Spatio-temporal Statistical Modeling: Climate Impacts due to Bioenergy Crop Expansion

abstract: Large-scale cultivation of perennial bioenergy crops (e.g., miscanthus and switch- grass) offers unique opportunities to mitigate climate change through avoided fossil fuel use and associated greenhouse gas reduction. Although conversion of existing agriculturally intensive lands (e.g., m...

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Other Authors: Wang, Meng (Author)
Format: Doctoral Thesis
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/2286/R.I.50563
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spelling ndltd-asu.edu-item-505632018-10-02T03:01:11Z Spatio-temporal Statistical Modeling: Climate Impacts due to Bioenergy Crop Expansion abstract: Large-scale cultivation of perennial bioenergy crops (e.g., miscanthus and switch- grass) offers unique opportunities to mitigate climate change through avoided fossil fuel use and associated greenhouse gas reduction. Although conversion of existing agriculturally intensive lands (e.g., maize and soy) to perennial bioenergy cropping systems has been shown to reduce near-surface temperatures, unintended consequences on natural water resources via depletion of soil moisture may offset these benefits. In the effort of the cross-fertilization across the disciplines of physics-based modeling and spatio-temporal statistics, three topics are investigated in this dissertation aiming to provide a novel quantification and robust justifications of the hydroclimate impacts associated with bioenergy crop expansion. Topic 1 quantifies the hydroclimatic impacts associated with perennial bioenergy crop expansion over the contiguous United States using the Weather Research and Forecasting Model (WRF) dynamically coupled to a land surface model (LSM). A suite of continuous (2000–09) medium-range resolution (20-km grid spacing) ensemble-based simulations is conducted. Hovmöller and Taylor diagrams are utilized to evaluate simulated temperature and precipitation. In addition, Mann-Kendall modified trend tests and Sieve-bootstrap trend tests are performed to evaluate the statistical significance of trends in soil moisture differences. Finally, this research reveals potential hot spots of suitable deployment and regions to avoid. Topic 2 presents spatio-temporal Bayesian models which quantify the robustness of control simulation bias, as well as biofuel impacts, using three spatio-temporal correlation structures. A hierarchical model with spatially varying intercepts and slopes display satisfactory performance in capturing spatio-temporal associations. Simulated temperature impacts due to perennial bioenergy crop expansion are robust to physics parameterization schemes. Topic 3 further focuses on the accuracy and efficiency of spatial-temporal statistical modeling for large datasets. An ensemble of spatio-temporal eigenvector filtering algorithms (hereafter: STEF) is proposed to account for the spatio-temporal autocorrelation structure of the data while taking into account spatial confounding. Monte Carlo experiments are conducted. This method is then used to quantify the robustness of simulated hydroclimatic impacts associated with bioenergy crops to alternative physics parameterizations. Results are evaluated against those obtained from three alternative Bayesian spatio-temporal specifications. Dissertation/Thesis Wang, Meng (Author) Kamarianakis, Yiannis (Advisor) Georgescu, Matei (Advisor) Fotheringham, Stewart (Committee member) Moustaoui, Mohamed (Committee member) Reiser, Mark (Committee member) Arizona State University (Publisher) Statistics eng 216 pages Doctoral Dissertation Statistics 2018 Doctoral Dissertation http://hdl.handle.net/2286/R.I.50563 http://rightsstatements.org/vocab/InC/1.0/ 2018
collection NDLTD
language English
format Doctoral Thesis
sources NDLTD
topic Statistics
spellingShingle Statistics
Spatio-temporal Statistical Modeling: Climate Impacts due to Bioenergy Crop Expansion
description abstract: Large-scale cultivation of perennial bioenergy crops (e.g., miscanthus and switch- grass) offers unique opportunities to mitigate climate change through avoided fossil fuel use and associated greenhouse gas reduction. Although conversion of existing agriculturally intensive lands (e.g., maize and soy) to perennial bioenergy cropping systems has been shown to reduce near-surface temperatures, unintended consequences on natural water resources via depletion of soil moisture may offset these benefits. In the effort of the cross-fertilization across the disciplines of physics-based modeling and spatio-temporal statistics, three topics are investigated in this dissertation aiming to provide a novel quantification and robust justifications of the hydroclimate impacts associated with bioenergy crop expansion. Topic 1 quantifies the hydroclimatic impacts associated with perennial bioenergy crop expansion over the contiguous United States using the Weather Research and Forecasting Model (WRF) dynamically coupled to a land surface model (LSM). A suite of continuous (2000–09) medium-range resolution (20-km grid spacing) ensemble-based simulations is conducted. Hovmöller and Taylor diagrams are utilized to evaluate simulated temperature and precipitation. In addition, Mann-Kendall modified trend tests and Sieve-bootstrap trend tests are performed to evaluate the statistical significance of trends in soil moisture differences. Finally, this research reveals potential hot spots of suitable deployment and regions to avoid. Topic 2 presents spatio-temporal Bayesian models which quantify the robustness of control simulation bias, as well as biofuel impacts, using three spatio-temporal correlation structures. A hierarchical model with spatially varying intercepts and slopes display satisfactory performance in capturing spatio-temporal associations. Simulated temperature impacts due to perennial bioenergy crop expansion are robust to physics parameterization schemes. Topic 3 further focuses on the accuracy and efficiency of spatial-temporal statistical modeling for large datasets. An ensemble of spatio-temporal eigenvector filtering algorithms (hereafter: STEF) is proposed to account for the spatio-temporal autocorrelation structure of the data while taking into account spatial confounding. Monte Carlo experiments are conducted. This method is then used to quantify the robustness of simulated hydroclimatic impacts associated with bioenergy crops to alternative physics parameterizations. Results are evaluated against those obtained from three alternative Bayesian spatio-temporal specifications. === Dissertation/Thesis === Doctoral Dissertation Statistics 2018
author2 Wang, Meng (Author)
author_facet Wang, Meng (Author)
title Spatio-temporal Statistical Modeling: Climate Impacts due to Bioenergy Crop Expansion
title_short Spatio-temporal Statistical Modeling: Climate Impacts due to Bioenergy Crop Expansion
title_full Spatio-temporal Statistical Modeling: Climate Impacts due to Bioenergy Crop Expansion
title_fullStr Spatio-temporal Statistical Modeling: Climate Impacts due to Bioenergy Crop Expansion
title_full_unstemmed Spatio-temporal Statistical Modeling: Climate Impacts due to Bioenergy Crop Expansion
title_sort spatio-temporal statistical modeling: climate impacts due to bioenergy crop expansion
publishDate 2018
url http://hdl.handle.net/2286/R.I.50563
_version_ 1718757035591335936