Quantifying the Contributions of Environmental Factors to Wind Characteristics over 2000–2019 in China

Abstract

Global climate change and human activities have resulted in immense changes in the Earth’s ecosystem, and the interaction between the land surface and the atmosphere is one of the most important processes. Wind is a reference for studying atmospheric dynamics and climate change, analyzing the wind speed change characteristics in historical periods, and studying the influence of wind on the Earth-atmosphere interaction; additionally, studying the wind, contributes to analyzing and alleviating a series of problems, such as the energy crisis, environmental pollution, and ecological deterioration facing human beings. In this study, data from 697 meteorological stations in China from 2000 to 2019 were used to study the distribution and trend of wind speed over the past two decades. The relationships between wind speed and climate factors were explored using statistical methods; furthermore, combined with terrain, climate change, and human activities, we quantified the contribution of environmental factors to wind speed. The results show that a downward trend was recorded before 2011, but overall, there was an increasing trend that was not significant; moreover, the wind speed changes showed obvious seasonality and were more complicated on the monthly scale. The wind speed trend mainly increased in the western region, decreased in the eastern region, was higher in the northeastern, northwestern, and coastal areas, and was lower in the central area. Temperature, bright sunshine duration, evaporation, and precipitation had a strong influence, in which wind speed showed a significant negative correlation with temperature and precipitation and vice versa for sunshine and evapotranspiration. The influence of environmental factors is diverse, and these results could help to develop environmental management strategies across ecologically fragile areas and improve the design of wind power plants to make better use of wind energy.

Downscaling wind speed based on coupled environmental factors and machine learning

“…The elevations of the topography and landforms vary (Figure 1b), the climate is complex and heterogeneous (Figure 1c) (Beck et al, 2018) and there are large regional differences in land surface temperature (LST) variability. Sunshine duration (SUNT) is lower in the southeast and higher in the northwest and increases from southeast to northwest, showing strong regional differences (Lu et al, 2021). Thus, it is challenging to map and generate high‐resolution spatiotemporal surfaces showing the meteorological factors, for example, wind speed, for such a large region with considerable regional heterogeneity (Li, 2019).…”

“…As air flows over different types of surfaces, each surface feature affects airflow. Additionally, wind speed variations are distinctly seasonal, caused mainly by subsurface changes and human activities (Lu et al, 2021). In rural areas, the variation in the substrate is caused by the sowing, growing, harvesting and crop rotation.…”

“…The effects of the interactions between the surface and atmospheric processes at various temporal and spatial scales as well as the exchange of momentum, energy and mass are important (Stonestrom, 2014; Suni et al, 2015). Climate conditions have a substantial impact on the environment, and wind speed is very important for the study of land–atmosphere interactions (Lu et al, 2021; Zhang et al, 2004). Wind is the important parameter used in the basic system of equations describing the thermal and dynamic state of the atmosphere.…”

Method for Environmental Flows Regulation and Early Warning with Remote Sensing and Land Cover Data

“…To be consistent with the resolution of the precipitation and ET data, the majority sampling method was adopted to resample the land cover and land use products to a 1 km spatial resolution. Previously, Landsat TM/ETM data and Chinese HJ-1 satellite data were used to map land cover, with the acquired images classified into forestlands, grasslands, croplands, wetlands, built-up lands and other lands [49]. The overall accuracy of the first-class China Land Cover data exceeded 94% [50], and the overall accuracy of the second-class data exceeded 86% [51].…”

Interaction between wind speed and net radiation controls reference evapotranspiration variance in the inland river basin of Northwest China

“…RH affects the U 2 threshold by affecting the surface soil moisture and viscous force between sand particles (Yang et al, 2019). The decrease in the T difference between the poles and the equator may slow global air circulation; as a result, local U 2 may increase because of local T gradients in certain areas (Lu et al, 2021; Pirazzoli & Tomasin, 2003). Thus, it is important to quantitatively consider the interaction effect of climatic variables on ET 0 variability by improving the current attribution method.…”