## 3 Results
### Evaluation of Basic Wind Speed and Wind Pressure Height Variation Coefficients
However, due to the impact of climate change and other non-static factors, the standard return levels may no longer be accurate. Effective return levels consider factors that change over time, allowing return levels to change over time while keeping the probability of the event occurring constantly. This means that, over time, even though the physical level of the event (such as wind speed) changes, the probability of the same risk occurring remains unchanged [50], as shown in Figure 13.  
In the figure, the effective return level plot takes time points as the horizontal axis and uses different curves to depict different return periods. The black line represents the maximum wind speed that fluctuates over time. It adjusts the risk levels at different time points to reflect changes over time. However, considering the uncertainty of the data and potential future trends, when using effective return levels to make risk assessments, specific practical applications and focal points need to be considered. When setting design standards, to ensure safety under extreme conditions, we generally choose the least favorable conditions' effective level for calculation. In this study, we select the maximum value among the effective return levels as the basic wind speed for a specific return period, and we compare the results with those calculated using the stationary GEV model and China's \"Load Code for the Design of Building Structures\" GB50009-2012 [51]. The comparison is shown in Figure 14 and Table 8.  
Figure 12: Standard return level plot for the stationary GEV model. Figure 13: Effective return level plot for non-stationary GEV model variations in both location and scale parameters. From the results, it can be observed that the basic wind speeds calculated by the stationary GEV model for all return periods are lower than the calculated values in the standards. The results from the QQ plots in the previous section indicate that its capturing capability for the maximum wind speeds at medium to high quantiles is not excellent. In the GEV2 model, which simultaneously considers changes in both location and scale parameters, the calculated basic wind speeds overall exceed those calculated in the standards. Only at higher return periods do the values become relatively close (15.28-16.39). This might be attributed to its capture of more extreme values in the time series, where the dynamic return levels provide a more stable benchmark for risk assessment, indicating that the basic wind speed values calculated by the standards over long time series may be underestimated without considering trend effects. The basic wind speed values are obtained after adjusting the maximum wind speed to a height of 10 m. However, the wind profile analysis in Section 3.1 indicates that the relationship between wind speeds at different heights also varies over time (wind profile indices show an increasing trend). In the standard [51], calculating the design wind load for specific heights uses fixed wind pressure height variation coefficients. For terrain features categorized as Class C and Class D (urban terrain features defined in the standards [51]), the empirical formulas for wind pressure height variation coefficients are, respectively, given by Equation (17):  
\[ \begin{cases}\mu^{C}=0.544(\frac{z}{10})^{0.44}\\ \mu^{D}=0.262(\frac{z}{10})^{0.60}\end{cases}  \tag{17}\]  
where \(\mu^{C}\) represents the wind pressure height variation coefficient for Class C terrain features, \(\mu^{D}\) represents the wind pressure height variation coefficient for Class D terrain features, \(z\) corresponds to the height value, and the empirical coefficients 0.544 and 0.262, respectively, represent the surface roughness coefficients. The formulas are derived based on wind profile equations and wind pressure calculation formulas, and the specific derivation process is not elaborated here.