Dissertation/Thesis Abstract

Use of Historical Data to Assess and Forecast Regional Climate Change and Implications for Engineering Applications
by Lai, Yuchuan, Ph.D., Carnegie Mellon University, 2020, 436; 28149152
Abstract (Summary)

Future regional climate information under non-stationary climate change conditions is critical for civil and environmental engineering applications to ensure the safety, durability, and reliability of infrastructure. The applications of regional climate information in engineering requires an interdisciplinary process that can bridge future climate conditions provided by the climate science community with design procedures utilized in the engineering community. Due to the barriers posed by professional cultures, codes, and institutions, substantial challenges currently exist for engineers in terms of identifying and evaluating sources of regional climate observations and projections–projections from global climate models, or GCMs, specifically. There is limited engineering guidance for acquiring and applying regional future climate information, and a lack of comprehensive evaluation of underlying uncertainties of climate projections.

The main goal of this research was to develop methods for improved regional temperature and precipitation change information for engineering with the use of long-term historical city-level climate records. The utilization of historical climate data provides an alternative to the more commonly employed approach of applying GCM projections and downscaling techniques. Six research objectives were identified and pursued individually: 1) compile and assess long-term regional (city-specific) historical temperature and precipitation records; 2) develop a statistical near-term regional temperature and precipitation forecasting model based on historical observations; 3) identify and evaluate the commonly utilized procedures and corresponding uncertainties of applying climate model projections in engineering applications; 4) compare and assess the performance of near-term projections from the developed statistical forecasting model and a GCM downscaling product; 5) utilize historical and projected ambient air temperature to assess water temperature and water quality in drinking water distribution systems, as an example engineering application; and 6) develop and investigate the integration of GCM simulations and statistical forecasting for regional climate projections.

The results of this work demonstrate that long-term historical temperature and precipitation observations are available for many cities in the U.S. and are informative and useful for engineering applications. Station-level daily temperature and precipitation records starting from as early as 1870s were obtained for 210 U.S. cities through the compilation effort in this work. Evaluate of these data revealed strong temporal correlation and skewed distributions in some time series of temperature and precipitation indices. A statistical time series forecasting technique–the autoregressive integrated moving average (ARIMA) model–was consequently utilized and further developed to provide projections of annual averages, annual extremes, and daily simulations of temperature and precipitation at individual cities.

Evaluation of the current use of GCM projections in engineering suggested substantial challenges for engineers with respect to the underlying complexity and sources of uncertainties. Application of the ARIMA model for near-term regional climate projections showed efficiency and sometimes improved accuracy when compared to the GCM-downscaling approach such as the localized constructed analogs (LOCA) downscaling product assessed in this work.

A chain-of-models approach was developed to provide assessments of drinking water temperature and water quality in drinking water distribution systems as an example of linking regional climate projections with a particular engineering application. Moderate changes were estimated for drinking water temperature and water quality parameters for the assessed historical and projected near-term periods across different U.S. cities. Extreme scenarios such as the warmest days of a year and the distribution system locations with the largest water age values merit attention and further research.

Combining GCM simulations and statistical forecasting based on regional observations, a hybrid regional climate projection model coupled with the ARIMA model (G-ARIMA) was developed and investigated. The results showed that the hybrid G-ARIMA model can describe well the existing historical climate variability and climate change trend in historical observations for a particular location, and also reduce the model bias from GCMs with efficiency when providing regional climate projections.

Considering the increasing climate change impacts on infrastructure and the importance of obtaining improved location-specific climate change information for engineering applications, long-term historical climate observations at particular locations can serve as an essential source of information for infrastructure engineering. This work aimed to contribute to development of approaches for bringing climate records and climate models to making location-specific projections for use in engineering. Additional research and further progress are needed. With more research on engineering adaptation to climate change on the horizon, this work serves as an example of an integrative research effort to bring together regional climate observations and projections, statistical data analyses, and engineering practice to help lay a foundation of continuing development and progress on facilitating engineering with improved future regional climate information.

Indexing (document details)
Advisor: Dzombak, David A
Commitee: Pozzi, Matteo, Samaras, Constantine, Vaishnav, Parth
School: Carnegie Mellon University
Department: Civil and Environmental Engineering
School Location: United States -- Pennsylvania
Source: DAI-B 82/4(E), Dissertation Abstracts International
Subjects: Civil engineering, Environmental engineering
Keywords: Climate change, Drinking water, Engineering adaptation, Regional climate projection, Statistical forecasting
Publication Number: 28149152
ISBN: 9798678182258
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