Location
Hill Aerospace Museum, Theater
Start Date
5-6-2014 2:30 PM
Description
Increasing rates in sea-level rise imply drastic consequences for U.S. coastal populations, infrastructure, ecological systems, and natural resources in the coming decades. These direct impacts will lead to negative repercussions in public health, biodiversity, tourism, and other aspects of the global economy. Using hourly tide readings from the past 30 years at 38 gauges along the east coast, we wish to develop a model that will allow us to analyze the trends in this type of data and to accurately and precisely predict sea-level change along the east coast. The model developed is an iterative generalized additive model that will use spatial and temporal dependence between gauges and across time, allowing us to predict sea-level change all along the east coast, not only at the stations for which we have data. Here, the methodology and components of our current model will be discussed as well as an overview of results. We will also address the model's shortcomings and the work that is currently being done to improve the accuracy and efficiency of its predictions.
Analyzing Sea-Level Change on the East Coast with Spatiotemporally Correlated Data
Hill Aerospace Museum, Theater
Increasing rates in sea-level rise imply drastic consequences for U.S. coastal populations, infrastructure, ecological systems, and natural resources in the coming decades. These direct impacts will lead to negative repercussions in public health, biodiversity, tourism, and other aspects of the global economy. Using hourly tide readings from the past 30 years at 38 gauges along the east coast, we wish to develop a model that will allow us to analyze the trends in this type of data and to accurately and precisely predict sea-level change along the east coast. The model developed is an iterative generalized additive model that will use spatial and temporal dependence between gauges and across time, allowing us to predict sea-level change all along the east coast, not only at the stations for which we have data. Here, the methodology and components of our current model will be discussed as well as an overview of results. We will also address the model's shortcomings and the work that is currently being done to improve the accuracy and efficiency of its predictions.