Semester

Spring

Date of Graduation

2010

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Civil and Environmental Engineering

Committee Chair

Lian-Shin Lin

Abstract

Climate change on global scale has been studied by many researchers while regional studies are limited. The most recent international summary of climate change science (IPCC 2007) suggested a remarkable trend of increasing global surface temperature but no statistically significant trend in precipitation on a global average due to large variability in both space and time. Thus, this study focused on detecting climate changes and effects in the Mid-Atlantic Region, an important ecological system in the US and the world. Climate data from 25 stations with daily measurements for more than 100 years from the National Climate Data Center were analyzed.;The primary aim of this study was to examine the trends of the air temperature and precipitation time series for all the available stations in the region using the Mann Kendall statistical test. The Z statistic for each season as well as for the whole time period was calculated. The median slope of trends was estimated by Sen's method. Regional trends were formed by statistically combining the results of the Mann Kendall test for each individual trend. Extreme event indices were developed to study the trends of severe conditions. Winter and summer seasons were selected to study seasonal effects.;The secondary objective of this study was to detect relations between landscape attributes (e.g., elevation, altitude, forestry) and the long-term trend. The analyses were expected to provide information that can help answer questions related to regional climate changes such as elevation-dependent climate changes in this region. Stations were divided into three groups---low, medium, high according to their elevation. Kruskal-Wallis test was performed to detect the differences among the three groups.;All statistical analyses were conducted using SAS software 9.1 and SYSTAT 12. Results indicated variations between stations. Both negative and positive trends for all parameters were detected. Homogeneous trend was not found for the whole region. Our results showed coastal stations and inland stations have opposite results. One of the possible reasons could be the bigger cities and faster population growth rate at the coastal area which urbanization effect was more pronounced. Annual trends and seasonal trends were consistent for most parameters. The only differences was the increasing rate of warm days in winter compared with decreasing rate at most stations for summer and annual trends. Due to the insignificant of the differences among our three elevation groups, the dependency was not well established. Our study is the first look of the available data, more work are needed to justify the results.

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