4.4 Scale Dependence of Regional Rainfall Correlation

This section examines the inter-regional correlation patterns of rainfall across the 15 sub-regions of Eastern Zhejiang. Correlation matrices were computed for 3-month, 6-month, and 12-month aggregated series to reveal how spatial coherence evolves with temporal scale.

4.4.1 Three-Month Scale Correlation Analysis

Figure 24 presents the correlation matrix at the 3-month scale.

Figure 24. Inter-regional correlation matrix (2-month scale)

Statistical Overview and Spatial Patterns

The correlation analysis at the 3-month scale reveals an average correlation coefficient of 0.83 across the region, with values ranging from a minimum of 0.709 to a maximum of 1.000. The lowest correlation (0.709) is observed between the Yuyao Plain Mazhu Midstream Area and the Nansha Plain Area.

Geographically, distinct clusters emerge even at this short time scale. The Coastal Cluster exhibits high internal coherence; for instance, the correlation between Cixi East River and Cixi Midstream reaches 0.905. Similarly, the River Basin Cluster demonstrates strong hydrological connectivity, evidenced by the perfect correlation (1.000) between Yuyao Upstream and Downstream areas. However, the Yuyao Plain Mazhu Midstream Area stands out as an anomalous region, showing systematically lower correlations with most other regions (e.g., 0.709 with Nansha). This decoupling likely reflects its unique hydro-geographic conditions and high local variability, which are more pronounced at seasonal scales.

Interpretation

At the seasonal scale (3-month), local meteorological factors such as convective systems and terrain effects play a dominant role. This dominance results in significant spatial heterogeneity across the study area, leading to relatively lower inter-regional correlations compared to longer time scales.

4.4.2 Six-Month Scale Correlation Analysis

Figure 25 presents the correlation matrix at the 6-month scale.

Figure 25. Inter-regional correlation matrix (6-month scale)

Statistical Changes and Cluster Strengthening

As the aggregation scale extends to 6 months, the average correlation coefficient increases slightly to 0.85, representing a net gain of 0.02 compared to the 3-month scale. The minimum observed correlation also improves to 0.718.

This scale extension leads to a noticeable strengthening of regional clusters. The coastal cluster, in particular, shows significant improvement in internal coherence; for example, the correlation between Cixi Midstream and West River rises from 0.899 to 0.938. This enhancement suggests that semi-annual aggregation effectively filters out a portion of the high-frequency noise associated with short-term weather events, thereby revealing stronger underlying oceanic climate signals, especially in coastal zones.

4.4.3 Twelve-Month Scale Correlation Analysis

Figure 26 presents the correlation matrix at the 12-month (annual) scale, showing the highest overall spatial coherence.

Figure 26. Inter-regional correlation matrix (12-month scale)

Overall Coherence and Anomalous Patterns

The 12-month scale analysis demonstrates the highest level of spatial synchronization, with the average correlation reaching 0.87 (an increase of 0.04 from the 3-month scale). Correlation coefficients across the matrix range from 0.681 to 1.000.

A notable finding is the "Increase-then-Decrease" pattern observed in the Yuyao Mazhu Midstream Area. For several region pairs, such as with Nansha, the correlation first increases from the 3-month scale (0.709) to the 6-month scale (0.718), but then unexpectedly decreases at the 12-month scale (0.681). This anomaly suggests that while seasonal aggregation initially improves coherence, the annual total rainfall in the Mazhu area may be decoupled from regional patterns. This could be due to specific local hydrological processes, such as moisture recycling, which operate on different timescales than the broader regional climate.

In terms of spatial patterns, the Coastal Cluster reaches maximum coherence with an average correlation of approximately 0.93, driven by common large-scale marine climate forcing. In contrast, the Hilly Cluster shows the smallest increase in correlation, indicating that terrain-induced heterogeneity persists even when data are aggregated to annual scales.

4.4.4 Summary of Correlation Evolution

Table 4. Correlation Statistics Across Time Scales

Implications for Management

The scale-dependent correlation patterns have direct implications for water resource management. High-correlation regions, such as the Coastal and River Basin clusters, can be managed as coordinated systems for long-term planning, leveraging their synchronized climate response. Conversely, low-correlation regions like the Mazhu Midstream Area require independent operational strategies and robust risk management plans, as their hydrological behavior may diverge significantly from the regional average, especially during extreme years.