Risk Assessment Framework of Spatio-temporal System from Rainfall to Groundwater by Integrating Empirical Orthogonal Function, Cross Wavelet Transform, and Bayesian Network Modeling in Pingtung Plain
Date Issued
2015
Date
2015
Author(s)
Lin, Yuan-Chien
Abstract
The increasing frequency and intensity of extreme rainfall events has been observed recently in Taiwan. Particularly, Typhoon Morakot, Typhoon Fanapi, and Typhoon Megi consecutively brought record-breaking intensity and magnitude of rainfalls to different locations of Taiwan in these two years. However, records show the extreme rainfall events did not elevate the amount of annual rainfall accordingly. Conversely, the increasing frequency of droughts has also been occurring in Taiwan. The challenges have been confronted by governmental agencies and scientific communities to come up with effective adaptation strategies for natural disaster reduction and sustainable environment establishment. Groundwater has long been a reliable water source for a variety of domestic, agricultural, and industrial uses because of its stable quantity and quality. In Taiwan, groundwater accounts for the largest proportion of all water resources for about 40%. Bayesian networks (BN) is one of the probabilistic graphical models that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG), which can help us to connect the probabilistic causal relationship between random variables with those probability distributions evaluated from different methods. In this study, first, the Empirical Orthogonal Function (EOF) method is used for investigate the spatial relationship of groundwater, the area of confined and unconfined aquifer, and the most important recharge zone can be identified. Second, investigate the time-frequency relationship between rainfall and groundwater level signals by using wavelet coherence method in order to figure out the latent connections. Estimating the human and natural effect on groundwater recharge and discharge amount spatially. Further, the information of EOF and wavelet analysis is integrated into the framework of Bayesian Network. The risk assessment of low groundwater level based on the framework not only has a good performance of cross validation but also provides conditional probability map in Pingtung Plain indicating the possible direction of groundwater flow. The lateral groundwater flow primary from alluvial fans in the east side of mountains and flow into plain area then tends to flow along with the main rivers in Pingtung Plain.
Subjects
Groundwater level
Empirical Orthogonal Function
Cross Wavelet Analysis
Bayesian Network
Type
thesis