# Designing an Interactive Visual Analytics System for
## 2 Previous Work
In addition, an overall increasing trend in annual temperature was observed, although the maximum and minimum temperatures during the monsoon season showed a decline. [PERSON] and [PERSON] [23] examined evolving flooding patterns. They employed a block maximum approach to extract the largest daily values within each block. Regression analysis in conjunction with the Mann-Kendall test was employed to identify patterns in the frequency and magnitude of floods over time. They investigated seasonal patterns and the impact of temperature and precipitation as possible causes of the pattern changes. They found little evidence of significant changes in flood peak magnitudes but discovered strong evidence that the frequency of flood events rises. In particular, the central and eastern parts of the region experienced more frequent and severe consecutive wet days and heavy precipitation events. A higher flood frequency was due to seasonal rainfall patterns and temperature changes. The last stream of research focuses on analyzing specific precipitation events that address severity, frequency, and spatial variation. These studies attempt to improve the knowledge of extreme weather events and their impact on flood risk management and urban development. For example, [PERSON] et al. [24] examined spatial heterogeneity and frequency of extreme rainfall events in the Baltimore Metropolitan region. They created a comprehensive storm catalog that includes significant storm properties like rainfall intensity, duration, and spatial coverage using a 16-year high-resolution radar rainfall dataset. Spatial distribution and frequency of extreme rainstorm events were then assessed using the stochastic storm transposition (SST) method [25]. From examining two extreme storm events, they demonstrated that the storm catalog with SST effectively captures rainfall spatial and temporal variability. This combined approach provides an effective framework for rainfall intensity-duration-frequency (IDF) estimation in urban planning and flood risk management. [PERSON] et al. [26] analyzed rainfall intensity and duration data from 1996 to 2019 for the northeastern region of the Nagpur district, India. They applied three statistical distribution methods to develop IDF curves--Log-Normal, Gumbel, and Log-Pearson Type III. The Gumbel distribution was identified as the most suitable model through goodness-of-fit tests and the least sum of squares model identification criterion (LSSMIC). [PERSON] et al. [27] used a statistical functional data analysis (FDA) approach with visualization techniques to analyze daily rainfall data collected in the Taiz Region between 1998 and 2018. As a statistical approach, FDA is good for analyzing time series data as it displays data as smooth curves or functions. Discrete rainfall measurements were converted into continuous functional forms using Fourier basis functions. These functional data were then smoothed through penalized smoothing based on a generalized cross-validation (GCV) criterion to reduce noise and errors and estimate the curves. Subsequently, functional statistical measures--including the mean, standard deviation, covariance, and correlation--were computed to illustrate rainfall variation over time. In particular, singular value decomposition (SVD) [28] was applied to visualize patterns of the functional data. The study showed that the FDA and visualization methods can provide meaningful insights into rainfall patterns and future trends. Understanding rainfall event patterns is crucial for generating precipitation analysis models and designing urban drainage systems. However, it is difficult to identify independent rainfall events from continuous rainfall records, which consist of sequential pulse (rain periods). Inter-event time definition (IETD) is commonly used to isolate distinct events, which is the minimum dry period between two consecutive rainfall pulses (see Section 4 for details about the IETD analysis).