In situ data monitoring is one of the most accurate and reliable measurement methods, and the results of factors such as near-surface temperature, turbulence intensity, air humidity, soil temperature, wind speed, and others on the impacts of onshore wind farms on local climate indicators can be obtained by monitoring them over long time spans (Smith et al., 2013; Rajewski et al., 2014; Armstrong et al., 2016; Gao et al., 2020; Xia et al., 2022). In addition, on-site data monitoring can be used to obtain statistics on community and species changes, such as the mortality of bats, birds, and other animals, and the growth of vegetation in the local area (Barrios and Rodriguez, 2004; Arnett et al., 2008; Ma et al., 2019; Pătru-Stupariu et al., 2019; Xu et al., 2019; Liu et al., 2020; Urziceanu et al., 2021; Ferrer et al., 2022; Zhang et al., 2022). However, in situ data monitoring tools are limited by factors such as complex field conditions and monitoring time, making it more difficult to obtain long-term continuous observation data, which poses a challenge to parameterization-based studies.

Although in situ data monitoring has the advantages of high data accuracy and distinctive features, it is limited in scale and has a major disadvantage, especially in assessing the ecological and climatic impacts of onshore wind farms in a large-scale context. In situ data monitoring can provide reliable information for the inversion of remotely sensed data, such as inversely assessing near-surface temperatures at different times (Walsh-Thomas et al., 2012; Zhou et al., 2012; Zhou et al., 2013; Harris et al., 2014; Slawsky et al., 2015; Chang et al., 2016; Liu et al., 2021b; Luo et al., 2021; Liu et al., 2022a; Ma et al., 2022; Qin et al., 2022), vegetation indexes (Li et al., 2016; Tang et al., 2017; Xia and Zhou, 2017; Wu et al., 2019; Liu et al., 2022b; Aksoy et al., 2023), landscape patterns (Zhang et al., 2016; Diffendorfer et al., 2019; Guo et al., 2020) and other indicators in order to assess the impacts of onshore wind farms on the localized eco-climate.

Numerical model simulation can theoretically analyze and predict the climatic effects caused by the construction and operation of onshore wind farms, and facilitate research on the related mechanisms. Currently, numerical model simulations of the ecological and climatic impacts of onshore wind farms mainly focus on the impacts generated by the wind turbines themselves, such as wind speed and turbulence (Roy et al., 2004; Fitch et al., 2013; Bleeg et al., 2018; Ai et al., 2022; Kovalnogov et al., 2022), although some studies have also analyzed near-surface temperatures and vegetation growth using numerical model simulations (Li et al., 2018). Overall, relatively few studies have used numerical model simulations to analyze the eco-climatic impacts of onshore wind farms, and the problem that needs to be solved lies in the further optimization of model parameters against a background that fully considers additional factors such as land-use types and local climatic conditions.

The results of studies on the effect of onshore wind farms on the growth of vegetation communities are not consistent, but more studies have concluded that onshore wind farms have a significant inhibitory effect on vegetation growth. After the completion of onshore wind farms, some studies found that the indicators reflecting the growth of vegetation all decreased significantly. For example, the LAI, EVI, and NDVI decreased by 14.5%, 14.8%, and 8.9%, respectively, in the local area of the Damshang region in North China in summer (Tang et al., 2017). In two other studies within the land-based wind farms in the Inner Mongolia region, the Patrick richness index and Simpson dominance index of the vegetation, as well as the Pielou evenness index, Shannon-Wiener diversity index, aboveground biomass, and vegetation plant height and grassland all decreased, and the NDVI and proximity area showed changes (Wu et al., 2019; Liu et al., 2020). The effect of onshore wind farms on local vegetation in the southern mountainous region is the same as that in the northern region, and the NDVI and FVC values in a study area in Yunnan region were reduced by 7.04% and 10.02%, respectively, compared with those before the construction of onshore wind farms (Zhang et al., 2022).

The results of domestic and international studies on the impacts of onshore wind farms on local vegetation are more consistent. In the United States, 59% of the onshore wind farms showed decreasing trends in the internal NDVI index (Qin et al., 2022). The inhibition of vegetation growth led to a significant reduction in community vegetation productivity, with a direct vegetation productivity loss of 6.79 g m^-2 yr^-1 and an indirect vegetation productivity loss of 91.43 g m^-2 yr^-1 after the completion of onshore wind farms in the desert grassland area of Gansu (Ma et al., 2019). In another study, the inhibition of the summer gross primary production by onshore wind farms in Hebei after their completion was 8.9%, and the annual net primary production suppression was 4% (Tang et al., 2017). Although onshore wind farms inhibit vegetation growth, studies have found that the effect of onshore wind farm impacts was mainly concentrated inside the wind farms, and the most degraded areas were inside the wind farms, which was verified by diagnostic model tests (Qin et al., 2022; Aksoy et al., 2023).

Due to differences in the data sources and land use types in the study areas, some studies have concluded that onshore wind farms promote vegetation growth or have differential or insignificant effects. The daily average NDVI results of onshore wind farms and their neighboring areas in Inner Mongolia showed that onshore wind farms prolonged the growing season of local vegetation (Liu et al., 2022b). However, the mechanisms by which the wind farms influence the vegetation inside and outside the farms were found to be different. For example, the vegetation growth inside the wind farms was suppressed, while the vegetation growth in the upwind and downwind areas was promoted (Li et al., 2016); and although the NDVI in the study area showed an increasing trend, there was no significant difference compared with the neighboring areas (Luo et al., 2021). Since the measurement of vegetation growth indicators is based on the inversion of satellite remote sensing data, the accuracy of remote sensing data affects the reliability of the final conclusions. An analysis of NDVI and EVI of onshore wind farms in the USA revealed that the variations of both values are within the uncertainty of MODIS data, so the impact of onshore wind farms on vegetation may be related to the uncertainty and noise in the remote sensing data (Xia and Zhou, 2017).

There are relatively few studies on the effects of onshore wind farms on plant communities and functions, and their conclusions vary widely. In mountainous areas, onshore wind farms have little or no effect on the five indicators of species richness and composition of plant communities, and onshore wind farms do not affect the structure of plant communities (Pătru-Stupariu et al., 2019). In desert areas, onshore wind farms significantly improve the ecosystem functioning of the whole vegetation community due to a significant influence on the local microclimate, especially on the precipitation factor, which leads to a better physiological state of the individual plants in the wind farms, with shorter and denser plants and better cover conditions (Xu et al., 2019). However, in parts of Romania, only 40% of inventoried rare, endemic and stressed species survived in disturbed areas, while the species turnover was higher in non-disturbed areas than in disturbed areas, and onshore wind farms caused significant damage to the localized vegetation community, which still had not fully recovered within a decade (Urziceanu et al., 2021).

The construction and operation of onshore wind farms leads to an increase in maintenance roads, which encroach on the land beneath them, resulting in a decrease in the area of vegetation. In addition, the maintenance roads divide the vegetation community into numerous patches, resulting in increased patch density, shape complexity, and fragmentation of the vegetation community, which lead to increased landscape pattern indices, reduced landscape dominance, increased ecosystem vulnerability, and reduced ecosystem stability (Zhang et al., 2016; Diffendorfer et al., 2019). The cutting of the localized landscape pattern will also cause a significant increase in migratory resistance along the ecological corridors of a species, which in turn will lead to an increase in the length of the regional ecological corridors and reductions in corridor accessibility and landscape connectivity (Roscioni et al., 2014; Guo et al., 2020; Guan, 2023).

The impacts of onshore wind farms on birds and bats are focused on two aspects: the mortality of birds and bats due to collisions with turbines, and the indirect impacts on bird migration and roosting. Collisions between wind turbines and birds or bats are the direct cause of bird or bat mortality in onshore wind farm areas, and they have had a significant impact on bird and bat abundance, which has increased with the length of time that the onshore wind farms were in operation (Fielding et al., 2006; Stewart et al., 2007; Schaub, 2012; Bellebaum et al., 2013; Zhu et al., 2016). However, there is no clear relationship between species mortality and species abundance in areas where onshore wind farms are located, and the probability of bird or bat collisions depends on the flight altitude of a species, turbine height, and elevation (Lucas et al., 2008; Anderson et al., 2022). Because turbine construction locations conflict with the use of slopes for uplift by birds or bats, some studies have found that bird mortality is concentrated in the late summer and fall, as well as on nights with low wind speeds (Barrios and Rodriguez, 2004; Arnett et al., 2008). Because the turbines of onshore wind farms may be directly in the migratory paths of bird flights, there is a positive correlation between turbine height and bird mortality in general, but no direct effect in areas where turbine speeds are slower and bird flights are higher (Xu et al., 2010; de Lucas et al., 2012; Katzner et al., 2012; Loss et al., 2013). Research is still underway on the indirect effects of bird habitat occupation by onshore wind farms on bird roosting and foraging (Xu et al., 2010; Kiesecker et al., 2011; Cabrera-Cruz and Villegas-Patraca, 2016; Shaffer and Buhl, 2016). However, the closure of onshore wind farms was associated with a 61.7% reduction in mortality of local soaring birds (Ferrer et al., 2022), which suggests that the impacts of onshore wind farms on bird habitat and migration still need to be considered in a comprehensive manner.

Based on an analysis of data sources, there are three main types of data: remote sensing data, ground sensor data, and numerical model simulation data. Of the three, remote sensing data accounted for the majority of studies, and the remote sensing data are mostly MODIS data, although some studies use Landsat data, and the application of high- resolution data is relatively rare. Although studies based on numerical model simulations have concluded that onshore wind farms cause a decrease in land surface temperatures in mountain and canyon areas (Ai et al., 2022), most of the studies concluded that onshore wind farms have a significant warming effect on local land surface temperatures. One of the earlier and more influential studies showed that onshore wind farms in Texas, USA, led to a warming trend of 0.72 ℃ decade^-1 in land surface temperatures in their interior and adjacent areas compared to the adjacent non-disturbed areas, and the warming trend was especially significant at night (Zhou et al., 2012). The conclusions of existing studies on the tendency of onshore wind farms to have a warming effect on the near-surface are basically in agreement, but the specific warming amplitudes still vary, with a few suggesting that the warming amplitude can be as high as 4-8 ℃ (Walsh-Thomas et al., 2012), although most suggest that the warming does not exceed 1 ℃ in general. Studies on the timing of warming showed that the warming effect of onshore wind farms on the near-surface is concentrated at night, and is not significant during the day (Ma et al., 2022; Qin et al., 2022). A few studies have suggested that the nighttime temperature base value decreased compared to the pre-construction period (Luo et al., 2021; Liu et al., 2022a). The results of onshore wind farm warming effects in relation to seasons are inconsistent, with most studies suggesting that warming is most significant in summer and fall (Chang et al., 2016; Liu et al., 2021b; Zhang et al., 2023). The results from numerical model simulations and ground sensor measurements have further validated the findings from remote sensing data. Data from 101 ground and soil sensors at an onshore wind farm in a peatland in England show that onshore wind farms lead to a 0.18 ℃ increase in the near-surface air temperature (Armstrong et al., 2016). Monitoring of individual turbine wake data and changes in momentum, sensible heat, latent heat, and carbon dioxide passing through the turbines shows that onshore wind farms reduce the underlying vertical temperature gradient and enhance upward carbon dioxide fluxes, which in turn raise near-surface air temperatures (Smith et al., 2013; Rajewski et al., 2014). On the other hand, numerical model simulation studies have investigated small onshore wind farms, momentum sinks, and turbulent kinetic energy sources using three-dimensional climate models, the Regional Atmospheric Modeling System (RAMS), and the Mesoscale Meteorological Numerical Model (WRF), and the results suggest a warming effect of the onshore wind farms on the near-surface air temperature, with a maximum nighttime rotor bottom warming of 1 ℃ (Roy and Traiteur, 2010; Wang and Prinn, 2010; Fitch et al., 2013; Xia et al., 2016; Xia et al., 2019). To further analyze the relationships between land surface temperature variations and onshore wind farms, analyses of the spatial and temporal variability by comparing the land surface temperatures between wind pixels and non-wind pixels in a number of independent onshore wind farms have shown that the geographical distribution of the warming effect of the near-surface air is spatially coupled with the layout of the onshore wind farms (Zhou et al., 2012; Zhou et al., 2013; Harris et al., 2014; Slawsky et al., 2015).

Onshore wind farms have predominantly negative impacts on local soil physicochemical properties. Various studies have found a decrease in soil organic carbon, increases in soil bulk density, total porosity and pH, and decreases in soil conductivity, water content, total salt, total nitrogen, total carbon, total phosphorus, alkaline dissolved nitrogen, quick-acting phosphorus, quick-acting potassium, and organic matter in the disturbed areas inside and around the onshore wind farms (Smith et al., 2014; Li, 2015; Wang et al., 2015; Liu et al., 2020; Pekkan et al., 2021; Zhang et al., 2022) (Table 1).

Research on the impact of wind speed mainly includes the analysis of observational data from observation stations and numerical model simulations. Observation station data can reflect changes in the local wind speed more intuitively and accurately, while numerical model simulation explains the mechanism of the impact of onshore wind farms on wind speed from the perspective of basic principles. The data from meteorological observation towers and stations in Inner Mongolia and Hebei showed that the rates of change in the annual average wind speed and the average maximum wind speed after the completion of onshore wind farms were ‒1.5% and ‒18.5%, respectively, and the annual average wind speed showed a decreasing trend over time (Gu et al., 2016; Luo et al., 2021). The numerical model simulation results are similar, and the simulation results show a significant reduction in wind speed at the hub height level for onshore wind farms (Roy et al., 2004; Kovalnogov et al., 2022). In terms of the correlations between the timing of wind speed changes and seasonal distribution, the wind speed changes caused by onshore wind farms are mainly concentrated in spring and winter (14%), with a 10% reduction in daytime wind speeds and a 12% reduction in nighttime wind speeds (Gu et al., 2016; Liu et al., 2021b). The results of momentum sink and turbulent kinetic energy simulations show that the stable layer within the rotor region at night inhibits turbulent fusion with insufficient momentum, which leads to wake variability and larger wind speed reductions (Fitch et al., 2013). Regarding the spatial distribution of the affected wind speeds, the results are quite divergent, with some studies suggesting that upstream wind speeds are lower than downstream, and others suggesting that local wind speeds at onshore wind farms show a spatial characteristic of decreasing from upstream to downstream, small in the middle, and large on both sides under the interactions of large airflow fields (Bleeg et al., 2018; Gao et al., 2020). In addition to these patterns, onshore wind farms cause enhanced near-surface turbulence, and the increase is greater at night than during the day (Kovalnogov et al., 2022; Xia et al., 2022). Since studies on wind speed are based on numerical model simulations, and the selection of data sets and models may have a large impact on the results, different data sets may produce completely opposite simulation results (Li et al., 2023).

Onshore wind farms result in significant soil drying, with the most severe soil moisture decline of 4.4% inside the wind farms, and a negative correlation between soil moisture content and distance from the wind farms. Although the impact of onshore wind farms on local soil moisture showed seasonal and spatial variability, the exact magnitude of the impacts reported varied depending on the study area and data source (Jia et al., 2021; Liu et al., 2021b; Wang et al., 2023). The effects of onshore wind farms on localized soil moisture include effects on both soil evapotranspiration and precipitation. Onshore wind farms significantly promote localized soil evapotranspiration and crop transpiration, and the ET inside the wind farms significantly increases and is significantly higher than that in the adjacent areas. At the same time, onshore wind farms contribute to local precipitation regardless of their size, with a 1% increase in precipitation in the areas around the onshore wind farms based on the average precipitation over 62 warm seasons (Fiedler and Bukovsky, 2011). Climate models also show that onshore wind farms increase surface friction, reduce albedo, and promote vegetation growth to produce a positive albedo-precipitation-vegetation feedback loop that enhances localized precipitation (Li et al., 2018). These results show that onshore wind farms promote both localized precipitation and soil evapotranspiration, but the promotion of evapotranspiration is stronger than precipitation, which ultimately leads to a net decrease in localized soil moisture.

Land surface temperatures are influenced by two main factors in addition to wind farms, namely regional or large-scale weather and climatic conditions, and changes in topography and land-use types. The influence of a single element, onshore wind farms, on near-surface temperature changes is explained mainly in terms of changes in the atmospheric boundary layer. As the heating rate of the surface by the sun is greater than that of the air during the day, the near-surface air temperature is higher than the upper air temperature, forming a vertical distribution pattern of cold air above and warm air below, when the atmospheric boundary layer is deeper and more unstable. In the nighttime, solar radiation disappears, the surface cooling rate is greater than the cooling rate of the air, the near-surface air temperature is lower than the upper air temperature, a vertical distribution pattern forms with warm air in the upper part and cold air in the lower part, and at this time the atmospheric boundary layer is shallow and stable. Under these conditions, the vertical mixing effect generated by the wind turbine rotor wake results in a localized nighttime increase in the land surface temperature in onshore wind farms (Roy and Traiteur, 2010; Zhou et al., 2012). The enhanced vertical mixing produces a stronger nocturnal warming effect as wind speeds are greater at night than during the day, resulting in greater turbulence from the wind turbines. In addition, the air temperature changes induced by onshore wind farms are not only confined to the near-surface, but extend vertically above the hub height of the turbine and horizontally downwind (Xia et al., 2019). However, it is important to note that most of the existing studies consider the effects of large-scale weather and climatic conditions on land surface temperatures, but it is also important to note that the effects of onshore wind farms on local land surface temperatures are affected by the type of land use in the subsurface, and that land surface temperatures do not vary equally in the subsurface layers of deserts, grasslands, and mountains (Liu et al., 2022a). Therefore, to accurately analyze the impacts of onshore wind farms on the local climate, the differences in land use types in the subsurface must be considered.

Onshore wind farms mainly affect the local water cycle by influencing evapotranspiration (Luo et al., 2021). Although some studies have suggested that onshore wind farms promote localized precipitation, evapotranspiration is greater than precipitation when analyzed in terms of changes in soil water content (Li et al., 2018). It is important to note that onshore wind farms enhance land surface temperatures during the nighttime hours of the plant growing season, and the increase in land surface temperatures may further accelerate the reduction in soil water content, thereby inhibiting plant growth. Since plant growth is affected by both soil water content and land surface temperature, studies on the effects of onshore wind farms on both local land surface temperature and soil water content changes in grass-dominated northern China have shown that they result in a prolonged plant growing season, but with a reduction in the vegetation index, lower productivity, and an overall suppression of plant growth (Tang et al., 2017; Ma et al., 2019; Liu et al., 2020; Liu et al., 2022b). It should be noted that this change is not absolute, and in some of the study areas, the inhibition of plant growth only occurred inside the wind farms, while plant growth outside the wind farms was promoted (Li et al., 2016). Since different plants have different sensitivities to changes in land surface temperature and soil moisture content, and there are fewer studies on the effects of onshore wind farms on the growth of other ecosystems, future studies should focus on the variability of the ecosystems in the regions where they are located in order to accurately analyze the impacts of onshore wind farms on the local ecology.

Wind energy is a green and clean energy source that will play an increasingly important role in future socio-economic development, so it is necessary to consider its economic and social value as well as its possible ecological and climatic effects. Over the past 20 years, research on the local ecological and climatic impacts of onshore wind farms has made great breakthrough progress, especially in the observation and analysis of local microclimates and vegetation growth indices, and a large amount of data has been accumulated. However, these data are not sufficient to support a comprehensive understanding of the effects of onshore wind farms on the local ecological climate. The shortcomings of the existing studies are manifested in the visualization of the evaluation indicators, such as directly evaluating the vegetation index, land surface temperature, bird mortality rate and other intuitive indicators in the study area of onshore wind farms. Furthermore, very few studies have conducted in-depth analyses based on the intuitive indicators, so the conclusions of the studies are too straightforward, which is not conducive to the provision of guidance at the eco-climatic level. In addition, there is a limited understanding of the mechanisms between the indirect impacts of local microclimate changes and the direct impacts of wind farms, and most current studies have neglected the non-uniformity of land use types and the differences in climatic environments in the regions where the onshore wind farms are located. These limitations have led to large variations in the results of the current studies, so the conclusions of the studies are not universal, and it is difficult to clearly and accurately explain the differences in the results of the studies at the mechanistic level.

Based on this analysis of studies related to the local ecological and climatic impacts of onshore wind farms, the key directions for future development in this field are proposed.

(1) Measured data is an important basis for simulation studies, so the monitoring of indicators such as land use change, ecological corridor evolution, and others, and the monitoring of a series of key data such as land surface temperature, wind speed, soil moisture content, vegetation biomass, plant community characteristics, mortality of birds and bats, and others, should be strengthened. These efforts would further improve the accuracy, precision and continuity of measured data, and provide local ecological and climatic data that represent long time-series, large spatial scales, and high resolution. Onshore wind farms cover large areas, so even the use of all measured data is detached from reality and does not conform to the development trends. Therefore, remote sensing data with a long time span and high resolution are essential for analyzing the local ecological and climatic impacts of onshore wind farms. Specifically, it is necessary to strengthen the combination of ground- based measured data and high-precision remote sensing data, and select high-precision remote sensing data according to the ground-based monitoring indexes, in order to construct a three-level data acquisition system of air-sky-ground for the regions impacted by onshore wind farms. The data acquisition system for these three levels of the onshore wind farm area must be constructed to further improve the accuracy of the analysis. The construction of onshore wind power equipment does not have high requirements for the type of land use of the underlying surface, and the use scenarios are diversified, so it is necessary to strengthen the multi-dimensional testing of distributed and centralized onshore wind farms with different ecosystems and different land use types, in orders to grasp the impacts on local ecological and climatic indexes after their stable operation.

(2) It is crucial to propose targeted modeling systems for different scenarios that explain the impact mechanisms of onshore wind farms. Therefore, it is necessary to further improve our understanding of the local ecological and climatic impact mechanism of onshore wind farms; to strengthen the cross-disciplinary integration of energy, environmental sciences, ecology, and other disciplines; to reduce the use of empirical parameters; and to integrate indicators with regional characteristics, such as land use types, local microclimate indicators, vegetation species and other indicators into the ecological and climatic effect models in order to construct more accurate and detailed mathematical models. On this basis, we will continue to expand the scope of research, expand the scales of the model systems, and classify the models by combining ecosystems, land use and other indicators, in order to provide support for the macro understanding of the impacts of onshore wind farms on climate and ecology.

(3) Vigorously developing green energy represented by wind power will be an important choice for major countries around the world in the future. Clarifying the effects of onshore wind farms on the local ecological climate, while weighing the advantages of low carbon emission reduction brought by wind power as a green energy source and the impacts on ecological environmental protection, is an important basis for guiding the development of onshore wind farms in the next stage. Based on the model of the impacts of onshore wind farms on the local ecological climate, we can provide targeted guidance for the construction of onshore wind farms for different land use types and ecosystems, in order to realize the development goal of eco-friendliness and win-win energy development. The rational design of onshore wind farm construction locations, turbine heights, unit spacing, etc., the use of artificial ecological restoration and other means to repair the fragmentation of the landscape pattern caused by the construction, and the rational promotion of wind power industry development in areas where the onshore wind farm promotes local eco-climatic improvement will help us to realize the double enhancement of economic and ecological benefits.