Hydropower Reservoirs Research Articles

Hydro-morphological alteration and related effects on fish habitat induced by sediment management in a regulated Alpine river

Sediment pulses can cause relevant geomorphological alteration and fine sediment deposition, affecting the entire river ecosystem. Quantifying the impact of these events is becoming increasingly important, as sediment disturbance is predicted to intensify because of growing anthropogenic activity combined to the effects of current climate change. In this paper, the effects of a sediment pulse event on riverbed morphology and, in turn, on fish habitat availability are assessed in a regulated river, located in the southern European Alps. Specifically, four target fish species were selected: marble (Salmo marmoratus) and brown trout (Salmo trutta), grayling (Thymallus thymallus), and bullhead (Cottus gobio). The investigated event was triggered by the effects of localized landslides combined with an uncontrolled sediment release from a hydropower reservoir. A pre-post event integrated approach was applied, involving topographic surveys, fine sediment deposition measurements, and eco-hydraulic modeling. Habitat availability analysis was done using two-dimensional steady-state channel-flow simulation and standard habitat suitability modeling, with univariate suitability curves as biological input. The investigation focused on two river reaches, quite close each other, but characterized by significantly different streamflow patterns, due to the layout of the local hydropower system. The results showed that noticeable riverbed fining occurred at both study reaches after the pulse, with the maximum thickness of the fine sediment deposits up to one meter, and higher amounts of deposition detected in areas wetted at baseflow. Moreover, for both reaches, recovery to the pre-event riverbed grain-size condition occurred in about a year, but with differences related to different streamflow patterns. Pre-post topographic comparison showed that areas of erosion prevailed over areas of deposition, and fine sediment deposition was not systematically associated to the riverbed elevation raise. Simulated habitat availability was affected by both topographic changes and fine sediment deposition, with the latter predominantly impacting juveniles and the most bottom dwelling species, i.e., the bullhead. The study confirms the need for improving the management of the hydro-sedimentary regime of regulated rivers to preserve the habitat of fish species, particularly of those characterized by conservation status already endangered.

Development of an Optimal Water Allocation Model for Reservoir System Operation

Allocating adequate water supplies under the increasing frequency and severity of droughts is a challenge. This study develops an optimal reservoir system operation method to allocate water supplies from upstream reservoirs to meet the downstream water requirements; validates the proposed optimization model through the system operation of upstream reservoirs; and proposes new water supply policies that incorporate a transformed hydropower reservoir with an add-on water supply function and two multipurpose reservoirs. We use linear programming to develop an optimal water allocation model. This model provides an operational strategy for managing upstream reservoirs with different storage capacities. By integrating the effective storage ratio of each reservoir into the allocation estimation, the model ensures an optimal distribution of downstream water requirements. The results indicated well-balanced, effective storage ratios among the Chungju, Soyanggang, and Hwacheon Reservoirs across varying hydrological conditions. Specifically, during drought years, the average effective storage rates were 20.5%, 20.6%, and 19.07%, respectively. In normal years, these figures, respectively, were 59.3%, 68.6%, and 52.4%, while in wet years, the rates stood at 64.08%, 62.90%, and 54.61%. This study enriches the reservoir operation literature by offering adaptable solutions for collaborative reservoir management and presents efficient strategies for reservoir operations.

Aquaculture in Hydropower Reservoirs

There is an undoubtedly existing relationship between water, energy, and food and their interaction with the natural environment and mankind. Climate change is the talk of the day in global development discussions as it intensifies water shortages, energy, poverty, food shortages, and environmental degradation. Currently, most of the hydropower dams constructed in Africa are majorly built to store water for agricultural purposes, control flooding, and generate electricity. Notable projects include the seven forks hydropower stations located on the Tana River in Kenya; Aswan high dam in Egypt; Grand Renaissance dam in Ethiopia; Kariba dam in Zimbabwe and Akosombo in Ghana. Since agriculture is the major source of livelihood in Africa, the construction of dams is key for socio-economic development. Many countries in Africa have adopted crop farming and livestock farming but have neglected fish farming. Fish farming is mainly carried out in the natural water resources which highly makes it to be at risk within a climate change ecosystem. Climate change has led to the high and low fluctuation of water levels in the natural water bodies. This highly affects fish farming negatively and has led to the extinction of some fish species. To curb the problems faced by fish farming arising from climate change there is a need to create mitigation measures to ensure an adequate supply of fish in Africa. One of the mitigation measures is incorporating caged aquaculture in hydropower dams. Having caged fish farming will not only increase the fish supply but also increase income generation and food security across Africa. The paper followed a desktop research approach and provide detailed analysis of the relations between the availability of water, energy, and food in regard to fish production. A proposed conceptual framework for caged aquaculture systems was presented in this study as a means of promoting sustainability in food and agricultural production. The study further provided feasible climate-friendly food systems and blue justice approaches that can be adopted by the key stakeholders.

Enhancing the representation of water management in global hydrological models

Abstract. This study enhances an existing global hydrological model (GHM), Xanthos, by adding a new water management module that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We remapped reservoirs in the Global Reservoir and Dam (GRanD) database to the 0.5∘ spatial resolution in Xanthos so that a single lumped reservoir exists per grid cell, which yielded 3790 large reservoirs. We implemented unique operation rules for each reservoir type, based on their primary purposes. In particular, hydropower reservoirs have been treated as flood control reservoirs in previous GHM studies, while here, we determined the operation rules for hydropower reservoirs via optimization that maximizes long-term hydropower production. We conducted global simulations using the enhanced Xanthos and validated monthly streamflow for 91 large river basins, where high-quality observed streamflow data were available. A total of 1878 (296 hydropower, 486 irrigation, and 1096 flood control and others) out of the 3790 reservoirs are located in the 91 basins and are part of our reported results. The Kling–Gupta efficiency (KGE) value (after adding the new water management) is ≥ 0.5 and ≥ 0.0 in 39 and 81 basins, respectively. After adding the new water management module, model performance improved for 75 out of 91 basins and worsened for only 7. To measure the relative difference between explicitly representing hydropower reservoirs and representing hydropower reservoirs as flood control reservoirs (as is commonly done in other GHMs), we use the normalized root mean square error (NRMSE) and the coefficient of determination (R2). Out of the 296 hydropower reservoirs, the NRMSE is > 0.25 (i.e., considering 0.25 to represent a moderate difference) for over 44 % of the 296 reservoirs when comparing both the simulated reservoir releases and storage time series between the two simulations. We suggest that correctly representing hydropower reservoirs in GHMs could have important implications for our understanding and management of freshwater resource challenges at regional-to-global scales. This enhanced global water management modeling framework will allow the analysis of future global reservoir development and management from a coupled human–earth system perspective.

Analysis of the impact of demand response on the Norwegian energy system

AbstractEuropean CO2 reduction goals have led to an increase in variable energy sources such as wind and solar, and consequently to an energy system that will need more flexibility in the future. In Norway, the hydropower reservoirs will enable the country to play a crucial role in European electrification by delivering flexibility to countries in Northern Europe. A further source of flexibility is demand response (DR) accumulated in residential, commercial, and industrial sectors. The paper discusses DR, load shifting, and load shedding based on the application of a stochastic TIMES model and it evaluates the role of DR in the Norwegian energy system towards 2050. The analysis shows that cost-efficient DR operation primarily comes from space heating in residential buildings. The use of DR, which is season-dependent, increases the volume of electricity trade, including electricity export and import to neighboring countries, and it smooths electricity prices. The implementation of DR in Norway leads to decreases in expected electricity price and total system cost by exporting flexible electricity and importing low price electricity. Additionally, it affects hydropower and reservoir management.

Enhanced machine learning model via twin support vector regression for streamflow time series forecasting of hydropower reservoir

The non-stationary, complex, and non-linear characteristics of streamflow time series have a significant impact on the simulation results of the conventional hydrological forecasting models. To improve the performances, this paper develops an enhanced machine learning model for streamflow time series forecasting, where the twin support vector regression (TSVR) is combined with singular spectrum analysis (SSA) and grey wolf optimizer (GWO). Specially, the SSA method is set as the data preprocessing tool for pattern identification; the TSVR model is set as the basic forecasting module for each pattern and the GWO method is used as the optimizer to select feasible parameter combination. Multi-step-ahead streamflow forecasting tasks are used to examine the feasibility and predictability of the proposed model. The results indicate that the proposed model can yield superior results compared with the traditional forecasting models. Thus, a robust and reliable tool is provided for streamflow time series forecasting under uncertainty.

Monthly virtual water transfers on the U.S. electric grid

Water consumed by power plants is transferred virtually from producers to consumers on the electric grid. This network of virtual transfers varies spatially and temporally on a sub-annual scale. In this study, we focused on cooling water consumed by thermoelectric power plants and water evaporated from hydropower reservoirs. We analyzed blue and grey virtual water flows between balancing authorities in the United States electric grid from 2016 to 2021. Transfers were calculated using thermoelectric water consumption volumes reported in Form EIA-923, power plant data from Form EIA-860, water consumption factors from literature, and electricity transfer data from Form EIA-930. The results indicate that virtual water transfers follow seasonal trends. Virtual blue water transfers are dominated by evaporation from hydropower reservoirs in high evaporation regions and peak around November. Virtual grey watertransfers reach a maximum peak during the summer months and a smaller peak during the winter. Notable virtual blue water transfers occur between Arizona and California as well as surrounding regions in the Southwest. Virtual grey water transfers are greatest in the Eastern United States where older, once-through cooling systems are still in operation. Understanding the spatial and temporal transfer of water resources has important policy, water management, and equity implications for understanding burden shifts between regions.

Development of Upper Euphrates Basin hydro-economic model and hydropower generation optimization

Abstract Hydro-economic optimization models are common in hydropower reservoir modeling to aid system operators and planners. In these models, operations are driven by the economic value and constrained by the availability of water. The objective is to either minimize total costs or maximize total benefits. In this study, a hydro-economic optimization model for the integrated reservoir system of the Upper Euphrates Basin, with major tributaries providing water flow to the Euphrates River, is introduced. These model the 10 large-scale reservoirs of the basin with a total installed capacity of 3,255 MW. Water management and hydropower decision-making operations are evaluated with a piecewise linear programming algorithm in monthly time steps using a 45-year historical hydrology between 1971 and 2016. The model aims to maximize hydropower revenue over a long-term time horizon with energy prices varying by month. Reservoir storage and turbine release decisions are optimized for multiple hydropower plants connected in serial or parallel. Hydropower generation, revenue, reservoir storage, capacity ratios and generation reliability results are analyzed. Results show that these hydropower plants generate about 9,481 Gigawatt hour (GWh) of energy with an average turbine capacity use of 36% and obtain a revenue of 620 million $ per year.

Climate Policy and Strategic Operations in a Hydro-Thermal Power System

Decarbonisation of the Nordic power sector entails substantial variable renewable energy (VRE) adoption. While Nordic hydropower reservoirs can mitigate VRE output’s intermittency, strategic hydro producers may leverage increased flexibility requirements to exert market power. Using a Nash-Cournot model, we find that even the current Nordic power system could yield modest gains from strategic reservoir operations regardless of a prohibition on “spilling” water to increase prices. Instead, strategic hydro producers could shift generation from peak to offpeak seasons. Such temporal arbitrage becomes more attractive under a climate package with a €100/t CO2 price and doubled VRE capacity. Since the package increases generation variability, lowers average prices, and makes fossil-fuelled plants unprofitable, strategic hydro producers face lower opportunity costs in shifting output from peak to off-peak seasons and encounter muted responses from price-taking fossil-fuelled plants. Hence, a climate package that curtails CO2 emissions may also bolster strategic hydro producers’ leverage.

Balancing-oriented hydropower operation makes the clean energy transition more affordable and simultaneously boosts water security

Reservoir hydropower offers a compelling combination of stability and flexibility services for modern water and power grids. However, its operating flexibility is poorly characterized in energy system planning, missing opportunities to cost-effectively uptake variable renewable energy (VRE) for a clean energy transition. In this study, we have developed a fully coupled reservoir operation and energy expansion model to quantify the economic and environmental benefits attained from adaptive hydropower operation in a high VRE future. Our case study of the China Southern Power Grid reveals that, in a 2050 net-zero grid, simply adapting hydropower operations to balance VRE can reduce 2018–2050 total system costs by 7% (that is, US$28.2 billion) and simultaneously save 123.8 km3 of water each year (that is, more than three times the reservoir capacity of the Three Gorges Dam). These vast, yet overlooked, cost- and water-saving potentials highlight the importance of incorporating balancing-oriented hydropower operation into future pathways to jointly decarbonize and secure power and water grids.

Multi-objective solution and decision-making framework for coordinating the short-term hydropeaking-navigation-production conflict of cascade hydropower reservoirs

The transformation of energy structure motivates hydropower to participate in peak shaving operations for grid stability, which conflicts with the multiple uses of cascade hydropower reservoirs. To coordinate the principal contradictory tasks of peak shaving, ship navigation, and power generation in day-ahead scheduling, a mixed integer linear programming model for the short-term multi-objective optimal scheduling is constructed. Specifically, a novel power release-based indicator is adopted to describe the navigation objective, while the minimization of the peak-valley difference of the residual load and the total water released are taken as the other two objective functions. Constraint aggregation and rectangular meshing methods are employed to handle the challenging nonlinear and nonconvex constraints. Subsequently, the Normalized Normal Constraint method, integrated with the mathematical optimization solver Gurobi, generates a set of well-distributed Pareto solutions, each representing a scheduling scheme. Finally, the Combined Weighting - Technique for Order Preference by Similarity to Ideal Solution is utilized as a decision-maker to trade off and determine a compromise solution for final implementation. Case studies on the cascade hydropower reservoirs of the Wujiang River in China demonstrate that the proposed model can adequately characterize the demands of various stakeholders, and the suggested framework can obtain an even and extensive Pareto front, mediate conflicts, and make reasonable decisions. Compared to the original peak-shaving scheme, the finalized compromise scheme reduces the peak-shaving effect by just 27.82% but offers simultaneous boosts of 96.54% in navigation benefits and 10.88% in power generation benefits. Consequently, the presented framework can be a promising alternative for conflict resolution of cascade hydropower reservoirs.

Aerial insectivorous bat responses to 30 years of forest insularization in a dam-created Amazonian archipelagic landscape

Mega dams result in habitat loss and fragmentation in lowland tropical forests, compromising the diversity and ecosystem functioning in remnant habitat islands. We investigated the structure of aerial insectivorous bat assemblages within insular forest patches created by a vast ∼30-year-old hydropower reservoir and adjacent mainland continuous forests in Central Amazonia. Bats were surveyed using passive bat recorders across 34 sites. We assessed bat assemblage responses to landscape insularization and estimated the contribution of assemblage-wide components of β-diversity. Additionally, we assessed the effects of local vegetation, and both patch and landscape variables on bat species diversity. Continuous forest sites in the mainland retained higher species richness and bat activity compared to islands, leading to divergent species composition between forest sites. Larger islands (>100 ha) and continuous forests tended to share a similar assemblage composition manly drive by species richness differences with a minor contribution of species replacements. Moreover, local vegetation structure, island size, and landscape edge area exerted significant effects on species richness, bat activity, and species composition, with small degraded islands and landscapes dominated by forest edges exhibiting pervasive species loss. Our study highlights the detrimental impacts of forest fragmentation induced by large dams on aerial insectivorous bat assemblages. If new dam projects are inevitable, we recommend the creation of extensive protected areas in land-bridge island systems and their adjacent continuous forests. Those protected sites can minimize the negative impacts of small islands, safeguarding forest quality for forest-dependent bats, which ensures the continuity of their ecosystem services.

A LSTM-based approximate dynamic programming method for hydropower reservoir operation optimization

Dynamic programming (DP) is a classical method developed to address the multi-stage hydropower reservoir operation problem, but still suffers from the serious dimensionality problem where the computational burden increases exponentially with the number of state variables. To improve the DP performance, this paper proposes a LSTM-based approximate dynamic programming (ADP) method for complex hydropower reservoir operation optimization. In ADP, the long short-term memory (LSTM) is treated as the response surface model to reduce redundant computations of power outputs in DP’s recursive equation, making obvious improvements in the execution efficiency. To fully assess its feasibility, the ADP method is used to find the scheduling schemes of a real-world reservoir system in China. Simulation results show that compared with the standard DP method, ADP effectively reduces the execution time while guarantee the solution quality in different cases. In the 1000-state and wet-year scenario, the ADP method achieves approximately 86.7% and 85.8% reductions in DP’s computation time for Longyangxia and Laxiwa reservoir with the goal of maximizing power generation. Thus, the LSTM-based response surface model is an effective tool to improve the DP performance in the hydropower reservoir operation problem.

Determining return levels of extreme daily precipitation, reservoir inflow, and dry spells

Return level calculations are widely used to determine the risks that extreme events may pose to infrastructure, including hydropower site operations. Extreme events (e.g., extreme precipitation and droughts) are expected to increase in frequency and intensity in the future, but not necessarily in a homogenous way across regions. This makes localized assessment important for understanding risk changes to specific sites. However, for sites with relatively small datasets, selecting an applicable method for return level calculations is not straightforward. This study focuses on the application of traditional univariate extreme value approaches (Generalized Extreme Value and Generalized Pareto) as well as two more recent approaches (extended Generalized Pareto and Metastatistical Extreme Value distributions), that are specifically suited for application to small datasets. These methods are used to calculate return levels of extreme precipitation at six Alpine stations and high reservoir inflow events for a hydropower reservoir. In addition, return levels of meteorological drought and low inflow periods (dry spells) are determined using a non-parametric approach. Return levels for return periods of 10- and 20- years were calculated using 10-, 20-, and 40- years of data for each method. The results show that even shorter timeseries can give similar return levels as longer timeseries for most methods. However, the GEV has greater sensitivity to sparse data and tended to give lower estimates for precipitation return levels. The MEV is only to be preferred over GPD if the underlying distribution fits the data well. The result is used to assemble a profile of 10- and 20-year return levels estimated with various statistical approaches, for extreme high precipitation/inflow and low precipitation/inflow events. The findings of the study may be helpful to researchers and practitioners alike in deciding which statistical approach to use to assess local extreme precipitation and inflow risks to individual reservoirs.

Optimal Scheduling of Cascade Reservoirs Based on an Integrated Multistrategy Particle Swarm Algorithm

The optimal scheduling of cascade reservoirs is an important water resource management and regulation method. In the actual operation process, its nonlinear, high-dimensional, and coupled characteristics become increasingly apparent under the influence of multiple constraints. In this study, an integrated multistrategy particle swarm optimization (IMPSO) algorithm is proposed to realize the optimal operation of mid- and long-term power generation in cascade reservoirs according to the solution problem in the scheduling process of cascade reservoirs. In IMPSO, a variety of effective improvement strategies are used, which are combined with the standard PSO algorithm in different steps, among which beta distribution initialization improves population diversity, parameter adaptive adjustment accelerates convergence speed, and the Lévy flight mechanism and adaptive variable spiral search strategy balance the global and local search capabilities of the algorithm. To handle complex constraints effectively, an explicit–implicit coupled constraint handling technique based on constraint normalization is designed to guide the update process into the feasible domain of the search space. The feasibility of the proposed method is verified in the mid- and long-term power generation optimization scheduling of the lower reaches of the Jinsha River–Three Gorges cascade hydropower reservoirs. The results show that the proposed method outperforms the other methods in terms of search accuracy and has the potential to improve hydropower resource utilization and power generation efficiency significantly.

Deriving hydropower reservoir operation policy using data-driven artificial intelligence model based on pattern recognition and metaheuristic optimizer

Robust reservoir operation policies are crucial in ensuring the effective utilization of water resources. However, owing to multiple complicated and changeable factors in practice, it is difficult for standalone approaches to derive reasonable reservoir operation policy. To address the practical requirement, this research proposes a novel artificial intelligence method for deriving reservoir operation policy. The proposed method uses the fuzzy clustering iteration method to identify multiple typical operation patterns from the influencing factors; secondary, for all the samples within each pattern, the novel twin support vector regression (TSVR) is utilized to model the nonlinear mapping relationship between the influence inputs and the target outputs, while the emerging equilibrium optimizer is chosen to determine suitable computation parameters for the TSVR model. The feasibility of the proposed method is fully evaluated on two real-world huge hydropower reservoirs in China. The simulations demonstrate that the developed method can yield better comprehensive benefits than several control methods in deriving reservoir operation policy under uncertain environments. Hence, the experiments confirm that metaheuristic algorithms and pattern recognition techniques can enhance the performance of a standalone artificial intelligence methods in deriving reservoir operation policy.

Slope Stability Analysis Based on the Coupling of SA and Limit Equilibrium Mechanics

The limit equilibrium strip method of slope has become mature, but because of the complexity of slope instability with many degrees of freedom and high nonlinear, a more three-dimensional and mature method is needed for slope problems. Based on the overall force balance and moment balance of slope, a unified model of three dimensional limit balance methods is established in this paper. Given different assumptions, the analytical expressions of each traditional model are obtained to avoid the problem of difficult boundary treatment when the original method is divided into bars and columns. The influence of the trailing edge point B and shear outlet A on the central axis of the sliding body, and the control arc radius variable t on the calculated value of the three-dimensional slope stability coefficient is discussed in detail. Then, based on the simulated annealing algorithm, the state generating function, state accepting function and temperature updating function are constructed, and the calculation method of optimizing the sliding surface search of the slope by using the simulated annealing algorithm is proposed, and the stability analysis of the slope of a hydropower reservoir dam area in Guangxi is carried out. The results show that the position of the sliding surface obtained by searching around the design value K = 1.10 is basically consistent with the actual one, which proves that the mechanical analysis method of coupling SA and limit equilibrium is convenient and efficient in the slope stability analysis.

EDNA metabarcoding reveals differences in fish diversity and community structure in heterogeneous habitat areas shaped by cascade hydropower.

Freshwater ecosystems are under great threat from humans, among which habitat heterogeneity is the most obvious, being one of the important reasons for the decline of fish diversity. This phenomenon is particularly prominent in the Wujiang River, where the continuous rapids of the mainstream have been divided into 12 mutually isolated sections by 11 cascade hydropower reservoirs. Based on the fact that conventional survey methods are more harmful to the ecological environment, the efficient and noninvasive environmental DNA metabarcoding (eDNA) approach was used in this study to conduct an aquatic ecological survey of the 12 river sections of the mainstream of the Wujiang River. A total of 2299 operational taxonomic units (OTUs) were obtained, corresponding to 97 species, including four nationally protected fish species and 12 alien species. The results indicate that the fish community structure of the Wujiang River mainstream, which was originally dominated by rheophilic fish species, has been changed. And there are differences in fish species diversity and species composition among the reservoir areas of the mainstream of the Wujiang River. The fish species in the area have gradually declined under the influence of anthropogenic factors such as terraced hydropower and overfishing. The fish populations consequently have demonstrated a tendency to be species miniaturized, and the indigenous fish are severely threatened. In addition, the fish composition monitored by the eDNA approach was found to be close to the fish composition of historical information on the Wujiang River, indicating that eDNA approach may be used as a complementary tool to conventional methods in this basin.

The Assessment of Sedimentation Problems in Kenyir Hydropower Reservoir, Malaysia

Lakes may take a while to respond to management interventions because of the management implications of incremental development and degradation issues. This includes the requirement for the ongoing participation of key lake basin management institutions and their operations. This study’s objective is to assess the impacts of land use activities along the Kenyir Lake Basin based on the sedimentation problem level. There are a few hydrological methods that are necessary indicators to measure the level of sediment production, such as Total Suspended Solid (TSS), area of sub-catchment, river discharge measurement, and annual sediment load production. The results showed that the sub-catchment of Besar River released the lowest annual average estimation at 3833.70 kg/km2/year, and the sub-catchment of Kenyir River produced the highest annual average estimation at 128,070.86 kg/km2/year for annual sediment load flow produced from tributary rivers into Kenyir Lake. Kenyir Lake Basin’s downstream and midstream regions had higher sediment load values than its upstream regions. This study highlighted the significance of the effects of anthropogenic factors, hydrological, geomorphological, growth, and developmental factors, and climate changes as the key variables attributing to the sedimentation phenomenon along the Kenyir Lake Basin. The construction of a long-term lake or reservoir catchment development and management plan, combined with the formation of a vision and comprehensive strategic plan, are vital components of sound management practice. The efficient implementation of the suggested watershed management programmes depends on the active involvement of all significant catchment stakeholders.

Diversity in reservoir surface morphology and climate limits ability to compare and upscale estimates of greenhouse gas emissions

Greenhouse gas (GHG) emissions from reservoirs are influenced by many factors, including the reservoir's morphology, watershed, and local climate. Failure to account for diversity in waterbody characteristics contributes to uncertainties in estimates of total waterbody GHG emissions and limits the ability to extrapolate patterns from one set of reservoirs to another. Hydropower reservoirs are of particular interest given recent studies that show variable – and sometimes very high – measurements and estimates of emissions. This study uses characteristics describing reservoir surface morphology and location within the watershed to identify US hydropower reservoir archetypes that represent the diversity of reservoir features relevant to GHG emissions. The majority of reservoirs are characterized by smaller watersheds, smaller surface areas, and lower elevations. Downscaled climate projections of temperature and precipitation mapped onto the archetypes show large variability in hydroclimate stresses (i.e., changes in precipitation and air temperature) within and across different reservoir types. Average air temperatures are projected to increase for all reservoirs by the end of the century, relative to historical conditions, while projected precipitation is much more variable across all archetypes. Variability in projected climate suggests that despite similar morphology-related traits, reservoirs may experience different shifts in climate, potentially resulting in a divergence in carbon processing and GHG emissions from historical conditions. Low representation in published GHG emission measurements among several reservoir archetypes (roughly 14 % of the population of hydropower reservoirs), highlights a potential limit to the generalization of current measurements and models. This multi-dimensional analysis of waterbodies and their local hydroclimate provides valuable context for the growing body of GHG accounting literature and ongoing empirical and modeling studies.