# Designing an Interactive Visual Analytics System for
## 6 Case Studies
### Case Study: Analyzing Extreme Precipitation Variability  
Multiple regions across the United States experience extreme precipitation variability. For example, the Southern California region alternates between severe drought and atmospheric river flooding [66]; Texas (particularly the Houston area) has experienced several catastrophic flood events amid drought periods [67; 68]; the Colorado Front Range shows extreme precipitation gradients from mountains to plains [69; 70]; and Oklahoma/Kansas experiences dramatic seasonal shifts and tornado-related precipitation [71; 72]. Many other regions across the country also show substantial precipitation variability due to diverse climatic and geographic influences [73]. We conducted a case study analysis of the Houston area in Texas. Historically, the region has experienced numerous significant flooding events. Spring and early summer events include the Memorial Day Flood (May 2015), the Tax Day Flood (April 2016), the 1994 Flood (November 1994), and the 1935 Flood (December 1935). Also, various flooding happened during the hurricane season as Hurricane Harvey (August 2017), Tropical Storm Imelda (September 2019), Hurricane Ike (September 2008), Tropical Storm Allison (June 2001), and the 1979 Flood from Tropical Storm Claudette (July 1979), among others [67; 74; 75]. Our designed system successfully identified most historical events by analyzing that region's precipitation. We also found that the system can detect such flooding events as possible anomalies. Figure 13 shows example analyses conducted with the system using an IETD threshold of (\(\theta=2\) h). With the seasonal PCA analysis (Figure 13A), we found an interesting pattern where many precipitation events form a cluster positioned in the left center. As discussed previously, several features are extracted and used to create a PCA analysis visualization by projecting all events on a scatterplot. The visualization shows a clear pattern forming a cluster and identifying several possible anomalies that appear outside of the cluster. Among the anomalies, we found two flooding events in 1979 and 2017, which appeared as extreme anomalies outside of the cluster. In 1979, Tropical Storm Henri moved inland near Corpus Christi and tracked northeast across Texas [76]. At that time, parts of the Houston area received 10-15 inches of rain over four days (17-21 September), which was significant enough to cause flooding problems. Although this was significant enough to cause flooding, this flooding received less historical attention than the July Claudette event in the same year due to its comparatively smaller impact [77]. In 2017, Hurricane Harvey impacted the Houston metropolitan area and southeast Texas by pouring over 60 inches for approximately 4-5 days (25-30 August 2017) [78]. In detail, we identified approximately 63 events (55 events in \(5.0\sim 10.0\) inches, 5 events in \(10.0\sim 15.0\) inches, and 3 events in \(20.0\sim\infty\) inches) in the volume-based hydrology analysis visualization that likely caused flooding problems (Figure 13B). Through anomaly analysis (Figure 13C), we found that most of these events appeared as possible anomalies using the gamma distribution. Specifically, using \(p<0.01\) and \(p<0.05\), 20 and 93 events were determined as possible anomalies, respectively. Using the normal distribution, about 176 events are identified as possible anomalies with \(z>2.0\). The Weibull distribution identified two possible extreme anomalies: Hurricane Harvey (August 2017) and the Tax Day Flood (April 2016) (Figure 13D). The Tax Day Flood was considered a significant flooding event that occurred 17-18 April 2016, during which approximately 12-20 inches of rain fell within a 24 h period [79]. We found interesting results with the monthly average precipitation visualization, showing two high peaks in 2016 (Figure 13E). The first peak appeared in April, indicating the Tax Day Flood.