Onshore Wind Research Articles

Tidal, density and wind-driven subtidal circulation in a hypersaline coastal lagoon

Subtidal circulation patterns were studied along an elongated hypersaline system. Month-long data were collected using three acoustic Doppler current profilers. These profilers were moored at depths of around 15 m in three locations: 10 km inland from the mouth of the lagoon, within the central region, and near the head of the lagoon. The variability of temporal and spatial subtidal circulation was explored using Empirical Orthogonal Functions analysis, which explained 93% of the variability through the first three modes. The first mode was influenced by tidal advection, resulting in a tidally-driven residual circulation for elongated channels, with inflows occurring over the shoals and outflows in the deeper channel sections. During the neap-tide cycle, inverse density gradients became relevant, coinciding with the peaks of mode 2 amplitude that accounted for 15% of the total variability. The gravitational circulation exhibited its highest intensity near the head, characterized by a vertically sheared flow with inflow occurring near the surface and outflow underneath. Predominant onshore wind stresses had a significant impact on the subtidal circulation and displayed a strong correlation with the mode 3 amplitude, explaining 11% of the total variability. Wind-driven circulation exhibited a vertically sheared profile, characterized by downwind near-surface flow and upwind flow underneath in the lagoon mouth and head, and a homogeneous downwind profile in the central part of the lagoon. It is concluded that tidal stresses governed the subtidal circulation, followed by density gradients, and wind stresses modified spatial circulation patterns induced by tides and density gradients. This research provides new observational evidence on tidal residuals in long channels and contributes to the understanding of the interactions between the main subtidal circulation drivers in hypersaline lagoons.

Technoeconomic Analysis of a Wind Power Generation System and Wind Resource Mapping Using GIS Tools: The Case of Twelve Locations in the Commune of Evodoula, Cameroon

This paper performs a technoeconomic analysis of the wind power potential and evaluates the cost of wind power generation in Evodoula, comparing two methods, the conventional method and the uncertainty method based on a comparative spatiotemporal approach using the geographic information system (GIS) software tool. This study is based on satellite wind data measured at 10 m above ground level (AGL) over a 40-year period (1980-2019), by the meteorological service NASA (National Aeronautics and Space Administration)/Goddard Space Flight Center (GSFC). The main objectives are to obtain an appropriate design for a proven optimal location and to assess the viability of wind power at Evodoula. For this type of study, there is little literature available. The optimization of an onshore wind farm and deployment in the wind energy development interest area (ZIDEE) is carried out to obtain a minimum and parsimonious discounted cost production unitary (CPU) of the electricity produced. The results showed that in Okok, a location with a large energy deficit, an onshore wind farm with an electricity generation capacity of 12.5 MW with 5 NORDEX N100-2.5MW wind turbines would have a total energy production (TEP) of about 64.0254825 TWh and a selling price of electricity that would be 0.0034 CAD$/kWh, which is very low compared to the utility price (about 98% cheaper). The total cost of the wind farm would be about $11 million, with a net present cost of about $218 million. The annual profit generated by the wind farm would be over $6 billion. The return on investment (ROI) of the project is estimated at 2880.882%. The constructed onshore wind farm would avoid CO2 emission at over 11 MtCO2,eq/year as the energy generated is from the atmosphere. The wind farm would realize an average annual cash flow estimated at nearly $30 million after 20 years of operation. These savings would allow the installation of CO2 capture systems in conventional power plants. In addition, the analysis of uncertainties and risks was identified and quantified to estimate the confidence levels of the project development results. The risks have been assessed, and we recommend that the total uncertainty of the project is around 15%. The energy values in P75 and P90 are 10.08% and 19.22%, respectively, lower than the energy value in P50 of 11.60 GWh/An. Finally, the main policy recommendations for an inclusive design process are highlighted. The contribution of this study is to assist policy makers in making appropriate decisions in the development and implementation of energy and environmental policy in Cameroon and in many continental areas.

Comprehensive onshore wind energy assessment in Malawi based on the WRF downscaling with ERA5 reanalysis data, optimal site selection, and energy production

Malawi, despite its minimal CO2 emissions, faces huge climate impacts and a low electrification rate. Recognizing these challenges, this study presents a comprehensive and nationwide assessment of Malawi's wind energy potential, leveraging the high-resolution capabilities of the Weather Research and Forecasting (WRF) model. Unlike most site-specific studies, this study covers the entire country, factoring in different turbine hub heights. Through sensitivity analysis, the optimal configuration for the WRF model in Malawi was determined and ERA5 reanalysis data was used for initial and boundary conditions. The study's findings were validated using MERRA-2 reanalysis and actual observational datasets. A combination of GIS and the fuzzy analytic hierarchy process (AHP) was employed, integrating key factors like land use and topographical complexity to determine optimal wind farm sites. Notably, the wind suitability map uncovered significant potential for wind farm development. Furthermore, in the evaluation of a 50 MW wind farm, 80 m hub heights consistently outperformed 100 m elevations, both technically and economically. An 80 m wind farm at Site A can potentially power up to 32,931 urban and 84,475 rural households. Economically, 80 m hub heights showcased lower levelized cost of electricity (LCOE) values and faster payback periods than 100 m ones. Furthermore, these proposed wind projects can markedly reduce CO2 emissions, positioning Malawi in tandem with global sustainability aspirations. Overall, this study provides a key foundation for Malawi's renewable energy strategies, highlighting the potential for improved electrification and better climate change mitigation outcomes.

Assessing worldwide future potentials of renewable electricity generation: Installable capacity, full load hours and costs

Several studies calculate the potential of renewable energies throughout the world. However, these studies vary in the underlying assumptions and do not consider several technologies within a uniform methodology, which affects comparability. In this paper, we calculate the worldwide potentials of ground-mounted photovoltaic (PV), concentrated solar power (CSP), and wind onshore and offshore on a 6.5 by 6.5 km grid. The results consist of installable capacity, generated electricity, and specific cost of electricity generation. Our methodology combines high-resolution data for land uses, slope, and hourly weather data with a thorough analysis of different factors for wind power, like hub heights and rotor diameters, and solar power plants, like tilt angle, and temperature-related loss of efficiency. The results show that the summed-up potential capacity of ground-mounted PV surpasses the amount of CSP, wind onshore and offshore combined with 450 TW. Given the cost-potential curve, ground-mounted PV reaches 560,000 TWh at costs below 20 €/MWh, which is twice the projected worldwide primary energy supply of the World Energy Outlook 2022 for the year 2050 with 205,557 TWh. The calculated results can function as input factors for energy system models, which are an important tool to analyse the energy transition.

A real options approach to renewable energy module end-of-life decisions under multiple uncertainties: Application to PV and wind in China

The issue of large-scale retirements of renewable energy modules has attracted considerable attention from governments and the public worldwide. Using the Chinese wind and photovoltaic industries as examples, this paper, for the first time, employs a real options approach to explore the optimal time for energy firms to retire their power plants and to predict the future trend of renewable energy wastage from a bottom-up perspective. The results show that the optimal retirement time for the four subdivided categories ranges from 16 to 23 years. The later the investments are made, the earlier the decommissioning time comes and the lower the project returns. Furthermore, wind projects have been found to generate more revenue than PV projects for the same size, and the gap continues to widen. Offshore wind technology always has the longest optimal service life (18.62 years in 2022) and the greatest project returns (10.84 million CNY in 2022). Annual utilization hours, the renewable electricity price and the waste price can accelerate the retirement of plants and generate more revenue for energy companies, while the initial investment cost has a completely opposite effect. The first wave of PV retirements will occur in 2035 at 305.82 GW, while onshore wind will be in 2036 at 530.77 GW and offshore wind will not explode until 2040 at 65.84 GW. An assessment of potential policies suggests that for every 100 CNY increase in incentive strength, the retirement wave arrives 1 year earlier. In contrast, for every 100 CNY increase in the strength of penalties, the retirement wave is delayed by 1 year. In addition, we verify that the size of the plants does not affect the forecast of the retirement trend, indicating that the assumptions about installed capacity do not cause bias.

Foundations of onshore Wind turbines: current situation and trends

The commitments by the governments for the net zero emissions for the coming decades will be a huge challenge for the renewable sector all over the planet. Wind energy will play a significant role in this transition and scale-up, in both offshore and onshore, is a crucial step to move forward. The quick development of the new onshore wind turbine models in the race for the most powerful machine, the gradual increase in the hub height, and the unitary power of the new onshore wind turbines are producing modifications in different components of the turbines. This work presents a review of the onshore wind turbines foundations, especially about the different foundation design concepts available in the onshore business, the analysis of some of them and the trends.

Optimization of Non-Uniform Onshore Wind Farm Layout Using Modified Electric Charged Particles Optimization Algorithm Considering Different Terrain Characteristics

Designing an onshore wind farm layout poses several challenges, including the effects of terrain and landscape characteristics. An accurate model should be developed to obtain the optimal wind farm layout. This study introduces a novel metaheuristic algorithm called Modified Electric Charged Particles Optimization (MECPO) to maximize wind farms’ annual energy production (AEP) by considering the different terrain and landscape characteristics of the sites. Some non-uniform scenarios are applied to the optimization process to find the best combination of decision variables in the wind farm design. The study was initiated by a uniform wind farm layout optimization employing identical wind turbine hub heights and diameters. Following this, these parameters underwent further optimization based on some non-uniform scenarios, with the optimal layout from the initial uniform wind farm serving as the reference design. Three real onshore sites located in South Sulawesi, Indonesia, were selected to validate the performance of the proposed algorithm. The wind characteristics for each site were derived from WAsP CFD, accounting for the terrain and landscape effects. The results show that the non-uniform wind farm performs better than its uniform counterpart only when using varying hub heights. Considering the impacts of the terrain and landscape characteristics, it is observed that sites with a higher elevation, slope index, and roughness length exhibit a lower wake effect than those with lower ones. Moreover, the proposed algorithm, MECPO, consistently outperforms other algorithms, achieving the highest AEP across all simulations, with a 100% success rate in all eight instances. These results underscore the algorithm’s robustness and effectiveness in optimizing wind farm layouts, offering a promising avenue for advancing sustainable wind energy practices.

Comparing energy system optimization models and integrated assessment models: Relevance for energy policy advice.

The transition to a climate neutral society such as that envisaged in the European Union Green Deal requires careful and comprehensive planning. Integrated assessment models (IAMs) and energy system optimisation models (ESOMs) are both commonly used for policy advice and in the process of policy design. In Europe, a vast landscape of these models has emerged and both kinds of models have been part of numerous model comparison and model linking exercises. However, IAMs and ESOMs have rarely been compared or linked with one another. This study conducts an explorative comparison and identifies possible flows of information between 11 of the integrated assessment and energy system models in the European Climate and Energy Modelling Forum. The study identifies and compares regional aggregations and commonly reported variables. We define harmonised regions and a subset of shared result variables that enable the comparison of scenario results across the models. The results highlight how power generation and demand development are related and driven by regional and sectoral drivers. They also show that demand developments like for hydrogen can be linked with power generation potentials such as onshore wind power. Lastly, the results show that the role of nuclear power is related to the availability of wind resources. This comparison and analysis of modelling results across model type boundaries provides modellers and policymakers with a better understanding of how to interpret both IAM and ESOM results. It also highlights the need for community standards for region definitions and information about reported variables to facilitate future comparisons of this kind. The comparison shows that regional aggregations might conceal differences within regions that are potentially of interest for national policy makers thereby indicating a need for national-level analysis.

DeepFore: A Deep Reinforcement Learning Approach for Power Forecasting in Renewable Energy Systems

An open network known as the “energy internet” links every component of the whole energy supply chains, from the generations. Due to their ability to mimic regional flow dynamics that have an impact on wind farm production, regional meteorological models are increasingly being used as a general tool for wind resource forecasting. In this study, higher vertical and horizontal resolutions WRF (weather research and forecasting) paradigm simulation are used to anticipate and validate production for a genuine onshore wind farm. This paper proposed a DeepFore which is a power forecasting system for hybrid renewable energy systems. Initially, the dataset is generated by the hybrid system. This data is preprocessed to improve the quality of the data by incorporating, filtering and outlier detection techniques. Then, this enriched data is fed into K++ means clustering algorithm to separate the normal data from faulty data. With the normal and original data, Teaching-Learning based optimization algorithm attempts to realize the optimal features which are important for forecasting. Finally, Deep SARSA which is deep reinforcement learning algorithm is incorporated to determine the power generated by the hybrid system. Better winds energy prediction estimations enable more efficient utilization of the produced electricity, according to computational models.

Assessing and enhancing the regional sustainability of electricity generation technologies in an energy-importing megaregion in China

Decision-makers are increasingly concerned about the sustainability of power generation technologies to achieve a secure and sustainable electricity supply in the future. This study aims to assess the sustainability of the eight key electricity generation technologies in the energy-importing Yangtze River Delta region of China and further enhance the regional sustainability of the electricity generation mix. We employed the multi-criteria decision-making process to rank the sustainability of the eight electricity generation technologiesfrom the perspective of various decision-makers. First, the results revealed that no technology is absolutely most sustainable. Second, the subjective findings show that hydropower is the most sustainable technology among the assessed ones, followed by nuclear and onshore wind power. Third, policymakers regard fossil fuel energy as more sustainable than investors and experts. Furthermore, local renewable diffusion and substantial electricity imports from renewable-rich areas can enhance the sustainability of the localelectricity system. In addition,an interregional electricity transmission grid with the neighbor Anhui province as the primary backup power supplier can sufficiently enhance the sustainability of the electricity system in the Yangtze River Delta region.

Effects of the yaw error and the fault conditions on the dynamic characteristics of the 15 MW offshore semi-submersible wind turbine

The floating offshore wind turbines (FOWTs) have a higher failure rate compared to onshore wind turbines, which could lead to aerodynamic imbalance on the floating systems. In addition, yaw error presents an inevitable challenge for FOWTs. This study aims to investigate the effects of the yaw error and the fault conditions on the dynamic properties of the IEA 15 MW semi-submersible FOWT using OpenFAST. The fault conditions involve blade seize without shutdown, blade seize with shutdown and grid loss. The platform motions and structure loads under different yaw errors (−20°, −8°, 0°, 8°, 20°) for different fault conditions were investigated. The dynamic response resulting from the fault was compared with the normal condition. Moreover, the optimal yaw angle under fault conditions was investigated in this paper. The results revealed that the fault shutdown conditions significantly impact the motions and loads of the wind turbine, with substantial reverse tower top and base bending moments. The yaw error, conversely, has less effect on the motions of the wind turbine, but can ameliorate the transient effect in the fault shutdown conditions. Additionally, it is recommended to operate the wind turbine at a yaw angle of 25–30° for wind speeds greater than 12 m/s.

Small Disturbance Stability Analysis of Onshore Wind Power All-DC Power Generation System Based on Impedance Method

The Onshore Wind Power All-DC Generation System (OWDCG) is designed to integrate with renewable energy sources by modifying the grid structure. This adaptation supports the grid infrastructure and addresses the challenges of large-scale wind power AC collection and harmonic resonance during transmission. Crucially, small disturbance stability parameters are essential for ensuring the system’s stable operation. Unlike conventional power systems, the OWDCG exhibits strong coupling between subsystems, accentuating the small disturbance stability issue due to the dynamic nature of its converter control system. The impedance method facilitates the decomposition of such systems into subsystems, offering insights into the destabilization mechanism through the lens of negative impedance contribution. This approach is conducive to conducting small disturbance stabilization analyses. To tackle this issue, the initial step involves deriving the input and output equivalent impedance models of the subsystem, considering the topological structure, control features, and operational dynamics of the OWDCG. Subsequently, the impact of circuit and control parameters on the system’s impedance characteristics and small-disturbance stability is examined through Bode diagrams and Nyquist curves. This analysis identifies critical parameters for small-disturbance stability, guiding the stable operation and parameter optimization of the OWDCG. The analysis highlights that the main control strategies for stability are the Modular Multilevel Converter (MMC) DC voltage control and the inner-loop current control gain. Validation of the theoretical findings is achieved through simulation results using PSCAD/EMTDC.

LiDAR‐based automated UAV inspection of wind turbine rotor blades

AbstractThe global trend indicates that overall wind energy production, both onshore and offshore, will increase drastically in the next decade. Therefore, presently, much effort is focused on optimizing the operation and maintenance of wind turbines, since these are quite challenging and cost‐intensive. To aid or even completely fulfill a specific inspection task, an automated solution is proposed in this paper. The prototype is built on an M300 drone platform from DJI Technology Co. and is presented here. It requires a single, additional 2D‐LiDAR sensor mounted on an upwards frame. The proposed control and path planning algorithms have been tested in the AirSim simulation environment, as well as in local model airfields and at real onshore and offshore wind turbines. As a result, a comprehensive sequential‐phased mission is presented, which reduces the total time required for the inspection routine to approximately 14 min, representing about half the time an expert pilot may need for the same task. Additionally, a platform prototype that may be deployed on a ship's deck for a safe landing is presented. It guarantees instant adhesion upon contact and avoids unwanted drone backlash due to sudden and unexpected ship movement during the landing approach. Further work will focus mainly on additional offshore flight probes, optimizing the landing platform, and tuning the flight algorithms.

Exploring the efficacy of renewable energy support policies in uncertain environments: A real options analysis

This paper explores the impact of various renewable energy support policies on investor behavior and environmental value. Specifically, the study investigates the effects of fixed feed-in tariff (FIT) and tradable green certificates (TGC) on investor’s flexibility and environmental value under conditions of uncertainty. Real options theory and envelope theory are employed to analyze these uncertainties, with an empirical analysis of China’s electricity market serving as the basis for the study. The results of the analysis demonstrate that the use of TGC can lead to higher environmental value for utility-scale photovoltaic (PV) projects in China. For onshore wind, substituting FIT with TGC may be more appropriate. However, for offshore wind projects, FIT may be more suitable due to their increased vulnerability to uncertainty. Additionally, the study finds that greater support is required for projects deployed under FIT policies.

Onshore wind in Portugal: History, supporting policies, economic viability and future perspectives

Onshore wind energy is currently playing a crucial role in Europe’s decarbonization efforts. Among other European countries, Portugal is recognized as a successful example of large-scale wind energy implementation thanks to its supportive policies in the 00 s. However, the government's financial support for onshore wind technologies has faced criticism, with accusations that feed-in tariffs were set too high and that farm owners were overpaid for their energy production. As the end of feed-in tariff regimes approaches for onshore wind farms in Portugal, it is unclear whether farm owners will choose to pursue repowering and life extension strategies, or opt to shut down operations. The present paper provides a techno-economic assessment to onshore wind farms in Portugal to assess their historical financial performance based on power production estimates and applicable feed-in tariff legal regimes. Based on the estimated investment costs, farm yield and established feed-in tariffs, the results suggest that there is no evidence of exceptional profits in onshore wind projects in Portugal. The results also suggest significant economic benefits for Portugal in prioritizing future investments in onshore wind, by pointing towards a strategic focus on life extension and repowering strategies as key components of this forward-looking approach.

A feasibility study of green hydrogen liquefaction for hydrogen refueling station: Multi-criteria based integrative assessment

Considering an important energy vector, hydrogen (H2) has earned enormous attention due to its cleanliness and potential role in decarbonizing the existing systems, e.g., transportation, industrial sector, and commercial units. This is the reason why developing the H2 economy is one of the most crucial aspects under research and numerous studies forecast that the H2 demand will increase many folds in the near future. However, the production of H2 and its transportation have serious challenges due to the fact of energy and cost intensiveness. In this context, we have made an effort to assess the potential feasibility of producing green H2 and simultaneously integrate it with a liquefaction unit to make it ready for transportation. Moreover, the proposed process is evaluated based on renewable energy resources to estimate the potential solution for future energy goals. The proposed model for liquefied H2 (LH2) production claimed high energy efficiency and the value in terms of specific energy consumption is obtained as 5.418 kWh kg LH2−1 for the capacity of 10 ton per day of LH2. A detailed techno-economic analysis provides levelized cost of LH2 by varying criteria for economic feasibility. Environmental assessment results in the lowest environmental potential during green LH2 production, which reveals that the solid oxide electrolysis system is a potential candidate for green LH2 production. As per multi-criteria decision analysis, results showed that the onshore wind with alkaline water electrolysis and proton exchange membrane water electrolysis have high capabilities to become the potential technology for green LH2 production.

Near-Surface Wind Profiling in a Utility-Scale Onshore Wind Farm Using Scanning Doppler Lidar: Quality Control and Validation

Wind profiling within operating wind farms is important for both wind resource assessment and wind power prediction. With increasing wind turbine size, it is getting difficult to obtain wind profiles covering the turbine-affecting area due to the limited height of wind towers. In this study, a stepwise quality control and optimizing process for deriving high-quality near-surface wind profiles within wind farms is proposed. The method is based on the radial wind speed obtained by the Doppler Wind Lidar velocity-azimuth display (VAD) technique. The method is used to obtain the whole wind profile from ground level to the height affected by wind turbines within a utility-scale onshore wind farm, in northern China. Compared with the traditional carrier-to-noise ratio (CNR) filter-based quality control method, the proposed data processing method can significantly improve the accuracy of the derived wind. For a 10 m wind speed, an increase in coefficient of determination (R2) from 0.826 to 0.932, and a decrease in mean absolute error (MAE) from 1.231% to 0.927% are obtained; while for 70 m wind speed, R2 increased from 0.926 to 0.958, and MAE decreased from 1.023% to 0.771%. For wind direction, R2 increased from 0.978 to 0.992 at 10 m, and increased from 0.983 to 0.995 at 70 m. The optimized method also presents advantages in improving the accuracy of derived wind under complex wind environments, e.g., inside a wind farm, and increasing the data availability during clear nights. The proposed method could be used to derive wind profiles from below the minimum range of a vertically operating scanning Doppler Lidar to a height affected by wind turbines. Combined with Doppler beam-swinging (DBS) scanning data, the method could be used to obtain the complete wind profile in the boundary layer. These wind profiles could be further used to predict wind power and evaluate the climate and environmental effects of wind farms.

Predicting the Displacement Variation of Rehabilitated Foundation of Onshore Wind Turbines Using Machine Learning Models

The rehabilitation of wind turbine foundations after damage is increasingly common. However, limited research exists on the deformation of wind turbine foundations after rehabilitation. Artificial intelligence methods can be used to analyze future deformation state and predict post-rehabilitation deformation of foundations. This paper focuses on analyzing the stability of damaged wind turbine foundations after rehabilitation, as well as establishing and evaluating machine learning models. Specifically, Decision Tree (DT), Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), and Long Short-Term Memory Network (LSTM) models are utilized to predict the vertical displacement of the rehabilitated foundation. Hence, the stability of the rehabilitated foundation is discussed in correlation with the measured wind speed, based on the foundation vertical displacement data. During the development of the machine learning model, the most suitable combination of hyperparameters is determined. The prediction performance of the SVR and LSTM models, which exhibit good performance, is compared to further evaluate their effectiveness. Furthermore, the models are analyzed and validated. The results indicate that the vertical displacements of the rehabilitated foundations gradually get close to a state of steady fluctuation over time. The SVR model is identified as the most effective in predicting the vertical displacements of wind turbine foundations after rehabilitation. This study aims to analyze and predict the vertical displacement of wind turbine foundations after rehabilitation based on extensive field monitoring data and powerful machine learning models.

Identification Uncertainties of Bending Modes of an Onshore Wind Turbine for Vibration-Based Monitoring

This study considers the identification uncertainties of closely spaced bending modes of an operating onshore concrete-steel hybrid wind turbine tower. The knowledge gained contributes to making mode shapes applicable to wind turbine tower monitoring rather than just mode tracking. One reason is that closely spaced modes make it difficult to determine reliable mode shapes for them. For example, the well-known covariance-driven stochastic subspace identification (SSI-COV) yields complex mode shapes with multiple mean phases in the complex plane, which does not allow error-free transformation to the real space. In contrast, the Bayesian Operational Modal Analysis (BAYOMA) allows the determination of real mode shapes. The application of BAYOMA presents a further challenge when quantifying the associated uncertainties, as the typical assumption of a linear, time-invariant system is violated. Therefore, validity is not self-evident and a comprehensive investigation and comparison of results is required. It has already been shown in a previous study that the significant part of the uncertainty in the mode shapes corresponds to their orientation in the mode subspace (MSS). Despite all the challenges mentioned, there is still a great need to develop reliable monitoring parameters (MPs) for Structural Health Monitoring (SHM). This study contributes to this by analysing metrics for comparing mode shapes. In addition to the well-known Modal Assurance Criteria (MAC), the Second-Order MAC (S2MAC) is also used to eliminate the alignment uncertainty by comparing the mode shape with a MSS. In addition, the mode shape identification uncertainties of BAYOMA are also considered. Including uncertainties is also essential for the typically used natural frequencies and damping ratios, which can be more appropriately used if the identification uncertainty is known.

Prioritizing landscapes for mitigating the impacts of onshore wind farms on multidimensional waterbird diversity in the Yellow Sea

Abstract Ongoing wind energy developments play a key role in mitigating the global effects of climate change and the energy crisis; however, they have complex ecological consequences for many flying animals. The Yellow Sea coast is considered as an ecological bottleneck for migratory waterbirds along the East Asian–Australasian flyway (EAAF), and is also an important wind farm base in China. However, the effects of large-scale onshore wind farms along the EAAF on multidimensional waterbird diversity, and how to mitigate these effects, remain unclear. Here we examined how wind farms and their surrounding landscapes affected multidimensional waterbird diversity along the Yellow Sea coast. Taxonomic, functional, and phylogenetic diversity of the waterbird assemblages, and mean pairwise distances and nearest taxon distances with null models were quantified in relation to 4 different wind turbine densities. We also measured 6 landscape variables. Multi-dimensional waterbird diversity (taxonomic, functional, and phylogenetic diversity) significantly decreased with increasing wind turbine density. Functional and phylogenetic structures tended to be clustered in waterbird communities, and environmental filtering drove waterbird community assemblages. Furthermore, waterbird diversity was regulated by a combination of wind turbine density and landscape variables, with edge density of aquaculture ponds, in addition to wind turbine density, having the greatest independent contribution to waterbird diversity. These results suggest that attempts to mitigate the impact of wind farms on waterbird diversity could involve the landscape transformation of wind farm regions, for example, by including high-edge-density aquaculture ponds (i.e., industrial ponds) around wind farms, instead of traditional low-edge-density aquaculture ponds.