case study, and the model was investigated under three scenarios, including the normal, wet, and dry years. Results showed the expanded MOFA-KNN reduced the number of original simulation model calls compared to the total number of simulations in MOQQROM by more than 99%, indicating its efficacy in significantly reducing execution time. The three most desired operating policies for meeting each objective were selected for investigation. Results showed that the operation policy with the best value for the second objective could be chosen as a compromise policy to balance the two conflicting goals of improving quality and supplying the demand in normal and wet scenarios. In terms of contamination mass, this policy was, on average, 16% worse than the first policy and 40% better than the third policy in the normal scenario. In the wet scenario, it was, on average, 55% worse than the first policy and 16% better than the third policy. The outflow temperature of this policy was, on average, only 8.35% different from the inflow temperature in the normal scenario and 0.93% different in the wet scenario. The performance of the developed model is satisfactory.

Optimal Scheduling of a Cascade Hydropower Energy Storage System for Solar and Wind Energy Accommodation

The massive grid integration of renewable energy necessitates frequent and rapid response of hydropower output, which has brought enormous challenges to the hydropower operation and new opportunities for hydropower development. To investigate feasible solutions for complementary systems to cope with the energy transition in the context of the constantly changing role of the hydropower plant and the rapid evolution of wind and solar power, the short-term coordinated scheduling model is developed for the wind–solar–hydro hybrid pumped storage (WSHPS) system with peak shaving operation. The effects of different reservoir inflow conditions, different wind and solar power forecast output, and installed capacity of pumping station on the performance of WSHPS system are analyzed. The results show that compared with the wind–solar–hydro hybrid (WSH) system, the total power generation of the WSHPS system in the dry, normal, and wet year increased by 10.69%, 11.40%, and 11.27% respectively. The solar curtailment decreased by 68.97%, 61.61%, and 48.43%, respectively, and the wind curtailment decreased by 76.14%, 58.48%, and 50.91%, respectively. The high proportion of wind and solar energy connected to the grid in summer leads to large net load fluctuations and serious energy curtailment. The increase in the installed capacity of the pumping station will promote the consumption of wind and solar energy in the WSHPS system. The model proposed in this paper can improve the operational flexibility of hydropower station and promote the consumption of wind and solar energy, which provides a reference for the research of cascade hydropower energy storage system.

Coupling Reservoir Operation and Rainfall‐Runoff Processes for Streamflow Simulation in Watersheds

AbstractWe assess the overall watershed system representation via fully coupling a generic reservoir operation model with a conceptual rainfall‐runoff model. The performance of the coupled model is evaluated comprehensively by examining watershed outflow simulations, model parameter values, and a key internal flux of the watershed model (here reservoir inflow). Five published generic reservoir operation models are coupled with a watershed rainfall‐runoff model, and results are compared across the coupled models and one additional model called ResIgnore that ignores reservoir operation. Traditional loosely coupled watershed hydrologic models (where calibrated inflow is routed through reservoir operation models) are used as baselines to examine the differences in simulation performance and parameterization obtained from the fully coupled models. We find that fully coupling the Generic Data‐Driven Reservoir Operation Model (GDROM) and the Dynamically Zoned Target Release (DZTR) reservoir operation models with the rainfall‐runoff model obtains robust simulations of watershed outflow with realistic parameterization, suggesting that they can be reliably integrated into large‐scale hydrological models for simulating streamflow in heavily dammed watersheds. Our results also show that compared to ResIgnore, the fully coupled watershed models more accurately simulate the entire distribution of watershed outflow, obtain more realistic values of model parameters, and simulate reservoir inflow with higher accuracy. Finally, we note that the prediction intervals of watershed outflow obtained from the GDROM‐ and DZTR‐based fully coupled models consistently envelop observed watershed outflow across the study watersheds, indicating that GDROM and DZTR can be suitable reservoir components of large‐scale hydrology models.

Comparative Study of Low Flow Frequency Analysis Using Bivariate Copula Model at Soyanggang Dam and Chungju Dam

A univariate analysis that relies solely on precipitation data in low flow frequency analysis is a technique to express meteorological drought, so it is limited to analyzing the characteristics of hydrological drought related to available water resources. In addition, if the data for the model calibration are insufficient, the uncertainty of a single variable limits the construction of a reliable model. To improve this problem, a frequency analysis was performed by constructing a bivariate copula model as a multivariate model with a high correlation between variables targeting reservoir inflows. The methodology utilizes the theory of runs to identify low flow events, establishing a threshold based on the mandatory regional water supply plan, and determining the low flow duration and cumulative water deficit. The Gumbel copula function, effective in capturing correlations between hydrological variables, was applied to derive a joint bivariate probability distribution, facilitating the calculation of combined low flow event return periods. This study compared low flow frequencies at Soyanggang dam (’74–’22) and Chungju dam (’86–‘22), which are in the same Han River basin but have different capacities and water demands, using a bivariate copula model. The top four extreme low flow events for the two adjacent dam basins did not occur in the same year and, in the years of the extreme low flow events at one of the two dam basins, there was an insignificant magnitude at the remaining dam basin. This result is noteworthy because it shows that the possibility of extreme low flow events appearing simultaneously in both watersheds is not as high as expected. The operational efficiency can be improved by setting the coordinated operation rules of the two reservoirs using the copula dependency structure.

Analyzing inflow to Faye reservoir sensitivity to climate change using CMIP6 and random forest algorithm

ABSTRACT In the era of Climate Change and Climate Variability (CC and CV), renewable energy sources such as Hydropower (HP) have a significant role to play in mitigation. However, inflow to reservoir which is the key fuel for HP generation is vulnerable to CC and CV. Thus, there is a need to investigate the potential impacts of CC and CV on HP systems in the future. This study attempts to assess the potential impacts of CC and CV on the Faye reservoir inflow using the Random Forest (RF) algorithm. For this purpose, bias-adjusted precipitation and temperature data of thirteen climate model outputs and their ensemble mean from Coupled Model Inter-comparison Project Phase 6 (CMIP6) under three Shared Socioeconomic Pathways scenarios (SSP1-2.6; SSP2-4.5 and SSP5-8.5) were used as predictors. The potential changes in reservoir inflows were evaluated in the near (2025–2049), mid (2050–2074) and far (2075–2099) futures relative to the reference period (1990–2014). The results show the good performance of the RF algorithm in simulating reservoir inflows with Cor > 0.6 for all models. The annual inflows to the Faye reservoir are noted to increase in the future compared to the reference period despite the potential decrease in future precipitation probably due to land use/cover change. For the ensemble mean of models, this projected increase is estimated to around 16%, 23% and 10%, respectively under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios for all projection periods. The largest annual increase is noted under the SSP2-4.5 scenario while the lowest increase is noted under the SSP5-8.5 scenario for all projection periods. This study could help the small dam managers better consider the implications of CC and CV on inflow management.