Cascade reservoirs on the Drina River (Bosnia and Herzegovina) are heavily modified water bodies that require reliable biological tools for assessing trophic status and ecological potential. Under the Water Framework Directive (WFD), assessments of surface water ecological status and potential rely on biological quality elements, since aquatic communities integrate and respond to prevailing environmental conditions and thus serve as reliable indicators of water quality. This study aims to (i) describe phytoplankton diversity, biomass, and functional-group composition along the Drina reservoir cascade, (ii) examine monthly changes across the studied reservoirs, (iii) determine trophic status and ecological potential, and (iv) provide a preliminary estimate of total phosphorus thresholds that may support future setting of ecological potential boundaries. Phytoplankton composition and functional groups were analysed in three longitudinally connected reservoirs of the Drina River during four monthly surveys in 2024. A total of 80 phytoplankton taxa were recorded, with diatoms dominating most of the study period. The highest biomasses were recorded for Fragilaria crotonensis, Dinobryon divergens, Acanthoceras zachariasii and Sphaerocystis sp., while the dominant functional groups were P, E, A, and F. Phytoplankton assemblage structure showed moderate spatial differentiation among the reservoirs. Mean chlorophyll a and Carlson’s Trophic State Index indicated eutrophic conditions in the Višegrad Reservoir and mesotrophic conditions in the Perućac and Zvornik reservoirs, while biomass showed a pronounced summer maximum, particularly in Perućac. Ecological potential was generally classified as good or better, except for a moderate classification in the Zvornik Reservoir in late summer. The good/moderate TP boundary was estimated at 39 µg L−1, linking EQR-based ecological assessment with the onset of eutrophic conditions. Overall, this study represents the first application of the phytoplankton functional group approach in cascade reservoirs in Bosnia and Herzegovina and may provide a valuable basis for the development of a phytoplankton-based monitoring framework in lakes and reservoirs, which is currently lacking.

State-based monthly operational rules for hydropower reservoirs to balance benefits, reliability, and vulnerability in hourly power supply

Large hydropower reservoirs in Russia can act as net anthropogenic sinks of carbon-based greenhouse gases.

Land Use/Land Cover (LULC) change is a fundamental driver of hydrological process changes, yet its immediate impact on soil moisture dynamics in tropical highland basins remains uncertain. Building on previous research that documented striking LULC changes in the Gojeb River sub-basin between 2000 and 2024, this study investigated the consequent impacts on the spatiotemporal dynamics of surface soil moisture. Using a 2000, 2011, and 2024 time series of Landsat imagery, the Normalized Difference Moisture Index (NDMI) was applied as a suitable proxy for soil surface water and vegetation water content. The study revealed an extreme anthropogenic modification of the soil moisture regimes of the basin with a decline in its peak NDMI values by 35.5% and significant contraction of its dynamic hydrological range. These trends indicate a drastic reduction in the water retention capacity of the landscape and an eroding ecological resilience. The strong correlation between Normalized Difference Moisture Index (NDVI) and NDMI indicated that 83% to 89% of the variance in vegetation water content is explained by the variance in vegetation greenness and health, justifying NDVI as a reliable indicator of NDMI in the study area. Spatially, the most significant losses in moisture were closely associated with areas where natural vegetation and wetlands were converted into agricultural fields and settlements. The degradation of these natural ecosystems, which are key hydrological regulators, has reduced the water retention capacity of the soil in the basin. This depletion of soil moisture has serious consequences on agricultural productivity, groundwater recharge, and base flow sustainability, besides increasing surface runoff and erosion. These ultimately threaten the long-term function of the downstream Gibe III hydropower reservoir through increased sedimentation. The findings urge prompt soil moisture-sensitive land use planning and conservation strategies to contribute towards water security and improved sustainable watershed management in the region.

The integration of floating solar photovoltaic (FPV) systems on hydropower reservoirs presents a promising dual-use solution for enhancing renewable energy capacity in Malaysia while optimizing existing water resources. This study conducts a comprehensive techno-economic analysis of deploying grid-connected FPV systems on major Malaysian hydropower reservoirs such as Kenyir, Bakun, and Temengor. Technically, the FPV systems offer advantages including higher energy yield due to natural water-based cooling effects, reduced land usage conflicts, and synergistic operation with hydropower for improved grid flexibility. The proposed hybrid configuration enables peak shaving, seasonal balancing, and more stable voltage-frequency regulation through coordinated dispatch strategies. A combination of HOMER Pro and PVSyst simulations is used to assess energy output, capacity factors, and grid interconnection constraints under Malaysia's irradiance and humidity conditions. Economically, the Levelized Cost of Electricity (LCOE) of FPV systems is evaluated and benchmarked against ground-mounted PV and conventional hydropower. Sensitivity analyses consider key factors such as module degradation, anchoring costs, BESS integration, and policy incentives (e.g., Net Energy Metering 3.0). Results indicate that a 50 MW FPV system co-located on a hydropower reservoir can achieve an LCOE as low as RM0.22/kWh, with payback periods under 8 years, assuming optimal deployment and access to green financing. Additionally, environmental co-benefits include reduced water evaporation, lower algal growth, and enhanced aquatic biodiversity. This study concludes that FPV-hydro hybrid systems represent a viable and sustainable pathway for accelerating Malaysia's low-carbon energy transition and meeting its RE capacity targets under the National Energy Transition Roadmap (NETR).

Optimization of interpretable hydropower reservoir operation rules by denoising diffusion probabilistic model, parallel chaotic cooperation search algorithm and liquid neural network

Accurate reservoir inflow forecasting is critical for real-time water management in monsoon-dominated basins. This study develops a weighted Voting Ensemble model to predict daily inflow to the A Luoi hydropower reservoir in central Vietnam using multi-station rainfall and lagged inflow data. Five machine learning models MLP, RF, KNN, XGB, and Ridge Regression, were trained on a unified feature set containing current and lagged rainfall inputs and three runoff memory terms, and subsequently combined using performance-based weights derived from time-series cross-validation errors. Evaluation using MSE, RMSE, and NSE shows that the ensemble outperforms all standalone learners, reducing RMSE by 12–25% and improving NSE from 0.70–0.91 (best individual models) to 0.92 on the test set. SHAP analysis is also used to explain model predictions and highlight the most influential features. During an independent verification period, the ensemble maintained strong performance (NSE ≈ 0.98), accurately capturing rising and recession limbs and minimizing peak-flow underestimation. These results demonstrate the robustness and operational feasibility of weighted ensemble learning for short-term inflow forecasting, offering valuable support for reservoir operation, flood mitigation, and water allocation in data-rich reservoir systems.

A scenario-based multi-agent reinforcement learning approach for efficient solving to long-term optimization of cascade hydropower reservoirs

This study investigates the role of water level fluctuations (WLF) in shaping phytoplankton community structure and gross primary productivity (GPP) in Hongfeng Reservoir, a karst reservoir in Southwest China, through long-term monitoring (2016–2023) across seven sampling sites. Results revealed substantial seasonal and interannual variations in water levels (maximum fluctuation range: 9.78 m) and nutrient concentrations, with total nitrogen (TN) and phosphorus (TP) peaking during wet seasons. Chlorophyta dominated species diversity (42–48%), while Cyanophyta exhibited the highest relative abundance. GPP fluctuated markedly between the dry (586.09 mg C/m²/d) and wet seasons (3018.66 mg C/m²/d), driven by phytoplankton abundance and community stability. The Bray-Curtis (BC) dissimilarity index declined over time, showing a negative correlation with WLF (r = -0.03) and a significant negative impact on GPP. WLF indirectly enhanced GPP by altering community dynamics. Findings highlight WLF as a critical regulator of phytoplankton structure and productivity, emphasizing the importance of hydrological management in mitigating algal blooms and balancing ecological stability. This study provides actionable insights for optimizing water level regulation to sustain reservoir ecosystem health in karst regions.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-25966-6.

Abstract. Understanding monthly runoff variability, its spatio–temporal characteristics, and key drivers under climate change and human impacts is crucial for long-term water resource management. However, current knowledge remains limited, especially in high-elevation, seasonally cold regions. Focusing on 10 sub-basins along an elevation gradient (1000 to 5900 ma.s.l.) in the hydrologically complex Yalong River basin, China, this study developed an extended Budyko framework based on monthly water balances (2002–2016), explicitly separating snow storage dynamics (ΔSsnow) from other terrestrial water storage changes (ΔS′), including those related to hydropower reservoir construction. Results showed that snow accumulation and snowmelt are main drivers of runoff seasonality in the upper sub-catchments, and their effects propagate to the lower-elevation snow-free sub-catchments, which are also subject to additional influence from hydropower reservoirs. This created pronounced altitudinal heterogeneity in drivers of monthly runoff, a phenomenon suggested but rarely quantified at high spatio–temporal resolution in other global regions. Furthermore, a decrease in runoff seasonality in the Yalong River at its Yangtze River outlet (that receives water from all 10 investigated sub-basins) was observed, this change appeared unrelated to snow storage changes and was more likely driven by trends in unfrozen precipitation seasonality and/or flow-modulating impacts of reservoirs, natural lakes and groundwater. Future snow thinning may exacerbate these trends. Implementing the variance decomposition method within the extended Budyko framework, the intra-annual runoff variability (σR2) was captured by calculating the variance and covariance of influencing factors, achieving R2 values above 0.9 in most sub-basins, and the rainfall (Pr) and ΔS′variances were identified as the main contributors. Methodologically, we have verified the substantial contribution of hydropower reservoir storage changes on total storage changes by independent analysis of reservoir storage data. These findings supported the applicability of the extended monthly Budyko framework for identifying dominant processes in the context of runoff generation and the rapid environmental changes that the Yalong River basin and other cold regions (not least of the Tibetan plateau) are currently experiencing.

Abstract In hydropower (HP) reservoirs, the large and rapid variations in water level can destabilize the ice cover, posing hazards to people and wildlife crossing the surface. Monitoring ice conditions to control safety in such systems is often challenging, and the role of water level regulation strategies on ice stability is not yet fully understood, especially in reservoirs with complex bathymetry. In this study, we inspected the state of the ice cover of two Norwegian HP reservoirs with complex bathymetry over nine winters (2014–2023) using multi‐sensor remote sensing data (Synthetic Aperture Radar and optical). Water level and meteorological data were analyzed to identify the primary driver and mechanism of cracking. Simple mechanical and thermal expansion models were used to interpret the results of data analysis and to isolate the effects of changes in water level from those of temperature fluctuations. Large cracks in the ice cover were consistently detected during early winter, propagating from sharp bathymetric features, and were still observable at the end of the ice season. Our results suggest that the primary cause of cracking in our study sites is the stress concentration over bathymetric obstacles during water level descent. This work highlights the importance of investigating the role of focused modulation of the HP operations to ensure ice cover integrity during the critical period for crack formation. Future studies should extend these findings to other systems with complex bathymetry to provide a solid background for informed management of HP reservoirs.

Abstract This study focuses on the net radiative forcing caused by the impoundment of an 85‐km 2 hydroelectric reservoir in the subarctic Côte‐Nord, Quebec, Canada (50.69°N, 63.24°W, mean depth of 44 m). Using spectral bands from the Landsat 7 ETM+ and Landsat 8 OLI/TIRS satellites, we observed spatial and temporal variations in albedo and surface temperature on 104 dates between 2000 and 2023. By integrating these data with ERA5‐Land meteorological reanalyses and in situ measurements, we investigated seasonal and interannual variations in shortwave and longwave radiative fluxes and net radiation before (2000–2013) and after (2015–2023) reservoir impoundment. The flooded area initially consisted of 79% coniferous forest (mainly black spruce), 15% water, 4% dry soil, and 2% wet soil. Before the impoundment, the average winter albedo was 0.25 ± 0.02 and the summer albedo was 0.08 ± 0.002. After the impoundment, the winter albedo increased significantly, reaching between 0.70 and 0.90 in the presence of fresh snow on the ice (January to May), while in summer it decreased to about 0.05 ± 0.003. Surface temperatures exhibited pronounced pre‐ to postimpoundment contrasts, with the reservoir consistently 2–7°C warmer in autumn and winter but 1–5°C cooler in spring and summer. The reservoir also reaches its maximum temperature 3–5 weeks later. The impoundment induced a net radiative cooling of −21 W m −2 compared to forest, featuring a pronounced winter deficit (−60 to −140 W m −2 ) due to snow cover albedo effects, partially compensated by summer surpluses (+5 to +50 W m −2 ).

A handful of Sphagnum species and their ecosystems find their southernmost occurrence in the Pyrenees, and these small, relict units are endangered through anthropic activities and climatic change. A number of hydropower reservoirs covered former mire systems with water or let them ashore. These infrastructures will eventually become useless and abandoned, and the mires could possibly be restored, but there have been no known experiments in the Pyrenees in this field. The removal of the dam of a small reservoir in the Central Pyrenees in 2012 uncovered bare ground that was appropriate for testing mire restoration. In 2017, we started the restoration of two Habitats of Community Interest (HCIs), i.e., transition mires and quaking bogs (HCI 7140) and active raised bogs (HCI 7110*). To restore HCI 7140, we set a Carex rostrata population by planting cuttings and then small tufts of two Sphagnum species within the sedge sward. In parallel, we set small clumps of two other Sphagnum species intended to grow into hummocks (HCI 7110*). After seven growing seasons, HCI 7140 reached a good progression level, with a prosperous C. rostrata sward and progressive expansion of the Sphagnum populations. HCI 7110* turfs had varying performance, exhibiting moderate survivorship and positive expansion of the remaining turfs. The varying performance of the restored populations illustrates the possibilities of restoring mire communities in suboptimal environments. Interestingly, such restorative actions are appropriate for enhancing populations of species under threat, such as Sphagnum divinum.

This paper introduces an Integrated Neuro-Fuzzy Expert System for controlling hydro-power reservoir systems, combining neural networks and fuzzy logic to improve decision-making in water level and flow rate management.Python libraries were utilized for data preprocessing, training, and prediction.A triangular membership function established 15 rule-base for input and output variables, with the dataset split into 70% for training and 30% for testing, ensuring reproducibility.A convolutional neural network was built using TensorFlow's Keras API, featuring 64 neurons and ReLU activation in the input and hidden layers, and a double neuron output layer.The model was trained for 150 epochs with an Adam optimizer, a batch size of 50, and a 30% validation split.The training set achieved a Mean Absolute Error (MAE) of 35.3623 and Mean Squared Error (MSE) of 2935.7104, while the test set had MAE and MSE values of 35.0812 and 1458.8833,respectively, with an RMSE of 38.2.Lower test loss and MSE compared to training MSE suggest the model generalizes reasonably well, which is crucial for reliable water reservoir control decisions.To prevent overflow, specific water levels and rates of change were set, predicting reservoir ranges of 0.07%, 0.10%, and 0.17% for the respective conditions.

Techno-economic and environmental assessment of hydrogen production utilizing captured methane from hydropower reservoirs

Interpretable hybrid artificial intelligence model for predicting daily hydropower generation of cascade hydropower reservoirs

Ecological operation of reservoirs to control the nutrient transport has been a new hotspot problem in the recent reservoir operation research. Previous studies have focused more on optimization and mechanisms, neglecting the practicality of real-time operation rules, which makes it difficult for reservoir managers to conduct real-time ecological operation of reservoirs for nutrient transport, and such real-time operation is more difficult due to the hydraulic connections between upstream and downstream reservoirs in cascade reservoirs. Therefore, this paper, taking Lancang River cascade reservoirs as an example, combining total phosphorus (TP) mass balance with the multiobjective operation model, which is based on operation charts and optimized by NSGA-II, proposes a new operation chart, i.e., the TP export operation chart (TOC), by adding three types of parameters to the conventional operation chart (COC), to guide real-time TP export ecological operation of reservoirs, thereby reducing TP retention in reservoirs and making the real-time ecological operation for cascade reservoirs easier. The multiobjective optimization results allow decision-makers to better balance power generation and TP export by selecting appropriate solutions on the Pareto front. First, TOC-specific parameters are derived from the comparison between Pareto front results taking COC and operation processes as decision variables. Then, TOC is taken as decision variables to optimize the reservoir operation, and the rationality of the optimization results is analyzed. Finally, typical solutions are selected from the TOC Pareto solution set to simulate the long-term operation and examine the practical application effect of TOC. The results indicate that TOC can greatly compensate for the shortcomings of the COC and significantly improve the TP export amount. When applying the optimal solutions for TP export, TOC can reduce the TP retention by 5% on average compared to COC, because TOC can guide the unique characteristics of operation processes that are beneficial for TP export through the extra discharge operations defined by new parameters, which COCs cannot achieve. The simulated operation results and optimized operation results of TOC are in good agreement, which means the TOC optimization results have good applicability. This paper made up for the lack of research of real-time operation rules of TP export operation of reservoirs and has important guiding significance for the formulation of rules for reservoir management.

Abstract This study examines the influence of the El Niño–Southern Oscillation on the statistical distribution of daily inflows to the 15 most important hydropower reservoirs across Colombia over the period 2000–2024. Twenty-five years of daily streamflow data were analyzed to determine whether El Niño–Southern Oscillation phases (La Niña, Normal, El Niño) produce statistically distinct hydrological signatures. Using the Oceanic Niño Index to classify each month into one of the three El Niño–Southern Oscillation phases, daily inflow records were grouped accordingly and applied the Anderson–Darling test to evaluate statistical differences among distributions. The analysis was performed independently for each river and calendar month, resulting in 540 pairwise comparisons. Six of eight possible outcome patterns were observed, with the most frequent pattern–rejection of the null hypothesis in all three comparisons–occurring in nearly 70% of the 180 river-month cases, indicating strong phase-dependent hydrological differentiation. The results exhibit clear spatial and temporal variability in El Niño–Southern Oscillation influence, with strongest signals during December–March and considerable complementarity among major reservoirs. By comparing monthly and daily streamflow statistics, the analysis demonstrates that daily resolution provides richer and more operationally relevant information, especially for reservoir management. These findings highlight the value of incorporating El Niño–Southern Oscillation-phase-resolved diagnostics into water resources planning and energy system operations in tropical regions influenced by climate variability.

This study investigates the spatial and seasonal variations in CO2 and CH4 emissions from the Kenyir hydropower reservoir and its downstream Terengganu River system in Malaysia. Understanding these variations is crucial for assessing whether the greenhouse gas (GHG) budget for this aquatic continuum significantly contributes to global emissions. Malaysia’s distinct monsoonal climate presents a unique opportunity to explore the influence of seasonal hydrological changes on GHG emission dynamics in inland waters. Five sampling campaigns were performed at the reservoir to investigate this, involving three longitudinal surveys from the reservoir downstream along the Terengganu River, and two time-series samplings at the estuary between November 2017 and August 2019. Our findings reveal that GHG emissions from the Kenyir Reservoir are notably higher during the wet season (97 mmol CO2 m−2 d−1 and 2 mmol CH4 m−2 d−1) than during the dry season (54 mmol CO2 m−2 d−1 and 0.8 mmol CH4 m−2 d−1). This increase coincides with increased wind speed and potential surface mixing during the wet season. Despite operating since 1985, the Kenyir Reservoir’s total GHG emissions remain high compared to other global reservoirs, likely due to its tropical location and high organic carbon content. Elevated GHG emissions were recorded along the Terengganu River, near the dam discharge outlets, with gradual reductions observed downstream. Despite the estuary’s smaller surface area, more GHGs are emitted there than in the river. Overall, the Terengganu River catchment emits approximately 572 Gg CO2-equivalent annually, with the Kenyir Reservoir accounting for the majority (94%). The river and the estuary contribute 0.5% and 5.5%, respectively. This study highlights the substantial role of tropical hydropower reservoirs and their downstream river networks in the global GHG budget, emphasizing the need for further investigation into the factors influencing GHG dynamics in tropical river systems.

Continuous and frequent monitoring of surface water variations in hydropower reservoirs is essential for effective energy production, flood mitigation, and water management across agricultural, industrial, and domestic sectors. This study focuses on monitoring water volume variations in the Nam Ngum hydropower reservoir, the largest water body in the Lao People's Democratic Republic (Lao PDR), from August 2022 to August 2024. The reservoir’s surface water extent was estimated using imagery acquired from optical sensors onboard Sentinel-2, Landsat-8 and Landsat-9 satellites, as well as radar sensors onboard Sentinel-1 satellite for cloud-covered conditions. The reservoir’s water level was derived from satellite altimetry data provided by the HydroWEB and G-REALM databases. In situ measurements of water level and volume, provided free of charge by the reservoir operating company, were used for validation. Results indicated that water levels derived from satellite altimetry data ranged from 200÷212 m, exhibiting an extremely high correlation with in situ measurements (R = 99.94% and RMSE = 0.1418 m). The reservoir’s water extent varied between 350 and 485 km2, with a strong correlation to water level records (R = 98.48%). Finally, the estimated water volume variations of the reservoir closely followed the in situ observations (R = 99.63%). These findings demonstrate the effectiveness of satellite data in monitoring variations in water levels at hydropower dams. Such information is crucial for water resource management, particularly in downstream regions during storm seasons. However, this study has some key limitations: (1) the reliance on optical imagery restricts its applicability during cloudy periods, which are common in tropical regions; and (2) the HydroWEB and G-REALM only provide data for a limited number of lakes with surface areas larger than 100 km2.