# Designing an Interactive Visual Analytics System for
## 6 Case Studies
### Case Study: Analyzing Extreme Precipitation Variability
The second peak represents another flooding event in June 2016, the Brazos River Flooding, which occurred in early to mid-June 2016. The overall rainfall was 5 to 12 inches, but it caused flooding due to approximately two weeks of continuous rainfall. This flooding event was not detected as a possible anomaly in the precipitation anomaly detection because it does not consider event duration when detecting anomalies. However, it was recognized as a possible flooding event in the monthly average precipitation visualization, which considers overall rainfall amount and duration information when performing monthly average precipitation analysis. By evaluating these two flooding events, we found clear distinctions between them. The measured errors are displayed when the user highlights each glyph (see Figure 13D). We found a higher SD (\(\pm\)1.8907) for the Tax Day Flood, indicating more significant variability in rainfall amounts across the study area during the April event. In contrast, the Brazos River Flooding showed a lower SD (\(\pm\)1.0727), representing a more uniform rainfall distribution during the June event. This aligns with characterizing the June event as a persistent, widespread rainfall pattern rather than intense localized downpours [80]. Because more consistent rainfall occurred across the region, it contributed to riverine flooding. For SE  
Figure 13: Comprehensive analysis of precipitation events in Houston using IETD threshold of (\(\theta=2\) h). (**A**) Seasonal PCA analysis showing event clustering and identification of significant anomalies, including flooding events from 1979 and 2017. (**B**) Volume-based hydrology analysis highlighting 63 potential flooding events across different intensity levels. (**C**) Anomaly detection using gamma distribution identifying multiple possible anomalies at different probability thresholds. (**D**) Weibull distribution analysis emphasizing extreme anomalies like Hurricane Harvey (2017) and the Tax Day Flood (2016). (**E**) Monthly average precipitation visualization for 2016 showing peaks corresponding to the Tax Day Flood (April) and Brazos River Flooding (June) with their respective statistical measures. (**F**) Monthly average precipitation analysis for 2017 highlighting Hurricane Harvey with its distinctive statistical characteristics (lower SD, very low SE, narrow CI) despite causing catastrophic flooding. measurements, we found greater uncertainty in estimating the true mean precipitation for the Tax Day Flood due to highly variable rainfall patterns (\(\pm\)0.2818). However, the June event showed a lower SE (\(\pm\)0.1517), indicating more reliable patterns because of consistent spatial rainfall distribution. The CI showed wider and narrower confidence intervals for the Tax Day Flood (\(\pm\)0.5524) and the Brazos River Flooding (\(\pm\)0.2973), respectively. These results show that the June event had a more consistent, persistent rainfall pattern than the more variable April event. We also performed monthly average precipitation analysis on 2017 data (Figure 13F). As Hurricane Harvey is considered one of the most catastrophic and well-documented rainfall events in U.S. history [81], evaluating these data helped determine the usefulness of our designed system. With the system, the average monthly precipitation for the Hurricane Harvey event was found to be 5.54 inches. Historically, it is known that some regions had 40-60 inches [78,82]. It is important to note that the 5.54 inch value represents the average monthly precipitation measured at the Texas Houston International Airport based on IETD precipitation data. Although Hurricane Harvey caused significant flooding through heavy rainfall, it showed a relatively low SD (\(\pm\)0.9388) compared to the 2016 events, representing a more uniform spatial distribution of precipitation. This reflects the hurricane's unique meteorological characteristics as a stalled tropical system [81] rather than a concentrated band of extreme precipitation like the Tax Day Flood (\(\pm\)1.8907).