## 2 Materials and Methods
### Methods of Analysis
#### 2.3.3 Methods of Meteorological Analysis  
Linear regression: Taking the reservoir operation as the boundary, the climatic tendency rate method was used to calculate the variability in temperature and precipitation elements in the two periods of 10 a before and 10 an after the start of water storage in each reservoir. The change in climate propensity before and after impoundment can remove the influence of the general background of long-term changes in the elements, and together with whether or not the elements change suddenly during impoundment, it can reflect the influence of impoundment on the elements. The slope of the linear regression model was used to describe the trend of each meteorological variable (Figure 2b).  
Climate-type M-K mutation test to obtain the climate jumping points, tested at the significance level of 0.05: The Mann-Kendall mutation test is a nonparametric statistical test with the advantage that it is not only easy to compute but also identifies the moment of mutation onset and indicates the region of the mutation period.  
Correlation Analysis Between Basic Reservoir Attributes and Climate Factors  
In this study, we analyzed the correlation between basic reservoir attributes and climate factors using the Pearson correlation coefficient (Figure 2d). The Pearson correlation coefficient measures the strength and direction of the linear relationship between two variables and is calculated as follows:  
\[R=\frac{\sum_{i=1}^{n}{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}}{\sqrt{\sum_ {i=1}^{n}{(X_{i}-\overline{X})}^{2}\sqrt{\sum_{i=1}^{n}{(Y_{i}-\overline{Y})} ^{2}}}} \tag{3}\]  
where \(X_{i}\) and \(Y_{i}\) are the ith observation of variables X and Y, respectively; \(\overline{X}\) and \(\overline{Y}\) are the means of variables X and Y; and \(n\) is the total number of observations. R is the correlation coefficient (\(-1\leq\rm{R}\leq 1\)), and the absolute value of \(R\) indicates the strength of the linear correlation.  
After analyzing the basic characteristics of the reservoir and its associated reservoirs, we identified seven quantitative reservoir control factors (RCFs): catchment area (CATCH), surface area (AREA), storage volume (CAP), mean depth (DEP), normal storage level (FSL), long-term average flow (DIS), and year of construction (YEAR). Additionally, two commonly used meteorological variables, precipitation (P) and temperature (T), were selected to represent local climate change.  
To quantify the relationship between these seven reservoir control factors and the 12 meteorological variables, we designed four metrics for each meteorological variable: T1, T2, T3, and T4. The specific definitions are as follows:  
T1: Variance in temperature series after reservoir construction.  
T2: Variance difference in temperature series before and after reservoir construction.  
T3: Long-term annual average value (LAAV) of temperature series after reservoir construction.  
T4: Difference in the LAAV of temperature series before and after reservoir construction.  
These indicators were used to analyze changes in annual (A), winter (W), and summer (S) temperatures. For precipitation (P), four similar indicators (P1-P4) were developed. The primary considerations in designing these metrics are as follows: (1) this study focuses on the long-term annual average (LAAV) and variance of each meteorological variable, which are key attributes in time series analysis, and (2) the study aims to determine whether reservoir construction has a static effect (i.e., changes in meteorological variables after reservoir construction) or a dynamic effect (i.e., differences in meteorological variables before and after reservoir construction) on these variables. Additionally, to explore the relationship between reservoir management and land use changes, the relative value of land type changes before and after construction was used as an indicator of the extent of temporal land use change.