## 4 Results and Discussion
### Influence Factors
#### 4.2.3 Comparison of the Effects of Climate Factor and Human Activities on Water-Surface Area  
In comparing the effects of climate factors and human activities on the water-surface area of the Honghu Lake, multiple linear regression models are developed, one for the April-May period and one for the June-October period. Before modeling, these effect factors are standardized using \(Z\)-score transforms based on the IBM SPSS Statistics 20. The results of the regression analysis are shown in Table 3.  
\[Y=\texttt{a}P+b(\texttt{P}-\texttt{E})+cR_{\texttt{i}}+dR_{\texttt{2}}+eR_{ \texttt{3}} \tag{9}\]  
where \(Y\) is the water-surface area, \(P\) is the monthly precipitation, \(P-E\) is the difference between the monthly precipitation and monthly evaporation, \(R_{\texttt{i}}\) is the water demand of drought crops, \(R_{\texttt{2}}\) is the water demand of paddy rice and \(R_{\texttt{3}}\) is the water demand of aquaculture.  
Accordingly, these regression models have statistical significance (\(p<0.05\)) that suggests the capability of the models to indicate the degree of influence of each factor on the water-surface area in different periods. As expected, the fitness of the equations in the April-May periods is higher than that in the June-October periods. Comparing the influence coefficient of each factor, we can observe the impact of climate factors and human activities on the water-surface area variations. In the April-May period, the sum of the water demand of paddy rice and that of aquaculture has a negative effect on the water-surface area; the effect of the sum of these factors is greater than the positive effect of the difference between the monthly precipitation and monthly evaporation. However, the precipitation from June to October has positive impacts on the water-surface area of the lake; such impacts exceed the negative effects of the water demand of agriculture, except in August and September. In August, the negative effects of the water demand of paddy areas outweigh the positive influence of precipitation. Meanwhile, in September, the negative influence of the water demand of dryland outweighs the positive influence of precipitation. Moreover, the water demand of the same land use type has different degrees of impact in each month depending on the water consumption during the different growth stages. Therefore, the water-surface area during June-October period is larger than that in the April-May period but does not indicate a pronounced tendency to increase with the increase in precipitation.  
\begin{table}
\begin{tabular}{c c c} \hline
**Period** & **Multiple Linear Regression Model** & \(\boldsymbol{R^{2}}\) \\ \hline April–May & \(Y=0.219(\texttt{P}-\texttt{E})-0.520(\ R_{\texttt{2}}+R_{\texttt{3}})\) & 0.684 \\ June & \(Y=0.716\) P \(-1.174\,R_{\texttt{1}}+0.564\,R_{\texttt{2}}+1.43\,1R_{\texttt{3}}\) & 0.643 \\ July & \(Y=0.603\) P \(+0.386\,R_{\texttt{1}}-0.49\,1R_{\texttt{2}}-0.01\,R_{\texttt{3}}\) & 0.606 \\ August & \(Y=0.405\) P \(+0.525\,R_{\texttt{1}}-0.587\,R_{\texttt{2}}-0.130\,R_{\texttt{3}}\) & 0.518 \\ September & \(Y=0.365\) P \(-0.540\,R_{\texttt{1}}-0.036\,R_{\texttt{2}}+0.296\,R_{\texttt{3}}\) & 0.513 \\ October & \(Y=0.638\) P \(+0.104\,R_{\texttt{1}}+0.584\,R_{\texttt{2}}\) & 0.234 \\ \hline \end{tabular}
\end{table}
Table 3: Regression analysis results from April to October.