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Precipitation is one of the many important natural factors impacting agriculture and natural resource management. Although statistics have been applied to investigate the non-stationary trend and the unpredictable variances of precipitation under climate change, existing methods usually lack a sound physical basis that can be generally applied in any location and at any time for future extrapolation, especially in tropical areas. Physically, the formation of precipitation is a result of ascending air which reduces air pressure and condenses moisture into drops, either by irregular terrain or atmospheric phenomena (e.g., via frontal lifting). Thus, in this paper, pressure change events (PCEs) will be used as a physical indicator of the stability of atmospheric systems to reveal the impact of temperature on precipitation in the tropical areas of Florida. By using data from both national and regional weather observation networks, this study segments the continuous observation series into PCE sequences for further analysis divided by dry and wet seasons. The results reveal that the frequency and intensity of PCE are highly associated with the occurrences of weather events. Decreasing pressure favors precipitation, and may turn extreme when the temperature and air moisture are sufficient to fuel the process. With similar intensity, decreasing pressure change events (DePCEs) generally bear a higher probability of precipitation (POP) and precipitation depth (PD) than increasing pressure change events (InPCEs). The frequency of alternating between InPCEs and DePCEs is subject to the temperature of the season and climate. Due to the seasonal fluctuations of weather characteristics, such as temperature and relative humidity, the dependence of extreme precipitation on these characteristics can be interpreted via PCE. A 7% increase rate of precipitation vs. temperature rise, determined by the Clausius—Clapeyron (C—C) relationship, can be observed from extreme precipitation with variances in the season and PCE types. Although indicated by other research, active vertical movement of air caused by a phase change in water at the frozen point is not pronounced in Florida. The response patterns of humidity to precipitation also vary by season and PCE types in extreme conditions. In summary, PCEs demonstrate reliable physical evidence of precipitation formation and can better associate the occurrence and intensity of extreme weather with other characteristics. In turn, such associations embody the underlying physical concepts present at any location in the world. [ABSTRACT FROM AUTHOR]

• Visibility graph algorithm (VGA) may be adapted to detect the time lag between two hydrogeological time series. • Cross-correlograms may yield incorrect results if the times series are not pre-whitened; VGA based approach will identify the correct lags without the pre-whitening step. • Multivariate simulations revealed that VGA-based approach detects the lags correctly even for small sample sizes if underlying models are of the same type. • VGA based approach detected time lags of 4 days as the most prominent lag between rainfall and water level fluctuations in Lake Okeechobee, FL. Identifying time-lag between two hydrogeological time series for planning and management of water resources has a long history and is of continuing research interest. Many hydrogeological studies in the past have used visual inspection and cross-correlogram techniques for quantifying the time lag. Cross-correlogram techniques, if not done under the transfer function framework, could lead to ambiguous results. In order to conduct cross-correlogram analysis under the transfer function framework, careful pre-processing steps have to be undertaken, which are often ignored in practice. In this paper, we propose a new approach to compare two sets of hydrogeological time series data using the visibility graph algorithm and show the advantages of using the new approach over the traditional approach. Application of the new approach is demonstrated by assessing the lags between rainfall and water level fluctuation in Lake Okeechobee, Florida. We also present simulation studies to better understand the performance of the method for different sample sizes, different underlying models and in the presence of missing values. [ABSTRACT FROM AUTHOR]