Analyzing Sea-Level Change on the East Coast with Spatiotemporally Correlated Data

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\u27s shortcomings and the work that is currently being done to improve the accuracy and efficiency of its predictions

An Empirical Mode-Spatial Model for Environmental Data Imputation

Complete and accurate data are necessary for analyzing and understanding trends in time-series datasets; however, many of the available time-series datasets have gaps that affect the analysis, especially in the earth sciences. As most available data have missing values, researchers use various interpolation methods or ad hoc approaches to data imputation. Since the analysis based on inaccurate data can lead to inaccurate conclusions, more accurate data imputation methods can provide accurate analysis. We present a spatial-temporal data imputation method using Empirical Mode Decomposition (EMD) based on spatial correlations. We call this method EMD-spatial data imputation or EMD-SDI. Though this method is applicable to other time-series data sets, here we demonstrate the method using temperature data. The EMD algorithm decomposes data into periodic components called intrinsic mode functions (IMF) and exactly reconstructs the original signal by summing these IMFs. EMD-SDI initially decomposes the data from the target station and other stations in the region into IMFs. EMD-SDI evaluates each IMF from the target station in turn and selects the IMF from other stations in the region with periodic behavior most correlated to target IMF. EMD-SDI then replaces a section of missing data in the target station IMF with the section from the most closely correlated IMF from the regional stations. We found that EMD-SDI selects the IMFs used for reconstruction from different stations throughout the region, not necessarily the station closest in the geographic sense. EMD-SDI accurately filled data gaps from 3 months to 5 years in length in our tests and favorably compares to a simple temporal method. EMD-SDI leverages regional correlation and the fact that different stations can be subject to different periodic behaviors. In addition to data imputation, the EMD-SDI method provides IMFs that can be used to better understand regional correlations and processes

Spatial and Covariate-Varying Relationships Among Dominant Tree Species in Utah

The presence and establishment of a tree species at a particular spatial location is influenced by multiple physiological and environmental filters such as propagule pressure (seed availability), light and moisture availability, and slope and elevation. However, a less understood environmental filter to species-specific establishment is competition or facilitation between dominant tree species. For example, certain tree species may compete for resources at spatial locations where such resources are scarce while less competition may occur at resource-rich areas. Using data from the Forest Inventory and Analysis (FIA) program of the United States Department of Agriculture (USDA) Forest Service, we develop a multivariate spatial Bernoulli model to investigate the space-varying relationship between extant tree species in Utah. Additionally, we propose a novel modeling strategy that explains the spatially varying relationships by regressing the associated between-species correlation matrix on available covariate data. Positive definite conditions of the covariate-varying correlation matrix are ensured by defining the regression in terms of the unique partial correlation matrix. Results indicate that correlations between species are dependent upon elevation