Hydropower is a renewable energy source critical for balancing the electricity grid and integrating variable wind and solar energy. However, its clean energy credentials are increasingly scrutinised due to its potential greenhouse gas (GHG) emissions, particularly methane—a potent GHG. While some reservoirs act as carbon sinks, others are significant emission sources. Accurately quantifying and addressing these emissions is essential to ensure hydropower’s role as a low-carbon energy source and to mitigate the climate-finance risks associated with reservoir emissions.

This paper critically analyzes existing methodologies for estimating hydropower-related GHG emissions, including direct field measurements, empirical and machine learning (ML) models, and simplified emission factors. These methods vary in their strengths, limitations, and uncertainties, with emissions being highly site-specific and influenced by climatic conditions, reservoir characteristics, water quality, and operational practices.

The analysis highlights how ML and hybrid modeling approaches can improve accuracy, providing more dynamic and scalable predictions of GHG emissions. These advancements enable the identification of high-emission reservoirs and inform the development of targeted mitigation strategies.

By advancing the understanding of hydropower emissions, this research supports the sustainable integration of hydropower into the energy mix, ensuring it displaces fossil fuel generation while maintaining its low-carbon status. Additionally, it provides actionable insights for policymakers to design strategies that promote low-carbon hydropower development, aligning with broader climate objectives.

How to cite: Seth, M., Aparicio, M. U., Santos, C. D., Nakomcic-Smargdakis, B., Brboric, M., Calamita, E., and Dasic, T.: Greenhouse gas emissions from Hydropower: Challenges and Opportunities review, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6715, https://doi.org/10.5194/egusphere-egu25-6715, 2025.

The tidal energy resource in the San Francisco Bay (USA) is investigated using of high-resolution numerical modelling and spatial analysis. The system is analyzed for tidal energy potential under various conditions including low, mean, average and high freshwater inflows. The study approached the problem using high resolution numerical modeling that followed a robust moel validation effort and demonstrated the applicability of  numerical modelling  for identifying the most appropriate areas for tidal stream energy conversion. Future work will incorporate the effects of tidal energy converters on the circulation regime within the san Francisco Bay Estuary and the quantification of ecological impacts.

How to cite: Savant, G.: Tidal Energy Potential in the San Francisco Bay, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5166, https://doi.org/10.5194/egusphere-egu25-5166, 2025.

Large-scale hydro-wind-solar complementary systems (HWSCSs) present a promising approach for integrating variable wind and solar power through the flexibility of hydro units and the storage capacity of reservoirs. These hybrid renewable energy systems, driven by climatological variables, are highly sensitive to climate change and may encounter periods of significantly reduced energy production. Such periods, termed "energy droughts," occur when energy generation falls below load demand or a prespecified threshold, posing critical challenges to system reliability and energy security. However, how energy droughts in HWSCSs will evolve under climate change and how to mitigate such events through strategic operations remain unexplored. Thus, this study proposes a generic framework for evaluating and mitigating energy droughts in HWSCSs under climate change. First, specific metrics for assessing energy droughts in HWSCSs are developed. Next, an adaptive operating rule for mitigating energy droughts is proposed and validated in both historical and projected future climate scenarios. A large-scale HWSCS in southwest China is selected as a case study. The results show robust improvements in reducing the frequency, duration, and severity of energy droughts across various climate scenarios. This study provides valuable insights for the sustainable management of HWSCSs in the face of climate change.

How to cite: Cheng, Q. and Liu, P.: Adaptive operating rules for mitigating energy droughts in large-scale hydro-wind-solar complementary systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10266, https://doi.org/10.5194/egusphere-egu25-10266, 2025.

With the increasing promotion of renewable energy sources, hydropower is expected to play a crucial role in energy storage and grid balancing, supplementing intermittent solar and wind power. However, the complex topography and variability of design parameters often lead to underutilization of small hydropower potential or sub-optimal designs. This study presents a new simplified algorithm for GIS-based positioning optimization and preliminary planning of small hydropower plants.

The practical problem addressed with this algorithm is that during the preliminary design phase of a small run-of-river hydropower plant, the designers are required to preselect, along a given river, the water intake point, the location of the power generation station, and the tailrace discharge point back to the river, as well as the conduit route between the intake and the station. The latter is quite complex, as it may consist of a section of open channel and the remaining section of closed conduit (penstock). The open channel is significantly cheaper than the penstock, but it needs to practically follow the contour line of the intake on suitable ground. The penstock does not have problems with steep slopes, but it is generally much more expensive per unit length than the open channel, especially if it is made of steel. At the same time, during the routing process, areas where pipelines are not allowed to pass must be excluded, e.g., natural reserve areas, or residential areas, or areas with intense geological phenomena. Simultaneously, the expected electricity production and the cost for each candidate design should be considered, in order to examine the technical and economic viability of the project.

Developed in Python within the QGIS environment, which is an open but well-established geographical information system software package, the algorithm operates in raster format and uses as input the digital terrain model, the flow direction and flow accumulation grids, the examined river reaches in raster format, characteristic discharge rate for each cell of the examined river, the open channel and the penstock cost per unit length in raster format. Areas where pipelines are not allowed to pass are designated with a very high unit length cost.

After reading the input data and initializing the required output raster data objects, the algorithm creates the lefthand and righthand contour lines for each upstream cell. Then it iterates between all the possible downstream cells for this upstream cell. For each upstream-downstream positions couple it iterates through all the possible combinations of open channel and penstock and creates a list with the combinations having the lower cost. For each optimal solution it stores all the characteristics in a list. After finalizing all the searches the algorithm sorts the list with the optimal solutions considering the produced energy/cost ratio.

While such algorithms typically exhibit O(n³) complexity, meaning that as the size of the area increases the computation time will become prohibiting, a key characteristic of the proposed algorithm is that includes novel search functions minimising the searched cells and required repetitions making the execution time reasonable in larger areas.

How to cite: Soulis, X., Soulis, K., Vasiliki-Eleftheria, S., and Tsekouras, G.: Development of a new simplified algorithm facilitating GIS-based preliminary planning of small hydropower plants, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11376, https://doi.org/10.5194/egusphere-egu25-11376, 2025.

Hydropower is a crucial renewable source reliant on water availability, making it vulnerable to climate change and hydroclimatic extremes such as droughts. Studying the connection between climate, streamflow, and hydropower generation is especially critical for hydro-dependent energy systems. However, analysing drought and climate change impacts on hydropower generation requires detailed data on both hydropower plant attributes (e.g. plant type and head) and reservoir characteristics (e.g. area, depth, and volume). Existing open-source datasets lack integration: hydropower plant datasets often lack reservoir information, while reservoir datasets frequently omit hydropower plant information.

To addresses this, we developed GloHydroRes, a new global dataset that combines existing open-source hydropower plant and reservoir datasets. GloHydroRes includes plant attributes (e.g., location, head, type) and reservoir details (e.g., dam and reservoir location, height, reservoir depth, area, volume) for 7,775 plants across 128 countries, covering 79% and 81% of the global installed capacity reported by the EIA (2022) and IRENA (2023), respectively.

Leveraging GloHydroRes, we developed a hybrid hydropower modelling framework that integrates physical model simulations with machine learning techniques to predict hydropower generation at plant level. Our validation results show that, the hybrid model outperforms the physical hydropower model. For instance, hybrid model results in 40% reduction in root mean squared error on average compared to the physical model across all plants.  

Our results reveal a significant reduction in hydropower generation during drought periods in regions worldwide, highlighting the vulnerability of hydropower systems to hydroclimatic extremes. By integrating detailed plant and reservoir data from GloHydroRes with physically-based and advanced machine learning methods, we enhance the accuracy of hydropower simulations while providing a valuable tool to support hydropower and water management and decision making within the water-energy nexus.

How to cite: Shah, J., Hu, J., Edelenbosch, O., and van Vliet, M.: Impact of Droughts on Hydropower Generation using a new Global Hydropower Plant and Reservoir dataset (GloHydroRes) and Hybrid Modelling , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12135, https://doi.org/10.5194/egusphere-egu25-12135, 2025.

Hydropower plants are the primary source of renewable energy globally, but their performance and reliability naturally degrade over time due to aging equipment, changing environmental conditions, and shifts in operational demands. By 2030, nearly 20% of global hydropower turbines, totaling about 154 GW, will be over 55 years old, while in the United States, the average turbine age will exceed 60 years, leading to significant efficiency challenges.

Retrofitting aging hydropower plants is crucial for ensuring optimal performance and offers opportunities to improve the adaptability of these plants to changing conditions. Traditionally, retrofitting has relied on identical turbine replacements, with newer models replicating original designs optimized for the conditions at the time of construction. While these upgrades offer marginal efficiency gains, they fail to address evolving challenges, as original designs may become suboptimal in the face of a changing climate and evolving grid demands. Given that these turbines are expected to operate for 40 to 50 years in an increasingly uncertain future, adopting turbine designs optimized for future conditions presents a more effective solution. To guide this transition, a well-defined methodology is needed to determine when and how to upgrade turbines, ensuring optimal and sustainable outcomes.

This study addresses this need for large-scale hydropower upgrades by using a newly developed toolbox to determine optimal turbine replacement strategies under uncertain inflow, demand, and energy price scenarios. It combines multi-objective optimization and advanced simulations for detailed comparisons between existing and optimized turbine configurations. The optimization focuses on maximizing capacity during peak demand, enhancing energy generation across fluctuating reservoir levels, addressing risks and costs associated with aging turbines, and ensuring efficient operation under low-flow conditions to support environmental releases. The toolbox is applied to the Hoover Hydropower Plant (HPP) in the Colorado River Basin, which operates 17 Francis turbines installed in 1936. These turbines were initially replaced with identical models around 50 years later (1986–1993). However, by 2012–2015, declining reservoir levels made the original turbines inefficient, leading to the replacement of five turbines with lower-head models, a rare example of non-identical replacement to adapt to changing conditions.

Preliminary results from a retrospective analysis of Hoover HPP highlight the benefits of optimizing replacement strategies. Optimized configurations across multiple objectives generally recommend (1) earlier turbine replacements to reduce efficiency losses (2) lower-head turbines to accommodate fluctuating reservoir levels especially due to droughts, (3) require a lower number of turbine replacement overall while increasing annual energy generation.   

How to cite: Yildiz, V. and Zaniolo, M.: Retrofitting Reservoir-Based Hydropower Plants: Turbine Upgrades for Enhanced Efficiency and Resilience, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13500, https://doi.org/10.5194/egusphere-egu25-13500, 2025.

The society is adapted to the current seasonality and variations in water balance and water availability. In Norway more than 90% of the electricity production is based on hydropower, and to meet the energy demand, reservoirs are used to store water across seasons as runoff is generally lowest in winter when the energy demand is the highest. The aim for electricity production and operation of hydropower reservoirs is to maximize income for hydropower companies. The day-to-day decision of power production is based on energy demand, electricity prices and water availability. The main constraints for reservoir operations are minimum and maximum water levels as well as minimum flow requirements downstream. To make the best possible decisions for the future, hydrological models are used to provide expected runoff that is used by an energy marked model to suggest reservoir operations. A changing climate might result in changes in both annual runoff and seasonality of runoff, that might lead to changes in energy production and reservoir management.

Here, as part of the HorizonEurope project STARS4Water, we aim to assess how climate changes might impact reservoir operations and water stress in the Drammen River basin. This will be achieved by using two gridded hydrologic models (HBV and LISFLOOD) to simulate runoff for a reference period and a future period under downscaled climate scenarios. . Thereafter the energy marked model EOPS will be used to simulate reservoir operations for the two climate periods assuming that the electricity prices are unchanged. EOPS is used for sub-areas or river basins, has a detailed representation of the hydropower system, and requires reservoir inflows and energy prices as inputs. When prioritizing between the different constraints, the strongest ones are the minimum and maximum water levels in the reservoirs. During droughts, EOPS might deliver less water than required for environmental flows to avoid violating other requirements or limitations.

To assess climate change impacts, the changes in reservoir inflow, water levels and periods with water stress (i.e. the minimum flow requirements are not met) and full reservoirs that might increase flood risk, will be compared.  

How to cite: Engeland, K., Gelati, E., Hegdahl, T. J., Huang, S., and Veie, C. A.: Climate change impacts on reservoir operations and water availability – a case study from Drammen river basin in Norway , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15110, https://doi.org/10.5194/egusphere-egu25-15110, 2025.

Hydrological alterations caused by hydropower dams significantly impact river ecosystems. In Nordic rivers where regulation of rivers for hydropower dominates flow alterations, the hydropower operations often introduce more frequent flow fluctuations directly linked to energy demand. In cascaded run-of-river hydropower plants (ROR-HPPs), upstream regulation directly affects downstream flow characteristics, leading to complex interactions between upstream and downstream regulation dynamics. Although free-flowing tributaries downstream of the hydropower plants dampen the flow pulsations due to regulation, cascading ROR-HPPs amplify the hydrological alterations in the regulated river. This study investigates the temporal dynamics of hydrological alterations in cascaded ROR-HPPs, analysing the interdependencies between upstream flow regulation and downstream flow patterns. Through hydrological modelling and flow routing, the study examines the downstream propagation of regulated flows from one hydropower plant to another in a cascade, and the changes in hydrological alterations introduced by the successive ROR-HPPs. By analysing the temporal dynamics of flow regulation between hydropower dams in a cascade, the study highlights the need for integrated hydropower management strategies that account for cascading effects and balancing energy production with ecological sustainability.

How to cite: Nakigudde, C. K., Patro, E. R., and Haghighi, A. T.: Temporal dynamics of short-term regulation in run-of-river hydropower cascades, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15541, https://doi.org/10.5194/egusphere-egu25-15541, 2025.

The flexibility of conventional hydropower stations and pumped storage power stations is regarded as a promising approach to integrating more intermittent photovoltaic (PV) power into the grid. However, directly implementing medium- and long-term operations of hydro-PV-pumped storage integrated energy bases (HPPEBs) is challenging due to the daily regulation capability of pumped storage power stations, an aspect that has been infrequently studied. To tackle this issue, a short-term operation model is developed to quantify power loss, including energy loss caused by the efficiency of pumped storage units and power curtailment due to load demand and channel capacity. Then, an accurate method for calculating power generation in HPPEBs during mid- and long-term operations is proposed, considering the short-term power loss patterns. A HPPEB located in the Lancang River Basin is selected as a case study. The results indicate that: (1) power generation is overestimated in the direct medium- and long-term operation, leading to higher water levels in the cascade reservoirs; (2) both energy loss of pumped storage and power curtailment exhibit a significant linear correlation with hydropower output, with coefficients of determination above 0.85 for each PV output range; (3) the proposed method can accurately calculate medium- and long-term power generation, with errors in total and daily power generation amounts of 0.06% and 1.22%, respectively, during the validation period. From the hydropower perspective, this study quantifies the short-term power loss patterns, providing a practical tool for the accurate mid- and long-term operation of HPPEBs.

How to cite: Liu, Z. and Liu, P.: Quantification and extraction of power loss patterns in hydro-photovoltaic-pumped storage integrated energy bases, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14368, https://doi.org/10.5194/egusphere-egu25-14368, 2025.

As climate change exacerbates the frequency of extreme weather events, urban water distribution networks face increased challenges. This paper investigates the water-energy nexus and the potential for energy harvesting in European water systems to address these challenges. The study focuses on vortex-induced vibrations (VIV) technology to recover energy from water flow, powering monitoring sensors and early warning systems. We analyzed data from case studies in several European cities, including Barcelona, Verona, Izmir, Ferlach, Ivancice, Rangárvellir, and Turin, to identify velocity profiles and energy recovery potential. A comprehensive database was created – including velocity, pressure, and temperature data from these networks – and used to model optimal energy harvesting conditions. Capacity factors, power outputs, and intermittency indicators were calculated to assess energy harvester feasibility. The results reveal that energy recovery potential varies significantly between different network types. For instance, drinking water networks in cities like Barcelona and Verona exhibit daily fluctuations – lower velocities at night – while district heating systems like those in Rangárvellir are more stable. The most promising case studies, such as Izmir and Ferlach, demonstrate higher energy outputs, with estimated productions ranging from 45 kWh to 6550 kWh over 20 years. Energy harvesting in water networks provides a sustainable solution to power remote sensors and early warning systems, improving resilience to climate-related events. We conclude that energy recovery in water networks could generate significant energy, offering a practical approach to enhance climate adaptation and resource management. 

How to cite: Bronkema, B., Gudlaugsson, B., Bermejo, D., Escaler, X., and Finger, D. C.: Addressing the Water-Energy Nexus: Renewable Energy Harvesting for Enhanced Monitoring and Sustainability in Water Networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16202, https://doi.org/10.5194/egusphere-egu25-16202, 2025.

In recent years, hydropower has rapidly developed as an efficient and clean peak-shaving energy source to accommodate the large-scale integration of wind and solar power. However, the operations of upstream hydropower plants significantly alter inflow processes for downstream plants. For daily regulation hydropower plants, the limited regulation capacity amplifies the impact of inflow variability on power generation efficiency. Thus, adjustments to the operational scheduling of such plants are urgently required. This study proposes a research framework to quantify the influence of upstream hydropower plants on downstream daily regulation plants and to establish operational scheduling rules in response. Firstly, a flow routing model is developed to simulate both dynamic and diffusion waves in river flow propagation. Secondly, a two-stage short-term peak-shaving scheduling model is constructed by integrating the flow routing model with the daily peak-shaving operations of hydropower plants. A dynamic control strategy for the initial and final water levels is innovatively incorporated into the scheduling model. Finally, the Alpha shapes algorithm is used to derive operational scheduling rules for daily regulation hydropower plants. Taking the upstream cascade hydropower stations of the Han River as an example, the study concludes that newly constructed hydropower plants shorten the flow routing time between existing cascade plants. Coordinating peaking times reduces water level fluctuations and boosts downstream plants’ power generation. When the full generation discharge of upstream plants exceeds that of downstream plants, the multi-year average power generation of downstream plants decreases. Additionally, specific scheduling rules are established for downstream daily regulation hydropower plants to mitigate the impacts of upstream operations. These results provide scientific decision support for operators of downstream hydropower plants affected by upstream reservoir construction and can be extended to similar hydropower systems worldwide.

How to cite: Wang, Y., Zhang, J., Guo, A., and Niu, C.: Quantitative analysis and operation strategies for daily-regulation hydropower plants impacted by upstream plant, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10191, https://doi.org/10.5194/egusphere-egu25-10191, 2025.